diff --git a/Microsoft.ML.sln b/Microsoft.ML.sln index 4974ca4887..63a15c977c 100644 --- a/Microsoft.ML.sln +++ b/Microsoft.ML.sln @@ -30,8 +30,6 @@ Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.KMeansClusteri EndProject Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.PCA", "src\Microsoft.ML.PCA\Microsoft.ML.PCA.csproj", "{58E06735-1129-4DD5-86E0-6BBFF049AAD9}" EndProject -Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.Maml", "src\Microsoft.ML.Maml\Microsoft.ML.Maml.csproj", "{D956E291-F6E5-4474-9023-91793F45ABEB}" -EndProject Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.Api", "src\Microsoft.ML.Api\Microsoft.ML.Api.csproj", "{2F636A2C-062C-49F4-85F3-60DCADAB6A43}" EndProject Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.Tests", "test\Microsoft.ML.Tests\Microsoft.ML.Tests.csproj", "{64BC22D3-1E76-41EF-94D8-C79E471FF2DD}" @@ -56,36 +54,6 @@ Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.Parquet", "src EndProject Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.Sweeper", "src\Microsoft.ML.Sweeper\Microsoft.ML.Sweeper.csproj", "{55C8122D-79EA-48AB-85D0-EB551FC1C427}" EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "docs", "docs", "{E20AF96D-3F66-4065-8A89-BEE479D74536}" - ProjectSection(SolutionItems) = preProject - docs\README.md = docs\README.md - docs\release-notes\0.1\release-0.1.md = docs\release-notes\0.1\release-0.1.md - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "project-docs", "project-docs", "{52794B40-AB8A-41AF-9EF7-799C80D6E0BC}" - ProjectSection(SolutionItems) = preProject - docs\project-docs\contributing.md = docs\project-docs\contributing.md - docs\project-docs\developer-guide.md = docs\project-docs\developer-guide.md - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Solution Items", "Solution Items", "{76F579E4-B9D2-4A0C-A511-EEFA4B2B829F}" - ProjectSection(SolutionItems) = preProject - CONTRIBUTING.md = CONTRIBUTING.md - README.md = README.md - ROADMAP.md = ROADMAP.md - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "building", "building", "{DB751004-5D49-4B88-B78F-29CA9887087D}" - ProjectSection(SolutionItems) = preProject - docs\building\unix-instructions.md = docs\building\unix-instructions.md - docs\building\windows-instructions.md = docs\building\windows-instructions.md - EndProjectSection -EndProject -Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "specs", "specs", "{2DEFC784-F2B5-44EA-ABBB-0DCF3E689DAC}" - ProjectSection(SolutionItems) = preProject - docs\specs\mvp.md = docs\specs\mvp.md - EndProjectSection -EndProject Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "pkg", "pkg", "{D3D38B03-B557-484D-8348-8BADEE4DF592}" ProjectSection(SolutionItems) = preProject pkg\Directory.Build.props = pkg\Directory.Build.props @@ -104,6 +72,24 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Microsoft.ML.Parquet", "Mic EndProject Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.Benchmarks", "test\Microsoft.ML.Benchmarks\Microsoft.ML.Benchmarks.csproj", "{7A9DB75F-2CA5-4184-9EF5-1F17EB39483F}" EndProject +Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.Maml", "src\Microsoft.ML.Maml\Microsoft.ML.Maml.csproj", "{64F40A0D-D4C2-4AA7-8470-E9CC437827E4}" +EndProject +Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.Console", "src\Microsoft.ML.Console\Microsoft.ML.Console.csproj", "{362A98CF-FBF7-4EBB-A11B-990BBF845B15}" +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "build", "build", "{487213C9-E8A9-4F94-85D7-28A05DBBFE3A}" +EndProject +Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "netstandard2.0", "netstandard2.0", "{9252A8EB-ABFB-440C-AB4D-1D562753CE0F}" + ProjectSection(SolutionItems) = preProject + pkg\Microsoft.ML\build\netstandard2.0\Microsoft.ML.props = pkg\Microsoft.ML\build\netstandard2.0\Microsoft.ML.props + pkg\Microsoft.ML\build\netstandard2.0\Microsoft.ML.targets = pkg\Microsoft.ML\build\netstandard2.0\Microsoft.ML.targets + EndProjectSection +EndProject +Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.Sweeper.Tests", "test\Microsoft.ML.Sweeper.Tests\Microsoft.ML.Sweeper.Tests.csproj", "{3DEB504D-7A07-48CE-91A2-8047461CB3D4}" +EndProject +Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.LightGBM", "src\Microsoft.ML.LightGBM\Microsoft.ML.LightGBM.csproj", "{001F3B4E-FBE4-4001-AFD2-A6A989CD1C25}" +EndProject +Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.Ensemble", "src\Microsoft.ML.Ensemble\Microsoft.ML.Ensemble.csproj", "{DCF46B79-1FDB-4DBA-A263-D3D64E3AAA27}" +EndProject Global GlobalSection(SolutionConfigurationPlatforms) = preSolution Debug|Any CPU = Debug|Any CPU @@ -158,10 +144,6 @@ Global {58E06735-1129-4DD5-86E0-6BBFF049AAD9}.Debug|Any CPU.Build.0 = Debug|Any CPU {58E06735-1129-4DD5-86E0-6BBFF049AAD9}.Release|Any CPU.ActiveCfg = Release|Any CPU {58E06735-1129-4DD5-86E0-6BBFF049AAD9}.Release|Any CPU.Build.0 = Release|Any CPU - {D956E291-F6E5-4474-9023-91793F45ABEB}.Debug|Any CPU.ActiveCfg = Debug|Any CPU - {D956E291-F6E5-4474-9023-91793F45ABEB}.Debug|Any CPU.Build.0 = Debug|Any CPU - {D956E291-F6E5-4474-9023-91793F45ABEB}.Release|Any CPU.ActiveCfg = Release|Any CPU - {D956E291-F6E5-4474-9023-91793F45ABEB}.Release|Any CPU.Build.0 = Release|Any CPU {2F636A2C-062C-49F4-85F3-60DCADAB6A43}.Debug|Any CPU.ActiveCfg = Debug|Any CPU {2F636A2C-062C-49F4-85F3-60DCADAB6A43}.Debug|Any CPU.Build.0 = Debug|Any CPU {2F636A2C-062C-49F4-85F3-60DCADAB6A43}.Release|Any CPU.ActiveCfg = Release|Any CPU @@ -202,6 +184,26 @@ Global {7A9DB75F-2CA5-4184-9EF5-1F17EB39483F}.Debug|Any CPU.Build.0 = Debug|Any CPU {7A9DB75F-2CA5-4184-9EF5-1F17EB39483F}.Release|Any CPU.ActiveCfg = Release|Any CPU {7A9DB75F-2CA5-4184-9EF5-1F17EB39483F}.Release|Any CPU.Build.0 = Release|Any CPU + {64F40A0D-D4C2-4AA7-8470-E9CC437827E4}.Debug|Any CPU.ActiveCfg = Debug|Any CPU + {64F40A0D-D4C2-4AA7-8470-E9CC437827E4}.Debug|Any CPU.Build.0 = Debug|Any CPU + {64F40A0D-D4C2-4AA7-8470-E9CC437827E4}.Release|Any CPU.ActiveCfg = Release|Any CPU + {64F40A0D-D4C2-4AA7-8470-E9CC437827E4}.Release|Any CPU.Build.0 = Release|Any CPU + {362A98CF-FBF7-4EBB-A11B-990BBF845B15}.Debug|Any CPU.ActiveCfg = Debug|Any CPU + {362A98CF-FBF7-4EBB-A11B-990BBF845B15}.Debug|Any CPU.Build.0 = Debug|Any CPU + {362A98CF-FBF7-4EBB-A11B-990BBF845B15}.Release|Any CPU.ActiveCfg = Release|Any CPU + {362A98CF-FBF7-4EBB-A11B-990BBF845B15}.Release|Any CPU.Build.0 = Release|Any CPU + {3DEB504D-7A07-48CE-91A2-8047461CB3D4}.Debug|Any CPU.ActiveCfg = Debug|Any CPU + {3DEB504D-7A07-48CE-91A2-8047461CB3D4}.Debug|Any CPU.Build.0 = Debug|Any CPU + {3DEB504D-7A07-48CE-91A2-8047461CB3D4}.Release|Any CPU.ActiveCfg = Release|Any CPU + {3DEB504D-7A07-48CE-91A2-8047461CB3D4}.Release|Any CPU.Build.0 = Release|Any CPU + {001F3B4E-FBE4-4001-AFD2-A6A989CD1C25}.Debug|Any CPU.ActiveCfg = Debug|Any CPU + {001F3B4E-FBE4-4001-AFD2-A6A989CD1C25}.Debug|Any CPU.Build.0 = Debug|Any CPU + {001F3B4E-FBE4-4001-AFD2-A6A989CD1C25}.Release|Any CPU.ActiveCfg = Release|Any CPU + {001F3B4E-FBE4-4001-AFD2-A6A989CD1C25}.Release|Any CPU.Build.0 = Release|Any CPU + {DCF46B79-1FDB-4DBA-A263-D3D64E3AAA27}.Debug|Any CPU.ActiveCfg = Debug|Any CPU + {DCF46B79-1FDB-4DBA-A263-D3D64E3AAA27}.Debug|Any CPU.Build.0 = Debug|Any CPU + {DCF46B79-1FDB-4DBA-A263-D3D64E3AAA27}.Release|Any CPU.ActiveCfg = Release|Any CPU + {DCF46B79-1FDB-4DBA-A263-D3D64E3AAA27}.Release|Any CPU.Build.0 = Release|Any CPU EndGlobalSection GlobalSection(SolutionProperties) = preSolution HideSolutionNode = FALSE @@ -219,7 +221,6 @@ Global {7288C084-11C0-43BE-AC7F-45DCFEAEEBF6} = {09EADF06-BE25-4228-AB53-95AE3E15B530} {F1CAE3AB-4F86-4BC0-BBA8-C4A58E7E8A4A} = {09EADF06-BE25-4228-AB53-95AE3E15B530} {58E06735-1129-4DD5-86E0-6BBFF049AAD9} = {09EADF06-BE25-4228-AB53-95AE3E15B530} - {D956E291-F6E5-4474-9023-91793F45ABEB} = {09EADF06-BE25-4228-AB53-95AE3E15B530} {2F636A2C-062C-49F4-85F3-60DCADAB6A43} = {09EADF06-BE25-4228-AB53-95AE3E15B530} {64BC22D3-1E76-41EF-94D8-C79E471FF2DD} = {AED9C836-31E3-4F3F-8ABC-929555D3F3C4} {FDA2FD2C-A708-43AC-A941-4D941B0853BF} = {AED9C836-31E3-4F3F-8ABC-929555D3F3C4} @@ -230,12 +231,16 @@ Global {B7B593C5-FB8C-4ADA-A638-5B53B47D087E} = {09EADF06-BE25-4228-AB53-95AE3E15B530} {16BB1454-2108-40E5-B3A6-594654005303} = {09EADF06-BE25-4228-AB53-95AE3E15B530} {55C8122D-79EA-48AB-85D0-EB551FC1C427} = {09EADF06-BE25-4228-AB53-95AE3E15B530} - {52794B40-AB8A-41AF-9EF7-799C80D6E0BC} = {E20AF96D-3F66-4065-8A89-BEE479D74536} - {DB751004-5D49-4B88-B78F-29CA9887087D} = {E20AF96D-3F66-4065-8A89-BEE479D74536} - {2DEFC784-F2B5-44EA-ABBB-0DCF3E689DAC} = {E20AF96D-3F66-4065-8A89-BEE479D74536} {DEC8F776-49F7-4D87-836C-FE4DC057D08C} = {D3D38B03-B557-484D-8348-8BADEE4DF592} {6C95FC87-F5F2-4EEF-BB97-567F2F5DD141} = {D3D38B03-B557-484D-8348-8BADEE4DF592} {7A9DB75F-2CA5-4184-9EF5-1F17EB39483F} = {AED9C836-31E3-4F3F-8ABC-929555D3F3C4} + {64F40A0D-D4C2-4AA7-8470-E9CC437827E4} = {09EADF06-BE25-4228-AB53-95AE3E15B530} + {362A98CF-FBF7-4EBB-A11B-990BBF845B15} = {09EADF06-BE25-4228-AB53-95AE3E15B530} + {487213C9-E8A9-4F94-85D7-28A05DBBFE3A} = {DEC8F776-49F7-4D87-836C-FE4DC057D08C} + {9252A8EB-ABFB-440C-AB4D-1D562753CE0F} = {487213C9-E8A9-4F94-85D7-28A05DBBFE3A} + {3DEB504D-7A07-48CE-91A2-8047461CB3D4} = {AED9C836-31E3-4F3F-8ABC-929555D3F3C4} + {001F3B4E-FBE4-4001-AFD2-A6A989CD1C25} = {09EADF06-BE25-4228-AB53-95AE3E15B530} + {DCF46B79-1FDB-4DBA-A263-D3D64E3AAA27} = {09EADF06-BE25-4228-AB53-95AE3E15B530} EndGlobalSection GlobalSection(ExtensibilityGlobals) = postSolution SolutionGuid = {41165AF1-35BB-4832-A189-73060F82B01D} diff --git a/README.md b/README.md index 891f39af3b..43f14bba69 100644 --- a/README.md +++ b/README.md @@ -18,7 +18,7 @@ Along with these ML capabilities this first release of ML.NET also brings the fi ML.NET runs on Windows, Linux, and macOS - any platform where 64 bit [.NET Core](https://github.com/dotnet/core) or later is available. -The current release is 0.1. Check out the [release notes](docs/release-notes/0.1/release-0.1.md). +The current release is 0.2. Check out the [release notes](docs/release-notes/0.2/release-0.2.md). First ensure you have installed [.NET Core 2.0](https://www.microsoft.com/net/learn/get-started) or later. ML.NET also works on the .NET Framework. Note that ML.NET currently must run in a 64 bit process. @@ -66,7 +66,7 @@ Here's an example of code to train a model to predict sentiment from text sample ```C# var pipeline = new LearningPipeline(); -pipeline.Add(new TextLoader(dataPath, separator: ",")); +pipeline.Add(new TextLoader(dataPath).CreateFrom(separator: ',')); pipeline.Add(new TextFeaturizer("Features", "SentimentText")); pipeline.Add(new FastTreeBinaryClassifier()); var model = pipeline.Train(); @@ -84,6 +84,9 @@ SentimentPrediction prediction = model.Predict(data); Console.WriteLine("prediction: " + prediction.Sentiment); ``` +## Samples + +We have a [repo of samples](https://github.com/dotnet/machinelearning-samples) that you can look at. ## License diff --git a/build/BranchInfo.props b/build/BranchInfo.props index b1d073b0bc..00dbd8e318 100644 --- a/build/BranchInfo.props +++ b/build/BranchInfo.props @@ -1,8 +1,8 @@ 0 - 2 + 3 0 preview - \ No newline at end of file + diff --git a/build/Dependencies.props b/build/Dependencies.props index 5a528bcaa9..21e65c9007 100644 --- a/build/Dependencies.props +++ b/build/Dependencies.props @@ -6,7 +6,7 @@ 4.8.0 4.4.0 4.3.0 - 4.4.0 1.0.0-beta-62824-02 + 2.1.2.2 diff --git a/docs/building/unix-instructions.md b/docs/building/unix-instructions.md index 0110ae6b8f..855c6b470f 100644 --- a/docs/building/unix-instructions.md +++ b/docs/building/unix-instructions.md @@ -42,9 +42,11 @@ macOS 10.12 or higher is needed to build dotnet/machinelearning. On macOS a few components are needed which are not provided by a default developer setup: * cmake 3.10.3 +* gcc * All the requirements necessary to run .NET Core 2.0 applications. To view macOS prerequisites click [here](https://docs.microsoft.com/en-us/dotnet/core/macos-prerequisites?tabs=netcore2x). -One way of obtaining CMake is via [Homebrew](http://brew.sh): +One way of obtaining CMake and gcc is via [Homebrew](http://brew.sh): ```sh $ brew install cmake +$ brew install gcc ``` diff --git a/docs/code/EntryPoints.md b/docs/code/EntryPoints.md new file mode 100644 index 0000000000..dbcc4e6bc9 --- /dev/null +++ b/docs/code/EntryPoints.md @@ -0,0 +1,231 @@ +# Entry Points And Helper Classes + +## Overview + +Entry points are a way to interface with ML.NET components, by specifying an execution graph of connected inputs and outputs of those components. +Both the manifest describing available components and their inputs/outputs, and an "experiment" graph description, are expressed in JSON. +The recommended way of interacting with ML.NET through other, non-.NET programming languages, is by composing, and exchanging pipelines or experiment graphs. + +Through the documentation, we also refer to entry points as 'entry points nodes', and that is because they are the nodes of the graph representing the experiment. +The graph 'variables', the various values of the experiment graph JSON properties, serve to describe the relationship between the entry point nodes. +The 'variables' are therefore the edges of the DAG (Directed Acyclic Graph). + +All of ML.NET entry points are described by their manifest. The manifest is another JSON object that documents and describes the structure of an entry points. +Manifests are referenced to understand what an entry point does, and how it should be constructed, in a graph. + +This document briefly describes the structure of the entry points, the structure of an entry point manifest, and mentions the ML.NET classes that help construct an entry point graph. + +## EntryPoint manifest - the definition of an entry point + +The components manifest is build by scanning the ML.NET assemblies through reflection and searching for types having the: `SignatureEntryPointModule` signature in their `LoadableClass` assembly attribute definition. +An example of an entry point manifest object, specifically for the `ColumnTypeConverter` transform, is: + +```javascript +{ + "Name": "Transforms.ColumnTypeConverter", + "Desc": "Converts a column to a different type, using standard conversions.", + "FriendlyName": "Convert Transform", + "ShortName": "Convert", + "Inputs": [ + { "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "ResultType", + "Type": { + "Kind": "Enum", + "Values": [ "I1","I2","U2","I4","U4","I8","U8","R4","Num","R8","TX","Text","TXT","BL","Bool","TimeSpan","TS","DT","DateTime","DZ","DateTimeZone","UG","U16" ] + }, + "Desc": "The result type", + "Aliases": [ "type" ], + "Required": false, + "SortOrder": 150, + "IsNullable": true, + "Default": null + }, + { "Name": "Range", + "Type": "String", + "Desc": "For a key column, this defines the range of values", + "Aliases": [ "key" ], + "Required": false, + "SortOrder": 150, + "IsNullable": false, + "Default": null + }, + { "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ "name" ], + "Required": false, + "SortOrder": 150, + "IsNullable": false, + "Default": null + }, + { "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ "src" ], + "Required": false, + "SortOrder": 150, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:type:src)", + "Aliases": [ "col" ], + "Required": true, + "SortOrder": 1, + "IsNullable": false + }, + { "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 2, + "IsNullable": false + }, + { "Name": "ResultType", + "Type": { + "Kind": "Enum", + "Values": [ "I1","I2","U2","I4","U4","I8","U8","R4","Num","R8","TX","Text","TXT","BL","Bool","TimeSpan","TS","DT","DateTime","DZ","DateTimeZone","UG","U16" ] + }, + "Desc": "The result type", + "Aliases": [ "type" ], + "Required": false, + "SortOrder": 2, + "IsNullable": true, + "Default": null + }, + { "Name": "Range", + "Type": "String", + "Desc": "For a key column, this defines the range of values", + "Aliases": [ "key" ], + "Required": false, + "SortOrder": 150, + "IsNullable": false, + "Default": null + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": ["ITransformInput" ], + "OutputKind": [ "ITransformOutput" ] +} +``` + +The respective entry point, constructed based on this manifest would be: + +```javascript + { + "Name": "Transforms.ColumnTypeConverter", + "Inputs": { + "Column": [{ + "Name": "Features", + "Source": "Features" + }], + "Data": "$data0", + "ResultType": "R4" + }, + "Outputs": { + "OutputData": "$Convert_Output", + "Model": "$Convert_TransformModel" + } + } +``` + +## `EntryPointGraph` + +This class encapsulates the list of nodes (`EntryPointNode`) and edges +(`EntryPointVariable` inside a `RunContext`) of the graph. + +## `EntryPointNode` + +This class represents a node in the graph, and wraps an entry point call. It +has methods for creating and running entry points. It also has a reference to +the `RunContext` to allow it to get and set values from `EntryPointVariable`s. + +To express the inputs that are set through variables, a set of dictionaries +are used. The `InputBindingMap` maps an input parameter name to a list of +`ParameterBinding`s. The `InputMap` maps a `ParameterBinding` to a +`VariableBinding`. For example, if the JSON looks like this: + +```javascript +'foo': '$bar' +``` + +the `InputBindingMap` will have one entry that maps the string "foo" to a list +that has only one element, a `SimpleParameterBinding` with the name "foo" and +the `InputMap` will map the `SimpleParameterBinding` to a +`SimpleVariableBinding` with the name "bar". For a more complicated example, +let's say we have this JSON: + +```javascript +'foo': [ '$bar[3]', '$baz'] +``` + +the `InputBindingMap` will have one entry that maps the string "foo" to a list +that has two elements, an `ArrayIndexParameterBinding` with the name "foo" and +index 0 and another one with index 1. The `InputMap` will map the first +`ArrayIndexParameterBinding` to an `ArrayIndexVariableBinding` with name "bar" +and index 3 and the second `ArrayIndexParameterBinding` to a +`SimpleVariableBinding` with the name "baz". + +For outputs, a node assumes that an output is mapped to a variable, so the +`OutputMap` is a simple dictionary from string to string. + +## `EntryPointVariable` + +This class represents an edge in the entry point graph. It has a name, a type +and a value. Variables can be simple, arrays and/or dictionaries. Currently, +only data views, file handles, predictor models and transform models are +allowed as element types for a variable. + +## `RunContext` + +This class is just a container for all the variables in a graph. + +## `VariableBinding` and Derived Classes + +The abstract base class represents a "pointer to a (part of a) variable". It +is used in conjunction with `ParameterBinding`s to specify inputs to an entry +point node. The `SimpleVariableBinding` is a pointer to an entire variable, +the `ArrayIndexVariableBinding` is a pointer to a specific index in an array +variable, and the `DictionaryKeyVariableBinding` is a pointer to a specific +key in a dictionary variable. + +## `ParameterBinding` and Derived Classes + +The abstract base class represents a "pointer to a (part of a) parameter". It +parallels the `VariableBinding` hierarchy and it is used to specify the inputs +to an entry point node. The `SimpleParameterBinding` is a pointer to a +non-array, non-dictionary parameter, the `ArrayIndexParameterBinding` is a +pointer to a specific index of an array parameter and the +`DictionaryKeyParameterBinding` is a pointer to a specific key of a dictionary +parameter. + +## How to create an entry point for an existing ML.NET component + +The steps to take, to create an entry point for an existing ML.NET component, are: +1. Add the `SignatureEntryPointModule` signature to the `LoadableClass` assembly attribute. +2. Create a public static method, that: + a. Takes as input, among others, an object representing the arguments of the component you want to expose. + b. Initializes and run the components, returning one of the nested classes of `Microsoft.ML.Runtime.EntryPoints.CommonOutputs` + c. Is annotated with the `TlcModule.EntryPoint` attribute + +Based on the type of entry point being created, there are further conventions on the name of the method, for example, the Trainers entry points are typically called: 'TrainMultiClass', 'TrainBinary' etc, based on the task. +Look at [OnlineGradientDescent](../../src/Microsoft.ML.StandardLearners/Standard/Online/OnlineGradientDescent.cs) for an example of a component and its entry point. \ No newline at end of file diff --git a/docs/code/GraphRunner.md b/docs/code/GraphRunner.md new file mode 100644 index 0000000000..b7fddc9476 --- /dev/null +++ b/docs/code/GraphRunner.md @@ -0,0 +1,128 @@ +# Entry Point JSON Graph format + +The entry point graph in ML.NET is an array of _nodes_. More information about the definition of entry points and classes that help construct entry point graphs +can be found in the [EntryPoint.md document](./EntryPoints.md). + +Each node is an object with the following fields: + +- _name_: string. Required. Name of the entry point. +- _inputs_: object. Optional. Specifies non-default inputs to the entry point. +Note that if the entry point has required inputs (which is very common), the _inputs_ field is required. +- _outputs_: object. Optional. Specifies the variables that will hold the node's outputs. + +## Input and output types +The following types are supported in JSON graphs: + +- `string`. Represented as a JSON string, maps to a C# string. +- `float`. Represented as a JSON float, maps to a C# float or double. +- `bool`. Represented as a JSON bool, maps to a C# bool. +- `enum`. Represented as a JSON string, maps to a C# enum. The allowed values are those of the C# enum (they are also listed in the manifest). +- `int`. Represented as a JSON integer, maps to a C# int or long. +- `array` of the above. Represented as a JSON array, maps to a C# array. +- `dictionary`. Currently not implemented. Represented as a JSON object, maps to a C# `Dictionary`. +- `component`. Represented as a JSON object with 2 fields: _name_:string and _settings_:object. + +## Variables +The following input/output types can not be represented as a JSON value: +- `IDataView` +- `IFileHandle` +- `ITransformModel` +- `IPredictorModel` + +These must be passed as _variables_. The variable is represented as a JSON string that begins with `$`. +Note the following rules: + +- A variable can appear in the _outputs_ only once per graph. That is, the variable can be 'assigned' only once. +- If the variable is present in _inputs_ of one node and in the _outputs_ of another node, this signifies a graph 'edge'. +The same variable can participate in many edges. +- If the variable is present only in _inputs_, but never in _outputs_, it is a _graph input_. All graph inputs must be provided before +a graph can be run. +- The variable has a type, which is the type of inputs (and, optionally, output) that it appears in. If the type of the variable is +ambiguous, ML.NET throws an exception. +- Circular references. The experiment graph is expected to be a DAG. If the circular dependency is detected, ML.NET throws an exception. +_Currently, this is done lazily: if we couldn't ever run a node because it's waiting for inputs, we throw._ + +### Variables for arrays and dictionaries. +It is allowed to define variables for arrays and dictionaries, as long as the item types are valid variable types (the four types listed above). +They are treated the same way as regular 'scalar' variables. + +If we want to reference an item of the collection, we can use the `[]` syntax: +- `$var[5]` denotes 5th element of an array variable. +- `$var[foo]` and `$var['foo']` both denote the element with key 'foo' of a dictionary variable. +_This is not yet implemented._ + +Conversely, if we want to build a collection (array or dictionary) of variables, we can do it using JSON arrays and objects: +- `["$v1", "$v2", "$v3"]` denotes an array containing 3 variables. +- `{"foo": "$v1", "bar": "$v2"}` denotes a collection containing 2 key-value pairs. +_This is also not yet implemented._ + +## Example of a JSON entry point manifest object, and the respective entry point graph node +Let's consider the following manifest snippet, describing an entry point _'CVSplit.Split'_: + +```javascript + { + "name": "CVSplit.Split", + "desc": "Split the dataset into the specified number of cross-validation folds (train and test sets)", + "inputs": [ + { + "name": "Data", + "type": "DataView", + "desc": "Input dataset", + "required": true + }, + { + "name": "NumFolds", + "type": "Int", + "desc": "Number of folds to split into", + "required": false, + "default": 2 + }, + { + "name": "StratificationColumn", + "type": "String", + "desc": "Stratification column", + "aliases": [ + "strat" + ], + "required": false, + "default": null + } + ], + "outputs": [ + { + "name": "TrainData", + "type": { + "kind": "Array", + "itemType": "DataView" + }, + "desc": "Training data (one dataset per fold)" + }, + { + "name": "TestData", + "type": { + "kind": "Array", + "itemType": "DataView" + }, + "desc": "Testing data (one dataset per fold)" + } + ] + } +``` + +As we can see, the entry point has 3 inputs (one of them required), and 2 outputs. +The following is a correct graph containing call to this entry point: + +```javascript +{ + "nodes": [ + { + "name": "CVSplit.Split", + "inputs": { + "Data": "$data1" + }, + "outputs": { + "TrainData": "$cv" + } + }] +} +``` \ No newline at end of file diff --git a/pkg/Microsoft.ML.LightGBM/Microsoft.ML.LightGBM.nupkgproj b/pkg/Microsoft.ML.LightGBM/Microsoft.ML.LightGBM.nupkgproj new file mode 100644 index 0000000000..8cddd1719e --- /dev/null +++ b/pkg/Microsoft.ML.LightGBM/Microsoft.ML.LightGBM.nupkgproj @@ -0,0 +1,13 @@ + + + + netstandard2.0 + ML.NET component for LightGBM + + + + + + + + diff --git a/pkg/Microsoft.ML.LightGBM/Microsoft.ML.LightGBM.symbols.nupkgproj b/pkg/Microsoft.ML.LightGBM/Microsoft.ML.LightGBM.symbols.nupkgproj new file mode 100644 index 0000000000..d7710ff60a --- /dev/null +++ b/pkg/Microsoft.ML.LightGBM/Microsoft.ML.LightGBM.symbols.nupkgproj @@ -0,0 +1,5 @@ + + + + + diff --git a/pkg/Microsoft.ML/Microsoft.ML.nupkgproj b/pkg/Microsoft.ML/Microsoft.ML.nupkgproj index b0cedd6ad4..e28b198bcf 100644 --- a/pkg/Microsoft.ML/Microsoft.ML.nupkgproj +++ b/pkg/Microsoft.ML/Microsoft.ML.nupkgproj @@ -11,7 +11,6 @@ - diff --git a/pkg/Microsoft.ML/build/Microsoft.ML.props b/pkg/Microsoft.ML/build/netstandard2.0/Microsoft.ML.props similarity index 87% rename from pkg/Microsoft.ML/build/Microsoft.ML.props rename to pkg/Microsoft.ML/build/netstandard2.0/Microsoft.ML.props index 3970c4d664..fa15d15eb5 100644 --- a/pkg/Microsoft.ML/build/Microsoft.ML.props +++ b/pkg/Microsoft.ML/build/netstandard2.0/Microsoft.ML.props @@ -7,7 +7,7 @@ - PreserveNewest false diff --git a/pkg/Microsoft.ML/build/Microsoft.ML.targets b/pkg/Microsoft.ML/build/netstandard2.0/Microsoft.ML.targets similarity index 100% rename from pkg/Microsoft.ML/build/Microsoft.ML.targets rename to pkg/Microsoft.ML/build/netstandard2.0/Microsoft.ML.targets diff --git a/src/Microsoft.ML.Console/Console.cs b/src/Microsoft.ML.Console/Console.cs new file mode 100644 index 0000000000..12e6254cce --- /dev/null +++ b/src/Microsoft.ML.Console/Console.cs @@ -0,0 +1,11 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +namespace Microsoft.ML.Runtime.Tools.Console +{ + public static class Console + { + public static int Main(string[] args) => Maml.Main(args); + } +} diff --git a/src/Microsoft.ML.Console/Microsoft.ML.Console.csproj b/src/Microsoft.ML.Console/Microsoft.ML.Console.csproj new file mode 100644 index 0000000000..8039371695 --- /dev/null +++ b/src/Microsoft.ML.Console/Microsoft.ML.Console.csproj @@ -0,0 +1,20 @@ + + + + true + CORECLR + Microsoft.ML + netcoreapp2.0 + Exe + MML + Microsoft.ML.Runtime.Tools.Console.Console + + + + + + + + + + \ No newline at end of file diff --git a/src/Microsoft.ML.Core/EntryPoints/ModuleCatalog.cs b/src/Microsoft.ML.Core/EntryPoints/ModuleCatalog.cs index 586f6a4b02..498a75c9e5 100644 --- a/src/Microsoft.ML.Core/EntryPoints/ModuleCatalog.cs +++ b/src/Microsoft.ML.Core/EntryPoints/ModuleCatalog.cs @@ -51,7 +51,7 @@ public sealed class EntryPointInfo public readonly Type[] OutputKinds; public readonly ObsoleteAttribute ObsoleteAttribute; - internal EntryPointInfo(IExceptionContext ectx, MethodInfo method, + internal EntryPointInfo(IExceptionContext ectx, MethodInfo method, TlcModule.EntryPointAttribute attribute, ObsoleteAttribute obsoleteAttribute) { Contracts.AssertValueOrNull(ectx); @@ -187,7 +187,7 @@ private ModuleCatalog(IExceptionContext ectx) if (attr == null) continue; - var info = new EntryPointInfo(ectx, methodInfo, attr, + var info = new EntryPointInfo(ectx, methodInfo, attr, methodInfo.GetCustomAttributes(typeof(ObsoleteAttribute), false).FirstOrDefault() as ObsoleteAttribute); entryPoints.Add(info); @@ -315,7 +315,7 @@ public bool TryFindComponent(Type interfaceType, Type argumentType, out Componen Contracts.CheckParam(interfaceType.IsInterface, nameof(interfaceType), "Must be interface"); Contracts.CheckValue(argumentType, nameof(argumentType)); - component = _components.FirstOrDefault(x => x.InterfaceType == interfaceType && x.ArgumentType == argumentType); + component = _components.FirstOrDefault(x => x.InterfaceType == interfaceType && x.ArgumentType == argumentType); return component != null; } diff --git a/src/Microsoft.ML.Core/Prediction/ISweeper.cs b/src/Microsoft.ML.Core/Prediction/ISweeper.cs index a0a1850be0..b3dd0dc3da 100644 --- a/src/Microsoft.ML.Core/Prediction/ISweeper.cs +++ b/src/Microsoft.ML.Core/Prediction/ISweeper.cs @@ -174,6 +174,11 @@ public override string ToString() { return string.Join(" ", _parameterValues.Select(kvp => string.Format("{0}={1}", kvp.Value.Name, kvp.Value.ValueText)).ToArray()); } + + public override int GetHashCode() + { + return _hash; + } } /// diff --git a/src/Microsoft.ML.Core/Prediction/ITrainer.cs b/src/Microsoft.ML.Core/Prediction/ITrainer.cs index cd8c6d12c8..6e04a30e6f 100644 --- a/src/Microsoft.ML.Core/Prediction/ITrainer.cs +++ b/src/Microsoft.ML.Core/Prediction/ITrainer.cs @@ -74,6 +74,8 @@ public interface IModelCombiner TPredictor CombineModels(IEnumerable models); } + public delegate void SignatureModelCombiner(PredictionKind kind); + /// /// Weakly typed interface for a trainer "session" that produces a predictor. /// diff --git a/src/Microsoft.ML.Core/Utilities/HybridMemoryStream.cs b/src/Microsoft.ML.Core/Utilities/HybridMemoryStream.cs index 8825369455..73b4c4a828 100644 --- a/src/Microsoft.ML.Core/Utilities/HybridMemoryStream.cs +++ b/src/Microsoft.ML.Core/Utilities/HybridMemoryStream.cs @@ -19,26 +19,24 @@ public sealed class HybridMemoryStream : Stream { private MemoryStream _memStream; private Stream _overflowStream; - private string _overflowPath; private readonly int _overflowBoundary; private const int _defaultMaxLen = 1 << 30; private bool _disposed; - private Stream MyStream { get { return _memStream ?? _overflowStream; } } + private Stream MyStream => _memStream ?? _overflowStream; - private bool IsMemory { get { return _memStream != null; } } + private bool IsMemory => _memStream != null; - public override long Position - { - get { return MyStream.Position; } - set { Seek(value, SeekOrigin.Begin); } + public override long Position { + get => MyStream.Position; + set => Seek(value, SeekOrigin.Begin); } - public override long Length { get { return MyStream.Length; } } - public override bool CanWrite { get { return MyStream.CanWrite; } } - public override bool CanSeek { get { return MyStream.CanSeek; } } - public override bool CanRead { get { return MyStream.CanRead; } } + public override long Length => MyStream.Length; + public override bool CanWrite => MyStream.CanWrite; + public override bool CanSeek => MyStream.CanSeek; + public override bool CanRead => MyStream.CanRead; /// /// Constructs an initially empty read-write stream. Once the number of @@ -123,27 +121,24 @@ protected override void Dispose(bool disposing) var overflow = _overflowStream; _overflowStream = null; overflow.Dispose(); - Contracts.AssertValue(_overflowPath); - File.Delete(_overflowPath); - _overflowPath = null; } _disposed = true; AssertInvariants(); + base.Dispose(disposing); } } public override void Close() { AssertInvariants(); - if (MyStream != null) - MyStream.Close(); + // The base Stream class Close will call Dispose(bool). + base.Close(); } public override void Flush() { AssertInvariants(); - if (MyStream != null) - MyStream.Flush(); + MyStream?.Flush(); AssertInvariants(); } @@ -164,9 +159,9 @@ private void EnsureOverflow() // been closed. Contracts.Check(_memStream.CanRead, "attempt to perform operation on closed stream"); - Contracts.Assert(_overflowPath == null); - _overflowPath = Path.GetTempFileName(); - _overflowStream = new FileStream(_overflowPath, FileMode.Open, FileAccess.ReadWrite); + string overflowPath = Path.GetTempFileName(); + _overflowStream = new FileStream(overflowPath, FileMode.Open, FileAccess.ReadWrite, + FileShare.None, bufferSize: 4096, FileOptions.DeleteOnClose); // The documentation is not clear on this point, but the source code for // memory stream makes clear that this buffer is exposable for a memory diff --git a/src/Microsoft.ML.Core/Utilities/PathUtils.cs b/src/Microsoft.ML.Core/Utilities/PathUtils.cs index e1129bf9c5..74ccec30c0 100644 --- a/src/Microsoft.ML.Core/Utilities/PathUtils.cs +++ b/src/Microsoft.ML.Core/Utilities/PathUtils.cs @@ -1,4 +1,4 @@ -// Licensed to the .NET Foundation under one or more agreements. +// Licensed to the .NET Foundation under one or more agreements. // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. @@ -67,13 +67,17 @@ public static string FindExistentFileOrNull(string fileName, string folderPrefix // 1. Search in customSearchDir. if (!string.IsNullOrWhiteSpace(customSearchDir) && TryFindFile(fileName, folderPrefix, customSearchDir, out candidate)) - return candidate; + { + return candidate; + } // 2. Search in the path specified by the environment variable. var envDir = Environment.GetEnvironmentVariable(CustomSearchDirEnvVariable); if (!string.IsNullOrWhiteSpace(envDir) && TryFindFile(fileName, folderPrefix, envDir, out candidate)) - return candidate; + { + return candidate; + } // 3. Search in the path specified by the assemblyForBasePath. if (assemblyForBasePath != null) diff --git a/src/Microsoft.ML.Core/Utilities/TextReaderStream.cs b/src/Microsoft.ML.Core/Utilities/TextReaderStream.cs index ab83bf40f0..5c05275ba7 100644 --- a/src/Microsoft.ML.Core/Utilities/TextReaderStream.cs +++ b/src/Microsoft.ML.Core/Utilities/TextReaderStream.cs @@ -14,7 +14,7 @@ namespace Microsoft.ML.Runtime.Internal.Utilities /// compensates by inserting \n line feed characters at the end of every /// input line, including the last one. /// - public class TextReaderStream : Stream + public sealed class TextReaderStream : Stream { private readonly TextReader _baseReader; private readonly Encoding _encoding; @@ -38,19 +38,11 @@ public class TextReaderStream : Stream public override bool CanWrite => false; public override long Length - { - get - { - throw Contracts.ExceptNotSupp("Stream cannot determine length."); - } - } + => throw Contracts.ExceptNotSupp("Stream cannot determine length."); public override long Position { - get - { - return _position; - } + get => _position; set { if (value != Position) @@ -96,6 +88,7 @@ public override void Close() protected override void Dispose(bool disposing) { _baseReader.Dispose(); + base.Dispose(disposing); } public override void Flush() @@ -182,18 +175,12 @@ public override int ReadByte() } public override long Seek(long offset, SeekOrigin origin) - { - throw Contracts.ExceptNotSupp("Stream cannot seek."); - } + => throw Contracts.ExceptNotSupp("Stream cannot seek."); public override void Write(byte[] buffer, int offset, int count) - { - throw Contracts.ExceptNotSupp("Stream is not writable."); - } + => throw Contracts.ExceptNotSupp("Stream is not writable."); public override void SetLength(long value) - { - throw Contracts.ExceptNotSupp("Stream is not writable."); - } + => throw Contracts.ExceptNotSupp("Stream is not writable."); } } \ No newline at end of file diff --git a/src/Microsoft.ML.CpuMath/AssemblyInfo.cs b/src/Microsoft.ML.CpuMath/AssemblyInfo.cs new file mode 100644 index 0000000000..cb45bf5608 --- /dev/null +++ b/src/Microsoft.ML.CpuMath/AssemblyInfo.cs @@ -0,0 +1,9 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System.Reflection; +using System.Runtime.CompilerServices; +using System.Runtime.InteropServices; + +[assembly: InternalsVisibleTo("Microsoft.ML.StandardLearners, PublicKey=00240000048000009400000006020000002400005253413100040000010001004b86c4cb78549b34bab61a3b1800e23bfeb5b3ec390074041536a7e3cbd97f5f04cf0f857155a8928eaa29ebfd11cfbbad3ba70efea7bda3226c6a8d370a4cd303f714486b6ebc225985a638471e6ef571cc92a4613c00b8fa65d61ccee0cbe5f36330c9a01f4183559f1bef24cc2917c6d913e3a541333a1d05d9bed22b38cb")] \ No newline at end of file diff --git a/src/Microsoft.ML.Data/Commands/DataCommand.cs b/src/Microsoft.ML.Data/Commands/DataCommand.cs index a68dd2022a..435c25bf5b 100644 --- a/src/Microsoft.ML.Data/Commands/DataCommand.cs +++ b/src/Microsoft.ML.Data/Commands/DataCommand.cs @@ -20,38 +20,38 @@ public static class DataCommand { public abstract class ArgumentsBase { - [Argument(ArgumentType.Multiple, HelpText = "The data loader", ShortName = "loader", SortOrder = 1, NullName = "")] + [Argument(ArgumentType.Multiple, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, HelpText = "The data loader", ShortName = "loader", SortOrder = 1, NullName = "")] public SubComponent Loader; [Argument(ArgumentType.AtMostOnce, IsInputFileName = true, HelpText = "The data file", ShortName = "data", SortOrder = 0)] public string DataFile; - [Argument(ArgumentType.AtMostOnce, HelpText = "Model file to save", ShortName = "out")] + [Argument(ArgumentType.AtMostOnce, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, HelpText = "Model file to save", ShortName = "out")] public string OutputModelFile; - [Argument(ArgumentType.AtMostOnce, IsInputFileName = true, HelpText = "Model file to load", ShortName = "in", SortOrder = 90)] + [Argument(ArgumentType.AtMostOnce, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, IsInputFileName = true, HelpText = "Model file to load", ShortName = "in", SortOrder = 90)] public string InputModelFile; - [Argument(ArgumentType.Multiple, HelpText = "Load transforms from model file?", ShortName = "loadTrans", SortOrder = 91)] + [Argument(ArgumentType.Multiple, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, HelpText = "Load transforms from model file?", ShortName = "loadTrans", SortOrder = 91)] public bool? LoadTransforms; - [Argument(ArgumentType.AtMostOnce, HelpText = "Random seed", ShortName = "seed", SortOrder = 101)] + [Argument(ArgumentType.AtMostOnce, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, HelpText = "Random seed", ShortName = "seed", SortOrder = 101)] public int? RandomSeed; - [Argument(ArgumentType.AtMostOnce, HelpText = "Verbose?", ShortName = "v", Hide = true)] + [Argument(ArgumentType.AtMostOnce, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, HelpText = "Verbose?", ShortName = "v", Hide = true)] public bool? Verbose; - [Argument(ArgumentType.AtMostOnce, HelpText = "The web server to publish the RESTful API", Hide = true)] + [Argument(ArgumentType.AtMostOnce, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, HelpText = "The web server to publish the RESTful API", Hide = true)] public ServerChannel.IServerFactory Server; // This is actually an advisory value. The implementations themselves are responsible for // determining what they consider appropriate, and the actual heuristics is a bit more // complex than just this. - [Argument(ArgumentType.LastOccurenceWins, + [Argument(ArgumentType.LastOccurenceWins, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, HelpText = "Desired degree of parallelism in the data pipeline", ShortName = "n")] public int? Parallel; - [Argument(ArgumentType.Multiple, HelpText = "Transform", ShortName = "xf")] + [Argument(ArgumentType.Multiple, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, HelpText = "Transform", ShortName = "xf")] public KeyValuePair>[] Transform; } @@ -396,6 +396,20 @@ public static void SaveLoader(IDataLoader loader, IFileHandle file) Contracts.CheckParam(file.CanWrite, nameof(file), "Must be writable"); using (var stream = file.CreateWriteStream()) + { + SaveLoader(loader, stream); + } + } + + /// + /// Saves to the specified . + /// + public static void SaveLoader(IDataLoader loader, Stream stream) + { + Contracts.CheckValue(loader, nameof(loader)); + Contracts.CheckValue(stream, nameof(stream)); + Contracts.CheckParam(stream.CanWrite, nameof(stream), "Must be writable"); + using (var rep = RepositoryWriter.CreateNew(stream)) { ModelSaveContext.SaveModel(rep, loader, ModelFileUtils.DirDataLoaderModel); diff --git a/src/Microsoft.ML.Data/DataLoadSave/PartitionedFileLoader.cs b/src/Microsoft.ML.Data/DataLoadSave/PartitionedFileLoader.cs new file mode 100644 index 0000000000..69eb3bbb3b --- /dev/null +++ b/src/Microsoft.ML.Data/DataLoadSave/PartitionedFileLoader.cs @@ -0,0 +1,755 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Collections.Generic; +using System.IO; +using System.Linq; +using System.Text; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Data.Conversion; +using Microsoft.ML.Runtime.Data.IO; +using Microsoft.ML.Runtime.Data.Utilities; +using Microsoft.ML.Runtime.Internal.Utilities; +using Microsoft.ML.Runtime.Model; + +[assembly: LoadableClass(PartitionedFileLoader.Summary, typeof(PartitionedFileLoader), typeof(PartitionedFileLoader.Arguments), typeof(SignatureDataLoader), + PartitionedFileLoader.UserName, PartitionedFileLoader.LoadName, PartitionedFileLoader.ShortName)] + +[assembly: LoadableClass(PartitionedFileLoader.Summary, typeof(PartitionedFileLoader), null, typeof(SignatureLoadDataLoader), + PartitionedFileLoader.UserName, PartitionedFileLoader.LoadName, PartitionedFileLoader.ShortName)] + +namespace Microsoft.ML.Runtime.Data +{ + /// + /// Loads a set of directory partitioned files into an IDataView. + /// The directories of the file will treated as column data and the underlying files are loaded using the data loader. + /// The first file will be used as the basis for all follow-up file paths and schemas. Any files that don't match + /// the expected path or schema will be skipped. + /// + /// + /// Sample directory structure: + /// + /// Data/ + /// Year=2017/ + /// Month=01/ + /// data1.parquet + /// data1.parquet + /// Month=02/ + /// data1.parquet + /// data1.parquet + /// Year=2018/ + /// Month=01/ + /// data1.parquet + /// data1.parquet + /// + public sealed class PartitionedFileLoader : IDataLoader + { + internal const string Summary = "Loads a horizontally partitioned file set."; + internal const string UserName = "Partitioned Loader"; + public const string LoadName = "PartitionedLoader"; + public const string ShortName = "Part"; + + private static VersionInfo GetVersionInfo() + { + return new VersionInfo( + modelSignature: "PARTLOAD", + verWrittenCur: 0x00010001, // Initial + verReadableCur: 0x00010001, + verWeCanReadBack: 0x00010001, + loaderSignature: LoadName); + } + + public class Arguments + { + [Argument(ArgumentType.Required, HelpText = "Base path to the directory of your partitioned files.", ShortName = "bp")] + public string BasePath; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Append a column with the file path.", ShortName = "path")] + public bool IncludePathColumn = false; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Path parser to extract column name/value pairs from the file path.", ShortName = "parser")] + public IPartitionedPathParserFactory PathParserFactory = new ParquetPartitionedPathParserFactory(); + + [Argument(ArgumentType.Multiple, HelpText = "The data loader.")] + public SubComponent Loader; + } + + public sealed class Column + { + [Argument(ArgumentType.Required, HelpText = "Name of the column.")] + public string Name; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Data type of the column.")] + public DataKind? Type; + + [Argument(ArgumentType.Required, HelpText = "Index of the directory representing this column.")] + public int Source; + + public static Column Parse(string str) + { + Contracts.AssertNonEmpty(str); + + if (TryParse(str, out Column column)) + { + return column; + } + + return null; + } + + public static bool TryParse(string str, out Column column) + { + column = null; + + if (string.IsNullOrEmpty(str)) + { + return false; + } + + if (!ColumnParsingUtils.TryParse(str, out string name, out string sourceStr, out string kindStr)) + { + return false; + } + + DataKind? kind = null; + if (kindStr != null && TypeParsingUtils.TryParseDataKind(kindStr, out DataKind parsedKind, out KeyRange range)) + { + kind = parsedKind; + } + + if (!int.TryParse(sourceStr, out int source)) + { + return false; + } + + column = new Column() + { + Name = name, + Source = source, + Type = kind + }; + + return true; + } + + public bool TryUnparse(StringBuilder sb) + { + Contracts.AssertValue(sb); + + sb.Append($"{Name}"); + + if (Type.HasValue) + { + sb.Append($":{Type}"); + } + + sb.Append($":{Source}"); + + return true; + } + } + + private readonly IHost _host; + private readonly IMultiStreamSource _files; + private readonly int[] _srcDirIndex; + private readonly byte[] _subLoaderBytes; + + // Number of tailing directories to include. + private readonly int _tailingDirCount; + + private readonly IPartitionedPathParser _pathParser; + + private const string RegistrationName = LoadName; + private const string FilePathSpecCtxName = "FilePathSpec"; + private const string SchemaCtxName = "Schema.idv"; + private const int FilePathColIndex = -1; + + public PartitionedFileLoader(IHostEnvironment env, Arguments args, IMultiStreamSource files) + { + Contracts.CheckValue(env, nameof(env)); + _host = env.Register(RegistrationName); + _host.CheckValue(args, nameof(args)); + _host.CheckValue(files, nameof(files)); + + _pathParser = args.PathParserFactory.CreateComponent(_host); + _host.CheckUserArg(_pathParser != null, nameof(args.PathParserFactory), "Failed to create the FilePathSpec."); + + _files = files; + + var subLoader = args.Loader.CreateInstance(_host, _files); + _subLoaderBytes = SaveLoaderToBytes(subLoader); + + string relativePath = GetRelativePath(args.BasePath, files); + var columns = ParseColumns(relativePath).ToArray(); + _tailingDirCount = GetDirectoryCount(relativePath); + + if (args.IncludePathColumn) + { + var pathCol = new Column() + { + Name = "Path", + Source = FilePathColIndex, + Type = DataKind.Text + }; + + columns = columns.Concat(new[] { pathCol }).ToArray(); + } + + _srcDirIndex = columns.Select(c => c.Source).ToArray(); + Schema = CreateSchema(_host, columns, subLoader); + } + + private PartitionedFileLoader(IHost host, ModelLoadContext ctx, IMultiStreamSource files) + { + Contracts.AssertValue(host); + _host = host; + _host.AssertValue(ctx); + _host.AssertValue(files); + + // ** Binary format ** + // int: tailing directory count + // Schema of the loader + // int[]: srcColumns + // byte[]: subloader + // model: file path spec + + _tailingDirCount = ctx.Reader.ReadInt32(); + + // Load the schema + byte[] buffer = null; + if (!ctx.TryLoadBinaryStream(SchemaCtxName, r => buffer = r.ReadByteArray())) + throw _host.ExceptDecode(); + BinaryLoader loader = null; + var strm = new MemoryStream(buffer, writable: false); + loader = new BinaryLoader(_host, new BinaryLoader.Arguments(), strm); + Schema = loader.Schema; + + _srcDirIndex = ctx.Reader.ReadIntArray(); + _subLoaderBytes = ctx.Reader.ReadByteArray(); + + ctx.LoadModel(_host, out _pathParser, FilePathSpecCtxName); + + _files = files; + } + + public static PartitionedFileLoader Create(IHostEnvironment env, ModelLoadContext ctx, IMultiStreamSource files) + { + Contracts.CheckValue(env, nameof(env)); + IHost host = env.Register(RegistrationName); + + env.CheckValue(ctx, nameof(ctx)); + ctx.CheckAtModel(GetVersionInfo()); + env.CheckValue(files, nameof(files)); + + return host.Apply("Loading Model", + ch => new PartitionedFileLoader(host, ctx, files)); + } + + public void Save(ModelSaveContext ctx) + { + Contracts.CheckValue(ctx, nameof(ctx)); + ctx.CheckAtModel(); + ctx.SetVersionInfo(GetVersionInfo()); + + // ** Binary format ** + // int: tailing directory count + // Schema of the loader + // int[]: srcColumns + // byte[]: subloader + // model: file path spec + + ctx.Writer.Write(_tailingDirCount); + + // Save the schema + var noRows = new EmptyDataView(_host, Schema); + var saverArgs = new BinarySaver.Arguments(); + saverArgs.Silent = true; + var saver = new BinarySaver(_host, saverArgs); + using (var strm = new MemoryStream()) + { + var allColumns = Enumerable.Range(0, Schema.ColumnCount).ToArray(); + saver.SaveData(strm, noRows, allColumns); + ctx.SaveBinaryStream(SchemaCtxName, w => w.WriteByteArray(strm.ToArray())); + } + ctx.Writer.WriteIntArray(_srcDirIndex); + + ctx.Writer.WriteByteArray(_subLoaderBytes); + ctx.SaveModel(_pathParser, FilePathSpecCtxName); + } + + public bool CanShuffle => true; + + public ISchema Schema { get; } + + public long? GetRowCount(bool lazy = true) + { + return null; + } + + public IRowCursor GetRowCursor(Func needCol, IRandom rand = null) + { + return new Cursor(_host, this, _files, needCol, rand); + } + + public IRowCursor[] GetRowCursorSet(out IRowCursorConsolidator consolidator, Func needCol, int n, IRandom rand = null) + { + consolidator = null; + var cursor = new Cursor(_host, this, _files, needCol, rand); + return new IRowCursor[] { cursor }; + } + + /// + /// Create a composite schema of both the partitioned columns and the underlying loader columns. + /// + /// The exception context. + /// The partitioned columns. + /// The sub loader. + /// The resulting schema. + private ISchema CreateSchema(IExceptionContext ectx, Column[] cols, IDataLoader subLoader) + { + Contracts.AssertValue(cols); + Contracts.AssertValue(subLoader); + + var columnNameTypes = cols.Select((col) => new KeyValuePair(col.Name, PrimitiveType.FromKind(col.Type.Value))); + var colSchema = new SimpleSchema(ectx, columnNameTypes.ToArray()); + + var subSchema = subLoader.Schema; + + if (subSchema.ColumnCount == 0) + { + return colSchema; + } + else + { + var schemas = new ISchema[] + { + subSchema, + colSchema + }; + + return new CompositeSchema(schemas); + } + } + + private byte [] SaveLoaderToBytes(IDataLoader loader) + { + Contracts.CheckValue(loader, nameof(loader)); + + using (var stream = new MemoryStream()) + { + LoaderUtils.SaveLoader(loader, stream); + return stream.GetBuffer(); + } + } + + private IDataLoader CreateLoaderFromBytes(byte [] loaderBytes, IMultiStreamSource files) + { + Contracts.CheckValue(loaderBytes, nameof(loaderBytes)); + Contracts.CheckValue(files, nameof(files)); + + using (var stream = new MemoryStream(loaderBytes)) + using (var rep = RepositoryReader.Open(stream, _host)) + { + return ModelFileUtils.LoadLoader(_host, rep, files, false); + } + } + + private sealed class Cursor : RootCursorBase, IRowCursor + { + private PartitionedFileLoader _parent; + + private bool[] _active; + private bool[] _subActive; // Active columns of the sub-cursor. + private Delegate[] _getters; + private Delegate[] _subGetters; // Cached getters of the sub-cursor. + + private DvText[] _colValues; // Column values cached from the file path. + private IRowCursor _subCursor; // Sub cursor of the current file. + + private IEnumerator _fileOrder; + + public Cursor(IChannelProvider provider, PartitionedFileLoader parent, IMultiStreamSource files, Func predicate, IRandom rand) + : base(provider) + { + Contracts.AssertValue(parent); + Contracts.AssertValue(files); + Contracts.AssertValue(predicate); + + _parent = parent; + + _active = Utils.BuildArray(Schema.ColumnCount, predicate); + _subActive = _active.Take(SubColumnCount).ToArray(); + _colValues = new DvText[Schema.ColumnCount - SubColumnCount]; + + _subGetters = new Delegate[SubColumnCount]; + _getters = CreateGetters(); + + _fileOrder = CreateFileOrder(rand).GetEnumerator(); + } + + public override long Batch => 0; + + public ISchema Schema => _parent.Schema; + + public ValueGetter GetGetter(int col) + { + Ch.Check(IsColumnActive(col)); + + var getter = _getters[col] as ValueGetter; + if (getter == null) + { + throw Ch.Except("Invalid TValue: '{0}'", typeof(TValue)); + } + + return getter; + } + + public override ValueGetter GetIdGetter() + { + return + (ref UInt128 val) => + { + Ch.Check(IsGood, "Cannot call ID getter in current state"); + + val = new UInt128(0, (ulong)Position); + }; + } + + public bool IsColumnActive(int col) + { + Ch.Check(0 <= col && col < Schema.ColumnCount); + return _active[col]; + } + + protected override bool MoveNextCore() + { + // Iterate sub cursor or move to the next file. + while (_subCursor == null || !_subCursor.MoveNext()) + { + // Cleanup old sub cursor + if (_subCursor != null) + { + _subCursor.Dispose(); + _subCursor = null; + } + + if (!TryGetNextPathAndValues(out string path, out string relativePath, out List values)) + { + return false; + } + + IDataLoader loader = null; + try + { + // Load the sub cursor and reset the data. + loader = _parent.CreateLoaderFromBytes(_parent._subLoaderBytes, new MultiFileSource(path)); + } + catch (Exception e) + { + Ch.Warning($"Failed to load file {path} due to a loader exception. Moving on to the next file. Ex: {e.Message}"); + continue; + } + + _subCursor = loader.GetRowCursor(col => _subActive[col]); + + try + { + UpdateSubGetters(); + UpdateColumnValues(relativePath, values); + } + catch (InvalidOperationException e) + { + // Failed to load this file so skip. + Ch.Warning(MessageSensitivity.Schema, e.Message); + if (_subCursor != null) + { + _subCursor.Dispose(); + _subCursor = null; + } + } + } + + return true; + } + + private bool TryGetNextPathAndValues(out string path, out string relativePath, out List values) + { + path = null; + relativePath = null; + values = null; + + do + { + // No more files to load. + if (!_fileOrder.MoveNext()) + { + return false; + } + + // Get next file and parse the column values from the file path. + string curPath = _parent._files.GetPathOrNull(_fileOrder.Current); + if (String.IsNullOrEmpty(curPath)) + { + Ch.Warning($"File at index {_fileOrder.Current} is missing a path. Loading of file is being skipped."); + continue; + } + + if (!TryTruncatePath(_parent._tailingDirCount, curPath, out relativePath)) + { + continue; + } + + if (!TryParseValuesFromPath(relativePath, out values)) + { + continue; + } + + path = curPath; + + } while (String.IsNullOrEmpty(path)); + + return true; + } + + private void UpdateSubGetters() + { + // Reset getters for the subcursor. + for (int i = 0; i < SubColumnCount; i++) + { + if (_subActive[i]) + { + var type = _subCursor.Schema.GetColumnType(i); + _subGetters[i] = MarshalGetter(_subCursor.GetGetter, type.RawType, i); + } + } + } + + private void UpdateColumnValues(string path, List values) + { + // Cache the column values for future Getter calls. + for (int i = 0; i < _colValues.Length; i++) + { + var source = _parent._srcDirIndex[i]; + if (source >= 0 && source < values.Count) + { + _colValues[i] = new DvText(values[source]); + } + else if (source == FilePathColIndex) + { + // Force Unix path for consistency. + var cleanPath = path.Replace(@"\", @"/"); + _colValues[i] = new DvText(cleanPath); + } + } + } + + private Delegate[] CreateGetters() + { + Delegate[] getters = new Delegate[Schema.ColumnCount]; + for (int i = 0; i < getters.Length; i++) + { + if (!_active[i]) + { + continue; + } + + var type = Schema.GetColumnType(i); + + // Use sub-cursor for all sub-columns. + if (IsSubColumn(i)) + { + getters[i] = Utils.MarshalInvoke(CreateSubGetterDelegateCore, type.RawType, i); + } + else + { + int idx = i - SubColumnCount; + getters[i] = Utils.MarshalInvoke(CreateGetterDelegateCore, type.RawType, idx, type); + } + } + + return getters; + } + + private Delegate CreateSubGetterDelegateCore(int col) + { + return (Delegate)SubGetterDelegateCore(col); + } + + private ValueGetter SubGetterDelegateCore(int col) + { + Ch.Check(col >= 0 && col < SubColumnCount); + + return (ref TValue value) => + { + // SubCursor may change so always requery the getter. + ValueGetter getter = _subGetters[col] as ValueGetter; + getter?.Invoke(ref value); + }; + } + + private Delegate CreateGetterDelegateCore(int col, ColumnType type) + { + return (Delegate)GetterDelegateCore(col, type); + } + + private ValueGetter GetterDelegateCore(int col, ColumnType type) + { + Ch.Check(col >= 0 && col < _colValues.Length); + Ch.AssertValue(type); + + var conv = Conversions.Instance.GetStandardConversion(TextType.Instance, type) as ValueMapper; + if (conv == null) + { + throw Ch.Except("Invalid TValue: '{0}' of the conversion.", typeof(TValue)); + } + + return (ref TValue value) => + { + conv(ref _colValues[col], ref value); + }; + } + + private bool IsSubColumn(int col) + { + return col < SubColumnCount; + } + + private int SubColumnCount => Schema.ColumnCount - _parent._srcDirIndex.Length; + + private IEnumerable CreateFileOrder(IRandom rand) + { + if (rand == null) + { + return Enumerable.Range(0, _parent._files.Count); + } + else + { + return Utils.GetRandomPermutation(rand, _parent._files.Count); + } + } + + private bool SchemasMatch(ISchema schema1, ISchema schema2) + { + if (schema1.ColumnCount != schema2.ColumnCount) + { + return false; + } + + int colLim = schema1.ColumnCount; + for (int col = 0; col < colLim; col++) + { + var type1 = schema1.GetColumnType(col); + var type2 = schema2.GetColumnType(col); + if (!type1.Equals(type2)) + { + return false; + } + } + + return true; + } + + private Delegate MarshalGetter(Func> func, Type type, int col) + { + var returnType = typeof(ValueGetter<>).MakeGenericType(type); + var meth = func.Method; + + var typedMeth = meth.GetGenericMethodDefinition().MakeGenericMethod(type); + return (Delegate)typedMeth.Invoke(func.Target, new object[] { col }); + } + + /// + /// Truncate path to the specified number of trailing directories. + /// + /// Number of directories to retain. + /// Path to truncate. + /// The resulting truncated path. + /// true if the truncation was successful. + private bool TryTruncatePath(int dirCount, string path, out string truncPath) + { + truncPath = null; + + // Remove directories that shouldn't be parsed. + var segments = PartitionedPathUtils.SplitDirectories(path); + segments = segments.Skip(segments.Count() - dirCount - 1); + + if (segments.Count() < dirCount - 1) + { + Ch.Warning($"Path {path} did not have {dirCount} directories necessary for parsing."); + return false; + } + + // Rejoin segments to create a valid path. + truncPath = String.Join(Path.DirectorySeparatorChar.ToString(), segments); + return true; + } + + + /// + /// Parse all column values from the directory path. + /// + /// The directory path to parse for name/value pairs. + /// The resulting name value pairs. + /// true if the parsing was successfull. + private bool TryParseValuesFromPath(string path, out List results) + { + Contracts.CheckNonWhiteSpace(path, nameof(path)); + + results = null; + + try + { + results = _parent._pathParser.ParseValues(path).ToList(); + return true; + } + catch (InvalidOperationException e) + { + Ch.Warning($"Could not parse column values from the path {path}. Ex: {e.Message}"); + results = null; + return false; + } + } + } + + /// + /// Get a path relative to the base path. + /// + /// A base path. + /// A list of files under the base path. + /// A realtive file path. + private string GetRelativePath(string basepath, IMultiStreamSource files) + { + Contracts.CheckNonEmpty(basepath, nameof(basepath)); + + string path = files.GetPathOrNull(0); + _host.CheckNonEmpty(path, nameof(path)); + + var relativePath = PartitionedPathUtils.MakePathRelative(basepath, path); + return relativePath; + } + + /// + /// Parse the column definitions using a path parser. + /// + /// The path to a file. + /// The resulting Columns. + private IEnumerable ParseColumns(string path) + { + return _pathParser.ParseColumns(path).ToArray(); + } + + /// + /// Get the number of directories in the file path. + /// + /// A file path. + /// The number of directories + private int GetDirectoryCount(string path) + { + return PartitionedPathUtils.SplitDirectories(path).Count() - 1; + } + } +} diff --git a/src/Microsoft.ML.Data/DataLoadSave/PartitionedPathParser.cs b/src/Microsoft.ML.Data/DataLoadSave/PartitionedPathParser.cs new file mode 100644 index 0000000000..70d8f898ab --- /dev/null +++ b/src/Microsoft.ML.Data/DataLoadSave/PartitionedPathParser.cs @@ -0,0 +1,385 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Collections.Generic; +using System.Linq; +using System.Text; +using System.Web; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Data.Utilities; +using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.Model; + +[assembly: LoadableClass(SimplePartitionedPathParser.Summary, typeof(SimplePartitionedPathParser), typeof(SimplePartitionedPathParser.Arguments), typeof(PartitionedPathParser), + SimplePartitionedPathParser.UserName, SimplePartitionedPathParser.LoadName, SimplePartitionedPathParser.ShortName)] +[assembly: LoadableClass(ParquetPartitionedPathParser.Summary, typeof(ParquetPartitionedPathParser), null, typeof(PartitionedPathParser), + ParquetPartitionedPathParser.UserName, ParquetPartitionedPathParser.LoadName, ParquetPartitionedPathParser.ShortName)] + +// This is for deserialization +[assembly: LoadableClass(SimplePartitionedPathParser.Summary, typeof(SimplePartitionedPathParser), null, typeof(SignatureLoadModel), + SimplePartitionedPathParser.UserName, SimplePartitionedPathParser.LoadName, SimplePartitionedPathParser.ShortName)] +[assembly: LoadableClass(ParquetPartitionedPathParser.Summary, typeof(ParquetPartitionedPathParser), null, typeof(SignatureLoadModel), + ParquetPartitionedPathParser.UserName, ParquetPartitionedPathParser.LoadName, ParquetPartitionedPathParser.ShortName)] + +[assembly: EntryPointModule(typeof(SimplePartitionedPathParser.Arguments))] +[assembly: EntryPointModule(typeof(ParquetPartitionedPathParserFactory))] + +namespace Microsoft.ML.Runtime.Data +{ + /// + /// Delegate signature for a partitioned path parser. + /// + public delegate void PartitionedPathParser(); + + /// + /// Supports extracting column names and values from a path string. + /// + public interface IPartitionedPathParser + { + /// + /// Extract the column definitions from a file path. + /// + /// The file path. + /// The resulting column definitions. + /// Thrown when parsing fails. + IEnumerable ParseColumns(string path); + + /// + /// Extract the column values from a file path. + /// + /// The file path. + /// The resulting column values. + /// Thrown when parsing fails. + IEnumerable ParseValues(string path); + } + + [TlcModule.ComponentKind("PartitionedPathParser")] + public interface IPartitionedPathParserFactory : IComponentFactory + { + new IPartitionedPathParser CreateComponent(IHostEnvironment env); + } + + public sealed class SimplePartitionedPathParser : IPartitionedPathParser, ICanSaveModel + { + internal const string Summary = "A simple parser that extracts directory names as column values. Column names are defined as arguments."; + internal const string UserName = "Simple Partitioned Path Parser"; + public const string LoadName = "SimplePathParser"; + public const string ShortName = "SmplPP"; + + [TlcModule.Component(Name = SimplePartitionedPathParser.LoadName, FriendlyName = SimplePartitionedPathParser.UserName, + Desc = SimplePartitionedPathParser.Summary, Alias = SimplePartitionedPathParser.ShortName)] + public class Arguments : IPartitionedPathParserFactory + { + [Argument(ArgumentType.Multiple, HelpText = "Column definitions used to override the Partitioned Path Parser. Expected with the format name:type:numeric-source, e.g. col=MyFeature:R4:1", + ShortName = "col", SortOrder = 1)] + public PartitionedFileLoader.Column[] Columns; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Data type of each column.")] + public DataKind Type = DataKind.Text; + + public IPartitionedPathParser CreateComponent(IHostEnvironment env) => new SimplePartitionedPathParser(env, this); + } + + private static VersionInfo GetVersionInfo() + { + return new VersionInfo( + modelSignature: "SMPLPARS", + verWrittenCur: 0x00010001, // Initial + verReadableCur: 0x00010001, + verWeCanReadBack: 0x00010001, + loaderSignature: LoadName); + } + + private IHost _host; + private PartitionedFileLoader.Column[] _columns; + + public SimplePartitionedPathParser(IHostEnvironment env, Arguments args) + { + _host = env.Register(LoadName); + + _columns = args.Columns; + foreach (var col in _columns) + { + if (!col.Type.HasValue) + { + col.Type = args.Type; + } + } + } + + private SimplePartitionedPathParser(IHost host, ModelLoadContext ctx) + { + Contracts.AssertValue(host); + _host = host; + _host.AssertValue(ctx); + + // ** Binary format ** + // int: number of columns + // foreach column: + // string: column representation + + int numColumns = ctx.Reader.ReadInt32(); + _host.CheckDecode(numColumns >= 0); + + _columns = new PartitionedFileLoader.Column[numColumns]; + for (int i = 0; i < numColumns; i++) + { + var column = PartitionedFileLoader.Column.Parse(ctx.LoadString()); + _host.CheckDecode(column != null); + _columns[i] = column; + } + } + + public static SimplePartitionedPathParser Create(IHostEnvironment env, ModelLoadContext ctx) + { + Contracts.CheckValue(ctx, nameof(ctx)); + IHost host = env.Register(LoadName); + ctx.CheckAtModel(GetVersionInfo()); + + return host.Apply("Loading Parser", + ch => new SimplePartitionedPathParser(host, ctx)); + } + + public void Save(ModelSaveContext ctx) + { + Contracts.CheckValue(ctx, nameof(ctx)); + ctx.SetVersionInfo(GetVersionInfo()); + + // ** Binary format ** + // int: number of columns + // foreach column: + // string: column representation + + ctx.Writer.Write(_columns.Length); + StringBuilder sb = new StringBuilder(); + foreach (var col in _columns) + { + sb.Clear(); + _host.Check(col.TryUnparse(sb)); + ctx.SaveString(sb.ToString()); + } + } + + public IEnumerable ParseColumns(string path) + { + Contracts.AssertNonEmpty(path); + + // Verify that path matches the columns expectations. + var values = ParseValues(path); + foreach (var col in _columns) + { + if (col.Source < 0 || col.Source >= values.Count()) + { + throw Contracts.Except($"Column definition {col} is outside the bounds of path {path}."); + } + } + + return _columns; + } + + public IEnumerable ParseValues(string path) + { + Contracts.AssertNonEmpty(path); + + var dirs = PartitionedPathUtils.SplitDirectories(path); + return dirs.Take(dirs.Count() - 1); // Ignore last directory which is the file name. + } + } + + [TlcModule.Component(Name = ParquetPartitionedPathParser.LoadName, FriendlyName = ParquetPartitionedPathParser.UserName, + Desc = ParquetPartitionedPathParser.Summary, Alias = ParquetPartitionedPathParser.ShortName)] + public class ParquetPartitionedPathParserFactory : IPartitionedPathParserFactory + { + public IPartitionedPathParser CreateComponent(IHostEnvironment env) => new ParquetPartitionedPathParser(); + } + + public sealed class ParquetPartitionedPathParser : IPartitionedPathParser, ICanSaveModel + { + internal const string Summary = "Extract name/value pairs from Parquet formatted directory names. Example path: Year=2018/Month=12/data1.parquet"; + internal const string UserName = "Parquet Partitioned Path Parser"; + public const string LoadName = "ParquetPathParser"; + public const string ShortName = "ParqPP"; + + private IHost _host; + private PartitionedFileLoader.Column[] _columns; + + private static VersionInfo GetVersionInfo() + { + return new VersionInfo( + modelSignature: "PARQPARS", + verWrittenCur: 0x00010001, // Initial + verReadableCur: 0x00010001, + verWeCanReadBack: 0x00010001, + loaderSignature: LoadName); + } + + public ParquetPartitionedPathParser() + { + _columns = new PartitionedFileLoader.Column[0]; + } + + private ParquetPartitionedPathParser(IHost host, ModelLoadContext ctx) + { + Contracts.AssertValue(host); + _host = host; + _host.AssertValue(ctx); + + // ** Binary format ** + // int: number of columns + // foreach column: + // string: column representation + + int numColumns = ctx.Reader.ReadInt32(); + _host.CheckDecode(numColumns >= 0); + + _columns = new PartitionedFileLoader.Column[numColumns]; + for (int i = 0; i < numColumns; i++) + { + var column = PartitionedFileLoader.Column.Parse(ctx.LoadString()); + _host.CheckDecode(column != null); + _columns[i] = column; + } + } + + public static ParquetPartitionedPathParser Create(IHostEnvironment env, ModelLoadContext ctx) + { + Contracts.CheckValue(ctx, nameof(ctx)); + IHost host = env.Register(LoadName); + ctx.CheckAtModel(GetVersionInfo()); + + return host.Apply("Loading Parser", + ch => new ParquetPartitionedPathParser(host, ctx)); + } + + public void Save(ModelSaveContext ctx) + { + Contracts.CheckValue(ctx, nameof(ctx)); + ctx.SetVersionInfo(GetVersionInfo()); + + // ** Binary format ** + // int: number of columns + // foreach column: + // string: column representation + + ctx.Writer.Write(_columns.Length); + StringBuilder sb = new StringBuilder(); + foreach (var col in _columns) + { + sb.Clear(); + _host.Check(col.TryUnparse(sb)); + ctx.SaveString(sb.ToString()); + }; + } + + public IEnumerable ParseColumns(string path) + { + if (!TryParseNames(path, out List names)) + { + throw Contracts.Except($"Failed to parse names from path {path}. Expected directory names with the format 'Name=Value'."); + } + + _columns = new PartitionedFileLoader.Column[names.Count]; + for (int i = 0; i < names.Count; i++) + { + _columns[i] = new PartitionedFileLoader.Column() + { + Name = names[i], + Source = i, + Type = DataKind.Text + }; + } + + return _columns; + } + + public IEnumerable ParseValues(string path) + { + if (!TryParseValues(path, out List values)) + { + throw Contracts.Except($"Failed to parse names from path {path}. Expected directory names with the format 'Name=Value'."); + } + + if (values.Count != _columns.Length) + { + throw Contracts.Except($"The extracted value count of {values.Count} does not match the expected Column count of {_columns.Length} for path {path}"); + } + + return values; + } + + public bool TryParseNames(string path, out List names) + { + return TryParseNamesAndValues(path, out names, out List values); + } + + public bool TryParseValues(string path, out List values) + { + return TryParseNamesAndValues(path, out List names, out values); + } + + public bool TryParseNamesAndValues(string path, out List names, out List values) + { + names = null; + values = null; + + if (string.IsNullOrEmpty(path)) + { + return false; + } + + var dirs = PartitionedPathUtils.SplitDirectories(path); + dirs = dirs.Take(dirs.Count() - 1); // Ignore last directory which is the file name. + + names = new List(dirs.Count()); + values = new List(dirs.Count()); + + foreach (var dir in dirs) + { + if (!TryParseNameValueFromDir(dir, out string name, out string value)) + { + return false; + } + + names.Add(name); + values.Add(value); + } + + return true; + } + + /// + /// Parse the name/value pair from a partitioned directory name. + /// + /// The directory name. + /// The resulting name. + /// The resulting value. + /// true if the parsing was successfull. + private static bool TryParseNameValueFromDir(string dir, out string name, out string value) + { + const char nameValueSeparator = '='; + + name = null; + value = null; + + if (string.IsNullOrEmpty(dir)) + { + return false; + } + + var nameValue = dir.Split(nameValueSeparator); + if (nameValue.Length != 2) + { + return false; + } + + name = nameValue[0]; + value = HttpUtility.UrlDecode(nameValue[1]); + + return true; + } + } +} diff --git a/src/Microsoft.ML.Data/DataView/AppendRowsDataView.cs b/src/Microsoft.ML.Data/DataView/AppendRowsDataView.cs index e7b1ff6741..6633e2535f 100644 --- a/src/Microsoft.ML.Data/DataView/AppendRowsDataView.cs +++ b/src/Microsoft.ML.Data/DataView/AppendRowsDataView.cs @@ -190,6 +190,8 @@ protected Delegate CreateGetter(int col) public ValueGetter GetGetter(int col) { Ch.Check(IsColumnActive(col), "The column must be active against the defined predicate."); + if (!(Getters[col] is ValueGetter)) + throw Ch.Except($"Invalid TValue in GetGetter: '{typeof(TValue)}'"); return Getters[col] as ValueGetter; } diff --git a/src/Microsoft.ML.Data/DataView/CompositeSchema.cs b/src/Microsoft.ML.Data/DataView/CompositeSchema.cs new file mode 100644 index 0000000000..4d387de1d5 --- /dev/null +++ b/src/Microsoft.ML.Data/DataView/CompositeSchema.cs @@ -0,0 +1,119 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Collections.Generic; +using Microsoft.ML.Runtime.Internal.Utilities; + +namespace Microsoft.ML.Runtime.Data +{ + /// + /// A convenience class for concatenating several schemas together. + /// This would be necessary when combining IDataViews through any type of combining operation, e.g. zip. + /// + internal sealed class CompositeSchema : ISchema + { + private readonly ISchema[] _sources; + + // Zero followed by cumulative column counts. Zero being used for the empty case. + private readonly int[] _cumulativeColCounts; + + public CompositeSchema(ISchema[] sources) + { + Contracts.AssertNonEmpty(sources); + _sources = sources; + _cumulativeColCounts = new int[_sources.Length + 1]; + _cumulativeColCounts[0] = 0; + + for (int i = 0; i < sources.Length; i++) + { + var schema = sources[i]; + _cumulativeColCounts[i + 1] = _cumulativeColCounts[i] + schema.ColumnCount; + } + } + + public int ColumnCount => _cumulativeColCounts[_cumulativeColCounts.Length - 1]; + + /// + /// Returns an array of input predicated for sources, corresponding to the input predicate. + /// The returned array size is equal to the number of sources, but if a given source is not needed at all, + /// the corresponding predicate will be null. + /// + public Func[] GetInputPredicates(Func predicate) + { + Contracts.AssertValue(predicate); + var result = new Func[_sources.Length]; + for (int i = 0; i < _sources.Length; i++) + { + var lastColCount = _cumulativeColCounts[i]; + result[i] = srcCol => predicate(srcCol + lastColCount); + } + + return result; + } + + /// + /// Checks whether the column index is in range. + /// + public void CheckColumnInRange(int col) + { + Contracts.CheckParam(0 <= col && col < _cumulativeColCounts[_cumulativeColCounts.Length - 1], nameof(col), "Column index out of range"); + } + + public void GetColumnSource(int col, out int srcIndex, out int srcCol) + { + CheckColumnInRange(col); + if (!_cumulativeColCounts.TryFindIndexSorted(0, _cumulativeColCounts.Length, col, out srcIndex)) + srcIndex--; + Contracts.Assert(0 <= srcIndex && srcIndex < _cumulativeColCounts.Length); + srcCol = col - _cumulativeColCounts[srcIndex]; + Contracts.Assert(0 <= srcCol && srcCol < _sources[srcIndex].ColumnCount); + } + + public bool TryGetColumnIndex(string name, out int col) + { + for (int i = _sources.Length; --i >= 0;) + { + if (_sources[i].TryGetColumnIndex(name, out col)) + { + col += _cumulativeColCounts[i]; + return true; + } + } + + col = -1; + return false; + } + + public string GetColumnName(int col) + { + GetColumnSource(col, out int dv, out int srcCol); + return _sources[dv].GetColumnName(srcCol); + } + + public ColumnType GetColumnType(int col) + { + GetColumnSource(col, out int dv, out int srcCol); + return _sources[dv].GetColumnType(srcCol); + } + + public IEnumerable> GetMetadataTypes(int col) + { + GetColumnSource(col, out int dv, out int srcCol); + return _sources[dv].GetMetadataTypes(srcCol); + } + + public ColumnType GetMetadataTypeOrNull(string kind, int col) + { + GetColumnSource(col, out int dv, out int srcCol); + return _sources[dv].GetMetadataTypeOrNull(kind, srcCol); + } + + public void GetMetadata(string kind, int col, ref TValue value) + { + GetColumnSource(col, out int dv, out int srcCol); + _sources[dv].GetMetadata(kind, srcCol, ref value); + } + } +} diff --git a/src/Microsoft.ML.Data/DataView/ZipDataView.cs b/src/Microsoft.ML.Data/DataView/ZipDataView.cs index d10e4f3223..9a7e79bab8 100644 --- a/src/Microsoft.ML.Data/DataView/ZipDataView.cs +++ b/src/Microsoft.ML.Data/DataView/ZipDataView.cs @@ -1,4 +1,4 @@ -// Licensed to the .NET Foundation under one or more agreements. +// Licensed to the .NET Foundation under one or more agreements. // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. @@ -25,7 +25,7 @@ public sealed class ZipDataView : IDataView private readonly IHost _host; private readonly IDataView[] _sources; - private readonly ZipSchema _schema; + private readonly CompositeSchema _schema; public static IDataView Create(IHostEnvironment env, IEnumerable sources) { @@ -47,7 +47,7 @@ private ZipDataView(IHost host, IDataView[] sources) _host.Assert(Utils.Size(sources) > 1); _sources = sources; - _schema = new ZipSchema(_sources.Select(x => x.Schema).ToArray()); + _schema = new CompositeSchema(_sources.Select(x => x.Schema).ToArray()); } public bool CanShuffle { get { return false; } } @@ -104,127 +104,10 @@ public IRowCursor[] GetRowCursorSet(out IRowCursorConsolidator consolidator, Fun return new IRowCursor[] { GetRowCursor(predicate, rand) }; } - /// - /// This is a result of appending several schema together. - /// - internal sealed class ZipSchema : ISchema - { - private readonly ISchema[] _sources; - // Zero followed by cumulative column counts. - private readonly int[] _cumulativeColCounts; - - public ZipSchema(ISchema[] sources) - { - Contracts.AssertNonEmpty(sources); - _sources = sources; - _cumulativeColCounts = new int[_sources.Length + 1]; - _cumulativeColCounts[0] = 0; - - for (int i = 0; i < sources.Length; i++) - { - var schema = sources[i]; - _cumulativeColCounts[i + 1] = _cumulativeColCounts[i] + schema.ColumnCount; - } - } - - /// - /// Returns an array of input predicated for sources, corresponding to the input predicate. - /// The returned array size is equal to the number of sources, but if a given source is not needed at all, - /// the corresponding predicate will be null. - /// - public Func[] GetInputPredicates(Func predicate) - { - Contracts.AssertValue(predicate); - var result = new Func[_sources.Length]; - for (int i = 0; i < _sources.Length; i++) - { - var lastColCount = _cumulativeColCounts[i]; - result[i] = srcCol => predicate(srcCol + lastColCount); - } - - return result; - } - - /// - /// Checks whether the column index is in range. - /// - public void CheckColumnInRange(int col) - { - Contracts.CheckParam(0 <= col && col < _cumulativeColCounts[_cumulativeColCounts.Length - 1], nameof(col), "Column index out of range"); - } - - public void GetColumnSource(int col, out int srcIndex, out int srcCol) - { - CheckColumnInRange(col); - if (!_cumulativeColCounts.TryFindIndexSorted(0, _cumulativeColCounts.Length, col, out srcIndex)) - srcIndex--; - Contracts.Assert(0 <= srcIndex && srcIndex < _cumulativeColCounts.Length); - srcCol = col - _cumulativeColCounts[srcIndex]; - Contracts.Assert(0 <= srcCol && srcCol < _sources[srcIndex].ColumnCount); - } - - public int ColumnCount { get { return _cumulativeColCounts[_cumulativeColCounts.Length - 1]; } } - - public bool TryGetColumnIndex(string name, out int col) - { - for (int i = _sources.Length; --i >= 0; ) - { - if (_sources[i].TryGetColumnIndex(name, out col)) - { - col += _cumulativeColCounts[i]; - return true; - } - } - - col = -1; - return false; - } - - public string GetColumnName(int col) - { - int dv; - int srcCol; - GetColumnSource(col, out dv, out srcCol); - return _sources[dv].GetColumnName(srcCol); - } - - public ColumnType GetColumnType(int col) - { - int dv; - int srcCol; - GetColumnSource(col, out dv, out srcCol); - return _sources[dv].GetColumnType(srcCol); - } - - public IEnumerable> GetMetadataTypes(int col) - { - int dv; - int srcCol; - GetColumnSource(col, out dv, out srcCol); - return _sources[dv].GetMetadataTypes(srcCol); - } - - public ColumnType GetMetadataTypeOrNull(string kind, int col) - { - int dv; - int srcCol; - GetColumnSource(col, out dv, out srcCol); - return _sources[dv].GetMetadataTypeOrNull(kind, srcCol); - } - - public void GetMetadata(string kind, int col, ref TValue value) - { - int dv; - int srcCol; - GetColumnSource(col, out dv, out srcCol); - _sources[dv].GetMetadata(kind, srcCol, ref value); - } - } - private sealed class Cursor : RootCursorBase, IRowCursor { private readonly IRowCursor[] _cursors; - private readonly ZipSchema _schema; + private readonly CompositeSchema _schema; private readonly bool[] _isColumnActive; public override long Batch { get { return 0; } } diff --git a/src/Microsoft.ML.Data/EntryPoints/EntryPointNode.cs b/src/Microsoft.ML.Data/EntryPoints/EntryPointNode.cs index 2230329c78..1ff3daee02 100644 --- a/src/Microsoft.ML.Data/EntryPoints/EntryPointNode.cs +++ b/src/Microsoft.ML.Data/EntryPoints/EntryPointNode.cs @@ -473,9 +473,9 @@ public float Cost } } - public EntryPointNode(IHostEnvironment env, ModuleCatalog moduleCatalog, RunContext context, + private EntryPointNode(IHostEnvironment env, IChannel ch, ModuleCatalog moduleCatalog, RunContext context, string id, string entryPointName, JObject inputs, JObject outputs, bool checkpoint = false, - string stageId = "", float cost = float.NaN) + string stageId = "", float cost = float.NaN, string label = null, string group = null, string weight = null) { Contracts.AssertValue(env); env.AssertNonEmpty(id); @@ -497,6 +497,7 @@ public EntryPointNode(IHostEnvironment env, ModuleCatalog moduleCatalog, RunCont _inputMap = new Dictionary(); _inputBindingMap = new Dictionary>(); _inputBuilder = new InputBuilder(_host, _entryPoint.InputType, moduleCatalog); + // REVIEW: This logic should move out of Node eventually and be delegated to // a class that can nest to handle Components with variables. if (inputs != null) @@ -508,6 +509,51 @@ public EntryPointNode(IHostEnvironment env, ModuleCatalog moduleCatalog, RunCont if (missing.Length > 0) throw _host.Except($"The following required inputs were not provided: {String.Join(", ", missing)}"); + var inputInstance = _inputBuilder.GetInstance(); + var warning = "Different {0} column specified in trainer and in macro: '{1}', '{2}'." + + " Using column '{2}'. To column use '{1}' instead, please specify this name in" + + "the trainer node arguments."; + if (!string.IsNullOrEmpty(label) && Utils.Size(_entryPoint.InputKinds) > 0 && + _entryPoint.InputKinds.Contains(typeof(CommonInputs.ITrainerInputWithLabel))) + { + var labelColField = _inputBuilder.GetFieldNameOrNull("LabelColumn"); + ch.AssertNonEmpty(labelColField); + var labelColFieldType = _inputBuilder.GetFieldTypeOrNull(labelColField); + ch.Assert(labelColFieldType == typeof(string)); + var inputLabel = inputInstance.GetType().GetField(labelColField).GetValue(inputInstance); + if (label != (string)inputLabel) + ch.Warning(warning, "label", label, inputLabel); + else + _inputBuilder.TrySetValue(labelColField, label); + } + if (!string.IsNullOrEmpty(group) && Utils.Size(_entryPoint.InputKinds) > 0 && + _entryPoint.InputKinds.Contains(typeof(CommonInputs.ITrainerInputWithGroupId))) + { + var groupColField = _inputBuilder.GetFieldNameOrNull("GroupIdColumn"); + ch.AssertNonEmpty(groupColField); + var groupColFieldType = _inputBuilder.GetFieldTypeOrNull(groupColField); + ch.Assert(groupColFieldType == typeof(string)); + var inputGroup = inputInstance.GetType().GetField(groupColField).GetValue(inputInstance); + if (group != (Optional)inputGroup) + ch.Warning(warning, "group Id", label, inputGroup); + else + _inputBuilder.TrySetValue(groupColField, label); + } + if (!string.IsNullOrEmpty(weight) && Utils.Size(_entryPoint.InputKinds) > 0 && + (_entryPoint.InputKinds.Contains(typeof(CommonInputs.ITrainerInputWithWeight)) || + _entryPoint.InputKinds.Contains(typeof(CommonInputs.IUnsupervisedTrainerWithWeight)))) + { + var weightColField = _inputBuilder.GetFieldNameOrNull("WeightColumn"); + ch.AssertNonEmpty(weightColField); + var weightColFieldType = _inputBuilder.GetFieldTypeOrNull(weightColField); + ch.Assert(weightColFieldType == typeof(string)); + var inputWeight = inputInstance.GetType().GetField(weightColField).GetValue(inputInstance); + if (weight != (Optional)inputWeight) + ch.Warning(warning, "weight", label, inputWeight); + else + _inputBuilder.TrySetValue(weightColField, label); + } + // Validate outputs. _outputHelper = new OutputHelper(_host, _entryPoint.OutputType); _outputMap = new Dictionary(); @@ -550,10 +596,15 @@ public static EntryPointNode Create( var inputBuilder = new InputBuilder(env, info.InputType, catalog); var outputHelper = new OutputHelper(env, info.OutputType); - var entryPointNode = new EntryPointNode(env, catalog, context, context.GenerateId(entryPointName), entryPointName, - inputBuilder.GetJsonObject(arguments, inputBindingMap, inputMap), - outputHelper.GetJsonObject(outputMap), checkpoint, stageId, cost); - return entryPointNode; + using (var ch = env.Start("Create EntryPointNode")) + { + var entryPointNode = new EntryPointNode(env, ch, catalog, context, context.GenerateId(entryPointName), entryPointName, + inputBuilder.GetJsonObject(arguments, inputBindingMap, inputMap), + outputHelper.GetJsonObject(outputMap), checkpoint, stageId, cost); + + ch.Done(); + return entryPointNode; + } } public static EntryPointNode Create( @@ -850,7 +901,8 @@ private object BuildParameterValue(List bindings) throw _host.ExceptNotImpl("Unsupported ParameterBinding"); } - public static List ValidateNodes(IHostEnvironment env, RunContext context, JArray nodes, ModuleCatalog moduleCatalog) + public static List ValidateNodes(IHostEnvironment env, RunContext context, JArray nodes, + ModuleCatalog moduleCatalog, string label = null, string group = null, string weight = null) { Contracts.AssertValue(env); env.AssertValue(context); @@ -890,8 +942,10 @@ public static List ValidateNodes(IHostEnvironment env, RunContex ch.Warning("Node '{0}' has unexpected fields that are ignored: {1}", id, string.Join(", ", unexpectedFields.Select(x => x.Name))); } - result.Add(new EntryPointNode(env, moduleCatalog, context, id, name, inputs, outputs, checkpoint, stageId, cost)); + result.Add(new EntryPointNode(env, ch, moduleCatalog, context, id, name, inputs, outputs, checkpoint, stageId, cost, label, group, weight)); } + + ch.Done(); } return result; } diff --git a/src/Microsoft.ML.Data/EntryPoints/InputBuilder.cs b/src/Microsoft.ML.Data/EntryPoints/InputBuilder.cs index 3ec23a01bf..e5afd8dbb5 100644 --- a/src/Microsoft.ML.Data/EntryPoints/InputBuilder.cs +++ b/src/Microsoft.ML.Data/EntryPoints/InputBuilder.cs @@ -4,10 +4,10 @@ using System; using System.Collections.Generic; -using System.Diagnostics.CodeAnalysis; using System.Linq; using System.Reflection; using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.Internal.Utilities; using Newtonsoft.Json.Linq; @@ -404,7 +404,11 @@ private static object ParseJsonValue(IExceptionContext ectx, Type type, Attribut return null; if (type.IsGenericType && (type.GetGenericTypeDefinition() == typeof(Optional<>) || type.GetGenericTypeDefinition() == typeof(Nullable<>))) + { + if (type.GetGenericTypeDefinition() == typeof(Optional<>) && value.HasValues) + value = value.Values().FirstOrDefault(); type = type.GetGenericArguments()[0]; + } if (type.IsGenericType && (type.GetGenericTypeDefinition() == typeof(Var<>))) { @@ -425,81 +429,81 @@ private static object ParseJsonValue(IExceptionContext ectx, Type type, Attribut { switch (dt) { - case TlcModule.DataKind.Bool: - return value.Value(); - case TlcModule.DataKind.String: - return value.Value(); - case TlcModule.DataKind.Char: - return value.Value(); - case TlcModule.DataKind.Enum: - if (!Enum.IsDefined(type, value.Value())) - throw ectx.Except($"Requested value '{value.Value()}' is not a member of the Enum type '{type.Name}'"); - return Enum.Parse(type, value.Value()); - case TlcModule.DataKind.Float: - if (type == typeof(double)) - return value.Value(); - else if (type == typeof(float)) - return value.Value(); - else - { + case TlcModule.DataKind.Bool: + return value.Value(); + case TlcModule.DataKind.String: + return value.Value(); + case TlcModule.DataKind.Char: + return value.Value(); + case TlcModule.DataKind.Enum: + if (!Enum.IsDefined(type, value.Value())) + throw ectx.Except($"Requested value '{value.Value()}' is not a member of the Enum type '{type.Name}'"); + return Enum.Parse(type, value.Value()); + case TlcModule.DataKind.Float: + if (type == typeof(double)) + return value.Value(); + else if (type == typeof(float)) + return value.Value(); + else + { + ectx.Assert(false); + throw ectx.ExceptNotSupp(); + } + case TlcModule.DataKind.Array: + var ja = value as JArray; + ectx.Check(ja != null, "Expected array value"); + Func makeArray = MakeArray; + return Utils.MarshalInvoke(makeArray, type.GetElementType(), ectx, ja, attributes, catalog); + case TlcModule.DataKind.Int: + if (type == typeof(long)) + return value.Value(); + if (type == typeof(int)) + return value.Value(); ectx.Assert(false); throw ectx.ExceptNotSupp(); - } - case TlcModule.DataKind.Array: - var ja = value as JArray; - ectx.Check(ja != null, "Expected array value"); - Func makeArray = MakeArray; - return Utils.MarshalInvoke(makeArray, type.GetElementType(), ectx, ja, attributes, catalog); - case TlcModule.DataKind.Int: - if (type == typeof(long)) - return value.Value(); - if (type == typeof(int)) - return value.Value(); - ectx.Assert(false); - throw ectx.ExceptNotSupp(); - case TlcModule.DataKind.UInt: - if (type == typeof(ulong)) - return value.Value(); - if (type == typeof(uint)) - return value.Value(); - ectx.Assert(false); - throw ectx.ExceptNotSupp(); - case TlcModule.DataKind.Dictionary: - ectx.Check(value is JObject, "Expected object value"); - Func makeDict = MakeDictionary; - return Utils.MarshalInvoke(makeDict, type.GetGenericArguments()[1], ectx, (JObject)value, attributes, catalog); - case TlcModule.DataKind.Component: - var jo = value as JObject; - ectx.Check(jo != null, "Expected object value"); - // REVIEW: consider accepting strings alone. - var jName = jo[FieldNames.Name]; - ectx.Check(jName != null, "Field '" + FieldNames.Name + "' is required for component."); - ectx.Check(jName is JValue, "Expected '" + FieldNames.Name + "' field to be a string."); - var name = jName.Value(); - ectx.Check(jo[FieldNames.Settings] == null || jo[FieldNames.Settings] is JObject, - "Expected '" + FieldNames.Settings + "' field to be an object"); - return GetComponentJson(ectx, type, name, jo[FieldNames.Settings] as JObject, catalog); - default: - var settings = value as JObject; - ectx.Check(settings != null, "Expected object value"); - var inputBuilder = new InputBuilder(ectx, type, catalog); - - if (inputBuilder._fields.Length == 0) - throw ectx.Except($"Unsupported input type: {dt}"); - - if (settings != null) - { - foreach (var pair in settings) + case TlcModule.DataKind.UInt: + if (type == typeof(ulong)) + return value.Value(); + if (type == typeof(uint)) + return value.Value(); + ectx.Assert(false); + throw ectx.ExceptNotSupp(); + case TlcModule.DataKind.Dictionary: + ectx.Check(value is JObject, "Expected object value"); + Func makeDict = MakeDictionary; + return Utils.MarshalInvoke(makeDict, type.GetGenericArguments()[1], ectx, (JObject)value, attributes, catalog); + case TlcModule.DataKind.Component: + var jo = value as JObject; + ectx.Check(jo != null, "Expected object value"); + // REVIEW: consider accepting strings alone. + var jName = jo[FieldNames.Name]; + ectx.Check(jName != null, "Field '" + FieldNames.Name + "' is required for component."); + ectx.Check(jName is JValue, "Expected '" + FieldNames.Name + "' field to be a string."); + var name = jName.Value(); + ectx.Check(jo[FieldNames.Settings] == null || jo[FieldNames.Settings] is JObject, + "Expected '" + FieldNames.Settings + "' field to be an object"); + return GetComponentJson(ectx, type, name, jo[FieldNames.Settings] as JObject, catalog); + default: + var settings = value as JObject; + ectx.Check(settings != null, "Expected object value"); + var inputBuilder = new InputBuilder(ectx, type, catalog); + + if (inputBuilder._fields.Length == 0) + throw ectx.Except($"Unsupported input type: {dt}"); + + if (settings != null) { - if (!inputBuilder.TrySetValueJson(pair.Key, pair.Value)) - throw ectx.Except($"Unexpected value for component '{type}', field '{pair.Key}': '{pair.Value}'"); + foreach (var pair in settings) + { + if (!inputBuilder.TrySetValueJson(pair.Key, pair.Value)) + throw ectx.Except($"Unexpected value for component '{type}', field '{pair.Key}': '{pair.Value}'"); + } } - } - var missing = inputBuilder.GetMissingValues().ToArray(); - if (missing.Length > 0) - throw ectx.Except($"The following required inputs were not provided for component '{type}': {string.Join(", ", missing)}"); - return inputBuilder.GetInstance(); + var missing = inputBuilder.GetMissingValues().ToArray(); + if (missing.Length > 0) + throw ectx.Except($"The following required inputs were not provided for component '{type}': {string.Join(", ", missing)}"); + return inputBuilder.GetInstance(); } } catch (FormatException ex) @@ -832,35 +836,35 @@ public static class SweepableDiscreteParam public static class PipelineSweeperSupportedMetrics { public new static string ToString() => "SupportedMetric"; - public const string Auc = "AUC"; - public const string AccuracyMicro = "AccuracyMicro"; - public const string AccuracyMacro = "AccuracyMacro"; - public const string F1 = "F1"; - public const string AuPrc = "AUPRC"; - public const string TopKAccuracy = "TopKAccuracy"; - public const string L1 = "L1"; - public const string L2 = "L2"; - public const string Rms = "RMS"; - public const string LossFn = "LossFn"; - public const string RSquared = "RSquared"; - public const string LogLoss = "LogLoss"; - public const string LogLossReduction = "LogLossReduction"; - public const string Ndcg = "NDCG"; - public const string Dcg = "DCG"; - public const string PositivePrecision = "PositivePrecision"; - public const string PositiveRecall = "PositiveRecall"; - public const string NegativePrecision = "NegativePrecision"; - public const string NegativeRecall = "NegativeRecall"; - public const string DrAtK = "DrAtK"; - public const string DrAtPFpr = "DrAtPFpr"; - public const string DrAtNumPos = "DrAtNumPos"; - public const string NumAnomalies = "NumAnomalies"; - public const string ThreshAtK = "ThreshAtK"; - public const string ThreshAtP = "ThreshAtP"; - public const string ThreshAtNumPos = "ThreshAtNumPos"; - public const string Nmi = "NMI"; - public const string AvgMinScore = "AvgMinScore"; - public const string Dbi = "DBI"; + public const string Auc = BinaryClassifierEvaluator.Auc; + public const string AccuracyMicro = Data.MultiClassClassifierEvaluator.AccuracyMicro; + public const string AccuracyMacro = MultiClassClassifierEvaluator.AccuracyMacro; + public const string F1 = BinaryClassifierEvaluator.F1; + public const string AuPrc = BinaryClassifierEvaluator.AuPrc; + public const string TopKAccuracy = MultiClassClassifierEvaluator.TopKAccuracy; + public const string L1 = RegressionLossEvaluatorBase.L1; + public const string L2 = RegressionLossEvaluatorBase.L2; + public const string Rms = RegressionLossEvaluatorBase.Rms; + public const string LossFn = RegressionLossEvaluatorBase.Loss; + public const string RSquared = RegressionLossEvaluatorBase.RSquared; + public const string LogLoss = BinaryClassifierEvaluator.LogLoss; + public const string LogLossReduction = BinaryClassifierEvaluator.LogLossReduction; + public const string Ndcg = RankerEvaluator.Ndcg; + public const string Dcg = RankerEvaluator.Dcg; + public const string PositivePrecision = BinaryClassifierEvaluator.PosPrecName; + public const string PositiveRecall = BinaryClassifierEvaluator.PosRecallName; + public const string NegativePrecision = BinaryClassifierEvaluator.NegPrecName; + public const string NegativeRecall = BinaryClassifierEvaluator.NegRecallName; + public const string DrAtK = AnomalyDetectionEvaluator.OverallMetrics.DrAtK; + public const string DrAtPFpr = AnomalyDetectionEvaluator.OverallMetrics.DrAtPFpr; + public const string DrAtNumPos = AnomalyDetectionEvaluator.OverallMetrics.DrAtNumPos; + public const string NumAnomalies = AnomalyDetectionEvaluator.OverallMetrics.NumAnomalies; + public const string ThreshAtK = AnomalyDetectionEvaluator.OverallMetrics.ThreshAtK; + public const string ThreshAtP = AnomalyDetectionEvaluator.OverallMetrics.ThreshAtP; + public const string ThreshAtNumPos = AnomalyDetectionEvaluator.OverallMetrics.ThreshAtNumPos; + public const string Nmi = ClusteringEvaluator.Nmi; + public const string AvgMinScore = ClusteringEvaluator.AvgMinScore; + public const string Dbi = ClusteringEvaluator.Dbi; } } } diff --git a/src/Microsoft.ML.Data/EntryPoints/ScoreModel.cs b/src/Microsoft.ML.Data/EntryPoints/ScoreModel.cs index de0a8208b0..96ce0acac9 100644 --- a/src/Microsoft.ML.Data/EntryPoints/ScoreModel.cs +++ b/src/Microsoft.ML.Data/EntryPoints/ScoreModel.cs @@ -72,11 +72,8 @@ public static Output Score(IHostEnvironment env, Input input) host.CheckValue(input, nameof(input)); EntryPointUtils.CheckInputArgs(host, input); - - IPredictor predictor; var inputData = input.Data; - RoleMappedData data; - input.PredictorModel.PrepareData(host, inputData, out data, out predictor); + input.PredictorModel.PrepareData(host, inputData, out RoleMappedData data, out IPredictor predictor); IDataView scoredPipe; using (var ch = host.Start("Creating scoring pipeline")) diff --git a/src/Microsoft.ML.Data/Evaluators/ClusteringEvaluator.cs b/src/Microsoft.ML.Data/Evaluators/ClusteringEvaluator.cs index 9a8d0d9fd4..907760649f 100644 --- a/src/Microsoft.ML.Data/Evaluators/ClusteringEvaluator.cs +++ b/src/Microsoft.ML.Data/Evaluators/ClusteringEvaluator.cs @@ -38,9 +38,9 @@ public sealed class Arguments public const string LoadName = "ClusteringEvaluator"; - private const string Nmi = "NMI"; - private const string AvgMinScore = "AvgMinScore"; - private const string Dbi = "DBI"; + public const string Nmi = "NMI"; + public const string AvgMinScore = "AvgMinScore"; + public const string Dbi = "DBI"; private readonly bool _calculateDbi; diff --git a/src/Microsoft.ML.Data/Evaluators/EvaluatorUtils.cs b/src/Microsoft.ML.Data/Evaluators/EvaluatorUtils.cs index 0e2de21530..4b2655ee5b 100644 --- a/src/Microsoft.ML.Data/Evaluators/EvaluatorUtils.cs +++ b/src/Microsoft.ML.Data/Evaluators/EvaluatorUtils.cs @@ -552,14 +552,15 @@ public static IDataView AddFoldIndex(IHostEnvironment env, IDataView input, int } } - private static int[][] MapKeys(ISchema[] schemas, string columnName, bool isVec, - out int[] indices, out Dictionary reconciledKeyNames) + private static int[][] MapKeys(ISchema[] schemas, string columnName, bool isVec, + int[] indices, Dictionary reconciledKeyNames) { + Contracts.AssertValue(indices); + Contracts.AssertValue(reconciledKeyNames); + var dvCount = schemas.Length; var keyValueMappers = new int[dvCount][]; - var keyNamesCur = default(VBuffer); - indices = new int[dvCount]; - reconciledKeyNames = new Dictionary(); + var keyNamesCur = default(VBuffer); for (int i = 0; i < dvCount; i++) { var schema = schemas[i]; @@ -567,10 +568,11 @@ private static int[][] MapKeys(ISchema[] schemas, string columnName, bool isVec, throw Contracts.Except($"Schema number {i} does not contain column '{columnName}'"); var type = schema.GetColumnType(indices[i]); + var keyValueType = schema.GetMetadataTypeOrNull(MetadataUtils.Kinds.KeyValues, indices[i]); if (type.IsVector != isVec) throw Contracts.Except($"Column '{columnName}' in schema number {i} does not have the correct type"); - if (!schema.HasKeyNames(indices[i], type.ItemType.KeyCount)) - throw Contracts.Except($"Column '{columnName}' in schema number {i} does not have text key values"); + if (keyValueType == null || keyValueType.ItemType.RawType != typeof(T)) + throw Contracts.Except($"Column '{columnName}' in schema number {i} does not have the correct type of key values"); if (!type.ItemType.IsKey || type.ItemType.RawKind != DataKind.U4) throw Contracts.Except($"Column '{columnName}' must be a U4 key type, but is '{type.ItemType}'"); @@ -580,7 +582,7 @@ private static int[][] MapKeys(ISchema[] schemas, string columnName, bool isVec, foreach (var kvp in keyNamesCur.Items(true)) { var key = kvp.Key; - var name = kvp.Value; + var name = new DvText(kvp.Value.ToString()); if (!reconciledKeyNames.ContainsKey(name)) reconciledKeyNames[name] = reconciledKeyNames.Count; keyValueMappers[i][key] = reconciledKeyNames[name]; @@ -595,17 +597,18 @@ private static int[][] MapKeys(ISchema[] schemas, string columnName, bool isVec, /// data view, with the union of the key values as the new key values. For each data view, the value in the output column is the value /// corresponding to the key value in the original column. /// - public static void ReconcileKeyValues(IHostEnvironment env, IDataView[] views, string columnName) + public static void ReconcileKeyValues(IHostEnvironment env, IDataView[] views, string columnName, ColumnType keyValueType) { Contracts.CheckNonEmpty(views, nameof(views)); Contracts.CheckNonEmpty(columnName, nameof(columnName)); var dvCount = views.Length; - Dictionary keyNames; - int[] indices; // Create mappings from the original key types to the reconciled key type. - var keyValueMappers = MapKeys(views.Select(view => view.Schema).ToArray(), columnName, false, out indices, out keyNames); + var indices = new int[dvCount]; + var keyNames = new Dictionary(); + // We use MarshalInvoke so that we can call MapKeys with the correct generic: keyValueType.RawType. + var keyValueMappers = Utils.MarshalInvoke(MapKeys, keyValueType.RawType, views.Select(view => view.Schema).ToArray(), columnName, false, indices, keyNames); var keyType = new KeyType(DataKind.U4, 0, keyNames.Count); var keyNamesVBuffer = new VBuffer(keyNames.Count, keyNames.Keys.ToArray()); ValueGetter> keyValueGetter = @@ -629,20 +632,51 @@ public static void ReconcileKeyValues(IHostEnvironment env, IDataView[] views, s } } + /// + /// This method takes an array of data views and a specified input key column, and adds a new output column to each of the data views. + /// First, we find the union set of the key values in the different data views. Next we define a new key column for each + /// data view, with the union of the key values as the new key values. For each data view, the value in the output column is the value + /// corresponding to the key value in the original column. + /// + public static void ReconcileKeyValuesWithNoNames(IHostEnvironment env, IDataView[] views, string columnName, int keyCount) + { + Contracts.CheckNonEmpty(views, nameof(views)); + Contracts.CheckNonEmpty(columnName, nameof(columnName)); + + var keyType = new KeyType(DataKind.U4, 0, keyCount); + + // For each input data view, create the reconciled key column by wrapping it in a LambdaColumnMapper. + for (int i = 0; i < views.Length; i++) + { + if (!views[i].Schema.TryGetColumnIndex(columnName, out var index)) + throw env.Except($"Data view {i} doesn't contain a column '{columnName}'"); + ValueMapper mapper = + (ref uint src, ref uint dst) => + { + if (src == 0 || src > keyCount) + dst = 0; + else + dst = src + 1; + }; + views[i] = LambdaColumnMapper.Create(env, "ReconcileKeyValues", views[i], columnName, columnName, + views[i].Schema.GetColumnType(index), keyType, mapper); + } + } + /// /// This method is similar to , but it reconciles the key values over vector /// input columns. /// - public static void ReconcileVectorKeyValues(IHostEnvironment env, IDataView[] views, string columnName) + public static void ReconcileVectorKeyValues(IHostEnvironment env, IDataView[] views, string columnName, ColumnType keyValueType) { Contracts.CheckNonEmpty(views, nameof(views)); Contracts.CheckNonEmpty(columnName, nameof(columnName)); var dvCount = views.Length; - Dictionary keyNames; - int[] columnIndices; - var keyValueMappers = MapKeys(views.Select(view => view.Schema).ToArray(), columnName, true, out columnIndices, out keyNames); + var keyNames = new Dictionary(); + var columnIndices = new int[dvCount]; + var keyValueMappers = Utils.MarshalInvoke(MapKeys, keyValueType.RawType, views.Select(view => view.Schema).ToArray(), columnName, true, columnIndices, keyNames); var keyType = new KeyType(DataKind.U4, 0, keyNames.Count); var keyNamesVBuffer = new VBuffer(keyNames.Count, keyNames.Keys.ToArray()); ValueGetter> keyValueGetter = @@ -736,7 +770,7 @@ public static IDataView[] ConcatenatePerInstanceDataViews(IHostEnvironment env, var foldDataViews = perInstance.Select(getPerInstance).ToArray(); if (collate) { - var combined = AppendPerInstanceDataViews(env, foldDataViews, out variableSizeVectorColumnNames); + var combined = AppendPerInstanceDataViews(env, perInstance[0].Schema.Label?.Name, foldDataViews, out variableSizeVectorColumnNames); return new[] { combined }; } else @@ -767,7 +801,8 @@ public static IDataView ConcatenateOverallMetrics(IHostEnvironment env, IDataVie return AppendRowsDataView.Create(env, overallList[0].Schema, overallList.ToArray()); } - private static IDataView AppendPerInstanceDataViews(IHostEnvironment env, IEnumerable foldDataViews, out string[] variableSizeVectorColumnNames) + private static IDataView AppendPerInstanceDataViews(IHostEnvironment env, string labelColName, + IEnumerable foldDataViews, out string[] variableSizeVectorColumnNames) { Contracts.AssertValue(env); env.AssertValue(foldDataViews); @@ -776,7 +811,9 @@ private static IDataView AppendPerInstanceDataViews(IHostEnvironment env, IEnume // This is a dictionary from the column name to its vector size. var vectorSizes = new Dictionary(); var firstDvSlotNames = new Dictionary>(); - var firstDvKeyColumns = new List(); + ColumnType labelColKeyValuesType = null; + var firstDvKeyWithNamesColumns = new List(); + var firstDvKeyNoNamesColumns = new Dictionary(); var firstDvVectorKeyColumns = new List(); var variableSizeVectorColumnNamesList = new List(); var list = new List(); @@ -822,10 +859,20 @@ private static IDataView AppendPerInstanceDataViews(IHostEnvironment env, IEnume else vectorSizes.Add(name, type.VectorSize); } - else if (dvNumber == 0 && dv.Schema.HasKeyNames(i, type.KeyCount)) + else if (dvNumber == 0 && name == labelColName) { // The label column can be a key. Reconcile the key values, and wrap with a KeyToValue transform. - firstDvKeyColumns.Add(name); + labelColKeyValuesType = dv.Schema.GetMetadataTypeOrNull(MetadataUtils.Kinds.KeyValues, i); + } + else if (dvNumber == 0 && dv.Schema.HasKeyNames(i, type.KeyCount)) + firstDvKeyWithNamesColumns.Add(name); + else if (type.KeyCount > 0 && name != labelColName) + { + // For any other key column (such as GroupId) we do not reconcile the key values, we only convert to U4. + if (!firstDvKeyNoNamesColumns.ContainsKey(name)) + firstDvKeyNoNamesColumns[name] = type.KeyCount; + if (firstDvKeyNoNamesColumns[name] < type.KeyCount) + firstDvKeyNoNamesColumns[name] = type.KeyCount; } } var idv = dv; @@ -839,26 +886,34 @@ private static IDataView AppendPerInstanceDataViews(IHostEnvironment env, IEnume list.Add(idv); dvNumber++; } - variableSizeVectorColumnNames = variableSizeVectorColumnNamesList.ToArray(); - if (variableSizeVectorColumnNamesList.Count == 0 && firstDvKeyColumns.Count == 0) - return AppendRowsDataView.Create(env, null, list.ToArray()); var views = list.ToArray(); - foreach (var keyCol in firstDvKeyColumns) - ReconcileKeyValues(env, views, keyCol); + foreach (var keyCol in firstDvKeyWithNamesColumns) + ReconcileKeyValues(env, views, keyCol, TextType.Instance); + if (labelColKeyValuesType != null) + ReconcileKeyValues(env, views, labelColName, labelColKeyValuesType.ItemType); + foreach (var keyCol in firstDvKeyNoNamesColumns) + ReconcileKeyValuesWithNoNames(env, views, keyCol.Key, keyCol.Value); foreach (var vectorKeyCol in firstDvVectorKeyColumns) - ReconcileVectorKeyValues(env, views, vectorKeyCol); + ReconcileVectorKeyValues(env, views, vectorKeyCol, TextType.Instance); Func keyToValue = (idv, i) => { - foreach (var keyCol in firstDvKeyColumns.Concat(firstDvVectorKeyColumns)) + foreach (var keyCol in firstDvVectorKeyColumns.Prepend(labelColName)) { + if (keyCol == labelColName && labelColKeyValuesType == null) + continue; idv = new KeyToValueTransform(env, new KeyToValueTransform.Arguments() { Column = new[] { new KeyToValueTransform.Column() { Name = keyCol }, } }, idv); var hidden = FindHiddenColumns(idv.Schema, keyCol); idv = new ChooseColumnsByIndexTransform(env, new ChooseColumnsByIndexTransform.Arguments() { Drop = true, Index = hidden.ToArray() }, idv); } + foreach (var keyCol in firstDvKeyNoNamesColumns) + { + var hidden = FindHiddenColumns(idv.Schema, keyCol.Key); + idv = new ChooseColumnsByIndexTransform(env, new ChooseColumnsByIndexTransform.Arguments() { Drop = true, Index = hidden.ToArray() }, idv); + } return idv; }; diff --git a/src/Microsoft.ML.Data/Evaluators/MulticlassClassifierEvaluator.cs b/src/Microsoft.ML.Data/Evaluators/MulticlassClassifierEvaluator.cs index 5507176fb4..94c920c3c6 100644 --- a/src/Microsoft.ML.Data/Evaluators/MulticlassClassifierEvaluator.cs +++ b/src/Microsoft.ML.Data/Evaluators/MulticlassClassifierEvaluator.cs @@ -256,6 +256,8 @@ public Double MacroAvgAccuracy { get { + if (_numInstances == 0) + return 0; Double macroAvgAccuracy = 0; int countOfNonEmptyClasses = 0; for (int i = 0; i < _numClasses; ++i) @@ -267,8 +269,7 @@ public Double MacroAvgAccuracy } } - Contracts.Assert(countOfNonEmptyClasses > 0); - return macroAvgAccuracy / countOfNonEmptyClasses; + return countOfNonEmptyClasses > 0 ? macroAvgAccuracy / countOfNonEmptyClasses : 0; } } diff --git a/src/Microsoft.ML.Data/Evaluators/RankerEvaluator.cs b/src/Microsoft.ML.Data/Evaluators/RankerEvaluator.cs index a383f835fd..ae9c2a8594 100644 --- a/src/Microsoft.ML.Data/Evaluators/RankerEvaluator.cs +++ b/src/Microsoft.ML.Data/Evaluators/RankerEvaluator.cs @@ -43,9 +43,9 @@ public sealed class Arguments public const string LoadName = "RankingEvaluator"; - private const string Ndcg = "NDCG"; - private const string Dcg = "DCG"; - private const string MaxDcg = "MaxDCG"; + public const string Ndcg = "NDCG"; + public const string Dcg = "DCG"; + public const string MaxDcg = "MaxDCG"; /// /// The ranking evaluator outputs a data view by this name, which contains metrics aggregated per group. diff --git a/src/Microsoft.ML.Data/Model/Onnx/OnnxContext.cs b/src/Microsoft.ML.Data/Model/Onnx/OnnxContext.cs index 4681f6943b..46c083173f 100644 --- a/src/Microsoft.ML.Data/Model/Onnx/OnnxContext.cs +++ b/src/Microsoft.ML.Data/Model/Onnx/OnnxContext.cs @@ -11,7 +11,7 @@ namespace Microsoft.ML.Runtime.Model.Onnx { /// - /// A context for defining a lotusIR output. + /// A context for defining a ONNX output. /// public sealed class OnnxContext { @@ -23,10 +23,14 @@ public sealed class OnnxContext private readonly HashSet _variableMap; private readonly HashSet _nodeNames; private readonly string _name; + private readonly string _producerName; private readonly IHost _host; private readonly string _domain; + private readonly string _producerVersion; + private readonly long _modelVersion; - public OnnxContext(IHostEnvironment env, string name, string domain) + public OnnxContext(IHostEnvironment env, string name, string producerName, + string producerVersion, long modelVersion, string domain) { Contracts.CheckValue(env, nameof(env)); Contracts.CheckValue(name, nameof(name)); @@ -41,6 +45,9 @@ public OnnxContext(IHostEnvironment env, string name, string domain) _variableMap = new HashSet(); _nodeNames = new HashSet(); _name = name; + _producerName = producerName; + _producerVersion = producerVersion; + _modelVersion = modelVersion; _domain = domain; } @@ -234,6 +241,6 @@ public void AddInputVariable(ColumnType type, string colName) /// Makes the ONNX model based on the context. /// public ModelProto MakeModel() - => OnnxUtils.MakeModel(_nodes, _name, _name, _domain, _inputs, _outputs, _intermediateValues); + => OnnxUtils.MakeModel(_nodes, _producerName, _name, _domain, _producerVersion, _modelVersion, _inputs, _outputs, _intermediateValues); } } diff --git a/src/Microsoft.ML.Data/Model/Onnx/OnnxUtils.cs b/src/Microsoft.ML.Data/Model/Onnx/OnnxUtils.cs index 667787c990..8ad9f40c20 100644 --- a/src/Microsoft.ML.Data/Model/Onnx/OnnxUtils.cs +++ b/src/Microsoft.ML.Data/Model/Onnx/OnnxUtils.cs @@ -153,7 +153,7 @@ private static AttributeProto MakeAttribute(string key, IEnumerable private static AttributeProto MakeAttribute(string key, bool value) => MakeAttribute(key, value ? 1 : 0); - public static NodeProto MakeNode(string opType, List inputs, List outputs, string name) + public static NodeProto MakeNode(string opType, List inputs, List outputs, string name, string domain = null) { Contracts.CheckNonEmpty(opType, nameof(opType)); Contracts.CheckValue(inputs, nameof(inputs)); @@ -165,7 +165,7 @@ public static NodeProto MakeNode(string opType, List inputs, List nodes, string producerName, string name, string domain, List inputs, + public static ModelProto MakeModel(List nodes, string producerName, string name, + string domain, string producerVersion, long modelVersion, List inputs, List outputs, List intermediateValues) { Contracts.CheckValue(nodes, nameof(nodes)); @@ -261,10 +262,16 @@ public static ModelProto MakeModel(List nodes, string producerName, s Contracts.CheckNonEmpty(producerName, nameof(producerName)); Contracts.CheckNonEmpty(name, nameof(name)); Contracts.CheckNonEmpty(domain, nameof(domain)); + Contracts.CheckNonEmpty(producerVersion, nameof(producerVersion)); var model = new ModelProto(); model.Domain = domain; model.ProducerName = producerName; + model.ProducerVersion = producerVersion; + model.IrVersion = (long)UniversalModelFormat.Onnx.Version.IrVersion; + model.ModelVersion = modelVersion; + model.OpsetImport.Add(new OperatorSetIdProto() { Domain = "ai.onnx.ml", Version = 1 }); + model.OpsetImport.Add(new OperatorSetIdProto() { Domain = "ai.onnx", Version = 6 }); model.Graph = new GraphProto(); var graph = model.Graph; graph.Node.Add(nodes); diff --git a/src/Microsoft.ML.Data/Model/Onnx/SaveOnnxCommand.cs b/src/Microsoft.ML.Data/Model/Onnx/SaveOnnxCommand.cs index 730916b031..d2dfc93fde 100644 --- a/src/Microsoft.ML.Data/Model/Onnx/SaveOnnxCommand.cs +++ b/src/Microsoft.ML.Data/Model/Onnx/SaveOnnxCommand.cs @@ -9,13 +9,17 @@ using Microsoft.ML.Runtime.Command; using Microsoft.ML.Runtime.CommandLine; using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.EntryPoints; using Microsoft.ML.Runtime.Internal.Utilities; using Microsoft.ML.Runtime.Model.Onnx; +using Microsoft.ML.Runtime.UniversalModelFormat.Onnx; using Newtonsoft.Json; [assembly: LoadableClass(SaveOnnxCommand.Summary, typeof(SaveOnnxCommand), typeof(SaveOnnxCommand.Arguments), typeof(SignatureCommand), "Save ONNX", "SaveOnnx", DocName = "command/SaveOnnx.md")] +[assembly: LoadableClass(typeof(void), typeof(SaveOnnxCommand), null, typeof(SignatureEntryPointModule), "SaveOnnx")] + namespace Microsoft.ML.Runtime.Model.Onnx { public sealed class SaveOnnxCommand : DataCommand.ImplBase @@ -37,14 +41,23 @@ public sealed class Arguments : DataCommand.ArgumentsBase [Argument(ArgumentType.AtMostOnce, HelpText = "The 'domain' property in the output ONNX.", NullName = "", SortOrder = 4)] public string Domain; - [Argument(ArgumentType.AtMostOnce, HelpText = "Comma delimited list of input column names to drop", ShortName = "idrop", SortOrder = 5)] + [Argument(ArgumentType.AtMostOnce, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, HelpText = "Comma delimited list of input column names to drop", ShortName = "idrop", SortOrder = 5)] public string InputsToDrop; - [Argument(ArgumentType.AtMostOnce, HelpText = "Comma delimited list of output column names to drop", ShortName = "odrop", SortOrder = 6)] + [Argument(ArgumentType.AtMostOnce, Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly, HelpText = "Array of input column names to drop", Name = nameof(InputsToDrop), SortOrder = 6)] + public string[] InputsToDropArray; + + [Argument(ArgumentType.AtMostOnce, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, HelpText = "Comma delimited list of output column names to drop", ShortName = "odrop", SortOrder = 7)] public string OutputsToDrop; - [Argument(ArgumentType.AtMostOnce, HelpText = "Whether we should attempt to load the predictor and attach the scorer to the pipeline if one is present.", ShortName = "pred", SortOrder = 7)] + [Argument(ArgumentType.AtMostOnce, Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly, HelpText = "Array of output column names to drop", Name = nameof(OutputsToDrop), SortOrder = 8)] + public string[] OutputsToDropArray; + + [Argument(ArgumentType.AtMostOnce, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly, HelpText = "Whether we should attempt to load the predictor and attach the scorer to the pipeline if one is present.", ShortName = "pred", SortOrder = 9)] public bool? LoadPredictor; + + [Argument(ArgumentType.Required, Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly, HelpText = "Model that needs to be converted to ONNX format.", SortOrder = 10)] + public ITransformModel Model; } private readonly string _outputModelPath; @@ -54,6 +67,10 @@ public sealed class Arguments : DataCommand.ArgumentsBase private readonly bool? _loadPredictor; private readonly HashSet _inputsToDrop; private readonly HashSet _outputsToDrop; + private readonly ITransformModel _model; + private const string ProducerName = "ML.NET"; + private const string ProducerVersion = "0.2.0.0000"; + private const long ModelVersion = 0; public SaveOnnxCommand(IHostEnvironment env, Arguments args) : base(env, args, LoadName) @@ -68,16 +85,18 @@ public SaveOnnxCommand(IHostEnvironment env, Arguments args) _name = args.Name; _loadPredictor = args.LoadPredictor; - _inputsToDrop = CreateDropMap(args.InputsToDrop); - _outputsToDrop = CreateDropMap(args.OutputsToDrop); + _inputsToDrop = CreateDropMap(args.InputsToDropArray ?? args.InputsToDrop?.Split(',')); + _outputsToDrop = CreateDropMap(args.OutputsToDropArray ?? args.OutputsToDrop?.Split(',')); _domain = args.Domain; + _model = args.Model; } - private static HashSet CreateDropMap(string toDrop) + private static HashSet CreateDropMap(string[] toDrop) { - if (string.IsNullOrWhiteSpace(toDrop)) + if (toDrop == null) return new HashSet(); - return new HashSet(toDrop.Split(',')); + + return new HashSet(toDrop); } public override void Run() @@ -115,29 +134,37 @@ private void GetPipe(IChannel ch, IDataView end, out IDataView source, out IData private void Run(IChannel ch) { - IDataLoader loader; - IPredictor rawPred; - RoleMappedSchema trainSchema; + IDataLoader loader = null; + IPredictor rawPred = null; + IDataView view; + RoleMappedSchema trainSchema = null; - if (string.IsNullOrEmpty(Args.InputModelFile)) + if (_model == null) { - loader = CreateLoader(); - rawPred = null; - trainSchema = null; - Host.CheckUserArg(Args.LoadPredictor != true, nameof(Args.LoadPredictor), - "Cannot be set to true unless " + nameof(Args.InputModelFile) + " is also specifified."); + if (string.IsNullOrEmpty(Args.InputModelFile)) + { + loader = CreateLoader(); + rawPred = null; + trainSchema = null; + Host.CheckUserArg(Args.LoadPredictor != true, nameof(Args.LoadPredictor), + "Cannot be set to true unless " + nameof(Args.InputModelFile) + " is also specifified."); + } + else + LoadModelObjects(ch, _loadPredictor, out rawPred, true, out trainSchema, out loader); + + view = loader; } else - LoadModelObjects(ch, _loadPredictor, out rawPred, true, out trainSchema, out loader); + view = _model.Apply(Host, new EmptyDataView(Host, _model.InputSchema)); // Get the transform chain. IDataView source; IDataView end; LinkedList transforms; - GetPipe(ch, loader, out source, out end, out transforms); + GetPipe(ch, view, out source, out end, out transforms); Host.Assert(transforms.Count == 0 || transforms.Last.Value == end); - var ctx = new OnnxContext(Host, _name, _domain); + var ctx = new OnnxContext(Host, _name, ProducerName, ProducerVersion, ModelVersion, _domain); // If we have a predictor, try to get the scorer for it. if (rawPred != null) { @@ -228,10 +255,24 @@ private void Run(IChannel ch) if (!string.IsNullOrWhiteSpace(Args.OutputModelFile)) { + Contracts.Assert(loader != null); + ch.Trace("Saving the data pipe"); // Should probably include "end"? SaveLoader(loader, Args.OutputModelFile); } } + + public sealed class Output + { + } + + [TlcModule.EntryPoint(Name = "Models.OnnxConverter", Desc = "Converts the model to ONNX format.", UserName = "ONNX Converter.")] + public static Output Apply(IHostEnvironment env, Arguments input) + { + new SaveOnnxCommand(env, input).Run(); + return new Output(); + } + } } diff --git a/src/Microsoft.ML.Data/Model/Repository.cs b/src/Microsoft.ML.Data/Model/Repository.cs index 7556cc970e..b19fbc8eba 100644 --- a/src/Microsoft.ML.Data/Model/Repository.cs +++ b/src/Microsoft.ML.Data/Model/Repository.cs @@ -6,7 +6,6 @@ using System.Collections.Generic; using System.IO; using System.IO.Compression; -using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.Internal.Utilities; namespace Microsoft.ML.Runtime.Model @@ -73,7 +72,7 @@ public void Dispose() } } - // These are the open entries that may contain streams into our _dirTemp. + // These are the open entries that may contain streams into our DirTemp. private List _open; private bool _disposed; @@ -108,19 +107,37 @@ internal Repository(bool needDir, IExceptionContext ectx) PathMap = new Dictionary(); _open = new List(); if (needDir) - { - DirTemp = GetTempPath(); - Directory.CreateDirectory(DirTemp); - } + DirTemp = GetShortTempDir(); else GC.SuppressFinalize(this); } - // REVIEW: This should use host environment functionality. - private static string GetTempPath() + private static string GetShortTempDir() + { + var rnd = RandomUtils.Create(); + string path; + do + { + path = Path.Combine(Path.GetTempPath(), "TLC_" + rnd.Next().ToString("X")); + path = Path.GetFullPath(path); + Directory.CreateDirectory(path); + } + while (!EnsureDirectory(path)); + return path; + } + + private static bool EnsureDirectory(string path) { - Guid guid = Guid.NewGuid(); - return Path.GetFullPath(Path.Combine(Path.GetTempPath(), "TLC_" + guid.ToString())); + path = Path.GetFullPath(Path.Combine(path, ".lock")); + try + { + using (var stream = new FileStream(path, FileMode.CreateNew)) + return true; + } + catch + { + return false; + } } ~Repository() @@ -232,7 +249,7 @@ protected void GetPath(out string pathEnt, out string pathTemp, string dir, stri _ectx.CheckParam(!name.Contains(".."), nameof(name)); // The gymnastics below are meant to deal with bad invocations including absolute paths, etc. - // That's why we go through it even if _dirTemp is null. + // That's why we go through it even if DirTemp is null. string root = Path.GetFullPath(DirTemp ?? @"x:\dummy"); string entityPath = Path.Combine(root, dir ?? "", name); entityPath = Path.GetFullPath(entityPath); diff --git a/src/Microsoft.ML.Data/Prediction/Calibrator.cs b/src/Microsoft.ML.Data/Prediction/Calibrator.cs index 9827cda193..487726572b 100644 --- a/src/Microsoft.ML.Data/Prediction/Calibrator.cs +++ b/src/Microsoft.ML.Data/Prediction/Calibrator.cs @@ -746,13 +746,10 @@ private static bool NeedCalibration(IHostEnvironment env, IChannel ch, ICalibrat /// The trainer used to train the predictor. /// The predictor that needs calibration. /// The examples to used for calibrator training. - /// Indicates whether the predictor returned needs to be an . - /// This parameter is needed for OVA that uses the predictors as s. If it is false, - /// The predictor returned is an an . /// The original predictor, if no calibration is needed, /// or a metapredictor that wraps the original predictor and the newly trained calibrator. public static IPredictor TrainCalibratorIfNeeded(IHostEnvironment env, IChannel ch, ICalibratorTrainer calibrator, - int maxRows, ITrainer trainer, IPredictor predictor, RoleMappedData data, bool needValueMapper = false) + int maxRows, ITrainer trainer, IPredictor predictor, RoleMappedData data) { Contracts.CheckValue(env, nameof(env)); env.CheckValue(ch, nameof(ch)); @@ -763,7 +760,7 @@ public static IPredictor TrainCalibratorIfNeeded(IHostEnvironment env, IChannel if (!NeedCalibration(env, ch, calibrator, trainer, predictor, data.Schema)) return predictor; - return TrainCalibrator(env, ch, calibrator, maxRows, predictor, data, needValueMapper); + return TrainCalibrator(env, ch, calibrator, maxRows, predictor, data); } /// @@ -775,13 +772,10 @@ public static IPredictor TrainCalibratorIfNeeded(IHostEnvironment env, IChannel /// The maximum rows to use for calibrator training. /// The predictor that needs calibration. /// The examples to used for calibrator training. - /// Indicates whether the predictor returned needs to be an . - /// This parameter is needed for OVA that uses the predictors as s. If it is false, - /// The predictor returned is an an . /// The original predictor, if no calibration is needed, /// or a metapredictor that wraps the original predictor and the newly trained calibrator. public static IPredictor TrainCalibrator(IHostEnvironment env, IChannel ch, ICalibratorTrainer caliTrainer, - int maxRows, IPredictor predictor, RoleMappedData data, bool needValueMapper = false) + int maxRows, IPredictor predictor, RoleMappedData data) { Contracts.CheckValue(env, nameof(env)); env.CheckValue(ch, nameof(ch)); @@ -834,10 +828,10 @@ public static IPredictor TrainCalibrator(IHostEnvironment env, IChannel ch, ICal } } var cali = caliTrainer.FinishTraining(ch); - return CreateCalibratedPredictor(env, (IPredictorProducing)predictor, cali, needValueMapper); + return CreateCalibratedPredictor(env, (IPredictorProducing)predictor, cali); } - public static IPredictorProducing CreateCalibratedPredictor(IHostEnvironment env, IPredictorProducing predictor, ICalibrator cali, bool needValueMapper = false) + public static IPredictorProducing CreateCalibratedPredictor(IHostEnvironment env, IPredictorProducing predictor, ICalibrator cali) { Contracts.Assert(predictor != null); if (cali == null) @@ -853,7 +847,7 @@ public static IPredictorProducing CreateCalibratedPredictor(IHostEnvironm var predWithFeatureScores = predictor as IPredictorWithFeatureWeights; if (predWithFeatureScores != null && predictor is IParameterMixer && cali is IParameterMixer) return new ParameterMixingCalibratedPredictor(env, predWithFeatureScores, cali); - if (needValueMapper) + if (predictor is IValueMapper) return new CalibratedPredictor(env, predictor, cali); return new SchemaBindableCalibratedPredictor(env, predictor, cali); } @@ -1349,8 +1343,6 @@ private static VersionInfo GetVersionInfo() public Double ParamA { get; } public Double ParamB { get; } public bool CanSavePfa => true; - public bool CanSaveLotusVNext => true; - public bool CanSaveOnnx => true; public PlattCalibrator(IHostEnvironment env, Double paramA, Double paramB) @@ -1446,9 +1438,8 @@ public bool SaveAsOnnx(OnnxContext ctx, string[] scoreProbablityColumnNames, str string opType = "Affine"; string linearOutput = ctx.AddIntermediateVariable(null, "linearOutput", true); var node = OnnxUtils.MakeNode(opType, new List { scoreProbablityColumnNames[0] }, - new List { linearOutput }, ctx.GetNodeName(opType)); + new List { linearOutput }, ctx.GetNodeName(opType), "ai.onnx"); - node.Domain = ""; OnnxUtils.NodeAddAttributes(node, "alpha", ParamA * -1); OnnxUtils.NodeAddAttributes(node, "beta", -0.0000001); @@ -1456,9 +1447,8 @@ public bool SaveAsOnnx(OnnxContext ctx, string[] scoreProbablityColumnNames, str opType = "Sigmoid"; node = OnnxUtils.MakeNode(opType, new List { linearOutput }, - new List { scoreProbablityColumnNames[1] }, ctx.GetNodeName(opType)); + new List { scoreProbablityColumnNames[1] }, ctx.GetNodeName(opType), "ai.onnx"); - node.Domain = ""; ctx.AddNode(node); return true; diff --git a/src/Microsoft.ML.Data/Transforms/TransformBase.cs b/src/Microsoft.ML.Data/Transforms/TransformBase.cs index 445833696d..263a3cf4ca 100644 --- a/src/Microsoft.ML.Data/Transforms/TransformBase.cs +++ b/src/Microsoft.ML.Data/Transforms/TransformBase.cs @@ -471,8 +471,6 @@ private sealed class ColumnTmp : OneToOneColumn public virtual bool CanSaveOnnx => false; - public virtual bool CanSaveLotusVNext => false; - protected OneToOneTransformBase(IHostEnvironment env, string name, OneToOneColumn[] column, IDataView input, Func testType) : base(env, name, input) diff --git a/src/Microsoft.ML.Data/Utilities/PartitionedPathUtils.cs b/src/Microsoft.ML.Data/Utilities/PartitionedPathUtils.cs new file mode 100644 index 0000000000..b13a0d5cee --- /dev/null +++ b/src/Microsoft.ML.Data/Utilities/PartitionedPathUtils.cs @@ -0,0 +1,73 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Collections.Generic; +using System.IO; + +namespace Microsoft.ML.Runtime.Data.Utilities +{ + internal static class PartitionedPathUtils + { + /// + /// Make a full path realtive to a base path. + /// + /// The base path, assumed to be a directory. + /// The full path. + /// The relative path. + /// If the paths are not relative. + internal static string MakePathRelative(string basepath, string path) + { + Contracts.AssertNonEmpty(basepath); + Contracts.AssertNonEmpty(path); + + Uri baseUri = new Uri(basepath); + Uri uri = new Uri(path); + + if (baseUri.Scheme != uri.Scheme) + { + throw Contracts.ExceptParam(nameof(basepath), "Paths cannot be made relative as they are of different schemes."); + } + + string relativePath; + try + { + if (!baseUri.AbsoluteUri.EndsWith("/")) + { + baseUri = new Uri(baseUri.AbsoluteUri + "/"); + } + + relativePath = Uri.UnescapeDataString(baseUri.MakeRelativeUri(uri).ToString()); + } + catch (ArgumentNullException e) + { + throw Contracts.Except(e, "Paths could not be made relative."); + } + catch (InvalidOperationException e) + { + throw Contracts.Except(e, "Paths could not be made relative."); + } + + if (uri.Scheme.Equals("file", StringComparison.OrdinalIgnoreCase)) + { + relativePath = relativePath.Replace(Path.AltDirectorySeparatorChar, Path.DirectorySeparatorChar); + } + + return relativePath; + } + + /// + /// Split a path string into an enumerable list of the directories. + /// + /// The path string to split. + /// An enumerable list of all non-empty directories. + internal static IEnumerable SplitDirectories(string path) + { + char[] separators = { Path.DirectorySeparatorChar }; + + var cleanPath = path.Replace(Path.AltDirectorySeparatorChar, Path.DirectorySeparatorChar); + return cleanPath.Split(separators, StringSplitOptions.RemoveEmptyEntries); + } + } +} diff --git a/src/Microsoft.ML.Data/Utilities/StreamUtils.cs b/src/Microsoft.ML.Data/Utilities/StreamUtils.cs index 45a808c55e..ac05684a8e 100644 --- a/src/Microsoft.ML.Data/Utilities/StreamUtils.cs +++ b/src/Microsoft.ML.Data/Utilities/StreamUtils.cs @@ -4,6 +4,7 @@ using System.Collections.Generic; using System.IO; +using System.Linq; namespace Microsoft.ML.Runtime.Internal.Utilities { @@ -94,7 +95,7 @@ private static string[] Expand(string pattern) try { // this is actually incorrect, for 3-char extensions: *** - var files = Directory.GetFiles(dir, right); + var files = Directory.GetFiles(dir, right).OrderBy(f => f).ToArray(); if (pathEmpty) { for (int i = 0; i < files.Length; i++) @@ -104,7 +105,7 @@ private static string[] Expand(string pattern) } } matchList.AddRange(files); - var subs = Directory.GetDirectories(dir); + var subs = Directory.GetDirectories(dir).OrderBy(f => f).ToArray(); for (var i = subs.Length - 1; i >= 0; i--) dirsLeft.Push(subs[i]); } @@ -125,7 +126,7 @@ private static string[] Expand(string pattern) // watch for lack of access: try { - var files = Directory.GetFiles(path, Path.GetFileName(currentPattern)); + var files = Directory.GetFiles(path, Path.GetFileName(currentPattern)).OrderBy(f => f).ToArray(); if (pathEmpty) { for (int i = 0; i < files.Length; i++) diff --git a/src/Microsoft.ML.Ensemble/Batch.cs b/src/Microsoft.ML.Ensemble/Batch.cs new file mode 100644 index 0000000000..e9c8fcf179 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/Batch.cs @@ -0,0 +1,22 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Runtime.Data; + +namespace Microsoft.ML.Runtime.Ensemble +{ + public sealed class Batch + { + public readonly RoleMappedData TrainInstances; + public readonly RoleMappedData TestInstances; + + public Batch(RoleMappedData trainData, RoleMappedData testData) + { + Contracts.CheckValue(trainData, nameof(trainData)); + Contracts.CheckValue(testData, nameof(testData)); + TrainInstances = trainData; + TestInstances = testData; + } + } +} diff --git a/src/Microsoft.ML.Ensemble/EnsembleUtils.cs b/src/Microsoft.ML.Ensemble/EnsembleUtils.cs new file mode 100644 index 0000000000..d5134e48c9 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/EnsembleUtils.cs @@ -0,0 +1,114 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Collections; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Internal.Utilities; + +namespace Microsoft.ML.Runtime.Ensemble +{ + internal static class EnsembleUtils + { + /// + /// Return a dataset with non-selected features zeroed out. + /// + public static RoleMappedData SelectFeatures(IHost host, RoleMappedData data, BitArray features) + { + Contracts.AssertValue(host); + Contracts.AssertValue(data); + Contracts.Assert(data.Schema.Feature != null); + Contracts.AssertValue(features); + + var type = data.Schema.Feature.Type; + Contracts.Assert(features.Length == type.VectorSize); + int card = Utils.GetCardinality(features); + if (card == type.VectorSize) + return data; + + // REVIEW: This doesn't preserve metadata on the features column. Should it? + var name = data.Schema.Feature.Name; + var view = LambdaColumnMapper.Create( + host, "FeatureSelector", data.Data, name, name, type, type, + (ref VBuffer src, ref VBuffer dst) => SelectFeatures(ref src, features, card, ref dst)); + + var res = RoleMappedData.Create(view, data.Schema.GetColumnRoleNames()); + return res; + } + + /// + /// Fill dst with values selected from src if the indices of the src values are set in includedIndices, + /// otherwise assign default(T). The length of dst will be equal to src.Length. + /// + public static void SelectFeatures(ref VBuffer src, BitArray includedIndices, int cardinality, ref VBuffer dst) + { + Contracts.Assert(Utils.Size(includedIndices) == src.Length); + Contracts.Assert(cardinality == Utils.GetCardinality(includedIndices)); + Contracts.Assert(cardinality < src.Length); + + var values = dst.Values; + var indices = dst.Indices; + + if (src.IsDense) + { + if (cardinality >= src.Length / 2) + { + T defaultValue = default; + if (Utils.Size(values) < src.Length) + values = new T[src.Length]; + for (int i = 0; i < src.Length; i++) + values[i] = !includedIndices[i] ? defaultValue : src.Values[i]; + dst = new VBuffer(src.Length, values, indices); + } + else + { + if (Utils.Size(values) < cardinality) + values = new T[cardinality]; + if (Utils.Size(indices) < cardinality) + indices = new int[cardinality]; + + int count = 0; + for (int i = 0; i < src.Length; i++) + { + if (includedIndices[i]) + { + Contracts.Assert(count < cardinality); + values[count] = src.Values[i]; + indices[count] = i; + count++; + } + } + + Contracts.Assert(count == cardinality); + dst = new VBuffer(src.Length, count, values, indices); + } + } + else + { + int valuesSize = Utils.Size(values); + int indicesSize = Utils.Size(indices); + if (valuesSize < src.Count || indicesSize < src.Count) + { + if (valuesSize < cardinality) + values = new T[cardinality]; + if (indicesSize < cardinality) + indices = new int[cardinality]; + } + + int count = 0; + for (int i = 0; i < src.Count; i++) + { + if (includedIndices[src.Indices[i]]) + { + values[count] = src.Values[i]; + indices[count] = src.Indices[i]; + count++; + } + } + + dst = new VBuffer(src.Length, count, values, indices); + } + } + } +} diff --git a/src/Microsoft.ML.Ensemble/EntryPoints/CreateEnsemble.cs b/src/Microsoft.ML.Ensemble/EntryPoints/CreateEnsemble.cs new file mode 100644 index 0000000000..7978ffb761 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/EntryPoints/CreateEnsemble.cs @@ -0,0 +1,406 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Collections.Generic; +using System.IO; +using System.IO.Compression; +using System.Linq; +using Microsoft.ML.Ensemble.EntryPoints; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Ensemble; +using Microsoft.ML.Runtime.Ensemble.OutputCombiners; +using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.Internal.Utilities; + +[assembly: LoadableClass(typeof(void), typeof(EnsembleCreator), null, typeof(SignatureEntryPointModule), "CreateEnsemble")] + +namespace Microsoft.ML.Runtime.EntryPoints +{ + /// + /// A component to combine given models into an ensemble model. + /// + public static class EnsembleCreator + { + /// + /// These are the combiner options for binary and multi class classifiers. + /// + public enum ClassifierCombiner + { + Median, + Average, + Vote, + } + + /// + /// These are the combiner options for regression and anomaly detection. + /// + public enum ScoreCombiner + { + Median, + Average, + } + + public abstract class PipelineInputBase + { + [Argument(ArgumentType.Required, ShortName = "models", HelpText = "The models to combine into an ensemble", SortOrder = 1)] + public IPredictorModel[] Models; + } + + public abstract class InputBase + { + [Argument(ArgumentType.Required, ShortName = "models", HelpText = "The models to combine into an ensemble", SortOrder = 1)] + public IPredictorModel[] Models; + + [Argument(ArgumentType.AtMostOnce, ShortName = "validate", HelpText = "Whether to validate that all the pipelines are identical", SortOrder = 5)] + public bool ValidatePipelines = true; + } + + public sealed class ClassifierInput : InputBase + { + [Argument(ArgumentType.AtMostOnce, ShortName = "combiner", HelpText = "The combiner used to combine the scores", SortOrder = 2)] + public ClassifierCombiner ModelCombiner = ClassifierCombiner.Median; + } + + public sealed class PipelineClassifierInput : PipelineInputBase + { + [Argument(ArgumentType.AtMostOnce, ShortName = "combiner", HelpText = "The combiner used to combine the scores", SortOrder = 2)] + public ClassifierCombiner ModelCombiner = ClassifierCombiner.Median; + } + + public sealed class RegressionInput : InputBase + { + [Argument(ArgumentType.AtMostOnce, ShortName = "combiner", HelpText = "The combiner used to combine the scores", SortOrder = 2)] + public ScoreCombiner ModelCombiner = ScoreCombiner.Median; + } + + public sealed class PipelineRegressionInput : PipelineInputBase + { + [Argument(ArgumentType.AtMostOnce, ShortName = "combiner", HelpText = "The combiner used to combine the scores", SortOrder = 2)] + public ScoreCombiner ModelCombiner = ScoreCombiner.Median; + } + + public sealed class PipelineAnomalyInput : PipelineInputBase + { + [Argument(ArgumentType.AtMostOnce, ShortName = "combiner", HelpText = "The combiner used to combine the scores", SortOrder = 2)] + public ScoreCombiner ModelCombiner = ScoreCombiner.Average; + } + + private static void GetPipeline(IHostEnvironment env, InputBase input, out IDataView startingData, out RoleMappedData transformedData) + { + Contracts.AssertValue(env); + env.AssertValue(input); + env.AssertNonEmpty(input.Models); + + ISchema inputSchema = null; + startingData = null; + transformedData = null; + byte[][] transformedDataSerialized = null; + string[] transformedDataZipEntryNames = null; + for (int i = 0; i < input.Models.Length; i++) + { + var model = input.Models[i]; + + var inputData = new EmptyDataView(env, model.TransformModel.InputSchema); + model.PrepareData(env, inputData, out RoleMappedData transformedDataCur, out IPredictor pred); + + if (inputSchema == null) + { + env.Assert(i == 0); + inputSchema = model.TransformModel.InputSchema; + startingData = inputData; + transformedData = transformedDataCur; + } + else if (input.ValidatePipelines) + { + using (var ch = env.Start("Validating pipeline")) + { + if (transformedDataSerialized == null) + { + ch.Assert(transformedDataZipEntryNames == null); + SerializeRoleMappedData(env, ch, transformedData, out transformedDataSerialized, + out transformedDataZipEntryNames); + } + CheckSamePipeline(env, ch, transformedDataCur, transformedDataSerialized, transformedDataZipEntryNames); + ch.Done(); + } + } + } + } + + [TlcModule.EntryPoint(Name = "Models.BinaryEnsemble", Desc = "Combine binary classifiers into an ensemble", UserName = EnsembleTrainer.UserNameValue)] + public static CommonOutputs.BinaryClassificationOutput CreateBinaryEnsemble(IHostEnvironment env, ClassifierInput input) + { + Contracts.CheckValue(env, nameof(env)); + var host = env.Register("CombineModels"); + host.CheckValue(input, nameof(input)); + host.CheckNonEmpty(input.Models, nameof(input.Models)); + + GetPipeline(host, input, out IDataView startingData, out RoleMappedData transformedData); + + var args = new EnsembleTrainer.Arguments(); + switch (input.ModelCombiner) + { + case ClassifierCombiner.Median: + args.OutputCombiner = new MedianFactory(); + break; + case ClassifierCombiner.Average: + args.OutputCombiner = new AverageFactory(); + break; + case ClassifierCombiner.Vote: + args.OutputCombiner = new VotingFactory(); + break; + default: + throw host.Except("Unknown combiner kind"); + } + + var trainer = new EnsembleTrainer(host, args); + var ensemble = trainer.CombineModels(input.Models.Select(pm => pm.Predictor as IPredictorProducing)); + + var predictorModel = new PredictorModel(host, transformedData, startingData, ensemble); + + var output = new CommonOutputs.BinaryClassificationOutput { PredictorModel = predictorModel }; + return output; + } + + [TlcModule.EntryPoint(Name = "Models.RegressionEnsemble", Desc = "Combine regression models into an ensemble", UserName = RegressionEnsembleTrainer.UserNameValue)] + public static CommonOutputs.RegressionOutput CreateRegressionEnsemble(IHostEnvironment env, RegressionInput input) + { + Contracts.CheckValue(env, nameof(env)); + var host = env.Register("CombineModels"); + host.CheckValue(input, nameof(input)); + host.CheckNonEmpty(input.Models, nameof(input.Models)); + + GetPipeline(host, input, out IDataView startingData, out RoleMappedData transformedData); + + var args = new RegressionEnsembleTrainer.Arguments(); + switch (input.ModelCombiner) + { + case ScoreCombiner.Median: + args.OutputCombiner = new MedianFactory(); + break; + case ScoreCombiner.Average: + args.OutputCombiner = new AverageFactory(); + break; + default: + throw host.Except("Unknown combiner kind"); + } + + var trainer = new RegressionEnsembleTrainer(host, args); + var ensemble = trainer.CombineModels(input.Models.Select(pm => pm.Predictor as IPredictorProducing)); + + var predictorModel = new PredictorModel(host, transformedData, startingData, ensemble); + + var output = new CommonOutputs.RegressionOutput { PredictorModel = predictorModel }; + return output; + } + + [TlcModule.EntryPoint(Name = "Models.BinaryPipelineEnsemble", Desc = "Combine binary classification models into an ensemble")] + public static CommonOutputs.BinaryClassificationOutput CreateBinaryPipelineEnsemble(IHostEnvironment env, PipelineClassifierInput input) + { + Contracts.CheckValue(env, nameof(env)); + var host = env.Register("CombineModels"); + host.CheckValue(input, nameof(input)); + host.CheckNonEmpty(input.Models, nameof(input.Models)); + + IBinaryOutputCombiner combiner; + switch (input.ModelCombiner) + { + case ClassifierCombiner.Median: + combiner = new Median(host); + break; + case ClassifierCombiner.Average: + combiner = new Average(host); + break; + case ClassifierCombiner.Vote: + combiner = new Voting(host); + break; + default: + throw host.Except("Unknown combiner kind"); + } + var ensemble = SchemaBindablePipelineEnsembleBase.Create(host, input.Models, combiner, MetadataUtils.Const.ScoreColumnKind.BinaryClassification); + return CreatePipelineEnsemble(host, input.Models, ensemble); + } + + [TlcModule.EntryPoint(Name = "Models.RegressionPipelineEnsemble", Desc = "Combine regression models into an ensemble")] + public static CommonOutputs.RegressionOutput CreateRegressionPipelineEnsemble(IHostEnvironment env, PipelineRegressionInput input) + { + Contracts.CheckValue(env, nameof(env)); + var host = env.Register("CombineModels"); + host.CheckValue(input, nameof(input)); + host.CheckNonEmpty(input.Models, nameof(input.Models)); + + IRegressionOutputCombiner combiner; + switch (input.ModelCombiner) + { + case ScoreCombiner.Median: + combiner = new Median(host); + break; + case ScoreCombiner.Average: + combiner = new Average(host); + break; + default: + throw host.Except("Unknown combiner kind"); + } + var ensemble = SchemaBindablePipelineEnsembleBase.Create(host, input.Models, combiner, MetadataUtils.Const.ScoreColumnKind.Regression); + return CreatePipelineEnsemble(host, input.Models, ensemble); + } + + [TlcModule.EntryPoint(Name = "Models.MultiClassPipelineEnsemble", Desc = "Combine multiclass classifiers into an ensemble")] + public static CommonOutputs.MulticlassClassificationOutput CreateMultiClassPipelineEnsemble(IHostEnvironment env, PipelineClassifierInput input) + { + Contracts.CheckValue(env, nameof(env)); + var host = env.Register("CombineModels"); + host.CheckValue(input, nameof(input)); + host.CheckNonEmpty(input.Models, nameof(input.Models)); + + IOutputCombiner> combiner; + switch (input.ModelCombiner) + { + case ClassifierCombiner.Median: + combiner = new MultiMedian(host, new MultiMedian.Arguments() { Normalize = true }); + break; + case ClassifierCombiner.Average: + combiner = new MultiAverage(host, new MultiAverage.Arguments() { Normalize = true }); + break; + case ClassifierCombiner.Vote: + combiner = new MultiVoting(host, new MultiVoting.Arguments()); + break; + default: + throw host.Except("Unknown combiner kind"); + } + var ensemble = SchemaBindablePipelineEnsembleBase.Create(host, input.Models, combiner, MetadataUtils.Const.ScoreColumnKind.MultiClassClassification); + return CreatePipelineEnsemble(host, input.Models, ensemble); + } + + [TlcModule.EntryPoint(Name = "Models.AnomalyPipelineEnsemble", Desc = "Combine anomaly detection models into an ensemble")] + public static CommonOutputs.AnomalyDetectionOutput CreateAnomalyPipelineEnsemble(IHostEnvironment env, PipelineAnomalyInput input) + { + Contracts.CheckValue(env, nameof(env)); + var host = env.Register("CombineModels"); + host.CheckValue(input, nameof(input)); + host.CheckNonEmpty(input.Models, nameof(input.Models)); + + IRegressionOutputCombiner combiner; + switch (input.ModelCombiner) + { + case ScoreCombiner.Median: + combiner = new Median(host); + break; + case ScoreCombiner.Average: + combiner = new Average(host); + break; + default: + throw host.Except("Unknown combiner kind"); + } + var ensemble = SchemaBindablePipelineEnsembleBase.Create(host, input.Models, combiner, MetadataUtils.Const.ScoreColumnKind.AnomalyDetection); + return CreatePipelineEnsemble(host, input.Models, ensemble); + } + + private static TOut CreatePipelineEnsemble(IHostEnvironment env, IPredictorModel[] predictors, SchemaBindablePipelineEnsembleBase ensemble) + where TOut : CommonOutputs.TrainerOutput, new() + { + var inputSchema = predictors[0].TransformModel.InputSchema; + var dv = new EmptyDataView(env, inputSchema); + + // The role mappings are specific to the individual predictors. + var rmd = RoleMappedData.Create(dv); + var predictorModel = new PredictorModel(env, rmd, dv, ensemble); + + var output = new TOut { PredictorModel = predictorModel }; + return output; + } + + /// + /// This method takes a as input, saves it as an in-memory + /// and returns two arrays indexed by the entries in the zip: + /// 1. An array of byte arrays, containing the byte sequences of each entry. + /// 2. An array of strings, containing the name of each entry. + /// + /// This method is used for comparing pipelines. Its outputs can be passed to + /// to check if this pipeline is identical to another pipeline. + /// + public static void SerializeRoleMappedData(IHostEnvironment env, IChannel ch, RoleMappedData data, + out byte[][] dataSerialized, out string[] dataZipEntryNames) + { + Contracts.CheckValue(env, nameof(env)); + env.CheckValue(ch, nameof(ch)); + ch.CheckValue(data, nameof(data)); + + using (var ms = new MemoryStream()) + { + TrainUtils.SaveModel(env, ch, ms, null, data); + var zip = new ZipArchive(ms); + var entries = zip.Entries.OrderBy(e => e.FullName).ToArray(); + dataSerialized = new byte[Utils.Size(entries)][]; + dataZipEntryNames = new string[Utils.Size(entries)]; + for (int i = 0; i < Utils.Size(entries); i++) + { + dataZipEntryNames[i] = entries[i].FullName; + dataSerialized[i] = new byte[entries[i].Length]; + using (var s = entries[i].Open()) + s.Read(dataSerialized[i], 0, (int)entries[i].Length); + } + } + } + + /// + /// This method compares two pipelines to make sure they are identical. The first pipeline is passed + /// as a , and the second as a double byte array and a string array. The double + /// byte array and the string array are obtained by calling on the + /// second pipeline. + /// The comparison is done by saving as an in-memory , + /// and for each entry in it, comparing its name, and the byte sequence to the corresponding entries in + /// and . + /// This method throws if for any of the entries the name/byte sequence are not identical. + /// + public static void CheckSamePipeline(IHostEnvironment env, IChannel ch, + RoleMappedData dataToCompare, byte[][] dataSerialized, string[] dataZipEntryNames) + { + Contracts.CheckValue(env, nameof(env)); + env.CheckValue(ch, nameof(ch)); + ch.CheckValue(dataToCompare, nameof(dataToCompare)); + ch.CheckValue(dataSerialized, nameof(dataSerialized)); + ch.CheckValue(dataZipEntryNames, nameof(dataZipEntryNames)); + if (dataZipEntryNames.Length != dataSerialized.Length) + { + throw ch.ExceptParam(nameof(dataSerialized), + $"The length of {nameof(dataSerialized)} must be equal to the length of {nameof(dataZipEntryNames)}"); + } + + using (var ms = new MemoryStream()) + { + // REVIEW: This can be done more efficiently by adding a custom type of repository that + // doesn't actually save the data, but upon stream closure compares the results to the given repository + // and then discards it. Currently, however, this cannot be done because ModelSaveContext does not use + // an abstract class/interface, but rather the RepositoryWriter class. + TrainUtils.SaveModel(env, ch, ms, null, dataToCompare); + + string errorMsg = "Models contain different pipelines, cannot ensemble them."; + var zip = new ZipArchive(ms); + var entries = zip.Entries.OrderBy(e => e.FullName).ToArray(); + ch.Check(dataSerialized.Length == Utils.Size(entries)); + byte[] buffer = null; + for (int i = 0; i < dataSerialized.Length; i++) + { + ch.Check(dataZipEntryNames[i] == entries[i].FullName, errorMsg); + int len = dataSerialized[i].Length; + if (Utils.Size(buffer) < len) + buffer = new byte[len]; + using (var s = entries[i].Open()) + { + int bytesRead = s.Read(buffer, 0, len); + ch.Check(bytesRead == len, errorMsg); + for (int j = 0; j < len; j++) + ch.Check(buffer[j] == dataSerialized[i][j], errorMsg); + if (s.Read(buffer, 0, 1) > 0) + throw env.Except(errorMsg); + } + } + } + } + } +} diff --git a/src/Microsoft.ML.Ensemble/EntryPoints/DiversityMeasure.cs b/src/Microsoft.ML.Ensemble/EntryPoints/DiversityMeasure.cs new file mode 100644 index 0000000000..889ba80411 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/EntryPoints/DiversityMeasure.cs @@ -0,0 +1,37 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using Microsoft.ML.Ensemble.EntryPoints; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Ensemble.Selector; +using Microsoft.ML.Runtime.Ensemble.Selector.DiversityMeasure; +using Microsoft.ML.Runtime.EntryPoints; + +[assembly: EntryPointModule(typeof(DisagreementDiversityFactory))] +[assembly: EntryPointModule(typeof(RegressionDisagreementDiversityFactory))] +[assembly: EntryPointModule(typeof(MultiDisagreementDiversityFactory))] + +namespace Microsoft.ML.Ensemble.EntryPoints +{ + [TlcModule.Component(Name = DisagreementDiversityMeasure.LoadName, FriendlyName = DisagreementDiversityMeasure.UserName)] + public sealed class DisagreementDiversityFactory : ISupportDiversityMeasureFactory + { + public IDiversityMeasure CreateComponent(IHostEnvironment env) => new DisagreementDiversityMeasure(); + } + + [TlcModule.Component(Name = RegressionDisagreementDiversityMeasure.LoadName, FriendlyName = DisagreementDiversityMeasure.UserName)] + public sealed class RegressionDisagreementDiversityFactory : ISupportDiversityMeasureFactory + { + public IDiversityMeasure CreateComponent(IHostEnvironment env) => new RegressionDisagreementDiversityMeasure(); + } + + [TlcModule.Component(Name = MultiDisagreementDiversityMeasure.LoadName, FriendlyName = DisagreementDiversityMeasure.UserName)] + public sealed class MultiDisagreementDiversityFactory : ISupportDiversityMeasureFactory> + { + public IDiversityMeasure> CreateComponent(IHostEnvironment env) => new MultiDisagreementDiversityMeasure(); + } + +} diff --git a/src/Microsoft.ML.Ensemble/EntryPoints/Ensemble.cs b/src/Microsoft.ML.Ensemble/EntryPoints/Ensemble.cs new file mode 100644 index 0000000000..728cccb1f6 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/EntryPoints/Ensemble.cs @@ -0,0 +1,55 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Ensemble.EntryPoints; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Ensemble; +using Microsoft.ML.Runtime.EntryPoints; + +[assembly: LoadableClass(typeof(void), typeof(Ensemble), null, typeof(SignatureEntryPointModule), "TrainEnsemble")] + +namespace Microsoft.ML.Ensemble.EntryPoints +{ + public static class Ensemble + { + [TlcModule.EntryPoint(Name = "Trainers.EnsembleBinaryClassifier", Desc = "Train binary ensemble.", UserName = EnsembleTrainer.UserNameValue)] + public static CommonOutputs.BinaryClassificationOutput CreateBinaryEnsemble(IHostEnvironment env, EnsembleTrainer.Arguments input) + { + Contracts.CheckValue(env, nameof(env)); + var host = env.Register("TrainBinaryEnsemble"); + host.CheckValue(input, nameof(input)); + EntryPointUtils.CheckInputArgs(host, input); + + return LearnerEntryPointsUtils.Train(host, input, + () => new EnsembleTrainer(host, input), + () => LearnerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.LabelColumn)); + } + + [TlcModule.EntryPoint(Name = "Trainers.EnsembleClassification", Desc = "Train multiclass ensemble.", UserName = EnsembleTrainer.UserNameValue)] + public static CommonOutputs.MulticlassClassificationOutput CreateMultiClassEnsemble(IHostEnvironment env, MulticlassDataPartitionEnsembleTrainer.Arguments input) + { + Contracts.CheckValue(env, nameof(env)); + var host = env.Register("TrainMultiClassEnsemble"); + host.CheckValue(input, nameof(input)); + EntryPointUtils.CheckInputArgs(host, input); + + return LearnerEntryPointsUtils.Train(host, input, + () => new MulticlassDataPartitionEnsembleTrainer(host, input), + () => LearnerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.LabelColumn)); + } + + [TlcModule.EntryPoint(Name = "Trainers.EnsembleRegression", Desc = "Train regression ensemble.", UserName = EnsembleTrainer.UserNameValue)] + public static CommonOutputs.RegressionOutput CreateRegressionEnsemble(IHostEnvironment env, RegressionEnsembleTrainer.Arguments input) + { + Contracts.CheckValue(env, nameof(env)); + var host = env.Register("TrainRegressionEnsemble"); + host.CheckValue(input, nameof(input)); + EntryPointUtils.CheckInputArgs(host, input); + + return LearnerEntryPointsUtils.Train(host, input, + () => new RegressionEnsembleTrainer(host, input), + () => LearnerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.LabelColumn)); + } + } +} diff --git a/src/Microsoft.ML.Ensemble/EntryPoints/FeatureSelector.cs b/src/Microsoft.ML.Ensemble/EntryPoints/FeatureSelector.cs new file mode 100644 index 0000000000..65ca5e9d06 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/EntryPoints/FeatureSelector.cs @@ -0,0 +1,22 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Ensemble.EntryPoints; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Ensemble.Selector; +using Microsoft.ML.Runtime.Ensemble.Selector.FeatureSelector; +using Microsoft.ML.Runtime.EntryPoints; + +[assembly: EntryPointModule(typeof(AllFeatureSelectorFactory))] +[assembly: EntryPointModule(typeof(RandomFeatureSelector))] + +namespace Microsoft.ML.Ensemble.EntryPoints +{ + [TlcModule.Component(Name = AllFeatureSelector.LoadName, FriendlyName = AllFeatureSelector.UserName)] + public sealed class AllFeatureSelectorFactory : ISupportFeatureSelectorFactory + { + IFeatureSelector IComponentFactory.CreateComponent(IHostEnvironment env) => new AllFeatureSelector(env); + } + +} diff --git a/src/Microsoft.ML.Ensemble/EntryPoints/OutputCombiner.cs b/src/Microsoft.ML.Ensemble/EntryPoints/OutputCombiner.cs new file mode 100644 index 0000000000..4f4107639a --- /dev/null +++ b/src/Microsoft.ML.Ensemble/EntryPoints/OutputCombiner.cs @@ -0,0 +1,46 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using Microsoft.ML.Ensemble.EntryPoints; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Ensemble.OutputCombiners; +using Microsoft.ML.Runtime.EntryPoints; + +[assembly: EntryPointModule(typeof(AverageFactory))] +[assembly: EntryPointModule(typeof(MedianFactory))] +[assembly: EntryPointModule(typeof(MultiAverage))] +[assembly: EntryPointModule(typeof(MultiMedian))] +[assembly: EntryPointModule(typeof(MultiStacking))] +[assembly: EntryPointModule(typeof(MultiVoting))] +[assembly: EntryPointModule(typeof(MultiWeightedAverage))] +[assembly: EntryPointModule(typeof(RegressionStacking))] +[assembly: EntryPointModule(typeof(Stacking))] +[assembly: EntryPointModule(typeof(VotingFactory))] +[assembly: EntryPointModule(typeof(WeightedAverage))] + +namespace Microsoft.ML.Ensemble.EntryPoints +{ + [TlcModule.Component(Name = Average.LoadName, FriendlyName = Average.UserName)] + public sealed class AverageFactory : ISupportBinaryOutputCombinerFactory, ISupportRegressionOutputCombinerFactory + { + public IRegressionOutputCombiner CreateComponent(IHostEnvironment env) => new Average(env); + + IBinaryOutputCombiner IComponentFactory.CreateComponent(IHostEnvironment env) => new Average(env); + } + + [TlcModule.Component(Name = Median.LoadName, FriendlyName = Median.UserName)] + public sealed class MedianFactory : ISupportBinaryOutputCombinerFactory, ISupportRegressionOutputCombinerFactory + { + public IRegressionOutputCombiner CreateComponent(IHostEnvironment env) => new Median(env); + + IBinaryOutputCombiner IComponentFactory.CreateComponent(IHostEnvironment env) => new Median(env); + } + + [TlcModule.Component(Name = Voting.LoadName, FriendlyName = Voting.UserName)] + public sealed class VotingFactory : ISupportBinaryOutputCombinerFactory + { + IBinaryOutputCombiner IComponentFactory.CreateComponent(IHostEnvironment env) => new Voting(env); + } +} diff --git a/src/Microsoft.ML.Ensemble/EntryPoints/PipelineEnsemble.cs b/src/Microsoft.ML.Ensemble/EntryPoints/PipelineEnsemble.cs new file mode 100644 index 0000000000..bcfaaefb89 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/EntryPoints/PipelineEnsemble.cs @@ -0,0 +1,59 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Ensemble.EntryPoints; +using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.Internal.Calibration; + +[assembly: EntryPointModule(typeof(PipelineEnsemble))] + +namespace Microsoft.ML.Runtime.Ensemble.EntryPoints +{ + public static class PipelineEnsemble + { + public sealed class SummaryOutput + { + [TlcModule.Output(Desc = "The summaries of the individual predictors")] + public IDataView[] Summaries; + + [TlcModule.Output(Desc = "The model statistics of the individual predictors")] + public IDataView[] Stats; + } + + [TlcModule.EntryPoint(Name = "Models.EnsembleSummary", Desc = "Summarize a pipeline ensemble predictor.")] + public static SummaryOutput Summarize(IHostEnvironment env, SummarizePredictor.Input input) + { + Contracts.CheckValue(env, nameof(env)); + var host = env.Register("PipelineEnsemblePredictor"); + host.CheckValue(input, nameof(input)); + EntryPointUtils.CheckInputArgs(host, input); + + input.PredictorModel.PrepareData(host, + new EmptyDataView(host, input.PredictorModel.TransformModel.InputSchema), + out RoleMappedData rmd, out IPredictor predictor +); + + var calibrated = predictor as CalibratedPredictorBase; + while (calibrated != null) + { + predictor = calibrated.SubPredictor; + calibrated = predictor as CalibratedPredictorBase; + } + var ensemble = predictor as SchemaBindablePipelineEnsembleBase; + host.CheckUserArg(ensemble != null, nameof(input.PredictorModel.Predictor), "Predictor is not a pipeline ensemble predictor"); + + var summaries = new IDataView[ensemble.PredictorModels.Length]; + var stats = new IDataView[ensemble.PredictorModels.Length]; + for (int i = 0; i < ensemble.PredictorModels.Length; i++) + { + var pm = ensemble.PredictorModels[i]; + + pm.PrepareData(host, new EmptyDataView(host, pm.TransformModel.InputSchema), out rmd, out IPredictor pred); + summaries[i] = SummarizePredictor.GetSummaryAndStats(host, pred, rmd.Schema, out stats[i]); + } + return new SummaryOutput() { Summaries = summaries, Stats = stats }; + } + } +} diff --git a/src/Microsoft.ML.Ensemble/EntryPoints/SubModelSelector.cs b/src/Microsoft.ML.Ensemble/EntryPoints/SubModelSelector.cs new file mode 100644 index 0000000000..57001190ac --- /dev/null +++ b/src/Microsoft.ML.Ensemble/EntryPoints/SubModelSelector.cs @@ -0,0 +1,37 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using Microsoft.ML.Ensemble.EntryPoints; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Ensemble.Selector; +using Microsoft.ML.Runtime.Ensemble.Selector.SubModelSelector; +using Microsoft.ML.Runtime.EntryPoints; + +[assembly: EntryPointModule(typeof(AllSelectorFactory))] +[assembly: EntryPointModule(typeof(AllSelectorMultiClassFactory))] +[assembly: EntryPointModule(typeof(BestDiverseSelectorBinary))] +[assembly: EntryPointModule(typeof(BestDiverseSelectorMultiClass))] +[assembly: EntryPointModule(typeof(BestDiverseSelectorRegression))] +[assembly: EntryPointModule(typeof(BestPerformanceRegressionSelector))] +[assembly: EntryPointModule(typeof(BestPerformanceSelector))] +[assembly: EntryPointModule(typeof(BestPerformanceSelectorMultiClass))] + +namespace Microsoft.ML.Ensemble.EntryPoints +{ + [TlcModule.Component(Name = AllSelector.LoadName, FriendlyName = AllSelector.UserName)] + public sealed class AllSelectorFactory : ISupportBinarySubModelSelectorFactory, ISupportRegressionSubModelSelectorFactory + { + IBinarySubModelSelector IComponentFactory.CreateComponent(IHostEnvironment env) => new AllSelector(env); + + IRegressionSubModelSelector IComponentFactory.CreateComponent(IHostEnvironment env) => new AllSelector(env); + } + + [TlcModule.Component(Name = AllSelectorMultiClass.LoadName, FriendlyName = AllSelectorMultiClass.UserName)] + public sealed class AllSelectorMultiClassFactory : ISupportMulticlassSubModelSelectorFactory + { + IMulticlassSubModelSelector IComponentFactory.CreateComponent(IHostEnvironment env) => new AllSelectorMultiClass(env); + } +} diff --git a/src/Microsoft.ML.Ensemble/FeatureSubsetModel.cs b/src/Microsoft.ML.Ensemble/FeatureSubsetModel.cs new file mode 100644 index 0000000000..4518666d34 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/FeatureSubsetModel.cs @@ -0,0 +1,32 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System.Collections; +using System.Collections.Generic; +using Microsoft.ML.Runtime.Internal.Utilities; + +namespace Microsoft.ML.Runtime.Ensemble +{ + public sealed class FeatureSubsetModel where TPredictor : IPredictor + { + public readonly TPredictor Predictor; + public readonly BitArray SelectedFeatures; + public readonly int Cardinality; + + public KeyValuePair[] Metrics { get; set; } + + public FeatureSubsetModel(TPredictor predictor, BitArray features = null, + KeyValuePair[] metrics = null) + { + Predictor = predictor; + int card; + if (features != null && (card = Utils.GetCardinality(features)) < features.Count) + { + SelectedFeatures = features; + Cardinality = card; + } + Metrics = metrics; + } + } +} diff --git a/src/Microsoft.ML.Ensemble/Microsoft.ML.Ensemble.csproj b/src/Microsoft.ML.Ensemble/Microsoft.ML.Ensemble.csproj new file mode 100644 index 0000000000..ddd4557788 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/Microsoft.ML.Ensemble.csproj @@ -0,0 +1,15 @@ + + + + netstandard2.0 + Microsoft.ML.Ensemble + CORECLR + + + + + + + + + diff --git a/src/Microsoft.ML.Ensemble/OutputCombiners/Average.cs b/src/Microsoft.ML.Ensemble/OutputCombiners/Average.cs new file mode 100644 index 0000000000..45cd764d13 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/OutputCombiners/Average.cs @@ -0,0 +1,62 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Ensemble.OutputCombiners; +using Microsoft.ML.Runtime.Model; + +[assembly: LoadableClass(typeof(Average), null, typeof(SignatureCombiner), Average.UserName)] +[assembly: LoadableClass(typeof(Average), null, typeof(SignatureLoadModel), Average.UserName, Average.LoaderSignature)] + +namespace Microsoft.ML.Runtime.Ensemble.OutputCombiners +{ + public sealed class Average : BaseAverager, ICanSaveModel, IRegressionOutputCombiner + { + public const string UserName = "Average"; + public const string LoadName = "Average"; + public const string LoaderSignature = "AverageCombiner"; + + private static VersionInfo GetVersionInfo() + { + return new VersionInfo( + modelSignature: "AVG COMB", + verWrittenCur: 0x00010001, + verReadableCur: 0x00010001, + verWeCanReadBack: 0x00010001, + loaderSignature: LoaderSignature); + } + + public Average(IHostEnvironment env) + : base(env, LoaderSignature) + { + } + + private Average(IHostEnvironment env, ModelLoadContext ctx) + : base(env, LoaderSignature, ctx) + { + } + + public static Average Create(IHostEnvironment env, ModelLoadContext ctx) + { + Contracts.CheckValue(env, nameof(env)); + env.CheckValue(ctx, nameof(ctx)); + ctx.CheckAtModel(GetVersionInfo()); + return new Average(env, ctx); + } + + protected override void SaveCore(ModelSaveContext ctx) + { + base.SaveCore(ctx); + ctx.SetVersionInfo(GetVersionInfo()); + } + + public override Combiner GetCombiner() + { + // Force the weights to null. + return(ref Single dst, Single[] src, Single[] weights) => + CombineCore(ref dst, src, null); + } + } +} diff --git a/src/Microsoft.ML.Ensemble/OutputCombiners/BaseAverager.cs b/src/Microsoft.ML.Ensemble/OutputCombiners/BaseAverager.cs new file mode 100644 index 0000000000..824300e594 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/OutputCombiners/BaseAverager.cs @@ -0,0 +1,78 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using Microsoft.ML.Runtime.Model; + +namespace Microsoft.ML.Runtime.Ensemble.OutputCombiners +{ + public abstract class BaseAverager : IBinaryOutputCombiner + { + protected readonly IHost Host; + public BaseAverager(IHostEnvironment env, string name) + { + Contracts.CheckValue(env, nameof(env)); + env.CheckNonWhiteSpace(name, nameof(name)); + Host = env.Register(name); + } + + protected BaseAverager(IHostEnvironment env, string name, ModelLoadContext ctx) + { + Contracts.AssertValue(env); + env.AssertNonWhiteSpace(name); + Host = env.Register(name); + Host.CheckValue(ctx, nameof(ctx)); + + // *** Binary format *** + // int: sizeof(Single) + int cbFloat = ctx.Reader.ReadInt32(); + Host.CheckDecode(cbFloat == sizeof(Single)); + } + + public void Save(ModelSaveContext ctx) + { + Host.CheckValue(ctx, nameof(ctx)); + ctx.CheckAtModel(); + SaveCore(ctx); + } + + protected virtual void SaveCore(ModelSaveContext ctx) + { + // *** Binary format *** + // int: sizeof(Single) + ctx.Writer.Write(sizeof(Single)); + } + + public abstract Combiner GetCombiner(); + + protected void CombineCore(ref Single dst, Single[] src, Single[] weights = null) + { + Single sum = 0; + Single weightTotal = 0; + if (weights == null) + { + for (int i = 0; i < src.Length; i++) + { + if (!Single.IsNaN(src[i])) + { + sum += src[i]; + weightTotal++; + } + } + } + else + { + for (int i = 0; i < src.Length; i++) + { + if (!Single.IsNaN(src[i])) + { + sum += weights[i] * src[i]; + weightTotal += weights[i]; + } + } + } + dst = sum / weightTotal; + } + } +} diff --git a/src/Microsoft.ML.Ensemble/OutputCombiners/BaseMultiAverager.cs b/src/Microsoft.ML.Ensemble/OutputCombiners/BaseMultiAverager.cs new file mode 100644 index 0000000000..64ec41d613 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/OutputCombiners/BaseMultiAverager.cs @@ -0,0 +1,68 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Internal.Utilities; +using Microsoft.ML.Runtime.Model; +using Microsoft.ML.Runtime.Numeric; + +namespace Microsoft.ML.Runtime.Ensemble.OutputCombiners +{ + public abstract class BaseMultiAverager : BaseMultiCombiner + { + internal BaseMultiAverager(IHostEnvironment env, string name, ArgumentsBase args) + : base(env, name, args) + { + } + + internal BaseMultiAverager(IHostEnvironment env, string name, ModelLoadContext ctx) + : base(env, name, ctx) + { + } + + protected void CombineCore(ref VBuffer dst, VBuffer[] src, Single[] weights = null) + { + Host.AssertNonEmpty(src); + Host.Assert(weights == null || Utils.Size(weights) == Utils.Size(src)); + + // REVIEW: Should this be tolerant of NaNs? + int len = GetClassCount(src); + if (!TryNormalize(src)) + { + GetNaNOutput(ref dst, len); + return; + } + + var values = dst.Values; + if (Utils.Size(values) < len) + values = new Single[len]; + else + Array.Clear(values, 0, len); + + // Set the output to values. + dst = new VBuffer(len, values, dst.Indices); + + Single weightTotal; + if (weights == null) + { + weightTotal = src.Length; + for (int i = 0; i < src.Length; i++) + VectorUtils.Add(ref src[i], ref dst); + } + else + { + weightTotal = 0; + for (int i = 0; i < src.Length; i++) + { + var w = weights[i]; + weightTotal += w; + VectorUtils.AddMult(ref src[i], w, ref dst); + } + } + + VectorUtils.ScaleBy(ref dst, 1 / weightTotal); + } + } +} diff --git a/src/Microsoft.ML.Ensemble/OutputCombiners/BaseMultiCombiner.cs b/src/Microsoft.ML.Ensemble/OutputCombiners/BaseMultiCombiner.cs new file mode 100644 index 0000000000..1258313df1 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/OutputCombiners/BaseMultiCombiner.cs @@ -0,0 +1,109 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Internal.Utilities; +using Microsoft.ML.Runtime.Model; +using Microsoft.ML.Runtime.Numeric; + +namespace Microsoft.ML.Runtime.Ensemble.OutputCombiners +{ + public abstract class BaseMultiCombiner : IMultiClassOutputCombiner + { + protected readonly IHost Host; + + public abstract class ArgumentsBase + { + [Argument(ArgumentType.AtMostOnce, HelpText = "Whether to normalize the output of base models before combining them", + ShortName = "norm", SortOrder = 50)] + public bool Normalize = true; + } + + protected readonly bool Normalize; + + internal BaseMultiCombiner(IHostEnvironment env, string name, ArgumentsBase args) + { + Contracts.AssertValue(env); + env.AssertNonWhiteSpace(name); + Host = env.Register(name); + Host.CheckValue(args, nameof(args)); + + Normalize = args.Normalize; + } + + internal BaseMultiCombiner(IHostEnvironment env, string name, ModelLoadContext ctx) + { + Contracts.AssertValue(env); + env.AssertNonWhiteSpace(name); + Host = env.Register(name); + Host.AssertValue(ctx); + + // *** Binary format *** + // int: sizeof(Single) + // bool: _normalize + int cbFloat = ctx.Reader.ReadInt32(); + Host.CheckDecode(cbFloat == sizeof(Single)); + Normalize = ctx.Reader.ReadBoolByte(); + } + + public void Save(ModelSaveContext ctx) + { + Host.CheckValue(ctx, nameof(ctx)); + ctx.CheckAtModel(); + SaveCore(ctx); + } + + protected virtual void SaveCore(ModelSaveContext ctx) + { + // *** Binary format *** + // int: sizeof(Single) + // bool: _normalize + ctx.Writer.Write(sizeof(Single)); + ctx.Writer.WriteBoolByte(Normalize); + } + + public abstract Combiner> GetCombiner(); + + protected int GetClassCount(VBuffer[] values) + { + int len = 0; + foreach (var item in values) + { + if (len < item.Length) + len = item.Length; + } + return len; + } + + protected bool TryNormalize(VBuffer[] values) + { + if (!Normalize) + return true; + + for (int i = 0; i < values.Length; i++) + { + // Leave a zero vector as all zeros. Otherwise, make the L1 norm equal to 1. + var sum = VectorUtils.L1Norm(ref values[i]); + if (!FloatUtils.IsFinite(sum)) + return false; + if (sum > 0) + VectorUtils.ScaleBy(ref values[i], 1 / sum); + } + return true; + } + + protected void GetNaNOutput(ref VBuffer dst, int len) + { + Contracts.Assert(len >= 0); + var values = dst.Values; + if (Utils.Size(values) < len) + values = new Single[len]; + for (int i = 0; i < len; i++) + values[i] = Single.NaN; + dst = new VBuffer(len, values, dst.Indices); + } + } +} diff --git a/src/Microsoft.ML.Ensemble/OutputCombiners/BaseScalarStacking.cs b/src/Microsoft.ML.Ensemble/OutputCombiners/BaseScalarStacking.cs new file mode 100644 index 0000000000..a5c9c757a4 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/OutputCombiners/BaseScalarStacking.cs @@ -0,0 +1,35 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Internal.Utilities; +using Microsoft.ML.Runtime.Model; + +namespace Microsoft.ML.Runtime.Ensemble.OutputCombiners +{ + public abstract class BaseScalarStacking : BaseStacking + { + internal BaseScalarStacking(IHostEnvironment env, string name, ArgumentsBase args) + : base(env, name, args) + { + } + + internal BaseScalarStacking(IHostEnvironment env, string name, ModelLoadContext ctx) + : base(env, name, ctx) + { + } + + protected override void FillFeatureBuffer(Single[] src, ref VBuffer dst) + { + Contracts.AssertNonEmpty(src); + int len = src.Length; + var values = dst.Values; + if (Utils.Size(values) < len) + values = new Single[len]; + Array.Copy(src, values, len); + dst = new VBuffer(len, values, dst.Indices); + } + } +} diff --git a/src/Microsoft.ML.Ensemble/OutputCombiners/BaseStacking.cs b/src/Microsoft.ML.Ensemble/OutputCombiners/BaseStacking.cs new file mode 100644 index 0000000000..7435958e62 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/OutputCombiners/BaseStacking.cs @@ -0,0 +1,201 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Collections.Generic; +using System.Threading.Tasks; +using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Internal.Internallearn; +using Microsoft.ML.Runtime.Internal.Utilities; +using Microsoft.ML.Runtime.Model; +using Microsoft.ML.Runtime.Training; + +namespace Microsoft.ML.Runtime.Ensemble.OutputCombiners +{ + using ColumnRole = RoleMappedSchema.ColumnRole; + public abstract class BaseStacking : IStackingTrainer + { + public abstract class ArgumentsBase + { + [Argument(ArgumentType.AtMostOnce, ShortName = "vp", SortOrder = 50, + HelpText = "The proportion of instances to be selected to test the individual base learner. If it is 0, it uses training set")] + [TGUI(Label = "Validation Dataset Proportion")] + public Single ValidationDatasetProportion = 0.3f; + + [Argument(ArgumentType.Multiple, HelpText = "Base predictor for meta learning", ShortName = "bp", SortOrder = 50, + Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly)] + [TGUI(Label = "Base predictor")] + public SubComponent>, TSigBase> BasePredictorType; + } + + protected readonly SubComponent>, TSigBase> BasePredictorType; + protected readonly IHost Host; + protected IPredictorProducing Meta; + + public Single ValidationDatasetProportion { get; } + + internal BaseStacking(IHostEnvironment env, string name, ArgumentsBase args) + { + Contracts.AssertValue(env); + env.AssertNonWhiteSpace(name); + Host = env.Register(name); + Host.AssertValue(args, "args"); + Host.CheckUserArg(0 <= args.ValidationDatasetProportion && args.ValidationDatasetProportion < 1, + nameof(args.ValidationDatasetProportion), + "The validation proportion for stacking should be greater than or equal to 0 and less than 1"); + Host.CheckUserArg(args.BasePredictorType.IsGood(), nameof(args.BasePredictorType)); + + ValidationDatasetProportion = args.ValidationDatasetProportion; + BasePredictorType = args.BasePredictorType; + } + + internal BaseStacking(IHostEnvironment env, string name, ModelLoadContext ctx) + { + Contracts.AssertValue(env); + env.AssertNonWhiteSpace(name); + Host = env.Register(name); + Host.AssertValue(ctx); + + // *** Binary format *** + // int: sizeof(Single) + // Float: _validationDatasetProportion + int cbFloat = ctx.Reader.ReadInt32(); + env.CheckDecode(cbFloat == sizeof(Single)); + ValidationDatasetProportion = ctx.Reader.ReadFloat(); + env.CheckDecode(0 <= ValidationDatasetProportion && ValidationDatasetProportion < 1); + + ctx.LoadModel, SignatureLoadModel>(env, out Meta, "MetaPredictor"); + CheckMeta(); + } + + public void Save(ModelSaveContext ctx) + { + Host.Check(Meta != null, "Can't save an untrained Stacking combiner"); + Host.CheckValue(ctx, nameof(ctx)); + ctx.CheckAtModel(); + SaveCore(ctx); + } + + protected virtual void SaveCore(ModelSaveContext ctx) + { + Host.Assert(Meta != null); + + // *** Binary format *** + // int: sizeof(Single) + // Float: _validationDatasetProportion + ctx.Writer.Write(sizeof(Single)); + ctx.Writer.Write(ValidationDatasetProportion); + + ctx.SaveModel(Meta, "MetaPredictor"); + } + + public Combiner GetCombiner() + { + Contracts.Check(Meta != null, "Training of stacking combiner not complete"); + + // Subtle point: We shouldn't get the ValueMapper delegate and cache it in a field + // since generally ValueMappers cannot be assumed to be thread safe - they often + // capture buffers needed for efficient operation. + var mapper = (IValueMapper)Meta; + var map = mapper.GetMapper, TOutput>(); + + var feat = default(VBuffer); + Combiner res = + (ref TOutput dst, TOutput[] src, Single[] weights) => + { + FillFeatureBuffer(src, ref feat); + map(ref feat, ref dst); + }; + return res; + } + + protected abstract void FillFeatureBuffer(TOutput[] src, ref VBuffer dst); + + private void CheckMeta() + { + Contracts.Assert(Meta != null); + + var ivm = Meta as IValueMapper; + Contracts.Check(ivm != null, "Stacking predictor doesn't implement the expected interface"); + if (!ivm.InputType.IsVector || ivm.InputType.ItemType != NumberType.Float) + throw Contracts.Except("Stacking predictor input type is unsupported: {0}", ivm.InputType); + if (ivm.OutputType.RawType != typeof(TOutput)) + throw Contracts.Except("Stacking predictor output type is unsupported: {0}", ivm.OutputType); + } + + public void Train(List>> models, RoleMappedData data, IHostEnvironment env) + { + Contracts.CheckValue(env, nameof(env)); + var host = env.Register(Stacking.LoadName); + host.CheckValue(models, nameof(models)); + host.CheckValue(data, nameof(data)); + + using (var ch = host.Start("Training stacked model")) + { + ch.Check(Meta == null, "Train called multiple times"); + ch.Check(BasePredictorType != null); + + var maps = new ValueMapper, TOutput>[models.Count]; + for (int i = 0; i < maps.Length; i++) + { + Contracts.Assert(models[i].Predictor is IValueMapper); + var m = (IValueMapper)models[i].Predictor; + maps[i] = m.GetMapper, TOutput>(); + } + + // REVIEW: Should implement this better.... + var labels = new Single[100]; + var features = new VBuffer[100]; + int count = 0; + // REVIEW: Should this include bad values or filter them? + using (var cursor = new FloatLabelCursor(data, CursOpt.AllFeatures | CursOpt.AllLabels)) + { + TOutput[] predictions = new TOutput[maps.Length]; + var vBuffers = new VBuffer[maps.Length]; + while (cursor.MoveNext()) + { + Parallel.For(0, maps.Length, i => + { + var model = models[i]; + if (model.SelectedFeatures != null) + { + EnsembleUtils.SelectFeatures(ref cursor.Features, model.SelectedFeatures, model.Cardinality, ref vBuffers[i]); + maps[i](ref vBuffers[i], ref predictions[i]); + } + else + maps[i](ref cursor.Features, ref predictions[i]); + }); + + Utils.EnsureSize(ref labels, count + 1); + Utils.EnsureSize(ref features, count + 1); + labels[count] = cursor.Label; + FillFeatureBuffer(predictions, ref features[count]); + count++; + } + } + + ch.Info("The number of instances used for stacking trainer is {0}", count); + + var bldr = new ArrayDataViewBuilder(host); + Array.Resize(ref labels, count); + Array.Resize(ref features, count); + bldr.AddColumn("Label", NumberType.Float, labels); + bldr.AddColumn("Features", NumberType.Float, features); + + var view = bldr.GetDataView(); + var rmd = RoleMappedData.Create(view, ColumnRole.Label.Bind("Label"), ColumnRole.Feature.Bind("Features")); + + var trainer = BasePredictorType.CreateInstance(host); + if (trainer is ITrainerEx ex && ex.NeedNormalization) + ch.Warning("The trainer specified for stacking wants normalization, but we do not currently allow this."); + trainer.Train(rmd); + Meta = trainer.CreatePredictor(); + CheckMeta(); + + ch.Done(); + } + } + } +} diff --git a/src/Microsoft.ML.Ensemble/OutputCombiners/IOutputCombiner.cs b/src/Microsoft.ML.Ensemble/OutputCombiners/IOutputCombiner.cs new file mode 100644 index 0000000000..d6a83070ba --- /dev/null +++ b/src/Microsoft.ML.Ensemble/OutputCombiners/IOutputCombiner.cs @@ -0,0 +1,72 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Collections.Generic; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.EntryPoints; + +namespace Microsoft.ML.Runtime.Ensemble.OutputCombiners +{ + /// + /// Signature for combiners. + /// + public delegate void SignatureCombiner(); + + public delegate void Combiner(ref TOutput dst, TOutput[] src, Single[] weights); + + public interface IOutputCombiner + { + } + + /// + /// Generic interface for combining outputs of multiple models + /// + public interface IOutputCombiner : IOutputCombiner + { + Combiner GetCombiner(); + } + + public interface IStackingTrainer + { + void Train(List>> models, RoleMappedData data, IHostEnvironment env); + Single ValidationDatasetProportion { get; } + } + + public interface IRegressionOutputCombiner : IOutputCombiner + { + } + + public interface IBinaryOutputCombiner : IOutputCombiner + { + } + + public interface IMultiClassOutputCombiner : IOutputCombiner> + { + } + + + [TlcModule.ComponentKind("EnsembleMulticlassOutputCombiner")] + public interface ISupportMulticlassOutputCombinerFactory : IComponentFactory + { + } + + [TlcModule.ComponentKind("EnsembleBinaryOutputCombiner")] + public interface ISupportBinaryOutputCombinerFactory : IComponentFactory + { + + } + + [TlcModule.ComponentKind("EnsembleRegressionOutputCombiner")] + public interface ISupportRegressionOutputCombinerFactory : IComponentFactory + { + + } + + public interface IWeightedAverager + { + string WeightageMetricName { get; } + } + +} \ No newline at end of file diff --git a/src/Microsoft.ML.Ensemble/OutputCombiners/Median.cs b/src/Microsoft.ML.Ensemble/OutputCombiners/Median.cs new file mode 100644 index 0000000000..de8d950de4 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/OutputCombiners/Median.cs @@ -0,0 +1,87 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Ensemble.OutputCombiners; +using Microsoft.ML.Runtime.Internal.Utilities; +using Microsoft.ML.Runtime.Model; + +[assembly: LoadableClass(typeof(Median), null, typeof(SignatureCombiner), Median.UserName, Median.LoadName)] +[assembly: LoadableClass(typeof(Median), null, typeof(SignatureLoadModel), Median.UserName, Median.LoaderSignature)] + +namespace Microsoft.ML.Runtime.Ensemble.OutputCombiners +{ + /// + /// Generic interface for combining outputs of multiple models + /// + public sealed class Median : IRegressionOutputCombiner, IBinaryOutputCombiner, ICanSaveModel + { + private readonly IHost _host; + public const string UserName = "Median"; + public const string LoadName = "Median"; + public const string LoaderSignature = "MedianCombiner"; + + private static VersionInfo GetVersionInfo() + { + return new VersionInfo( + modelSignature: "MEDICOMB", + verWrittenCur: 0x00010001, + verReadableCur: 0x00010001, + verWeCanReadBack: 0x00010001, + loaderSignature: LoaderSignature); + } + + public Median(IHostEnvironment env) + { + Contracts.CheckValue(env, nameof(env)); + _host = env.Register(LoaderSignature); + } + + private Median(IHostEnvironment env, ModelLoadContext ctx) + { + Contracts.AssertValue(env); + _host = env.Register(LoaderSignature); + + // *** Binary format *** + // int: sizeof(Single) + int cbFloat = ctx.Reader.ReadInt32(); + _host.CheckDecode(cbFloat == sizeof(Single)); + } + + public static Median Create(IHostEnvironment env, ModelLoadContext ctx) + { + Contracts.CheckValue(env, nameof(env)); + env.CheckValue(ctx, nameof(ctx)); + ctx.CheckAtModel(GetVersionInfo()); + return new Median(env, ctx); + } + + public void Save(ModelSaveContext ctx) + { + _host.CheckValue(ctx, nameof(ctx)); + ctx.CheckAtModel(); + ctx.SetVersionInfo(GetVersionInfo()); + + // *** Binary format *** + // int: sizeof(Float) + ctx.Writer.Write(sizeof(Single)); + } + + public Combiner GetCombiner() + { + return CombineCore; + } + + private void CombineCore(ref Single dst, Single[] src, Single[] weights) + { + // REVIEW: This mutates "src". We need to ensure that the documentation of + // combiners makes it clear that combiners are allowed to do this. Note that "normalization" + // in the multi-class case also mutates. + _host.AssertNonEmpty(src); + _host.Assert(weights == null || Utils.Size(weights) == Utils.Size(src)); + dst = MathUtils.GetMedianInPlace(src, src.Length); + } + } +} diff --git a/src/Microsoft.ML.Ensemble/OutputCombiners/MultiAverage.cs b/src/Microsoft.ML.Ensemble/OutputCombiners/MultiAverage.cs new file mode 100644 index 0000000000..c147f932f3 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/OutputCombiners/MultiAverage.cs @@ -0,0 +1,72 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Ensemble.OutputCombiners; +using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.Model; + +[assembly: LoadableClass(typeof(MultiAverage), typeof(MultiAverage.Arguments), typeof(SignatureCombiner), + Average.UserName, MultiAverage.LoadName)] +[assembly: LoadableClass(typeof(MultiAverage), null, typeof(SignatureLoadModel), Average.UserName, + MultiAverage.LoadName, MultiAverage.LoaderSignature)] + +namespace Microsoft.ML.Runtime.Ensemble.OutputCombiners +{ + public sealed class MultiAverage : BaseMultiAverager, ICanSaveModel + { + public const string LoadName = "MultiAverage"; + public const string LoaderSignature = "MultiAverageCombiner"; + + private static VersionInfo GetVersionInfo() + { + return new VersionInfo( + modelSignature: "MAVGCOMB", + verWrittenCur: 0x00010001, + verReadableCur: 0x00010001, + verWeCanReadBack: 0x00010001, + loaderSignature: LoaderSignature); + } + + [TlcModule.Component(Name = LoadName, FriendlyName = Average.UserName)] + public sealed class Arguments : ArgumentsBase, ISupportMulticlassOutputCombinerFactory + { + public IMultiClassOutputCombiner CreateComponent(IHostEnvironment env) => new MultiAverage(env, this); + } + + public MultiAverage(IHostEnvironment env, Arguments args) + : base(env, LoaderSignature, args) + { + } + + private MultiAverage(IHostEnvironment env, ModelLoadContext ctx) + : base(env, LoaderSignature, ctx) + { + } + + public static MultiAverage Create(IHostEnvironment env, ModelLoadContext ctx) + { + Contracts.CheckValue(env, nameof(env)); + env.CheckValue(ctx, nameof(ctx)); + ctx.CheckAtModel(GetVersionInfo()); + return new MultiAverage(env, ctx); + } + + protected override void SaveCore(ModelSaveContext ctx) + { + base.SaveCore(ctx); + ctx.SetVersionInfo(GetVersionInfo()); + } + + public override Combiner> GetCombiner() + { + // Force the weights to null. + return + (ref VBuffer dst, VBuffer[] src, Single[] weights) => + CombineCore(ref dst, src, null); + } + } +} diff --git a/src/Microsoft.ML.Ensemble/OutputCombiners/MultiMedian.cs b/src/Microsoft.ML.Ensemble/OutputCombiners/MultiMedian.cs new file mode 100644 index 0000000000..c3e6869d69 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/OutputCombiners/MultiMedian.cs @@ -0,0 +1,102 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Ensemble.OutputCombiners; +using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.Internal.Utilities; +using Microsoft.ML.Runtime.Model; + +[assembly: LoadableClass(typeof(MultiMedian), typeof(MultiMedian.Arguments), typeof(SignatureCombiner), + Median.UserName, MultiMedian.LoadName)] +[assembly: LoadableClass(typeof(MultiMedian), null, typeof(SignatureLoadModel), Median.UserName, MultiMedian.LoaderSignature)] + +namespace Microsoft.ML.Runtime.Ensemble.OutputCombiners +{ + /// + /// Generic interface for combining outputs of multiple models + /// + public sealed class MultiMedian : BaseMultiCombiner, ICanSaveModel + { + public const string LoadName = "MultiMedian"; + public const string LoaderSignature = "MultiMedianCombiner"; + + private static VersionInfo GetVersionInfo() + { + return new VersionInfo( + modelSignature: "MMEDCOMB", + verWrittenCur: 0x00010001, + verReadableCur: 0x00010001, + verWeCanReadBack: 0x00010001, + loaderSignature: LoaderSignature); + } + + [TlcModule.Component(Name = LoadName, FriendlyName = Median.UserName)] + public sealed class Arguments : ArgumentsBase, ISupportMulticlassOutputCombinerFactory + { + public IMultiClassOutputCombiner CreateComponent(IHostEnvironment env) => new MultiMedian(env, this); + } + + public MultiMedian(IHostEnvironment env, Arguments args) + : base(env, LoaderSignature, args) + { + } + + private MultiMedian(IHostEnvironment env, ModelLoadContext ctx) + : base(env, LoaderSignature, ctx) + { + } + + public static MultiMedian Create(IHostEnvironment env, ModelLoadContext ctx) + { + Contracts.CheckValue(env, nameof(env)); + env.CheckValue(ctx, nameof(ctx)); + ctx.CheckAtModel(GetVersionInfo()); + return new MultiMedian(env, ctx); + } + + protected override void SaveCore(ModelSaveContext ctx) + { + base.SaveCore(ctx); + ctx.SetVersionInfo(GetVersionInfo()); + } + + public override Combiner> GetCombiner() + { + Single[] raw = null; + return + (ref VBuffer dst, VBuffer[] src, Single[] weights) => + { + Host.AssertNonEmpty(src); + Host.Assert(weights == null || Utils.Size(weights) == Utils.Size(src)); + + int len = GetClassCount(src); + if (!TryNormalize(src)) + { + GetNaNOutput(ref dst, len); + return; + } + + var values = dst.Values; + if (Utils.Size(values) < len) + values = new Single[len]; + + int count = src.Length; + if (Utils.Size(raw) < count) + raw = new Single[count]; + for (int i = 0; i < len; i++) + { + for (int j = 0; j < count; j++) + raw[j] = i < src[j].Length ? src[j].GetItemOrDefault(i) : 0; + values[i] = MathUtils.GetMedianInPlace(raw, count); + } + + // Set the output to values. + dst = new VBuffer(len, values, dst.Indices); + }; + } + } +} diff --git a/src/Microsoft.ML.Ensemble/OutputCombiners/MultiStacking.cs b/src/Microsoft.ML.Ensemble/OutputCombiners/MultiStacking.cs new file mode 100644 index 0000000000..588dd89508 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/OutputCombiners/MultiStacking.cs @@ -0,0 +1,99 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Ensemble.OutputCombiners; +using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.Internal.Utilities; +using Microsoft.ML.Runtime.Model; + +[assembly: LoadableClass(typeof(MultiStacking), typeof(MultiStacking.Arguments), typeof(SignatureCombiner), + Stacking.UserName, MultiStacking.LoadName)] + +[assembly: LoadableClass(typeof(MultiStacking), null, typeof(SignatureLoadModel), + Stacking.UserName, MultiStacking.LoaderSignature)] + +namespace Microsoft.ML.Runtime.Ensemble.OutputCombiners +{ + using TVectorPredictor = IPredictorProducing>; + public sealed class MultiStacking : BaseStacking, SignatureMultiClassClassifierTrainer>, ICanSaveModel, IMultiClassOutputCombiner + { + public const string LoadName = "MultiStacking"; + public const string LoaderSignature = "MultiStackingCombiner"; + + private static VersionInfo GetVersionInfo() + { + return new VersionInfo( + modelSignature: "MSTACK C", + verWrittenCur: 0x00010001, // Initial + verReadableCur: 0x00010001, + verWeCanReadBack: 0x00010001, + loaderSignature: LoaderSignature); + } + + [TlcModule.Component(Name = LoadName, FriendlyName = Stacking.UserName)] + public sealed class Arguments : ArgumentsBase, ISupportMulticlassOutputCombinerFactory + { + public IMultiClassOutputCombiner CreateComponent(IHostEnvironment env) => new MultiStacking(env, this); + + public Arguments() + { + // REVIEW: Perhaps we can have a better non-parametetric learner. + BasePredictorType = new SubComponent, SignatureMultiClassClassifierTrainer>( + "OVA", "p=FastTreeBinaryClassification"); + } + } + + public MultiStacking(IHostEnvironment env, Arguments args) + : base(env, LoaderSignature, args) + { + } + + private MultiStacking(IHostEnvironment env, ModelLoadContext ctx) + : base(env, LoaderSignature, ctx) + { + } + + public static MultiStacking Create(IHostEnvironment env, ModelLoadContext ctx) + { + Contracts.CheckValue(env, nameof(env)); + env.CheckValue(ctx, nameof(ctx)); + ctx.CheckAtModel(GetVersionInfo()); + return new MultiStacking(env, ctx); + } + + protected override void SaveCore(ModelSaveContext ctx) + { + base.SaveCore(ctx); + ctx.SetVersionInfo(GetVersionInfo()); + } + + protected override void FillFeatureBuffer(VBuffer[] src, ref VBuffer dst) + { + Contracts.AssertNonEmpty(src); + + // REVIEW: Would there be any value in ever making dst sparse? + int len = 0; + for (int i = 0; i < src.Length; i++) + len += src[i].Length; + + var values = dst.Values; + if (Utils.Size(values) < len) + values = new Single[len]; + dst = new VBuffer(len, values, dst.Indices); + + int iv = 0; + for (int i = 0; i < src.Length; i++) + { + src[i].CopyTo(values, iv); + iv += src[i].Length; + Contracts.Assert(iv <= len); + } + Contracts.Assert(iv == len); + } + } +} diff --git a/src/Microsoft.ML.Ensemble/OutputCombiners/MultiVoting.cs b/src/Microsoft.ML.Ensemble/OutputCombiners/MultiVoting.cs new file mode 100644 index 0000000000..0cd36c9e47 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/OutputCombiners/MultiVoting.cs @@ -0,0 +1,114 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Ensemble.OutputCombiners; +using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.Internal.Utilities; +using Microsoft.ML.Runtime.Model; +using Microsoft.ML.Runtime.Numeric; + +[assembly: LoadableClass(typeof(MultiVoting), typeof(MultiVoting.Arguments), typeof(SignatureCombiner), Voting.UserName, MultiVoting.LoadName)] +[assembly: LoadableClass(typeof(MultiVoting), null, typeof(SignatureLoadModel), Voting.UserName, MultiVoting.LoaderSignature)] + +namespace Microsoft.ML.Runtime.Ensemble.OutputCombiners +{ + // REVIEW: Why is MultiVoting based on BaseMultiCombiner? Normalizing the model outputs + // is senseless, so the base adds no real functionality. + public sealed class MultiVoting : BaseMultiCombiner, ICanSaveModel + { + public const string LoadName = "MultiVoting"; + public const string LoaderSignature = "MultiVotingCombiner"; + + private static VersionInfo GetVersionInfo() + { + return new VersionInfo( + modelSignature: "MVOTCOMB", + verWrittenCur: 0x00010001, + verReadableCur: 0x00010001, + verWeCanReadBack: 0x00010001, + loaderSignature: LoaderSignature); + } + + [TlcModule.Component(Name = LoadName, FriendlyName = Voting.UserName)] + public sealed class Arguments : ArgumentsBase, ISupportMulticlassOutputCombinerFactory + { + public new bool Normalize = false; + + public IMultiClassOutputCombiner CreateComponent(IHostEnvironment env) => new MultiVoting(env, this); + } + + public MultiVoting(IHostEnvironment env, Arguments args) + : base(env, LoaderSignature, args) + { + Host.Assert(!Normalize); + } + + private MultiVoting(IHostEnvironment env, ModelLoadContext ctx) + : base(env, LoaderSignature, ctx) + { + Host.CheckDecode(!Normalize); + } + + public static MultiVoting Create(IHostEnvironment env, ModelLoadContext ctx) + { + Contracts.CheckValue(env, nameof(env)); + env.CheckValue(ctx, nameof(ctx)); + ctx.CheckAtModel(GetVersionInfo()); + return new MultiVoting(env, ctx); + } + + protected override void SaveCore(ModelSaveContext ctx) + { + Contracts.Assert(!Normalize); + base.SaveCore(ctx); + ctx.SetVersionInfo(GetVersionInfo()); + } + + public override Combiner> GetCombiner() + { + return CombineCore; + } + + private void CombineCore(ref VBuffer dst, VBuffer[] src, Single[] weights = null) + { + Host.AssertNonEmpty(src); + Host.Assert(weights == null || Utils.Size(weights) == Utils.Size(src)); + + int count = Utils.Size(src); + if (count == 0) + { + dst = new VBuffer(0, dst.Values, dst.Indices); + return; + } + + int len = GetClassCount(src); + var values = dst.Values; + if (Utils.Size(values) < len) + values = new Single[len]; + else + Array.Clear(values, 0, len); + + int voteCount = 0; + for (int i = 0; i < count; i++) + { + int index = VectorUtils.ArgMax(ref src[i]); + if (index >= 0) + { + values[index]++; + voteCount++; + } + } + + // Normalize by dividing by the number of votes. + for (int i = 0; i < len; i++) + values[i] /= voteCount; + + // Set the output to values. + dst = new VBuffer(len, values, dst.Indices); + } + } +} diff --git a/src/Microsoft.ML.Ensemble/OutputCombiners/MultiWeightedAverage.cs b/src/Microsoft.ML.Ensemble/OutputCombiners/MultiWeightedAverage.cs new file mode 100644 index 0000000000..9bda1d151a --- /dev/null +++ b/src/Microsoft.ML.Ensemble/OutputCombiners/MultiWeightedAverage.cs @@ -0,0 +1,104 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Ensemble.OutputCombiners; +using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.Internal.Internallearn; +using Microsoft.ML.Runtime.Model; + +[assembly: LoadableClass(typeof(MultiWeightedAverage), typeof(MultiWeightedAverage.Arguments), typeof(SignatureCombiner), + MultiWeightedAverage.UserName, MultiWeightedAverage.LoadName)] + +[assembly: LoadableClass(typeof(MultiWeightedAverage), null, typeof(SignatureLoadModel), + MultiWeightedAverage.UserName, MultiWeightedAverage.LoaderSignature)] + +namespace Microsoft.ML.Runtime.Ensemble.OutputCombiners +{ + /// + /// Generic interface for combining outputs of multiple models + /// + public sealed class MultiWeightedAverage : BaseMultiAverager, IWeightedAverager, ICanSaveModel + { + public const string UserName = "Multi Weighted Average"; + public const string LoadName = "MultiWeightedAverage"; + public const string LoaderSignature = "MultiWeightedAverageComb"; + + private static VersionInfo GetVersionInfo() + { + return new VersionInfo( + modelSignature: "MWAVCOMB", + verWrittenCur: 0x00010001, + verReadableCur: 0x00010001, + verWeCanReadBack: 0x00010001, + loaderSignature: LoaderSignature); + } + + [TlcModule.Component(Name = LoadName, FriendlyName = UserName)] + public sealed class Arguments : ArgumentsBase, ISupportMulticlassOutputCombinerFactory + { + IMultiClassOutputCombiner IComponentFactory.CreateComponent(IHostEnvironment env) => new MultiWeightedAverage(env, this); + + [Argument(ArgumentType.AtMostOnce, HelpText = "The metric type to be used to find the weights for each model", ShortName = "wn", SortOrder = 50)] + [TGUI(Label = "Metric Name", Description = "The weights are calculated according to the selected metric")] + public MultiWeightageKind WeightageName = MultiWeightageKind.AccuracyMicroAvg; + } + + private readonly MultiWeightageKind _weightageKind; + public string WeightageMetricName { get { return _weightageKind.ToString(); } } + + public MultiWeightedAverage(IHostEnvironment env, Arguments args) + : base(env, LoaderSignature, args) + { + _weightageKind = args.WeightageName; + Host.CheckUserArg(Enum.IsDefined(typeof(MultiWeightageKind), _weightageKind), nameof(args.WeightageName)); + } + + private MultiWeightedAverage(IHostEnvironment env, ModelLoadContext ctx) + : base(env, LoaderSignature, ctx) + { + // *** Binary format *** + // int: _weightageKind + + _weightageKind = (MultiWeightageKind)ctx.Reader.ReadInt32(); + Host.CheckDecode(Enum.IsDefined(typeof(MultiWeightageKind), _weightageKind)); + } + + public static MultiWeightedAverage Create(IHostEnvironment env, ModelLoadContext ctx) + { + Contracts.CheckValue(env, nameof(env)); + env.CheckValue(ctx, nameof(ctx)); + ctx.CheckAtModel(GetVersionInfo()); + return new MultiWeightedAverage(env, ctx); + } + + protected override void SaveCore(ModelSaveContext ctx) + { + base.SaveCore(ctx); + ctx.SetVersionInfo(GetVersionInfo()); + // *** Binary format *** + // int: _weightageKind + + Host.Assert(Enum.IsDefined(typeof(MultiWeightageKind), _weightageKind)); + ctx.Writer.Write((int)_weightageKind); + } + + public override Combiner> GetCombiner() + { + return CombineCore; + } + } + + // These values are serialized, so should not be changed. + public enum MultiWeightageKind + { + [TGUI(Label = MultiClassClassifierEvaluator.AccuracyMicro)] + AccuracyMicroAvg = 0, + [TGUI(Label = MultiClassClassifierEvaluator.AccuracyMacro)] + AccuracyMacroAvg = 1 + } +} diff --git a/src/Microsoft.ML.Ensemble/OutputCombiners/RegressionStacking.cs b/src/Microsoft.ML.Ensemble/OutputCombiners/RegressionStacking.cs new file mode 100644 index 0000000000..aeb011a51b --- /dev/null +++ b/src/Microsoft.ML.Ensemble/OutputCombiners/RegressionStacking.cs @@ -0,0 +1,72 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. +using System; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Ensemble.OutputCombiners; +using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.Model; + +[assembly: LoadableClass(typeof(RegressionStacking), typeof(RegressionStacking.Arguments), typeof(SignatureCombiner), + Stacking.UserName, RegressionStacking.LoadName)] + +[assembly: LoadableClass(typeof(RegressionStacking), null, typeof(SignatureLoadModel), + Stacking.UserName, RegressionStacking.LoaderSignature)] + +namespace Microsoft.ML.Runtime.Ensemble.OutputCombiners +{ + using TScalarPredictor = IPredictorProducing; + + public sealed class RegressionStacking : BaseScalarStacking, IRegressionOutputCombiner, ICanSaveModel + { + public const string LoadName = "RegressionStacking"; + public const string LoaderSignature = "RegressionStacking"; + + private static VersionInfo GetVersionInfo() + { + return new VersionInfo( + modelSignature: "RSTACK C", + verWrittenCur: 0x00010001, // Initial + verReadableCur: 0x00010001, + verWeCanReadBack: 0x00010001, + loaderSignature: LoaderSignature); + } + + [TlcModule.Component(Name = LoadName, FriendlyName = Stacking.UserName)] + public sealed class Arguments : ArgumentsBase, ISupportRegressionOutputCombinerFactory + { + public Arguments() + { + BasePredictorType = new SubComponent, SignatureRegressorTrainer>("FastTreeRegression"); + } + + public IRegressionOutputCombiner CreateComponent(IHostEnvironment env) => new RegressionStacking(env, this); + } + + public RegressionStacking(IHostEnvironment env, Arguments args) + : base(env, LoaderSignature, args) + { + } + + private RegressionStacking(IHostEnvironment env, ModelLoadContext ctx) + : base(env, LoaderSignature, ctx) + { + } + + public static RegressionStacking Create(IHostEnvironment env, ModelLoadContext ctx) + { + Contracts.CheckValue(env, nameof(env)); + env.CheckValue(ctx, nameof(ctx)); + ctx.CheckAtModel(GetVersionInfo()); + return new RegressionStacking(env, ctx); + } + + protected override void SaveCore(ModelSaveContext ctx) + { + base.SaveCore(ctx); + ctx.SetVersionInfo(GetVersionInfo()); + } + } +} diff --git a/src/Microsoft.ML.Ensemble/OutputCombiners/Stacking.cs b/src/Microsoft.ML.Ensemble/OutputCombiners/Stacking.cs new file mode 100644 index 0000000000..afd6e3f958 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/OutputCombiners/Stacking.cs @@ -0,0 +1,70 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Ensemble.OutputCombiners; +using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.Model; + +[assembly: LoadableClass(typeof(Stacking), typeof(Stacking.Arguments), typeof(SignatureCombiner), Stacking.UserName, Stacking.LoadName)] +[assembly: LoadableClass(typeof(Stacking), null, typeof(SignatureLoadModel), Stacking.UserName, Stacking.LoaderSignature)] + +namespace Microsoft.ML.Runtime.Ensemble.OutputCombiners +{ + using TScalarPredictor = IPredictorProducing; + public sealed class Stacking : BaseScalarStacking, IBinaryOutputCombiner, ICanSaveModel + { + public const string UserName = "Stacking"; + public const string LoadName = "Stacking"; + public const string LoaderSignature = "StackingCombiner"; + + private static VersionInfo GetVersionInfo() + { + return new VersionInfo( + modelSignature: " STACK C", + verWrittenCur: 0x00010001, // Initial + verReadableCur: 0x00010001, + verWeCanReadBack: 0x00010001, + loaderSignature: LoaderSignature); + } + + [TlcModule.Component(Name = LoadName, FriendlyName = UserName)] + public sealed class Arguments : ArgumentsBase, ISupportBinaryOutputCombinerFactory + { + public Arguments() + { + BasePredictorType = new SubComponent, SignatureBinaryClassifierTrainer>("FastTreeBinaryClassification"); + } + + public IBinaryOutputCombiner CreateComponent(IHostEnvironment env) => new Stacking(env, this); + } + + public Stacking(IHostEnvironment env, Arguments args) + : base(env, LoaderSignature, args) + { + } + + private Stacking(IHostEnvironment env, ModelLoadContext ctx) + : base(env, LoaderSignature, ctx) + { + } + + public static Stacking Create(IHostEnvironment env, ModelLoadContext ctx) + { + Contracts.CheckValue(env, nameof(env)); + env.CheckValue(ctx, nameof(ctx)); + ctx.CheckAtModel(GetVersionInfo()); + return new Stacking(env, ctx); + } + + protected override void SaveCore(ModelSaveContext ctx) + { + base.SaveCore(ctx); + ctx.SetVersionInfo(GetVersionInfo()); + } + } +} diff --git a/src/Microsoft.ML.Ensemble/OutputCombiners/Voting.cs b/src/Microsoft.ML.Ensemble/OutputCombiners/Voting.cs new file mode 100644 index 0000000000..932f99d93a --- /dev/null +++ b/src/Microsoft.ML.Ensemble/OutputCombiners/Voting.cs @@ -0,0 +1,94 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Ensemble.OutputCombiners; +using Microsoft.ML.Runtime.Internal.Utilities; +using Microsoft.ML.Runtime.Model; + +[assembly: LoadableClass(typeof(Voting), null, typeof(SignatureCombiner), Voting.UserName, Voting.LoadName)] +[assembly: LoadableClass(typeof(Voting), null, typeof(SignatureLoadModel), Voting.UserName, Voting.LoaderSignature)] + +namespace Microsoft.ML.Runtime.Ensemble.OutputCombiners +{ + public sealed class Voting : IBinaryOutputCombiner, ICanSaveModel + { + private readonly IHost _host; + public const string UserName = "Voting"; + public const string LoadName = "Voting"; + public const string LoaderSignature = "VotingCombiner"; + + private static VersionInfo GetVersionInfo() + { + return new VersionInfo( + modelSignature: "VOT COMB", + verWrittenCur: 0x00010001, + verReadableCur: 0x00010001, + verWeCanReadBack: 0x00010001, + loaderSignature: LoaderSignature); + } + + public Voting(IHostEnvironment env) + { + Contracts.CheckValue(env, nameof(env)); + _host = env.Register(LoaderSignature); + } + + private Voting(IHostEnvironment env, ModelLoadContext ctx) + { + Contracts.AssertValue(env); + _host = env.Register(LoaderSignature); + _host.AssertValue(ctx); + + // *** Binary format *** + // int: sizeof(Single) + int cbFloat = ctx.Reader.ReadInt32(); + _host.CheckDecode(cbFloat == sizeof(Single)); + } + + public static Voting Create(IHostEnvironment env, ModelLoadContext ctx) + { + Contracts.CheckValue(env, nameof(env)); + env.CheckValue(ctx, nameof(ctx)); + ctx.CheckAtModel(GetVersionInfo()); + return new Voting(env, ctx); + } + + public void Save(ModelSaveContext ctx) + { + _host.CheckValue(ctx, nameof(ctx)); + ctx.CheckAtModel(); + ctx.SetVersionInfo(GetVersionInfo()); + + // *** Binary format *** + // int: sizeof(Single) + ctx.Writer.Write(sizeof(Single)); + } + + public Combiner GetCombiner() + { + return CombineCore; + } + + private void CombineCore(ref Single dst, Single[] src, Single[] weights) + { + _host.AssertNonEmpty(src); + _host.Assert(weights == null || Utils.Size(weights) == Utils.Size(src)); + + int len = Utils.Size(src); + int pos = 0; + int neg = 0; + for (int i = 0; i < src.Length; i++) + { + var v = src[i]; + if (v > 0) + pos++; + else if (v <= 0) + neg++; + } + dst = (Single)(pos - neg) / (pos + neg); + } + } +} diff --git a/src/Microsoft.ML.Ensemble/OutputCombiners/WeightedAverage.cs b/src/Microsoft.ML.Ensemble/OutputCombiners/WeightedAverage.cs new file mode 100644 index 0000000000..4a824235a7 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/OutputCombiners/WeightedAverage.cs @@ -0,0 +1,111 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Ensemble.OutputCombiners; +using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.Internal.Internallearn; +using Microsoft.ML.Runtime.Model; + +[assembly: LoadableClass(typeof(WeightedAverage), typeof(WeightedAverage.Arguments), typeof(SignatureCombiner), + WeightedAverage.UserName, WeightedAverage.LoadName)] + +[assembly: LoadableClass(typeof(WeightedAverage), null, typeof(SignatureLoadModel), + WeightedAverage.UserName, WeightedAverage.LoaderSignature)] + +namespace Microsoft.ML.Runtime.Ensemble.OutputCombiners +{ + public sealed class WeightedAverage : BaseAverager, IWeightedAverager, ICanSaveModel + { + public const string UserName = "Weighted Average"; + public const string LoadName = "WeightedAverage"; + public const string LoaderSignature = "WeightedAverageCombiner"; + + private static VersionInfo GetVersionInfo() + { + return new VersionInfo( + modelSignature: "WAVGCOMB", + verWrittenCur: 0x00010001, + verReadableCur: 0x00010001, + verWeCanReadBack: 0x00010001, + loaderSignature: LoaderSignature); + } + + [TlcModule.Component(Name = LoadName, FriendlyName = Stacking.UserName)] + public sealed class Arguments: ISupportBinaryOutputCombinerFactory + { + [Argument(ArgumentType.AtMostOnce, HelpText = "The metric type to be used to find the weights for each model", ShortName = "wn", SortOrder = 50)] + [TGUI(Label = "Weightage Name", Description = "The weights are calculated according to the selected metric")] + public WeightageKind WeightageName = WeightageKind.Auc; + + public IBinaryOutputCombiner CreateComponent(IHostEnvironment env) => new WeightedAverage(env, this); + } + + private WeightageKind _weightageKind; + + public string WeightageMetricName { get { return _weightageKind.ToString(); } } + + public WeightedAverage(IHostEnvironment env, Arguments args) + : base(env, LoaderSignature) + { + _weightageKind = args.WeightageName; + Host.CheckUserArg(Enum.IsDefined(typeof(WeightageKind), _weightageKind), nameof(args.WeightageName)); + } + + private WeightedAverage(IHostEnvironment env, ModelLoadContext ctx) + : base(env, LoaderSignature, ctx) + { + // *** Binary format *** + // int: _weightageKind + _weightageKind = (WeightageKind)ctx.Reader.ReadInt32(); + Host.CheckDecode(Enum.IsDefined(typeof(WeightageKind), _weightageKind)); + } + + public static WeightedAverage Create(IHostEnvironment env, ModelLoadContext ctx) + { + Contracts.CheckValue(env, nameof(env)); + env.CheckValue(ctx, nameof(ctx)); + ctx.CheckAtModel(GetVersionInfo()); + return new WeightedAverage(env, ctx); + } + + protected override void SaveCore(ModelSaveContext ctx) + { + base.SaveCore(ctx); + ctx.SetVersionInfo(GetVersionInfo()); + + // *** Binary format *** + // int: _weightageKind + + Contracts.Assert(Enum.IsDefined(typeof(WeightageKind), _weightageKind)); + ctx.Writer.Write((int)_weightageKind); + } + + public override Combiner GetCombiner() + { + return CombineCore; + } + } + + // These values are serialized, so should not be changed. + public enum WeightageKind + { + [TGUI(Label = BinaryClassifierEvaluator.Accuracy)] + Accuracy = 0, + [TGUI(Label = BinaryClassifierEvaluator.Auc)] + Auc = 1, + [TGUI(Label = BinaryClassifierEvaluator.PosPrecName)] + PosPrecision = 2, + [TGUI(Label = BinaryClassifierEvaluator.PosRecallName)] + PosRecall = 3, + [TGUI(Label = BinaryClassifierEvaluator.NegPrecName)] + NegPrecision = 4, + [TGUI(Label = BinaryClassifierEvaluator.NegRecallName)] + NegRecall = 5, + } + +} diff --git a/src/Microsoft.ML.Ensemble/PipelineEnsemble.cs b/src/Microsoft.ML.Ensemble/PipelineEnsemble.cs new file mode 100644 index 0000000000..43956c608f --- /dev/null +++ b/src/Microsoft.ML.Ensemble/PipelineEnsemble.cs @@ -0,0 +1,749 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Collections.Generic; +using System.IO; +using System.Linq; +using System.Text; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Ensemble; +using Microsoft.ML.Runtime.Ensemble.OutputCombiners; +using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.Internal.Calibration; +using Microsoft.ML.Runtime.Internal.Internallearn; +using Microsoft.ML.Runtime.Internal.Utilities; +using Microsoft.ML.Runtime.Model; + +[assembly: LoadableClass(typeof(SchemaBindablePipelineEnsembleBase), null, typeof(SignatureLoadModel), + SchemaBindablePipelineEnsembleBase.UserName, SchemaBindablePipelineEnsembleBase.LoaderSignature)] + +namespace Microsoft.ML.Runtime.Ensemble +{ + /// + /// This class represents an ensemble predictor, where each predictor has its own featurization pipeline. It is + /// useful for the distributed training scenario, where the featurization includes trainable transforms (for example, + /// categorical transform, or normalization). + /// + public abstract class SchemaBindablePipelineEnsembleBase : ICanGetTrainingLabelNames, ICanSaveModel, + ISchemaBindableMapper, ICanSaveSummary, ICanGetSummaryInKeyValuePairs + { + private abstract class BoundBase : ISchemaBoundRowMapper + { + protected readonly SchemaBindablePipelineEnsembleBase Parent; + private readonly HashSet _inputColIndices; + + protected readonly ISchemaBoundRowMapper[] Mappers; + protected readonly IRowToRowMapper[] BoundPipelines; + protected readonly int[] ScoreCols; + + public ISchemaBindableMapper Bindable => Parent; + public RoleMappedSchema InputSchema { get; } + public ISchema OutputSchema { get; } + + public BoundBase(SchemaBindablePipelineEnsembleBase parent, RoleMappedSchema schema) + { + Parent = parent; + InputSchema = schema; + OutputSchema = new ScoreMapperSchema(Parent.ScoreType, Parent._scoreColumnKind); + _inputColIndices = new HashSet(); + for (int i = 0; i < Parent._inputCols.Length; i++) + { + var name = Parent._inputCols[i]; + if (!InputSchema.Schema.TryGetColumnIndex(name, out int col)) + throw Parent.Host.Except("Schema does not contain required input column '{0}'", name); + _inputColIndices.Add(col); + } + + Mappers = new ISchemaBoundRowMapper[Parent.PredictorModels.Length]; + BoundPipelines = new IRowToRowMapper[Parent.PredictorModels.Length]; + ScoreCols = new int[Parent.PredictorModels.Length]; + for (int i = 0; i < Mappers.Length; i++) + { + // Get the RoleMappedSchema to pass to the predictor. + var emptyDv = new EmptyDataView(Parent.Host, schema.Schema); + Parent.PredictorModels[i].PrepareData(Parent.Host, emptyDv, out RoleMappedData rmd, out IPredictor predictor); + + // Get the predictor as a bindable mapper, and bind it to the RoleMappedSchema found above. + var bindable = ScoreUtils.GetSchemaBindableMapper(Parent.Host, Parent.PredictorModels[i].Predictor, null); + Mappers[i] = bindable.Bind(Parent.Host, rmd.Schema) as ISchemaBoundRowMapper; + if (Mappers[i] == null) + throw Parent.Host.Except("Predictor {0} is not a row to row mapper", i); + + // Make sure there is a score column, and remember its index. + if (!Mappers[i].OutputSchema.TryGetColumnIndex(MetadataUtils.Const.ScoreValueKind.Score, out ScoreCols[i])) + throw Parent.Host.Except("Predictor {0} does not contain a score column", i); + + // Get the pipeline. + var dv = new EmptyDataView(Parent.Host, schema.Schema); + var tm = new TransformModel(Parent.Host, dv, dv); + var pipeline = Parent.PredictorModels[i].TransformModel.Apply(Parent.Host, tm); + BoundPipelines[i] = pipeline.AsRowToRowMapper(Parent.Host); + if (BoundPipelines[i] == null) + throw Parent.Host.Except("Transform pipeline {0} contains transforms that do not implement IRowToRowMapper", i); + } + } + + public Func GetDependencies(Func predicate) + { + for (int i = 0; i < OutputSchema.ColumnCount; i++) + { + if (predicate(i)) + return col => _inputColIndices.Contains(col); + } + return col => false; + } + + public IEnumerable> GetInputColumnRoles() + { + yield break; + } + + public IRow GetOutputRow(IRow input, Func predicate, out Action disposer) + { + return new SimpleRow(OutputSchema, input, new[] { CreateScoreGetter(input, predicate, out disposer) }); + } + + public abstract Delegate CreateScoreGetter(IRow input, Func mapperPredicate, out Action disposer); + } + + // A generic base class for pipeline ensembles. This class contains the combiner. + private abstract class SchemaBindablePipelineEnsemble : SchemaBindablePipelineEnsembleBase, IPredictorProducing + { + protected sealed class Bound : BoundBase + { + private readonly IOutputCombiner _combiner; + + public Bound(SchemaBindablePipelineEnsemble parent, RoleMappedSchema schema) + : base(parent, schema) + { + _combiner = parent.Combiner; + } + + public override Delegate CreateScoreGetter(IRow input, Func mapperPredicate, out Action disposer) + { + disposer = null; + + if (!mapperPredicate(0)) + return null; + + var getters = new ValueGetter[Mappers.Length]; + for (int i = 0; i < Mappers.Length; i++) + { + // First get the output row from the pipelines. The input predicate of the predictor + // is the output predicate of the pipeline. + var inputPredicate = Mappers[i].GetDependencies(mapperPredicate); + var pipelineRow = BoundPipelines[i].GetRow(input, inputPredicate, out Action disp); + disposer += disp; + + // Next we get the output row from the predictors. We activate the score column as output predicate. + var predictorRow = Mappers[i].GetOutputRow(pipelineRow, col => col == ScoreCols[i], out disp); + disposer += disp; + getters[i] = predictorRow.GetGetter(ScoreCols[i]); + } + + var comb = _combiner.GetCombiner(); + var buffer = new T[Mappers.Length]; + ValueGetter scoreGetter = + (ref T dst) => + { + for (int i = 0; i < Mappers.Length; i++) + getters[i](ref buffer[i]); + comb(ref dst, buffer, null); + }; + return scoreGetter; + } + + public ValueGetter GetLabelGetter(IRow input, int i, out Action disposer) + { + Parent.Host.Assert(0 <= i && i < Mappers.Length); + Parent.Host.Check(Mappers[i].InputSchema.Label != null, "Mapper was not trained using a label column"); + + // The label should be in the output row of the i'th pipeline + var pipelineRow = BoundPipelines[i].GetRow(input, col => col == Mappers[i].InputSchema.Label.Index, out disposer); + return RowCursorUtils.GetLabelGetter(pipelineRow, Mappers[i].InputSchema.Label.Index); + } + + public ValueGetter GetWeightGetter(IRow input, int i, out Action disposer) + { + Parent.Host.Assert(0 <= i && i < Mappers.Length); + + if (Mappers[i].InputSchema.Weight == null) + { + ValueGetter weight = (ref Single dst) => dst = 1; + disposer = null; + return weight; + } + // The weight should be in the output row of the i'th pipeline if it exists. + var inputPredicate = Mappers[i].GetDependencies(col => col == Mappers[i].InputSchema.Weight.Index); + var pipelineRow = BoundPipelines[i].GetRow(input, inputPredicate, out disposer); + return pipelineRow.GetGetter(Mappers[i].InputSchema.Weight.Index); + } + } + + protected readonly IOutputCombiner Combiner; + + protected SchemaBindablePipelineEnsemble(IHostEnvironment env, IPredictorModel[] predictors, + IOutputCombiner combiner, string registrationName, string scoreColumnKind) + : base(env, predictors, registrationName, scoreColumnKind) + { + Combiner = combiner; + } + + protected SchemaBindablePipelineEnsemble(IHostEnvironment env, ModelLoadContext ctx, string scoreColumnKind) + : base(env, ctx, scoreColumnKind) + { + // *** Binary format *** + // + // The combiner + + ctx.LoadModel, SignatureLoadModel>(Host, out Combiner, "Combiner"); + } + + protected override void SaveCore(ModelSaveContext ctx) + { + Host.AssertValue(ctx); + + // *** Binary format *** + // + // The combiner + + ctx.SaveModel(Combiner, "Combiner"); + } + + public override ISchemaBoundMapper Bind(IHostEnvironment env, RoleMappedSchema schema) + { + return new Bound(this, schema); + } + } + + // This is an implementation of pipeline ensembles that combines scores of type float (regression and anomaly detection). + private sealed class ImplOne : SchemaBindablePipelineEnsemble + { + protected override ColumnType ScoreType => NumberType.R4; + + public override PredictionKind PredictionKind + { + get + { + if (_scoreColumnKind == MetadataUtils.Const.ScoreColumnKind.Regression) + return PredictionKind.Regression; + if (_scoreColumnKind == MetadataUtils.Const.ScoreColumnKind.AnomalyDetection) + return PredictionKind.AnomalyDetection; + throw Host.Except("Unknown prediction kind"); + } + } + + public ImplOne(IHostEnvironment env, IPredictorModel[] predictors, IRegressionOutputCombiner combiner, string scoreColumnKind) + : base(env, predictors, combiner, LoaderSignature, scoreColumnKind) + { + } + + public ImplOne(IHostEnvironment env, ModelLoadContext ctx, string scoreColumnKind) + : base(env, ctx, scoreColumnKind) + { + } + } + + // This is an implementation of pipeline ensemble that combines scores of type vectors of float (multi-class). + private sealed class ImplVec : SchemaBindablePipelineEnsemble> + { + protected override ColumnType ScoreType { get { return _scoreType; } } + + public override PredictionKind PredictionKind + { + get + { + if (_scoreColumnKind == MetadataUtils.Const.ScoreColumnKind.MultiClassClassification) + return PredictionKind.MultiClassClassification; + throw Host.Except("Unknown prediction kind"); + } + } + + private readonly VectorType _scoreType; + + public ImplVec(IHostEnvironment env, IPredictorModel[] predictors, IMultiClassOutputCombiner combiner) + : base(env, predictors, combiner, LoaderSignature, MetadataUtils.Const.ScoreColumnKind.MultiClassClassification) + { + int classCount = CheckLabelColumn(Host, predictors, false); + _scoreType = new VectorType(NumberType.R4, classCount); + } + + public ImplVec(IHostEnvironment env, ModelLoadContext ctx, string scoreColumnKind) + : base(env, ctx, scoreColumnKind) + { + int classCount = CheckLabelColumn(Host, PredictorModels, false); + _scoreType = new VectorType(NumberType.R4, classCount); + } + } + + // This is an implementation of pipeline ensembles that combines scores of type float, and also provides calibration (for binary classification). + private sealed class ImplOneWithCalibrator : SchemaBindablePipelineEnsemble, ISelfCalibratingPredictor + { + protected override ColumnType ScoreType { get { return NumberType.R4; } } + + public override PredictionKind PredictionKind { get { return PredictionKind.BinaryClassification; } } + + public ImplOneWithCalibrator(IHostEnvironment env, IPredictorModel[] predictors, IBinaryOutputCombiner combiner) + : base(env, predictors, combiner, LoaderSignature, MetadataUtils.Const.ScoreColumnKind.BinaryClassification) + { + Host.Assert(_scoreColumnKind == MetadataUtils.Const.ScoreColumnKind.BinaryClassification); + CheckBinaryLabel(true, Host, PredictorModels); + } + + public ImplOneWithCalibrator(IHostEnvironment env, ModelLoadContext ctx, string scoreColumnKind) + : base(env, ctx, scoreColumnKind) + { + Host.Assert(_scoreColumnKind == MetadataUtils.Const.ScoreColumnKind.BinaryClassification); + CheckBinaryLabel(false, Host, PredictorModels); + } + + private static void CheckBinaryLabel(bool user, IHostEnvironment env, IPredictorModel[] predictors) + { + int classCount = CheckLabelColumn(env, predictors, true); + if (classCount != 2) + { + var error = string.Format("Expected label to have exactly 2 classes, instead has {0}", classCount); + throw user ? env.ExceptParam(nameof(predictors), error) : env.ExceptDecode(error); + } + } + + public IPredictor Calibrate(IChannel ch, IDataView data, ICalibratorTrainer caliTrainer, int maxRows) + { + Host.CheckValue(ch, nameof(ch)); + ch.CheckValue(data, nameof(data)); + ch.CheckValue(caliTrainer, nameof(caliTrainer)); + + if (caliTrainer.NeedsTraining) + { + var bound = new Bound(this, RoleMappedSchema.Create(data.Schema)); + using (var curs = data.GetRowCursor(col => true)) + { + var scoreGetter = (ValueGetter)bound.CreateScoreGetter(curs, col => true, out Action disposer); + + // We assume that we can use the label column of the first predictor, since if the labels are not identical + // then the whole model is garbage anyway. + var labelGetter = bound.GetLabelGetter(curs, 0, out Action disp); + disposer += disp; + var weightGetter = bound.GetWeightGetter(curs, 0, out disp); + disposer += disp; + try + { + int num = 0; + while (curs.MoveNext()) + { + Single label = 0; + labelGetter(ref label); + if (!FloatUtils.IsFinite(label)) + continue; + Single score = 0; + scoreGetter(ref score); + if (!FloatUtils.IsFinite(score)) + continue; + Single weight = 0; + weightGetter(ref weight); + if (!FloatUtils.IsFinite(weight)) + continue; + + caliTrainer.ProcessTrainingExample(score, label > 0, weight); + + if (maxRows > 0 && ++num >= maxRows) + break; + } + } + finally + { + disposer?.Invoke(); + } + } + } + + var calibrator = caliTrainer.FinishTraining(ch); + return CalibratorUtils.CreateCalibratedPredictor(Host, this, calibrator); + } + } + + private readonly string[] _inputCols; + + protected readonly IHost Host; + + private static VersionInfo GetVersionInfo() + { + return new VersionInfo( + modelSignature: "PIPELNEN", + //verWrittenCur: 0x00010001, // Initial + verWrittenCur: 0x00010002, // Save predictor models in a subdirectory + verReadableCur: 0x00010002, + verWeCanReadBack: 0x00010001, + loaderSignature: LoaderSignature); + } + public const string UserName = "Pipeline Ensemble"; + public const string LoaderSignature = "PipelineEnsemble"; + + private readonly string _scoreColumnKind; + + protected abstract ColumnType ScoreType { get; } + + public abstract PredictionKind PredictionKind { get; } + + internal IPredictorModel[] PredictorModels { get; } + + private SchemaBindablePipelineEnsembleBase(IHostEnvironment env, IPredictorModel[] predictors, string registrationName, string scoreColumnKind) + { + Contracts.CheckValue(env, nameof(env)); + Host = env.Register(registrationName); + Host.CheckNonEmpty(predictors, nameof(predictors)); + Host.CheckNonWhiteSpace(scoreColumnKind, nameof(scoreColumnKind)); + + PredictorModels = predictors; + _scoreColumnKind = scoreColumnKind; + + HashSet inputCols = null; + for (int i = 0; i < predictors.Length; i++) + { + var predModel = predictors[i]; + + // Get the input column names. + var inputSchema = predModel.TransformModel.InputSchema; + if (inputCols == null) + { + inputCols = new HashSet(); + for (int j = 0; j < inputSchema.ColumnCount; j++) + { + if (inputSchema.IsHidden(j)) + continue; + inputCols.Add(inputSchema.GetColumnName(j)); + } + _inputCols = inputCols.ToArray(); + } + else + { + int nonHiddenCols = 0; + for (int j = 0; j < inputSchema.ColumnCount; j++) + { + if (inputSchema.IsHidden(j)) + continue; + var name = inputSchema.GetColumnName(j); + if (!inputCols.Contains(name)) + throw Host.Except("Inconsistent schemas: Some schemas do not contain the column '{0}'", name); + nonHiddenCols++; + } + Host.Check(nonHiddenCols == _inputCols.Length, + "Inconsistent schemas: not all schemas have the same number of columns"); + } + } + } + + protected SchemaBindablePipelineEnsembleBase(IHostEnvironment env, ModelLoadContext ctx, string scoreColumnKind) + { + Host = env.Register(LoaderSignature); + Host.AssertNonEmpty(scoreColumnKind); + + _scoreColumnKind = scoreColumnKind; + + // *** Binary format *** + // int: id of _scoreColumnKind (loaded in the Create method) + // int: number of predictors + // The predictor models + // int: the number of input columns + // for each input column: + // int: id of the column name + + var length = ctx.Reader.ReadInt32(); + Host.CheckDecode(length > 0); + PredictorModels = new IPredictorModel[length]; + for (int i = 0; i < PredictorModels.Length; i++) + { + string dir = + ctx.Header.ModelVerWritten == 0x00010001 + ? "PredictorModels" + : Path.Combine(ctx.Directory, "PredictorModels"); + using (var ent = ctx.Repository.OpenEntry(dir, $"PredictorModel_{i:000}")) + PredictorModels[i] = new PredictorModel(Host, ent.Stream); + } + + length = ctx.Reader.ReadInt32(); + Host.CheckDecode(length >= 0); + _inputCols = new string[length]; + for (int i = 0; i < length; i++) + _inputCols[i] = ctx.LoadNonEmptyString(); + } + + public void Save(ModelSaveContext ctx) + { + Host.AssertValue(ctx); + ctx.SetVersionInfo(GetVersionInfo()); + + // *** Binary format *** + // int: id of _scoreColumnKind (loaded in the Create method) + // int: number of predictors + // The predictor models + // int: the number of input columns + // for each input column: + // int: id of the column name + + ctx.SaveNonEmptyString(_scoreColumnKind); + + Host.AssertNonEmpty(PredictorModels); + ctx.Writer.Write(PredictorModels.Length); + + for (int i = 0; i < PredictorModels.Length; i++) + { + var dir = Path.Combine(ctx.Directory, "PredictorModels"); + using (var ent = ctx.Repository.CreateEntry(dir, $"PredictorModel_{i:000}")) + PredictorModels[i].Save(Host, ent.Stream); + } + + Contracts.AssertValue(_inputCols); + ctx.Writer.Write(_inputCols.Length); + foreach (var name in _inputCols) + ctx.SaveNonEmptyString(name); + + SaveCore(ctx); + } + + protected abstract void SaveCore(ModelSaveContext ctx); + + public static SchemaBindablePipelineEnsembleBase Create(IHostEnvironment env, IPredictorModel[] predictors, IOutputCombiner combiner, string scoreColumnKind) + { + switch (scoreColumnKind) + { + case MetadataUtils.Const.ScoreColumnKind.BinaryClassification: + var binaryCombiner = combiner as IBinaryOutputCombiner; + if (binaryCombiner == null) + throw env.Except("Combiner type incompatible with score column kind"); + return new ImplOneWithCalibrator(env, predictors, binaryCombiner); + case MetadataUtils.Const.ScoreColumnKind.Regression: + case MetadataUtils.Const.ScoreColumnKind.AnomalyDetection: + var regressionCombiner = combiner as IRegressionOutputCombiner; + if (regressionCombiner == null) + throw env.Except("Combiner type incompatible with score column kind"); + return new ImplOne(env, predictors, regressionCombiner, scoreColumnKind); + case MetadataUtils.Const.ScoreColumnKind.MultiClassClassification: + var vectorCombiner = combiner as IMultiClassOutputCombiner; + if (vectorCombiner == null) + throw env.Except("Combiner type incompatible with score column kind"); + return new ImplVec(env, predictors, vectorCombiner); + default: + throw env.Except("Unknown score kind"); + } + } + + public static SchemaBindablePipelineEnsembleBase Create(IHostEnvironment env, ModelLoadContext ctx) + { + Contracts.CheckValue(env, nameof(env)); + env.CheckValue(ctx, nameof(ctx)); + ctx.CheckAtModel(GetVersionInfo()); + + var scoreColumnKind = ctx.LoadNonEmptyString(); + switch (scoreColumnKind) + { + case MetadataUtils.Const.ScoreColumnKind.BinaryClassification: + return new ImplOneWithCalibrator(env, ctx, scoreColumnKind); + case MetadataUtils.Const.ScoreColumnKind.Regression: + case MetadataUtils.Const.ScoreColumnKind.AnomalyDetection: + return new ImplOne(env, ctx, scoreColumnKind); + case MetadataUtils.Const.ScoreColumnKind.MultiClassClassification: + return new ImplVec(env, ctx, scoreColumnKind); + default: + throw env.Except("Unknown score kind"); + } + } + + public abstract ISchemaBoundMapper Bind(IHostEnvironment env, RoleMappedSchema schema); + + public void SaveSummary(TextWriter writer, RoleMappedSchema schema) + { + for (int i = 0; i < PredictorModels.Length; i++) + { + writer.WriteLine("Partition model {0} summary:", i); + + if (!(PredictorModels[i].Predictor is ICanSaveSummary summaryModel)) + { + writer.WriteLine("Model of type {0}", PredictorModels[i].Predictor.GetType().Name); + continue; + } + + // Load the feature names for the i'th model. + var dv = new EmptyDataView(Host, PredictorModels[i].TransformModel.InputSchema); + PredictorModels[i].PrepareData(Host, dv, out RoleMappedData rmd, out IPredictor pred); + summaryModel.SaveSummary(writer, rmd.Schema); + } + } + + // Checks that the predictors have matching label columns, and returns the number of classes in all predictors. + protected static int CheckLabelColumn(IHostEnvironment env, IPredictorModel[] models, bool isBinary) + { + Contracts.CheckValue(env, nameof(env)); + env.CheckNonEmpty(models, nameof(models)); + + var model = models[0]; + var edv = new EmptyDataView(env, model.TransformModel.InputSchema); + model.PrepareData(env, edv, out RoleMappedData rmd, out IPredictor pred); + var labelInfo = rmd.Schema.Label; + if (labelInfo == null) + throw env.Except("Training schema for model 0 does not have a label column"); + + var labelType = rmd.Schema.Schema.GetColumnType(rmd.Schema.Label.Index); + if (!labelType.IsKey) + return CheckNonKeyLabelColumnCore(env, pred, models, isBinary, labelType); + + if (isBinary && labelType.KeyCount != 2) + throw env.Except("Label is not binary"); + var schema = rmd.Schema.Schema; + var mdType = schema.GetMetadataTypeOrNull(MetadataUtils.Kinds.KeyValues, labelInfo.Index); + if (mdType == null || !mdType.IsKnownSizeVector) + throw env.Except("Label column of type key must have a vector of key values metadata"); + + return Utils.MarshalInvoke(CheckKeyLabelColumnCore, mdType.ItemType.RawType, env, models, labelType.AsKey, schema, labelInfo.Index, mdType); + } + + // When the label column is not a key, we check that the number of classes is the same for all the predictors, by checking the + // OutputType property of the IValueMapper. + // If any of the predictors do not implement IValueMapper we throw an exception. Returns the class count. + private static int CheckNonKeyLabelColumnCore(IHostEnvironment env, IPredictor pred, IPredictorModel[] models, bool isBinary, ColumnType labelType) + { + env.Assert(!labelType.IsKey); + env.AssertNonEmpty(models); + + if (isBinary) + return 2; + + // The label is numeric, we just have to check that the number of classes is the same. + if (!(pred is IValueMapper vm)) + throw env.Except("Cannot determine the number of classes the predictor outputs"); + var classCount = vm.OutputType.VectorSize; + + for (int i = 1; i < models.Length; i++) + { + var model = models[i]; + var edv = new EmptyDataView(env, model.TransformModel.InputSchema); + model.PrepareData(env, edv, out RoleMappedData rmd, out pred); + vm = pred as IValueMapper; + if (vm.OutputType.VectorSize != classCount) + throw env.Except("Label of model {0} has different number of classes than model 0", i); + } + return classCount; + } + + // Checks that all the label columns of the model have the same key type as their label column - including the same + // cardinality and the same key values, and returns the cardinality of the label column key. + private static int CheckKeyLabelColumnCore(IHostEnvironment env, IPredictorModel[] models, KeyType labelType, ISchema schema, int labelIndex, ColumnType keyValuesType) + where T : IEquatable + { + env.Assert(keyValuesType.ItemType.RawType == typeof(T)); + env.AssertNonEmpty(models); + var labelNames = default(VBuffer); + schema.GetMetadata(MetadataUtils.Kinds.KeyValues, labelIndex, ref labelNames); + var classCount = labelNames.Length; + + var curLabelNames = default(VBuffer); + for (int i = 1; i < models.Length; i++) + { + var model = models[i]; + var edv = new EmptyDataView(env, model.TransformModel.InputSchema); + model.PrepareData(env, edv, out RoleMappedData rmd, out IPredictor pred); + var labelInfo = rmd.Schema.Label; + if (labelInfo == null) + throw env.Except("Training schema for model {0} does not have a label column", i); + + var curLabelType = rmd.Schema.Schema.GetColumnType(rmd.Schema.Label.Index); + if (!labelType.Equals(curLabelType.AsKey)) + throw env.Except("Label column of model {0} has different type than model 0", i); + + var mdType = rmd.Schema.Schema.GetMetadataTypeOrNull(MetadataUtils.Kinds.KeyValues, labelInfo.Index); + if (!mdType.Equals(keyValuesType)) + throw env.Except("Label column of model {0} has different key value type than model 0", i); + rmd.Schema.Schema.GetMetadata(MetadataUtils.Kinds.KeyValues, labelInfo.Index, ref curLabelNames); + if (!AreEqual(ref labelNames, ref curLabelNames)) + throw env.Except("Label of model {0} has different values than model 0", i); + } + return classCount; + } + + private static bool AreEqual(ref VBuffer v1, ref VBuffer v2) + where T : IEquatable + { + if (v1.Length != v2.Length) + return false; + return v1.DenseValues().Zip(v2.DenseValues(), (x1, x2) => x1.Equals(x2)).All(b => b); + } + + /// + /// This method outputs a Key-Value Pair (kvp) per model in the ensemble. + /// * The key is the model number such as "Partition model 0 summary". If the model implements + /// then this string is followed by the first line of the model summary (the first line contains a description specific to the + /// model kind, such as "Feature gains" for FastTree or "Feature weights" for linear). + /// * The value: + /// - If the model implements then the value is the list of Key-Value pairs + /// containing the detailed summary for that model (for example, linear models have a list containing kvps where the keys + /// are the feature names and the values are the weights. FastTree has a similar list with the feature gains as values). + /// - If the model does not implement but does implement , + /// the value is a string containing the summary of that model. + /// - If neither of those interfaces are implemented then the value is a string containing the name of the type of model. + /// + /// + public IList> GetSummaryInKeyValuePairs(RoleMappedSchema schema) + { + Host.CheckValueOrNull(schema); + + var list = new List>(); + + var sb = new StringBuilder(); + for (int i = 0; i < PredictorModels.Length; i++) + { + var key = string.Format("Partition model {0} summary:", i); + var summaryKvps = PredictorModels[i].Predictor as ICanGetSummaryInKeyValuePairs; + var summaryModel = PredictorModels[i].Predictor as ICanSaveSummary; + if (summaryKvps == null && summaryModel == null) + { + list.Add(new KeyValuePair(key, PredictorModels[i].Predictor.GetType().Name)); + continue; + } + + // Load the feature names for the i'th model. + var dv = new EmptyDataView(Host, PredictorModels[i].TransformModel.InputSchema); + PredictorModels[i].PrepareData(Host, dv, out RoleMappedData rmd, out IPredictor pred); + + if (summaryModel != null) + { + sb.Clear(); + using (StringWriter sw = new StringWriter(sb)) + summaryModel.SaveSummary(sw, rmd.Schema); + } + + if (summaryKvps != null) + { + var listCur = summaryKvps.GetSummaryInKeyValuePairs(rmd.Schema); + if (summaryModel != null) + { + using (var reader = new StringReader(sb.ToString())) + { + string firstLine = null; + while (string.IsNullOrEmpty(firstLine)) + firstLine = reader.ReadLine(); + if (!string.IsNullOrEmpty(firstLine)) + key += ("\r\n" + firstLine); + } + } + list.Add(new KeyValuePair(key, listCur)); + } + else + { + Host.AssertValue(summaryModel); + list.Add(new KeyValuePair(key, sb.ToString())); + } + + } + return list; + } + + public string[] GetLabelNamesOrNull(out ColumnType labelType) + { + Host.AssertNonEmpty(PredictorModels); + return PredictorModels[0].GetLabelInfo(Host, out labelType); + } + } +} diff --git a/src/Microsoft.ML.Ensemble/Selector/DiversityMeasure/BaseDisagreementDiversityMeasure.cs b/src/Microsoft.ML.Ensemble/Selector/DiversityMeasure/BaseDisagreementDiversityMeasure.cs new file mode 100644 index 0000000000..6a34da757a --- /dev/null +++ b/src/Microsoft.ML.Ensemble/Selector/DiversityMeasure/BaseDisagreementDiversityMeasure.cs @@ -0,0 +1,45 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Collections.Concurrent; +using System.Collections.Generic; + +namespace Microsoft.ML.Runtime.Ensemble.Selector.DiversityMeasure +{ + public abstract class BaseDisagreementDiversityMeasure : IDiversityMeasure + { + public List> CalculateDiversityMeasure(IList>> models, + ConcurrentDictionary>, TOutput[]> predictions) + { + Contracts.Assert(models.Count > 1); + Contracts.Assert(predictions.Count == models.Count); + + var diversityValues = new List>(); + + for (int i = 0; i < (models.Count - 1); i++) + { + for (int j = i + 1; j < models.Count; j++) + { + Single differencesCount = 0; + var modelXOutputs = predictions[models[i]]; + var modelYOutputs = predictions[models[j]]; + for (int k = 0; k < modelXOutputs.Length; k++) + { + differencesCount += GetDifference(ref modelXOutputs[k], ref modelYOutputs[k]); + } + diversityValues.Add(new ModelDiversityMetric() + { + DiversityNumber = differencesCount, + ModelX = models[i], + ModelY = models[j] + }); + } + } + return diversityValues; + } + + protected abstract Single GetDifference(ref TOutput tOutput1, ref TOutput tOutput2); + } +} diff --git a/src/Microsoft.ML.Ensemble/Selector/DiversityMeasure/DisagreementDiversityMeasure.cs b/src/Microsoft.ML.Ensemble/Selector/DiversityMeasure/DisagreementDiversityMeasure.cs new file mode 100644 index 0000000000..8c41953d6b --- /dev/null +++ b/src/Microsoft.ML.Ensemble/Selector/DiversityMeasure/DisagreementDiversityMeasure.cs @@ -0,0 +1,25 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Ensemble.Selector; +using Microsoft.ML.Runtime.Ensemble.Selector.DiversityMeasure; + +[assembly: LoadableClass(typeof(DisagreementDiversityMeasure), null, typeof(SignatureEnsembleDiversityMeasure), + DisagreementDiversityMeasure.UserName, DisagreementDiversityMeasure.LoadName)] + +namespace Microsoft.ML.Runtime.Ensemble.Selector.DiversityMeasure +{ + public class DisagreementDiversityMeasure : BaseDisagreementDiversityMeasure + { + public const string UserName = "Disagreement Diversity Measure"; + public const string LoadName = "DisagreementDiversityMeasure"; + + protected override Single GetDifference(ref Single valueX, ref Single valueY) + { + return (valueX > 0 && valueY < 0 || valueX < 0 && valueY > 0) ? 1 : 0; + } + } +} diff --git a/src/Microsoft.ML.Ensemble/Selector/DiversityMeasure/ModelDiversityMetric.cs b/src/Microsoft.ML.Ensemble/Selector/DiversityMeasure/ModelDiversityMetric.cs new file mode 100644 index 0000000000..1ee03a9489 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/Selector/DiversityMeasure/ModelDiversityMetric.cs @@ -0,0 +1,15 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; + +namespace Microsoft.ML.Runtime.Ensemble.Selector.DiversityMeasure +{ + public class ModelDiversityMetric + { + public FeatureSubsetModel> ModelX { get; set; } + public FeatureSubsetModel> ModelY { get; set; } + public Single DiversityNumber { get; set; } + } +} diff --git a/src/Microsoft.ML.Ensemble/Selector/DiversityMeasure/MultiDisagreementDiversityMeasure.cs b/src/Microsoft.ML.Ensemble/Selector/DiversityMeasure/MultiDisagreementDiversityMeasure.cs new file mode 100644 index 0000000000..1ccf9b2f65 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/Selector/DiversityMeasure/MultiDisagreementDiversityMeasure.cs @@ -0,0 +1,26 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Ensemble.Selector; +using Microsoft.ML.Runtime.Ensemble.Selector.DiversityMeasure; +using Microsoft.ML.Runtime.Numeric; + +[assembly: LoadableClass(typeof(MultiDisagreementDiversityMeasure), null, typeof(SignatureEnsembleDiversityMeasure), + DisagreementDiversityMeasure.UserName, MultiDisagreementDiversityMeasure.LoadName)] + +namespace Microsoft.ML.Runtime.Ensemble.Selector.DiversityMeasure +{ + public class MultiDisagreementDiversityMeasure : BaseDisagreementDiversityMeasure> + { + public const string LoadName = "MultiDisagreementDiversityMeasure"; + + protected override Single GetDifference(ref VBuffer valueX, ref VBuffer valueY) + { + return (VectorUtils.ArgMax(ref valueX) != VectorUtils.ArgMax(ref valueY)) ? 1 : 0; + } + } +} diff --git a/src/Microsoft.ML.Ensemble/Selector/DiversityMeasure/RegressionDisagreementDiversityMeasure.cs b/src/Microsoft.ML.Ensemble/Selector/DiversityMeasure/RegressionDisagreementDiversityMeasure.cs new file mode 100644 index 0000000000..5a55545fe0 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/Selector/DiversityMeasure/RegressionDisagreementDiversityMeasure.cs @@ -0,0 +1,24 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Ensemble.Selector; +using Microsoft.ML.Runtime.Ensemble.Selector.DiversityMeasure; + +[assembly: LoadableClass(typeof(RegressionDisagreementDiversityMeasure), null, typeof(SignatureEnsembleDiversityMeasure), + DisagreementDiversityMeasure.UserName, RegressionDisagreementDiversityMeasure.LoadName)] + +namespace Microsoft.ML.Runtime.Ensemble.Selector.DiversityMeasure +{ + public class RegressionDisagreementDiversityMeasure : BaseDisagreementDiversityMeasure + { + public const string LoadName = "RegressionDisagreementDiversityMeasure"; + + protected override Single GetDifference(ref Single valueX, ref Single valueY) + { + return Math.Abs(valueX - valueY); + } + } +} diff --git a/src/Microsoft.ML.Ensemble/Selector/FeatureSelector/AllFeatureSelector.cs b/src/Microsoft.ML.Ensemble/Selector/FeatureSelector/AllFeatureSelector.cs new file mode 100644 index 0000000000..84a70a6ee4 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/Selector/FeatureSelector/AllFeatureSelector.cs @@ -0,0 +1,29 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Ensemble.Selector; +using Microsoft.ML.Runtime.Ensemble.Selector.FeatureSelector; + +[assembly: LoadableClass(typeof(AllFeatureSelector), null, typeof(SignatureEnsembleFeatureSelector), + AllFeatureSelector.UserName, AllFeatureSelector.LoadName)] + +namespace Microsoft.ML.Runtime.Ensemble.Selector.FeatureSelector +{ + public sealed class AllFeatureSelector : IFeatureSelector + { + public const string UserName = "All Feature Selector"; + public const string LoadName = "AllFeatureSelector"; + + public AllFeatureSelector(IHostEnvironment env) + { + } + + public Subset SelectFeatures(RoleMappedData data, IRandom rand) + { + return new Subset(data); + } + } +} diff --git a/src/Microsoft.ML.Ensemble/Selector/FeatureSelector/RandomFeatureSelector.cs b/src/Microsoft.ML.Ensemble/Selector/FeatureSelector/RandomFeatureSelector.cs new file mode 100644 index 0000000000..c0c9b8968f --- /dev/null +++ b/src/Microsoft.ML.Ensemble/Selector/FeatureSelector/RandomFeatureSelector.cs @@ -0,0 +1,62 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Collections; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Ensemble.Selector; +using Microsoft.ML.Runtime.Ensemble.Selector.FeatureSelector; +using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.Training; + +[assembly: LoadableClass(typeof(RandomFeatureSelector), typeof(RandomFeatureSelector.Arguments), + typeof(SignatureEnsembleFeatureSelector), RandomFeatureSelector.UserName, RandomFeatureSelector.LoadName)] + +namespace Microsoft.ML.Runtime.Ensemble.Selector.FeatureSelector +{ + public class RandomFeatureSelector : IFeatureSelector + { + public const string UserName = "Random Feature Selector"; + public const string LoadName = "RandomFeatureSelector"; + + [TlcModule.Component(Name = LoadName, FriendlyName = UserName)] + public sealed class Arguments: ISupportFeatureSelectorFactory + { + [Argument(ArgumentType.AtMostOnce, HelpText = "The proportion of features to be selected. The range is 0.0-1.0", ShortName = "fp", SortOrder = 50)] + public Single FeaturesSelectionProportion = 0.8f; + + public IFeatureSelector CreateComponent(IHostEnvironment env) => new RandomFeatureSelector(env, this); + } + + private readonly Arguments _args; + private readonly IHost _host; + + public RandomFeatureSelector(IHostEnvironment env, Arguments args) + { + Contracts.CheckValue(env, nameof(env)); + env.CheckValue(args, nameof(args)); + + _host = env.Register(LoadName); + _args = args; + _host.Check(0 < _args.FeaturesSelectionProportion && _args.FeaturesSelectionProportion < 1, + "The feature proportion for RandomFeatureSelector should be greater than 0 and lesser than 1"); + } + + public Subset SelectFeatures(RoleMappedData data, IRandom rand) + { + _host.CheckValue(data, nameof(data)); + data.CheckFeatureFloatVector(); + + var type = data.Schema.Feature.Type; + int len = type.VectorSize; + var features = new BitArray(len); + for (int j = 0; j < len; j++) + features[j] = rand.NextDouble() < _args.FeaturesSelectionProportion; + var dataNew = EnsembleUtils.SelectFeatures(_host, data, features); + return new Subset(dataNew, features); + } + } +} diff --git a/src/Microsoft.ML.Ensemble/Selector/IDiversityMeasure.cs b/src/Microsoft.ML.Ensemble/Selector/IDiversityMeasure.cs new file mode 100644 index 0000000000..814cbe317c --- /dev/null +++ b/src/Microsoft.ML.Ensemble/Selector/IDiversityMeasure.cs @@ -0,0 +1,24 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System.Collections.Concurrent; +using System.Collections.Generic; +using Microsoft.ML.Runtime.Ensemble.Selector.DiversityMeasure; +using Microsoft.ML.Runtime.EntryPoints; + +namespace Microsoft.ML.Runtime.Ensemble.Selector +{ + public interface IDiversityMeasure + { + List> CalculateDiversityMeasure(IList>> models, + ConcurrentDictionary>, TOutput[]> predictions); + } + + public delegate void SignatureEnsembleDiversityMeasure(); + + [TlcModule.ComponentKind("EnsembleDiversityMeasure")] + public interface ISupportDiversityMeasureFactory : IComponentFactory> + { + } +} diff --git a/src/Microsoft.ML.Ensemble/Selector/IFeatureSelector.cs b/src/Microsoft.ML.Ensemble/Selector/IFeatureSelector.cs new file mode 100644 index 0000000000..99e90c5c01 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/Selector/IFeatureSelector.cs @@ -0,0 +1,21 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.EntryPoints; + +namespace Microsoft.ML.Runtime.Ensemble.Selector +{ + public interface IFeatureSelector + { + Subset SelectFeatures(RoleMappedData data, IRandom rand); + } + + public delegate void SignatureEnsembleFeatureSelector(); + + [TlcModule.ComponentKind("EnsembleFeatureSelector")] + public interface ISupportFeatureSelectorFactory : IComponentFactory + { + } +} diff --git a/src/Microsoft.ML.Ensemble/Selector/ISubModelSelector.cs b/src/Microsoft.ML.Ensemble/Selector/ISubModelSelector.cs new file mode 100644 index 0000000000..96e9f5b886 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/Selector/ISubModelSelector.cs @@ -0,0 +1,52 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.EntryPoints; +using System; +using System.Collections.Generic; + +namespace Microsoft.ML.Runtime.Ensemble.Selector +{ + public interface ISubModelSelector + { + IList>> Prune(IList>> models); + + void CalculateMetrics(FeatureSubsetModel> model, ISubsetSelector subsetSelector, Subset subset, + Batch batch, bool needMetrics); + + Single ValidationDatasetProportion { get; } + } + + public interface IRegressionSubModelSelector : ISubModelSelector + { + } + + public interface IBinarySubModelSelector : ISubModelSelector + { + } + + public interface IMulticlassSubModelSelector : ISubModelSelector> + { + } + + public delegate void SignatureEnsembleSubModelSelector(); + + [TlcModule.ComponentKind("EnsembleMulticlassSubModelSelector")] + public interface ISupportMulticlassSubModelSelectorFactory : IComponentFactory + { + } + + [TlcModule.ComponentKind("EnsembleBinarySubModelSelector")] + public interface ISupportBinarySubModelSelectorFactory: IComponentFactory + { + + } + + [TlcModule.ComponentKind("EnsembleRegressionSubModelSelector")] + public interface ISupportRegressionSubModelSelectorFactory : IComponentFactory + { + + } +} diff --git a/src/Microsoft.ML.Ensemble/Selector/ISubsetSelector.cs b/src/Microsoft.ML.Ensemble/Selector/ISubsetSelector.cs new file mode 100644 index 0000000000..2a0f088219 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/Selector/ISubsetSelector.cs @@ -0,0 +1,26 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Collections.Generic; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.EntryPoints; + +namespace Microsoft.ML.Runtime.Ensemble.Selector +{ + public interface ISubsetSelector + { + void Initialize(RoleMappedData data, int size, int batchSize, Single validationDatasetProportion); + IEnumerable GetBatches(IRandom rand); + IEnumerable GetSubsets(Batch batch, IRandom rand); + RoleMappedData GetTestData(Subset subset, Batch batch); + } + + public delegate void SignatureEnsembleDataSelector(); + + [TlcModule.ComponentKind("EnsembleSubsetSelector")] + public interface ISupportSubsetSelectorFactory : IComponentFactory + { + } +} diff --git a/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/AllSelector.cs b/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/AllSelector.cs new file mode 100644 index 0000000000..4196ab3558 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/AllSelector.cs @@ -0,0 +1,28 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Ensemble.Selector; +using Microsoft.ML.Runtime.Ensemble.Selector.SubModelSelector; + +[assembly: LoadableClass(typeof(AllSelector), null, typeof(SignatureEnsembleSubModelSelector), AllSelector.UserName, AllSelector.LoadName)] + +namespace Microsoft.ML.Runtime.Ensemble.Selector.SubModelSelector +{ + public class AllSelector : BaseSubModelSelector, IBinarySubModelSelector, IRegressionSubModelSelector + { + public const string UserName = "All Selector"; + public const string LoadName = "AllSelector"; + + public override Single ValidationDatasetProportion => 0; + + protected override PredictionKind PredictionKind => PredictionKind.BinaryClassification; + + public AllSelector(IHostEnvironment env) + : base(env, LoadName) + { + } + } +} diff --git a/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/AllSelectorMultiClass.cs b/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/AllSelectorMultiClass.cs new file mode 100644 index 0000000000..6c82fc25f5 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/AllSelectorMultiClass.cs @@ -0,0 +1,30 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Ensemble.Selector; +using Microsoft.ML.Runtime.Ensemble.Selector.SubModelSelector; + +[assembly: LoadableClass(typeof(AllSelectorMultiClass), null, typeof(SignatureEnsembleSubModelSelector), + AllSelectorMultiClass.UserName, AllSelectorMultiClass.LoadName)] + +namespace Microsoft.ML.Runtime.Ensemble.Selector.SubModelSelector +{ + public class AllSelectorMultiClass : BaseSubModelSelector>, IMulticlassSubModelSelector + { + public const string UserName = "All Selector"; + public const string LoadName = "AllSelectorMultiClass"; + + public override Single ValidationDatasetProportion => 0; + + protected override PredictionKind PredictionKind => PredictionKind.MultiClassClassification; + + public AllSelectorMultiClass(IHostEnvironment env) + : base(env, LoadName) + { + } + } +} diff --git a/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/BaseBestPerformanceSelector.cs b/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/BaseBestPerformanceSelector.cs new file mode 100644 index 0000000000..8701e2833c --- /dev/null +++ b/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/BaseBestPerformanceSelector.cs @@ -0,0 +1,121 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Collections.Generic; +using System.Linq; +using System.Reflection; +using Microsoft.ML.Runtime.CommandLine; + +namespace Microsoft.ML.Runtime.Ensemble.Selector.SubModelSelector +{ + public abstract class BaseBestPerformanceSelector : SubModelDataSelector + { + protected abstract string MetricName { get; } + + protected virtual bool IsAscMetric => true; + + protected BaseBestPerformanceSelector(ArgumentsBase args, IHostEnvironment env, string name) + : base(args, env, name) + { + } + + public override void CalculateMetrics(FeatureSubsetModel> model, + ISubsetSelector subsetSelector, Subset subset, Batch batch, bool needMetrics) + { + base.CalculateMetrics(model, subsetSelector, subset, batch, true); + } + + public override IList>> Prune(IList>> models) + { + using (var ch = Host.Start("Pruning")) + { + var sortedModels = models.ToArray(); + Array.Sort(sortedModels, new ModelPerformanceComparer(MetricName, IsAscMetric)); + Print(ch, sortedModels, MetricName); + int modelCountToBeSelected = (int)(models.Count * LearnersSelectionProportion); + if (modelCountToBeSelected == 0) + modelCountToBeSelected = 1; + + var retval = sortedModels.Where(m => m != null).Take(modelCountToBeSelected).ToList(); + ch.Done(); + return retval; + } + } + + protected static string FindMetricName(Type type, object value) + { + Contracts.Assert(type.IsEnum); + Contracts.Assert(value.GetType() == type); + + foreach (var field in type.GetFields(BindingFlags.Public | BindingFlags.Static | BindingFlags.DeclaredOnly)) + { + if (field.FieldType != type) + continue; + if (field.GetCustomAttribute() != null) + continue; + var displayAttr = field.GetCustomAttribute(); + if (displayAttr != null) + { + var valCur = field.GetValue(null); + if (value.Equals(valCur)) + return displayAttr.Name; + } + } + Contracts.Assert(false); + return null; + } + + private sealed class ModelPerformanceComparer : IComparer>> + { + private readonly string _metricName; + private readonly bool _isAscMetric; + + public ModelPerformanceComparer(string metricName, bool isAscMetric) + { + Contracts.AssertValue(metricName); + + _metricName = metricName; + _isAscMetric = isAscMetric; + } + + public int Compare(FeatureSubsetModel> x, FeatureSubsetModel> y) + { + if (x == null || y == null) + return (x == null ? 0 : 1) - (y == null ? 0 : 1); + double xValue = 0; + var found = false; + foreach (var kvp in x.Metrics) + { + if (_metricName == kvp.Key) + { + xValue = kvp.Value; + found = true; + break; + } + } + if (!found) + throw Contracts.Except("Metrics did not contain the requested metric '{0}'", _metricName); + double yValue = 0; + found = false; + foreach (var kvp in y.Metrics) + { + if (_metricName == kvp.Key) + { + yValue = kvp.Value; + found = true; + break; + } + } + if (!found) + throw Contracts.Except("Metrics did not contain the requested metric '{0}'", _metricName); + if (xValue > yValue) + return _isAscMetric ? -1 : 1; + if (yValue > xValue) + return _isAscMetric ? 1 : -1; + return 0; + } + } + } +} diff --git a/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/BaseDiverseSelector.cs b/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/BaseDiverseSelector.cs new file mode 100644 index 0000000000..fbf3c02839 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/BaseDiverseSelector.cs @@ -0,0 +1,141 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Collections.Concurrent; +using System.Collections.Generic; +using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Ensemble.Selector.DiversityMeasure; +using Microsoft.ML.Runtime.Internal.Internallearn; +using Microsoft.ML.Runtime.Internal.Utilities; +using Microsoft.ML.Runtime.Training; + +namespace Microsoft.ML.Runtime.Ensemble.Selector.SubModelSelector +{ + public abstract class BaseDiverseSelector : SubModelDataSelector + where TDiversityMetric : class, IDiversityMeasure + { + public abstract class DiverseSelectorArguments : ArgumentsBase + { + [Argument(ArgumentType.Multiple, HelpText = "The metric type to be used to find the diversity among base learners", ShortName = "dm", SortOrder = 50)] + [TGUI(Label = "Diversity Measure Type")] + public ISupportDiversityMeasureFactory DiversityMetricType; + } + + private readonly ISupportDiversityMeasureFactory _diversityMetricType; + private ConcurrentDictionary>, TOutput[]> _predictions; + + protected abstract ISupportDiversityMeasureFactory DefaultDiversityMetricType { get; } + + protected internal BaseDiverseSelector(IHostEnvironment env, DiverseSelectorArguments args, string name) + : base(args, env, name) + { + _diversityMetricType = args.DiversityMetricType; + _predictions = new ConcurrentDictionary>, TOutput[]>(); + } + + protected IDiversityMeasure CreateDiversityMetric() + { + if (_diversityMetricType == null) + return DefaultDiversityMetricType.CreateComponent(Host); + return _diversityMetricType.CreateComponent(Host); + } + + public override void CalculateMetrics(FeatureSubsetModel> model, + ISubsetSelector subsetSelector, Subset subset, Batch batch, bool needMetrics) + { + base.CalculateMetrics(model, subsetSelector, subset, batch, needMetrics); + + var vm = model.Predictor as IValueMapper; + Host.Check(vm != null, "Predictor doesn't implement the expected interface"); + var map = vm.GetMapper, TOutput>(); + + TOutput[] preds = new TOutput[100]; + int count = 0; + var data = subsetSelector.GetTestData(subset, batch); + using (var cursor = new FeatureFloatVectorCursor(data, CursOpt.AllFeatures)) + { + while (cursor.MoveNext()) + { + Utils.EnsureSize(ref preds, count + 1); + map(ref cursor.Features, ref preds[count]); + count++; + } + } + Array.Resize(ref preds, count); + _predictions[model] = preds; + } + + /// + /// This calculates the diversity by calculating the disagreement measure which is defined as the sum of number of instances correctly(incorrectly) + /// classified by first classifier and incorrectly(correctly) classified by the second classifier over the total number of instances. + /// All the pairwise classifiers are sorted out to take the most divers classifiers. + /// + /// + /// + public override IList>> Prune(IList>> models) + { + if (models.Count <= 1) + return models; + + // 1. Find the disagreement number + List> diversityValues = CalculateDiversityMeasure(models, _predictions); + _predictions.Clear(); + + // 2. Sort all the pairwise classifiers + var sortedModels = diversityValues.ToArray(); + Array.Sort(sortedModels, new ModelDiversityComparer()); + var modelCountToBeSelected = (int)(models.Count * LearnersSelectionProportion); + + if (modelCountToBeSelected == 0) + modelCountToBeSelected++; + + // 3. Take the most diverse classifiers + var selectedModels = new List>>(); + foreach (var item in sortedModels) + { + if (selectedModels.Count < modelCountToBeSelected) + { + if (!selectedModels.Contains(item.ModelX)) + { + selectedModels.Add(item.ModelX); + } + } + + if (selectedModels.Count < modelCountToBeSelected) + { + if (!selectedModels.Contains(item.ModelY)) + { + selectedModels.Add(item.ModelY); + continue; + } + } + else + { + break; + } + } + + return selectedModels; + } + + public abstract List> CalculateDiversityMeasure(IList>> models, + ConcurrentDictionary>, TOutput[]> predictions); + + public class ModelDiversityComparer : IComparer> + { + public int Compare(ModelDiversityMetric x, ModelDiversityMetric y) + { + if (x == null || y == null) + return 0; + if (x.DiversityNumber > y.DiversityNumber) + return -1; + if (y.DiversityNumber > x.DiversityNumber) + return 1; + return 0; + } + } + } +} diff --git a/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/BaseSubModelSelector.cs b/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/BaseSubModelSelector.cs new file mode 100644 index 0000000000..2976214cfe --- /dev/null +++ b/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/BaseSubModelSelector.cs @@ -0,0 +1,137 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Collections.Generic; +using System.Linq; +using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.Data; + +namespace Microsoft.ML.Runtime.Ensemble.Selector.SubModelSelector +{ + public abstract class BaseSubModelSelector : ISubModelSelector + { + protected readonly IHost Host; + + public abstract Single ValidationDatasetProportion { get; } + + protected abstract PredictionKind PredictionKind { get; } + + protected BaseSubModelSelector(IHostEnvironment env, string name) + { + Contracts.CheckValue(env, nameof(env)); + env.CheckNonWhiteSpace(name, nameof(name)); + Host = env.Register(name); + } + + protected void Print(IChannel ch, IList>> models, string metricName) + { + // REVIEW: The output format was faithfully reproduced from the original format, but it's unclear + // to me that this is right. Why have two bars in the header line, but only one bar in the results? + ch.Info("List of models and the metrics after sorted"); + ch.Info("| {0}(Sorted) || Name of Model |", metricName); + foreach (var model in models) + { + var metric = 0.0; + var found = false; + foreach (var kvp in model.Metrics) + { + if (kvp.Key == metricName) + { + metric = kvp.Value; + found = true; + } + } + if (!found) + throw ch.Except("Metrics did not contain the requested metric '{0}'", metricName); + ch.Info("| {0} |{1}", metric, model.Predictor.GetType().Name); + } + } + + public virtual IList>> Prune(IList>> models) + { + return models; + } + + private SubComponent GetEvaluatorSubComponent() + { + switch (PredictionKind) + { + case PredictionKind.BinaryClassification: + return new SubComponent(BinaryClassifierEvaluator.LoadName); + case PredictionKind.Regression: + return new SubComponent(RegressionEvaluator.LoadName); + case PredictionKind.MultiClassClassification: + return new SubComponent(MultiClassClassifierEvaluator.LoadName); + default: + throw Host.Except("Unrecognized prediction kind '{0}'", PredictionKind); + } + } + + public virtual void CalculateMetrics(FeatureSubsetModel> model, + ISubsetSelector subsetSelector, Subset subset, Batch batch, bool needMetrics) + { + if (!needMetrics || model == null || model.Metrics != null) + return; + + using (var ch = Host.Start("Calculate metrics")) + { + RoleMappedData testData = subsetSelector.GetTestData(subset, batch); + // Because the training and test datasets are drawn from the same base dataset, the test data role mappings + // are the same as for the train data. + IDataScorerTransform scorePipe = ScoreUtils.GetScorer(model.Predictor, testData, Host, testData.Schema); + // REVIEW: Should we somehow allow the user to customize the evaluator? + // By what mechanism should we allow that? + var evalComp = GetEvaluatorSubComponent(); + RoleMappedData scoredTestData = RoleMappedData.Create(scorePipe, + GetColumnRoles(testData.Schema, scorePipe.Schema)); + IEvaluator evaluator = evalComp.CreateInstance(Host); + // REVIEW: with the new evaluators, metrics of individual models are no longer + // printed to the Console. Consider adding an option on the combiner to print them. + // REVIEW: Consider adding an option to the combiner to save a data view + // containing all the results of the individual models. + var metricsDict = evaluator.Evaluate(scoredTestData); + if (!metricsDict.TryGetValue(MetricKinds.OverallMetrics, out IDataView metricsView)) + throw Host.Except("Evaluator did not produce any overall metrics"); + // REVIEW: We're assuming that the metrics of interest are always doubles here. + var metrics = EvaluateUtils.GetMetrics(metricsView, getVectorMetrics: false); + model.Metrics = metrics.ToArray(); + ch.Done(); + } + } + + private IEnumerable> GetColumnRoles( + RoleMappedSchema testSchema, ISchema scoredSchema) + { + switch (PredictionKind) + { + case PredictionKind.BinaryClassification: + yield return RoleMappedSchema.CreatePair(RoleMappedSchema.ColumnRole.Label, testSchema.Label.Name); + var scoreInfo = EvaluateUtils.GetScoreColumnInfo(Host, scoredSchema, null, nameof(BinaryClassifierMamlEvaluator.ArgumentsBase.ScoreColumn), + MetadataUtils.Const.ScoreColumnKind.BinaryClassification); + yield return RoleMappedSchema.CreatePair(MetadataUtils.Const.ScoreValueKind.Score, scoreInfo.Name); + // Get the optional probability column. + var probInfo = EvaluateUtils.GetOptAuxScoreColumnInfo(Host, scoredSchema, null, nameof(BinaryClassifierMamlEvaluator.Arguments.ProbabilityColumn), + scoreInfo.Index, MetadataUtils.Const.ScoreValueKind.Probability, t => t == NumberType.Float); + if (probInfo != null) + yield return RoleMappedSchema.CreatePair(MetadataUtils.Const.ScoreValueKind.Probability, probInfo.Name); + yield break; + case PredictionKind.Regression: + yield return RoleMappedSchema.CreatePair(RoleMappedSchema.ColumnRole.Label, testSchema.Label.Name); + scoreInfo = EvaluateUtils.GetScoreColumnInfo(Host, scoredSchema, null, nameof(RegressionMamlEvaluator.Arguments.ScoreColumn), + MetadataUtils.Const.ScoreColumnKind.Regression); + yield return RoleMappedSchema.CreatePair(MetadataUtils.Const.ScoreValueKind.Score, scoreInfo.Name); + yield break; + case PredictionKind.MultiClassClassification: + yield return RoleMappedSchema.CreatePair(RoleMappedSchema.ColumnRole.Label, testSchema.Label.Name); + scoreInfo = EvaluateUtils.GetScoreColumnInfo(Host, scoredSchema, null, nameof(MultiClassMamlEvaluator.Arguments.ScoreColumn), + MetadataUtils.Const.ScoreColumnKind.MultiClassClassification); + yield return RoleMappedSchema.CreatePair(MetadataUtils.Const.ScoreValueKind.Score, scoreInfo.Name); + yield break; + default: + throw Host.Except("Unrecognized prediction kind '{0}'", PredictionKind); + } + } + } +} diff --git a/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/BestDiverseSelectorBinary.cs b/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/BestDiverseSelectorBinary.cs new file mode 100644 index 0000000000..b3deb891f0 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/BestDiverseSelectorBinary.cs @@ -0,0 +1,49 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Collections.Concurrent; +using System.Collections.Generic; +using Microsoft.ML.Ensemble.EntryPoints; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Ensemble.Selector; +using Microsoft.ML.Runtime.Ensemble.Selector.DiversityMeasure; +using Microsoft.ML.Runtime.Ensemble.Selector.SubModelSelector; +using Microsoft.ML.Runtime.EntryPoints; + +[assembly: LoadableClass(typeof(BestDiverseSelectorBinary), typeof(BestDiverseSelectorBinary.Arguments), + typeof(SignatureEnsembleSubModelSelector), BestDiverseSelectorBinary.UserName, BestDiverseSelectorBinary.LoadName)] + +namespace Microsoft.ML.Runtime.Ensemble.Selector.SubModelSelector +{ + using TScalarPredictor = IPredictorProducing; + + public sealed class BestDiverseSelectorBinary : BaseDiverseSelector, IBinarySubModelSelector + { + public const string UserName = "Best Diverse Selector"; + public const string LoadName = "BestDiverseSelector"; + + protected override ISupportDiversityMeasureFactory DefaultDiversityMetricType => new DisagreementDiversityFactory(); + + [TlcModule.Component(Name = LoadName, FriendlyName = UserName)] + public sealed class Arguments : DiverseSelectorArguments, ISupportBinarySubModelSelectorFactory + { + public IBinarySubModelSelector CreateComponent(IHostEnvironment env) => new BestDiverseSelectorBinary(env, this); + } + + public BestDiverseSelectorBinary(IHostEnvironment env, Arguments args) + : base(env, args, LoadName) + { + } + + public override List> CalculateDiversityMeasure(IList> models, + ConcurrentDictionary, Single[]> predictions) + { + var diversityMetric = CreateDiversityMetric(); + return diversityMetric.CalculateDiversityMeasure(models, predictions); + } + + protected override PredictionKind PredictionKind => PredictionKind.BinaryClassification; + } +} diff --git a/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/BestDiverseSelectorMultiClass.cs b/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/BestDiverseSelectorMultiClass.cs new file mode 100644 index 0000000000..853297970d --- /dev/null +++ b/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/BestDiverseSelectorMultiClass.cs @@ -0,0 +1,52 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Collections.Concurrent; +using System.Collections.Generic; +using Microsoft.ML.Ensemble.EntryPoints; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Ensemble.Selector; +using Microsoft.ML.Runtime.Ensemble.Selector.DiversityMeasure; +using Microsoft.ML.Runtime.Ensemble.Selector.SubModelSelector; +using Microsoft.ML.Runtime.EntryPoints; + +[assembly: LoadableClass(typeof(BestDiverseSelectorMultiClass), typeof(BestDiverseSelectorMultiClass.Arguments), + typeof(SignatureEnsembleSubModelSelector), BestDiverseSelectorMultiClass.UserName, BestDiverseSelectorMultiClass.LoadName)] + +namespace Microsoft.ML.Runtime.Ensemble.Selector.SubModelSelector +{ + using TVectorPredictor = IPredictorProducing>; + + public sealed class BestDiverseSelectorMultiClass : BaseDiverseSelector, IDiversityMeasure>>, IMulticlassSubModelSelector + { + public const string UserName = "Best Diverse Selector"; + public const string LoadName = "BestDiverseSelectorMultiClass"; + protected override ISupportDiversityMeasureFactory> DefaultDiversityMetricType => new MultiDisagreementDiversityFactory(); + + [TlcModule.Component(Name = BestDiverseSelectorMultiClass.LoadName, FriendlyName = UserName)] + public sealed class Arguments : DiverseSelectorArguments, ISupportMulticlassSubModelSelectorFactory + { + public IMulticlassSubModelSelector CreateComponent(IHostEnvironment env) => new BestDiverseSelectorMultiClass(env, this); + } + + public BestDiverseSelectorMultiClass(IHostEnvironment env, Arguments args) + : base(env, args, LoadName) + { + } + + protected override PredictionKind PredictionKind => PredictionKind.MultiClassClassification; + + public override List>> CalculateDiversityMeasure(IList> models, + ConcurrentDictionary, VBuffer[]> predictions) + { + Host.Assert(models.Count > 1); + Host.Assert(predictions.Count == models.Count); + + var diversityMetric = CreateDiversityMetric(); + return diversityMetric.CalculateDiversityMeasure(models, predictions); + } + } +} diff --git a/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/BestDiverseSelectorRegression.cs b/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/BestDiverseSelectorRegression.cs new file mode 100644 index 0000000000..d4d6979fe1 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/BestDiverseSelectorRegression.cs @@ -0,0 +1,49 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Collections.Concurrent; +using System.Collections.Generic; +using Microsoft.ML.Ensemble.EntryPoints; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Ensemble.Selector; +using Microsoft.ML.Runtime.Ensemble.Selector.DiversityMeasure; +using Microsoft.ML.Runtime.Ensemble.Selector.SubModelSelector; +using Microsoft.ML.Runtime.EntryPoints; + +[assembly: LoadableClass(typeof(BestDiverseSelectorRegression), typeof(BestDiverseSelectorRegression.Arguments), + typeof(SignatureEnsembleSubModelSelector), BestDiverseSelectorRegression.UserName, BestDiverseSelectorRegression.LoadName)] + +namespace Microsoft.ML.Runtime.Ensemble.Selector.SubModelSelector +{ + using TScalarPredictor = IPredictorProducing; + + public sealed class BestDiverseSelectorRegression : BaseDiverseSelector, IRegressionSubModelSelector + { + public const string UserName = "Best Diverse Selector"; + public const string LoadName = "BestDiverseSelectorRegression"; + + protected override ISupportDiversityMeasureFactory DefaultDiversityMetricType => new RegressionDisagreementDiversityFactory(); + + [TlcModule.Component(Name = LoadName, FriendlyName = UserName)] + public sealed class Arguments : DiverseSelectorArguments, ISupportRegressionSubModelSelectorFactory + { + public IRegressionSubModelSelector CreateComponent(IHostEnvironment env) => new BestDiverseSelectorRegression(env, this); + } + + public BestDiverseSelectorRegression(IHostEnvironment env, Arguments args) + : base(env, args, LoadName) + { + } + + public override List> CalculateDiversityMeasure(IList> models, + ConcurrentDictionary, Single[]> predictions) + { + var diversityMetric = CreateDiversityMetric(); + return diversityMetric.CalculateDiversityMeasure(models, predictions); + } + + protected override PredictionKind PredictionKind => PredictionKind.Regression; + } +} diff --git a/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/BestPerformanceRegressionSelector.cs b/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/BestPerformanceRegressionSelector.cs new file mode 100644 index 0000000000..46f13e9cd1 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/BestPerformanceRegressionSelector.cs @@ -0,0 +1,53 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Ensemble.Selector; +using Microsoft.ML.Runtime.Ensemble.Selector.SubModelSelector; +using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.Internal.Internallearn; + +[assembly: LoadableClass(typeof(BestPerformanceRegressionSelector), typeof(BestPerformanceRegressionSelector.Arguments), + typeof(SignatureEnsembleSubModelSelector), BestPerformanceRegressionSelector.UserName, BestPerformanceRegressionSelector.LoadName)] + +namespace Microsoft.ML.Runtime.Ensemble.Selector.SubModelSelector +{ + public sealed class BestPerformanceRegressionSelector : BaseBestPerformanceSelector, IRegressionSubModelSelector + { + [TlcModule.Component(Name = LoadName, FriendlyName = UserName)] + public sealed class Arguments : ArgumentsBase, ISupportRegressionSubModelSelectorFactory + { + [Argument(ArgumentType.AtMostOnce, HelpText = "The metric type to be used to find the best performance", ShortName = "mn", SortOrder = 50)] + [TGUI(Label = "Metric Name")] + public RegressionEvaluator.Metrics MetricName = RegressionEvaluator.Metrics.L1; + + public IRegressionSubModelSelector CreateComponent(IHostEnvironment env) => new BestPerformanceRegressionSelector(env, this); + } + + public const string UserName = "Best Performance Selector"; + public const string LoadName = "BestPerformanceRegressionSelector"; + + private readonly RegressionEvaluator.Metrics _metric; + + private readonly string _metricName; + + public BestPerformanceRegressionSelector(IHostEnvironment env, Arguments args) + : base(args, env, LoadName) + { + Host.CheckUserArg(Enum.IsDefined(typeof(RegressionEvaluator.Metrics), args.MetricName), nameof(args.MetricName), "Undefined metric name"); + _metric = args.MetricName; + _metricName = FindMetricName(typeof(RegressionEvaluator.Metrics), _metric); + Host.Assert(!string.IsNullOrEmpty(_metricName)); + } + + protected override string MetricName => _metricName; + + protected override bool IsAscMetric => false; + + protected override PredictionKind PredictionKind => PredictionKind.Regression; + } +} diff --git a/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/BestPerformanceSelector.cs b/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/BestPerformanceSelector.cs new file mode 100644 index 0000000000..76742ad0ec --- /dev/null +++ b/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/BestPerformanceSelector.cs @@ -0,0 +1,53 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Ensemble.Selector; +using Microsoft.ML.Runtime.Ensemble.Selector.SubModelSelector; +using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.Internal.Internallearn; + +[assembly: LoadableClass(typeof(BestPerformanceSelector), typeof(BestPerformanceSelector.Arguments), + typeof(SignatureEnsembleSubModelSelector), BestPerformanceSelector.UserName, BestPerformanceSelector.LoadName)] + +namespace Microsoft.ML.Runtime.Ensemble.Selector.SubModelSelector +{ + public sealed class BestPerformanceSelector : BaseBestPerformanceSelector, IBinarySubModelSelector + { + [TlcModule.Component(Name = LoadName, FriendlyName = UserName)] + public sealed class Arguments : ArgumentsBase, ISupportBinarySubModelSelectorFactory + { + [Argument(ArgumentType.AtMostOnce, HelpText = "The metric type to be used to find the best performance", ShortName = "mn", SortOrder = 50)] + [TGUI(Label = "Metric Name")] + public BinaryClassifierEvaluator.Metrics MetricName = BinaryClassifierEvaluator.Metrics.Auc; + + public IBinarySubModelSelector CreateComponent(IHostEnvironment env) => new BestPerformanceSelector(env, this); + } + + public const string UserName = "Best Performance Selector"; + public const string LoadName = "BestPerformanceSelector"; + + private readonly BinaryClassifierEvaluator.Metrics _metric; + private readonly string _metricName; + + public BestPerformanceSelector(IHostEnvironment env, Arguments args) + : base(args, env, LoadName) + { + Host.CheckUserArg(Enum.IsDefined(typeof(BinaryClassifierEvaluator.Metrics), args.MetricName), + nameof(args.MetricName), "Undefined metric name"); + _metric = args.MetricName; + _metricName = FindMetricName(typeof(BinaryClassifierEvaluator.Metrics), _metric); + Host.Assert(!string.IsNullOrEmpty(_metricName)); + } + + protected override string MetricName => _metricName; + + protected override bool IsAscMetric => _metric != BinaryClassifierEvaluator.Metrics.LogLoss; + + protected override PredictionKind PredictionKind => PredictionKind.BinaryClassification; + } +} diff --git a/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/BestPerformanceSelectorMultiClass.cs b/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/BestPerformanceSelectorMultiClass.cs new file mode 100644 index 0000000000..0a9b9ac497 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/BestPerformanceSelectorMultiClass.cs @@ -0,0 +1,53 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Ensemble.Selector; +using Microsoft.ML.Runtime.Ensemble.Selector.SubModelSelector; +using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.Internal.Internallearn; + +[assembly: LoadableClass(typeof(BestPerformanceSelectorMultiClass), typeof(BestPerformanceSelectorMultiClass.Arguments), + typeof(SignatureEnsembleSubModelSelector), BestPerformanceSelectorMultiClass.UserName, BestPerformanceSelectorMultiClass.LoadName)] + +namespace Microsoft.ML.Runtime.Ensemble.Selector.SubModelSelector +{ + public class BestPerformanceSelectorMultiClass : BaseBestPerformanceSelector>, IMulticlassSubModelSelector + { + [TlcModule.Component(Name = LoadName, FriendlyName = UserName)] + public sealed class Arguments : ArgumentsBase, ISupportMulticlassSubModelSelectorFactory + { + [Argument(ArgumentType.AtMostOnce, HelpText = "The metric type to be used to find the best performance", ShortName = "mn", SortOrder = 50)] + [TGUI(Label = "Metric Name")] + public MultiClassClassifierEvaluator.Metrics MetricName = MultiClassClassifierEvaluator.Metrics.AccuracyMicro; + + IMulticlassSubModelSelector IComponentFactory.CreateComponent(IHostEnvironment env) => new BestPerformanceSelectorMultiClass(env, this); + } + + public const string UserName = "Best Performance Selector"; + public const string LoadName = "BestPerformanceSelectorMultiClass"; + + private readonly MultiClassClassifierEvaluator.Metrics _metric; + private readonly string _metricName; + + public BestPerformanceSelectorMultiClass(IHostEnvironment env, Arguments args) + : base(args, env, LoadName) + { + Host.CheckUserArg(Enum.IsDefined(typeof(MultiClassClassifierEvaluator.Metrics), args.MetricName), + nameof(args.MetricName), "Undefined metric name"); + _metric = args.MetricName; + _metricName = FindMetricName(typeof(MultiClassClassifierEvaluator.Metrics), _metric); + Host.Assert(!string.IsNullOrEmpty(_metricName)); + } + + protected override PredictionKind PredictionKind => PredictionKind.MultiClassClassification; + + protected override bool IsAscMetric => _metric != MultiClassClassifierEvaluator.Metrics.LogLoss; + + protected override string MetricName => _metricName; + } +} diff --git a/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/SubModelDataSelector.cs b/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/SubModelDataSelector.cs new file mode 100644 index 0000000000..5953b30d97 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/Selector/SubModelSelector/SubModelDataSelector.cs @@ -0,0 +1,44 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.Internal.Internallearn; + +namespace Microsoft.ML.Runtime.Ensemble.Selector.SubModelSelector +{ + public abstract class SubModelDataSelector : BaseSubModelSelector + { + public abstract class ArgumentsBase + { + [Argument(ArgumentType.AtMostOnce, ShortName = "lp", SortOrder = 50, + HelpText = "The proportion of best base learners to be selected. The range is 0.0-1.0")] + [TGUI(Label = "Learners Selection Proportion")] + public Single LearnersSelectionProportion = 0.5f; + + [Argument(ArgumentType.AtMostOnce, ShortName = "vp", SortOrder = 50, + HelpText = "The proportion of instances to be selected to test the individual base learner. If it is 0, it uses training set")] + [TGUI(Label = "Validation Dataset Proportion")] + public Single ValidationDatasetProportion = 0.3f; + } + + public Single LearnersSelectionProportion { get; } + + public override Single ValidationDatasetProportion { get; } + + protected SubModelDataSelector(ArgumentsBase args, IHostEnvironment env, string name) + : base(env, name) + { + Host.CheckValue(args, nameof(args)); + Host.CheckParam(0 <= args.ValidationDatasetProportion && args.ValidationDatasetProportion < 1, + nameof(args.ValidationDatasetProportion), + "Should be greater than or equal to 0 and less than 1"); + Host.CheckParam(0 < args.LearnersSelectionProportion && args.LearnersSelectionProportion < 1, + nameof(args.LearnersSelectionProportion), + "Should be greater than 0 and less than 1"); + LearnersSelectionProportion = args.LearnersSelectionProportion; + ValidationDatasetProportion = args.ValidationDatasetProportion; + } + } +} diff --git a/src/Microsoft.ML.Ensemble/Selector/SubsetSelector/AllInstanceSelector.cs b/src/Microsoft.ML.Ensemble/Selector/SubsetSelector/AllInstanceSelector.cs new file mode 100644 index 0000000000..98e6eeb8eb --- /dev/null +++ b/src/Microsoft.ML.Ensemble/Selector/SubsetSelector/AllInstanceSelector.cs @@ -0,0 +1,40 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System.Collections.Generic; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Ensemble.Selector; +using Microsoft.ML.Runtime.Ensemble.Selector.SubsetSelector; +using Microsoft.ML.Runtime.EntryPoints; + +[assembly: LoadableClass(typeof(AllInstanceSelector), typeof(AllInstanceSelector.Arguments), + typeof(SignatureEnsembleDataSelector), AllInstanceSelector.UserName, AllInstanceSelector.LoadName)] + +[assembly: EntryPointModule(typeof(AllInstanceSelector))] + +namespace Microsoft.ML.Runtime.Ensemble.Selector.SubsetSelector +{ + public sealed class AllInstanceSelector : BaseSubsetSelector + { + public const string UserName = "All Instance Selector"; + public const string LoadName = "AllInstanceSelector"; + + [TlcModule.Component(Name = LoadName, FriendlyName = UserName)] + public sealed class Arguments : ArgumentsBase, ISupportSubsetSelectorFactory + { + public ISubsetSelector CreateComponent(IHostEnvironment env) => new AllInstanceSelector(env, this); + } + + public AllInstanceSelector(IHostEnvironment env, Arguments args) + : base(args, env, LoadName) + { + } + + public override IEnumerable GetSubsets(Batch batch, IRandom rand) + { + for (int i = 0; i < Size; i++) + yield return FeatureSelector.SelectFeatures(batch.TrainInstances, rand); + } + } +} diff --git a/src/Microsoft.ML.Ensemble/Selector/SubsetSelector/BaseSubsetSelector.cs b/src/Microsoft.ML.Ensemble/Selector/SubsetSelector/BaseSubsetSelector.cs new file mode 100644 index 0000000000..d31439b79d --- /dev/null +++ b/src/Microsoft.ML.Ensemble/Selector/SubsetSelector/BaseSubsetSelector.cs @@ -0,0 +1,104 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Collections.Generic; +using Microsoft.ML.Ensemble.EntryPoints; +using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.Data; + +namespace Microsoft.ML.Runtime.Ensemble.Selector.SubsetSelector +{ + public abstract class BaseSubsetSelector : ISubsetSelector + where TArgs : BaseSubsetSelector.ArgumentsBase + { + public abstract class ArgumentsBase + { + [Argument(ArgumentType.Multiple, HelpText = "The Feature selector", ShortName = "fs", SortOrder = 1)] + public ISupportFeatureSelectorFactory FeatureSelector = new AllFeatureSelectorFactory(); + } + + protected readonly IHost Host; + protected readonly TArgs Args; + protected readonly IFeatureSelector FeatureSelector; + + protected int Size; + protected RoleMappedData Data; + protected int BatchSize; + protected Single ValidationDatasetProportion; + + protected BaseSubsetSelector(TArgs args, IHostEnvironment env, string name) + { + Contracts.CheckValue(env, nameof(env)); + env.CheckValue(args, nameof(args)); + env.CheckNonWhiteSpace(name, nameof(name)); + + Host = env.Register(name); + Args = args; + FeatureSelector = Args.FeatureSelector.CreateComponent(Host); + } + + public void Initialize(RoleMappedData data, int size, int batchSize, Single validationDatasetProportion) + { + Host.CheckValue(data, nameof(data)); + Host.CheckParam(size > 0, nameof(size)); + Host.CheckParam(0 <= validationDatasetProportion && validationDatasetProportion < 1, + nameof(validationDatasetProportion), "Should be greater than or equal to 0 and less than 1"); + Data = data; + Size = size; + BatchSize = batchSize; + ValidationDatasetProportion = validationDatasetProportion; + } + + public abstract IEnumerable GetSubsets(Batch batch, IRandom rand); + + public IEnumerable GetBatches(IRandom rand) + { + Host.Assert(Data != null, "Must call Initialize first!"); + Host.AssertValue(rand); + + using (var ch = Host.Start("Getting batches")) + { + RoleMappedData dataTest; + RoleMappedData dataTrain; + + // Split the data, if needed. + if (!(ValidationDatasetProportion > 0)) + dataTest = dataTrain = Data; + else + { + // Split the data into train and test sets. + string name = Data.Data.Schema.GetTempColumnName(); + var args = new GenerateNumberTransform.Arguments(); + args.Column = new[] { new GenerateNumberTransform.Column() { Name = name } }; + args.Seed = (uint)rand.Next(); + var view = new GenerateNumberTransform(Host, args, Data.Data); + var viewTest = new RangeFilter(Host, new RangeFilter.Arguments() { Column = name, Max = ValidationDatasetProportion }, view); + var viewTrain = new RangeFilter(Host, new RangeFilter.Arguments() { Column = name, Max = ValidationDatasetProportion, Complement = true }, view); + dataTest = RoleMappedData.Create(viewTest, Data.Schema.GetColumnRoleNames()); + dataTrain = RoleMappedData.Create(viewTrain, Data.Schema.GetColumnRoleNames()); + } + + if (BatchSize > 0) + { + // REVIEW: How should we carve the data into batches? + ch.Warning("Batch support is temporarily disabled"); + } + + yield return new Batch(dataTrain, dataTest); + ch.Done(); + } + } + + public virtual RoleMappedData GetTestData(Subset subset, Batch batch) + { + Host.CheckValueOrNull(subset); + Host.CheckValue(batch.TestInstances, nameof(batch), "Batch does not have test data"); + + if (subset == null || subset.SelectedFeatures == null) + return batch.TestInstances; + return EnsembleUtils.SelectFeatures(Host, batch.TestInstances, subset.SelectedFeatures); + } + } +} diff --git a/src/Microsoft.ML.Ensemble/Selector/SubsetSelector/BootstrapSelector.cs b/src/Microsoft.ML.Ensemble/Selector/SubsetSelector/BootstrapSelector.cs new file mode 100644 index 0000000000..25e7f8d64d --- /dev/null +++ b/src/Microsoft.ML.Ensemble/Selector/SubsetSelector/BootstrapSelector.cs @@ -0,0 +1,54 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System.Collections.Generic; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Ensemble.Selector; +using Microsoft.ML.Runtime.Ensemble.Selector.SubsetSelector; +using Microsoft.ML.Runtime.EntryPoints; + +[assembly: LoadableClass(typeof(BootstrapSelector), typeof(BootstrapSelector.Arguments), + typeof(SignatureEnsembleDataSelector), BootstrapSelector.UserName, BootstrapSelector.LoadName)] + +[assembly: EntryPointModule(typeof(BootstrapSelector))] + +namespace Microsoft.ML.Runtime.Ensemble.Selector.SubsetSelector +{ + public sealed class BootstrapSelector : BaseSubsetSelector + { + public const string UserName = "Bootstrap Selector"; + public const string LoadName = "BootstrapSelector"; + + [TlcModule.Component(Name = LoadName, FriendlyName = UserName)] + public sealed class Arguments : ArgumentsBase, ISupportSubsetSelectorFactory + { + // REVIEW: This could be reintroduced by having the transform counting the + // proportions of each label, then adjusting the lambdas accordingly. However, at + // the current point in time supporting this non-default action is not considered + // a priority. +#if OLD_ENSEMBLE + [Argument(ArgumentType.AtMostOnce, HelpText = "If checked, the classes will be balanced by over sampling of minority classes", ShortName = "cb", SortOrder = 50)] + public bool balanced = false; +#endif + public ISubsetSelector CreateComponent(IHostEnvironment env) => new BootstrapSelector(env, this); + } + + public BootstrapSelector(IHostEnvironment env, Arguments args) + : base(args, env, LoadName) + { + } + + public override IEnumerable GetSubsets(Batch batch, IRandom rand) + { + for (int i = 0; i < Size; i++) + { + // REVIEW: Consider ways to reintroduce "balanced" samples. + var viewTrain = new BootstrapSampleTransform(Host, new BootstrapSampleTransform.Arguments(), Data.Data); + var dataTrain = RoleMappedData.Create(viewTrain, Data.Schema.GetColumnRoleNames()); + yield return FeatureSelector.SelectFeatures(dataTrain, rand); + } + } + } +} diff --git a/src/Microsoft.ML.Ensemble/Selector/SubsetSelector/RandomPartitionSelector.cs b/src/Microsoft.ML.Ensemble/Selector/SubsetSelector/RandomPartitionSelector.cs new file mode 100644 index 0000000000..322a2133dc --- /dev/null +++ b/src/Microsoft.ML.Ensemble/Selector/SubsetSelector/RandomPartitionSelector.cs @@ -0,0 +1,53 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Collections.Generic; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Ensemble.Selector; +using Microsoft.ML.Runtime.Ensemble.Selector.SubsetSelector; +using Microsoft.ML.Runtime.EntryPoints; + +[assembly: LoadableClass(typeof(RandomPartitionSelector),typeof(RandomPartitionSelector.Arguments), + typeof(SignatureEnsembleDataSelector),RandomPartitionSelector.UserName, RandomPartitionSelector.LoadName)] + +[assembly: EntryPointModule(typeof(RandomPartitionSelector))] + +namespace Microsoft.ML.Runtime.Ensemble.Selector.SubsetSelector +{ + public sealed class RandomPartitionSelector : BaseSubsetSelector + { + public const string UserName = "Random Partition Selector"; + public const string LoadName = "RandomPartitionSelector"; + + [TlcModule.Component(Name = LoadName, FriendlyName = UserName)] + public sealed class Arguments : ArgumentsBase, ISupportSubsetSelectorFactory + { + public ISubsetSelector CreateComponent(IHostEnvironment env) => new RandomPartitionSelector(env, this); + } + + public RandomPartitionSelector(IHostEnvironment env, Arguments args) + : base(args, env, LoadName) + { + } + + public override IEnumerable GetSubsets(Batch batch, IRandom rand) + { + string name = Data.Data.Schema.GetTempColumnName(); + var args = new GenerateNumberTransform.Arguments(); + args.Column = new[] { new GenerateNumberTransform.Column() { Name = name } }; + args.Seed = (uint)rand.Next(); + IDataTransform view = new GenerateNumberTransform(Host, args, Data.Data); + + // REVIEW: This won't be very efficient when Size is large. + for (int i = 0; i < Size; i++) + { + var viewTrain = new RangeFilter(Host, new RangeFilter.Arguments() { Column = name, Min = (Double)i / Size, Max = (Double)(i + 1) / Size }, view); + var dataTrain = RoleMappedData.Create(viewTrain, Data.Schema.GetColumnRoleNames()); + yield return FeatureSelector.SelectFeatures(dataTrain, rand); + } + } + } +} diff --git a/src/Microsoft.ML.Ensemble/Subset.cs b/src/Microsoft.ML.Ensemble/Subset.cs new file mode 100644 index 0000000000..77183de6f3 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/Subset.cs @@ -0,0 +1,22 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System.Collections; +using Microsoft.ML.Runtime.Data; + +namespace Microsoft.ML.Runtime.Ensemble +{ + public sealed class Subset + { + public readonly RoleMappedData Data; + public readonly BitArray SelectedFeatures; + + public Subset(RoleMappedData data, BitArray features = null) + { + Contracts.AssertValue(data); + Data = data; + SelectedFeatures = features; + } + } +} \ No newline at end of file diff --git a/src/Microsoft.ML.Ensemble/Trainer/Binary/EnsembleTrainer.cs b/src/Microsoft.ML.Ensemble/Trainer/Binary/EnsembleTrainer.cs new file mode 100644 index 0000000000..80fe4cbdee --- /dev/null +++ b/src/Microsoft.ML.Ensemble/Trainer/Binary/EnsembleTrainer.cs @@ -0,0 +1,95 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Collections.Generic; +using System.Linq; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Ensemble; +using Microsoft.ML.Runtime.Ensemble.OutputCombiners; +using Microsoft.ML.Runtime.Ensemble.Selector; +using Microsoft.ML.Ensemble.EntryPoints; +using Microsoft.ML.Runtime.Internal.Internallearn; + +[assembly: LoadableClass(EnsembleTrainer.Summary, typeof(EnsembleTrainer), typeof(EnsembleTrainer.Arguments), + new[] { typeof(SignatureBinaryClassifierTrainer), typeof(SignatureTrainer) }, + EnsembleTrainer.UserNameValue, EnsembleTrainer.LoadNameValue, "pe", "ParallelEnsemble")] + +namespace Microsoft.ML.Runtime.Ensemble +{ + using TDistPredictor = IDistPredictorProducing; + using TScalarPredictor = IPredictorProducing; + /// + /// A generic ensemble trainer for binary classification. + /// + public sealed class EnsembleTrainer : EnsembleTrainerBase, + IModelCombiner + { + public const string LoadNameValue = "WeightedEnsemble"; + public const string UserNameValue = "Parallel Ensemble (bagging, stacking, etc)"; + public const string Summary = "A generic ensemble classifier for binary classification."; + + public sealed class Arguments : ArgumentsBase + { + [Argument(ArgumentType.Multiple, HelpText = "Algorithm to prune the base learners for selective Ensemble", ShortName = "pt", SortOrder = 4)] + [TGUI(Label = "Sub-Model Selector(pruning) Type", + Description = "Algorithm to prune the base learners for selective Ensemble")] + public ISupportBinarySubModelSelectorFactory SubModelSelectorType = new AllSelectorFactory(); + + [Argument(ArgumentType.Multiple, HelpText = "Output combiner", ShortName = "oc", SortOrder = 5)] + [TGUI(Label = "Output combiner", Description = "Output combiner type")] + public ISupportBinaryOutputCombinerFactory OutputCombiner = new MedianFactory(); + + public Arguments() + { + BasePredictors = new[] { new SubComponent, SignatureBinaryClassifierTrainer>("LinearSVM") }; + } + } + + private readonly ISupportBinaryOutputCombinerFactory _outputCombiner; + + public EnsembleTrainer(IHostEnvironment env, Arguments args) + : base(args, env, LoadNameValue) + { + SubModelSelector = args.SubModelSelectorType.CreateComponent(Host); + _outputCombiner = args.OutputCombiner; + Combiner = args.OutputCombiner.CreateComponent(Host); + } + + public override PredictionKind PredictionKind + { + get { return PredictionKind.BinaryClassification; } + } + + public override TScalarPredictor CreatePredictor() + { + if (Models.All(m => m.Predictor is TDistPredictor)) + return new EnsembleDistributionPredictor(Host, PredictionKind, CreateModels(), Combiner); + return new EnsemblePredictor(Host, PredictionKind, CreateModels(), Combiner); + } + + public TScalarPredictor CombineModels(IEnumerable models) + { + var combiner = _outputCombiner.CreateComponent(Host); + var p = models.First(); + + TScalarPredictor predictor = null; + if (p is TDistPredictor) + { + predictor = new EnsembleDistributionPredictor(Host, p.PredictionKind, + models.Select(k => new FeatureSubsetModel((TDistPredictor)k)).ToArray(), combiner); + } + else + { + predictor = new EnsemblePredictor(Host, p.PredictionKind, + models.Select(k => new FeatureSubsetModel(k)).ToArray(), combiner); + } + + return predictor; + } + } +} \ No newline at end of file diff --git a/src/Microsoft.ML.Ensemble/Trainer/EnsembleDistributionPredictor.cs b/src/Microsoft.ML.Ensemble/Trainer/EnsembleDistributionPredictor.cs new file mode 100644 index 0000000000..547800c152 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/Trainer/EnsembleDistributionPredictor.cs @@ -0,0 +1,226 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Collections.Generic; +using System.Linq; +using System.Threading.Tasks; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Ensemble; +using Microsoft.ML.Runtime.Ensemble.OutputCombiners; +using Microsoft.ML.Runtime.Internal.Utilities; +using Microsoft.ML.Runtime.Model; + +// These are for deserialization from a model repository. +[assembly: LoadableClass(typeof(EnsembleDistributionPredictor), null, typeof(SignatureLoadModel), + EnsembleDistributionPredictor.UserName, EnsembleDistributionPredictor.LoaderSignature)] + +namespace Microsoft.ML.Runtime.Ensemble +{ + using TDistPredictor = IDistPredictorProducing; + + public sealed class EnsembleDistributionPredictor : EnsemblePredictorBase, + TDistPredictor, IValueMapperDist + { + public const string UserName = "Ensemble Distribution Executor"; + public const string LoaderSignature = "EnsemDbExec"; + public const string RegistrationName = "EnsembleDistributionPredictor"; + + private static VersionInfo GetVersionInfo() + { + return new VersionInfo( + modelSignature: "ENSEM DB", + // verWrittenCur: 0x00010001, // Initial + //verWrittenCur: 0x00010002, // Metrics and subset info into main stream, after each predictor + verWrittenCur: 0x00010003, // Don't serialize the "IsAveraged" property of the metrics + verReadableCur: 0x00010003, + verWeCanReadBack: 0x00010002, + loaderSignature: LoaderSignature); + } + + private readonly Single[] _averagedWeights; + private readonly Median _probabilityCombiner; + private readonly IValueMapperDist[] _mappers; + + public ColumnType InputType { get; } + public ColumnType OutputType => NumberType.Float; + public ColumnType DistType => NumberType.Float; + + public override PredictionKind PredictionKind { get; } + + internal EnsembleDistributionPredictor(IHostEnvironment env, PredictionKind kind, + FeatureSubsetModel[] models, IOutputCombiner combiner, Single[] weights = null) + : base(env, RegistrationName, models, combiner, weights) + { + PredictionKind = kind; + _probabilityCombiner = new Median(env); + InputType = InitializeMappers(out _mappers); + ComputeAveragedWeights(out _averagedWeights); + } + + private EnsembleDistributionPredictor(IHostEnvironment env, ModelLoadContext ctx) + : base(env, RegistrationName, ctx) + { + PredictionKind = (PredictionKind)ctx.Reader.ReadInt32(); + _probabilityCombiner = new Median(env); + InputType = InitializeMappers(out _mappers); + ComputeAveragedWeights(out _averagedWeights); + } + + private ColumnType InitializeMappers(out IValueMapperDist[] mappers) + { + Host.AssertNonEmpty(Models); + + mappers = new IValueMapperDist[Models.Length]; + ColumnType inputType = null; + for (int i = 0; i < Models.Length; i++) + { + var vmd = Models[i].Predictor as IValueMapperDist; + if (!IsValid(vmd)) + throw Host.Except("Predictor does not implement expected interface"); + if (vmd.InputType.VectorSize > 0) + { + if (inputType == null) + inputType = vmd.InputType; + else if (vmd.InputType.VectorSize != inputType.VectorSize) + throw Host.Except("Predictor input type mismatch"); + } + mappers[i] = vmd; + } + return inputType ?? new VectorType(NumberType.Float); + } + + private bool IsValid(IValueMapperDist mapper) + { + return mapper != null + && mapper.InputType.IsVector && mapper.InputType.ItemType == NumberType.Float + && mapper.OutputType == NumberType.Float + && mapper.DistType == NumberType.Float; + } + + public static EnsembleDistributionPredictor Create(IHostEnvironment env, ModelLoadContext ctx) + { + Contracts.CheckValue(env, nameof(env)); + env.CheckValue(ctx, nameof(ctx)); + ctx.CheckAtModel(GetVersionInfo()); + return new EnsembleDistributionPredictor(env, ctx); + } + + protected override void SaveCore(ModelSaveContext ctx) + { + base.SaveCore(ctx); + ctx.SetVersionInfo(GetVersionInfo()); + + // *** Binary format *** + // int: PredictionKind + ctx.Writer.Write((int)PredictionKind); + } + + public ValueMapper GetMapper() + { + Host.Check(typeof(TIn) == typeof(VBuffer)); + Host.Check(typeof(TOut) == typeof(Single)); + + var combine = Combiner.GetCombiner(); + var maps = GetMaps(); + var predictions = new Single[_mappers.Length]; + var probabilities = new Single[_mappers.Length]; + var vBuffers = new VBuffer[_mappers.Length]; + ValueMapper, Single> del = + (ref VBuffer src, ref Single dst) => + { + if (InputType.VectorSize > 0) + Host.Check(src.Length == InputType.VectorSize); + + var tmp = src; + Parallel.For(0, maps.Length, i => + { + var model = Models[i]; + if (model.SelectedFeatures != null) + { + EnsembleUtils.SelectFeatures(ref tmp, model.SelectedFeatures, model.Cardinality, ref vBuffers[i]); + maps[i](ref vBuffers[i], ref predictions[i], ref probabilities[i]); + } + else + maps[i](ref tmp, ref predictions[i], ref probabilities[i]); + }); + + // REVIEW: DistributionEnsemble - AveragedWeights are used only in one of the two PredictDistributions overloads + combine(ref dst, predictions, Weights); + }; + + return (ValueMapper)(Delegate)del; + } + + public ValueMapper GetMapper() + { + Host.Check(typeof(TIn) == typeof(VBuffer)); + Host.Check(typeof(TOut) == typeof(Single)); + Host.Check(typeof(TDist) == typeof(Single)); + + var combine = Combiner.GetCombiner(); + var combineProb = _probabilityCombiner.GetCombiner(); + var maps = GetMaps(); + var predictions = new Single[_mappers.Length]; + var probabilities = new Single[_mappers.Length]; + var vBuffers = new VBuffer[_mappers.Length]; + ValueMapper, Single, Single> del = + (ref VBuffer src, ref Single score, ref Single prob) => + { + if (InputType.VectorSize > 0) + Host.Check(src.Length == InputType.VectorSize); + + var tmp = src; + Parallel.For(0, maps.Length, i => + { + var model = Models[i]; + if (model.SelectedFeatures != null) + { + EnsembleUtils.SelectFeatures(ref tmp, model.SelectedFeatures, model.Cardinality, ref vBuffers[i]); + maps[i](ref vBuffers[i], ref predictions[i], ref probabilities[i]); + } + else + maps[i](ref tmp, ref predictions[i], ref probabilities[i]); + }); + + combine(ref score, predictions, _averagedWeights); + combineProb(ref prob, probabilities, _averagedWeights); + }; + + return (ValueMapper)(Delegate)del; + } + + private ValueMapper, Single, Single>[] GetMaps() + { + Host.AssertValue(_mappers); + + var maps = new ValueMapper, Single, Single>[_mappers.Length]; + for (int i = 0; i < _mappers.Length; i++) + maps[i] = _mappers[i].GetMapper, Single, Single>(); + return maps; + } + + private void ComputeAveragedWeights(out Single[] averagedWeights) + { + averagedWeights = Weights; + if (Combiner is IWeightedAverager weightedAverager && averagedWeights == null && Models[0].Metrics != null) + { + var metric = default(KeyValuePair); + bool found = false; + foreach (var m in Models[0].Metrics) + { + metric = m; + if (Utils.ExtractLettersAndNumbers(m.Key).ToLower().Equals(weightedAverager.WeightageMetricName.ToLower())) + { + found = true; + break; + } + } + if (found) + averagedWeights = Models.SelectMany(model => model.Metrics).Where(m => m.Key == metric.Key).Select(m => (Single)m.Value).ToArray(); + } + } + } +} diff --git a/src/Microsoft.ML.Ensemble/Trainer/EnsemblePredictor.cs b/src/Microsoft.ML.Ensemble/Trainer/EnsemblePredictor.cs new file mode 100644 index 0000000000..08c8f0dd8d --- /dev/null +++ b/src/Microsoft.ML.Ensemble/Trainer/EnsemblePredictor.cs @@ -0,0 +1,148 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Threading.Tasks; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Ensemble; +using Microsoft.ML.Runtime.Model; +using Microsoft.ML.Runtime.Ensemble.OutputCombiners; +using Microsoft.ML.Runtime.EntryPoints; + +[assembly: LoadableClass(typeof(EnsemblePredictor), null, typeof(SignatureLoadModel), EnsemblePredictor.UserName, + EnsemblePredictor.LoaderSignature)] + +[assembly: EntryPointModule(typeof(EnsemblePredictor))] + +namespace Microsoft.ML.Runtime.Ensemble +{ + using TScalarPredictor = IPredictorProducing; + + public sealed class EnsemblePredictor : EnsemblePredictorBase, IValueMapper + { + public const string UserName = "Ensemble Executor"; + public const string LoaderSignature = "EnsembleFloatExec"; + public const string RegistrationName = "EnsemblePredictor"; + + private static VersionInfo GetVersionInfo() + { + return new VersionInfo( + modelSignature: "ENSEM XX", + // verWrittenCur: 0x00010001, // Initial + //verWrittenCur: 0x00010002, // Metrics and subset info into main stream, after each predictor + verWrittenCur: 0x00010003, // Don't serialize the "IsAveraged" property of the metrics + verReadableCur: 0x00010003, + verWeCanReadBack: 0x00010002, + loaderSignature: LoaderSignature); + } + + private readonly IValueMapper[] _mappers; + + public ColumnType InputType { get; } + public ColumnType OutputType => NumberType.Float; + public override PredictionKind PredictionKind { get; } + + internal EnsemblePredictor(IHostEnvironment env, PredictionKind kind, + FeatureSubsetModel[] models, IOutputCombiner combiner, Single[] weights = null) + : base(env, LoaderSignature, models, combiner, weights) + { + PredictionKind = kind; + InputType = InitializeMappers(out _mappers); + } + + private EnsemblePredictor(IHostEnvironment env, ModelLoadContext ctx) + : base(env, RegistrationName, ctx) + { + PredictionKind = (PredictionKind)ctx.Reader.ReadInt32(); + InputType = InitializeMappers(out _mappers); + } + + private ColumnType InitializeMappers(out IValueMapper[] mappers) + { + Host.AssertNonEmpty(Models); + + mappers = new IValueMapper[Models.Length]; + ColumnType inputType = null; + for (int i = 0; i < Models.Length; i++) + { + var vm = Models[i].Predictor as IValueMapper; + if (!IsValid(vm)) + throw Host.Except("Predictor does not implement expected interface"); + if (vm.InputType.VectorSize > 0) + { + if (inputType == null) + inputType = vm.InputType; + else if (vm.InputType.VectorSize != inputType.VectorSize) + throw Host.Except("Predictor input type mismatch"); + } + mappers[i] = vm; + } + + return inputType ?? new VectorType(NumberType.Float); + } + + private bool IsValid(IValueMapper mapper) + { + return mapper != null + && mapper.InputType.IsVector && mapper.InputType.ItemType == NumberType.Float + && mapper.OutputType == NumberType.Float; + } + + public static EnsemblePredictor Create(IHostEnvironment env, ModelLoadContext ctx) + { + Contracts.CheckValue(env, nameof(env)); + env.CheckValue(ctx, nameof(ctx)); + ctx.CheckAtModel(GetVersionInfo()); + return new EnsemblePredictor(env, ctx); + } + + protected override void SaveCore(ModelSaveContext ctx) + { + base.SaveCore(ctx); + ctx.SetVersionInfo(GetVersionInfo()); + + // *** Binary format *** + // int: _kind + ctx.Writer.Write((int)PredictionKind); + } + + public ValueMapper GetMapper() + { + Host.Check(typeof(TIn) == typeof(VBuffer)); + Host.Check(typeof(TOut) == typeof(Single)); + + var combine = Combiner.GetCombiner(); + var predictions = new Single[_mappers.Length]; + var buffers = new VBuffer[_mappers.Length]; + var maps = new ValueMapper, Single>[_mappers.Length]; + for (int i = 0; i < _mappers.Length; i++) + maps[i] = _mappers[i].GetMapper, Single>(); + + ValueMapper, Single> del = + (ref VBuffer src, ref Single dst) => + { + if (InputType.VectorSize > 0) + Host.Check(src.Length == InputType.VectorSize); + + var tmp = src; + Parallel.For(0, maps.Length, i => + { + var model = Models[i]; + if (model.SelectedFeatures != null) + { + EnsembleUtils.SelectFeatures(ref tmp, model.SelectedFeatures, model.Cardinality, ref buffers[i]); + maps[i](ref buffers[i], ref predictions[i]); + } + else + maps[i](ref tmp, ref predictions[i]); + }); + + combine(ref dst, predictions, Weights); + }; + + return (ValueMapper)(Delegate)del; + } + } +} \ No newline at end of file diff --git a/src/Microsoft.ML.Ensemble/Trainer/EnsemblePredictorBase.cs b/src/Microsoft.ML.Ensemble/Trainer/EnsemblePredictorBase.cs new file mode 100644 index 0000000000..9f2ebfb804 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/Trainer/EnsemblePredictorBase.cs @@ -0,0 +1,164 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Collections.Generic; +using System.IO; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Ensemble.OutputCombiners; +using Microsoft.ML.Runtime.Internal.Internallearn; +using Microsoft.ML.Runtime.Internal.Utilities; +using Microsoft.ML.Runtime.Model; + +namespace Microsoft.ML.Runtime.Ensemble +{ + public abstract class EnsemblePredictorBase : PredictorBase, + IPredictorProducing, ICanSaveInTextFormat, ICanSaveModel, ICanSaveSummary + where TPredictor : class, IPredictorProducing + { + private const string SubPredictorFmt = "SubPredictor_{0:000}"; + + protected readonly FeatureSubsetModel[] Models; + protected readonly IOutputCombiner Combiner; + protected readonly Single[] Weights; + + private const uint VerOld = 0x00010002; + + protected EnsemblePredictorBase(IHostEnvironment env, string name, FeatureSubsetModel[] models, + IOutputCombiner combiner, Single[] weights) + : base(env, name) + { + + Host.Check(Utils.Size(models) > 0, "Ensemble was created with no models."); + Host.Check(weights == null || weights.Length == models.Length); + + Models = models; + Combiner = combiner; + Weights = weights; + } + + protected EnsemblePredictorBase(IHostEnvironment env, string name, ModelLoadContext ctx) + : base(env, name, ctx) + { + // *** Binary format *** + // int: model count + // int: weight count (0 or model count) + // Float[]: weights + // for each model: + // int: number of SelectedFeatures (in bits) + // byte[]: selected features (as many as needed for number of bits == (numSelectedFeatures + 7) / 8) + // int: number of Metric values + // for each Metric: + // Float: metric value + // int: metric name (id of the metric name in the string table) + // in version 0x0001x0002: + // bool: is the metric averaged + + int count = ctx.Reader.ReadInt32(); + Host.CheckDecode(count > 0); + + int weightCount = ctx.Reader.ReadInt32(); + Host.CheckDecode(weightCount == 0 || weightCount == count); + Weights = ctx.Reader.ReadFloatArray(weightCount); + + Models = new FeatureSubsetModel[count]; + var ver = ctx.Header.ModelVerWritten; + for (int i = 0; i < count; i++) + { + ctx.LoadModel(Host, out IPredictor p, string.Format(SubPredictorFmt, i)); + var predictor = p as TPredictor; + Host.Check(p != null, "Inner predictor type not compatible with the ensemble type."); + var features = ctx.Reader.ReadBitArray(); + int numMetrics = ctx.Reader.ReadInt32(); + Host.CheckDecode(numMetrics >= 0); + var metrics = new KeyValuePair[numMetrics]; + for (int j = 0; j < numMetrics; j++) + { + var metricValue = ctx.Reader.ReadFloat(); + var metricName = ctx.LoadStringOrNull(); + if (ver == VerOld) + ctx.Reader.ReadBoolByte(); + metrics[j] = new KeyValuePair(metricName, metricValue); + } + Models[i] = new FeatureSubsetModel(predictor, features, metrics); + } + ctx.LoadModel, SignatureLoadModel>(Host, out Combiner, @"Combiner"); + } + + protected override void SaveCore(ModelSaveContext ctx) + { + base.SaveCore(ctx); + + // *** Binary format *** + // int: model count + // int: weight count (0 or model count) + // Single[]: weights + // for each model: + // int: number of SelectedFeatures (in bits) + // byte[]: selected features (as many as needed for number of bits == (numSelectedFeatures + 7) / 8) + // int: number of Metric values + // for each Metric: + // Single: metric value + // int: metric name (id of the metric name in the string table) + + ctx.Writer.Write(Models.Length); + ctx.Writer.WriteFloatArray(Weights); + + // Save other streams. + for (int i = 0; i < Models.Length; i++) + { + var model = Models[i]; + ctx.SaveModel(model.Predictor, string.Format(SubPredictorFmt, i)); + Host.AssertValueOrNull(model.SelectedFeatures); + ctx.Writer.WriteBitArray(model.SelectedFeatures); + Host.AssertValueOrNull(model.Metrics); + int numMetrics = Utils.Size(model.Metrics); + ctx.Writer.Write(numMetrics); + for (int j = 0; j < numMetrics; j++) + { + var metric = model.Metrics[j]; + ctx.Writer.Write((Single)metric.Value); + ctx.SaveStringOrNull(metric.Key); + } + } + ctx.SaveModel(Combiner, @"Combiner"); + } + + /// + /// Output the INI model to a given writer + /// + public void SaveAsText(TextWriter writer, RoleMappedSchema schema) + { + using (var ch = Host.Start("SaveAsText")) + { + for (int i = 0; i < Models.Length; i++) + { + writer.WriteLine(";; Partition model {0}", i); + writer.WriteLine(";; Weight={0}", (Weights != null ? Weights[i] : 1)); + PredictorUtils.SaveText(ch, Models[i].Predictor, schema, writer); + } + ch.Done(); + } + } + + /// + /// Saves the model summary + /// + public void SaveSummary(TextWriter writer, RoleMappedSchema schema) + { + for (int i = 0; i < Models.Length; i++) + { + writer.WriteLine(";; Partition model {0}", i); + writer.WriteLine(";; Weight={0}", (Weights != null ? Weights[i] : 1)); + + // REVIEW: The featureName Collection names may vary for different base learners. + // How do we get the right collection for the base learners? + if (Models[i].Predictor is ICanSaveSummary summaryModel) + summaryModel.SaveSummary(writer, schema); + else + writer.WriteLine("The Model {0} does not support saving summaries", Models[i].GetType().Name); + } + } + } +} diff --git a/src/Microsoft.ML.Ensemble/Trainer/EnsembleTrainerBase.cs b/src/Microsoft.ML.Ensemble/Trainer/EnsembleTrainerBase.cs new file mode 100644 index 0000000000..776b1f5f53 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/Trainer/EnsembleTrainerBase.cs @@ -0,0 +1,241 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Collections.Generic; +using System.Linq; +using System.Threading.Tasks; +using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Ensemble.OutputCombiners; +using Microsoft.ML.Runtime.Ensemble.Selector; +using Microsoft.ML.Runtime.Ensemble.Selector.SubsetSelector; +using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.Internal.Internallearn; +using Microsoft.ML.Runtime.Internal.Utilities; +using Microsoft.ML.Runtime.Training; + +namespace Microsoft.ML.Runtime.Ensemble +{ + using Stopwatch = System.Diagnostics.Stopwatch; + + public abstract class EnsembleTrainerBase : TrainerBase + where TPredictor : class, IPredictorProducing + where TSelector : class, ISubModelSelector + where TCombiner : class, IOutputCombiner + { + public abstract class ArgumentsBase : LearnerInputBaseWithLabel + { + [Argument(ArgumentType.AtMostOnce, + HelpText = "Number of models per batch. If not specified, will default to 50 if there is only one base predictor, " + + "or the number of base predictors otherwise.", ShortName = "nm", SortOrder = 3)] + [TGUI(Label = "Number of Models per batch")] + public int? NumModels; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Batch size", ShortName = "bs", SortOrder = 107)] + [TGUI(Label = "Batch Size", + Description = + "Number of instances to be loaded in memory to create an ensemble out of it. All the instances will be loaded if the value is -1.")] + public int BatchSize = -1; + + [Argument(ArgumentType.Multiple, HelpText = "Sampling Type", ShortName = "st", SortOrder = 2)] + [TGUI(Label = "Sampling Type", Description = "Subset Selection Algorithm to induce the base learner.Sub-settings can be used to select the features")] + public ISupportSubsetSelectorFactory SamplingType = new BootstrapSelector.Arguments(); + + [Argument(ArgumentType.AtMostOnce, HelpText = "All the base learners will run asynchronously if the value is true", ShortName = "tp", SortOrder = 106)] + [TGUI(Label = "Train parallel", Description = "All the base learners will run asynchronously if the value is true")] + public bool TrainParallel; + + [Argument(ArgumentType.AtMostOnce, + HelpText = "True, if metrics for each model need to be evaluated and shown in comparison table. This is done by using validation set if available or the training set", + ShortName = "sm", SortOrder = 108)] + [TGUI(Label = "Show Sub-Model Metrics")] + public bool ShowMetrics; + + [Argument(ArgumentType.Multiple, HelpText = "Base predictor type", ShortName = "bp,basePredictorTypes", SortOrder = 1, Visibility = ArgumentAttribute.VisibilityType.CmdLineOnly)] + public SubComponent>, TSig>[] BasePredictors; + } + + private const int DefaultNumModels = 50; + /// Command-line arguments + protected readonly ArgumentsBase Args; + protected readonly int NumModels; + + /// Ensemble members + protected readonly ITrainer>[] Trainers; + + private readonly ISubsetSelector _subsetSelector; + protected ISubModelSelector SubModelSelector; + protected IOutputCombiner Combiner; + + protected List>> Models; + + private readonly bool _needNorm; + private readonly bool _needCalibration; + + internal EnsembleTrainerBase(ArgumentsBase args, IHostEnvironment env, string name) + : base(env, name) + { + Args = args; + + using (var ch = Host.Start("Init")) + { + ch.CheckUserArg(Utils.Size(Args.BasePredictors) > 0, nameof(Args.BasePredictors), "This should have at-least one value"); + + NumModels = Args.NumModels ?? + (Args.BasePredictors.Length == 1 ? DefaultNumModels : Args.BasePredictors.Length); + + ch.CheckUserArg(NumModels > 0, nameof(Args.NumModels), "Must be positive, or null to indicate numModels is the number of base predictors"); + + if (Utils.Size(Args.BasePredictors) > NumModels) + ch.Warning("The base predictor count is greater than models count. Some of the base predictors will be ignored."); + + _subsetSelector = Args.SamplingType.CreateComponent(Host); + + Trainers = new ITrainer>[NumModels]; + for (int i = 0; i < Trainers.Length; i++) + Trainers[i] = Args.BasePredictors[i % Args.BasePredictors.Length].CreateInstance(Host); + _needNorm = Trainers.Any( + t => + { + return t is ITrainerEx nn && nn.NeedNormalization; + }); + _needCalibration = Trainers.Any( + t => + { + return t is ITrainerEx nn && nn.NeedCalibration; + }); + ch.Done(); + } + } + + public override bool NeedNormalization => _needNorm; + + public override bool NeedCalibration => _needCalibration; + + // No matter the internal predictors, we are performing multiple passes over the data + // so it is probably appropriate to always cache. + public override bool WantCaching => true; + + public override void Train(RoleMappedData data) + { + using (var ch = Host.Start("Training")) + { + TrainCore(ch, data); + ch.Done(); + } + } + + private void TrainCore(IChannel ch, RoleMappedData data) + { + Host.AssertValue(ch); + ch.AssertValue(data); + + // 1. Subset Selection + var stackingTrainer = Combiner as IStackingTrainer; + + //REVIEW: Implement stacking for Batch mode. + ch.CheckUserArg(stackingTrainer == null || Args.BatchSize <= 0, nameof(Args.BatchSize), "Stacking works only with Non-batch mode"); + + var validationDataSetProportion = SubModelSelector.ValidationDatasetProportion; + if (stackingTrainer != null) + validationDataSetProportion = Math.Max(validationDataSetProportion, stackingTrainer.ValidationDatasetProportion); + + var needMetrics = Args.ShowMetrics || Combiner is IWeightedAverager; + + _subsetSelector.Initialize(data, NumModels, Args.BatchSize, validationDataSetProportion); + int batchNumber = 1; + foreach (var batch in _subsetSelector.GetBatches(Host.Rand)) + { + // 2. Core train + ch.Info("Training {0} learners for the batch {1}", Trainers.Length, batchNumber++); + var models = new FeatureSubsetModel>[Trainers.Length]; + + Parallel.ForEach(_subsetSelector.GetSubsets(batch, Host.Rand), + new ParallelOptions() { MaxDegreeOfParallelism = Args.TrainParallel ? -1 : 1 }, + (subset, state, index) => + { + ch.Info("Beginning training model {0} of {1}", index + 1, Trainers.Length); + Stopwatch sw = Stopwatch.StartNew(); + try + { + if (EnsureMinimumFeaturesSelected(subset)) + { + Trainers[(int)index].Train(subset.Data); + + var model = new FeatureSubsetModel>( + Trainers[(int)index].CreatePredictor(), + subset.SelectedFeatures, + null); + SubModelSelector.CalculateMetrics(model, _subsetSelector, subset, batch, needMetrics); + models[(int)index] = model; + } + } + catch (Exception ex) + { + ch.Assert(models[(int)index] == null); + ch.Warning(ex.Sensitivity(), "Trainer {0} of {1} was not learned properly due to the exception '{2}' and will not be added to models.", + index + 1, Trainers.Length, ex.Message); + } + ch.Info("Trainer {0} of {1} finished in {2}", index + 1, Trainers.Length, sw.Elapsed); + }); + + var modelsList = models.Where(m => m != null).ToList(); + if (Args.ShowMetrics) + PrintMetrics(ch, modelsList); + + modelsList = SubModelSelector.Prune(modelsList).ToList(); + + if (stackingTrainer != null) + stackingTrainer.Train(modelsList, _subsetSelector.GetTestData(null, batch), Host); + + foreach (var model in modelsList) + Utils.Add(ref Models, model); + int modelSize = Utils.Size(Models); + if (modelSize < Utils.Size(Trainers)) + ch.Warning("{0} of {1} trainings failed.", Utils.Size(Trainers) - modelSize, Utils.Size(Trainers)); + ch.Check(modelSize > 0, "Ensemble training resulted in no valid models."); + } + } + + private bool EnsureMinimumFeaturesSelected(Subset subset) + { + if (subset.SelectedFeatures == null) + return true; + for (int i = 0; i < subset.SelectedFeatures.Count; i++) + { + if (subset.SelectedFeatures[i]) + return true; + } + + return false; + } + + protected virtual void PrintMetrics(IChannel ch, List>> models) + { + // REVIEW: The formatting of this method is bizarre and seemingly not even self-consistent + // w.r.t. its usage of |. Is this intentional? + if (models.Count == 0 || models[0].Metrics == null) + return; + + ch.Info("{0}| Name of Model |", string.Join("", models[0].Metrics.Select(m => string.Format("| {0} |", m.Key)))); + + foreach (var model in models) + ch.Info("{0}{1}", string.Join("", model.Metrics.Select(m => string.Format("| {0} |", m.Value))), model.Predictor.GetType().Name); + } + + protected FeatureSubsetModel[] CreateModels() where T : IPredictor + { + var models = new FeatureSubsetModel[Models.Count]; + for (int i = 0; i < Models.Count; i++) + { + models[i] = new FeatureSubsetModel( + (T)Models[i].Predictor, + Models[i].SelectedFeatures, + Models[i].Metrics); + } + return models; + } + } +} diff --git a/src/Microsoft.ML.Ensemble/Trainer/Multiclass/EnsembleMultiClassPredictor.cs b/src/Microsoft.ML.Ensemble/Trainer/Multiclass/EnsembleMultiClassPredictor.cs new file mode 100644 index 0000000000..f846d3f80a --- /dev/null +++ b/src/Microsoft.ML.Ensemble/Trainer/Multiclass/EnsembleMultiClassPredictor.cs @@ -0,0 +1,152 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Threading.Tasks; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Ensemble.OutputCombiners; +using Microsoft.ML.Runtime.Model; + +namespace Microsoft.ML.Runtime.Ensemble +{ + using TVectorPredictor = IPredictorProducing>; + public sealed class EnsembleMultiClassPredictor : + EnsemblePredictorBase>, + IValueMapper + { + public const string LoaderSignature = "EnsemMcExec"; + public const string RegistrationName = "EnsembleMultiClassPredictor"; + + private static VersionInfo GetVersionInfo() + { + return new VersionInfo( + modelSignature: "ENSEM MC", + // verWrittenCur: 0x00010001, // Initial + //verWrittenCur: 0x00010002, // Metrics and subset info into main stream, after each predictor + verWrittenCur: 0x00010003, // Don't serialize the "IsAveraged" property of the metrics + verReadableCur: 0x00010003, + verWeCanReadBack: 0x00010002, + loaderSignature: LoaderSignature); + } + + private readonly ColumnType _inputType; + private readonly ColumnType _outputType; + private readonly IValueMapper[] _mappers; + + public ColumnType InputType { get { return _inputType; } } + public ColumnType OutputType { get { return _outputType; } } + + internal EnsembleMultiClassPredictor(IHostEnvironment env, FeatureSubsetModel[] models, + IMultiClassOutputCombiner combiner, Single[] weights = null) + : base(env, RegistrationName, models, combiner, weights) + { + InitializeMappers(out _mappers, out _inputType, out _outputType); + } + + private EnsembleMultiClassPredictor(IHostEnvironment env, ModelLoadContext ctx) + : base(env, RegistrationName, ctx) + { + InitializeMappers(out _mappers, out _inputType, out _outputType); + } + + private void InitializeMappers(out IValueMapper[] mappers, out ColumnType inputType, out ColumnType outputType) + { + Host.AssertNonEmpty(Models); + + mappers = new IValueMapper[Models.Length]; + inputType = null; + outputType = null; + for (int i = 0; i < Models.Length; i++) + { + var vm = Models[i].Predictor as IValueMapper; + if (!IsValid(vm)) + throw Host.Except("Predictor does not implement expected interface"); + if (vm.InputType.VectorSize > 0) + { + if (inputType == null) + inputType = vm.InputType; + else if (vm.InputType.VectorSize != inputType.VectorSize) + throw Host.Except("Predictor input type mismatch"); + } + + if (outputType == null || vm.OutputType.VectorSize > outputType.VectorSize) + outputType = vm.OutputType; + + mappers[i] = vm; + } + Host.AssertValue(outputType); + + if (inputType == null) + inputType = new VectorType(NumberType.Float); + } + + public static EnsembleMultiClassPredictor Create(IHostEnvironment env, ModelLoadContext ctx) + { + Contracts.CheckValue(env, nameof(env)); + env.CheckValue(ctx, nameof(ctx)); + ctx.CheckAtModel(GetVersionInfo()); + return new EnsembleMultiClassPredictor(env, ctx); + } + + protected override void SaveCore(ModelSaveContext ctx) + { + base.SaveCore(ctx); + ctx.SetVersionInfo(GetVersionInfo()); + } + + public override PredictionKind PredictionKind { get { return PredictionKind.MultiClassClassification; } } + + public ValueMapper GetMapper() + { + Host.Check(typeof(TIn) == typeof(VBuffer)); + Host.Check(typeof(TOut) == typeof(VBuffer)); + + var combine = Combiner.GetCombiner(); + var features = new VBuffer[_mappers.Length]; + var predictions = new VBuffer[_mappers.Length]; + var maps = new ValueMapper, VBuffer>[_mappers.Length]; + for (int i = 0; i < _mappers.Length; i++) + { + // IsValid method ensures we go this else path only if the OutputType.VectorSize of + // all _mappers is greater than zero + Host.Assert(_mappers[i].OutputType.VectorSize > 0); + maps[i] = _mappers[i].GetMapper, VBuffer>(); + } + + ValueMapper, VBuffer> del = + (ref VBuffer src, ref VBuffer dst) => + { + if (_inputType.VectorSize > 0) + Host.Check(src.Length == _inputType.VectorSize); + + var tmp = src; + Parallel.For(0, maps.Length, i => + { + var model = Models[i]; + if (model.SelectedFeatures != null) + { + EnsembleUtils.SelectFeatures(ref tmp, model.SelectedFeatures, model.Cardinality, ref features[i]); + maps[i](ref features[i], ref predictions[i]); + } + else + maps[i](ref tmp, ref predictions[i]); + + // individual maps delegates will return always the same VBuffer length + Host.Check(predictions[i].Length == _mappers[i].OutputType.VectorSize); + }); + + combine(ref dst, predictions, Weights); + }; + + return (ValueMapper)(Delegate)del; + } + + private bool IsValid(IValueMapper mapper) + { + return mapper != null + && mapper.InputType.IsVector && mapper.InputType.ItemType == NumberType.Float + && mapper.OutputType.VectorSize > 0 && mapper.OutputType.ItemType == NumberType.Float; + } + } +} diff --git a/src/Microsoft.ML.Ensemble/Trainer/Multiclass/MulticlassDataPartitionEnsembleTrainer.cs b/src/Microsoft.ML.Ensemble/Trainer/Multiclass/MulticlassDataPartitionEnsembleTrainer.cs new file mode 100644 index 0000000000..0e6b4f6a53 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/Trainer/Multiclass/MulticlassDataPartitionEnsembleTrainer.cs @@ -0,0 +1,81 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Collections.Generic; +using System.Linq; +using Microsoft.ML.Ensemble.EntryPoints; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Ensemble; +using Microsoft.ML.Runtime.Ensemble.OutputCombiners; +using Microsoft.ML.Runtime.Ensemble.Selector; +using Microsoft.ML.Runtime.Internal.Internallearn; + +[assembly: LoadableClass(MulticlassDataPartitionEnsembleTrainer.Summary, typeof(MulticlassDataPartitionEnsembleTrainer), + typeof(MulticlassDataPartitionEnsembleTrainer.Arguments), + new[] { typeof(SignatureMultiClassClassifierTrainer), typeof(SignatureTrainer) }, + MulticlassDataPartitionEnsembleTrainer.UserNameValue, + MulticlassDataPartitionEnsembleTrainer.LoadNameValue)] + +namespace Microsoft.ML.Runtime.Ensemble +{ + using TVectorPredictor = IPredictorProducing>; + /// + /// A generic ensemble classifier for multi-class classification + /// + public sealed class MulticlassDataPartitionEnsembleTrainer : + EnsembleTrainerBase, EnsembleMultiClassPredictor, + IMulticlassSubModelSelector, IMultiClassOutputCombiner, SignatureMultiClassClassifierTrainer>, + IModelCombiner + { + public const string LoadNameValue = "WeightedEnsembleMulticlass"; + public const string UserNameValue = "Multi-class Parallel Ensemble (bagging, stacking, etc)"; + public const string Summary = "A generic ensemble classifier for multi-class classification."; + + public sealed class Arguments : ArgumentsBase + { + [Argument(ArgumentType.Multiple, HelpText = "Algorithm to prune the base learners for selective Ensemble", ShortName = "pt", SortOrder = 4)] + [TGUI(Label = "Sub-Model Selector(pruning) Type", Description = "Algorithm to prune the base learners for selective Ensemble")] + public ISupportMulticlassSubModelSelectorFactory SubModelSelectorType = new AllSelectorMultiClassFactory(); + + [Argument(ArgumentType.Multiple, HelpText = "Output combiner", ShortName = "oc", SortOrder = 5)] + [TGUI(Label = "Output combiner", Description = "Output combiner type")] + public ISupportMulticlassOutputCombinerFactory OutputCombiner = new MultiMedian.Arguments(); + + public Arguments() + { + BasePredictors = new[] { new SubComponent, SignatureMultiClassClassifierTrainer>("MultiClassLogisticRegression") }; + } + } + + private readonly ISupportMulticlassOutputCombinerFactory _outputCombiner; + + public MulticlassDataPartitionEnsembleTrainer(IHostEnvironment env, Arguments args) + : base(args, env, LoadNameValue) + { + SubModelSelector = args.SubModelSelectorType.CreateComponent(Host); + _outputCombiner = args.OutputCombiner; + Combiner = args.OutputCombiner.CreateComponent(Host); + } + + public override PredictionKind PredictionKind { get { return PredictionKind.MultiClassClassification; } } + + public override EnsembleMultiClassPredictor CreatePredictor() + { + var combiner = Combiner; + return new EnsembleMultiClassPredictor(Host, CreateModels(), combiner as IMultiClassOutputCombiner); + } + + public TVectorPredictor CombineModels(IEnumerable models) + { + var predictor = new EnsembleMultiClassPredictor(Host, + models.Select(k => new FeatureSubsetModel(k)).ToArray(), + _outputCombiner.CreateComponent(Host)); + + return predictor; + } + } +} diff --git a/src/Microsoft.ML.Ensemble/Trainer/Regression/RegressionEnsembleTrainer.cs b/src/Microsoft.ML.Ensemble/Trainer/Regression/RegressionEnsembleTrainer.cs new file mode 100644 index 0000000000..322c1e02a1 --- /dev/null +++ b/src/Microsoft.ML.Ensemble/Trainer/Regression/RegressionEnsembleTrainer.cs @@ -0,0 +1,79 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Collections.Generic; +using System.Linq; +using Microsoft.ML.Ensemble.EntryPoints; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Ensemble; +using Microsoft.ML.Runtime.Ensemble.OutputCombiners; +using Microsoft.ML.Runtime.Ensemble.Selector; +using Microsoft.ML.Runtime.Internal.Internallearn; + +[assembly: LoadableClass(typeof(RegressionEnsembleTrainer), typeof(RegressionEnsembleTrainer.Arguments), + new[] { typeof(SignatureRegressorTrainer), typeof(SignatureTrainer) }, + RegressionEnsembleTrainer.UserNameValue, + RegressionEnsembleTrainer.LoadNameValue)] + +namespace Microsoft.ML.Runtime.Ensemble +{ + using TScalarPredictor = IPredictorProducing; + public sealed class RegressionEnsembleTrainer : EnsembleTrainerBase, + IModelCombiner + { + public const string LoadNameValue = "EnsembleRegression"; + public const string UserNameValue = "Regression Ensemble (bagging, stacking, etc)"; + + public sealed class Arguments : ArgumentsBase + { + [Argument(ArgumentType.Multiple, HelpText = "Algorithm to prune the base learners for selective Ensemble", ShortName = "pt", SortOrder = 4)] + [TGUI(Label = "Sub-Model Selector(pruning) Type", Description = "Algorithm to prune the base learners for selective Ensemble")] + public ISupportRegressionSubModelSelectorFactory SubModelSelectorType = new AllSelectorFactory(); + + [Argument(ArgumentType.Multiple, HelpText = "Output combiner", ShortName = "oc", SortOrder = 5)] + [TGUI(Label = "Output combiner", Description = "Output combiner type")] + public ISupportRegressionOutputCombinerFactory OutputCombiner = new MedianFactory(); + + public Arguments() + { + BasePredictors = new[] { new SubComponent, SignatureRegressorTrainer>("OnlineGradientDescent") }; + } + } + + private readonly ISupportRegressionOutputCombinerFactory _outputCombiner; + + public RegressionEnsembleTrainer(IHostEnvironment env, Arguments args) + : base(args, env, LoadNameValue) + { + SubModelSelector = args.SubModelSelectorType.CreateComponent(Host); + _outputCombiner = args.OutputCombiner; + Combiner = args.OutputCombiner.CreateComponent(Host); + } + + public override PredictionKind PredictionKind + { + get { return PredictionKind.Regression; } + } + + public override TScalarPredictor CreatePredictor() + { + return new EnsemblePredictor(Host, PredictionKind, CreateModels(), Combiner); + } + + public TScalarPredictor CombineModels(IEnumerable models) + { + var combiner = _outputCombiner.CreateComponent(Host); + var p = models.First(); + + var predictor = new EnsemblePredictor(Host, p.PredictionKind, + models.Select(k => new FeatureSubsetModel(k)).ToArray(), combiner); + + return predictor; + } + } +} diff --git a/src/Microsoft.ML.FastTree/FastTree.cs b/src/Microsoft.ML.FastTree/FastTree.cs index 00060c44b5..654735c4b6 100644 --- a/src/Microsoft.ML.FastTree/FastTree.cs +++ b/src/Microsoft.ML.FastTree/FastTree.cs @@ -2789,8 +2789,7 @@ public abstract class FastTreePredictionWrapper : IWhatTheFeatureValueMapper, ICanGetSummaryAsIRow, ISingleCanSavePfa, - ISingleCanSaveOnnx/*, - ISingleCanSaveLotusVNext*/ + ISingleCanSaveOnnx { //The below two properties are necessary for tree Visualizer public Ensemble TrainedEnsemble { get; } @@ -2816,7 +2815,6 @@ public abstract class FastTreePredictionWrapper : public ColumnType OutputType => NumberType.Float; public bool CanSavePfa => true; public bool CanSaveOnnx => true; - public bool CanSaveLotusVNext => true; protected internal FastTreePredictionWrapper(IHostEnvironment env, string name, Ensemble trainedEnsemble, int numFeatures, string innerArgs) : base(env, name) @@ -3134,27 +3132,6 @@ public virtual bool SaveAsOnnx(OnnxContext ctx, string[] outputNames, string fea return true; } - /*public void SaveAsLotusVNext(LotusVNextContext ctx, string featuresVariableName, string outputColumnName) - { - Host.CheckValue(ctx, nameof(ctx)); - Host.CheckValue(featuresVariableName, nameof(featuresVariableName)); - var tempVariables = new List(); - foreach (RegressionTree tree in TrainedEnsemble.Trees) - { - var tempVariable = ctx.DeclareVariable(null, LotusVNextUtils.MakeFloatLiteral(0)); - tempVariables.Add(tempVariable); - tree.SaveAsLotusVNext(ctx, featuresVariableName, tempVariable); - } - - var sumExpression = LotusVNextUtils.MakeVariableReference(tempVariables[0]); - for (int i = 1; i < tempVariables.Count; i++) - sumExpression = LotusVNextUtils.MakeCall("plus", - sumExpression, - LotusVNextUtils.MakeVariableReference(tempVariables[i])); - - ctx.DeclareVariable(outputColumnName, sumExpression); - }*/ - public void SaveSummary(TextWriter writer, RoleMappedSchema schema) { writer.WriteLine(); diff --git a/src/Microsoft.ML.FastTree/FastTreeArguments.cs b/src/Microsoft.ML.FastTree/FastTreeArguments.cs index e6420153f4..7262659e43 100644 --- a/src/Microsoft.ML.FastTree/FastTreeArguments.cs +++ b/src/Microsoft.ML.FastTree/FastTreeArguments.cs @@ -238,7 +238,7 @@ public abstract class TreeArgs : LearnerInputBaseWithGroupId public int MinDocumentsInLeafs = 10; // REVIEW: Different shortname than FastRank module. Same as the TLC FRWrapper. - [Argument(ArgumentType.LastOccurenceWins, HelpText = "Number of weak hypotheses in the ensemble", ShortName = "iter", SortOrder = 1)] + [Argument(ArgumentType.LastOccurenceWins, HelpText = "Total number of decision trees to create in the ensemble", ShortName = "iter", SortOrder = 1)] [TGUI(Description = "Total number of trees constructed", SuggestedSweeps = "20,100,500")] [TlcModule.SweepableDiscreteParamAttribute("NumTrees", new object[] { 20, 100, 500 })] public int NumTrees = 100; diff --git a/src/Microsoft.ML.FastTree/FastTreeRanking.cs b/src/Microsoft.ML.FastTree/FastTreeRanking.cs index 7f89858e00..2263d2541e 100644 --- a/src/Microsoft.ML.FastTree/FastTreeRanking.cs +++ b/src/Microsoft.ML.FastTree/FastTreeRanking.cs @@ -343,6 +343,9 @@ protected override void CustomizedTrainingIteration(RegressionTree tree) /// standard test for the dataset private Test CreateStandardTest(Dataset dataset) { + if (Utils.Size(dataset.MaxDcg) == 0) + dataset.Skeleton.RecomputeMaxDcg(10); + return new NdcgTest( ConstructScoreTracker(dataset), dataset.Ratings, @@ -764,7 +767,7 @@ protected override void GetGradientInOneQuery(int query, int threadIndex) { // calculates the permutation that orders "scores" in descending order, without modifying "scores" Array.Copy(_oneTwoThree, permutation, numDocuments); -#if USE_FASTTREENATIVE2 +#if USE_FASTTREENATIVE PermutationSort(permutation, scoresToUse, labels, numDocuments, begin); // Get how far about baseline our current @@ -815,12 +818,14 @@ protected override void GetGradientInOneQuery(int query, int threadIndex) if (!_trainDcg && (_costFunctionParam == 'c' || _useShiftedNdcg)) { PermutationSort(permutation, scoresToUse, labels, numDocuments, begin); - inverseMaxDcg = 1.0 / DCGCalculator.MaxDCGQuery(labels, begin, numDocuments, numDocuments, _labelCounts[query]); + inverseMaxDcg = 1.0 / DcgCalculator.MaxDcgQuery(labels, begin, numDocuments, numDocuments, _labelCounts[query]); } - C_GetDerivatives(numDocuments, begin, pPermutation, pLabels, + // A constant related to secondary labels, which does not exist in the current codebase. + const bool secondaryIsolabelExclusive = false; + GetDerivatives(numDocuments, begin, pPermutation, pLabels, pScores, pLambdas, pWeights, pDiscount, inverseMaxDcg, pGainLabels, - _secondaryMetricShare, _secondaryIsolabelExclusive, secondaryInverseMaxDcg, pSecondaryGains, + _secondaryMetricShare, secondaryIsolabelExclusive, secondaryInverseMaxDcg, pSecondaryGains, pSigmoidTable, _minScore, _maxScore, _sigmoidTable.Length, _scoreToSigmoidTableFactor, _costFunctionParam, _distanceWeight2, numActualResults, &lambdaSum, double.MinValue, _baselineAlphaCurrent, baselineDcgGap); @@ -828,11 +833,11 @@ protected override void GetGradientInOneQuery(int query, int threadIndex) // For computing the "ideal" case of the DCGs. if (_baselineDcg != null) { - if (scoresToUse == _scores) - Array.Copy(_scores, begin, _scoresCopy, begin, numDocuments); + if (scoresToUse == Scores) + Array.Copy(Scores, begin, _scoresCopy, begin, numDocuments); for (int i = begin; i < begin + numDocuments; ++i) { - _scoresCopy[i] += _gradient[i] / _weights[i]; + _scoresCopy[i] += Gradient[i] / Weights[i]; } Array.Copy(_oneTwoThree, permutation, numDocuments); PermutationSort(permutation, _scoresCopy, labels, numDocuments, begin); diff --git a/src/Microsoft.ML.FastTree/Microsoft.ML.FastTree.csproj b/src/Microsoft.ML.FastTree/Microsoft.ML.FastTree.csproj index 0441b77e48..2a701542f0 100644 --- a/src/Microsoft.ML.FastTree/Microsoft.ML.FastTree.csproj +++ b/src/Microsoft.ML.FastTree/Microsoft.ML.FastTree.csproj @@ -57,6 +57,7 @@ + diff --git a/src/Microsoft.ML.FastTree/TreeEnsemble/RegressionTree.cs b/src/Microsoft.ML.FastTree/TreeEnsemble/RegressionTree.cs index 2496ed52b9..d2edeb796a 100644 --- a/src/Microsoft.ML.FastTree/TreeEnsemble/RegressionTree.cs +++ b/src/Microsoft.ML.FastTree/TreeEnsemble/RegressionTree.cs @@ -13,9 +13,6 @@ using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.Internal.Utilities; using Microsoft.ML.Runtime.Model; -/*LOTUS -using Microsoft.ML.Runtime.Model.LotusVNext; -using LotusvNext.Expressions;*/ using Microsoft.ML.Runtime.Model.Pfa; using Microsoft.ML.Runtime.Internal.Internallearn; using Newtonsoft.Json.Linq; @@ -108,22 +105,21 @@ public RegressionTree(byte[] buffer, ref int position) LteChild = buffer.ToIntArray(ref position); GtChild = buffer.ToIntArray(ref position); SplitFeatures = buffer.ToIntArray(ref position); - int[] categoricalNodeIndices = buffer.ToIntArray(ref position); - CategoricalSplit = GetCategoricalSplitFromIndices(categoricalNodeIndices); - if (categoricalNodeIndices?.Length > 0) + byte[] categoricalSplitAsBytes = buffer.ToByteArray(ref position); + CategoricalSplit = categoricalSplitAsBytes.Select(b => b > 0).ToArray(); + if (CategoricalSplit.Any(b => b)) { CategoricalSplitFeatures = new int[NumNodes][]; CategoricalSplitFeatureRanges = new int[NumNodes][]; - foreach (var index in categoricalNodeIndices) + for (int index = 0; index < NumNodes; index++) { - Contracts.Assert(CategoricalSplit[index]); - CategoricalSplitFeatures[index] = buffer.ToIntArray(ref position); - CategoricalSplitFeatureRanges[index] = buffer.ToIntArray(ref position, 2); + CategoricalSplitFeatureRanges[index] = buffer.ToIntArray(ref position); } } Thresholds = buffer.ToUIntArray(ref position); + RawThresholds = buffer.ToFloatArray(ref position); _splitGain = buffer.ToDoubleArray(ref position); _gainPValue = buffer.ToDoubleArray(ref position); _previousLeafValue = buffer.ToDoubleArray(ref position); @@ -147,6 +143,23 @@ private bool[] GetCategoricalSplitFromIndices(int[] indices) return categoricalSplit; } + private bool[] GetCategoricalSplitFromBytes(byte[] indices) + { + bool[] categoricalSplit = new bool[NumNodes]; + if (indices == null) + return categoricalSplit; + + Contracts.Assert(indices.Length <= NumNodes); + + foreach (int index in indices) + { + Contracts.Assert(index >= 0 && index < NumNodes); + categoricalSplit[index] = true; + } + + return categoricalSplit; + } + /// /// Create a Regression Tree object from raw tree contents. /// @@ -195,7 +208,7 @@ internal RegressionTree(int[] splitFeatures, Double[] splitGain, Double[] gainPV LeafValues = leafValues; CategoricalSplitFeatures = categoricalSplitFeatures; CategoricalSplitFeatureRanges = new int[CategoricalSplitFeatures.Length][]; - for(int i= 0; i < CategoricalSplitFeatures.Length; ++i) + for (int i = 0; i < CategoricalSplitFeatures.Length; ++i) { if (CategoricalSplitFeatures[i] != null && CategoricalSplitFeatures[i].Length > 0) { @@ -503,6 +516,7 @@ public virtual int SizeInBytes() NumNodes * sizeof(int) + CategoricalSplit.Length * sizeof(bool) + Thresholds.SizeInBytes() + + RawThresholds.SizeInBytes() + _splitGain.SizeInBytes() + _gainPValue.SizeInBytes() + _previousLeafValue.SizeInBytes() + @@ -517,22 +531,22 @@ public virtual void ToByteArray(byte[] buffer, ref int position) LteChild.ToByteArray(buffer, ref position); GtChild.ToByteArray(buffer, ref position); SplitFeatures.ToByteArray(buffer, ref position); + CategoricalSplit.Length.ToByteArray(buffer, ref position); foreach (var split in CategoricalSplit) Convert.ToByte(split).ToByteArray(buffer, ref position); if (CategoricalSplitFeatures != null) { - foreach (var splits in CategoricalSplitFeatures) - splits.ToByteArray(buffer, ref position); - } - - if (CategoricalSplitFeatureRanges != null) - { - foreach (var ranges in CategoricalSplitFeatureRanges) - ranges.ToByteArray(buffer, ref position); + Contracts.AssertValue(CategoricalSplitFeatureRanges); + for (int i = 0; i < CategoricalSplitFeatures.Length; i++) + { + CategoricalSplitFeatures[i].ToByteArray(buffer, ref position); + CategoricalSplitFeatureRanges[i].ToByteArray(buffer, ref position); + } } Thresholds.ToByteArray(buffer, ref position); + RawThresholds.ToByteArray(buffer, ref position); _splitGain.ToByteArray(buffer, ref position); _gainPValue.ToByteArray(buffer, ref position); _previousLeafValue.ToByteArray(buffer, ref position); @@ -1295,54 +1309,6 @@ private JToken AsPfaCore(JToken feat, int node) return PfaUtils.If(PfaUtils.Call("<=", PfaUtils.Index(feat, SplitFeatures[node]), RawThresholds[node]), lte, gt); } - /*LOTUS - internal void SaveAsLotusVNext(LotusVNextContext ctx, string featuresVariableName, string treeOutputVariable) - { - ctx.AddExpression(SaveAsLotusVNext(0, featuresVariableName, treeOutputVariable)); - } - - private Expression SaveAsLotusVNext(int node, string featuresVariableName, string treeOutputVariable) - { - if (node < 0) - { - return LotusVNextUtils.MakeSet(treeOutputVariable, - LotusVNextUtils.MakeFloatLiteral((float)LeafValue(~node))); - } - - Expression cond; - if (CategoricalSplit[node]) - { - cond = LotusVNextUtils.MakeCall("gt", - LotusVNextUtils.MakeAttr(featuresVariableName, CategoricalSplitFeatures[node][0]), - LotusVNextUtils.MakeFloatLiteral(0.5f) - ); - - for (int i = 1; i < CategoricalSplitFeatures[node].Length; i++) - { - cond = LotusVNextUtils.MakeCall("or", - cond, - LotusVNextUtils.MakeCall("gt", - LotusVNextUtils.MakeAttr(featuresVariableName, CategoricalSplitFeatures[node][i]), - LotusVNextUtils.MakeFloatLiteral(0.5f) - ) - ); - } - } - else - { - cond = LotusVNextUtils.MakeCall("gt", - LotusVNextUtils.MakeAttr(featuresVariableName, SplitFeature(node)), - LotusVNextUtils.MakeFloatLiteral(RawThreshold(node)) - ); - } - - return LotusVNextUtils.MakeIf( - cond, - new[] { SaveAsLotusVNext(GetGtChildForNode(node), featuresVariableName, treeOutputVariable) }, - new[] { SaveAsLotusVNext(GetLteChildForNode(node), featuresVariableName, treeOutputVariable) } - ); - }*/ - public FeatureToGainMap GainMap { get diff --git a/src/Microsoft.ML.FastTree/TreeEnsemble/TreeEnsembleCombiner.cs b/src/Microsoft.ML.FastTree/TreeEnsemble/TreeEnsembleCombiner.cs new file mode 100644 index 0000000000..a8cce616d1 --- /dev/null +++ b/src/Microsoft.ML.FastTree/TreeEnsemble/TreeEnsembleCombiner.cs @@ -0,0 +1,115 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System.Collections.Generic; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.FastTree.Internal; +using Microsoft.ML.Runtime.Internal.Calibration; + +[assembly: LoadableClass(typeof(TreeEnsembleCombiner), null, typeof(SignatureModelCombiner), "Fast Tree Model Combiner", "FastTreeCombiner")] + +namespace Microsoft.ML.Runtime.FastTree.Internal +{ + public sealed class TreeEnsembleCombiner : IModelCombiner, IPredictorProducing> + { + private readonly IHost _host; + private readonly PredictionKind _kind; + + public TreeEnsembleCombiner(IHostEnvironment env, PredictionKind kind) + { + _host = env.Register("TreeEnsembleCombiner"); + switch (kind) + { + case PredictionKind.BinaryClassification: + case PredictionKind.Regression: + case PredictionKind.Ranking: + _kind = kind; + break; + default: + throw _host.ExceptUserArg(nameof(kind), $"Tree ensembles can be either of type {nameof(PredictionKind.BinaryClassification)}, " + + $"{nameof(PredictionKind.Regression)} or {nameof(PredictionKind.Ranking)}"); + } + } + + public IPredictorProducing CombineModels(IEnumerable> models) + { + _host.CheckValue(models, nameof(models)); + + var ensemble = new Ensemble(); + int modelCount = 0; + int featureCount = -1; + bool binaryClassifier = false; + foreach (var model in models) + { + modelCount++; + + var predictor = model; + _host.CheckValue(predictor, nameof(models), "One of the models is null"); + + var calibrated = predictor as CalibratedPredictorBase; + double paramA = 1; + if (calibrated != null) + { + _host.Check(calibrated.Calibrator is PlattCalibrator, + "Combining FastTree models can only be done when the models are calibrated with Platt calibrator"); + predictor = calibrated.SubPredictor; + paramA = -(calibrated.Calibrator as PlattCalibrator).ParamA; + } + var tree = predictor as FastTreePredictionWrapper; + if (tree == null) + throw _host.Except("Model is not a tree ensemble"); + foreach (var t in tree.TrainedEnsemble.Trees) + { + var bytes = new byte[t.SizeInBytes()]; + int position = -1; + t.ToByteArray(bytes, ref position); + position = -1; + var tNew = new RegressionTree(bytes, ref position); + if (paramA != 1) + { + for (int i = 0; i < tNew.NumLeaves; i++) + tNew.SetOutput(i, tNew.LeafValues[i] * paramA); + } + ensemble.AddTree(tNew); + } + + if (modelCount == 1) + { + binaryClassifier = calibrated != null; + featureCount = tree.InputType.ValueCount; + } + else + { + _host.Check((calibrated != null) == binaryClassifier, "Ensemble contains both calibrated and uncalibrated models"); + _host.Check(featureCount == tree.InputType.ValueCount, "Found models with different number of features"); + } + } + + var scale = 1 / (double)modelCount; + + foreach (var t in ensemble.Trees) + { + for (int i = 0; i < t.NumLeaves; i++) + t.SetOutput(i, t.LeafValues[i] * scale); + } + + switch (_kind) + { + case PredictionKind.BinaryClassification: + if (!binaryClassifier) + return new FastTreeBinaryPredictor(_host, ensemble, featureCount, null); + + var cali = new PlattCalibrator(_host, -1, 0); + return new FeatureWeightsCalibratedPredictor(_host, new FastTreeBinaryPredictor(_host, ensemble, featureCount, null), cali); + case PredictionKind.Regression: + return new FastTreeRegressionPredictor(_host, ensemble, featureCount, null); + case PredictionKind.Ranking: + return new FastTreeRankingPredictor(_host, ensemble, featureCount, null); + default: + _host.Assert(false); + throw _host.ExceptNotSupp(); + } + } + } +} diff --git a/src/Microsoft.ML.FastTree/Utils/ToByteArrayExtensions.cs b/src/Microsoft.ML.FastTree/Utils/ToByteArrayExtensions.cs index e3aa3cce83..5763e3c9dd 100644 --- a/src/Microsoft.ML.FastTree/Utils/ToByteArrayExtensions.cs +++ b/src/Microsoft.ML.FastTree/Utils/ToByteArrayExtensions.cs @@ -5,6 +5,7 @@ using System; using System.Linq; using System.Text; +using Microsoft.ML.Runtime.Internal.Utilities; namespace Microsoft.ML.Runtime.FastTree.Internal { @@ -290,7 +291,7 @@ public static string ToString(this byte[] buffer, ref int position) public static int SizeInBytes(this byte[] a) { - return sizeof(int) + a.Length * sizeof(byte); + return sizeof(int) + Utils.Size(a) * sizeof(byte); } public static void ToByteArray(this byte[] a, byte[] buffer, ref int position) @@ -314,7 +315,7 @@ public static byte[] ToByteArray(this byte[] buffer, ref int position) public static int SizeInBytes(this short[] a) { - return sizeof(int) + a.Length * sizeof(short); + return sizeof(int) + Utils.Size(a) * sizeof(short); } public unsafe static void ToByteArray(this short[] a, byte[] buffer, ref int position) @@ -353,7 +354,7 @@ public unsafe static short[] ToShortArray(this byte[] buffer, ref int position) public static int SizeInBytes(this ushort[] a) { - return sizeof(int) + a.Length * sizeof(ushort); + return sizeof(int) + Utils.Size(a) * sizeof(ushort); } public unsafe static void ToByteArray(this ushort[] a, byte[] buffer, ref int position) @@ -392,12 +393,12 @@ public unsafe static ushort[] ToUShortArray(this byte[] buffer, ref int position public static int SizeInBytes(this int[] array) { - return sizeof(int) + array.Length * sizeof(int); + return sizeof(int) + Utils.Size(array) * sizeof(int); } public unsafe static void ToByteArray(this int[] a, byte[] buffer, ref int position) { - int length = a.Length; + int length = Utils.Size(a); length.ToByteArray(buffer, ref position); fixed (byte* tmpBuffer = buffer) @@ -415,6 +416,9 @@ public unsafe static int[] ToIntArray(this byte[] buffer, ref int position) public unsafe static int[] ToIntArray(this byte[] buffer, ref int position, int length) { + if (length == 0) + return null; + int[] a = new int[length]; fixed (byte* tmpBuffer = buffer) @@ -433,7 +437,7 @@ public unsafe static int[] ToIntArray(this byte[] buffer, ref int position, int public static int SizeInBytes(this uint[] array) { - return sizeof(int) + array.Length * sizeof(uint); + return sizeof(int) + Utils.Size(array) * sizeof(uint); } public unsafe static void ToByteArray(this uint[] a, byte[] buffer, ref int position) @@ -472,7 +476,7 @@ public unsafe static uint[] ToUIntArray(this byte[] buffer, ref int position) public static int SizeInBytes(this long[] array) { - return sizeof(int) + array.Length * sizeof(long); + return sizeof(int) + Utils.Size(array) * sizeof(long); } public unsafe static void ToByteArray(this long[] a, byte[] buffer, ref int position) @@ -511,7 +515,7 @@ public unsafe static long[] ToLongArray(this byte[] buffer, ref int position) public static int SizeInBytes(this ulong[] array) { - return sizeof(int) + array.Length * sizeof(ulong); + return sizeof(int) + Utils.Size(array) * sizeof(ulong); } public unsafe static void ToByteArray(this ulong[] a, byte[] buffer, ref int position) @@ -550,7 +554,7 @@ public unsafe static ulong[] ToULongArray(this byte[] buffer, ref int position) public static int SizeInBytes(this MD5Hash[] array) { - return sizeof(int) + array.Length * MD5Hash.SizeInBytes(); + return sizeof(int) + Utils.Size(array) * MD5Hash.SizeInBytes(); } public static void ToByteArray(this MD5Hash[] a, byte[] buffer, ref int position) @@ -577,7 +581,7 @@ public unsafe static MD5Hash[] ToUInt128Array(this byte[] buffer, ref int positi public static int SizeInBytes(this float[] array) { - return sizeof(int) + array.Length * sizeof(float); + return sizeof(int) + Utils.Size(array) * sizeof(float); } public unsafe static void ToByteArray(this float[] a, byte[] buffer, ref int position) @@ -616,7 +620,7 @@ public unsafe static float[] ToFloatArray(this byte[] buffer, ref int position) public static int SizeInBytes(this double[] array) { - return sizeof(int) + array.Length * sizeof(double); + return sizeof(int) + Utils.Size(array) * sizeof(double); } public unsafe static void ToByteArray(this double[] a, byte[] buffer, ref int position) @@ -655,6 +659,8 @@ public unsafe static double[] ToDoubleArray(this byte[] buffer, ref int position public static int SizeInBytes(this double[][] array) { + if (Utils.Size(array) == 0) + return sizeof(int); return sizeof(int) + array.Sum(x => x.SizeInBytes()); } @@ -683,7 +689,7 @@ public static double[][] ToDoubleJaggedArray(this byte[] buffer, ref int positio public static long SizeInBytes(this string[] array) { long length = sizeof(int); - for (int i = 0; i < array.Length; ++i) + for (int i = 0; i < Utils.Size(array); ++i) { length += array[i].SizeInBytes(); } @@ -692,8 +698,8 @@ public static long SizeInBytes(this string[] array) public static void ToByteArray(this string[] a, byte[] buffer, ref int position) { - a.Length.ToByteArray(buffer, ref position); - for (int i = 0; i < a.Length; ++i) + Utils.Size(a).ToByteArray(buffer, ref position); + for (int i = 0; i < Utils.Size(a); ++i) { a[i].ToByteArray(buffer, ref position); } diff --git a/src/Microsoft.ML.LightGBM/LightGbmArguments.cs b/src/Microsoft.ML.LightGBM/LightGbmArguments.cs new file mode 100644 index 0000000000..e6c5ed360a --- /dev/null +++ b/src/Microsoft.ML.LightGBM/LightGbmArguments.cs @@ -0,0 +1,414 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System.Collections.Generic; +using System.Text; +using System.Reflection; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.Internal.Internallearn; +using Microsoft.ML.Runtime.LightGBM; + +[assembly: LoadableClass(typeof(LightGbmArguments.TreeBooster), typeof(LightGbmArguments.TreeBooster.Arguments), + typeof(SignatureLightGBMBooster), LightGbmArguments.TreeBooster.FriendlyName, LightGbmArguments.TreeBooster.Name)] +[assembly: LoadableClass(typeof(LightGbmArguments.DartBooster), typeof(LightGbmArguments.DartBooster.Arguments), + typeof(SignatureLightGBMBooster), LightGbmArguments.DartBooster.FriendlyName, LightGbmArguments.DartBooster.Name)] +[assembly: LoadableClass(typeof(LightGbmArguments.GossBooster), typeof(LightGbmArguments.GossBooster.Arguments), + typeof(SignatureLightGBMBooster), LightGbmArguments.GossBooster.FriendlyName, LightGbmArguments.GossBooster.Name)] + +[assembly: EntryPointModule(typeof(LightGbmArguments.TreeBooster.Arguments))] +[assembly: EntryPointModule(typeof(LightGbmArguments.DartBooster.Arguments))] +[assembly: EntryPointModule(typeof(LightGbmArguments.GossBooster.Arguments))] + +namespace Microsoft.ML.Runtime.LightGBM +{ + public delegate void SignatureLightGBMBooster(); + + [TlcModule.ComponentKind("BoosterParameterFunction")] + public interface ISupportBoosterParameterFactory : IComponentFactory + { + } + public interface IBoosterParameter + { + void UpdateParameters(Dictionary res); + } + + /// + /// Parameters names comes from LightGBM library. + /// See https://github.com/Microsoft/LightGBM/blob/master/docs/Parameters.rst. + /// + public sealed class LightGbmArguments : LearnerInputBaseWithGroupId + { + public abstract class BoosterParameter : IBoosterParameter + where TArgs : class, new() + { + protected TArgs Args { get; } + + protected BoosterParameter(TArgs args) + { + Args = args; + } + + /// + /// Update the parameters by specific Booster, will update parameters into "res" directly. + /// + public virtual void UpdateParameters(Dictionary res) + { + FieldInfo[] fields = Args.GetType().GetFields(); + foreach (var field in fields) + res[GetArgName(field.Name)] = field.GetValue(Args).ToString(); + } + } + + private static string GetArgName(string name) + { + StringBuilder strBuf = new StringBuilder(); + bool first = true; + foreach (char c in name) + { + if (char.IsUpper(c)) + { + if (first) + first = false; + else + strBuf.Append('_'); + strBuf.Append(char.ToLower(c)); + } + else + strBuf.Append(c); + } + return strBuf.ToString(); + } + + public sealed class TreeBooster : BoosterParameter + { + public const string Name = "gbdt"; + public const string FriendlyName = "Tree Booster"; + + [TlcModule.Component(Name = Name, FriendlyName = FriendlyName, Desc = "Traditional Gradient Boosting Decision Tree.")] + public class Arguments : ISupportBoosterParameterFactory + { + [Argument(ArgumentType.AtMostOnce, HelpText = "Use for binary classification when classes are not balanced.", ShortName = "us")] + public bool UnbalancedSets = false; + + [Argument(ArgumentType.AtMostOnce, + HelpText = "Minimum loss reduction required to make a further partition on a leaf node of the tree. the larger, " + + "the more conservative the algorithm will be.")] + [TlcModule.Range(Min = 0.0)] + public double MinSplitGain = 0; + + [Argument(ArgumentType.AtMostOnce, + HelpText = "Maximum depth of a tree. 0 means no limit. However, tree still grows by best-first.")] + [TlcModule.Range(Min = 0, Max = int.MaxValue)] + public int MaxDepth = 0; + + [Argument(ArgumentType.AtMostOnce, + HelpText = "Minimum sum of instance weight(hessian) needed in a child. If the tree partition step results in a leaf " + + "node with the sum of instance weight less than min_child_weight, then the building process will give up further partitioning. In linear regression mode, " + + "this simply corresponds to minimum number of instances needed to be in each node. The larger, the more conservative the algorithm will be.")] + [TlcModule.Range(Min = 0.0)] + public double MinChildWeight = 0.1; + + [Argument(ArgumentType.AtMostOnce, + HelpText = "Subsample frequency. 0 means no subsample. " + + "If subsampleFreq > 0, it will use a subset(ratio=subsample) to train. And the subset will be updated on every Subsample iteratinos.")] + [TlcModule.Range(Min = 0, Max = int.MaxValue)] + public int SubsampleFreq = 0; + + [Argument(ArgumentType.AtMostOnce, + HelpText = "Subsample ratio of the training instance. Setting it to 0.5 means that LightGBM randomly collected " + + "half of the data instances to grow trees and this will prevent overfitting. Range: (0,1].")] + [TlcModule.Range(Inf = 0.0, Max = 1.0)] + public double Subsample = 1; + + [Argument(ArgumentType.AtMostOnce, + HelpText = "Subsample ratio of columns when constructing each tree. Range: (0,1].", + ShortName = "ff")] + [TlcModule.Range(Inf = 0.0, Max = 1.0)] + public double FeatureFraction = 1; + + [Argument(ArgumentType.AtMostOnce, + HelpText = "L2 regularization term on weights, increasing this value will make model more conservative.", + ShortName = "l2")] + [TlcModule.Range(Min = 0.0)] + [TGUI(Label = "Lambda(L2)", SuggestedSweeps = "0,0.5,1")] + [TlcModule.SweepableDiscreteParam("RegLambda", new object[] { 0f, 0.5f, 1f })] + public double RegLambda = 0.01; + + [Argument(ArgumentType.AtMostOnce, + HelpText = "L1 regularization term on weights, increase this value will make model more conservative.", + ShortName = "l1")] + [TlcModule.Range(Min = 0.0)] + [TGUI(Label = "Alpha(L1)", SuggestedSweeps = "0,0.5,1")] + [TlcModule.SweepableDiscreteParam("RegAlpha", new object[] { 0f, 0.5f, 1f })] + public double RegAlpha = 0; + + [Argument(ArgumentType.AtMostOnce, + HelpText = "Control the balance of positive and negative weights, useful for unbalanced classes." + + " A typical value to consider: sum(negative cases) / sum(positive cases).")] + public double ScalePosWeight = 1; + + public virtual IBoosterParameter CreateComponent(IHostEnvironment env) => new TreeBooster(this); + } + + public TreeBooster(Arguments args) + : base(args) + { + Contracts.CheckUserArg(Args.MinSplitGain >= 0, nameof(Args.MinSplitGain), "must be >= 0."); + Contracts.CheckUserArg(Args.MinChildWeight >= 0, nameof(Args.MinChildWeight), "must be >= 0."); + Contracts.CheckUserArg(Args.Subsample > 0 && Args.Subsample <= 1, nameof(Args.Subsample), "must be in (0,1]."); + Contracts.CheckUserArg(Args.FeatureFraction > 0 && Args.FeatureFraction <= 1, nameof(Args.FeatureFraction), "must be in (0,1]."); + Contracts.CheckUserArg(Args.ScalePosWeight > 0 && Args.ScalePosWeight <= 1, nameof(Args.ScalePosWeight), "must be in (0,1]."); + } + + public override void UpdateParameters(Dictionary res) + { + base.UpdateParameters(res); + res["boosting_type"] = Name; + } + } + + public class DartBooster : BoosterParameter + { + public const string Name = "dart"; + public const string FriendlyName = "Tree Dropout Tree Booster"; + + [TlcModule.Component(Name = Name, FriendlyName = FriendlyName, Desc = "Dropouts meet Multiple Additive Regresion Trees. See https://arxiv.org/abs/1505.01866")] + public class Arguments : TreeBooster.Arguments + { + [Argument(ArgumentType.AtMostOnce, HelpText = "Drop ratio for trees. Range:(0,1).")] + [TlcModule.Range(Inf = 0.0, Max = 1.0)] + public double DropRate = 0.1; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Max number of dropped tree in a boosting round.")] + [TlcModule.Range(Inf = 0, Max = int.MaxValue)] + public int MaxDrop = 1; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Probability for not perform dropping in a boosting round.")] + [TlcModule.Range(Inf = 0.0, Max = 1.0)] + public double SkipDrop = 0.5; + + [Argument(ArgumentType.AtMostOnce, HelpText = "True will enable xgboost dart mode.")] + public bool XgboostDartMode = false; + + [Argument(ArgumentType.AtMostOnce, HelpText = "True will enable uniform drop.")] + public bool UniformDrop = false; + + public override IBoosterParameter CreateComponent(IHostEnvironment env) => new DartBooster(this); + } + + public DartBooster(Arguments args) + : base(args) + { + Contracts.CheckUserArg(Args.DropRate > 0 && Args.DropRate < 1, nameof(Args.DropRate), "must be in (0,1)."); + Contracts.CheckUserArg(Args.MaxDrop > 0, nameof(Args.MaxDrop), "must be > 0."); + Contracts.CheckUserArg(Args.SkipDrop >= 0 && Args.SkipDrop < 1, nameof(Args.SkipDrop), "must be in [0,1)."); + } + + public override void UpdateParameters(Dictionary res) + { + base.UpdateParameters(res); + res["boosting_type"] = Name; + } + } + + public class GossBooster : BoosterParameter + { + public const string Name = "goss"; + public const string FriendlyName = "Gradient-based One-Size Sampling"; + + [TlcModule.Component(Name = Name, FriendlyName = FriendlyName, Desc = "Gradient-based One-Side Sampling.")] + public class Arguments : TreeBooster.Arguments + { + [Argument(ArgumentType.AtMostOnce, + HelpText = "Retain ratio for large gradient instances.")] + [TlcModule.Range(Inf = 0.0, Max = 1.0)] + public double TopRate = 0.2; + + [Argument(ArgumentType.AtMostOnce, + HelpText = + "Retain ratio for small gradient instances.")] + [TlcModule.Range(Inf = 0.0, Max = 1.0)] + public double OtherRate = 0.1; + + public override IBoosterParameter CreateComponent(IHostEnvironment env) => new GossBooster(this); + } + + public GossBooster(Arguments args) + : base(args) + { + Contracts.CheckUserArg(Args.TopRate > 0 && Args.TopRate < 1, nameof(Args.TopRate), "must be in (0,1)."); + Contracts.CheckUserArg(Args.OtherRate >= 0 && Args.OtherRate < 1, nameof(Args.TopRate), "must be in [0,1)."); + Contracts.Check(Args.TopRate + Args.OtherRate <= 1, "Sum of topRate and otherRate cannot be larger than 1."); + } + + public override void UpdateParameters(Dictionary res) + { + base.UpdateParameters(res); + res["boosting_type"] = Name; + } + } + + public enum EvalMetricType + { + DefaultMetric, + Rmse, + Mae, + Logloss, + Error, + Merror, + Mlogloss, + Auc, + Ndcg, + Map + }; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Number of iterations.", SortOrder = 1, ShortName = "iter")] + [TGUI(Label = "Number of boosting iterations", SuggestedSweeps = "10,20,50,100,150,200")] + [TlcModule.SweepableDiscreteParam("NumBoostRound", new object[] { 10, 20, 50, 100, 150, 200 })] + public int NumBoostRound = 100; + + [Argument(ArgumentType.AtMostOnce, + HelpText = "Shrinkage rate for trees, used to prevent over-fitting. Range: (0,1].", + SortOrder = 2, ShortName = "lr", NullName = "")] + [TGUI(Label = "Learning Rate", SuggestedSweeps = "0.025-0.4;log")] + [TlcModule.SweepableFloatParamAttribute("LearningRate", 0.025f, 0.4f, isLogScale: true)] + public double? LearningRate; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Maximum leaves for trees.", + SortOrder = 2, ShortName = "nl", NullName = "")] + [TGUI(Description = "The maximum number of leaves per tree", SuggestedSweeps = "2-128;log;inc:4")] + [TlcModule.SweepableLongParamAttribute("NumLeaves", 2, 128, isLogScale: true, stepSize: 4)] + public int? NumLeaves; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Minimum number of instances needed in a child.", + SortOrder = 2, ShortName = "mil", NullName = "")] + [TGUI(Label = "Min Documents In Leaves", SuggestedSweeps = "1,10,20,50 ")] + [TlcModule.SweepableDiscreteParamAttribute("MinDataPerLeaf", new object[] { 1, 10, 20, 50 })] + public int? MinDataPerLeaf; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Max number of bucket bin for features.", ShortName = "mb")] + public int MaxBin = 255; + + [Argument(ArgumentType.Multiple, HelpText = "Which booster to use, can be gbtree, gblinear or dart. gbtree and dart use tree based model while gblinear uses linear function.", SortOrder = 3)] + public ISupportBoosterParameterFactory Booster = new TreeBooster.Arguments(); + + [Argument(ArgumentType.AtMostOnce, HelpText = "Verbose", ShortName = "v")] + public bool VerboseEval = false; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Printing running messages.")] + public bool Silent = true; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Number of parallel threads used to run LightGBM.", ShortName = "nt")] + public int? NThread; + + [Argument(ArgumentType.AtMostOnce, + HelpText = "Evaluation metrics.", + ShortName = "em")] + public EvalMetricType EvalMetric = EvalMetricType.DefaultMetric; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Use softmax loss for the multi classification.")] + [TlcModule.SweepableDiscreteParam("UseSoftmax", new object[] { true, false})] + public bool? UseSoftmax; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Rounds of early stopping, 0 will disable it.", + ShortName = "es")] + public int EarlyStoppingRound = 0; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Comma seperated list of gains associated to each relevance label.", ShortName = "gains")] + [TGUI(Label = "Ranking Label Gain")] + public string CustomGains = "0,3,7,15,31,63,127,255,511,1023,2047,4095"; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Number of entries in a batch when loading data.", Hide = true)] + public int BatchSize = 1 << 20; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Enable categorical split or not.", ShortName = "cat")] + [TlcModule.SweepableDiscreteParam("UseCat", new object[] { true, false })] + public bool? UseCat; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Enable missing value auto infer or not.")] + [TlcModule.SweepableDiscreteParam("UseMissing", new object[] { true, false })] + public bool UseMissing = false; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Min number of instances per categorical group.", ShortName = "mdpg")] + [TlcModule.Range(Inf = 0, Max = int.MaxValue)] + [TlcModule.SweepableDiscreteParam("MinDataPerGroup", new object[] { 10, 50, 100, 200 })] + public int MinDataPerGroup = 100; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Max number of categorical thresholds.", ShortName = "maxcat")] + [TlcModule.Range(Inf = 0, Max = int.MaxValue)] + [TlcModule.SweepableDiscreteParam("MaxCatThreshold", new object[] { 8, 16, 32, 64 })] + public int MaxCatThreshold = 32; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Lapalace smooth term in categorical feature spilt. Avoid the bias of small categories.")] + [TlcModule.Range(Min = 0.0)] + [TlcModule.SweepableDiscreteParam("CatSmooth", new object[] { 1, 10, 20 })] + public double CatSmooth = 10; + + [Argument(ArgumentType.AtMostOnce, HelpText = "L2 Regularization for categorical split.")] + [TlcModule.Range(Min = 0.0)] + [TlcModule.SweepableDiscreteParam("CatL2", new object[] { 0.1, 0.5, 1, 5, 10 })] + public double CatL2 = 10; + + [Argument(ArgumentType.Multiple, HelpText = "Parallel LightGBM Learning Algorithm", ShortName = "parag")] + public ISupportParallel ParallelTrainer = new SingleTrainerFactory(); + + internal Dictionary ToDictionary(IHost host) + { + Contracts.CheckValue(host, nameof(host)); + Contracts.CheckUserArg(MaxBin > 0, nameof(MaxBin), "must be > 0."); + Dictionary res = new Dictionary(); + + var boosterParams = Booster.CreateComponent(host); + boosterParams.UpdateParameters(res); + + res[GetArgName(nameof(MaxBin))] = MaxBin.ToString(); + + res["verbose"] = Silent ? "-1" : "1"; + if (NThread.HasValue) + res["nthread"] = NThread.Value.ToString(); + + res["seed"] = host.Rand.Next().ToString(); + + string metric = null; + switch (EvalMetric) + { + case EvalMetricType.DefaultMetric: + break; + case EvalMetricType.Mae: + metric = "l1"; + break; + case EvalMetricType.Logloss: + metric = "binary_logloss"; + break; + case EvalMetricType.Error: + metric = "binary_error"; + break; + case EvalMetricType.Merror: + metric = "multi_error"; + break; + case EvalMetricType.Mlogloss: + metric = "multi_logloss"; + break; + case EvalMetricType.Rmse: + case EvalMetricType.Auc: + case EvalMetricType.Ndcg: + case EvalMetricType.Map: + metric = EvalMetric.ToString().ToLower(); + break; + } + if (!string.IsNullOrEmpty(metric)) + res["metric"] = metric; + res["sigmoid"] = "0.5"; + res["label_gain"] = CustomGains; + res[GetArgName(nameof(UseMissing))] = UseMissing.ToString(); + res[GetArgName(nameof(MinDataPerGroup))] = MinDataPerGroup.ToString(); + res[GetArgName(nameof(MaxCatThreshold))] = MaxCatThreshold.ToString(); + res[GetArgName(nameof(CatSmooth))] = CatSmooth.ToString(); + res[GetArgName(nameof(CatL2))] = CatL2.ToString(); + return res; + } + } +} diff --git a/src/Microsoft.ML.LightGBM/LightGbmBinaryTrainer.cs b/src/Microsoft.ML.LightGBM/LightGbmBinaryTrainer.cs new file mode 100644 index 0000000000..9cecb0da6b --- /dev/null +++ b/src/Microsoft.ML.LightGBM/LightGbmBinaryTrainer.cs @@ -0,0 +1,147 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.FastTree; +using Microsoft.ML.Runtime.Internal.Calibration; +using Microsoft.ML.Runtime.Internal.Internallearn; +using Microsoft.ML.Runtime.LightGBM; +using Microsoft.ML.Runtime.Model; + +[assembly: LoadableClass(LightGbmBinaryTrainer.Summary, typeof(LightGbmBinaryTrainer), typeof(LightGbmArguments), + new[] { typeof(SignatureBinaryClassifierTrainer), typeof(SignatureTrainer), typeof(SignatureTreeEnsembleTrainer) }, + "LightGBM Binary Classification", LightGbmBinaryTrainer.LoadNameValue, LightGbmBinaryTrainer.ShortName, DocName = "trainer/LightGBM.md")] + +[assembly: LoadableClass(typeof(IPredictorProducing), typeof(LightGbmBinaryPredictor), null, typeof(SignatureLoadModel), + "LightGBM Binary Executor", + LightGbmBinaryPredictor.LoaderSignature)] + +[assembly: LoadableClass(typeof(void), typeof(LightGbm), null, typeof(SignatureEntryPointModule), "LightGBM")] + +namespace Microsoft.ML.Runtime.LightGBM +{ + public sealed class LightGbmBinaryPredictor : FastTreePredictionWrapper + { + public const string LoaderSignature = "LightGBMBinaryExec"; + public const string RegistrationName = "LightGBMBinaryPredictor"; + private static VersionInfo GetVersionInfo() + { + // REVIEW: can we decouple the version from FastTree predictor version ? + return new VersionInfo( + modelSignature: "LGBBINCL", + // verWrittenCur: 0x00010001, // Initial + // verWrittenCur: 0x00010002, // _numFeatures serialized + // verWrittenCur: 0x00010003, // Ini content out of predictor + //verWrittenCur: 0x00010004, // Add _defaultValueForMissing + verWrittenCur: 0x00010005, // Categorical splits. + verReadableCur: 0x00010004, + verWeCanReadBack: 0x00010001, + loaderSignature: LoaderSignature); + } + + protected override uint VerNumFeaturesSerialized { get { return 0x00010002; } } + + protected override uint VerDefaultValueSerialized { get { return 0x00010004; } } + + protected override uint VerCategoricalSplitSerialized { get { return 0x00010005; } } + + internal LightGbmBinaryPredictor(IHostEnvironment env, FastTree.Internal.Ensemble trainedEnsemble, int featureCount, string innerArgs) + : base(env, RegistrationName, trainedEnsemble, featureCount, innerArgs) + { + } + + private LightGbmBinaryPredictor(IHostEnvironment env, ModelLoadContext ctx) + : base(env, RegistrationName, ctx, GetVersionInfo()) + { + } + + protected override void SaveCore(ModelSaveContext ctx) + { + base.SaveCore(ctx); + ctx.SetVersionInfo(GetVersionInfo()); + } + + public static IPredictorProducing Create(IHostEnvironment env, ModelLoadContext ctx) + { + Contracts.CheckValue(env, nameof(env)); + env.CheckValue(ctx, nameof(ctx)); + ctx.CheckAtModel(GetVersionInfo()); + var predictor = new LightGbmBinaryPredictor(env, ctx); + ICalibrator calibrator; + ctx.LoadModelOrNull(env, out calibrator, @"Calibrator"); + if (calibrator == null) + return predictor; + return new CalibratedPredictor(env, predictor, calibrator); + } + + public override PredictionKind PredictionKind { get { return PredictionKind.BinaryClassification; } } + } + + public sealed class LightGbmBinaryTrainer : LightGbmTrainerBase> + { + public const string Summary = "LightGBM Binary Classifier"; + public const string LoadNameValue = "LightGBMBinary"; + public const string ShortName = "LightGBM"; + + public LightGbmBinaryTrainer(IHostEnvironment env, LightGbmArguments args) + : base(env, args, PredictionKind.BinaryClassification, "LGBBINCL") + { + } + + public override IPredictorWithFeatureWeights CreatePredictor() + { + Host.Check(TrainedEnsemble != null, "The predictor cannot be created before training is complete"); + var innerArgs = LightGbmInterfaceUtils.JoinParameters(Options); + var pred = new LightGbmBinaryPredictor(Host, TrainedEnsemble, FeatureCount, innerArgs); + var cali = new PlattCalibrator(Host, -0.5, 0); + return new FeatureWeightsCalibratedPredictor(Host, pred, cali); + } + + protected override void CheckDataValid(IChannel ch, RoleMappedData data) + { + Host.AssertValue(ch); + base.CheckDataValid(ch, data); + var labelType = data.Schema.Label.Type; + if (!(labelType.IsBool || labelType.IsKey || labelType == NumberType.R4)) + { + throw ch.ExceptParam(nameof(data), + $"Label column '{data.Schema.Label.Name}' is of type '{labelType}', but must be key, boolean or R4."); + } + } + + protected override void CheckAndUpdateParametersBeforeTraining(IChannel ch, RoleMappedData data, float[] labels, int[] groups) + { + Options["objective"] = "binary"; + // Add default metric. + if (!Options.ContainsKey("metric")) + Options["metric"] = "binary_logloss"; + } + } + + /// + /// A component to train an LightGBM model. + /// + public static partial class LightGbm + { + [TlcModule.EntryPoint( + Name = "Trainers.LightGbmBinaryClassifier", + Desc = "Train an LightGBM binary class model", + UserName = LightGbmBinaryTrainer.Summary, + ShortName = LightGbmBinaryTrainer.ShortName)] + public static CommonOutputs.BinaryClassificationOutput TrainBinary(IHostEnvironment env, LightGbmArguments input) + { + Contracts.CheckValue(env, nameof(env)); + var host = env.Register("TrainLightGBM"); + host.CheckValue(input, nameof(input)); + EntryPointUtils.CheckInputArgs(host, input); + + return LearnerEntryPointsUtils.Train(host, input, + () => new LightGbmBinaryTrainer(host, input), + getLabel: () => LearnerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.LabelColumn), + getWeight: () => LearnerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.WeightColumn)); + } + } +} diff --git a/src/Microsoft.ML.LightGBM/LightGbmMulticlassTrainer.cs b/src/Microsoft.ML.LightGBM/LightGbmMulticlassTrainer.cs new file mode 100644 index 0000000000..5acd90e83d --- /dev/null +++ b/src/Microsoft.ML.LightGBM/LightGbmMulticlassTrainer.cs @@ -0,0 +1,199 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.Internal.Calibration; +using Microsoft.ML.Runtime.Learners; +using Microsoft.ML.Runtime.LightGBM; + +[assembly: LoadableClass(LightGbmMulticlassTrainer.Summary, typeof(LightGbmMulticlassTrainer), typeof(LightGbmArguments), + new[] { typeof(SignatureMultiClassClassifierTrainer), typeof(SignatureTrainer) }, + "LightGBM Multi-class Classifier", LightGbmMulticlassTrainer.LoadNameValue, LightGbmMulticlassTrainer.ShortName, DocName = "trainer/LightGBM.md")] + +namespace Microsoft.ML.Runtime.LightGBM +{ + + public sealed class LightGbmMulticlassTrainer : LightGbmTrainerBase, OvaPredictor> + { + public const string Summary = "LightGBM Multi Class Classifier"; + public const string LoadNameValue = "LightGBMMulticlass"; + public const string ShortName = "LightGBMMC"; + private const int _minDataToUseSoftmax = 50000; + + private const double _maxNumClass = 1e6; + private int _numClass; + private int _tlcNumClass; + + public LightGbmMulticlassTrainer(IHostEnvironment env, LightGbmArguments args) + : base(env, args, PredictionKind.MultiClassClassification, "LightGBMMulticlass") + { + _numClass = -1; + } + + private FastTree.Internal.Ensemble GetBinaryEnsemble(int classID) + { + FastTree.Internal.Ensemble res = new FastTree.Internal.Ensemble(); + for (int i = classID; i < TrainedEnsemble.NumTrees; i += _numClass) + { + // Ignore dummy trees. + if (TrainedEnsemble.GetTreeAt(i).NumLeaves > 1) + res.AddTree(TrainedEnsemble.GetTreeAt(i)); + } + return res; + } + + private LightGbmBinaryPredictor CreateBinaryPredictor(int classID, string innerArgs) + { + return new LightGbmBinaryPredictor(Host, GetBinaryEnsemble(classID), FeatureCount, innerArgs); + } + + public override OvaPredictor CreatePredictor() + { + Host.Check(TrainedEnsemble != null, "The predictor cannot be created before training is complete."); + + Host.Assert(_numClass > 1, "Must know the number of classes before creating a predictor."); + Host.Assert(TrainedEnsemble.NumTrees % _numClass == 0, "Number of trees should be a multiple of number of classes."); + + var innerArgs = LightGbmInterfaceUtils.JoinParameters(Options); + IPredictorProducing[] predictors = new IPredictorProducing[_tlcNumClass]; + for (int i = 0; i < _tlcNumClass; ++i) + { + var pred = CreateBinaryPredictor(i, innerArgs); + var cali = new PlattCalibrator(Host, -0.5, 0); + predictors[i] = new FeatureWeightsCalibratedPredictor(Host, pred, cali); + } + return OvaPredictor.Create(Host, predictors); + } + + protected override void CheckDataValid(IChannel ch, RoleMappedData data) + { + Host.AssertValue(ch); + base.CheckDataValid(ch, data); + var labelType = data.Schema.Label.Type; + if (!(labelType.IsBool || labelType.IsKey || labelType == NumberType.R4)) + { + throw ch.ExceptParam(nameof(data), + $"Label column '{data.Schema.Label.Name}' is of type '{labelType}', but must be key, boolean or R4."); + } + } + + protected override void ConvertNaNLabels(IChannel ch, RoleMappedData data, float[] labels) + { + // Only initialize one time. + if (_numClass < 0) + { + float minLabel = float.MaxValue; + float maxLabel = float.MinValue; + bool hasNaNLabel = false; + foreach (var label in labels) + { + if (float.IsNaN(label)) + hasNaNLabel = true; + else + { + minLabel = Math.Min(minLabel, label); + maxLabel = Math.Max(maxLabel, label); + } + } + ch.CheckParam(minLabel >= 0, nameof(data), "min label cannot be negative"); + if (maxLabel >= _maxNumClass) + throw ch.ExceptParam(nameof(data), $"max label cannot exceed {_maxNumClass}"); + + if (data.Schema.Label.Type.IsKey) + { + ch.Check(data.Schema.Label.Type.AsKey.Contiguous, "label value should be contiguous"); + if (hasNaNLabel) + _numClass = data.Schema.Label.Type.AsKey.Count + 1; + else + _numClass = data.Schema.Label.Type.AsKey.Count; + _tlcNumClass = data.Schema.Label.Type.AsKey.Count; + } + else + { + if (hasNaNLabel) + _numClass = (int)maxLabel + 2; + else + _numClass = (int)maxLabel + 1; + _tlcNumClass = (int)maxLabel + 1; + } + } + float defaultLabel = _numClass - 1; + for (int i = 0; i < labels.Length; ++i) + if (float.IsNaN(labels[i])) + labels[i] = defaultLabel; + } + + protected override void GetDefaultParameters(IChannel ch, int numRow, bool hasCategorical, int totalCats, bool hiddenMsg=false) + { + base.GetDefaultParameters(ch, numRow, hasCategorical, totalCats, true); + int numLeaves = int.Parse(Options["num_leaves"]); + int minDataPerLeaf = Args.MinDataPerLeaf ?? DefaultMinDataPerLeaf(numRow, numLeaves, _numClass); + Options["min_data_per_leaf"] = minDataPerLeaf.ToString(); + if (!hiddenMsg) + { + if (!Args.LearningRate.HasValue) + ch.Info("Auto-tuning parameters: " + nameof(Args.LearningRate) + " = " + Options["learning_rate"]); + if (!Args.NumLeaves.HasValue) + ch.Info("Auto-tuning parameters: " + nameof(Args.NumLeaves) + " = " + numLeaves); + if (!Args.MinDataPerLeaf.HasValue) + ch.Info("Auto-tuning parameters: " + nameof(Args.MinDataPerLeaf) + " = " + minDataPerLeaf); + } + } + + protected override void CheckAndUpdateParametersBeforeTraining(IChannel ch, RoleMappedData data, float[] labels, int[] groups) + { + Host.AssertValue(ch); + ch.Assert(PredictionKind == PredictionKind.MultiClassClassification); + ch.Assert(_numClass > 1); + Options["num_class"] = _numClass.ToString(); + bool useSoftmax = false; + + if (Args.UseSoftmax.HasValue) + useSoftmax = Args.UseSoftmax.Value; + else + { + if (labels.Length >= _minDataToUseSoftmax) + useSoftmax = true; + + ch.Info("Auto-tuning parameters: " + nameof(Args.UseSoftmax) + " = " + useSoftmax); + } + + if (useSoftmax) + Options["objective"] = "multiclass"; + else + Options["objective"] = "multiclassova"; + + // Add default metric. + if (!Options.ContainsKey("metric")) + Options["metric"] = "multi_error"; + } + } + + /// + /// A component to train an LightGBM model. + /// + public static partial class LightGbm + { + [TlcModule.EntryPoint( + Name = "Trainers.LightGbmClassifier", + Desc = "Train an LightGBM multi class model", + UserName = LightGbmMulticlassTrainer.Summary, + ShortName = LightGbmMulticlassTrainer.ShortName)] + public static CommonOutputs.MulticlassClassificationOutput TrainMultiClass(IHostEnvironment env, LightGbmArguments input) + { + Contracts.CheckValue(env, nameof(env)); + var host = env.Register("TrainLightGBM"); + host.CheckValue(input, nameof(input)); + EntryPointUtils.CheckInputArgs(host, input); + + return LearnerEntryPointsUtils.Train(host, input, + () => new LightGbmMulticlassTrainer(host, input), + getLabel: () => LearnerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.LabelColumn), + getWeight: () => LearnerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.WeightColumn)); + } + } +} diff --git a/src/Microsoft.ML.LightGBM/LightGbmRankingTrainer.cs b/src/Microsoft.ML.LightGBM/LightGbmRankingTrainer.cs new file mode 100644 index 0000000000..b2b82fbd24 --- /dev/null +++ b/src/Microsoft.ML.LightGBM/LightGbmRankingTrainer.cs @@ -0,0 +1,145 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.FastTree; +using Microsoft.ML.Runtime.FastTree.Internal; +using Microsoft.ML.Runtime.LightGBM; +using Microsoft.ML.Runtime.Model; + +[assembly: LoadableClass(LightGbmRankingTrainer.Summary, typeof(LightGbmRankingTrainer), typeof(LightGbmArguments), + new[] { typeof(SignatureRankerTrainer), typeof(SignatureTrainer), typeof(SignatureTreeEnsembleTrainer) }, + "LightGBM Ranking", LightGbmRankingTrainer.LoadNameValue, LightGbmRankingTrainer.ShortName, DocName = "trainer/LightGBM.md")] + +[assembly: LoadableClass(typeof(LightGbmRankingPredictor), null, typeof(SignatureLoadModel), + "LightGBM Ranking Executor", + LightGbmRankingPredictor.LoaderSignature)] + +namespace Microsoft.ML.Runtime.LightGBM +{ + + public sealed class LightGbmRankingPredictor : FastTreePredictionWrapper + { + public const string LoaderSignature = "LightGBMRankerExec"; + public const string RegistrationName = "LightGBMRankingPredictor"; + + private static VersionInfo GetVersionInfo() + { + // REVIEW tfinley(guoke): can we decouple the version from FastTree predictor version ? + return new VersionInfo( + modelSignature: "LGBMRANK", + // verWrittenCur: 0x00010001, // Initial + // verWrittenCur: 0x00010002, // _numFeatures serialized + // verWrittenCur: 0x00010003, // Ini content out of predictor + // verWrittenCur: 0x00010004, // Add _defaultValueForMissing + verWrittenCur: 0x00010005, // Categorical splits. + verReadableCur: 0x00010004, + verWeCanReadBack: 0x00010001, + loaderSignature: LoaderSignature); + } + + protected override uint VerNumFeaturesSerialized { get { return 0x00010002; } } + + protected override uint VerDefaultValueSerialized { get { return 0x00010004; } } + + protected override uint VerCategoricalSplitSerialized { get { return 0x00010005; } } + + internal LightGbmRankingPredictor(IHostEnvironment env, FastTree.Internal.Ensemble trainedEnsemble, int featureCount, string innerArgs) + : base(env, RegistrationName, trainedEnsemble, featureCount, innerArgs) + { + } + + private LightGbmRankingPredictor(IHostEnvironment env, ModelLoadContext ctx) + : base(env, RegistrationName, ctx, GetVersionInfo()) + { + } + + protected override void SaveCore(ModelSaveContext ctx) + { + base.SaveCore(ctx); + ctx.SetVersionInfo(GetVersionInfo()); + } + + public static LightGbmRankingPredictor Create(IHostEnvironment env, ModelLoadContext ctx) + { + return new LightGbmRankingPredictor(env, ctx); + } + + public override PredictionKind PredictionKind { get { return PredictionKind.Ranking; } } + } + + public sealed class LightGbmRankingTrainer : LightGbmTrainerBase + { + public const string Summary = "LightGBM Ranking"; + public const string LoadNameValue = "LightGBMRanking"; + public const string ShortName = "LightGBMRank"; + + public LightGbmRankingTrainer(IHostEnvironment env, LightGbmArguments args) + : base(env, args, PredictionKind.Ranking, "LightGBMRanking") + { + } + + protected override void CheckDataValid(IChannel ch, RoleMappedData data) + { + Host.AssertValue(ch); + base.CheckDataValid(ch, data); + // Check label types. + var labelType = data.Schema.Label.Type; + if (!(labelType.IsKey || labelType == NumberType.R4)) + { + throw ch.ExceptParam(nameof(data), + $"Label column '{data.Schema.Label.Name}' is of type '{labelType}', but must be key or R4."); + } + // Check group types. + var groupType = data.Schema.Group.Type; + if (!(groupType == NumberType.U4 || groupType.IsKey)) + { + throw ch.ExceptParam(nameof(data), + $"Group column '{data.Schema.Group.Name}' is of type '{groupType}', but must be U4 or a Key."); + } + } + + public override LightGbmRankingPredictor CreatePredictor() + { + Host.Check(TrainedEnsemble != null, "The predictor cannot be created before training is complete"); + var innerArgs = LightGbmInterfaceUtils.JoinParameters(Options); + return new LightGbmRankingPredictor(Host, TrainedEnsemble, FeatureCount, innerArgs); + } + + protected override void CheckAndUpdateParametersBeforeTraining(IChannel ch, RoleMappedData data, float[] labels, int[] groups) + { + Host.AssertValue(ch); + Options["objective"] = "lambdarank"; + ch.CheckValue(groups, nameof(groups)); + // Add default metric. + if (!Options.ContainsKey("metric")) + Options["metric"] = "ndcg"; + // Only output one ndcg score. + Options["eval_at"] = "5"; + } + } + + /// + /// A component to train an LightGBM model. + /// + public static partial class LightGbm + { + [TlcModule.EntryPoint(Name = "Trainers.LightGbmRanker", Desc = "Train an LightGBM ranking model", UserName = LightGbmRankingTrainer.Summary, ShortName = LightGbmRankingTrainer.ShortName)] + public static CommonOutputs.RankingOutput TrainRanking(IHostEnvironment env, LightGbmArguments input) + { + Contracts.CheckValue(env, nameof(env)); + var host = env.Register("TrainLightGBM"); + host.CheckValue(input, nameof(input)); + EntryPointUtils.CheckInputArgs(host, input); + + return LearnerEntryPointsUtils.Train(host, input, + () => new LightGbmRankingTrainer(host, input), + getLabel: () => LearnerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.LabelColumn), + getWeight: () => LearnerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.WeightColumn), + getGroup: () => LearnerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.GroupIdColumn)); + } + } +} diff --git a/src/Microsoft.ML.LightGBM/LightGbmRegressionTrainer.cs b/src/Microsoft.ML.LightGBM/LightGbmRegressionTrainer.cs new file mode 100644 index 0000000000..5024009f98 --- /dev/null +++ b/src/Microsoft.ML.LightGBM/LightGbmRegressionTrainer.cs @@ -0,0 +1,137 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.FastTree; +using Microsoft.ML.Runtime.FastTree.Internal; +using Microsoft.ML.Runtime.LightGBM; +using Microsoft.ML.Runtime.Model; + +[assembly: LoadableClass(LightGbmRegressorTrainer.Summary, typeof(LightGbmRegressorTrainer), typeof(LightGbmArguments), + new[] { typeof(SignatureRegressorTrainer), typeof(SignatureTrainer), typeof(SignatureTreeEnsembleTrainer) }, + LightGbmRegressorTrainer.UserNameValue, LightGbmRegressorTrainer.LoadNameValue, LightGbmRegressorTrainer.ShortName, DocName = "trainer/LightGBM.md")] + +[assembly: LoadableClass(typeof(LightGbmRegressionPredictor), null, typeof(SignatureLoadModel), + "LightGBM Regression Executor", + LightGbmRegressionPredictor.LoaderSignature)] + +namespace Microsoft.ML.Runtime.LightGBM +{ + + public sealed class LightGbmRegressionPredictor : FastTreePredictionWrapper + { + public const string LoaderSignature = "LightGBMRegressionExec"; + public const string RegistrationName = "LightGBMRegressionPredictor"; + + private static VersionInfo GetVersionInfo() + { + // REVIEW tfinley(guoke): can we decouple the version from FastTree predictor version ? + return new VersionInfo( + modelSignature: "LGBSIREG", + // verWrittenCur: 0x00010001, // Initial + // verWrittenCur: 0x00010002, // _numFeatures serialized + // verWrittenCur: 0x00010003, // Ini content out of predictor + // verWrittenCur: 0x00010004, // Add _defaultValueForMissing + verWrittenCur: 0x00010005, // Categorical splits. + verReadableCur: 0x00010004, + verWeCanReadBack: 0x00010001, + loaderSignature: LoaderSignature); + } + + protected override uint VerNumFeaturesSerialized { get { return 0x00010002; } } + + protected override uint VerDefaultValueSerialized { get { return 0x00010004; } } + + protected override uint VerCategoricalSplitSerialized { get { return 0x00010005; } } + + internal LightGbmRegressionPredictor(IHostEnvironment env, FastTree.Internal.Ensemble trainedEnsemble, int featureCount, string innerArgs) + : base(env, RegistrationName, trainedEnsemble, featureCount, innerArgs) + { + } + + private LightGbmRegressionPredictor(IHostEnvironment env, ModelLoadContext ctx) + : base(env, RegistrationName, ctx, GetVersionInfo()) + { + } + + protected override void SaveCore(ModelSaveContext ctx) + { + base.SaveCore(ctx); + ctx.SetVersionInfo(GetVersionInfo()); + } + + public static LightGbmRegressionPredictor Create(IHostEnvironment env, ModelLoadContext ctx) + { + Contracts.CheckValue(env, nameof(env)); + env.CheckValue(ctx, nameof(ctx)); + ctx.CheckAtModel(GetVersionInfo()); + return new LightGbmRegressionPredictor(env, ctx); + } + + public override PredictionKind PredictionKind { get { return PredictionKind.Regression; } } + } + + public sealed class LightGbmRegressorTrainer : LightGbmTrainerBase + { + public const string Summary = "LightGBM Regression"; + public const string LoadNameValue = "LightGBMRegression"; + public const string ShortName = "LightGBMR"; + public const string UserNameValue = "LightGBM Regressor"; + + public LightGbmRegressorTrainer(IHostEnvironment env, LightGbmArguments args) + : base(env, args, PredictionKind.Regression, "LightGBMRegressor") + { + } + + public override LightGbmRegressionPredictor CreatePredictor() + { + Host.Check(TrainedEnsemble != null, + "The predictor cannot be created before training is complete"); + var innerArgs = LightGbmInterfaceUtils.JoinParameters(Options); + return new LightGbmRegressionPredictor(Host, TrainedEnsemble, FeatureCount, innerArgs); + } + + protected override void CheckDataValid(IChannel ch, RoleMappedData data) + { + Host.AssertValue(ch); + base.CheckDataValid(ch, data); + var labelType = data.Schema.Label.Type; + if (!(labelType.IsBool || labelType.IsKey || labelType == NumberType.R4)) + { + throw ch.ExceptParam(nameof(data), + $"Label column '{data.Schema.Label.Name}' is of type '{labelType}', but must be key, boolean or R4."); + } + } + + protected override void CheckAndUpdateParametersBeforeTraining(IChannel ch, RoleMappedData data, float[] labels, int[] groups) + { + Options["objective"] = "regression"; + // Add default metric. + if (!Options.ContainsKey("metric")) + Options["metric"] = "l2"; + } + } + + /// + /// A component to train an LightGBM model. + /// + public static partial class LightGbm + { + [TlcModule.EntryPoint(Name = "Trainers.LightGbmRegressor", Desc = LightGbmRegressorTrainer.Summary, UserName = LightGbmRegressorTrainer.UserNameValue, ShortName = LightGbmRegressorTrainer.ShortName)] + public static CommonOutputs.RegressionOutput TrainRegression(IHostEnvironment env, LightGbmArguments input) + { + Contracts.CheckValue(env, nameof(env)); + var host = env.Register("TrainLightGBM"); + host.CheckValue(input, nameof(input)); + EntryPointUtils.CheckInputArgs(host, input); + + return LearnerEntryPointsUtils.Train(host, input, + () => new LightGbmRegressorTrainer(host, input), + getLabel: () => LearnerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.LabelColumn), + getWeight: () => LearnerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.WeightColumn)); + } + } +} diff --git a/src/Microsoft.ML.LightGBM/LightGbmTrainerBase.cs b/src/Microsoft.ML.LightGBM/LightGbmTrainerBase.cs new file mode 100644 index 0000000000..aff3befa0d --- /dev/null +++ b/src/Microsoft.ML.LightGBM/LightGbmTrainerBase.cs @@ -0,0 +1,886 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Linq; +using System.Threading; +using System.Threading.Tasks; +using System.Collections.Generic; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Internal.Utilities; +using Microsoft.ML.Runtime.Training; +using Microsoft.ML.Runtime.FastTree.Internal; + +namespace Microsoft.ML.Runtime.LightGBM +{ + /// + /// Lock for LightGBM trainer. + /// + internal static class LightGbmShared + { + // Lock for the operations that are multi-threading inside in LightGBM DLL. + public static readonly object LockForMultiThreadingInside = new object(); + // Lock for the sampling stage, this can reduce the peak memory usage. + public static readonly object SampleLock = new object(); + } + + /// + /// Base class for all training with LightGBM. + /// + public abstract class LightGbmTrainerBase : + ITrainer, + IValidatingTrainer + where TPredictor : IPredictorProducing + { + private sealed class CategoricalMetaData + { + public int NumCol; + public int TotalCats; + public int[] CategoricalBoudaries; + public int[] OnehotIndices; + public int[] OnehotBias; + public bool[] IsCategoricalFeature; + } + + #region members + private readonly IHostEnvironment _env; + private readonly PredictionKind _predictionKind; + + protected readonly IHost Host; + protected readonly LightGbmArguments Args; + protected readonly Dictionary Options; + protected readonly IParallel ParallelTraining; + + // Store _featureCount and _trainedEnsemble to construct predictor. + protected int FeatureCount; + protected FastTree.Internal.Ensemble TrainedEnsemble; + + #endregion + + protected LightGbmTrainerBase(IHostEnvironment env, LightGbmArguments args, PredictionKind predictionKind, string name) + { + Contracts.CheckValue(env, nameof(env)); + env.CheckNonWhiteSpace(name, nameof(name)); + + Host = env.Register(name); + Host.CheckValue(args, nameof(args)); + + Args = args; + Options = Args.ToDictionary(Host); + _predictionKind = predictionKind; + _env = env; + ParallelTraining = Args.ParallelTrainer != null ? Args.ParallelTrainer.CreateComponent(env) : new SingleTrainer(); + InitParallelTraining(); + } + + public void Train(RoleMappedData data) + { + Dataset dtrain; + CategoricalMetaData catMetaData; + using (var ch = Host.Start("Loading data for LightGBM")) + { + using (var pch = Host.StartProgressChannel("Loading data for LightGBM")) + dtrain = LoadTrainingData(ch, data, out catMetaData); + ch.Done(); + } + using (var ch = Host.Start("Training with LightGBM")) + { + using (var pch = Host.StartProgressChannel("Training with LightGBM")) + TrainCore(ch, pch, dtrain, catMetaData); + ch.Done(); + } + dtrain.Dispose(); + DisposeParallelTraining(); + } + + public void Train(RoleMappedData data, RoleMappedData validData) + { + Dataset dtrain; + Dataset dvalid; + CategoricalMetaData catMetaData; + using (var ch = Host.Start("Loading data for LightGBM")) + { + using (var pch = Host.StartProgressChannel("Loading data for LightGBM")) + { + dtrain = LoadTrainingData(ch, data, out catMetaData); + dvalid = LoadValidationData(ch, dtrain, validData, catMetaData); + } + ch.Done(); + } + using (var ch = Host.Start("Training with LightGBM")) + { + using (var pch = Host.StartProgressChannel("Training with LightGBM")) + TrainCore(ch, pch, dtrain, catMetaData, dvalid); + ch.Done(); + } + dtrain.Dispose(); + dvalid.Dispose(); + DisposeParallelTraining(); + } + + private void InitParallelTraining() + { + if (ParallelTraining.ParallelType() != "serial" && ParallelTraining.NumMachines() > 1) + { + Options["tree_learner"] = ParallelTraining.ParallelType(); + var otherParams = ParallelTraining.AdditionalParams(); + if (otherParams != null) + { + foreach (var pair in otherParams) + Options[pair.Key] = pair.Value; + } + + Contracts.CheckValue(ParallelTraining.GetReduceScatterFunction(), nameof(ParallelTraining.GetReduceScatterFunction)); + Contracts.CheckValue(ParallelTraining.GetAllgatherFunction(), nameof(ParallelTraining.GetAllgatherFunction)); + LightGbmInterfaceUtils.Check(WrappedLightGbmInterface.NetworkInitWithFunctions( + ParallelTraining.NumMachines(), + ParallelTraining.Rank(), + ParallelTraining.GetReduceScatterFunction(), + ParallelTraining.GetAllgatherFunction() + )); + } + } + + private void DisposeParallelTraining() + { + if (ParallelTraining.NumMachines() > 1) + LightGbmInterfaceUtils.Check(WrappedLightGbmInterface.NetworkFree()); + } + + protected virtual void CheckDataValid(IChannel ch, RoleMappedData data) + { + data.CheckFeatureFloatVector(); + ch.CheckParam(data.Schema.Label != null, nameof(data), "Need a label column"); + } + + protected virtual void GetDefaultParameters(IChannel ch, int numRow, bool hasCategarical, int totalCats, bool hiddenMsg=false) + { + double learningRate = Args.LearningRate ?? DefaultLearningRate(numRow, hasCategarical, totalCats); + int numLeaves = Args.NumLeaves ?? DefaultNumLeaves(numRow, hasCategarical, totalCats); + int minDataPerLeaf = Args.MinDataPerLeaf ?? DefaultMinDataPerLeaf(numRow, numLeaves, 1); + Options["learning_rate"] = learningRate.ToString(); + Options["num_leaves"] = numLeaves.ToString(); + Options["min_data_per_leaf"] = minDataPerLeaf.ToString(); + if (!hiddenMsg) + { + if (!Args.LearningRate.HasValue) + ch.Info("Auto-tuning parameters: " + nameof(Args.LearningRate) + " = " + learningRate); + if (!Args.NumLeaves.HasValue) + ch.Info("Auto-tuning parameters: " + nameof(Args.NumLeaves) + " = " + numLeaves); + if (!Args.MinDataPerLeaf.HasValue) + ch.Info("Auto-tuning parameters: " + nameof(Args.MinDataPerLeaf) + " = " + minDataPerLeaf); + } + } + + private FloatLabelCursor.Factory CreateCursorFactory(RoleMappedData data) + { + var loadFlags = CursOpt.AllLabels | CursOpt.AllWeights | CursOpt.Features; + if (_predictionKind == PredictionKind.Ranking) + loadFlags |= CursOpt.Group; + + var factory = new FloatLabelCursor.Factory(data, loadFlags); + return factory; + } + + private static List GetCategoricalBoundires(int[] categoricalFeatures, int rawNumCol) + { + List catBoundaries = new List { 0 }; + int curFidx = 0; + int j = 0; + while (curFidx < rawNumCol) + { + if (j < categoricalFeatures.Length && curFidx == categoricalFeatures[j]) + { + if (curFidx > catBoundaries.Last()) + catBoundaries.Add(curFidx); + if (categoricalFeatures[j + 1] - categoricalFeatures[j] >= 0) + { + curFidx = categoricalFeatures[j + 1] + 1; + catBoundaries.Add(curFidx); + } + else + { + for (int i = curFidx + 1; i <= categoricalFeatures[j + 1] + 1; ++i) + catBoundaries.Add(i); + curFidx = categoricalFeatures[j + 1] + 1; + } + j += 2; + } + else + { + catBoundaries.Add(curFidx + 1); + ++curFidx; + } + } + return catBoundaries; + } + + private static List ConstructCategoricalFeatureMetaData(int[] categoricalFeatures, int rawNumCol, ref CategoricalMetaData catMetaData) + { + List catBoundaries = GetCategoricalBoundires(categoricalFeatures, rawNumCol); + catMetaData.NumCol = catBoundaries.Count() - 1; + catMetaData.CategoricalBoudaries = catBoundaries.ToArray(); + catMetaData.IsCategoricalFeature = new bool[catMetaData.NumCol]; + catMetaData.OnehotIndices = new int[rawNumCol]; + catMetaData.OnehotBias = new int[rawNumCol]; + List catIndices = new List(); + int j = 0; + for (int i = 0; i < catMetaData.NumCol; ++i) + { + var numCat = catMetaData.CategoricalBoudaries[i + 1] - catMetaData.CategoricalBoudaries[i]; + if (numCat > 1) + { + catMetaData.TotalCats += numCat; + catMetaData.IsCategoricalFeature[i] = true; + catIndices.Add(i.ToString()); + for (int k = catMetaData.CategoricalBoudaries[i]; k < catMetaData.CategoricalBoudaries[i + 1]; ++k) + { + catMetaData.OnehotIndices[j] = i; + catMetaData.OnehotBias[j] = k - catMetaData.CategoricalBoudaries[i]; + ++j; + } + } + else + { + catMetaData.IsCategoricalFeature[i] = false; + catMetaData.OnehotIndices[j] = i; + catMetaData.OnehotBias[j] = 0; + ++j; + } + } + return catIndices; + } + + private CategoricalMetaData GetCategoricalMetaData(IChannel ch, RoleMappedData trainData, int numRow) + { + CategoricalMetaData catMetaData = new CategoricalMetaData(); + int[] categoricalFeatures = null; + const int useCatThreshold = 50000; + // Disable cat when data is too small, reduce the overfitting. + bool useCat = Args.UseCat ?? numRow > useCatThreshold; + if (!Args.UseCat.HasValue) + ch.Info("Auto-tuning parameters: " + nameof(Args.UseCat) + " = " + useCat); + if (useCat) + { + trainData.Schema.Schema.TryGetColumnIndex(DefaultColumnNames.Features, out int featureIndex); + MetadataUtils.TryGetCategoricalFeatureIndices(trainData.Schema.Schema, featureIndex, out categoricalFeatures); + } + var colType = trainData.Schema.Feature.Type; + int rawNumCol = colType.VectorSize; + FeatureCount = rawNumCol; + catMetaData.TotalCats = 0; + if (categoricalFeatures == null) + { + catMetaData.CategoricalBoudaries = null; + catMetaData.NumCol = rawNumCol; + } + else + { + var catIndices = ConstructCategoricalFeatureMetaData(categoricalFeatures, rawNumCol, ref catMetaData); + // Set categorical features + Options["categorical_feature"] = String.Join(",", catIndices); + } + return catMetaData; + } + + private Dataset LoadTrainingData(IChannel ch, RoleMappedData trainData, out CategoricalMetaData catMetaData) + { + // Verifications. + Host.AssertValue(ch); + ch.CheckValue(trainData, nameof(trainData)); + + CheckDataValid(ch, trainData); + + // Load metadata first. + var factory = CreateCursorFactory(trainData); + GetMetainfo(ch, factory, out int numRow, out float[] labels, out float[] weights, out int[] groups); + catMetaData = GetCategoricalMetaData(ch, trainData, numRow); + GetDefaultParameters(ch, numRow, catMetaData.CategoricalBoudaries != null, catMetaData.TotalCats); + + Dataset dtrain; + string param = LightGbmInterfaceUtils.JoinParameters(Options); + + // To reduce peak memory usage, only enable one sampling task at any given time. + lock (LightGbmShared.SampleLock) + { + CreateDatasetFromSamplingData(ch, factory, numRow, + param, labels, weights, groups, catMetaData, out dtrain); + } + + // Push rows into dataset. + LoadDataset(ch, factory, dtrain, numRow, Args.BatchSize, catMetaData); + + // Some checks. + CheckAndUpdateParametersBeforeTraining(ch, trainData, labels, groups); + return dtrain; + } + + private Dataset LoadValidationData(IChannel ch, Dataset dtrain, RoleMappedData validData, CategoricalMetaData catMetaData) + { + // Verifications. + Host.AssertValue(ch); + + ch.CheckValue(validData, nameof(validData)); + + CheckDataValid(ch, validData); + + // Load meta info first. + var factory = CreateCursorFactory(validData); + GetMetainfo(ch, factory, out int numRow, out float[] labels, out float[] weights, out int[] groups); + + // Construct validation dataset. + Dataset dvalid = new Dataset(dtrain, numRow, labels, weights, groups); + + // Push rows into dataset. + LoadDataset(ch, factory, dvalid, numRow, Args.BatchSize, catMetaData); + + return dvalid; + } + + private void TrainCore(IChannel ch, IProgressChannel pch, Dataset dtrain, CategoricalMetaData catMetaData, Dataset dvalid = null) + { + Host.AssertValue(ch); + Host.AssertValue(pch); + Host.AssertValue(dtrain); + Host.AssertValueOrNull(dvalid); + // For multi class, the number of labels is required. + ch.Assert(PredictionKind != PredictionKind.MultiClassClassification || Options.ContainsKey("num_class"), + "LightGBM requires the number of classes to be specified in the parameters."); + + // Only enable one trainer to run at one time. + lock (LightGbmShared.LockForMultiThreadingInside) + { + ch.Info("LightGBM objective={0}", Options["objective"]); + using (Booster bst = WrappedLightGbmTraining.Train(ch, pch, Options, dtrain, + dvalid: dvalid, numIteration: Args.NumBoostRound, + verboseEval: Args.VerboseEval, earlyStoppingRound: Args.EarlyStoppingRound)) + { + TrainedEnsemble = bst.GetModel(catMetaData.CategoricalBoudaries); + } + } + } + + /// + /// Calculate the density of data. Only use top 1000 rows to calculate. + /// + private static double DetectDensity(FloatLabelCursor.Factory factory, int numRows = 1000) + { + int nonZeroCount = 0; + int totalCount = 0; + using (var cursor = factory.Create()) + { + while (cursor.MoveNext() && numRows > 0) + { + nonZeroCount += cursor.Features.Count; + totalCount += cursor.Features.Length; + --numRows; + } + } + return (double)nonZeroCount / totalCount; + } + + /// + /// Compute row count, list of labels, weights and group counts of the dataset. + /// + private void GetMetainfo(IChannel ch, FloatLabelCursor.Factory factory, + out int numRow, out float[] labels, out float[] weights, out int[] groups) + { + ch.Check(factory.Data.Schema.Label != null, "The data should have label."); + List labelList = new List(); + bool hasWeights = factory.Data.Schema.Weight != null; + bool hasGroup = false; + if (_predictionKind == PredictionKind.Ranking) + { + ch.Check(factory.Data.Schema != null, "The data for ranking task should have group field."); + hasGroup = true; + } + List weightList = hasWeights ? new List() : null; + List cursorGroups = hasGroup ? new List() : null; + + using (var cursor = factory.Create()) + { + while (cursor.MoveNext()) + { + if (labelList.Count == Utils.ArrayMaxSize) + throw ch.Except($"Dataset row count exceeded the maximum count of {Utils.ArrayMaxSize}"); + labelList.Add(cursor.Label); + if (hasWeights) + { + // Default weight = 1. + if (float.IsNaN(cursor.Weight)) + weightList.Add(1); + else + weightList.Add(cursor.Weight); + } + if (hasGroup) + cursorGroups.Add(cursor.Group); + } + } + labels = labelList.ToArray(); + ConvertNaNLabels(ch, factory.Data, labels); + numRow = labels.Length; + ch.Check(numRow > 0, "Cannot use empty dataset."); + weights = hasWeights ? weightList.ToArray() : null; + groups = null; + if (hasGroup) + { + List groupList = new List(); + int lastGroup = -1; + for (int i = 0; i < numRow; ++i) + { + if (i == 0 || cursorGroups[i] != cursorGroups[i - 1]) + { + groupList.Add(1); + ++lastGroup; + } + else + ++groupList[lastGroup]; + } + groups = groupList.ToArray(); + } + } + + /// + /// Convert Nan labels. Default way is converting them to zero. + /// + protected virtual void ConvertNaNLabels(IChannel ch, RoleMappedData data, float[] labels) + { + for (int i = 0; i < labels.Length; ++i) + { + if (float.IsNaN(labels[i])) + labels[i] = 0; + } + } + + private static bool MoveMany(FloatLabelCursor cursor, long count) + { + for (long i = 0; i < count; ++i) + { + if (!cursor.MoveNext()) + return false; + } + return true; + } + + private void GetFeatureValueDense(IChannel ch, FloatLabelCursor cursor, CategoricalMetaData catMetaData, IRandom rand, out float[] featureValues) + { + if (catMetaData.CategoricalBoudaries != null) + { + featureValues = new float[catMetaData.NumCol]; + for (int i = 0; i < catMetaData.NumCol; ++i) + { + float fv = cursor.Features.Values[catMetaData.CategoricalBoudaries[i]]; + if (catMetaData.IsCategoricalFeature[i]) + { + int hotIdx = catMetaData.CategoricalBoudaries[i] - 1; + int nhot = 0; + for (int j = catMetaData.CategoricalBoudaries[i]; j < catMetaData.CategoricalBoudaries[i + 1]; ++j) + { + if (cursor.Features.Values[j] > 0) + { + // Reservoir Sampling. + nhot++; + var prob = rand.NextSingle(); + if (prob < 1.0f / nhot) + hotIdx = j; + } + } + // All-Zero is category 0. + fv = hotIdx - catMetaData.CategoricalBoudaries[i] + 1; + } + featureValues[i] = fv; + } + } + else + { + featureValues = cursor.Features.Values; + } + } + + private void GetFeatureValueSparse(IChannel ch, FloatLabelCursor cursor, + CategoricalMetaData catMetaData, IRandom rand, out int[] indices, + out float[] featureValues, out int cnt) + { + if (catMetaData.CategoricalBoudaries != null) + { + List featureIndices = new List(); + List values = new List(); + int lastIdx = -1; + int nhot = 0; + for (int i = 0; i < cursor.Features.Count; ++i) + { + float fv = cursor.Features.Values[i]; + int colIdx = cursor.Features.Indices[i]; + int newColIdx = catMetaData.OnehotIndices[colIdx]; + if (catMetaData.IsCategoricalFeature[newColIdx]) + fv = catMetaData.OnehotBias[colIdx] + 1; + if (newColIdx != lastIdx) + { + featureIndices.Push(newColIdx); + values.Push(fv); + nhot = 1; + } + else + { + // Multi-hot. + ++nhot; + var prob = rand.NextSingle(); + if (prob < 1.0f / nhot) + values[values.Count() - 1] = fv; + } + lastIdx = newColIdx; + } + indices = featureIndices.ToArray(); + featureValues = values.ToArray(); + cnt = featureIndices.Count(); + } + else + { + indices = cursor.Features.Indices; + featureValues = cursor.Features.Values; + cnt = cursor.Features.Count; + } + } + + /// + /// Create a dataset from the sampling data. + /// + private void CreateDatasetFromSamplingData(IChannel ch, FloatLabelCursor.Factory factory, + int numRow, string param, float[] labels, float[] weights, int[] groups, CategoricalMetaData catMetaData, + out Dataset dataset) + { + Host.AssertValue(ch); + + int numSampleRow = GetNumSampleRow(numRow, FeatureCount); + + var rand = Host.Rand; + double averageStep = (double)numRow / numSampleRow; + int totalIdx = 0; + int sampleIdx = 0; + double density = DetectDensity(factory); + + double[][] sampleValuePerColumn = new double[catMetaData.NumCol][]; + int[][] sampleIndicesPerColumn = new int[catMetaData.NumCol][]; + int[] nonZeroCntPerColumn = new int[catMetaData.NumCol]; + int estimateNonZeroCnt = (int)(numSampleRow * density); + estimateNonZeroCnt = Math.Max(1, estimateNonZeroCnt); + for(int i = 0; i < catMetaData.NumCol; i++) + { + nonZeroCntPerColumn[i] = 0; + sampleValuePerColumn[i] = new double[estimateNonZeroCnt]; + sampleIndicesPerColumn[i] = new int[estimateNonZeroCnt]; + }; + using (var cursor = factory.Create()) + { + int step = 1; + if (averageStep > 1) + step = rand.Next((int)(2 * averageStep - 1)) + 1; + while (MoveMany(cursor, step)) + { + if (cursor.Features.IsDense) + { + GetFeatureValueDense(ch, cursor, catMetaData, rand, out float[] featureValues); + for (int i = 0; i < catMetaData.NumCol; ++i) + { + float fv = featureValues[i]; + if (fv == 0) + continue; + int curNonZeroCnt = nonZeroCntPerColumn[i]; + Utils.EnsureSize(ref sampleValuePerColumn[i], curNonZeroCnt + 1); + Utils.EnsureSize(ref sampleIndicesPerColumn[i], curNonZeroCnt + 1); + sampleValuePerColumn[i][curNonZeroCnt] = fv; + sampleIndicesPerColumn[i][curNonZeroCnt] = sampleIdx; + nonZeroCntPerColumn[i] = curNonZeroCnt + 1; + } + } + else + { + GetFeatureValueSparse(ch, cursor, catMetaData, rand, out int[] featureIndices, out float[] featureValues, out int cnt); + for (int i = 0; i < cnt; ++i) + { + int colIdx = featureIndices[i]; + float fv = featureValues[i]; + if (fv == 0) + continue; + int curNonZeroCnt = nonZeroCntPerColumn[colIdx]; + Utils.EnsureSize(ref sampleValuePerColumn[colIdx], curNonZeroCnt + 1); + Utils.EnsureSize(ref sampleIndicesPerColumn[colIdx], curNonZeroCnt + 1); + sampleValuePerColumn[colIdx][curNonZeroCnt] = fv; + sampleIndicesPerColumn[colIdx][curNonZeroCnt] = sampleIdx; + nonZeroCntPerColumn[colIdx] = curNonZeroCnt + 1; + } + } + totalIdx += step; + ++sampleIdx; + if (numSampleRow == sampleIdx || numRow == totalIdx) + break; + averageStep = (double)(numRow - totalIdx) / (numSampleRow - sampleIdx); + step = 1; + if (averageStep > 1) + step = rand.Next((int)(2 * averageStep - 1)) + 1; + } + } + dataset = new Dataset(sampleValuePerColumn, sampleIndicesPerColumn, catMetaData.NumCol, nonZeroCntPerColumn, sampleIdx, numRow, param, labels, weights, groups); + } + + /// + /// Load dataset. Use row batch way to reduce peak memory cost. + /// + private void LoadDataset(IChannel ch, FloatLabelCursor.Factory factory, Dataset dataset, int numRow, int batchSize, CategoricalMetaData catMetaData) + { + Host.AssertValue(ch); + ch.AssertValue(factory); + ch.AssertValue(dataset); + ch.Assert(dataset.GetNumRows() == numRow); + ch.Assert(dataset.GetNumCols() == catMetaData.NumCol); + var rand = Host.Rand; + // To avoid array resize, batch size should bigger than size of one row. + batchSize = Math.Max(batchSize, catMetaData.NumCol); + double density = DetectDensity(factory); + int numElem = 0; + int totalRowCount = 0; + int curRowCount = 0; + + if (density >= 0.5) + { + int batchRow = batchSize / catMetaData.NumCol; + batchRow = Math.Max(1, batchRow); + if (batchRow > numRow) + batchRow = numRow; + + // This can only happen if the size of ONE example(row) exceeds the max array size. This looks like a very unlikely case. + if ((long)catMetaData.NumCol * batchRow > Utils.ArrayMaxSize) + throw ch.Except("Size of array exceeded the " + nameof(Utils.ArrayMaxSize)); + + float[] features = new float[catMetaData.NumCol * batchRow]; + + using (var cursor = factory.Create()) + { + while (cursor.MoveNext()) + { + ch.Assert(totalRowCount < numRow); + CopyToArray(ch, cursor, features, catMetaData, rand, ref numElem); + ++totalRowCount; + ++curRowCount; + if (batchRow == curRowCount) + { + ch.Assert(numElem == curRowCount * catMetaData.NumCol); + // PushRows is run by multi-threading inside, so lock here. + lock (LightGbmShared.LockForMultiThreadingInside) + dataset.PushRows(features, curRowCount, catMetaData.NumCol, totalRowCount - curRowCount); + curRowCount = 0; + numElem = 0; + } + } + ch.Assert(totalRowCount == numRow); + if (curRowCount > 0) + { + ch.Assert(numElem == curRowCount * catMetaData.NumCol); + // PushRows is run by multi-threading inside, so lock here. + lock (LightGbmShared.LockForMultiThreadingInside) + dataset.PushRows(features, curRowCount, catMetaData.NumCol, totalRowCount - curRowCount); + } + } + } + else + { + int esimateBatchRow = (int)(batchSize / (catMetaData.NumCol * density)); + esimateBatchRow = Math.Max(1, esimateBatchRow); + float[] features = new float[batchSize]; + int[] indices = new int[batchSize]; + int[] indptr = new int[esimateBatchRow + 1]; + + using (var cursor = factory.Create()) + { + while (cursor.MoveNext()) + { + ch.Assert(totalRowCount < numRow); + // Need push rows to LightGBM. + if (numElem + cursor.Features.Count > features.Length) + { + // Mini batch size is greater than size of one row. + // So, at least we have the data of one row. + ch.Assert(curRowCount > 0); + Utils.EnsureSize(ref indptr, curRowCount + 1); + indptr[curRowCount] = numElem; + // PushRows is run by multi-threading inside, so lock here. + lock (LightGbmShared.LockForMultiThreadingInside) + { + dataset.PushRows(indptr, indices, features, + curRowCount + 1, numElem, catMetaData.NumCol, totalRowCount - curRowCount); + } + curRowCount = 0; + numElem = 0; + } + Utils.EnsureSize(ref indptr, curRowCount + 1); + indptr[curRowCount] = numElem; + CopyToCsr(ch, cursor, indices, features, catMetaData, rand, ref numElem); + ++totalRowCount; + ++curRowCount; + } + ch.Assert(totalRowCount == numRow); + if (curRowCount > 0) + { + Utils.EnsureSize(ref indptr, curRowCount + 1); + indptr[curRowCount] = numElem; + // PushRows is run by multi-threading inside, so lock here. + lock (LightGbmShared.LockForMultiThreadingInside) + { + dataset.PushRows(indptr, indices, features, curRowCount + 1, + numElem, catMetaData.NumCol, totalRowCount - curRowCount); + } + } + } + } + } + + private void CopyToArray(IChannel ch, FloatLabelCursor cursor, float[] features, CategoricalMetaData catMetaData, IRandom rand, ref int numElem) + { + ch.Assert(features.Length >= numElem + catMetaData.NumCol); + if (catMetaData.CategoricalBoudaries != null) + { + if (cursor.Features.IsDense) + { + GetFeatureValueDense(ch, cursor, catMetaData, rand, out float[] featureValues); + for (int i = 0; i < catMetaData.NumCol; ++i) + features[numElem + i] = featureValues[i]; + numElem += catMetaData.NumCol; + } + else + { + GetFeatureValueSparse(ch, cursor, catMetaData, rand, out int[] indices, out float[] featureValues, out int cnt); + int lastIdx = 0; + for (int i = 0; i < cnt; i++) + { + int slot = indices[i]; + float fv = featureValues[i]; + Contracts.Assert(slot >= lastIdx); + while (lastIdx < slot) + features[numElem + lastIdx++] = 0.0f; + Contracts.Assert(lastIdx == slot); + features[numElem + lastIdx++] = fv; + } + while (lastIdx < catMetaData.NumCol) + features[numElem + lastIdx++] = 0.0f; + numElem += catMetaData.NumCol; + } + } + else + { + cursor.Features.CopyTo(features, numElem, 0.0f); + numElem += catMetaData.NumCol; + } + } + + private void CopyToCsr(IChannel ch, FloatLabelCursor cursor, + int[] indices, float[] features, CategoricalMetaData catMetaData, IRandom rand, ref int numElem) + { + int numValue = cursor.Features.Count; + if (numValue > 0) + { + ch.Assert(indices.Length >= numElem + numValue); + ch.Assert(features.Length >= numElem + numValue); + + if (cursor.Features.IsDense) + { + GetFeatureValueDense(ch, cursor, catMetaData, rand, out float[] featureValues); + for (int i = 0; i < catMetaData.NumCol; ++i) + { + float fv = featureValues[i]; + if (fv == 0) + continue; + features[numElem] = fv; + indices[numElem] = i; + ++numElem; + } + } + else + { + GetFeatureValueSparse(ch, cursor, catMetaData, rand, out int[] featureIndices, out float[] featureValues, out int cnt); + for (int i = 0; i < cnt; ++i) + { + int colIdx = featureIndices[i]; + float fv = featureValues[i]; + if (fv == 0) + continue; + features[numElem] = fv; + indices[numElem] = colIdx; + ++numElem; + } + } + } + } + + private static double DefaultLearningRate(int numRow, bool useCat, int totalCats) + { + if (useCat) + { + if (totalCats < 1e6) + return 0.1; + else + return 0.15; + } + else if (numRow <= 100000) + return 0.2; + else + return 0.25; + } + + private static int DefaultNumLeaves(int numRow, bool useCat, int totalCats) + { + if (useCat && totalCats > 100) + { + if (totalCats < 1e6) + return 20; + else + return 30; + } + else if (numRow <= 100000) + return 20; + else + return 30; + } + + protected static int DefaultMinDataPerLeaf(int numRow, int numLeaves, int numClass) + { + if (numClass > 1) + { + int ret = numRow / numLeaves / numClass / 10; + ret = Math.Max(ret, 5); + ret = Math.Min(ret, 50); + return ret; + } + else + { + return 20; + } + } + + private static int GetNumSampleRow(int numRow, int numCol) + { + // Default is 65536. + int ret = 1 << 16; + // If have many features, use more sampling data. + if (numCol >= 100000) + ret *= 4; + ret = Math.Min(ret, numRow); + return ret; + } + + public PredictionKind PredictionKind => _predictionKind; + + IPredictor ITrainer.CreatePredictor() + { + return CreatePredictor(); + } + + public abstract TPredictor CreatePredictor(); + + /// + /// This function will be called before training. It will check the label/group and add parameters for specific applications. + /// + protected abstract void CheckAndUpdateParametersBeforeTraining(IChannel ch, + RoleMappedData data, float[] labels, int[] groups); + } +} diff --git a/src/Microsoft.ML.LightGBM/Microsoft.ML.LightGBM.csproj b/src/Microsoft.ML.LightGBM/Microsoft.ML.LightGBM.csproj new file mode 100644 index 0000000000..3757c00743 --- /dev/null +++ b/src/Microsoft.ML.LightGBM/Microsoft.ML.LightGBM.csproj @@ -0,0 +1,21 @@ + + + + netstandard2.0 + Microsoft.ML.LightGBM + true + + + + + + + + + + + + + + + diff --git a/src/Microsoft.ML.LightGBM/Parallel/IParallel.cs b/src/Microsoft.ML.LightGBM/Parallel/IParallel.cs new file mode 100644 index 0000000000..469beae930 --- /dev/null +++ b/src/Microsoft.ML.LightGBM/Parallel/IParallel.cs @@ -0,0 +1,74 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Collections.Generic; +using System.Runtime.InteropServices; +using Microsoft.ML.Runtime.EntryPoints; + +namespace Microsoft.ML.Runtime.LightGBM +{ + /// + /// Signature of LightGBM IAllreduce + /// + public delegate void SignatureParallelTrainer(); + + /// + /// Reduce function define in LightGBM Cpp side + /// + public unsafe delegate void ReduceFunction(byte* src, byte* output, int typeSize, int arraySize); + + /// + /// Definition of ReduceScatter funtion + /// + public delegate void ReduceScatterFunction([MarshalAs(UnmanagedType.LPArray, SizeParamIndex = 1)]byte[] input, int inputSize, int typeSize, + [MarshalAs(UnmanagedType.LPArray, SizeParamIndex = 5)]int[] blockStart, [MarshalAs(UnmanagedType.LPArray, SizeParamIndex = 5)]int[] blockLen, int numBlock, + [MarshalAs(UnmanagedType.LPArray, SizeParamIndex = 7)]byte[] output, int outputSize, + IntPtr reducer); + + /// + /// Definition of Allgather funtion + /// + public delegate void AllgatherFunction([MarshalAs(UnmanagedType.LPArray, SizeParamIndex = 1)]byte[] input, int inputSize, + [MarshalAs(UnmanagedType.LPArray, SizeParamIndex = 4)]int[] blockStart, [MarshalAs(UnmanagedType.LPArray, SizeParamIndex = 4)]int[] blockLen, int numBlock, + [MarshalAs(UnmanagedType.LPArray, SizeParamIndex = 6)]byte[] output, int outputSize); + + public interface IParallel + { + /// + /// Type of parallel + /// + string ParallelType(); + + /// + /// Number of machines + /// + int NumMachines(); + + /// + /// Rank of local machine + /// + int Rank(); + + /// + /// ReduceScatter Function + /// + ReduceScatterFunction GetReduceScatterFunction(); + + /// + /// Allgather Function + /// + AllgatherFunction GetAllgatherFunction(); + + /// + /// Additional parameteres + /// + Dictionary AdditionalParams(); + } + + [TlcModule.ComponentKind("ParallelLightGBM")] + public interface ISupportParallel : IComponentFactory + { + } +} diff --git a/src/Microsoft.ML.LightGBM/Parallel/SingleTrainer.cs b/src/Microsoft.ML.LightGBM/Parallel/SingleTrainer.cs new file mode 100644 index 0000000000..4c61bd65c6 --- /dev/null +++ b/src/Microsoft.ML.LightGBM/Parallel/SingleTrainer.cs @@ -0,0 +1,54 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System.Collections.Generic; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.EntryPoints; + +[assembly: LoadableClass(typeof(Microsoft.ML.Runtime.LightGBM.SingleTrainer), + null, typeof(Microsoft.ML.Runtime.LightGBM.SignatureParallelTrainer), "single")] + +[assembly: EntryPointModule(typeof(Microsoft.ML.Runtime.LightGBM.SingleTrainerFactory))] + +namespace Microsoft.ML.Runtime.LightGBM +{ + public sealed class SingleTrainer : IParallel + { + public AllgatherFunction GetAllgatherFunction() + { + return null; + } + + public ReduceScatterFunction GetReduceScatterFunction() + { + return null; + } + + public int NumMachines() + { + return 1; + } + + public string ParallelType() + { + return "serial"; + } + + public int Rank() + { + return 0; + } + + public Dictionary AdditionalParams() + { + return null; + } + } + + [TlcModule.Component(Name = "Single", Desc = "Single node machine learning process.")] + public sealed class SingleTrainerFactory : ISupportParallel + { + public IParallel CreateComponent(IHostEnvironment env) => new SingleTrainer(); + } +} diff --git a/src/Microsoft.ML.LightGBM/WrappedLightGbmBooster.cs b/src/Microsoft.ML.LightGBM/WrappedLightGbmBooster.cs new file mode 100644 index 0000000000..116fd1c4a9 --- /dev/null +++ b/src/Microsoft.ML.LightGBM/WrappedLightGbmBooster.cs @@ -0,0 +1,276 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Collections.Generic; +using System.Linq; +using Microsoft.ML.Runtime.FastTree.Internal; + +namespace Microsoft.ML.Runtime.LightGBM +{ + /// + /// Wrapper of Booster object of LightGBM. + /// + internal sealed class Booster : IDisposable + { + private readonly bool _hasValid; + private readonly bool _hasMetric; + + public IntPtr Handle { get; private set; } + public int BestIteration { get; set; } + + public Booster(Dictionary parameters, Dataset trainset, Dataset validset = null) + { + var param = LightGbmInterfaceUtils.JoinParameters(parameters); + var handle = IntPtr.Zero; + LightGbmInterfaceUtils.Check(WrappedLightGbmInterface.BoosterCreate(trainset.Handle, param, ref handle)); + Handle = handle; + if (validset != null) + { + LightGbmInterfaceUtils.Check(WrappedLightGbmInterface.BoosterAddValidData(Handle, validset.Handle)); + _hasValid = true; + } + + int numEval = 0; + BestIteration = -1; + LightGbmInterfaceUtils.Check(WrappedLightGbmInterface.BoosterGetEvalCounts(Handle, ref numEval)); + // At most one metric in ML.NET. + Contracts.Assert(numEval <= 1); + if (numEval == 1) + _hasMetric = true; + } + + public bool Update() + { + int isFinished = 0; + LightGbmInterfaceUtils.Check(WrappedLightGbmInterface.BoosterUpdateOneIter(Handle, ref isFinished)); + return isFinished == 1; + } + + public double EvalTrain() + { + return Eval(0); + } + + public double EvalValid() + { + if (_hasValid) + return Eval(1); + else + return double.NaN; + } + + private unsafe double Eval(int dataIdx) + { + if (!_hasMetric) + return double.NaN; + int outLen = 0; + double[] res = new double[1]; + fixed (double* ptr = res) + LightGbmInterfaceUtils.Check(WrappedLightGbmInterface.BoosterGetEval(Handle, dataIdx, ref outLen, ptr)); + return res[0]; + } + + private unsafe string GetModelString() + { + int bufLen = 2 << 15; + byte[] buffer = new byte[bufLen]; + int size = 0; + fixed (byte* ptr = buffer) + LightGbmInterfaceUtils.Check(WrappedLightGbmInterface.BoosterSaveModelToString(Handle, BestIteration, bufLen, ref size, ptr)); + // If buffer size is not enough, reallocate buffer and get again. + if (size > bufLen) + { + bufLen = size; + buffer = new byte[bufLen]; + fixed (byte* ptr = buffer) + LightGbmInterfaceUtils.Check(WrappedLightGbmInterface.BoosterSaveModelToString(Handle, BestIteration, bufLen, ref size, ptr)); + } + byte[] content = new byte[size]; + Array.Copy(buffer, content, size); + fixed (byte* ptr = content) + return LightGbmInterfaceUtils.GetString((IntPtr)ptr); + } + + private static double[] Str2DoubleArray(string str, char delimiter) + { + return str.Split(delimiter).Select( + x => { double t; double.TryParse(x, out t); return t; } + ).ToArray(); + + } + + private static int[] Str2IntArray(string str, char delimiter) + { + return str.Split(delimiter).Select(int.Parse).ToArray(); + } + + private static UInt32[] Str2UIntArray(string str, char delimiter) + { + return str.Split(delimiter).Select(UInt32.Parse).ToArray(); + } + + private static bool GetIsDefaultLeft(UInt32 decisionType) + { + // The second bit. + return (decisionType & 2) > 0; + } + + private static bool GetIsCategoricalSplit(UInt32 decisionType) + { + // The first bit. + return (decisionType & 1) > 0; + } + + private static bool GetHasMissing(UInt32 decisionType) + { + // The 3rd and 4th bits. + return ((decisionType >> 2) & 3) > 0; + } + + private static double[] GetDefalutValue(double[] threshold, UInt32[] decisionType) + { + double[] ret = new double[threshold.Length]; + for (int i = 0; i < threshold.Length; ++i) + { + if (GetHasMissing(decisionType[i]) && !GetIsCategoricalSplit(decisionType[i])) + { + if (GetIsDefaultLeft(decisionType[i])) + ret[i] = threshold[i]; + else + ret[i] = threshold[i] + 1; + } + } + return ret; + } + + private static bool FindInBitset(UInt32[] bits, int start, int end, int pos) + { + int i1 = pos / 32; + if (start + i1 >= end) + return false; + int i2 = pos % 32; + return ((bits[start + i1] >> i2) & 1) > 0; + } + + private static int[] GetCatThresholds(UInt32[] catThreshold, int lowerBound, int upperBound) + { + List cats = new List(); + for (int j = lowerBound; j < upperBound; ++j) + { + // 32 bits. + for (int k = 0; k < 32; ++k) + { + int cat = (j - lowerBound) * 32 + k; + if (FindInBitset(catThreshold, lowerBound, upperBound, cat) && cat > 0) + cats.Add(cat); + } + } + return cats.ToArray(); + } + + public FastTree.Internal.Ensemble GetModel(int[] categoricalFeatureBoudaries) + { + FastTree.Internal.Ensemble res = new FastTree.Internal.Ensemble(); + string modelString = GetModelString(); + string[] lines = modelString.Split('\n'); + int i = 0; + for (; i < lines.Length;) + { + if (lines[i].StartsWith("Tree=")) + { + Dictionary kvPairs = new Dictionary(); + ++i; + while (!lines[i].StartsWith("Tree=") && lines[i].Trim().Length != 0) + { + string[] kv = lines[i].Split('='); + Contracts.Check(kv.Length == 2); + kvPairs[kv[0].Trim()] = kv[1].Trim(); + ++i; + } + int numLeaves = int.Parse(kvPairs["num_leaves"]); + int numCat = int.Parse(kvPairs["num_cat"]); + if (numLeaves > 1) + { + var leftChild = Str2IntArray(kvPairs["left_child"], ' '); + var rightChild = Str2IntArray(kvPairs["right_child"], ' '); + var splitFeature = Str2IntArray(kvPairs["split_feature"], ' '); + var threshold = Str2DoubleArray(kvPairs["threshold"], ' '); + var splitGain = Str2DoubleArray(kvPairs["split_gain"], ' '); + var leafOutput = Str2DoubleArray(kvPairs["leaf_value"], ' '); + var decisionType = Str2UIntArray(kvPairs["decision_type"], ' '); + var defaultValue = GetDefalutValue(threshold, decisionType); + var categoricalSplitFeatures = new int[numLeaves - 1][]; + var categoricalSplit = new bool[numLeaves - 1]; + if (categoricalFeatureBoudaries != null) + { + // Add offsets to split features. + for (int node = 0; node < numLeaves - 1; ++node) + splitFeature[node] = categoricalFeatureBoudaries[splitFeature[node]]; + } + + if (numCat > 0) + { + var catBoundaries = Str2IntArray(kvPairs["cat_boundaries"], ' '); + var catThreshold = Str2UIntArray(kvPairs["cat_threshold"], ' '); + for (int node = 0; node < numLeaves - 1; ++node) + { + if (GetIsCategoricalSplit(decisionType[node])) + { + int catIdx = (int)threshold[node]; + var cats = GetCatThresholds(catThreshold, catBoundaries[catIdx], catBoundaries[catIdx + 1]); + categoricalSplitFeatures[node] = new int[cats.Length]; + // Convert Cat thresholds to feature indices. + for (int j = 0; j < cats.Length; ++j) + categoricalSplitFeatures[node][j] = splitFeature[node] + cats[j] - 1; + + splitFeature[node] = -1; + categoricalSplit[node] = true; + // Swap left and right child. + int t = leftChild[node]; + leftChild[node] = rightChild[node]; + rightChild[node] = t; + } + else + { + categoricalSplit[node] = false; + } + } + } + RegressionTree tree = RegressionTree.Create(numLeaves, splitFeature, splitGain, + threshold.Select(x => (float)(x)).ToArray(), defaultValue.Select(x => (float)(x)).ToArray(), leftChild, rightChild, leafOutput, + categoricalSplitFeatures, categoricalSplit); + res.AddTree(tree); + } + else + { + RegressionTree tree = new RegressionTree(2); + var leafOutput = Str2DoubleArray(kvPairs["leaf_value"], ' '); + if (leafOutput[0] != 0) + { + // Convert Constant tree to Two-leaf tree, avoid being filter by TLC. + var categoricalSplitFeatures = new int[1][]; + var categoricalSplit = new bool[1]; + tree = RegressionTree.Create(2, new int[] { 0 }, new double[] { 0 }, + new float[] { 0 }, new float[] { 0 }, new int[] { -1 }, new int[] { -2 }, new double[] { leafOutput[0], leafOutput[0] }, + categoricalSplitFeatures, categoricalSplit); + } + res.AddTree(tree); + } + } + else + ++i; + } + return res; + } + #region IDisposable Support + public void Dispose() + { + if (Handle != IntPtr.Zero) + LightGbmInterfaceUtils.Check(WrappedLightGbmInterface.BoosterFree(Handle)); + Handle = IntPtr.Zero; + } + #endregion + } +} diff --git a/src/Microsoft.ML.LightGBM/WrappedLightGbmDataset.cs b/src/Microsoft.ML.LightGBM/WrappedLightGbmDataset.cs new file mode 100644 index 0000000000..991aadfb14 --- /dev/null +++ b/src/Microsoft.ML.LightGBM/WrappedLightGbmDataset.cs @@ -0,0 +1,181 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Threading.Tasks; +using System.Runtime.InteropServices; + +namespace Microsoft.ML.Runtime.LightGBM +{ + /// + /// Wrapper of Dataset object of LightGBM. + /// + internal sealed class Dataset : IDisposable + { + private IntPtr _handle; + private int _lastPushedRowID; + public IntPtr Handle => _handle; + + public unsafe Dataset(double[][] sampleValuePerColumn, + int[][] sampleIndicesPerColumn, + int numCol, + int[] sampleNonZeroCntPerColumn, + int numSampleRow, + int numTotalRow, + string param, float[] labels, float[] weights = null, int[] groups = null) + { + _handle = IntPtr.Zero; + + // Use GCHandle to pin the memory, avoid the memory relocation. + GCHandle[] gcValues = new GCHandle[numCol]; + GCHandle[] gcIndices = new GCHandle[numCol]; + try + { + double*[] ptrArrayValues = new double*[numCol]; + int*[] ptrArrayIndices = new int*[numCol]; + for(int i = 0; i < numCol; i++) + { + gcValues[i] = GCHandle.Alloc(sampleValuePerColumn[i], GCHandleType.Pinned); + ptrArrayValues[i] = (double*)gcValues[i].AddrOfPinnedObject().ToPointer(); + gcIndices[i] = GCHandle.Alloc(sampleIndicesPerColumn[i], GCHandleType.Pinned); + ptrArrayIndices[i] = (int*)gcIndices[i].AddrOfPinnedObject().ToPointer(); + }; + fixed (double** ptrValues = ptrArrayValues) + fixed (int** ptrIndices = ptrArrayIndices) + { + LightGbmInterfaceUtils.Check(WrappedLightGbmInterface.DatasetCreateFromSampledColumn( + (IntPtr)ptrValues, (IntPtr)ptrIndices, numCol, sampleNonZeroCntPerColumn, numSampleRow, numTotalRow, + param, ref _handle)); + } + } + finally + { + for (int i = 0; i < numCol; i++) + { + if (gcValues[i].IsAllocated) + gcValues[i].Free(); + if (gcIndices[i].IsAllocated) + gcIndices[i].Free(); + }; + } + SetLabel(labels); + SetWeights(weights); + SetGroup(groups); + + Contracts.Assert(GetNumCols() == numCol); + Contracts.Assert(GetNumRows() == numTotalRow); + } + + public Dataset(Dataset reference, int numTotalRow, float[] labels, float[] weights = null, int[] groups = null) + { + IntPtr refHandle = IntPtr.Zero; + if (reference != null) + refHandle = reference.Handle; + + LightGbmInterfaceUtils.Check(WrappedLightGbmInterface.DatasetCreateByReference(refHandle, numTotalRow, ref _handle)); + + SetLabel(labels); + SetWeights(weights); + SetGroup(groups); + } + + public void Dispose() + { + if (_handle != IntPtr.Zero) + LightGbmInterfaceUtils.Check(WrappedLightGbmInterface.DatasetFree(_handle)); + _handle = IntPtr.Zero; + } + + public void PushRows(float[] data, int numRow, int numCol, int startRowIdx) + { + Contracts.Assert(startRowIdx == _lastPushedRowID); + Contracts.Assert(numCol == GetNumCols()); + Contracts.Assert(numRow > 0); + Contracts.Assert(startRowIdx <= GetNumRows() - numRow); + LightGbmInterfaceUtils.Check(WrappedLightGbmInterface.DatasetPushRows(_handle, data, numRow, numCol, startRowIdx)); + _lastPushedRowID = startRowIdx + numRow; + } + + public void PushRows(int[] indPtr, int[] indices, float[] data, int nIndptr, + long numElem, int numCol, int startRowIdx) + { + Contracts.Assert(startRowIdx == _lastPushedRowID); + Contracts.Assert(numCol == GetNumCols()); + Contracts.Assert(startRowIdx < GetNumRows()); + LightGbmInterfaceUtils.Check( + WrappedLightGbmInterface.DatasetPushRowsByCsr( + _handle, indPtr, indices, data, nIndptr, numElem, numCol, startRowIdx)); + _lastPushedRowID = startRowIdx + nIndptr - 1; + } + + public int GetNumRows() + { + int res = 0; + LightGbmInterfaceUtils.Check(WrappedLightGbmInterface.DatasetGetNumData(_handle, ref res)); + return res; + } + + public int GetNumCols() + { + int res = 0; + LightGbmInterfaceUtils.Check(WrappedLightGbmInterface.DatasetGetNumFeature(_handle, ref res)); + return res; + } + + public unsafe void SetLabel(float[] labels) + { + Contracts.AssertValue(labels); + Contracts.Assert(labels.Length == GetNumRows()); + fixed (float* ptr = labels) + LightGbmInterfaceUtils.Check(WrappedLightGbmInterface.DatasetSetField(_handle, "label", (IntPtr)ptr, labels.Length, + WrappedLightGbmInterface.CApiDType.Float32)); + } + + public unsafe void SetWeights(float[] weights) + { + if (weights != null) + { + Contracts.Assert(weights.Length == GetNumRows()); + // Skip SetWeights if all weights are same. + bool allSame = true; + for (int i = 1; i < weights.Length; ++i) + { + if (weights[i] != weights[0]) + { + allSame = false; + break; + } + } + if (!allSame) + { + fixed (float* ptr = weights) + LightGbmInterfaceUtils.Check(WrappedLightGbmInterface.DatasetSetField(_handle, "weight", (IntPtr)ptr, weights.Length, + WrappedLightGbmInterface.CApiDType.Float32)); + } + } + } + + public unsafe void SetGroup(int[] groups) + { + if (groups != null) + { + fixed (int* ptr = groups) + LightGbmInterfaceUtils.Check(WrappedLightGbmInterface.DatasetSetField(_handle, "group", (IntPtr)ptr, groups.Length, + WrappedLightGbmInterface.CApiDType.Int32)); + } + } + + // Not used now. Can use for the continued train. + public unsafe void SetInitScore(double[] initScores) + { + if (initScores != null) + { + Contracts.Assert(initScores.Length % GetNumRows() == 0); + fixed (double* ptr = initScores) + LightGbmInterfaceUtils.Check(WrappedLightGbmInterface.DatasetSetField(_handle, "init_score", (IntPtr)ptr, initScores.Length, + WrappedLightGbmInterface.CApiDType.Float64)); + } + } + } +} diff --git a/src/Microsoft.ML.LightGBM/WrappedLightGbmInterface.cs b/src/Microsoft.ML.LightGBM/WrappedLightGbmInterface.cs new file mode 100644 index 0000000000..6d5e13bbeb --- /dev/null +++ b/src/Microsoft.ML.LightGBM/WrappedLightGbmInterface.cs @@ -0,0 +1,220 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Runtime.InteropServices; +using System.Collections.Generic; + +namespace Microsoft.ML.Runtime.LightGBM +{ + /// + /// Wrapper of the c interfaces of LightGBM. + /// Refer to https://github.com/Microsoft/LightGBM/blob/master/include/LightGBM/c_api.h to get the details. + /// + internal static class WrappedLightGbmInterface + { + public enum CApiDType : int + { + Float32 = 0, + Float64 = 1, + Int32 = 2, + Int64 = 3 + } + + private const string DllName = "lib_lightgbm"; + + #region API Array + + [DllImport(DllName, EntryPoint = "LGBM_AllocateArray", CallingConvention = CallingConvention.StdCall)] + public static extern int AllocateArray( + long len, + int type, + ref IntPtr ret); + + [DllImport(DllName, EntryPoint = "LGBM_CopyToArray", CallingConvention = CallingConvention.StdCall)] + public static extern int CopyToArray( + IntPtr arr, + int type, + long startIdx, + IntPtr src, + long len); + + [DllImport(DllName, EntryPoint = "LGBM_FreeArray", CallingConvention = CallingConvention.StdCall)] + public static extern int FreeArray( + IntPtr ret, + int type); + + #endregion + + #region API ERROR + + [DllImport(DllName, EntryPoint = "LGBM_GetLastError", CallingConvention = CallingConvention.StdCall)] + public static extern IntPtr GetLastError(); + + #endregion + + #region API Dataset + + [DllImport(DllName, EntryPoint = "LGBM_DatasetCreateFromSampledColumn", CallingConvention = CallingConvention.StdCall)] + public static extern int DatasetCreateFromSampledColumn(IntPtr sampleValuePerColumn, + IntPtr sampleIndicesPerColumn, + int numCol, + int[] sampleNonZeroCntPerColumn, + int numSampleRow, + int numTotalRow, + [MarshalAs(UnmanagedType.LPStr)]string parameters, + ref IntPtr ret); + + [DllImport(DllName, EntryPoint = "LGBM_DatasetCreateByReference", CallingConvention = CallingConvention.StdCall)] + public static extern int DatasetCreateByReference(IntPtr reference, + long numRow, + ref IntPtr ret); + + [DllImport(DllName, EntryPoint = "LGBM_DatasetPushRows", CallingConvention = CallingConvention.StdCall)] + private static extern int DatasetPushRows(IntPtr dataset, + float[] data, + CApiDType dataType, + int numRow, + int numCol, + int startRowIdx); + + public static int DatasetPushRows(IntPtr dataset, + float[] data, + int numRow, + int numCol, + int startRowIdx) + { + return DatasetPushRows(dataset, data, CApiDType.Float32, numRow, numCol, startRowIdx); + } + + [DllImport(DllName, EntryPoint = "LGBM_DatasetPushRowsByCSR", CallingConvention = CallingConvention.StdCall)] + private static extern int DatasetPushRowsByCsr(IntPtr dataset, + int[] indPtr, + CApiDType indPtrType, + int[] indices, + float[] data, + CApiDType dataType, + long nIndPtr, + long numElem, + long numCol, + long startRowIdx); + + public static int DatasetPushRowsByCsr(IntPtr dataset, + int[] indPtr, + int[] indices, + float[] data, + long nIndPtr, + long numElem, + long numCol, + long startRowIdx) + { + return DatasetPushRowsByCsr(dataset, + indPtr, CApiDType.Int32, + indices, data, CApiDType.Float32, + nIndPtr, numElem, numCol, startRowIdx); + } + + [DllImport(DllName, EntryPoint = "LGBM_DatasetFree", CallingConvention = CallingConvention.StdCall)] + public static extern int DatasetFree(IntPtr handle); + + [DllImport(DllName, EntryPoint = "LGBM_DatasetSetField", CallingConvention = CallingConvention.StdCall)] + public static extern int DatasetSetField( + IntPtr handle, + [MarshalAs(UnmanagedType.LPStr)]string field, + IntPtr array, + int len, + CApiDType type); + + [DllImport(DllName, EntryPoint = "LGBM_DatasetGetNumData", CallingConvention = CallingConvention.StdCall)] + public static extern int DatasetGetNumData(IntPtr handle, ref int res); + + [DllImport(DllName, EntryPoint = "LGBM_DatasetGetNumFeature", CallingConvention = CallingConvention.StdCall)] + public static extern int DatasetGetNumFeature(IntPtr handle, ref int res); + + #endregion + + #region API Booster + + [DllImport(DllName, EntryPoint = "LGBM_BoosterCreate", CallingConvention = CallingConvention.StdCall)] + public static extern int BoosterCreate(IntPtr trainset, + [MarshalAs(UnmanagedType.LPStr)]string param, + ref IntPtr res); + + [DllImport(DllName, EntryPoint = "LGBM_BoosterFree", CallingConvention = CallingConvention.StdCall)] + public static extern int BoosterFree(IntPtr handle); + + [DllImport(DllName, EntryPoint = "LGBM_BoosterAddValidData", CallingConvention = CallingConvention.StdCall)] + public static extern int BoosterAddValidData(IntPtr handle, IntPtr validset); + + [DllImport(DllName, EntryPoint = "LGBM_BoosterSaveModelToString", CallingConvention = CallingConvention.StdCall)] + public unsafe static extern int BoosterSaveModelToString(IntPtr handle, + int numIteration, + int bufferLen, + ref int outLen, + byte* outStr); + + #endregion + + #region API train + + [DllImport(DllName, EntryPoint = "LGBM_BoosterUpdateOneIter", CallingConvention = CallingConvention.StdCall)] + public static extern int BoosterUpdateOneIter(IntPtr handle, ref int isFinished); + + [DllImport(DllName, EntryPoint = "LGBM_BoosterGetEvalCounts", CallingConvention = CallingConvention.StdCall)] + public static extern int BoosterGetEvalCounts(IntPtr handle, ref int outLen); + + [DllImport(DllName, EntryPoint = "LGBM_BoosterGetEval", CallingConvention = CallingConvention.StdCall)] + public unsafe static extern int BoosterGetEval(IntPtr handle, int dataIdx, + ref int outLen, double* outResult); + + #endregion + + #region API parallel + + [DllImport(DllName, EntryPoint = "LGBM_NetworkInitWithFunctions", CallingConvention = CallingConvention.StdCall)] + public static extern int NetworkInitWithFunctions(int numMachines, int rank, ReduceScatterFunction reduceScatterFuncPtr, AllgatherFunction allgatherFuncPtr); + + [DllImport(DllName, EntryPoint = "LGBM_NetworkFree", CallingConvention = CallingConvention.StdCall)] + public static extern int NetworkFree(); + + #endregion + } + + internal static class LightGbmInterfaceUtils + { + /// + /// Checks if LightGBM has a pending error message. Raises an exception in that case. + /// + public static void Check(int res) + { + if (res != 0) + { + var charPtr = WrappedLightGbmInterface.GetLastError(); + string mes = Marshal.PtrToStringAnsi(charPtr); + throw Contracts.Except("LightGBM Error, code is {0}, error message is '{1}'.", res, mes); + } + } + + /// + /// Join the parameters to key=value format. + /// + public static string JoinParameters(Dictionary parameters) + { + if (parameters == null) + return ""; + List res = new List(); + foreach (var keyVal in parameters) + res.Add(keyVal.Key + "=" + keyVal.Value); + return string.Join(" ", res); + } + + /// + /// Convert the pointer of c string to c# string. + /// + public static string GetString(IntPtr src) + { + return Marshal.PtrToStringAnsi(src); + } + } +} diff --git a/src/Microsoft.ML.LightGBM/WrappedLightGbmTraining.cs b/src/Microsoft.ML.LightGBM/WrappedLightGbmTraining.cs new file mode 100644 index 0000000000..9699581a13 --- /dev/null +++ b/src/Microsoft.ML.LightGBM/WrappedLightGbmTraining.cs @@ -0,0 +1,117 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Collections.Generic; + +namespace Microsoft.ML.Runtime.LightGBM +{ + /// + /// Helpers to train a booster with given parameters. + /// + internal static class WrappedLightGbmTraining + { + /// + /// Train and return a booster. + /// + public static Booster Train(IChannel ch, IProgressChannel pch, + Dictionary parameters, Dataset dtrain, Dataset dvalid = null, int numIteration = 100, + bool verboseEval = true, int earlyStoppingRound = 0) + { + // create Booster. + Booster bst = new Booster(parameters, dtrain, dvalid); + + // Disable early stopping if we don't have validation data. + if (dvalid == null && earlyStoppingRound > 0) + { + earlyStoppingRound = 0; + ch.Warning("Validation dataset not present, early stopping will be disabled."); + } + + int bestIter = 0; + double bestScore = double.MaxValue; + double factorToSmallerBetter = 1.0; + + if (earlyStoppingRound > 0 && (parameters["metric"] == "auc" + || parameters["metric"] == "ndcg" + || parameters["metric"] == "map")) + { + factorToSmallerBetter = -1.0; + } + + const int evalFreq = 50; + + var metrics = new List() { "Iteration" }; + var units = new List() { "iterations" }; + + if (verboseEval) + { + ch.Assert(parameters.ContainsKey("metric")); + metrics.Add("Training-" + parameters["metric"]); + if (dvalid != null) + metrics.Add("Validation-" + parameters["metric"]); + } + + var header = new ProgressHeader(metrics.ToArray(), units.ToArray()); + + int iter = 0; + double trainError = double.NaN; + double validError = double.NaN; + pch.SetHeader(header, e => + { + e.SetProgress(0, iter, numIteration); + if (verboseEval) + { + e.SetProgress(1, trainError); + if (dvalid != null) + e.SetProgress(2, validError); + } + }); + for (iter = 0; iter < numIteration; ++iter) + { + if (bst.Update()) + break; + + if (earlyStoppingRound > 0) + { + validError = bst.EvalValid(); + if (validError * factorToSmallerBetter < bestScore) + { + bestScore = validError * factorToSmallerBetter; + bestIter = iter; + } + if (iter - bestIter >= earlyStoppingRound) + { + ch.Info($"Met early stopping, best iteration: {bestIter + 1}, best score: {bestScore / factorToSmallerBetter}"); + break; + } + } + if ((iter + 1) % evalFreq == 0) + { + if (verboseEval) + { + trainError = bst.EvalTrain(); + if (dvalid == null) + pch.Checkpoint(new double?[] { iter + 1, trainError }); + else + { + if (earlyStoppingRound == 0) + validError = bst.EvalValid(); + pch.Checkpoint(new double?[] { iter + 1, + trainError, validError }); + } + } + else + pch.Checkpoint(new double?[] { iter + 1 }); + } + } + // Set the BestIteration. + if (iter != numIteration && earlyStoppingRound > 0) + { + bst.BestIteration = bestIter + 1; + } + return bst; + } + } +} diff --git a/src/Microsoft.ML.Maml/Microsoft.ML.Maml.csproj b/src/Microsoft.ML.Maml/Microsoft.ML.Maml.csproj index 2ddbf9c7f7..88b1e9e55b 100644 --- a/src/Microsoft.ML.Maml/Microsoft.ML.Maml.csproj +++ b/src/Microsoft.ML.Maml/Microsoft.ML.Maml.csproj @@ -3,8 +3,8 @@ true CORECLR - netstandard2.0 Microsoft.ML + netstandard2.0 diff --git a/src/Microsoft.ML.PCA/PcaTrainer.cs b/src/Microsoft.ML.PCA/PcaTrainer.cs index 1d34db4b21..0945b04041 100644 --- a/src/Microsoft.ML.PCA/PcaTrainer.cs +++ b/src/Microsoft.ML.PCA/PcaTrainer.cs @@ -307,6 +307,7 @@ public static CommonOutputs.AnomalyDetectionOutput TrainPcaAnomaly(IHostEnvironm // REVIEW: move the predictor to a different file and fold EigenUtils.cs to this file. public sealed class PcaPredictor : PredictorBase, IValueMapper, + ICanGetSummaryAsIDataView, ICanSaveInTextFormat, ICanSaveModel, ICanSaveSummary { public const string LoaderSignature = "pcaAnomExec"; @@ -468,6 +469,26 @@ public void SaveAsText(TextWriter writer, RoleMappedSchema schema) } } + public IDataView GetSummaryDataView(RoleMappedSchema schema) + { + var bldr = new ArrayDataViewBuilder(Host); + + var cols = new VBuffer[_rank + 1]; + var names = new string[_rank + 1]; + for (var i = 0; i < _rank; ++i) + { + names[i] = "EigenVector" + i; + cols[i] = _eigenVectors[i]; + } + names[_rank] = "MeanVector"; + cols[_rank] = _mean; + + bldr.AddColumn("VectorName", names); + bldr.AddColumn("VectorData", NumberType.R4, cols); + + return bldr.GetDataView(); + } + public ColumnType InputType { get { return _inputType; } diff --git a/src/Microsoft.ML.PipelineInference/AutoInference.cs b/src/Microsoft.ML.PipelineInference/AutoInference.cs index 642ff4d0d7..2c94c58348 100644 --- a/src/Microsoft.ML.PipelineInference/AutoInference.cs +++ b/src/Microsoft.ML.PipelineInference/AutoInference.cs @@ -98,12 +98,13 @@ private SupportedMetric(string name, bool isMaximizing) public static SupportedMetric ByName(string name) { var fields = - typeof(SupportedMetric).GetMembers(BindingFlags.Static | BindingFlags.Public) - .Where(s => s.MemberType == MemberTypes.Field); + typeof(SupportedMetric).GetFields(BindingFlags.Static | BindingFlags.Public); + foreach (var field in fields) { - if (name.Equals(field.Name, StringComparison.OrdinalIgnoreCase)) - return (SupportedMetric)typeof(SupportedMetric).GetField(field.Name).GetValue(null); + var metric = (SupportedMetric)field.GetValue(Auc); + if (name.Equals(metric.Name, StringComparison.OrdinalIgnoreCase)) + return metric; } throw new NotSupportedException($"Metric '{name}' not supported."); } @@ -353,7 +354,7 @@ private void ProcessPipeline(Sweeper.Algorithms.SweeperProbabilityUtils utils, S testMetricVal += 1e-10; // Save performance score - candidate.PerformanceSummary = + candidate.PerformanceSummary = new RunSummary(testMetricVal, randomizedNumberOfRows, stopwatch.ElapsedMilliseconds, trainMetricVal); _sortedSampledElements.Add(candidate.PerformanceSummary.MetricValue, candidate); _history.Add(candidate); diff --git a/src/Microsoft.ML.PipelineInference/AutoMlEngines/RocketEngine.cs b/src/Microsoft.ML.PipelineInference/AutoMlEngines/RocketEngine.cs index 27b7fa54b7..80388eb53e 100644 --- a/src/Microsoft.ML.PipelineInference/AutoMlEngines/RocketEngine.cs +++ b/src/Microsoft.ML.PipelineInference/AutoMlEngines/RocketEngine.cs @@ -196,21 +196,10 @@ private TransformInference.SuggestedTransform[] SampleTransforms(RecipeInference private RecipeInference.SuggestedRecipe.SuggestedLearner[] GetTopLearners(IEnumerable history) { var weights = LearnerHistoryToWeights(history.ToArray(), IsMaximizingMetric); - var topKTuples = new Tuple[_topK]; - - for (int i = 0; i < weights.Length; i++) - { - if (i < _topK) - topKTuples[i] = new Tuple(weights[i], i); - else - { - for (int j = 0; j < topKTuples.Length; j++) - if (weights[i] > topKTuples[j].Item1) - topKTuples[j] = new Tuple(weights[i], i); - } - } - - return topKTuples.Select(t => AvailableLearners[t.Item2]).ToArray(); + return weights.Select((w, i) => new { Weight = w, Index = i }) + .OrderByDescending(x => x.Weight) + .Take(_topK) + .Select(t=>AvailableLearners[t.Index]).ToArray(); } public override PipelinePattern[] GetNextCandidates(IEnumerable history, int numCandidates) diff --git a/src/Microsoft.ML.PipelineInference/AutoMlUtils.cs b/src/Microsoft.ML.PipelineInference/AutoMlUtils.cs index a0aae16a63..6aec714618 100644 --- a/src/Microsoft.ML.PipelineInference/AutoMlUtils.cs +++ b/src/Microsoft.ML.PipelineInference/AutoMlUtils.cs @@ -15,7 +15,7 @@ namespace Microsoft.ML.Runtime.PipelineInference { public static class AutoMlUtils { - public static double ExtractValueFromIDV(IHostEnvironment env, IDataView result, string columnName) + public static double ExtractValueFromIdv(IHostEnvironment env, IDataView result, string columnName) { Contracts.CheckValue(env, nameof(env)); env.CheckValue(result, nameof(result)); @@ -40,8 +40,8 @@ public static double ExtractValueFromIDV(IHostEnvironment env, IDataView result, public static AutoInference.RunSummary ExtractRunSummary(IHostEnvironment env, IDataView result, string metricColumnName, IDataView trainResult = null) { - double testingMetricValue = ExtractValueFromIDV(env, result, metricColumnName); - double trainingMetricValue = trainResult != null ? ExtractValueFromIDV(env, trainResult, metricColumnName) : double.MinValue; + double testingMetricValue = ExtractValueFromIdv(env, result, metricColumnName); + double trainingMetricValue = trainResult != null ? ExtractValueFromIdv(env, trainResult, metricColumnName) : double.MinValue; return new AutoInference.RunSummary(testingMetricValue, 0, 0, trainingMetricValue); } diff --git a/src/Microsoft.ML.PipelineInference/DatasetFeaturesInference.cs b/src/Microsoft.ML.PipelineInference/DatasetFeaturesInference.cs index f0d173de13..e8d40fc6c8 100644 --- a/src/Microsoft.ML.PipelineInference/DatasetFeaturesInference.cs +++ b/src/Microsoft.ML.PipelineInference/DatasetFeaturesInference.cs @@ -507,7 +507,7 @@ public static string InferDatasetFeatures(IHostEnvironment env, Arguments args) } if (args.PrettyPrint) - jsonString = JsonConvert.SerializeObject(features, Formatting.Indented); + jsonString = JsonConvert.SerializeObject(features, Newtonsoft.Json.Formatting.Indented); else jsonString = JsonConvert.SerializeObject(features); diff --git a/src/Microsoft.ML.PipelineInference/PipelinePattern.cs b/src/Microsoft.ML.PipelineInference/PipelinePattern.cs index c6b4de44fa..6fdf922a68 100644 --- a/src/Microsoft.ML.PipelineInference/PipelinePattern.cs +++ b/src/Microsoft.ML.PipelineInference/PipelinePattern.cs @@ -213,8 +213,8 @@ public void RunTrainTestExperiment(IDataView trainData, IDataView testData, var dataOut = experiment.GetOutput(trainTestOutput.OverallMetrics); var dataOutTraining = experiment.GetOutput(trainTestOutput.TrainingOverallMetrics); - testMetricValue = AutoMlUtils.ExtractValueFromIDV(_env, dataOut, metric.Name); - trainMetricValue = AutoMlUtils.ExtractValueFromIDV(_env, dataOutTraining, metric.Name); + testMetricValue = AutoMlUtils.ExtractValueFromIdv(_env, dataOut, metric.Name); + trainMetricValue = AutoMlUtils.ExtractValueFromIdv(_env, dataOutTraining, metric.Name); } public static PipelineResultRow[] ExtractResults(IHostEnvironment env, IDataView data, diff --git a/src/Microsoft.ML.PipelineInference/TransformInference.cs b/src/Microsoft.ML.PipelineInference/TransformInference.cs index 988b56eedf..6390139030 100644 --- a/src/Microsoft.ML.PipelineInference/TransformInference.cs +++ b/src/Microsoft.ML.PipelineInference/TransformInference.cs @@ -6,7 +6,6 @@ using System.Collections.Generic; using System.Linq; using System.Text; -using Microsoft.ML; using Microsoft.ML.Runtime.CommandLine; using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.EntryPoints; @@ -712,7 +711,7 @@ public override IEnumerable Apply(IntermediateColumn[] colum { Name = columnNameQuoted.ToString(), Source = columnNameQuoted.ToString(), - ResultType = ML.Transforms.DataKind.R4 + ResultType = ML.Data.DataKind.R4 }); } @@ -721,7 +720,7 @@ public override IEnumerable Apply(IntermediateColumn[] colum ch.Info("Suggested conversion to numeric for boolean features."); var args = new SubComponent("Convert", new[] { $"{columnArgument}type=R4" }); - var epInput = new ML.Transforms.ColumnTypeConverter { Column = epColumns.ToArray(), ResultType = ML.Transforms.DataKind.R4 }; + var epInput = new ML.Transforms.ColumnTypeConverter { Column = epColumns.ToArray(), ResultType = ML.Data.DataKind.R4 }; ColumnRoutingStructure.AnnotatedName[] columnsSource = epColumns.Select(c => new ColumnRoutingStructure.AnnotatedName { IsNumeric = false, Name = c.Name }).ToArray(); ColumnRoutingStructure.AnnotatedName[] columnsDest = diff --git a/src/Microsoft.ML.StandardLearners/FactorizationMachine/FactorizationMachineInterface.cs b/src/Microsoft.ML.StandardLearners/FactorizationMachine/FactorizationMachineInterface.cs new file mode 100644 index 0000000000..b5fdbd0262 --- /dev/null +++ b/src/Microsoft.ML.StandardLearners/FactorizationMachine/FactorizationMachineInterface.cs @@ -0,0 +1,94 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Runtime.Internal.CpuMath; +using Microsoft.ML.Runtime.Internal.Utilities; +using System.Runtime.InteropServices; + +using System.Security; + +namespace Microsoft.ML.Runtime.FactorizationMachine +{ + internal unsafe static class FieldAwareFactorizationMachineInterface + { + internal const string NativePath = "FactorizationMachineNative"; + public const int CbAlign = 16; + + private static bool Compat(AlignedArray a) + { + Contracts.AssertValue(a); + Contracts.Assert(a.Size > 0); + return a.CbAlign == CbAlign; + } + + private unsafe static float* Ptr(AlignedArray a, float* p) + { + Contracts.AssertValue(a); + float* q = p + a.GetBase((long)p); + Contracts.Assert(((long)q & (CbAlign - 1)) == 0); + return q; + } + + [DllImport(NativePath), SuppressUnmanagedCodeSecurity] + public static extern void CalculateIntermediateVariablesNative(int fieldCount, int latentDim, int count, int* /*const*/ fieldIndices, int* /*const*/ featureIndices, + float* /*const*/ featureValues, float* /*const*/ linearWeights, float* /*const*/ latentWeights, float* latentSum, float* response); + + [DllImport(NativePath), SuppressUnmanagedCodeSecurity] + public static extern void CalculateGradientAndUpdateNative(float lambdaLinear, float lambdaLatent, float learningRate, int fieldCount, int latentDim, float weight, + int count, int* /*const*/ fieldIndices, int* /*const*/ featureIndices, float* /*const*/ featureValues, float* /*const*/ latentSum, float slope, + float* linearWeights, float* latentWeights, float* linearAccumulatedSquaredGrads, float* latentAccumulatedSquaredGrads); + + public static void CalculateIntermediateVariables(int fieldCount, int latentDim, int count, int[] fieldIndices, int[] featureIndices, float[] featureValues, + float[] linearWeights, AlignedArray latentWeights, AlignedArray latentSum, ref float response) + { + Contracts.AssertNonEmpty(fieldIndices); + Contracts.AssertNonEmpty(featureValues); + Contracts.AssertNonEmpty(featureIndices); + Contracts.AssertNonEmpty(linearWeights); + Contracts.Assert(Compat(latentWeights)); + Contracts.Assert(Compat(latentSum)); + + unsafe + { + fixed (int* pf = &fieldIndices[0]) + fixed (int* pi = &featureIndices[0]) + fixed (float* px = &featureValues[0]) + fixed (float* pw = &linearWeights[0]) + fixed (float* pv = &latentWeights.Items[0]) + fixed (float* pq = &latentSum.Items[0]) + fixed (float* pr = &response) + CalculateIntermediateVariablesNative(fieldCount, latentDim, count, pf, pi, px, pw, Ptr(latentWeights, pv), Ptr(latentSum, pq), pr); + } + } + + public static void CalculateGradientAndUpdate(float lambdaLinear, float lambdaLatent, float learningRate, int fieldCount, int latentDim, + float weight, int count, int[] fieldIndices, int[] featureIndices, float[] featureValues, AlignedArray latentSum, float slope, + float[] linearWeights, AlignedArray latentWeights, float[] linearAccumulatedSquaredGrads, AlignedArray latentAccumulatedSquaredGrads) + { + Contracts.AssertNonEmpty(fieldIndices); + Contracts.AssertNonEmpty(featureIndices); + Contracts.AssertNonEmpty(featureValues); + Contracts.Assert(Compat(latentSum)); + Contracts.AssertNonEmpty(linearWeights); + Contracts.Assert(Compat(latentWeights)); + Contracts.AssertNonEmpty(linearAccumulatedSquaredGrads); + Contracts.Assert(Compat(latentAccumulatedSquaredGrads)); + + unsafe + { + fixed (int* pf = &fieldIndices[0]) + fixed (int* pi = &featureIndices[0]) + fixed (float* px = &featureValues[0]) + fixed (float* pq = &latentSum.Items[0]) + fixed (float* pw = &linearWeights[0]) + fixed (float* pv = &latentWeights.Items[0]) + fixed (float* phw = &linearAccumulatedSquaredGrads[0]) + fixed (float* phv = &latentAccumulatedSquaredGrads.Items[0]) + CalculateGradientAndUpdateNative(lambdaLinear, lambdaLatent, learningRate, fieldCount, latentDim, weight, count, pf, pi, px, + Ptr(latentSum, pq), slope, pw, Ptr(latentWeights, pv), phw, Ptr(latentAccumulatedSquaredGrads, phv)); + } + + } + } +} diff --git a/src/Microsoft.ML.StandardLearners/FactorizationMachine/FactorizationMachineTrainer.cs b/src/Microsoft.ML.StandardLearners/FactorizationMachine/FactorizationMachineTrainer.cs new file mode 100644 index 0000000000..87d72471d9 --- /dev/null +++ b/src/Microsoft.ML.StandardLearners/FactorizationMachine/FactorizationMachineTrainer.cs @@ -0,0 +1,418 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Collections.Generic; +using System.Linq; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.FactorizationMachine; +using Microsoft.ML.Runtime.Internal.CpuMath; +using Microsoft.ML.Runtime.Internal.Internallearn; +using Microsoft.ML.Runtime.Internal.Utilities; +using Microsoft.ML.Runtime.Model; +using Microsoft.ML.Runtime.Training; + +[assembly: LoadableClass(FieldAwareFactorizationMachineTrainer.Summary, typeof(FieldAwareFactorizationMachineTrainer), typeof(FieldAwareFactorizationMachineTrainer.Arguments), + new[] { typeof(SignatureBinaryClassifierTrainer), typeof(SignatureTrainer) }, FieldAwareFactorizationMachineTrainer.UserName, FieldAwareFactorizationMachineTrainer.LoadName, + FieldAwareFactorizationMachineTrainer.ShortName, DocName = "trainer/FactorizationMachine.md")] + +[assembly: LoadableClass(typeof(void), typeof(FieldAwareFactorizationMachineTrainer), null, typeof(SignatureEntryPointModule), FieldAwareFactorizationMachineTrainer.LoadName)] + +namespace Microsoft.ML.Runtime.FactorizationMachine +{ + /// + /// Train a field-aware factorization machine using ADAGRAD (an advanced stochastic gradient method). See references below + /// for details. This trainer is essentially faster the one introduced in [2] because of some implemtation tricks[3]. + /// [1] http://jmlr.org/papers/volume12/duchi11a/duchi11a.pdf + /// [2] http://www.csie.ntu.edu.tw/~cjlin/papers/ffm.pdf + /// [3] https://github.com/wschin/fast-ffm/blob/master/fast-ffm.pdf + /// + public sealed class FieldAwareFactorizationMachineTrainer : TrainerBase, + IIncrementalTrainer, IValidatingTrainer, + IIncrementalValidatingTrainer + { + public const string Summary = "Train a field-aware factorization machine for binary classification"; + public const string UserName = "Field-aware Factorization Machine"; + public const string LoadName = "FieldAwareFactorizationMachine"; + public const string ShortName = "ffm"; + + public sealed class Arguments : LearnerInputBaseWithLabel + { + [Argument(ArgumentType.AtMostOnce, HelpText = "Initial learning rate", ShortName = "lr", SortOrder = 1)] + [TlcModule.SweepableFloatParam(0.001f, 1.0f, isLogScale: true)] + public float LearningRate = (float)0.1; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Number of training iterations", ShortName = "iter", SortOrder = 2)] + [TlcModule.SweepableLongParam(1, 100)] + public int Iters = 5; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Latent space dimension", ShortName = "d", SortOrder = 3)] + [TlcModule.SweepableLongParam(4, 100)] + public int LatentDim = 20; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Regularization coefficient of linear weights", ShortName = "lambdaLinear", SortOrder = 4)] + [TlcModule.SweepableFloatParam(1e-8f, 1f, isLogScale: true)] + public float LambdaLinear = 0.0001f; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Regularization coefficient of latent weights", ShortName = "lambdaLatent", SortOrder = 5)] + [TlcModule.SweepableFloatParam(1e-8f, 1f, isLogScale: true)] + public float LambdaLatent = 0.0001f; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Whether to normalize the input vectors so that the concatenation of all fields' feature vectors is unit-length", ShortName = "norm", SortOrder = 6)] + public bool Norm = true; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Whether to shuffle for each training iteration", ShortName = "shuf", SortOrder = 90)] + public bool Shuffle = true; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Report traning progress or not", ShortName = "verbose", SortOrder = 91)] + public bool Verbose = true; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Radius of initial latent factors", ShortName = "rad", SortOrder = 110)] + [TlcModule.SweepableFloatParam(0.1f, 1f)] + public float Radius = 0.5f; + } + + public override PredictionKind PredictionKind => PredictionKind.BinaryClassification; + public override bool NeedNormalization => true; + public override bool NeedCalibration => false; + public override bool WantCaching => true; + private readonly int _latentDim; + private readonly int _latentDimAligned; + private readonly float _lambdaLinear; + private readonly float _lambdaLatent; + private readonly float _learningRate; + private readonly int _numIterations; + private readonly bool _norm; + private readonly bool _shuffle; + private readonly bool _verbose; + private readonly float _radius; + private FieldAwareFactorizationMachinePredictor _pred; + + public FieldAwareFactorizationMachineTrainer(IHostEnvironment env, Arguments args) : base(env, LoadName) + { + Host.CheckUserArg(args.LatentDim > 0, nameof(args.LatentDim), "Must be positive"); + Host.CheckUserArg(args.LambdaLinear >= 0, nameof(args.LambdaLinear), "Must be non-negative"); + Host.CheckUserArg(args.LambdaLatent >= 0, nameof(args.LambdaLatent), "Must be non-negative"); + Host.CheckUserArg(args.LearningRate > 0, nameof(args.LearningRate), "Must be positive"); + Host.CheckUserArg(args.Iters >= 0, nameof(args.Iters), "Must be non-negative"); + _latentDim = args.LatentDim; + _latentDimAligned = FieldAwareFactorizationMachineUtils.GetAlignedVectorLength(_latentDim); + _lambdaLinear = args.LambdaLinear; + _lambdaLatent = args.LambdaLatent; + _learningRate = args.LearningRate; + _numIterations = args.Iters; + _norm = args.Norm; + _shuffle = args.Shuffle; + _verbose = args.Verbose; + _radius = args.Radius; + } + + private void InitializeTrainingState(int fieldCount, int featureCount, FieldAwareFactorizationMachinePredictor predictor, out float[] linearWeights, + out AlignedArray latentWeightsAligned, out float[] linearAccumulatedSquaredGrads, out AlignedArray latentAccumulatedSquaredGradsAligned) + { + linearWeights = new float[featureCount]; + latentWeightsAligned = new AlignedArray(featureCount * fieldCount * _latentDimAligned, 16); + linearAccumulatedSquaredGrads = new float[featureCount]; + latentAccumulatedSquaredGradsAligned = new AlignedArray(featureCount * fieldCount * _latentDimAligned, 16); + + if (predictor == null) + { + var rng = Host.Rand; + for (int j = 0; j < featureCount; j++) + { + linearWeights[j] = 0; + linearAccumulatedSquaredGrads[j] = 1; + for (int f = 0; f < fieldCount; f++) + { + int vBias = j * fieldCount * _latentDimAligned + f * _latentDimAligned; + for (int k = 0; k < _latentDimAligned; k++) + { + if (k < _latentDim) + latentWeightsAligned[vBias + k] = _radius * (float)rng.NextDouble(); + else + latentWeightsAligned[vBias + k] = 0; + latentAccumulatedSquaredGradsAligned[vBias + k] = 1; + } + } + } + } + else + { + predictor.CopyLinearWeightsTo(linearWeights); + predictor.CopyLatentWeightsTo(latentWeightsAligned); + for (int j = 0; j < featureCount; j++) + { + linearAccumulatedSquaredGrads[j] = 1; + for (int f = 0; f < fieldCount; f++) + { + int vBias = j * fieldCount * _latentDimAligned + f * _latentDimAligned; + for (int k = 0; k < _latentDimAligned; k++) + latentAccumulatedSquaredGradsAligned[vBias + k] = 1; + } + } + } + } + + private static float CalculateLoss(float label, float modelResponse) + { + float margin = label > 0 ? modelResponse : -modelResponse; + if (margin > 0) + return MathUtils.Log(1 + MathUtils.ExpSlow(-margin)); + else + return -margin + MathUtils.Log(1 + MathUtils.ExpSlow(margin)); + } + + private static float CalculateLossSlope(float label, float modelResponse) + { + float sign = label > 0 ? 1 : -1; + float margin = sign * modelResponse; + return -sign * MathUtils.Sigmoid(-margin); + } + + private static double CalculateAvgLoss(IChannel ch, RoleMappedData data, bool norm, float[] linearWeights, AlignedArray latentWeightsAligned, + int latentDimAligned, AlignedArray latentSum, int[] featureFieldBuffer, int[] featureIndexBuffer, float[] featureValueBuffer, VBuffer buffer, ref long badExampleCount) + { + var featureColumns = data.Schema.GetColumns(RoleMappedSchema.ColumnRole.Feature); + Func pred = c => featureColumns.Select(ci => ci.Index).Contains(c) || c == data.Schema.Label.Index || (data.Schema.Weight != null && c == data.Schema.Weight.Index); + var getters = new ValueGetter>[featureColumns.Count]; + float label = 0; + float weight = 1; + double loss = 0; + float modelResponse = 0; + long exampleCount = 0; + badExampleCount = 0; + int count = 0; + using (var cursor = data.Data.GetRowCursor(pred)) + { + var labelGetter = cursor.GetGetter(data.Schema.Label.Index); + var weightGetter = data.Schema.Weight == null ? null : cursor.GetGetter(data.Schema.Weight.Index); + for (int f = 0; f < featureColumns.Count; f++) + getters[f] = cursor.GetGetter>(featureColumns[f].Index); + while (cursor.MoveNext()) + { + labelGetter(ref label); + weightGetter?.Invoke(ref weight); + float annihilation = label - label + weight - weight; + if (!FloatUtils.IsFinite(annihilation)) + { + badExampleCount++; + continue; + } + if (!FieldAwareFactorizationMachineUtils.LoadOneExampleIntoBuffer(getters, buffer, norm, ref count, + featureFieldBuffer, featureIndexBuffer, featureValueBuffer)) + { + badExampleCount++; + continue; + } + FieldAwareFactorizationMachineInterface.CalculateIntermediateVariables(featureColumns.Count, latentDimAligned, count, + featureFieldBuffer, featureIndexBuffer, featureValueBuffer, linearWeights, latentWeightsAligned, latentSum, ref modelResponse); + loss += weight * CalculateLoss(label, modelResponse); + exampleCount++; + } + } + return loss / exampleCount; + } + + private void TrainCore(IChannel ch, IProgressChannel pch, RoleMappedData data, RoleMappedData validData, FieldAwareFactorizationMachinePredictor predictor) + { + Host.AssertValue(ch); + Host.AssertValue(pch); + + data.CheckBinaryLabel(); + var featureColumns = data.Schema.GetColumns(RoleMappedSchema.ColumnRole.Feature); + int fieldCount = featureColumns.Count; + int totalFeatureCount = 0; + int[] fieldColumnIndexes = new int[fieldCount]; + for (int f = 0; f < fieldCount; f++) + { + var col = featureColumns[f]; + Host.Assert(col.Type.AsVector.VectorSize > 0); + if (col == null) + throw ch.ExceptParam(nameof(data), "Empty feature column not allowed"); + Host.Assert(!data.Schema.Schema.IsHidden(col.Index)); + if (!col.Type.IsKnownSizeVector || col.Type.ItemType != NumberType.Float) + throw ch.ExceptParam(nameof(data), "Training feature column '{0}' must be a known-size vector of R4, but has type: {1}.", col.Name, col.Type); + fieldColumnIndexes[f] = col.Index; + totalFeatureCount += col.Type.AsVector.VectorSize; + } + ch.Check(checked(totalFeatureCount * fieldCount * _latentDimAligned) <= Utils.ArrayMaxSize, "Latent dimension or the number of fields too large"); + if (predictor != null) + { + ch.Check(predictor.FeatureCount == totalFeatureCount, "Input model's feature count mismatches training feature count"); + ch.Check(predictor.LatentDim == _latentDim, "Input model's latent dimension mismatches trainer's"); + } + if (validData != null) + { + validData.CheckBinaryLabel(); + var validFeatureColumns = data.Schema.GetColumns(RoleMappedSchema.ColumnRole.Feature); + Host.Assert(fieldCount == validFeatureColumns.Count); + for (int f = 0; f < fieldCount; f++) + Host.Assert(featureColumns[f] == validFeatureColumns[f]); + } + bool shuffle = _shuffle; + if (shuffle && !data.Data.CanShuffle) + { + ch.Warning("Training data does not support shuffling, so ignoring request to shuffle"); + shuffle = false; + } + var rng = shuffle ? Host.Rand : null; + var featureGetters = new ValueGetter>[fieldCount]; + var featureBuffer = new VBuffer(); + var featureValueBuffer = new float[totalFeatureCount]; + var featureIndexBuffer = new int[totalFeatureCount]; + var featureFieldBuffer = new int[totalFeatureCount]; + var latentSum = new AlignedArray(fieldCount * fieldCount * _latentDimAligned, 16); + var metricNames = new List() { "Training-loss" }; + if (validData != null) + metricNames.Add("Validation-loss"); + int iter = 0; + long exampleCount = 0; + long badExampleCount = 0; + long validBadExampleCount = 0; + double loss = 0; + double validLoss = 0; + pch.SetHeader(new ProgressHeader(metricNames.ToArray(), new string[] { "iterations", "examples" }), entry => + { + entry.SetProgress(0, iter, _numIterations); + entry.SetProgress(1, exampleCount); + }); + Func pred = c => fieldColumnIndexes.Contains(c) || c == data.Schema.Label.Index || (data.Schema.Weight != null && c == data.Schema.Weight.Index); + InitializeTrainingState(fieldCount, totalFeatureCount, predictor, out float[] linearWeights, + out AlignedArray latentWeightsAligned, out float[] linearAccSqGrads, out AlignedArray latentAccSqGradsAligned); + + // refer to Algorithm 3 in https://github.com/wschin/fast-ffm/blob/master/fast-ffm.pdf + while (iter++ < _numIterations) + { + using (var cursor = data.Data.GetRowCursor(pred, rng)) + { + var labelGetter = RowCursorUtils.GetLabelGetter(cursor, data.Schema.Label.Index); + var weightGetter = data.Schema.Weight == null ? null : RowCursorUtils.GetGetterAs(NumberType.R4, cursor, data.Schema.Weight.Index); + for (int i = 0; i < fieldCount; i++) + featureGetters[i] = cursor.GetGetter>(fieldColumnIndexes[i]); + loss = 0; + exampleCount = 0; + badExampleCount = 0; + while (cursor.MoveNext()) + { + float label = 0; + float weight = 1; + int count = 0; + float modelResponse = 0; + labelGetter(ref label); + weightGetter?.Invoke(ref weight); + float annihilation = label - label + weight - weight; + if (!FloatUtils.IsFinite(annihilation)) + { + badExampleCount++; + continue; + } + if (!FieldAwareFactorizationMachineUtils.LoadOneExampleIntoBuffer(featureGetters, featureBuffer, _norm, ref count, + featureFieldBuffer, featureIndexBuffer, featureValueBuffer)) + { + badExampleCount++; + continue; + } + + // refer to Algorithm 1 in [3] https://github.com/wschin/fast-ffm/blob/master/fast-ffm.pdf + FieldAwareFactorizationMachineInterface.CalculateIntermediateVariables(fieldCount, _latentDimAligned, count, + featureFieldBuffer, featureIndexBuffer, featureValueBuffer, linearWeights, latentWeightsAligned, latentSum, ref modelResponse); + var slope = CalculateLossSlope(label, modelResponse); + + // refer to Algorithm 2 in [3] https://github.com/wschin/fast-ffm/blob/master/fast-ffm.pdf + FieldAwareFactorizationMachineInterface.CalculateGradientAndUpdate(_lambdaLinear, _lambdaLatent, _learningRate, fieldCount, _latentDimAligned, weight, count, + featureFieldBuffer, featureIndexBuffer, featureValueBuffer, latentSum, slope, linearWeights, latentWeightsAligned, linearAccSqGrads, latentAccSqGradsAligned); + loss += weight * CalculateLoss(label, modelResponse); + exampleCount++; + } + loss /= exampleCount; + } + + if (_verbose) + { + if (validData == null) + pch.Checkpoint(loss, iter, exampleCount); + else + { + validLoss = CalculateAvgLoss(ch, validData, _norm, linearWeights, latentWeightsAligned, _latentDimAligned, latentSum, + featureFieldBuffer, featureIndexBuffer, featureValueBuffer, featureBuffer, ref validBadExampleCount); + pch.Checkpoint(loss, validLoss, iter, exampleCount); + } + } + } + if (badExampleCount != 0) + ch.Warning($"Skipped {badExampleCount} examples with bad label/weight/features in training set"); + if (validBadExampleCount != 0) + ch.Warning($"Skipped {validBadExampleCount} examples with bad label/weight/features in validation set"); + _pred = new FieldAwareFactorizationMachinePredictor(Host, _norm, fieldCount, totalFeatureCount, _latentDim, linearWeights, latentWeightsAligned); + } + + public override void Train(RoleMappedData data) + { + Host.CheckValue(data, nameof(data)); + using (var ch = Host.Start("Training")) + using (var pch = Host.StartProgressChannel("Training")) + { + TrainCore(ch, pch, data, null, null); + ch.Done(); + } + } + + public void Train(RoleMappedData data, RoleMappedData validData) + { + Host.CheckValue(data, nameof(data)); + Host.CheckValue(validData, nameof(validData)); + using (var ch = Host.Start("Training")) + using (var pch = Host.StartProgressChannel("Training")) + { + TrainCore(ch, pch, data, validData, null); + ch.Done(); + } + } + + public void Train(RoleMappedData data, FieldAwareFactorizationMachinePredictor predictor) + { + Host.CheckValue(data, nameof(data)); + Host.CheckValue(predictor, nameof(predictor)); + using (var ch = Host.Start("Training")) + using (var pch = Host.StartProgressChannel("Training")) + { + TrainCore(ch, pch, data, null, predictor); + ch.Done(); + } + } + + public void Train(RoleMappedData data, RoleMappedData validData, FieldAwareFactorizationMachinePredictor predictor) + { + Host.CheckValue(data, nameof(data)); + Host.CheckValue(data, nameof(validData)); + Host.CheckValue(predictor, nameof(predictor)); + using (var ch = Host.Start("Training")) + using (var pch = Host.StartProgressChannel("Training")) + { + TrainCore(ch, pch, data, validData, predictor); + ch.Done(); + } + } + + public override FieldAwareFactorizationMachinePredictor CreatePredictor() + { + Host.Check(_pred != null, nameof(Train) + " has not yet been called"); + return _pred; + } + + [TlcModule.EntryPoint(Name = "Trainers.FieldAwareFactorizationMachineBinaryClassifier", Desc = FieldAwareFactorizationMachineTrainer.Summary, UserName = FieldAwareFactorizationMachineTrainer.UserName, ShortName = FieldAwareFactorizationMachineTrainer.ShortName)] + public static CommonOutputs.BinaryClassificationOutput TrainBinary(IHostEnvironment env, Arguments input) + { + Contracts.CheckValue(env, nameof(env)); + var host = env.Register("Train a field-aware factorization machine"); + host.CheckValue(input, nameof(input)); + EntryPointUtils.CheckInputArgs(host, input); + return LearnerEntryPointsUtils.Train(host, input, () => new FieldAwareFactorizationMachineTrainer(host, input), + () => LearnerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.LabelColumn)); + } + } +} diff --git a/src/Microsoft.ML.StandardLearners/FactorizationMachine/FieldAwareFactorizationMachinePredictor.cs b/src/Microsoft.ML.StandardLearners/FactorizationMachine/FieldAwareFactorizationMachinePredictor.cs new file mode 100644 index 0000000000..37261cb55b --- /dev/null +++ b/src/Microsoft.ML.StandardLearners/FactorizationMachine/FieldAwareFactorizationMachinePredictor.cs @@ -0,0 +1,184 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.FactorizationMachine; +using Microsoft.ML.Runtime.Internal.CpuMath; +using Microsoft.ML.Runtime.Internal.Internallearn; +using Microsoft.ML.Runtime.Internal.Utilities; +using Microsoft.ML.Runtime.Model; + +[assembly: LoadableClass(typeof(FieldAwareFactorizationMachinePredictor), null, typeof(SignatureLoadModel), "Field Aware Factorization Machine", FieldAwareFactorizationMachinePredictor.LoaderSignature)] + +namespace Microsoft.ML.Runtime.FactorizationMachine +{ + public sealed class FieldAwareFactorizationMachinePredictor : PredictorBase, ISchemaBindableMapper, ICanSaveModel + { + public const string LoaderSignature = "FieldAwareFactMacPredict"; + public override PredictionKind PredictionKind => PredictionKind.BinaryClassification; + private bool _norm; + internal int FieldCount { get; } + internal int FeatureCount { get; } + internal int LatentDim { get; } + internal int LatentDimAligned { get; } + private readonly float[] _linearWeights; + private readonly AlignedArray _latentWeightsAligned; + + private static VersionInfo GetVersionInfo() + { + return new VersionInfo( + modelSignature: "FAFAMAPD", + verWrittenCur: 0x00010001, + verReadableCur: 0x00010001, + verWeCanReadBack: 0x00010001, + loaderSignature: LoaderSignature); + } + + internal FieldAwareFactorizationMachinePredictor(IHostEnvironment env, bool norm, int fieldCount, int featureCount, int latentDim, + float[] linearWeights, AlignedArray latentWeightsAligned) : base(env, LoaderSignature) + { + Host.Assert(fieldCount > 0); + Host.Assert(featureCount > 0); + Host.Assert(latentDim > 0); + Host.Assert(Utils.Size(linearWeights) == featureCount); + LatentDimAligned = FieldAwareFactorizationMachineUtils.GetAlignedVectorLength(latentDim); + Host.Assert(latentWeightsAligned.Size == checked(featureCount * fieldCount * LatentDimAligned)); + + _norm = norm; + FieldCount = fieldCount; + FeatureCount = featureCount; + LatentDim = latentDim; + _linearWeights = linearWeights; + _latentWeightsAligned = latentWeightsAligned; + } + + private FieldAwareFactorizationMachinePredictor(IHostEnvironment env, ModelLoadContext ctx) : base(env, LoaderSignature) + { + Host.AssertValue(ctx); + + // *** Binary format *** + // bool: whether to normalize feature vectors + // int: number of fields + // int: number of features + // int: latent dimension + // float[]: linear coefficients + // float[]: latent representation of features + + var norm = ctx.Reader.ReadBoolean(); + var fieldCount = ctx.Reader.ReadInt32(); + Host.CheckDecode(fieldCount > 0); + var featureCount = ctx.Reader.ReadInt32(); + Host.CheckDecode(featureCount > 0); + var latentDim = ctx.Reader.ReadInt32(); + Host.CheckDecode(latentDim > 0); + LatentDimAligned = FieldAwareFactorizationMachineUtils.GetAlignedVectorLength(latentDim); + Host.Check(checked(featureCount * fieldCount * LatentDimAligned) <= Utils.ArrayMaxSize, "Latent dimension too large"); + var linearWeights = ctx.Reader.ReadFloatArray(); + Host.CheckDecode(Utils.Size(linearWeights) == featureCount); + var latentWeights = ctx.Reader.ReadFloatArray(); + Host.CheckDecode(Utils.Size(latentWeights) == featureCount * fieldCount * latentDim); + + _norm = norm; + FieldCount = fieldCount; + FeatureCount = featureCount; + LatentDim = latentDim; + _linearWeights = linearWeights; + _latentWeightsAligned = new AlignedArray(FeatureCount * FieldCount * LatentDimAligned, 16); + for (int j = 0; j < FeatureCount; j++) + { + for (int f = 0; f < FieldCount; f++) + { + int vBias = j * FieldCount * LatentDim + f * LatentDim; + int vBiasAligned = j * FieldCount * LatentDimAligned + f * LatentDimAligned; + for (int k = 0; k < LatentDimAligned; k++) + { + if (k < LatentDim) + _latentWeightsAligned[vBiasAligned + k] = latentWeights[vBias + k]; + else + _latentWeightsAligned[vBiasAligned + k] = 0; + } + } + } + } + + public static FieldAwareFactorizationMachinePredictor Create(IHostEnvironment env, ModelLoadContext ctx) + { + Contracts.CheckValue(env, nameof(env)); + env.CheckValue(ctx, nameof(ctx)); + ctx.CheckAtModel(GetVersionInfo()); + return new FieldAwareFactorizationMachinePredictor(env, ctx); + } + + protected override void SaveCore(ModelSaveContext ctx) + { + Host.AssertValue(ctx); + ctx.SetVersionInfo(GetVersionInfo()); + + // *** Binary format *** + // bool: whether to normalize feature vectors + // int: number of fields + // int: number of features + // int: latent dimension + // float[]: linear coefficients + // float[]: latent representation of features + + Host.Assert(FieldCount > 0); + Host.Assert(FeatureCount > 0); + Host.Assert(LatentDim > 0); + Host.Assert(Utils.Size(_linearWeights) == FeatureCount); + Host.Assert(_latentWeightsAligned.Size == FeatureCount * FieldCount * LatentDimAligned); + + ctx.Writer.Write(_norm); + ctx.Writer.Write(FieldCount); + ctx.Writer.Write(FeatureCount); + ctx.Writer.Write(LatentDim); + ctx.Writer.WriteFloatArray(_linearWeights); + float[] latentWeights = new float[FeatureCount * FieldCount * LatentDim]; + for (int j = 0; j < FeatureCount; j++) + { + for (int f = 0; f < FieldCount; f++) + { + int vBias = j * FieldCount * LatentDim + f * LatentDim; + int vBiasAligned = j * FieldCount * LatentDimAligned + f * LatentDimAligned; + for (int k = 0; k < LatentDim; k++) + latentWeights[vBias + k] = _latentWeightsAligned[vBiasAligned + k]; + } + } + ctx.Writer.WriteFloatArray(latentWeights); + } + + internal float CalculateResponse(ValueGetter>[] getters, VBuffer featureBuffer, + int[] featureFieldBuffer, int[] featureIndexBuffer, float[] featureValueBuffer, AlignedArray latentSum) + { + int count = 0; + float modelResponse = 0; + FieldAwareFactorizationMachineUtils.LoadOneExampleIntoBuffer(getters, featureBuffer, _norm, ref count, + featureFieldBuffer, featureIndexBuffer, featureValueBuffer); + FieldAwareFactorizationMachineInterface.CalculateIntermediateVariables(FieldCount, LatentDimAligned, count, + featureFieldBuffer, featureIndexBuffer, featureValueBuffer, _linearWeights, _latentWeightsAligned, latentSum, ref modelResponse); + return modelResponse; + } + + public ISchemaBoundMapper Bind(IHostEnvironment env, RoleMappedSchema schema) + { + return new FieldAwareFactorizationMachineScalarRowMapper(env, schema, new BinaryClassifierSchema(), this); + } + + internal void CopyLinearWeightsTo(float[] linearWeights) + { + Host.AssertValue(_linearWeights); + Host.AssertValue(linearWeights); + Array.Copy(_linearWeights, linearWeights, _linearWeights.Length); + } + + internal void CopyLatentWeightsTo(AlignedArray latentWeights) + { + Host.AssertValue(_latentWeightsAligned); + Host.AssertValue(latentWeights); + latentWeights.CopyFrom(_latentWeightsAligned); + } + } +} diff --git a/src/Microsoft.ML.StandardLearners/FactorizationMachine/FieldAwareFactorizationMachineUtils.cs b/src/Microsoft.ML.StandardLearners/FactorizationMachine/FieldAwareFactorizationMachineUtils.cs new file mode 100644 index 0000000000..0438fcc49b --- /dev/null +++ b/src/Microsoft.ML.StandardLearners/FactorizationMachine/FieldAwareFactorizationMachineUtils.cs @@ -0,0 +1,144 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Collections.Generic; +using System.Linq; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Internal.CpuMath; +using Microsoft.ML.Runtime.Internal.Utilities; + +namespace Microsoft.ML.Runtime.FactorizationMachine +{ + internal sealed class FieldAwareFactorizationMachineUtils + { + internal static int GetAlignedVectorLength(int length) + { + int res = length % 4; + if (res == 0) + return length; + else + return length + (4 - res); + } + + internal static bool LoadOneExampleIntoBuffer(ValueGetter>[] getters, VBuffer featureBuffer, bool norm, ref int count, + int[] fieldIndexBuffer, int[] featureIndexBuffer, float[] featureValueBuffer) + { + count = 0; + float featureNorm = 0; + int bias = 0; + float annihilation = 0; + for (int f = 0; f < getters.Length; f++) + { + getters[f](ref featureBuffer); + foreach (var pair in featureBuffer.Items()) + { + fieldIndexBuffer[count] = f; + featureIndexBuffer[count] = bias + pair.Key; + featureValueBuffer[count] = pair.Value; + featureNorm += pair.Value * pair.Value; + annihilation += pair.Value - pair.Value; + count++; + } + bias += featureBuffer.Length; + } + featureNorm = MathUtils.Sqrt(featureNorm); + if (norm) + { + for (int i = 0; i < count; i++) + featureValueBuffer[i] /= featureNorm; + } + return FloatUtils.IsFinite(annihilation); + } + } + + + internal sealed class FieldAwareFactorizationMachineScalarRowMapper : ISchemaBoundRowMapper + { + private readonly FieldAwareFactorizationMachinePredictor _pred; + + public RoleMappedSchema InputSchema { get; } + + public ISchema OutputSchema { get; } + + public ISchemaBindableMapper Bindable => _pred; + + private readonly ColumnInfo[] _columns; + private readonly List _inputColumnIndexes; + private readonly IHostEnvironment _env; + + public FieldAwareFactorizationMachineScalarRowMapper(IHostEnvironment env, RoleMappedSchema schema, + ISchema outputSchema, FieldAwareFactorizationMachinePredictor pred) + { + Contracts.AssertValue(env); + Contracts.AssertValue(schema); + Contracts.CheckParam(outputSchema.ColumnCount == 2, nameof(outputSchema)); + Contracts.CheckParam(outputSchema.GetColumnType(0).IsNumber, nameof(outputSchema)); + Contracts.CheckParam(outputSchema.GetColumnType(1).IsNumber, nameof(outputSchema)); + Contracts.AssertValue(pred); + + _env = env; + _columns = schema.GetColumns(RoleMappedSchema.ColumnRole.Feature).ToArray(); + _pred = pred; + + var inputFeatureColumns = _columns.Select(c => new KeyValuePair(RoleMappedSchema.ColumnRole.Feature, c.Name)).ToList(); + InputSchema = RoleMappedSchema.Create(schema.Schema, inputFeatureColumns); + OutputSchema = outputSchema; + + _inputColumnIndexes = new List(); + foreach (var kvp in inputFeatureColumns) + { + if (schema.Schema.TryGetColumnIndex(kvp.Value, out int index)) + _inputColumnIndexes.Add(index); + } + } + + public IRow GetOutputRow(IRow input, Func predicate, out Action action) + { + var latentSum = new AlignedArray(_pred.FieldCount * _pred.FieldCount * _pred.LatentDimAligned, 16); + var featureBuffer = new VBuffer(); + var featureFieldBuffer = new int[_pred.FeatureCount]; + var featureIndexBuffer = new int[_pred.FeatureCount]; + var featureValueBuffer = new float[_pred.FeatureCount]; + var inputGetters = new ValueGetter>[_pred.FieldCount]; + for (int f = 0; f < _pred.FieldCount; f++) + inputGetters[f] = input.GetGetter>(_inputColumnIndexes[f]); + + action = null; + var getters = new Delegate[2]; + if (predicate(0)) + { + ValueGetter responseGetter = (ref float value) => + { + value = _pred.CalculateResponse(inputGetters, featureBuffer, featureFieldBuffer, featureIndexBuffer, featureValueBuffer, latentSum); + }; + getters[0] = responseGetter; + } + if (predicate(1)) + { + ValueGetter probGetter = (ref float value) => + { + value = _pred.CalculateResponse(inputGetters, featureBuffer, featureFieldBuffer, featureIndexBuffer, featureValueBuffer, latentSum); + value = MathUtils.SigmoidSlow(value); + }; + getters[1] = probGetter; + } + + return new SimpleRow(OutputSchema, input, getters); + } + + public Func GetDependencies(Func predicate) + { + if (Enumerable.Range(0, OutputSchema.ColumnCount).Any(predicate)) + return index => _inputColumnIndexes.Any(c => c == index); + else + return index => false; + } + + public IEnumerable> GetInputColumnRoles() + { + return InputSchema.GetColumnRoles().Select(kvp => new KeyValuePair(kvp.Key, kvp.Value.Name)); + } + } +} diff --git a/src/Microsoft.ML.StandardLearners/Microsoft.ML.StandardLearners.csproj b/src/Microsoft.ML.StandardLearners/Microsoft.ML.StandardLearners.csproj index 303995dd5b..6bada43299 100644 --- a/src/Microsoft.ML.StandardLearners/Microsoft.ML.StandardLearners.csproj +++ b/src/Microsoft.ML.StandardLearners/Microsoft.ML.StandardLearners.csproj @@ -1,8 +1,9 @@ - + netstandard2.0 Microsoft.ML + true diff --git a/src/Microsoft.ML.StandardLearners/Standard/LinearPredictor.cs b/src/Microsoft.ML.StandardLearners/Standard/LinearPredictor.cs index ad1564c33d..a3ac7ce72e 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/LinearPredictor.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/LinearPredictor.cs @@ -44,6 +44,7 @@ public abstract class LinearPredictor : PredictorBase, ICanSaveInTextFormat, ICanSaveInSourceCode, ICanSaveModel, + ICanGetSummaryAsIRow, ICanSaveSummary, IPredictorWithFeatureWeights, IWhatTheFeatureValueMapper, @@ -343,6 +344,30 @@ public void SaveAsCode(TextWriter writer, RoleMappedSchema schema) public abstract void SaveSummary(TextWriter writer, RoleMappedSchema schema); + public virtual IRow GetSummaryIRowOrNull(RoleMappedSchema schema) + { + var cols = new List(); + + var names = default(VBuffer); + MetadataUtils.GetSlotNames(schema, RoleMappedSchema.ColumnRole.Feature, Weight.Length, ref names); + var slotNamesCol = RowColumnUtils.GetColumn(MetadataUtils.Kinds.SlotNames, + new VectorType(TextType.Instance, Weight.Length), ref names); + var slotNamesRow = RowColumnUtils.GetRow(null, slotNamesCol); + var colType = new VectorType(NumberType.R4, Weight.Length); + + // Add the bias and the weight columns. + var bias = Bias; + cols.Add(RowColumnUtils.GetColumn("Bias", NumberType.R4, ref bias)); + var weights = Weight; + cols.Add(RowColumnUtils.GetColumn("Weights", colType, ref weights, slotNamesRow)); + return RowColumnUtils.GetRow(null, cols.ToArray()); + } + + public virtual IRow GetStatsIRowOrNull(RoleMappedSchema schema) + { + return null; + } + public abstract void SaveAsIni(TextWriter writer, RoleMappedSchema schema, ICalibrator calibrator = null); public virtual void GetFeatureWeights(ref VBuffer weights) @@ -366,8 +391,7 @@ public ValueMapper> GetWhatTheFeatureMapper, - ICanGetSummaryAsIRow + IParameterMixer { public const string LoaderSignature = "Linear2CExec"; public const string RegistrationName = "LinearBinaryPredictor"; @@ -503,26 +527,7 @@ public IList> GetSummaryInKeyValuePairs(RoleMappedS return results; } - public IRow GetSummaryIRowOrNull(RoleMappedSchema schema) - { - var cols = new List(); - - var names = default(VBuffer); - MetadataUtils.GetSlotNames(schema, RoleMappedSchema.ColumnRole.Feature, Weight.Length, ref names); - var slotNamesCol = RowColumnUtils.GetColumn(MetadataUtils.Kinds.SlotNames, - new VectorType(TextType.Instance, Weight.Length), ref names); - var slotNamesRow = RowColumnUtils.GetRow(null, slotNamesCol); - var colType = new VectorType(NumberType.R4, Weight.Length); - - // Add the bias and the weight columns. - var bias = Bias; - cols.Add(RowColumnUtils.GetColumn("Bias", NumberType.R4, ref bias)); - var weights = Weight; - cols.Add(RowColumnUtils.GetColumn("Weights", colType, ref weights, slotNamesRow)); - return RowColumnUtils.GetRow(null, cols.ToArray()); - } - - public IRow GetStatsIRowOrNull(RoleMappedSchema schema) + public override IRow GetStatsIRowOrNull(RoleMappedSchema schema) { if (_stats == null) return null; @@ -582,8 +587,7 @@ public override void SaveAsIni(TextWriter writer, RoleMappedSchema schema, ICali public sealed class LinearRegressionPredictor : RegressionPredictor, IParameterMixer, - ICanGetSummaryInKeyValuePairs, - ICanGetSummaryAsIRow + ICanGetSummaryInKeyValuePairs { public const string LoaderSignature = "LinearRegressionExec"; public const string RegistrationName = "LinearRegressionPredictor"; @@ -663,30 +667,6 @@ public IList> GetSummaryInKeyValuePairs(RoleMappedS return results; } - - public IRow GetSummaryIRowOrNull(RoleMappedSchema schema) - { - var cols = new List(); - - var names = default(VBuffer); - MetadataUtils.GetSlotNames(schema, RoleMappedSchema.ColumnRole.Feature, Weight.Length, ref names); - var slotNamesCol = RowColumnUtils.GetColumn(MetadataUtils.Kinds.SlotNames, - new VectorType(TextType.Instance, Weight.Length), ref names); - var slotNamesRow = RowColumnUtils.GetRow(null, slotNamesCol); - var colType = new VectorType(NumberType.R4, Weight.Length); - - // Add the bias and the weight columns. - var bias = Bias; - cols.Add(RowColumnUtils.GetColumn("Bias", NumberType.R4, ref bias)); - var weights = Weight; - cols.Add(RowColumnUtils.GetColumn("Weights", colType, ref weights, slotNamesRow)); - return RowColumnUtils.GetRow(null, cols.ToArray()); - } - - public IRow GetStatsIRowOrNull(RoleMappedSchema schema) - { - return null; - } } public sealed class PoissonRegressionPredictor : RegressionPredictor, IParameterMixer diff --git a/src/Microsoft.ML.StandardLearners/Standard/MultiClass/Ova.cs b/src/Microsoft.ML.StandardLearners/Standard/MultiClass/Ova.cs index 895dcea7cf..8aa6e4b6e0 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/MultiClass/Ova.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/MultiClass/Ova.cs @@ -92,7 +92,7 @@ private TScalarPredictor TrainOne(IChannel ch, TScalarTrainer trainer, RoleMappe else calibrator = Args.Calibrator.CreateInstance(Host); var res = CalibratorUtils.TrainCalibratorIfNeeded(Host, ch, calibrator, Args.MaxCalibrationExamples, - trainer, predictor, td, true); + trainer, predictor, td); predictor = res as TScalarPredictor; Host.Check(predictor != null, "Calibrated predictor does not implement the expected interface"); } @@ -200,10 +200,13 @@ public static ModelOperations.PredictorModelOutput CombineOvaModels(IHostEnviron host.CheckValue(input, nameof(input)); EntryPointUtils.CheckInputArgs(host, input); host.CheckNonEmpty(input.ModelArray, nameof(input.ModelArray)); - + // Something tells me we should put normalization as part of macro expansion, but since i get + // subgraph instead of learner it's a bit tricky to get learner and decide should we add + // normalization node or not, plus everywhere in code we leave that reposnsibility to TransformModel. + var normalizedView = input.ModelArray[0].TransformModel.Apply(host, input.TrainingData); using (var ch = host.Start("CombineOvaModels")) { - ISchema schema = input.TrainingData.Schema; + ISchema schema = normalizedView.Schema; var label = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(input.LabelColumn), input.LabelColumn, DefaultColumnNames.Label); @@ -211,7 +214,7 @@ public static ModelOperations.PredictorModelOutput CombineOvaModels(IHostEnviron input.FeatureColumn, DefaultColumnNames.Features); var weight = TrainUtils.MatchNameOrDefaultOrNull(ch, schema, nameof(input.WeightColumn), input.WeightColumn, DefaultColumnNames.Weight); - var data = TrainUtils.CreateExamples(input.TrainingData, label, feature, null, weight); + var data = TrainUtils.CreateExamples(normalizedView, label, feature, null, weight); return new ModelOperations.PredictorModelOutput { diff --git a/src/Microsoft.ML.StandardLearners/Standard/OlsLinearRegression.cs b/src/Microsoft.ML.StandardLearners/Standard/OlsLinearRegression.cs index c9ff35a8eb..7ea557159e 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/OlsLinearRegression.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/OlsLinearRegression.cs @@ -28,8 +28,6 @@ "OLS Linear Regression Executor", OlsLinearRegressionPredictor.LoaderSignature)] -[assembly: LoadableClass(typeof(void), typeof(OlsLinearRegressionTrainer), null, typeof(SignatureEntryPointModule), OlsLinearRegressionTrainer.LoadNameValue)] - namespace Microsoft.ML.Runtime.Learners { public sealed class OlsLinearRegressionTrainer : TrainerBase @@ -491,20 +489,6 @@ public static void Pptri(Layout layout, UpLo uplo, int n, Double[] ap) } } } - - [TlcModule.EntryPoint(Name= "Trainers.OrdinaryLeastSquaresRegressor", Desc = "Train an OLS regression model.", UserName = UserNameValue, ShortName = ShortName)] - public static CommonOutputs.RegressionOutput TrainRegression(IHostEnvironment env, Arguments input) - { - Contracts.CheckValue(env, nameof(env)); - var host = env.Register("TrainOLS"); - host.CheckValue(input, nameof(input)); - EntryPointUtils.CheckInputArgs(host, input); - - return LearnerEntryPointsUtils.Train(host, input, - () => new OlsLinearRegressionTrainer(host, input), - () => LearnerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.LabelColumn), - () => LearnerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.WeightColumn)); - } } /// diff --git a/src/Microsoft.ML.Sweeper/Algorithms/Grid.cs b/src/Microsoft.ML.Sweeper/Algorithms/Grid.cs index 6cb2b2b4c2..53e7046ebd 100644 --- a/src/Microsoft.ML.Sweeper/Algorithms/Grid.cs +++ b/src/Microsoft.ML.Sweeper/Algorithms/Grid.cs @@ -2,16 +2,18 @@ // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. -using Float = System.Single; - -using System; using System.Collections.Generic; using System.Linq; -using Microsoft.ML; +using Microsoft.ML.Runtime; using Microsoft.ML.Runtime.CommandLine; using Microsoft.ML.Runtime.Internal.Utilities; using Microsoft.ML.Runtime.Sweeper; +[assembly: LoadableClass(typeof(RandomGridSweeper), typeof(RandomGridSweeper.Arguments), typeof(SignatureSweeper), + "Random Grid Sweeper", "RandomGridSweeper", "RandomGrid")] +[assembly: LoadableClass(typeof(RandomGridSweeper), typeof(RandomGridSweeper.Arguments), typeof(SignatureSweeperFromParameterList), + "Random Grid Sweeper", "RandomGridSweeperParamList", "RandomGridpl")] + namespace Microsoft.ML.Runtime.Sweeper { /// @@ -62,10 +64,10 @@ protected SweeperBase(ArgumentsBase args, IHostEnvironment env, IValueGenerator[ SweepParameters = sweepParameters; } - public virtual ParameterSet[] ProposeSweeps(int maxSweeps, IEnumerable previousRuns) + public virtual ParameterSet[] ProposeSweeps(int maxSweeps, IEnumerable previousRuns = null) { var prevParamSets = previousRuns?.Select(r => r.ParameterSet).ToList() ?? new List(); - var result = new List(); + var result = new HashSet(); for (int i = 0; i < maxSweeps; i++) { ParameterSet paramSet; @@ -148,12 +150,12 @@ public RandomGridSweeper(IHostEnvironment env, Arguments args, IValueGenerator[] } } - public override ParameterSet[] ProposeSweeps(int maxSweeps, IEnumerable previousRuns) + public override ParameterSet[] ProposeSweeps(int maxSweeps, IEnumerable previousRuns = null) { if (_nGridPoints == 0) return base.ProposeSweeps(maxSweeps, previousRuns); - var result = new List(); + var result = new HashSet(); var prevParamSets = (previousRuns != null) ? previousRuns.Select(r => r.ParameterSet).ToList() : new List(); diff --git a/src/Microsoft.ML.Sweeper/Algorithms/Random.cs b/src/Microsoft.ML.Sweeper/Algorithms/Random.cs index e6673bb38e..fd40de3cda 100644 --- a/src/Microsoft.ML.Sweeper/Algorithms/Random.cs +++ b/src/Microsoft.ML.Sweeper/Algorithms/Random.cs @@ -2,14 +2,8 @@ // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. -using Float = System.Single; - -using System; -using System.Collections.Generic; using System.Linq; -using Microsoft.ML; using Microsoft.ML.Runtime; -using Microsoft.ML.Runtime.CommandLine; using Microsoft.ML.Runtime.Sweeper; [assembly: LoadableClass(typeof(UniformRandomSweeper), typeof(SweeperBase.ArgumentsBase), typeof(SignatureSweeper), diff --git a/src/Microsoft.ML.Sweeper/AsyncSweeper.cs b/src/Microsoft.ML.Sweeper/AsyncSweeper.cs index f13ad02531..e325846f6f 100644 --- a/src/Microsoft.ML.Sweeper/AsyncSweeper.cs +++ b/src/Microsoft.ML.Sweeper/AsyncSweeper.cs @@ -8,12 +8,15 @@ using System.Threading.Tasks; using System.Threading.Tasks.Dataflow; -using Microsoft.ML; using Microsoft.ML.Runtime; using Microsoft.ML.Runtime.CommandLine; using Microsoft.ML.Runtime.Internal.Utilities; using Microsoft.ML.Runtime.Sweeper; +[assembly: LoadableClass(typeof(SimpleAsyncSweeper), typeof(SweeperBase.ArgumentsBase), typeof(SignatureAsyncSweeper), + "Asynchronous Uniform Random Sweeper", "UniformRandomSweeper", "UniformRandom")] +[assembly: LoadableClass(typeof(SimpleAsyncSweeper), typeof(RandomGridSweeper.Arguments), typeof(SignatureAsyncSweeper), + "Asynchronous Random Grid Sweeper", "RandomGridSweeper", "RandomGrid")] [assembly: LoadableClass(typeof(DeterministicSweeperAsync), typeof(DeterministicSweeperAsync.Arguments), typeof(SignatureAsyncSweeper), "Asynchronous and Deterministic Sweeper", "DeterministicSweeper", "Deterministic")] @@ -63,7 +66,7 @@ public interface IAsyncSweeper /// Expose existing s as with no synchronization over the past runs. /// Nelder-Mead requires synchronization so is not compatible with SimpleAsyncSweeperBase. /// - public class SimpleAsyncSweeper : IAsyncSweeper, IDisposable + public partial class SimpleAsyncSweeper : IAsyncSweeper, IDisposable { private readonly List _results; private readonly object _lock; @@ -85,6 +88,16 @@ private SimpleAsyncSweeper(ISweeper baseSweeper) _results = new List(); } + public SimpleAsyncSweeper(IHostEnvironment env, UniformRandomSweeper.ArgumentsBase args) + : this(new UniformRandomSweeper(env, args)) + { + } + + public SimpleAsyncSweeper(IHostEnvironment env, RandomGridSweeper.Arguments args) + : this(new UniformRandomSweeper(env, args)) + { + } + public void Update(int id, IRunResult result) { Contracts.CheckParam(0 <= id && id < _numGenerated, nameof(id), "Invalid run id"); diff --git a/src/Microsoft.ML.Transforms/EntryPoints/TextAnalytics.cs b/src/Microsoft.ML.Transforms/EntryPoints/TextAnalytics.cs index 3ce5102fa1..d6de490d52 100644 --- a/src/Microsoft.ML.Transforms/EntryPoints/TextAnalytics.cs +++ b/src/Microsoft.ML.Transforms/EntryPoints/TextAnalytics.cs @@ -92,5 +92,20 @@ public static CommonOutputs.TransformOutput CharTokenize(IHostEnvironment env, C OutputData = view }; } + + [TlcModule.EntryPoint(Name = "Transforms.LightLda", Desc = LdaTransform.Summary, UserName = LdaTransform.UserName, ShortName = LdaTransform.ShortName)] + public static CommonOutputs.TransformOutput LightLda(IHostEnvironment env, LdaTransform.Arguments input) + { + Contracts.CheckValue(env, nameof(env)); + env.CheckValue(input, nameof(input)); + + var h = EntryPointUtils.CheckArgsAndCreateHost(env, "LightLda", input); + var view = new LdaTransform(h, input, input.Data); + return new CommonOutputs.TransformOutput() + { + Model = new TransformModel(h, view, input.Data), + OutputData = view + }; + } } } diff --git a/src/Microsoft.ML.Transforms/NAReplaceTransform.cs b/src/Microsoft.ML.Transforms/NAReplaceTransform.cs index 522237e303..52989472fc 100644 --- a/src/Microsoft.ML.Transforms/NAReplaceTransform.cs +++ b/src/Microsoft.ML.Transforms/NAReplaceTransform.cs @@ -43,6 +43,7 @@ public enum ReplacementKind Mean, Minimum, Maximum, + SpecifiedValue, [HideEnumValue] Def = DefaultValue, @@ -53,8 +54,6 @@ public enum ReplacementKind [HideEnumValue] Max = Maximum, - [HideEnumValue] - SpecifiedValue, [HideEnumValue] Val = SpecifiedValue, [HideEnumValue] diff --git a/src/Microsoft.ML.Transforms/NormalizeTransform.cs b/src/Microsoft.ML.Transforms/NormalizeTransform.cs index 7c1584a4e6..e5602918b6 100644 --- a/src/Microsoft.ML.Transforms/NormalizeTransform.cs +++ b/src/Microsoft.ML.Transforms/NormalizeTransform.cs @@ -93,7 +93,6 @@ public string TestType(ColumnType type) public override bool CanSavePfa => true; public override bool CanSaveOnnx => true; - public override bool CanSaveLotusVNext => true; public const string LoaderSignature = "NormalizeTransform"; internal const string LoaderSignatureOld = "NormalizeFunction"; private static VersionInfo GetVersionInfo() diff --git a/src/Microsoft.ML.Transforms/Text/LdaSingleBox.cs b/src/Microsoft.ML.Transforms/Text/LdaSingleBox.cs new file mode 100644 index 0000000000..36e55099d9 --- /dev/null +++ b/src/Microsoft.ML.Transforms/Text/LdaSingleBox.cs @@ -0,0 +1,357 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Collections.Generic; +using System.Linq; +using System.Runtime.InteropServices; +using System.Security; + +namespace Microsoft.ML.Runtime.TextAnalytics +{ + + internal static class LdaInterface + { + public struct LdaEngine + { + public IntPtr Ptr; + } + + private const string NativeDll = "LdaNative"; + [DllImport(NativeDll), SuppressUnmanagedCodeSecurity] + internal static extern LdaEngine CreateEngine(int numTopic, int numVocab, float alphaSum, float beta, int numIter, + int likelihoodInterval, int numThread, int mhstep, int maxDocToken); + + [DllImport(NativeDll), SuppressUnmanagedCodeSecurity] + internal static extern void AllocateModelMemory(LdaEngine engine, int numTopic, int numVocab, long tableSize, long aliasTableSize); + + [DllImport(NativeDll), SuppressUnmanagedCodeSecurity] + internal static extern void AllocateDataMemory(LdaEngine engine, int docNum, long corpusSize); + + [DllImport(NativeDll, CharSet = CharSet.Ansi), SuppressUnmanagedCodeSecurity] + internal static extern void Train(LdaEngine engine, string trainOutput); + + [DllImport(NativeDll), SuppressUnmanagedCodeSecurity] + internal static extern void GetModelStat(LdaEngine engine, out long memBlockSize, out long aliasMemBlockSize); + + [DllImport(NativeDll), SuppressUnmanagedCodeSecurity] + internal static extern void Test(LdaEngine engine, int numBurninIter, float[] pLogLikelihood); + + [DllImport(NativeDll), SuppressUnmanagedCodeSecurity] + internal static extern void CleanData(LdaEngine engine); + + [DllImport(NativeDll), SuppressUnmanagedCodeSecurity] + internal static extern void CleanModel(LdaEngine engine); + + [DllImport(NativeDll), SuppressUnmanagedCodeSecurity] + internal static extern void DestroyEngine(LdaEngine engine); + + [DllImport(NativeDll), SuppressUnmanagedCodeSecurity] + internal static extern void GetWordTopic(LdaEngine engine, int wordId, int[] pTopic, int[] pProb, ref int length); + + [DllImport(NativeDll), SuppressUnmanagedCodeSecurity] + internal static extern void SetWordTopic(LdaEngine engine, int wordId, int[] pTopic, int[] pProb, int length); + + [DllImport(NativeDll), SuppressUnmanagedCodeSecurity] + internal static extern void SetAlphaSum(LdaEngine engine, float avgDocLength); + + [DllImport(NativeDll), SuppressUnmanagedCodeSecurity] + internal static extern int FeedInData(LdaEngine engine, int[] termId, int[] termFreq, int termNum, int numVocab); + + [DllImport(NativeDll), SuppressUnmanagedCodeSecurity] + internal static extern int FeedInDataDense(LdaEngine engine, int[] termFreq, int termNum, int numVocab); + + [DllImport(NativeDll), SuppressUnmanagedCodeSecurity] + internal static extern void GetDocTopic(LdaEngine engine, int docId, int[] pTopic, int[] pProb, ref int numTopicReturn); + + [DllImport(NativeDll), SuppressUnmanagedCodeSecurity] + internal static extern void GetTopicSummary(LdaEngine engine, int topicId, int[] pWords, float[] pProb, ref int numTopicReturn); + + [DllImport(NativeDll), SuppressUnmanagedCodeSecurity] + internal static extern void TestOneDoc(LdaEngine engine, int[] termId, int[] termFreq, int termNum, int[] pTopics, int[] pProbs, ref int numTopicsMax, int numBurnIter, bool reset); + + [DllImport(NativeDll), SuppressUnmanagedCodeSecurity] + internal static extern void TestOneDocDense(LdaEngine engine, int[] termFreq, int termNum, int[] pTopics, int[] pProbs, ref int numTopicsMax, int numBurninIter, bool reset); + + [DllImport(NativeDll), SuppressUnmanagedCodeSecurity] + internal static extern void InitializeBeforeTrain(LdaEngine engine); + + [DllImport(NativeDll), SuppressUnmanagedCodeSecurity] + internal static extern void InitializeBeforeTest(LdaEngine engine); + } + + internal sealed class LdaSingleBox : IDisposable + { + private LdaInterface.LdaEngine _engine; + private bool _isDisposed; + private int[] _topics; + private int[] _probabilities; + private int[] _summaryTerm; + private float[] _summaryTermProb; + private readonly int _likelihoodInterval; + private readonly float _alpha; + private readonly float _beta; + private readonly int _mhStep; + private readonly int _numThread; + private readonly int _numSummaryTerms; + private readonly bool _denseOutput; + + public readonly int NumTopic; + public readonly int NumVocab; + public LdaSingleBox(int numTopic, int numVocab, float alpha, + float beta, int numIter, int likelihoodInterval, int numThread, + int mhstep, int numSummaryTerms, bool denseOutput, int maxDocToken) + { + NumTopic = numTopic; + NumVocab = numVocab; + _alpha = alpha; + _beta = beta; + _mhStep = mhstep; + _numSummaryTerms = numSummaryTerms; + _denseOutput = denseOutput; + _likelihoodInterval = likelihoodInterval; + _numThread = numThread; + + _topics = new int[numTopic]; + _probabilities = new int[numTopic]; + + _summaryTerm = new int[_numSummaryTerms]; + _summaryTermProb = new float[_numSummaryTerms]; + + _engine = LdaInterface.CreateEngine(numTopic, numVocab, alpha, beta, numIter, likelihoodInterval, numThread, mhstep, maxDocToken); + } + + public void AllocateModelMemory(int numTopic, int numVocab, long tableSize, long aliasTableSize) + { + Contracts.Check(numTopic >= 0); + Contracts.Check(numVocab >= 0); + Contracts.Check(tableSize >= 0); + Contracts.Check(aliasTableSize >= 0); + LdaInterface.AllocateModelMemory(_engine, numVocab, numTopic, tableSize, aliasTableSize); + } + + public void AllocateDataMemory(int docNum, long corpusSize) + { + Contracts.Check(docNum >= 0); + Contracts.Check(corpusSize >= 0); + LdaInterface.AllocateDataMemory(_engine, docNum, corpusSize); + } + + public void Train(string trainOutput) + { + if (string.IsNullOrWhiteSpace(trainOutput)) + LdaInterface.Train(_engine, null); + else + LdaInterface.Train(_engine, trainOutput); + } + + public void GetModelStat(out long memBlockSize, out long aliasMemBlockSize) + { + LdaInterface.GetModelStat(_engine, out memBlockSize, out aliasMemBlockSize); + } + + public void Test(int numBurninIter, float[] logLikelihood) + { + Contracts.Check(numBurninIter >= 0); + var pLogLikelihood = new float[numBurninIter]; + LdaInterface.Test(_engine, numBurninIter, pLogLikelihood); + logLikelihood = pLogLikelihood.Select(item => (float)item).ToArray(); + } + + public void CleanData() + { + LdaInterface.CleanData(_engine); + } + + public void CleanModel() + { + LdaInterface.CleanModel(_engine); + } + + public void CopyModel(LdaSingleBox trainer, int wordId) + { + int length = NumTopic; + LdaInterface.GetWordTopic(trainer._engine, wordId, _topics, _probabilities, ref length); + LdaInterface.SetWordTopic(_engine, wordId, _topics, _probabilities, length); + } + + public void SetAlphaSum(float averageDocLength) + { + LdaInterface.SetAlphaSum(_engine, averageDocLength); + } + + public int LoadDoc(int[] termID, double[] termVal, int termNum, int numVocab) + { + Contracts.Check(numVocab == NumVocab); + Contracts.Check(termNum > 0); + Contracts.Check(termID.Length >= termNum); + Contracts.Check(termVal.Length >= termNum); + + int[] pID = new int[termNum]; + int[] pVal = termVal.Select(item => (int)item).ToArray(); + Array.Copy(termID, pID, termNum); + return LdaInterface.FeedInData(_engine, pID, pVal, termNum, NumVocab); + } + + public int LoadDocDense(double[] termVal, int termNum, int numVocab) + { + Contracts.Check(numVocab == NumVocab); + Contracts.Check(termNum > 0); + + Contracts.Check(termVal.Length >= termNum); + + int[] pID = new int[termNum]; + int[] pVal = termVal.Select(item => (int)item).ToArray(); + return LdaInterface.FeedInDataDense(_engine, pVal, termNum, NumVocab); + + } + + public List> GetDocTopicVector(int docID) + { + int numTopicReturn = NumTopic; + LdaInterface.GetDocTopic(_engine, docID, _topics, _probabilities, ref numTopicReturn); + var topicRet = new List>(); + int currentTopic = 0; + for (int i = 0; i < numTopicReturn; i++) + { + if (_denseOutput) + { + while (currentTopic < _topics[i]) + { + //use a value to smooth the count so that we get dense output on each topic + //the smooth value is usually set to 0.1 + topicRet.Add(new KeyValuePair(currentTopic, (float)_alpha)); + currentTopic++; + } + topicRet.Add(new KeyValuePair(_topics[i], _probabilities[i] + (float)_alpha)); + currentTopic++; + } + else + { + topicRet.Add(new KeyValuePair(_topics[i], (float)_probabilities[i])); + } + } + + if (_denseOutput) + { + while (currentTopic < NumTopic) + { + topicRet.Add(new KeyValuePair(currentTopic, (float)_alpha)); + currentTopic++; + } + } + return topicRet; + } + + public List> TestDoc(int[] termID, double[] termVal, int termNum, int numBurninIter, bool reset) + { + Contracts.Check(termNum > 0); + Contracts.Check(termVal.Length >= termNum); + Contracts.Check(termID.Length >= termNum); + + int[] pID = new int[termNum]; + int[] pVal = termVal.Select(item => (int)item).ToArray(); + int[] pTopic = new int[NumTopic]; + int[] pProb = new int[NumTopic]; + Array.Copy(termID, pID, termNum); + + int numTopicReturn = NumTopic; + + LdaInterface.TestOneDoc(_engine, pID, pVal, termNum, pTopic, pProb, ref numTopicReturn, numBurninIter, reset); + + // PREfast suspects that the value of numTopicReturn could be changed in _engine->TestOneDoc, which might result in read overrun in the following loop. + if (numTopicReturn > NumTopic) + { + Contracts.Check(false); + numTopicReturn = NumTopic; + } + + var topicRet = new List>(); + for (int i = 0; i < numTopicReturn; i++) + topicRet.Add(new KeyValuePair(pTopic[i], (float)pProb[i])); + return topicRet; + } + + public List> TestDocDense(double[] termVal, int termNum, int numBurninIter, bool reset) + { + Contracts.Check(termNum > 0); + Contracts.Check(numBurninIter > 0); + Contracts.Check(termVal.Length >= termNum); + int[] pVal = termVal.Select(item => (int)item).ToArray(); + int[] pTopic = new int[NumTopic]; + int[] pProb = new int[NumTopic]; + + int numTopicReturn = NumTopic; + + // There are two versions of TestOneDoc interfaces + // (1) TestOneDoc + // (2) TestOneDocRestart + // The second one is the same as the first one except that it will reset + // the states of the internal random number generator, so that it yields reproducable results for the same input + LdaInterface.TestOneDocDense(_engine, pVal, termNum, pTopic, pProb, ref numTopicReturn, numBurninIter, reset); + + // PREfast suspects that the value of numTopicReturn could be changed in _engine->TestOneDoc, which might result in read overrun in the following loop. + if (numTopicReturn > NumTopic) + { + Contracts.Check(false); + numTopicReturn = NumTopic; + } + + var topicRet = new List>(); + for (int i = 0; i < numTopicReturn; i++) + topicRet.Add(new KeyValuePair(pTopic[i], (float)pProb[i])); + return topicRet; + } + + public void InitializeBeforeTrain() + { + LdaInterface.InitializeBeforeTrain(_engine); + } + + public void InitializeBeforeTest() + { + LdaInterface.InitializeBeforeTest(_engine); + } + + public KeyValuePair[] GetModel(int wordId) + { + int length = NumTopic; + LdaInterface.GetWordTopic(_engine, wordId, _topics, _probabilities, ref length); + var wordTopicVector = new KeyValuePair[length]; + + for (int i = 0; i < length; i++) + wordTopicVector[i] = new KeyValuePair(_topics[i], _probabilities[i]); + return wordTopicVector; + } + + public KeyValuePair[] GetTopicSummary(int topicId) + { + int length = _numSummaryTerms; + LdaInterface.GetTopicSummary(_engine, topicId, _summaryTerm, _summaryTermProb, ref length); + var topicSummary = new KeyValuePair[length]; + + for (int i = 0; i < length; i++) + topicSummary[i] = new KeyValuePair(_summaryTerm[i], _summaryTermProb[i]); + return topicSummary; + } + + public void SetModel(int termID, int[] topicID, int[] topicProb, int topicNum) + { + Contracts.Check(termID >= 0); + Contracts.Check(topicNum <= NumTopic); + Array.Copy(topicID, _topics, topicNum); + Array.Copy(topicProb, _probabilities, topicNum); + LdaInterface.SetWordTopic(_engine, termID, _topics, _probabilities, topicNum); + } + + public void Dispose() + { + if (_isDisposed) + return; + _isDisposed = true; + LdaInterface.DestroyEngine(_engine); + _engine.Ptr = IntPtr.Zero; + } + } +} diff --git a/src/Microsoft.ML.Transforms/Text/LdaTransform.cs b/src/Microsoft.ML.Transforms/Text/LdaTransform.cs new file mode 100644 index 0000000000..1267f634cd --- /dev/null +++ b/src/Microsoft.ML.Transforms/Text/LdaTransform.cs @@ -0,0 +1,962 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Float = System.Single; + +using System; +using System.Collections.Generic; +using System.IO; +using System.Linq; +using System.Text; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.Internal.Internallearn; +using Microsoft.ML.Runtime.Internal.Utilities; +using Microsoft.ML.Runtime.Model; +using Microsoft.ML.Runtime.TextAnalytics; + +[assembly: LoadableClass(typeof(LdaTransform), typeof(LdaTransform.Arguments), typeof(SignatureDataTransform), + LdaTransform.UserName, LdaTransform.LoaderSignature, LdaTransform.ShortName, DocName = "transform/LdaTransform.md")] + +[assembly: LoadableClass(typeof(LdaTransform), null, typeof(SignatureLoadDataTransform), + LdaTransform.UserName, LdaTransform.LoaderSignature)] + +namespace Microsoft.ML.Runtime.TextAnalytics +{ + /// + /// The latent Dirichlet allocation (LDA) transform. + /// http://arxiv.org/abs/1412.1576 + /// + public sealed class LdaTransform : OneToOneTransformBase + { + public sealed class Arguments : TransformInputBase + { + [Argument(ArgumentType.Multiple | ArgumentType.Required, HelpText = "New column definition(s) (optional form: name:srcs)", ShortName = "col", SortOrder = 49)] + public Column[] Column; + + [Argument(ArgumentType.AtMostOnce, HelpText = "The number of topics in the LDA", SortOrder = 50)] + [TGUI(SuggestedSweeps = "20,40,100,200")] + [TlcModule.SweepableDiscreteParam("NumTopic", new object[] { 20, 40, 100, 200 })] + public int NumTopic = 100; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Dirichlet prior on document-topic vectors")] + [TGUI(SuggestedSweeps = "1,10,100,200")] + [TlcModule.SweepableDiscreteParam("AlphaSum", new object[] { 1, 10, 100, 200 })] + public Single AlphaSum = 100; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Dirichlet prior on vocab-topic vectors")] + [TGUI(SuggestedSweeps = "0.01,0.015,0.07,0.02")] + [TlcModule.SweepableDiscreteParam("Beta", new object[] { 0.01f, 0.015f, 0.07f, 0.02f })] + public Single Beta = 0.01f; + + [Argument(ArgumentType.Multiple, HelpText = "Number of Metropolis Hasting step")] + [TGUI(SuggestedSweeps = "2,4,8,16")] + [TlcModule.SweepableDiscreteParam("Mhstep", new object[] { 2, 4, 8, 16 })] + public int Mhstep = 4; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Number of iterations", ShortName = "iter")] + [TGUI(SuggestedSweeps = "100,200,300,400")] + [TlcModule.SweepableDiscreteParam("NumIterations", new object[] { 100, 200, 300, 400 })] + public int NumIterations = 200; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Compute log likelihood over local dataset on this iteration interval", ShortName = "llInterval")] + public int LikelihoodInterval = 5; + + [Argument(ArgumentType.AtMostOnce, HelpText = "The threshold of maximum count of tokens per doc", ShortName = "maxNumToken", SortOrder = 50)] + public int NumMaxDocToken = 512; + + // REVIEW: Should change the default when multi-threading support is optimized. + [Argument(ArgumentType.AtMostOnce, HelpText = "The number of training threads. Default value depends on number of logical processors.", ShortName = "t", SortOrder = 50)] + public int? NumThreads; + + [Argument(ArgumentType.AtMostOnce, HelpText = "The number of words to summarize the topic", ShortName = "ns")] + public int NumSummaryTermPerTopic = 10; + + [Argument(ArgumentType.AtMostOnce, HelpText = "The number of burn-in iterations", ShortName = "burninIter")] + [TGUI(SuggestedSweeps = "10,20,30,40")] + [TlcModule.SweepableDiscreteParam("NumBurninIterations", new object[] { 10, 20, 30, 40 })] + public int NumBurninIterations = 10; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Reset the random number generator for each document", ShortName = "reset")] + public bool ResetRandomGenerator; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Whether to output the topic-word summary in text format", ShortName = "summary")] + public bool OutputTopicWordSummary; + } + + public sealed class Column : OneToOneColumn + { + [Argument(ArgumentType.AtMostOnce, HelpText = "The number of topics in the LDA")] + public int? NumTopic; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Dirichlet prior on document-topic vectors")] + public Single? AlphaSum; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Dirichlet prior on vocab-topic vectors")] + public Single? Beta; + + [Argument(ArgumentType.Multiple, HelpText = "Number of Metropolis Hasting step")] + public int? Mhstep; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Number of iterations", ShortName = "iter")] + public int? NumIterations; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Compute log likelihood over local dataset on this iteration interval", ShortName = "llInterval")] + public int? LikelihoodInterval; + + [Argument(ArgumentType.AtMostOnce, HelpText = "The number of training threads", ShortName = "t")] + public int? NumThreads; + + [Argument(ArgumentType.AtMostOnce, HelpText = "The threshold of maximum count of tokens per doc", ShortName = "maxNumToken")] + public int? NumMaxDocToken; + + [Argument(ArgumentType.AtMostOnce, HelpText = "The number of words to summarize the topic", ShortName = "ns")] + public int? NumSummaryTermPerTopic; + + [Argument(ArgumentType.AtMostOnce, HelpText = "The number of burn-in iterations", ShortName = "burninIter")] + public int? NumBurninIterations = 10; + + [Argument(ArgumentType.AtMostOnce, HelpText = "Reset the random number generator for each document", ShortName = "reset")] + public bool? ResetRandomGenerator; + + public static Column Parse(string str) + { + Contracts.AssertNonEmpty(str); + + var res = new Column(); + if (res.TryParse(str)) + return res; + return null; + } + + public bool TryUnparse(StringBuilder sb) + { + Contracts.AssertValue(sb); + if (NumTopic != null || AlphaSum != null || Beta != null || Mhstep != null || NumIterations != null || LikelihoodInterval != null || + NumThreads != null || NumMaxDocToken != null || NumSummaryTermPerTopic != null || ResetRandomGenerator != null) + return false; + return TryUnparseCore(sb); + } + } + + private sealed class ColInfoEx + { + public readonly int NumTopic; + public readonly Single AlphaSum; + public readonly Single Beta; + public readonly int MHStep; + public readonly int NumIter; + public readonly int LikelihoodInterval; + public readonly int NumThread; + public readonly int NumMaxDocToken; + public readonly int NumSummaryTermPerTopic; + public readonly int NumBurninIter; + public readonly bool ResetRandomGenerator; + + public ColInfoEx(IExceptionContext ectx, Column item, Arguments args) + { + Contracts.AssertValue(ectx); + + NumTopic = item.NumTopic ?? args.NumTopic; + Contracts.CheckUserArg(NumTopic > 0, nameof(item.NumTopic), "Must be positive."); + + AlphaSum = item.AlphaSum ?? args.AlphaSum; + + Beta = item.Beta ?? args.Beta; + + MHStep = item.Mhstep ?? args.Mhstep; + ectx.CheckUserArg(MHStep > 0, nameof(item.Mhstep), "Must be positive."); + + NumIter = item.NumIterations ?? args.NumIterations; + ectx.CheckUserArg(NumIter > 0, nameof(item.NumIterations), "Must be positive."); + + LikelihoodInterval = item.LikelihoodInterval ?? args.LikelihoodInterval; + ectx.CheckUserArg(LikelihoodInterval > 0, nameof(item.LikelihoodInterval), "Must be positive."); + + NumThread = item.NumThreads ?? args.NumThreads ?? 0; + ectx.CheckUserArg(NumThread >= 0, nameof(item.NumThreads), "Must be positive or zero."); + + NumMaxDocToken = item.NumMaxDocToken ?? args.NumMaxDocToken; + ectx.CheckUserArg(NumMaxDocToken > 0, nameof(item.NumMaxDocToken), "Must be positive."); + + NumSummaryTermPerTopic = item.NumSummaryTermPerTopic ?? args.NumSummaryTermPerTopic; + ectx.CheckUserArg(NumSummaryTermPerTopic > 0, nameof(item.NumSummaryTermPerTopic), "Must be positive"); + + NumBurninIter = item.NumBurninIterations ?? args.NumBurninIterations; + ectx.CheckUserArg(NumBurninIter >= 0, nameof(item.NumBurninIterations), "Must be non-negative."); + + ResetRandomGenerator = item.ResetRandomGenerator ?? args.ResetRandomGenerator; + } + + public ColInfoEx(IExceptionContext ectx, ModelLoadContext ctx) + { + Contracts.AssertValue(ectx); + ectx.AssertValue(ctx); + + // *** Binary format *** + // int NumTopic; + // Single AlphaSum; + // Single Beta; + // int MHStep; + // int NumIter; + // int LikelihoodInterval; + // int NumThread; + // int NumMaxDocToken; + // int NumSummaryTermPerTopic; + // int NumBurninIter; + // byte ResetRandomGenerator; + + NumTopic = ctx.Reader.ReadInt32(); + ectx.CheckDecode(NumTopic > 0); + + AlphaSum = ctx.Reader.ReadSingle(); + + Beta = ctx.Reader.ReadSingle(); + + MHStep = ctx.Reader.ReadInt32(); + ectx.CheckDecode(MHStep > 0); + + NumIter = ctx.Reader.ReadInt32(); + ectx.CheckDecode(NumIter > 0); + + LikelihoodInterval = ctx.Reader.ReadInt32(); + ectx.CheckDecode(LikelihoodInterval > 0); + + NumThread = ctx.Reader.ReadInt32(); + ectx.CheckDecode(NumThread >= 0); + + NumMaxDocToken = ctx.Reader.ReadInt32(); + ectx.CheckDecode(NumMaxDocToken > 0); + + NumSummaryTermPerTopic = ctx.Reader.ReadInt32(); + ectx.CheckDecode(NumSummaryTermPerTopic > 0); + + NumBurninIter = ctx.Reader.ReadInt32(); + ectx.CheckDecode(NumBurninIter >= 0); + + ResetRandomGenerator = ctx.Reader.ReadBoolByte(); + } + + public void Save(ModelSaveContext ctx) + { + Contracts.AssertValue(ctx); + + // *** Binary format *** + // int NumTopic; + // Single AlphaSum; + // Single Beta; + // int MHStep; + // int NumIter; + // int LikelihoodInterval; + // int NumThread; + // int NumMaxDocToken; + // int NumSummaryTermPerTopic; + // int NumBurninIter; + // byte ResetRandomGenerator; + + ctx.Writer.Write(NumTopic); + ctx.Writer.Write(AlphaSum); + ctx.Writer.Write(Beta); + ctx.Writer.Write(MHStep); + ctx.Writer.Write(NumIter); + ctx.Writer.Write(LikelihoodInterval); + ctx.Writer.Write(NumThread); + ctx.Writer.Write(NumMaxDocToken); + ctx.Writer.Write(NumSummaryTermPerTopic); + ctx.Writer.Write(NumBurninIter); + ctx.Writer.WriteBoolByte(ResetRandomGenerator); + } + } + + public const string LoaderSignature = "LdaTransform"; + private static VersionInfo GetVersionInfo() + { + return new VersionInfo( + modelSignature: "LIGHTLDA", + verWrittenCur: 0x00010001, // Initial + verReadableCur: 0x00010001, + verWeCanReadBack: 0x00010001, + loaderSignature: LoaderSignature); + } + + private readonly ColInfoEx[] _exes; + private readonly LdaState[] _ldas; + private readonly ColumnType[] _types; + private readonly bool _saveText; + + private const string RegistrationName = "LightLda"; + private const string WordTopicModelFilename = "word_topic_summary.txt"; + internal const string Summary = "The LDA transform implements LightLDA, a state-of-the-art implementation of Latent Dirichlet Allocation."; + internal const string UserName = "Latent Dirichlet Allocation Transform"; + internal const string ShortName = "LightLda"; + + public LdaTransform(IHostEnvironment env, Arguments args, IDataView input) + : base(env, RegistrationName, args.Column, input, TestType) + { + Host.CheckValue(args, nameof(args)); + Host.CheckUserArg(args.NumTopic > 0, nameof(args.NumTopic), "Must be positive."); + Host.CheckValue(input, nameof(input)); + Host.CheckUserArg(Utils.Size(args.Column) > 0, nameof(args.Column)); + _exes = new ColInfoEx[Infos.Length]; + _types = new ColumnType[Infos.Length]; + _ldas = new LdaState[Infos.Length]; + _saveText = args.OutputTopicWordSummary; + for (int i = 0; i < Infos.Length; i++) + { + var ex = new ColInfoEx(Host, args.Column[i], args); + _exes[i] = ex; + _types[i] = new VectorType(NumberType.Float, ex.NumTopic); + } + using (var ch = Host.Start("Train")) + { + Train(ch, input, _ldas); + ch.Done(); + } + Metadata.Seal(); + } + + private void Dispose(bool disposing) + { + if (_ldas != null) + { + foreach (var state in _ldas) + state?.Dispose(); + } + if (disposing) + GC.SuppressFinalize(this); + } + + public void Dispose() + { + Dispose(true); + } + + ~LdaTransform() + { + Dispose(false); + } + + private LdaTransform(IHost host, ModelLoadContext ctx, IDataView input) + : base(host, ctx, input, TestType) + { + Host.AssertValue(ctx); + + // *** Binary format *** + // + // + // ldaState[num infos]: The LDA parameters + + // Note: infos.length would be just one in most cases. + _exes = new ColInfoEx[Infos.Length]; + _ldas = new LdaState[Infos.Length]; + _types = new ColumnType[Infos.Length]; + for (int i = 0; i < _ldas.Length; i++) + { + _ldas[i] = new LdaState(Host, ctx); + _exes[i] = _ldas[i].InfoEx; + _types[i] = new VectorType(NumberType.Float, _ldas[i].InfoEx.NumTopic); + } + using (var ent = ctx.Repository.OpenEntryOrNull("model", WordTopicModelFilename)) + { + _saveText = ent != null; + } + Metadata.Seal(); + } + + public static LdaTransform Create(IHostEnvironment env, ModelLoadContext ctx, IDataView input) + { + Contracts.CheckValue(env, nameof(env)); + var h = env.Register(RegistrationName); + + h.CheckValue(ctx, nameof(ctx)); + ctx.CheckAtModel(GetVersionInfo()); + h.CheckValue(input, nameof(input)); + + return h.Apply( + "Loading Model", + ch => + { + // *** Binary Format *** + // int: sizeof(Float) + // + int cbFloat = ctx.Reader.ReadInt32(); + h.CheckDecode(cbFloat == sizeof(Float)); + return new LdaTransform(h, ctx, input); + }); + } + + public string GetTopicSummary() + { + StringWriter writer = new StringWriter(); + VBuffer slotNames = default(VBuffer); + for (int i = 0; i < _ldas.Length; i++) + { + GetSlotNames(i, ref slotNames); + _ldas[i].GetTopicSummaryWriter(slotNames)(writer); + writer.WriteLine(); + } + return writer.ToString(); + } + + public override void Save(ModelSaveContext ctx) + { + Host.CheckValue(ctx, nameof(ctx)); + ctx.CheckAtModel(); + ctx.SetVersionInfo(GetVersionInfo()); + + // *** Binary format *** + // int: sizeof(Float) + // + // ldaState[num infos]: The LDA parameters + + ctx.Writer.Write(sizeof(Float)); + SaveBase(ctx); + Host.Assert(_ldas.Length == Infos.Length); + VBuffer slotNames = default(VBuffer); + for (int i = 0; i < _ldas.Length; i++) + { + GetSlotNames(i, ref slotNames); + _ldas[i].Save(ctx, _saveText, slotNames); + } + } + + private void GetSlotNames(int iinfo, ref VBuffer dst) + { + Host.Assert(0 <= iinfo && iinfo < Infos.Length); + if (Source.Schema.HasSlotNames(Infos[iinfo].Source, Infos[iinfo].TypeSrc.ValueCount)) + Source.Schema.GetMetadata(MetadataUtils.Kinds.SlotNames, Infos[iinfo].Source, ref dst); + else + dst = default(VBuffer); + } + + private static string TestType(ColumnType t) + { + // LDA consumes term frequency vectors, so I am assuming VBuffer is an appropriate input type. + // It must also be of known size for the sake of the LDA trainer initialization. + if (t.IsKnownSizeVector && t.ItemType is NumberType) + return null; + return "Expected vector of number type of known size."; + } + + private static int GetFrequency(double value) + { + int result = (int)value; + if (!(result == value && result >= 0)) + return -1; + return result; + } + + private void Train(IChannel ch, IDataView trainingData, LdaState[] states) + { + Host.AssertValue(ch); + ch.AssertValue(trainingData); + ch.AssertValue(states); + ch.Assert(states.Length == Infos.Length); + + bool[] activeColumns = new bool[trainingData.Schema.ColumnCount]; + int[] numVocabs = new int[Infos.Length]; + + for (int i = 0; i < Infos.Length; i++) + { + activeColumns[Infos[i].Source] = true; + numVocabs[i] = 0; + } + + //the current lda needs the memory allocation before feedin data, so needs two sweeping of the data, + //one for the pre-calc memory, one for feedin data really + //another solution can be prepare these two value externally and put them in the beginning of the input file. + long[] corpusSize = new long[Infos.Length]; + int[] numDocArray = new int[Infos.Length]; + + using (var cursor = trainingData.GetRowCursor(col => activeColumns[col])) + { + var getters = new ValueGetter>[Utils.Size(Infos)]; + for (int i = 0; i < Infos.Length; i++) + { + corpusSize[i] = 0; + numDocArray[i] = 0; + getters[i] = RowCursorUtils.GetVecGetterAs(NumberType.R8, cursor, Infos[i].Source); + } + VBuffer src = default(VBuffer); + long rowCount = 0; + + while (cursor.MoveNext()) + { + ++rowCount; + for (int i = 0; i < Infos.Length; i++) + { + int docSize = 0; + getters[i](ref src); + + // compute term, doc instance#. + for (int termID = 0; termID < src.Count; termID++) + { + int termFreq = GetFrequency(src.Values[termID]); + if (termFreq < 0) + { + // Ignore this row. + docSize = 0; + break; + } + + if (docSize >= _exes[i].NumMaxDocToken - termFreq) + break; //control the document length + + //if legal then add the term + docSize += termFreq; + } + + // Ignore empty doc + if (docSize == 0) + continue; + + numDocArray[i]++; + corpusSize[i] += docSize * 2 + 1; // in the beggining of each doc, there is a cursor variable + + // increase numVocab if needed. + if (numVocabs[i] < src.Length) + numVocabs[i] = src.Length; + } + } + + for (int i = 0; i < Infos.Length; ++i) + { + if (numDocArray[i] != rowCount) + { + ch.Assert(numDocArray[i] < rowCount); + ch.Warning($"Column '{Infos[i].Name}' has skipped {rowCount - numDocArray[i]} of {rowCount} rows either empty or with negative, non-finite, or fractional values."); + } + } + } + + // Initialize all LDA states + for (int i = 0; i < Infos.Length; i++) + { + var state = new LdaState(Host, _exes[i], numVocabs[i]); + if (numDocArray[i] == 0 || corpusSize[i] == 0) + throw ch.Except("The specified documents are all empty in column '{0}'.", Infos[i].Name); + + state.AllocateDataMemory(numDocArray[i], corpusSize[i]); + states[i] = state; + } + + using (var cursor = trainingData.GetRowCursor(col => activeColumns[col])) + { + int[] docSizeCheck = new int[Infos.Length]; + // This could be optimized so that if multiple trainers consume the same column, it is + // fed into the train method once. + var getters = new ValueGetter>[Utils.Size(Infos)]; + for (int i = 0; i < Infos.Length; i++) + { + docSizeCheck[i] = 0; + getters[i] = RowCursorUtils.GetVecGetterAs(NumberType.R8, cursor, Infos[i].Source); + } + + VBuffer src = default(VBuffer); + + while (cursor.MoveNext()) + { + for (int i = 0; i < Infos.Length; i++) + { + getters[i](ref src); + docSizeCheck[i] += states[i].FeedTrain(Host, ref src); + } + } + for (int i = 0; i < Infos.Length; i++) + { + Host.Assert(corpusSize[i] == docSizeCheck[i]); + states[i].CompleteTrain(); + } + } + } + + private sealed class LdaState : IDisposable + { + public readonly ColInfoEx InfoEx; + private readonly int _numVocab; + private readonly object _preparationSyncRoot; + private readonly object _testSyncRoot; + private bool _predictionPreparationDone; + private LdaSingleBox _ldaTrainer; + + private LdaState() + { + _preparationSyncRoot = new object(); + _testSyncRoot = new object(); + } + + public LdaState(IExceptionContext ectx, ColInfoEx ex, int numVocab) + : this() + { + Contracts.AssertValue(ectx); + ectx.AssertValue(ex, "ex"); + + ectx.Assert(numVocab >= 0); + InfoEx = ex; + _numVocab = numVocab; + + _ldaTrainer = new LdaSingleBox( + InfoEx.NumTopic, + numVocab, /* Need to set number of vocabulary here */ + InfoEx.AlphaSum, + InfoEx.Beta, + InfoEx.NumIter, + InfoEx.LikelihoodInterval, + InfoEx.NumThread, + InfoEx.MHStep, + InfoEx.NumSummaryTermPerTopic, + false, + InfoEx.NumMaxDocToken); + } + + public LdaState(IExceptionContext ectx, ModelLoadContext ctx) + : this() + { + ectx.AssertValue(ctx); + + // *** Binary format *** + // + // int: vocabnum + // long: memblocksize + // long: aliasMemBlockSize + // (serializing term by term, for one term) + // int: term_id, int: topic_num, KeyValuePair[]: termTopicVector + + InfoEx = new ColInfoEx(ectx, ctx); + + _numVocab = ctx.Reader.ReadInt32(); + ectx.CheckDecode(_numVocab > 0); + + long memBlockSize = ctx.Reader.ReadInt64(); + ectx.CheckDecode(memBlockSize > 0); + + long aliasMemBlockSize = ctx.Reader.ReadInt64(); + ectx.CheckDecode(aliasMemBlockSize > 0); + + _ldaTrainer = new LdaSingleBox( + InfoEx.NumTopic, + _numVocab, /* Need to set number of vocabulary here */ + InfoEx.AlphaSum, + InfoEx.Beta, + InfoEx.NumIter, + InfoEx.LikelihoodInterval, + InfoEx.NumThread, + InfoEx.MHStep, + InfoEx.NumSummaryTermPerTopic, + false, + InfoEx.NumMaxDocToken); + + _ldaTrainer.AllocateModelMemory(_numVocab, InfoEx.NumTopic, memBlockSize, aliasMemBlockSize); + + for (int i = 0; i < _numVocab; i++) + { + int termID = ctx.Reader.ReadInt32(); + ectx.CheckDecode(termID >= 0); + int termTopicNum = ctx.Reader.ReadInt32(); + ectx.CheckDecode(termTopicNum >= 0); + + int[] topicId = new int[termTopicNum]; + int[] topicProb = new int[termTopicNum]; + + for (int j = 0; j < termTopicNum; j++) + { + topicId[j] = ctx.Reader.ReadInt32(); + topicProb[j] = ctx.Reader.ReadInt32(); + } + + //set the topic into _ldaTrainer inner topic table + _ldaTrainer.SetModel(termID, topicId, topicProb, termTopicNum); + } + + //do the preparation + if (!_predictionPreparationDone) + { + _ldaTrainer.InitializeBeforeTest(); + _predictionPreparationDone = true; + } + } + + public Action GetTopicSummaryWriter(VBuffer mapping) + { + Action writeAction; + + if (mapping.Length == 0) + { + writeAction = + writer => + { + for (int i = 0; i < _ldaTrainer.NumTopic; i++) + { + KeyValuePair[] topicSummaryVector = _ldaTrainer.GetTopicSummary(i); + writer.Write("{0}\t{1}\t", i, topicSummaryVector.Length); + foreach (KeyValuePair p in topicSummaryVector) + writer.Write("{0}:{1}\t", p.Key, p.Value); + writer.WriteLine(); + } + }; + } + else + { + writeAction = + writer => + { + DvText slotName = default(DvText); + for (int i = 0; i < _ldaTrainer.NumTopic; i++) + { + KeyValuePair[] topicSummaryVector = _ldaTrainer.GetTopicSummary(i); + writer.Write("{0}\t{1}\t", i, topicSummaryVector.Length); + foreach (KeyValuePair p in topicSummaryVector) + { + mapping.GetItemOrDefault(p.Key, ref slotName); + writer.Write("{0}[{1}]:{2}\t", p.Key, slotName, p.Value); + } + writer.WriteLine(); + } + }; + } + + return writeAction; + } + + public void Save(ModelSaveContext ctx, bool saveText, VBuffer mapping) + { + Contracts.AssertValue(ctx); + long memBlockSize = 0; + long aliasMemBlockSize = 0; + _ldaTrainer.GetModelStat(out memBlockSize, out aliasMemBlockSize); + + // *** Binary format *** + // + // int: vocabnum + // long: memblocksize + // long: aliasMemBlockSize + // (serializing term by term, for one term) + // int: term_id, int: topic_num, KeyValuePair[]: termTopicVector + + InfoEx.Save(ctx); + ctx.Writer.Write(_ldaTrainer.NumVocab); + ctx.Writer.Write(memBlockSize); + ctx.Writer.Write(aliasMemBlockSize); + + //save model from this interface + for (int i = 0; i < _ldaTrainer.NumVocab; i++) + { + KeyValuePair[] termTopicVector = _ldaTrainer.GetModel(i); + + //write the topic to disk through ctx + ctx.Writer.Write(i); //term_id + ctx.Writer.Write(termTopicVector.Length); + + foreach (KeyValuePair p in termTopicVector) + { + ctx.Writer.Write(p.Key); + ctx.Writer.Write(p.Value); + } + } + + var writeAction = GetTopicSummaryWriter(mapping); + + // save word-topic summary in text + if (saveText) + ctx.SaveTextStream(WordTopicModelFilename, writeAction); + } + + public void AllocateDataMemory(int docNum, long corpusSize) + { + _ldaTrainer.AllocateDataMemory(docNum, corpusSize); + } + + public int FeedTrain(IExceptionContext ectx, ref VBuffer input) + { + Contracts.AssertValue(ectx); + + // REVIEW: Input the counts to your trainer here. This + // is called multiple times. + + int docSize = 0; + int termNum = 0; + + for (int i = 0; i < input.Count; i++) + { + int termFreq = GetFrequency(input.Values[i]); + if (termFreq < 0) + { + // Ignore this row. + return 0; + } + if (docSize >= InfoEx.NumMaxDocToken - termFreq) + break; + + // If legal then add the term. + docSize += termFreq; + termNum++; + } + + // Ignore empty doc. + if (docSize == 0) + return 0; + + int actualSize = 0; + if (input.IsDense) + actualSize = _ldaTrainer.LoadDocDense(input.Values, termNum, input.Length); + else + actualSize = _ldaTrainer.LoadDoc(input.Indices, input.Values, termNum, input.Length); + + ectx.Assert(actualSize == 2 * docSize + 1, string.Format("The doc size are distinct. Actual: {0}, Expected: {1}", actualSize, 2 * docSize + 1)); + return actualSize; + } + + public void CompleteTrain() + { + //allocate all kinds of in memory sample tables + _ldaTrainer.InitializeBeforeTrain(); + + //call native lda trainer to perform the multi-thread training + _ldaTrainer.Train(""); /* Need to pass in an empty string */ + } + + public void Output(ref VBuffer src, ref VBuffer dst, int numBurninIter, bool reset) + { + // Prediction for a single document. + // LdaSingleBox.InitializeBeforeTest() is NOT thread-safe. + if (!_predictionPreparationDone) + { + lock (_preparationSyncRoot) + { + if (!_predictionPreparationDone) + { + //do some preparation for building tables in native c++ + _ldaTrainer.InitializeBeforeTest(); + _predictionPreparationDone = true; + } + } + } + + int len = InfoEx.NumTopic; + var values = dst.Values; + var indices = dst.Indices; + if (src.Count == 0) + { + dst = new VBuffer(len, 0, values, indices); + return; + } + + // Make sure all the frequencies are valid and truncate if the sum gets too large. + int docSize = 0; + int termNum = 0; + for (int i = 0; i < src.Count; i++) + { + int termFreq = GetFrequency(src.Values[i]); + if (termFreq < 0) + { + // REVIEW: Should this log a warning message? And what should it produce? + // It currently produces a vbuffer of all NA values. + // REVIEW: Need a utility method to do this... + if (Utils.Size(values) < len) + values = new Float[len]; + for (int k = 0; k < len; k++) + values[k] = Float.NaN; + dst = new VBuffer(len, values, indices); + return; + } + + if (docSize >= InfoEx.NumMaxDocToken - termFreq) + break; + + docSize += termFreq; + termNum++; + } + + // REVIEW: Too much memory allocation here on each prediction. + List> retTopics; + if (src.IsDense) + retTopics = _ldaTrainer.TestDocDense(src.Values, termNum, numBurninIter, reset); + else + retTopics = _ldaTrainer.TestDoc(src.Indices.Take(src.Count).ToArray(), src.Values.Take(src.Count).ToArray(), termNum, numBurninIter, reset); + + int count = retTopics.Count; + Contracts.Assert(count <= len); + if (Utils.Size(values) < count) + values = new Float[count]; + if (count < len && Utils.Size(indices) < count) + indices = new int[count]; + + double normalizer = 0; + for (int i = 0; i < count; i++) + { + int index = retTopics[i].Key; + Float value = retTopics[i].Value; + Contracts.Assert(value >= 0); + Contracts.Assert(0 <= index && index < len); + if (count < len) + { + Contracts.Assert(i == 0 || indices[i - 1] < index); + indices[i] = index; + } + else + Contracts.Assert(index == i); + + values[i] = value; + normalizer += value; + } + + if (normalizer > 0) + { + for (int i = 0; i < count; i++) + values[i] = (Float)(values[i] / normalizer); + } + dst = new VBuffer(len, count, values, indices); + } + + public void Dispose() + { + _ldaTrainer.Dispose(); + } + } + + private ColumnType[] InitColumnTypes(int numTopics) + { + Host.Assert(Utils.Size(Infos) > 0); + var types = new ColumnType[Infos.Length]; + for (int c = 0; c < Infos.Length; c++) + types[c] = new VectorType(NumberType.Float, numTopics); + return types; + } + + protected override ColumnType GetColumnTypeCore(int iinfo) + { + Host.Assert(0 <= iinfo & iinfo < Utils.Size(_types)); + return _types[iinfo]; + } + + protected override Delegate GetGetterCore(IChannel ch, IRow input, int iinfo, out Action disposer) + { + Host.AssertValueOrNull(ch); + Host.AssertValue(input); + Host.Assert(0 <= iinfo && iinfo < Infos.Length); + disposer = null; + + return GetTopic(input, iinfo); + } + + private ValueGetter> GetTopic(IRow input, int iinfo) + { + var getSrc = RowCursorUtils.GetVecGetterAs(NumberType.R8, input, Infos[iinfo].Source); + var src = default(VBuffer); + var lda = _ldas[iinfo]; + int numBurninIter = lda.InfoEx.NumBurninIter; + bool reset = lda.InfoEx.ResetRandomGenerator; + return + (ref VBuffer dst) => + { + // REVIEW: This will work, but there are opportunities for caching + // based on input.Counter that are probably worthwhile given how long inference takes. + getSrc(ref src); + lda.Output(ref src, ref dst, numBurninIter, reset); + }; + } + } +} diff --git a/src/Microsoft.ML.UniversalModelFormat/LotusIR/Graph.cs b/src/Microsoft.ML.UniversalModelFormat/LotusIR/Graph.cs deleted file mode 100644 index 9261f236a3..0000000000 --- a/src/Microsoft.ML.UniversalModelFormat/LotusIR/Graph.cs +++ /dev/null @@ -1,6375 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: graph.proto -#pragma warning disable 1591, 0612, 3021 -#region Designer generated code - -using pb = global::Google.Protobuf; -using pbc = global::Google.Protobuf.Collections; -using pbr = global::Google.Protobuf.Reflection; -using scg = global::System.Collections.Generic; -namespace LotusIR { - - /// Holder for reflection information generated from graph.proto - public static partial class GraphReflection { - - #region Descriptor - /// File descriptor for graph.proto - public static pbr::FileDescriptor Descriptor { - get { return descriptor; } - } - private static pbr::FileDescriptor descriptor; - - static GraphReflection() { - byte[] descriptorData = global::System.Convert.FromBase64String( - string.Concat( - "CgtncmFwaC5wcm90bxIHTG90dXNJUiKpAwoOQXR0cmlidXRlUHJvdG8SDAoE", - "bmFtZRgBIAEoCRIJCgFmGAIgASgCEgkKAWkYAyABKAMSCQoBcxgEIAEoDBIf", - "CgF0GAUgASgLMhQuTG90dXNJUi5UZW5zb3JQcm90bxIeCgFnGAYgASgLMhMu", - "TG90dXNJUi5HcmFwaFByb3RvEg4KBmZsb2F0cxgHIAMoAhIMCgRpbnRzGAgg", - 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new pbr::GeneratedClrTypeInfo(typeof(global::LotusIR.GraphProto), global::LotusIR.GraphProto.Parser, new[]{ "Node", "Name", "Initializer", "DocString", "Input", "Output", "ValueInfo", "Function", "Operator", "ImportedLibraries" }, null, null, null), - new pbr::GeneratedClrTypeInfo(typeof(global::LotusIR.TensorProto), global::LotusIR.TensorProto.Parser, new[]{ "Dims", "DataType", "Segment", "FloatData", "Int32Data", "StringData", "Int64Data", "Name", "RawData", "DoubleData", "Uint64Data" }, null, new[]{ typeof(global::LotusIR.TensorProto.Types.DataType) }, new pbr::GeneratedClrTypeInfo[] { new pbr::GeneratedClrTypeInfo(typeof(global::LotusIR.TensorProto.Types.Segment), global::LotusIR.TensorProto.Types.Segment.Parser, new[]{ "Begin", "End" }, null, null, null)}), - new pbr::GeneratedClrTypeInfo(typeof(global::LotusIR.SparseTensorProto), global::LotusIR.SparseTensorProto.Parser, new[]{ "Dims", "Indices", "Values" }, null, null, null), - new pbr::GeneratedClrTypeInfo(typeof(global::LotusIR.TypeProto), global::LotusIR.TypeProto.Parser, new[]{ "TensorType", "SparseTensorType", "HandleType", "TupleType", "SeqType", "MapType" }, new[]{ "Value" }, null, new pbr::GeneratedClrTypeInfo[] { new pbr::GeneratedClrTypeInfo(typeof(global::LotusIR.TypeProto.Types.TensorShapeProto), global::LotusIR.TypeProto.Types.TensorShapeProto.Parser, new[]{ "Dim" }, null, null, new pbr::GeneratedClrTypeInfo[] { new pbr::GeneratedClrTypeInfo(typeof(global::LotusIR.TypeProto.Types.TensorShapeProto.Types.Dimension), global::LotusIR.TypeProto.Types.TensorShapeProto.Types.Dimension.Parser, new[]{ "DimValue", "DimParam" }, new[]{ "Value" }, null, null)}), - new pbr::GeneratedClrTypeInfo(typeof(global::LotusIR.TypeProto.Types.TensorTypeProto), global::LotusIR.TypeProto.Types.TensorTypeProto.Parser, new[]{ "ElemType", "Shape" }, null, null, null), - new pbr::GeneratedClrTypeInfo(typeof(global::LotusIR.TypeProto.Types.SparseTensorTypeProto), global::LotusIR.TypeProto.Types.SparseTensorTypeProto.Parser, new[]{ "ElemType", "Shape" }, null, null, null), - new pbr::GeneratedClrTypeInfo(typeof(global::LotusIR.TypeProto.Types.HandleTypeProto), global::LotusIR.TypeProto.Types.HandleTypeProto.Parser, null, null, null, null), - new pbr::GeneratedClrTypeInfo(typeof(global::LotusIR.TypeProto.Types.TupleTypeProto), global::LotusIR.TypeProto.Types.TupleTypeProto.Parser, new[]{ "ElemType" }, null, null, null), - new pbr::GeneratedClrTypeInfo(typeof(global::LotusIR.TypeProto.Types.SeqTypeProto), global::LotusIR.TypeProto.Types.SeqTypeProto.Parser, new[]{ "ElemType" }, null, null, null), - new pbr::GeneratedClrTypeInfo(typeof(global::LotusIR.TypeProto.Types.MapTypeProto), global::LotusIR.TypeProto.Types.MapTypeProto.Parser, new[]{ "KeyType", "ValueType" }, null, null, null)}), - new pbr::GeneratedClrTypeInfo(typeof(global::LotusIR.ValueProto), global::LotusIR.ValueProto.Parser, new[]{ "DenseTensor", "SparseTensor", "Handle", "Tuple", "Seq", "Map" }, new[]{ "Value" }, null, new pbr::GeneratedClrTypeInfo[] { new pbr::GeneratedClrTypeInfo(typeof(global::LotusIR.ValueProto.Types.HandleProto), global::LotusIR.ValueProto.Types.HandleProto.Parser, new[]{ "Uid" }, null, null, null), - new pbr::GeneratedClrTypeInfo(typeof(global::LotusIR.ValueProto.Types.TupleProto), global::LotusIR.ValueProto.Types.TupleProto.Parser, new[]{ "Elems" }, null, null, null), - new pbr::GeneratedClrTypeInfo(typeof(global::LotusIR.ValueProto.Types.SequenceProto), global::LotusIR.ValueProto.Types.SequenceProto.Parser, new[]{ "Elems" }, null, null, null), - new pbr::GeneratedClrTypeInfo(typeof(global::LotusIR.ValueProto.Types.MapProto), global::LotusIR.ValueProto.Types.MapProto.Parser, new[]{ "Keys", "Values" }, null, null, null)}), - new pbr::GeneratedClrTypeInfo(typeof(global::LotusIR.ParameterDeclProto), global::LotusIR.ParameterDeclProto.Parser, new[]{ "Name", "Type", "DocString" }, null, null, null), - new pbr::GeneratedClrTypeInfo(typeof(global::LotusIR.FunctionDefProto), global::LotusIR.FunctionDefProto.Parser, new[]{ "Name", "InputParams", "OutputParams", "Node", "Attr", "DocString" }, null, null, null), - new pbr::GeneratedClrTypeInfo(typeof(global::LotusIR.SignatureDeclProto), global::LotusIR.SignatureDeclProto.Parser, new[]{ "InputParams", "OutputParams", "InputAttributes", "DocString" }, null, null, null), - new pbr::GeneratedClrTypeInfo(typeof(global::LotusIR.OperatorDeclProto), global::LotusIR.OperatorDeclProto.Parser, new[]{ "Name", "Signature", "DocString" }, null, null, null), - new pbr::GeneratedClrTypeInfo(typeof(global::LotusIR.LibraryProto), global::LotusIR.LibraryProto.Parser, new[]{ "IrVersion", "ProducerVersion", "ProducerTag", "ModelVersion", "ModelAuthor", "ModelLicense", "Name", "Domain", "DocString", "Operator", "Function", "ImportedLibraries" }, null, null, null) - })); - } - #endregion - - } - #region Enums - /// - /// To be compatible with both proto2 and proto3, we will use a version number - /// that is not defined by the default value but an explicit enum number. - /// - public enum Version { - /// - /// Just for proto3 compatibility - /// - [pbr::OriginalName("NONE")] None = 0, - /// - /// The version field is always serialized and we will use it to store the - /// version that the graph is generated from. This helps us set up version - /// control. We should use version as - /// xx(major) - xx(minor) - xxxx(bugfix) - /// and we are starting with 00000001. - /// - [pbr::OriginalName("IR_VERSION")] IrVersion = 1, - } - - #endregion - - #region Messages - /// - /// A named attribute containing either singular float, integer, string - /// and tensor values, or repeated float, integer, string and tensor values. - /// An AttributeProto MUST contain the name field, and *only one* of the - /// following content fields, effectively enforcing a C/C++ union equivalent. - /// - public sealed partial class AttributeProto : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new AttributeProto()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::LotusIR.GraphReflection.Descriptor.MessageTypes[0]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public AttributeProto() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public AttributeProto(AttributeProto other) : this() { - name_ = other.name_; - f_ = other.f_; - i_ = other.i_; - s_ = other.s_; - T = other.t_ != null ? other.T.Clone() : null; - G = other.g_ != null ? other.G.Clone() : null; - floats_ = other.floats_.Clone(); - ints_ = other.ints_.Clone(); - strings_ = other.strings_.Clone(); - tensors_ = other.tensors_.Clone(); - graphs_ = other.graphs_.Clone(); - Type = other.type_ != null ? other.Type.Clone() : null; - types_ = other.types_.Clone(); - Shape = other.shape_ != null ? other.Shape.Clone() : null; - shapes_ = other.shapes_.Clone(); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public AttributeProto Clone() { - return new AttributeProto(this); - } - - /// Field number for the "name" field. - public const int NameFieldNumber = 1; - private string name_ = ""; - /// - /// The name field MUST be present for this version of the IR. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string Name { - get { return name_; } - set { - name_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "f" field. - public const int FFieldNumber = 2; - private float f_; - /// - /// float - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public float F { - get { return f_; } - set { - f_ = value; - } - } - - /// Field number for the "i" field. - public const int IFieldNumber = 3; - private long i_; - /// - /// int - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public long I { - get { return i_; } - set { - i_ = value; - } - } - - /// Field number for the "s" field. - public const int SFieldNumber = 4; - private pb::ByteString s_ = pb::ByteString.Empty; - /// - /// UTF-8 string - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pb::ByteString S { - get { return s_; } - set { - s_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "t" field. - public const int TFieldNumber = 5; - private global::LotusIR.TensorProto t_; - /// - /// tensor value - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::LotusIR.TensorProto T { - get { return t_; } - set { - t_ = value; - } - } - - /// Field number for the "g" field. - public const int GFieldNumber = 6; - private global::LotusIR.GraphProto g_; - /// - /// graph - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::LotusIR.GraphProto G { - get { return g_; } - set { - g_ = value; - } - } - - /// Field number for the "floats" field. - public const int FloatsFieldNumber = 7; - private static readonly pb::FieldCodec _repeated_floats_codec - = pb::FieldCodec.ForFloat(58); - private readonly pbc::RepeatedField floats_ = new pbc::RepeatedField(); - /// - /// list of floats - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Floats { - get { return floats_; } - } - - /// Field number for the "ints" field. - public const int IntsFieldNumber = 8; - private static readonly pb::FieldCodec _repeated_ints_codec - = pb::FieldCodec.ForInt64(66); - private readonly pbc::RepeatedField ints_ = new pbc::RepeatedField(); - /// - /// list of ints - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Ints { - get { return ints_; } - } - - /// Field number for the "strings" field. - public const int StringsFieldNumber = 9; - private static readonly pb::FieldCodec _repeated_strings_codec - = pb::FieldCodec.ForBytes(74); - private readonly pbc::RepeatedField strings_ = new pbc::RepeatedField(); - /// - /// list of UTF-8 strings - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Strings { - get { return strings_; } - } - - /// Field number for the "tensors" field. - public const int TensorsFieldNumber = 10; - private static readonly pb::FieldCodec _repeated_tensors_codec - = pb::FieldCodec.ForMessage(82, global::LotusIR.TensorProto.Parser); - private readonly pbc::RepeatedField tensors_ = new pbc::RepeatedField(); - /// - /// list of tensors - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Tensors { - get { return tensors_; } - } - - /// Field number for the "graphs" field. - public const int GraphsFieldNumber = 11; - private static readonly pb::FieldCodec _repeated_graphs_codec - = pb::FieldCodec.ForMessage(90, global::LotusIR.GraphProto.Parser); - private readonly pbc::RepeatedField graphs_ = new pbc::RepeatedField(); - /// - /// list of graph - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Graphs { - get { return graphs_; } - } - - /// Field number for the "type" field. - public const int TypeFieldNumber = 51; - private global::LotusIR.TypeProto type_; - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::LotusIR.TypeProto Type { - get { return type_; } - set { - type_ = value; - } - } - - /// Field number for the "types" field. - public const int Types_FieldNumber = 52; - private static readonly pb::FieldCodec _repeated_types_codec - = pb::FieldCodec.ForMessage(418, global::LotusIR.TypeProto.Parser); - private readonly pbc::RepeatedField types_ = new pbc::RepeatedField(); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Types_ { - get { return types_; } - } - - /// Field number for the "shape" field. - public const int ShapeFieldNumber = 53; - private global::LotusIR.TypeProto.Types.TensorShapeProto shape_; - /// - ///ISSUE:13807134,dbox: Do we ever see shape showing up as an attribute value? - /// If so, won't it always be accompanied by a TypeProto? - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::LotusIR.TypeProto.Types.TensorShapeProto Shape { - get { return shape_; } - set { - shape_ = value; - } - } - - /// Field number for the "shapes" field. - public const int ShapesFieldNumber = 54; - private static readonly pb::FieldCodec _repeated_shapes_codec - = pb::FieldCodec.ForMessage(434, global::LotusIR.TypeProto.Types.TensorShapeProto.Parser); - private readonly pbc::RepeatedField shapes_ = new pbc::RepeatedField(); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Shapes { - get { return shapes_; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as AttributeProto); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(AttributeProto other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if (Name != other.Name) return false; - if (F != other.F) return false; - if (I != other.I) return false; - if (S != other.S) return false; - if (!object.Equals(T, other.T)) return false; - if (!object.Equals(G, other.G)) return false; - if(!floats_.Equals(other.floats_)) return false; - if(!ints_.Equals(other.ints_)) return false; - if(!strings_.Equals(other.strings_)) return false; - if(!tensors_.Equals(other.tensors_)) return false; - if(!graphs_.Equals(other.graphs_)) return false; - if (!object.Equals(Type, other.Type)) return false; - if(!types_.Equals(other.types_)) return false; - if (!object.Equals(Shape, other.Shape)) return false; - if(!shapes_.Equals(other.shapes_)) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - if (Name.Length != 0) hash ^= Name.GetHashCode(); - if (F != 0F) hash ^= F.GetHashCode(); - if (I != 0L) hash ^= I.GetHashCode(); - if (S.Length != 0) hash ^= S.GetHashCode(); - if (t_ != null) hash ^= T.GetHashCode(); - if (g_ != null) hash ^= G.GetHashCode(); - hash ^= floats_.GetHashCode(); - hash ^= ints_.GetHashCode(); - hash ^= strings_.GetHashCode(); - hash ^= tensors_.GetHashCode(); - hash ^= graphs_.GetHashCode(); - if (type_ != null) hash ^= Type.GetHashCode(); - hash ^= types_.GetHashCode(); - if (shape_ != null) hash ^= Shape.GetHashCode(); - hash ^= shapes_.GetHashCode(); - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - if (Name.Length != 0) { - output.WriteRawTag(10); - output.WriteString(Name); - } - if (F != 0F) { - output.WriteRawTag(21); - output.WriteFloat(F); - } - if (I != 0L) { - output.WriteRawTag(24); - output.WriteInt64(I); - } - if (S.Length != 0) { - output.WriteRawTag(34); - output.WriteBytes(S); - } - if (t_ != null) { - output.WriteRawTag(42); - output.WriteMessage(T); - } - if (g_ != null) { - output.WriteRawTag(50); - output.WriteMessage(G); - } - floats_.WriteTo(output, _repeated_floats_codec); - ints_.WriteTo(output, _repeated_ints_codec); - strings_.WriteTo(output, _repeated_strings_codec); - tensors_.WriteTo(output, _repeated_tensors_codec); - graphs_.WriteTo(output, _repeated_graphs_codec); - if (type_ != null) { - output.WriteRawTag(154, 3); - output.WriteMessage(Type); - } - types_.WriteTo(output, _repeated_types_codec); - if (shape_ != null) { - output.WriteRawTag(170, 3); - output.WriteMessage(Shape); - } - shapes_.WriteTo(output, _repeated_shapes_codec); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - if (Name.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(Name); - } - if (F != 0F) { - size += 1 + 4; - } - if (I != 0L) { - size += 1 + pb::CodedOutputStream.ComputeInt64Size(I); - } - if (S.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeBytesSize(S); - } - if (t_ != null) { - size += 1 + pb::CodedOutputStream.ComputeMessageSize(T); - } - if (g_ != null) { - size += 1 + pb::CodedOutputStream.ComputeMessageSize(G); - } - size += floats_.CalculateSize(_repeated_floats_codec); - size += ints_.CalculateSize(_repeated_ints_codec); - size += strings_.CalculateSize(_repeated_strings_codec); - size += tensors_.CalculateSize(_repeated_tensors_codec); - size += graphs_.CalculateSize(_repeated_graphs_codec); - if (type_ != null) { - size += 2 + pb::CodedOutputStream.ComputeMessageSize(Type); - } - size += types_.CalculateSize(_repeated_types_codec); - if (shape_ != null) { - size += 2 + pb::CodedOutputStream.ComputeMessageSize(Shape); - } - size += shapes_.CalculateSize(_repeated_shapes_codec); - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(AttributeProto other) { - if (other == null) { - return; - } - if (other.Name.Length != 0) { - Name = other.Name; - } - if (other.F != 0F) { - F = other.F; - } - if (other.I != 0L) { - I = other.I; - } - if (other.S.Length != 0) { - S = other.S; - } - if (other.t_ != null) { - if (t_ == null) { - t_ = new global::LotusIR.TensorProto(); - } - T.MergeFrom(other.T); - } - if (other.g_ != null) { - if (g_ == null) { - g_ = new global::LotusIR.GraphProto(); - } - G.MergeFrom(other.G); - } - floats_.Add(other.floats_); - ints_.Add(other.ints_); - strings_.Add(other.strings_); - tensors_.Add(other.tensors_); - graphs_.Add(other.graphs_); - if (other.type_ != null) { - if (type_ == null) { - type_ = new global::LotusIR.TypeProto(); - } - Type.MergeFrom(other.Type); - } - types_.Add(other.types_); - if (other.shape_ != null) { - if (shape_ == null) { - shape_ = new global::LotusIR.TypeProto.Types.TensorShapeProto(); - } - Shape.MergeFrom(other.Shape); - } - shapes_.Add(other.shapes_); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 10: { - Name = input.ReadString(); - break; - } - case 21: { - F = input.ReadFloat(); - break; - } - case 24: { - I = input.ReadInt64(); - break; - } - case 34: { - S = input.ReadBytes(); - break; - } - case 42: { - if (t_ == null) { - t_ = new global::LotusIR.TensorProto(); - } - input.ReadMessage(t_); - break; - } - case 50: { - if (g_ == null) { - g_ = new global::LotusIR.GraphProto(); - } - input.ReadMessage(g_); - break; - } - case 58: - case 61: { - floats_.AddEntriesFrom(input, _repeated_floats_codec); - break; - } - case 66: - case 64: { - ints_.AddEntriesFrom(input, _repeated_ints_codec); - break; - } - case 74: { - strings_.AddEntriesFrom(input, _repeated_strings_codec); - break; - } - case 82: { - tensors_.AddEntriesFrom(input, _repeated_tensors_codec); - break; - } - case 90: { - graphs_.AddEntriesFrom(input, _repeated_graphs_codec); - break; - } - case 410: { - if (type_ == null) { - type_ = new global::LotusIR.TypeProto(); - } - input.ReadMessage(type_); - break; - } - case 418: { - types_.AddEntriesFrom(input, _repeated_types_codec); - break; - } - case 426: { - if (shape_ == null) { - shape_ = new global::LotusIR.TypeProto.Types.TensorShapeProto(); - } - input.ReadMessage(shape_); - break; - } - case 434: { - shapes_.AddEntriesFrom(input, _repeated_shapes_codec); - break; - } - } - } - } - - } - - /// - /// Defines information on value, including the name, the type, and - /// the shape of the value. - /// - public sealed partial class ValueInfoProto : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new ValueInfoProto()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::LotusIR.GraphReflection.Descriptor.MessageTypes[1]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public ValueInfoProto() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public ValueInfoProto(ValueInfoProto other) : this() { - name_ = other.name_; - Type = other.type_ != null ? other.Type.Clone() : null; - docString_ = other.docString_; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public ValueInfoProto Clone() { - return new ValueInfoProto(this); - } - - /// Field number for the "name" field. - public const int NameFieldNumber = 1; - private string name_ = ""; - /// - /// This field MUST be present in this version of the IR. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string Name { - get { return name_; } - set { - name_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "type" field. - public const int TypeFieldNumber = 2; - private global::LotusIR.TypeProto type_; - /// - /// This field MUST be present in this version of the IR. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::LotusIR.TypeProto Type { - get { return type_; } - set { - type_ = value; - } - } - - /// Field number for the "doc_string" field. - public const int DocStringFieldNumber = 3; - private string docString_ = ""; - /// - /// An human-readable documentation for this node in the graph. - /// This text MAY contain Markdown markup that conforms to http://commonmark.org/. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string DocString { - get { return docString_; } - set { - docString_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as ValueInfoProto); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(ValueInfoProto other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if (Name != other.Name) return false; - if (!object.Equals(Type, other.Type)) return false; - if (DocString != other.DocString) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - if (Name.Length != 0) hash ^= Name.GetHashCode(); - if (type_ != null) hash ^= Type.GetHashCode(); - if (DocString.Length != 0) hash ^= DocString.GetHashCode(); - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - if (Name.Length != 0) { - output.WriteRawTag(10); - output.WriteString(Name); - } - if (type_ != null) { - output.WriteRawTag(18); - output.WriteMessage(Type); - } - if (DocString.Length != 0) { - output.WriteRawTag(26); - output.WriteString(DocString); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - if (Name.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(Name); - } - if (type_ != null) { - size += 1 + pb::CodedOutputStream.ComputeMessageSize(Type); - } - if (DocString.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(DocString); - } - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(ValueInfoProto other) { - if (other == null) { - return; - } - if (other.Name.Length != 0) { - Name = other.Name; - } - if (other.type_ != null) { - if (type_ == null) { - type_ = new global::LotusIR.TypeProto(); - } - Type.MergeFrom(other.Type); - } - if (other.DocString.Length != 0) { - DocString = other.DocString; - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 10: { - Name = input.ReadString(); - break; - } - case 18: { - if (type_ == null) { - type_ = new global::LotusIR.TypeProto(); - } - input.ReadMessage(type_); - break; - } - case 26: { - DocString = input.ReadString(); - break; - } - } - } - } - - } - - /// - /// Defines a node in a computation graph. Each graph node is either an - /// operator or a function call. A node that is similar to the notion of "layer" - /// or "operator" in many deep learning frameworks. For example, it can be a - /// node of type "Conv" that takes in an image, a filter tensor and a bias - /// tensor, and produces the convolved output. - /// - /// NOTE: Control flow is defined by two built-in operators: - /// - /// Cond(p, true_input, false_input) takes three inputs, where p is a - /// boolean scalar tensor, true_input is the list of inputs to the true - /// branch of cond, and false_input is the list of inputs to the false - /// branch of cond. The true and false branches are defined as - /// functions that takes true_input and false_input as inputs respectively. - /// The two functions must have the same number of outputs, and each - /// corresponding output must have the same types, and have compatible - /// shapes. - /// - /// While(vars, consts) takes two inputs, where vars are the initial - /// values of the loop variables and consts are the values of constants - /// used inside the loop. The loop condition and loop body are defined - /// as functions. The functions take both vars and consts as inputs. - /// The loop condition function returns a boolean scalar tensor. The - /// loop body function has the form: body(vars, consts) = new_vars, - /// where new_vars are the new values of the loop variables after one - /// iteration so must match vars in terms of types and shapes. - /// - public sealed partial class NodeProto : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new NodeProto()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::LotusIR.GraphReflection.Descriptor.MessageTypes[2]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public NodeProto() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public NodeProto(NodeProto other) : this() { - input_ = other.input_.Clone(); - output_ = other.output_.Clone(); - name_ = other.name_; - opType_ = other.opType_; - attribute_ = other.attribute_.Clone(); - docString_ = other.docString_; - inputArgCount_ = other.inputArgCount_.Clone(); - controlInput_ = other.controlInput_.Clone(); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public NodeProto Clone() { - return new NodeProto(this); - } - - /// Field number for the "input" field. - public const int InputFieldNumber = 1; - private static readonly pb::FieldCodec _repeated_input_codec - = pb::FieldCodec.ForString(10); - private readonly pbc::RepeatedField input_ = new pbc::RepeatedField(); - /// - /// The named inputs of the node. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Input { - get { return input_; } - } - - /// Field number for the "output" field. - public const int OutputFieldNumber = 2; - private static readonly pb::FieldCodec _repeated_output_codec - = pb::FieldCodec.ForString(18); - private readonly pbc::RepeatedField output_ = new pbc::RepeatedField(); - /// - /// The named outputs of the node. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Output { - get { return output_; } - } - - /// Field number for the "name" field. - public const int NameFieldNumber = 3; - private string name_ = ""; - /// - /// The name of this node. - /// This field is and used to uniquely identify nodes in the graph. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string Name { - get { return name_; } - set { - name_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "op_type" field. - public const int OpTypeFieldNumber = 4; - private string opType_ = ""; - /// - /// The name of the operator/function called by the node. - /// This field MUST be present for this version of the IR. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string OpType { - get { return opType_; } - set { - opType_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "attribute" field. - public const int AttributeFieldNumber = 5; - private static readonly pb::FieldCodec _repeated_attribute_codec - = pb::FieldCodec.ForMessage(42, global::LotusIR.AttributeProto.Parser); - private readonly pbc::RepeatedField attribute_ = new pbc::RepeatedField(); - /// - /// Additional named attributes. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Attribute { - get { return attribute_; } - } - - /// Field number for the "doc_string" field. - public const int DocStringFieldNumber = 6; - private string docString_ = ""; - /// - /// An human-readable documentation for this node in the graph. - /// This text MAY contain Markdown markup that conforms to http://commonmark.org/. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string DocString { - get { return docString_; } - set { - docString_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "input_arg_count" field. - public const int InputArgCountFieldNumber = 50; - private static readonly pb::FieldCodec _repeated_inputArgCount_codec - = pb::FieldCodec.ForInt32(402); - private readonly pbc::RepeatedField inputArgCount_ = new pbc::RepeatedField(); - /// - /// The number of inputs for each argument of the operator/function. - /// A formal parameter of the op may take a variable number of inputs - /// that is only known when this node is constructed. - ///BUG:13806939,dbox: I'm assuming that this field is like input_arg_info in that - /// a zero element/missing array implies that one needs to crawl - /// the graph to figure out the input counts, yes? Confirm and I'll - /// make clear. Otherwise, we need to require it to be present - /// and accurate. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField InputArgCount { - get { return inputArgCount_; } - } - - /// Field number for the "control_input" field. - public const int ControlInputFieldNumber = 51; - private static readonly pb::FieldCodec _repeated_controlInput_codec - = pb::FieldCodec.ForString(410); - private readonly pbc::RepeatedField controlInput_ = new pbc::RepeatedField(); - /// - /// Specify a list of named nodes that must be executed before this node. - /// Framework may use this to give users the ability to impose additional - /// execution orders for the operations. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField ControlInput { - get { return controlInput_; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as NodeProto); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(NodeProto other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if(!input_.Equals(other.input_)) return false; - if(!output_.Equals(other.output_)) return false; - if (Name != other.Name) return false; - if (OpType != other.OpType) return false; - if(!attribute_.Equals(other.attribute_)) return false; - if (DocString != other.DocString) return false; - if(!inputArgCount_.Equals(other.inputArgCount_)) return false; - if(!controlInput_.Equals(other.controlInput_)) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - hash ^= input_.GetHashCode(); - hash ^= output_.GetHashCode(); - if (Name.Length != 0) hash ^= Name.GetHashCode(); - if (OpType.Length != 0) hash ^= OpType.GetHashCode(); - hash ^= attribute_.GetHashCode(); - if (DocString.Length != 0) hash ^= DocString.GetHashCode(); - hash ^= inputArgCount_.GetHashCode(); - hash ^= controlInput_.GetHashCode(); - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - input_.WriteTo(output, _repeated_input_codec); - output_.WriteTo(output, _repeated_output_codec); - if (Name.Length != 0) { - output.WriteRawTag(26); - output.WriteString(Name); - } - if (OpType.Length != 0) { - output.WriteRawTag(34); - output.WriteString(OpType); - } - attribute_.WriteTo(output, _repeated_attribute_codec); - if (DocString.Length != 0) { - output.WriteRawTag(50); - output.WriteString(DocString); - } - inputArgCount_.WriteTo(output, _repeated_inputArgCount_codec); - controlInput_.WriteTo(output, _repeated_controlInput_codec); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - size += input_.CalculateSize(_repeated_input_codec); - size += output_.CalculateSize(_repeated_output_codec); - if (Name.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(Name); - } - if (OpType.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(OpType); - } - size += attribute_.CalculateSize(_repeated_attribute_codec); - if (DocString.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(DocString); - } - size += inputArgCount_.CalculateSize(_repeated_inputArgCount_codec); - size += controlInput_.CalculateSize(_repeated_controlInput_codec); - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(NodeProto other) { - if (other == null) { - return; - } - input_.Add(other.input_); - output_.Add(other.output_); - if (other.Name.Length != 0) { - Name = other.Name; - } - if (other.OpType.Length != 0) { - OpType = other.OpType; - } - attribute_.Add(other.attribute_); - if (other.DocString.Length != 0) { - DocString = other.DocString; - } - inputArgCount_.Add(other.inputArgCount_); - controlInput_.Add(other.controlInput_); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 10: { - input_.AddEntriesFrom(input, _repeated_input_codec); - break; - } - case 18: { - output_.AddEntriesFrom(input, _repeated_output_codec); - break; - } - case 26: { - Name = input.ReadString(); - break; - } - case 34: { - OpType = input.ReadString(); - break; - } - case 42: { - attribute_.AddEntriesFrom(input, _repeated_attribute_codec); - break; - } - case 50: { - DocString = input.ReadString(); - break; - } - case 402: - case 400: { - inputArgCount_.AddEntriesFrom(input, _repeated_inputArgCount_codec); - break; - } - case 410: { - controlInput_.AddEntriesFrom(input, _repeated_controlInput_codec); - break; - } - } - } - } - - } - - /// - /// ModelProto is a top-level file/container format for bundling a ML model. - /// The semantics of the model are described by the GraphProto that represents - /// a parameterized computation graph against a set of named operators that are - /// defined independently from the graph. - /// - public sealed partial class ModelProto : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new ModelProto()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::LotusIR.GraphReflection.Descriptor.MessageTypes[3]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public ModelProto() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public ModelProto(ModelProto other) : this() { - irVersion_ = other.irVersion_; - producerName_ = other.producerName_; - producerVersion_ = other.producerVersion_; - domain_ = other.domain_; - modelVersion_ = other.modelVersion_; - docString_ = other.docString_; - Graph = other.graph_ != null ? other.Graph.Clone() : null; - modelAuthor_ = other.modelAuthor_; - modelLicense_ = other.modelLicense_; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public ModelProto Clone() { - return new ModelProto(this); - } - - /// Field number for the "ir_version" field. - public const int IrVersionFieldNumber = 1; - private long irVersion_; - /// - /// The version of the IR this model targets. See Version enum above. - /// This field MUST be present. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public long IrVersion { - get { return irVersion_; } - set { - irVersion_ = value; - } - } - - /// Field number for the "producer_name" field. - public const int ProducerNameFieldNumber = 2; - private string producerName_ = ""; - /// - /// The name of the framework or tool used to generate this model. - /// This field SHOULD be present to indicate which implementation/tool/framework - /// emitted the model. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string ProducerName { - get { return producerName_; } - set { - producerName_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "producer_version" field. - public const int ProducerVersionFieldNumber = 3; - private string producerVersion_ = ""; - /// - /// The version of the framework or tool used to generate this model. - /// This field SHOULD be present to indicate which implementation/tool/framework - /// emitted the model. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string ProducerVersion { - get { return producerVersion_; } - set { - producerVersion_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "domain" field. - public const int DomainFieldNumber = 4; - private string domain_ = ""; - /// - /// Domain name of the model. - /// We use reverse domain names as name space indicators. For example: - /// `com.facebook.fair` or `com.microsoft.cognitiveservices` - /// - /// Together with `model_version` and GraphProto.name, this forms the unique identity of - /// the graph. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string Domain { - get { return domain_; } - set { - domain_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "model_version" field. - public const int ModelVersionFieldNumber = 5; - private long modelVersion_; - /// - /// The version of the graph encoded. See Version enum below. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public long ModelVersion { - get { return modelVersion_; } - set { - modelVersion_ = value; - } - } - - /// Field number for the "doc_string" field. - public const int DocStringFieldNumber = 6; - private string docString_ = ""; - /// - /// A human-readable documentation for this model. Markdown is allowed. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string DocString { - get { return docString_; } - set { - docString_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "graph" field. - public const int GraphFieldNumber = 7; - private global::LotusIR.GraphProto graph_; - /// - /// The parameterized graph that is evaluated to execute the model. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::LotusIR.GraphProto Graph { - get { return graph_; } - set { - graph_ = value; - } - } - - /// Field number for the "model_author" field. - public const int ModelAuthorFieldNumber = 50; - private string modelAuthor_ = ""; - /// - /// The name of the author who created the graph. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string ModelAuthor { - get { return modelAuthor_; } - set { - modelAuthor_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "model_license" field. - public const int ModelLicenseFieldNumber = 51; - private string modelLicense_ = ""; - /// - /// licensing information concerning use or origination of the graph. - /// This text MAY contain Markdown markup that conforms to http://commonmark.org/. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string ModelLicense { - get { return modelLicense_; } - set { - modelLicense_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as ModelProto); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(ModelProto other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if (IrVersion != other.IrVersion) return false; - if (ProducerName != other.ProducerName) return false; - if (ProducerVersion != other.ProducerVersion) return false; - if (Domain != other.Domain) return false; - if (ModelVersion != other.ModelVersion) return false; - if (DocString != other.DocString) return false; - if (!object.Equals(Graph, other.Graph)) return false; - if (ModelAuthor != other.ModelAuthor) return false; - if (ModelLicense != other.ModelLicense) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - if (IrVersion != 0L) hash ^= IrVersion.GetHashCode(); - if (ProducerName.Length != 0) hash ^= ProducerName.GetHashCode(); - if (ProducerVersion.Length != 0) hash ^= ProducerVersion.GetHashCode(); - if (Domain.Length != 0) hash ^= Domain.GetHashCode(); - if (ModelVersion != 0L) hash ^= ModelVersion.GetHashCode(); - if (DocString.Length != 0) hash ^= DocString.GetHashCode(); - if (graph_ != null) hash ^= Graph.GetHashCode(); - if (ModelAuthor.Length != 0) hash ^= ModelAuthor.GetHashCode(); - if (ModelLicense.Length != 0) hash ^= ModelLicense.GetHashCode(); - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - if (IrVersion != 0L) { - output.WriteRawTag(8); - output.WriteInt64(IrVersion); - } - if (ProducerName.Length != 0) { - output.WriteRawTag(18); - output.WriteString(ProducerName); - } - if (ProducerVersion.Length != 0) { - output.WriteRawTag(26); - output.WriteString(ProducerVersion); - } - if (Domain.Length != 0) { - output.WriteRawTag(34); - output.WriteString(Domain); - } - if (ModelVersion != 0L) { - output.WriteRawTag(40); - output.WriteInt64(ModelVersion); - } - if (DocString.Length != 0) { - output.WriteRawTag(50); - output.WriteString(DocString); - } - if (graph_ != null) { - output.WriteRawTag(58); - output.WriteMessage(Graph); - } - if (ModelAuthor.Length != 0) { - output.WriteRawTag(146, 3); - output.WriteString(ModelAuthor); - } - if (ModelLicense.Length != 0) { - output.WriteRawTag(154, 3); - output.WriteString(ModelLicense); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - if (IrVersion != 0L) { - size += 1 + pb::CodedOutputStream.ComputeInt64Size(IrVersion); - } - if (ProducerName.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(ProducerName); - } - if (ProducerVersion.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(ProducerVersion); - } - if (Domain.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(Domain); - } - if (ModelVersion != 0L) { - size += 1 + pb::CodedOutputStream.ComputeInt64Size(ModelVersion); - } - if (DocString.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(DocString); - } - if (graph_ != null) { - size += 1 + pb::CodedOutputStream.ComputeMessageSize(Graph); - } - if (ModelAuthor.Length != 0) { - size += 2 + pb::CodedOutputStream.ComputeStringSize(ModelAuthor); - } - if (ModelLicense.Length != 0) { - size += 2 + pb::CodedOutputStream.ComputeStringSize(ModelLicense); - } - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(ModelProto other) { - if (other == null) { - return; - } - if (other.IrVersion != 0L) { - IrVersion = other.IrVersion; - } - if (other.ProducerName.Length != 0) { - ProducerName = other.ProducerName; - } - if (other.ProducerVersion.Length != 0) { - ProducerVersion = other.ProducerVersion; - } - if (other.Domain.Length != 0) { - Domain = other.Domain; - } - if (other.ModelVersion != 0L) { - ModelVersion = other.ModelVersion; - } - if (other.DocString.Length != 0) { - DocString = other.DocString; - } - if (other.graph_ != null) { - if (graph_ == null) { - graph_ = new global::LotusIR.GraphProto(); - } - Graph.MergeFrom(other.Graph); - } - if (other.ModelAuthor.Length != 0) { - ModelAuthor = other.ModelAuthor; - } - if (other.ModelLicense.Length != 0) { - ModelLicense = other.ModelLicense; - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 8: { - IrVersion = input.ReadInt64(); - break; - } - case 18: { - ProducerName = input.ReadString(); - break; - } - case 26: { - ProducerVersion = input.ReadString(); - break; - } - case 34: { - Domain = input.ReadString(); - break; - } - case 40: { - ModelVersion = input.ReadInt64(); - break; - } - case 50: { - DocString = input.ReadString(); - break; - } - case 58: { - if (graph_ == null) { - graph_ = new global::LotusIR.GraphProto(); - } - input.ReadMessage(graph_); - break; - } - case 402: { - ModelAuthor = input.ReadString(); - break; - } - case 410: { - ModelLicense = input.ReadString(); - break; - } - } - } - } - - } - - /// - /// GraphProto defines a parameterized series of nodes to form a directed acyclic graph. - /// This is the equivalent of the "network" and "graph" in many deep learning - /// frameworks. - /// All the input/output tensors are explicitly named so a framework can - /// run any subgraph of the graph by feeding and fetching the named tensors. - /// - public sealed partial class GraphProto : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new GraphProto()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::LotusIR.GraphReflection.Descriptor.MessageTypes[4]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public GraphProto() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public GraphProto(GraphProto other) : this() { - node_ = other.node_.Clone(); - name_ = other.name_; - initializer_ = other.initializer_.Clone(); - docString_ = other.docString_; - input_ = other.input_.Clone(); - output_ = other.output_.Clone(); - valueInfo_ = other.valueInfo_.Clone(); - function_ = other.function_.Clone(); - operator_ = other.operator_.Clone(); - importedLibraries_ = other.importedLibraries_.Clone(); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public GraphProto Clone() { - return new GraphProto(this); - } - - /// Field number for the "node" field. - public const int NodeFieldNumber = 1; - private static readonly pb::FieldCodec _repeated_node_codec - = pb::FieldCodec.ForMessage(10, global::LotusIR.NodeProto.Parser); - private readonly pbc::RepeatedField node_ = new pbc::RepeatedField(); - /// - /// The nodes in the graph. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Node { - get { return node_; } - } - - /// Field number for the "name" field. - public const int NameFieldNumber = 2; - private string name_ = ""; - /// - /// The name of the graph. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string Name { - get { return name_; } - set { - name_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "initializer" field. - public const int InitializerFieldNumber = 5; - private static readonly pb::FieldCodec _repeated_initializer_codec - = pb::FieldCodec.ForMessage(42, global::LotusIR.TensorProto.Parser); - private readonly pbc::RepeatedField initializer_ = new pbc::RepeatedField(); - /// - /// A list of named tensor values (constants), used to specify default - /// values for some of the inputs of the graph. - /// Each TensorProto entry must have a distinct name (within the list) that - /// also appears in the input list. - /// In an evaluation, the default value specified here is used if and only if - /// user specifies no value for the corresponding input parameter. - /// May be used to pass serialized parameters for networks. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Initializer { - get { return initializer_; } - } - - /// Field number for the "doc_string" field. - public const int DocStringFieldNumber = 10; - private string docString_ = ""; - /// - /// A human-readable documentation for this graph. Markdown is allowed. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string DocString { - get { return docString_; } - set { - docString_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "input" field. - public const int InputFieldNumber = 11; - private static readonly pb::FieldCodec _repeated_input_codec - = pb::FieldCodec.ForMessage(90, global::LotusIR.ValueInfoProto.Parser); - private readonly pbc::RepeatedField input_ = new pbc::RepeatedField(); - /// - /// The inputs and outputs of the graph. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Input { - get { return input_; } - } - - /// Field number for the "output" field. - public const int OutputFieldNumber = 12; - private static readonly pb::FieldCodec _repeated_output_codec - = pb::FieldCodec.ForMessage(98, global::LotusIR.ValueInfoProto.Parser); - private readonly pbc::RepeatedField output_ = new pbc::RepeatedField(); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Output { - get { return output_; } - } - - /// Field number for the "value_info" field. - public const int ValueInfoFieldNumber = 13; - private static readonly pb::FieldCodec _repeated_valueInfo_codec - = pb::FieldCodec.ForMessage(106, global::LotusIR.ValueInfoProto.Parser); - private readonly pbc::RepeatedField valueInfo_ = new pbc::RepeatedField(); - /// - /// Information for the values in the graph. The ValueInfoProto.name's - /// must be distinct. It is for a value to appear in value_info list. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField ValueInfo { - get { return valueInfo_; } - } - - /// Field number for the "function" field. - public const int FunctionFieldNumber = 50; - private static readonly pb::FieldCodec _repeated_function_codec - = pb::FieldCodec.ForMessage(402, global::LotusIR.FunctionDefProto.Parser); - private readonly pbc::RepeatedField function_ = new pbc::RepeatedField(); - /// - /// The function definitions of the graph. They can only only be used - /// (i.e., called) in this graph. - /// Each FunctionDefProto in function MUST have a unique name. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Function { - get { return function_; } - } - - /// Field number for the "operator" field. - public const int OperatorFieldNumber = 51; - private static readonly pb::FieldCodec _repeated_operator_codec - = pb::FieldCodec.ForMessage(410, global::LotusIR.OperatorDeclProto.Parser); - private readonly pbc::RepeatedField operator_ = new pbc::RepeatedField(); - /// - /// The externally defined operators declared by this graph. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Operator { - get { return operator_; } - } - - /// Field number for the "imported_libraries" field. - public const int ImportedLibrariesFieldNumber = 52; - private static readonly pb::FieldCodec _repeated_importedLibraries_codec - = pb::FieldCodec.ForString(418); - private readonly pbc::RepeatedField importedLibraries_ = new pbc::RepeatedField(); - /// - /// Imported libraries are referenced as a collection of strings in the form of absolute - /// URIs or relative paths. Where such relative paths are rooted is defined by tools and - /// runtime implementations. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField ImportedLibraries { - get { return importedLibraries_; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as GraphProto); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(GraphProto other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if(!node_.Equals(other.node_)) return false; - if (Name != other.Name) return false; - if(!initializer_.Equals(other.initializer_)) return false; - if (DocString != other.DocString) return false; - if(!input_.Equals(other.input_)) return false; - if(!output_.Equals(other.output_)) return false; - if(!valueInfo_.Equals(other.valueInfo_)) return false; - if(!function_.Equals(other.function_)) return false; - if(!operator_.Equals(other.operator_)) return false; - if(!importedLibraries_.Equals(other.importedLibraries_)) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - hash ^= node_.GetHashCode(); - if (Name.Length != 0) hash ^= Name.GetHashCode(); - hash ^= initializer_.GetHashCode(); - if (DocString.Length != 0) hash ^= DocString.GetHashCode(); - hash ^= input_.GetHashCode(); - hash ^= output_.GetHashCode(); - hash ^= valueInfo_.GetHashCode(); - hash ^= function_.GetHashCode(); - hash ^= operator_.GetHashCode(); - hash ^= importedLibraries_.GetHashCode(); - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - node_.WriteTo(output, _repeated_node_codec); - if (Name.Length != 0) { - output.WriteRawTag(18); - output.WriteString(Name); - } - initializer_.WriteTo(output, _repeated_initializer_codec); - if (DocString.Length != 0) { - output.WriteRawTag(82); - output.WriteString(DocString); - } - input_.WriteTo(output, _repeated_input_codec); - output_.WriteTo(output, _repeated_output_codec); - valueInfo_.WriteTo(output, _repeated_valueInfo_codec); - function_.WriteTo(output, _repeated_function_codec); - operator_.WriteTo(output, _repeated_operator_codec); - importedLibraries_.WriteTo(output, _repeated_importedLibraries_codec); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - size += node_.CalculateSize(_repeated_node_codec); - if (Name.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(Name); - } - size += initializer_.CalculateSize(_repeated_initializer_codec); - if (DocString.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(DocString); - } - size += input_.CalculateSize(_repeated_input_codec); - size += output_.CalculateSize(_repeated_output_codec); - size += valueInfo_.CalculateSize(_repeated_valueInfo_codec); - size += function_.CalculateSize(_repeated_function_codec); - size += operator_.CalculateSize(_repeated_operator_codec); - size += importedLibraries_.CalculateSize(_repeated_importedLibraries_codec); - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(GraphProto other) { - if (other == null) { - return; - } - node_.Add(other.node_); - if (other.Name.Length != 0) { - Name = other.Name; - } - initializer_.Add(other.initializer_); - if (other.DocString.Length != 0) { - DocString = other.DocString; - } - input_.Add(other.input_); - output_.Add(other.output_); - valueInfo_.Add(other.valueInfo_); - function_.Add(other.function_); - operator_.Add(other.operator_); - importedLibraries_.Add(other.importedLibraries_); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 10: { - node_.AddEntriesFrom(input, _repeated_node_codec); - break; - } - case 18: { - Name = input.ReadString(); - break; - } - case 42: { - initializer_.AddEntriesFrom(input, _repeated_initializer_codec); - break; - } - case 82: { - DocString = input.ReadString(); - break; - } - case 90: { - input_.AddEntriesFrom(input, _repeated_input_codec); - break; - } - case 98: { - output_.AddEntriesFrom(input, _repeated_output_codec); - break; - } - case 106: { - valueInfo_.AddEntriesFrom(input, _repeated_valueInfo_codec); - break; - } - case 402: { - function_.AddEntriesFrom(input, _repeated_function_codec); - break; - } - case 410: { - operator_.AddEntriesFrom(input, _repeated_operator_codec); - break; - } - case 418: { - importedLibraries_.AddEntriesFrom(input, _repeated_importedLibraries_codec); - break; - } - } - } - } - - } - - /// - /// A message defined to store a tensor in its serialized format. - /// - public sealed partial class TensorProto : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new TensorProto()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::LotusIR.GraphReflection.Descriptor.MessageTypes[5]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public TensorProto() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public TensorProto(TensorProto other) : this() { - dims_ = other.dims_.Clone(); - dataType_ = other.dataType_; - Segment = other.segment_ != null ? other.Segment.Clone() : null; - floatData_ = other.floatData_.Clone(); - int32Data_ = other.int32Data_.Clone(); - stringData_ = other.stringData_.Clone(); - int64Data_ = other.int64Data_.Clone(); - name_ = other.name_; - rawData_ = other.rawData_; - doubleData_ = other.doubleData_.Clone(); - uint64Data_ = other.uint64Data_.Clone(); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public TensorProto Clone() { - return new TensorProto(this); - } - - /// Field number for the "dims" field. - public const int DimsFieldNumber = 1; - private static readonly pb::FieldCodec _repeated_dims_codec - = pb::FieldCodec.ForInt64(10); - private readonly pbc::RepeatedField dims_ = new pbc::RepeatedField(); - /// - /// The shape of the tensor. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Dims { - get { return dims_; } - } - - /// Field number for the "data_type" field. - public const int DataTypeFieldNumber = 2; - private global::LotusIR.TensorProto.Types.DataType dataType_ = 0; - /// - /// The data type of the tensor. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::LotusIR.TensorProto.Types.DataType DataType { - get { return dataType_; } - set { - dataType_ = value; - } - } - - /// Field number for the "segment" field. - public const int SegmentFieldNumber = 3; - private global::LotusIR.TensorProto.Types.Segment segment_; - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::LotusIR.TensorProto.Types.Segment Segment { - get { return segment_; } - set { - segment_ = value; - } - } - - /// Field number for the "float_data" field. - public const int FloatDataFieldNumber = 4; - private static readonly pb::FieldCodec _repeated_floatData_codec - = pb::FieldCodec.ForFloat(34); - private readonly pbc::RepeatedField floatData_ = new pbc::RepeatedField(); - /// - /// For float and complex64 values - /// Complex64 tensors are encoded as a single array of floats, - /// with the real components appearing in odd numbered positions, - /// and the corresponding imaginary component apparing in the - /// subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i] - /// is encoded as [1.0, 2.0 ,3.0 ,4.0] - /// When this field is present, the data_type field MUST be FLOAT or COMPLEX64. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField FloatData { - get { return floatData_; } - } - - /// Field number for the "int32_data" field. - public const int Int32DataFieldNumber = 5; - private static readonly pb::FieldCodec _repeated_int32Data_codec - = pb::FieldCodec.ForInt32(42); - private readonly pbc::RepeatedField int32Data_ = new pbc::RepeatedField(); - /// - /// For int32, uint8, int8, uint16, int16, bool, and float16 values - /// float16 values must be bit-wise converted to an uint16_t prior - /// to writing to the buffer. - /// When this field is present, the data_type field MUST be - /// INT32, INT16, INT8, UINT16, INT8, BOOL, or FLOAT32 - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Int32Data { - get { return int32Data_; } - } - - /// Field number for the "string_data" field. - public const int StringDataFieldNumber = 6; - private static readonly pb::FieldCodec _repeated_stringData_codec - = pb::FieldCodec.ForBytes(50); - private readonly pbc::RepeatedField stringData_ = new pbc::RepeatedField(); - /// - /// For strings. - /// Each element of string_data is a UTF-8 encoded Unicode - /// string. No trailing null, no leading BOM. The protobuf "string" - /// scalar type is not used to match ML community conventions. - /// When this field is present, the data_type field MUST be STRING - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField StringData { - get { return stringData_; } - } - - /// Field number for the "int64_data" field. - public const int Int64DataFieldNumber = 7; - private static readonly pb::FieldCodec _repeated_int64Data_codec - = pb::FieldCodec.ForInt64(58); - private readonly pbc::RepeatedField int64Data_ = new pbc::RepeatedField(); - /// - /// For int64. - /// When this field is present, the data_type field MUST be INT64 - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Int64Data { - get { return int64Data_; } - } - - /// Field number for the "name" field. - public const int NameFieldNumber = 8; - private string name_ = ""; - /// - /// ly, a name for the tensor. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string Name { - get { return name_; } - set { - name_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "raw_data" field. - public const int RawDataFieldNumber = 9; - private pb::ByteString rawData_ = pb::ByteString.Empty; - /// - /// Serializations can either use one of the fields above, or use this - /// raw bytes field. The only exception is the string case, where one is - /// to store the content in the repeated bytes string_data field. - /// - /// When this raw_data field is used to store tensor value, elements MUST - /// be stored in as fixed-width, little-endian order. - /// Floating-point data types MUST be stored in IEEE 754 format. - /// Complex64 elements must be written as two consecutive FLOAT values, real component first. - /// Complex128 elements must be written as two consecutive DOUBLE values, real component first. - /// Boolean type MUST be written one byte per tensor element (00000001 for true, 00000000 for false). - /// - /// Note: the advantage of specific field rather than the raw_data field is - /// that in some cases (e.g. int data), protobuf does a better packing via - /// variable length storage, and may lead to smaller binary footprint. - /// When this field is present, the data_type field MUST NOT be STRING or UNDEFINED - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pb::ByteString RawData { - get { return rawData_; } - set { - rawData_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "double_data" field. - public const int DoubleDataFieldNumber = 10; - private static readonly pb::FieldCodec _repeated_doubleData_codec - = pb::FieldCodec.ForDouble(82); - private readonly pbc::RepeatedField doubleData_ = new pbc::RepeatedField(); - /// - /// For double - /// Complex64 tensors are encoded as a single array of doubles, - /// with the real components appearing in odd numbered positions, - /// and the corresponding imaginary component apparing in the - /// subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i] - /// is encoded as [1.0, 2.0 ,3.0 ,4.0] - /// When this field is present, the data_type field MUST be DOUBLE or COMPLEX128 - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField DoubleData { - get { return doubleData_; } - } - - /// Field number for the "uint64_data" field. - public const int Uint64DataFieldNumber = 11; - private static readonly pb::FieldCodec _repeated_uint64Data_codec - = pb::FieldCodec.ForUInt64(90); - private readonly pbc::RepeatedField uint64Data_ = new pbc::RepeatedField(); - /// - /// For uint64 and uint32 values - /// When this field is present, the data_type field MUST be - /// UINT32 or UINT64 - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Uint64Data { - get { return uint64Data_; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as TensorProto); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(TensorProto other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if(!dims_.Equals(other.dims_)) return false; - if (DataType != other.DataType) return false; - if (!object.Equals(Segment, other.Segment)) return false; - if(!floatData_.Equals(other.floatData_)) return false; - if(!int32Data_.Equals(other.int32Data_)) return false; - if(!stringData_.Equals(other.stringData_)) return false; - if(!int64Data_.Equals(other.int64Data_)) return false; - if (Name != other.Name) return false; - if (RawData != other.RawData) return false; - if(!doubleData_.Equals(other.doubleData_)) return false; - if(!uint64Data_.Equals(other.uint64Data_)) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - hash ^= dims_.GetHashCode(); - if (DataType != 0) hash ^= DataType.GetHashCode(); - if (segment_ != null) hash ^= Segment.GetHashCode(); - hash ^= floatData_.GetHashCode(); - hash ^= int32Data_.GetHashCode(); - hash ^= stringData_.GetHashCode(); - hash ^= int64Data_.GetHashCode(); - if (Name.Length != 0) hash ^= Name.GetHashCode(); - if (RawData.Length != 0) hash ^= RawData.GetHashCode(); - hash ^= doubleData_.GetHashCode(); - hash ^= uint64Data_.GetHashCode(); - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - dims_.WriteTo(output, _repeated_dims_codec); - if (DataType != 0) { - output.WriteRawTag(16); - output.WriteEnum((int) DataType); - } - if (segment_ != null) { - output.WriteRawTag(26); - output.WriteMessage(Segment); - } - floatData_.WriteTo(output, _repeated_floatData_codec); - int32Data_.WriteTo(output, _repeated_int32Data_codec); - stringData_.WriteTo(output, _repeated_stringData_codec); - int64Data_.WriteTo(output, _repeated_int64Data_codec); - if (Name.Length != 0) { - output.WriteRawTag(66); - output.WriteString(Name); - } - if (RawData.Length != 0) { - output.WriteRawTag(74); - output.WriteBytes(RawData); - } - doubleData_.WriteTo(output, _repeated_doubleData_codec); - uint64Data_.WriteTo(output, _repeated_uint64Data_codec); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - size += dims_.CalculateSize(_repeated_dims_codec); - if (DataType != 0) { - size += 1 + pb::CodedOutputStream.ComputeEnumSize((int) DataType); - } - if (segment_ != null) { - size += 1 + pb::CodedOutputStream.ComputeMessageSize(Segment); - } - size += floatData_.CalculateSize(_repeated_floatData_codec); - size += int32Data_.CalculateSize(_repeated_int32Data_codec); - size += stringData_.CalculateSize(_repeated_stringData_codec); - size += int64Data_.CalculateSize(_repeated_int64Data_codec); - if (Name.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(Name); - } - if (RawData.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeBytesSize(RawData); - } - size += doubleData_.CalculateSize(_repeated_doubleData_codec); - size += uint64Data_.CalculateSize(_repeated_uint64Data_codec); - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(TensorProto other) { - if (other == null) { - return; - } - dims_.Add(other.dims_); - if (other.DataType != 0) { - DataType = other.DataType; - } - if (other.segment_ != null) { - if (segment_ == null) { - segment_ = new global::LotusIR.TensorProto.Types.Segment(); - } - Segment.MergeFrom(other.Segment); - } - floatData_.Add(other.floatData_); - int32Data_.Add(other.int32Data_); - stringData_.Add(other.stringData_); - int64Data_.Add(other.int64Data_); - if (other.Name.Length != 0) { - Name = other.Name; - } - if (other.RawData.Length != 0) { - RawData = other.RawData; - } - doubleData_.Add(other.doubleData_); - uint64Data_.Add(other.uint64Data_); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 10: - case 8: { - dims_.AddEntriesFrom(input, _repeated_dims_codec); - break; - } - case 16: { - dataType_ = (global::LotusIR.TensorProto.Types.DataType) input.ReadEnum(); - break; - } - case 26: { - if (segment_ == null) { - segment_ = new global::LotusIR.TensorProto.Types.Segment(); - } - input.ReadMessage(segment_); - break; - } - case 34: - case 37: { - floatData_.AddEntriesFrom(input, _repeated_floatData_codec); - break; - } - case 42: - case 40: { - int32Data_.AddEntriesFrom(input, _repeated_int32Data_codec); - break; - } - case 50: { - stringData_.AddEntriesFrom(input, _repeated_stringData_codec); - break; - } - case 58: - case 56: { - int64Data_.AddEntriesFrom(input, _repeated_int64Data_codec); - break; - } - case 66: { - Name = input.ReadString(); - break; - } - case 74: { - RawData = input.ReadBytes(); - break; - } - case 82: - case 81: { - doubleData_.AddEntriesFrom(input, _repeated_doubleData_codec); - break; - } - case 90: - case 88: { - uint64Data_.AddEntriesFrom(input, _repeated_uint64Data_codec); - break; - } - } - } - } - - #region Nested types - /// Container for nested types declared in the TensorProto message type. - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static partial class Types { - public enum DataType { - [pbr::OriginalName("UNDEFINED")] Undefined = 0, - /// - /// Basic types. - /// - [pbr::OriginalName("FLOAT")] Float = 1, - /// - /// uint8_t - /// - [pbr::OriginalName("UINT8")] Uint8 = 2, - /// - /// int8_t - /// - [pbr::OriginalName("INT8")] Int8 = 3, - /// - /// uint16_t - /// - [pbr::OriginalName("UINT16")] Uint16 = 4, - /// - /// int16_t - /// - [pbr::OriginalName("INT16")] Int16 = 5, - /// - /// int32_t - /// - [pbr::OriginalName("INT32")] Int32 = 6, - /// - /// int64_t - /// - [pbr::OriginalName("INT64")] Int64 = 7, - /// - /// string - /// - [pbr::OriginalName("STRING")] String = 8, - /// - /// bool - /// - [pbr::OriginalName("BOOL")] Bool = 9, - /// - /// Advanced types - /// - [pbr::OriginalName("FLOAT16")] Float16 = 10, - [pbr::OriginalName("DOUBLE")] Double = 11, - [pbr::OriginalName("UINT32")] Uint32 = 12, - [pbr::OriginalName("UINT64")] Uint64 = 13, - /// - /// complex with float32 real and imaginary components - /// - [pbr::OriginalName("COMPLEX64")] Complex64 = 14, - /// - /// complex with float64 real and imaginary components - /// - [pbr::OriginalName("COMPLEX128")] Complex128 = 15, - } - - /// - /// For very large tensors, we may want to store them in chunks, in which - /// case the following fields will specify the segment that is stored in - /// the current TensorProto. - /// - public sealed partial class Segment : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new Segment()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::LotusIR.TensorProto.Descriptor.NestedTypes[0]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public Segment() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public Segment(Segment other) : this() { - begin_ = other.begin_; - end_ = other.end_; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public Segment Clone() { - return new Segment(this); - } - - /// Field number for the "begin" field. - public const int BeginFieldNumber = 1; - private long begin_; - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public long Begin { - get { return begin_; } - set { - begin_ = value; - } - } - - /// Field number for the "end" field. - public const int EndFieldNumber = 2; - private long end_; - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public long End { - get { return end_; } - set { - end_ = value; - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as Segment); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(Segment other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if (Begin != other.Begin) return false; - if (End != other.End) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - if (Begin != 0L) hash ^= Begin.GetHashCode(); - if (End != 0L) hash ^= End.GetHashCode(); - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - if (Begin != 0L) { - output.WriteRawTag(8); - output.WriteInt64(Begin); - } - if (End != 0L) { - output.WriteRawTag(16); - output.WriteInt64(End); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - if (Begin != 0L) { - size += 1 + pb::CodedOutputStream.ComputeInt64Size(Begin); - } - if (End != 0L) { - size += 1 + pb::CodedOutputStream.ComputeInt64Size(End); - } - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(Segment other) { - if (other == null) { - return; - } - if (other.Begin != 0L) { - Begin = other.Begin; - } - if (other.End != 0L) { - End = other.End; - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 8: { - Begin = input.ReadInt64(); - break; - } - case 16: { - End = input.ReadInt64(); - break; - } - } - } - } - - } - - } - #endregion - - } - - /// - /// A sparse tensor must be stored as three dense tensors: - /// 1. dims: The shape of the original dense tensor. - /// 2. indices: A 2-D tensor specifying the indices of the nonzero elements. - /// 3. values: A 1-D tensor containing the values of the nonzero elements. - /// - public sealed partial class SparseTensorProto : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new SparseTensorProto()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::LotusIR.GraphReflection.Descriptor.MessageTypes[6]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public SparseTensorProto() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public SparseTensorProto(SparseTensorProto other) : this() { - dims_ = other.dims_.Clone(); - Indices = other.indices_ != null ? other.Indices.Clone() : null; - Values = other.values_ != null ? other.Values.Clone() : null; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public SparseTensorProto Clone() { - return new SparseTensorProto(this); - } - - /// Field number for the "dims" field. - public const int DimsFieldNumber = 1; - private static readonly pb::FieldCodec _repeated_dims_codec - = pb::FieldCodec.ForInt64(10); - private readonly pbc::RepeatedField dims_ = new pbc::RepeatedField(); - /// - /// The dimensions in the tensor. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Dims { - get { return dims_; } - } - - /// Field number for the "indices" field. - public const int IndicesFieldNumber = 2; - private global::LotusIR.TensorProto indices_; - /// - /// This field MUST be present this version of the IR. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::LotusIR.TensorProto Indices { - get { return indices_; } - set { - indices_ = value; - } - } - - /// Field number for the "values" field. - public const int ValuesFieldNumber = 3; - private global::LotusIR.TensorProto values_; - /// - /// This field MUST be present this version of the IR. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::LotusIR.TensorProto Values { - get { return values_; } - set { - values_ = value; - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as SparseTensorProto); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(SparseTensorProto other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if(!dims_.Equals(other.dims_)) return false; - if (!object.Equals(Indices, other.Indices)) return false; - if (!object.Equals(Values, other.Values)) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - hash ^= dims_.GetHashCode(); - if (indices_ != null) hash ^= Indices.GetHashCode(); - if (values_ != null) hash ^= Values.GetHashCode(); - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - dims_.WriteTo(output, _repeated_dims_codec); - if (indices_ != null) { - output.WriteRawTag(18); - output.WriteMessage(Indices); - } - if (values_ != null) { - output.WriteRawTag(26); - output.WriteMessage(Values); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - size += dims_.CalculateSize(_repeated_dims_codec); - if (indices_ != null) { - size += 1 + pb::CodedOutputStream.ComputeMessageSize(Indices); - } - if (values_ != null) { - size += 1 + pb::CodedOutputStream.ComputeMessageSize(Values); - } - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(SparseTensorProto other) { - if (other == null) { - return; - } - dims_.Add(other.dims_); - if (other.indices_ != null) { - if (indices_ == null) { - indices_ = new global::LotusIR.TensorProto(); - } - Indices.MergeFrom(other.Indices); - } - if (other.values_ != null) { - if (values_ == null) { - values_ = new global::LotusIR.TensorProto(); - } - Values.MergeFrom(other.Values); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 10: - case 8: { - dims_.AddEntriesFrom(input, _repeated_dims_codec); - break; - } - case 18: { - if (indices_ == null) { - indices_ = new global::LotusIR.TensorProto(); - } - input.ReadMessage(indices_); - break; - } - case 26: { - if (values_ == null) { - values_ = new global::LotusIR.TensorProto(); - } - input.ReadMessage(values_); - break; - } - } - } - } - - } - - /// - /// Define the types. - /// - public sealed partial class TypeProto : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new TypeProto()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::LotusIR.GraphReflection.Descriptor.MessageTypes[7]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public TypeProto() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public TypeProto(TypeProto other) : this() { - switch (other.ValueCase) { - case ValueOneofCase.TensorType: - TensorType = other.TensorType.Clone(); - break; - case ValueOneofCase.SparseTensorType: - SparseTensorType = other.SparseTensorType.Clone(); - break; - case ValueOneofCase.HandleType: - HandleType = other.HandleType.Clone(); - break; - case ValueOneofCase.TupleType: - TupleType = other.TupleType.Clone(); - break; - case ValueOneofCase.SeqType: - SeqType = other.SeqType.Clone(); - break; - case ValueOneofCase.MapType: - MapType = other.MapType.Clone(); - break; - } - - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public TypeProto Clone() { - return new TypeProto(this); - } - - /// Field number for the "tensor_type" field. - public const int TensorTypeFieldNumber = 1; - /// - /// The type of a tensor. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::LotusIR.TypeProto.Types.TensorTypeProto TensorType { - get { return valueCase_ == ValueOneofCase.TensorType ? (global::LotusIR.TypeProto.Types.TensorTypeProto) value_ : null; } - set { - value_ = value; - valueCase_ = value == null ? ValueOneofCase.None : ValueOneofCase.TensorType; - } - } - - /// Field number for the "sparse_tensor_type" field. - public const int SparseTensorTypeFieldNumber = 2; - /// - /// The type of a sparse tensor. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::LotusIR.TypeProto.Types.SparseTensorTypeProto SparseTensorType { - get { return valueCase_ == ValueOneofCase.SparseTensorType ? (global::LotusIR.TypeProto.Types.SparseTensorTypeProto) value_ : null; } - set { - value_ = value; - valueCase_ = value == null ? ValueOneofCase.None : ValueOneofCase.SparseTensorType; - } - } - - /// Field number for the "handle_type" field. - public const int HandleTypeFieldNumber = 3; - /// - /// The type of an opaque handle. A handle is used to represent a - /// reference to a resource managed by the framework runtime. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::LotusIR.TypeProto.Types.HandleTypeProto HandleType { - get { return valueCase_ == ValueOneofCase.HandleType ? (global::LotusIR.TypeProto.Types.HandleTypeProto) value_ : null; } - set { - value_ = value; - valueCase_ = value == null ? ValueOneofCase.None : ValueOneofCase.HandleType; - } - } - - /// Field number for the "tuple_type" field. - public const int TupleTypeFieldNumber = 4; - /// - /// The type of a tuple. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::LotusIR.TypeProto.Types.TupleTypeProto TupleType { - get { return valueCase_ == ValueOneofCase.TupleType ? (global::LotusIR.TypeProto.Types.TupleTypeProto) value_ : null; } - set { - value_ = value; - valueCase_ = value == null ? ValueOneofCase.None : ValueOneofCase.TupleType; - } - } - - /// Field number for the "seq_type" field. - public const int SeqTypeFieldNumber = 5; - /// - /// The type of a sequence. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::LotusIR.TypeProto.Types.SeqTypeProto SeqType { - get { return valueCase_ == ValueOneofCase.SeqType ? (global::LotusIR.TypeProto.Types.SeqTypeProto) value_ : null; } - set { - value_ = value; - valueCase_ = value == null ? ValueOneofCase.None : ValueOneofCase.SeqType; - } - } - - /// Field number for the "map_type" field. - public const int MapTypeFieldNumber = 6; - /// - /// The type of a map. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::LotusIR.TypeProto.Types.MapTypeProto MapType { - get { return valueCase_ == ValueOneofCase.MapType ? (global::LotusIR.TypeProto.Types.MapTypeProto) value_ : null; } - set { - value_ = value; - valueCase_ = value == null ? ValueOneofCase.None : ValueOneofCase.MapType; - } - } - - private object value_; - /// Enum of possible cases for the "value" oneof. - public enum ValueOneofCase { - None = 0, - TensorType = 1, - SparseTensorType = 2, - HandleType = 3, - TupleType = 4, - SeqType = 5, - MapType = 6, - } - private ValueOneofCase valueCase_ = ValueOneofCase.None; - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public ValueOneofCase ValueCase { - get { return valueCase_; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void ClearValue() { - valueCase_ = ValueOneofCase.None; - value_ = null; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as TypeProto); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(TypeProto other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if (!object.Equals(TensorType, other.TensorType)) return false; - if (!object.Equals(SparseTensorType, other.SparseTensorType)) return false; - if (!object.Equals(HandleType, other.HandleType)) return false; - if (!object.Equals(TupleType, other.TupleType)) return false; - if (!object.Equals(SeqType, other.SeqType)) return false; - if (!object.Equals(MapType, other.MapType)) return false; - if (ValueCase != other.ValueCase) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - if (valueCase_ == ValueOneofCase.TensorType) hash ^= TensorType.GetHashCode(); - if (valueCase_ == ValueOneofCase.SparseTensorType) hash ^= SparseTensorType.GetHashCode(); - if (valueCase_ == ValueOneofCase.HandleType) hash ^= HandleType.GetHashCode(); - if (valueCase_ == ValueOneofCase.TupleType) hash ^= TupleType.GetHashCode(); - if (valueCase_ == ValueOneofCase.SeqType) hash ^= SeqType.GetHashCode(); - if (valueCase_ == ValueOneofCase.MapType) hash ^= MapType.GetHashCode(); - hash ^= (int) valueCase_; - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - if (valueCase_ == ValueOneofCase.TensorType) { - output.WriteRawTag(10); - output.WriteMessage(TensorType); - } - if (valueCase_ == ValueOneofCase.SparseTensorType) { - output.WriteRawTag(18); - output.WriteMessage(SparseTensorType); - } - if (valueCase_ == ValueOneofCase.HandleType) { - output.WriteRawTag(26); - output.WriteMessage(HandleType); - } - if (valueCase_ == ValueOneofCase.TupleType) { - output.WriteRawTag(34); - output.WriteMessage(TupleType); - } - if (valueCase_ == ValueOneofCase.SeqType) { - output.WriteRawTag(42); - output.WriteMessage(SeqType); - } - if (valueCase_ == ValueOneofCase.MapType) { - output.WriteRawTag(50); - output.WriteMessage(MapType); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - if (valueCase_ == ValueOneofCase.TensorType) { - size += 1 + pb::CodedOutputStream.ComputeMessageSize(TensorType); - } - if (valueCase_ == ValueOneofCase.SparseTensorType) { - size += 1 + pb::CodedOutputStream.ComputeMessageSize(SparseTensorType); - } - if (valueCase_ == ValueOneofCase.HandleType) { - size += 1 + pb::CodedOutputStream.ComputeMessageSize(HandleType); - } - if (valueCase_ == ValueOneofCase.TupleType) { - size += 1 + pb::CodedOutputStream.ComputeMessageSize(TupleType); - } - if (valueCase_ == ValueOneofCase.SeqType) { - size += 1 + pb::CodedOutputStream.ComputeMessageSize(SeqType); - } - if (valueCase_ == ValueOneofCase.MapType) { - size += 1 + pb::CodedOutputStream.ComputeMessageSize(MapType); - } - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(TypeProto other) { - if (other == null) { - return; - } - switch (other.ValueCase) { - case ValueOneofCase.TensorType: - TensorType = other.TensorType; - break; - case ValueOneofCase.SparseTensorType: - SparseTensorType = other.SparseTensorType; - break; - case ValueOneofCase.HandleType: - HandleType = other.HandleType; - break; - case ValueOneofCase.TupleType: - TupleType = other.TupleType; - break; - case ValueOneofCase.SeqType: - SeqType = other.SeqType; - break; - case ValueOneofCase.MapType: - MapType = other.MapType; - break; - } - - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 10: { - global::LotusIR.TypeProto.Types.TensorTypeProto subBuilder = new global::LotusIR.TypeProto.Types.TensorTypeProto(); - if (valueCase_ == ValueOneofCase.TensorType) { - subBuilder.MergeFrom(TensorType); - } - input.ReadMessage(subBuilder); - TensorType = subBuilder; - break; - } - case 18: { - global::LotusIR.TypeProto.Types.SparseTensorTypeProto subBuilder = new global::LotusIR.TypeProto.Types.SparseTensorTypeProto(); - if (valueCase_ == ValueOneofCase.SparseTensorType) { - subBuilder.MergeFrom(SparseTensorType); - } - input.ReadMessage(subBuilder); - SparseTensorType = subBuilder; - break; - } - case 26: { - global::LotusIR.TypeProto.Types.HandleTypeProto subBuilder = new global::LotusIR.TypeProto.Types.HandleTypeProto(); - if (valueCase_ == ValueOneofCase.HandleType) { - subBuilder.MergeFrom(HandleType); - } - input.ReadMessage(subBuilder); - HandleType = subBuilder; - break; - } - case 34: { - global::LotusIR.TypeProto.Types.TupleTypeProto subBuilder = new global::LotusIR.TypeProto.Types.TupleTypeProto(); - if (valueCase_ == ValueOneofCase.TupleType) { - subBuilder.MergeFrom(TupleType); - } - input.ReadMessage(subBuilder); - TupleType = subBuilder; - break; - } - case 42: { - global::LotusIR.TypeProto.Types.SeqTypeProto subBuilder = new global::LotusIR.TypeProto.Types.SeqTypeProto(); - if (valueCase_ == ValueOneofCase.SeqType) { - subBuilder.MergeFrom(SeqType); - } - input.ReadMessage(subBuilder); - SeqType = subBuilder; - break; - } - case 50: { - global::LotusIR.TypeProto.Types.MapTypeProto subBuilder = new global::LotusIR.TypeProto.Types.MapTypeProto(); - if (valueCase_ == ValueOneofCase.MapType) { - subBuilder.MergeFrom(MapType); - } - input.ReadMessage(subBuilder); - MapType = subBuilder; - break; - } - } - } - } - - #region Nested types - /// Container for nested types declared in the TypeProto message type. - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static partial class Types { - /// - /// Defines a tensor shape. A dimension can be either an integer value - /// or a symbolic variable. A symbolic variable represents an unknown - /// dimension. - /// - public sealed partial class TensorShapeProto : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new TensorShapeProto()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::LotusIR.TypeProto.Descriptor.NestedTypes[0]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public TensorShapeProto() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public TensorShapeProto(TensorShapeProto other) : this() { - dim_ = other.dim_.Clone(); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public TensorShapeProto Clone() { - return new TensorShapeProto(this); - } - - /// Field number for the "dim" field. - public const int DimFieldNumber = 1; - private static readonly pb::FieldCodec _repeated_dim_codec - = pb::FieldCodec.ForMessage(10, global::LotusIR.TypeProto.Types.TensorShapeProto.Types.Dimension.Parser); - private readonly pbc::RepeatedField dim_ = new pbc::RepeatedField(); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Dim { - get { return dim_; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as TensorShapeProto); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(TensorShapeProto other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if(!dim_.Equals(other.dim_)) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - hash ^= dim_.GetHashCode(); - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - dim_.WriteTo(output, _repeated_dim_codec); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - size += dim_.CalculateSize(_repeated_dim_codec); - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(TensorShapeProto other) { - if (other == null) { - return; - } - dim_.Add(other.dim_); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 10: { - dim_.AddEntriesFrom(input, _repeated_dim_codec); - break; - } - } - } - } - - #region Nested types - /// Container for nested types declared in the TensorShapeProto message type. - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static partial class Types { - public sealed partial class Dimension : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new Dimension()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::LotusIR.TypeProto.Types.TensorShapeProto.Descriptor.NestedTypes[0]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public Dimension() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public Dimension(Dimension other) : this() { - switch (other.ValueCase) { - case ValueOneofCase.DimValue: - DimValue = other.DimValue; - break; - case ValueOneofCase.DimParam: - DimParam = other.DimParam; - break; - } - - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public Dimension Clone() { - return new Dimension(this); - } - - /// Field number for the "dim_value" field. - public const int DimValueFieldNumber = 1; - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public long DimValue { - get { return valueCase_ == ValueOneofCase.DimValue ? (long) value_ : 0L; } - set { - value_ = value; - valueCase_ = ValueOneofCase.DimValue; - } - } - - /// Field number for the "dim_param" field. - public const int DimParamFieldNumber = 2; - /// - /// namespace Shape - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string DimParam { - get { return valueCase_ == ValueOneofCase.DimParam ? (string) value_ : ""; } - set { - value_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - valueCase_ = ValueOneofCase.DimParam; - } - } - - private object value_; - /// Enum of possible cases for the "value" oneof. - public enum ValueOneofCase { - None = 0, - DimValue = 1, - DimParam = 2, - } - private ValueOneofCase valueCase_ = ValueOneofCase.None; - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public ValueOneofCase ValueCase { - get { return valueCase_; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void ClearValue() { - valueCase_ = ValueOneofCase.None; - value_ = null; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as Dimension); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(Dimension other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if (DimValue != other.DimValue) return false; - if (DimParam != other.DimParam) return false; - if (ValueCase != other.ValueCase) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - if (valueCase_ == ValueOneofCase.DimValue) hash ^= DimValue.GetHashCode(); - if (valueCase_ == ValueOneofCase.DimParam) hash ^= DimParam.GetHashCode(); - hash ^= (int) valueCase_; - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - if (valueCase_ == ValueOneofCase.DimValue) { - output.WriteRawTag(8); - output.WriteInt64(DimValue); - } - if (valueCase_ == ValueOneofCase.DimParam) { - output.WriteRawTag(18); - output.WriteString(DimParam); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - if (valueCase_ == ValueOneofCase.DimValue) { - size += 1 + pb::CodedOutputStream.ComputeInt64Size(DimValue); - } - if (valueCase_ == ValueOneofCase.DimParam) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(DimParam); - } - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(Dimension other) { - if (other == null) { - return; - } - switch (other.ValueCase) { - case ValueOneofCase.DimValue: - DimValue = other.DimValue; - break; - case ValueOneofCase.DimParam: - DimParam = other.DimParam; - break; - } - - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 8: { - DimValue = input.ReadInt64(); - break; - } - case 18: { - DimParam = input.ReadString(); - break; - } - } - } - } - - } - - } - #endregion - - } - - public sealed partial class TensorTypeProto : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new TensorTypeProto()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::LotusIR.TypeProto.Descriptor.NestedTypes[1]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public TensorTypeProto() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public TensorTypeProto(TensorTypeProto other) : this() { - elemType_ = other.elemType_; - Shape = other.shape_ != null ? other.Shape.Clone() : null; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public TensorTypeProto Clone() { - return new TensorTypeProto(this); - } - - /// Field number for the "elem_type" field. - public const int ElemTypeFieldNumber = 1; - private global::LotusIR.TensorProto.Types.DataType elemType_ = 0; - /// - /// This field MUST NOT have the value of UNDEFINED - /// This field MUST be present for this version of the IR. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::LotusIR.TensorProto.Types.DataType ElemType { - get { return elemType_; } - set { - elemType_ = value; - } - } - - /// Field number for the "shape" field. - public const int ShapeFieldNumber = 2; - private global::LotusIR.TypeProto.Types.TensorShapeProto shape_; - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::LotusIR.TypeProto.Types.TensorShapeProto Shape { - get { return shape_; } - set { - shape_ = value; - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as TensorTypeProto); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(TensorTypeProto other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if (ElemType != other.ElemType) return false; - if (!object.Equals(Shape, other.Shape)) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - if (ElemType != 0) hash ^= ElemType.GetHashCode(); - if (shape_ != null) hash ^= Shape.GetHashCode(); - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - if (ElemType != 0) { - output.WriteRawTag(8); - output.WriteEnum((int) ElemType); - } - if (shape_ != null) { - output.WriteRawTag(18); - output.WriteMessage(Shape); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - if (ElemType != 0) { - size += 1 + pb::CodedOutputStream.ComputeEnumSize((int) ElemType); - } - if (shape_ != null) { - size += 1 + pb::CodedOutputStream.ComputeMessageSize(Shape); - } - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(TensorTypeProto other) { - if (other == null) { - return; - } - if (other.ElemType != 0) { - ElemType = other.ElemType; - } - if (other.shape_ != null) { - if (shape_ == null) { - shape_ = new global::LotusIR.TypeProto.Types.TensorShapeProto(); - } - Shape.MergeFrom(other.Shape); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 8: { - elemType_ = (global::LotusIR.TensorProto.Types.DataType) input.ReadEnum(); - break; - } - case 18: { - if (shape_ == null) { - shape_ = new global::LotusIR.TypeProto.Types.TensorShapeProto(); - } - input.ReadMessage(shape_); - break; - } - } - } - } - - } - - public sealed partial class SparseTensorTypeProto : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new SparseTensorTypeProto()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::LotusIR.TypeProto.Descriptor.NestedTypes[2]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public SparseTensorTypeProto() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public SparseTensorTypeProto(SparseTensorTypeProto other) : this() { - elemType_ = other.elemType_; - Shape = other.shape_ != null ? other.Shape.Clone() : null; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public SparseTensorTypeProto Clone() { - return new SparseTensorTypeProto(this); - } - - /// Field number for the "elem_type" field. - public const int ElemTypeFieldNumber = 1; - private global::LotusIR.TensorProto.Types.DataType elemType_ = 0; - /// - /// This field MUST NOT have the value of UNDEFINED - /// This field MUST be present for this version of the IR. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::LotusIR.TensorProto.Types.DataType ElemType { - get { return elemType_; } - set { - elemType_ = value; - } - } - - /// Field number for the "shape" field. - public const int ShapeFieldNumber = 2; - private global::LotusIR.TypeProto.Types.TensorShapeProto shape_; - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::LotusIR.TypeProto.Types.TensorShapeProto Shape { - get { return shape_; } - set { - shape_ = value; - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as SparseTensorTypeProto); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(SparseTensorTypeProto other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if (ElemType != other.ElemType) return false; - if (!object.Equals(Shape, other.Shape)) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - if (ElemType != 0) hash ^= ElemType.GetHashCode(); - if (shape_ != null) hash ^= Shape.GetHashCode(); - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - if (ElemType != 0) { - output.WriteRawTag(8); - output.WriteEnum((int) ElemType); - } - if (shape_ != null) { - output.WriteRawTag(18); - output.WriteMessage(Shape); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - if (ElemType != 0) { - size += 1 + pb::CodedOutputStream.ComputeEnumSize((int) ElemType); - } - if (shape_ != null) { - size += 1 + pb::CodedOutputStream.ComputeMessageSize(Shape); - } - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(SparseTensorTypeProto other) { - if (other == null) { - return; - } - if (other.ElemType != 0) { - ElemType = other.ElemType; - } - if (other.shape_ != null) { - if (shape_ == null) { - shape_ = new global::LotusIR.TypeProto.Types.TensorShapeProto(); - } - Shape.MergeFrom(other.Shape); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 8: { - elemType_ = (global::LotusIR.TensorProto.Types.DataType) input.ReadEnum(); - break; - } - case 18: { - if (shape_ == null) { - shape_ = new global::LotusIR.TypeProto.Types.TensorShapeProto(); - } - input.ReadMessage(shape_); - break; - } - } - } - } - - } - - public sealed partial class HandleTypeProto : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new HandleTypeProto()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::LotusIR.TypeProto.Descriptor.NestedTypes[3]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public HandleTypeProto() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public HandleTypeProto(HandleTypeProto other) : this() { - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public HandleTypeProto Clone() { - return new HandleTypeProto(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as HandleTypeProto); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(HandleTypeProto other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(HandleTypeProto other) { - if (other == null) { - return; - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - } - } - } - - } - - public sealed partial class TupleTypeProto : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new TupleTypeProto()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::LotusIR.TypeProto.Descriptor.NestedTypes[4]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public TupleTypeProto() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public TupleTypeProto(TupleTypeProto other) : this() { - elemType_ = other.elemType_.Clone(); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public TupleTypeProto Clone() { - return new TupleTypeProto(this); - } - - /// Field number for the "elem_type" field. - public const int ElemTypeFieldNumber = 1; - private static readonly pb::FieldCodec _repeated_elemType_codec - = pb::FieldCodec.ForMessage(10, global::LotusIR.TypeProto.Parser); - private readonly pbc::RepeatedField elemType_ = new pbc::RepeatedField(); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField ElemType { - get { return elemType_; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as TupleTypeProto); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(TupleTypeProto other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if(!elemType_.Equals(other.elemType_)) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - hash ^= elemType_.GetHashCode(); - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - elemType_.WriteTo(output, _repeated_elemType_codec); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - size += elemType_.CalculateSize(_repeated_elemType_codec); - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(TupleTypeProto other) { - if (other == null) { - return; - } - elemType_.Add(other.elemType_); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 10: { - elemType_.AddEntriesFrom(input, _repeated_elemType_codec); - break; - } - } - } - } - - } - - public sealed partial class SeqTypeProto : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new SeqTypeProto()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::LotusIR.TypeProto.Descriptor.NestedTypes[5]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public SeqTypeProto() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public SeqTypeProto(SeqTypeProto other) : this() { - ElemType = other.elemType_ != null ? other.ElemType.Clone() : null; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public SeqTypeProto Clone() { - return new SeqTypeProto(this); - } - - /// Field number for the "elem_type" field. - public const int ElemTypeFieldNumber = 1; - private global::LotusIR.TypeProto elemType_; - /// - /// This field MUST be present for this version of the IR. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::LotusIR.TypeProto ElemType { - get { return elemType_; } - set { - elemType_ = value; - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as SeqTypeProto); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(SeqTypeProto other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if (!object.Equals(ElemType, other.ElemType)) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - if (elemType_ != null) hash ^= ElemType.GetHashCode(); - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - if (elemType_ != null) { - output.WriteRawTag(10); - output.WriteMessage(ElemType); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - if (elemType_ != null) { - size += 1 + pb::CodedOutputStream.ComputeMessageSize(ElemType); - } - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(SeqTypeProto other) { - if (other == null) { - return; - } - if (other.elemType_ != null) { - if (elemType_ == null) { - elemType_ = new global::LotusIR.TypeProto(); - } - ElemType.MergeFrom(other.ElemType); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 10: { - if (elemType_ == null) { - elemType_ = new global::LotusIR.TypeProto(); - } - input.ReadMessage(elemType_); - break; - } - } - } - } - - } - - public sealed partial class MapTypeProto : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new MapTypeProto()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::LotusIR.TypeProto.Descriptor.NestedTypes[6]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public MapTypeProto() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public MapTypeProto(MapTypeProto other) : this() { - keyType_ = other.keyType_; - valueType_ = other.valueType_; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public MapTypeProto Clone() { - return new MapTypeProto(this); - } - - /// Field number for the "key_type" field. - public const int KeyTypeFieldNumber = 1; - private global::LotusIR.TensorProto.Types.DataType keyType_ = 0; - /// - /// This field MUST be present for this version of the IR. - /// This field MUST refer to an integral type ([U]INT{8|16|32|64}) or STRING - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::LotusIR.TensorProto.Types.DataType KeyType { - get { return keyType_; } - set { - keyType_ = value; - } - } - - /// Field number for the "value_type" field. - public const int ValueTypeFieldNumber = 2; - private global::LotusIR.TensorProto.Types.DataType valueType_ = 0; - /// - /// This field MUST be present for this version of the IR. - /// This field MUST NOT refer to UNDEFINED - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::LotusIR.TensorProto.Types.DataType ValueType { - get { return valueType_; } - set { - valueType_ = value; - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as MapTypeProto); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(MapTypeProto other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if (KeyType != other.KeyType) return false; - if (ValueType != other.ValueType) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - if (KeyType != 0) hash ^= KeyType.GetHashCode(); - if (ValueType != 0) hash ^= ValueType.GetHashCode(); - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - if (KeyType != 0) { - output.WriteRawTag(8); - output.WriteEnum((int) KeyType); - } - if (ValueType != 0) { - output.WriteRawTag(16); - output.WriteEnum((int) ValueType); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - if (KeyType != 0) { - size += 1 + pb::CodedOutputStream.ComputeEnumSize((int) KeyType); - } - if (ValueType != 0) { - size += 1 + pb::CodedOutputStream.ComputeEnumSize((int) ValueType); - } - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(MapTypeProto other) { - if (other == null) { - return; - } - if (other.KeyType != 0) { - KeyType = other.KeyType; - } - if (other.ValueType != 0) { - ValueType = other.ValueType; - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 8: { - keyType_ = (global::LotusIR.TensorProto.Types.DataType) input.ReadEnum(); - break; - } - case 16: { - valueType_ = (global::LotusIR.TensorProto.Types.DataType) input.ReadEnum(); - break; - } - } - } - } - - } - - } - #endregion - - } - - public sealed partial class ValueProto : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new ValueProto()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::LotusIR.GraphReflection.Descriptor.MessageTypes[8]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public ValueProto() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public ValueProto(ValueProto other) : this() { - switch (other.ValueCase) { - case ValueOneofCase.DenseTensor: - DenseTensor = other.DenseTensor.Clone(); - break; - case ValueOneofCase.SparseTensor: - SparseTensor = other.SparseTensor.Clone(); - break; - case ValueOneofCase.Handle: - Handle = other.Handle.Clone(); - break; - case ValueOneofCase.Tuple: - Tuple = other.Tuple.Clone(); - break; - case ValueOneofCase.Seq: - Seq = other.Seq.Clone(); - break; - case ValueOneofCase.Map: - Map = other.Map.Clone(); - break; - } - - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public ValueProto Clone() { - return new ValueProto(this); - } - - /// Field number for the "dense_tensor" field. - public const int DenseTensorFieldNumber = 1; - /// - /// A dense tensor. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::LotusIR.TensorProto DenseTensor { - get { return valueCase_ == ValueOneofCase.DenseTensor ? (global::LotusIR.TensorProto) value_ : null; } - set { - value_ = value; - valueCase_ = value == null ? ValueOneofCase.None : ValueOneofCase.DenseTensor; - } - } - - /// Field number for the "sparse_tensor" field. - public const int SparseTensorFieldNumber = 2; - /// - /// A sparse tensor. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::LotusIR.SparseTensorProto SparseTensor { - get { return valueCase_ == ValueOneofCase.SparseTensor ? (global::LotusIR.SparseTensorProto) value_ : null; } - set { - value_ = value; - valueCase_ = value == null ? ValueOneofCase.None : ValueOneofCase.SparseTensor; - } - } - - /// Field number for the "handle" field. - public const int HandleFieldNumber = 3; - /// - /// A handle. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::LotusIR.ValueProto.Types.HandleProto Handle { - get { return valueCase_ == ValueOneofCase.Handle ? (global::LotusIR.ValueProto.Types.HandleProto) value_ : null; } - set { - value_ = value; - valueCase_ = value == null ? ValueOneofCase.None : ValueOneofCase.Handle; - } - } - - /// Field number for the "tuple" field. - public const int TupleFieldNumber = 4; - /// - /// A tuple. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::LotusIR.ValueProto.Types.TupleProto Tuple { - get { return valueCase_ == ValueOneofCase.Tuple ? (global::LotusIR.ValueProto.Types.TupleProto) value_ : null; } - set { - value_ = value; - valueCase_ = value == null ? ValueOneofCase.None : ValueOneofCase.Tuple; - } - } - - /// Field number for the "seq" field. - public const int SeqFieldNumber = 5; - /// - /// A sequence. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::LotusIR.ValueProto.Types.SequenceProto Seq { - get { return valueCase_ == ValueOneofCase.Seq ? (global::LotusIR.ValueProto.Types.SequenceProto) value_ : null; } - set { - value_ = value; - valueCase_ = value == null ? ValueOneofCase.None : ValueOneofCase.Seq; - } - } - - /// Field number for the "map" field. - public const int MapFieldNumber = 6; - /// - /// A map. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::LotusIR.ValueProto.Types.MapProto Map { - get { return valueCase_ == ValueOneofCase.Map ? (global::LotusIR.ValueProto.Types.MapProto) value_ : null; } - set { - value_ = value; - valueCase_ = value == null ? ValueOneofCase.None : ValueOneofCase.Map; - } - } - - private object value_; - /// Enum of possible cases for the "value" oneof. - public enum ValueOneofCase { - None = 0, - DenseTensor = 1, - SparseTensor = 2, - Handle = 3, - Tuple = 4, - Seq = 5, - Map = 6, - } - private ValueOneofCase valueCase_ = ValueOneofCase.None; - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public ValueOneofCase ValueCase { - get { return valueCase_; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void ClearValue() { - valueCase_ = ValueOneofCase.None; - value_ = null; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as ValueProto); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(ValueProto other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if (!object.Equals(DenseTensor, other.DenseTensor)) return false; - if (!object.Equals(SparseTensor, other.SparseTensor)) return false; - if (!object.Equals(Handle, other.Handle)) return false; - if (!object.Equals(Tuple, other.Tuple)) return false; - if (!object.Equals(Seq, other.Seq)) return false; - if (!object.Equals(Map, other.Map)) return false; - if (ValueCase != other.ValueCase) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - if (valueCase_ == ValueOneofCase.DenseTensor) hash ^= DenseTensor.GetHashCode(); - if (valueCase_ == ValueOneofCase.SparseTensor) hash ^= SparseTensor.GetHashCode(); - if (valueCase_ == ValueOneofCase.Handle) hash ^= Handle.GetHashCode(); - if (valueCase_ == ValueOneofCase.Tuple) hash ^= Tuple.GetHashCode(); - if (valueCase_ == ValueOneofCase.Seq) hash ^= Seq.GetHashCode(); - if (valueCase_ == ValueOneofCase.Map) hash ^= Map.GetHashCode(); - hash ^= (int) valueCase_; - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - if (valueCase_ == ValueOneofCase.DenseTensor) { - output.WriteRawTag(10); - output.WriteMessage(DenseTensor); - } - if (valueCase_ == ValueOneofCase.SparseTensor) { - output.WriteRawTag(18); - output.WriteMessage(SparseTensor); - } - if (valueCase_ == ValueOneofCase.Handle) { - output.WriteRawTag(26); - output.WriteMessage(Handle); - } - if (valueCase_ == ValueOneofCase.Tuple) { - output.WriteRawTag(34); - output.WriteMessage(Tuple); - } - if (valueCase_ == ValueOneofCase.Seq) { - output.WriteRawTag(42); - output.WriteMessage(Seq); - } - if (valueCase_ == ValueOneofCase.Map) { - output.WriteRawTag(50); - output.WriteMessage(Map); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - if (valueCase_ == ValueOneofCase.DenseTensor) { - size += 1 + pb::CodedOutputStream.ComputeMessageSize(DenseTensor); - } - if (valueCase_ == ValueOneofCase.SparseTensor) { - size += 1 + pb::CodedOutputStream.ComputeMessageSize(SparseTensor); - } - if (valueCase_ == ValueOneofCase.Handle) { - size += 1 + pb::CodedOutputStream.ComputeMessageSize(Handle); - } - if (valueCase_ == ValueOneofCase.Tuple) { - size += 1 + pb::CodedOutputStream.ComputeMessageSize(Tuple); - } - if (valueCase_ == ValueOneofCase.Seq) { - size += 1 + pb::CodedOutputStream.ComputeMessageSize(Seq); - } - if (valueCase_ == ValueOneofCase.Map) { - size += 1 + pb::CodedOutputStream.ComputeMessageSize(Map); - } - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(ValueProto other) { - if (other == null) { - return; - } - switch (other.ValueCase) { - case ValueOneofCase.DenseTensor: - DenseTensor = other.DenseTensor; - break; - case ValueOneofCase.SparseTensor: - SparseTensor = other.SparseTensor; - break; - case ValueOneofCase.Handle: - Handle = other.Handle; - break; - case ValueOneofCase.Tuple: - Tuple = other.Tuple; - break; - case ValueOneofCase.Seq: - Seq = other.Seq; - break; - case ValueOneofCase.Map: - Map = other.Map; - break; - } - - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 10: { - global::LotusIR.TensorProto subBuilder = new global::LotusIR.TensorProto(); - if (valueCase_ == ValueOneofCase.DenseTensor) { - subBuilder.MergeFrom(DenseTensor); - } - input.ReadMessage(subBuilder); - DenseTensor = subBuilder; - break; - } - case 18: { - global::LotusIR.SparseTensorProto subBuilder = new global::LotusIR.SparseTensorProto(); - if (valueCase_ == ValueOneofCase.SparseTensor) { - subBuilder.MergeFrom(SparseTensor); - } - input.ReadMessage(subBuilder); - SparseTensor = subBuilder; - break; - } - case 26: { - global::LotusIR.ValueProto.Types.HandleProto subBuilder = new global::LotusIR.ValueProto.Types.HandleProto(); - if (valueCase_ == ValueOneofCase.Handle) { - subBuilder.MergeFrom(Handle); - } - input.ReadMessage(subBuilder); - Handle = subBuilder; - break; - } - case 34: { - global::LotusIR.ValueProto.Types.TupleProto subBuilder = new global::LotusIR.ValueProto.Types.TupleProto(); - if (valueCase_ == ValueOneofCase.Tuple) { - subBuilder.MergeFrom(Tuple); - } - input.ReadMessage(subBuilder); - Tuple = subBuilder; - break; - } - case 42: { - global::LotusIR.ValueProto.Types.SequenceProto subBuilder = new global::LotusIR.ValueProto.Types.SequenceProto(); - if (valueCase_ == ValueOneofCase.Seq) { - subBuilder.MergeFrom(Seq); - } - input.ReadMessage(subBuilder); - Seq = subBuilder; - break; - } - case 50: { - global::LotusIR.ValueProto.Types.MapProto subBuilder = new global::LotusIR.ValueProto.Types.MapProto(); - if (valueCase_ == ValueOneofCase.Map) { - subBuilder.MergeFrom(Map); - } - input.ReadMessage(subBuilder); - Map = subBuilder; - break; - } - } - } - } - - #region Nested types - /// Container for nested types declared in the ValueProto message type. - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static partial class Types { - /// - /// Defines a handle in its serialized format. - /// - public sealed partial class HandleProto : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new HandleProto()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::LotusIR.ValueProto.Descriptor.NestedTypes[0]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public HandleProto() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public HandleProto(HandleProto other) : this() { - uid_ = other.uid_; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public HandleProto Clone() { - return new HandleProto(this); - } - - /// Field number for the "uid" field. - public const int UidFieldNumber = 1; - private long uid_; - /// - /// This field MUST be present this version of the IR. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public long Uid { - get { return uid_; } - set { - uid_ = value; - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as HandleProto); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(HandleProto other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if (Uid != other.Uid) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - if (Uid != 0L) hash ^= Uid.GetHashCode(); - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - if (Uid != 0L) { - output.WriteRawTag(8); - output.WriteInt64(Uid); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - if (Uid != 0L) { - size += 1 + pb::CodedOutputStream.ComputeInt64Size(Uid); - } - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(HandleProto other) { - if (other == null) { - return; - } - if (other.Uid != 0L) { - Uid = other.Uid; - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 8: { - Uid = input.ReadInt64(); - break; - } - } - } - } - - } - - /// - /// Defines a tuple in its serialized format. - /// - public sealed partial class TupleProto : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new TupleProto()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::LotusIR.ValueProto.Descriptor.NestedTypes[1]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public TupleProto() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public TupleProto(TupleProto other) : this() { - elems_ = other.elems_.Clone(); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public TupleProto Clone() { - return new TupleProto(this); - } - - /// Field number for the "elems" field. - public const int ElemsFieldNumber = 1; - private static readonly pb::FieldCodec _repeated_elems_codec - = pb::FieldCodec.ForMessage(10, global::LotusIR.ValueProto.Parser); - private readonly pbc::RepeatedField elems_ = new pbc::RepeatedField(); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Elems { - get { return elems_; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as TupleProto); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(TupleProto other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if(!elems_.Equals(other.elems_)) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - hash ^= elems_.GetHashCode(); - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - elems_.WriteTo(output, _repeated_elems_codec); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - size += elems_.CalculateSize(_repeated_elems_codec); - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(TupleProto other) { - if (other == null) { - return; - } - elems_.Add(other.elems_); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 10: { - elems_.AddEntriesFrom(input, _repeated_elems_codec); - break; - } - } - } - } - - } - - /// - /// Defines a sequence in its serialized format. - /// - public sealed partial class SequenceProto : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new SequenceProto()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::LotusIR.ValueProto.Descriptor.NestedTypes[2]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public SequenceProto() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public SequenceProto(SequenceProto other) : this() { - elems_ = other.elems_.Clone(); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public SequenceProto Clone() { - return new SequenceProto(this); - } - - /// Field number for the "elems" field. - public const int ElemsFieldNumber = 1; - private static readonly pb::FieldCodec _repeated_elems_codec - = pb::FieldCodec.ForMessage(10, global::LotusIR.ValueProto.Parser); - private readonly pbc::RepeatedField elems_ = new pbc::RepeatedField(); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Elems { - get { return elems_; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as SequenceProto); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(SequenceProto other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if(!elems_.Equals(other.elems_)) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - hash ^= elems_.GetHashCode(); - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - elems_.WriteTo(output, _repeated_elems_codec); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - size += elems_.CalculateSize(_repeated_elems_codec); - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(SequenceProto other) { - if (other == null) { - return; - } - elems_.Add(other.elems_); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 10: { - elems_.AddEntriesFrom(input, _repeated_elems_codec); - break; - } - } - } - } - - } - - /// - /// Defines a map in its serialized format. - /// Maps are serialized as two single-dimensional tensors - /// for storage efficiency. The dimensions of each tensor MUST be identical - /// and the key at position N corresponds to the value at position N. - /// Keys SHOULD be unique. When a given key appears multiple times, - /// the value that corresponds last occurance of the key is the value. - /// This is consistent with protobuf3 encoding rules for map. - /// - public sealed partial class MapProto : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new MapProto()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::LotusIR.ValueProto.Descriptor.NestedTypes[3]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public MapProto() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public MapProto(MapProto other) : this() { - Keys = other.keys_ != null ? other.Keys.Clone() : null; - Values = other.values_ != null ? other.Values.Clone() : null; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public MapProto Clone() { - return new MapProto(this); - } - - /// Field number for the "keys" field. - public const int KeysFieldNumber = 1; - private global::LotusIR.TensorProto keys_; - /// - /// This field MUST be present for this version of the IR. - /// The data type of the tensor MUST refer to an integral type ([U]INT{8|16|32|64}) or STRING - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::LotusIR.TensorProto Keys { - get { return keys_; } - set { - keys_ = value; - } - } - - /// Field number for the "values" field. - public const int ValuesFieldNumber = 2; - private global::LotusIR.TensorProto values_; - /// - /// This field MUST be present for this version of the IR. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::LotusIR.TensorProto Values { - get { return values_; } - set { - values_ = value; - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as MapProto); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(MapProto other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if (!object.Equals(Keys, other.Keys)) return false; - if (!object.Equals(Values, other.Values)) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - if (keys_ != null) hash ^= Keys.GetHashCode(); - if (values_ != null) hash ^= Values.GetHashCode(); - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - if (keys_ != null) { - output.WriteRawTag(10); - output.WriteMessage(Keys); - } - if (values_ != null) { - output.WriteRawTag(18); - output.WriteMessage(Values); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - if (keys_ != null) { - size += 1 + pb::CodedOutputStream.ComputeMessageSize(Keys); - } - if (values_ != null) { - size += 1 + pb::CodedOutputStream.ComputeMessageSize(Values); - } - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(MapProto other) { - if (other == null) { - return; - } - if (other.keys_ != null) { - if (keys_ == null) { - keys_ = new global::LotusIR.TensorProto(); - } - Keys.MergeFrom(other.Keys); - } - if (other.values_ != null) { - if (values_ == null) { - values_ = new global::LotusIR.TensorProto(); - } - Values.MergeFrom(other.Values); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 10: { - if (keys_ == null) { - keys_ = new global::LotusIR.TensorProto(); - } - input.ReadMessage(keys_); - break; - } - case 18: { - if (values_ == null) { - values_ = new global::LotusIR.TensorProto(); - } - input.ReadMessage(values_); - break; - } - } - } - } - - } - - } - #endregion - - } - - public sealed partial class ParameterDeclProto : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new ParameterDeclProto()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::LotusIR.GraphReflection.Descriptor.MessageTypes[9]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public ParameterDeclProto() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public ParameterDeclProto(ParameterDeclProto other) : this() { - name_ = other.name_; - Type = other.type_ != null ? other.Type.Clone() : null; - docString_ = other.docString_; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public ParameterDeclProto Clone() { - return new ParameterDeclProto(this); - } - - /// Field number for the "name" field. - public const int NameFieldNumber = 1; - private string name_ = ""; - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string Name { - get { return name_; } - set { - name_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "type" field. - public const int TypeFieldNumber = 2; - private global::LotusIR.TypeProto type_; - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::LotusIR.TypeProto Type { - get { return type_; } - set { - type_ = value; - } - } - - /// Field number for the "doc_string" field. - public const int DocStringFieldNumber = 3; - private string docString_ = ""; - /// - /// An human-readable documentation for this parameter. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string DocString { - get { return docString_; } - set { - docString_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as ParameterDeclProto); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(ParameterDeclProto other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if (Name != other.Name) return false; - if (!object.Equals(Type, other.Type)) return false; - if (DocString != other.DocString) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - if (Name.Length != 0) hash ^= Name.GetHashCode(); - if (type_ != null) hash ^= Type.GetHashCode(); - if (DocString.Length != 0) hash ^= DocString.GetHashCode(); - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - if (Name.Length != 0) { - output.WriteRawTag(10); - output.WriteString(Name); - } - if (type_ != null) { - output.WriteRawTag(18); - output.WriteMessage(Type); - } - if (DocString.Length != 0) { - output.WriteRawTag(26); - output.WriteString(DocString); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - if (Name.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(Name); - } - if (type_ != null) { - size += 1 + pb::CodedOutputStream.ComputeMessageSize(Type); - } - if (DocString.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(DocString); - } - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(ParameterDeclProto other) { - if (other == null) { - return; - } - if (other.Name.Length != 0) { - Name = other.Name; - } - if (other.type_ != null) { - if (type_ == null) { - type_ = new global::LotusIR.TypeProto(); - } - Type.MergeFrom(other.Type); - } - if (other.DocString.Length != 0) { - DocString = other.DocString; - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 10: { - Name = input.ReadString(); - break; - } - case 18: { - if (type_ == null) { - type_ = new global::LotusIR.TypeProto(); - } - input.ReadMessage(type_); - break; - } - case 26: { - DocString = input.ReadString(); - break; - } - } - } - } - - } - - /// - /// Defines a function. - /// - public sealed partial class FunctionDefProto : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new FunctionDefProto()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::LotusIR.GraphReflection.Descriptor.MessageTypes[10]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public FunctionDefProto() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public FunctionDefProto(FunctionDefProto other) : this() { - name_ = other.name_; - inputParams_ = other.inputParams_.Clone(); - outputParams_ = other.outputParams_.Clone(); - node_ = other.node_.Clone(); - attr_ = other.attr_.Clone(); - docString_ = other.docString_; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public FunctionDefProto Clone() { - return new FunctionDefProto(this); - } - - /// Field number for the "name" field. - public const int NameFieldNumber = 1; - private string name_ = ""; - /// - /// The name of the function. - /// This field MUST be present for this version of the IR. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string Name { - get { return name_; } - set { - name_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "input_params" field. - public const int InputParamsFieldNumber = 2; - private static readonly pb::FieldCodec _repeated_inputParams_codec - = pb::FieldCodec.ForMessage(18, global::LotusIR.ParameterDeclProto.Parser); - private readonly pbc::RepeatedField inputParams_ = new pbc::RepeatedField(); - /// - /// The input parameters of the function. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField InputParams { - get { return inputParams_; } - } - - /// Field number for the "output_params" field. - public const int OutputParamsFieldNumber = 3; - private static readonly pb::FieldCodec _repeated_outputParams_codec - = pb::FieldCodec.ForMessage(26, global::LotusIR.ParameterDeclProto.Parser); - private readonly pbc::RepeatedField outputParams_ = new pbc::RepeatedField(); - /// - /// The output parameters of the function. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField OutputParams { - get { return outputParams_; } - } - - /// Field number for the "node" field. - public const int NodeFieldNumber = 4; - private static readonly pb::FieldCodec _repeated_node_codec - = pb::FieldCodec.ForMessage(34, global::LotusIR.NodeProto.Parser); - private readonly pbc::RepeatedField node_ = new pbc::RepeatedField(); - /// - /// The body of the function. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Node { - get { return node_; } - } - - /// Field number for the "attr" field. - public const int AttrFieldNumber = 5; - private static readonly pb::FieldCodec _repeated_attr_codec - = pb::FieldCodec.ForMessage(42, global::LotusIR.AttributeProto.Parser); - private readonly pbc::RepeatedField attr_ = new pbc::RepeatedField(); - /// - /// The named attributes of the function. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Attr { - get { return attr_; } - } - - /// Field number for the "doc_string" field. - public const int DocStringFieldNumber = 6; - private string docString_ = ""; - /// - /// An human-readable documentation for this node in the graph. - /// This text MAY contain Markdown markup that conforms to http://commonmark.org/. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string DocString { - get { return docString_; } - set { - docString_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as FunctionDefProto); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(FunctionDefProto other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if (Name != other.Name) return false; - if(!inputParams_.Equals(other.inputParams_)) return false; - if(!outputParams_.Equals(other.outputParams_)) return false; - if(!node_.Equals(other.node_)) return false; - if(!attr_.Equals(other.attr_)) return false; - if (DocString != other.DocString) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - if (Name.Length != 0) hash ^= Name.GetHashCode(); - hash ^= inputParams_.GetHashCode(); - hash ^= outputParams_.GetHashCode(); - hash ^= node_.GetHashCode(); - hash ^= attr_.GetHashCode(); - if (DocString.Length != 0) hash ^= DocString.GetHashCode(); - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - if (Name.Length != 0) { - output.WriteRawTag(10); - output.WriteString(Name); - } - inputParams_.WriteTo(output, _repeated_inputParams_codec); - outputParams_.WriteTo(output, _repeated_outputParams_codec); - node_.WriteTo(output, _repeated_node_codec); - attr_.WriteTo(output, _repeated_attr_codec); - if (DocString.Length != 0) { - output.WriteRawTag(50); - output.WriteString(DocString); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - if (Name.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(Name); - } - size += inputParams_.CalculateSize(_repeated_inputParams_codec); - size += outputParams_.CalculateSize(_repeated_outputParams_codec); - size += node_.CalculateSize(_repeated_node_codec); - size += attr_.CalculateSize(_repeated_attr_codec); - if (DocString.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(DocString); - } - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(FunctionDefProto other) { - if (other == null) { - return; - } - if (other.Name.Length != 0) { - Name = other.Name; - } - inputParams_.Add(other.inputParams_); - outputParams_.Add(other.outputParams_); - node_.Add(other.node_); - attr_.Add(other.attr_); - if (other.DocString.Length != 0) { - DocString = other.DocString; - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 10: { - Name = input.ReadString(); - break; - } - case 18: { - inputParams_.AddEntriesFrom(input, _repeated_inputParams_codec); - break; - } - case 26: { - outputParams_.AddEntriesFrom(input, _repeated_outputParams_codec); - break; - } - case 34: { - node_.AddEntriesFrom(input, _repeated_node_codec); - break; - } - case 42: { - attr_.AddEntriesFrom(input, _repeated_attr_codec); - break; - } - case 50: { - DocString = input.ReadString(); - break; - } - } - } - } - - } - - public sealed partial class SignatureDeclProto : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new SignatureDeclProto()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::LotusIR.GraphReflection.Descriptor.MessageTypes[11]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public SignatureDeclProto() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public SignatureDeclProto(SignatureDeclProto other) : this() { - inputParams_ = other.inputParams_.Clone(); - outputParams_ = other.outputParams_.Clone(); - inputAttributes_ = other.inputAttributes_.Clone(); - docString_ = other.docString_; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public SignatureDeclProto Clone() { - return new SignatureDeclProto(this); - } - - /// Field number for the "input_params" field. - public const int InputParamsFieldNumber = 1; - private static readonly pb::FieldCodec _repeated_inputParams_codec - = pb::FieldCodec.ForMessage(10, global::LotusIR.ParameterDeclProto.Parser); - private readonly pbc::RepeatedField inputParams_ = new pbc::RepeatedField(); - /// - /// The formal input parameters to the operation or function - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField InputParams { - get { return inputParams_; } - } - - /// Field number for the "output_params" field. - public const int OutputParamsFieldNumber = 2; - private static readonly pb::FieldCodec _repeated_outputParams_codec - = pb::FieldCodec.ForMessage(18, global::LotusIR.ParameterDeclProto.Parser); - private readonly pbc::RepeatedField outputParams_ = new pbc::RepeatedField(); - /// - /// The formal output parameters to the operation or function - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField OutputParams { - get { return outputParams_; } - } - - /// Field number for the "input_attributes" field. - public const int InputAttributesFieldNumber = 3; - private static readonly pb::FieldCodec _repeated_inputAttributes_codec - = pb::FieldCodec.ForMessage(26, global::LotusIR.ParameterDeclProto.Parser); - private readonly pbc::RepeatedField inputAttributes_ = new pbc::RepeatedField(); - /// - /// The declaration of expected attributes to the operation or function - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField InputAttributes { - get { return inputAttributes_; } - } - - /// Field number for the "doc_string" field. - public const int DocStringFieldNumber = 4; - private string docString_ = ""; - /// - /// An human-readable documentation for this signature. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string DocString { - get { return docString_; } - set { - docString_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as SignatureDeclProto); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(SignatureDeclProto other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if(!inputParams_.Equals(other.inputParams_)) return false; - if(!outputParams_.Equals(other.outputParams_)) return false; - if(!inputAttributes_.Equals(other.inputAttributes_)) return false; - if (DocString != other.DocString) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - hash ^= inputParams_.GetHashCode(); - hash ^= outputParams_.GetHashCode(); - hash ^= inputAttributes_.GetHashCode(); - if (DocString.Length != 0) hash ^= DocString.GetHashCode(); - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - inputParams_.WriteTo(output, _repeated_inputParams_codec); - outputParams_.WriteTo(output, _repeated_outputParams_codec); - inputAttributes_.WriteTo(output, _repeated_inputAttributes_codec); - if (DocString.Length != 0) { - output.WriteRawTag(34); - output.WriteString(DocString); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - size += inputParams_.CalculateSize(_repeated_inputParams_codec); - size += outputParams_.CalculateSize(_repeated_outputParams_codec); - size += inputAttributes_.CalculateSize(_repeated_inputAttributes_codec); - if (DocString.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(DocString); - } - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(SignatureDeclProto other) { - if (other == null) { - return; - } - inputParams_.Add(other.inputParams_); - outputParams_.Add(other.outputParams_); - inputAttributes_.Add(other.inputAttributes_); - if (other.DocString.Length != 0) { - DocString = other.DocString; - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 10: { - inputParams_.AddEntriesFrom(input, _repeated_inputParams_codec); - break; - } - case 18: { - outputParams_.AddEntriesFrom(input, _repeated_outputParams_codec); - break; - } - case 26: { - inputAttributes_.AddEntriesFrom(input, _repeated_inputAttributes_codec); - break; - } - case 34: { - DocString = input.ReadString(); - break; - } - } - } - } - - } - - public sealed partial class OperatorDeclProto : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new OperatorDeclProto()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::LotusIR.GraphReflection.Descriptor.MessageTypes[12]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public OperatorDeclProto() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public OperatorDeclProto(OperatorDeclProto other) : this() { - name_ = other.name_; - signature_ = other.signature_.Clone(); - docString_ = other.docString_; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public OperatorDeclProto Clone() { - return new OperatorDeclProto(this); - } - - /// Field number for the "name" field. - public const int NameFieldNumber = 1; - private string name_ = ""; - /// - /// This field MUST be present for this version of the IR. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string Name { - get { return name_; } - set { - name_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "signature" field. - public const int SignatureFieldNumber = 2; - private static readonly pb::FieldCodec _repeated_signature_codec - = pb::FieldCodec.ForMessage(18, global::LotusIR.SignatureDeclProto.Parser); - private readonly pbc::RepeatedField signature_ = new pbc::RepeatedField(); - /// - /// This field MUST contain at least one SignatureDeclProto. - /// This field MAY contain multiple SignatureDeclProtos, one - /// per type signature supported by this operator. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Signature { - get { return signature_; } - } - - /// Field number for the "doc_string" field. - public const int DocStringFieldNumber = 3; - private string docString_ = ""; - /// - /// An human-readable documentation for this operator. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string DocString { - get { return docString_; } - set { - docString_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as OperatorDeclProto); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(OperatorDeclProto other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if (Name != other.Name) return false; - if(!signature_.Equals(other.signature_)) return false; - if (DocString != other.DocString) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - if (Name.Length != 0) hash ^= Name.GetHashCode(); - hash ^= signature_.GetHashCode(); - if (DocString.Length != 0) hash ^= DocString.GetHashCode(); - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - if (Name.Length != 0) { - output.WriteRawTag(10); - output.WriteString(Name); - } - signature_.WriteTo(output, _repeated_signature_codec); - if (DocString.Length != 0) { - output.WriteRawTag(26); - output.WriteString(DocString); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - if (Name.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(Name); - } - size += signature_.CalculateSize(_repeated_signature_codec); - if (DocString.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(DocString); - } - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(OperatorDeclProto other) { - if (other == null) { - return; - } - if (other.Name.Length != 0) { - Name = other.Name; - } - signature_.Add(other.signature_); - if (other.DocString.Length != 0) { - DocString = other.DocString; - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 10: { - Name = input.ReadString(); - break; - } - case 18: { - signature_.AddEntriesFrom(input, _repeated_signature_codec); - break; - } - case 26: { - DocString = input.ReadString(); - break; - } - } - } - } - - } - - /// - /// A library is a top-level format that contains the declaration - /// of operators and the definition of functions. - /// - public sealed partial class LibraryProto : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new LibraryProto()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::LotusIR.GraphReflection.Descriptor.MessageTypes[13]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public LibraryProto() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public LibraryProto(LibraryProto other) : this() { - irVersion_ = other.irVersion_; - producerVersion_ = other.producerVersion_; - producerTag_ = other.producerTag_; - modelVersion_ = other.modelVersion_; - modelAuthor_ = other.modelAuthor_; - modelLicense_ = other.modelLicense_; - name_ = other.name_; - domain_ = other.domain_; - docString_ = other.docString_; - operator_ = other.operator_.Clone(); - function_ = other.function_.Clone(); - importedLibraries_ = other.importedLibraries_.Clone(); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public LibraryProto Clone() { - return new LibraryProto(this); - } - - /// Field number for the "ir_version" field. - public const int IrVersionFieldNumber = 1; - private long irVersion_; - /// - /// The version of the IR this graph targets. See Version enum below. - /// This field MUST be present this version of the IR. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public long IrVersion { - get { return irVersion_; } - set { - irVersion_ = value; - } - } - - /// Field number for the "producer_version" field. - public const int ProducerVersionFieldNumber = 2; - private long producerVersion_; - /// - /// The version of the framework runtime that generates this graph. - /// This producer_version has the same format as ir_version. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public long ProducerVersion { - get { return producerVersion_; } - set { - producerVersion_ = value; - } - } - - /// Field number for the "producer_tag" field. - public const int ProducerTagFieldNumber = 3; - private string producerTag_ = ""; - /// - /// The name of the framework used to generate this graph in the form - /// "framework_name[-tag]". Tag is and provides additional - /// information such as `alpha` or `beta` or `rc3`. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string ProducerTag { - get { return producerTag_; } - set { - producerTag_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "model_version" field. - public const int ModelVersionFieldNumber = 4; - private long modelVersion_; - /// - /// An version identifier used to track evolution of this library. - /// This model_version has the same format as ir_version. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public long ModelVersion { - get { return modelVersion_; } - set { - modelVersion_ = value; - } - } - - /// Field number for the "model_author" field. - public const int ModelAuthorFieldNumber = 5; - private string modelAuthor_ = ""; - /// - /// The name of the author who created the library. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string ModelAuthor { - get { return modelAuthor_; } - set { - modelAuthor_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "model_license" field. - public const int ModelLicenseFieldNumber = 6; - private string modelLicense_ = ""; - /// - /// licensing information concerning use or origination of the library. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string ModelLicense { - get { return modelLicense_; } - set { - modelLicense_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "name" field. - public const int NameFieldNumber = 7; - private string name_ = ""; - /// - /// The name of the library. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string Name { - get { return name_; } - set { - name_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "domain" field. - public const int DomainFieldNumber = 8; - private string domain_ = ""; - /// - /// Domain of the graph. - /// We use reverse domain names as name space indicators. For example: - /// `com.facebook.fair` or `com.microsoft.cognitiveservices` - /// - /// Together with `name` and `model_version`, this forms the unique identity of - /// the library. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string Domain { - get { return domain_; } - set { - domain_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "doc_string" field. - public const int DocStringFieldNumber = 9; - private string docString_ = ""; - /// - /// An human-readable documentation for this graph. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string DocString { - get { return docString_; } - set { - docString_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "operator" field. - public const int OperatorFieldNumber = 10; - private static readonly pb::FieldCodec _repeated_operator_codec - = pb::FieldCodec.ForMessage(82, global::LotusIR.OperatorDeclProto.Parser); - private readonly pbc::RepeatedField operator_ = new pbc::RepeatedField(); - /// - /// The operators declared by this library. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Operator { - get { return operator_; } - } - - /// Field number for the "function" field. - public const int FunctionFieldNumber = 11; - private static readonly pb::FieldCodec _repeated_function_codec - = pb::FieldCodec.ForMessage(90, global::LotusIR.FunctionDefProto.Parser); - private readonly pbc::RepeatedField function_ = new pbc::RepeatedField(); - /// - /// The function definitions of the library. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Function { - get { return function_; } - } - - /// Field number for the "imported_libraries" field. - public const int ImportedLibrariesFieldNumber = 12; - private static readonly pb::FieldCodec _repeated_importedLibraries_codec - = pb::FieldCodec.ForString(98); - private readonly pbc::RepeatedField importedLibraries_ = new pbc::RepeatedField(); - /// - /// Imported libraries are referenced as a collection of strings in the form of absolute - /// URIs or relative paths. Where such relative paths are rooted is defined by tools and - /// runtime implementations. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField ImportedLibraries { - get { return importedLibraries_; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as LibraryProto); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(LibraryProto other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if (IrVersion != other.IrVersion) return false; - if (ProducerVersion != other.ProducerVersion) return false; - if (ProducerTag != other.ProducerTag) return false; - if (ModelVersion != other.ModelVersion) return false; - if (ModelAuthor != other.ModelAuthor) return false; - if (ModelLicense != other.ModelLicense) return false; - if (Name != other.Name) return false; - if (Domain != other.Domain) return false; - if (DocString != other.DocString) return false; - if(!operator_.Equals(other.operator_)) return false; - if(!function_.Equals(other.function_)) return false; - if(!importedLibraries_.Equals(other.importedLibraries_)) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - if (IrVersion != 0L) hash ^= IrVersion.GetHashCode(); - if (ProducerVersion != 0L) hash ^= ProducerVersion.GetHashCode(); - if (ProducerTag.Length != 0) hash ^= ProducerTag.GetHashCode(); - if (ModelVersion != 0L) hash ^= ModelVersion.GetHashCode(); - if (ModelAuthor.Length != 0) hash ^= ModelAuthor.GetHashCode(); - if (ModelLicense.Length != 0) hash ^= ModelLicense.GetHashCode(); - if (Name.Length != 0) hash ^= Name.GetHashCode(); - if (Domain.Length != 0) hash ^= Domain.GetHashCode(); - if (DocString.Length != 0) hash ^= DocString.GetHashCode(); - hash ^= operator_.GetHashCode(); - hash ^= function_.GetHashCode(); - hash ^= importedLibraries_.GetHashCode(); - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - if (IrVersion != 0L) { - output.WriteRawTag(8); - output.WriteInt64(IrVersion); - } - if (ProducerVersion != 0L) { - output.WriteRawTag(16); - output.WriteInt64(ProducerVersion); - } - if (ProducerTag.Length != 0) { - output.WriteRawTag(26); - output.WriteString(ProducerTag); - } - if (ModelVersion != 0L) { - output.WriteRawTag(32); - output.WriteInt64(ModelVersion); - } - if (ModelAuthor.Length != 0) { - output.WriteRawTag(42); - output.WriteString(ModelAuthor); - } - if (ModelLicense.Length != 0) { - output.WriteRawTag(50); - output.WriteString(ModelLicense); - } - if (Name.Length != 0) { - output.WriteRawTag(58); - output.WriteString(Name); - } - if (Domain.Length != 0) { - output.WriteRawTag(66); - output.WriteString(Domain); - } - if (DocString.Length != 0) { - output.WriteRawTag(74); - output.WriteString(DocString); - } - operator_.WriteTo(output, _repeated_operator_codec); - function_.WriteTo(output, _repeated_function_codec); - importedLibraries_.WriteTo(output, _repeated_importedLibraries_codec); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - if (IrVersion != 0L) { - size += 1 + pb::CodedOutputStream.ComputeInt64Size(IrVersion); - } - if (ProducerVersion != 0L) { - size += 1 + pb::CodedOutputStream.ComputeInt64Size(ProducerVersion); - } - if (ProducerTag.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(ProducerTag); - } - if (ModelVersion != 0L) { - size += 1 + pb::CodedOutputStream.ComputeInt64Size(ModelVersion); - } - if (ModelAuthor.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(ModelAuthor); - } - if (ModelLicense.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(ModelLicense); - } - if (Name.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(Name); - } - if (Domain.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(Domain); - } - if (DocString.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(DocString); - } - size += operator_.CalculateSize(_repeated_operator_codec); - size += function_.CalculateSize(_repeated_function_codec); - size += importedLibraries_.CalculateSize(_repeated_importedLibraries_codec); - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(LibraryProto other) { - if (other == null) { - return; - } - if (other.IrVersion != 0L) { - IrVersion = other.IrVersion; - } - if (other.ProducerVersion != 0L) { - ProducerVersion = other.ProducerVersion; - } - if (other.ProducerTag.Length != 0) { - ProducerTag = other.ProducerTag; - } - if (other.ModelVersion != 0L) { - ModelVersion = other.ModelVersion; - } - if (other.ModelAuthor.Length != 0) { - ModelAuthor = other.ModelAuthor; - } - if (other.ModelLicense.Length != 0) { - ModelLicense = other.ModelLicense; - } - if (other.Name.Length != 0) { - Name = other.Name; - } - if (other.Domain.Length != 0) { - Domain = other.Domain; - } - if (other.DocString.Length != 0) { - DocString = other.DocString; - } - operator_.Add(other.operator_); - function_.Add(other.function_); - importedLibraries_.Add(other.importedLibraries_); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 8: { - IrVersion = input.ReadInt64(); - break; - } - case 16: { - ProducerVersion = input.ReadInt64(); - break; - } - case 26: { - ProducerTag = input.ReadString(); - break; - } - case 32: { - ModelVersion = input.ReadInt64(); - break; - } - case 42: { - ModelAuthor = input.ReadString(); - break; - } - case 50: { - ModelLicense = input.ReadString(); - break; - } - case 58: { - Name = input.ReadString(); - break; - } - case 66: { - Domain = input.ReadString(); - break; - } - case 74: { - DocString = input.ReadString(); - break; - } - case 82: { - operator_.AddEntriesFrom(input, _repeated_operator_codec); - break; - } - case 90: { - function_.AddEntriesFrom(input, _repeated_function_codec); - break; - } - case 98: { - importedLibraries_.AddEntriesFrom(input, _repeated_importedLibraries_codec); - break; - } - } - } - } - - } - - #endregion - -} - -#endregion Designer generated code diff --git a/src/Microsoft.ML.UniversalModelFormat/LotusIR/OnnxMl.cs b/src/Microsoft.ML.UniversalModelFormat/LotusIR/OnnxMl.cs deleted file mode 100644 index 8bf193202e..0000000000 --- a/src/Microsoft.ML.UniversalModelFormat/LotusIR/OnnxMl.cs +++ /dev/null @@ -1,3752 +0,0 @@ -// Generated by the protocol buffer compiler. DO NOT EDIT! -// source: onnx-ml.proto3 -#pragma warning disable 1591, 0612, 3021 -#region Designer generated code - -using pb = global::Google.Protobuf; -using pbc = global::Google.Protobuf.Collections; -using pbr = global::Google.Protobuf.Reflection; -using scg = global::System.Collections.Generic; -namespace Microsoft.ML.Runtime.UniversalModelFormat.Onnx { - - /// Holder for reflection information generated from onnx-ml.proto3 - public static partial class OnnxMlReflection { - - #region Descriptor - /// File descriptor for onnx-ml.proto3 - public static pbr::FileDescriptor Descriptor { - get { return descriptor; } - } - private static pbr::FileDescriptor descriptor; - - static OnnxMlReflection() { - byte[] descriptorData = global::System.Convert.FromBase64String( - string.Concat( - "Cg5vbm54LW1sLnByb3RvMxIOT05OWF9OQU1FU1BBQ0Ui+wMKDkF0dHJpYnV0", - "ZVByb3RvEgwKBG5hbWUYASABKAkSEgoKZG9jX3N0cmluZxgNIAEoCRI6CgR0", - "eXBlGBQgASgOMiwuT05OWF9OQU1FU1BBQ0UuQXR0cmlidXRlUHJvdG8uQXR0", - "cmlidXRlVHlwZRIJCgFmGAIgASgCEgkKAWkYAyABKAMSCQoBcxgEIAEoDBIm", - 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new pbr::GeneratedClrTypeInfo(typeof(global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.AttributeProto), global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.AttributeProto.Parser, new[]{ "Name", "DocString", "Type", "F", "I", "S", "T", "G", "Floats", "Ints", "Strings", "Tensors", "Graphs" }, null, new[]{ typeof(global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.AttributeProto.Types.AttributeType) }, null), - new pbr::GeneratedClrTypeInfo(typeof(global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.ValueInfoProto), global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.ValueInfoProto.Parser, new[]{ "Name", "Type", "DocString" }, null, null, null), - new pbr::GeneratedClrTypeInfo(typeof(global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.NodeProto), global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.NodeProto.Parser, new[]{ "Input", "Output", "Name", "OpType", "Domain", "Attribute", "DocString" }, null, null, null), - new pbr::GeneratedClrTypeInfo(typeof(global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.ModelProto), global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.ModelProto.Parser, new[]{ "IrVersion", "OpsetImport", "ProducerName", "ProducerVersion", "Domain", "ModelVersion", "DocString", "Graph", "MetadataProps" }, null, null, null), - new pbr::GeneratedClrTypeInfo(typeof(global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.StringStringEntryProto), global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.StringStringEntryProto.Parser, new[]{ "Key", "Value" }, null, null, null), - new pbr::GeneratedClrTypeInfo(typeof(global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.GraphProto), global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.GraphProto.Parser, new[]{ "Node", "Name", "Initializer", "DocString", "Input", "Output", "ValueInfo" }, null, null, null), - new pbr::GeneratedClrTypeInfo(typeof(global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto), global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Parser, new[]{ "Dims", "DataType", "Segment", "FloatData", "Int32Data", "StringData", "Int64Data", "Name", "DocString", "RawData", "DoubleData", "Uint64Data" }, null, new[]{ typeof(global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Types.DataType) }, new pbr::GeneratedClrTypeInfo[] { new pbr::GeneratedClrTypeInfo(typeof(global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Types.Segment), global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Types.Segment.Parser, new[]{ "Begin", "End" }, null, null, null)}), - new pbr::GeneratedClrTypeInfo(typeof(global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorShapeProto), global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorShapeProto.Parser, new[]{ "Dim" }, null, null, new pbr::GeneratedClrTypeInfo[] { new pbr::GeneratedClrTypeInfo(typeof(global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorShapeProto.Types.Dimension), global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorShapeProto.Types.Dimension.Parser, new[]{ "DimValue", "DimParam" }, new[]{ "Value" }, null, null)}), - new pbr::GeneratedClrTypeInfo(typeof(global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto), global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Parser, new[]{ "TensorType", "SequenceType", "MapType" }, new[]{ "Value" }, null, new pbr::GeneratedClrTypeInfo[] { new pbr::GeneratedClrTypeInfo(typeof(global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Types.Tensor), global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Types.Tensor.Parser, new[]{ "ElemType", "Shape" }, null, null, null), - new pbr::GeneratedClrTypeInfo(typeof(global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Types.Sequence), global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Types.Sequence.Parser, new[]{ "ElemType" }, null, null, null), - new pbr::GeneratedClrTypeInfo(typeof(global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Types.Map), global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Types.Map.Parser, new[]{ "KeyType", "ValueType" }, null, null, null)}), - new pbr::GeneratedClrTypeInfo(typeof(global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.OperatorSetIdProto), global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.OperatorSetIdProto.Parser, new[]{ "Domain", "Version" }, null, null, null) - })); - } - #endregion - - } - #region Enums - /// - /// To be compatible with both proto2 and proto3, we will use a version number - /// that is not defined by the default value but an explicit enum number. - /// - public enum Version { - /// - /// proto3 requires the first enum value to be zero. - /// We add this just to appease the compiler. - /// - [pbr::OriginalName("_START_VERSION")] StartVersion = 0, - /// - /// The version field is always serialized and we will use it to store the - /// version that the graph is generated from. This helps us set up version - /// control. We should use version as - /// xx(major) - xx(minor) - xxxx(bugfix) - /// and we are starting with 0x00000001 (0.0.1), which was the - /// version we published on Oct 10, 2017. - /// - [pbr::OriginalName("IR_VERSION_2017_10_10")] IrVersion20171010 = 1, - /// - /// IR_VERSION 0.0.2 published on Oct 30, 2017 - /// - Added type discriminator to AttributeProto to support proto3 users - /// - [pbr::OriginalName("IR_VERSION_2017_10_30")] IrVersion20171030 = 2, - /// - /// IR VERSION 0.0.3 published on Nov 3, 2017 - /// - For operator versioning: - /// - Added new message OperatorSetIdProto - /// - Added opset_import in ModelProto - /// - For vendor extensions, added domain in NodeProto - /// - [pbr::OriginalName("IR_VERSION")] IrVersion = 3, - } - - #endregion - - #region Messages - /// - /// A named attribute containing either singular float, integer, string - /// and tensor values, or repeated float, integer, string and tensor values. - /// An AttributeProto MUST contain the name field, and *only one* of the - /// following content fields, effectively enforcing a C/C++ union equivalent. - /// - public sealed partial class AttributeProto : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new AttributeProto()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.OnnxMlReflection.Descriptor.MessageTypes[0]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public AttributeProto() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public AttributeProto(AttributeProto other) : this() { - name_ = other.name_; - docString_ = other.docString_; - type_ = other.type_; - f_ = other.f_; - i_ = other.i_; - s_ = other.s_; - T = other.t_ != null ? other.T.Clone() : null; - G = other.g_ != null ? other.G.Clone() : null; - floats_ = other.floats_.Clone(); - ints_ = other.ints_.Clone(); - strings_ = other.strings_.Clone(); - tensors_ = other.tensors_.Clone(); - graphs_ = other.graphs_.Clone(); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public AttributeProto Clone() { - return new AttributeProto(this); - } - - /// Field number for the "name" field. - public const int NameFieldNumber = 1; - private string name_ = ""; - /// - /// The name field MUST be present for this version of the IR. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string Name { - get { return name_; } - set { - name_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "doc_string" field. - public const int DocStringFieldNumber = 13; - private string docString_ = ""; - /// - /// A human-readable documentation for this attribute. Markdown is allowed. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string DocString { - get { return docString_; } - set { - docString_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "type" field. - public const int TypeFieldNumber = 20; - private global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.AttributeProto.Types.AttributeType type_ = 0; - /// - /// The type field MUST be present for this version of the IR. - /// For 0.0.1 versions of the IR, this field was not defined, and - /// implementations needed to use has_field hueristics to determine - /// which value field was in use. For IR_VERSION 0.0.2 or later, this - /// field MUST be set and match the f|i|s|t|... field in use. This - /// change was made to accomodate proto3 implementations. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.AttributeProto.Types.AttributeType Type { - get { return type_; } - set { - type_ = value; - } - } - - /// Field number for the "f" field. - public const int FFieldNumber = 2; - private float f_; - /// - /// Exactly ONE of the following fields must be present for this version of the IR - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public float F { - get { return f_; } - set { - f_ = value; - } - } - - /// Field number for the "i" field. - public const int IFieldNumber = 3; - private long i_; - /// - /// int - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public long I { - get { return i_; } - set { - i_ = value; - } - } - - /// Field number for the "s" field. - public const int SFieldNumber = 4; - private pb::ByteString s_ = pb::ByteString.Empty; - /// - /// UTF-8 string - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pb::ByteString S { - get { return s_; } - set { - s_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "t" field. - public const int TFieldNumber = 5; - private global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto t_; - /// - /// tensor value - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto T { - get { return t_; } - set { - t_ = value; - } - } - - /// Field number for the "g" field. - public const int GFieldNumber = 6; - private global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.GraphProto g_; - /// - /// graph - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.GraphProto G { - get { return g_; } - set { - g_ = value; - } - } - - /// Field number for the "floats" field. - public const int FloatsFieldNumber = 7; - private static readonly pb::FieldCodec _repeated_floats_codec - = pb::FieldCodec.ForFloat(58); - private readonly pbc::RepeatedField floats_ = new pbc::RepeatedField(); - /// - /// list of floats - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Floats { - get { return floats_; } - } - - /// Field number for the "ints" field. - public const int IntsFieldNumber = 8; - private static readonly pb::FieldCodec _repeated_ints_codec - = pb::FieldCodec.ForInt64(66); - private readonly pbc::RepeatedField ints_ = new pbc::RepeatedField(); - /// - /// list of ints - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Ints { - get { return ints_; } - } - - /// Field number for the "strings" field. - public const int StringsFieldNumber = 9; - private static readonly pb::FieldCodec _repeated_strings_codec - = pb::FieldCodec.ForBytes(74); - private readonly pbc::RepeatedField strings_ = new pbc::RepeatedField(); - /// - /// list of UTF-8 strings - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Strings { - get { return strings_; } - } - - /// Field number for the "tensors" field. - public const int TensorsFieldNumber = 10; - private static readonly pb::FieldCodec _repeated_tensors_codec - = pb::FieldCodec.ForMessage(82, global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Parser); - private readonly pbc::RepeatedField tensors_ = new pbc::RepeatedField(); - /// - /// list of tensors - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Tensors { - get { return tensors_; } - } - - /// Field number for the "graphs" field. - public const int GraphsFieldNumber = 11; - private static readonly pb::FieldCodec _repeated_graphs_codec - = pb::FieldCodec.ForMessage(90, global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.GraphProto.Parser); - private readonly pbc::RepeatedField graphs_ = new pbc::RepeatedField(); - /// - /// list of graph - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Graphs { - get { return graphs_; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as AttributeProto); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(AttributeProto other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if (Name != other.Name) return false; - if (DocString != other.DocString) return false; - if (Type != other.Type) return false; - if (F != other.F) return false; - if (I != other.I) return false; - if (S != other.S) return false; - if (!object.Equals(T, other.T)) return false; - if (!object.Equals(G, other.G)) return false; - if(!floats_.Equals(other.floats_)) return false; - if(!ints_.Equals(other.ints_)) return false; - if(!strings_.Equals(other.strings_)) return false; - if(!tensors_.Equals(other.tensors_)) return false; - if(!graphs_.Equals(other.graphs_)) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - if (Name.Length != 0) hash ^= Name.GetHashCode(); - if (DocString.Length != 0) hash ^= DocString.GetHashCode(); - if (Type != 0) hash ^= Type.GetHashCode(); - if (F != 0F) hash ^= F.GetHashCode(); - if (I != 0L) hash ^= I.GetHashCode(); - if (S.Length != 0) hash ^= S.GetHashCode(); - if (t_ != null) hash ^= T.GetHashCode(); - if (g_ != null) hash ^= G.GetHashCode(); - hash ^= floats_.GetHashCode(); - hash ^= ints_.GetHashCode(); - hash ^= strings_.GetHashCode(); - hash ^= tensors_.GetHashCode(); - hash ^= graphs_.GetHashCode(); - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - if (Name.Length != 0) { - output.WriteRawTag(10); - output.WriteString(Name); - } - if (F != 0F) { - output.WriteRawTag(21); - output.WriteFloat(F); - } - if (I != 0L) { - output.WriteRawTag(24); - output.WriteInt64(I); - } - if (S.Length != 0) { - output.WriteRawTag(34); - output.WriteBytes(S); - } - if (t_ != null) { - output.WriteRawTag(42); - output.WriteMessage(T); - } - if (g_ != null) { - output.WriteRawTag(50); - output.WriteMessage(G); - } - floats_.WriteTo(output, _repeated_floats_codec); - ints_.WriteTo(output, _repeated_ints_codec); - strings_.WriteTo(output, _repeated_strings_codec); - tensors_.WriteTo(output, _repeated_tensors_codec); - graphs_.WriteTo(output, _repeated_graphs_codec); - if (DocString.Length != 0) { - output.WriteRawTag(106); - output.WriteString(DocString); - } - if (Type != 0) { - output.WriteRawTag(160, 1); - output.WriteEnum((int) Type); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - if (Name.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(Name); - } - if (DocString.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(DocString); - } - if (Type != 0) { - size += 2 + pb::CodedOutputStream.ComputeEnumSize((int) Type); - } - if (F != 0F) { - size += 1 + 4; - } - if (I != 0L) { - size += 1 + pb::CodedOutputStream.ComputeInt64Size(I); - } - if (S.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeBytesSize(S); - } - if (t_ != null) { - size += 1 + pb::CodedOutputStream.ComputeMessageSize(T); - } - if (g_ != null) { - size += 1 + pb::CodedOutputStream.ComputeMessageSize(G); - } - size += floats_.CalculateSize(_repeated_floats_codec); - size += ints_.CalculateSize(_repeated_ints_codec); - size += strings_.CalculateSize(_repeated_strings_codec); - size += tensors_.CalculateSize(_repeated_tensors_codec); - size += graphs_.CalculateSize(_repeated_graphs_codec); - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(AttributeProto other) { - if (other == null) { - return; - } - if (other.Name.Length != 0) { - Name = other.Name; - } - if (other.DocString.Length != 0) { - DocString = other.DocString; - } - if (other.Type != 0) { - Type = other.Type; - } - if (other.F != 0F) { - F = other.F; - } - if (other.I != 0L) { - I = other.I; - } - if (other.S.Length != 0) { - S = other.S; - } - if (other.t_ != null) { - if (t_ == null) { - t_ = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto(); - } - T.MergeFrom(other.T); - } - if (other.g_ != null) { - if (g_ == null) { - g_ = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.GraphProto(); - } - G.MergeFrom(other.G); - } - floats_.Add(other.floats_); - ints_.Add(other.ints_); - strings_.Add(other.strings_); - tensors_.Add(other.tensors_); - graphs_.Add(other.graphs_); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 10: { - Name = input.ReadString(); - break; - } - case 21: { - F = input.ReadFloat(); - break; - } - case 24: { - I = input.ReadInt64(); - break; - } - case 34: { - S = input.ReadBytes(); - break; - } - case 42: { - if (t_ == null) { - t_ = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto(); - } - input.ReadMessage(t_); - break; - } - case 50: { - if (g_ == null) { - g_ = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.GraphProto(); - } - input.ReadMessage(g_); - break; - } - case 58: - case 61: { - floats_.AddEntriesFrom(input, _repeated_floats_codec); - break; - } - case 66: - case 64: { - ints_.AddEntriesFrom(input, _repeated_ints_codec); - break; - } - case 74: { - strings_.AddEntriesFrom(input, _repeated_strings_codec); - break; - } - case 82: { - tensors_.AddEntriesFrom(input, _repeated_tensors_codec); - break; - } - case 90: { - graphs_.AddEntriesFrom(input, _repeated_graphs_codec); - break; - } - case 106: { - DocString = input.ReadString(); - break; - } - case 160: { - type_ = (global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.AttributeProto.Types.AttributeType) input.ReadEnum(); - break; - } - } - } - } - - #region Nested types - /// Container for nested types declared in the AttributeProto message type. - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static partial class Types { - /// - /// Note: this enum is structurally identical to the OpSchema::AttrType - /// enum defined in schema.h. If you rev one, you likely need to rev the other. - /// - public enum AttributeType { - [pbr::OriginalName("UNDEFINED")] Undefined = 0, - [pbr::OriginalName("FLOAT")] Float = 1, - [pbr::OriginalName("INT")] Int = 2, - [pbr::OriginalName("STRING")] String = 3, - [pbr::OriginalName("TENSOR")] Tensor = 4, - [pbr::OriginalName("GRAPH")] Graph = 5, - [pbr::OriginalName("FLOATS")] Floats = 6, - [pbr::OriginalName("INTS")] Ints = 7, - [pbr::OriginalName("STRINGS")] Strings = 8, - [pbr::OriginalName("TENSORS")] Tensors = 9, - [pbr::OriginalName("GRAPHS")] Graphs = 10, - } - - } - #endregion - - } - - /// - /// Defines information on value, including the name, the type, and - /// the shape of the value. - /// - public sealed partial class ValueInfoProto : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new ValueInfoProto()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.OnnxMlReflection.Descriptor.MessageTypes[1]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public ValueInfoProto() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public ValueInfoProto(ValueInfoProto other) : this() { - name_ = other.name_; - Type = other.type_ != null ? other.Type.Clone() : null; - docString_ = other.docString_; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public ValueInfoProto Clone() { - return new ValueInfoProto(this); - } - - /// Field number for the "name" field. - public const int NameFieldNumber = 1; - private string name_ = ""; - /// - /// This field MUST be present in this version of the IR. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string Name { - get { return name_; } - set { - name_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "type" field. - public const int TypeFieldNumber = 2; - private global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto type_; - /// - /// This field MUST be present in this version of the IR. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto Type { - get { return type_; } - set { - type_ = value; - } - } - - /// Field number for the "doc_string" field. - public const int DocStringFieldNumber = 3; - private string docString_ = ""; - /// - /// A human-readable documentation for this value. Markdown is allowed. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string DocString { - get { return docString_; } - set { - docString_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as ValueInfoProto); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(ValueInfoProto other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if (Name != other.Name) return false; - if (!object.Equals(Type, other.Type)) return false; - if (DocString != other.DocString) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - if (Name.Length != 0) hash ^= Name.GetHashCode(); - if (type_ != null) hash ^= Type.GetHashCode(); - if (DocString.Length != 0) hash ^= DocString.GetHashCode(); - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - if (Name.Length != 0) { - output.WriteRawTag(10); - output.WriteString(Name); - } - if (type_ != null) { - output.WriteRawTag(18); - output.WriteMessage(Type); - } - if (DocString.Length != 0) { - output.WriteRawTag(26); - output.WriteString(DocString); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - if (Name.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(Name); - } - if (type_ != null) { - size += 1 + pb::CodedOutputStream.ComputeMessageSize(Type); - } - if (DocString.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(DocString); - } - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(ValueInfoProto other) { - if (other == null) { - return; - } - if (other.Name.Length != 0) { - Name = other.Name; - } - if (other.type_ != null) { - if (type_ == null) { - type_ = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto(); - } - Type.MergeFrom(other.Type); - } - if (other.DocString.Length != 0) { - DocString = other.DocString; - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 10: { - Name = input.ReadString(); - break; - } - case 18: { - if (type_ == null) { - type_ = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto(); - } - input.ReadMessage(type_); - break; - } - case 26: { - DocString = input.ReadString(); - break; - } - } - } - } - - } - - /// - /// NodeProto stores a node that is similar to the notion of "layer" - /// or "operator" in many deep learning frameworks. For example, it can be a - /// node of type "Conv" that takes in an image, a filter tensor and a bias - /// tensor, and produces the convolved output. - /// - public sealed partial class NodeProto : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new NodeProto()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.OnnxMlReflection.Descriptor.MessageTypes[2]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public NodeProto() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public NodeProto(NodeProto other) : this() { - input_ = other.input_.Clone(); - output_ = other.output_.Clone(); - name_ = other.name_; - opType_ = other.opType_; - domain_ = other.domain_; - attribute_ = other.attribute_.Clone(); - docString_ = other.docString_; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public NodeProto Clone() { - return new NodeProto(this); - } - - /// Field number for the "input" field. - public const int InputFieldNumber = 1; - private static readonly pb::FieldCodec _repeated_input_codec - = pb::FieldCodec.ForString(10); - private readonly pbc::RepeatedField input_ = new pbc::RepeatedField(); - /// - /// namespace Value - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Input { - get { return input_; } - } - - /// Field number for the "output" field. - public const int OutputFieldNumber = 2; - private static readonly pb::FieldCodec _repeated_output_codec - = pb::FieldCodec.ForString(18); - private readonly pbc::RepeatedField output_ = new pbc::RepeatedField(); - /// - /// namespace Value - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Output { - get { return output_; } - } - - /// Field number for the "name" field. - public const int NameFieldNumber = 3; - private string name_ = ""; - /// - /// An optional identifier for this node in a graph. - /// This field MAY be absent in ths version of the IR. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string Name { - get { return name_; } - set { - name_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "op_type" field. - public const int OpTypeFieldNumber = 4; - private string opType_ = ""; - /// - /// The symbolic identifier of the Operator to execute. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string OpType { - get { return opType_; } - set { - opType_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "domain" field. - public const int DomainFieldNumber = 7; - private string domain_ = ""; - /// - /// The domain of the OperatorSet that specifies the operator named by op_type. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string Domain { - get { return domain_; } - set { - domain_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "attribute" field. - public const int AttributeFieldNumber = 5; - private static readonly pb::FieldCodec _repeated_attribute_codec - = pb::FieldCodec.ForMessage(42, global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.AttributeProto.Parser); - private readonly pbc::RepeatedField attribute_ = new pbc::RepeatedField(); - /// - /// Additional named attributes. - /// NOTE: Simply using ValueProto.NameValuePairProto is the most general - /// solution. I kept AttributeProto to minimize churn on CI results. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Attribute { - get { return attribute_; } - } - - /// Field number for the "doc_string" field. - public const int DocStringFieldNumber = 6; - private string docString_ = ""; - /// - /// A human-readable documentation for this node. Markdown is allowed. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string DocString { - get { return docString_; } - set { - docString_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as NodeProto); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(NodeProto other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if(!input_.Equals(other.input_)) return false; - if(!output_.Equals(other.output_)) return false; - if (Name != other.Name) return false; - if (OpType != other.OpType) return false; - if (Domain != other.Domain) return false; - if(!attribute_.Equals(other.attribute_)) return false; - if (DocString != other.DocString) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - hash ^= input_.GetHashCode(); - hash ^= output_.GetHashCode(); - if (Name.Length != 0) hash ^= Name.GetHashCode(); - if (OpType.Length != 0) hash ^= OpType.GetHashCode(); - if (Domain.Length != 0) hash ^= Domain.GetHashCode(); - hash ^= attribute_.GetHashCode(); - if (DocString.Length != 0) hash ^= DocString.GetHashCode(); - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - input_.WriteTo(output, _repeated_input_codec); - output_.WriteTo(output, _repeated_output_codec); - if (Name.Length != 0) { - output.WriteRawTag(26); - output.WriteString(Name); - } - if (OpType.Length != 0) { - output.WriteRawTag(34); - output.WriteString(OpType); - } - attribute_.WriteTo(output, _repeated_attribute_codec); - if (DocString.Length != 0) { - output.WriteRawTag(50); - output.WriteString(DocString); - } - if (Domain.Length != 0) { - output.WriteRawTag(58); - output.WriteString(Domain); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - size += input_.CalculateSize(_repeated_input_codec); - size += output_.CalculateSize(_repeated_output_codec); - if (Name.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(Name); - } - if (OpType.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(OpType); - } - if (Domain.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(Domain); - } - size += attribute_.CalculateSize(_repeated_attribute_codec); - if (DocString.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(DocString); - } - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(NodeProto other) { - if (other == null) { - return; - } - input_.Add(other.input_); - output_.Add(other.output_); - if (other.Name.Length != 0) { - Name = other.Name; - } - if (other.OpType.Length != 0) { - OpType = other.OpType; - } - if (other.Domain.Length != 0) { - Domain = other.Domain; - } - attribute_.Add(other.attribute_); - if (other.DocString.Length != 0) { - DocString = other.DocString; - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 10: { - input_.AddEntriesFrom(input, _repeated_input_codec); - break; - } - case 18: { - output_.AddEntriesFrom(input, _repeated_output_codec); - break; - } - case 26: { - Name = input.ReadString(); - break; - } - case 34: { - OpType = input.ReadString(); - break; - } - case 42: { - attribute_.AddEntriesFrom(input, _repeated_attribute_codec); - break; - } - case 50: { - DocString = input.ReadString(); - break; - } - case 58: { - Domain = input.ReadString(); - break; - } - } - } - } - - } - - /// - /// ModelProto is a top-level file/container format for bundling a ML model. - /// The semantics of the model are described by the GraphProto that represents - /// a parameterized computation graph against a set of named operators that are - /// defined independently from the graph. - /// - public sealed partial class ModelProto : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new ModelProto()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.OnnxMlReflection.Descriptor.MessageTypes[3]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public ModelProto() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public ModelProto(ModelProto other) : this() { - irVersion_ = other.irVersion_; - opsetImport_ = other.opsetImport_.Clone(); - producerName_ = other.producerName_; - producerVersion_ = other.producerVersion_; - domain_ = other.domain_; - modelVersion_ = other.modelVersion_; - docString_ = other.docString_; - Graph = other.graph_ != null ? other.Graph.Clone() : null; - metadataProps_ = other.metadataProps_.Clone(); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public ModelProto Clone() { - return new ModelProto(this); - } - - /// Field number for the "ir_version" field. - public const int IrVersionFieldNumber = 1; - private long irVersion_; - /// - /// The version of the IR this model targets. See Version enum above. - /// This field MUST be present. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public long IrVersion { - get { return irVersion_; } - set { - irVersion_ = value; - } - } - - /// Field number for the "opset_import" field. - public const int OpsetImportFieldNumber = 8; - private static readonly pb::FieldCodec _repeated_opsetImport_codec - = pb::FieldCodec.ForMessage(66, global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.OperatorSetIdProto.Parser); - private readonly pbc::RepeatedField opsetImport_ = new pbc::RepeatedField(); - /// - /// The OperatorSets this model relies on. - /// All ModelProtos MUST have at least one entry that - /// specifies which version of the ONNX OperatorSet is - /// being imported. - /// - /// All nodes in the ModelProto's graph will bind against the operator - /// with the same-domain/same-op_type operator with the HIGHEST version - /// in the referenced operator sets. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField OpsetImport { - get { return opsetImport_; } - } - - /// Field number for the "producer_name" field. - public const int ProducerNameFieldNumber = 2; - private string producerName_ = ""; - /// - /// The name of the framework or tool used to generate this model. - /// This field SHOULD be present to indicate which implementation/tool/framework - /// emitted the model. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string ProducerName { - get { return producerName_; } - set { - producerName_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "producer_version" field. - public const int ProducerVersionFieldNumber = 3; - private string producerVersion_ = ""; - /// - /// The version of the framework or tool used to generate this model. - /// This field SHOULD be present to indicate which implementation/tool/framework - /// emitted the model. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string ProducerVersion { - get { return producerVersion_; } - set { - producerVersion_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "domain" field. - public const int DomainFieldNumber = 4; - private string domain_ = ""; - /// - /// Domain name of the model. - /// We use reverse domain names as name space indicators. For example: - /// `com.facebook.fair` or `com.microsoft.cognitiveservices` - /// - /// Together with `model_version` and GraphProto.name, this forms the unique identity of - /// the graph. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string Domain { - get { return domain_; } - set { - domain_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "model_version" field. - public const int ModelVersionFieldNumber = 5; - private long modelVersion_; - /// - /// The version of the graph encoded. See Version enum below. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public long ModelVersion { - get { return modelVersion_; } - set { - modelVersion_ = value; - } - } - - /// Field number for the "doc_string" field. - public const int DocStringFieldNumber = 6; - private string docString_ = ""; - /// - /// A human-readable documentation for this model. Markdown is allowed. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string DocString { - get { return docString_; } - set { - docString_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "graph" field. - public const int GraphFieldNumber = 7; - private global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.GraphProto graph_; - /// - /// The parameterized graph that is evaluated to execute the model. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.GraphProto Graph { - get { return graph_; } - set { - graph_ = value; - } - } - - /// Field number for the "metadata_props" field. - public const int MetadataPropsFieldNumber = 14; - private static readonly pb::FieldCodec _repeated_metadataProps_codec - = pb::FieldCodec.ForMessage(114, global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.StringStringEntryProto.Parser); - private readonly pbc::RepeatedField metadataProps_ = new pbc::RepeatedField(); - /// - /// Named metadata values; keys should be distinct. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField MetadataProps { - get { return metadataProps_; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as ModelProto); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(ModelProto other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if (IrVersion != other.IrVersion) return false; - if(!opsetImport_.Equals(other.opsetImport_)) return false; - if (ProducerName != other.ProducerName) return false; - if (ProducerVersion != other.ProducerVersion) return false; - if (Domain != other.Domain) return false; - if (ModelVersion != other.ModelVersion) return false; - if (DocString != other.DocString) return false; - if (!object.Equals(Graph, other.Graph)) return false; - if(!metadataProps_.Equals(other.metadataProps_)) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - if (IrVersion != 0L) hash ^= IrVersion.GetHashCode(); - hash ^= opsetImport_.GetHashCode(); - if (ProducerName.Length != 0) hash ^= ProducerName.GetHashCode(); - if (ProducerVersion.Length != 0) hash ^= ProducerVersion.GetHashCode(); - if (Domain.Length != 0) hash ^= Domain.GetHashCode(); - if (ModelVersion != 0L) hash ^= ModelVersion.GetHashCode(); - if (DocString.Length != 0) hash ^= DocString.GetHashCode(); - if (graph_ != null) hash ^= Graph.GetHashCode(); - hash ^= metadataProps_.GetHashCode(); - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - if (IrVersion != 0L) { - output.WriteRawTag(8); - output.WriteInt64(IrVersion); - } - if (ProducerName.Length != 0) { - output.WriteRawTag(18); - output.WriteString(ProducerName); - } - if (ProducerVersion.Length != 0) { - output.WriteRawTag(26); - output.WriteString(ProducerVersion); - } - if (Domain.Length != 0) { - output.WriteRawTag(34); - output.WriteString(Domain); - } - if (ModelVersion != 0L) { - output.WriteRawTag(40); - output.WriteInt64(ModelVersion); - } - if (DocString.Length != 0) { - output.WriteRawTag(50); - output.WriteString(DocString); - } - if (graph_ != null) { - output.WriteRawTag(58); - output.WriteMessage(Graph); - } - opsetImport_.WriteTo(output, _repeated_opsetImport_codec); - metadataProps_.WriteTo(output, _repeated_metadataProps_codec); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - if (IrVersion != 0L) { - size += 1 + pb::CodedOutputStream.ComputeInt64Size(IrVersion); - } - size += opsetImport_.CalculateSize(_repeated_opsetImport_codec); - if (ProducerName.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(ProducerName); - } - if (ProducerVersion.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(ProducerVersion); - } - if (Domain.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(Domain); - } - if (ModelVersion != 0L) { - size += 1 + pb::CodedOutputStream.ComputeInt64Size(ModelVersion); - } - if (DocString.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(DocString); - } - if (graph_ != null) { - size += 1 + pb::CodedOutputStream.ComputeMessageSize(Graph); - } - size += metadataProps_.CalculateSize(_repeated_metadataProps_codec); - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(ModelProto other) { - if (other == null) { - return; - } - if (other.IrVersion != 0L) { - IrVersion = other.IrVersion; - } - opsetImport_.Add(other.opsetImport_); - if (other.ProducerName.Length != 0) { - ProducerName = other.ProducerName; - } - if (other.ProducerVersion.Length != 0) { - ProducerVersion = other.ProducerVersion; - } - if (other.Domain.Length != 0) { - Domain = other.Domain; - } - if (other.ModelVersion != 0L) { - ModelVersion = other.ModelVersion; - } - if (other.DocString.Length != 0) { - DocString = other.DocString; - } - if (other.graph_ != null) { - if (graph_ == null) { - graph_ = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.GraphProto(); - } - Graph.MergeFrom(other.Graph); - } - metadataProps_.Add(other.metadataProps_); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 8: { - IrVersion = input.ReadInt64(); - break; - } - case 18: { - ProducerName = input.ReadString(); - break; - } - case 26: { - ProducerVersion = input.ReadString(); - break; - } - case 34: { - Domain = input.ReadString(); - break; - } - case 40: { - ModelVersion = input.ReadInt64(); - break; - } - case 50: { - DocString = input.ReadString(); - break; - } - case 58: { - if (graph_ == null) { - graph_ = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.GraphProto(); - } - input.ReadMessage(graph_); - break; - } - case 66: { - opsetImport_.AddEntriesFrom(input, _repeated_opsetImport_codec); - break; - } - case 114: { - metadataProps_.AddEntriesFrom(input, _repeated_metadataProps_codec); - break; - } - } - } - } - - } - - /// - /// StringStringEntryProto follows the pattern for cross-proto-version maps. - /// See https://developers.google.com/protocol-buffers/docs/proto3#maps - /// - public sealed partial class StringStringEntryProto : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new StringStringEntryProto()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.OnnxMlReflection.Descriptor.MessageTypes[4]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public StringStringEntryProto() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public StringStringEntryProto(StringStringEntryProto other) : this() { - key_ = other.key_; - value_ = other.value_; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public StringStringEntryProto Clone() { - return new StringStringEntryProto(this); - } - - /// Field number for the "key" field. - public const int KeyFieldNumber = 1; - private string key_ = ""; - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string Key { - get { return key_; } - set { - key_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "value" field. - public const int ValueFieldNumber = 2; - private string value_ = ""; - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string Value { - get { return value_; } - set { - value_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as StringStringEntryProto); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(StringStringEntryProto other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if (Key != other.Key) return false; - if (Value != other.Value) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - if (Key.Length != 0) hash ^= Key.GetHashCode(); - if (Value.Length != 0) hash ^= Value.GetHashCode(); - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - if (Key.Length != 0) { - output.WriteRawTag(10); - output.WriteString(Key); - } - if (Value.Length != 0) { - output.WriteRawTag(18); - output.WriteString(Value); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - if (Key.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(Key); - } - if (Value.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(Value); - } - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(StringStringEntryProto other) { - if (other == null) { - return; - } - if (other.Key.Length != 0) { - Key = other.Key; - } - if (other.Value.Length != 0) { - Value = other.Value; - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 10: { - Key = input.ReadString(); - break; - } - case 18: { - Value = input.ReadString(); - break; - } - } - } - } - - } - - /// - /// GraphProto defines a parameterized series of nodes to form a directed acyclic graph. - /// This is the equivalent of the "network" and "graph" in many deep learning - /// frameworks. - /// - public sealed partial class GraphProto : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new GraphProto()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.OnnxMlReflection.Descriptor.MessageTypes[5]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public GraphProto() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public GraphProto(GraphProto other) : this() { - node_ = other.node_.Clone(); - name_ = other.name_; - initializer_ = other.initializer_.Clone(); - docString_ = other.docString_; - input_ = other.input_.Clone(); - output_ = other.output_.Clone(); - valueInfo_ = other.valueInfo_.Clone(); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public GraphProto Clone() { - return new GraphProto(this); - } - - /// Field number for the "node" field. - public const int NodeFieldNumber = 1; - private static readonly pb::FieldCodec _repeated_node_codec - = pb::FieldCodec.ForMessage(10, global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.NodeProto.Parser); - private readonly pbc::RepeatedField node_ = new pbc::RepeatedField(); - /// - /// The nodes in the graph. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Node { - get { return node_; } - } - - /// Field number for the "name" field. - public const int NameFieldNumber = 2; - private string name_ = ""; - /// - /// The name of the graph. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string Name { - get { return name_; } - set { - name_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "initializer" field. - public const int InitializerFieldNumber = 5; - private static readonly pb::FieldCodec _repeated_initializer_codec - = pb::FieldCodec.ForMessage(42, global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Parser); - private readonly pbc::RepeatedField initializer_ = new pbc::RepeatedField(); - /// - /// A list of named tensor values (constants), used to specify default - /// values for some of the inputs of the graph. - /// Each TensorProto entry must have a distinct name (within the list) that - /// also appears in the input list. - /// In an evaluation, the default value specified here is used if and only if - /// user specifies no value for the corresponding input parameter. - /// May be used to pass serialized parameters for networks. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Initializer { - get { return initializer_; } - } - - /// Field number for the "doc_string" field. - public const int DocStringFieldNumber = 10; - private string docString_ = ""; - /// - /// A human-readable documentation for this graph. Markdown is allowed. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string DocString { - get { return docString_; } - set { - docString_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "input" field. - public const int InputFieldNumber = 11; - private static readonly pb::FieldCodec _repeated_input_codec - = pb::FieldCodec.ForMessage(90, global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.ValueInfoProto.Parser); - private readonly pbc::RepeatedField input_ = new pbc::RepeatedField(); - /// - /// The inputs and outputs of the graph. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Input { - get { return input_; } - } - - /// Field number for the "output" field. - public const int OutputFieldNumber = 12; - private static readonly pb::FieldCodec _repeated_output_codec - = pb::FieldCodec.ForMessage(98, global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.ValueInfoProto.Parser); - private readonly pbc::RepeatedField output_ = new pbc::RepeatedField(); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Output { - get { return output_; } - } - - /// Field number for the "value_info" field. - public const int ValueInfoFieldNumber = 13; - private static readonly pb::FieldCodec _repeated_valueInfo_codec - = pb::FieldCodec.ForMessage(106, global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.ValueInfoProto.Parser); - private readonly pbc::RepeatedField valueInfo_ = new pbc::RepeatedField(); - /// - /// Information for the values in the graph. The ValueInfoProto.name's - /// must be distinct. It is optional for a value to appear in value_info list. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField ValueInfo { - get { return valueInfo_; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as GraphProto); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(GraphProto other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if(!node_.Equals(other.node_)) return false; - if (Name != other.Name) return false; - if(!initializer_.Equals(other.initializer_)) return false; - if (DocString != other.DocString) return false; - if(!input_.Equals(other.input_)) return false; - if(!output_.Equals(other.output_)) return false; - if(!valueInfo_.Equals(other.valueInfo_)) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - hash ^= node_.GetHashCode(); - if (Name.Length != 0) hash ^= Name.GetHashCode(); - hash ^= initializer_.GetHashCode(); - if (DocString.Length != 0) hash ^= DocString.GetHashCode(); - hash ^= input_.GetHashCode(); - hash ^= output_.GetHashCode(); - hash ^= valueInfo_.GetHashCode(); - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - node_.WriteTo(output, _repeated_node_codec); - if (Name.Length != 0) { - output.WriteRawTag(18); - output.WriteString(Name); - } - initializer_.WriteTo(output, _repeated_initializer_codec); - if (DocString.Length != 0) { - output.WriteRawTag(82); - output.WriteString(DocString); - } - input_.WriteTo(output, _repeated_input_codec); - output_.WriteTo(output, _repeated_output_codec); - valueInfo_.WriteTo(output, _repeated_valueInfo_codec); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - size += node_.CalculateSize(_repeated_node_codec); - if (Name.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(Name); - } - size += initializer_.CalculateSize(_repeated_initializer_codec); - if (DocString.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(DocString); - } - size += input_.CalculateSize(_repeated_input_codec); - size += output_.CalculateSize(_repeated_output_codec); - size += valueInfo_.CalculateSize(_repeated_valueInfo_codec); - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(GraphProto other) { - if (other == null) { - return; - } - node_.Add(other.node_); - if (other.Name.Length != 0) { - Name = other.Name; - } - initializer_.Add(other.initializer_); - if (other.DocString.Length != 0) { - DocString = other.DocString; - } - input_.Add(other.input_); - output_.Add(other.output_); - valueInfo_.Add(other.valueInfo_); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 10: { - node_.AddEntriesFrom(input, _repeated_node_codec); - break; - } - case 18: { - Name = input.ReadString(); - break; - } - case 42: { - initializer_.AddEntriesFrom(input, _repeated_initializer_codec); - break; - } - case 82: { - DocString = input.ReadString(); - break; - } - case 90: { - input_.AddEntriesFrom(input, _repeated_input_codec); - break; - } - case 98: { - output_.AddEntriesFrom(input, _repeated_output_codec); - break; - } - case 106: { - valueInfo_.AddEntriesFrom(input, _repeated_valueInfo_codec); - break; - } - } - } - } - - } - - /// - /// A message defined to store a tensor in its serialized format. - /// - public sealed partial class TensorProto : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new TensorProto()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.OnnxMlReflection.Descriptor.MessageTypes[6]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public TensorProto() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public TensorProto(TensorProto other) : this() { - dims_ = other.dims_.Clone(); - dataType_ = other.dataType_; - Segment = other.segment_ != null ? other.Segment.Clone() : null; - floatData_ = other.floatData_.Clone(); - int32Data_ = other.int32Data_.Clone(); - stringData_ = other.stringData_.Clone(); - int64Data_ = other.int64Data_.Clone(); - name_ = other.name_; - docString_ = other.docString_; - rawData_ = other.rawData_; - doubleData_ = other.doubleData_.Clone(); - uint64Data_ = other.uint64Data_.Clone(); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public TensorProto Clone() { - return new TensorProto(this); - } - - /// Field number for the "dims" field. - public const int DimsFieldNumber = 1; - private static readonly pb::FieldCodec _repeated_dims_codec - = pb::FieldCodec.ForInt64(10); - private readonly pbc::RepeatedField dims_ = new pbc::RepeatedField(); - /// - /// The shape of the tensor. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Dims { - get { return dims_; } - } - - /// Field number for the "data_type" field. - public const int DataTypeFieldNumber = 2; - private global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Types.DataType dataType_ = 0; - /// - /// The data type of the tensor. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Types.DataType DataType { - get { return dataType_; } - set { - dataType_ = value; - } - } - - /// Field number for the "segment" field. - public const int SegmentFieldNumber = 3; - private global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Types.Segment segment_; - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Types.Segment Segment { - get { return segment_; } - set { - segment_ = value; - } - } - - /// Field number for the "float_data" field. - public const int FloatDataFieldNumber = 4; - private static readonly pb::FieldCodec _repeated_floatData_codec - = pb::FieldCodec.ForFloat(34); - private readonly pbc::RepeatedField floatData_ = new pbc::RepeatedField(); - /// - /// For float and complex64 values - /// Complex64 tensors are encoded as a single array of floats, - /// with the real components appearing in odd numbered positions, - /// and the corresponding imaginary component apparing in the - /// subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i] - /// is encoded as [1.0, 2.0 ,3.0 ,4.0] - /// When this field is present, the data_type field MUST be FLOAT or COMPLEX64. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField FloatData { - get { return floatData_; } - } - - /// Field number for the "int32_data" field. - public const int Int32DataFieldNumber = 5; - private static readonly pb::FieldCodec _repeated_int32Data_codec - = pb::FieldCodec.ForInt32(42); - private readonly pbc::RepeatedField int32Data_ = new pbc::RepeatedField(); - /// - /// For int32, uint8, int8, uint16, int16, bool, and float16 values - /// float16 values must be bit-wise converted to an uint16_t prior - /// to writing to the buffer. - /// When this field is present, the data_type field MUST be - /// INT32, INT16, INT8, UINT16, INT8, BOOL, or FLOAT32 - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Int32Data { - get { return int32Data_; } - } - - /// Field number for the "string_data" field. - public const int StringDataFieldNumber = 6; - private static readonly pb::FieldCodec _repeated_stringData_codec - = pb::FieldCodec.ForBytes(50); - private readonly pbc::RepeatedField stringData_ = new pbc::RepeatedField(); - /// - /// For strings. - /// Each element of string_data is a UTF-8 encoded Unicode - /// string. No trailing null, no leading BOM. The protobuf "string" - /// scalar type is not used to match ML community conventions. - /// When this field is present, the data_type field MUST be STRING - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField StringData { - get { return stringData_; } - } - - /// Field number for the "int64_data" field. - public const int Int64DataFieldNumber = 7; - private static readonly pb::FieldCodec _repeated_int64Data_codec - = pb::FieldCodec.ForInt64(58); - private readonly pbc::RepeatedField int64Data_ = new pbc::RepeatedField(); - /// - /// For int64. - /// When this field is present, the data_type field MUST be INT64 - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Int64Data { - get { return int64Data_; } - } - - /// Field number for the "name" field. - public const int NameFieldNumber = 8; - private string name_ = ""; - /// - /// Optionally, a name for the tensor. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string Name { - get { return name_; } - set { - name_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "doc_string" field. - public const int DocStringFieldNumber = 12; - private string docString_ = ""; - /// - /// A human-readable documentation for this tensor. Markdown is allowed. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string DocString { - get { return docString_; } - set { - docString_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "raw_data" field. - public const int RawDataFieldNumber = 9; - private pb::ByteString rawData_ = pb::ByteString.Empty; - /// - /// Serializations can either use one of the fields above, or use this - /// raw bytes field. The only exception is the string case, where one is - /// required to store the content in the repeated bytes string_data field. - /// - /// When this raw_data field is used to store tensor value, elements MUST - /// be stored in as fixed-width, little-endian order. - /// Floating-point data types MUST be stored in IEEE 754 format. - /// Complex64 elements must be written as two consecutive FLOAT values, real component first. - /// Complex128 elements must be written as two consecutive DOUBLE values, real component first. - /// Boolean type MUST be written one byte per tensor element (00000001 for true, 00000000 for false). - /// - /// Note: the advantage of specific field rather than the raw_data field is - /// that in some cases (e.g. int data), protobuf does a better packing via - /// variable length storage, and may lead to smaller binary footprint. - /// When this field is present, the data_type field MUST NOT be STRING or UNDEFINED - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pb::ByteString RawData { - get { return rawData_; } - set { - rawData_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "double_data" field. - public const int DoubleDataFieldNumber = 10; - private static readonly pb::FieldCodec _repeated_doubleData_codec - = pb::FieldCodec.ForDouble(82); - private readonly pbc::RepeatedField doubleData_ = new pbc::RepeatedField(); - /// - /// For double - /// Complex64 tensors are encoded as a single array of doubles, - /// with the real components appearing in odd numbered positions, - /// and the corresponding imaginary component apparing in the - /// subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i] - /// is encoded as [1.0, 2.0 ,3.0 ,4.0] - /// When this field is present, the data_type field MUST be DOUBLE or COMPLEX128 - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField DoubleData { - get { return doubleData_; } - } - - /// Field number for the "uint64_data" field. - public const int Uint64DataFieldNumber = 11; - private static readonly pb::FieldCodec _repeated_uint64Data_codec - = pb::FieldCodec.ForUInt64(90); - private readonly pbc::RepeatedField uint64Data_ = new pbc::RepeatedField(); - /// - /// For uint64 and uint32 values - /// When this field is present, the data_type field MUST be - /// UINT32 or UINT64 - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Uint64Data { - get { return uint64Data_; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as TensorProto); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(TensorProto other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if(!dims_.Equals(other.dims_)) return false; - if (DataType != other.DataType) return false; - if (!object.Equals(Segment, other.Segment)) return false; - if(!floatData_.Equals(other.floatData_)) return false; - if(!int32Data_.Equals(other.int32Data_)) return false; - if(!stringData_.Equals(other.stringData_)) return false; - if(!int64Data_.Equals(other.int64Data_)) return false; - if (Name != other.Name) return false; - if (DocString != other.DocString) return false; - if (RawData != other.RawData) return false; - if(!doubleData_.Equals(other.doubleData_)) return false; - if(!uint64Data_.Equals(other.uint64Data_)) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - hash ^= dims_.GetHashCode(); - if (DataType != 0) hash ^= DataType.GetHashCode(); - if (segment_ != null) hash ^= Segment.GetHashCode(); - hash ^= floatData_.GetHashCode(); - hash ^= int32Data_.GetHashCode(); - hash ^= stringData_.GetHashCode(); - hash ^= int64Data_.GetHashCode(); - if (Name.Length != 0) hash ^= Name.GetHashCode(); - if (DocString.Length != 0) hash ^= DocString.GetHashCode(); - if (RawData.Length != 0) hash ^= RawData.GetHashCode(); - hash ^= doubleData_.GetHashCode(); - hash ^= uint64Data_.GetHashCode(); - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - dims_.WriteTo(output, _repeated_dims_codec); - if (DataType != 0) { - output.WriteRawTag(16); - output.WriteEnum((int) DataType); - } - if (segment_ != null) { - output.WriteRawTag(26); - output.WriteMessage(Segment); - } - floatData_.WriteTo(output, _repeated_floatData_codec); - int32Data_.WriteTo(output, _repeated_int32Data_codec); - stringData_.WriteTo(output, _repeated_stringData_codec); - int64Data_.WriteTo(output, _repeated_int64Data_codec); - if (Name.Length != 0) { - output.WriteRawTag(66); - output.WriteString(Name); - } - if (RawData.Length != 0) { - output.WriteRawTag(74); - output.WriteBytes(RawData); - } - doubleData_.WriteTo(output, _repeated_doubleData_codec); - uint64Data_.WriteTo(output, _repeated_uint64Data_codec); - if (DocString.Length != 0) { - output.WriteRawTag(98); - output.WriteString(DocString); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - size += dims_.CalculateSize(_repeated_dims_codec); - if (DataType != 0) { - size += 1 + pb::CodedOutputStream.ComputeEnumSize((int) DataType); - } - if (segment_ != null) { - size += 1 + pb::CodedOutputStream.ComputeMessageSize(Segment); - } - size += floatData_.CalculateSize(_repeated_floatData_codec); - size += int32Data_.CalculateSize(_repeated_int32Data_codec); - size += stringData_.CalculateSize(_repeated_stringData_codec); - size += int64Data_.CalculateSize(_repeated_int64Data_codec); - if (Name.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(Name); - } - if (DocString.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(DocString); - } - if (RawData.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeBytesSize(RawData); - } - size += doubleData_.CalculateSize(_repeated_doubleData_codec); - size += uint64Data_.CalculateSize(_repeated_uint64Data_codec); - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(TensorProto other) { - if (other == null) { - return; - } - dims_.Add(other.dims_); - if (other.DataType != 0) { - DataType = other.DataType; - } - if (other.segment_ != null) { - if (segment_ == null) { - segment_ = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Types.Segment(); - } - Segment.MergeFrom(other.Segment); - } - floatData_.Add(other.floatData_); - int32Data_.Add(other.int32Data_); - stringData_.Add(other.stringData_); - int64Data_.Add(other.int64Data_); - if (other.Name.Length != 0) { - Name = other.Name; - } - if (other.DocString.Length != 0) { - DocString = other.DocString; - } - if (other.RawData.Length != 0) { - RawData = other.RawData; - } - doubleData_.Add(other.doubleData_); - uint64Data_.Add(other.uint64Data_); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 10: - case 8: { - dims_.AddEntriesFrom(input, _repeated_dims_codec); - break; - } - case 16: { - dataType_ = (global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Types.DataType) input.ReadEnum(); - break; - } - case 26: { - if (segment_ == null) { - segment_ = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Types.Segment(); - } - input.ReadMessage(segment_); - break; - } - case 34: - case 37: { - floatData_.AddEntriesFrom(input, _repeated_floatData_codec); - break; - } - case 42: - case 40: { - int32Data_.AddEntriesFrom(input, _repeated_int32Data_codec); - break; - } - case 50: { - stringData_.AddEntriesFrom(input, _repeated_stringData_codec); - break; - } - case 58: - case 56: { - int64Data_.AddEntriesFrom(input, _repeated_int64Data_codec); - break; - } - case 66: { - Name = input.ReadString(); - break; - } - case 74: { - RawData = input.ReadBytes(); - break; - } - case 82: - case 81: { - doubleData_.AddEntriesFrom(input, _repeated_doubleData_codec); - break; - } - case 90: - case 88: { - uint64Data_.AddEntriesFrom(input, _repeated_uint64Data_codec); - break; - } - case 98: { - DocString = input.ReadString(); - break; - } - } - } - } - - #region Nested types - /// Container for nested types declared in the TensorProto message type. - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static partial class Types { - public enum DataType { - [pbr::OriginalName("UNDEFINED")] Undefined = 0, - /// - /// Basic types. - /// - [pbr::OriginalName("FLOAT")] Float = 1, - /// - /// uint8_t - /// - [pbr::OriginalName("UINT8")] Uint8 = 2, - /// - /// int8_t - /// - [pbr::OriginalName("INT8")] Int8 = 3, - /// - /// uint16_t - /// - [pbr::OriginalName("UINT16")] Uint16 = 4, - /// - /// int16_t - /// - [pbr::OriginalName("INT16")] Int16 = 5, - /// - /// int32_t - /// - [pbr::OriginalName("INT32")] Int32 = 6, - /// - /// int64_t - /// - [pbr::OriginalName("INT64")] Int64 = 7, - /// - /// string - /// - [pbr::OriginalName("STRING")] String = 8, - /// - /// bool - /// - [pbr::OriginalName("BOOL")] Bool = 9, - /// - /// Advanced types - /// - [pbr::OriginalName("FLOAT16")] Float16 = 10, - [pbr::OriginalName("DOUBLE")] Double = 11, - [pbr::OriginalName("UINT32")] Uint32 = 12, - [pbr::OriginalName("UINT64")] Uint64 = 13, - /// - /// complex with float32 real and imaginary components - /// - [pbr::OriginalName("COMPLEX64")] Complex64 = 14, - /// - /// complex with float64 real and imaginary components - /// - [pbr::OriginalName("COMPLEX128")] Complex128 = 15, - } - - /// - /// For very large tensors, we may want to store them in chunks, in which - /// case the following fields will specify the segment that is stored in - /// the current TensorProto. - /// - public sealed partial class Segment : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new Segment()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Descriptor.NestedTypes[0]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public Segment() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public Segment(Segment other) : this() { - begin_ = other.begin_; - end_ = other.end_; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public Segment Clone() { - return new Segment(this); - } - - /// Field number for the "begin" field. - public const int BeginFieldNumber = 1; - private long begin_; - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public long Begin { - get { return begin_; } - set { - begin_ = value; - } - } - - /// Field number for the "end" field. - public const int EndFieldNumber = 2; - private long end_; - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public long End { - get { return end_; } - set { - end_ = value; - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as Segment); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(Segment other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if (Begin != other.Begin) return false; - if (End != other.End) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - if (Begin != 0L) hash ^= Begin.GetHashCode(); - if (End != 0L) hash ^= End.GetHashCode(); - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - if (Begin != 0L) { - output.WriteRawTag(8); - output.WriteInt64(Begin); - } - if (End != 0L) { - output.WriteRawTag(16); - output.WriteInt64(End); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - if (Begin != 0L) { - size += 1 + pb::CodedOutputStream.ComputeInt64Size(Begin); - } - if (End != 0L) { - size += 1 + pb::CodedOutputStream.ComputeInt64Size(End); - } - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(Segment other) { - if (other == null) { - return; - } - if (other.Begin != 0L) { - Begin = other.Begin; - } - if (other.End != 0L) { - End = other.End; - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 8: { - Begin = input.ReadInt64(); - break; - } - case 16: { - End = input.ReadInt64(); - break; - } - } - } - } - - } - - } - #endregion - - } - - /// - /// Defines a tensor shape. A dimension can be either an integer value - /// or a symbolic variable. A symbolic variable represents an unknown - /// dimension. - /// - public sealed partial class TensorShapeProto : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new TensorShapeProto()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.OnnxMlReflection.Descriptor.MessageTypes[7]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public TensorShapeProto() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public TensorShapeProto(TensorShapeProto other) : this() { - dim_ = other.dim_.Clone(); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public TensorShapeProto Clone() { - return new TensorShapeProto(this); - } - - /// Field number for the "dim" field. - public const int DimFieldNumber = 1; - private static readonly pb::FieldCodec _repeated_dim_codec - = pb::FieldCodec.ForMessage(10, global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorShapeProto.Types.Dimension.Parser); - private readonly pbc::RepeatedField dim_ = new pbc::RepeatedField(); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public pbc::RepeatedField Dim { - get { return dim_; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as TensorShapeProto); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(TensorShapeProto other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if(!dim_.Equals(other.dim_)) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - hash ^= dim_.GetHashCode(); - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - dim_.WriteTo(output, _repeated_dim_codec); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - size += dim_.CalculateSize(_repeated_dim_codec); - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(TensorShapeProto other) { - if (other == null) { - return; - } - dim_.Add(other.dim_); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 10: { - dim_.AddEntriesFrom(input, _repeated_dim_codec); - break; - } - } - } - } - - #region Nested types - /// Container for nested types declared in the TensorShapeProto message type. - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static partial class Types { - public sealed partial class Dimension : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new Dimension()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorShapeProto.Descriptor.NestedTypes[0]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public Dimension() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public Dimension(Dimension other) : this() { - switch (other.ValueCase) { - case ValueOneofCase.DimValue: - DimValue = other.DimValue; - break; - case ValueOneofCase.DimParam: - DimParam = other.DimParam; - break; - } - - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public Dimension Clone() { - return new Dimension(this); - } - - /// Field number for the "dim_value" field. - public const int DimValueFieldNumber = 1; - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public long DimValue { - get { return valueCase_ == ValueOneofCase.DimValue ? (long) value_ : 0L; } - set { - value_ = value; - valueCase_ = ValueOneofCase.DimValue; - } - } - - /// Field number for the "dim_param" field. - public const int DimParamFieldNumber = 2; - /// - /// namespace Shape - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string DimParam { - get { return valueCase_ == ValueOneofCase.DimParam ? (string) value_ : ""; } - set { - value_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - valueCase_ = ValueOneofCase.DimParam; - } - } - - private object value_; - /// Enum of possible cases for the "value" oneof. - public enum ValueOneofCase { - None = 0, - DimValue = 1, - DimParam = 2, - } - private ValueOneofCase valueCase_ = ValueOneofCase.None; - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public ValueOneofCase ValueCase { - get { return valueCase_; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void ClearValue() { - valueCase_ = ValueOneofCase.None; - value_ = null; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as Dimension); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(Dimension other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if (DimValue != other.DimValue) return false; - if (DimParam != other.DimParam) return false; - if (ValueCase != other.ValueCase) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - if (valueCase_ == ValueOneofCase.DimValue) hash ^= DimValue.GetHashCode(); - if (valueCase_ == ValueOneofCase.DimParam) hash ^= DimParam.GetHashCode(); - hash ^= (int) valueCase_; - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - if (valueCase_ == ValueOneofCase.DimValue) { - output.WriteRawTag(8); - output.WriteInt64(DimValue); - } - if (valueCase_ == ValueOneofCase.DimParam) { - output.WriteRawTag(18); - output.WriteString(DimParam); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - if (valueCase_ == ValueOneofCase.DimValue) { - size += 1 + pb::CodedOutputStream.ComputeInt64Size(DimValue); - } - if (valueCase_ == ValueOneofCase.DimParam) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(DimParam); - } - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(Dimension other) { - if (other == null) { - return; - } - switch (other.ValueCase) { - case ValueOneofCase.DimValue: - DimValue = other.DimValue; - break; - case ValueOneofCase.DimParam: - DimParam = other.DimParam; - break; - } - - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 8: { - DimValue = input.ReadInt64(); - break; - } - case 18: { - DimParam = input.ReadString(); - break; - } - } - } - } - - } - - } - #endregion - - } - - /// - /// Define the types. - /// - public sealed partial class TypeProto : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new TypeProto()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.OnnxMlReflection.Descriptor.MessageTypes[8]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public TypeProto() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public TypeProto(TypeProto other) : this() { - switch (other.ValueCase) { - case ValueOneofCase.TensorType: - TensorType = other.TensorType.Clone(); - break; - case ValueOneofCase.SequenceType: - SequenceType = other.SequenceType.Clone(); - break; - case ValueOneofCase.MapType: - MapType = other.MapType.Clone(); - break; - } - - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public TypeProto Clone() { - return new TypeProto(this); - } - - /// Field number for the "tensor_type" field. - public const int TensorTypeFieldNumber = 1; - /// - /// The type of a tensor. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Types.Tensor TensorType { - get { return valueCase_ == ValueOneofCase.TensorType ? (global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Types.Tensor) value_ : null; } - set { - value_ = value; - valueCase_ = value == null ? ValueOneofCase.None : ValueOneofCase.TensorType; - } - } - - /// Field number for the "sequence_type" field. - public const int SequenceTypeFieldNumber = 4; - /// - /// The type of a sequence. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Types.Sequence SequenceType { - get { return valueCase_ == ValueOneofCase.SequenceType ? (global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Types.Sequence) value_ : null; } - set { - value_ = value; - valueCase_ = value == null ? ValueOneofCase.None : ValueOneofCase.SequenceType; - } - } - - /// Field number for the "map_type" field. - public const int MapTypeFieldNumber = 5; - /// - /// The type of a map. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Types.Map MapType { - get { return valueCase_ == ValueOneofCase.MapType ? (global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Types.Map) value_ : null; } - set { - value_ = value; - valueCase_ = value == null ? ValueOneofCase.None : ValueOneofCase.MapType; - } - } - - private object value_; - /// Enum of possible cases for the "value" oneof. - public enum ValueOneofCase { - None = 0, - TensorType = 1, - SequenceType = 4, - MapType = 5, - } - private ValueOneofCase valueCase_ = ValueOneofCase.None; - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public ValueOneofCase ValueCase { - get { return valueCase_; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void ClearValue() { - valueCase_ = ValueOneofCase.None; - value_ = null; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as TypeProto); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(TypeProto other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if (!object.Equals(TensorType, other.TensorType)) return false; - if (!object.Equals(SequenceType, other.SequenceType)) return false; - if (!object.Equals(MapType, other.MapType)) return false; - if (ValueCase != other.ValueCase) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - if (valueCase_ == ValueOneofCase.TensorType) hash ^= TensorType.GetHashCode(); - if (valueCase_ == ValueOneofCase.SequenceType) hash ^= SequenceType.GetHashCode(); - if (valueCase_ == ValueOneofCase.MapType) hash ^= MapType.GetHashCode(); - hash ^= (int) valueCase_; - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - if (valueCase_ == ValueOneofCase.TensorType) { - output.WriteRawTag(10); - output.WriteMessage(TensorType); - } - if (valueCase_ == ValueOneofCase.SequenceType) { - output.WriteRawTag(34); - output.WriteMessage(SequenceType); - } - if (valueCase_ == ValueOneofCase.MapType) { - output.WriteRawTag(42); - output.WriteMessage(MapType); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - if (valueCase_ == ValueOneofCase.TensorType) { - size += 1 + pb::CodedOutputStream.ComputeMessageSize(TensorType); - } - if (valueCase_ == ValueOneofCase.SequenceType) { - size += 1 + pb::CodedOutputStream.ComputeMessageSize(SequenceType); - } - if (valueCase_ == ValueOneofCase.MapType) { - size += 1 + pb::CodedOutputStream.ComputeMessageSize(MapType); - } - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(TypeProto other) { - if (other == null) { - return; - } - switch (other.ValueCase) { - case ValueOneofCase.TensorType: - TensorType = other.TensorType; - break; - case ValueOneofCase.SequenceType: - SequenceType = other.SequenceType; - break; - case ValueOneofCase.MapType: - MapType = other.MapType; - break; - } - - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 10: { - global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Types.Tensor subBuilder = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Types.Tensor(); - if (valueCase_ == ValueOneofCase.TensorType) { - subBuilder.MergeFrom(TensorType); - } - input.ReadMessage(subBuilder); - TensorType = subBuilder; - break; - } - case 34: { - global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Types.Sequence subBuilder = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Types.Sequence(); - if (valueCase_ == ValueOneofCase.SequenceType) { - subBuilder.MergeFrom(SequenceType); - } - input.ReadMessage(subBuilder); - SequenceType = subBuilder; - break; - } - case 42: { - global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Types.Map subBuilder = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Types.Map(); - if (valueCase_ == ValueOneofCase.MapType) { - subBuilder.MergeFrom(MapType); - } - input.ReadMessage(subBuilder); - MapType = subBuilder; - break; - } - } - } - } - - #region Nested types - /// Container for nested types declared in the TypeProto message type. - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static partial class Types { - public sealed partial class Tensor : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new Tensor()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Descriptor.NestedTypes[0]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public Tensor() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public Tensor(Tensor other) : this() { - elemType_ = other.elemType_; - Shape = other.shape_ != null ? other.Shape.Clone() : null; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public Tensor Clone() { - return new Tensor(this); - } - - /// Field number for the "elem_type" field. - public const int ElemTypeFieldNumber = 1; - private global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Types.DataType elemType_ = 0; - /// - /// This field MUST NOT have the value of UNDEFINED - /// This field MUST be present for this version of the IR. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Types.DataType ElemType { - get { return elemType_; } - set { - elemType_ = value; - } - } - - /// Field number for the "shape" field. - public const int ShapeFieldNumber = 2; - private global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorShapeProto shape_; - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorShapeProto Shape { - get { return shape_; } - set { - shape_ = value; - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as Tensor); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(Tensor other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if (ElemType != other.ElemType) return false; - if (!object.Equals(Shape, other.Shape)) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - if (ElemType != 0) hash ^= ElemType.GetHashCode(); - if (shape_ != null) hash ^= Shape.GetHashCode(); - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - if (ElemType != 0) { - output.WriteRawTag(8); - output.WriteEnum((int) ElemType); - } - if (shape_ != null) { - output.WriteRawTag(18); - output.WriteMessage(Shape); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - if (ElemType != 0) { - size += 1 + pb::CodedOutputStream.ComputeEnumSize((int) ElemType); - } - if (shape_ != null) { - size += 1 + pb::CodedOutputStream.ComputeMessageSize(Shape); - } - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(Tensor other) { - if (other == null) { - return; - } - if (other.ElemType != 0) { - ElemType = other.ElemType; - } - if (other.shape_ != null) { - if (shape_ == null) { - shape_ = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorShapeProto(); - } - Shape.MergeFrom(other.Shape); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 8: { - elemType_ = (global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Types.DataType) input.ReadEnum(); - break; - } - case 18: { - if (shape_ == null) { - shape_ = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorShapeProto(); - } - input.ReadMessage(shape_); - break; - } - } - } - } - - } - - /// - /// repeated T - /// - public sealed partial class Sequence : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new Sequence()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Descriptor.NestedTypes[1]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public Sequence() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public Sequence(Sequence other) : this() { - ElemType = other.elemType_ != null ? other.ElemType.Clone() : null; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public Sequence Clone() { - return new Sequence(this); - } - - /// Field number for the "elem_type" field. - public const int ElemTypeFieldNumber = 1; - private global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto elemType_; - /// - /// The type and optional shape of each element of the sequence. - /// This field MUST be present for this version of the IR. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto ElemType { - get { return elemType_; } - set { - elemType_ = value; - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as Sequence); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(Sequence other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if (!object.Equals(ElemType, other.ElemType)) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - if (elemType_ != null) hash ^= ElemType.GetHashCode(); - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - if (elemType_ != null) { - output.WriteRawTag(10); - output.WriteMessage(ElemType); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - if (elemType_ != null) { - size += 1 + pb::CodedOutputStream.ComputeMessageSize(ElemType); - } - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(Sequence other) { - if (other == null) { - return; - } - if (other.elemType_ != null) { - if (elemType_ == null) { - elemType_ = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto(); - } - ElemType.MergeFrom(other.ElemType); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 10: { - if (elemType_ == null) { - elemType_ = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto(); - } - input.ReadMessage(elemType_); - break; - } - } - } - } - - } - - /// - /// map<K,V> - /// - public sealed partial class Map : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new Map()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Descriptor.NestedTypes[2]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public Map() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public Map(Map other) : this() { - keyType_ = other.keyType_; - ValueType = other.valueType_ != null ? other.ValueType.Clone() : null; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public Map Clone() { - return new Map(this); - } - - /// Field number for the "key_type" field. - public const int KeyTypeFieldNumber = 1; - private global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Types.DataType keyType_ = 0; - /// - /// This field MUST be present for this version of the IR. - /// This field MUST refer to an integral type ([U]INT{8|16|32|64}) or STRING - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Types.DataType KeyType { - get { return keyType_; } - set { - keyType_ = value; - } - } - - /// Field number for the "value_type" field. - public const int ValueTypeFieldNumber = 2; - private global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto valueType_; - /// - /// This field MUST be present for this version of the IR. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto ValueType { - get { return valueType_; } - set { - valueType_ = value; - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as Map); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(Map other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if (KeyType != other.KeyType) return false; - if (!object.Equals(ValueType, other.ValueType)) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - if (KeyType != 0) hash ^= KeyType.GetHashCode(); - if (valueType_ != null) hash ^= ValueType.GetHashCode(); - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - if (KeyType != 0) { - output.WriteRawTag(8); - output.WriteEnum((int) KeyType); - } - if (valueType_ != null) { - output.WriteRawTag(18); - output.WriteMessage(ValueType); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - if (KeyType != 0) { - size += 1 + pb::CodedOutputStream.ComputeEnumSize((int) KeyType); - } - if (valueType_ != null) { - size += 1 + pb::CodedOutputStream.ComputeMessageSize(ValueType); - } - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(Map other) { - if (other == null) { - return; - } - if (other.KeyType != 0) { - KeyType = other.KeyType; - } - if (other.valueType_ != null) { - if (valueType_ == null) { - valueType_ = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto(); - } - ValueType.MergeFrom(other.ValueType); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 8: { - keyType_ = (global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Types.DataType) input.ReadEnum(); - break; - } - case 18: { - if (valueType_ == null) { - valueType_ = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto(); - } - input.ReadMessage(valueType_); - break; - } - } - } - } - - } - - } - #endregion - - } - - /// - /// OperatorSets are uniquely identified by a (domain, opset_version) pair. - /// - public sealed partial class OperatorSetIdProto : pb::IMessage { - private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new OperatorSetIdProto()); - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pb::MessageParser Parser { get { return _parser; } } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public static pbr::MessageDescriptor Descriptor { - get { return global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.OnnxMlReflection.Descriptor.MessageTypes[9]; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - pbr::MessageDescriptor pb::IMessage.Descriptor { - get { return Descriptor; } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public OperatorSetIdProto() { - OnConstruction(); - } - - partial void OnConstruction(); - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public OperatorSetIdProto(OperatorSetIdProto other) : this() { - domain_ = other.domain_; - version_ = other.version_; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public OperatorSetIdProto Clone() { - return new OperatorSetIdProto(this); - } - - /// Field number for the "domain" field. - public const int DomainFieldNumber = 1; - private string domain_ = ""; - /// - /// The domain of the operator set being identified. - /// The empty string ("") or absence of this field implies the operator - /// set that is defined as part of the ONNX specification. - /// This field MUST be present in this version of the IR when referring to any other operator set. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public string Domain { - get { return domain_; } - set { - domain_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); - } - } - - /// Field number for the "version" field. - public const int VersionFieldNumber = 2; - private long version_; - /// - /// The version of the operator set being identified. - /// This field MUST be present in this version of the IR. - /// - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public long Version { - get { return version_; } - set { - version_ = value; - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override bool Equals(object other) { - return Equals(other as OperatorSetIdProto); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public bool Equals(OperatorSetIdProto other) { - if (ReferenceEquals(other, null)) { - return false; - } - if (ReferenceEquals(other, this)) { - return true; - } - if (Domain != other.Domain) return false; - if (Version != other.Version) return false; - return true; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override int GetHashCode() { - int hash = 1; - if (Domain.Length != 0) hash ^= Domain.GetHashCode(); - if (Version != 0L) hash ^= Version.GetHashCode(); - return hash; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public override string ToString() { - return pb::JsonFormatter.ToDiagnosticString(this); - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void WriteTo(pb::CodedOutputStream output) { - if (Domain.Length != 0) { - output.WriteRawTag(10); - output.WriteString(Domain); - } - if (Version != 0L) { - output.WriteRawTag(16); - output.WriteInt64(Version); - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public int CalculateSize() { - int size = 0; - if (Domain.Length != 0) { - size += 1 + pb::CodedOutputStream.ComputeStringSize(Domain); - } - if (Version != 0L) { - size += 1 + pb::CodedOutputStream.ComputeInt64Size(Version); - } - return size; - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(OperatorSetIdProto other) { - if (other == null) { - return; - } - if (other.Domain.Length != 0) { - Domain = other.Domain; - } - if (other.Version != 0L) { - Version = other.Version; - } - } - - [global::System.Diagnostics.DebuggerNonUserCodeAttribute] - public void MergeFrom(pb::CodedInputStream input) { - uint tag; - while ((tag = input.ReadTag()) != 0) { - switch(tag) { - default: - input.SkipLastField(); - break; - case 10: { - Domain = input.ReadString(); - break; - } - case 16: { - Version = input.ReadInt64(); - break; - } - } - } - } - - } - - #endregion - -} - -#endregion Designer generated code diff --git a/src/Microsoft.ML.UniversalModelFormat/Onnx/OnnxMl.cs b/src/Microsoft.ML.UniversalModelFormat/Onnx/OnnxMl.cs new file mode 100644 index 0000000000..0117712614 --- /dev/null +++ b/src/Microsoft.ML.UniversalModelFormat/Onnx/OnnxMl.cs @@ -0,0 +1,4756 @@ +// +// Generated by the protocol buffer compiler. DO NOT EDIT! +// source: onnx-ml.proto3 +// +#pragma warning disable 1591, 0612, 3021 +#region Designer generated code + +using pb = global::Google.Protobuf; +using pbc = global::Google.Protobuf.Collections; +using pbr = global::Google.Protobuf.Reflection; +using scg = global::System.Collections.Generic; +namespace Microsoft.ML.Runtime.UniversalModelFormat.Onnx +{ + + /// Holder for reflection information generated from onnx-ml.proto3 + public static partial class OnnxMlReflection + { + + #region Descriptor + /// File descriptor for onnx-ml.proto3 + public static pbr::FileDescriptor Descriptor + { + get { return descriptor; } + } + private static pbr::FileDescriptor descriptor; + + static OnnxMlReflection() + { + byte[] descriptorData = global::System.Convert.FromBase64String( + string.Concat( + "Cg5vbm54LW1sLnByb3RvMxIEb25ueCLgAwoOQXR0cmlidXRlUHJvdG8SDAoE", + "bmFtZRgBIAEoCRIVCg1yZWZfYXR0cl9uYW1lGBUgASgJEhIKCmRvY19zdHJp", + "bmcYDSABKAkSMAoEdHlwZRgUIAEoDjIiLm9ubnguQXR0cmlidXRlUHJvdG8u", + "QXR0cmlidXRlVHlwZRIJCgFmGAIgASgCEgkKAWkYAyABKAMSCQoBcxgEIAEo", + "DBIcCgF0GAUgASgLMhEub25ueC5UZW5zb3JQcm90bxIbCgFnGAYgASgLMhAu", + "b25ueC5HcmFwaFByb3RvEg4KBmZsb2F0cxgHIAMoAhIMCgRpbnRzGAggAygD", + "Eg8KB3N0cmluZ3MYCSADKAwSIgoHdGVuc29ycxgKIAMoCzIRLm9ubnguVGVu", + "c29yUHJvdG8SIAoGZ3JhcGhzGAsgAygLMhAub25ueC5HcmFwaFByb3RvIpEB", + "Cg1BdHRyaWJ1dGVUeXBlEg0KCVVOREVGSU5FRBAAEgkKBUZMT0FUEAESBwoD", + "SU5UEAISCgoGU1RSSU5HEAMSCgoGVEVOU09SEAQSCQoFR1JBUEgQBRIKCgZG", + "TE9BVFMQBhIICgRJTlRTEAcSCwoHU1RSSU5HUxAIEgsKB1RFTlNPUlMQCRIK", + "CgZHUkFQSFMQCiJRCg5WYWx1ZUluZm9Qcm90bxIMCgRuYW1lGAEgASgJEh0K", + "BHR5cGUYAiABKAsyDy5vbm54LlR5cGVQcm90bxISCgpkb2Nfc3RyaW5nGAMg", + "ASgJIpYBCglOb2RlUHJvdG8SDQoFaW5wdXQYASADKAkSDgoGb3V0cHV0GAIg", + "AygJEgwKBG5hbWUYAyABKAkSDwoHb3BfdHlwZRgEIAEoCRIOCgZkb21haW4Y", + "ByABKAkSJwoJYXR0cmlidXRlGAUgAygLMhQub25ueC5BdHRyaWJ1dGVQcm90", + "bxISCgpkb2Nfc3RyaW5nGAYgASgJIpMCCgpNb2RlbFByb3RvEhIKCmlyX3Zl", + "cnNpb24YASABKAMSLgoMb3BzZXRfaW1wb3J0GAggAygLMhgub25ueC5PcGVy", + "YXRvclNldElkUHJvdG8SFQoNcHJvZHVjZXJfbmFtZRgCIAEoCRIYChBwcm9k", + "dWNlcl92ZXJzaW9uGAMgASgJEg4KBmRvbWFpbhgEIAEoCRIVCg1tb2RlbF92", + "ZXJzaW9uGAUgASgDEhIKCmRvY19zdHJpbmcYBiABKAkSHwoFZ3JhcGgYByAB", + "KAsyEC5vbm54LkdyYXBoUHJvdG8SNAoObWV0YWRhdGFfcHJvcHMYDiADKAsy", + "HC5vbm54LlN0cmluZ1N0cmluZ0VudHJ5UHJvdG8iNAoWU3RyaW5nU3RyaW5n", + "RW50cnlQcm90bxILCgNrZXkYASABKAkSDQoFdmFsdWUYAiABKAki6gEKCkdy", + "YXBoUHJvdG8SHQoEbm9kZRgBIAMoCzIPLm9ubnguTm9kZVByb3RvEgwKBG5h", + "bWUYAiABKAkSJgoLaW5pdGlhbGl6ZXIYBSADKAsyES5vbm54LlRlbnNvclBy", + "b3RvEhIKCmRvY19zdHJpbmcYCiABKAkSIwoFaW5wdXQYCyADKAsyFC5vbm54", + "LlZhbHVlSW5mb1Byb3RvEiQKBm91dHB1dBgMIAMoCzIULm9ubnguVmFsdWVJ", + "bmZvUHJvdG8SKAoKdmFsdWVfaW5mbxgNIAMoCzIULm9ubnguVmFsdWVJbmZv", + "UHJvdG8irwQKC1RlbnNvclByb3RvEgwKBGRpbXMYASADKAMSLQoJZGF0YV90", + "eXBlGAIgASgOMhoub25ueC5UZW5zb3JQcm90by5EYXRhVHlwZRIqCgdzZWdt", + "ZW50GAMgASgLMhkub25ueC5UZW5zb3JQcm90by5TZWdtZW50EhYKCmZsb2F0", + "X2RhdGEYBCADKAJCAhABEhYKCmludDMyX2RhdGEYBSADKAVCAhABEhMKC3N0", + "cmluZ19kYXRhGAYgAygMEhYKCmludDY0X2RhdGEYByADKANCAhABEgwKBG5h", + "bWUYCCABKAkSEgoKZG9jX3N0cmluZxgMIAEoCRIQCghyYXdfZGF0YRgJIAEo", + "DBIXCgtkb3VibGVfZGF0YRgKIAMoAUICEAESFwoLdWludDY0X2RhdGEYCyAD", + "KARCAhABGiUKB1NlZ21lbnQSDQoFYmVnaW4YASABKAMSCwoDZW5kGAIgASgD", + "IswBCghEYXRhVHlwZRINCglVTkRFRklORUQQABIJCgVGTE9BVBABEgkKBVVJ", + "TlQ4EAISCAoESU5UOBADEgoKBlVJTlQxNhAEEgkKBUlOVDE2EAUSCQoFSU5U", + "MzIQBhIJCgVJTlQ2NBAHEgoKBlNUUklORxAIEggKBEJPT0wQCRILCgdGTE9B", + "VDE2EAoSCgoGRE9VQkxFEAsSCgoGVUlOVDMyEAwSCgoGVUlOVDY0EA0SDQoJ", + "Q09NUExFWDY0EA4SDgoKQ09NUExFWDEyOBAPIpUBChBUZW5zb3JTaGFwZVBy", + "b3RvEi0KA2RpbRgBIAMoCzIgLm9ubnguVGVuc29yU2hhcGVQcm90by5EaW1l", + "bnNpb24aUgoJRGltZW5zaW9uEhMKCWRpbV92YWx1ZRgBIAEoA0gAEhMKCWRp", + "bV9wYXJhbRgCIAEoCUgAEhIKCmRlbm90YXRpb24YAyABKAlCBwoFdmFsdWUi", + "nQMKCVR5cGVQcm90bxItCgt0ZW5zb3JfdHlwZRgBIAEoCzIWLm9ubnguVHlw", + "ZVByb3RvLlRlbnNvckgAEjEKDXNlcXVlbmNlX3R5cGUYBCABKAsyGC5vbm54", + "LlR5cGVQcm90by5TZXF1ZW5jZUgAEicKCG1hcF90eXBlGAUgASgLMhMub25u", + "eC5UeXBlUHJvdG8uTWFwSAASEgoKZGVub3RhdGlvbhgGIAEoCRpeCgZUZW5z", + "b3ISLQoJZWxlbV90eXBlGAEgASgOMhoub25ueC5UZW5zb3JQcm90by5EYXRh", + "VHlwZRIlCgVzaGFwZRgCIAEoCzIWLm9ubnguVGVuc29yU2hhcGVQcm90bxou", + "CghTZXF1ZW5jZRIiCgllbGVtX3R5cGUYASABKAsyDy5vbm54LlR5cGVQcm90", + "bxpYCgNNYXASLAoIa2V5X3R5cGUYASABKA4yGi5vbm54LlRlbnNvclByb3Rv", + "LkRhdGFUeXBlEiMKCnZhbHVlX3R5cGUYAiABKAsyDy5vbm54LlR5cGVQcm90", + "b0IHCgV2YWx1ZSI1ChJPcGVyYXRvclNldElkUHJvdG8SDgoGZG9tYWluGAEg", + "ASgJEg8KB3ZlcnNpb24YAiABKAMqYwoHVmVyc2lvbhISCg5fU1RBUlRfVkVS", + "U0lPThAAEhkKFUlSX1ZFUlNJT05fMjAxN18xMF8xMBABEhkKFUlSX1ZFUlNJ", + "T05fMjAxN18xMF8zMBACEg4KCklSX1ZFUlNJT04QA0IxqgIuTWljcm9zb2Z0", + "Lk1MLlJ1bnRpbWUuVW5pdmVyc2FsTW9kZWxGb3JtYXQuT25ueGIGcHJvdG8z")); + descriptor = pbr::FileDescriptor.FromGeneratedCode(descriptorData, + new pbr::FileDescriptor[] { }, + new pbr::GeneratedClrTypeInfo(new[] { typeof(global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.Version), }, new pbr::GeneratedClrTypeInfo[] { + new pbr::GeneratedClrTypeInfo(typeof(global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.AttributeProto), global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.AttributeProto.Parser, new[]{ "Name", "RefAttrName", "DocString", "Type", "F", "I", "S", "T", "G", "Floats", "Ints", "Strings", "Tensors", "Graphs" }, null, new[]{ typeof(global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.AttributeProto.Types.AttributeType) }, null), + new pbr::GeneratedClrTypeInfo(typeof(global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.ValueInfoProto), global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.ValueInfoProto.Parser, new[]{ "Name", "Type", "DocString" }, null, null, null), + new pbr::GeneratedClrTypeInfo(typeof(global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.NodeProto), global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.NodeProto.Parser, new[]{ "Input", "Output", "Name", "OpType", "Domain", "Attribute", "DocString" }, null, null, null), + new pbr::GeneratedClrTypeInfo(typeof(global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.ModelProto), global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.ModelProto.Parser, new[]{ "IrVersion", "OpsetImport", "ProducerName", "ProducerVersion", "Domain", "ModelVersion", "DocString", "Graph", "MetadataProps" }, null, null, null), + new pbr::GeneratedClrTypeInfo(typeof(global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.StringStringEntryProto), global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.StringStringEntryProto.Parser, new[]{ "Key", "Value" }, null, null, null), + new pbr::GeneratedClrTypeInfo(typeof(global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.GraphProto), global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.GraphProto.Parser, new[]{ "Node", "Name", "Initializer", "DocString", "Input", "Output", "ValueInfo" }, null, null, null), + new pbr::GeneratedClrTypeInfo(typeof(global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto), global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Parser, new[]{ "Dims", "DataType", "Segment", "FloatData", "Int32Data", "StringData", "Int64Data", "Name", "DocString", "RawData", "DoubleData", "Uint64Data" }, null, new[]{ typeof(global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Types.DataType) }, new pbr::GeneratedClrTypeInfo[] { new pbr::GeneratedClrTypeInfo(typeof(global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Types.Segment), global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Types.Segment.Parser, new[]{ "Begin", "End" }, null, null, null)}), + new pbr::GeneratedClrTypeInfo(typeof(global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorShapeProto), global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorShapeProto.Parser, new[]{ "Dim" }, null, null, new pbr::GeneratedClrTypeInfo[] { new pbr::GeneratedClrTypeInfo(typeof(global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorShapeProto.Types.Dimension), global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorShapeProto.Types.Dimension.Parser, new[]{ "DimValue", "DimParam", "Denotation" }, new[]{ "Value" }, null, null)}), + new pbr::GeneratedClrTypeInfo(typeof(global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto), global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Parser, new[]{ "TensorType", "SequenceType", "MapType", "Denotation" }, new[]{ "Value" }, null, new pbr::GeneratedClrTypeInfo[] { new pbr::GeneratedClrTypeInfo(typeof(global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Types.Tensor), global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Types.Tensor.Parser, new[]{ "ElemType", "Shape" }, null, null, null), + new pbr::GeneratedClrTypeInfo(typeof(global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Types.Sequence), global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Types.Sequence.Parser, new[]{ "ElemType" }, null, null, null), + new pbr::GeneratedClrTypeInfo(typeof(global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Types.Map), global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Types.Map.Parser, new[]{ "KeyType", "ValueType" }, null, null, null)}), + new pbr::GeneratedClrTypeInfo(typeof(global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.OperatorSetIdProto), global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.OperatorSetIdProto.Parser, new[]{ "Domain", "Version" }, null, null, null) + })); + } + #endregion + + } + #region Enums + /// + /// Versioning + /// + /// ONNX versioning is specified in docs/IR.md and elaborated on in docs/Versioning.md + /// + /// To be compatible with both proto2 and proto3, we will use a version number + /// that is not defined by the default value but an explicit enum number. + /// + public enum Version + { + /// + /// proto3 requires the first enum value to be zero. + /// We add this just to appease the compiler. + /// + [pbr::OriginalName("_START_VERSION")] StartVersion = 0, + /// + /// The version field is always serialized and we will use it to store the + /// version that the graph is generated from. This helps us set up version + /// control. + /// For the IR, we are using simple numbers starting with with 0x00000001, + /// which was the version we published on Oct 10, 2017. + /// + [pbr::OriginalName("IR_VERSION_2017_10_10")] IrVersion20171010 = 1, + /// + /// IR_VERSION 2 published on Oct 30, 2017 + /// - Added type discriminator to AttributeProto to support proto3 users + /// + [pbr::OriginalName("IR_VERSION_2017_10_30")] IrVersion20171030 = 2, + /// + /// IR VERSION 3 published on Nov 3, 2017 + /// - For operator versioning: + /// - Added new message OperatorSetIdProto + /// - Added opset_import in ModelProto + /// - For vendor extensions, added domain in NodeProto + /// + [pbr::OriginalName("IR_VERSION")] IrVersion = 3, + } + + #endregion + + #region Messages + /// + /// Attributes + /// + /// A named attribute containing either singular float, integer, string, graph, + /// and tensor values, or repeated float, integer, string, graph, and tensor values. + /// An AttributeProto MUST contain the name field, and *only one* of the + /// following content fields, effectively enforcing a C/C++ union equivalent. + /// + public sealed partial class AttributeProto : pb::IMessage + { + private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new AttributeProto()); + private pb::UnknownFieldSet _unknownFields; + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public static pb::MessageParser Parser { get { return _parser; } } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public static pbr::MessageDescriptor Descriptor + { + get { return global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.OnnxMlReflection.Descriptor.MessageTypes[0]; } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + pbr::MessageDescriptor pb::IMessage.Descriptor + { + get { return Descriptor; } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public AttributeProto() + { + OnConstruction(); + } + + partial void OnConstruction(); + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public AttributeProto(AttributeProto other) : this() + { + name_ = other.name_; + refAttrName_ = other.refAttrName_; + docString_ = other.docString_; + type_ = other.type_; + f_ = other.f_; + i_ = other.i_; + s_ = other.s_; + T = other.t_ != null ? other.T.Clone() : null; + G = other.g_ != null ? other.G.Clone() : null; + floats_ = other.floats_.Clone(); + ints_ = other.ints_.Clone(); + strings_ = other.strings_.Clone(); + tensors_ = other.tensors_.Clone(); + graphs_ = other.graphs_.Clone(); + _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public AttributeProto Clone() + { + return new AttributeProto(this); + } + + /// Field number for the "name" field. + public const int NameFieldNumber = 1; + private string name_ = ""; + /// + /// The name field MUST be present for this version of the IR. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public string Name + { + get { return name_; } + set + { + name_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); + } + } + + /// Field number for the "ref_attr_name" field. + public const int RefAttrNameFieldNumber = 21; + private string refAttrName_ = ""; + /// + /// if ref_attr_name is not empty, ref_attr_name is the attribute name in parent function. + /// In this case, this AttributeProto does not contain data, and it's a reference of attribute + /// in parent scope. + /// NOTE: This should ONLY be used in function (sub-graph). It's invalid to be used in main graph. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public string RefAttrName + { + get { return refAttrName_; } + set + { + refAttrName_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); + } + } + + /// Field number for the "doc_string" field. + public const int DocStringFieldNumber = 13; + private string docString_ = ""; + /// + /// A human-readable documentation for this attribute. Markdown is allowed. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public string DocString + { + get { return docString_; } + set + { + docString_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); + } + } + + /// Field number for the "type" field. + public const int TypeFieldNumber = 20; + private global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.AttributeProto.Types.AttributeType type_ = 0; + /// + /// The type field MUST be present for this version of the IR. + /// For 0.0.1 versions of the IR, this field was not defined, and + /// implementations needed to use has_field hueristics to determine + /// which value field was in use. For IR_VERSION 0.0.2 or later, this + /// field MUST be set and match the f|i|s|t|... field in use. This + /// change was made to accomodate proto3 implementations. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.AttributeProto.Types.AttributeType Type + { + get { return type_; } + set + { + type_ = value; + } + } + + /// Field number for the "f" field. + public const int FFieldNumber = 2; + private float f_; + /// + /// Exactly ONE of the following fields must be present for this version of the IR + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public float F + { + get { return f_; } + set + { + f_ = value; + } + } + + /// Field number for the "i" field. + public const int IFieldNumber = 3; + private long i_; + /// + /// int + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public long I + { + get { return i_; } + set + { + i_ = value; + } + } + + /// Field number for the "s" field. + public const int SFieldNumber = 4; + private pb::ByteString s_ = pb::ByteString.Empty; + /// + /// UTF-8 string + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public pb::ByteString S + { + get { return s_; } + set + { + s_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); + } + } + + /// Field number for the "t" field. + public const int TFieldNumber = 5; + private global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto t_; + /// + /// tensor value + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto T + { + get { return t_; } + set + { + t_ = value; + } + } + + /// Field number for the "g" field. + public const int GFieldNumber = 6; + private global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.GraphProto g_; + /// + /// graph + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.GraphProto G + { + get { return g_; } + set + { + g_ = value; + } + } + + /// Field number for the "floats" field. + public const int FloatsFieldNumber = 7; + private static readonly pb::FieldCodec _repeated_floats_codec + = pb::FieldCodec.ForFloat(58); + private readonly pbc::RepeatedField floats_ = new pbc::RepeatedField(); + /// + /// list of floats + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public pbc::RepeatedField Floats + { + get { return floats_; } + } + + /// Field number for the "ints" field. + public const int IntsFieldNumber = 8; + private static readonly pb::FieldCodec _repeated_ints_codec + = pb::FieldCodec.ForInt64(66); + private readonly pbc::RepeatedField ints_ = new pbc::RepeatedField(); + /// + /// list of ints + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public pbc::RepeatedField Ints + { + get { return ints_; } + } + + /// Field number for the "strings" field. + public const int StringsFieldNumber = 9; + private static readonly pb::FieldCodec _repeated_strings_codec + = pb::FieldCodec.ForBytes(74); + private readonly pbc::RepeatedField strings_ = new pbc::RepeatedField(); + /// + /// list of UTF-8 strings + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public pbc::RepeatedField Strings + { + get { return strings_; } + } + + /// Field number for the "tensors" field. + public const int TensorsFieldNumber = 10; + private static readonly pb::FieldCodec _repeated_tensors_codec + = pb::FieldCodec.ForMessage(82, global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Parser); + private readonly pbc::RepeatedField tensors_ = new pbc::RepeatedField(); + /// + /// list of tensors + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public pbc::RepeatedField Tensors + { + get { return tensors_; } + } + + /// Field number for the "graphs" field. + public const int GraphsFieldNumber = 11; + private static readonly pb::FieldCodec _repeated_graphs_codec + = pb::FieldCodec.ForMessage(90, global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.GraphProto.Parser); + private readonly pbc::RepeatedField graphs_ = new pbc::RepeatedField(); + /// + /// list of graph + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public pbc::RepeatedField Graphs + { + get { return graphs_; } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override bool Equals(object other) + { + return Equals(other as AttributeProto); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public bool Equals(AttributeProto other) + { + if (ReferenceEquals(other, null)) + { + return false; + } + if (ReferenceEquals(other, this)) + { + return true; + } + if (Name != other.Name) return false; + if (RefAttrName != other.RefAttrName) return false; + if (DocString != other.DocString) return false; + if (Type != other.Type) return false; + if (!pbc::ProtobufEqualityComparers.BitwiseSingleEqualityComparer.Equals(F, other.F)) return false; + if (I != other.I) return false; + if (S != other.S) return false; + if (!object.Equals(T, other.T)) return false; + if (!object.Equals(G, other.G)) return false; + if (!floats_.Equals(other.floats_)) return false; + if (!ints_.Equals(other.ints_)) return false; + if (!strings_.Equals(other.strings_)) return false; + if (!tensors_.Equals(other.tensors_)) return false; + if (!graphs_.Equals(other.graphs_)) return false; + return Equals(_unknownFields, other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override int GetHashCode() + { + int hash = 1; + if (Name.Length != 0) hash ^= Name.GetHashCode(); + if (RefAttrName.Length != 0) hash ^= RefAttrName.GetHashCode(); + if (DocString.Length != 0) hash ^= DocString.GetHashCode(); + if (Type != 0) hash ^= Type.GetHashCode(); + if (F != 0F) hash ^= pbc::ProtobufEqualityComparers.BitwiseSingleEqualityComparer.GetHashCode(F); + if (I != 0L) hash ^= I.GetHashCode(); + if (S.Length != 0) hash ^= S.GetHashCode(); + if (t_ != null) hash ^= T.GetHashCode(); + if (g_ != null) hash ^= G.GetHashCode(); + hash ^= floats_.GetHashCode(); + hash ^= ints_.GetHashCode(); + hash ^= strings_.GetHashCode(); + hash ^= tensors_.GetHashCode(); + hash ^= graphs_.GetHashCode(); + if (_unknownFields != null) + { + hash ^= _unknownFields.GetHashCode(); + } + return hash; + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override string ToString() + { + return pb::JsonFormatter.ToDiagnosticString(this); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void WriteTo(pb::CodedOutputStream output) + { + if (Name.Length != 0) + { + output.WriteRawTag(10); + output.WriteString(Name); + } + if (F != 0F) + { + output.WriteRawTag(21); + output.WriteFloat(F); + } + if (I != 0L) + { + output.WriteRawTag(24); + output.WriteInt64(I); + } + if (S.Length != 0) + { + output.WriteRawTag(34); + output.WriteBytes(S); + } + if (t_ != null) + { + output.WriteRawTag(42); + output.WriteMessage(T); + } + if (g_ != null) + { + output.WriteRawTag(50); + output.WriteMessage(G); + } + floats_.WriteTo(output, _repeated_floats_codec); + ints_.WriteTo(output, _repeated_ints_codec); + strings_.WriteTo(output, _repeated_strings_codec); + tensors_.WriteTo(output, _repeated_tensors_codec); + graphs_.WriteTo(output, _repeated_graphs_codec); + if (DocString.Length != 0) + { + output.WriteRawTag(106); + output.WriteString(DocString); + } + if (Type != 0) + { + output.WriteRawTag(160, 1); + output.WriteEnum((int)Type); + } + if (RefAttrName.Length != 0) + { + output.WriteRawTag(170, 1); + output.WriteString(RefAttrName); + } + if (_unknownFields != null) + { + _unknownFields.WriteTo(output); + } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public int CalculateSize() + { + int size = 0; + if (Name.Length != 0) + { + size += 1 + pb::CodedOutputStream.ComputeStringSize(Name); + } + if (RefAttrName.Length != 0) + { + size += 2 + pb::CodedOutputStream.ComputeStringSize(RefAttrName); + } + if (DocString.Length != 0) + { + size += 1 + pb::CodedOutputStream.ComputeStringSize(DocString); + } + if (Type != 0) + { + size += 2 + pb::CodedOutputStream.ComputeEnumSize((int)Type); + } + if (F != 0F) + { + size += 1 + 4; + } + if (I != 0L) + { + size += 1 + pb::CodedOutputStream.ComputeInt64Size(I); + } + if (S.Length != 0) + { + size += 1 + pb::CodedOutputStream.ComputeBytesSize(S); + } + if (t_ != null) + { + size += 1 + pb::CodedOutputStream.ComputeMessageSize(T); + } + if (g_ != null) + { + size += 1 + pb::CodedOutputStream.ComputeMessageSize(G); + } + size += floats_.CalculateSize(_repeated_floats_codec); + size += ints_.CalculateSize(_repeated_ints_codec); + size += strings_.CalculateSize(_repeated_strings_codec); + size += tensors_.CalculateSize(_repeated_tensors_codec); + size += graphs_.CalculateSize(_repeated_graphs_codec); + if (_unknownFields != null) + { + size += _unknownFields.CalculateSize(); + } + return size; + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void MergeFrom(AttributeProto other) + { + if (other == null) + { + return; + } + if (other.Name.Length != 0) + { + Name = other.Name; + } + if (other.RefAttrName.Length != 0) + { + RefAttrName = other.RefAttrName; + } + if (other.DocString.Length != 0) + { + DocString = other.DocString; + } + if (other.Type != 0) + { + Type = other.Type; + } + if (other.F != 0F) + { + F = other.F; + } + if (other.I != 0L) + { + I = other.I; + } + if (other.S.Length != 0) + { + S = other.S; + } + if (other.t_ != null) + { + if (t_ == null) + { + t_ = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto(); + } + T.MergeFrom(other.T); + } + if (other.g_ != null) + { + if (g_ == null) + { + g_ = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.GraphProto(); + } + G.MergeFrom(other.G); + } + floats_.Add(other.floats_); + ints_.Add(other.ints_); + strings_.Add(other.strings_); + tensors_.Add(other.tensors_); + graphs_.Add(other.graphs_); + _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void MergeFrom(pb::CodedInputStream input) + { + uint tag; + while ((tag = input.ReadTag()) != 0) + { + switch (tag) + { + default: + _unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input); + break; + case 10: + { + Name = input.ReadString(); + break; + } + case 21: + { + F = input.ReadFloat(); + break; + } + case 24: + { + I = input.ReadInt64(); + break; + } + case 34: + { + S = input.ReadBytes(); + break; + } + case 42: + { + if (t_ == null) + { + t_ = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto(); + } + input.ReadMessage(t_); + break; + } + case 50: + { + if (g_ == null) + { + g_ = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.GraphProto(); + } + input.ReadMessage(g_); + break; + } + case 58: + case 61: + { + floats_.AddEntriesFrom(input, _repeated_floats_codec); + break; + } + case 66: + case 64: + { + ints_.AddEntriesFrom(input, _repeated_ints_codec); + break; + } + case 74: + { + strings_.AddEntriesFrom(input, _repeated_strings_codec); + break; + } + case 82: + { + tensors_.AddEntriesFrom(input, _repeated_tensors_codec); + break; + } + case 90: + { + graphs_.AddEntriesFrom(input, _repeated_graphs_codec); + break; + } + case 106: + { + DocString = input.ReadString(); + break; + } + case 160: + { + type_ = (global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.AttributeProto.Types.AttributeType)input.ReadEnum(); + break; + } + case 170: + { + RefAttrName = input.ReadString(); + break; + } + } + } + } + + #region Nested types + /// Container for nested types declared in the AttributeProto message type. + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public static partial class Types + { + /// + /// Note: this enum is structurally identical to the OpSchema::AttrType + /// enum defined in schema.h. If you rev one, you likely need to rev the other. + /// + public enum AttributeType + { + [pbr::OriginalName("UNDEFINED")] Undefined = 0, + [pbr::OriginalName("FLOAT")] Float = 1, + [pbr::OriginalName("INT")] Int = 2, + [pbr::OriginalName("STRING")] String = 3, + [pbr::OriginalName("TENSOR")] Tensor = 4, + [pbr::OriginalName("GRAPH")] Graph = 5, + [pbr::OriginalName("FLOATS")] Floats = 6, + [pbr::OriginalName("INTS")] Ints = 7, + [pbr::OriginalName("STRINGS")] Strings = 8, + [pbr::OriginalName("TENSORS")] Tensors = 9, + [pbr::OriginalName("GRAPHS")] Graphs = 10, + } + + } + #endregion + + } + + /// + /// Defines information on value, including the name, the type, and + /// the shape of the value. + /// + public sealed partial class ValueInfoProto : pb::IMessage + { + private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new ValueInfoProto()); + private pb::UnknownFieldSet _unknownFields; + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public static pb::MessageParser Parser { get { return _parser; } } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public static pbr::MessageDescriptor Descriptor + { + get { return global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.OnnxMlReflection.Descriptor.MessageTypes[1]; } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + pbr::MessageDescriptor pb::IMessage.Descriptor + { + get { return Descriptor; } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public ValueInfoProto() + { + OnConstruction(); + } + + partial void OnConstruction(); + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public ValueInfoProto(ValueInfoProto other) : this() + { + name_ = other.name_; + Type = other.type_ != null ? other.Type.Clone() : null; + docString_ = other.docString_; + _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public ValueInfoProto Clone() + { + return new ValueInfoProto(this); + } + + /// Field number for the "name" field. + public const int NameFieldNumber = 1; + private string name_ = ""; + /// + /// This field MUST be present in this version of the IR. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public string Name + { + get { return name_; } + set + { + name_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); + } + } + + /// Field number for the "type" field. + public const int TypeFieldNumber = 2; + private global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto type_; + /// + /// This field MUST be present in this version of the IR. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto Type + { + get { return type_; } + set + { + type_ = value; + } + } + + /// Field number for the "doc_string" field. + public const int DocStringFieldNumber = 3; + private string docString_ = ""; + /// + /// A human-readable documentation for this value. Markdown is allowed. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public string DocString + { + get { return docString_; } + set + { + docString_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); + } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override bool Equals(object other) + { + return Equals(other as ValueInfoProto); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public bool Equals(ValueInfoProto other) + { + if (ReferenceEquals(other, null)) + { + return false; + } + if (ReferenceEquals(other, this)) + { + return true; + } + if (Name != other.Name) return false; + if (!object.Equals(Type, other.Type)) return false; + if (DocString != other.DocString) return false; + return Equals(_unknownFields, other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override int GetHashCode() + { + int hash = 1; + if (Name.Length != 0) hash ^= Name.GetHashCode(); + if (type_ != null) hash ^= Type.GetHashCode(); + if (DocString.Length != 0) hash ^= DocString.GetHashCode(); + if (_unknownFields != null) + { + hash ^= _unknownFields.GetHashCode(); + } + return hash; + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override string ToString() + { + return pb::JsonFormatter.ToDiagnosticString(this); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void WriteTo(pb::CodedOutputStream output) + { + if (Name.Length != 0) + { + output.WriteRawTag(10); + output.WriteString(Name); + } + if (type_ != null) + { + output.WriteRawTag(18); + output.WriteMessage(Type); + } + if (DocString.Length != 0) + { + output.WriteRawTag(26); + output.WriteString(DocString); + } + if (_unknownFields != null) + { + _unknownFields.WriteTo(output); + } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public int CalculateSize() + { + int size = 0; + if (Name.Length != 0) + { + size += 1 + pb::CodedOutputStream.ComputeStringSize(Name); + } + if (type_ != null) + { + size += 1 + pb::CodedOutputStream.ComputeMessageSize(Type); + } + if (DocString.Length != 0) + { + size += 1 + pb::CodedOutputStream.ComputeStringSize(DocString); + } + if (_unknownFields != null) + { + size += _unknownFields.CalculateSize(); + } + return size; + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void MergeFrom(ValueInfoProto other) + { + if (other == null) + { + return; + } + if (other.Name.Length != 0) + { + Name = other.Name; + } + if (other.type_ != null) + { + if (type_ == null) + { + type_ = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto(); + } + Type.MergeFrom(other.Type); + } + if (other.DocString.Length != 0) + { + DocString = other.DocString; + } + _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void MergeFrom(pb::CodedInputStream input) + { + uint tag; + while ((tag = input.ReadTag()) != 0) + { + switch (tag) + { + default: + _unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input); + break; + case 10: + { + Name = input.ReadString(); + break; + } + case 18: + { + if (type_ == null) + { + type_ = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto(); + } + input.ReadMessage(type_); + break; + } + case 26: + { + DocString = input.ReadString(); + break; + } + } + } + } + + } + + /// + /// Nodes + /// + /// Computation graphs are made up of a DAG of nodes, which represent what is + /// commonly called a "layer" or "pipeline stage" in machine learning frameworks. + /// + /// For example, it can be a node of type "Conv" that takes in an image, a filter + /// tensor and a bias tensor, and produces the convolved output. + /// + public sealed partial class NodeProto : pb::IMessage + { + private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new NodeProto()); + private pb::UnknownFieldSet _unknownFields; + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public static pb::MessageParser Parser { get { return _parser; } } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public static pbr::MessageDescriptor Descriptor + { + get { return global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.OnnxMlReflection.Descriptor.MessageTypes[2]; } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + pbr::MessageDescriptor pb::IMessage.Descriptor + { + get { return Descriptor; } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public NodeProto() + { + OnConstruction(); + } + + partial void OnConstruction(); + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public NodeProto(NodeProto other) : this() + { + input_ = other.input_.Clone(); + output_ = other.output_.Clone(); + name_ = other.name_; + opType_ = other.opType_; + domain_ = other.domain_; + attribute_ = other.attribute_.Clone(); + docString_ = other.docString_; + _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public NodeProto Clone() + { + return new NodeProto(this); + } + + /// Field number for the "input" field. + public const int InputFieldNumber = 1; + private static readonly pb::FieldCodec _repeated_input_codec + = pb::FieldCodec.ForString(10); + private readonly pbc::RepeatedField input_ = new pbc::RepeatedField(); + /// + /// namespace Value + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public pbc::RepeatedField Input + { + get { return input_; } + } + + /// Field number for the "output" field. + public const int OutputFieldNumber = 2; + private static readonly pb::FieldCodec _repeated_output_codec + = pb::FieldCodec.ForString(18); + private readonly pbc::RepeatedField output_ = new pbc::RepeatedField(); + /// + /// namespace Value + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public pbc::RepeatedField Output + { + get { return output_; } + } + + /// Field number for the "name" field. + public const int NameFieldNumber = 3; + private string name_ = ""; + /// + /// An optional identifier for this node in a graph. + /// This field MAY be absent in ths version of the IR. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public string Name + { + get { return name_; } + set + { + name_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); + } + } + + /// Field number for the "op_type" field. + public const int OpTypeFieldNumber = 4; + private string opType_ = ""; + /// + /// The symbolic identifier of the Operator to execute. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public string OpType + { + get { return opType_; } + set + { + opType_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); + } + } + + /// Field number for the "domain" field. + public const int DomainFieldNumber = 7; + private string domain_ = ""; + /// + /// The domain of the OperatorSet that specifies the operator named by op_type. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public string Domain + { + get { return domain_; } + set + { + domain_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); + } + } + + /// Field number for the "attribute" field. + public const int AttributeFieldNumber = 5; + private static readonly pb::FieldCodec _repeated_attribute_codec + = pb::FieldCodec.ForMessage(42, global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.AttributeProto.Parser); + private readonly pbc::RepeatedField attribute_ = new pbc::RepeatedField(); + /// + /// Additional named attributes. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public pbc::RepeatedField Attribute + { + get { return attribute_; } + } + + /// Field number for the "doc_string" field. + public const int DocStringFieldNumber = 6; + private string docString_ = ""; + /// + /// A human-readable documentation for this node. Markdown is allowed. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public string DocString + { + get { return docString_; } + set + { + docString_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); + } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override bool Equals(object other) + { + return Equals(other as NodeProto); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public bool Equals(NodeProto other) + { + if (ReferenceEquals(other, null)) + { + return false; + } + if (ReferenceEquals(other, this)) + { + return true; + } + if (!input_.Equals(other.input_)) return false; + if (!output_.Equals(other.output_)) return false; + if (Name != other.Name) return false; + if (OpType != other.OpType) return false; + if (Domain != other.Domain) return false; + if (!attribute_.Equals(other.attribute_)) return false; + if (DocString != other.DocString) return false; + return Equals(_unknownFields, other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override int GetHashCode() + { + int hash = 1; + hash ^= input_.GetHashCode(); + hash ^= output_.GetHashCode(); + if (Name.Length != 0) hash ^= Name.GetHashCode(); + if (OpType.Length != 0) hash ^= OpType.GetHashCode(); + if (Domain.Length != 0) hash ^= Domain.GetHashCode(); + hash ^= attribute_.GetHashCode(); + if (DocString.Length != 0) hash ^= DocString.GetHashCode(); + if (_unknownFields != null) + { + hash ^= _unknownFields.GetHashCode(); + } + return hash; + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override string ToString() + { + return pb::JsonFormatter.ToDiagnosticString(this); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void WriteTo(pb::CodedOutputStream output) + { + input_.WriteTo(output, _repeated_input_codec); + output_.WriteTo(output, _repeated_output_codec); + if (Name.Length != 0) + { + output.WriteRawTag(26); + output.WriteString(Name); + } + if (OpType.Length != 0) + { + output.WriteRawTag(34); + output.WriteString(OpType); + } + attribute_.WriteTo(output, _repeated_attribute_codec); + if (DocString.Length != 0) + { + output.WriteRawTag(50); + output.WriteString(DocString); + } + if (Domain.Length != 0) + { + output.WriteRawTag(58); + output.WriteString(Domain); + } + if (_unknownFields != null) + { + _unknownFields.WriteTo(output); + } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public int CalculateSize() + { + int size = 0; + size += input_.CalculateSize(_repeated_input_codec); + size += output_.CalculateSize(_repeated_output_codec); + if (Name.Length != 0) + { + size += 1 + pb::CodedOutputStream.ComputeStringSize(Name); + } + if (OpType.Length != 0) + { + size += 1 + pb::CodedOutputStream.ComputeStringSize(OpType); + } + if (Domain.Length != 0) + { + size += 1 + pb::CodedOutputStream.ComputeStringSize(Domain); + } + size += attribute_.CalculateSize(_repeated_attribute_codec); + if (DocString.Length != 0) + { + size += 1 + pb::CodedOutputStream.ComputeStringSize(DocString); + } + if (_unknownFields != null) + { + size += _unknownFields.CalculateSize(); + } + return size; + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void MergeFrom(NodeProto other) + { + if (other == null) + { + return; + } + input_.Add(other.input_); + output_.Add(other.output_); + if (other.Name.Length != 0) + { + Name = other.Name; + } + if (other.OpType.Length != 0) + { + OpType = other.OpType; + } + if (other.Domain.Length != 0) + { + Domain = other.Domain; + } + attribute_.Add(other.attribute_); + if (other.DocString.Length != 0) + { + DocString = other.DocString; + } + _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void MergeFrom(pb::CodedInputStream input) + { + uint tag; + while ((tag = input.ReadTag()) != 0) + { + switch (tag) + { + default: + _unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input); + break; + case 10: + { + input_.AddEntriesFrom(input, _repeated_input_codec); + break; + } + case 18: + { + output_.AddEntriesFrom(input, _repeated_output_codec); + break; + } + case 26: + { + Name = input.ReadString(); + break; + } + case 34: + { + OpType = input.ReadString(); + break; + } + case 42: + { + attribute_.AddEntriesFrom(input, _repeated_attribute_codec); + break; + } + case 50: + { + DocString = input.ReadString(); + break; + } + case 58: + { + Domain = input.ReadString(); + break; + } + } + } + } + + } + + /// + /// Models + /// + /// ModelProto is a top-level file/container format for bundling a ML model and + /// associating its computation graph with metadata. + /// + /// The semantics of the model are described by the associated GraphProto. + /// + public sealed partial class ModelProto : pb::IMessage + { + private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new ModelProto()); + private pb::UnknownFieldSet _unknownFields; + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public static pb::MessageParser Parser { get { return _parser; } } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public static pbr::MessageDescriptor Descriptor + { + get { return global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.OnnxMlReflection.Descriptor.MessageTypes[3]; } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + pbr::MessageDescriptor pb::IMessage.Descriptor + { + get { return Descriptor; } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public ModelProto() + { + OnConstruction(); + } + + partial void OnConstruction(); + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public ModelProto(ModelProto other) : this() + { + irVersion_ = other.irVersion_; + opsetImport_ = other.opsetImport_.Clone(); + producerName_ = other.producerName_; + producerVersion_ = other.producerVersion_; + domain_ = other.domain_; + modelVersion_ = other.modelVersion_; + docString_ = other.docString_; + Graph = other.graph_ != null ? other.Graph.Clone() : null; + metadataProps_ = other.metadataProps_.Clone(); + _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public ModelProto Clone() + { + return new ModelProto(this); + } + + /// Field number for the "ir_version" field. + public const int IrVersionFieldNumber = 1; + private long irVersion_; + /// + /// The version of the IR this model targets. See Version enum above. + /// This field MUST be present. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public long IrVersion + { + get { return irVersion_; } + set + { + irVersion_ = value; + } + } + + /// Field number for the "opset_import" field. + public const int OpsetImportFieldNumber = 8; + private static readonly pb::FieldCodec _repeated_opsetImport_codec + = pb::FieldCodec.ForMessage(66, global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.OperatorSetIdProto.Parser); + private readonly pbc::RepeatedField opsetImport_ = new pbc::RepeatedField(); + /// + /// The OperatorSets this model relies on. + /// All ModelProtos MUST have at least one entry that + /// specifies which version of the ONNX OperatorSet is + /// being imported. + /// + /// All nodes in the ModelProto's graph will bind against the operator + /// with the same-domain/same-op_type operator with the HIGHEST version + /// in the referenced operator sets. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public pbc::RepeatedField OpsetImport + { + get { return opsetImport_; } + } + + /// Field number for the "producer_name" field. + public const int ProducerNameFieldNumber = 2; + private string producerName_ = ""; + /// + /// The name of the framework or tool used to generate this model. + /// This field SHOULD be present to indicate which implementation/tool/framework + /// emitted the model. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public string ProducerName + { + get { return producerName_; } + set + { + producerName_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); + } + } + + /// Field number for the "producer_version" field. + public const int ProducerVersionFieldNumber = 3; + private string producerVersion_ = ""; + /// + /// The version of the framework or tool used to generate this model. + /// This field SHOULD be present to indicate which implementation/tool/framework + /// emitted the model. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public string ProducerVersion + { + get { return producerVersion_; } + set + { + producerVersion_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); + } + } + + /// Field number for the "domain" field. + public const int DomainFieldNumber = 4; + private string domain_ = ""; + /// + /// Domain name of the model. + /// We use reverse domain names as name space indicators. For example: + /// `com.facebook.fair` or `com.microsoft.cognitiveservices` + /// + /// Together with `model_version` and GraphProto.name, this forms the unique identity of + /// the graph. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public string Domain + { + get { return domain_; } + set + { + domain_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); + } + } + + /// Field number for the "model_version" field. + public const int ModelVersionFieldNumber = 5; + private long modelVersion_; + /// + /// The version of the graph encoded. See Version enum below. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public long ModelVersion + { + get { return modelVersion_; } + set + { + modelVersion_ = value; + } + } + + /// Field number for the "doc_string" field. + public const int DocStringFieldNumber = 6; + private string docString_ = ""; + /// + /// A human-readable documentation for this model. Markdown is allowed. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public string DocString + { + get { return docString_; } + set + { + docString_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); + } + } + + /// Field number for the "graph" field. + public const int GraphFieldNumber = 7; + private global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.GraphProto graph_; + /// + /// The parameterized graph that is evaluated to execute the model. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.GraphProto Graph + { + get { return graph_; } + set + { + graph_ = value; + } + } + + /// Field number for the "metadata_props" field. + public const int MetadataPropsFieldNumber = 14; + private static readonly pb::FieldCodec _repeated_metadataProps_codec + = pb::FieldCodec.ForMessage(114, global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.StringStringEntryProto.Parser); + private readonly pbc::RepeatedField metadataProps_ = new pbc::RepeatedField(); + /// + /// Named metadata values; keys should be distinct. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public pbc::RepeatedField MetadataProps + { + get { return metadataProps_; } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override bool Equals(object other) + { + return Equals(other as ModelProto); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public bool Equals(ModelProto other) + { + if (ReferenceEquals(other, null)) + { + return false; + } + if (ReferenceEquals(other, this)) + { + return true; + } + if (IrVersion != other.IrVersion) return false; + if (!opsetImport_.Equals(other.opsetImport_)) return false; + if (ProducerName != other.ProducerName) return false; + if (ProducerVersion != other.ProducerVersion) return false; + if (Domain != other.Domain) return false; + if (ModelVersion != other.ModelVersion) return false; + if (DocString != other.DocString) return false; + if (!object.Equals(Graph, other.Graph)) return false; + if (!metadataProps_.Equals(other.metadataProps_)) return false; + return Equals(_unknownFields, other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override int GetHashCode() + { + int hash = 1; + if (IrVersion != 0L) hash ^= IrVersion.GetHashCode(); + hash ^= opsetImport_.GetHashCode(); + if (ProducerName.Length != 0) hash ^= ProducerName.GetHashCode(); + if (ProducerVersion.Length != 0) hash ^= ProducerVersion.GetHashCode(); + if (Domain.Length != 0) hash ^= Domain.GetHashCode(); + if (ModelVersion != 0L) hash ^= ModelVersion.GetHashCode(); + if (DocString.Length != 0) hash ^= DocString.GetHashCode(); + if (graph_ != null) hash ^= Graph.GetHashCode(); + hash ^= metadataProps_.GetHashCode(); + if (_unknownFields != null) + { + hash ^= _unknownFields.GetHashCode(); + } + return hash; + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override string ToString() + { + return pb::JsonFormatter.ToDiagnosticString(this); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void WriteTo(pb::CodedOutputStream output) + { + if (IrVersion != 0L) + { + output.WriteRawTag(8); + output.WriteInt64(IrVersion); + } + if (ProducerName.Length != 0) + { + output.WriteRawTag(18); + output.WriteString(ProducerName); + } + if (ProducerVersion.Length != 0) + { + output.WriteRawTag(26); + output.WriteString(ProducerVersion); + } + if (Domain.Length != 0) + { + output.WriteRawTag(34); + output.WriteString(Domain); + } + if (ModelVersion != 0L) + { + output.WriteRawTag(40); + output.WriteInt64(ModelVersion); + } + if (DocString.Length != 0) + { + output.WriteRawTag(50); + output.WriteString(DocString); + } + if (graph_ != null) + { + output.WriteRawTag(58); + output.WriteMessage(Graph); + } + opsetImport_.WriteTo(output, _repeated_opsetImport_codec); + metadataProps_.WriteTo(output, _repeated_metadataProps_codec); + if (_unknownFields != null) + { + _unknownFields.WriteTo(output); + } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public int CalculateSize() + { + int size = 0; + if (IrVersion != 0L) + { + size += 1 + pb::CodedOutputStream.ComputeInt64Size(IrVersion); + } + size += opsetImport_.CalculateSize(_repeated_opsetImport_codec); + if (ProducerName.Length != 0) + { + size += 1 + pb::CodedOutputStream.ComputeStringSize(ProducerName); + } + if (ProducerVersion.Length != 0) + { + size += 1 + pb::CodedOutputStream.ComputeStringSize(ProducerVersion); + } + if (Domain.Length != 0) + { + size += 1 + pb::CodedOutputStream.ComputeStringSize(Domain); + } + if (ModelVersion != 0L) + { + size += 1 + pb::CodedOutputStream.ComputeInt64Size(ModelVersion); + } + if (DocString.Length != 0) + { + size += 1 + pb::CodedOutputStream.ComputeStringSize(DocString); + } + if (graph_ != null) + { + size += 1 + pb::CodedOutputStream.ComputeMessageSize(Graph); + } + size += metadataProps_.CalculateSize(_repeated_metadataProps_codec); + if (_unknownFields != null) + { + size += _unknownFields.CalculateSize(); + } + return size; + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void MergeFrom(ModelProto other) + { + if (other == null) + { + return; + } + if (other.IrVersion != 0L) + { + IrVersion = other.IrVersion; + } + opsetImport_.Add(other.opsetImport_); + if (other.ProducerName.Length != 0) + { + ProducerName = other.ProducerName; + } + if (other.ProducerVersion.Length != 0) + { + ProducerVersion = other.ProducerVersion; + } + if (other.Domain.Length != 0) + { + Domain = other.Domain; + } + if (other.ModelVersion != 0L) + { + ModelVersion = other.ModelVersion; + } + if (other.DocString.Length != 0) + { + DocString = other.DocString; + } + if (other.graph_ != null) + { + if (graph_ == null) + { + graph_ = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.GraphProto(); + } + Graph.MergeFrom(other.Graph); + } + metadataProps_.Add(other.metadataProps_); + _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void MergeFrom(pb::CodedInputStream input) + { + uint tag; + while ((tag = input.ReadTag()) != 0) + { + switch (tag) + { + default: + _unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input); + break; + case 8: + { + IrVersion = input.ReadInt64(); + break; + } + case 18: + { + ProducerName = input.ReadString(); + break; + } + case 26: + { + ProducerVersion = input.ReadString(); + break; + } + case 34: + { + Domain = input.ReadString(); + break; + } + case 40: + { + ModelVersion = input.ReadInt64(); + break; + } + case 50: + { + DocString = input.ReadString(); + break; + } + case 58: + { + if (graph_ == null) + { + graph_ = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.GraphProto(); + } + input.ReadMessage(graph_); + break; + } + case 66: + { + opsetImport_.AddEntriesFrom(input, _repeated_opsetImport_codec); + break; + } + case 114: + { + metadataProps_.AddEntriesFrom(input, _repeated_metadataProps_codec); + break; + } + } + } + } + + } + + /// + /// StringStringEntryProto follows the pattern for cross-proto-version maps. + /// See https://developers.google.com/protocol-buffers/docs/proto3#maps + /// + public sealed partial class StringStringEntryProto : pb::IMessage + { + private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new StringStringEntryProto()); + private pb::UnknownFieldSet _unknownFields; + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public static pb::MessageParser Parser { get { return _parser; } } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public static pbr::MessageDescriptor Descriptor + { + get { return global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.OnnxMlReflection.Descriptor.MessageTypes[4]; } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + pbr::MessageDescriptor pb::IMessage.Descriptor + { + get { return Descriptor; } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public StringStringEntryProto() + { + OnConstruction(); + } + + partial void OnConstruction(); + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public StringStringEntryProto(StringStringEntryProto other) : this() + { + key_ = other.key_; + value_ = other.value_; + _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public StringStringEntryProto Clone() + { + return new StringStringEntryProto(this); + } + + /// Field number for the "key" field. + public const int KeyFieldNumber = 1; + private string key_ = ""; + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public string Key + { + get { return key_; } + set + { + key_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); + } + } + + /// Field number for the "value" field. + public const int ValueFieldNumber = 2; + private string value_ = ""; + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public string Value + { + get { return value_; } + set + { + value_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); + } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override bool Equals(object other) + { + return Equals(other as StringStringEntryProto); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public bool Equals(StringStringEntryProto other) + { + if (ReferenceEquals(other, null)) + { + return false; + } + if (ReferenceEquals(other, this)) + { + return true; + } + if (Key != other.Key) return false; + if (Value != other.Value) return false; + return Equals(_unknownFields, other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override int GetHashCode() + { + int hash = 1; + if (Key.Length != 0) hash ^= Key.GetHashCode(); + if (Value.Length != 0) hash ^= Value.GetHashCode(); + if (_unknownFields != null) + { + hash ^= _unknownFields.GetHashCode(); + } + return hash; + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override string ToString() + { + return pb::JsonFormatter.ToDiagnosticString(this); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void WriteTo(pb::CodedOutputStream output) + { + if (Key.Length != 0) + { + output.WriteRawTag(10); + output.WriteString(Key); + } + if (Value.Length != 0) + { + output.WriteRawTag(18); + output.WriteString(Value); + } + if (_unknownFields != null) + { + _unknownFields.WriteTo(output); + } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public int CalculateSize() + { + int size = 0; + if (Key.Length != 0) + { + size += 1 + pb::CodedOutputStream.ComputeStringSize(Key); + } + if (Value.Length != 0) + { + size += 1 + pb::CodedOutputStream.ComputeStringSize(Value); + } + if (_unknownFields != null) + { + size += _unknownFields.CalculateSize(); + } + return size; + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void MergeFrom(StringStringEntryProto other) + { + if (other == null) + { + return; + } + if (other.Key.Length != 0) + { + Key = other.Key; + } + if (other.Value.Length != 0) + { + Value = other.Value; + } + _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void MergeFrom(pb::CodedInputStream input) + { + uint tag; + while ((tag = input.ReadTag()) != 0) + { + switch (tag) + { + default: + _unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input); + break; + case 10: + { + Key = input.ReadString(); + break; + } + case 18: + { + Value = input.ReadString(); + break; + } + } + } + } + + } + + /// + /// Graphs + /// + /// A graph defines the computational logic of a model and is comprised of a parameterized + /// list of nodes that form a directed acyclic graph based on their inputs and outputs. + /// This is the equivalent of the "network" or "graph" in many deep learning + /// frameworks. + /// + public sealed partial class GraphProto : pb::IMessage + { + private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new GraphProto()); + private pb::UnknownFieldSet _unknownFields; + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public static pb::MessageParser Parser { get { return _parser; } } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public static pbr::MessageDescriptor Descriptor + { + get { return global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.OnnxMlReflection.Descriptor.MessageTypes[5]; } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + pbr::MessageDescriptor pb::IMessage.Descriptor + { + get { return Descriptor; } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public GraphProto() + { + OnConstruction(); + } + + partial void OnConstruction(); + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public GraphProto(GraphProto other) : this() + { + node_ = other.node_.Clone(); + name_ = other.name_; + initializer_ = other.initializer_.Clone(); + docString_ = other.docString_; + input_ = other.input_.Clone(); + output_ = other.output_.Clone(); + valueInfo_ = other.valueInfo_.Clone(); + _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public GraphProto Clone() + { + return new GraphProto(this); + } + + /// Field number for the "node" field. + public const int NodeFieldNumber = 1; + private static readonly pb::FieldCodec _repeated_node_codec + = pb::FieldCodec.ForMessage(10, global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.NodeProto.Parser); + private readonly pbc::RepeatedField node_ = new pbc::RepeatedField(); + /// + /// The nodes in the graph, sorted topologically. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public pbc::RepeatedField Node + { + get { return node_; } + } + + /// Field number for the "name" field. + public const int NameFieldNumber = 2; + private string name_ = ""; + /// + /// The name of the graph. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public string Name + { + get { return name_; } + set + { + name_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); + } + } + + /// Field number for the "initializer" field. + public const int InitializerFieldNumber = 5; + private static readonly pb::FieldCodec _repeated_initializer_codec + = pb::FieldCodec.ForMessage(42, global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Parser); + private readonly pbc::RepeatedField initializer_ = new pbc::RepeatedField(); + /// + /// A list of named tensor values, used to specify constant inputs of the graph. + /// Each TensorProto entry must have a distinct name (within the list) that + /// also appears in the input list. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public pbc::RepeatedField Initializer + { + get { return initializer_; } + } + + /// Field number for the "doc_string" field. + public const int DocStringFieldNumber = 10; + private string docString_ = ""; + /// + /// A human-readable documentation for this graph. Markdown is allowed. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public string DocString + { + get { return docString_; } + set + { + docString_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); + } + } + + /// Field number for the "input" field. + public const int InputFieldNumber = 11; + private static readonly pb::FieldCodec _repeated_input_codec + = pb::FieldCodec.ForMessage(90, global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.ValueInfoProto.Parser); + private readonly pbc::RepeatedField input_ = new pbc::RepeatedField(); + /// + /// The inputs and outputs of the graph. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public pbc::RepeatedField Input + { + get { return input_; } + } + + /// Field number for the "output" field. + public const int OutputFieldNumber = 12; + private static readonly pb::FieldCodec _repeated_output_codec + = pb::FieldCodec.ForMessage(98, global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.ValueInfoProto.Parser); + private readonly pbc::RepeatedField output_ = new pbc::RepeatedField(); + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public pbc::RepeatedField Output + { + get { return output_; } + } + + /// Field number for the "value_info" field. + public const int ValueInfoFieldNumber = 13; + private static readonly pb::FieldCodec _repeated_valueInfo_codec + = pb::FieldCodec.ForMessage(106, global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.ValueInfoProto.Parser); + private readonly pbc::RepeatedField valueInfo_ = new pbc::RepeatedField(); + /// + /// Information for the values in the graph. The ValueInfoProto.name's + /// must be distinct. It is optional for a value to appear in value_info list. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public pbc::RepeatedField ValueInfo + { + get { return valueInfo_; } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override bool Equals(object other) + { + return Equals(other as GraphProto); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public bool Equals(GraphProto other) + { + if (ReferenceEquals(other, null)) + { + return false; + } + if (ReferenceEquals(other, this)) + { + return true; + } + if (!node_.Equals(other.node_)) return false; + if (Name != other.Name) return false; + if (!initializer_.Equals(other.initializer_)) return false; + if (DocString != other.DocString) return false; + if (!input_.Equals(other.input_)) return false; + if (!output_.Equals(other.output_)) return false; + if (!valueInfo_.Equals(other.valueInfo_)) return false; + return Equals(_unknownFields, other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override int GetHashCode() + { + int hash = 1; + hash ^= node_.GetHashCode(); + if (Name.Length != 0) hash ^= Name.GetHashCode(); + hash ^= initializer_.GetHashCode(); + if (DocString.Length != 0) hash ^= DocString.GetHashCode(); + hash ^= input_.GetHashCode(); + hash ^= output_.GetHashCode(); + hash ^= valueInfo_.GetHashCode(); + if (_unknownFields != null) + { + hash ^= _unknownFields.GetHashCode(); + } + return hash; + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override string ToString() + { + return pb::JsonFormatter.ToDiagnosticString(this); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void WriteTo(pb::CodedOutputStream output) + { + node_.WriteTo(output, _repeated_node_codec); + if (Name.Length != 0) + { + output.WriteRawTag(18); + output.WriteString(Name); + } + initializer_.WriteTo(output, _repeated_initializer_codec); + if (DocString.Length != 0) + { + output.WriteRawTag(82); + output.WriteString(DocString); + } + input_.WriteTo(output, _repeated_input_codec); + output_.WriteTo(output, _repeated_output_codec); + valueInfo_.WriteTo(output, _repeated_valueInfo_codec); + if (_unknownFields != null) + { + _unknownFields.WriteTo(output); + } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public int CalculateSize() + { + int size = 0; + size += node_.CalculateSize(_repeated_node_codec); + if (Name.Length != 0) + { + size += 1 + pb::CodedOutputStream.ComputeStringSize(Name); + } + size += initializer_.CalculateSize(_repeated_initializer_codec); + if (DocString.Length != 0) + { + size += 1 + pb::CodedOutputStream.ComputeStringSize(DocString); + } + size += input_.CalculateSize(_repeated_input_codec); + size += output_.CalculateSize(_repeated_output_codec); + size += valueInfo_.CalculateSize(_repeated_valueInfo_codec); + if (_unknownFields != null) + { + size += _unknownFields.CalculateSize(); + } + return size; + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void MergeFrom(GraphProto other) + { + if (other == null) + { + return; + } + node_.Add(other.node_); + if (other.Name.Length != 0) + { + Name = other.Name; + } + initializer_.Add(other.initializer_); + if (other.DocString.Length != 0) + { + DocString = other.DocString; + } + input_.Add(other.input_); + output_.Add(other.output_); + valueInfo_.Add(other.valueInfo_); + _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void MergeFrom(pb::CodedInputStream input) + { + uint tag; + while ((tag = input.ReadTag()) != 0) + { + switch (tag) + { + default: + _unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input); + break; + case 10: + { + node_.AddEntriesFrom(input, _repeated_node_codec); + break; + } + case 18: + { + Name = input.ReadString(); + break; + } + case 42: + { + initializer_.AddEntriesFrom(input, _repeated_initializer_codec); + break; + } + case 82: + { + DocString = input.ReadString(); + break; + } + case 90: + { + input_.AddEntriesFrom(input, _repeated_input_codec); + break; + } + case 98: + { + output_.AddEntriesFrom(input, _repeated_output_codec); + break; + } + case 106: + { + valueInfo_.AddEntriesFrom(input, _repeated_valueInfo_codec); + break; + } + } + } + } + + } + + /// + /// Tensors + /// + /// A serialized tensor value. + /// + public sealed partial class TensorProto : pb::IMessage + { + private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new TensorProto()); + private pb::UnknownFieldSet _unknownFields; + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public static pb::MessageParser Parser { get { return _parser; } } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public static pbr::MessageDescriptor Descriptor + { + get { return global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.OnnxMlReflection.Descriptor.MessageTypes[6]; } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + pbr::MessageDescriptor pb::IMessage.Descriptor + { + get { return Descriptor; } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public TensorProto() + { + OnConstruction(); + } + + partial void OnConstruction(); + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public TensorProto(TensorProto other) : this() + { + dims_ = other.dims_.Clone(); + dataType_ = other.dataType_; + Segment = other.segment_ != null ? other.Segment.Clone() : null; + floatData_ = other.floatData_.Clone(); + int32Data_ = other.int32Data_.Clone(); + stringData_ = other.stringData_.Clone(); + int64Data_ = other.int64Data_.Clone(); + name_ = other.name_; + docString_ = other.docString_; + rawData_ = other.rawData_; + doubleData_ = other.doubleData_.Clone(); + uint64Data_ = other.uint64Data_.Clone(); + _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public TensorProto Clone() + { + return new TensorProto(this); + } + + /// Field number for the "dims" field. + public const int DimsFieldNumber = 1; + private static readonly pb::FieldCodec _repeated_dims_codec + = pb::FieldCodec.ForInt64(10); + private readonly pbc::RepeatedField dims_ = new pbc::RepeatedField(); + /// + /// The shape of the tensor. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public pbc::RepeatedField Dims + { + get { return dims_; } + } + + /// Field number for the "data_type" field. + public const int DataTypeFieldNumber = 2; + private global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Types.DataType dataType_ = 0; + /// + /// The data type of the tensor. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Types.DataType DataType + { + get { return dataType_; } + set + { + dataType_ = value; + } + } + + /// Field number for the "segment" field. + public const int SegmentFieldNumber = 3; + private global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Types.Segment segment_; + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Types.Segment Segment + { + get { return segment_; } + set + { + segment_ = value; + } + } + + /// Field number for the "float_data" field. + public const int FloatDataFieldNumber = 4; + private static readonly pb::FieldCodec _repeated_floatData_codec + = pb::FieldCodec.ForFloat(34); + private readonly pbc::RepeatedField floatData_ = new pbc::RepeatedField(); + /// + /// For float and complex64 values + /// Complex64 tensors are encoded as a single array of floats, + /// with the real components appearing in odd numbered positions, + /// and the corresponding imaginary component apparing in the + /// subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i] + /// is encoded as [1.0, 2.0 ,3.0 ,4.0] + /// When this field is present, the data_type field MUST be FLOAT or COMPLEX64. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public pbc::RepeatedField FloatData + { + get { return floatData_; } + } + + /// Field number for the "int32_data" field. + public const int Int32DataFieldNumber = 5; + private static readonly pb::FieldCodec _repeated_int32Data_codec + = pb::FieldCodec.ForInt32(42); + private readonly pbc::RepeatedField int32Data_ = new pbc::RepeatedField(); + /// + /// For int32, uint8, int8, uint16, int16, bool, and float16 values + /// float16 values must be bit-wise converted to an uint16_t prior + /// to writing to the buffer. + /// When this field is present, the data_type field MUST be + /// INT32, INT16, INT8, UINT16, INT8, BOOL, or FLOAT16 + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public pbc::RepeatedField Int32Data + { + get { return int32Data_; } + } + + /// Field number for the "string_data" field. + public const int StringDataFieldNumber = 6; + private static readonly pb::FieldCodec _repeated_stringData_codec + = pb::FieldCodec.ForBytes(50); + private readonly pbc::RepeatedField stringData_ = new pbc::RepeatedField(); + /// + /// For strings. + /// Each element of string_data is a UTF-8 encoded Unicode + /// string. No trailing null, no leading BOM. The protobuf "string" + /// scalar type is not used to match ML community conventions. + /// When this field is present, the data_type field MUST be STRING + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public pbc::RepeatedField StringData + { + get { return stringData_; } + } + + /// Field number for the "int64_data" field. + public const int Int64DataFieldNumber = 7; + private static readonly pb::FieldCodec _repeated_int64Data_codec + = pb::FieldCodec.ForInt64(58); + private readonly pbc::RepeatedField int64Data_ = new pbc::RepeatedField(); + /// + /// For int64. + /// When this field is present, the data_type field MUST be INT64 + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public pbc::RepeatedField Int64Data + { + get { return int64Data_; } + } + + /// Field number for the "name" field. + public const int NameFieldNumber = 8; + private string name_ = ""; + /// + /// Optionally, a name for the tensor. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public string Name + { + get { return name_; } + set + { + name_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); + } + } + + /// Field number for the "doc_string" field. + public const int DocStringFieldNumber = 12; + private string docString_ = ""; + /// + /// A human-readable documentation for this tensor. Markdown is allowed. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public string DocString + { + get { return docString_; } + set + { + docString_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); + } + } + + /// Field number for the "raw_data" field. + public const int RawDataFieldNumber = 9; + private pb::ByteString rawData_ = pb::ByteString.Empty; + /// + /// Serializations can either use one of the fields above, or use this + /// raw bytes field. The only exception is the string case, where one is + /// required to store the content in the repeated bytes string_data field. + /// + /// When this raw_data field is used to store tensor value, elements MUST + /// be stored in as fixed-width, little-endian order. + /// Floating-point data types MUST be stored in IEEE 754 format. + /// Complex64 elements must be written as two consecutive FLOAT values, real component first. + /// Complex128 elements must be written as two consecutive DOUBLE values, real component first. + /// Boolean type MUST be written one byte per tensor element (00000001 for true, 00000000 for false). + /// + /// Note: the advantage of specific field rather than the raw_data field is + /// that in some cases (e.g. int data), protobuf does a better packing via + /// variable length storage, and may lead to smaller binary footprint. + /// When this field is present, the data_type field MUST NOT be STRING or UNDEFINED + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public pb::ByteString RawData + { + get { return rawData_; } + set + { + rawData_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); + } + } + + /// Field number for the "double_data" field. + public const int DoubleDataFieldNumber = 10; + private static readonly pb::FieldCodec _repeated_doubleData_codec + = pb::FieldCodec.ForDouble(82); + private readonly pbc::RepeatedField doubleData_ = new pbc::RepeatedField(); + /// + /// For double + /// Complex64 tensors are encoded as a single array of doubles, + /// with the real components appearing in odd numbered positions, + /// and the corresponding imaginary component apparing in the + /// subsequent even numbered position. (e.g., [1.0 + 2.0i, 3.0 + 4.0i] + /// is encoded as [1.0, 2.0 ,3.0 ,4.0] + /// When this field is present, the data_type field MUST be DOUBLE or COMPLEX128 + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public pbc::RepeatedField DoubleData + { + get { return doubleData_; } + } + + /// Field number for the "uint64_data" field. + public const int Uint64DataFieldNumber = 11; + private static readonly pb::FieldCodec _repeated_uint64Data_codec + = pb::FieldCodec.ForUInt64(90); + private readonly pbc::RepeatedField uint64Data_ = new pbc::RepeatedField(); + /// + /// For uint64 and uint32 values + /// When this field is present, the data_type field MUST be + /// UINT32 or UINT64 + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public pbc::RepeatedField Uint64Data + { + get { return uint64Data_; } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override bool Equals(object other) + { + return Equals(other as TensorProto); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public bool Equals(TensorProto other) + { + if (ReferenceEquals(other, null)) + { + return false; + } + if (ReferenceEquals(other, this)) + { + return true; + } + if (!dims_.Equals(other.dims_)) return false; + if (DataType != other.DataType) return false; + if (!object.Equals(Segment, other.Segment)) return false; + if (!floatData_.Equals(other.floatData_)) return false; + if (!int32Data_.Equals(other.int32Data_)) return false; + if (!stringData_.Equals(other.stringData_)) return false; + if (!int64Data_.Equals(other.int64Data_)) return false; + if (Name != other.Name) return false; + if (DocString != other.DocString) return false; + if (RawData != other.RawData) return false; + if (!doubleData_.Equals(other.doubleData_)) return false; + if (!uint64Data_.Equals(other.uint64Data_)) return false; + return Equals(_unknownFields, other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override int GetHashCode() + { + int hash = 1; + hash ^= dims_.GetHashCode(); + if (DataType != 0) hash ^= DataType.GetHashCode(); + if (segment_ != null) hash ^= Segment.GetHashCode(); + hash ^= floatData_.GetHashCode(); + hash ^= int32Data_.GetHashCode(); + hash ^= stringData_.GetHashCode(); + hash ^= int64Data_.GetHashCode(); + if (Name.Length != 0) hash ^= Name.GetHashCode(); + if (DocString.Length != 0) hash ^= DocString.GetHashCode(); + if (RawData.Length != 0) hash ^= RawData.GetHashCode(); + hash ^= doubleData_.GetHashCode(); + hash ^= uint64Data_.GetHashCode(); + if (_unknownFields != null) + { + hash ^= _unknownFields.GetHashCode(); + } + return hash; + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override string ToString() + { + return pb::JsonFormatter.ToDiagnosticString(this); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void WriteTo(pb::CodedOutputStream output) + { + dims_.WriteTo(output, _repeated_dims_codec); + if (DataType != 0) + { + output.WriteRawTag(16); + output.WriteEnum((int)DataType); + } + if (segment_ != null) + { + output.WriteRawTag(26); + output.WriteMessage(Segment); + } + floatData_.WriteTo(output, _repeated_floatData_codec); + int32Data_.WriteTo(output, _repeated_int32Data_codec); + stringData_.WriteTo(output, _repeated_stringData_codec); + int64Data_.WriteTo(output, _repeated_int64Data_codec); + if (Name.Length != 0) + { + output.WriteRawTag(66); + output.WriteString(Name); + } + if (RawData.Length != 0) + { + output.WriteRawTag(74); + output.WriteBytes(RawData); + } + doubleData_.WriteTo(output, _repeated_doubleData_codec); + uint64Data_.WriteTo(output, _repeated_uint64Data_codec); + if (DocString.Length != 0) + { + output.WriteRawTag(98); + output.WriteString(DocString); + } + if (_unknownFields != null) + { + _unknownFields.WriteTo(output); + } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public int CalculateSize() + { + int size = 0; + size += dims_.CalculateSize(_repeated_dims_codec); + if (DataType != 0) + { + size += 1 + pb::CodedOutputStream.ComputeEnumSize((int)DataType); + } + if (segment_ != null) + { + size += 1 + pb::CodedOutputStream.ComputeMessageSize(Segment); + } + size += floatData_.CalculateSize(_repeated_floatData_codec); + size += int32Data_.CalculateSize(_repeated_int32Data_codec); + size += stringData_.CalculateSize(_repeated_stringData_codec); + size += int64Data_.CalculateSize(_repeated_int64Data_codec); + if (Name.Length != 0) + { + size += 1 + pb::CodedOutputStream.ComputeStringSize(Name); + } + if (DocString.Length != 0) + { + size += 1 + pb::CodedOutputStream.ComputeStringSize(DocString); + } + if (RawData.Length != 0) + { + size += 1 + pb::CodedOutputStream.ComputeBytesSize(RawData); + } + size += doubleData_.CalculateSize(_repeated_doubleData_codec); + size += uint64Data_.CalculateSize(_repeated_uint64Data_codec); + if (_unknownFields != null) + { + size += _unknownFields.CalculateSize(); + } + return size; + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void MergeFrom(TensorProto other) + { + if (other == null) + { + return; + } + dims_.Add(other.dims_); + if (other.DataType != 0) + { + DataType = other.DataType; + } + if (other.segment_ != null) + { + if (segment_ == null) + { + segment_ = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Types.Segment(); + } + Segment.MergeFrom(other.Segment); + } + floatData_.Add(other.floatData_); + int32Data_.Add(other.int32Data_); + stringData_.Add(other.stringData_); + int64Data_.Add(other.int64Data_); + if (other.Name.Length != 0) + { + Name = other.Name; + } + if (other.DocString.Length != 0) + { + DocString = other.DocString; + } + if (other.RawData.Length != 0) + { + RawData = other.RawData; + } + doubleData_.Add(other.doubleData_); + uint64Data_.Add(other.uint64Data_); + _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void MergeFrom(pb::CodedInputStream input) + { + uint tag; + while ((tag = input.ReadTag()) != 0) + { + switch (tag) + { + default: + _unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input); + break; + case 10: + case 8: + { + dims_.AddEntriesFrom(input, _repeated_dims_codec); + break; + } + case 16: + { + dataType_ = (global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Types.DataType)input.ReadEnum(); + break; + } + case 26: + { + if (segment_ == null) + { + segment_ = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Types.Segment(); + } + input.ReadMessage(segment_); + break; + } + case 34: + case 37: + { + floatData_.AddEntriesFrom(input, _repeated_floatData_codec); + break; + } + case 42: + case 40: + { + int32Data_.AddEntriesFrom(input, _repeated_int32Data_codec); + break; + } + case 50: + { + stringData_.AddEntriesFrom(input, _repeated_stringData_codec); + break; + } + case 58: + case 56: + { + int64Data_.AddEntriesFrom(input, _repeated_int64Data_codec); + break; + } + case 66: + { + Name = input.ReadString(); + break; + } + case 74: + { + RawData = input.ReadBytes(); + break; + } + case 82: + case 81: + { + doubleData_.AddEntriesFrom(input, _repeated_doubleData_codec); + break; + } + case 90: + case 88: + { + uint64Data_.AddEntriesFrom(input, _repeated_uint64Data_codec); + break; + } + case 98: + { + DocString = input.ReadString(); + break; + } + } + } + } + + #region Nested types + /// Container for nested types declared in the TensorProto message type. + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public static partial class Types + { + public enum DataType + { + [pbr::OriginalName("UNDEFINED")] Undefined = 0, + /// + /// Basic types. + /// + [pbr::OriginalName("FLOAT")] Float = 1, + /// + /// uint8_t + /// + [pbr::OriginalName("UINT8")] Uint8 = 2, + /// + /// int8_t + /// + [pbr::OriginalName("INT8")] Int8 = 3, + /// + /// uint16_t + /// + [pbr::OriginalName("UINT16")] Uint16 = 4, + /// + /// int16_t + /// + [pbr::OriginalName("INT16")] Int16 = 5, + /// + /// int32_t + /// + [pbr::OriginalName("INT32")] Int32 = 6, + /// + /// int64_t + /// + [pbr::OriginalName("INT64")] Int64 = 7, + /// + /// string + /// + [pbr::OriginalName("STRING")] String = 8, + /// + /// bool + /// + [pbr::OriginalName("BOOL")] Bool = 9, + /// + /// Advanced types + /// + [pbr::OriginalName("FLOAT16")] Float16 = 10, + [pbr::OriginalName("DOUBLE")] Double = 11, + [pbr::OriginalName("UINT32")] Uint32 = 12, + [pbr::OriginalName("UINT64")] Uint64 = 13, + /// + /// complex with float32 real and imaginary components + /// + [pbr::OriginalName("COMPLEX64")] Complex64 = 14, + /// + /// complex with float64 real and imaginary components + /// + [pbr::OriginalName("COMPLEX128")] Complex128 = 15, + } + + /// + /// For very large tensors, we may want to store them in chunks, in which + /// case the following fields will specify the segment that is stored in + /// the current TensorProto. + /// + public sealed partial class Segment : pb::IMessage + { + private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new Segment()); + private pb::UnknownFieldSet _unknownFields; + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public static pb::MessageParser Parser { get { return _parser; } } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public static pbr::MessageDescriptor Descriptor + { + get { return global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Descriptor.NestedTypes[0]; } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + pbr::MessageDescriptor pb::IMessage.Descriptor + { + get { return Descriptor; } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public Segment() + { + OnConstruction(); + } + + partial void OnConstruction(); + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public Segment(Segment other) : this() + { + begin_ = other.begin_; + end_ = other.end_; + _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public Segment Clone() + { + return new Segment(this); + } + + /// Field number for the "begin" field. + public const int BeginFieldNumber = 1; + private long begin_; + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public long Begin + { + get { return begin_; } + set + { + begin_ = value; + } + } + + /// Field number for the "end" field. + public const int EndFieldNumber = 2; + private long end_; + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public long End + { + get { return end_; } + set + { + end_ = value; + } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override bool Equals(object other) + { + return Equals(other as Segment); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public bool Equals(Segment other) + { + if (ReferenceEquals(other, null)) + { + return false; + } + if (ReferenceEquals(other, this)) + { + return true; + } + if (Begin != other.Begin) return false; + if (End != other.End) return false; + return Equals(_unknownFields, other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override int GetHashCode() + { + int hash = 1; + if (Begin != 0L) hash ^= Begin.GetHashCode(); + if (End != 0L) hash ^= End.GetHashCode(); + if (_unknownFields != null) + { + hash ^= _unknownFields.GetHashCode(); + } + return hash; + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override string ToString() + { + return pb::JsonFormatter.ToDiagnosticString(this); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void WriteTo(pb::CodedOutputStream output) + { + if (Begin != 0L) + { + output.WriteRawTag(8); + output.WriteInt64(Begin); + } + if (End != 0L) + { + output.WriteRawTag(16); + output.WriteInt64(End); + } + if (_unknownFields != null) + { + _unknownFields.WriteTo(output); + } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public int CalculateSize() + { + int size = 0; + if (Begin != 0L) + { + size += 1 + pb::CodedOutputStream.ComputeInt64Size(Begin); + } + if (End != 0L) + { + size += 1 + pb::CodedOutputStream.ComputeInt64Size(End); + } + if (_unknownFields != null) + { + size += _unknownFields.CalculateSize(); + } + return size; + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void MergeFrom(Segment other) + { + if (other == null) + { + return; + } + if (other.Begin != 0L) + { + Begin = other.Begin; + } + if (other.End != 0L) + { + End = other.End; + } + _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void MergeFrom(pb::CodedInputStream input) + { + uint tag; + while ((tag = input.ReadTag()) != 0) + { + switch (tag) + { + default: + _unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input); + break; + case 8: + { + Begin = input.ReadInt64(); + break; + } + case 16: + { + End = input.ReadInt64(); + break; + } + } + } + } + + } + + } + #endregion + + } + + /// + /// Defines a tensor shape. A dimension can be either an integer value + /// or a symbolic variable. A symbolic variable represents an unknown + /// dimension. + /// + public sealed partial class TensorShapeProto : pb::IMessage + { + private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new TensorShapeProto()); + private pb::UnknownFieldSet _unknownFields; + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public static pb::MessageParser Parser { get { return _parser; } } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public static pbr::MessageDescriptor Descriptor + { + get { return global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.OnnxMlReflection.Descriptor.MessageTypes[7]; } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + pbr::MessageDescriptor pb::IMessage.Descriptor + { + get { return Descriptor; } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public TensorShapeProto() + { + OnConstruction(); + } + + partial void OnConstruction(); + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public TensorShapeProto(TensorShapeProto other) : this() + { + dim_ = other.dim_.Clone(); + _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public TensorShapeProto Clone() + { + return new TensorShapeProto(this); + } + + /// Field number for the "dim" field. + public const int DimFieldNumber = 1; + private static readonly pb::FieldCodec _repeated_dim_codec + = pb::FieldCodec.ForMessage(10, global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorShapeProto.Types.Dimension.Parser); + private readonly pbc::RepeatedField dim_ = new pbc::RepeatedField(); + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public pbc::RepeatedField Dim + { + get { return dim_; } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override bool Equals(object other) + { + return Equals(other as TensorShapeProto); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public bool Equals(TensorShapeProto other) + { + if (ReferenceEquals(other, null)) + { + return false; + } + if (ReferenceEquals(other, this)) + { + return true; + } + if (!dim_.Equals(other.dim_)) return false; + return Equals(_unknownFields, other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override int GetHashCode() + { + int hash = 1; + hash ^= dim_.GetHashCode(); + if (_unknownFields != null) + { + hash ^= _unknownFields.GetHashCode(); + } + return hash; + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override string ToString() + { + return pb::JsonFormatter.ToDiagnosticString(this); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void WriteTo(pb::CodedOutputStream output) + { + dim_.WriteTo(output, _repeated_dim_codec); + if (_unknownFields != null) + { + _unknownFields.WriteTo(output); + } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public int CalculateSize() + { + int size = 0; + size += dim_.CalculateSize(_repeated_dim_codec); + if (_unknownFields != null) + { + size += _unknownFields.CalculateSize(); + } + return size; + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void MergeFrom(TensorShapeProto other) + { + if (other == null) + { + return; + } + dim_.Add(other.dim_); + _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void MergeFrom(pb::CodedInputStream input) + { + uint tag; + while ((tag = input.ReadTag()) != 0) + { + switch (tag) + { + default: + _unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input); + break; + case 10: + { + dim_.AddEntriesFrom(input, _repeated_dim_codec); + break; + } + } + } + } + + #region Nested types + /// Container for nested types declared in the TensorShapeProto message type. + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public static partial class Types + { + public sealed partial class Dimension : pb::IMessage + { + private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new Dimension()); + private pb::UnknownFieldSet _unknownFields; + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public static pb::MessageParser Parser { get { return _parser; } } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public static pbr::MessageDescriptor Descriptor + { + get { return global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorShapeProto.Descriptor.NestedTypes[0]; } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + pbr::MessageDescriptor pb::IMessage.Descriptor + { + get { return Descriptor; } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public Dimension() + { + OnConstruction(); + } + + partial void OnConstruction(); + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public Dimension(Dimension other) : this() + { + denotation_ = other.denotation_; + switch (other.ValueCase) + { + case ValueOneofCase.DimValue: + DimValue = other.DimValue; + break; + case ValueOneofCase.DimParam: + DimParam = other.DimParam; + break; + } + + _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public Dimension Clone() + { + return new Dimension(this); + } + + /// Field number for the "dim_value" field. + public const int DimValueFieldNumber = 1; + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public long DimValue + { + get { return valueCase_ == ValueOneofCase.DimValue ? (long)value_ : 0L; } + set + { + value_ = value; + valueCase_ = ValueOneofCase.DimValue; + } + } + + /// Field number for the "dim_param" field. + public const int DimParamFieldNumber = 2; + /// + /// namespace Shape + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public string DimParam + { + get { return valueCase_ == ValueOneofCase.DimParam ? (string)value_ : ""; } + set + { + value_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); + valueCase_ = ValueOneofCase.DimParam; + } + } + + /// Field number for the "denotation" field. + public const int DenotationFieldNumber = 3; + private string denotation_ = ""; + /// + /// Standard denotation can optionally be used to denote tensor + /// dimensions with standard semantic descriptions to ensure + /// that operations are applied to the correct axis of a tensor. + /// Refer to https://github.com/onnx/onnx/blob/master/docs/DimensionDenotation.md#denotation-definition + /// for pre-defined dimension denotations. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public string Denotation + { + get { return denotation_; } + set + { + denotation_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); + } + } + + private object value_; + /// Enum of possible cases for the "value" oneof. + public enum ValueOneofCase + { + None = 0, + DimValue = 1, + DimParam = 2, + } + private ValueOneofCase valueCase_ = ValueOneofCase.None; + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public ValueOneofCase ValueCase + { + get { return valueCase_; } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void ClearValue() + { + valueCase_ = ValueOneofCase.None; + value_ = null; + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override bool Equals(object other) + { + return Equals(other as Dimension); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public bool Equals(Dimension other) + { + if (ReferenceEquals(other, null)) + { + return false; + } + if (ReferenceEquals(other, this)) + { + return true; + } + if (DimValue != other.DimValue) return false; + if (DimParam != other.DimParam) return false; + if (Denotation != other.Denotation) return false; + if (ValueCase != other.ValueCase) return false; + return Equals(_unknownFields, other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override int GetHashCode() + { + int hash = 1; + if (valueCase_ == ValueOneofCase.DimValue) hash ^= DimValue.GetHashCode(); + if (valueCase_ == ValueOneofCase.DimParam) hash ^= DimParam.GetHashCode(); + if (Denotation.Length != 0) hash ^= Denotation.GetHashCode(); + hash ^= (int)valueCase_; + if (_unknownFields != null) + { + hash ^= _unknownFields.GetHashCode(); + } + return hash; + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override string ToString() + { + return pb::JsonFormatter.ToDiagnosticString(this); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void WriteTo(pb::CodedOutputStream output) + { + if (valueCase_ == ValueOneofCase.DimValue) + { + output.WriteRawTag(8); + output.WriteInt64(DimValue); + } + if (valueCase_ == ValueOneofCase.DimParam) + { + output.WriteRawTag(18); + output.WriteString(DimParam); + } + if (Denotation.Length != 0) + { + output.WriteRawTag(26); + output.WriteString(Denotation); + } + if (_unknownFields != null) + { + _unknownFields.WriteTo(output); + } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public int CalculateSize() + { + int size = 0; + if (valueCase_ == ValueOneofCase.DimValue) + { + size += 1 + pb::CodedOutputStream.ComputeInt64Size(DimValue); + } + if (valueCase_ == ValueOneofCase.DimParam) + { + size += 1 + pb::CodedOutputStream.ComputeStringSize(DimParam); + } + if (Denotation.Length != 0) + { + size += 1 + pb::CodedOutputStream.ComputeStringSize(Denotation); + } + if (_unknownFields != null) + { + size += _unknownFields.CalculateSize(); + } + return size; + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void MergeFrom(Dimension other) + { + if (other == null) + { + return; + } + if (other.Denotation.Length != 0) + { + Denotation = other.Denotation; + } + switch (other.ValueCase) + { + case ValueOneofCase.DimValue: + DimValue = other.DimValue; + break; + case ValueOneofCase.DimParam: + DimParam = other.DimParam; + break; + } + + _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void MergeFrom(pb::CodedInputStream input) + { + uint tag; + while ((tag = input.ReadTag()) != 0) + { + switch (tag) + { + default: + _unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input); + break; + case 8: + { + DimValue = input.ReadInt64(); + break; + } + case 18: + { + DimParam = input.ReadString(); + break; + } + case 26: + { + Denotation = input.ReadString(); + break; + } + } + } + } + + } + + } + #endregion + + } + + /// + /// Types + /// + /// The standard ONNX data types. + /// + public sealed partial class TypeProto : pb::IMessage + { + private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new TypeProto()); + private pb::UnknownFieldSet _unknownFields; + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public static pb::MessageParser Parser { get { return _parser; } } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public static pbr::MessageDescriptor Descriptor + { + get { return global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.OnnxMlReflection.Descriptor.MessageTypes[8]; } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + pbr::MessageDescriptor pb::IMessage.Descriptor + { + get { return Descriptor; } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public TypeProto() + { + OnConstruction(); + } + + partial void OnConstruction(); + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public TypeProto(TypeProto other) : this() + { + denotation_ = other.denotation_; + switch (other.ValueCase) + { + case ValueOneofCase.TensorType: + TensorType = other.TensorType.Clone(); + break; + case ValueOneofCase.SequenceType: + SequenceType = other.SequenceType.Clone(); + break; + case ValueOneofCase.MapType: + MapType = other.MapType.Clone(); + break; + } + + _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public TypeProto Clone() + { + return new TypeProto(this); + } + + /// Field number for the "tensor_type" field. + public const int TensorTypeFieldNumber = 1; + /// + /// The type of a tensor. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Types.Tensor TensorType + { + get { return valueCase_ == ValueOneofCase.TensorType ? (global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Types.Tensor)value_ : null; } + set + { + value_ = value; + valueCase_ = value == null ? ValueOneofCase.None : ValueOneofCase.TensorType; + } + } + + /// Field number for the "sequence_type" field. + public const int SequenceTypeFieldNumber = 4; + /// + /// The type of a sequence. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Types.Sequence SequenceType + { + get { return valueCase_ == ValueOneofCase.SequenceType ? (global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Types.Sequence)value_ : null; } + set + { + value_ = value; + valueCase_ = value == null ? ValueOneofCase.None : ValueOneofCase.SequenceType; + } + } + + /// Field number for the "map_type" field. + public const int MapTypeFieldNumber = 5; + /// + /// The type of a map. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Types.Map MapType + { + get { return valueCase_ == ValueOneofCase.MapType ? (global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Types.Map)value_ : null; } + set + { + value_ = value; + valueCase_ = value == null ? ValueOneofCase.None : ValueOneofCase.MapType; + } + } + + /// Field number for the "denotation" field. + public const int DenotationFieldNumber = 6; + private string denotation_ = ""; + /// + /// An optional denotation can be used to denote the whole + /// type with a standard semantic description as to what is + /// stored inside. Refer to https://github.com/onnx/onnx/blob/master/docs/TypeDenotation.md#type-denotation-definition + /// for pre-defined type denotations. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public string Denotation + { + get { return denotation_; } + set + { + denotation_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); + } + } + + private object value_; + /// Enum of possible cases for the "value" oneof. + public enum ValueOneofCase + { + None = 0, + TensorType = 1, + SequenceType = 4, + MapType = 5, + } + private ValueOneofCase valueCase_ = ValueOneofCase.None; + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public ValueOneofCase ValueCase + { + get { return valueCase_; } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void ClearValue() + { + valueCase_ = ValueOneofCase.None; + value_ = null; + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override bool Equals(object other) + { + return Equals(other as TypeProto); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public bool Equals(TypeProto other) + { + if (ReferenceEquals(other, null)) + { + return false; + } + if (ReferenceEquals(other, this)) + { + return true; + } + if (!object.Equals(TensorType, other.TensorType)) return false; + if (!object.Equals(SequenceType, other.SequenceType)) return false; + if (!object.Equals(MapType, other.MapType)) return false; + if (Denotation != other.Denotation) return false; + if (ValueCase != other.ValueCase) return false; + return Equals(_unknownFields, other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override int GetHashCode() + { + int hash = 1; + if (valueCase_ == ValueOneofCase.TensorType) hash ^= TensorType.GetHashCode(); + if (valueCase_ == ValueOneofCase.SequenceType) hash ^= SequenceType.GetHashCode(); + if (valueCase_ == ValueOneofCase.MapType) hash ^= MapType.GetHashCode(); + if (Denotation.Length != 0) hash ^= Denotation.GetHashCode(); + hash ^= (int)valueCase_; + if (_unknownFields != null) + { + hash ^= _unknownFields.GetHashCode(); + } + return hash; + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override string ToString() + { + return pb::JsonFormatter.ToDiagnosticString(this); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void WriteTo(pb::CodedOutputStream output) + { + if (valueCase_ == ValueOneofCase.TensorType) + { + output.WriteRawTag(10); + output.WriteMessage(TensorType); + } + if (valueCase_ == ValueOneofCase.SequenceType) + { + output.WriteRawTag(34); + output.WriteMessage(SequenceType); + } + if (valueCase_ == ValueOneofCase.MapType) + { + output.WriteRawTag(42); + output.WriteMessage(MapType); + } + if (Denotation.Length != 0) + { + output.WriteRawTag(50); + output.WriteString(Denotation); + } + if (_unknownFields != null) + { + _unknownFields.WriteTo(output); + } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public int CalculateSize() + { + int size = 0; + if (valueCase_ == ValueOneofCase.TensorType) + { + size += 1 + pb::CodedOutputStream.ComputeMessageSize(TensorType); + } + if (valueCase_ == ValueOneofCase.SequenceType) + { + size += 1 + pb::CodedOutputStream.ComputeMessageSize(SequenceType); + } + if (valueCase_ == ValueOneofCase.MapType) + { + size += 1 + pb::CodedOutputStream.ComputeMessageSize(MapType); + } + if (Denotation.Length != 0) + { + size += 1 + pb::CodedOutputStream.ComputeStringSize(Denotation); + } + if (_unknownFields != null) + { + size += _unknownFields.CalculateSize(); + } + return size; + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void MergeFrom(TypeProto other) + { + if (other == null) + { + return; + } + if (other.Denotation.Length != 0) + { + Denotation = other.Denotation; + } + switch (other.ValueCase) + { + case ValueOneofCase.TensorType: + if (TensorType == null) + { + TensorType = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Types.Tensor(); + } + TensorType.MergeFrom(other.TensorType); + break; + case ValueOneofCase.SequenceType: + if (SequenceType == null) + { + SequenceType = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Types.Sequence(); + } + SequenceType.MergeFrom(other.SequenceType); + break; + case ValueOneofCase.MapType: + if (MapType == null) + { + MapType = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Types.Map(); + } + MapType.MergeFrom(other.MapType); + break; + } + + _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void MergeFrom(pb::CodedInputStream input) + { + uint tag; + while ((tag = input.ReadTag()) != 0) + { + switch (tag) + { + default: + _unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input); + break; + case 10: + { + global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Types.Tensor subBuilder = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Types.Tensor(); + if (valueCase_ == ValueOneofCase.TensorType) + { + subBuilder.MergeFrom(TensorType); + } + input.ReadMessage(subBuilder); + TensorType = subBuilder; + break; + } + case 34: + { + global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Types.Sequence subBuilder = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Types.Sequence(); + if (valueCase_ == ValueOneofCase.SequenceType) + { + subBuilder.MergeFrom(SequenceType); + } + input.ReadMessage(subBuilder); + SequenceType = subBuilder; + break; + } + case 42: + { + global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Types.Map subBuilder = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Types.Map(); + if (valueCase_ == ValueOneofCase.MapType) + { + subBuilder.MergeFrom(MapType); + } + input.ReadMessage(subBuilder); + MapType = subBuilder; + break; + } + case 50: + { + Denotation = input.ReadString(); + break; + } + } + } + } + + #region Nested types + /// Container for nested types declared in the TypeProto message type. + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public static partial class Types + { + public sealed partial class Tensor : pb::IMessage + { + private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new Tensor()); + private pb::UnknownFieldSet _unknownFields; + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public static pb::MessageParser Parser { get { return _parser; } } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public static pbr::MessageDescriptor Descriptor + { + get { return global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Descriptor.NestedTypes[0]; } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + pbr::MessageDescriptor pb::IMessage.Descriptor + { + get { return Descriptor; } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public Tensor() + { + OnConstruction(); + } + + partial void OnConstruction(); + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public Tensor(Tensor other) : this() + { + elemType_ = other.elemType_; + Shape = other.shape_ != null ? other.Shape.Clone() : null; + _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public Tensor Clone() + { + return new Tensor(this); + } + + /// Field number for the "elem_type" field. + public const int ElemTypeFieldNumber = 1; + private global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Types.DataType elemType_ = 0; + /// + /// This field MUST NOT have the value of UNDEFINED + /// This field MUST be present for this version of the IR. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Types.DataType ElemType + { + get { return elemType_; } + set + { + elemType_ = value; + } + } + + /// Field number for the "shape" field. + public const int ShapeFieldNumber = 2; + private global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorShapeProto shape_; + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorShapeProto Shape + { + get { return shape_; } + set + { + shape_ = value; + } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override bool Equals(object other) + { + return Equals(other as Tensor); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public bool Equals(Tensor other) + { + if (ReferenceEquals(other, null)) + { + return false; + } + if (ReferenceEquals(other, this)) + { + return true; + } + if (ElemType != other.ElemType) return false; + if (!object.Equals(Shape, other.Shape)) return false; + return Equals(_unknownFields, other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override int GetHashCode() + { + int hash = 1; + if (ElemType != 0) hash ^= ElemType.GetHashCode(); + if (shape_ != null) hash ^= Shape.GetHashCode(); + if (_unknownFields != null) + { + hash ^= _unknownFields.GetHashCode(); + } + return hash; + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override string ToString() + { + return pb::JsonFormatter.ToDiagnosticString(this); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void WriteTo(pb::CodedOutputStream output) + { + if (ElemType != 0) + { + output.WriteRawTag(8); + output.WriteEnum((int)ElemType); + } + if (shape_ != null) + { + output.WriteRawTag(18); + output.WriteMessage(Shape); + } + if (_unknownFields != null) + { + _unknownFields.WriteTo(output); + } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public int CalculateSize() + { + int size = 0; + if (ElemType != 0) + { + size += 1 + pb::CodedOutputStream.ComputeEnumSize((int)ElemType); + } + if (shape_ != null) + { + size += 1 + pb::CodedOutputStream.ComputeMessageSize(Shape); + } + if (_unknownFields != null) + { + size += _unknownFields.CalculateSize(); + } + return size; + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void MergeFrom(Tensor other) + { + if (other == null) + { + return; + } + if (other.ElemType != 0) + { + ElemType = other.ElemType; + } + if (other.shape_ != null) + { + if (shape_ == null) + { + shape_ = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorShapeProto(); + } + Shape.MergeFrom(other.Shape); + } + _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void MergeFrom(pb::CodedInputStream input) + { + uint tag; + while ((tag = input.ReadTag()) != 0) + { + switch (tag) + { + default: + _unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input); + break; + case 8: + { + elemType_ = (global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Types.DataType)input.ReadEnum(); + break; + } + case 18: + { + if (shape_ == null) + { + shape_ = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorShapeProto(); + } + input.ReadMessage(shape_); + break; + } + } + } + } + + } + + /// + /// repeated T + /// + public sealed partial class Sequence : pb::IMessage + { + private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new Sequence()); + private pb::UnknownFieldSet _unknownFields; + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public static pb::MessageParser Parser { get { return _parser; } } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public static pbr::MessageDescriptor Descriptor + { + get { return global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Descriptor.NestedTypes[1]; } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + pbr::MessageDescriptor pb::IMessage.Descriptor + { + get { return Descriptor; } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public Sequence() + { + OnConstruction(); + } + + partial void OnConstruction(); + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public Sequence(Sequence other) : this() + { + ElemType = other.elemType_ != null ? other.ElemType.Clone() : null; + _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public Sequence Clone() + { + return new Sequence(this); + } + + /// Field number for the "elem_type" field. + public const int ElemTypeFieldNumber = 1; + private global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto elemType_; + /// + /// The type and optional shape of each element of the sequence. + /// This field MUST be present for this version of the IR. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto ElemType + { + get { return elemType_; } + set + { + elemType_ = value; + } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override bool Equals(object other) + { + return Equals(other as Sequence); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public bool Equals(Sequence other) + { + if (ReferenceEquals(other, null)) + { + return false; + } + if (ReferenceEquals(other, this)) + { + return true; + } + if (!object.Equals(ElemType, other.ElemType)) return false; + return Equals(_unknownFields, other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override int GetHashCode() + { + int hash = 1; + if (elemType_ != null) hash ^= ElemType.GetHashCode(); + if (_unknownFields != null) + { + hash ^= _unknownFields.GetHashCode(); + } + return hash; + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override string ToString() + { + return pb::JsonFormatter.ToDiagnosticString(this); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void WriteTo(pb::CodedOutputStream output) + { + if (elemType_ != null) + { + output.WriteRawTag(10); + output.WriteMessage(ElemType); + } + if (_unknownFields != null) + { + _unknownFields.WriteTo(output); + } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public int CalculateSize() + { + int size = 0; + if (elemType_ != null) + { + size += 1 + pb::CodedOutputStream.ComputeMessageSize(ElemType); + } + if (_unknownFields != null) + { + size += _unknownFields.CalculateSize(); + } + return size; + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void MergeFrom(Sequence other) + { + if (other == null) + { + return; + } + if (other.elemType_ != null) + { + if (elemType_ == null) + { + elemType_ = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto(); + } + ElemType.MergeFrom(other.ElemType); + } + _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void MergeFrom(pb::CodedInputStream input) + { + uint tag; + while ((tag = input.ReadTag()) != 0) + { + switch (tag) + { + default: + _unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input); + break; + case 10: + { + if (elemType_ == null) + { + elemType_ = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto(); + } + input.ReadMessage(elemType_); + break; + } + } + } + } + + } + + /// + /// map<K,V> + /// + public sealed partial class Map : pb::IMessage + { + private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new Map()); + private pb::UnknownFieldSet _unknownFields; + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public static pb::MessageParser Parser { get { return _parser; } } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public static pbr::MessageDescriptor Descriptor + { + get { return global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto.Descriptor.NestedTypes[2]; } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + pbr::MessageDescriptor pb::IMessage.Descriptor + { + get { return Descriptor; } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public Map() + { + OnConstruction(); + } + + partial void OnConstruction(); + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public Map(Map other) : this() + { + keyType_ = other.keyType_; + ValueType = other.valueType_ != null ? other.ValueType.Clone() : null; + _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public Map Clone() + { + return new Map(this); + } + + /// Field number for the "key_type" field. + public const int KeyTypeFieldNumber = 1; + private global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Types.DataType keyType_ = 0; + /// + /// This field MUST be present for this version of the IR. + /// This field MUST refer to an integral type ([U]INT{8|16|32|64}) or STRING + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Types.DataType KeyType + { + get { return keyType_; } + set + { + keyType_ = value; + } + } + + /// Field number for the "value_type" field. + public const int ValueTypeFieldNumber = 2; + private global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto valueType_; + /// + /// This field MUST be present for this version of the IR. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto ValueType + { + get { return valueType_; } + set + { + valueType_ = value; + } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override bool Equals(object other) + { + return Equals(other as Map); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public bool Equals(Map other) + { + if (ReferenceEquals(other, null)) + { + return false; + } + if (ReferenceEquals(other, this)) + { + return true; + } + if (KeyType != other.KeyType) return false; + if (!object.Equals(ValueType, other.ValueType)) return false; + return Equals(_unknownFields, other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override int GetHashCode() + { + int hash = 1; + if (KeyType != 0) hash ^= KeyType.GetHashCode(); + if (valueType_ != null) hash ^= ValueType.GetHashCode(); + if (_unknownFields != null) + { + hash ^= _unknownFields.GetHashCode(); + } + return hash; + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override string ToString() + { + return pb::JsonFormatter.ToDiagnosticString(this); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void WriteTo(pb::CodedOutputStream output) + { + if (KeyType != 0) + { + output.WriteRawTag(8); + output.WriteEnum((int)KeyType); + } + if (valueType_ != null) + { + output.WriteRawTag(18); + output.WriteMessage(ValueType); + } + if (_unknownFields != null) + { + _unknownFields.WriteTo(output); + } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public int CalculateSize() + { + int size = 0; + if (KeyType != 0) + { + size += 1 + pb::CodedOutputStream.ComputeEnumSize((int)KeyType); + } + if (valueType_ != null) + { + size += 1 + pb::CodedOutputStream.ComputeMessageSize(ValueType); + } + if (_unknownFields != null) + { + size += _unknownFields.CalculateSize(); + } + return size; + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void MergeFrom(Map other) + { + if (other == null) + { + return; + } + if (other.KeyType != 0) + { + KeyType = other.KeyType; + } + if (other.valueType_ != null) + { + if (valueType_ == null) + { + valueType_ = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto(); + } + ValueType.MergeFrom(other.ValueType); + } + _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void MergeFrom(pb::CodedInputStream input) + { + uint tag; + while ((tag = input.ReadTag()) != 0) + { + switch (tag) + { + default: + _unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input); + break; + case 8: + { + keyType_ = (global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TensorProto.Types.DataType)input.ReadEnum(); + break; + } + case 18: + { + if (valueType_ == null) + { + valueType_ = new global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.TypeProto(); + } + input.ReadMessage(valueType_); + break; + } + } + } + } + + } + + } + #endregion + + } + + /// + /// Operator Sets + /// + /// OperatorSets are uniquely identified by a (domain, opset_version) pair. + /// + public sealed partial class OperatorSetIdProto : pb::IMessage + { + private static readonly pb::MessageParser _parser = new pb::MessageParser(() => new OperatorSetIdProto()); + private pb::UnknownFieldSet _unknownFields; + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public static pb::MessageParser Parser { get { return _parser; } } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public static pbr::MessageDescriptor Descriptor + { + get { return global::Microsoft.ML.Runtime.UniversalModelFormat.Onnx.OnnxMlReflection.Descriptor.MessageTypes[9]; } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + pbr::MessageDescriptor pb::IMessage.Descriptor + { + get { return Descriptor; } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public OperatorSetIdProto() + { + OnConstruction(); + } + + partial void OnConstruction(); + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public OperatorSetIdProto(OperatorSetIdProto other) : this() + { + domain_ = other.domain_; + version_ = other.version_; + _unknownFields = pb::UnknownFieldSet.Clone(other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public OperatorSetIdProto Clone() + { + return new OperatorSetIdProto(this); + } + + /// Field number for the "domain" field. + public const int DomainFieldNumber = 1; + private string domain_ = ""; + /// + /// The domain of the operator set being identified. + /// The empty string ("") or absence of this field implies the operator + /// set that is defined as part of the ONNX specification. + /// This field MUST be present in this version of the IR when referring to any other operator set. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public string Domain + { + get { return domain_; } + set + { + domain_ = pb::ProtoPreconditions.CheckNotNull(value, "value"); + } + } + + /// Field number for the "version" field. + public const int VersionFieldNumber = 2; + private long version_; + /// + /// The version of the operator set being identified. + /// This field MUST be present in this version of the IR. + /// + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public long Version + { + get { return version_; } + set + { + version_ = value; + } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override bool Equals(object other) + { + return Equals(other as OperatorSetIdProto); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public bool Equals(OperatorSetIdProto other) + { + if (ReferenceEquals(other, null)) + { + return false; + } + if (ReferenceEquals(other, this)) + { + return true; + } + if (Domain != other.Domain) return false; + if (Version != other.Version) return false; + return Equals(_unknownFields, other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override int GetHashCode() + { + int hash = 1; + if (Domain.Length != 0) hash ^= Domain.GetHashCode(); + if (Version != 0L) hash ^= Version.GetHashCode(); + if (_unknownFields != null) + { + hash ^= _unknownFields.GetHashCode(); + } + return hash; + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public override string ToString() + { + return pb::JsonFormatter.ToDiagnosticString(this); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void WriteTo(pb::CodedOutputStream output) + { + if (Domain.Length != 0) + { + output.WriteRawTag(10); + output.WriteString(Domain); + } + if (Version != 0L) + { + output.WriteRawTag(16); + output.WriteInt64(Version); + } + if (_unknownFields != null) + { + _unknownFields.WriteTo(output); + } + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public int CalculateSize() + { + int size = 0; + if (Domain.Length != 0) + { + size += 1 + pb::CodedOutputStream.ComputeStringSize(Domain); + } + if (Version != 0L) + { + size += 1 + pb::CodedOutputStream.ComputeInt64Size(Version); + } + if (_unknownFields != null) + { + size += _unknownFields.CalculateSize(); + } + return size; + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void MergeFrom(OperatorSetIdProto other) + { + if (other == null) + { + return; + } + if (other.Domain.Length != 0) + { + Domain = other.Domain; + } + if (other.Version != 0L) + { + Version = other.Version; + } + _unknownFields = pb::UnknownFieldSet.MergeFrom(_unknownFields, other._unknownFields); + } + + [global::System.Diagnostics.DebuggerNonUserCodeAttribute] + public void MergeFrom(pb::CodedInputStream input) + { + uint tag; + while ((tag = input.ReadTag()) != 0) + { + switch (tag) + { + default: + _unknownFields = pb::UnknownFieldSet.MergeFieldFrom(_unknownFields, input); + break; + case 10: + { + Domain = input.ReadString(); + break; + } + case 16: + { + Version = input.ReadInt64(); + break; + } + } + } + } + + } + + #endregion + +} + +#endregion Designer generated code diff --git a/src/Microsoft.ML.UniversalModelFormat/LotusIR/OnnxMl.md b/src/Microsoft.ML.UniversalModelFormat/Onnx/OnnxMl.md similarity index 66% rename from src/Microsoft.ML.UniversalModelFormat/LotusIR/OnnxMl.md rename to src/Microsoft.ML.UniversalModelFormat/Onnx/OnnxMl.md index 4dd0a0fccd..c6b8a5283a 100644 --- a/src/Microsoft.ML.UniversalModelFormat/LotusIR/OnnxMl.md +++ b/src/Microsoft.ML.UniversalModelFormat/Onnx/OnnxMl.md @@ -4,11 +4,11 @@ 2. Download protobuf C# compiler version 3.0 or greater from https://github.com/google/protobuf/tree/master/csharp 3. Add `option csharp_namespace = - "Microsoft.MachineLearning.Runtime.UniversalModelFormat.Onnx";` to `onnx-ml.proto3` right below `package ONNX_NAMESPACE;` + "Microsoft.ML.Runtime.UniversalModelFormat.Onnx";` to `onnx-ml.proto3` right below `package ONNX_NAMESPACE;` 4. Assuming the compiler and proto file are saved at `E:\protobuf-csharp-port\lib` then run the following in command line to get C# code from the proto file: ``` protoc.exe -I="E:\protobuf-csharp-port\lib" --csharp_out="E:\protobuf-csharp-port\lib" "E:\protobuf-csharp-port\lib\onnx-ml.proto3" ``` -## The proto3 file is current as of 02/07/2018. \ No newline at end of file +## The proto3 file is current as of 06/01/2018 and generated from onnx-ml.proto3 based on the following commit https://github.com/onnx/onnx/commit/33e9cd4182fe468675241fba4ae8a16c2f0bd82f \ No newline at end of file diff --git a/src/Microsoft.ML/CSharpApi.cs b/src/Microsoft.ML/CSharpApi.cs index 058e8bafe3..12402b3993 100644 --- a/src/Microsoft.ML/CSharpApi.cs +++ b/src/Microsoft.ML/CSharpApi.cs @@ -106,6 +106,18 @@ public void Add(Microsoft.ML.Models.AnomalyDetectionEvaluator input, Microsoft.M _jsonNodes.Add(Serialize("Models.AnomalyDetectionEvaluator", input, output)); } + public Microsoft.ML.Models.AnomalyPipelineEnsemble.Output Add(Microsoft.ML.Models.AnomalyPipelineEnsemble input) + { + var output = new Microsoft.ML.Models.AnomalyPipelineEnsemble.Output(); + Add(input, output); + return output; + } + + public void Add(Microsoft.ML.Models.AnomalyPipelineEnsemble input, Microsoft.ML.Models.AnomalyPipelineEnsemble.Output output) + { + _jsonNodes.Add(Serialize("Models.AnomalyPipelineEnsemble", input, output)); + } + public Microsoft.ML.Models.BinaryClassificationEvaluator.Output Add(Microsoft.ML.Models.BinaryClassificationEvaluator input) { var output = new Microsoft.ML.Models.BinaryClassificationEvaluator.Output(); @@ -130,6 +142,30 @@ public void Add(Microsoft.ML.Models.BinaryCrossValidator input, Microsoft.ML.Mod _jsonNodes.Add(Serialize("Models.BinaryCrossValidator", input, output)); } + public Microsoft.ML.Models.BinaryEnsemble.Output Add(Microsoft.ML.Models.BinaryEnsemble input) + { + var output = new Microsoft.ML.Models.BinaryEnsemble.Output(); + Add(input, output); + return output; + } + + public void Add(Microsoft.ML.Models.BinaryEnsemble input, Microsoft.ML.Models.BinaryEnsemble.Output output) + { + _jsonNodes.Add(Serialize("Models.BinaryEnsemble", input, output)); + } + + public Microsoft.ML.Models.BinaryPipelineEnsemble.Output Add(Microsoft.ML.Models.BinaryPipelineEnsemble input) + { + var output = new Microsoft.ML.Models.BinaryPipelineEnsemble.Output(); + Add(input, output); + return output; + } + + public void Add(Microsoft.ML.Models.BinaryPipelineEnsemble input, Microsoft.ML.Models.BinaryPipelineEnsemble.Output output) + { + _jsonNodes.Add(Serialize("Models.BinaryPipelineEnsemble", input, output)); + } + public Microsoft.ML.Models.ClassificationEvaluator.Output Add(Microsoft.ML.Models.ClassificationEvaluator input) { var output = new Microsoft.ML.Models.ClassificationEvaluator.Output(); @@ -202,6 +238,18 @@ public void Add(Microsoft.ML.Models.DatasetTransformer input, Microsoft.ML.Model _jsonNodes.Add(Serialize("Models.DatasetTransformer", input, output)); } + public Microsoft.ML.Models.EnsembleSummary.Output Add(Microsoft.ML.Models.EnsembleSummary input) + { + var output = new Microsoft.ML.Models.EnsembleSummary.Output(); + Add(input, output); + return output; + } + + public void Add(Microsoft.ML.Models.EnsembleSummary input, Microsoft.ML.Models.EnsembleSummary.Output output) + { + _jsonNodes.Add(Serialize("Models.EnsembleSummary", input, output)); + } + public Microsoft.ML.Models.FixedPlattCalibrator.Output Add(Microsoft.ML.Models.FixedPlattCalibrator input) { var output = new Microsoft.ML.Models.FixedPlattCalibrator.Output(); @@ -214,6 +262,18 @@ public void Add(Microsoft.ML.Models.FixedPlattCalibrator input, Microsoft.ML.Mod _jsonNodes.Add(Serialize("Models.FixedPlattCalibrator", input, output)); } + public Microsoft.ML.Models.MultiClassPipelineEnsemble.Output Add(Microsoft.ML.Models.MultiClassPipelineEnsemble input) + { + var output = new Microsoft.ML.Models.MultiClassPipelineEnsemble.Output(); + Add(input, output); + return output; + } + + public void Add(Microsoft.ML.Models.MultiClassPipelineEnsemble input, Microsoft.ML.Models.MultiClassPipelineEnsemble.Output output) + { + _jsonNodes.Add(Serialize("Models.MultiClassPipelineEnsemble", input, output)); + } + public Microsoft.ML.Models.MultiOutputRegressionEvaluator.Output Add(Microsoft.ML.Models.MultiOutputRegressionEvaluator input) { var output = new Microsoft.ML.Models.MultiOutputRegressionEvaluator.Output(); @@ -250,6 +310,18 @@ public void Add(Microsoft.ML.Models.OneVersusAll input, Microsoft.ML.Models.OneV _jsonNodes.Add(Serialize("Models.OneVersusAll", input, output)); } + public Microsoft.ML.Models.OnnxConverter.Output Add(Microsoft.ML.Models.OnnxConverter input) + { + var output = new Microsoft.ML.Models.OnnxConverter.Output(); + Add(input, output); + return output; + } + + public void Add(Microsoft.ML.Models.OnnxConverter input, Microsoft.ML.Models.OnnxConverter.Output output) + { + _jsonNodes.Add(Serialize("Models.OnnxConverter", input, output)); + } + public Microsoft.ML.Models.OvaModelCombiner.Output Add(Microsoft.ML.Models.OvaModelCombiner input) { var output = new Microsoft.ML.Models.OvaModelCombiner.Output(); @@ -322,6 +394,18 @@ public void Add(Microsoft.ML.Models.RankerEvaluator input, Microsoft.ML.Models.R _jsonNodes.Add(Serialize("Models.RankerEvaluator", input, output)); } + public Microsoft.ML.Models.RegressionEnsemble.Output Add(Microsoft.ML.Models.RegressionEnsemble input) + { + var output = new Microsoft.ML.Models.RegressionEnsemble.Output(); + Add(input, output); + return output; + } + + public void Add(Microsoft.ML.Models.RegressionEnsemble input, Microsoft.ML.Models.RegressionEnsemble.Output output) + { + _jsonNodes.Add(Serialize("Models.RegressionEnsemble", input, output)); + } + public Microsoft.ML.Models.RegressionEvaluator.Output Add(Microsoft.ML.Models.RegressionEvaluator input) { var output = new Microsoft.ML.Models.RegressionEvaluator.Output(); @@ -334,6 +418,18 @@ public void Add(Microsoft.ML.Models.RegressionEvaluator input, Microsoft.ML.Mode _jsonNodes.Add(Serialize("Models.RegressionEvaluator", input, output)); } + public Microsoft.ML.Models.RegressionPipelineEnsemble.Output Add(Microsoft.ML.Models.RegressionPipelineEnsemble input) + { + var output = new Microsoft.ML.Models.RegressionPipelineEnsemble.Output(); + Add(input, output); + return output; + } + + public void Add(Microsoft.ML.Models.RegressionPipelineEnsemble input, Microsoft.ML.Models.RegressionPipelineEnsemble.Output output) + { + _jsonNodes.Add(Serialize("Models.RegressionPipelineEnsemble", input, output)); + } + public Microsoft.ML.Models.Summarizer.Output Add(Microsoft.ML.Models.Summarizer input) { var output = new Microsoft.ML.Models.Summarizer.Output(); @@ -394,6 +490,42 @@ public void Add(Microsoft.ML.Trainers.AveragedPerceptronBinaryClassifier input, _jsonNodes.Add(Serialize("Trainers.AveragedPerceptronBinaryClassifier", input, output)); } + public Microsoft.ML.Trainers.EnsembleBinaryClassifier.Output Add(Microsoft.ML.Trainers.EnsembleBinaryClassifier input) + { + var output = new Microsoft.ML.Trainers.EnsembleBinaryClassifier.Output(); + Add(input, output); + return output; + } + + public void Add(Microsoft.ML.Trainers.EnsembleBinaryClassifier input, Microsoft.ML.Trainers.EnsembleBinaryClassifier.Output output) + { + _jsonNodes.Add(Serialize("Trainers.EnsembleBinaryClassifier", input, output)); + } + + public Microsoft.ML.Trainers.EnsembleClassification.Output Add(Microsoft.ML.Trainers.EnsembleClassification input) + { + var output = new Microsoft.ML.Trainers.EnsembleClassification.Output(); + Add(input, output); + return output; + } + + public void Add(Microsoft.ML.Trainers.EnsembleClassification input, Microsoft.ML.Trainers.EnsembleClassification.Output output) + { + _jsonNodes.Add(Serialize("Trainers.EnsembleClassification", input, output)); + } + + public Microsoft.ML.Trainers.EnsembleRegression.Output Add(Microsoft.ML.Trainers.EnsembleRegression input) + { + var output = new Microsoft.ML.Trainers.EnsembleRegression.Output(); + Add(input, output); + return output; + } + + public void Add(Microsoft.ML.Trainers.EnsembleRegression input, Microsoft.ML.Trainers.EnsembleRegression.Output output) + { + _jsonNodes.Add(Serialize("Trainers.EnsembleRegression", input, output)); + } + public Microsoft.ML.Trainers.FastForestBinaryClassifier.Output Add(Microsoft.ML.Trainers.FastForestBinaryClassifier input) { var output = new Microsoft.ML.Trainers.FastForestBinaryClassifier.Output(); @@ -466,6 +598,18 @@ public void Add(Microsoft.ML.Trainers.FastTreeTweedieRegressor input, Microsoft. _jsonNodes.Add(Serialize("Trainers.FastTreeTweedieRegressor", input, output)); } + public Microsoft.ML.Trainers.FieldAwareFactorizationMachineBinaryClassifier.Output Add(Microsoft.ML.Trainers.FieldAwareFactorizationMachineBinaryClassifier input) + { + var output = new Microsoft.ML.Trainers.FieldAwareFactorizationMachineBinaryClassifier.Output(); + Add(input, output); + return output; + } + + public void Add(Microsoft.ML.Trainers.FieldAwareFactorizationMachineBinaryClassifier input, Microsoft.ML.Trainers.FieldAwareFactorizationMachineBinaryClassifier.Output output) + { + _jsonNodes.Add(Serialize("Trainers.FieldAwareFactorizationMachineBinaryClassifier", input, output)); + } + public Microsoft.ML.Trainers.GeneralizedAdditiveModelBinaryClassifier.Output Add(Microsoft.ML.Trainers.GeneralizedAdditiveModelBinaryClassifier input) { var output = new Microsoft.ML.Trainers.GeneralizedAdditiveModelBinaryClassifier.Output(); @@ -502,6 +646,54 @@ public void Add(Microsoft.ML.Trainers.KMeansPlusPlusClusterer input, Microsoft.M _jsonNodes.Add(Serialize("Trainers.KMeansPlusPlusClusterer", input, output)); } + public Microsoft.ML.Trainers.LightGbmBinaryClassifier.Output Add(Microsoft.ML.Trainers.LightGbmBinaryClassifier input) + { + var output = new Microsoft.ML.Trainers.LightGbmBinaryClassifier.Output(); + Add(input, output); + return output; + } + + public void Add(Microsoft.ML.Trainers.LightGbmBinaryClassifier input, Microsoft.ML.Trainers.LightGbmBinaryClassifier.Output output) + { + _jsonNodes.Add(Serialize("Trainers.LightGbmBinaryClassifier", input, output)); + } + + public Microsoft.ML.Trainers.LightGbmClassifier.Output Add(Microsoft.ML.Trainers.LightGbmClassifier input) + { + var output = new Microsoft.ML.Trainers.LightGbmClassifier.Output(); + Add(input, output); + return output; + } + + public void Add(Microsoft.ML.Trainers.LightGbmClassifier input, Microsoft.ML.Trainers.LightGbmClassifier.Output output) + { + _jsonNodes.Add(Serialize("Trainers.LightGbmClassifier", input, output)); + } + + public Microsoft.ML.Trainers.LightGbmRanker.Output Add(Microsoft.ML.Trainers.LightGbmRanker input) + { + var output = new Microsoft.ML.Trainers.LightGbmRanker.Output(); + Add(input, output); + return output; + } + + public void Add(Microsoft.ML.Trainers.LightGbmRanker input, Microsoft.ML.Trainers.LightGbmRanker.Output output) + { + _jsonNodes.Add(Serialize("Trainers.LightGbmRanker", input, output)); + } + + public Microsoft.ML.Trainers.LightGbmRegressor.Output Add(Microsoft.ML.Trainers.LightGbmRegressor input) + { + var output = new Microsoft.ML.Trainers.LightGbmRegressor.Output(); + Add(input, output); + return output; + } + + public void Add(Microsoft.ML.Trainers.LightGbmRegressor input, Microsoft.ML.Trainers.LightGbmRegressor.Output output) + { + _jsonNodes.Add(Serialize("Trainers.LightGbmRegressor", input, output)); + } + public Microsoft.ML.Trainers.LinearSvmBinaryClassifier.Output Add(Microsoft.ML.Trainers.LinearSvmBinaryClassifier input) { var output = new Microsoft.ML.Trainers.LinearSvmBinaryClassifier.Output(); @@ -562,18 +754,6 @@ public void Add(Microsoft.ML.Trainers.OnlineGradientDescentRegressor input, Micr _jsonNodes.Add(Serialize("Trainers.OnlineGradientDescentRegressor", input, output)); } - public Microsoft.ML.Trainers.OrdinaryLeastSquaresRegressor.Output Add(Microsoft.ML.Trainers.OrdinaryLeastSquaresRegressor input) - { - var output = new Microsoft.ML.Trainers.OrdinaryLeastSquaresRegressor.Output(); - Add(input, output); - return output; - } - - public void Add(Microsoft.ML.Trainers.OrdinaryLeastSquaresRegressor input, Microsoft.ML.Trainers.OrdinaryLeastSquaresRegressor.Output output) - { - _jsonNodes.Add(Serialize("Trainers.OrdinaryLeastSquaresRegressor", input, output)); - } - public Microsoft.ML.Trainers.PcaAnomalyDetector.Output Add(Microsoft.ML.Trainers.PcaAnomalyDetector input) { var output = new Microsoft.ML.Trainers.PcaAnomalyDetector.Output(); @@ -958,6 +1138,18 @@ public void Add(Microsoft.ML.Transforms.LabelToFloatConverter input, Microsoft.M _jsonNodes.Add(Serialize("Transforms.LabelToFloatConverter", input, output)); } + public Microsoft.ML.Transforms.LightLda.Output Add(Microsoft.ML.Transforms.LightLda input) + { + var output = new Microsoft.ML.Transforms.LightLda.Output(); + Add(input, output); + return output; + } + + public void Add(Microsoft.ML.Transforms.LightLda input, Microsoft.ML.Transforms.LightLda.Output output) + { + _jsonNodes.Add(Serialize("Transforms.LightLda", input, output)); + } + public Microsoft.ML.Transforms.LogMeanVarianceNormalizer.Output Add(Microsoft.ML.Transforms.LogMeanVarianceNormalizer input) { var output = new Microsoft.ML.Transforms.LogMeanVarianceNormalizer.Output(); @@ -1461,57 +1653,84 @@ public sealed class Output namespace Data { - public sealed partial class TextLoaderArguments + public enum DataKind : byte { - /// - /// Use separate parsing threads? - /// - public bool UseThreads { get; set; } = true; + I1 = 1, + U1 = 2, + I2 = 3, + U2 = 4, + I4 = 5, + U4 = 6, + I8 = 7, + U8 = 8, + R4 = 9, + Num = 9, + R8 = 10, + TX = 11, + Text = 11, + TXT = 11, + BL = 12, + Bool = 12, + TimeSpan = 13, + TS = 13, + DT = 14, + DateTime = 14, + DZ = 15, + DateTimeZone = 15, + UG = 16, + U16 = 16 + } + public sealed partial class TextLoaderRange + { /// - /// File containing a header with feature names. If specified, header defined in the data file (header+) is ignored. + /// First index in the range /// - public string HeaderFile { get; set; } + public int Min { get; set; } /// - /// Maximum number of rows to produce + /// Last index in the range /// - public long? MaxRows { get; set; } + public int? Max { get; set; } /// - /// Whether the input may include quoted values, which can contain separator characters, colons, and distinguish empty values from missing values. When true, consecutive separators denote a missing value and an empty value is denoted by "". When false, consecutive separators denote an empty value. + /// This range extends to the end of the line, but should be a fixed number of items /// - public bool AllowQuoting { get; set; } = true; + public bool AutoEnd { get; set; } = false; /// - /// Whether the input may include sparse representations + /// This range extends to the end of the line, which can vary from line to line /// - public bool AllowSparse { get; set; } = true; + public bool VariableEnd { get; set; } = false; /// - /// Number of source columns in the text data. Default is that sparse rows contain their size information. + /// This range includes only other indices not specified /// - public int? InputSize { get; set; } + public bool AllOther { get; set; } = false; /// - /// Source column separator. + /// Force scalar columns to be treated as vectors of length one /// - public char[] Separator { get; set; } = { '\t' }; + public bool ForceVector { get; set; } = false; + + } + public sealed partial class KeyRange + { /// - /// Column groups. Each group is specified as name:type:numeric-ranges, eg, col=Features:R4:1-17,26,35-40 + /// First index in the range /// - public TextLoaderColumn[] Column { get; set; } + public ulong Min { get; set; } = 0; /// - /// Remove trailing whitespace from lines + /// Last index in the range /// - public bool TrimWhitespace { get; set; } = false; + public ulong? Max { get; set; } /// - /// Data file has header with feature names. Header is read only if options 'hs' and 'hf' are not specified. + /// Whether the key is contiguous /// - public bool HasHeader { get; set; } = false; + public bool Contiguous { get; set; } = true; } @@ -1539,56 +1758,57 @@ public sealed partial class TextLoaderColumn } - public sealed partial class TextLoaderRange + public sealed partial class TextLoaderArguments { /// - /// First index in the range + /// Use separate parsing threads? /// - public int Min { get; set; } + public bool UseThreads { get; set; } = true; /// - /// Last index in the range + /// File containing a header with feature names. If specified, header defined in the data file (header+) is ignored. /// - public int? Max { get; set; } + public string HeaderFile { get; set; } /// - /// This range extends to the end of the line, but should be a fixed number of items + /// Maximum number of rows to produce /// - public bool AutoEnd { get; set; } = false; + public long? MaxRows { get; set; } /// - /// This range extends to the end of the line, which can vary from line to line + /// Whether the input may include quoted values, which can contain separator characters, colons, and distinguish empty values from missing values. When true, consecutive separators denote a missing value and an empty value is denoted by "". When false, consecutive separators denote an empty value. /// - public bool VariableEnd { get; set; } = false; + public bool AllowQuoting { get; set; } = true; /// - /// This range includes only other indices not specified + /// Whether the input may include sparse representations /// - public bool AllOther { get; set; } = false; + public bool AllowSparse { get; set; } = true; /// - /// Force scalar columns to be treated as vectors of length one + /// Number of source columns in the text data. Default is that sparse rows contain their size information. /// - public bool ForceVector { get; set; } = false; - - } + public int? InputSize { get; set; } - public sealed partial class KeyRange - { /// - /// First index in the range + /// Source column separator. /// - public ulong Min { get; set; } = 0; + public char[] Separator { get; set; } = { '\t' }; /// - /// Last index in the range + /// Column groups. Each group is specified as name:type:numeric-ranges, eg, col=Features:R4:1-17,26,35-40 /// - public ulong? Max { get; set; } + public TextLoaderColumn[] Column { get; set; } /// - /// Whether the key is contiguous + /// Remove trailing whitespace from lines /// - public bool Contiguous { get; set; } = true; + public bool TrimWhitespace { get; set; } = false; + + /// + /// Data file has header with feature names. Header is read only if options 'hs' and 'hf' are not specified. + /// + public bool HasHeader { get; set; } = false; } @@ -1640,7 +1860,7 @@ public TextLoaderPipelineStep (Output output) /// /// Arguments /// - public Data.TextLoaderArguments Arguments { get; set; } = new Data.TextLoaderArguments(); + public TextLoaderArguments Arguments { get; set; } = new TextLoaderArguments(); public sealed class Output @@ -1768,6 +1988,44 @@ public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IEva } } + namespace Models + { + public enum EnsembleCreatorScoreCombiner + { + Median = 0, + Average = 1 + } + + + /// + /// Combine anomaly detection models into an ensemble + /// + public sealed partial class AnomalyPipelineEnsemble + { + + + /// + /// The combiner used to combine the scores + /// + public EnsembleCreatorScoreCombiner ModelCombiner { get; set; } = EnsembleCreatorScoreCombiner.Average; + + /// + /// The models to combine into an ensemble + /// + public ArrayVar Models { get; set; } = new ArrayVar(); + + + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IAnomalyDetectionOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput + { + /// + /// The trained model + /// + public Var PredictorModel { get; set; } = new Var(); + + } + } + } + namespace Models { @@ -1906,12 +2164,12 @@ public sealed partial class BinaryCrossValidator /// /// The training subgraph inputs /// - public Models.CrossValidationBinaryMacroSubGraphInput Inputs { get; set; } = new Models.CrossValidationBinaryMacroSubGraphInput(); + public CrossValidationBinaryMacroSubGraphInput Inputs { get; set; } = new CrossValidationBinaryMacroSubGraphInput(); /// /// The training subgraph outputs /// - public Models.CrossValidationBinaryMacroSubGraphOutput Outputs { get; set; } = new Models.CrossValidationBinaryMacroSubGraphOutput(); + public CrossValidationBinaryMacroSubGraphOutput Outputs { get; set; } = new CrossValidationBinaryMacroSubGraphOutput(); /// /// Column to use for stratification @@ -1955,6 +2213,82 @@ public sealed class Output } } + namespace Models + { + public enum EnsembleCreatorClassifierCombiner + { + Median = 0, + Average = 1, + Vote = 2 + } + + + /// + /// Combine binary classifiers into an ensemble + /// + public sealed partial class BinaryEnsemble + { + + + /// + /// The combiner used to combine the scores + /// + public EnsembleCreatorClassifierCombiner ModelCombiner { get; set; } = EnsembleCreatorClassifierCombiner.Median; + + /// + /// The models to combine into an ensemble + /// + public ArrayVar Models { get; set; } = new ArrayVar(); + + /// + /// Whether to validate that all the pipelines are identical + /// + public bool ValidatePipelines { get; set; } = true; + + + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IBinaryClassificationOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput + { + /// + /// The trained model + /// + public Var PredictorModel { get; set; } = new Var(); + + } + } + } + + namespace Models + { + + /// + /// Combine binary classification models into an ensemble + /// + public sealed partial class BinaryPipelineEnsemble + { + + + /// + /// The combiner used to combine the scores + /// + public EnsembleCreatorClassifierCombiner ModelCombiner { get; set; } = EnsembleCreatorClassifierCombiner.Median; + + /// + /// The models to combine into an ensemble + /// + public ArrayVar Models { get; set; } = new ArrayVar(); + + + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IBinaryClassificationOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput + { + /// + /// The trained model + /// + public Var PredictorModel { get; set; } = new Var(); + + } + } + } + namespace Models { @@ -2165,10 +2499,20 @@ public sealed partial class CrossValidationResultsCombiner /// public string LabelColumn { get; set; } = "Label"; + /// + /// Column to use for example weight + /// + public Microsoft.ML.Runtime.EntryPoints.Optional WeightColumn { get; set; } + + /// + /// Column to use for grouping + /// + public Microsoft.ML.Runtime.EntryPoints.Optional GroupColumn { get; set; } + /// /// Specifies the trainer kind, which determines the evaluator to be used. /// - public Models.MacroUtilsTrainerKinds Kind { get; set; } = Models.MacroUtilsTrainerKinds.SignatureBinaryClassifierTrainer; + public MacroUtilsTrainerKinds Kind { get; set; } = MacroUtilsTrainerKinds.SignatureBinaryClassifierTrainer; public sealed class Output @@ -2248,12 +2592,12 @@ public sealed partial class CrossValidator /// /// The training subgraph inputs /// - public Models.CrossValidationMacroSubGraphInput Inputs { get; set; } = new Models.CrossValidationMacroSubGraphInput(); + public CrossValidationMacroSubGraphInput Inputs { get; set; } = new CrossValidationMacroSubGraphInput(); /// /// The training subgraph outputs /// - public Models.CrossValidationMacroSubGraphOutput Outputs { get; set; } = new Models.CrossValidationMacroSubGraphOutput(); + public CrossValidationMacroSubGraphOutput Outputs { get; set; } = new CrossValidationMacroSubGraphOutput(); /// /// Column to use for stratification @@ -2268,7 +2612,22 @@ public sealed partial class CrossValidator /// /// Specifies the trainer kind, which determines the evaluator to be used. /// - public Models.MacroUtilsTrainerKinds Kind { get; set; } = Models.MacroUtilsTrainerKinds.SignatureBinaryClassifierTrainer; + public MacroUtilsTrainerKinds Kind { get; set; } = MacroUtilsTrainerKinds.SignatureBinaryClassifierTrainer; + + /// + /// Column to use for labels + /// + public string LabelColumn { get; set; } = "Label"; + + /// + /// Column to use for example weight + /// + public Microsoft.ML.Runtime.EntryPoints.Optional WeightColumn { get; set; } + + /// + /// Column to use for grouping + /// + public Microsoft.ML.Runtime.EntryPoints.Optional GroupColumn { get; set; } public sealed class Output @@ -2408,6 +2767,38 @@ public DatasetTransformerPipelineStep(Output output) } } + namespace Models + { + + /// + /// Summarize a pipeline ensemble predictor. + /// + public sealed partial class EnsembleSummary + { + + + /// + /// The predictor to summarize + /// + public Var PredictorModel { get; set; } = new Var(); + + + public sealed class Output + { + /// + /// The summaries of the individual predictors + /// + public ArrayVar Summaries { get; set; } = new ArrayVar(); + + /// + /// The model statistics of the individual predictors + /// + public ArrayVar Stats { get; set; } = new ArrayVar(); + + } + } + } + namespace Models { @@ -2482,6 +2873,38 @@ public FixedPlattCalibratorPipelineStep(Output output) } } + namespace Models + { + + /// + /// Combine multiclass classifiers into an ensemble + /// + public sealed partial class MultiClassPipelineEnsemble + { + + + /// + /// The combiner used to combine the scores + /// + public EnsembleCreatorClassifierCombiner ModelCombiner { get; set; } = EnsembleCreatorClassifierCombiner.Median; + + /// + /// The models to combine into an ensemble + /// + public ArrayVar Models { get; set; } = new ArrayVar(); + + + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IMulticlassClassificationOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput + { + /// + /// The trained model + /// + public Var PredictorModel { get; set; } = new Var(); + + } + } + } + namespace Models { @@ -2662,7 +3085,7 @@ public sealed partial class OneVersusAll : Microsoft.ML.Runtime.EntryPoints.Comm /// /// The training subgraph output. /// - public Models.OneVersusAllMacroSubGraphOutput OutputForSubGraph { get; set; } = new Models.OneVersusAllMacroSubGraphOutput(); + public OneVersusAllMacroSubGraphOutput OutputForSubGraph { get; set; } = new OneVersusAllMacroSubGraphOutput(); /// /// Use probabilities in OVA combiner @@ -2692,12 +3115,12 @@ public sealed partial class OneVersusAll : Microsoft.ML.Runtime.EntryPoints.Comm /// /// Normalize option for the feature column /// - public Models.NormalizeOption NormalizeFeatures { get; set; } = Models.NormalizeOption.Auto; + public NormalizeOption NormalizeFeatures { get; set; } = NormalizeOption.Auto; /// /// Whether learner should cache input training data /// - public Models.CachingOptions Caching { get; set; } = Models.CachingOptions.Auto; + public CachingOptions Caching { get; set; } = CachingOptions.Auto; public sealed class Output @@ -2737,6 +3160,63 @@ public OneVersusAllPipelineStep(Output output) } } + namespace Models + { + + /// + /// Converts the model to ONNX format. + /// + public sealed partial class OnnxConverter + { + + + /// + /// The path to write the output ONNX to. + /// + public string Onnx { get; set; } + + /// + /// The path to write the output JSON to. + /// + public string Json { get; set; } + + /// + /// The 'name' property in the output ONNX. By default this will be the ONNX extension-less name. + /// + public string Name { get; set; } + + /// + /// The 'domain' property in the output ONNX. + /// + public string Domain { get; set; } + + /// + /// Array of input column names to drop + /// + public string[] InputsToDrop { get; set; } + + /// + /// Array of output column names to drop + /// + public string[] OutputsToDrop { get; set; } + + /// + /// Model that needs to be converted to ONNX format. + /// + public Var Model { get; set; } = new Var(); + + /// + /// The data file + /// + public string DataFile { get; set; } + + + public sealed class Output + { + } + } + } + namespace Models { @@ -2780,12 +3260,12 @@ public sealed partial class OvaModelCombiner : Microsoft.ML.Runtime.EntryPoints. /// /// Normalize option for the feature column /// - public Models.NormalizeOption NormalizeFeatures { get; set; } = Models.NormalizeOption.Auto; + public NormalizeOption NormalizeFeatures { get; set; } = NormalizeOption.Auto; /// /// Whether learner should cache input training data /// - public Models.CachingOptions Caching { get; set; } = Models.CachingOptions.Auto; + public CachingOptions Caching { get; set; } = CachingOptions.Auto; public sealed class Output @@ -3161,6 +3641,43 @@ public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IEva } } + namespace Models + { + + /// + /// Combine regression models into an ensemble + /// + public sealed partial class RegressionEnsemble + { + + + /// + /// The combiner used to combine the scores + /// + public EnsembleCreatorScoreCombiner ModelCombiner { get; set; } = EnsembleCreatorScoreCombiner.Median; + + /// + /// The models to combine into an ensemble + /// + public ArrayVar Models { get; set; } = new ArrayVar(); + + /// + /// Whether to validate that all the pipelines are identical + /// + public bool ValidatePipelines { get; set; } = true; + + + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IRegressionOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput + { + /// + /// The trained model + /// + public Var PredictorModel { get; set; } = new Var(); + + } + } + } + namespace Models { @@ -3233,29 +3750,29 @@ namespace Models { /// - /// Summarize a linear regression predictor. + /// Combine regression models into an ensemble /// - public sealed partial class Summarizer + public sealed partial class RegressionPipelineEnsemble { /// - /// The predictor to summarize + /// The combiner used to combine the scores /// - public Var PredictorModel { get; set; } = new Var(); + public EnsembleCreatorScoreCombiner ModelCombiner { get; set; } = EnsembleCreatorScoreCombiner.Median; + /// + /// The models to combine into an ensemble + /// + public ArrayVar Models { get; set; } = new ArrayVar(); - public sealed class Output - { - /// - /// The summary of a predictor - /// - public Var Summary { get; set; } = new Var(); + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IRegressionOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput + { /// - /// The training set statistics. Note that this output can be null. + /// The trained model /// - public Var Stats { get; set; } = new Var(); + public Var PredictorModel { get; set; } = new Var(); } } @@ -3265,7 +3782,39 @@ namespace Models { /// - /// Extracts the sweep result. + /// Summarize a linear regression predictor. + /// + public sealed partial class Summarizer + { + + + /// + /// The predictor to summarize + /// + public Var PredictorModel { get; set; } = new Var(); + + + public sealed class Output + { + /// + /// The summary of a predictor + /// + public Var Summary { get; set; } = new Var(); + + /// + /// The training set statistics. Note that this output can be null. + /// + public Var Stats { get; set; } = new Var(); + + } + } + } + + namespace Models + { + + /// + /// Extracts the sweep result. /// public sealed partial class SweepResultExtractor { @@ -3339,12 +3888,12 @@ public sealed partial class TrainTestBinaryEvaluator /// /// The training subgraph inputs /// - public Models.TrainTestBinaryMacroSubGraphInput Inputs { get; set; } = new Models.TrainTestBinaryMacroSubGraphInput(); + public TrainTestBinaryMacroSubGraphInput Inputs { get; set; } = new TrainTestBinaryMacroSubGraphInput(); /// /// The training subgraph outputs /// - public Models.TrainTestBinaryMacroSubGraphOutput Outputs { get; set; } = new Models.TrainTestBinaryMacroSubGraphOutput(); + public TrainTestBinaryMacroSubGraphOutput Outputs { get; set; } = new TrainTestBinaryMacroSubGraphOutput(); public sealed class Output @@ -3434,17 +3983,17 @@ public sealed partial class TrainTestEvaluator /// /// The training subgraph inputs /// - public Models.TrainTestMacroSubGraphInput Inputs { get; set; } = new Models.TrainTestMacroSubGraphInput(); + public TrainTestMacroSubGraphInput Inputs { get; set; } = new TrainTestMacroSubGraphInput(); /// /// The training subgraph outputs /// - public Models.TrainTestMacroSubGraphOutput Outputs { get; set; } = new Models.TrainTestMacroSubGraphOutput(); + public TrainTestMacroSubGraphOutput Outputs { get; set; } = new TrainTestMacroSubGraphOutput(); /// /// Specifies the trainer kind, which determines the evaluator to be used. /// - public Models.MacroUtilsTrainerKinds Kind { get; set; } = Models.MacroUtilsTrainerKinds.SignatureBinaryClassifierTrainer; + public MacroUtilsTrainerKinds Kind { get; set; } = MacroUtilsTrainerKinds.SignatureBinaryClassifierTrainer; /// /// Identifies which pipeline was run for this train test. @@ -3456,6 +4005,21 @@ public sealed partial class TrainTestEvaluator /// public bool IncludeTrainingMetrics { get; set; } = false; + /// + /// Column to use for labels + /// + public string LabelColumn { get; set; } = "Label"; + + /// + /// Column to use for example weight + /// + public Microsoft.ML.Runtime.EntryPoints.Optional WeightColumn { get; set; } + + /// + /// Column to use for grouping + /// + public Microsoft.ML.Runtime.EntryPoints.Optional GroupColumn { get; set; } + public sealed class Output { @@ -3634,12 +4198,12 @@ public sealed partial class AveragedPerceptronBinaryClassifier : Microsoft.ML.Ru /// /// Normalize option for the feature column /// - public Models.NormalizeOption NormalizeFeatures { get; set; } = Models.NormalizeOption.Auto; + public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; /// /// Whether learner should cache input training data /// - public Models.CachingOptions Caching { get; set; } = Models.CachingOptions.Auto; + public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IBinaryClassificationOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput @@ -3681,210 +4245,543 @@ public AveragedPerceptronBinaryClassifierPipelineStep(Output output) namespace Trainers { - public enum Bundle : byte - { - None = 0, - AggregateLowPopulation = 1, - Adjacent = 2 - } - /// - /// Uses a random forest learner to perform binary classification. + /// Train binary ensemble. /// - public sealed partial class FastForestBinaryClassifier : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithGroupId, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithWeight, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithLabel, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class EnsembleBinaryClassifier : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithLabel, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInput, Microsoft.ML.ILearningPipelineItem { /// - /// Upper bound on absolute value of single tree output + /// Algorithm to prune the base learners for selective Ensemble /// - public double MaxTreeOutput { get; set; } = 100d; + [JsonConverter(typeof(ComponentSerializer))] + public EnsembleBinarySubModelSelector SubModelSelectorType { get; set; } = new AllSelectorEnsembleBinarySubModelSelector(); /// - /// The calibrator kind to apply to the predictor. Specify null for no calibration + /// Output combiner /// [JsonConverter(typeof(ComponentSerializer))] - public CalibratorTrainer Calibrator { get; set; } = new PlattCalibratorCalibratorTrainer(); + public EnsembleBinaryOutputCombiner OutputCombiner { get; set; } = new MedianEnsembleBinaryOutputCombiner(); /// - /// The maximum number of examples to use when training the calibrator + /// Number of models per batch. If not specified, will default to 50 if there is only one base predictor, or the number of base predictors otherwise. /// - public int MaxCalibrationExamples { get; set; } = 1000000; + public int? NumModels { get; set; } /// - /// Number of labels to be sampled from each leaf to make the distribtuion + /// Batch size /// - public int QuantileSampleCount { get; set; } = 100; + public int BatchSize { get; set; } = -1; /// - /// Allows to choose Parallel FastTree Learning Algorithm + /// Sampling Type /// [JsonConverter(typeof(ComponentSerializer))] - public ParallelTraining ParallelTrainer { get; set; } = new SingleParallelTraining(); + public EnsembleSubsetSelector SamplingType { get; set; } = new BootstrapSelectorEnsembleSubsetSelector(); /// - /// The number of threads to use + /// All the base learners will run asynchronously if the value is true /// - public int? NumThreads { get; set; } + public bool TrainParallel { get; set; } = false; /// - /// The seed of the random number generator + /// True, if metrics for each model need to be evaluated and shown in comparison table. This is done by using validation set if available or the training set /// - public int RngSeed { get; set; } = 123; + public bool ShowMetrics { get; set; } = false; /// - /// The seed of the active feature selection + /// Column to use for labels /// - public int FeatureSelectSeed { get; set; } = 123; + public string LabelColumn { get; set; } = "Label"; /// - /// The entropy (regularization) coefficient between 0 and 1 + /// The data to be used for training /// - public double EntropyCoefficient { get; set; } + public Var TrainingData { get; set; } = new Var(); /// - /// The number of histograms in the pool (between 2 and numLeaves) + /// Column to use for features /// - public int HistogramPoolSize { get; set; } = -1; + public string FeatureColumn { get; set; } = "Features"; /// - /// Whether to utilize the disk or the data's native transposition facilities (where applicable) when performing the transpose + /// Normalize option for the feature column /// - public bool? DiskTranspose { get; set; } + public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; /// - /// Whether to collectivize features during dataset preparation to speed up training + /// Whether learner should cache input training data /// - public bool FeatureFlocks { get; set; } = true; + public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; - /// - /// Whether to do split based on multiple categorical feature values. - /// - public bool CategoricalSplit { get; set; } = false; - /// - /// Maximum categorical split groups to consider when splitting on a categorical feature. Split groups are a collection of split points. This is used to reduce overfitting when there many categorical features. - /// - public int MaxCategoricalGroupsPerNode { get; set; } = 64; + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IBinaryClassificationOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput + { + /// + /// The trained model + /// + public Var PredictorModel { get; set; } = new Var(); + + } + public Var GetInputData() => TrainingData; + + public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) + { + if (previousStep != null) + { + if (!(previousStep is ILearningPipelineDataStep dataStep)) + { + throw new InvalidOperationException($"{ nameof(EnsembleBinaryClassifier)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + } + + TrainingData = dataStep.Data; + } + Output output = experiment.Add(this); + return new EnsembleBinaryClassifierPipelineStep(output); + } + + private class EnsembleBinaryClassifierPipelineStep : ILearningPipelinePredictorStep + { + public EnsembleBinaryClassifierPipelineStep(Output output) + { + Model = output.PredictorModel; + } + + public Var Model { get; } + } + } + } + + namespace Trainers + { + + /// + /// Train multiclass ensemble. + /// + public sealed partial class EnsembleClassification : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithLabel, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInput, Microsoft.ML.ILearningPipelineItem + { + /// - /// Maximum categorical split points to consider when splitting on a categorical feature. + /// Algorithm to prune the base learners for selective Ensemble /// - public int MaxCategoricalSplitPoints { get; set; } = 64; + [JsonConverter(typeof(ComponentSerializer))] + public EnsembleMulticlassSubModelSelector SubModelSelectorType { get; set; } = new AllSelectorMultiClassEnsembleMulticlassSubModelSelector(); /// - /// Minimum categorical docs percentage in a bin to consider for a split. + /// Output combiner /// - public double MinDocsPercentageForCategoricalSplit { get; set; } = 0.001d; + [JsonConverter(typeof(ComponentSerializer))] + public EnsembleMulticlassOutputCombiner OutputCombiner { get; set; } = new MultiMedianEnsembleMulticlassOutputCombiner(); /// - /// Minimum categorical doc count in a bin to consider for a split. + /// Number of models per batch. If not specified, will default to 50 if there is only one base predictor, or the number of base predictors otherwise. /// - public int MinDocsForCategoricalSplit { get; set; } = 100; + public int? NumModels { get; set; } /// - /// Bias for calculating gradient for each feature bin for a categorical feature. + /// Batch size /// - public double Bias { get; set; } + public int BatchSize { get; set; } = -1; /// - /// Bundle low population bins. Bundle.None(0): no bundling, Bundle.AggregateLowPopulation(1): Bundle low population, Bundle.Adjacent(2): Neighbor low population bundle. + /// Sampling Type /// - public Trainers.Bundle Bundling { get; set; } = Trainers.Bundle.None; + [JsonConverter(typeof(ComponentSerializer))] + public EnsembleSubsetSelector SamplingType { get; set; } = new BootstrapSelectorEnsembleSubsetSelector(); /// - /// Maximum number of distinct values (bins) per feature + /// All the base learners will run asynchronously if the value is true /// - public int MaxBins { get; set; } = 255; + public bool TrainParallel { get; set; } = false; /// - /// Sparsity level needed to use sparse feature representation + /// True, if metrics for each model need to be evaluated and shown in comparison table. This is done by using validation set if available or the training set /// - public double SparsifyThreshold { get; set; } = 0.7d; + public bool ShowMetrics { get; set; } = false; /// - /// The feature first use penalty coefficient + /// Column to use for labels /// - public double FeatureFirstUsePenalty { get; set; } + public string LabelColumn { get; set; } = "Label"; /// - /// The feature re-use penalty (regularization) coefficient + /// The data to be used for training /// - public double FeatureReusePenalty { get; set; } + public Var TrainingData { get; set; } = new Var(); /// - /// Tree fitting gain confidence requirement (should be in the range [0,1) ). + /// Column to use for features /// - public double GainConfidenceLevel { get; set; } + public string FeatureColumn { get; set; } = "Features"; /// - /// The temperature of the randomized softmax distribution for choosing the feature + /// Normalize option for the feature column /// - public double SoftmaxTemperature { get; set; } + public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; /// - /// Print execution time breakdown to stdout + /// Whether learner should cache input training data /// - public bool ExecutionTimes { get; set; } = false; + public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; + + + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IMulticlassClassificationOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput + { + /// + /// The trained model + /// + public Var PredictorModel { get; set; } = new Var(); + + } + public Var GetInputData() => TrainingData; + + public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) + { + if (previousStep != null) + { + if (!(previousStep is ILearningPipelineDataStep dataStep)) + { + throw new InvalidOperationException($"{ nameof(EnsembleClassification)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + } + + TrainingData = dataStep.Data; + } + Output output = experiment.Add(this); + return new EnsembleClassificationPipelineStep(output); + } + + private class EnsembleClassificationPipelineStep : ILearningPipelinePredictorStep + { + public EnsembleClassificationPipelineStep(Output output) + { + Model = output.PredictorModel; + } + + public Var Model { get; } + } + } + } + + namespace Trainers + { + + /// + /// Train regression ensemble. + /// + public sealed partial class EnsembleRegression : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithLabel, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInput, Microsoft.ML.ILearningPipelineItem + { + /// - /// The max number of leaves in each regression tree + /// Algorithm to prune the base learners for selective Ensemble /// - [TlcModule.SweepableLongParamAttribute("NumLeaves", 2, 128, stepSize:4, isLogScale:true)] - public int NumLeaves { get; set; } = 20; + [JsonConverter(typeof(ComponentSerializer))] + public EnsembleRegressionSubModelSelector SubModelSelectorType { get; set; } = new AllSelectorEnsembleRegressionSubModelSelector(); /// - /// The minimal number of documents allowed in a leaf of a regression tree, out of the subsampled data + /// Output combiner /// - [TlcModule.SweepableDiscreteParamAttribute("MinDocumentsInLeafs", new object[]{1, 10, 50})] - public int MinDocumentsInLeafs { get; set; } = 10; + [JsonConverter(typeof(ComponentSerializer))] + public EnsembleRegressionOutputCombiner OutputCombiner { get; set; } = new MedianEnsembleRegressionOutputCombiner(); /// - /// Number of weak hypotheses in the ensemble + /// Number of models per batch. If not specified, will default to 50 if there is only one base predictor, or the number of base predictors otherwise. /// - [TlcModule.SweepableDiscreteParamAttribute("NumTrees", new object[]{20, 100, 500})] - public int NumTrees { get; set; } = 100; + public int? NumModels { get; set; } /// - /// The fraction of features (chosen randomly) to use on each iteration + /// Batch size /// - public double FeatureFraction { get; set; } = 0.7d; + public int BatchSize { get; set; } = -1; /// - /// Number of trees in each bag (0 for disabling bagging) + /// Sampling Type /// - public int BaggingSize { get; set; } = 1; + [JsonConverter(typeof(ComponentSerializer))] + public EnsembleSubsetSelector SamplingType { get; set; } = new BootstrapSelectorEnsembleSubsetSelector(); /// - /// Percentage of training examples used in each bag + /// All the base learners will run asynchronously if the value is true /// - public double BaggingTrainFraction { get; set; } = 0.7d; + public bool TrainParallel { get; set; } = false; /// - /// The fraction of features (chosen randomly) to use on each split + /// True, if metrics for each model need to be evaluated and shown in comparison table. This is done by using validation set if available or the training set /// - public double SplitFraction { get; set; } = 0.7d; + public bool ShowMetrics { get; set; } = false; /// - /// Smoothing paramter for tree regularization + /// Column to use for labels /// - public double Smoothing { get; set; } + public string LabelColumn { get; set; } = "Label"; /// - /// When a root split is impossible, allow training to proceed + /// The data to be used for training /// - public bool AllowEmptyTrees { get; set; } = true; + public Var TrainingData { get; set; } = new Var(); /// - /// The level of feature compression to use + /// Column to use for features /// - public int FeatureCompressionLevel { get; set; } = 1; + public string FeatureColumn { get; set; } = "Features"; /// - /// Compress the tree Ensemble + /// Normalize option for the feature column /// - public bool CompressEnsemble { get; set; } = false; + public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; + + /// + /// Whether learner should cache input training data + /// + public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; + + + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IRegressionOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput + { + /// + /// The trained model + /// + public Var PredictorModel { get; set; } = new Var(); + + } + public Var GetInputData() => TrainingData; + + public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) + { + if (previousStep != null) + { + if (!(previousStep is ILearningPipelineDataStep dataStep)) + { + throw new InvalidOperationException($"{ nameof(EnsembleRegression)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + } + + TrainingData = dataStep.Data; + } + Output output = experiment.Add(this); + return new EnsembleRegressionPipelineStep(output); + } + + private class EnsembleRegressionPipelineStep : ILearningPipelinePredictorStep + { + public EnsembleRegressionPipelineStep(Output output) + { + Model = output.PredictorModel; + } + + public Var Model { get; } + } + } + } + + namespace Trainers + { + public enum Bundle : byte + { + None = 0, + AggregateLowPopulation = 1, + Adjacent = 2 + } + + + /// + /// Uses a random forest learner to perform binary classification. + /// + public sealed partial class FastForestBinaryClassifier : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithGroupId, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithWeight, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithLabel, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInput, Microsoft.ML.ILearningPipelineItem + { + + + /// + /// Upper bound on absolute value of single tree output + /// + public double MaxTreeOutput { get; set; } = 100d; + + /// + /// The calibrator kind to apply to the predictor. Specify null for no calibration + /// + [JsonConverter(typeof(ComponentSerializer))] + public CalibratorTrainer Calibrator { get; set; } = new PlattCalibratorCalibratorTrainer(); + + /// + /// The maximum number of examples to use when training the calibrator + /// + public int MaxCalibrationExamples { get; set; } = 1000000; + + /// + /// Number of labels to be sampled from each leaf to make the distribtuion + /// + public int QuantileSampleCount { get; set; } = 100; + + /// + /// Allows to choose Parallel FastTree Learning Algorithm + /// + [JsonConverter(typeof(ComponentSerializer))] + public ParallelTraining ParallelTrainer { get; set; } = new SingleParallelTraining(); + + /// + /// The number of threads to use + /// + public int? NumThreads { get; set; } + + /// + /// The seed of the random number generator + /// + public int RngSeed { get; set; } = 123; + + /// + /// The seed of the active feature selection + /// + public int FeatureSelectSeed { get; set; } = 123; + + /// + /// The entropy (regularization) coefficient between 0 and 1 + /// + public double EntropyCoefficient { get; set; } + + /// + /// The number of histograms in the pool (between 2 and numLeaves) + /// + public int HistogramPoolSize { get; set; } = -1; + + /// + /// Whether to utilize the disk or the data's native transposition facilities (where applicable) when performing the transpose + /// + public bool? DiskTranspose { get; set; } + + /// + /// Whether to collectivize features during dataset preparation to speed up training + /// + public bool FeatureFlocks { get; set; } = true; + + /// + /// Whether to do split based on multiple categorical feature values. + /// + public bool CategoricalSplit { get; set; } = false; + + /// + /// Maximum categorical split groups to consider when splitting on a categorical feature. Split groups are a collection of split points. This is used to reduce overfitting when there many categorical features. + /// + public int MaxCategoricalGroupsPerNode { get; set; } = 64; + + /// + /// Maximum categorical split points to consider when splitting on a categorical feature. + /// + public int MaxCategoricalSplitPoints { get; set; } = 64; + + /// + /// Minimum categorical docs percentage in a bin to consider for a split. + /// + public double MinDocsPercentageForCategoricalSplit { get; set; } = 0.001d; + + /// + /// Minimum categorical doc count in a bin to consider for a split. + /// + public int MinDocsForCategoricalSplit { get; set; } = 100; + + /// + /// Bias for calculating gradient for each feature bin for a categorical feature. + /// + public double Bias { get; set; } + + /// + /// Bundle low population bins. Bundle.None(0): no bundling, Bundle.AggregateLowPopulation(1): Bundle low population, Bundle.Adjacent(2): Neighbor low population bundle. + /// + public Bundle Bundling { get; set; } = Bundle.None; + + /// + /// Maximum number of distinct values (bins) per feature + /// + public int MaxBins { get; set; } = 255; + + /// + /// Sparsity level needed to use sparse feature representation + /// + public double SparsifyThreshold { get; set; } = 0.7d; + + /// + /// The feature first use penalty coefficient + /// + public double FeatureFirstUsePenalty { get; set; } + + /// + /// The feature re-use penalty (regularization) coefficient + /// + public double FeatureReusePenalty { get; set; } + + /// + /// Tree fitting gain confidence requirement (should be in the range [0,1) ). + /// + public double GainConfidenceLevel { get; set; } + + /// + /// The temperature of the randomized softmax distribution for choosing the feature + /// + public double SoftmaxTemperature { get; set; } + + /// + /// Print execution time breakdown to stdout + /// + public bool ExecutionTimes { get; set; } = false; + + /// + /// The max number of leaves in each regression tree + /// + [TlcModule.SweepableLongParamAttribute("NumLeaves", 2, 128, stepSize:4, isLogScale:true)] + public int NumLeaves { get; set; } = 20; + + /// + /// The minimal number of documents allowed in a leaf of a regression tree, out of the subsampled data + /// + [TlcModule.SweepableDiscreteParamAttribute("MinDocumentsInLeafs", new object[]{1, 10, 50})] + public int MinDocumentsInLeafs { get; set; } = 10; + + /// + /// Total number of decision trees to create in the ensemble + /// + [TlcModule.SweepableDiscreteParamAttribute("NumTrees", new object[]{20, 100, 500})] + public int NumTrees { get; set; } = 100; + + /// + /// The fraction of features (chosen randomly) to use on each iteration + /// + public double FeatureFraction { get; set; } = 0.7d; + + /// + /// Number of trees in each bag (0 for disabling bagging) + /// + public int BaggingSize { get; set; } = 1; + + /// + /// Percentage of training examples used in each bag + /// + public double BaggingTrainFraction { get; set; } = 0.7d; + + /// + /// The fraction of features (chosen randomly) to use on each split + /// + public double SplitFraction { get; set; } = 0.7d; + + /// + /// Smoothing paramter for tree regularization + /// + public double Smoothing { get; set; } + + /// + /// When a root split is impossible, allow training to proceed + /// + public bool AllowEmptyTrees { get; set; } = true; + + /// + /// The level of feature compression to use + /// + public int FeatureCompressionLevel { get; set; } = 1; + + /// + /// Compress the tree Ensemble + /// + public bool CompressEnsemble { get; set; } = false; /// /// Maximum Number of trees after compression @@ -3934,12 +4831,12 @@ public sealed partial class FastForestBinaryClassifier : Microsoft.ML.Runtime.En /// /// Normalize option for the feature column /// - public Models.NormalizeOption NormalizeFeatures { get; set; } = Models.NormalizeOption.Auto; + public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; /// /// Whether learner should cache input training data /// - public Models.CachingOptions Caching { get; set; } = Models.CachingOptions.Auto; + public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IBinaryClassificationOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput @@ -4073,7 +4970,7 @@ public sealed partial class FastForestRegressor : Microsoft.ML.Runtime.EntryPoin /// /// Bundle low population bins. Bundle.None(0): no bundling, Bundle.AggregateLowPopulation(1): Bundle low population, Bundle.Adjacent(2): Neighbor low population bundle. /// - public Trainers.Bundle Bundling { get; set; } = Trainers.Bundle.None; + public Bundle Bundling { get; set; } = Bundle.None; /// /// Maximum number of distinct values (bins) per feature @@ -4123,7 +5020,7 @@ public sealed partial class FastForestRegressor : Microsoft.ML.Runtime.EntryPoin public int MinDocumentsInLeafs { get; set; } = 10; /// - /// Number of weak hypotheses in the ensemble + /// Total number of decision trees to create in the ensemble /// [TlcModule.SweepableDiscreteParamAttribute("NumTrees", new object[]{20, 100, 500})] public int NumTrees { get; set; } = 100; @@ -4216,12 +5113,12 @@ public sealed partial class FastForestRegressor : Microsoft.ML.Runtime.EntryPoin /// /// Normalize option for the feature column /// - public Models.NormalizeOption NormalizeFeatures { get; set; } = Models.NormalizeOption.Auto; + public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; /// /// Whether learner should cache input training data /// - public Models.CachingOptions Caching { get; set; } = Models.CachingOptions.Auto; + public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IRegressionOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput @@ -4306,7 +5203,7 @@ public sealed partial class FastTreeBinaryClassifier : Microsoft.ML.Runtime.Entr /// /// Optimization algorithm to be used (GradientDescent, AcceleratedGradientDescent) /// - public Trainers.BoostedTreeArgsOptimizationAlgorithmType OptimizationAlgorithm { get; set; } = Trainers.BoostedTreeArgsOptimizationAlgorithmType.GradientDescent; + public BoostedTreeArgsOptimizationAlgorithmType OptimizationAlgorithm { get; set; } = BoostedTreeArgsOptimizationAlgorithmType.GradientDescent; /// /// Early stopping rule. (Validation set (/valid) is required.) @@ -4471,7 +5368,7 @@ public sealed partial class FastTreeBinaryClassifier : Microsoft.ML.Runtime.Entr /// /// Bundle low population bins. Bundle.None(0): no bundling, Bundle.AggregateLowPopulation(1): Bundle low population, Bundle.Adjacent(2): Neighbor low population bundle. /// - public Trainers.Bundle Bundling { get; set; } = Trainers.Bundle.None; + public Bundle Bundling { get; set; } = Bundle.None; /// /// Maximum number of distinct values (bins) per feature @@ -4521,7 +5418,7 @@ public sealed partial class FastTreeBinaryClassifier : Microsoft.ML.Runtime.Entr public int MinDocumentsInLeafs { get; set; } = 10; /// - /// Number of weak hypotheses in the ensemble + /// Total number of decision trees to create in the ensemble /// [TlcModule.SweepableDiscreteParamAttribute("NumTrees", new object[]{20, 100, 500})] public int NumTrees { get; set; } = 100; @@ -4614,12 +5511,12 @@ public sealed partial class FastTreeBinaryClassifier : Microsoft.ML.Runtime.Entr /// /// Normalize option for the feature column /// - public Models.NormalizeOption NormalizeFeatures { get; set; } = Models.NormalizeOption.Auto; + public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; /// /// Whether learner should cache input training data /// - public Models.CachingOptions Caching { get; set; } = Models.CachingOptions.Auto; + public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IBinaryClassificationOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput @@ -4732,7 +5629,7 @@ public sealed partial class FastTreeRanker : Microsoft.ML.Runtime.EntryPoints.Co /// /// Optimization algorithm to be used (GradientDescent, AcceleratedGradientDescent) /// - public Trainers.BoostedTreeArgsOptimizationAlgorithmType OptimizationAlgorithm { get; set; } = Trainers.BoostedTreeArgsOptimizationAlgorithmType.GradientDescent; + public BoostedTreeArgsOptimizationAlgorithmType OptimizationAlgorithm { get; set; } = BoostedTreeArgsOptimizationAlgorithmType.GradientDescent; /// /// Early stopping rule. (Validation set (/valid) is required.) @@ -4897,7 +5794,7 @@ public sealed partial class FastTreeRanker : Microsoft.ML.Runtime.EntryPoints.Co /// /// Bundle low population bins. Bundle.None(0): no bundling, Bundle.AggregateLowPopulation(1): Bundle low population, Bundle.Adjacent(2): Neighbor low population bundle. /// - public Trainers.Bundle Bundling { get; set; } = Trainers.Bundle.None; + public Bundle Bundling { get; set; } = Bundle.None; /// /// Maximum number of distinct values (bins) per feature @@ -4947,7 +5844,7 @@ public sealed partial class FastTreeRanker : Microsoft.ML.Runtime.EntryPoints.Co public int MinDocumentsInLeafs { get; set; } = 10; /// - /// Number of weak hypotheses in the ensemble + /// Total number of decision trees to create in the ensemble /// [TlcModule.SweepableDiscreteParamAttribute("NumTrees", new object[]{20, 100, 500})] public int NumTrees { get; set; } = 100; @@ -5040,12 +5937,12 @@ public sealed partial class FastTreeRanker : Microsoft.ML.Runtime.EntryPoints.Co /// /// Normalize option for the feature column /// - public Models.NormalizeOption NormalizeFeatures { get; set; } = Models.NormalizeOption.Auto; + public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; /// /// Whether learner should cache input training data /// - public Models.CachingOptions Caching { get; set; } = Models.CachingOptions.Auto; + public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IRankingOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput @@ -5118,7 +6015,7 @@ public sealed partial class FastTreeRegressor : Microsoft.ML.Runtime.EntryPoints /// /// Optimization algorithm to be used (GradientDescent, AcceleratedGradientDescent) /// - public Trainers.BoostedTreeArgsOptimizationAlgorithmType OptimizationAlgorithm { get; set; } = Trainers.BoostedTreeArgsOptimizationAlgorithmType.GradientDescent; + public BoostedTreeArgsOptimizationAlgorithmType OptimizationAlgorithm { get; set; } = BoostedTreeArgsOptimizationAlgorithmType.GradientDescent; /// /// Early stopping rule. (Validation set (/valid) is required.) @@ -5283,7 +6180,7 @@ public sealed partial class FastTreeRegressor : Microsoft.ML.Runtime.EntryPoints /// /// Bundle low population bins. Bundle.None(0): no bundling, Bundle.AggregateLowPopulation(1): Bundle low population, Bundle.Adjacent(2): Neighbor low population bundle. /// - public Trainers.Bundle Bundling { get; set; } = Trainers.Bundle.None; + public Bundle Bundling { get; set; } = Bundle.None; /// /// Maximum number of distinct values (bins) per feature @@ -5333,7 +6230,7 @@ public sealed partial class FastTreeRegressor : Microsoft.ML.Runtime.EntryPoints public int MinDocumentsInLeafs { get; set; } = 10; /// - /// Number of weak hypotheses in the ensemble + /// Total number of decision trees to create in the ensemble /// [TlcModule.SweepableDiscreteParamAttribute("NumTrees", new object[]{20, 100, 500})] public int NumTrees { get; set; } = 100; @@ -5426,12 +6323,12 @@ public sealed partial class FastTreeRegressor : Microsoft.ML.Runtime.EntryPoints /// /// Normalize option for the feature column /// - public Models.NormalizeOption NormalizeFeatures { get; set; } = Models.NormalizeOption.Auto; + public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; /// /// Whether learner should cache input training data /// - public Models.CachingOptions Caching { get; set; } = Models.CachingOptions.Auto; + public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IRegressionOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput @@ -5509,7 +6406,7 @@ public sealed partial class FastTreeTweedieRegressor : Microsoft.ML.Runtime.Entr /// /// Optimization algorithm to be used (GradientDescent, AcceleratedGradientDescent) /// - public Trainers.BoostedTreeArgsOptimizationAlgorithmType OptimizationAlgorithm { get; set; } = Trainers.BoostedTreeArgsOptimizationAlgorithmType.GradientDescent; + public BoostedTreeArgsOptimizationAlgorithmType OptimizationAlgorithm { get; set; } = BoostedTreeArgsOptimizationAlgorithmType.GradientDescent; /// /// Early stopping rule. (Validation set (/valid) is required.) @@ -5674,7 +6571,7 @@ public sealed partial class FastTreeTweedieRegressor : Microsoft.ML.Runtime.Entr /// /// Bundle low population bins. Bundle.None(0): no bundling, Bundle.AggregateLowPopulation(1): Bundle low population, Bundle.Adjacent(2): Neighbor low population bundle. /// - public Trainers.Bundle Bundling { get; set; } = Trainers.Bundle.None; + public Bundle Bundling { get; set; } = Bundle.None; /// /// Maximum number of distinct values (bins) per feature @@ -5724,7 +6621,7 @@ public sealed partial class FastTreeTweedieRegressor : Microsoft.ML.Runtime.Entr public int MinDocumentsInLeafs { get; set; } = 10; /// - /// Number of weak hypotheses in the ensemble + /// Total number of decision trees to create in the ensemble /// [TlcModule.SweepableDiscreteParamAttribute("NumTrees", new object[]{20, 100, 500})] public int NumTrees { get; set; } = 100; @@ -5817,12 +6714,12 @@ public sealed partial class FastTreeTweedieRegressor : Microsoft.ML.Runtime.Entr /// /// Normalize option for the feature column /// - public Models.NormalizeOption NormalizeFeatures { get; set; } = Models.NormalizeOption.Auto; + public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; /// /// Whether learner should cache input training data /// - public Models.CachingOptions Caching { get; set; } = Models.CachingOptions.Auto; + public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IRegressionOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput @@ -5862,6 +6759,130 @@ public FastTreeTweedieRegressorPipelineStep(Output output) } } + namespace Trainers + { + + /// + /// Train a field-aware factorization machine for binary classification + /// + public sealed partial class FieldAwareFactorizationMachineBinaryClassifier : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithLabel, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInput, Microsoft.ML.ILearningPipelineItem + { + + + /// + /// Initial learning rate + /// + [TlcModule.SweepableFloatParamAttribute("LearningRate", 0.001f, 1f, isLogScale:true)] + public float LearningRate { get; set; } = 0.1f; + + /// + /// Number of training iterations + /// + [TlcModule.SweepableLongParamAttribute("Iters", 1, 100)] + public int Iters { get; set; } = 5; + + /// + /// Latent space dimension + /// + [TlcModule.SweepableLongParamAttribute("LatentDim", 4, 100)] + public int LatentDim { get; set; } = 20; + + /// + /// Regularization coefficient of linear weights + /// + [TlcModule.SweepableFloatParamAttribute("LambdaLinear", 1E-08f, 1f, isLogScale:true)] + public float LambdaLinear { get; set; } = 0.0001f; + + /// + /// Regularization coefficient of latent weights + /// + [TlcModule.SweepableFloatParamAttribute("LambdaLatent", 1E-08f, 1f, isLogScale:true)] + public float LambdaLatent { get; set; } = 0.0001f; + + /// + /// Whether to normalize the input vectors so that the concatenation of all fields' feature vectors is unit-length + /// + public bool Norm { get; set; } = true; + + /// + /// Whether to shuffle for each training iteration + /// + public bool Shuffle { get; set; } = true; + + /// + /// Report traning progress or not + /// + public bool Verbose { get; set; } = true; + + /// + /// Radius of initial latent factors + /// + [TlcModule.SweepableFloatParamAttribute("Radius", 0.1f, 1f)] + public float Radius { get; set; } = 0.5f; + + /// + /// Column to use for labels + /// + public string LabelColumn { get; set; } = "Label"; + + /// + /// The data to be used for training + /// + public Var TrainingData { get; set; } = new Var(); + + /// + /// Column to use for features + /// + public string FeatureColumn { get; set; } = "Features"; + + /// + /// Normalize option for the feature column + /// + public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; + + /// + /// Whether learner should cache input training data + /// + public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; + + + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IBinaryClassificationOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput + { + /// + /// The trained model + /// + public Var PredictorModel { get; set; } = new Var(); + + } + public Var GetInputData() => TrainingData; + + public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) + { + if (previousStep != null) + { + if (!(previousStep is ILearningPipelineDataStep dataStep)) + { + throw new InvalidOperationException($"{ nameof(FieldAwareFactorizationMachineBinaryClassifier)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + } + + TrainingData = dataStep.Data; + } + Output output = experiment.Add(this); + return new FieldAwareFactorizationMachineBinaryClassifierPipelineStep(output); + } + + private class FieldAwareFactorizationMachineBinaryClassifierPipelineStep : ILearningPipelinePredictorStep + { + public FieldAwareFactorizationMachineBinaryClassifierPipelineStep(Output output) + { + Model = output.PredictorModel; + } + + public Var Model { get; } + } + } + } + namespace Trainers { @@ -5974,12 +6995,12 @@ public sealed partial class GeneralizedAdditiveModelBinaryClassifier : Microsoft /// /// Normalize option for the feature column /// - public Models.NormalizeOption NormalizeFeatures { get; set; } = Models.NormalizeOption.Auto; + public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; /// /// Whether learner should cache input training data /// - public Models.CachingOptions Caching { get; set; } = Models.CachingOptions.Auto; + public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IBinaryClassificationOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput @@ -6115,12 +7136,12 @@ public sealed partial class GeneralizedAdditiveModelRegressor : Microsoft.ML.Run /// /// Normalize option for the feature column /// - public Models.NormalizeOption NormalizeFeatures { get; set; } = Models.NormalizeOption.Auto; + public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; /// /// Whether learner should cache input training data /// - public Models.CachingOptions Caching { get; set; } = Models.CachingOptions.Auto; + public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IRegressionOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput @@ -6173,7 +7194,7 @@ public enum KMeansPlusPlusTrainerInitAlgorithm /// /// K-means is a popular clustering algorithm. With K-means, the data is clustered into a specified number of clusters in order to minimize the within-cluster sum of squares. K-means++ improves upon K-means by using a better method for choosing the initial cluster centers. /// - public sealed partial class KMeansPlusPlusClusterer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class KMeansPlusPlusClusterer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.IUnsupervisedTrainerWithWeight, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInput, Microsoft.ML.ILearningPipelineItem { @@ -6186,7 +7207,7 @@ public sealed partial class KMeansPlusPlusClusterer : Microsoft.ML.Runtime.Entry /// /// Cluster initialization algorithm /// - public Trainers.KMeansPlusPlusTrainerInitAlgorithm InitAlgorithm { get; set; } = Trainers.KMeansPlusPlusTrainerInitAlgorithm.KMeansParallel; + public KMeansPlusPlusTrainerInitAlgorithm InitAlgorithm { get; set; } = KMeansPlusPlusTrainerInitAlgorithm.KMeansParallel; /// /// Tolerance parameter for trainer convergence. Lower = slower, more accurate @@ -6208,6 +7229,11 @@ public sealed partial class KMeansPlusPlusClusterer : Microsoft.ML.Runtime.Entry /// public int? NumThreads { get; set; } + /// + /// Column to use for example weight + /// + public Microsoft.ML.Runtime.EntryPoints.Optional WeightColumn { get; set; } + /// /// The data to be used for training /// @@ -6221,12 +7247,12 @@ public sealed partial class KMeansPlusPlusClusterer : Microsoft.ML.Runtime.Entry /// /// Normalize option for the feature column /// - public Models.NormalizeOption NormalizeFeatures { get; set; } = Models.NormalizeOption.Auto; + public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; /// /// Whether learner should cache input training data /// - public Models.CachingOptions Caching { get; set; } = Models.CachingOptions.Auto; + public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IClusteringOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput @@ -6268,75 +7294,159 @@ public KMeansPlusPlusClustererPipelineStep(Output output) namespace Trainers { + public enum LightGbmArgumentsEvalMetricType + { + DefaultMetric = 0, + Rmse = 1, + Mae = 2, + Logloss = 3, + Error = 4, + Merror = 5, + Mlogloss = 6, + Auc = 7, + Ndcg = 8, + Map = 9 + } + /// - /// Train a linear SVM. + /// Train an LightGBM binary class model /// - public sealed partial class LinearSvmBinaryClassifier : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithLabel, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class LightGbmBinaryClassifier : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithGroupId, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithWeight, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithLabel, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInput, Microsoft.ML.ILearningPipelineItem { /// - /// Regularizer constant + /// Number of iterations. /// - [TlcModule.SweepableFloatParamAttribute("Lambda", 1E-05f, 0.1f, stepSize:10, isLogScale:true)] - public float Lambda { get; set; } = 0.001f; + [TlcModule.SweepableDiscreteParamAttribute("NumBoostRound", new object[]{10, 20, 50, 100, 150, 200})] + public int NumBoostRound { get; set; } = 100; /// - /// Batch size + /// Shrinkage rate for trees, used to prevent over-fitting. Range: (0,1]. /// - public int BatchSize { get; set; } = 1; + [TlcModule.SweepableFloatParamAttribute("LearningRate", 0.025f, 0.4f, isLogScale:true)] + public double? LearningRate { get; set; } /// - /// Perform projection to unit-ball? Typically used with batch size > 1. + /// Maximum leaves for trees. /// - [TlcModule.SweepableDiscreteParamAttribute("PerformProjection", new object[]{false, true})] - public bool PerformProjection { get; set; } = false; + [TlcModule.SweepableLongParamAttribute("NumLeaves", 2, 128, stepSize:4, isLogScale:true)] + public int? NumLeaves { get; set; } /// - /// No bias + /// Minimum number of instances needed in a child. /// - [TlcModule.SweepableDiscreteParamAttribute("NoBias", new object[]{false, true})] - public bool NoBias { get; set; } = false; + [TlcModule.SweepableDiscreteParamAttribute("MinDataPerLeaf", new object[]{1, 10, 20, 50})] + public int? MinDataPerLeaf { get; set; } /// - /// The calibrator kind to apply to the predictor. Specify null for no calibration + /// Max number of bucket bin for features. + /// + public int MaxBin { get; set; } = 255; + + /// + /// Which booster to use, can be gbtree, gblinear or dart. gbtree and dart use tree based model while gblinear uses linear function. /// [JsonConverter(typeof(ComponentSerializer))] - public CalibratorTrainer Calibrator { get; set; } = new PlattCalibratorCalibratorTrainer(); + public BoosterParameterFunction Booster { get; set; } = new GbdtBoosterParameterFunction(); /// - /// The maximum number of examples to use when training the calibrator + /// Verbose /// - public int MaxCalibrationExamples { get; set; } = 1000000; + public bool VerboseEval { get; set; } = false; /// - /// Number of iterations + /// Printing running messages. /// - [TlcModule.SweepableLongParamAttribute("NumIterations", 1, 100, stepSize:10, isLogScale:true)] - public int NumIterations { get; set; } = 1; + public bool Silent { get; set; } = true; /// - /// Initial Weights and bias, comma-separated + /// Number of parallel threads used to run LightGBM. /// - public string InitialWeights { get; set; } + public int? NThread { get; set; } /// - /// Init weights diameter + /// Evaluation metrics. /// - [TlcModule.SweepableFloatParamAttribute("InitWtsDiameter", 0f, 1f, numSteps:5)] - public float InitWtsDiameter { get; set; } + public LightGbmArgumentsEvalMetricType EvalMetric { get; set; } = LightGbmArgumentsEvalMetricType.DefaultMetric; /// - /// Whether to shuffle for each training iteration + /// Use softmax loss for the multi classification. /// - [TlcModule.SweepableDiscreteParamAttribute("Shuffle", new object[]{false, true})] - public bool Shuffle { get; set; } = true; + [TlcModule.SweepableDiscreteParamAttribute("UseSoftmax", new object[]{true, false})] + public bool? UseSoftmax { get; set; } /// - /// Size of cache when trained in Scope + /// Rounds of early stopping, 0 will disable it. /// - public int StreamingCacheSize { get; set; } = 1000000; + public int EarlyStoppingRound { get; set; } + + /// + /// Comma seperated list of gains associated to each relevance label. + /// + public string CustomGains { get; set; } = "0,3,7,15,31,63,127,255,511,1023,2047,4095"; + + /// + /// Number of entries in a batch when loading data. + /// + public int BatchSize { get; set; } = 1048576; + + /// + /// Enable categorical split or not. + /// + [TlcModule.SweepableDiscreteParamAttribute("UseCat", new object[]{true, false})] + public bool? UseCat { get; set; } + + /// + /// Enable missing value auto infer or not. + /// + [TlcModule.SweepableDiscreteParamAttribute("UseMissing", new object[]{true, false})] + public bool UseMissing { get; set; } = false; + + /// + /// Min number of instances per categorical group. + /// + [TlcModule.Range(Inf = 0, Max = 2147483647)] + [TlcModule.SweepableDiscreteParamAttribute("MinDataPerGroup", new object[]{10, 50, 100, 200})] + public int MinDataPerGroup { get; set; } = 100; + + /// + /// Max number of categorical thresholds. + /// + [TlcModule.Range(Inf = 0, Max = 2147483647)] + [TlcModule.SweepableDiscreteParamAttribute("MaxCatThreshold", new object[]{8, 16, 32, 64})] + public int MaxCatThreshold { get; set; } = 32; + + /// + /// Lapalace smooth term in categorical feature spilt. Avoid the bias of small categories. + /// + [TlcModule.Range(Min = 0d)] + [TlcModule.SweepableDiscreteParamAttribute("CatSmooth", new object[]{1, 10, 20})] + public double CatSmooth { get; set; } = 10d; + + /// + /// L2 Regularization for categorical split. + /// + [TlcModule.Range(Min = 0d)] + [TlcModule.SweepableDiscreteParamAttribute("CatL2", new object[]{0.1f, 0.5f, 1, 5, 10})] + public double CatL2 { get; set; } = 10d; + + /// + /// Parallel LightGBM Learning Algorithm + /// + [JsonConverter(typeof(ComponentSerializer))] + public ParallelLightGBM ParallelTrainer { get; set; } = new SingleParallelLightGBM(); + + /// + /// Column to use for example groupId + /// + public Microsoft.ML.Runtime.EntryPoints.Optional GroupIdColumn { get; set; } + + /// + /// Column to use for example weight + /// + public Microsoft.ML.Runtime.EntryPoints.Optional WeightColumn { get; set; } /// /// Column to use for labels @@ -6356,12 +7466,12 @@ public sealed partial class LinearSvmBinaryClassifier : Microsoft.ML.Runtime.Ent /// /// Normalize option for the feature column /// - public Models.NormalizeOption NormalizeFeatures { get; set; } = Models.NormalizeOption.Auto; + public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; /// /// Whether learner should cache input training data /// - public Models.CachingOptions Caching { get; set; } = Models.CachingOptions.Auto; + public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IBinaryClassificationOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput @@ -6380,18 +7490,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(LinearSvmBinaryClassifier)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(LightGbmBinaryClassifier)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } TrainingData = dataStep.Data; } Output output = experiment.Add(this); - return new LinearSvmBinaryClassifierPipelineStep(output); + return new LightGbmBinaryClassifierPipelineStep(output); } - private class LinearSvmBinaryClassifierPipelineStep : ILearningPipelinePredictorStep + private class LightGbmBinaryClassifierPipelineStep : ILearningPipelinePredictorStep { - public LinearSvmBinaryClassifierPipelineStep(Output output) + public LightGbmBinaryClassifierPipelineStep(Output output) { Model = output.PredictorModel; } @@ -6405,143 +7515,198 @@ namespace Trainers { /// - /// Train a logistic regression binary model + /// Train an LightGBM multi class model /// - public sealed partial class LogisticRegressionBinaryClassifier : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithWeight, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithLabel, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class LightGbmClassifier : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithGroupId, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithWeight, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithLabel, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInput, Microsoft.ML.ILearningPipelineItem { /// - /// Show statistics of training examples. + /// Number of iterations. /// - public bool ShowTrainingStats { get; set; } = false; + [TlcModule.SweepableDiscreteParamAttribute("NumBoostRound", new object[]{10, 20, 50, 100, 150, 200})] + public int NumBoostRound { get; set; } = 100; /// - /// L2 regularization weight + /// Shrinkage rate for trees, used to prevent over-fitting. Range: (0,1]. /// - [TlcModule.SweepableFloatParamAttribute("L2Weight", 0f, 1f, numSteps:4)] - public float L2Weight { get; set; } = 1f; + [TlcModule.SweepableFloatParamAttribute("LearningRate", 0.025f, 0.4f, isLogScale:true)] + public double? LearningRate { get; set; } /// - /// L1 regularization weight + /// Maximum leaves for trees. /// - [TlcModule.SweepableFloatParamAttribute("L1Weight", 0f, 1f, numSteps:4)] - public float L1Weight { get; set; } = 1f; + [TlcModule.SweepableLongParamAttribute("NumLeaves", 2, 128, stepSize:4, isLogScale:true)] + public int? NumLeaves { get; set; } /// - /// Tolerance parameter for optimization convergence. Lower = slower, more accurate + /// Minimum number of instances needed in a child. /// - [TlcModule.SweepableDiscreteParamAttribute("OptTol", new object[]{0.0001f, 1E-07f})] - public float OptTol { get; set; } = 1E-07f; + [TlcModule.SweepableDiscreteParamAttribute("MinDataPerLeaf", new object[]{1, 10, 20, 50})] + public int? MinDataPerLeaf { get; set; } /// - /// Memory size for L-BFGS. Lower=faster, less accurate + /// Max number of bucket bin for features. /// - [TlcModule.SweepableDiscreteParamAttribute("MemorySize", new object[]{5, 20, 50})] - public int MemorySize { get; set; } = 20; + public int MaxBin { get; set; } = 255; /// - /// Maximum iterations. + /// Which booster to use, can be gbtree, gblinear or dart. gbtree and dart use tree based model while gblinear uses linear function. /// - [TlcModule.SweepableLongParamAttribute("MaxIterations", 1, 2147483647)] - public int MaxIterations { get; set; } = 2147483647; + [JsonConverter(typeof(ComponentSerializer))] + public BoosterParameterFunction Booster { get; set; } = new GbdtBoosterParameterFunction(); /// - /// Run SGD to initialize LR weights, converging to this tolerance + /// Verbose /// - public float SgdInitializationTolerance { get; set; } + public bool VerboseEval { get; set; } = false; /// - /// If set to true, produce no output during training. + /// Printing running messages. /// - public bool Quiet { get; set; } = false; + public bool Silent { get; set; } = true; /// - /// Init weights diameter + /// Number of parallel threads used to run LightGBM. /// - [TlcModule.SweepableFloatParamAttribute("InitWtsDiameter", 0f, 1f, numSteps:5)] - public float InitWtsDiameter { get; set; } + public int? NThread { get; set; } /// - /// Whether or not to use threads. Default is true + /// Evaluation metrics. /// - public bool UseThreads { get; set; } = true; + public LightGbmArgumentsEvalMetricType EvalMetric { get; set; } = LightGbmArgumentsEvalMetricType.DefaultMetric; /// - /// Number of threads + /// Use softmax loss for the multi classification. /// - public int? NumThreads { get; set; } + [TlcModule.SweepableDiscreteParamAttribute("UseSoftmax", new object[]{true, false})] + public bool? UseSoftmax { get; set; } /// - /// Force densification of the internal optimization vectors + /// Rounds of early stopping, 0 will disable it. /// - [TlcModule.SweepableDiscreteParamAttribute("DenseOptimizer", new object[]{false, true})] - public bool DenseOptimizer { get; set; } = false; + public int EarlyStoppingRound { get; set; } /// - /// Enforce non-negative weights + /// Comma seperated list of gains associated to each relevance label. /// - public bool EnforceNonNegativity { get; set; } = false; + public string CustomGains { get; set; } = "0,3,7,15,31,63,127,255,511,1023,2047,4095"; /// - /// Column to use for example weight + /// Number of entries in a batch when loading data. /// - public Microsoft.ML.Runtime.EntryPoints.Optional WeightColumn { get; set; } + public int BatchSize { get; set; } = 1048576; /// - /// Column to use for labels + /// Enable categorical split or not. /// - public string LabelColumn { get; set; } = "Label"; + [TlcModule.SweepableDiscreteParamAttribute("UseCat", new object[]{true, false})] + public bool? UseCat { get; set; } /// - /// The data to be used for training + /// Enable missing value auto infer or not. /// - public Var TrainingData { get; set; } = new Var(); + [TlcModule.SweepableDiscreteParamAttribute("UseMissing", new object[]{true, false})] + public bool UseMissing { get; set; } = false; /// - /// Column to use for features + /// Min number of instances per categorical group. /// - public string FeatureColumn { get; set; } = "Features"; + [TlcModule.Range(Inf = 0, Max = 2147483647)] + [TlcModule.SweepableDiscreteParamAttribute("MinDataPerGroup", new object[]{10, 50, 100, 200})] + public int MinDataPerGroup { get; set; } = 100; /// - /// Normalize option for the feature column + /// Max number of categorical thresholds. /// - public Models.NormalizeOption NormalizeFeatures { get; set; } = Models.NormalizeOption.Auto; + [TlcModule.Range(Inf = 0, Max = 2147483647)] + [TlcModule.SweepableDiscreteParamAttribute("MaxCatThreshold", new object[]{8, 16, 32, 64})] + public int MaxCatThreshold { get; set; } = 32; /// - /// Whether learner should cache input training data + /// Lapalace smooth term in categorical feature spilt. Avoid the bias of small categories. /// - public Models.CachingOptions Caching { get; set; } = Models.CachingOptions.Auto; + [TlcModule.Range(Min = 0d)] + [TlcModule.SweepableDiscreteParamAttribute("CatSmooth", new object[]{1, 10, 20})] + public double CatSmooth { get; set; } = 10d; + /// + /// L2 Regularization for categorical split. + /// + [TlcModule.Range(Min = 0d)] + [TlcModule.SweepableDiscreteParamAttribute("CatL2", new object[]{0.1f, 0.5f, 1, 5, 10})] + public double CatL2 { get; set; } = 10d; - public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IBinaryClassificationOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput - { - /// - /// The trained model - /// - public Var PredictorModel { get; set; } = new Var(); + /// + /// Parallel LightGBM Learning Algorithm + /// + [JsonConverter(typeof(ComponentSerializer))] + public ParallelLightGBM ParallelTrainer { get; set; } = new SingleParallelLightGBM(); - } - public Var GetInputData() => TrainingData; - + /// + /// Column to use for example groupId + /// + public Microsoft.ML.Runtime.EntryPoints.Optional GroupIdColumn { get; set; } + + /// + /// Column to use for example weight + /// + public Microsoft.ML.Runtime.EntryPoints.Optional WeightColumn { get; set; } + + /// + /// Column to use for labels + /// + public string LabelColumn { get; set; } = "Label"; + + /// + /// The data to be used for training + /// + public Var TrainingData { get; set; } = new Var(); + + /// + /// Column to use for features + /// + public string FeatureColumn { get; set; } = "Features"; + + /// + /// Normalize option for the feature column + /// + public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; + + /// + /// Whether learner should cache input training data + /// + public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; + + + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IMulticlassClassificationOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput + { + /// + /// The trained model + /// + public Var PredictorModel { get; set; } = new Var(); + + } + public Var GetInputData() => TrainingData; + public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) { if (previousStep != null) { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(LogisticRegressionBinaryClassifier)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(LightGbmClassifier)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } TrainingData = dataStep.Data; } Output output = experiment.Add(this); - return new LogisticRegressionBinaryClassifierPipelineStep(output); + return new LightGbmClassifierPipelineStep(output); } - private class LogisticRegressionBinaryClassifierPipelineStep : ILearningPipelinePredictorStep + private class LightGbmClassifierPipelineStep : ILearningPipelinePredictorStep { - public LogisticRegressionBinaryClassifierPipelineStep(Output output) + public LightGbmClassifierPipelineStep(Output output) { Model = output.PredictorModel; } @@ -6555,161 +7720,143 @@ namespace Trainers { /// - /// Train a logistic regression multi class model + /// Train an LightGBM ranking model /// - public sealed partial class LogisticRegressionClassifier : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithWeight, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithLabel, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class LightGbmRanker : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithGroupId, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithWeight, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithLabel, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInput, Microsoft.ML.ILearningPipelineItem { /// - /// Show statistics of training examples. + /// Number of iterations. /// - public bool ShowTrainingStats { get; set; } = false; + [TlcModule.SweepableDiscreteParamAttribute("NumBoostRound", new object[]{10, 20, 50, 100, 150, 200})] + public int NumBoostRound { get; set; } = 100; /// - /// L2 regularization weight + /// Shrinkage rate for trees, used to prevent over-fitting. Range: (0,1]. /// - [TlcModule.SweepableFloatParamAttribute("L2Weight", 0f, 1f, numSteps:4)] - public float L2Weight { get; set; } = 1f; + [TlcModule.SweepableFloatParamAttribute("LearningRate", 0.025f, 0.4f, isLogScale:true)] + public double? LearningRate { get; set; } /// - /// L1 regularization weight + /// Maximum leaves for trees. /// - [TlcModule.SweepableFloatParamAttribute("L1Weight", 0f, 1f, numSteps:4)] - public float L1Weight { get; set; } = 1f; + [TlcModule.SweepableLongParamAttribute("NumLeaves", 2, 128, stepSize:4, isLogScale:true)] + public int? NumLeaves { get; set; } /// - /// Tolerance parameter for optimization convergence. Lower = slower, more accurate + /// Minimum number of instances needed in a child. /// - [TlcModule.SweepableDiscreteParamAttribute("OptTol", new object[]{0.0001f, 1E-07f})] - public float OptTol { get; set; } = 1E-07f; + [TlcModule.SweepableDiscreteParamAttribute("MinDataPerLeaf", new object[]{1, 10, 20, 50})] + public int? MinDataPerLeaf { get; set; } /// - /// Memory size for L-BFGS. Lower=faster, less accurate + /// Max number of bucket bin for features. /// - [TlcModule.SweepableDiscreteParamAttribute("MemorySize", new object[]{5, 20, 50})] - public int MemorySize { get; set; } = 20; + public int MaxBin { get; set; } = 255; /// - /// Maximum iterations. + /// Which booster to use, can be gbtree, gblinear or dart. gbtree and dart use tree based model while gblinear uses linear function. /// - [TlcModule.SweepableLongParamAttribute("MaxIterations", 1, 2147483647)] - public int MaxIterations { get; set; } = 2147483647; + [JsonConverter(typeof(ComponentSerializer))] + public BoosterParameterFunction Booster { get; set; } = new GbdtBoosterParameterFunction(); /// - /// Run SGD to initialize LR weights, converging to this tolerance + /// Verbose /// - public float SgdInitializationTolerance { get; set; } + public bool VerboseEval { get; set; } = false; /// - /// If set to true, produce no output during training. + /// Printing running messages. /// - public bool Quiet { get; set; } = false; + public bool Silent { get; set; } = true; /// - /// Init weights diameter + /// Number of parallel threads used to run LightGBM. /// - [TlcModule.SweepableFloatParamAttribute("InitWtsDiameter", 0f, 1f, numSteps:5)] - public float InitWtsDiameter { get; set; } + public int? NThread { get; set; } /// - /// Whether or not to use threads. Default is true + /// Evaluation metrics. /// - public bool UseThreads { get; set; } = true; + public LightGbmArgumentsEvalMetricType EvalMetric { get; set; } = LightGbmArgumentsEvalMetricType.DefaultMetric; /// - /// Number of threads + /// Use softmax loss for the multi classification. /// - public int? NumThreads { get; set; } + [TlcModule.SweepableDiscreteParamAttribute("UseSoftmax", new object[]{true, false})] + public bool? UseSoftmax { get; set; } /// - /// Force densification of the internal optimization vectors + /// Rounds of early stopping, 0 will disable it. /// - [TlcModule.SweepableDiscreteParamAttribute("DenseOptimizer", new object[]{false, true})] - public bool DenseOptimizer { get; set; } = false; + public int EarlyStoppingRound { get; set; } /// - /// Enforce non-negative weights + /// Comma seperated list of gains associated to each relevance label. /// - public bool EnforceNonNegativity { get; set; } = false; + public string CustomGains { get; set; } = "0,3,7,15,31,63,127,255,511,1023,2047,4095"; /// - /// Column to use for example weight + /// Number of entries in a batch when loading data. /// - public Microsoft.ML.Runtime.EntryPoints.Optional WeightColumn { get; set; } + public int BatchSize { get; set; } = 1048576; /// - /// Column to use for labels + /// Enable categorical split or not. /// - public string LabelColumn { get; set; } = "Label"; + [TlcModule.SweepableDiscreteParamAttribute("UseCat", new object[]{true, false})] + public bool? UseCat { get; set; } /// - /// The data to be used for training + /// Enable missing value auto infer or not. /// - public Var TrainingData { get; set; } = new Var(); + [TlcModule.SweepableDiscreteParamAttribute("UseMissing", new object[]{true, false})] + public bool UseMissing { get; set; } = false; /// - /// Column to use for features + /// Min number of instances per categorical group. /// - public string FeatureColumn { get; set; } = "Features"; + [TlcModule.Range(Inf = 0, Max = 2147483647)] + [TlcModule.SweepableDiscreteParamAttribute("MinDataPerGroup", new object[]{10, 50, 100, 200})] + public int MinDataPerGroup { get; set; } = 100; /// - /// Normalize option for the feature column + /// Max number of categorical thresholds. /// - public Models.NormalizeOption NormalizeFeatures { get; set; } = Models.NormalizeOption.Auto; + [TlcModule.Range(Inf = 0, Max = 2147483647)] + [TlcModule.SweepableDiscreteParamAttribute("MaxCatThreshold", new object[]{8, 16, 32, 64})] + public int MaxCatThreshold { get; set; } = 32; /// - /// Whether learner should cache input training data + /// Lapalace smooth term in categorical feature spilt. Avoid the bias of small categories. /// - public Models.CachingOptions Caching { get; set; } = Models.CachingOptions.Auto; - - - public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IMulticlassClassificationOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput - { - /// - /// The trained model - /// - public Var PredictorModel { get; set; } = new Var(); - - } - public Var GetInputData() => TrainingData; - - public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) - { - if (previousStep != null) - { - if (!(previousStep is ILearningPipelineDataStep dataStep)) - { - throw new InvalidOperationException($"{ nameof(LogisticRegressionClassifier)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); - } - - TrainingData = dataStep.Data; - } - Output output = experiment.Add(this); - return new LogisticRegressionClassifierPipelineStep(output); - } - - private class LogisticRegressionClassifierPipelineStep : ILearningPipelinePredictorStep - { - public LogisticRegressionClassifierPipelineStep(Output output) - { - Model = output.PredictorModel; - } + [TlcModule.Range(Min = 0d)] + [TlcModule.SweepableDiscreteParamAttribute("CatSmooth", new object[]{1, 10, 20})] + public double CatSmooth { get; set; } = 10d; - public Var Model { get; } - } - } - } + /// + /// L2 Regularization for categorical split. + /// + [TlcModule.Range(Min = 0d)] + [TlcModule.SweepableDiscreteParamAttribute("CatL2", new object[]{0.1f, 0.5f, 1, 5, 10})] + public double CatL2 { get; set; } = 10d; - namespace Trainers - { + /// + /// Parallel LightGBM Learning Algorithm + /// + [JsonConverter(typeof(ComponentSerializer))] + public ParallelLightGBM ParallelTrainer { get; set; } = new SingleParallelLightGBM(); - /// - /// Train a MultiClassNaiveBayesTrainer. - /// - public sealed partial class NaiveBayesClassifier : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithLabel, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInput, Microsoft.ML.ILearningPipelineItem - { + /// + /// Column to use for example groupId + /// + public Microsoft.ML.Runtime.EntryPoints.Optional GroupIdColumn { get; set; } + /// + /// Column to use for example weight + /// + public Microsoft.ML.Runtime.EntryPoints.Optional WeightColumn { get; set; } /// /// Column to use for labels @@ -6729,15 +7876,15 @@ public sealed partial class NaiveBayesClassifier : Microsoft.ML.Runtime.EntryPoi /// /// Normalize option for the feature column /// - public Models.NormalizeOption NormalizeFeatures { get; set; } = Models.NormalizeOption.Auto; + public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; /// /// Whether learner should cache input training data /// - public Models.CachingOptions Caching { get; set; } = Models.CachingOptions.Auto; + public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; - public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IMulticlassClassificationOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IRankingOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput { /// /// The trained model @@ -6753,18 +7900,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(NaiveBayesClassifier)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(LightGbmRanker)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } TrainingData = dataStep.Data; } Output output = experiment.Add(this); - return new NaiveBayesClassifierPipelineStep(output); + return new LightGbmRankerPipelineStep(output); } - private class NaiveBayesClassifierPipelineStep : ILearningPipelinePredictorStep + private class LightGbmRankerPipelineStep : ILearningPipelinePredictorStep { - public NaiveBayesClassifierPipelineStep(Output output) + public LightGbmRankerPipelineStep(Output output) { Model = output.PredictorModel; } @@ -6778,93 +7925,143 @@ namespace Trainers { /// - /// Train a Online gradient descent perceptron. + /// LightGBM Regression /// - public sealed partial class OnlineGradientDescentRegressor : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithLabel, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class LightGbmRegressor : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithGroupId, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithWeight, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithLabel, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInput, Microsoft.ML.ILearningPipelineItem { /// - /// Loss Function + /// Number of iterations. + /// + [TlcModule.SweepableDiscreteParamAttribute("NumBoostRound", new object[]{10, 20, 50, 100, 150, 200})] + public int NumBoostRound { get; set; } = 100; + + /// + /// Shrinkage rate for trees, used to prevent over-fitting. Range: (0,1]. + /// + [TlcModule.SweepableFloatParamAttribute("LearningRate", 0.025f, 0.4f, isLogScale:true)] + public double? LearningRate { get; set; } + + /// + /// Maximum leaves for trees. + /// + [TlcModule.SweepableLongParamAttribute("NumLeaves", 2, 128, stepSize:4, isLogScale:true)] + public int? NumLeaves { get; set; } + + /// + /// Minimum number of instances needed in a child. + /// + [TlcModule.SweepableDiscreteParamAttribute("MinDataPerLeaf", new object[]{1, 10, 20, 50})] + public int? MinDataPerLeaf { get; set; } + + /// + /// Max number of bucket bin for features. + /// + public int MaxBin { get; set; } = 255; + + /// + /// Which booster to use, can be gbtree, gblinear or dart. gbtree and dart use tree based model while gblinear uses linear function. /// [JsonConverter(typeof(ComponentSerializer))] - public RegressionLossFunction LossFunction { get; set; } = new SquaredLossRegressionLossFunction(); + public BoosterParameterFunction Booster { get; set; } = new GbdtBoosterParameterFunction(); /// - /// Learning rate + /// Verbose /// - [TlcModule.SweepableDiscreteParamAttribute("LearningRate", new object[]{0.01f, 0.1f, 0.5f, 1f})] - public float LearningRate { get; set; } = 0.1f; + public bool VerboseEval { get; set; } = false; /// - /// Decrease learning rate + /// Printing running messages. /// - [TlcModule.SweepableDiscreteParamAttribute("DecreaseLearningRate", new object[]{false, true})] - public bool DecreaseLearningRate { get; set; } = true; + public bool Silent { get; set; } = true; /// - /// Number of examples after which weights will be reset to the current average + /// Number of parallel threads used to run LightGBM. /// - public long? ResetWeightsAfterXExamples { get; set; } + public int? NThread { get; set; } /// - /// Instead of updating averaged weights on every example, only update when loss is nonzero + /// Evaluation metrics. /// - public bool DoLazyUpdates { get; set; } = true; + public LightGbmArgumentsEvalMetricType EvalMetric { get; set; } = LightGbmArgumentsEvalMetricType.DefaultMetric; /// - /// L2 Regularization Weight + /// Use softmax loss for the multi classification. /// - [TlcModule.SweepableFloatParamAttribute("L2RegularizerWeight", 0f, 0.5f)] - public float L2RegularizerWeight { get; set; } + [TlcModule.SweepableDiscreteParamAttribute("UseSoftmax", new object[]{true, false})] + public bool? UseSoftmax { get; set; } /// - /// Extra weight given to more recent updates + /// Rounds of early stopping, 0 will disable it. /// - public float RecencyGain { get; set; } + public int EarlyStoppingRound { get; set; } /// - /// Whether Recency Gain is multiplicative (vs. additive) + /// Comma seperated list of gains associated to each relevance label. /// - public bool RecencyGainMulti { get; set; } = false; + public string CustomGains { get; set; } = "0,3,7,15,31,63,127,255,511,1023,2047,4095"; /// - /// Do averaging? + /// Number of entries in a batch when loading data. /// - public bool Averaged { get; set; } = true; + public int BatchSize { get; set; } = 1048576; /// - /// The inexactness tolerance for averaging + /// Enable categorical split or not. /// - public float AveragedTolerance { get; set; } = 0.01f; + [TlcModule.SweepableDiscreteParamAttribute("UseCat", new object[]{true, false})] + public bool? UseCat { get; set; } /// - /// Number of iterations + /// Enable missing value auto infer or not. /// - [TlcModule.SweepableLongParamAttribute("NumIterations", 1, 100, stepSize:10, isLogScale:true)] - public int NumIterations { get; set; } = 1; + [TlcModule.SweepableDiscreteParamAttribute("UseMissing", new object[]{true, false})] + public bool UseMissing { get; set; } = false; /// - /// Initial Weights and bias, comma-separated + /// Min number of instances per categorical group. /// - public string InitialWeights { get; set; } + [TlcModule.Range(Inf = 0, Max = 2147483647)] + [TlcModule.SweepableDiscreteParamAttribute("MinDataPerGroup", new object[]{10, 50, 100, 200})] + public int MinDataPerGroup { get; set; } = 100; /// - /// Init weights diameter + /// Max number of categorical thresholds. /// - [TlcModule.SweepableFloatParamAttribute("InitWtsDiameter", 0f, 1f, numSteps:5)] - public float InitWtsDiameter { get; set; } + [TlcModule.Range(Inf = 0, Max = 2147483647)] + [TlcModule.SweepableDiscreteParamAttribute("MaxCatThreshold", new object[]{8, 16, 32, 64})] + public int MaxCatThreshold { get; set; } = 32; /// - /// Whether to shuffle for each training iteration + /// Lapalace smooth term in categorical feature spilt. Avoid the bias of small categories. /// - [TlcModule.SweepableDiscreteParamAttribute("Shuffle", new object[]{false, true})] - public bool Shuffle { get; set; } = true; + [TlcModule.Range(Min = 0d)] + [TlcModule.SweepableDiscreteParamAttribute("CatSmooth", new object[]{1, 10, 20})] + public double CatSmooth { get; set; } = 10d; /// - /// Size of cache when trained in Scope + /// L2 Regularization for categorical split. /// - public int StreamingCacheSize { get; set; } = 1000000; + [TlcModule.Range(Min = 0d)] + [TlcModule.SweepableDiscreteParamAttribute("CatL2", new object[]{0.1f, 0.5f, 1, 5, 10})] + public double CatL2 { get; set; } = 10d; + + /// + /// Parallel LightGBM Learning Algorithm + /// + [JsonConverter(typeof(ComponentSerializer))] + public ParallelLightGBM ParallelTrainer { get; set; } = new SingleParallelLightGBM(); + + /// + /// Column to use for example groupId + /// + public Microsoft.ML.Runtime.EntryPoints.Optional GroupIdColumn { get; set; } + + /// + /// Column to use for example weight + /// + public Microsoft.ML.Runtime.EntryPoints.Optional WeightColumn { get; set; } /// /// Column to use for labels @@ -6884,12 +8081,12 @@ public sealed partial class OnlineGradientDescentRegressor : Microsoft.ML.Runtim /// /// Normalize option for the feature column /// - public Models.NormalizeOption NormalizeFeatures { get; set; } = Models.NormalizeOption.Auto; + public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; /// /// Whether learner should cache input training data /// - public Models.CachingOptions Caching { get; set; } = Models.CachingOptions.Auto; + public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IRegressionOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput @@ -6908,18 +8105,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(OnlineGradientDescentRegressor)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(LightGbmRegressor)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } TrainingData = dataStep.Data; } Output output = experiment.Add(this); - return new OnlineGradientDescentRegressorPipelineStep(output); + return new LightGbmRegressorPipelineStep(output); } - private class OnlineGradientDescentRegressorPipelineStep : ILearningPipelinePredictorStep + private class LightGbmRegressorPipelineStep : ILearningPipelinePredictorStep { - public OnlineGradientDescentRegressorPipelineStep(Output output) + public LightGbmRegressorPipelineStep(Output output) { Model = output.PredictorModel; } @@ -6933,30 +8130,76 @@ namespace Trainers { /// - /// Train an OLS regression model. + /// Train a linear SVM. /// - public sealed partial class OrdinaryLeastSquaresRegressor : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithWeight, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithLabel, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class LinearSvmBinaryClassifier : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithLabel, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInput, Microsoft.ML.ILearningPipelineItem { /// - /// L2 regularization weight + /// Regularizer constant /// - [TlcModule.SweepableDiscreteParamAttribute("L2Weight", new object[]{1E-06f, 0.1f, 1f})] - public float L2Weight { get; set; } = 1E-06f; + [TlcModule.SweepableFloatParamAttribute("Lambda", 1E-05f, 0.1f, stepSize:10, isLogScale:true)] + public float Lambda { get; set; } = 0.001f; /// - /// Whether to calculate per parameter significance statistics + /// Batch size /// - public bool PerParameterSignificance { get; set; } = true; + public int BatchSize { get; set; } = 1; /// - /// Column to use for example weight + /// Perform projection to unit-ball? Typically used with batch size > 1. /// - public Microsoft.ML.Runtime.EntryPoints.Optional WeightColumn { get; set; } + [TlcModule.SweepableDiscreteParamAttribute("PerformProjection", new object[]{false, true})] + public bool PerformProjection { get; set; } = false; /// - /// Column to use for labels + /// No bias + /// + [TlcModule.SweepableDiscreteParamAttribute("NoBias", new object[]{false, true})] + public bool NoBias { get; set; } = false; + + /// + /// The calibrator kind to apply to the predictor. Specify null for no calibration + /// + [JsonConverter(typeof(ComponentSerializer))] + public CalibratorTrainer Calibrator { get; set; } = new PlattCalibratorCalibratorTrainer(); + + /// + /// The maximum number of examples to use when training the calibrator + /// + public int MaxCalibrationExamples { get; set; } = 1000000; + + /// + /// Number of iterations + /// + [TlcModule.SweepableLongParamAttribute("NumIterations", 1, 100, stepSize:10, isLogScale:true)] + public int NumIterations { get; set; } = 1; + + /// + /// Initial Weights and bias, comma-separated + /// + public string InitialWeights { get; set; } + + /// + /// Init weights diameter + /// + [TlcModule.SweepableFloatParamAttribute("InitWtsDiameter", 0f, 1f, numSteps:5)] + public float InitWtsDiameter { get; set; } + + /// + /// Whether to shuffle for each training iteration + /// + [TlcModule.SweepableDiscreteParamAttribute("Shuffle", new object[]{false, true})] + public bool Shuffle { get; set; } = true; + + /// + /// Size of cache when trained in Scope + /// + public int StreamingCacheSize { get; set; } = 1000000; + + /// + /// Column to use for labels /// public string LabelColumn { get; set; } = "Label"; @@ -6973,15 +8216,15 @@ public sealed partial class OrdinaryLeastSquaresRegressor : Microsoft.ML.Runtime /// /// Normalize option for the feature column /// - public Models.NormalizeOption NormalizeFeatures { get; set; } = Models.NormalizeOption.Auto; + public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; /// /// Whether learner should cache input training data /// - public Models.CachingOptions Caching { get; set; } = Models.CachingOptions.Auto; + public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; - public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IRegressionOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IBinaryClassificationOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput { /// /// The trained model @@ -6997,18 +8240,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(OrdinaryLeastSquaresRegressor)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(LinearSvmBinaryClassifier)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } TrainingData = dataStep.Data; } Output output = experiment.Add(this); - return new OrdinaryLeastSquaresRegressorPipelineStep(output); + return new LinearSvmBinaryClassifierPipelineStep(output); } - private class OrdinaryLeastSquaresRegressorPipelineStep : ILearningPipelinePredictorStep + private class LinearSvmBinaryClassifierPipelineStep : ILearningPipelinePredictorStep { - public OrdinaryLeastSquaresRegressorPipelineStep(Output output) + public LinearSvmBinaryClassifierPipelineStep(Output output) { Model = output.PredictorModel; } @@ -7022,40 +8265,94 @@ namespace Trainers { /// - /// Train an PCA Anomaly model. + /// Train a logistic regression binary model /// - public sealed partial class PcaAnomalyDetector : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class LogisticRegressionBinaryClassifier : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithWeight, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithLabel, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInput, Microsoft.ML.ILearningPipelineItem { /// - /// The number of components in the PCA + /// Show statistics of training examples. /// - [TlcModule.SweepableDiscreteParamAttribute("Rank", new object[]{10, 20, 40, 80})] - public int Rank { get; set; } = 20; + public bool ShowTrainingStats { get; set; } = false; /// - /// Oversampling parameter for randomized PCA training + /// L2 regularization weight /// - [TlcModule.SweepableDiscreteParamAttribute("Oversampling", new object[]{10, 20, 40})] - public int Oversampling { get; set; } = 20; + [TlcModule.SweepableFloatParamAttribute("L2Weight", 0f, 1f, numSteps:4)] + public float L2Weight { get; set; } = 1f; /// - /// If enabled, data is centered to be zero mean + /// L1 regularization weight /// - [TlcModule.SweepableDiscreteParamAttribute("Center", new object[]{false, true})] - public bool Center { get; set; } = true; + [TlcModule.SweepableFloatParamAttribute("L1Weight", 0f, 1f, numSteps:4)] + public float L1Weight { get; set; } = 1f; /// - /// The seed for random number generation + /// Tolerance parameter for optimization convergence. Lower = slower, more accurate /// - public int? Seed { get; set; } + [TlcModule.SweepableDiscreteParamAttribute("OptTol", new object[]{0.0001f, 1E-07f})] + public float OptTol { get; set; } = 1E-07f; + + /// + /// Memory size for L-BFGS. Lower=faster, less accurate + /// + [TlcModule.SweepableDiscreteParamAttribute("MemorySize", new object[]{5, 20, 50})] + public int MemorySize { get; set; } = 20; + + /// + /// Maximum iterations. + /// + [TlcModule.SweepableLongParamAttribute("MaxIterations", 1, 2147483647)] + public int MaxIterations { get; set; } = 2147483647; + + /// + /// Run SGD to initialize LR weights, converging to this tolerance + /// + public float SgdInitializationTolerance { get; set; } + + /// + /// If set to true, produce no output during training. + /// + public bool Quiet { get; set; } = false; + + /// + /// Init weights diameter + /// + [TlcModule.SweepableFloatParamAttribute("InitWtsDiameter", 0f, 1f, numSteps:5)] + public float InitWtsDiameter { get; set; } + + /// + /// Whether or not to use threads. Default is true + /// + public bool UseThreads { get; set; } = true; + + /// + /// Number of threads + /// + public int? NumThreads { get; set; } + + /// + /// Force densification of the internal optimization vectors + /// + [TlcModule.SweepableDiscreteParamAttribute("DenseOptimizer", new object[]{false, true})] + public bool DenseOptimizer { get; set; } = false; + + /// + /// Enforce non-negative weights + /// + public bool EnforceNonNegativity { get; set; } = false; /// /// Column to use for example weight /// public Microsoft.ML.Runtime.EntryPoints.Optional WeightColumn { get; set; } + /// + /// Column to use for labels + /// + public string LabelColumn { get; set; } = "Label"; + /// /// The data to be used for training /// @@ -7069,15 +8366,15 @@ public sealed partial class PcaAnomalyDetector : Microsoft.ML.Runtime.EntryPoint /// /// Normalize option for the feature column /// - public Models.NormalizeOption NormalizeFeatures { get; set; } = Models.NormalizeOption.Auto; + public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; /// /// Whether learner should cache input training data /// - public Models.CachingOptions Caching { get; set; } = Models.CachingOptions.Auto; + public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; - public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IAnomalyDetectionOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IBinaryClassificationOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput { /// /// The trained model @@ -7093,18 +8390,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(PcaAnomalyDetector)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(LogisticRegressionBinaryClassifier)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } TrainingData = dataStep.Data; } Output output = experiment.Add(this); - return new PcaAnomalyDetectorPipelineStep(output); + return new LogisticRegressionBinaryClassifierPipelineStep(output); } - private class PcaAnomalyDetectorPipelineStep : ILearningPipelinePredictorStep + private class LogisticRegressionBinaryClassifierPipelineStep : ILearningPipelinePredictorStep { - public PcaAnomalyDetectorPipelineStep(Output output) + public LogisticRegressionBinaryClassifierPipelineStep(Output output) { Model = output.PredictorModel; } @@ -7118,12 +8415,17 @@ namespace Trainers { /// - /// Train an Poisson regression model. + /// Train a logistic regression multi class model /// - public sealed partial class PoissonRegressor : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithWeight, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithLabel, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class LogisticRegressionClassifier : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithWeight, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithLabel, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInput, Microsoft.ML.ILearningPipelineItem { + /// + /// Show statistics of training examples. + /// + public bool ShowTrainingStats { get; set; } = false; + /// /// L2 regularization weight /// @@ -7214,15 +8516,15 @@ public sealed partial class PoissonRegressor : Microsoft.ML.Runtime.EntryPoints. /// /// Normalize option for the feature column /// - public Models.NormalizeOption NormalizeFeatures { get; set; } = Models.NormalizeOption.Auto; + public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; /// /// Whether learner should cache input training data /// - public Models.CachingOptions Caching { get; set; } = Models.CachingOptions.Auto; + public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; - public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IRegressionOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IMulticlassClassificationOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput { /// /// The trained model @@ -7238,18 +8540,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(PoissonRegressor)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(LogisticRegressionClassifier)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } TrainingData = dataStep.Data; } Output output = experiment.Add(this); - return new PoissonRegressorPipelineStep(output); + return new LogisticRegressionClassifierPipelineStep(output); } - private class PoissonRegressorPipelineStep : ILearningPipelinePredictorStep + private class LogisticRegressionClassifierPipelineStep : ILearningPipelinePredictorStep { - public PoissonRegressorPipelineStep(Output output) + public LogisticRegressionClassifierPipelineStep(Output output) { Model = output.PredictorModel; } @@ -7263,80 +8565,12 @@ namespace Trainers { /// - /// Train an SDCA binary model. + /// Train a MultiClassNaiveBayesTrainer. /// - public sealed partial class StochasticDualCoordinateAscentBinaryClassifier : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithLabel, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class NaiveBayesClassifier : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithLabel, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInput, Microsoft.ML.ILearningPipelineItem { - /// - /// Loss Function - /// - [JsonConverter(typeof(ComponentSerializer))] - public SDCAClassificationLossFunction LossFunction { get; set; } = new LogLossSDCAClassificationLossFunction(); - - /// - /// Apply weight to the positive class, for imbalanced data - /// - public float PositiveInstanceWeight { get; set; } = 1f; - - /// - /// The calibrator kind to apply to the predictor. Specify null for no calibration - /// - [JsonConverter(typeof(ComponentSerializer))] - public CalibratorTrainer Calibrator { get; set; } = new PlattCalibratorCalibratorTrainer(); - - /// - /// The maximum number of examples to use when training the calibrator - /// - public int MaxCalibrationExamples { get; set; } = 1000000; - - /// - /// L2 regularizer constant. By default the l2 constant is automatically inferred based on data set. - /// - [TlcModule.SweepableDiscreteParamAttribute("L2Const", new object[]{"", 1E-07f, 1E-06f, 1E-05f, 0.0001f, 0.001f, 0.01f})] - public float? L2Const { get; set; } - - /// - /// L1 soft threshold (L1/L2). Note that it is easier to control and sweep using the threshold parameter than the raw L1-regularizer constant. By default the l1 threshold is automatically inferred based on data set. - /// - [TlcModule.SweepableDiscreteParamAttribute("L1Threshold", new object[]{"", 0f, 0.25f, 0.5f, 0.75f, 1f})] - public float? L1Threshold { get; set; } - - /// - /// Degree of lock-free parallelism. Defaults to automatic. Determinism not guaranteed. - /// - public int? NumThreads { get; set; } - - /// - /// The tolerance for the ratio between duality gap and primal loss for convergence checking. - /// - [TlcModule.SweepableDiscreteParamAttribute("ConvergenceTolerance", new object[]{0.001f, 0.01f, 0.1f, 0.2f})] - public float ConvergenceTolerance { get; set; } = 0.1f; - - /// - /// Maximum number of iterations; set to 1 to simulate online learning. Defaults to automatic. - /// - [TlcModule.SweepableDiscreteParamAttribute("MaxIterations", new object[]{"", 10, 20, 100})] - public int? MaxIterations { get; set; } - - /// - /// Shuffle data every epoch? - /// - [TlcModule.SweepableDiscreteParamAttribute("Shuffle", new object[]{false, true})] - public bool Shuffle { get; set; } = true; - - /// - /// Convergence check frequency (in terms of number of iterations). Set as negative or zero for not checking at all. If left blank, it defaults to check after every 'numThreads' iterations. - /// - public int? CheckFrequency { get; set; } - - /// - /// The learning rate for adjusting bias from being regularized. - /// - [TlcModule.SweepableDiscreteParamAttribute("BiasLearningRate", new object[]{0f, 0.01f, 0.1f, 1f})] - public float BiasLearningRate { get; set; } - /// /// Column to use for labels /// @@ -7355,15 +8589,15 @@ public sealed partial class StochasticDualCoordinateAscentBinaryClassifier : Mic /// /// Normalize option for the feature column /// - public Models.NormalizeOption NormalizeFeatures { get; set; } = Models.NormalizeOption.Auto; + public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; /// /// Whether learner should cache input training data /// - public Models.CachingOptions Caching { get; set; } = Models.CachingOptions.Auto; + public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; - public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IBinaryClassificationOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IMulticlassClassificationOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput { /// /// The trained model @@ -7379,18 +8613,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(StochasticDualCoordinateAscentBinaryClassifier)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(NaiveBayesClassifier)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } TrainingData = dataStep.Data; } Output output = experiment.Add(this); - return new StochasticDualCoordinateAscentBinaryClassifierPipelineStep(output); + return new NaiveBayesClassifierPipelineStep(output); } - private class StochasticDualCoordinateAscentBinaryClassifierPipelineStep : ILearningPipelinePredictorStep + private class NaiveBayesClassifierPipelineStep : ILearningPipelinePredictorStep { - public StochasticDualCoordinateAscentBinaryClassifierPipelineStep(Output output) + public NaiveBayesClassifierPipelineStep(Output output) { Model = output.PredictorModel; } @@ -7404,9 +8638,9 @@ namespace Trainers { /// - /// Train an SDCA multi class model + /// Train a Online gradient descent perceptron. /// - public sealed partial class StochasticDualCoordinateAscentClassifier : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithLabel, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class OnlineGradientDescentRegressor : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithLabel, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInput, Microsoft.ML.ILearningPipelineItem { @@ -7414,53 +8648,83 @@ public sealed partial class StochasticDualCoordinateAscentClassifier : Microsoft /// Loss Function /// [JsonConverter(typeof(ComponentSerializer))] - public SDCAClassificationLossFunction LossFunction { get; set; } = new LogLossSDCAClassificationLossFunction(); + public RegressionLossFunction LossFunction { get; set; } = new SquaredLossRegressionLossFunction(); /// - /// L2 regularizer constant. By default the l2 constant is automatically inferred based on data set. + /// Learning rate /// - [TlcModule.SweepableDiscreteParamAttribute("L2Const", new object[]{"", 1E-07f, 1E-06f, 1E-05f, 0.0001f, 0.001f, 0.01f})] - public float? L2Const { get; set; } + [TlcModule.SweepableDiscreteParamAttribute("LearningRate", new object[]{0.01f, 0.1f, 0.5f, 1f})] + public float LearningRate { get; set; } = 0.1f; /// - /// L1 soft threshold (L1/L2). Note that it is easier to control and sweep using the threshold parameter than the raw L1-regularizer constant. By default the l1 threshold is automatically inferred based on data set. + /// Decrease learning rate /// - [TlcModule.SweepableDiscreteParamAttribute("L1Threshold", new object[]{"", 0f, 0.25f, 0.5f, 0.75f, 1f})] - public float? L1Threshold { get; set; } + [TlcModule.SweepableDiscreteParamAttribute("DecreaseLearningRate", new object[]{false, true})] + public bool DecreaseLearningRate { get; set; } = true; /// - /// Degree of lock-free parallelism. Defaults to automatic. Determinism not guaranteed. + /// Number of examples after which weights will be reset to the current average /// - public int? NumThreads { get; set; } + public long? ResetWeightsAfterXExamples { get; set; } /// - /// The tolerance for the ratio between duality gap and primal loss for convergence checking. + /// Instead of updating averaged weights on every example, only update when loss is nonzero /// - [TlcModule.SweepableDiscreteParamAttribute("ConvergenceTolerance", new object[]{0.001f, 0.01f, 0.1f, 0.2f})] - public float ConvergenceTolerance { get; set; } = 0.1f; + public bool DoLazyUpdates { get; set; } = true; /// - /// Maximum number of iterations; set to 1 to simulate online learning. Defaults to automatic. + /// L2 Regularization Weight /// - [TlcModule.SweepableDiscreteParamAttribute("MaxIterations", new object[]{"", 10, 20, 100})] - public int? MaxIterations { get; set; } + [TlcModule.SweepableFloatParamAttribute("L2RegularizerWeight", 0f, 0.5f)] + public float L2RegularizerWeight { get; set; } /// - /// Shuffle data every epoch? + /// Extra weight given to more recent updates /// - [TlcModule.SweepableDiscreteParamAttribute("Shuffle", new object[]{false, true})] - public bool Shuffle { get; set; } = true; + public float RecencyGain { get; set; } /// - /// Convergence check frequency (in terms of number of iterations). Set as negative or zero for not checking at all. If left blank, it defaults to check after every 'numThreads' iterations. + /// Whether Recency Gain is multiplicative (vs. additive) /// - public int? CheckFrequency { get; set; } + public bool RecencyGainMulti { get; set; } = false; /// - /// The learning rate for adjusting bias from being regularized. + /// Do averaging? /// - [TlcModule.SweepableDiscreteParamAttribute("BiasLearningRate", new object[]{0f, 0.01f, 0.1f, 1f})] - public float BiasLearningRate { get; set; } + public bool Averaged { get; set; } = true; + + /// + /// The inexactness tolerance for averaging + /// + public float AveragedTolerance { get; set; } = 0.01f; + + /// + /// Number of iterations + /// + [TlcModule.SweepableLongParamAttribute("NumIterations", 1, 100, stepSize:10, isLogScale:true)] + public int NumIterations { get; set; } = 1; + + /// + /// Initial Weights and bias, comma-separated + /// + public string InitialWeights { get; set; } + + /// + /// Init weights diameter + /// + [TlcModule.SweepableFloatParamAttribute("InitWtsDiameter", 0f, 1f, numSteps:5)] + public float InitWtsDiameter { get; set; } + + /// + /// Whether to shuffle for each training iteration + /// + [TlcModule.SweepableDiscreteParamAttribute("Shuffle", new object[]{false, true})] + public bool Shuffle { get; set; } = true; + + /// + /// Size of cache when trained in Scope + /// + public int StreamingCacheSize { get; set; } = 1000000; /// /// Column to use for labels @@ -7480,15 +8744,15 @@ public sealed partial class StochasticDualCoordinateAscentClassifier : Microsoft /// /// Normalize option for the feature column /// - public Models.NormalizeOption NormalizeFeatures { get; set; } = Models.NormalizeOption.Auto; + public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; /// /// Whether learner should cache input training data /// - public Models.CachingOptions Caching { get; set; } = Models.CachingOptions.Auto; + public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; - public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IMulticlassClassificationOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IRegressionOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput { /// /// The trained model @@ -7504,18 +8768,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(StochasticDualCoordinateAscentClassifier)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(OnlineGradientDescentRegressor)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } TrainingData = dataStep.Data; } Output output = experiment.Add(this); - return new StochasticDualCoordinateAscentClassifierPipelineStep(output); + return new OnlineGradientDescentRegressorPipelineStep(output); } - private class StochasticDualCoordinateAscentClassifierPipelineStep : ILearningPipelinePredictorStep + private class OnlineGradientDescentRegressorPipelineStep : ILearningPipelinePredictorStep { - public StochasticDualCoordinateAscentClassifierPipelineStep(Output output) + public OnlineGradientDescentRegressorPipelineStep(Output output) { Model = output.PredictorModel; } @@ -7529,68 +8793,39 @@ namespace Trainers { /// - /// Train an SDCA regression model + /// Train an PCA Anomaly model. /// - public sealed partial class StochasticDualCoordinateAscentRegressor : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithLabel, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class PcaAnomalyDetector : Microsoft.ML.Runtime.EntryPoints.CommonInputs.IUnsupervisedTrainerWithWeight, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInput, Microsoft.ML.ILearningPipelineItem { /// - /// Loss Function - /// - [JsonConverter(typeof(ComponentSerializer))] - public SDCARegressionLossFunction LossFunction { get; set; } = new SquaredLossSDCARegressionLossFunction(); - - /// - /// L2 regularizer constant. By default the l2 constant is automatically inferred based on data set. - /// - [TlcModule.SweepableDiscreteParamAttribute("L2Const", new object[]{"", 1E-07f, 1E-06f, 1E-05f, 0.0001f, 0.001f, 0.01f})] - public float? L2Const { get; set; } - - /// - /// L1 soft threshold (L1/L2). Note that it is easier to control and sweep using the threshold parameter than the raw L1-regularizer constant. By default the l1 threshold is automatically inferred based on data set. - /// - [TlcModule.SweepableDiscreteParamAttribute("L1Threshold", new object[]{"", 0f, 0.25f, 0.5f, 0.75f, 1f})] - public float? L1Threshold { get; set; } - - /// - /// Degree of lock-free parallelism. Defaults to automatic. Determinism not guaranteed. - /// - public int? NumThreads { get; set; } - - /// - /// The tolerance for the ratio between duality gap and primal loss for convergence checking. - /// - [TlcModule.SweepableDiscreteParamAttribute("ConvergenceTolerance", new object[]{0.001f, 0.01f, 0.1f, 0.2f})] - public float ConvergenceTolerance { get; set; } = 0.01f; - - /// - /// Maximum number of iterations; set to 1 to simulate online learning. Defaults to automatic. + /// The number of components in the PCA /// - [TlcModule.SweepableDiscreteParamAttribute("MaxIterations", new object[]{"", 10, 20, 100})] - public int? MaxIterations { get; set; } + [TlcModule.SweepableDiscreteParamAttribute("Rank", new object[]{10, 20, 40, 80})] + public int Rank { get; set; } = 20; /// - /// Shuffle data every epoch? + /// Oversampling parameter for randomized PCA training /// - [TlcModule.SweepableDiscreteParamAttribute("Shuffle", new object[]{false, true})] - public bool Shuffle { get; set; } = true; + [TlcModule.SweepableDiscreteParamAttribute("Oversampling", new object[]{10, 20, 40})] + public int Oversampling { get; set; } = 20; /// - /// Convergence check frequency (in terms of number of iterations). Set as negative or zero for not checking at all. If left blank, it defaults to check after every 'numThreads' iterations. + /// If enabled, data is centered to be zero mean /// - public int? CheckFrequency { get; set; } + [TlcModule.SweepableDiscreteParamAttribute("Center", new object[]{false, true})] + public bool Center { get; set; } = true; /// - /// The learning rate for adjusting bias from being regularized. + /// The seed for random number generation /// - [TlcModule.SweepableDiscreteParamAttribute("BiasLearningRate", new object[]{0f, 0.01f, 0.1f, 1f})] - public float BiasLearningRate { get; set; } = 1f; + public int? Seed { get; set; } /// - /// Column to use for labels + /// Column to use for example weight /// - public string LabelColumn { get; set; } = "Label"; + public Microsoft.ML.Runtime.EntryPoints.Optional WeightColumn { get; set; } /// /// The data to be used for training @@ -7605,15 +8840,15 @@ public sealed partial class StochasticDualCoordinateAscentRegressor : Microsoft. /// /// Normalize option for the feature column /// - public Models.NormalizeOption NormalizeFeatures { get; set; } = Models.NormalizeOption.Auto; + public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; /// /// Whether learner should cache input training data /// - public Models.CachingOptions Caching { get; set; } = Models.CachingOptions.Auto; + public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; - public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IRegressionOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IAnomalyDetectionOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput { /// /// The trained model @@ -7629,18 +8864,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(StochasticDualCoordinateAscentRegressor)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(PcaAnomalyDetector)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } TrainingData = dataStep.Data; } Output output = experiment.Add(this); - return new StochasticDualCoordinateAscentRegressorPipelineStep(output); + return new PcaAnomalyDetectorPipelineStep(output); } - private class StochasticDualCoordinateAscentRegressorPipelineStep : ILearningPipelinePredictorStep + private class PcaAnomalyDetectorPipelineStep : ILearningPipelinePredictorStep { - public StochasticDualCoordinateAscentRegressorPipelineStep(Output output) + public PcaAnomalyDetectorPipelineStep(Output output) { Model = output.PredictorModel; } @@ -7654,72 +8889,78 @@ namespace Trainers { /// - /// Train an Hogwild SGD binary model. + /// Train an Poisson regression model. /// - public sealed partial class StochasticGradientDescentBinaryClassifier : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithWeight, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithLabel, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class PoissonRegressor : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithWeight, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithLabel, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInput, Microsoft.ML.ILearningPipelineItem { /// - /// Loss Function + /// L2 regularization weight /// - [JsonConverter(typeof(ComponentSerializer))] - public ClassificationLossFunction LossFunction { get; set; } = new LogLossClassificationLossFunction(); + [TlcModule.SweepableFloatParamAttribute("L2Weight", 0f, 1f, numSteps:4)] + public float L2Weight { get; set; } = 1f; /// - /// L2 regularizer constant + /// L1 regularization weight /// - [TlcModule.SweepableDiscreteParamAttribute("L2Const", new object[]{1E-07f, 5E-07f, 1E-06f, 5E-06f, 1E-05f})] - public float L2Const { get; set; } = 1E-06f; + [TlcModule.SweepableFloatParamAttribute("L1Weight", 0f, 1f, numSteps:4)] + public float L1Weight { get; set; } = 1f; /// - /// Degree of lock-free parallelism. Defaults to automatic depending on data sparseness. Determinism not guaranteed. + /// Tolerance parameter for optimization convergence. Lower = slower, more accurate /// - public int? NumThreads { get; set; } + [TlcModule.SweepableDiscreteParamAttribute("OptTol", new object[]{0.0001f, 1E-07f})] + public float OptTol { get; set; } = 1E-07f; /// - /// Exponential moving averaged improvement tolerance for convergence + /// Memory size for L-BFGS. Lower=faster, less accurate /// - [TlcModule.SweepableDiscreteParamAttribute("ConvergenceTolerance", new object[]{0.01f, 0.001f, 0.0001f, 1E-05f})] - public double ConvergenceTolerance { get; set; } = 0.0001d; + [TlcModule.SweepableDiscreteParamAttribute("MemorySize", new object[]{5, 20, 50})] + public int MemorySize { get; set; } = 20; /// - /// Maximum number of iterations; set to 1 to simulate online learning. + /// Maximum iterations. /// - [TlcModule.SweepableDiscreteParamAttribute("MaxIterations", new object[]{1, 5, 10, 20})] - public int MaxIterations { get; set; } = 20; + [TlcModule.SweepableLongParamAttribute("MaxIterations", 1, 2147483647)] + public int MaxIterations { get; set; } = 2147483647; /// - /// Initial learning rate (only used by SGD) + /// Run SGD to initialize LR weights, converging to this tolerance /// - public double InitLearningRate { get; set; } = 0.01d; + public float SgdInitializationTolerance { get; set; } /// - /// Shuffle data every epoch? + /// If set to true, produce no output during training. /// - [TlcModule.SweepableDiscreteParamAttribute("Shuffle", new object[]{false, true})] - public bool Shuffle { get; set; } = true; + public bool Quiet { get; set; } = false; /// - /// Apply weight to the positive class, for imbalanced data + /// Init weights diameter /// - public float PositiveInstanceWeight { get; set; } = 1f; + [TlcModule.SweepableFloatParamAttribute("InitWtsDiameter", 0f, 1f, numSteps:5)] + public float InitWtsDiameter { get; set; } /// - /// Convergence check frequency (in terms of number of iterations). Default equals number of threads + /// Whether or not to use threads. Default is true /// - public int? CheckFrequency { get; set; } + public bool UseThreads { get; set; } = true; /// - /// The calibrator kind to apply to the predictor. Specify null for no calibration + /// Number of threads /// - [JsonConverter(typeof(ComponentSerializer))] - public CalibratorTrainer Calibrator { get; set; } = new PlattCalibratorCalibratorTrainer(); + public int? NumThreads { get; set; } /// - /// The maximum number of examples to use when training the calibrator + /// Force densification of the internal optimization vectors /// - public int MaxCalibrationExamples { get; set; } = 1000000; + [TlcModule.SweepableDiscreteParamAttribute("DenseOptimizer", new object[]{false, true})] + public bool DenseOptimizer { get; set; } = false; + + /// + /// Enforce non-negative weights + /// + public bool EnforceNonNegativity { get; set; } = false; /// /// Column to use for example weight @@ -7744,15 +8985,15 @@ public sealed partial class StochasticGradientDescentBinaryClassifier : Microsof /// /// Normalize option for the feature column /// - public Models.NormalizeOption NormalizeFeatures { get; set; } = Models.NormalizeOption.Auto; + public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; /// /// Whether learner should cache input training data /// - public Models.CachingOptions Caching { get; set; } = Models.CachingOptions.Auto; + public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; - public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IBinaryClassificationOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IRegressionOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput { /// /// The trained model @@ -7768,18 +9009,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(StochasticGradientDescentBinaryClassifier)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(PoissonRegressor)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } TrainingData = dataStep.Data; } Output output = experiment.Add(this); - return new StochasticGradientDescentBinaryClassifierPipelineStep(output); + return new PoissonRegressorPipelineStep(output); } - private class StochasticGradientDescentBinaryClassifierPipelineStep : ILearningPipelinePredictorStep + private class PoissonRegressorPipelineStep : ILearningPipelinePredictorStep { - public StochasticGradientDescentBinaryClassifierPipelineStep(Output output) + public PoissonRegressorPipelineStep(Output output) { Model = output.PredictorModel; } @@ -7789,121 +9030,119 @@ public StochasticGradientDescentBinaryClassifierPipelineStep(Output output) } } - namespace Transforms + namespace Trainers { /// - /// Approximate bootstrap sampling. + /// Train an SDCA binary model. /// - public sealed partial class ApproximateBootstrapSampler : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class StochasticDualCoordinateAscentBinaryClassifier : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithLabel, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInput, Microsoft.ML.ILearningPipelineItem { /// - /// Whether this is the out-of-bag sample, that is, all those rows that are not selected by the transform. + /// Loss Function /// - public bool Complement { get; set; } = false; + [JsonConverter(typeof(ComponentSerializer))] + public SDCAClassificationLossFunction LossFunction { get; set; } = new LogLossSDCAClassificationLossFunction(); /// - /// The random seed. If unspecified random state will be instead derived from the environment. + /// Apply weight to the positive class, for imbalanced data /// - public uint? Seed { get; set; } + public float PositiveInstanceWeight { get; set; } = 1f; /// - /// Whether we should attempt to shuffle the source data. By default on, but can be turned off for efficiency. + /// The calibrator kind to apply to the predictor. Specify null for no calibration /// - public bool ShuffleInput { get; set; } = true; + [JsonConverter(typeof(ComponentSerializer))] + public CalibratorTrainer Calibrator { get; set; } = new PlattCalibratorCalibratorTrainer(); /// - /// When shuffling the output, the number of output rows to keep in that pool. Note that shuffling of output is completely distinct from shuffling of input. + /// The maximum number of examples to use when training the calibrator /// - public int PoolSize { get; set; } = 1000; + public int MaxCalibrationExamples { get; set; } = 1000000; /// - /// Input dataset + /// L2 regularizer constant. By default the l2 constant is automatically inferred based on data set. /// - public Var Data { get; set; } = new Var(); + [TlcModule.SweepableDiscreteParamAttribute("L2Const", new object[]{"", 1E-07f, 1E-06f, 1E-05f, 0.0001f, 0.001f, 0.01f})] + public float? L2Const { get; set; } + /// + /// L1 soft threshold (L1/L2). Note that it is easier to control and sweep using the threshold parameter than the raw L1-regularizer constant. By default the l1 threshold is automatically inferred based on data set. + /// + [TlcModule.SweepableDiscreteParamAttribute("L1Threshold", new object[]{"", 0f, 0.25f, 0.5f, 0.75f, 1f})] + public float? L1Threshold { get; set; } - public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput - { - /// - /// Transformed dataset - /// - public Var OutputData { get; set; } = new Var(); + /// + /// Degree of lock-free parallelism. Defaults to automatic. Determinism not guaranteed. + /// + public int? NumThreads { get; set; } - /// - /// Transform model - /// - public Var Model { get; set; } = new Var(); + /// + /// The tolerance for the ratio between duality gap and primal loss for convergence checking. + /// + [TlcModule.SweepableDiscreteParamAttribute("ConvergenceTolerance", new object[]{0.001f, 0.01f, 0.1f, 0.2f})] + public float ConvergenceTolerance { get; set; } = 0.1f; - } - public Var GetInputData() => Data; - - public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) - { - if (previousStep != null) - { - if (!(previousStep is ILearningPipelineDataStep dataStep)) - { - throw new InvalidOperationException($"{ nameof(ApproximateBootstrapSampler)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); - } + /// + /// Maximum number of iterations; set to 1 to simulate online learning. Defaults to automatic. + /// + [TlcModule.SweepableDiscreteParamAttribute("MaxIterations", new object[]{"", 10, 20, 100})] + public int? MaxIterations { get; set; } - Data = dataStep.Data; - } - Output output = experiment.Add(this); - return new ApproximateBootstrapSamplerPipelineStep(output); - } + /// + /// Shuffle data every epoch? + /// + [TlcModule.SweepableDiscreteParamAttribute("Shuffle", new object[]{false, true})] + public bool Shuffle { get; set; } = true; - private class ApproximateBootstrapSamplerPipelineStep : ILearningPipelineDataStep - { - public ApproximateBootstrapSamplerPipelineStep(Output output) - { - Data = output.OutputData; - Model = output.Model; - } + /// + /// Convergence check frequency (in terms of number of iterations). Set as negative or zero for not checking at all. If left blank, it defaults to check after every 'numThreads' iterations. + /// + public int? CheckFrequency { get; set; } - public Var Data { get; } - public Var Model { get; } - } - } - } + /// + /// The learning rate for adjusting bias from being regularized. + /// + [TlcModule.SweepableDiscreteParamAttribute("BiasLearningRate", new object[]{0f, 0.01f, 0.1f, 1f})] + public float BiasLearningRate { get; set; } - namespace Transforms - { + /// + /// Column to use for labels + /// + public string LabelColumn { get; set; } = "Label"; - /// - /// For binary prediction, it renames the PredictedLabel and Score columns to include the name of the positive class. - /// - public sealed partial class BinaryPredictionScoreColumnsRenamer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem - { + /// + /// The data to be used for training + /// + public Var TrainingData { get; set; } = new Var(); + /// + /// Column to use for features + /// + public string FeatureColumn { get; set; } = "Features"; /// - /// The predictor model used in scoring + /// Normalize option for the feature column /// - public Var PredictorModel { get; set; } = new Var(); + public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; /// - /// Input dataset + /// Whether learner should cache input training data /// - public Var Data { get; set; } = new Var(); + public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; - public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IBinaryClassificationOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput { /// - /// Transformed dataset - /// - public Var OutputData { get; set; } = new Var(); - - /// - /// Transform model + /// The trained model /// - public Var Model { get; set; } = new Var(); + public Var PredictorModel { get; set; } = new Var(); } - public Var GetInputData() => Data; + public Var GetInputData() => TrainingData; public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) { @@ -7911,148 +9150,124 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(BinaryPredictionScoreColumnsRenamer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(StochasticDualCoordinateAscentBinaryClassifier)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } - Data = dataStep.Data; + TrainingData = dataStep.Data; } Output output = experiment.Add(this); - return new BinaryPredictionScoreColumnsRenamerPipelineStep(output); + return new StochasticDualCoordinateAscentBinaryClassifierPipelineStep(output); } - private class BinaryPredictionScoreColumnsRenamerPipelineStep : ILearningPipelineDataStep + private class StochasticDualCoordinateAscentBinaryClassifierPipelineStep : ILearningPipelinePredictorStep { - public BinaryPredictionScoreColumnsRenamerPipelineStep(Output output) + public StochasticDualCoordinateAscentBinaryClassifierPipelineStep(Output output) { - Data = output.OutputData; - Model = output.Model; + Model = output.PredictorModel; } - public Var Data { get; } - public Var Model { get; } + public Var Model { get; } } } } - namespace Transforms + namespace Trainers { - public sealed partial class NormalizeTransformBinColumn : OneToOneColumn, IOneToOneColumn + /// + /// Train an SDCA multi class model + /// + public sealed partial class StochasticDualCoordinateAscentClassifier : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithLabel, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInput, Microsoft.ML.ILearningPipelineItem { + + /// - /// Max number of bins, power of 2 recommended + /// Loss Function /// - public int? NumBins { get; set; } + [JsonConverter(typeof(ComponentSerializer))] + public SDCAClassificationLossFunction LossFunction { get; set; } = new LogLossSDCAClassificationLossFunction(); /// - /// Whether to map zero to zero, preserving sparsity + /// L2 regularizer constant. By default the l2 constant is automatically inferred based on data set. /// - public bool? FixZero { get; set; } + [TlcModule.SweepableDiscreteParamAttribute("L2Const", new object[]{"", 1E-07f, 1E-06f, 1E-05f, 0.0001f, 0.001f, 0.01f})] + public float? L2Const { get; set; } /// - /// Max number of examples used to train the normalizer + /// L1 soft threshold (L1/L2). Note that it is easier to control and sweep using the threshold parameter than the raw L1-regularizer constant. By default the l1 threshold is automatically inferred based on data set. /// - public long? MaxTrainingExamples { get; set; } + [TlcModule.SweepableDiscreteParamAttribute("L1Threshold", new object[]{"", 0f, 0.25f, 0.5f, 0.75f, 1f})] + public float? L1Threshold { get; set; } /// - /// Name of the new column + /// Degree of lock-free parallelism. Defaults to automatic. Determinism not guaranteed. /// - public string Name { get; set; } + public int? NumThreads { get; set; } /// - /// Name of the source column + /// The tolerance for the ratio between duality gap and primal loss for convergence checking. /// - public string Source { get; set; } - - } + [TlcModule.SweepableDiscreteParamAttribute("ConvergenceTolerance", new object[]{0.001f, 0.01f, 0.1f, 0.2f})] + public float ConvergenceTolerance { get; set; } = 0.1f; - /// - /// The values are assigned into equidensity bins and a value is mapped to its bin_number/number_of_bins. - /// - public sealed partial class BinNormalizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem - { + /// + /// Maximum number of iterations; set to 1 to simulate online learning. Defaults to automatic. + /// + [TlcModule.SweepableDiscreteParamAttribute("MaxIterations", new object[]{"", 10, 20, 100})] + public int? MaxIterations { get; set; } - public BinNormalizer() - { - } - - public BinNormalizer(params string[] inputColumns) - { - if (inputColumns != null) - { - foreach (string input in inputColumns) - { - AddColumn(input); - } - } - } - - public BinNormalizer(params ValueTuple[] inputOutputColumns) - { - if (inputOutputColumns != null) - { - foreach (ValueTuple inputOutput in inputOutputColumns) - { - AddColumn(inputOutput.Item2, inputOutput.Item1); - } - } - } - - public void AddColumn(string source) - { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(source)); - Column = list.ToArray(); - } + /// + /// Shuffle data every epoch? + /// + [TlcModule.SweepableDiscreteParamAttribute("Shuffle", new object[]{false, true})] + public bool Shuffle { get; set; } = true; - public void AddColumn(string name, string source) - { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(name, source)); - Column = list.ToArray(); - } + /// + /// Convergence check frequency (in terms of number of iterations). Set as negative or zero for not checking at all. If left blank, it defaults to check after every 'numThreads' iterations. + /// + public int? CheckFrequency { get; set; } + /// + /// The learning rate for adjusting bias from being regularized. + /// + [TlcModule.SweepableDiscreteParamAttribute("BiasLearningRate", new object[]{0f, 0.01f, 0.1f, 1f})] + public float BiasLearningRate { get; set; } /// - /// New column definition(s) (optional form: name:src) + /// Column to use for labels /// - public Transforms.NormalizeTransformBinColumn[] Column { get; set; } + public string LabelColumn { get; set; } = "Label"; /// - /// Max number of bins, power of 2 recommended + /// The data to be used for training /// - public int NumBins { get; set; } = 1024; + public Var TrainingData { get; set; } = new Var(); /// - /// Whether to map zero to zero, preserving sparsity + /// Column to use for features /// - public bool FixZero { get; set; } = true; + public string FeatureColumn { get; set; } = "Features"; /// - /// Max number of examples used to train the normalizer + /// Normalize option for the feature column /// - public long MaxTrainingExamples { get; set; } = 1000000000; + public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; /// - /// Input dataset + /// Whether learner should cache input training data /// - public Var Data { get; set; } = new Var(); + public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; - public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IMulticlassClassificationOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput { /// - /// Transformed dataset - /// - public Var OutputData { get; set; } = new Var(); - - /// - /// Transform model + /// The trained model /// - public Var Model { get; set; } = new Var(); + public Var PredictorModel { get; set; } = new Var(); } - public Var GetInputData() => Data; + public Var GetInputData() => TrainingData; public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) { @@ -8060,176 +9275,124 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(BinNormalizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(StochasticDualCoordinateAscentClassifier)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } - Data = dataStep.Data; + TrainingData = dataStep.Data; } Output output = experiment.Add(this); - return new BinNormalizerPipelineStep(output); + return new StochasticDualCoordinateAscentClassifierPipelineStep(output); } - private class BinNormalizerPipelineStep : ILearningPipelineDataStep + private class StochasticDualCoordinateAscentClassifierPipelineStep : ILearningPipelinePredictorStep { - public BinNormalizerPipelineStep(Output output) + public StochasticDualCoordinateAscentClassifierPipelineStep(Output output) { - Data = output.OutputData; - Model = output.Model; + Model = output.PredictorModel; } - public Var Data { get; } - public Var Model { get; } + public Var Model { get; } } } } - namespace Transforms + namespace Trainers { - public enum CategoricalTransformOutputKind : byte + + /// + /// Train an SDCA regression model + /// + public sealed partial class StochasticDualCoordinateAscentRegressor : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithLabel, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInput, Microsoft.ML.ILearningPipelineItem { - Bag = 1, - Ind = 2, - Key = 3, - Bin = 4 - } - public sealed partial class CategoricalHashTransformColumn : OneToOneColumn, IOneToOneColumn - { /// - /// The number of bits to hash into. Must be between 1 and 30, inclusive. + /// Loss Function /// - public int? HashBits { get; set; } + [JsonConverter(typeof(ComponentSerializer))] + public SDCARegressionLossFunction LossFunction { get; set; } = new SquaredLossSDCARegressionLossFunction(); /// - /// Hashing seed + /// L2 regularizer constant. By default the l2 constant is automatically inferred based on data set. /// - public uint? Seed { get; set; } + [TlcModule.SweepableDiscreteParamAttribute("L2Const", new object[]{"", 1E-07f, 1E-06f, 1E-05f, 0.0001f, 0.001f, 0.01f})] + public float? L2Const { get; set; } /// - /// Whether the position of each term should be included in the hash + /// L1 soft threshold (L1/L2). Note that it is easier to control and sweep using the threshold parameter than the raw L1-regularizer constant. By default the l1 threshold is automatically inferred based on data set. /// - public bool? Ordered { get; set; } + [TlcModule.SweepableDiscreteParamAttribute("L1Threshold", new object[]{"", 0f, 0.25f, 0.5f, 0.75f, 1f})] + public float? L1Threshold { get; set; } /// - /// Limit the number of keys used to generate the slot name to this many. 0 means no invert hashing, -1 means no limit. + /// Degree of lock-free parallelism. Defaults to automatic. Determinism not guaranteed. /// - public int? InvertHash { get; set; } + public int? NumThreads { get; set; } /// - /// Output kind: Bag (multi-set vector), Ind (indicator vector), or Key (index) + /// The tolerance for the ratio between duality gap and primal loss for convergence checking. /// - public Transforms.CategoricalTransformOutputKind? OutputKind { get; set; } + [TlcModule.SweepableDiscreteParamAttribute("ConvergenceTolerance", new object[]{0.001f, 0.01f, 0.1f, 0.2f})] + public float ConvergenceTolerance { get; set; } = 0.01f; /// - /// Name of the new column + /// Maximum number of iterations; set to 1 to simulate online learning. Defaults to automatic. /// - public string Name { get; set; } + [TlcModule.SweepableDiscreteParamAttribute("MaxIterations", new object[]{"", 10, 20, 100})] + public int? MaxIterations { get; set; } /// - /// Name of the source column + /// Shuffle data every epoch? /// - public string Source { get; set; } - - } - - /// - /// Encodes the categorical variable with hash-based encoding - /// - public sealed partial class CategoricalHashOneHotVectorizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem - { - - public CategoricalHashOneHotVectorizer() - { - } - - public CategoricalHashOneHotVectorizer(params string[] inputColumns) - { - if (inputColumns != null) - { - foreach (string input in inputColumns) - { - AddColumn(input); - } - } - } - - public CategoricalHashOneHotVectorizer(params ValueTuple[] inputOutputColumns) - { - if (inputOutputColumns != null) - { - foreach (ValueTuple inputOutput in inputOutputColumns) - { - AddColumn(inputOutput.Item2, inputOutput.Item1); - } - } - } - - public void AddColumn(string source) - { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(source)); - Column = list.ToArray(); - } - - public void AddColumn(string name, string source) - { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(name, source)); - Column = list.ToArray(); - } - + [TlcModule.SweepableDiscreteParamAttribute("Shuffle", new object[]{false, true})] + public bool Shuffle { get; set; } = true; /// - /// New column definition(s) (optional form: name:hashBits:src) + /// Convergence check frequency (in terms of number of iterations). Set as negative or zero for not checking at all. If left blank, it defaults to check after every 'numThreads' iterations. /// - public Transforms.CategoricalHashTransformColumn[] Column { get; set; } + public int? CheckFrequency { get; set; } /// - /// Number of bits to hash into. Must be between 1 and 30, inclusive. + /// The learning rate for adjusting bias from being regularized. /// - public int HashBits { get; set; } = 16; + [TlcModule.SweepableDiscreteParamAttribute("BiasLearningRate", new object[]{0f, 0.01f, 0.1f, 1f})] + public float BiasLearningRate { get; set; } = 1f; /// - /// Hashing seed + /// Column to use for labels /// - public uint Seed { get; set; } = 314489979; + public string LabelColumn { get; set; } = "Label"; /// - /// Whether the position of each term should be included in the hash + /// The data to be used for training /// - public bool Ordered { get; set; } = true; + public Var TrainingData { get; set; } = new Var(); /// - /// Limit the number of keys used to generate the slot name to this many. 0 means no invert hashing, -1 means no limit. + /// Column to use for features /// - public int InvertHash { get; set; } + public string FeatureColumn { get; set; } = "Features"; /// - /// Output kind: Bag (multi-set vector), Ind (indicator vector), or Key (index) + /// Normalize option for the feature column /// - public Transforms.CategoricalTransformOutputKind OutputKind { get; set; } = Transforms.CategoricalTransformOutputKind.Bag; + public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; /// - /// Input dataset + /// Whether learner should cache input training data /// - public Var Data { get; set; } = new Var(); + public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; - public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IRegressionOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput { /// - /// Transformed dataset - /// - public Var OutputData { get; set; } = new Var(); - - /// - /// Transform model + /// The trained model /// - public Var Model { get; set; } = new Var(); + public Var PredictorModel { get; set; } = new Var(); } - public Var GetInputData() => Data; + public Var GetInputData() => TrainingData; public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) { @@ -8237,174 +9400,138 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(CategoricalHashOneHotVectorizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(StochasticDualCoordinateAscentRegressor)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } - Data = dataStep.Data; + TrainingData = dataStep.Data; } Output output = experiment.Add(this); - return new CategoricalHashOneHotVectorizerPipelineStep(output); + return new StochasticDualCoordinateAscentRegressorPipelineStep(output); } - private class CategoricalHashOneHotVectorizerPipelineStep : ILearningPipelineDataStep + private class StochasticDualCoordinateAscentRegressorPipelineStep : ILearningPipelinePredictorStep { - public CategoricalHashOneHotVectorizerPipelineStep(Output output) + public StochasticDualCoordinateAscentRegressorPipelineStep(Output output) { - Data = output.OutputData; - Model = output.Model; + Model = output.PredictorModel; } - public Var Data { get; } - public Var Model { get; } + public Var Model { get; } } } } - namespace Transforms + namespace Trainers { - public enum TermTransformSortOrder : byte + + /// + /// Train an Hogwild SGD binary model. + /// + public sealed partial class StochasticGradientDescentBinaryClassifier : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithWeight, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithLabel, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInput, Microsoft.ML.ILearningPipelineItem { - Occurrence = 0, - Value = 1 - } - public sealed partial class CategoricalTransformColumn : OneToOneColumn, IOneToOneColumn - { /// - /// Output kind: Bag (multi-set vector), Ind (indicator vector), Key (index), or Binary encoded indicator vector + /// Loss Function /// - public Transforms.CategoricalTransformOutputKind? OutputKind { get; set; } + [JsonConverter(typeof(ComponentSerializer))] + public ClassificationLossFunction LossFunction { get; set; } = new LogLossClassificationLossFunction(); /// - /// Maximum number of terms to keep when auto-training + /// L2 regularizer constant /// - public int? MaxNumTerms { get; set; } + [TlcModule.SweepableDiscreteParamAttribute("L2Const", new object[]{1E-07f, 5E-07f, 1E-06f, 5E-06f, 1E-05f})] + public float L2Const { get; set; } = 1E-06f; /// - /// List of terms + /// Degree of lock-free parallelism. Defaults to automatic depending on data sparseness. Determinism not guaranteed. /// - public string[] Term { get; set; } + public int? NumThreads { get; set; } /// - /// How items should be ordered when vectorized. By default, they will be in the order encountered. If by value items are sorted according to their default comparison, e.g., text sorting will be case sensitive (e.g., 'A' then 'Z' then 'a'). + /// Exponential moving averaged improvement tolerance for convergence /// - public Transforms.TermTransformSortOrder? Sort { get; set; } + [TlcModule.SweepableDiscreteParamAttribute("ConvergenceTolerance", new object[]{0.01f, 0.001f, 0.0001f, 1E-05f})] + public double ConvergenceTolerance { get; set; } = 0.0001d; /// - /// Whether key value metadata should be text, regardless of the actual input type + /// Maximum number of iterations; set to 1 to simulate online learning. /// - public bool? TextKeyValues { get; set; } + [TlcModule.SweepableDiscreteParamAttribute("MaxIterations", new object[]{1, 5, 10, 20})] + public int MaxIterations { get; set; } = 20; /// - /// Name of the new column + /// Initial learning rate (only used by SGD) /// - public string Name { get; set; } + public double InitLearningRate { get; set; } = 0.01d; /// - /// Name of the source column + /// Shuffle data every epoch? /// - public string Source { get; set; } - - } - - /// - /// Encodes the categorical variable with one-hot encoding based on term dictionary - /// - public sealed partial class CategoricalOneHotVectorizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem - { - - public CategoricalOneHotVectorizer() - { - } - - public CategoricalOneHotVectorizer(params string[] inputColumns) - { - if (inputColumns != null) - { - foreach (string input in inputColumns) - { - AddColumn(input); - } - } - } - - public CategoricalOneHotVectorizer(params ValueTuple[] inputOutputColumns) - { - if (inputOutputColumns != null) - { - foreach (ValueTuple inputOutput in inputOutputColumns) - { - AddColumn(inputOutput.Item2, inputOutput.Item1); - } - } - } - - public void AddColumn(string source) - { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(source)); - Column = list.ToArray(); - } - - public void AddColumn(string name, string source) - { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(name, source)); - Column = list.ToArray(); - } - + [TlcModule.SweepableDiscreteParamAttribute("Shuffle", new object[]{false, true})] + public bool Shuffle { get; set; } = true; /// - /// New column definition(s) (optional form: name:src) + /// Apply weight to the positive class, for imbalanced data /// - public Transforms.CategoricalTransformColumn[] Column { get; set; } + public float PositiveInstanceWeight { get; set; } = 1f; /// - /// Output kind: Bag (multi-set vector), Ind (indicator vector), or Key (index) + /// Convergence check frequency (in terms of number of iterations). Default equals number of threads /// - public Transforms.CategoricalTransformOutputKind OutputKind { get; set; } = Transforms.CategoricalTransformOutputKind.Ind; + public int? CheckFrequency { get; set; } /// - /// Maximum number of terms to keep per column when auto-training + /// The calibrator kind to apply to the predictor. Specify null for no calibration /// - public int MaxNumTerms { get; set; } = 1000000; + [JsonConverter(typeof(ComponentSerializer))] + public CalibratorTrainer Calibrator { get; set; } = new PlattCalibratorCalibratorTrainer(); /// - /// List of terms + /// The maximum number of examples to use when training the calibrator /// - public string[] Term { get; set; } + public int MaxCalibrationExamples { get; set; } = 1000000; /// - /// How items should be ordered when vectorized. By default, they will be in the order encountered. If by value items are sorted according to their default comparison, e.g., text sorting will be case sensitive (e.g., 'A' then 'Z' then 'a'). + /// Column to use for example weight /// - public Transforms.TermTransformSortOrder Sort { get; set; } = Transforms.TermTransformSortOrder.Occurrence; + public Microsoft.ML.Runtime.EntryPoints.Optional WeightColumn { get; set; } /// - /// Whether key value metadata should be text, regardless of the actual input type + /// Column to use for labels /// - public bool TextKeyValues { get; set; } = true; + public string LabelColumn { get; set; } = "Label"; /// - /// Input dataset + /// The data to be used for training /// - public Var Data { get; set; } = new Var(); + public Var TrainingData { get; set; } = new Var(); + + /// + /// Column to use for features + /// + public string FeatureColumn { get; set; } = "Features"; + /// + /// Normalize option for the feature column + /// + public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; + + /// + /// Whether learner should cache input training data + /// + public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; - public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput - { - /// - /// Transformed dataset - /// - public Var OutputData { get; set; } = new Var(); + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IBinaryClassificationOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput + { /// - /// Transform model + /// The trained model /// - public Var Model { get; set; } = new Var(); + public Var PredictorModel { get; set; } = new Var(); } - public Var GetInputData() => Data; + public Var GetInputData() => TrainingData; public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) { @@ -8412,25 +9539,23 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(CategoricalOneHotVectorizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(StochasticGradientDescentBinaryClassifier)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } - Data = dataStep.Data; + TrainingData = dataStep.Data; } Output output = experiment.Add(this); - return new CategoricalOneHotVectorizerPipelineStep(output); + return new StochasticGradientDescentBinaryClassifierPipelineStep(output); } - private class CategoricalOneHotVectorizerPipelineStep : ILearningPipelineDataStep + private class StochasticGradientDescentBinaryClassifierPipelineStep : ILearningPipelinePredictorStep { - public CategoricalOneHotVectorizerPipelineStep(Output output) + public StochasticGradientDescentBinaryClassifierPipelineStep(Output output) { - Data = output.OutputData; - Model = output.Model; + Model = output.PredictorModel; } - public Var Data { get; } - public Var Model { get; } + public Var Model { get; } } } } @@ -8438,76 +9563,32 @@ public CategoricalOneHotVectorizerPipelineStep(Output output) namespace Transforms { - public sealed partial class CharTokenizeTransformColumn : OneToOneColumn, IOneToOneColumn - { - /// - /// Name of the new column - /// - public string Name { get; set; } - - /// - /// Name of the source column - /// - public string Source { get; set; } - - } - /// - /// Character-oriented tokenizer where text is considered a sequence of characters. + /// Approximate bootstrap sampling. /// - public sealed partial class CharacterTokenizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class ApproximateBootstrapSampler : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { - public CharacterTokenizer() - { - } - - public CharacterTokenizer(params string[] inputColumns) - { - if (inputColumns != null) - { - foreach (string input in inputColumns) - { - AddColumn(input); - } - } - } - - public CharacterTokenizer(params ValueTuple[] inputOutputColumns) - { - if (inputOutputColumns != null) - { - foreach (ValueTuple inputOutput in inputOutputColumns) - { - AddColumn(inputOutput.Item2, inputOutput.Item1); - } - } - } - - public void AddColumn(string source) - { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(source)); - Column = list.ToArray(); - } - public void AddColumn(string name, string source) - { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(name, source)); - Column = list.ToArray(); - } + /// + /// Whether this is the out-of-bag sample, that is, all those rows that are not selected by the transform. + /// + public bool Complement { get; set; } = false; + /// + /// The random seed. If unspecified random state will be instead derived from the environment. + /// + public uint? Seed { get; set; } /// - /// New column definition(s) (optional form: name:src) + /// Whether we should attempt to shuffle the source data. By default on, but can be turned off for efficiency. /// - public Transforms.CharTokenizeTransformColumn[] Column { get; set; } + public bool ShuffleInput { get; set; } = true; /// - /// Whether to mark the beginning/end of each row/slot with start of text character (0x02)/end of text character (0x03) + /// When shuffling the output, the number of output rows to keep in that pool. Note that shuffling of output is completely distinct from shuffling of input. /// - public bool UseMarkerChars { get; set; } = true; + public int PoolSize { get; set; } = 1000; /// /// Input dataset @@ -8536,18 +9617,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(CharacterTokenizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(ApproximateBootstrapSampler)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new CharacterTokenizerPipelineStep(output); + return new ApproximateBootstrapSamplerPipelineStep(output); } - private class CharacterTokenizerPipelineStep : ILearningPipelineDataStep + private class ApproximateBootstrapSamplerPipelineStep : ILearningPipelineDataStep { - public CharacterTokenizerPipelineStep(Output output) + public ApproximateBootstrapSamplerPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -8562,47 +9643,17 @@ public CharacterTokenizerPipelineStep(Output output) namespace Transforms { - public sealed partial class ConcatTransformColumn : ManyToOneColumn, IManyToOneColumn - { - /// - /// Name of the new column - /// - public string Name { get; set; } - - /// - /// Name of the source column - /// - public string[] Source { get; set; } - - } - /// - /// Concatenates two columns of the same item type. + /// For binary prediction, it renames the PredictedLabel and Score columns to include the name of the positive class. /// - public sealed partial class ColumnConcatenator : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class BinaryPredictionScoreColumnsRenamer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { - public ColumnConcatenator() - { - } - - public ColumnConcatenator(string outputColumn, params string[] inputColumns) - { - AddColumn(outputColumn, inputColumns); - } - - public void AddColumn(string name, params string[] source) - { - var list = Column == null ? new List() : new List(Column); - list.Add(ManyToOneColumn.Create(name, source)); - Column = list.ToArray(); - } - /// - /// New column definition(s) (optional form: name:srcs) + /// The predictor model used in scoring /// - public Transforms.ConcatTransformColumn[] Column { get; set; } + public Var PredictorModel { get; set; } = new Var(); /// /// Input dataset @@ -8631,18 +9682,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(ColumnConcatenator)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(BinaryPredictionScoreColumnsRenamer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new ColumnConcatenatorPipelineStep(output); + return new BinaryPredictionScoreColumnsRenamerPipelineStep(output); } - private class ColumnConcatenatorPipelineStep : ILearningPipelineDataStep + private class BinaryPredictionScoreColumnsRenamerPipelineStep : ILearningPipelineDataStep { - public ColumnConcatenatorPipelineStep(Output output) + public BinaryPredictionScoreColumnsRenamerPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -8657,8 +9708,23 @@ public ColumnConcatenatorPipelineStep(Output output) namespace Transforms { - public sealed partial class CopyColumnsTransformColumn : OneToOneColumn, IOneToOneColumn + public sealed partial class NormalizeTransformBinColumn : OneToOneColumn, IOneToOneColumn { + /// + /// Max number of bins, power of 2 recommended + /// + public int? NumBins { get; set; } + + /// + /// Whether to map zero to zero, preserving sparsity + /// + public bool? FixZero { get; set; } + + /// + /// Max number of examples used to train the normalizer + /// + public long? MaxTrainingExamples { get; set; } + /// /// Name of the new column /// @@ -8672,16 +9738,16 @@ public sealed partial class CopyColumnsTransformColumn : OneToOneColumn - /// Duplicates columns from the dataset + /// The values are assigned into equidensity bins and a value is mapped to its bin_number/number_of_bins. /// - public sealed partial class ColumnCopier : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class BinNormalizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { - public ColumnCopier() + public BinNormalizer() { } - public ColumnCopier(params string[] inputColumns) + public BinNormalizer(params string[] inputColumns) { if (inputColumns != null) { @@ -8692,28 +9758,28 @@ public ColumnCopier(params string[] inputColumns) } } - public ColumnCopier(params ValueTuple[] inputOutputColumns) + public BinNormalizer(params (string inputColumn, string outputColumn)[] inputOutputColumns) { if (inputOutputColumns != null) { - foreach (ValueTuple inputOutput in inputOutputColumns) + foreach (var inputOutput in inputOutputColumns) { - AddColumn(inputOutput.Item2, inputOutput.Item1); + AddColumn(inputOutput.outputColumn, inputOutput.inputColumn); } } } - public void AddColumn(string source) + public void AddColumn(string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(source)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); Column = list.ToArray(); } - public void AddColumn(string name, string source) + public void AddColumn(string outputColumn, string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(name, source)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); Column = list.ToArray(); } @@ -8721,7 +9787,22 @@ public void AddColumn(string name, string source) /// /// New column definition(s) (optional form: name:src) /// - public Transforms.CopyColumnsTransformColumn[] Column { get; set; } + public NormalizeTransformBinColumn[] Column { get; set; } + + /// + /// Max number of bins, power of 2 recommended + /// + public int NumBins { get; set; } = 1024; + + /// + /// Whether to map zero to zero, preserving sparsity + /// + public bool FixZero { get; set; } = true; + + /// + /// Max number of examples used to train the normalizer + /// + public long MaxTrainingExamples { get; set; } = 1000000000; /// /// Input dataset @@ -8750,18 +9831,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(ColumnCopier)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(BinNormalizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new ColumnCopierPipelineStep(output); + return new BinNormalizerPipelineStep(output); } - private class ColumnCopierPipelineStep : ILearningPipelineDataStep + private class BinNormalizerPipelineStep : ILearningPipelineDataStep { - public ColumnCopierPipelineStep(Output output) + public BinNormalizerPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -8775,83 +9856,130 @@ public ColumnCopierPipelineStep(Output output) namespace Transforms { - - /// - /// Drops columns from the dataset - /// - public sealed partial class ColumnDropper : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public enum CategoricalTransformOutputKind : byte { + Bag = 1, + Ind = 2, + Key = 3, + Bin = 4 + } + public sealed partial class CategoricalHashTransformColumn : OneToOneColumn, IOneToOneColumn + { /// - /// Column name to drop + /// The number of bits to hash into. Must be between 1 and 30, inclusive. /// - public string[] Column { get; set; } + public int? HashBits { get; set; } /// - /// Input dataset + /// Hashing seed /// - public Var Data { get; set; } = new Var(); + public uint? Seed { get; set; } + /// + /// Whether the position of each term should be included in the hash + /// + public bool? Ordered { get; set; } - public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput - { - /// - /// Transformed dataset - /// - public Var OutputData { get; set; } = new Var(); + /// + /// Limit the number of keys used to generate the slot name to this many. 0 means no invert hashing, -1 means no limit. + /// + public int? InvertHash { get; set; } - /// - /// Transform model - /// - public Var Model { get; set; } = new Var(); + /// + /// Output kind: Bag (multi-set vector), Ind (indicator vector), or Key (index) + /// + public CategoricalTransformOutputKind? OutputKind { get; set; } + + /// + /// Name of the new column + /// + public string Name { get; set; } + + /// + /// Name of the source column + /// + public string Source { get; set; } + + } + + /// + /// Encodes the categorical variable with hash-based encoding + /// + public sealed partial class CategoricalHashOneHotVectorizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + { + public CategoricalHashOneHotVectorizer() + { } - public Var GetInputData() => Data; - public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) + public CategoricalHashOneHotVectorizer(params string[] inputColumns) { - if (previousStep != null) + if (inputColumns != null) { - if (!(previousStep is ILearningPipelineDataStep dataStep)) + foreach (string input in inputColumns) { - throw new InvalidOperationException($"{ nameof(ColumnDropper)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + AddColumn(input); } - - Data = dataStep.Data; } - Output output = experiment.Add(this); - return new ColumnDropperPipelineStep(output); } - - private class ColumnDropperPipelineStep : ILearningPipelineDataStep + + public CategoricalHashOneHotVectorizer(params (string inputColumn, string outputColumn)[] inputOutputColumns) { - public ColumnDropperPipelineStep(Output output) + if (inputOutputColumns != null) { - Data = output.OutputData; - Model = output.Model; + foreach (var inputOutput in inputOutputColumns) + { + AddColumn(inputOutput.outputColumn, inputOutput.inputColumn); + } } + } + + public void AddColumn(string inputColumn) + { + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); + Column = list.ToArray(); + } - public Var Data { get; } - public Var Model { get; } + public void AddColumn(string outputColumn, string inputColumn) + { + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); + Column = list.ToArray(); } - } - } - namespace Transforms - { - /// - /// Selects a set of columns, dropping all others - /// - public sealed partial class ColumnSelector : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem - { + /// + /// New column definition(s) (optional form: name:hashBits:src) + /// + public CategoricalHashTransformColumn[] Column { get; set; } + /// + /// Number of bits to hash into. Must be between 1 and 30, inclusive. + /// + public int HashBits { get; set; } = 16; /// - /// Column name to keep + /// Hashing seed /// - public string[] Column { get; set; } + public uint Seed { get; set; } = 314489979; + + /// + /// Whether the position of each term should be included in the hash + /// + public bool Ordered { get; set; } = true; + + /// + /// Limit the number of keys used to generate the slot name to this many. 0 means no invert hashing, -1 means no limit. + /// + public int InvertHash { get; set; } + + /// + /// Output kind: Bag (multi-set vector), Ind (indicator vector), or Key (index) + /// + public CategoricalTransformOutputKind OutputKind { get; set; } = CategoricalTransformOutputKind.Bag; /// /// Input dataset @@ -8880,18 +10008,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(ColumnSelector)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(CategoricalHashOneHotVectorizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new ColumnSelectorPipelineStep(output); + return new CategoricalHashOneHotVectorizerPipelineStep(output); } - private class ColumnSelectorPipelineStep : ILearningPipelineDataStep + private class CategoricalHashOneHotVectorizerPipelineStep : ILearningPipelineDataStep { - public ColumnSelectorPipelineStep(Output output) + public CategoricalHashOneHotVectorizerPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -8905,46 +10033,39 @@ public ColumnSelectorPipelineStep(Output output) namespace Transforms { - public enum DataKind : byte + public enum TermTransformSortOrder : byte { - I1 = 1, - U1 = 2, - I2 = 3, - U2 = 4, - I4 = 5, - U4 = 6, - I8 = 7, - U8 = 8, - R4 = 9, - Num = 9, - R8 = 10, - TX = 11, - Text = 11, - TXT = 11, - BL = 12, - Bool = 12, - TimeSpan = 13, - TS = 13, - DT = 14, - DateTime = 14, - DZ = 15, - DateTimeZone = 15, - UG = 16, - U16 = 16 + Occurrence = 0, + Value = 1 } - public sealed partial class ConvertTransformColumn : OneToOneColumn, IOneToOneColumn + public sealed partial class CategoricalTransformColumn : OneToOneColumn, IOneToOneColumn { /// - /// The result type + /// Output kind: Bag (multi-set vector), Ind (indicator vector), Key (index), or Binary encoded indicator vector /// - public Transforms.DataKind? ResultType { get; set; } + public CategoricalTransformOutputKind? OutputKind { get; set; } /// - /// For a key column, this defines the range of values + /// Maximum number of terms to keep when auto-training /// - public string Range { get; set; } + public int? MaxNumTerms { get; set; } + + /// + /// List of terms + /// + public string[] Term { get; set; } + + /// + /// How items should be ordered when vectorized. By default, they will be in the order encountered. If by value items are sorted according to their default comparison, e.g., text sorting will be case sensitive (e.g., 'A' then 'Z' then 'a'). + /// + public TermTransformSortOrder? Sort { get; set; } + + /// + /// Whether key value metadata should be text, regardless of the actual input type + /// + public bool? TextKeyValues { get; set; } /// /// Name of the new column @@ -8959,16 +10080,16 @@ public sealed partial class ConvertTransformColumn : OneToOneColumn - /// Converts a column to a different type, using standard conversions. + /// Encodes the categorical variable with one-hot encoding based on term dictionary /// - public sealed partial class ColumnTypeConverter : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class CategoricalOneHotVectorizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { - public ColumnTypeConverter() + public CategoricalOneHotVectorizer() { } - public ColumnTypeConverter(params string[] inputColumns) + public CategoricalOneHotVectorizer(params string[] inputColumns) { if (inputColumns != null) { @@ -8979,46 +10100,61 @@ public ColumnTypeConverter(params string[] inputColumns) } } - public ColumnTypeConverter(params ValueTuple[] inputOutputColumns) + public CategoricalOneHotVectorizer(params (string inputColumn, string outputColumn)[] inputOutputColumns) { if (inputOutputColumns != null) { - foreach (ValueTuple inputOutput in inputOutputColumns) + foreach (var inputOutput in inputOutputColumns) { - AddColumn(inputOutput.Item2, inputOutput.Item1); + AddColumn(inputOutput.outputColumn, inputOutput.inputColumn); } } } - public void AddColumn(string source) + public void AddColumn(string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(source)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); Column = list.ToArray(); } - public void AddColumn(string name, string source) + public void AddColumn(string outputColumn, string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(name, source)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); Column = list.ToArray(); } /// - /// New column definition(s) (optional form: name:type:src) + /// New column definition(s) (optional form: name:src) /// - public Transforms.ConvertTransformColumn[] Column { get; set; } + public CategoricalTransformColumn[] Column { get; set; } /// - /// The result type + /// Output kind: Bag (multi-set vector), Ind (indicator vector), or Key (index) /// - public Transforms.DataKind? ResultType { get; set; } + public CategoricalTransformOutputKind OutputKind { get; set; } = CategoricalTransformOutputKind.Ind; /// - /// For a key column, this defines the range of values + /// Maximum number of terms to keep per column when auto-training /// - public string Range { get; set; } + public int MaxNumTerms { get; set; } = 1000000; + + /// + /// List of terms + /// + public string[] Term { get; set; } + + /// + /// How items should be ordered when vectorized. By default, they will be in the order encountered. If by value items are sorted according to their default comparison, e.g., text sorting will be case sensitive (e.g., 'A' then 'Z' then 'a'). + /// + public TermTransformSortOrder Sort { get; set; } = TermTransformSortOrder.Occurrence; + + /// + /// Whether key value metadata should be text, regardless of the actual input type + /// + public bool TextKeyValues { get; set; } = true; /// /// Input dataset @@ -9047,18 +10183,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(ColumnTypeConverter)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(CategoricalOneHotVectorizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new ColumnTypeConverterPipelineStep(output); + return new CategoricalOneHotVectorizerPipelineStep(output); } - private class ColumnTypeConverterPipelineStep : ILearningPipelineDataStep + private class CategoricalOneHotVectorizerPipelineStep : ILearningPipelineDataStep { - public ColumnTypeConverterPipelineStep(Output output) + public CategoricalOneHotVectorizerPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -9073,22 +10209,76 @@ public ColumnTypeConverterPipelineStep(Output output) namespace Transforms { + public sealed partial class CharTokenizeTransformColumn : OneToOneColumn, IOneToOneColumn + { + /// + /// Name of the new column + /// + public string Name { get; set; } + + /// + /// Name of the source column + /// + public string Source { get; set; } + + } + /// - /// Groups values of a scalar column into a vector, by a contiguous group ID + /// Character-oriented tokenizer where text is considered a sequence of characters. /// - public sealed partial class CombinerByContiguousGroupId : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class CharacterTokenizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { + public CharacterTokenizer() + { + } + + public CharacterTokenizer(params string[] inputColumns) + { + if (inputColumns != null) + { + foreach (string input in inputColumns) + { + AddColumn(input); + } + } + } + + public CharacterTokenizer(params (string inputColumn, string outputColumn)[] inputOutputColumns) + { + if (inputOutputColumns != null) + { + foreach (var inputOutput in inputOutputColumns) + { + AddColumn(inputOutput.outputColumn, inputOutput.inputColumn); + } + } + } + + public void AddColumn(string inputColumn) + { + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); + Column = list.ToArray(); + } + + public void AddColumn(string outputColumn, string inputColumn) + { + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); + Column = list.ToArray(); + } + /// - /// Columns to group by + /// New column definition(s) (optional form: name:src) /// - public string[] GroupKey { get; set; } + public CharTokenizeTransformColumn[] Column { get; set; } /// - /// Columns to group together + /// Whether to mark the beginning/end of each row/slot with start of text character (0x02)/end of text character (0x03) /// - public string[] Column { get; set; } + public bool UseMarkerChars { get; set; } = true; /// /// Input dataset @@ -9117,18 +10307,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(CombinerByContiguousGroupId)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(CharacterTokenizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new CombinerByContiguousGroupIdPipelineStep(output); + return new CharacterTokenizerPipelineStep(output); } - private class CombinerByContiguousGroupIdPipelineStep : ILearningPipelineDataStep + private class CharacterTokenizerPipelineStep : ILearningPipelineDataStep { - public CombinerByContiguousGroupIdPipelineStep(Output output) + public CharacterTokenizerPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -9143,18 +10333,103 @@ public CombinerByContiguousGroupIdPipelineStep(Output output) namespace Transforms { - public sealed partial class NormalizeTransformAffineColumn : OneToOneColumn, IOneToOneColumn + public sealed partial class ConcatTransformColumn : ManyToOneColumn, IManyToOneColumn { /// - /// Whether to map zero to zero, preserving sparsity + /// Name of the new column /// - public bool? FixZero { get; set; } + public string Name { get; set; } /// - /// Max number of examples used to train the normalizer + /// Name of the source column /// - public long? MaxTrainingExamples { get; set; } + public string[] Source { get; set; } + + } + + /// + /// Concatenates two columns of the same item type. + /// + public sealed partial class ColumnConcatenator : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + { + + public ColumnConcatenator() + { + } + + public ColumnConcatenator(string outputColumn, params string[] inputColumns) + { + AddColumn(outputColumn, inputColumns); + } + + public void AddColumn(string name, params string[] source) + { + var list = Column == null ? new List() : new List(Column); + list.Add(ManyToOneColumn.Create(name, source)); + Column = list.ToArray(); + } + + + /// + /// New column definition(s) (optional form: name:srcs) + /// + public ConcatTransformColumn[] Column { get; set; } + + /// + /// Input dataset + /// + public Var Data { get; set; } = new Var(); + + + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput + { + /// + /// Transformed dataset + /// + public Var OutputData { get; set; } = new Var(); + + /// + /// Transform model + /// + public Var Model { get; set; } = new Var(); + + } + public Var GetInputData() => Data; + + public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) + { + if (previousStep != null) + { + if (!(previousStep is ILearningPipelineDataStep dataStep)) + { + throw new InvalidOperationException($"{ nameof(ColumnConcatenator)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + } + + Data = dataStep.Data; + } + Output output = experiment.Add(this); + return new ColumnConcatenatorPipelineStep(output); + } + + private class ColumnConcatenatorPipelineStep : ILearningPipelineDataStep + { + public ColumnConcatenatorPipelineStep(Output output) + { + Data = output.OutputData; + Model = output.Model; + } + + public Var Data { get; } + public Var Model { get; } + } + } + } + + namespace Transforms + { + public sealed partial class CopyColumnsTransformColumn : OneToOneColumn, IOneToOneColumn + { /// /// Name of the new column /// @@ -9168,16 +10443,16 @@ public sealed partial class NormalizeTransformAffineColumn : OneToOneColumn - /// Normalize the columns only if needed + /// Duplicates columns from the dataset /// - public sealed partial class ConditionalNormalizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class ColumnCopier : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { - public ConditionalNormalizer() + public ColumnCopier() { } - public ConditionalNormalizer(params string[] inputColumns) + public ColumnCopier(params string[] inputColumns) { if (inputColumns != null) { @@ -9188,28 +10463,28 @@ public ConditionalNormalizer(params string[] inputColumns) } } - public ConditionalNormalizer(params ValueTuple[] inputOutputColumns) + public ColumnCopier(params (string inputColumn, string outputColumn)[] inputOutputColumns) { if (inputOutputColumns != null) { - foreach (ValueTuple inputOutput in inputOutputColumns) + foreach (var inputOutput in inputOutputColumns) { - AddColumn(inputOutput.Item2, inputOutput.Item1); + AddColumn(inputOutput.outputColumn, inputOutput.inputColumn); } } } - public void AddColumn(string source) + public void AddColumn(string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(source)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); Column = list.ToArray(); } - public void AddColumn(string name, string source) + public void AddColumn(string outputColumn, string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(name, source)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); Column = list.ToArray(); } @@ -9217,17 +10492,7 @@ public void AddColumn(string name, string source) /// /// New column definition(s) (optional form: name:src) /// - public Transforms.NormalizeTransformAffineColumn[] Column { get; set; } - - /// - /// Whether to map zero to zero, preserving sparsity - /// - public bool FixZero { get; set; } = true; - - /// - /// Max number of examples used to train the normalizer - /// - public long MaxTrainingExamples { get; set; } = 1000000000; + public CopyColumnsTransformColumn[] Column { get; set; } /// /// Input dataset @@ -9235,7 +10500,7 @@ public void AddColumn(string name, string source) public Var Data { get; set; } = new Var(); - public sealed class Output + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput { /// /// Transformed dataset @@ -9256,18 +10521,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(ConditionalNormalizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(ColumnCopier)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new ConditionalNormalizerPipelineStep(output); + return new ColumnCopierPipelineStep(output); } - private class ConditionalNormalizerPipelineStep : ILearningPipelineDataStep + private class ColumnCopierPipelineStep : ILearningPipelineDataStep { - public ConditionalNormalizerPipelineStep(Output output) + public ColumnCopierPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -9281,24 +10546,18 @@ public ConditionalNormalizerPipelineStep(Output output) namespace Transforms { - public enum CacheCachingType - { - Memory = 0, - Disk = 1 - } - /// - /// Caches using the specified cache option. + /// Drops columns from the dataset /// - public sealed partial class DataCache : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class ColumnDropper : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { /// - /// Caching strategy + /// Column name to drop /// - public Transforms.CacheCachingType Caching { get; set; } = Transforms.CacheCachingType.Memory; + public string[] Column { get; set; } /// /// Input dataset @@ -9306,13 +10565,18 @@ public sealed partial class DataCache : Microsoft.ML.Runtime.EntryPoints.CommonI public Var Data { get; set; } = new Var(); - public sealed class Output + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput { /// - /// Dataset + /// Transformed dataset /// public Var OutputData { get; set; } = new Var(); + /// + /// Transform model + /// + public Var Model { get; set; } = new Var(); + } public Var GetInputData() => Data; @@ -9322,20 +10586,21 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(DataCache)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(ColumnDropper)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new DataCachePipelineStep(output); + return new ColumnDropperPipelineStep(output); } - private class DataCachePipelineStep : ILearningPipelineDataStep + private class ColumnDropperPipelineStep : ILearningPipelineDataStep { - public DataCachePipelineStep(Output output) + public ColumnDropperPipelineStep(Output output) { Data = output.OutputData; + Model = output.Model; } public Var Data { get; } @@ -9348,77 +10613,63 @@ namespace Transforms { /// - /// Score a dataset with a predictor model + /// Selects a set of columns, dropping all others /// - public sealed partial class DatasetScorer + public sealed partial class ColumnSelector : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { /// - /// The dataset to be scored - /// - public Var Data { get; set; } = new Var(); - - /// - /// The predictor model to apply to data + /// Column name to keep /// - public Var PredictorModel { get; set; } = new Var(); + public string[] Column { get; set; } /// - /// Suffix to append to the score columns + /// Input dataset /// - public string Suffix { get; set; } + public Var Data { get; set; } = new Var(); - public sealed class Output + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput { /// - /// The scored dataset + /// Transformed dataset /// - public Var ScoredData { get; set; } = new Var(); + public Var OutputData { get; set; } = new Var(); /// - /// The scoring transform + /// Transform model /// - public Var ScoringTransform { get; set; } = new Var(); + public Var Model { get; set; } = new Var(); } - } - } - - namespace Transforms - { - - /// - /// Score a dataset with a transform model - /// - public sealed partial class DatasetTransformScorer - { - - - /// - /// The dataset to be scored - /// - public Var Data { get; set; } = new Var(); - - /// - /// The transform model to apply to data - /// - public Var TransformModel { get; set; } = new Var(); + public Var GetInputData() => Data; + + public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) + { + if (previousStep != null) + { + if (!(previousStep is ILearningPipelineDataStep dataStep)) + { + throw new InvalidOperationException($"{ nameof(ColumnSelector)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + } + Data = dataStep.Data; + } + Output output = experiment.Add(this); + return new ColumnSelectorPipelineStep(output); + } - public sealed class Output + private class ColumnSelectorPipelineStep : ILearningPipelineDataStep { - /// - /// The scored dataset - /// - public Var ScoredData { get; set; } = new Var(); - - /// - /// The scoring transform - /// - public Var ScoringTransform { get; set; } = new Var(); + public ColumnSelectorPipelineStep(Output output) + { + Data = output.OutputData; + Model = output.Model; + } + public Var Data { get; } + public Var Model { get; } } } } @@ -9426,27 +10677,17 @@ public sealed class Output namespace Transforms { - public sealed partial class TermTransformColumn : OneToOneColumn, IOneToOneColumn + public sealed partial class ConvertTransformColumn : OneToOneColumn, IOneToOneColumn { /// - /// Maximum number of terms to keep when auto-training - /// - public int? MaxNumTerms { get; set; } - - /// - /// List of terms - /// - public string[] Term { get; set; } - - /// - /// How items should be ordered when vectorized. By default, they will be in the order encountered. If by value items are sorted according to their default comparison, e.g., text sorting will be case sensitive (e.g., 'A' then 'Z' then 'a'). + /// The result type /// - public Transforms.TermTransformSortOrder? Sort { get; set; } + public Microsoft.ML.Data.DataKind? ResultType { get; set; } /// - /// Whether key value metadata should be text, regardless of the actual input type + /// For a key column, this defines the range of values /// - public bool? TextKeyValues { get; set; } + public string Range { get; set; } /// /// Name of the new column @@ -9461,16 +10702,16 @@ public sealed partial class TermTransformColumn : OneToOneColumn - /// Converts input values (words, numbers, etc.) to index in a dictionary. + /// Converts a column to a different type, using standard conversions. /// - public sealed partial class Dictionarizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class ColumnTypeConverter : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { - public Dictionarizer() + public ColumnTypeConverter() { } - public Dictionarizer(params string[] inputColumns) + public ColumnTypeConverter(params string[] inputColumns) { if (inputColumns != null) { @@ -9481,56 +10722,46 @@ public Dictionarizer(params string[] inputColumns) } } - public Dictionarizer(params ValueTuple[] inputOutputColumns) + public ColumnTypeConverter(params (string inputColumn, string outputColumn)[] inputOutputColumns) { if (inputOutputColumns != null) { - foreach (ValueTuple inputOutput in inputOutputColumns) + foreach (var inputOutput in inputOutputColumns) { - AddColumn(inputOutput.Item2, inputOutput.Item1); + AddColumn(inputOutput.outputColumn, inputOutput.inputColumn); } } } - public void AddColumn(string source) + public void AddColumn(string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(source)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); Column = list.ToArray(); } - public void AddColumn(string name, string source) + public void AddColumn(string outputColumn, string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(name, source)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); Column = list.ToArray(); } /// - /// New column definition(s) (optional form: name:src) + /// New column definition(s) (optional form: name:type:src) /// - public Transforms.TermTransformColumn[] Column { get; set; } + public ConvertTransformColumn[] Column { get; set; } /// - /// Maximum number of terms to keep per column when auto-training + /// The result type /// - public int MaxNumTerms { get; set; } = 1000000; + public Microsoft.ML.Data.DataKind? ResultType { get; set; } /// - /// List of terms - /// - public string[] Term { get; set; } - - /// - /// How items should be ordered when vectorized. By default, they will be in the order encountered. If by value items are sorted according to their default comparison, e.g., text sorting will be case sensitive (e.g., 'A' then 'Z' then 'a'). - /// - public Transforms.TermTransformSortOrder Sort { get; set; } = Transforms.TermTransformSortOrder.Occurrence; - - /// - /// Whether key value metadata should be text, regardless of the actual input type + /// For a key column, this defines the range of values /// - public bool TextKeyValues { get; set; } = false; + public string Range { get; set; } /// /// Input dataset @@ -9559,18 +10790,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(Dictionarizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(ColumnTypeConverter)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new DictionarizerPipelineStep(output); + return new ColumnTypeConverterPipelineStep(output); } - private class DictionarizerPipelineStep : ILearningPipelineDataStep + private class ColumnTypeConverterPipelineStep : ILearningPipelineDataStep { - public DictionarizerPipelineStep(Output output) + public ColumnTypeConverterPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -9586,16 +10817,21 @@ namespace Transforms { /// - /// Combines all the features into one feature column. + /// Groups values of a scalar column into a vector, by a contiguous group ID /// - public sealed partial class FeatureCombiner : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class CombinerByContiguousGroupId : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { /// - /// Features + /// Columns to group by /// - public string[] Features { get; set; } + public string[] GroupKey { get; set; } + + /// + /// Columns to group together + /// + public string[] Column { get; set; } /// /// Input dataset @@ -9624,18 +10860,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(FeatureCombiner)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(CombinerByContiguousGroupId)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new FeatureCombinerPipelineStep(output); + return new CombinerByContiguousGroupIdPipelineStep(output); } - private class FeatureCombinerPipelineStep : ILearningPipelineDataStep + private class CombinerByContiguousGroupIdPipelineStep : ILearningPipelineDataStep { - public FeatureCombinerPipelineStep(Output output) + public CombinerByContiguousGroupIdPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -9650,102 +10886,91 @@ public FeatureCombinerPipelineStep(Output output) namespace Transforms { - /// - /// Selects the slots for which the count of non-default values is greater than or equal to a threshold. - /// - public sealed partial class FeatureSelectorByCount : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class NormalizeTransformAffineColumn : OneToOneColumn, IOneToOneColumn { - - /// - /// Columns to use for feature selection + /// Whether to map zero to zero, preserving sparsity /// - public string[] Column { get; set; } + public bool? FixZero { get; set; } /// - /// If the count of non-default values for a slot is greater than or equal to this threshold, the slot is preserved + /// Max number of examples used to train the normalizer /// - public long Count { get; set; } = 1; + public long? MaxTrainingExamples { get; set; } /// - /// Input dataset + /// Name of the new column /// - public Var Data { get; set; } = new Var(); + public string Name { get; set; } + /// + /// Name of the source column + /// + public string Source { get; set; } - public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput - { - /// - /// Transformed dataset - /// - public Var OutputData { get; set; } = new Var(); + } - /// - /// Transform model - /// - public Var Model { get; set; } = new Var(); + /// + /// Normalize the columns only if needed + /// + public sealed partial class ConditionalNormalizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + { + public ConditionalNormalizer() + { } - public Var GetInputData() => Data; - public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) + public ConditionalNormalizer(params string[] inputColumns) { - if (previousStep != null) + if (inputColumns != null) { - if (!(previousStep is ILearningPipelineDataStep dataStep)) + foreach (string input in inputColumns) { - throw new InvalidOperationException($"{ nameof(FeatureSelectorByCount)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + AddColumn(input); } - - Data = dataStep.Data; } - Output output = experiment.Add(this); - return new FeatureSelectorByCountPipelineStep(output); } - - private class FeatureSelectorByCountPipelineStep : ILearningPipelineDataStep + + public ConditionalNormalizer(params (string inputColumn, string outputColumn)[] inputOutputColumns) { - public FeatureSelectorByCountPipelineStep(Output output) + if (inputOutputColumns != null) { - Data = output.OutputData; - Model = output.Model; + foreach (var inputOutput in inputOutputColumns) + { + AddColumn(inputOutput.outputColumn, inputOutput.inputColumn); + } } - - public Var Data { get; } - public Var Model { get; } } - } - } - - namespace Transforms - { - - /// - /// Selects the top k slots across all specified columns ordered by their mutual information with the label column. - /// - public sealed partial class FeatureSelectorByMutualInformation : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem - { + + public void AddColumn(string inputColumn) + { + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); + Column = list.ToArray(); + } + public void AddColumn(string outputColumn, string inputColumn) + { + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); + Column = list.ToArray(); + } - /// - /// Columns to use for feature selection - /// - public string[] Column { get; set; } /// - /// Column to use for labels + /// New column definition(s) (optional form: name:src) /// - public string LabelColumn { get; set; } = "Label"; + public NormalizeTransformAffineColumn[] Column { get; set; } /// - /// The maximum number of slots to preserve in output + /// Whether to map zero to zero, preserving sparsity /// - public int SlotsInOutput { get; set; } = 1000; + public bool FixZero { get; set; } = true; /// - /// Max number of bins for R4/R8 columns, power of 2 recommended + /// Max number of examples used to train the normalizer /// - public int NumBins { get; set; } = 256; + public long MaxTrainingExamples { get; set; } = 1000000000; /// /// Input dataset @@ -9753,7 +10978,7 @@ public sealed partial class FeatureSelectorByMutualInformation : Microsoft.ML.Ru public Var Data { get; set; } = new Var(); - public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput + public sealed class Output { /// /// Transformed dataset @@ -9774,18 +10999,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(FeatureSelectorByMutualInformation)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(ConditionalNormalizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new FeatureSelectorByMutualInformationPipelineStep(output); + return new ConditionalNormalizerPipelineStep(output); } - private class FeatureSelectorByMutualInformationPipelineStep : ILearningPipelineDataStep + private class ConditionalNormalizerPipelineStep : ILearningPipelineDataStep { - public FeatureSelectorByMutualInformationPipelineStep(Output output) + public ConditionalNormalizerPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -9799,102 +11024,24 @@ public FeatureSelectorByMutualInformationPipelineStep(Output output) namespace Transforms { - - public sealed partial class LpNormNormalizerTransformGcnColumn : OneToOneColumn, IOneToOneColumn + public enum CacheCachingType { - /// - /// Normalize by standard deviation rather than L2 norm - /// - public bool? UseStdDev { get; set; } - - /// - /// Scale features by this value - /// - public float? Scale { get; set; } - - /// - /// Subtract mean from each value before normalizing - /// - public bool? SubMean { get; set; } - - /// - /// Name of the new column - /// - public string Name { get; set; } - - /// - /// Name of the source column - /// - public string Source { get; set; } - + Memory = 0, + Disk = 1 } + /// - /// Performs a global contrast normalization on input values: Y = (s * X - M) / D, where s is a scale, M is mean and D is either L2 norm or standard deviation. + /// Caches using the specified cache option. /// - public sealed partial class GlobalContrastNormalizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class DataCache : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { - public GlobalContrastNormalizer() - { - } - - public GlobalContrastNormalizer(params string[] inputColumns) - { - if (inputColumns != null) - { - foreach (string input in inputColumns) - { - AddColumn(input); - } - } - } - - public GlobalContrastNormalizer(params ValueTuple[] inputOutputColumns) - { - if (inputOutputColumns != null) - { - foreach (ValueTuple inputOutput in inputOutputColumns) - { - AddColumn(inputOutput.Item2, inputOutput.Item1); - } - } - } - - public void AddColumn(string source) - { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(source)); - Column = list.ToArray(); - } - - public void AddColumn(string name, string source) - { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(name, source)); - Column = list.ToArray(); - } - - - /// - /// New column definition(s) (optional form: name:src) - /// - public Transforms.LpNormNormalizerTransformGcnColumn[] Column { get; set; } - - /// - /// Subtract mean from each value before normalizing - /// - public bool SubMean { get; set; } = true; - - /// - /// Normalize by standard deviation rather than L2 norm - /// - public bool UseStdDev { get; set; } = false; /// - /// Scale features by this value + /// Caching strategy /// - public float Scale { get; set; } = 1f; + public CacheCachingType Caching { get; set; } = CacheCachingType.Memory; /// /// Input dataset @@ -9902,18 +11049,13 @@ public void AddColumn(string name, string source) public Var Data { get; set; } = new Var(); - public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput + public sealed class Output { /// - /// Transformed dataset + /// Dataset /// public Var OutputData { get; set; } = new Var(); - /// - /// Transform model - /// - public Var Model { get; set; } = new Var(); - } public Var GetInputData() => Data; @@ -9923,21 +11065,20 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(GlobalContrastNormalizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(DataCache)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new GlobalContrastNormalizerPipelineStep(output); + return new DataCachePipelineStep(output); } - private class GlobalContrastNormalizerPipelineStep : ILearningPipelineDataStep + private class DataCachePipelineStep : ILearningPipelineDataStep { - public GlobalContrastNormalizerPipelineStep(Output output) + public DataCachePipelineStep(Output output) { Data = output.OutputData; - Model = output.Model; } public Var Data { get; } @@ -9949,32 +11090,106 @@ public GlobalContrastNormalizerPipelineStep(Output output) namespace Transforms { - public sealed partial class HashJoinTransformColumn : OneToOneColumn, IOneToOneColumn - { - /// - /// Whether the values need to be combined for a single hash + /// + /// Score a dataset with a predictor model + /// + public sealed partial class DatasetScorer + { + + + /// + /// The dataset to be scored /// - public bool? Join { get; set; } + public Var Data { get; set; } = new Var(); /// - /// Which slots should be combined together. Example: 0,3,5;0,1;3;2,1,0. Overrides 'join'. + /// The predictor model to apply to data /// - public string CustomSlotMap { get; set; } + public Var PredictorModel { get; set; } = new Var(); /// - /// Number of bits to hash into. Must be between 1 and 31, inclusive. + /// Suffix to append to the score columns /// - public int? HashBits { get; set; } + public string Suffix { get; set; } + + + public sealed class Output + { + /// + /// The scored dataset + /// + public Var ScoredData { get; set; } = new Var(); + + /// + /// The scoring transform + /// + public Var ScoringTransform { get; set; } = new Var(); + + } + } + } + + namespace Transforms + { + + /// + /// Score a dataset with a transform model + /// + public sealed partial class DatasetTransformScorer + { + /// - /// Hashing seed + /// The dataset to be scored /// - public uint? Seed { get; set; } + public Var Data { get; set; } = new Var(); /// - /// Whether the position of each term should be included in the hash + /// The transform model to apply to data /// - public bool? Ordered { get; set; } + public Var TransformModel { get; set; } = new Var(); + + + public sealed class Output + { + /// + /// The scored dataset + /// + public Var ScoredData { get; set; } = new Var(); + + /// + /// The scoring transform + /// + public Var ScoringTransform { get; set; } = new Var(); + + } + } + } + + namespace Transforms + { + + public sealed partial class TermTransformColumn : OneToOneColumn, IOneToOneColumn + { + /// + /// Maximum number of terms to keep when auto-training + /// + public int? MaxNumTerms { get; set; } + + /// + /// List of terms + /// + public string[] Term { get; set; } + + /// + /// How items should be ordered when vectorized. By default, they will be in the order encountered. If by value items are sorted according to their default comparison, e.g., text sorting will be case sensitive (e.g., 'A' then 'Z' then 'a'). + /// + public TermTransformSortOrder? Sort { get; set; } + + /// + /// Whether key value metadata should be text, regardless of the actual input type + /// + public bool? TextKeyValues { get; set; } /// /// Name of the new column @@ -9989,16 +11204,16 @@ public sealed partial class HashJoinTransformColumn : OneToOneColumn - /// Converts column values into hashes. This transform accepts both numeric and text inputs, both single and vector-valued columns. This is a part of the Dracula transform. + /// Converts input values (words, numbers, etc.) to index in a dictionary. /// - public sealed partial class HashConverter : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class Dictionarizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { - public HashConverter() + public Dictionarizer() { } - public HashConverter(params string[] inputColumns) + public Dictionarizer(params string[] inputColumns) { if (inputColumns != null) { @@ -10009,28 +11224,28 @@ public HashConverter(params string[] inputColumns) } } - public HashConverter(params ValueTuple[] inputOutputColumns) + public Dictionarizer(params (string inputColumn, string outputColumn)[] inputOutputColumns) { if (inputOutputColumns != null) { - foreach (ValueTuple inputOutput in inputOutputColumns) + foreach (var inputOutput in inputOutputColumns) { - AddColumn(inputOutput.Item2, inputOutput.Item1); + AddColumn(inputOutput.outputColumn, inputOutput.inputColumn); } } } - public void AddColumn(string source) + public void AddColumn(string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(source)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); Column = list.ToArray(); } - public void AddColumn(string name, string source) + public void AddColumn(string outputColumn, string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(name, source)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); Column = list.ToArray(); } @@ -10038,27 +11253,27 @@ public void AddColumn(string name, string source) /// /// New column definition(s) (optional form: name:src) /// - public Transforms.HashJoinTransformColumn[] Column { get; set; } + public TermTransformColumn[] Column { get; set; } /// - /// Whether the values need to be combined for a single hash + /// Maximum number of terms to keep per column when auto-training /// - public bool Join { get; set; } = true; + public int MaxNumTerms { get; set; } = 1000000; /// - /// Number of bits to hash into. Must be between 1 and 31, inclusive. + /// List of terms /// - public int HashBits { get; set; } = 31; + public string[] Term { get; set; } /// - /// Hashing seed + /// How items should be ordered when vectorized. By default, they will be in the order encountered. If by value items are sorted according to their default comparison, e.g., text sorting will be case sensitive (e.g., 'A' then 'Z' then 'a'). /// - public uint Seed { get; set; } = 314489979; + public TermTransformSortOrder Sort { get; set; } = TermTransformSortOrder.Occurrence; /// - /// Whether the position of each term should be included in the hash + /// Whether key value metadata should be text, regardless of the actual input type /// - public bool Ordered { get; set; } = true; + public bool TextKeyValues { get; set; } = false; /// /// Input dataset @@ -10087,18 +11302,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(HashConverter)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(Dictionarizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new HashConverterPipelineStep(output); + return new DictionarizerPipelineStep(output); } - private class HashConverterPipelineStep : ILearningPipelineDataStep + private class DictionarizerPipelineStep : ILearningPipelineDataStep { - public HashConverterPipelineStep(Output output) + public DictionarizerPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -10113,71 +11328,87 @@ public HashConverterPipelineStep(Output output) namespace Transforms { - public sealed partial class KeyToValueTransformColumn : OneToOneColumn, IOneToOneColumn + /// + /// Combines all the features into one feature column. + /// + public sealed partial class FeatureCombiner : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { + + /// - /// Name of the new column + /// Features /// - public string Name { get; set; } + public string[] Features { get; set; } /// - /// Name of the source column + /// Input dataset /// - public string Source { get; set; } - - } + public Var Data { get; set; } = new Var(); - /// - /// KeyToValueTransform utilizes KeyValues metadata to map key indices to the corresponding values in the KeyValues metadata. - /// - public sealed partial class KeyToTextConverter : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem - { - public KeyToTextConverter() + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput { + /// + /// Transformed dataset + /// + public Var OutputData { get; set; } = new Var(); + + /// + /// Transform model + /// + public Var Model { get; set; } = new Var(); + } + public Var GetInputData() => Data; - public KeyToTextConverter(params string[] inputColumns) + public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) { - if (inputColumns != null) + if (previousStep != null) { - foreach (string input in inputColumns) + if (!(previousStep is ILearningPipelineDataStep dataStep)) { - AddColumn(input); + throw new InvalidOperationException($"{ nameof(FeatureCombiner)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } + + Data = dataStep.Data; } + Output output = experiment.Add(this); + return new FeatureCombinerPipelineStep(output); } - - public KeyToTextConverter(params ValueTuple[] inputOutputColumns) + + private class FeatureCombinerPipelineStep : ILearningPipelineDataStep { - if (inputOutputColumns != null) + public FeatureCombinerPipelineStep(Output output) { - foreach (ValueTuple inputOutput in inputOutputColumns) - { - AddColumn(inputOutput.Item2, inputOutput.Item1); - } + Data = output.OutputData; + Model = output.Model; } - } - - public void AddColumn(string source) - { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(source)); - Column = list.ToArray(); - } - public void AddColumn(string name, string source) - { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(name, source)); - Column = list.ToArray(); + public Var Data { get; } + public Var Model { get; } } + } + } + + namespace Transforms + { + + /// + /// Selects the slots for which the count of non-default values is greater than or equal to a threshold. + /// + public sealed partial class FeatureSelectorByCount : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + { /// - /// New column definition(s) (optional form: name:src) + /// Columns to use for feature selection + /// + public string[] Column { get; set; } + + /// + /// If the count of non-default values for a slot is greater than or equal to this threshold, the slot is preserved /// - public Transforms.KeyToValueTransformColumn[] Column { get; set; } + public long Count { get; set; } = 1; /// /// Input dataset @@ -10206,18 +11437,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(KeyToTextConverter)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(FeatureSelectorByCount)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new KeyToTextConverterPipelineStep(output); + return new FeatureSelectorByCountPipelineStep(output); } - private class KeyToTextConverterPipelineStep : ILearningPipelineDataStep + private class FeatureSelectorByCountPipelineStep : ILearningPipelineDataStep { - public KeyToTextConverterPipelineStep(Output output) + public FeatureSelectorByCountPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -10233,21 +11464,31 @@ namespace Transforms { /// - /// Transforms the label to either key or bool (if needed) to make it suitable for classification. + /// Selects the top k slots across all specified columns ordered by their mutual information with the label column. /// - public sealed partial class LabelColumnKeyBooleanConverter : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class FeatureSelectorByMutualInformation : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { /// - /// Convert the key values to text + /// Columns to use for feature selection /// - public bool TextKeyValues { get; set; } = true; + public string[] Column { get; set; } /// - /// The label column + /// Column to use for labels /// - public string LabelColumn { get; set; } + public string LabelColumn { get; set; } = "Label"; + + /// + /// The maximum number of slots to preserve in output + /// + public int SlotsInOutput { get; set; } = 1000; + + /// + /// Max number of bins for R4/R8 columns, power of 2 recommended + /// + public int NumBins { get; set; } = 256; /// /// Input dataset @@ -10276,18 +11517,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(LabelColumnKeyBooleanConverter)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(FeatureSelectorByMutualInformation)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new LabelColumnKeyBooleanConverterPipelineStep(output); + return new FeatureSelectorByMutualInformationPipelineStep(output); } - private class LabelColumnKeyBooleanConverterPipelineStep : ILearningPipelineDataStep + private class FeatureSelectorByMutualInformationPipelineStep : ILearningPipelineDataStep { - public LabelColumnKeyBooleanConverterPipelineStep(Output output) + public FeatureSelectorByMutualInformationPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -10302,15 +11543,25 @@ public LabelColumnKeyBooleanConverterPipelineStep(Output output) namespace Transforms { - public sealed partial class LabelIndicatorTransformColumn : OneToOneColumn, IOneToOneColumn + public sealed partial class LpNormNormalizerTransformGcnColumn : OneToOneColumn, IOneToOneColumn { /// - /// The positive example class for binary classification. + /// Normalize by standard deviation rather than L2 norm /// - public int? ClassIndex { get; set; } + public bool? UseStdDev { get; set; } /// - /// Name of the new column + /// Scale features by this value + /// + public float? Scale { get; set; } + + /// + /// Subtract mean from each value before normalizing + /// + public bool? SubMean { get; set; } + + /// + /// Name of the new column /// public string Name { get; set; } @@ -10322,16 +11573,16 @@ public sealed partial class LabelIndicatorTransformColumn : OneToOneColumn - public sealed partial class LabelIndicator : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class GlobalContrastNormalizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { - public LabelIndicator() + public GlobalContrastNormalizer() { } - public LabelIndicator(params string[] inputColumns) + public GlobalContrastNormalizer(params string[] inputColumns) { if (inputColumns != null) { @@ -10342,28 +11593,28 @@ public LabelIndicator(params string[] inputColumns) } } - public LabelIndicator(params ValueTuple[] inputOutputColumns) + public GlobalContrastNormalizer(params (string inputColumn, string outputColumn)[] inputOutputColumns) { if (inputOutputColumns != null) { - foreach (ValueTuple inputOutput in inputOutputColumns) + foreach (var inputOutput in inputOutputColumns) { - AddColumn(inputOutput.Item2, inputOutput.Item1); + AddColumn(inputOutput.outputColumn, inputOutput.inputColumn); } } } - public void AddColumn(string source) + public void AddColumn(string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(source)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); Column = list.ToArray(); } - public void AddColumn(string name, string source) + public void AddColumn(string outputColumn, string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(name, source)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); Column = list.ToArray(); } @@ -10371,12 +11622,22 @@ public void AddColumn(string name, string source) /// /// New column definition(s) (optional form: name:src) /// - public Transforms.LabelIndicatorTransformColumn[] Column { get; set; } + public LpNormNormalizerTransformGcnColumn[] Column { get; set; } /// - /// Label of the positive class. + /// Subtract mean from each value before normalizing /// - public int ClassIndex { get; set; } + public bool SubMean { get; set; } = true; + + /// + /// Normalize by standard deviation rather than L2 norm + /// + public bool UseStdDev { get; set; } = false; + + /// + /// Scale features by this value + /// + public float Scale { get; set; } = 1f; /// /// Input dataset @@ -10405,18 +11666,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(LabelIndicator)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(GlobalContrastNormalizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new LabelIndicatorPipelineStep(output); + return new GlobalContrastNormalizerPipelineStep(output); } - private class LabelIndicatorPipelineStep : ILearningPipelineDataStep + private class GlobalContrastNormalizerPipelineStep : ILearningPipelineDataStep { - public LabelIndicatorPipelineStep(Output output) + public GlobalContrastNormalizerPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -10431,77 +11692,32 @@ public LabelIndicatorPipelineStep(Output output) namespace Transforms { - /// - /// Transforms the label to float to make it suitable for regression. - /// - public sealed partial class LabelToFloatConverter : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class HashJoinTransformColumn : OneToOneColumn, IOneToOneColumn { - - /// - /// The label column + /// Whether the values need to be combined for a single hash /// - public string LabelColumn { get; set; } + public bool? Join { get; set; } /// - /// Input dataset + /// Which slots should be combined together. Example: 0,3,5;0,1;3;2,1,0. Overrides 'join'. /// - public Var Data { get; set; } = new Var(); - - - public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput - { - /// - /// Transformed dataset - /// - public Var OutputData { get; set; } = new Var(); - - /// - /// Transform model - /// - public Var Model { get; set; } = new Var(); - - } - public Var GetInputData() => Data; - - public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) - { - if (previousStep != null) - { - if (!(previousStep is ILearningPipelineDataStep dataStep)) - { - throw new InvalidOperationException($"{ nameof(LabelToFloatConverter)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); - } - - Data = dataStep.Data; - } - Output output = experiment.Add(this); - return new LabelToFloatConverterPipelineStep(output); - } - - private class LabelToFloatConverterPipelineStep : ILearningPipelineDataStep - { - public LabelToFloatConverterPipelineStep(Output output) - { - Data = output.OutputData; - Model = output.Model; - } + public string CustomSlotMap { get; set; } - public Var Data { get; } - public Var Model { get; } - } - } - } + /// + /// Number of bits to hash into. Must be between 1 and 31, inclusive. + /// + public int? HashBits { get; set; } - namespace Transforms - { + /// + /// Hashing seed + /// + public uint? Seed { get; set; } - public sealed partial class NormalizeTransformLogNormalColumn : OneToOneColumn, IOneToOneColumn - { /// - /// Max number of examples used to train the normalizer + /// Whether the position of each term should be included in the hash /// - public long? MaxTrainingExamples { get; set; } + public bool? Ordered { get; set; } /// /// Name of the new column @@ -10516,16 +11732,16 @@ public sealed partial class NormalizeTransformLogNormalColumn : OneToOneColumn - /// Normalizes the data based on the computed mean and variance of the logarithm of the data. + /// Converts column values into hashes. This transform accepts both numeric and text inputs, both single and vector-valued columns. This is a part of the Dracula transform. /// - public sealed partial class LogMeanVarianceNormalizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class HashConverter : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { - public LogMeanVarianceNormalizer() + public HashConverter() { } - public LogMeanVarianceNormalizer(params string[] inputColumns) + public HashConverter(params string[] inputColumns) { if (inputColumns != null) { @@ -10536,46 +11752,56 @@ public LogMeanVarianceNormalizer(params string[] inputColumns) } } - public LogMeanVarianceNormalizer(params ValueTuple[] inputOutputColumns) + public HashConverter(params (string inputColumn, string outputColumn)[] inputOutputColumns) { if (inputOutputColumns != null) { - foreach (ValueTuple inputOutput in inputOutputColumns) + foreach (var inputOutput in inputOutputColumns) { - AddColumn(inputOutput.Item2, inputOutput.Item1); + AddColumn(inputOutput.outputColumn, inputOutput.inputColumn); } } } - public void AddColumn(string source) + public void AddColumn(string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(source)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); Column = list.ToArray(); } - public void AddColumn(string name, string source) + public void AddColumn(string outputColumn, string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(name, source)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); Column = list.ToArray(); } /// - /// Whether to use CDF as the output + /// New column definition(s) (optional form: name:src) /// - public bool UseCdf { get; set; } = true; + public HashJoinTransformColumn[] Column { get; set; } /// - /// New column definition(s) (optional form: name:src) + /// Whether the values need to be combined for a single hash /// - public Transforms.NormalizeTransformLogNormalColumn[] Column { get; set; } + public bool Join { get; set; } = true; /// - /// Max number of examples used to train the normalizer + /// Number of bits to hash into. Must be between 1 and 31, inclusive. /// - public long MaxTrainingExamples { get; set; } = 1000000000; + public int HashBits { get; set; } = 31; + + /// + /// Hashing seed + /// + public uint Seed { get; set; } = 314489979; + + /// + /// Whether the position of each term should be included in the hash + /// + public bool Ordered { get; set; } = true; /// /// Input dataset @@ -10604,18 +11830,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(LogMeanVarianceNormalizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(HashConverter)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new LogMeanVarianceNormalizerPipelineStep(output); + return new HashConverterPipelineStep(output); } - private class LogMeanVarianceNormalizerPipelineStep : ILearningPipelineDataStep + private class HashConverterPipelineStep : ILearningPipelineDataStep { - public LogMeanVarianceNormalizerPipelineStep(Output output) + public HashConverterPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -10629,27 +11855,9 @@ public LogMeanVarianceNormalizerPipelineStep(Output output) namespace Transforms { - public enum LpNormNormalizerTransformNormalizerKind : byte - { - L2Norm = 0, - StdDev = 1, - L1Norm = 2, - LInf = 3 - } - - public sealed partial class LpNormNormalizerTransformColumn : OneToOneColumn, IOneToOneColumn + public sealed partial class KeyToValueTransformColumn : OneToOneColumn, IOneToOneColumn { - /// - /// The norm to use to normalize each sample - /// - public Transforms.LpNormNormalizerTransformNormalizerKind? NormKind { get; set; } - - /// - /// Subtract mean from each value before normalizing - /// - public bool? SubMean { get; set; } - /// /// Name of the new column /// @@ -10663,16 +11871,16 @@ public sealed partial class LpNormNormalizerTransformColumn : OneToOneColumn - /// Normalize vectors (rows) individually by rescaling them to unit norm (L2, L1 or LInf). Performs the following operation on a vector X: Y = (X - M) / D, where M is mean and D is either L2 norm, L1 norm or LInf norm. + /// KeyToValueTransform utilizes KeyValues metadata to map key indices to the corresponding values in the KeyValues metadata. /// - public sealed partial class LpNormalizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class KeyToTextConverter : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { - public LpNormalizer() + public KeyToTextConverter() { } - public LpNormalizer(params string[] inputColumns) + public KeyToTextConverter(params string[] inputColumns) { if (inputColumns != null) { @@ -10683,28 +11891,28 @@ public LpNormalizer(params string[] inputColumns) } } - public LpNormalizer(params ValueTuple[] inputOutputColumns) + public KeyToTextConverter(params (string inputColumn, string outputColumn)[] inputOutputColumns) { if (inputOutputColumns != null) { - foreach (ValueTuple inputOutput in inputOutputColumns) + foreach (var inputOutput in inputOutputColumns) { - AddColumn(inputOutput.Item2, inputOutput.Item1); + AddColumn(inputOutput.outputColumn, inputOutput.inputColumn); } } } - public void AddColumn(string source) + public void AddColumn(string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(source)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); Column = list.ToArray(); } - public void AddColumn(string name, string source) + public void AddColumn(string outputColumn, string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(name, source)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); Column = list.ToArray(); } @@ -10712,17 +11920,7 @@ public void AddColumn(string name, string source) /// /// New column definition(s) (optional form: name:src) /// - public Transforms.LpNormNormalizerTransformColumn[] Column { get; set; } - - /// - /// The norm to use to normalize each sample - /// - public Transforms.LpNormNormalizerTransformNormalizerKind NormKind { get; set; } = Transforms.LpNormNormalizerTransformNormalizerKind.L2Norm; - - /// - /// Subtract mean from each value before normalizing - /// - public bool SubMean { get; set; } = false; + public KeyToValueTransformColumn[] Column { get; set; } /// /// Input dataset @@ -10751,18 +11949,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(LpNormalizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(KeyToTextConverter)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new LpNormalizerPipelineStep(output); + return new KeyToTextConverterPipelineStep(output); } - private class LpNormalizerPipelineStep : ILearningPipelineDataStep + private class KeyToTextConverterPipelineStep : ILearningPipelineDataStep { - public LpNormalizerPipelineStep(Output output) + public KeyToTextConverterPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -10778,103 +11976,150 @@ namespace Transforms { /// - /// Combines a sequence of TransformModels and a PredictorModel into a single PredictorModel. + /// Transforms the label to either key or bool (if needed) to make it suitable for classification. /// - public sealed partial class ManyHeterogeneousModelCombiner + public sealed partial class LabelColumnKeyBooleanConverter : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { /// - /// Transform model + /// Convert the key values to text /// - public ArrayVar TransformModels { get; set; } = new ArrayVar(); + public bool TextKeyValues { get; set; } = true; /// - /// Predictor model + /// The label column /// - public Var PredictorModel { get; set; } = new Var(); + public string LabelColumn { get; set; } + + /// + /// Input dataset + /// + public Var Data { get; set; } = new Var(); - public sealed class Output + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput { /// - /// Predictor model + /// Transformed dataset /// - public Var PredictorModel { get; set; } = new Var(); - - } - } - } - - namespace Transforms - { + public Var OutputData { get; set; } = new Var(); - /// - /// Normalizes the data based on the computed mean and variance of the data. - /// - public sealed partial class MeanVarianceNormalizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem - { + /// + /// Transform model + /// + public Var Model { get; set; } = new Var(); - public MeanVarianceNormalizer() - { } + public Var GetInputData() => Data; - public MeanVarianceNormalizer(params string[] inputColumns) + public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) { - if (inputColumns != null) + if (previousStep != null) { - foreach (string input in inputColumns) + if (!(previousStep is ILearningPipelineDataStep dataStep)) { - AddColumn(input); + throw new InvalidOperationException($"{ nameof(LabelColumnKeyBooleanConverter)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } + + Data = dataStep.Data; } + Output output = experiment.Add(this); + return new LabelColumnKeyBooleanConverterPipelineStep(output); } - - public MeanVarianceNormalizer(params ValueTuple[] inputOutputColumns) + + private class LabelColumnKeyBooleanConverterPipelineStep : ILearningPipelineDataStep { - if (inputOutputColumns != null) + public LabelColumnKeyBooleanConverterPipelineStep(Output output) { - foreach (ValueTuple inputOutput in inputOutputColumns) - { - AddColumn(inputOutput.Item2, inputOutput.Item1); - } + Data = output.OutputData; + Model = output.Model; + } + + public Var Data { get; } + public Var Model { get; } + } + } + } + + namespace Transforms + { + + public sealed partial class LabelIndicatorTransformColumn : OneToOneColumn, IOneToOneColumn + { + /// + /// The positive example class for binary classification. + /// + public int? ClassIndex { get; set; } + + /// + /// Name of the new column + /// + public string Name { get; set; } + + /// + /// Name of the source column + /// + public string Source { get; set; } + + } + + /// + /// Label remapper used by OVA + /// + public sealed partial class LabelIndicator : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + { + + public LabelIndicator() + { + } + + public LabelIndicator(params string[] inputColumns) + { + if (inputColumns != null) + { + foreach (string input in inputColumns) + { + AddColumn(input); + } + } + } + + public LabelIndicator(params (string inputColumn, string outputColumn)[] inputOutputColumns) + { + if (inputOutputColumns != null) + { + foreach (var inputOutput in inputOutputColumns) + { + AddColumn(inputOutput.outputColumn, inputOutput.inputColumn); + } } } - public void AddColumn(string source) + public void AddColumn(string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(source)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); Column = list.ToArray(); } - public void AddColumn(string name, string source) + public void AddColumn(string outputColumn, string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(name, source)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); Column = list.ToArray(); } - /// - /// Whether to use CDF as the output - /// - public bool UseCdf { get; set; } = false; - /// /// New column definition(s) (optional form: name:src) /// - public Transforms.NormalizeTransformAffineColumn[] Column { get; set; } - - /// - /// Whether to map zero to zero, preserving sparsity - /// - public bool FixZero { get; set; } = true; + public LabelIndicatorTransformColumn[] Column { get; set; } /// - /// Max number of examples used to train the normalizer + /// Label of the positive class. /// - public long MaxTrainingExamples { get; set; } = 1000000000; + public int ClassIndex { get; set; } /// /// Input dataset @@ -10903,18 +12148,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(MeanVarianceNormalizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(LabelIndicator)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new MeanVarianceNormalizerPipelineStep(output); + return new LabelIndicatorPipelineStep(output); } - private class MeanVarianceNormalizerPipelineStep : ILearningPipelineDataStep + private class LabelIndicatorPipelineStep : ILearningPipelineDataStep { - public MeanVarianceNormalizerPipelineStep(Output output) + public LabelIndicatorPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -10930,66 +12175,16 @@ namespace Transforms { /// - /// Normalizes the data based on the observed minimum and maximum values of the data. + /// Transforms the label to float to make it suitable for regression. /// - public sealed partial class MinMaxNormalizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class LabelToFloatConverter : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { - public MinMaxNormalizer() - { - } - - public MinMaxNormalizer(params string[] inputColumns) - { - if (inputColumns != null) - { - foreach (string input in inputColumns) - { - AddColumn(input); - } - } - } - - public MinMaxNormalizer(params ValueTuple[] inputOutputColumns) - { - if (inputOutputColumns != null) - { - foreach (ValueTuple inputOutput in inputOutputColumns) - { - AddColumn(inputOutput.Item2, inputOutput.Item1); - } - } - } - - public void AddColumn(string source) - { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(source)); - Column = list.ToArray(); - } - - public void AddColumn(string name, string source) - { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(name, source)); - Column = list.ToArray(); - } - - - /// - /// New column definition(s) (optional form: name:src) - /// - public Transforms.NormalizeTransformAffineColumn[] Column { get; set; } - - /// - /// Whether to map zero to zero, preserving sparsity - /// - public bool FixZero { get; set; } = true; /// - /// Max number of examples used to train the normalizer + /// The label column /// - public long MaxTrainingExamples { get; set; } = 1000000000; + public string LabelColumn { get; set; } /// /// Input dataset @@ -11018,18 +12213,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(MinMaxNormalizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(LabelToFloatConverter)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new MinMaxNormalizerPipelineStep(output); + return new LabelToFloatConverterPipelineStep(output); } - private class MinMaxNormalizerPipelineStep : ILearningPipelineDataStep + private class LabelToFloatConverterPipelineStep : ILearningPipelineDataStep { - public MinMaxNormalizerPipelineStep(Output output) + public LabelToFloatConverterPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -11043,35 +12238,63 @@ public MinMaxNormalizerPipelineStep(Output output) namespace Transforms { - public enum NAHandleTransformReplacementKind + + public sealed partial class LdaTransformColumn : OneToOneColumn, IOneToOneColumn { - Default = 0, - Def = 0, - DefaultValue = 0, - Mean = 1, - Minimum = 2, - Min = 2, - Maximum = 3, - Max = 3 - } + /// + /// The number of topics in the LDA + /// + public int? NumTopic { get; set; } + /// + /// Dirichlet prior on document-topic vectors + /// + public float? AlphaSum { get; set; } - public sealed partial class NAHandleTransformColumn : OneToOneColumn, IOneToOneColumn - { /// - /// The replacement method to utilize + /// Dirichlet prior on vocab-topic vectors /// - public Transforms.NAHandleTransformReplacementKind? Kind { get; set; } + public float? Beta { get; set; } /// - /// Whether to impute values by slot + /// Number of Metropolis Hasting step /// - public bool? ImputeBySlot { get; set; } + public int? Mhstep { get; set; } /// - /// Whether or not to concatenate an indicator vector column to the value column + /// Number of iterations /// - public bool? ConcatIndicator { get; set; } + public int? NumIterations { get; set; } + + /// + /// Compute log likelihood over local dataset on this iteration interval + /// + public int? LikelihoodInterval { get; set; } + + /// + /// The number of training threads + /// + public int? NumThreads { get; set; } + + /// + /// The threshold of maximum count of tokens per doc + /// + public int? NumMaxDocToken { get; set; } + + /// + /// The number of words to summarize the topic + /// + public int? NumSummaryTermPerTopic { get; set; } + + /// + /// The number of burn-in iterations + /// + public int? NumBurninIterations { get; set; } = 10; + + /// + /// Reset the random number generator for each document + /// + public bool? ResetRandomGenerator { get; set; } /// /// Name of the new column @@ -11086,16 +12309,16 @@ public sealed partial class NAHandleTransformColumn : OneToOneColumn - /// Handle missing values by replacing them with either the default value or the mean/min/max value (for non-text columns only). An indicator column can optionally be concatenated, if theinput column type is numeric. + /// The LDA transform implements LightLDA, a state-of-the-art implementation of Latent Dirichlet Allocation. /// - public sealed partial class MissingValueHandler : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class LightLda : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { - public MissingValueHandler() + public LightLda() { } - public MissingValueHandler(params string[] inputColumns) + public LightLda(params string[] inputColumns) { if (inputColumns != null) { @@ -11106,51 +12329,102 @@ public MissingValueHandler(params string[] inputColumns) } } - public MissingValueHandler(params ValueTuple[] inputOutputColumns) + public LightLda(params (string inputColumn, string outputColumn)[] inputOutputColumns) { if (inputOutputColumns != null) { - foreach (ValueTuple inputOutput in inputOutputColumns) + foreach (var inputOutput in inputOutputColumns) { - AddColumn(inputOutput.Item2, inputOutput.Item1); + AddColumn(inputOutput.outputColumn, inputOutput.inputColumn); } } } - public void AddColumn(string source) + public void AddColumn(string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(source)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); Column = list.ToArray(); } - public void AddColumn(string name, string source) + public void AddColumn(string outputColumn, string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(name, source)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); Column = list.ToArray(); } /// - /// New column definition(s) (optional form: name:rep:src) + /// New column definition(s) (optional form: name:srcs) /// - public Transforms.NAHandleTransformColumn[] Column { get; set; } + public LdaTransformColumn[] Column { get; set; } /// - /// The replacement method to utilize + /// The number of topics in the LDA /// - public Transforms.NAHandleTransformReplacementKind ReplaceWith { get; set; } = Transforms.NAHandleTransformReplacementKind.Def; + [TlcModule.SweepableDiscreteParamAttribute("NumTopic", new object[]{20, 40, 100, 200})] + public int NumTopic { get; set; } = 100; /// - /// Whether to impute values by slot + /// Dirichlet prior on document-topic vectors /// - public bool ImputeBySlot { get; set; } = true; + [TlcModule.SweepableDiscreteParamAttribute("AlphaSum", new object[]{1, 10, 100, 200})] + public float AlphaSum { get; set; } = 100f; /// - /// Whether or not to concatenate an indicator vector column to the value column + /// Dirichlet prior on vocab-topic vectors /// - public bool Concat { get; set; } = true; + [TlcModule.SweepableDiscreteParamAttribute("Beta", new object[]{0.01f, 0.015f, 0.07f, 0.02f})] + public float Beta { get; set; } = 0.01f; + + /// + /// Number of Metropolis Hasting step + /// + [TlcModule.SweepableDiscreteParamAttribute("Mhstep", new object[]{2, 4, 8, 16})] + public int Mhstep { get; set; } = 4; + + /// + /// Number of iterations + /// + [TlcModule.SweepableDiscreteParamAttribute("NumIterations", new object[]{100, 200, 300, 400})] + public int NumIterations { get; set; } = 200; + + /// + /// Compute log likelihood over local dataset on this iteration interval + /// + public int LikelihoodInterval { get; set; } = 5; + + /// + /// The threshold of maximum count of tokens per doc + /// + public int NumMaxDocToken { get; set; } = 512; + + /// + /// The number of training threads. Default value depends on number of logical processors. + /// + public int? NumThreads { get; set; } + + /// + /// The number of words to summarize the topic + /// + public int NumSummaryTermPerTopic { get; set; } = 10; + + /// + /// The number of burn-in iterations + /// + [TlcModule.SweepableDiscreteParamAttribute("NumBurninIterations", new object[]{10, 20, 30, 40})] + public int NumBurninIterations { get; set; } = 10; + + /// + /// Reset the random number generator for each document + /// + public bool ResetRandomGenerator { get; set; } = false; + + /// + /// Whether to output the topic-word summary in text format + /// + public bool OutputTopicWordSummary { get; set; } = false; /// /// Input dataset @@ -11179,18 +12453,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(MissingValueHandler)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(LightLda)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new MissingValueHandlerPipelineStep(output); + return new LightLdaPipelineStep(output); } - private class MissingValueHandlerPipelineStep : ILearningPipelineDataStep + private class LightLdaPipelineStep : ILearningPipelineDataStep { - public MissingValueHandlerPipelineStep(Output output) + public LightLdaPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -11205,8 +12479,13 @@ public MissingValueHandlerPipelineStep(Output output) namespace Transforms { - public sealed partial class NAIndicatorTransformColumn : OneToOneColumn, IOneToOneColumn + public sealed partial class NormalizeTransformLogNormalColumn : OneToOneColumn, IOneToOneColumn { + /// + /// Max number of examples used to train the normalizer + /// + public long? MaxTrainingExamples { get; set; } + /// /// Name of the new column /// @@ -11220,16 +12499,16 @@ public sealed partial class NAIndicatorTransformColumn : OneToOneColumn - /// Create a boolean output column with the same number of slots as the input column, where the output value is true if the value in the input column is missing. + /// Normalizes the data based on the computed mean and variance of the logarithm of the data. /// - public sealed partial class MissingValueIndicator : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class LogMeanVarianceNormalizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { - public MissingValueIndicator() + public LogMeanVarianceNormalizer() { } - public MissingValueIndicator(params string[] inputColumns) + public LogMeanVarianceNormalizer(params string[] inputColumns) { if (inputColumns != null) { @@ -11240,36 +12519,46 @@ public MissingValueIndicator(params string[] inputColumns) } } - public MissingValueIndicator(params ValueTuple[] inputOutputColumns) + public LogMeanVarianceNormalizer(params (string inputColumn, string outputColumn)[] inputOutputColumns) { if (inputOutputColumns != null) { - foreach (ValueTuple inputOutput in inputOutputColumns) + foreach (var inputOutput in inputOutputColumns) { - AddColumn(inputOutput.Item2, inputOutput.Item1); + AddColumn(inputOutput.outputColumn, inputOutput.inputColumn); } } } - public void AddColumn(string source) + public void AddColumn(string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(source)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); Column = list.ToArray(); } - public void AddColumn(string name, string source) + public void AddColumn(string outputColumn, string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(name, source)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); Column = list.ToArray(); } + /// + /// Whether to use CDF as the output + /// + public bool UseCdf { get; set; } = true; + /// /// New column definition(s) (optional form: name:src) /// - public Transforms.NAIndicatorTransformColumn[] Column { get; set; } + public NormalizeTransformLogNormalColumn[] Column { get; set; } + + /// + /// Max number of examples used to train the normalizer + /// + public long MaxTrainingExamples { get; set; } = 1000000000; /// /// Input dataset @@ -11298,18 +12587,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(MissingValueIndicator)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(LogMeanVarianceNormalizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new MissingValueIndicatorPipelineStep(output); + return new LogMeanVarianceNormalizerPipelineStep(output); } - private class MissingValueIndicatorPipelineStep : ILearningPipelineDataStep + private class LogMeanVarianceNormalizerPipelineStep : ILearningPipelineDataStep { - public MissingValueIndicatorPipelineStep(Output output) + public LogMeanVarianceNormalizerPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -11323,11 +12612,29 @@ public MissingValueIndicatorPipelineStep(Output output) namespace Transforms { - - public sealed partial class NADropTransformColumn : OneToOneColumn, IOneToOneColumn + public enum LpNormNormalizerTransformNormalizerKind : byte { - /// - /// Name of the new column + L2Norm = 0, + StdDev = 1, + L1Norm = 2, + LInf = 3 + } + + + public sealed partial class LpNormNormalizerTransformColumn : OneToOneColumn, IOneToOneColumn + { + /// + /// The norm to use to normalize each sample + /// + public LpNormNormalizerTransformNormalizerKind? NormKind { get; set; } + + /// + /// Subtract mean from each value before normalizing + /// + public bool? SubMean { get; set; } + + /// + /// Name of the new column /// public string Name { get; set; } @@ -11339,16 +12646,16 @@ public sealed partial class NADropTransformColumn : OneToOneColumn - /// Removes NAs from vector columns. + /// Normalize vectors (rows) individually by rescaling them to unit norm (L2, L1 or LInf). Performs the following operation on a vector X: Y = (X - M) / D, where M is mean and D is either L2 norm, L1 norm or LInf norm. /// - public sealed partial class MissingValuesDropper : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class LpNormalizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { - public MissingValuesDropper() + public LpNormalizer() { } - public MissingValuesDropper(params string[] inputColumns) + public LpNormalizer(params string[] inputColumns) { if (inputColumns != null) { @@ -11359,36 +12666,46 @@ public MissingValuesDropper(params string[] inputColumns) } } - public MissingValuesDropper(params ValueTuple[] inputOutputColumns) + public LpNormalizer(params (string inputColumn, string outputColumn)[] inputOutputColumns) { if (inputOutputColumns != null) { - foreach (ValueTuple inputOutput in inputOutputColumns) + foreach (var inputOutput in inputOutputColumns) { - AddColumn(inputOutput.Item2, inputOutput.Item1); + AddColumn(inputOutput.outputColumn, inputOutput.inputColumn); } } } - public void AddColumn(string source) + public void AddColumn(string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(source)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); Column = list.ToArray(); } - public void AddColumn(string name, string source) + public void AddColumn(string outputColumn, string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(name, source)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); Column = list.ToArray(); } /// - /// Columns to drop the NAs for + /// New column definition(s) (optional form: name:src) + /// + public LpNormNormalizerTransformColumn[] Column { get; set; } + + /// + /// The norm to use to normalize each sample + /// + public LpNormNormalizerTransformNormalizerKind NormKind { get; set; } = LpNormNormalizerTransformNormalizerKind.L2Norm; + + /// + /// Subtract mean from each value before normalizing /// - public Transforms.NADropTransformColumn[] Column { get; set; } + public bool SubMean { get; set; } = false; /// /// Input dataset @@ -11417,18 +12734,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(MissingValuesDropper)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(LpNormalizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new MissingValuesDropperPipelineStep(output); + return new LpNormalizerPipelineStep(output); } - private class MissingValuesDropperPipelineStep : ILearningPipelineDataStep + private class LpNormalizerPipelineStep : ILearningPipelineDataStep { - public MissingValuesDropperPipelineStep(Output output) + public LpNormalizerPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -11444,21 +12761,103 @@ namespace Transforms { /// - /// Filters out rows that contain missing values. + /// Combines a sequence of TransformModels and a PredictorModel into a single PredictorModel. /// - public sealed partial class MissingValuesRowDropper : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class ManyHeterogeneousModelCombiner { /// - /// Column + /// Transform model /// - public string[] Column { get; set; } + public ArrayVar TransformModels { get; set; } = new ArrayVar(); /// - /// If true, keep only rows that contain NA values, and filter the rest. + /// Predictor model /// - public bool Complement { get; set; } = false; + public Var PredictorModel { get; set; } = new Var(); + + + public sealed class Output + { + /// + /// Predictor model + /// + public Var PredictorModel { get; set; } = new Var(); + + } + } + } + + namespace Transforms + { + + /// + /// Normalizes the data based on the computed mean and variance of the data. + /// + public sealed partial class MeanVarianceNormalizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + { + + public MeanVarianceNormalizer() + { + } + + public MeanVarianceNormalizer(params string[] inputColumns) + { + if (inputColumns != null) + { + foreach (string input in inputColumns) + { + AddColumn(input); + } + } + } + + public MeanVarianceNormalizer(params (string inputColumn, string outputColumn)[] inputOutputColumns) + { + if (inputOutputColumns != null) + { + foreach (var inputOutput in inputOutputColumns) + { + AddColumn(inputOutput.outputColumn, inputOutput.inputColumn); + } + } + } + + public void AddColumn(string inputColumn) + { + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); + Column = list.ToArray(); + } + + public void AddColumn(string outputColumn, string inputColumn) + { + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); + Column = list.ToArray(); + } + + + /// + /// Whether to use CDF as the output + /// + public bool UseCdf { get; set; } = false; + + /// + /// New column definition(s) (optional form: name:src) + /// + public NormalizeTransformAffineColumn[] Column { get; set; } + + /// + /// Whether to map zero to zero, preserving sparsity + /// + public bool FixZero { get; set; } = true; + + /// + /// Max number of examples used to train the normalizer + /// + public long MaxTrainingExamples { get; set; } = 1000000000; /// /// Input dataset @@ -11487,18 +12886,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(MissingValuesRowDropper)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(MeanVarianceNormalizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new MissingValuesRowDropperPipelineStep(output); + return new MeanVarianceNormalizerPipelineStep(output); } - private class MissingValuesRowDropperPipelineStep : ILearningPipelineDataStep + private class MeanVarianceNormalizerPipelineStep : ILearningPipelineDataStep { - public MissingValuesRowDropperPipelineStep(Output output) + public MeanVarianceNormalizerPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -11512,62 +12911,18 @@ public MissingValuesRowDropperPipelineStep(Output output) namespace Transforms { - public enum NAReplaceTransformReplacementKind - { - Default = 0, - DefaultValue = 0, - Def = 0, - Mean = 1, - Min = 2, - Minimum = 2, - Max = 3, - Maximum = 3, - SpecifiedValue = 4, - Val = 4, - Value = 4 - } - - - public sealed partial class NAReplaceTransformColumn : OneToOneColumn, IOneToOneColumn - { - /// - /// Replacement value for NAs (uses default value if not given) - /// - public string ReplacementString { get; set; } - - /// - /// The replacement method to utilize - /// - public Transforms.NAReplaceTransformReplacementKind? Kind { get; set; } - - /// - /// Whether to impute values by slot - /// - public bool? Slot { get; set; } - - /// - /// Name of the new column - /// - public string Name { get; set; } - - /// - /// Name of the source column - /// - public string Source { get; set; } - - } /// - /// Create an output column of the same type and size of the input column, where missing values are replaced with either the default value or the mean/min/max value (for non-text columns only). + /// Normalizes the data based on the observed minimum and maximum values of the data. /// - public sealed partial class MissingValueSubstitutor : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class MinMaxNormalizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { - public MissingValueSubstitutor() + public MinMaxNormalizer() { } - public MissingValueSubstitutor(params string[] inputColumns) + public MinMaxNormalizer(params string[] inputColumns) { if (inputColumns != null) { @@ -11578,46 +12933,46 @@ public MissingValueSubstitutor(params string[] inputColumns) } } - public MissingValueSubstitutor(params ValueTuple[] inputOutputColumns) + public MinMaxNormalizer(params (string inputColumn, string outputColumn)[] inputOutputColumns) { if (inputOutputColumns != null) { - foreach (ValueTuple inputOutput in inputOutputColumns) + foreach (var inputOutput in inputOutputColumns) { - AddColumn(inputOutput.Item2, inputOutput.Item1); + AddColumn(inputOutput.outputColumn, inputOutput.inputColumn); } } } - public void AddColumn(string source) + public void AddColumn(string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(source)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); Column = list.ToArray(); } - public void AddColumn(string name, string source) + public void AddColumn(string outputColumn, string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(name, source)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); Column = list.ToArray(); } /// - /// New column definition(s) (optional form: name:rep:src) + /// New column definition(s) (optional form: name:src) /// - public Transforms.NAReplaceTransformColumn[] Column { get; set; } + public NormalizeTransformAffineColumn[] Column { get; set; } /// - /// The replacement method to utilize + /// Whether to map zero to zero, preserving sparsity /// - public Transforms.NAReplaceTransformReplacementKind ReplacementKind { get; set; } = Transforms.NAReplaceTransformReplacementKind.Def; + public bool FixZero { get; set; } = true; /// - /// Whether to impute values by slot + /// Max number of examples used to train the normalizer /// - public bool ImputeBySlot { get; set; } = true; + public long MaxTrainingExamples { get; set; } = 1000000000; /// /// Input dataset @@ -11646,18 +13001,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(MissingValueSubstitutor)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(MinMaxNormalizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new MissingValueSubstitutorPipelineStep(output); + return new MinMaxNormalizerPipelineStep(output); } - private class MissingValueSubstitutorPipelineStep : ILearningPipelineDataStep + private class MinMaxNormalizerPipelineStep : ILearningPipelineDataStep { - public MissingValueSubstitutorPipelineStep(Output output) + public MinMaxNormalizerPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -11671,91 +13026,55 @@ public MissingValueSubstitutorPipelineStep(Output output) namespace Transforms { - - /// - /// Combines a sequence of TransformModels into a single model - /// - public sealed partial class ModelCombiner - { - - - /// - /// Input models - /// - public ArrayVar Models { get; set; } = new ArrayVar(); - - - public sealed class Output - { - /// - /// Combined model - /// - public Var OutputModel { get; set; } = new Var(); - - } - } - } - - namespace Transforms - { - public enum NgramTransformWeightingCriteria + public enum NAHandleTransformReplacementKind { - Tf = 0, - Idf = 1, - TfIdf = 2 + DefaultValue = 0, + Mean = 1, + Minimum = 2, + Maximum = 3 } - public sealed partial class NgramTransformColumn : OneToOneColumn, IOneToOneColumn + public sealed partial class NAHandleTransformColumn : OneToOneColumn, IOneToOneColumn { /// - /// Maximum ngram length + /// The replacement method to utilize /// - public int? NgramLength { get; set; } + public NAHandleTransformReplacementKind? Kind { get; set; } /// - /// Whether to include all ngram lengths up to NgramLength or only NgramLength + /// Whether to impute values by slot /// - public bool? AllLengths { get; set; } + public bool? ImputeBySlot { get; set; } /// - /// Maximum number of tokens to skip when constructing an ngram + /// Whether or not to concatenate an indicator vector column to the value column /// - public int? SkipLength { get; set; } + public bool? ConcatIndicator { get; set; } /// - /// Maximum number of ngrams to store in the dictionary + /// Name of the new column /// - public int[] MaxNumTerms { get; set; } + public string Name { get; set; } /// - /// Statistical measure used to evaluate how important a word is to a document in a corpus + /// Name of the source column /// - public Transforms.NgramTransformWeightingCriteria? Weighting { get; set; } - - /// - /// Name of the new column - /// - public string Name { get; set; } - - /// - /// Name of the source column - /// - public string Source { get; set; } + public string Source { get; set; } } /// - /// Produces a bag of counts of ngrams (sequences of consecutive values of length 1-n) in a given vector of keys. It does so by building a dictionary of ngrams and using the id in the dictionary as the index in the bag. + /// Handle missing values by replacing them with either the default value or the mean/min/max value (for non-text columns only). An indicator column can optionally be concatenated, if theinput column type is numeric. /// - public sealed partial class NGramTranslator : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class MissingValueHandler : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { - public NGramTranslator() + public MissingValueHandler() { } - public NGramTranslator(params string[] inputColumns) + public MissingValueHandler(params string[] inputColumns) { if (inputColumns != null) { @@ -11766,61 +13085,51 @@ public NGramTranslator(params string[] inputColumns) } } - public NGramTranslator(params ValueTuple[] inputOutputColumns) + public MissingValueHandler(params (string inputColumn, string outputColumn)[] inputOutputColumns) { if (inputOutputColumns != null) { - foreach (ValueTuple inputOutput in inputOutputColumns) + foreach (var inputOutput in inputOutputColumns) { - AddColumn(inputOutput.Item2, inputOutput.Item1); + AddColumn(inputOutput.outputColumn, inputOutput.inputColumn); } } } - public void AddColumn(string source) + public void AddColumn(string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(source)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); Column = list.ToArray(); } - public void AddColumn(string name, string source) + public void AddColumn(string outputColumn, string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(name, source)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); Column = list.ToArray(); } /// - /// New column definition(s) (optional form: name:src) - /// - public Transforms.NgramTransformColumn[] Column { get; set; } - - /// - /// Maximum ngram length - /// - public int NgramLength { get; set; } = 2; - - /// - /// Whether to store all ngram lengths up to ngramLength, or only ngramLength + /// New column definition(s) (optional form: name:rep:src) /// - public bool AllLengths { get; set; } = true; + public NAHandleTransformColumn[] Column { get; set; } /// - /// Maximum number of tokens to skip when constructing an ngram + /// The replacement method to utilize /// - public int SkipLength { get; set; } + public NAHandleTransformReplacementKind ReplaceWith { get; set; } = NAHandleTransformReplacementKind.DefaultValue; /// - /// Maximum number of ngrams to store in the dictionary + /// Whether to impute values by slot /// - public int[] MaxNumTerms { get; set; } = { 10000000 }; + public bool ImputeBySlot { get; set; } = true; /// - /// The weighting criteria + /// Whether or not to concatenate an indicator vector column to the value column /// - public Transforms.NgramTransformWeightingCriteria Weighting { get; set; } = Transforms.NgramTransformWeightingCriteria.Tf; + public bool Concat { get; set; } = true; /// /// Input dataset @@ -11849,18 +13158,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(NGramTranslator)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(MissingValueHandler)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new NGramTranslatorPipelineStep(output); + return new MissingValueHandlerPipelineStep(output); } - private class NGramTranslatorPipelineStep : ILearningPipelineDataStep + private class MissingValueHandlerPipelineStep : ILearningPipelineDataStep { - public NGramTranslatorPipelineStep(Output output) + public MissingValueHandlerPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -11875,77 +13184,71 @@ public NGramTranslatorPipelineStep(Output output) namespace Transforms { - /// - /// Does nothing. - /// - public sealed partial class NoOperation : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class NAIndicatorTransformColumn : OneToOneColumn, IOneToOneColumn { - - /// - /// Input dataset + /// Name of the new column /// - public Var Data { get; set; } = new Var(); + public string Name { get; set; } + /// + /// Name of the source column + /// + public string Source { get; set; } - public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput - { - /// - /// Transformed dataset - /// - public Var OutputData { get; set; } = new Var(); + } - /// - /// Transform model - /// - public Var Model { get; set; } = new Var(); + /// + /// Create a boolean output column with the same number of slots as the input column, where the output value is true if the value in the input column is missing. + /// + public sealed partial class MissingValueIndicator : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + { + public MissingValueIndicator() + { } - public Var GetInputData() => Data; - public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) + public MissingValueIndicator(params string[] inputColumns) { - if (previousStep != null) + if (inputColumns != null) { - if (!(previousStep is ILearningPipelineDataStep dataStep)) + foreach (string input in inputColumns) { - throw new InvalidOperationException($"{ nameof(NoOperation)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + AddColumn(input); } - - Data = dataStep.Data; } - Output output = experiment.Add(this); - return new NoOperationPipelineStep(output); } - - private class NoOperationPipelineStep : ILearningPipelineDataStep + + public MissingValueIndicator(params (string inputColumn, string outputColumn)[] inputOutputColumns) { - public NoOperationPipelineStep(Output output) + if (inputOutputColumns != null) { - Data = output.OutputData; - Model = output.Model; + foreach (var inputOutput in inputOutputColumns) + { + AddColumn(inputOutput.outputColumn, inputOutput.inputColumn); + } } - - public Var Data { get; } - public Var Model { get; } } - } - } - - namespace Transforms - { + + public void AddColumn(string inputColumn) + { + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); + Column = list.ToArray(); + } - /// - /// If the source column does not exist after deserialization, create a column with the right type and default values. - /// - public sealed partial class OptionalColumnCreator : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem - { + public void AddColumn(string outputColumn, string inputColumn) + { + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); + Column = list.ToArray(); + } /// - /// New column definition(s) + /// New column definition(s) (optional form: name:src) /// - public string[] Column { get; set; } + public NAIndicatorTransformColumn[] Column { get; set; } /// /// Input dataset @@ -11974,18 +13277,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(OptionalColumnCreator)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(MissingValueIndicator)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new OptionalColumnCreatorPipelineStep(output); + return new MissingValueIndicatorPipelineStep(output); } - private class OptionalColumnCreatorPipelineStep : ILearningPipelineDataStep + private class MissingValueIndicatorPipelineStep : ILearningPipelineDataStep { - public OptionalColumnCreatorPipelineStep(Output output) + public MissingValueIndicatorPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -12000,33 +13303,8 @@ public OptionalColumnCreatorPipelineStep(Output output) namespace Transforms { - public sealed partial class PcaTransformColumn : OneToOneColumn, IOneToOneColumn + public sealed partial class NADropTransformColumn : OneToOneColumn, IOneToOneColumn { - /// - /// The name of the weight column - /// - public string WeightColumn { get; set; } - - /// - /// The number of components in the PCA - /// - public int? Rank { get; set; } - - /// - /// Oversampling parameter for randomized PCA training - /// - public int? Oversampling { get; set; } - - /// - /// If enabled, data is centered to be zero mean - /// - public bool? Center { get; set; } - - /// - /// The seed for random number generation - /// - public int? Seed { get; set; } - /// /// Name of the new column /// @@ -12040,16 +13318,16 @@ public sealed partial class PcaTransformColumn : OneToOneColumn - /// Train an PCA Anomaly model. + /// Removes NAs from vector columns. /// - public sealed partial class PcaCalculator : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class MissingValuesDropper : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { - public PcaCalculator() + public MissingValuesDropper() { } - public PcaCalculator(params string[] inputColumns) + public MissingValuesDropper(params string[] inputColumns) { if (inputColumns != null) { @@ -12060,61 +13338,36 @@ public PcaCalculator(params string[] inputColumns) } } - public PcaCalculator(params ValueTuple[] inputOutputColumns) + public MissingValuesDropper(params (string inputColumn, string outputColumn)[] inputOutputColumns) { if (inputOutputColumns != null) { - foreach (ValueTuple inputOutput in inputOutputColumns) + foreach (var inputOutput in inputOutputColumns) { - AddColumn(inputOutput.Item2, inputOutput.Item1); + AddColumn(inputOutput.outputColumn, inputOutput.inputColumn); } } } - public void AddColumn(string source) + public void AddColumn(string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(source)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); Column = list.ToArray(); } - public void AddColumn(string name, string source) + public void AddColumn(string outputColumn, string inputColumn) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(name, source)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); Column = list.ToArray(); } /// - /// New column definition(s) (optional form: name:src) - /// - public Transforms.PcaTransformColumn[] Column { get; set; } - - /// - /// The name of the weight column - /// - public string WeightColumn { get; set; } - - /// - /// The number of components in the PCA - /// - public int Rank { get; set; } = 20; - - /// - /// Oversampling parameter for randomized PCA training - /// - public int Oversampling { get; set; } = 20; - - /// - /// If enabled, data is centered to be zero mean - /// - public bool Center { get; set; } = true; - - /// - /// The seed for random number generation + /// Columns to drop the NAs for /// - public int Seed { get; set; } + public NADropTransformColumn[] Column { get; set; } /// /// Input dataset @@ -12143,18 +13396,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(PcaCalculator)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(MissingValuesDropper)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new PcaCalculatorPipelineStep(output); + return new MissingValuesDropperPipelineStep(output); } - private class PcaCalculatorPipelineStep : ILearningPipelineDataStep + private class MissingValuesDropperPipelineStep : ILearningPipelineDataStep { - public PcaCalculatorPipelineStep(Output output) + public MissingValuesDropperPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -12170,16 +13423,21 @@ namespace Transforms { /// - /// Transforms a predicted label column to its original values, unless it is of type bool. + /// Filters out rows that contain missing values. /// - public sealed partial class PredictedLabelColumnOriginalValueConverter : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class MissingValuesRowDropper : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { /// - /// The predicted label column + /// Column /// - public string PredictedLabelColumn { get; set; } + public string[] Column { get; set; } + + /// + /// If true, keep only rows that contain NA values, and filter the rest. + /// + public bool Complement { get; set; } = false; /// /// Input dataset @@ -12208,18 +13466,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(PredictedLabelColumnOriginalValueConverter)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(MissingValuesRowDropper)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new PredictedLabelColumnOriginalValueConverterPipelineStep(output); + return new MissingValuesRowDropperPipelineStep(output); } - private class PredictedLabelColumnOriginalValueConverterPipelineStep : ILearningPipelineDataStep + private class MissingValuesRowDropperPipelineStep : ILearningPipelineDataStep { - public PredictedLabelColumnOriginalValueConverterPipelineStep(Output output) + public MissingValuesRowDropperPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -12233,47 +13491,106 @@ public PredictedLabelColumnOriginalValueConverterPipelineStep(Output output) namespace Transforms { + public enum NAReplaceTransformReplacementKind + { + DefaultValue = 0, + Mean = 1, + Minimum = 2, + Maximum = 3, + SpecifiedValue = 4 + } - public sealed partial class GenerateNumberTransformColumn + + public sealed partial class NAReplaceTransformColumn : OneToOneColumn, IOneToOneColumn { /// - /// Name of the new column + /// Replacement value for NAs (uses default value if not given) /// - public string Name { get; set; } + public string ReplacementString { get; set; } /// - /// Use an auto-incremented integer starting at zero instead of a random number + /// The replacement method to utilize /// - public bool? UseCounter { get; set; } + public NAReplaceTransformReplacementKind? Kind { get; set; } /// - /// The random seed + /// Whether to impute values by slot /// - public uint? Seed { get; set; } + public bool? Slot { get; set; } + + /// + /// Name of the new column + /// + public string Name { get; set; } + + /// + /// Name of the source column + /// + public string Source { get; set; } } /// - /// Adds a column with a generated number sequence. + /// Create an output column of the same type and size of the input column, where missing values are replaced with either the default value or the mean/min/max value (for non-text columns only). /// - public sealed partial class RandomNumberGenerator : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class MissingValueSubstitutor : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { + public MissingValueSubstitutor() + { + } + + public MissingValueSubstitutor(params string[] inputColumns) + { + if (inputColumns != null) + { + foreach (string input in inputColumns) + { + AddColumn(input); + } + } + } + + public MissingValueSubstitutor(params (string inputColumn, string outputColumn)[] inputOutputColumns) + { + if (inputOutputColumns != null) + { + foreach (var inputOutput in inputOutputColumns) + { + AddColumn(inputOutput.outputColumn, inputOutput.inputColumn); + } + } + } + + public void AddColumn(string inputColumn) + { + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); + Column = list.ToArray(); + } + + public void AddColumn(string outputColumn, string inputColumn) + { + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); + Column = list.ToArray(); + } + /// - /// New column definition(s) (optional form: name:seed) + /// New column definition(s) (optional form: name:rep:src) /// - public Transforms.GenerateNumberTransformColumn[] Column { get; set; } + public NAReplaceTransformColumn[] Column { get; set; } /// - /// Use an auto-incremented integer starting at zero instead of a random number + /// The replacement method to utilize /// - public bool UseCounter { get; set; } = false; + public NAReplaceTransformReplacementKind ReplacementKind { get; set; } = NAReplaceTransformReplacementKind.DefaultValue; /// - /// The random seed + /// Whether to impute values by slot /// - public uint Seed { get; set; } = 42; + public bool ImputeBySlot { get; set; } = true; /// /// Input dataset @@ -12302,18 +13619,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(RandomNumberGenerator)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(MissingValueSubstitutor)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new RandomNumberGeneratorPipelineStep(output); + return new MissingValueSubstitutorPipelineStep(output); } - private class RandomNumberGeneratorPipelineStep : ILearningPipelineDataStep + private class MissingValueSubstitutorPipelineStep : ILearningPipelineDataStep { - public RandomNumberGeneratorPipelineStep(Output output) + public MissingValueSubstitutorPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -12329,111 +13646,154 @@ namespace Transforms { /// - /// Filters a dataview on a column of type Single, Double or Key (contiguous). Keeps the values that are in the specified min/max range. NaNs are always filtered out. If the input is a Key type, the min/max are considered percentages of the number of values. + /// Combines a sequence of TransformModels into a single model /// - public sealed partial class RowRangeFilter : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class ModelCombiner { /// - /// Column + /// Input models /// - public string Column { get; set; } + public ArrayVar Models { get; set; } = new ArrayVar(); + + + public sealed class Output + { + /// + /// Combined model + /// + public Var OutputModel { get; set; } = new Var(); + + } + } + } + + namespace Transforms + { + public enum NgramTransformWeightingCriteria + { + Tf = 0, + Idf = 1, + TfIdf = 2 + } + + public sealed partial class NgramTransformColumn : OneToOneColumn, IOneToOneColumn + { /// - /// Minimum value (0 to 1 for key types) + /// Maximum ngram length /// - public double? Min { get; set; } + public int? NgramLength { get; set; } /// - /// Maximum value (0 to 1 for key types) + /// Whether to include all ngram lengths up to NgramLength or only NgramLength /// - public double? Max { get; set; } + public bool? AllLengths { get; set; } /// - /// If true, keep the values that fall outside the range. + /// Maximum number of tokens to skip when constructing an ngram /// - public bool Complement { get; set; } = false; + public int? SkipLength { get; set; } /// - /// If true, include in the range the values that are equal to min. + /// Maximum number of ngrams to store in the dictionary /// - public bool IncludeMin { get; set; } = true; + public int[] MaxNumTerms { get; set; } /// - /// If true, include in the range the values that are equal to max. + /// Statistical measure used to evaluate how important a word is to a document in a corpus /// - public bool? IncludeMax { get; set; } + public NgramTransformWeightingCriteria? Weighting { get; set; } /// - /// Input dataset + /// Name of the new column /// - public Var Data { get; set; } = new Var(); + public string Name { get; set; } + /// + /// Name of the source column + /// + public string Source { get; set; } - public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput - { - /// - /// Transformed dataset - /// - public Var OutputData { get; set; } = new Var(); + } - /// - /// Transform model - /// - public Var Model { get; set; } = new Var(); + /// + /// Produces a bag of counts of ngrams (sequences of consecutive values of length 1-n) in a given vector of keys. It does so by building a dictionary of ngrams and using the id in the dictionary as the index in the bag. + /// + public sealed partial class NGramTranslator : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + { + public NGramTranslator() + { } - public Var GetInputData() => Data; - public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) + public NGramTranslator(params string[] inputColumns) { - if (previousStep != null) + if (inputColumns != null) { - if (!(previousStep is ILearningPipelineDataStep dataStep)) + foreach (string input in inputColumns) { - throw new InvalidOperationException($"{ nameof(RowRangeFilter)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + AddColumn(input); } - - Data = dataStep.Data; } - Output output = experiment.Add(this); - return new RowRangeFilterPipelineStep(output); } - - private class RowRangeFilterPipelineStep : ILearningPipelineDataStep + + public NGramTranslator(params (string inputColumn, string outputColumn)[] inputOutputColumns) { - public RowRangeFilterPipelineStep(Output output) + if (inputOutputColumns != null) { - Data = output.OutputData; - Model = output.Model; + foreach (var inputOutput in inputOutputColumns) + { + AddColumn(inputOutput.outputColumn, inputOutput.inputColumn); + } } + } + + public void AddColumn(string inputColumn) + { + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); + Column = list.ToArray(); + } - public Var Data { get; } - public Var Model { get; } + public void AddColumn(string outputColumn, string inputColumn) + { + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); + Column = list.ToArray(); } - } - } - namespace Transforms - { - /// - /// Allows limiting input to a subset of rows at an optional offset. Can be used to implement data paging. - /// - public sealed partial class RowSkipAndTakeFilter : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem - { + /// + /// New column definition(s) (optional form: name:src) + /// + public NgramTransformColumn[] Column { get; set; } + /// + /// Maximum ngram length + /// + public int NgramLength { get; set; } = 2; /// - /// Number of items to skip + /// Whether to store all ngram lengths up to ngramLength, or only ngramLength /// - public long? Skip { get; set; } + public bool AllLengths { get; set; } = true; /// - /// Number of items to take + /// Maximum number of tokens to skip when constructing an ngram /// - public long? Take { get; set; } + public int SkipLength { get; set; } + + /// + /// Maximum number of ngrams to store in the dictionary + /// + public int[] MaxNumTerms { get; set; } = { 10000000 }; + + /// + /// The weighting criteria + /// + public NgramTransformWeightingCriteria Weighting { get; set; } = NgramTransformWeightingCriteria.Tf; /// /// Input dataset @@ -12462,18 +13822,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(RowSkipAndTakeFilter)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(NGramTranslator)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new RowSkipAndTakeFilterPipelineStep(output); + return new NGramTranslatorPipelineStep(output); } - private class RowSkipAndTakeFilterPipelineStep : ILearningPipelineDataStep + private class NGramTranslatorPipelineStep : ILearningPipelineDataStep { - public RowSkipAndTakeFilterPipelineStep(Output output) + public NGramTranslatorPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -12489,17 +13849,12 @@ namespace Transforms { /// - /// Allows limiting input to a subset of rows by skipping a number of rows. + /// Does nothing. /// - public sealed partial class RowSkipFilter : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class NoOperation : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { - /// - /// Number of items to skip - /// - public long Count { get; set; } - /// /// Input dataset /// @@ -12527,18 +13882,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(RowSkipFilter)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(NoOperation)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new RowSkipFilterPipelineStep(output); + return new NoOperationPipelineStep(output); } - private class RowSkipFilterPipelineStep : ILearningPipelineDataStep + private class NoOperationPipelineStep : ILearningPipelineDataStep { - public RowSkipFilterPipelineStep(Output output) + public NoOperationPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -12554,16 +13909,16 @@ namespace Transforms { /// - /// Allows limiting input to a subset of rows by taking N first rows. + /// If the source column does not exist after deserialization, create a column with the right type and default values. /// - public sealed partial class RowTakeFilter : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class OptionalColumnCreator : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { /// - /// Number of items to take + /// New column definition(s) /// - public long Count { get; set; } = 9223372036854775807; + public string[] Column { get; set; } /// /// Input dataset @@ -12592,18 +13947,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(RowTakeFilter)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(OptionalColumnCreator)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new RowTakeFilterPipelineStep(output); + return new OptionalColumnCreatorPipelineStep(output); } - private class RowTakeFilterPipelineStep : ILearningPipelineDataStep + private class OptionalColumnCreatorPipelineStep : ILearningPipelineDataStep { - public RowTakeFilterPipelineStep(Output output) + public OptionalColumnCreatorPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -12618,126 +13973,121 @@ public RowTakeFilterPipelineStep(Output output) namespace Transforms { - /// - /// Selects only the last score columns and the extra columns specified in the arguments. - /// - public sealed partial class ScoreColumnSelector : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class PcaTransformColumn : OneToOneColumn, IOneToOneColumn { - + /// + /// The name of the weight column + /// + public string WeightColumn { get; set; } /// - /// Extra columns to write + /// The number of components in the PCA /// - public string[] ExtraColumns { get; set; } + public int? Rank { get; set; } /// - /// Input dataset + /// Oversampling parameter for randomized PCA training /// - public Var Data { get; set; } = new Var(); + public int? Oversampling { get; set; } + /// + /// If enabled, data is centered to be zero mean + /// + public bool? Center { get; set; } - public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput - { - /// - /// Transformed dataset - /// - public Var OutputData { get; set; } = new Var(); + /// + /// The seed for random number generation + /// + public int? Seed { get; set; } - /// - /// Transform model - /// - public Var Model { get; set; } = new Var(); + /// + /// Name of the new column + /// + public string Name { get; set; } + + /// + /// Name of the source column + /// + public string Source { get; set; } + + } + /// + /// Train an PCA Anomaly model. + /// + public sealed partial class PcaCalculator : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + { + + public PcaCalculator() + { } - public Var GetInputData() => Data; - public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) + public PcaCalculator(params string[] inputColumns) { - if (previousStep != null) + if (inputColumns != null) { - if (!(previousStep is ILearningPipelineDataStep dataStep)) + foreach (string input in inputColumns) { - throw new InvalidOperationException($"{ nameof(ScoreColumnSelector)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + AddColumn(input); } - - Data = dataStep.Data; } - Output output = experiment.Add(this); - return new ScoreColumnSelectorPipelineStep(output); } - - private class ScoreColumnSelectorPipelineStep : ILearningPipelineDataStep + + public PcaCalculator(params (string inputColumn, string outputColumn)[] inputOutputColumns) { - public ScoreColumnSelectorPipelineStep(Output output) + if (inputOutputColumns != null) { - Data = output.OutputData; - Model = output.Model; + foreach (var inputOutput in inputOutputColumns) + { + AddColumn(inputOutput.outputColumn, inputOutput.inputColumn); + } } - - public Var Data { get; } - public Var Model { get; } } - } - } - - namespace Transforms - { + + public void AddColumn(string inputColumn) + { + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); + Column = list.ToArray(); + } - /// - /// Turn the predictor model into a transform model - /// - public sealed partial class Scorer - { + public void AddColumn(string outputColumn, string inputColumn) + { + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); + Column = list.ToArray(); + } /// - /// The predictor model to turn into a transform + /// New column definition(s) (optional form: name:src) /// - public Var PredictorModel { get; set; } = new Var(); - - - public sealed class Output - { - /// - /// The scored dataset - /// - public Var ScoredData { get; set; } = new Var(); - - /// - /// The scoring transform - /// - public Var ScoringTransform { get; set; } = new Var(); - - } - } - } - - namespace Transforms - { - public enum UngroupTransformUngroupMode - { - Inner = 0, - Outer = 1, - First = 2 - } + public PcaTransformColumn[] Column { get; set; } + /// + /// The name of the weight column + /// + public string WeightColumn { get; set; } - /// - /// Un-groups vector columns into sequences of rows, inverse of Group transform - /// - public sealed partial class Segregator : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem - { + /// + /// The number of components in the PCA + /// + public int Rank { get; set; } = 20; + /// + /// Oversampling parameter for randomized PCA training + /// + public int Oversampling { get; set; } = 20; /// - /// Columns to unroll, or 'pivot' + /// If enabled, data is centered to be zero mean /// - public string[] Column { get; set; } + public bool Center { get; set; } = true; /// - /// Specifies how to unroll multiple pivot columns of different size. + /// The seed for random number generation /// - public Transforms.UngroupTransformUngroupMode Mode { get; set; } = Transforms.UngroupTransformUngroupMode.Inner; + public int Seed { get; set; } /// /// Input dataset @@ -12766,18 +14116,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(Segregator)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(PcaCalculator)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new SegregatorPipelineStep(output); + return new PcaCalculatorPipelineStep(output); } - private class SegregatorPipelineStep : ILearningPipelineDataStep + private class PcaCalculatorPipelineStep : ILearningPipelineDataStep { - public SegregatorPipelineStep(Output output) + public PcaCalculatorPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -12793,21 +14143,16 @@ namespace Transforms { /// - /// Uses a pretrained sentiment model to score input strings + /// Transforms a predicted label column to its original values, unless it is of type bool. /// - public sealed partial class SentimentAnalyzer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class PredictedLabelColumnOriginalValueConverter : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { /// - /// Name of the source column. - /// - public string Source { get; set; } - - /// - /// Name of the new column. + /// The predicted label column /// - public string Name { get; set; } + public string PredictedLabelColumn { get; set; } /// /// Input dataset @@ -12836,18 +14181,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(SentimentAnalyzer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(PredictedLabelColumnOriginalValueConverter)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new SentimentAnalyzerPipelineStep(output); + return new PredictedLabelColumnOriginalValueConverterPipelineStep(output); } - private class SentimentAnalyzerPipelineStep : ILearningPipelineDataStep + private class PredictedLabelColumnOriginalValueConverterPipelineStep : ILearningPipelineDataStep { - public SentimentAnalyzerPipelineStep(Output output) + public PredictedLabelColumnOriginalValueConverterPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -12862,82 +14207,46 @@ public SentimentAnalyzerPipelineStep(Output output) namespace Transforms { - /// - /// Similar to BinNormalizer, but calculates bins based on correlation with the label column, not equi-density. The new value is bin_number / number_of_bins. - /// - public sealed partial class SupervisedBinNormalizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class GenerateNumberTransformColumn { - - public SupervisedBinNormalizer() - { - } - - public SupervisedBinNormalizer(params string[] inputColumns) - { - if (inputColumns != null) - { - foreach (string input in inputColumns) - { - AddColumn(input); - } - } - } - - public SupervisedBinNormalizer(params ValueTuple[] inputOutputColumns) - { - if (inputOutputColumns != null) - { - foreach (ValueTuple inputOutput in inputOutputColumns) - { - AddColumn(inputOutput.Item2, inputOutput.Item1); - } - } - } - - public void AddColumn(string source) - { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(source)); - Column = list.ToArray(); - } - - public void AddColumn(string name, string source) - { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(name, source)); - Column = list.ToArray(); - } - - /// - /// Label column for supervised binning + /// Name of the new column /// - public string LabelColumn { get; set; } + public string Name { get; set; } /// - /// Minimum number of examples per bin + /// Use an auto-incremented integer starting at zero instead of a random number /// - public int MinBinSize { get; set; } = 10; + public bool? UseCounter { get; set; } /// - /// New column definition(s) (optional form: name:src) + /// The random seed /// - public Transforms.NormalizeTransformBinColumn[] Column { get; set; } + public uint? Seed { get; set; } + + } + + /// + /// Adds a column with a generated number sequence. + /// + public sealed partial class RandomNumberGenerator : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + { + /// - /// Max number of bins, power of 2 recommended + /// New column definition(s) (optional form: name:seed) /// - public int NumBins { get; set; } = 1024; + public GenerateNumberTransformColumn[] Column { get; set; } /// - /// Whether to map zero to zero, preserving sparsity + /// Use an auto-incremented integer starting at zero instead of a random number /// - public bool FixZero { get; set; } = true; + public bool UseCounter { get; set; } = false; /// - /// Max number of examples used to train the normalizer + /// The random seed /// - public long MaxTrainingExamples { get; set; } = 1000000000; + public uint Seed { get; set; } = 42; /// /// Input dataset @@ -12966,18 +14275,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(SupervisedBinNormalizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(RandomNumberGenerator)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new SupervisedBinNormalizerPipelineStep(output); + return new RandomNumberGeneratorPipelineStep(output); } - private class SupervisedBinNormalizerPipelineStep : ILearningPipelineDataStep + private class RandomNumberGeneratorPipelineStep : ILearningPipelineDataStep { - public SupervisedBinNormalizerPipelineStep(Output output) + public RandomNumberGeneratorPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -12991,149 +14300,43 @@ public SupervisedBinNormalizerPipelineStep(Output output) namespace Transforms { - public enum TextTransformLanguage - { - English = 1, - French = 2, - German = 3, - Dutch = 4, - Italian = 5, - Spanish = 6, - Japanese = 7 - } - public enum TextNormalizerTransformCaseNormalizationMode + /// + /// Filters a dataview on a column of type Single, Double or Key (contiguous). Keeps the values that are in the specified min/max range. NaNs are always filtered out. If the input is a Key type, the min/max are considered percentages of the number of values. + /// + public sealed partial class RowRangeFilter : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { - Lower = 0, - Upper = 1, - None = 2 - } - public enum TextTransformTextNormKind - { - None = 0, - L1 = 1, - L2 = 2, - LInf = 3 - } - - - public sealed partial class TextTransformColumn : ManyToOneColumn, IManyToOneColumn - { - /// - /// Name of the new column - /// - public string Name { get; set; } - - /// - /// Name of the source column - /// - public string[] Source { get; set; } - - } - - public sealed partial class TermLoaderArguments - { - /// - /// List of terms - /// - public string[] Term { get; set; } - - /// - /// How items should be ordered when vectorized. By default, they will be in the order encountered. If by value items are sorted according to their default comparison, e.g., text sorting will be case sensitive (e.g., 'A' then 'Z' then 'a'). - /// - public Transforms.TermTransformSortOrder Sort { get; set; } = Transforms.TermTransformSortOrder.Occurrence; - - /// - /// Drop unknown terms instead of mapping them to NA term. - /// - public bool DropUnknowns { get; set; } = false; - - } - - /// - /// A transform that turns a collection of text documents into numerical feature vectors. The feature vectors are normalized counts of (word and/or character) ngrams in a given tokenized text. - /// - public sealed partial class TextFeaturizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem - { - - public TextFeaturizer() - { - } - - public TextFeaturizer(string outputColumn, params string[] inputColumns) - { - AddColumn(outputColumn, inputColumns); - } - - public void AddColumn(string name, params string[] source) - { - Column = ManyToOneColumn.Create(name, source); - } - - - /// - /// New column definition (optional form: name:srcs). - /// - public Transforms.TextTransformColumn Column { get; set; } - - /// - /// Dataset language or 'AutoDetect' to detect language per row. - /// - public Transforms.TextTransformLanguage Language { get; set; } = Transforms.TextTransformLanguage.English; /// - /// Stopwords remover. - /// - [JsonConverter(typeof(ComponentSerializer))] - public StopWordsRemover StopWordsRemover { get; set; } - - /// - /// Casing text using the rules of the invariant culture. - /// - public Transforms.TextNormalizerTransformCaseNormalizationMode TextCase { get; set; } = Transforms.TextNormalizerTransformCaseNormalizationMode.Lower; - - /// - /// Whether to keep diacritical marks or remove them. - /// - public bool KeepDiacritics { get; set; } = false; - - /// - /// Whether to keep punctuation marks or remove them. - /// - public bool KeepPunctuations { get; set; } = true; - - /// - /// Whether to keep numbers or remove them. + /// Column /// - public bool KeepNumbers { get; set; } = true; + public string Column { get; set; } /// - /// Whether to output the transformed text tokens as an additional column. + /// Minimum value (0 to 1 for key types) /// - public bool OutputTokens { get; set; } = false; + public double? Min { get; set; } /// - /// A dictionary of whitelisted terms. + /// Maximum value (0 to 1 for key types) /// - public Transforms.TermLoaderArguments Dictionary { get; set; } + public double? Max { get; set; } /// - /// Ngram feature extractor to use for words (WordBag/WordHashBag). + /// If true, keep the values that fall outside the range. /// - [JsonConverter(typeof(ComponentSerializer))] - public NgramExtractor WordFeatureExtractor { get; set; } = new NGramNgramExtractor(); + public bool Complement { get; set; } = false; /// - /// Ngram feature extractor to use for characters (WordBag/WordHashBag). + /// If true, include in the range the values that are equal to min. /// - [JsonConverter(typeof(ComponentSerializer))] - public NgramExtractor CharFeatureExtractor { get; set; } = new NGramNgramExtractor() { NgramLength = 3, AllLengths = false }; + public bool IncludeMin { get; set; } = true; /// - /// Normalize vectors (rows) individually by rescaling them to unit norm. + /// If true, include in the range the values that are equal to max. /// - public Transforms.TextTransformTextNormKind VectorNormalizer { get; set; } = Transforms.TextTransformTextNormKind.L2; + public bool? IncludeMax { get; set; } /// /// Input dataset @@ -13162,18 +14365,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(TextFeaturizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(RowRangeFilter)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new TextFeaturizerPipelineStep(output); + return new RowRangeFilterPipelineStep(output); } - private class TextFeaturizerPipelineStep : ILearningPipelineDataStep + private class RowRangeFilterPipelineStep : ILearningPipelineDataStep { - public TextFeaturizerPipelineStep(Output output) + public RowRangeFilterPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -13189,76 +14392,21 @@ namespace Transforms { /// - /// Converts input values (words, numbers, etc.) to index in a dictionary. + /// Allows limiting input to a subset of rows at an optional offset. Can be used to implement data paging. /// - public sealed partial class TextToKeyConverter : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class RowSkipAndTakeFilter : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { - public TextToKeyConverter() - { - } - - public TextToKeyConverter(params string[] inputColumns) - { - if (inputColumns != null) - { - foreach (string input in inputColumns) - { - AddColumn(input); - } - } - } - - public TextToKeyConverter(params ValueTuple[] inputOutputColumns) - { - if (inputOutputColumns != null) - { - foreach (ValueTuple inputOutput in inputOutputColumns) - { - AddColumn(inputOutput.Item2, inputOutput.Item1); - } - } - } - - public void AddColumn(string source) - { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(source)); - Column = list.ToArray(); - } - - public void AddColumn(string name, string source) - { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(name, source)); - Column = list.ToArray(); - } - - - /// - /// New column definition(s) (optional form: name:src) - /// - public Transforms.TermTransformColumn[] Column { get; set; } - - /// - /// Maximum number of terms to keep per column when auto-training - /// - public int MaxNumTerms { get; set; } = 1000000; - - /// - /// List of terms - /// - public string[] Term { get; set; } /// - /// How items should be ordered when vectorized. By default, they will be in the order encountered. If by value items are sorted according to their default comparison, e.g., text sorting will be case sensitive (e.g., 'A' then 'Z' then 'a'). + /// Number of items to skip /// - public Transforms.TermTransformSortOrder Sort { get; set; } = Transforms.TermTransformSortOrder.Occurrence; + public long? Skip { get; set; } /// - /// Whether key value metadata should be text, regardless of the actual input type + /// Number of items to take /// - public bool TextKeyValues { get; set; } = false; + public long? Take { get; set; } /// /// Input dataset @@ -13287,18 +14435,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(TextToKeyConverter)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(RowSkipAndTakeFilter)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new TextToKeyConverterPipelineStep(output); + return new RowSkipAndTakeFilterPipelineStep(output); } - private class TextToKeyConverterPipelineStep : ILearningPipelineDataStep + private class RowSkipAndTakeFilterPipelineStep : ILearningPipelineDataStep { - public TextToKeyConverterPipelineStep(Output output) + public RowSkipAndTakeFilterPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -13314,68 +14462,16 @@ namespace Transforms { /// - /// Split the dataset into train and test sets - /// - public sealed partial class TrainTestDatasetSplitter - { - - - /// - /// Input dataset - /// - public Var Data { get; set; } = new Var(); - - /// - /// Fraction of training data - /// - public float Fraction { get; set; } = 0.8f; - - /// - /// Stratification column - /// - public string StratificationColumn { get; set; } - - - public sealed class Output - { - /// - /// Training data - /// - public Var TrainData { get; set; } = new Var(); - - /// - /// Testing data - /// - public Var TestData { get; set; } = new Var(); - - } - } - } - - namespace Transforms - { - - /// - /// Trains a tree ensemble, or loads it from a file, then maps a numeric feature vector to three outputs: 1. A vector containing the individual tree outputs of the tree ensemble. 2. A vector indicating the leaves that the feature vector falls on in the tree ensemble. 3. A vector indicating the paths that the feature vector falls on in the tree ensemble. If a both a model file and a trainer are specified - will use the model file. If neither are specified, will train a default FastTree model. This can handle key labels by training a regression model towards their optionally permuted indices. + /// Allows limiting input to a subset of rows by skipping a number of rows. /// - public sealed partial class TreeLeafFeaturizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.IFeaturizerInput, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + public sealed partial class RowSkipFilter : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { /// - /// Output column: The suffix to append to the default column names - /// - public string Suffix { get; set; } - - /// - /// If specified, determines the permutation seed for applying this featurizer to a multiclass problem. - /// - public int LabelPermutationSeed { get; set; } - - /// - /// Trainer to use + /// Number of items to skip /// - public Var PredictorModel { get; set; } = new Var(); + public long Count { get; set; } /// /// Input dataset @@ -13404,18 +14500,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(TreeLeafFeaturizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(RowSkipFilter)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new TreeLeafFeaturizerPipelineStep(output); + return new RowSkipFilterPipelineStep(output); } - private class TreeLeafFeaturizerPipelineStep : ILearningPipelineDataStep + private class RowSkipFilterPipelineStep : ILearningPipelineDataStep { - public TreeLeafFeaturizerPipelineStep(Output output) + public RowSkipFilterPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -13431,30 +14527,63 @@ namespace Transforms { /// - /// Combines a TransformModel and a PredictorModel into a single PredictorModel. + /// Allows limiting input to a subset of rows by taking N first rows. /// - public sealed partial class TwoHeterogeneousModelCombiner + public sealed partial class RowTakeFilter : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { /// - /// Transform model + /// Number of items to take /// - public Var TransformModel { get; set; } = new Var(); + public long Count { get; set; } = 9223372036854775807; /// - /// Predictor model + /// Input dataset /// - public Var PredictorModel { get; set; } = new Var(); + public Var Data { get; set; } = new Var(); - public sealed class Output + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput { /// - /// Predictor model + /// Transformed dataset /// - public Var PredictorModel { get; set; } = new Var(); + public Var OutputData { get; set; } = new Var(); + + /// + /// Transform model + /// + public Var Model { get; set; } = new Var(); + + } + public Var GetInputData() => Data; + + public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) + { + if (previousStep != null) + { + if (!(previousStep is ILearningPipelineDataStep dataStep)) + { + throw new InvalidOperationException($"{ nameof(RowTakeFilter)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + } + + Data = dataStep.Data; + } + Output output = experiment.Add(this); + return new RowTakeFilterPipelineStep(output); + } + + private class RowTakeFilterPipelineStep : ILearningPipelineDataStep + { + public RowTakeFilterPipelineStep(Output output) + { + Data = output.OutputData; + Model = output.Model; + } + public Var Data { get; } + public Var Model { get; } } } } @@ -13462,81 +14591,126 @@ public sealed class Output namespace Transforms { - public sealed partial class DelimitedTokenizeTransformColumn : OneToOneColumn, IOneToOneColumn + /// + /// Selects only the last score columns and the extra columns specified in the arguments. + /// + public sealed partial class ScoreColumnSelector : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { - /// - /// Comma separated set of term separator(s). Commonly: 'space', 'comma', 'semicolon' or other single character. - /// - public string TermSeparators { get; set; } + /// - /// Name of the new column + /// Extra columns to write /// - public string Name { get; set; } + public string[] ExtraColumns { get; set; } /// - /// Name of the source column + /// Input dataset /// - public string Source { get; set; } - - } + public Var Data { get; set; } = new Var(); - /// - /// The input to this transform is text, and the output is a vector of text containing the words (tokens) in the original text. The separator is space, but can be specified as any other character (or multiple characters) if needed. - /// - public sealed partial class WordTokenizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem - { - public WordTokenizer() + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput { + /// + /// Transformed dataset + /// + public Var OutputData { get; set; } = new Var(); + + /// + /// Transform model + /// + public Var Model { get; set; } = new Var(); + } + public Var GetInputData() => Data; - public WordTokenizer(params string[] inputColumns) + public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) { - if (inputColumns != null) + if (previousStep != null) { - foreach (string input in inputColumns) + if (!(previousStep is ILearningPipelineDataStep dataStep)) { - AddColumn(input); + throw new InvalidOperationException($"{ nameof(ScoreColumnSelector)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } + + Data = dataStep.Data; } + Output output = experiment.Add(this); + return new ScoreColumnSelectorPipelineStep(output); } - - public WordTokenizer(params ValueTuple[] inputOutputColumns) + + private class ScoreColumnSelectorPipelineStep : ILearningPipelineDataStep { - if (inputOutputColumns != null) + public ScoreColumnSelectorPipelineStep(Output output) { - foreach (ValueTuple inputOutput in inputOutputColumns) - { - AddColumn(inputOutput.Item2, inputOutput.Item1); - } + Data = output.OutputData; + Model = output.Model; } + + public Var Data { get; } + public Var Model { get; } } - - public void AddColumn(string source) - { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(source)); - Column = list.ToArray(); - } + } + } + + namespace Transforms + { + + /// + /// Turn the predictor model into a transform model + /// + public sealed partial class Scorer + { - public void AddColumn(string name, string source) + + /// + /// The predictor model to turn into a transform + /// + public Var PredictorModel { get; set; } = new Var(); + + + public sealed class Output { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(name, source)); - Column = list.ToArray(); + /// + /// The scored dataset + /// + public Var ScoredData { get; set; } = new Var(); + + /// + /// The scoring transform + /// + public Var ScoringTransform { get; set; } = new Var(); + } + } + } + + namespace Transforms + { + public enum UngroupTransformUngroupMode + { + Inner = 0, + Outer = 1, + First = 2 + } + + + /// + /// Un-groups vector columns into sequences of rows, inverse of Group transform + /// + public sealed partial class Segregator : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + { /// - /// New column definition(s) + /// Columns to unroll, or 'pivot' /// - public Transforms.DelimitedTokenizeTransformColumn[] Column { get; set; } + public string[] Column { get; set; } /// - /// Comma separated set of term separator(s). Commonly: 'space', 'comma', 'semicolon' or other single character. + /// Specifies how to unroll multiple pivot columns of different size. /// - public string TermSeparators { get; set; } = "space"; + public UngroupTransformUngroupMode Mode { get; set; } = UngroupTransformUngroupMode.Inner; /// /// Input dataset @@ -13565,18 +14739,18 @@ public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Exper { if (!(previousStep is ILearningPipelineDataStep dataStep)) { - throw new InvalidOperationException($"{ nameof(WordTokenizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + throw new InvalidOperationException($"{ nameof(Segregator)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); } Data = dataStep.Data; } Output output = experiment.Add(this); - return new WordTokenizerPipelineStep(output); + return new SegregatorPipelineStep(output); } - private class WordTokenizerPipelineStep : ILearningPipelineDataStep + private class SegregatorPipelineStep : ILearningPipelineDataStep { - public WordTokenizerPipelineStep(Output output) + public SegregatorPipelineStep(Output output) { Data = output.OutputData; Model = output.Model; @@ -13588,332 +14762,1810 @@ public WordTokenizerPipelineStep(Output output) } } - namespace Runtime + namespace Transforms { - public abstract class AutoMlEngine : ComponentKind {} - - /// - /// AutoML engine that returns learners with default settings. + /// Uses a pretrained sentiment model to score input strings /// - public sealed class DefaultsAutoMlEngine : AutoMlEngine + public sealed partial class SentimentAnalyzer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem { - internal override string ComponentName => "Defaults"; - } + /// + /// Name of the source column. + /// + public string Source { get; set; } - /// - /// AutoML engine that consists of distinct, hierarchical stages of operation. - /// - public sealed class RocketAutoMlEngine : AutoMlEngine - { /// - /// Number of learners to retain for second stage. + /// Name of the new column. /// - public int TopKLearners { get; set; } = 2; + public string Name { get; set; } /// - /// Number of trials for retained second stage learners. + /// Input dataset /// - public int SecondRoundTrialsPerLearner { get; set; } = 5; + public Var Data { get; set; } = new Var(); + + + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput + { + /// + /// Transformed dataset + /// + public Var OutputData { get; set; } = new Var(); + + /// + /// Transform model + /// + public Var Model { get; set; } = new Var(); + + } + public Var GetInputData() => Data; + + public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) + { + if (previousStep != null) + { + if (!(previousStep is ILearningPipelineDataStep dataStep)) + { + throw new InvalidOperationException($"{ nameof(SentimentAnalyzer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + } + + Data = dataStep.Data; + } + Output output = experiment.Add(this); + return new SentimentAnalyzerPipelineStep(output); + } + + private class SentimentAnalyzerPipelineStep : ILearningPipelineDataStep + { + public SentimentAnalyzerPipelineStep(Output output) + { + Data = output.OutputData; + Model = output.Model; + } + + public Var Data { get; } + public Var Model { get; } + } + } + } + + namespace Transforms + { + + /// + /// Similar to BinNormalizer, but calculates bins based on correlation with the label column, not equi-density. The new value is bin_number / number_of_bins. + /// + public sealed partial class SupervisedBinNormalizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + { + + public SupervisedBinNormalizer() + { + } + + public SupervisedBinNormalizer(params string[] inputColumns) + { + if (inputColumns != null) + { + foreach (string input in inputColumns) + { + AddColumn(input); + } + } + } + + public SupervisedBinNormalizer(params (string inputColumn, string outputColumn)[] inputOutputColumns) + { + if (inputOutputColumns != null) + { + foreach (var inputOutput in inputOutputColumns) + { + AddColumn(inputOutput.outputColumn, inputOutput.inputColumn); + } + } + } + + public void AddColumn(string inputColumn) + { + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); + Column = list.ToArray(); + } + + public void AddColumn(string outputColumn, string inputColumn) + { + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); + Column = list.ToArray(); + } + + + /// + /// Label column for supervised binning + /// + public string LabelColumn { get; set; } + + /// + /// Minimum number of examples per bin + /// + public int MinBinSize { get; set; } = 10; + + /// + /// New column definition(s) (optional form: name:src) + /// + public NormalizeTransformBinColumn[] Column { get; set; } + + /// + /// Max number of bins, power of 2 recommended + /// + public int NumBins { get; set; } = 1024; + + /// + /// Whether to map zero to zero, preserving sparsity + /// + public bool FixZero { get; set; } = true; + + /// + /// Max number of examples used to train the normalizer + /// + public long MaxTrainingExamples { get; set; } = 1000000000; + + /// + /// Input dataset + /// + public Var Data { get; set; } = new Var(); + + + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput + { + /// + /// Transformed dataset + /// + public Var OutputData { get; set; } = new Var(); + + /// + /// Transform model + /// + public Var Model { get; set; } = new Var(); + + } + public Var GetInputData() => Data; + + public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) + { + if (previousStep != null) + { + if (!(previousStep is ILearningPipelineDataStep dataStep)) + { + throw new InvalidOperationException($"{ nameof(SupervisedBinNormalizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + } + + Data = dataStep.Data; + } + Output output = experiment.Add(this); + return new SupervisedBinNormalizerPipelineStep(output); + } + + private class SupervisedBinNormalizerPipelineStep : ILearningPipelineDataStep + { + public SupervisedBinNormalizerPipelineStep(Output output) + { + Data = output.OutputData; + Model = output.Model; + } + + public Var Data { get; } + public Var Model { get; } + } + } + } + + namespace Transforms + { + public enum TextTransformLanguage + { + English = 1, + French = 2, + German = 3, + Dutch = 4, + Italian = 5, + Spanish = 6, + Japanese = 7 + } + + public enum TextNormalizerTransformCaseNormalizationMode + { + Lower = 0, + Upper = 1, + None = 2 + } + + public enum TextTransformTextNormKind + { + None = 0, + L1 = 1, + L2 = 2, + LInf = 3 + } + + + public sealed partial class TextTransformColumn : ManyToOneColumn, IManyToOneColumn + { + /// + /// Name of the new column + /// + public string Name { get; set; } + + /// + /// Name of the source column + /// + public string[] Source { get; set; } + + } + + public sealed partial class TermLoaderArguments + { + /// + /// List of terms + /// + public string[] Term { get; set; } + + /// + /// How items should be ordered when vectorized. By default, they will be in the order encountered. If by value items are sorted according to their default comparison, e.g., text sorting will be case sensitive (e.g., 'A' then 'Z' then 'a'). + /// + public TermTransformSortOrder Sort { get; set; } = TermTransformSortOrder.Occurrence; + + /// + /// Drop unknown terms instead of mapping them to NA term. + /// + public bool DropUnknowns { get; set; } = false; + + } + + /// + /// A transform that turns a collection of text documents into numerical feature vectors. The feature vectors are normalized counts of (word and/or character) ngrams in a given tokenized text. + /// + public sealed partial class TextFeaturizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + { + + public TextFeaturizer() + { + } + + public TextFeaturizer(string outputColumn, params string[] inputColumns) + { + AddColumn(outputColumn, inputColumns); + } + + public void AddColumn(string name, params string[] source) + { + Column = ManyToOneColumn.Create(name, source); + } + + + /// + /// New column definition (optional form: name:srcs). + /// + public TextTransformColumn Column { get; set; } + + /// + /// Dataset language or 'AutoDetect' to detect language per row. + /// + public TextTransformLanguage Language { get; set; } = TextTransformLanguage.English; + + /// + /// Stopwords remover. + /// + [JsonConverter(typeof(ComponentSerializer))] + public StopWordsRemover StopWordsRemover { get; set; } + + /// + /// Casing text using the rules of the invariant culture. + /// + public TextNormalizerTransformCaseNormalizationMode TextCase { get; set; } = TextNormalizerTransformCaseNormalizationMode.Lower; + + /// + /// Whether to keep diacritical marks or remove them. + /// + public bool KeepDiacritics { get; set; } = false; + + /// + /// Whether to keep punctuation marks or remove them. + /// + public bool KeepPunctuations { get; set; } = true; + + /// + /// Whether to keep numbers or remove them. + /// + public bool KeepNumbers { get; set; } = true; + + /// + /// Whether to output the transformed text tokens as an additional column. + /// + public bool OutputTokens { get; set; } = false; + + /// + /// A dictionary of whitelisted terms. + /// + public TermLoaderArguments Dictionary { get; set; } + + /// + /// Ngram feature extractor to use for words (WordBag/WordHashBag). + /// + [JsonConverter(typeof(ComponentSerializer))] + public NgramExtractor WordFeatureExtractor { get; set; } = new NGramNgramExtractor(); + + /// + /// Ngram feature extractor to use for characters (WordBag/WordHashBag). + /// + [JsonConverter(typeof(ComponentSerializer))] + public NgramExtractor CharFeatureExtractor { get; set; } = new NGramNgramExtractor() { NgramLength = 3, AllLengths = false }; + + /// + /// Normalize vectors (rows) individually by rescaling them to unit norm. + /// + public TextTransformTextNormKind VectorNormalizer { get; set; } = TextTransformTextNormKind.L2; + + /// + /// Input dataset + /// + public Var Data { get; set; } = new Var(); + + + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput + { + /// + /// Transformed dataset + /// + public Var OutputData { get; set; } = new Var(); + + /// + /// Transform model + /// + public Var Model { get; set; } = new Var(); + + } + public Var GetInputData() => Data; + + public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) + { + if (previousStep != null) + { + if (!(previousStep is ILearningPipelineDataStep dataStep)) + { + throw new InvalidOperationException($"{ nameof(TextFeaturizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + } + + Data = dataStep.Data; + } + Output output = experiment.Add(this); + return new TextFeaturizerPipelineStep(output); + } + + private class TextFeaturizerPipelineStep : ILearningPipelineDataStep + { + public TextFeaturizerPipelineStep(Output output) + { + Data = output.OutputData; + Model = output.Model; + } + + public Var Data { get; } + public Var Model { get; } + } + } + } + + namespace Transforms + { + + /// + /// Converts input values (words, numbers, etc.) to index in a dictionary. + /// + public sealed partial class TextToKeyConverter : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + { + + public TextToKeyConverter() + { + } + + public TextToKeyConverter(params string[] inputColumns) + { + if (inputColumns != null) + { + foreach (string input in inputColumns) + { + AddColumn(input); + } + } + } + + public TextToKeyConverter(params (string inputColumn, string outputColumn)[] inputOutputColumns) + { + if (inputOutputColumns != null) + { + foreach (var inputOutput in inputOutputColumns) + { + AddColumn(inputOutput.outputColumn, inputOutput.inputColumn); + } + } + } + + public void AddColumn(string inputColumn) + { + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); + Column = list.ToArray(); + } + + public void AddColumn(string outputColumn, string inputColumn) + { + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); + Column = list.ToArray(); + } + + + /// + /// New column definition(s) (optional form: name:src) + /// + public TermTransformColumn[] Column { get; set; } + + /// + /// Maximum number of terms to keep per column when auto-training + /// + public int MaxNumTerms { get; set; } = 1000000; + + /// + /// List of terms + /// + public string[] Term { get; set; } + + /// + /// How items should be ordered when vectorized. By default, they will be in the order encountered. If by value items are sorted according to their default comparison, e.g., text sorting will be case sensitive (e.g., 'A' then 'Z' then 'a'). + /// + public TermTransformSortOrder Sort { get; set; } = TermTransformSortOrder.Occurrence; + + /// + /// Whether key value metadata should be text, regardless of the actual input type + /// + public bool TextKeyValues { get; set; } = false; + + /// + /// Input dataset + /// + public Var Data { get; set; } = new Var(); + + + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput + { + /// + /// Transformed dataset + /// + public Var OutputData { get; set; } = new Var(); + + /// + /// Transform model + /// + public Var Model { get; set; } = new Var(); + + } + public Var GetInputData() => Data; + + public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) + { + if (previousStep != null) + { + if (!(previousStep is ILearningPipelineDataStep dataStep)) + { + throw new InvalidOperationException($"{ nameof(TextToKeyConverter)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + } + + Data = dataStep.Data; + } + Output output = experiment.Add(this); + return new TextToKeyConverterPipelineStep(output); + } + + private class TextToKeyConverterPipelineStep : ILearningPipelineDataStep + { + public TextToKeyConverterPipelineStep(Output output) + { + Data = output.OutputData; + Model = output.Model; + } + + public Var Data { get; } + public Var Model { get; } + } + } + } + + namespace Transforms + { + + /// + /// Split the dataset into train and test sets + /// + public sealed partial class TrainTestDatasetSplitter + { + + + /// + /// Input dataset + /// + public Var Data { get; set; } = new Var(); + + /// + /// Fraction of training data + /// + public float Fraction { get; set; } = 0.8f; + + /// + /// Stratification column + /// + public string StratificationColumn { get; set; } + + + public sealed class Output + { + /// + /// Training data + /// + public Var TrainData { get; set; } = new Var(); + + /// + /// Testing data + /// + public Var TestData { get; set; } = new Var(); + + } + } + } + + namespace Transforms + { + + /// + /// Trains a tree ensemble, or loads it from a file, then maps a numeric feature vector to three outputs: 1. A vector containing the individual tree outputs of the tree ensemble. 2. A vector indicating the leaves that the feature vector falls on in the tree ensemble. 3. A vector indicating the paths that the feature vector falls on in the tree ensemble. If a both a model file and a trainer are specified - will use the model file. If neither are specified, will train a default FastTree model. This can handle key labels by training a regression model towards their optionally permuted indices. + /// + public sealed partial class TreeLeafFeaturizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.IFeaturizerInput, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + { + + + /// + /// Output column: The suffix to append to the default column names + /// + public string Suffix { get; set; } + + /// + /// If specified, determines the permutation seed for applying this featurizer to a multiclass problem. + /// + public int LabelPermutationSeed { get; set; } + + /// + /// Trainer to use + /// + public Var PredictorModel { get; set; } = new Var(); + + /// + /// Input dataset + /// + public Var Data { get; set; } = new Var(); + + + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput + { + /// + /// Transformed dataset + /// + public Var OutputData { get; set; } = new Var(); + + /// + /// Transform model + /// + public Var Model { get; set; } = new Var(); + + } + public Var GetInputData() => Data; + + public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) + { + if (previousStep != null) + { + if (!(previousStep is ILearningPipelineDataStep dataStep)) + { + throw new InvalidOperationException($"{ nameof(TreeLeafFeaturizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + } + + Data = dataStep.Data; + } + Output output = experiment.Add(this); + return new TreeLeafFeaturizerPipelineStep(output); + } + + private class TreeLeafFeaturizerPipelineStep : ILearningPipelineDataStep + { + public TreeLeafFeaturizerPipelineStep(Output output) + { + Data = output.OutputData; + Model = output.Model; + } + + public Var Data { get; } + public Var Model { get; } + } + } + } + + namespace Transforms + { + + /// + /// Combines a TransformModel and a PredictorModel into a single PredictorModel. + /// + public sealed partial class TwoHeterogeneousModelCombiner + { + + + /// + /// Transform model + /// + public Var TransformModel { get; set; } = new Var(); + + /// + /// Predictor model + /// + public Var PredictorModel { get; set; } = new Var(); + + + public sealed class Output + { + /// + /// Predictor model + /// + public Var PredictorModel { get; set; } = new Var(); + + } + } + } + + namespace Transforms + { + + public sealed partial class DelimitedTokenizeTransformColumn : OneToOneColumn, IOneToOneColumn + { + /// + /// Comma separated set of term separator(s). Commonly: 'space', 'comma', 'semicolon' or other single character. + /// + public string TermSeparators { get; set; } + + /// + /// Name of the new column + /// + public string Name { get; set; } + + /// + /// Name of the source column + /// + public string Source { get; set; } + + } + + /// + /// The input to this transform is text, and the output is a vector of text containing the words (tokens) in the original text. The separator is space, but can be specified as any other character (or multiple characters) if needed. + /// + public sealed partial class WordTokenizer : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITransformInput, Microsoft.ML.ILearningPipelineItem + { + + public WordTokenizer() + { + } + + public WordTokenizer(params string[] inputColumns) + { + if (inputColumns != null) + { + foreach (string input in inputColumns) + { + AddColumn(input); + } + } + } + + public WordTokenizer(params (string inputColumn, string outputColumn)[] inputOutputColumns) + { + if (inputOutputColumns != null) + { + foreach (var inputOutput in inputOutputColumns) + { + AddColumn(inputOutput.outputColumn, inputOutput.inputColumn); + } + } + } + + public void AddColumn(string inputColumn) + { + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(inputColumn)); + Column = list.ToArray(); + } + + public void AddColumn(string outputColumn, string inputColumn) + { + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(outputColumn, inputColumn)); + Column = list.ToArray(); + } + + + /// + /// New column definition(s) + /// + public DelimitedTokenizeTransformColumn[] Column { get; set; } + + /// + /// Comma separated set of term separator(s). Commonly: 'space', 'comma', 'semicolon' or other single character. + /// + public string TermSeparators { get; set; } = "space"; + + /// + /// Input dataset + /// + public Var Data { get; set; } = new Var(); + + + public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITransformOutput + { + /// + /// Transformed dataset + /// + public Var OutputData { get; set; } = new Var(); + + /// + /// Transform model + /// + public Var Model { get; set; } = new Var(); + + } + public Var GetInputData() => Data; + + public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) + { + if (previousStep != null) + { + if (!(previousStep is ILearningPipelineDataStep dataStep)) + { + throw new InvalidOperationException($"{ nameof(WordTokenizer)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + } + + Data = dataStep.Data; + } + Output output = experiment.Add(this); + return new WordTokenizerPipelineStep(output); + } + + private class WordTokenizerPipelineStep : ILearningPipelineDataStep + { + public WordTokenizerPipelineStep(Output output) + { + Data = output.OutputData; + Model = output.Model; + } + + public Var Data { get; } + public Var Model { get; } + } + } + } + + namespace Runtime + { + public abstract class AutoMlEngine : ComponentKind {} + + + + /// + /// AutoML engine that returns learners with default settings. + /// + public sealed class DefaultsAutoMlEngine : AutoMlEngine + { + internal override string ComponentName => "Defaults"; + } + + + + /// + /// AutoML engine that consists of distinct, hierarchical stages of operation. + /// + public sealed class RocketAutoMlEngine : AutoMlEngine + { + /// + /// Number of learners to retain for second stage. + /// + public int TopKLearners { get; set; } = 2; + + /// + /// Number of trials for retained second stage learners. + /// + public int SecondRoundTrialsPerLearner { get; set; } = 5; + + /// + /// Use random initialization only. + /// + public bool RandomInitialization { get; set; } = false; + + /// + /// Number of initilization pipelines, used for random initialization only. + /// + public int NumInitializationPipelines { get; set; } = 20; + + internal override string ComponentName => "Rocket"; + } + + + + /// + /// AutoML engine using uniform random sampling. + /// + public sealed class UniformRandomAutoMlEngine : AutoMlEngine + { + internal override string ComponentName => "UniformRandom"; + } + + public abstract class AutoMlStateBase : ComponentKind {} + + public enum AutoInferenceAutoMlMlStateArgumentsMetrics + { + Auc = 0, + AccuracyMicro = 1, + AccuracyMacro = 2, + L2 = 3, + F1 = 4, + AuPrc = 5, + TopKAccuracy = 6, + Rms = 7, + LossFn = 8, + RSquared = 9, + LogLoss = 10, + LogLossReduction = 11, + Ndcg = 12, + Dcg = 13, + PositivePrecision = 14, + PositiveRecall = 15, + NegativePrecision = 16, + NegativeRecall = 17, + DrAtK = 18, + DrAtPFpr = 19, + DrAtNumPos = 20, + NumAnomalies = 21, + ThreshAtK = 22, + ThreshAtP = 23, + ThreshAtNumPos = 24, + Nmi = 25, + AvgMinScore = 26, + Dbi = 27 + } + + + + /// + /// State of an AutoML search and search space. + /// + public sealed class AutoMlStateAutoMlStateBase : AutoMlStateBase + { + /// + /// Supported metric for evaluator. + /// + public AutoInferenceAutoMlMlStateArgumentsMetrics Metric { get; set; } = AutoInferenceAutoMlMlStateArgumentsMetrics.Auc; + + /// + /// AutoML engine (pipeline optimizer) that generates next candidates. + /// + [JsonConverter(typeof(ComponentSerializer))] + public AutoMlEngine Engine { get; set; } + + /// + /// Kind of trainer for task, such as binary classification trainer, multiclass trainer, etc. + /// + public Microsoft.ML.Models.MacroUtilsTrainerKinds TrainerKind { get; set; } = Microsoft.ML.Models.MacroUtilsTrainerKinds.SignatureBinaryClassifierTrainer; + + /// + /// Arguments for creating terminator, which determines when to stop search. + /// + [JsonConverter(typeof(ComponentSerializer))] + public SearchTerminator TerminatorArgs { get; set; } + + /// + /// Learner set to sweep over (if available). + /// + public string[] RequestedLearners { get; set; } + + internal override string ComponentName => "AutoMlState"; + } + + public abstract class BoosterParameterFunction : ComponentKind {} + + + + /// + /// Dropouts meet Multiple Additive Regresion Trees. See https://arxiv.org/abs/1505.01866 + /// + public sealed class DartBoosterParameterFunction : BoosterParameterFunction + { + /// + /// Drop ratio for trees. Range:(0,1). + /// + [TlcModule.Range(Inf = 0d, Max = 1d)] + public double DropRate { get; set; } = 0.1d; + + /// + /// Max number of dropped tree in a boosting round. + /// + [TlcModule.Range(Inf = 0, Max = 2147483647)] + public int MaxDrop { get; set; } = 1; + + /// + /// Probability for not perform dropping in a boosting round. + /// + [TlcModule.Range(Inf = 0d, Max = 1d)] + public double SkipDrop { get; set; } = 0.5d; + + /// + /// True will enable xgboost dart mode. + /// + public bool XgboostDartMode { get; set; } = false; + + /// + /// True will enable uniform drop. + /// + public bool UniformDrop { get; set; } = false; + + /// + /// Use for binary classification when classes are not balanced. + /// + public bool UnbalancedSets { get; set; } = false; + + /// + /// Minimum loss reduction required to make a further partition on a leaf node of the tree. the larger, the more conservative the algorithm will be. + /// + [TlcModule.Range(Min = 0d)] + public double MinSplitGain { get; set; } + + /// + /// Maximum depth of a tree. 0 means no limit. However, tree still grows by best-first. + /// + [TlcModule.Range(Min = 0, Max = 2147483647)] + public int MaxDepth { get; set; } + + /// + /// Minimum sum of instance weight(hessian) needed in a child. If the tree partition step results in a leaf node with the sum of instance weight less than min_child_weight, then the building process will give up further partitioning. In linear regression mode, this simply corresponds to minimum number of instances needed to be in each node. The larger, the more conservative the algorithm will be. + /// + [TlcModule.Range(Min = 0d)] + public double MinChildWeight { get; set; } = 0.1d; + + /// + /// Subsample frequency. 0 means no subsample. If subsampleFreq > 0, it will use a subset(ratio=subsample) to train. And the subset will be updated on every Subsample iteratinos. + /// + [TlcModule.Range(Min = 0, Max = 2147483647)] + public int SubsampleFreq { get; set; } + + /// + /// Subsample ratio of the training instance. Setting it to 0.5 means that LightGBM randomly collected half of the data instances to grow trees and this will prevent overfitting. Range: (0,1]. + /// + [TlcModule.Range(Inf = 0d, Max = 1d)] + public double Subsample { get; set; } = 1d; + + /// + /// Subsample ratio of columns when constructing each tree. Range: (0,1]. + /// + [TlcModule.Range(Inf = 0d, Max = 1d)] + public double FeatureFraction { get; set; } = 1d; + + /// + /// L2 regularization term on weights, increasing this value will make model more conservative. + /// + [TlcModule.Range(Min = 0d)] + [TlcModule.SweepableDiscreteParamAttribute("RegLambda", new object[]{0f, 0.5f, 1f})] + public double RegLambda { get; set; } = 0.01d; + + /// + /// L1 regularization term on weights, increase this value will make model more conservative. + /// + [TlcModule.Range(Min = 0d)] + [TlcModule.SweepableDiscreteParamAttribute("RegAlpha", new object[]{0f, 0.5f, 1f})] + public double RegAlpha { get; set; } + + /// + /// Control the balance of positive and negative weights, useful for unbalanced classes. A typical value to consider: sum(negative cases) / sum(positive cases). + /// + public double ScalePosWeight { get; set; } = 1d; + + internal override string ComponentName => "dart"; + } + + + + /// + /// Traditional Gradient Boosting Decision Tree. + /// + public sealed class GbdtBoosterParameterFunction : BoosterParameterFunction + { + /// + /// Use for binary classification when classes are not balanced. + /// + public bool UnbalancedSets { get; set; } = false; + + /// + /// Minimum loss reduction required to make a further partition on a leaf node of the tree. the larger, the more conservative the algorithm will be. + /// + [TlcModule.Range(Min = 0d)] + public double MinSplitGain { get; set; } + + /// + /// Maximum depth of a tree. 0 means no limit. However, tree still grows by best-first. + /// + [TlcModule.Range(Min = 0, Max = 2147483647)] + public int MaxDepth { get; set; } + + /// + /// Minimum sum of instance weight(hessian) needed in a child. If the tree partition step results in a leaf node with the sum of instance weight less than min_child_weight, then the building process will give up further partitioning. In linear regression mode, this simply corresponds to minimum number of instances needed to be in each node. The larger, the more conservative the algorithm will be. + /// + [TlcModule.Range(Min = 0d)] + public double MinChildWeight { get; set; } = 0.1d; + + /// + /// Subsample frequency. 0 means no subsample. If subsampleFreq > 0, it will use a subset(ratio=subsample) to train. And the subset will be updated on every Subsample iteratinos. + /// + [TlcModule.Range(Min = 0, Max = 2147483647)] + public int SubsampleFreq { get; set; } + + /// + /// Subsample ratio of the training instance. Setting it to 0.5 means that LightGBM randomly collected half of the data instances to grow trees and this will prevent overfitting. Range: (0,1]. + /// + [TlcModule.Range(Inf = 0d, Max = 1d)] + public double Subsample { get; set; } = 1d; + + /// + /// Subsample ratio of columns when constructing each tree. Range: (0,1]. + /// + [TlcModule.Range(Inf = 0d, Max = 1d)] + public double FeatureFraction { get; set; } = 1d; + + /// + /// L2 regularization term on weights, increasing this value will make model more conservative. + /// + [TlcModule.Range(Min = 0d)] + [TlcModule.SweepableDiscreteParamAttribute("RegLambda", new object[]{0f, 0.5f, 1f})] + public double RegLambda { get; set; } = 0.01d; + + /// + /// L1 regularization term on weights, increase this value will make model more conservative. + /// + [TlcModule.Range(Min = 0d)] + [TlcModule.SweepableDiscreteParamAttribute("RegAlpha", new object[]{0f, 0.5f, 1f})] + public double RegAlpha { get; set; } + + /// + /// Control the balance of positive and negative weights, useful for unbalanced classes. A typical value to consider: sum(negative cases) / sum(positive cases). + /// + public double ScalePosWeight { get; set; } = 1d; + + internal override string ComponentName => "gbdt"; + } + + + + /// + /// Gradient-based One-Side Sampling. + /// + public sealed class GossBoosterParameterFunction : BoosterParameterFunction + { + /// + /// Retain ratio for large gradient instances. + /// + [TlcModule.Range(Inf = 0d, Max = 1d)] + public double TopRate { get; set; } = 0.2d; + + /// + /// Retain ratio for small gradient instances. + /// + [TlcModule.Range(Inf = 0d, Max = 1d)] + public double OtherRate { get; set; } = 0.1d; + + /// + /// Use for binary classification when classes are not balanced. + /// + public bool UnbalancedSets { get; set; } = false; + + /// + /// Minimum loss reduction required to make a further partition on a leaf node of the tree. the larger, the more conservative the algorithm will be. + /// + [TlcModule.Range(Min = 0d)] + public double MinSplitGain { get; set; } + + /// + /// Maximum depth of a tree. 0 means no limit. However, tree still grows by best-first. + /// + [TlcModule.Range(Min = 0, Max = 2147483647)] + public int MaxDepth { get; set; } + + /// + /// Minimum sum of instance weight(hessian) needed in a child. If the tree partition step results in a leaf node with the sum of instance weight less than min_child_weight, then the building process will give up further partitioning. In linear regression mode, this simply corresponds to minimum number of instances needed to be in each node. The larger, the more conservative the algorithm will be. + /// + [TlcModule.Range(Min = 0d)] + public double MinChildWeight { get; set; } = 0.1d; + + /// + /// Subsample frequency. 0 means no subsample. If subsampleFreq > 0, it will use a subset(ratio=subsample) to train. And the subset will be updated on every Subsample iteratinos. + /// + [TlcModule.Range(Min = 0, Max = 2147483647)] + public int SubsampleFreq { get; set; } + + /// + /// Subsample ratio of the training instance. Setting it to 0.5 means that LightGBM randomly collected half of the data instances to grow trees and this will prevent overfitting. Range: (0,1]. + /// + [TlcModule.Range(Inf = 0d, Max = 1d)] + public double Subsample { get; set; } = 1d; + + /// + /// Subsample ratio of columns when constructing each tree. Range: (0,1]. + /// + [TlcModule.Range(Inf = 0d, Max = 1d)] + public double FeatureFraction { get; set; } = 1d; + + /// + /// L2 regularization term on weights, increasing this value will make model more conservative. + /// + [TlcModule.Range(Min = 0d)] + [TlcModule.SweepableDiscreteParamAttribute("RegLambda", new object[]{0f, 0.5f, 1f})] + public double RegLambda { get; set; } = 0.01d; + + /// + /// L1 regularization term on weights, increase this value will make model more conservative. + /// + [TlcModule.Range(Min = 0d)] + [TlcModule.SweepableDiscreteParamAttribute("RegAlpha", new object[]{0f, 0.5f, 1f})] + public double RegAlpha { get; set; } + + /// + /// Control the balance of positive and negative weights, useful for unbalanced classes. A typical value to consider: sum(negative cases) / sum(positive cases). + /// + public double ScalePosWeight { get; set; } = 1d; + + internal override string ComponentName => "goss"; + } + + public abstract class CalibratorTrainer : ComponentKind {} + + + + public sealed class FixedPlattCalibratorCalibratorTrainer : CalibratorTrainer + { + /// + /// The slope parameter of f(x) = 1 / (1 + exp(-slope * x + offset) + /// + public double Slope { get; set; } = 1d; + + /// + /// The offset parameter of f(x) = 1 / (1 + exp(-slope * x + offset) + /// + public double Offset { get; set; } + + internal override string ComponentName => "FixedPlattCalibrator"; + } + + + + public sealed class NaiveCalibratorCalibratorTrainer : CalibratorTrainer + { + internal override string ComponentName => "NaiveCalibrator"; + } + + + + public sealed class PavCalibratorCalibratorTrainer : CalibratorTrainer + { + internal override string ComponentName => "PavCalibrator"; + } + + + + /// + /// Platt calibration. + /// + public sealed class PlattCalibratorCalibratorTrainer : CalibratorTrainer + { + internal override string ComponentName => "PlattCalibrator"; + } + + public abstract class ClassificationLossFunction : ComponentKind {} + + + + /// + /// Exponential loss. + /// + public sealed class ExpLossClassificationLossFunction : ClassificationLossFunction + { + /// + /// Beta (dilation) + /// + public float Beta { get; set; } = 1f; + + internal override string ComponentName => "ExpLoss"; + } + + + + /// + /// Hinge loss. + /// + public sealed class HingeLossClassificationLossFunction : ClassificationLossFunction + { + /// + /// Margin value + /// + public float Margin { get; set; } = 1f; + + internal override string ComponentName => "HingeLoss"; + } + + + + /// + /// Log loss. + /// + public sealed class LogLossClassificationLossFunction : ClassificationLossFunction + { + internal override string ComponentName => "LogLoss"; + } + + + + /// + /// Smoothed Hinge loss. + /// + public sealed class SmoothedHingeLossClassificationLossFunction : ClassificationLossFunction + { + /// + /// Smoothing constant + /// + public float SmoothingConst { get; set; } = 1f; + + internal override string ComponentName => "SmoothedHingeLoss"; + } + + public abstract class EarlyStoppingCriterion : ComponentKind {} + + + + /// + /// Stop in case of loss of generality. + /// + public sealed class GLEarlyStoppingCriterion : EarlyStoppingCriterion + { + /// + /// Threshold in range [0,1]. + /// + [TlcModule.Range(Min = 0f, Max = 1f)] + public float Threshold { get; set; } = 0.01f; + + internal override string ComponentName => "GL"; + } + + + + /// + /// Stops in case of low progress. + /// + public sealed class LPEarlyStoppingCriterion : EarlyStoppingCriterion + { + /// + /// Threshold in range [0,1]. + /// + [TlcModule.Range(Min = 0f, Max = 1f)] + public float Threshold { get; set; } = 0.01f; + + /// + /// The window size. + /// + [TlcModule.Range(Inf = 0)] + public int WindowSize { get; set; } = 5; + + internal override string ComponentName => "LP"; + } + + + + /// + /// Stops in case of generality to progress ration exceeds threshold. + /// + public sealed class PQEarlyStoppingCriterion : EarlyStoppingCriterion + { + /// + /// Threshold in range [0,1]. + /// + [TlcModule.Range(Min = 0f, Max = 1f)] + public float Threshold { get; set; } = 0.01f; + + /// + /// The window size. + /// + [TlcModule.Range(Inf = 0)] + public int WindowSize { get; set; } = 5; + + internal override string ComponentName => "PQ"; + } + + + + /// + /// Stop if validation score exceeds threshold value. + /// + public sealed class TREarlyStoppingCriterion : EarlyStoppingCriterion + { + /// + /// Tolerance threshold. (Non negative value) + /// + [TlcModule.Range(Min = 0f)] + public float Threshold { get; set; } = 0.01f; + + internal override string ComponentName => "TR"; + } + + + + /// + /// Stops in case of consecutive loss in generality. + /// + public sealed class UPEarlyStoppingCriterion : EarlyStoppingCriterion + { + /// + /// The window size. + /// + [TlcModule.Range(Inf = 0)] + public int WindowSize { get; set; } = 5; + + internal override string ComponentName => "UP"; + } + + public abstract class EnsembleBinaryOutputCombiner : ComponentKind {} + + + + public sealed class AverageEnsembleBinaryOutputCombiner : EnsembleBinaryOutputCombiner + { + internal override string ComponentName => "Average"; + } + + + + public sealed class MedianEnsembleBinaryOutputCombiner : EnsembleBinaryOutputCombiner + { + internal override string ComponentName => "Median"; + } + + + + public sealed class StackingEnsembleBinaryOutputCombiner : EnsembleBinaryOutputCombiner + { + /// + /// The proportion of instances to be selected to test the individual base learner. If it is 0, it uses training set + /// + public float ValidationDatasetProportion { get; set; } = 0.3f; + + internal override string ComponentName => "Stacking"; + } + + + + public sealed class VotingEnsembleBinaryOutputCombiner : EnsembleBinaryOutputCombiner + { + internal override string ComponentName => "Voting"; + } + + public enum WeightageKind + { + Accuracy = 0, + Auc = 1, + PosPrecision = 2, + PosRecall = 3, + NegPrecision = 4, + NegRecall = 5 + } + + + + public sealed class WeightedAverageEnsembleBinaryOutputCombiner : EnsembleBinaryOutputCombiner + { + /// + /// The metric type to be used to find the weights for each model + /// + public WeightageKind WeightageName { get; set; } = WeightageKind.Auc; + + internal override string ComponentName => "WeightedAverage"; + } + + public abstract class EnsembleBinarySubModelSelector : ComponentKind {} + + + + public sealed class AllSelectorEnsembleBinarySubModelSelector : EnsembleBinarySubModelSelector + { + internal override string ComponentName => "AllSelector"; + } + + + + public sealed class BestDiverseSelectorEnsembleBinarySubModelSelector : EnsembleBinarySubModelSelector + { + /// + /// The metric type to be used to find the diversity among base learners + /// + [JsonConverter(typeof(ComponentSerializer))] + public EnsembleDiversityMeasure DiversityMetricType { get; set; } + + /// + /// The proportion of best base learners to be selected. The range is 0.0-1.0 + /// + public float LearnersSelectionProportion { get; set; } = 0.5f; + + /// + /// The proportion of instances to be selected to test the individual base learner. If it is 0, it uses training set + /// + public float ValidationDatasetProportion { get; set; } = 0.3f; + + internal override string ComponentName => "BestDiverseSelector"; + } + + public enum BinaryClassifierEvaluatorMetrics + { + Accuracy = 0, + PosPrecName = 1, + PosRecallName = 2, + NegPrecName = 3, + NegRecallName = 4, + Auc = 5, + LogLoss = 6, + LogLossReduction = 7, + F1 = 8, + AuPrc = 9 + } + + + + public sealed class BestPerformanceSelectorEnsembleBinarySubModelSelector : EnsembleBinarySubModelSelector + { + /// + /// The metric type to be used to find the best performance + /// + public BinaryClassifierEvaluatorMetrics MetricName { get; set; } = BinaryClassifierEvaluatorMetrics.Auc; + + /// + /// The proportion of best base learners to be selected. The range is 0.0-1.0 + /// + public float LearnersSelectionProportion { get; set; } = 0.5f; + + /// + /// The proportion of instances to be selected to test the individual base learner. If it is 0, it uses training set + /// + public float ValidationDatasetProportion { get; set; } = 0.3f; + + internal override string ComponentName => "BestPerformanceSelector"; + } + + public abstract class EnsembleDiversityMeasure : ComponentKind {} + + + + public sealed class DisagreementDiversityMeasureEnsembleDiversityMeasure : EnsembleDiversityMeasure + { + internal override string ComponentName => "DisagreementDiversityMeasure"; + } + + + + public sealed class MultiDisagreementDiversityMeasureEnsembleDiversityMeasure : EnsembleDiversityMeasure + { + internal override string ComponentName => "MultiDisagreementDiversityMeasure"; + } + + + + public sealed class RegressionDisagreementDiversityMeasureEnsembleDiversityMeasure : EnsembleDiversityMeasure + { + internal override string ComponentName => "RegressionDisagreementDiversityMeasure"; + } + + public abstract class EnsembleFeatureSelector : ComponentKind {} + + + + public sealed class AllFeatureSelectorEnsembleFeatureSelector : EnsembleFeatureSelector + { + internal override string ComponentName => "AllFeatureSelector"; + } + + + + public sealed class RandomFeatureSelectorEnsembleFeatureSelector : EnsembleFeatureSelector + { + /// + /// The proportion of features to be selected. The range is 0.0-1.0 + /// + public float FeaturesSelectionProportion { get; set; } = 0.8f; + + internal override string ComponentName => "RandomFeatureSelector"; + } + + public abstract class EnsembleMulticlassOutputCombiner : ComponentKind {} + + + + public sealed class MultiAverageEnsembleMulticlassOutputCombiner : EnsembleMulticlassOutputCombiner + { + /// + /// Whether to normalize the output of base models before combining them + /// + public bool Normalize { get; set; } = true; + + internal override string ComponentName => "MultiAverage"; + } + + + public sealed class MultiMedianEnsembleMulticlassOutputCombiner : EnsembleMulticlassOutputCombiner + { /// - /// Use random initialization only. + /// Whether to normalize the output of base models before combining them /// - public bool RandomInitialization { get; set; } = false; + public bool Normalize { get; set; } = true; + + internal override string ComponentName => "MultiMedian"; + } + + + public sealed class MultiStackingEnsembleMulticlassOutputCombiner : EnsembleMulticlassOutputCombiner + { /// - /// Number of initilization pipelines, used for random initialization only. + /// The proportion of instances to be selected to test the individual base learner. If it is 0, it uses training set /// - public int NumInitializationPipelines { get; set; } = 20; + public float ValidationDatasetProportion { get; set; } = 0.3f; - internal override string ComponentName => "Rocket"; + internal override string ComponentName => "MultiStacking"; } - /// - /// AutoML engine using uniform random sampling. - /// - public sealed class UniformRandomAutoMlEngine : AutoMlEngine + public sealed class MultiVotingEnsembleMulticlassOutputCombiner : EnsembleMulticlassOutputCombiner { - internal override string ComponentName => "UniformRandom"; - } + /// + /// Whether to normalize the output of base models before combining them + /// + public bool Normalize { get; set; } = true; - public abstract class AutoMlStateBase : ComponentKind {} + internal override string ComponentName => "MultiVoting"; + } - public enum AutoInferenceAutoMlMlStateArgumentsMetrics + public enum MultiWeightageKind { - Auc = 0, - AccuracyMicro = 1, - AccuracyMacro = 2, - L2 = 3, - F1 = 4, - AuPrc = 5, - TopKAccuracy = 6, - Rms = 7, - LossFn = 8, - RSquared = 9, - LogLoss = 10, - LogLossReduction = 11, - Ndcg = 12, - Dcg = 13, - PositivePrecision = 14, - PositiveRecall = 15, - NegativePrecision = 16, - NegativeRecall = 17, - DrAtK = 18, - DrAtPFpr = 19, - DrAtNumPos = 20, - NumAnomalies = 21, - ThreshAtK = 22, - ThreshAtP = 23, - ThreshAtNumPos = 24, - Nmi = 25, - AvgMinScore = 26, - Dbi = 27 + AccuracyMicroAvg = 0, + AccuracyMacroAvg = 1 } - /// - /// State of an AutoML search and search space. - /// - public sealed class AutoMlStateAutoMlStateBase : AutoMlStateBase + public sealed class MultiWeightedAverageEnsembleMulticlassOutputCombiner : EnsembleMulticlassOutputCombiner { /// - /// Supported metric for evaluator. + /// The metric type to be used to find the weights for each model /// - public Microsoft.ML.Runtime.AutoInferenceAutoMlMlStateArgumentsMetrics Metric { get; set; } = Microsoft.ML.Runtime.AutoInferenceAutoMlMlStateArgumentsMetrics.Auc; + public MultiWeightageKind WeightageName { get; set; } = MultiWeightageKind.AccuracyMicroAvg; /// - /// AutoML engine (pipeline optimizer) that generates next candidates. + /// Whether to normalize the output of base models before combining them /// - [JsonConverter(typeof(ComponentSerializer))] - public AutoMlEngine Engine { get; set; } + public bool Normalize { get; set; } = true; + + internal override string ComponentName => "MultiWeightedAverage"; + } + + public abstract class EnsembleMulticlassSubModelSelector : ComponentKind {} + + + public sealed class AllSelectorMultiClassEnsembleMulticlassSubModelSelector : EnsembleMulticlassSubModelSelector + { + internal override string ComponentName => "AllSelectorMultiClass"; + } + + + + public sealed class BestDiverseSelectorMultiClassEnsembleMulticlassSubModelSelector : EnsembleMulticlassSubModelSelector + { /// - /// Kind of trainer for task, such as binary classification trainer, multiclass trainer, etc. + /// The metric type to be used to find the diversity among base learners /// - public Microsoft.ML.Models.MacroUtilsTrainerKinds TrainerKind { get; set; } = Microsoft.ML.Models.MacroUtilsTrainerKinds.SignatureBinaryClassifierTrainer; + [JsonConverter(typeof(ComponentSerializer))] + public EnsembleDiversityMeasure DiversityMetricType { get; set; } /// - /// Arguments for creating terminator, which determines when to stop search. + /// The proportion of best base learners to be selected. The range is 0.0-1.0 /// - [JsonConverter(typeof(ComponentSerializer))] - public SearchTerminator TerminatorArgs { get; set; } + public float LearnersSelectionProportion { get; set; } = 0.5f; /// - /// Learner set to sweep over (if available). + /// The proportion of instances to be selected to test the individual base learner. If it is 0, it uses training set /// - public string[] RequestedLearners { get; set; } + public float ValidationDatasetProportion { get; set; } = 0.3f; - internal override string ComponentName => "AutoMlState"; + internal override string ComponentName => "BestDiverseSelectorMultiClass"; } - public abstract class CalibratorTrainer : ComponentKind {} + public enum MultiClassClassifierEvaluatorMetrics + { + AccuracyMicro = 0, + AccuracyMacro = 1, + LogLoss = 2, + LogLossReduction = 3 + } - /// - /// - /// - public sealed class FixedPlattCalibratorCalibratorTrainer : CalibratorTrainer + public sealed class BestPerformanceSelectorMultiClassEnsembleMulticlassSubModelSelector : EnsembleMulticlassSubModelSelector { /// - /// The slope parameter of f(x) = 1 / (1 + exp(-slope * x + offset) + /// The metric type to be used to find the best performance /// - public double Slope { get; set; } = 1d; + public MultiClassClassifierEvaluatorMetrics MetricName { get; set; } = MultiClassClassifierEvaluatorMetrics.AccuracyMicro; /// - /// The offset parameter of f(x) = 1 / (1 + exp(-slope * x + offset) + /// The proportion of best base learners to be selected. The range is 0.0-1.0 /// - public double Offset { get; set; } - - internal override string ComponentName => "FixedPlattCalibrator"; - } - + public float LearnersSelectionProportion { get; set; } = 0.5f; + /// + /// The proportion of instances to be selected to test the individual base learner. If it is 0, it uses training set + /// + public float ValidationDatasetProportion { get; set; } = 0.3f; - /// - /// - /// - public sealed class NaiveCalibratorCalibratorTrainer : CalibratorTrainer - { - internal override string ComponentName => "NaiveCalibrator"; + internal override string ComponentName => "BestPerformanceSelectorMultiClass"; } + public abstract class EnsembleRegressionOutputCombiner : ComponentKind {} - /// - /// - /// - public sealed class PavCalibratorCalibratorTrainer : CalibratorTrainer + + public sealed class AverageEnsembleRegressionOutputCombiner : EnsembleRegressionOutputCombiner { - internal override string ComponentName => "PavCalibrator"; + internal override string ComponentName => "Average"; } - /// - /// Platt calibration. - /// - public sealed class PlattCalibratorCalibratorTrainer : CalibratorTrainer + public sealed class MedianEnsembleRegressionOutputCombiner : EnsembleRegressionOutputCombiner { - internal override string ComponentName => "PlattCalibrator"; + internal override string ComponentName => "Median"; } - public abstract class ClassificationLossFunction : ComponentKind {} - - /// - /// Exponential loss. - /// - public sealed class ExpLossClassificationLossFunction : ClassificationLossFunction + public sealed class RegressionStackingEnsembleRegressionOutputCombiner : EnsembleRegressionOutputCombiner { /// - /// Beta (dilation) + /// The proportion of instances to be selected to test the individual base learner. If it is 0, it uses training set /// - public float Beta { get; set; } = 1f; + public float ValidationDatasetProportion { get; set; } = 0.3f; - internal override string ComponentName => "ExpLoss"; + internal override string ComponentName => "RegressionStacking"; } + public abstract class EnsembleRegressionSubModelSelector : ComponentKind {} - /// - /// Hinge loss. - /// - public sealed class HingeLossClassificationLossFunction : ClassificationLossFunction - { - /// - /// Margin value - /// - public float Margin { get; set; } = 1f; - internal override string ComponentName => "HingeLoss"; + public sealed class AllSelectorEnsembleRegressionSubModelSelector : EnsembleRegressionSubModelSelector + { + internal override string ComponentName => "AllSelector"; } - /// - /// Log loss. - /// - public sealed class LogLossClassificationLossFunction : ClassificationLossFunction + public sealed class BestDiverseSelectorRegressionEnsembleRegressionSubModelSelector : EnsembleRegressionSubModelSelector { - internal override string ComponentName => "LogLoss"; - } - + /// + /// The metric type to be used to find the diversity among base learners + /// + [JsonConverter(typeof(ComponentSerializer))] + public EnsembleDiversityMeasure DiversityMetricType { get; set; } + /// + /// The proportion of best base learners to be selected. The range is 0.0-1.0 + /// + public float LearnersSelectionProportion { get; set; } = 0.5f; - /// - /// Smoothed Hinge loss. - /// - public sealed class SmoothedHingeLossClassificationLossFunction : ClassificationLossFunction - { /// - /// Smoothing constant + /// The proportion of instances to be selected to test the individual base learner. If it is 0, it uses training set /// - public float SmoothingConst { get; set; } = 1f; + public float ValidationDatasetProportion { get; set; } = 0.3f; - internal override string ComponentName => "SmoothedHingeLoss"; + internal override string ComponentName => "BestDiverseSelectorRegression"; } - public abstract class EarlyStoppingCriterion : ComponentKind {} + public enum RegressionEvaluatorMetrics + { + L1 = 0, + L2 = 1, + Rms = 2, + Loss = 3, + RSquared = 4 + } - /// - /// Stop in case of loss of generality. - /// - public sealed class GLEarlyStoppingCriterion : EarlyStoppingCriterion + public sealed class BestPerformanceRegressionSelectorEnsembleRegressionSubModelSelector : EnsembleRegressionSubModelSelector { /// - /// Threshold in range [0,1]. + /// The metric type to be used to find the best performance /// - [TlcModule.Range(Min = 0f, Max = 1f)] - public float Threshold { get; set; } = 0.01f; - - internal override string ComponentName => "GL"; - } + public RegressionEvaluatorMetrics MetricName { get; set; } = RegressionEvaluatorMetrics.L1; - - - /// - /// Stops in case of low progress. - /// - public sealed class LPEarlyStoppingCriterion : EarlyStoppingCriterion - { /// - /// Threshold in range [0,1]. + /// The proportion of best base learners to be selected. The range is 0.0-1.0 /// - [TlcModule.Range(Min = 0f, Max = 1f)] - public float Threshold { get; set; } = 0.01f; + public float LearnersSelectionProportion { get; set; } = 0.5f; /// - /// The window size. + /// The proportion of instances to be selected to test the individual base learner. If it is 0, it uses training set /// - [TlcModule.Range(Inf = 0)] - public int WindowSize { get; set; } = 5; + public float ValidationDatasetProportion { get; set; } = 0.3f; - internal override string ComponentName => "LP"; + internal override string ComponentName => "BestPerformanceRegressionSelector"; } + public abstract class EnsembleSubsetSelector : ComponentKind {} - /// - /// Stops in case of generality to progress ration exceeds threshold. - /// - public sealed class PQEarlyStoppingCriterion : EarlyStoppingCriterion - { - /// - /// Threshold in range [0,1]. - /// - [TlcModule.Range(Min = 0f, Max = 1f)] - public float Threshold { get; set; } = 0.01f; + public sealed class AllInstanceSelectorEnsembleSubsetSelector : EnsembleSubsetSelector + { /// - /// The window size. + /// The Feature selector /// - [TlcModule.Range(Inf = 0)] - public int WindowSize { get; set; } = 5; + [JsonConverter(typeof(ComponentSerializer))] + public EnsembleFeatureSelector FeatureSelector { get; set; } = new AllFeatureSelectorEnsembleFeatureSelector(); - internal override string ComponentName => "PQ"; + internal override string ComponentName => "AllInstanceSelector"; } - /// - /// Stop if validation score exceeds threshold value. - /// - public sealed class TREarlyStoppingCriterion : EarlyStoppingCriterion + public sealed class BootstrapSelectorEnsembleSubsetSelector : EnsembleSubsetSelector { /// - /// Tolerance threshold. (Non negative value) + /// The Feature selector /// - [TlcModule.Range(Min = 0f)] - public float Threshold { get; set; } = 0.01f; + [JsonConverter(typeof(ComponentSerializer))] + public EnsembleFeatureSelector FeatureSelector { get; set; } = new AllFeatureSelectorEnsembleFeatureSelector(); - internal override string ComponentName => "TR"; + internal override string ComponentName => "BootstrapSelector"; } - /// - /// Stops in case of consecutive loss in generality. - /// - public sealed class UPEarlyStoppingCriterion : EarlyStoppingCriterion + public sealed class RandomPartitionSelectorEnsembleSubsetSelector : EnsembleSubsetSelector { /// - /// The window size. + /// The Feature selector /// - [TlcModule.Range(Inf = 0)] - public int WindowSize { get; set; } = 5; + [JsonConverter(typeof(ComponentSerializer))] + public EnsembleFeatureSelector FeatureSelector { get; set; } = new AllFeatureSelectorEnsembleFeatureSelector(); - internal override string ComponentName => "UP"; + internal override string ComponentName => "RandomPartitionSelector"; } public abstract class FastTreeTrainer : ComponentKind {} @@ -14168,7 +16820,7 @@ public sealed class FastTreeBinaryClassificationFastTreeTrainer : FastTreeTraine public int MinDocumentsInLeafs { get; set; } = 10; /// - /// Number of weak hypotheses in the ensemble + /// Total number of decision trees to create in the ensemble /// [TlcModule.SweepableDiscreteParamAttribute("NumTrees", new object[]{20, 100, 500})] public int NumTrees { get; set; } = 100; @@ -14556,7 +17208,7 @@ public sealed class FastTreeRankingFastTreeTrainer : FastTreeTrainer public int MinDocumentsInLeafs { get; set; } = 10; /// - /// Number of weak hypotheses in the ensemble + /// Total number of decision trees to create in the ensemble /// [TlcModule.SweepableDiscreteParamAttribute("NumTrees", new object[]{20, 100, 500})] public int NumTrees { get; set; } = 100; @@ -14904,7 +17556,7 @@ public sealed class FastTreeRegressionFastTreeTrainer : FastTreeTrainer public int MinDocumentsInLeafs { get; set; } = 10; /// - /// Number of weak hypotheses in the ensemble + /// Total number of decision trees to create in the ensemble /// [TlcModule.SweepableDiscreteParamAttribute("NumTrees", new object[]{20, 100, 500})] public int NumTrees { get; set; } = 100; @@ -15257,7 +17909,7 @@ public sealed class FastTreeTweedieRegressionFastTreeTrainer : FastTreeTrainer public int MinDocumentsInLeafs { get; set; } = 10; /// - /// Number of weak hypotheses in the ensemble + /// Total number of decision trees to create in the ensemble /// [TlcModule.SweepableDiscreteParamAttribute("NumTrees", new object[]{20, 100, 500})] public int NumTrees { get; set; } = 100; @@ -15442,6 +18094,18 @@ public sealed class NGramHashNgramExtractor : NgramExtractor internal override string ComponentName => "NGramHash"; } + public abstract class ParallelLightGBM : ComponentKind {} + + + + /// + /// Single node machine learning process. + /// + public sealed class SingleParallelLightGBM : ParallelLightGBM + { + internal override string ComponentName => "Single"; + } + public abstract class ParallelTraining : ComponentKind {} @@ -15454,6 +18118,57 @@ public sealed class SingleParallelTraining : ParallelTraining internal override string ComponentName => "Single"; } + public abstract class PartitionedPathParser : ComponentKind {} + + + + /// + /// Extract name/value pairs from Parquet formatted directory names. Example path: Year=2018/Month=12/data1.parquet + /// + public sealed class ParquetPathParserPartitionedPathParser : PartitionedPathParser + { + internal override string ComponentName => "ParquetPathParser"; + } + + + public sealed partial class PartitionedFileLoaderColumn + { + /// + /// Name of the column. + /// + public string Name { get; set; } + + /// + /// Data type of the column. + /// + public Microsoft.ML.Data.DataKind? Type { get; set; } + + /// + /// Index of the directory representing this column. + /// + public int Source { get; set; } + + } + + + /// + /// A simple parser that extracts directory names as column values. Column names are defined as arguments. + /// + public sealed class SimplePathParserPartitionedPathParser : PartitionedPathParser + { + /// + /// Column definitions used to override the Partitioned Path Parser. Expected with the format name:type:numeric-source, e.g. col=MyFeature:R4:1 + /// + public PartitionedFileLoaderColumn[] Columns { get; set; } + + /// + /// Data type of each column. + /// + public Microsoft.ML.Data.DataKind Type { get; set; } = Microsoft.ML.Data.DataKind.TX; + + internal override string ComponentName => "SimplePathParser"; + } + public abstract class RegressionLossFunction : ComponentKind {} diff --git a/src/Microsoft.ML/Data/TextLoader.cs b/src/Microsoft.ML/Data/TextLoader.cs index 3c8550ef09..c9b16ad24e 100644 --- a/src/Microsoft.ML/Data/TextLoader.cs +++ b/src/Microsoft.ML/Data/TextLoader.cs @@ -2,6 +2,7 @@ // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. +using Microsoft.CSharp; using Microsoft.ML.Runtime; using Microsoft.ML.Runtime.Api; using Microsoft.ML.Runtime.Data; @@ -84,8 +85,6 @@ public TextLoader CreateFrom(bool useHeader = false, $"Valid characters are 0-9, *, - and ~"); var name = mappingAttr.Name ?? field.Name; - if (name.Any(c => !Char.IsLetterOrDigit(c))) - throw Contracts.Except($"{name} is not alphanumeric."); Runtime.Data.TextLoader.Range[] sources; if (!Runtime.Data.TextLoader.Column.TryParseSourceEx(mappingAttr.Ordinal, out sources)) diff --git a/src/Microsoft.ML/LearningPipeline.cs b/src/Microsoft.ML/LearningPipeline.cs index 0e554734ea..0637c5e65e 100644 --- a/src/Microsoft.ML/LearningPipeline.cs +++ b/src/Microsoft.ML/LearningPipeline.cs @@ -49,12 +49,27 @@ public ScorerPipelineStep(Var data, Var model) public class LearningPipeline : ICollection { private List Items { get; } = new List(); + private readonly int? _seed; + private readonly int _conc; /// /// Construct an empty object. /// public LearningPipeline() { + _seed = null; + _conc = 0; + } + + /// + /// Construct an empty object. + /// + /// Specify seed for random generator + /// Specify concurrency factor (default value - autoselection) + internal LearningPipeline(int? seed = null, int conc = 0) + { + _seed = seed; + _conc = conc; } /// @@ -94,6 +109,16 @@ public LearningPipeline() /// Any ML component (data loader, transform or trainer) defined as . public void Add(ILearningPipelineItem item) => Items.Add(item); + /// + /// Add a data loader, transform or trainer into the pipeline. + /// + /// Any ML component (data loader, transform or trainer) defined as . + /// Pipeline with added item + public LearningPipeline Append(ILearningPipelineItem item) + { + Add(item); + return this; + } /// /// Remove all the loaders/transforms/trainers from the pipeline. /// @@ -137,8 +162,7 @@ public PredictionModel Train() where TInput : class where TOutput : class, new() { - - using (var environment = new TlcEnvironment()) + using (var environment = new TlcEnvironment(seed: _seed, conc: _conc)) { Experiment experiment = environment.CreateExperiment(); ILearningPipelineStep step = null; diff --git a/src/Microsoft.ML/Microsoft.ML.csproj b/src/Microsoft.ML/Microsoft.ML.csproj index 7b370f2804..bd3d72ab68 100644 --- a/src/Microsoft.ML/Microsoft.ML.csproj +++ b/src/Microsoft.ML/Microsoft.ML.csproj @@ -9,7 +9,6 @@ - diff --git a/src/Microsoft.ML/Models/ClassificationEvaluator.cs b/src/Microsoft.ML/Models/ClassificationEvaluator.cs index 8fedc3fb4f..bc97a372a0 100644 --- a/src/Microsoft.ML/Models/ClassificationEvaluator.cs +++ b/src/Microsoft.ML/Models/ClassificationEvaluator.cs @@ -57,13 +57,13 @@ public ClassificationMetrics Evaluate(PredictionModel model, ILearningPipelineLo IDataView overallMetrics = experiment.GetOutput(evaluteOutput.OverallMetrics); if (overallMetrics == null) { - throw environment.Except($"Could not find OverallMetrics in the results returned in {nameof(BinaryClassificationEvaluator)} Evaluate."); + throw environment.Except($"Could not find OverallMetrics in the results returned in {nameof(ClassificationEvaluator)} Evaluate."); } IDataView confusionMatrix = experiment.GetOutput(evaluteOutput.ConfusionMatrix); if (confusionMatrix == null) { - throw environment.Except($"Could not find ConfusionMatrix in the results returned in {nameof(BinaryClassificationEvaluator)} Evaluate."); + throw environment.Except($"Could not find ConfusionMatrix in the results returned in {nameof(ClassificationEvaluator)} Evaluate."); } var metric = ClassificationMetrics.FromMetrics(environment, overallMetrics, confusionMatrix); diff --git a/src/Microsoft.ML/Models/ClusterEvaluator.cs b/src/Microsoft.ML/Models/ClusterEvaluator.cs new file mode 100644 index 0000000000..5aceca16f7 --- /dev/null +++ b/src/Microsoft.ML/Models/ClusterEvaluator.cs @@ -0,0 +1,71 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Transforms; + +namespace Microsoft.ML.Models +{ + public sealed partial class ClusterEvaluator + { + /// + /// Computes the quality metrics for the PredictionModel using the specified data set. + /// + /// + /// The trained PredictionModel to be evaluated. + /// + /// + /// The test data that will be predicted and used to evaluate the model. + /// + /// + /// A ClusterMetrics instance that describes how well the model performed against the test data. + /// + public ClusterMetrics Evaluate(PredictionModel model, ILearningPipelineLoader testData) + { + using (var environment = new TlcEnvironment()) + { + environment.CheckValue(model, nameof(model)); + environment.CheckValue(testData, nameof(testData)); + + Experiment experiment = environment.CreateExperiment(); + + ILearningPipelineStep testDataStep = testData.ApplyStep(previousStep: null, experiment); + if (!(testDataStep is ILearningPipelineDataStep testDataOutput)) + { + throw environment.Except($"The {nameof(ILearningPipelineLoader)} did not return a {nameof(ILearningPipelineDataStep)} from ApplyStep."); + } + + var datasetScorer = new DatasetTransformScorer + { + Data = testDataOutput.Data, + }; + DatasetTransformScorer.Output scoreOutput = experiment.Add(datasetScorer); + + Data = scoreOutput.ScoredData; + Output evaluteOutput = experiment.Add(this); + + experiment.Compile(); + + experiment.SetInput(datasetScorer.TransformModel, model.PredictorModel); + testData.SetInput(environment, experiment); + + experiment.Run(); + + IDataView overallMetrics = experiment.GetOutput(evaluteOutput.OverallMetrics); + + if (overallMetrics == null) + { + throw environment.Except($"Could not find OverallMetrics in the results returned in {nameof(ClusterEvaluator)} Evaluate."); + } + + var metric = ClusterMetrics.FromOverallMetrics(environment, overallMetrics); + + Contracts.Assert(metric.Count == 1, $"Exactly one metric set was expected but found {metric.Count} metrics"); + + return metric[0]; + } + } + } +} diff --git a/src/Microsoft.ML/Models/ClusterMetrics.cs b/src/Microsoft.ML/Models/ClusterMetrics.cs new file mode 100644 index 0000000000..7f88784ef8 --- /dev/null +++ b/src/Microsoft.ML/Models/ClusterMetrics.cs @@ -0,0 +1,94 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Api; +using Microsoft.ML.Runtime.Data; +using System; +using System.Collections.Generic; + +namespace Microsoft.ML.Models +{ + /// + /// This class contains the overall metrics computed by cluster evaluators. + /// + public sealed class ClusterMetrics + { + private ClusterMetrics() + { + } + + internal static List FromOverallMetrics(IHostEnvironment env, IDataView overallMetrics) + { + Contracts.AssertValue(env); + env.AssertValue(overallMetrics); + + var metricsEnumerable = overallMetrics.AsEnumerable(env, true, ignoreMissingColumns: true); + if (!metricsEnumerable.GetEnumerator().MoveNext()) + { + throw env.Except("The overall ClusteringMetrics didn't have any rows."); + } + + var metrics = new List(); + foreach (var metric in metricsEnumerable) + { + metrics.Add(new ClusterMetrics() + { + AvgMinScore = metric.AvgMinScore, + Nmi = metric.Nmi, + Dbi = metric.Dbi, + }); + } + + return metrics; + } + + /// + /// Davies-Bouldin Index. + /// + /// + /// DBI is a measure of the how much scatter is in the cluster and the cluster separation. + /// + public double Dbi { get; private set; } + + /// + /// Normalized Mutual Information + /// + /// + /// NMI is a measure of the mutual dependence between the true and predicted cluster labels for instances in the dataset. + /// NMI ranges between 0 and 1 where "0" indicates clustering is random and "1" indicates clustering is perfect w.r.t true labels. + /// + public double Nmi { get; private set; } + + /// + /// Average minimum score. + /// + /// + /// AvgMinScore is the average squared-distance of examples from the respective cluster centroids. + /// It is defined as + /// AvgMinScore = (1/m) * sum ((xi - c(xi))^2) + /// where m is the number of instances in the dataset. + /// xi is the i'th instance and c(xi) is the centriod of the predicted cluster for xi. + /// + public double AvgMinScore { get; private set; } + + /// + /// This class contains the public fields necessary to deserialize from IDataView. + /// + private sealed class SerializationClass + { +#pragma warning disable 649 // never assigned + [ColumnName(Runtime.Data.ClusteringEvaluator.Dbi)] + public Double Dbi; + + [ColumnName(Runtime.Data.ClusteringEvaluator.Nmi)] + public Double Nmi; + + [ColumnName(Runtime.Data.ClusteringEvaluator.AvgMinScore)] + public Double AvgMinScore; + +#pragma warning restore 649 // never assigned + } + } +} diff --git a/src/Microsoft.ML/Models/CrossValidator.cs b/src/Microsoft.ML/Models/CrossValidator.cs index 173e03916c..5f6e374e11 100644 --- a/src/Microsoft.ML/Models/CrossValidator.cs +++ b/src/Microsoft.ML/Models/CrossValidator.cs @@ -1,4 +1,8 @@ -using Microsoft.ML.Runtime; +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Runtime; using Microsoft.ML.Runtime.Api; using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.EntryPoints; @@ -19,7 +23,7 @@ public sealed partial class CrossValidator /// Class type that represents prediction schema. /// Machine learning pipeline may contain loader, transforms and at least one trainer. /// List containing metrics and predictor model for each fold - public CrossValidationOutput CrossValidate(LearningPipeline pipeline) + public CrossValidationOutput CrossValidate(LearningPipeline pipeline) where TInput : class where TOutput : class, new() { @@ -76,7 +80,7 @@ public CrossValidationOutput CrossValidate(Lea { PredictorModel = predictorModel }; - + var scorerOutput = subGraph.Add(scorer); lastTransformModel = scorerOutput.ScoringTransform; step = new ScorerPipelineStep(scorerOutput.ScoredData, scorerOutput.ScoringTransform); @@ -129,7 +133,7 @@ public CrossValidationOutput CrossValidate(Lea experiment.GetOutput(crossValidateOutput.OverallMetrics), experiment.GetOutput(crossValidateOutput.ConfusionMatrix), 2); } - else if(Kind == MacroUtilsTrainerKinds.SignatureMultiClassClassifierTrainer) + else if (Kind == MacroUtilsTrainerKinds.SignatureMultiClassClassifierTrainer) { cvOutput.ClassificationMetrics = ClassificationMetrics.FromMetrics( environment, @@ -142,6 +146,12 @@ public CrossValidationOutput CrossValidate(Lea environment, experiment.GetOutput(crossValidateOutput.OverallMetrics)); } + else if (Kind == MacroUtilsTrainerKinds.SignatureClusteringTrainer) + { + cvOutput.ClusterMetrics = ClusterMetrics.FromOverallMetrics( + environment, + experiment.GetOutput(crossValidateOutput.OverallMetrics)); + } else { //Implement metrics for ranking, clustering and anomaly detection. @@ -174,6 +184,7 @@ public class CrossValidationOutput public List BinaryClassificationMetrics; public List ClassificationMetrics; public List RegressionMetrics; + public List ClusterMetrics; public PredictionModel[] PredictorModels; //REVIEW: Add warnings and per instance results and implement diff --git a/src/Microsoft.ML/Models/OneVersusAll.cs b/src/Microsoft.ML/Models/OneVersusAll.cs new file mode 100644 index 0000000000..2f0a265dad --- /dev/null +++ b/src/Microsoft.ML/Models/OneVersusAll.cs @@ -0,0 +1,74 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.EntryPoints; +using static Microsoft.ML.Runtime.EntryPoints.CommonInputs; + +namespace Microsoft.ML.Models +{ + public sealed partial class OneVersusAll + { + /// + /// Create OneVersusAll multiclass trainer. + /// + /// Underlying binary trainer + /// "Use probabilities (vs. raw outputs) to identify top-score category + public static ILearningPipelineItem With(ITrainerInputWithLabel trainer, bool useProbabilities = true) + { + return new OvaPipelineItem(trainer, useProbabilities); + } + + private class OvaPipelineItem : ILearningPipelineItem + { + private Var _data; + private ITrainerInputWithLabel _trainer; + private bool _useProbabilities; + + public OvaPipelineItem(ITrainerInputWithLabel trainer, bool useProbabilities) + { + _trainer = trainer; + _useProbabilities = useProbabilities; + } + + public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) + { + using (var env = new TlcEnvironment()) + { + var subgraph = env.CreateExperiment(); + subgraph.Add(_trainer); + var ova = new OneVersusAll(); + if (previousStep != null) + { + if (!(previousStep is ILearningPipelineDataStep dataStep)) + { + throw new InvalidOperationException($"{ nameof(OneVersusAll)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); + } + + _data = dataStep.Data; + ova.TrainingData = dataStep.Data; + ova.UseProbabilities = _useProbabilities; + ova.Nodes = subgraph; + } + Output output = experiment.Add(ova); + return new OvaPipelineStep(output); + } + } + + public Var GetInputData() => _data; + } + + private class OvaPipelineStep : ILearningPipelinePredictorStep + { + public OvaPipelineStep(Output output) + { + Model = output.PredictorModel; + } + + public Var Model { get; } + } + } +} diff --git a/src/Microsoft.ML/Models/OnnxConverter.cs b/src/Microsoft.ML/Models/OnnxConverter.cs new file mode 100644 index 0000000000..dd8ca383fd --- /dev/null +++ b/src/Microsoft.ML/Models/OnnxConverter.cs @@ -0,0 +1,77 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.Data; + +namespace Microsoft.ML.Models +{ + public sealed partial class OnnxConverter + { + /// + /// ONNX is an intermediate representation format + /// for machine learning models. It is used to make models portable such that you can + /// train a model using a toolkit and run it in another tookit's runtime, for example, + /// you can create a model using ML.NET (or any ONNX compatible toolkit), convert it to ONNX and + /// then the ONNX model can be converted into say, CoreML, TensorFlow or WinML model + /// to run on the respective runtime. + /// + /// This API converts an ML.NET model to ONNX format by inspecting the transform pipeline + /// from the end, checking for components that know how to save themselves as ONNX. + /// The first item in the transform pipeline that does not know how to save itself + /// as ONNX, is considered the "input" to the ONNX pipeline. (Ideally this would be the + /// original loader itself, but this may not be possible if the user used unsavable + /// transforms in defining the pipe.) All the columns in the source that are a type the + /// ONNX knows how to deal with will be tracked. Intermediate transformations of the + /// data appearing as new columns will appear in the output block of the ONNX, with names + /// derived from the corresponding column names. The ONNX JSON will be serialized to a + /// path defined through the Json option. + /// + /// This API supports the following arguments: + /// indicates the file to write the ONNX protocol buffer file to. This is optional. + /// indicates the file to write the JSON representation of the ONNX model. This is optional. + /// indicates the name property in the ONNX model. If left unspecified, it will + /// be the extension-less name of the file specified in the onnx indicates the protocol buffer file + /// to write the ONNX representation to. + /// indicates the domain name of the model. ONNX uses reverse domain name space indicators. + /// For example com.microsoft.cognitiveservices. This is a required field. + /// is a string array of input column names to omit from the input mapping. + /// A common scenario might be to drop the label column, for instance, since it may not be practically + /// useful for the pipeline. Note that any columns depending on these naturally cannot be saved. + /// is similar, except for the output schema. Note that the pipeline handler + /// is currently not intelligent enough to drop intermediate calculations that produce this value: this will + /// merely omit that value from the actual output. + /// + /// Transforms that can be exported to ONNX + /// 1. Concat + /// 2. KeyToVector + /// 3. NAReplace + /// 4. Normalize + /// 5. Term + /// 6. Categorical + /// + /// Learners that can be exported to ONNX + /// 1. FastTree + /// 2. LightGBM + /// 3. Logistic Regression + /// + /// See + /// for an example on how to train a model and then convert that model to ONNX. + /// + /// Model that needs to be converted to ONNX format. + public void Convert(PredictionModel model) + { + using (var environment = new TlcEnvironment()) + { + environment.CheckValue(model, nameof(model)); + + Experiment experiment = environment.CreateExperiment(); + experiment.Add(this); + experiment.Compile(); + experiment.SetInput(Model, model.PredictorModel); + experiment.Run(); + } + } + } +} diff --git a/src/Microsoft.ML/Models/RegressionMetrics.cs b/src/Microsoft.ML/Models/RegressionMetrics.cs index 68f9af2feb..64500f5e6c 100644 --- a/src/Microsoft.ML/Models/RegressionMetrics.cs +++ b/src/Microsoft.ML/Models/RegressionMetrics.cs @@ -71,7 +71,7 @@ internal static List FromOverallMetrics(IHostEnvironment env, public double L2 { get; private set; } /// - /// Gets the root mean square loss (or RMC) which is the square root of the L2 loss. + /// Gets the root mean square loss (or RMS) which is the square root of the L2 loss. /// public double Rms { get; private set; } diff --git a/src/Microsoft.ML/Models/TrainTestEvaluator.cs b/src/Microsoft.ML/Models/TrainTestEvaluator.cs index 19261e82de..e3de7a4e50 100644 --- a/src/Microsoft.ML/Models/TrainTestEvaluator.cs +++ b/src/Microsoft.ML/Models/TrainTestEvaluator.cs @@ -1,4 +1,8 @@ -using Microsoft.ML.Runtime; +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Runtime; using Microsoft.ML.Runtime.Api; using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.EntryPoints; @@ -102,7 +106,7 @@ public TrainTestEvaluatorOutput TrainTestEvaluate TrainTestEvaluate TrainTestEvaluate TrainTestEvaluate predictor; using (var memoryStream = new MemoryStream()) { @@ -158,7 +168,7 @@ public TrainTestEvaluatorOutput TrainTestEvaluate(predictor, memoryStream); } - + return trainTestOutput; } } @@ -171,6 +181,7 @@ public class TrainTestEvaluatorOutput public BinaryClassificationMetrics BinaryClassificationMetrics; public ClassificationMetrics ClassificationMetrics; public RegressionMetrics RegressionMetrics; + public ClusterMetrics ClusterMetrics; public PredictionModel PredictorModels; //REVIEW: Add warnings and per instance results and implement diff --git a/src/Microsoft.ML/PredictionModel.cs b/src/Microsoft.ML/PredictionModel.cs index 6eb1b3c6f5..c1dded82b8 100644 --- a/src/Microsoft.ML/PredictionModel.cs +++ b/src/Microsoft.ML/PredictionModel.cs @@ -25,7 +25,7 @@ internal PredictionModel(Stream stream) _predictorModel = new Runtime.EntryPoints.TransformModel(_env, stream); } - internal Runtime.EntryPoints.TransformModel PredictorModel + internal TransformModel PredictorModel { get { return _predictorModel; } } @@ -44,7 +44,7 @@ public bool TryGetScoreLabelNames(out string[] names, string scoreColumnName = D int colIndex = -1; if (!schema.TryGetColumnIndex(scoreColumnName, out colIndex)) return false; - + int expectedLabelCount = schema.GetColumnType(colIndex).ValueCount; if (!schema.HasSlotNames(colIndex, expectedLabelCount)) return false; @@ -57,7 +57,7 @@ public bool TryGetScoreLabelNames(out string[] names, string scoreColumnName = D names = new string[expectedLabelCount]; int index = 0; - foreach(var label in labels.DenseValues()) + foreach (var label in labels.DenseValues()) names[index++] = label.ToString(); return true; diff --git a/src/Microsoft.ML/Properties/AssemblyInfo.cs b/src/Microsoft.ML/Properties/AssemblyInfo.cs new file mode 100644 index 0000000000..52835b2f81 --- /dev/null +++ b/src/Microsoft.ML/Properties/AssemblyInfo.cs @@ -0,0 +1,9 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System.Reflection; +using System.Runtime.CompilerServices; +using System.Runtime.InteropServices; + +[assembly: InternalsVisibleTo("Microsoft.ML.Tests, PublicKey=002400000480000094000000060200000024000052534131000400000100010015c01ae1f50e8cc09ba9eac9147cf8fd9fce2cfe9f8dce4f7301c4132ca9fb50ce8cbf1df4dc18dd4d210e4345c744ecb3365ed327efdbc52603faa5e21daa11234c8c4a73e51f03bf192544581ebe107adee3a34928e39d04e524a9ce729d5090bfd7dad9d10c722c0def9ccc08ff0a03790e48bcd1f9b6c476063e1966a1c4")] diff --git a/src/Microsoft.ML/Runtime/EntryPoints/CrossValidationBinaryMacro.cs b/src/Microsoft.ML/Runtime/EntryPoints/CrossValidationBinaryMacro.cs index fca8d3ac5b..cdf52cf076 100644 --- a/src/Microsoft.ML/Runtime/EntryPoints/CrossValidationBinaryMacro.cs +++ b/src/Microsoft.ML/Runtime/EntryPoints/CrossValidationBinaryMacro.cs @@ -256,7 +256,6 @@ public static ArrayITransformModelOutput MakeArray(IHostEnvironment env, ArrayIT return result; } - public sealed class ArrayIDataViewInput { [Argument(ArgumentType.Required, HelpText = "The data sets", SortOrder = 1)] diff --git a/src/Microsoft.ML/Runtime/EntryPoints/CrossValidationMacro.cs b/src/Microsoft.ML/Runtime/EntryPoints/CrossValidationMacro.cs index 569d0b3571..395f6d8178 100644 --- a/src/Microsoft.ML/Runtime/EntryPoints/CrossValidationMacro.cs +++ b/src/Microsoft.ML/Runtime/EntryPoints/CrossValidationMacro.cs @@ -31,7 +31,7 @@ public sealed class SubGraphOutput { [Argument(ArgumentType.AtMostOnce, HelpText = "The predictor model", SortOrder = 1)] public Var PredictorModel; - + [Argument(ArgumentType.AtMostOnce, HelpText = "The transform model", SortOrder = 2)] public Var TransformModel; } @@ -77,6 +77,15 @@ public sealed class Arguments // (and the same for the TrainTest macro). I currently do not know how to do this, so this should be revisited in the future. [Argument(ArgumentType.Required, HelpText = "Specifies the trainer kind, which determines the evaluator to be used.", SortOrder = 8)] public MacroUtils.TrainerKinds Kind = MacroUtils.TrainerKinds.SignatureBinaryClassifierTrainer; + + [Argument(ArgumentType.LastOccurenceWins, HelpText = "Column to use for labels", ShortName = "lab", SortOrder = 10)] + public string LabelColumn = DefaultColumnNames.Label; + + [Argument(ArgumentType.LastOccurenceWins, HelpText = "Column to use for example weight", ShortName = "weight", SortOrder = 11)] + public Optional WeightColumn = Optional.Implicit(DefaultColumnNames.Weight); + + [Argument(ArgumentType.LastOccurenceWins, HelpText = "Column to use for grouping", ShortName = "group", SortOrder = 12)] + public Optional GroupColumn = Optional.Implicit(DefaultColumnNames.GroupId); } // REVIEW: This output would be much better as an array of CommonOutputs.ClassificationEvaluateOutput, @@ -104,7 +113,6 @@ public sealed class Output public IDataView ConfusionMatrix; } - public sealed class CombineMetricsInput { [Argument(ArgumentType.Multiple, HelpText = "Overall metrics datasets", SortOrder = 1)] @@ -122,6 +130,12 @@ public sealed class CombineMetricsInput [Argument(ArgumentType.AtMostOnce, HelpText = "The label column name", ShortName = "Label", SortOrder = 5)] public string LabelColumn = DefaultColumnNames.Label; + [Argument(ArgumentType.LastOccurenceWins, HelpText = "Column to use for example weight", ShortName = "weight", SortOrder = 6)] + public Optional WeightColumn = Optional.Implicit(DefaultColumnNames.Weight); + + [Argument(ArgumentType.LastOccurenceWins, HelpText = "Column to use for grouping", ShortName = "group", SortOrder = 12)] + public Optional GroupColumn = Optional.Implicit(DefaultColumnNames.GroupId); + [Argument(ArgumentType.Required, HelpText = "Specifies the trainer kind, which determines the evaluator to be used.", SortOrder = 6)] public MacroUtils.TrainerKinds Kind = MacroUtils.TrainerKinds.SignatureBinaryClassifierTrainer; } @@ -189,7 +203,10 @@ public static CommonOutputs.MacroOutput CrossValidate( var args = new TrainTestMacro.Arguments { Nodes = new JArray(graph.Select(n => n.ToJson()).ToArray()), - TransformModel = null + TransformModel = null, + LabelColumn = input.LabelColumn, + GroupColumn = input.GroupColumn, + WeightColumn = input.WeightColumn }; if (transformModelVarName != null) @@ -219,7 +236,7 @@ public static CommonOutputs.MacroOutput CrossValidate( } else args.Outputs.TransformModel = null; - + // Set train/test trainer kind to match. args.Kind = input.Kind; @@ -357,10 +374,17 @@ public static CommonOutputs.MacroOutput CrossValidate( var combineArgs = new CombineMetricsInput(); combineArgs.Kind = input.Kind; + combineArgs.LabelColumn = input.LabelColumn; + combineArgs.WeightColumn = input.WeightColumn; + combineArgs.GroupColumn = input.GroupColumn; // Set the input bindings for the CombineMetrics entry point. var combineInputBindingMap = new Dictionary>(); var combineInputMap = new Dictionary(); + + var warningsArray = new SimpleParameterBinding(nameof(combineArgs.Warnings)); + combineInputBindingMap.Add(nameof(combineArgs.Warnings), new List { warningsArray }); + combineInputMap.Add(warningsArray, new SimpleVariableBinding(warningsOutput.OutputData.VarName)); var overallArray = new SimpleParameterBinding(nameof(combineArgs.OverallMetrics)); combineInputBindingMap.Add(nameof(combineArgs.OverallMetrics), new List { overallArray }); combineInputMap.Add(overallArray, new SimpleVariableBinding(overallMetricsOutput.OutputData.VarName)); @@ -384,10 +408,12 @@ public static CommonOutputs.MacroOutput CrossValidate( var combineInstanceMetric = new Var(); combineInstanceMetric.VarName = node.GetOutputVariableName(nameof(Output.PerInstanceMetrics)); combineOutputMap.Add(nameof(Output.PerInstanceMetrics), combineInstanceMetric.VarName); - var combineConfusionMatrix = new Var(); - combineConfusionMatrix.VarName = node.GetOutputVariableName(nameof(Output.ConfusionMatrix)); - combineOutputMap.Add(nameof(TrainTestMacro.Output.ConfusionMatrix), combineConfusionMatrix.VarName); - + if (confusionMatricesOutput != null) + { + var combineConfusionMatrix = new Var(); + combineConfusionMatrix.VarName = node.GetOutputVariableName(nameof(Output.ConfusionMatrix)); + combineOutputMap.Add(nameof(TrainTestMacro.Output.ConfusionMatrix), combineConfusionMatrix.VarName); + } subGraphNodes.AddRange(EntryPointNode.ValidateNodes(env, node.Context, exp.GetNodes(), node.Catalog)); subGraphNodes.Add(EntryPointNode.Create(env, "Models.CrossValidationResultsCombiner", combineArgs, node.Catalog, node.Context, combineInputBindingMap, combineInputMap, combineOutputMap)); return new CommonOutputs.MacroOutput() { Nodes = subGraphNodes }; @@ -399,7 +425,12 @@ public static CombinedOutput CombineMetrics(IHostEnvironment env, CombineMetrics var eval = GetEvaluator(env, input.Kind); var perInst = EvaluateUtils.ConcatenatePerInstanceDataViews(env, eval, true, true, input.PerInstanceMetrics.Select( - idv => RoleMappedData.Create(idv, RoleMappedSchema.CreatePair(RoleMappedSchema.ColumnRole.Label, input.LabelColumn))).ToArray(), + idv => RoleMappedData.CreateOpt(idv, new[] + { + RoleMappedSchema.CreatePair(RoleMappedSchema.ColumnRole.Label, input.LabelColumn), + RoleMappedSchema.CreatePair(RoleMappedSchema.ColumnRole.Weight, input.WeightColumn.Value), + RoleMappedSchema.CreatePair(RoleMappedSchema.ColumnRole.Group, input.GroupColumn.Value) + })).ToArray(), out var variableSizeVectorColumnNames); var warnings = input.Warnings != null ? new List(input.Warnings) : new List(); diff --git a/src/Microsoft.ML/Runtime/EntryPoints/ImportTextData.cs b/src/Microsoft.ML/Runtime/EntryPoints/ImportTextData.cs index 41048000d8..a3913ab160 100644 --- a/src/Microsoft.ML/Runtime/EntryPoints/ImportTextData.cs +++ b/src/Microsoft.ML/Runtime/EntryPoints/ImportTextData.cs @@ -64,7 +64,7 @@ public static Output TextLoader(IHostEnvironment env, LoaderInput input) var host = env.Register("ImportTextData"); env.CheckValue(input, nameof(input)); EntryPointUtils.CheckInputArgs(host, input); - var loader = host.CreateLoader(input.Arguments, new FileHandleSource(input.InputFile)); + var loader = host.CreateLoader(input.Arguments, new FileHandleSource(input.InputFile)); return new Output { Data = loader }; } } diff --git a/src/Microsoft.ML/Runtime/EntryPoints/JsonUtils/JsonManifestUtils.cs b/src/Microsoft.ML/Runtime/EntryPoints/JsonUtils/JsonManifestUtils.cs index 289adc6f75..fd76ad8565 100644 --- a/src/Microsoft.ML/Runtime/EntryPoints/JsonUtils/JsonManifestUtils.cs +++ b/src/Microsoft.ML/Runtime/EntryPoints/JsonUtils/JsonManifestUtils.cs @@ -7,6 +7,7 @@ using System.Linq; using System.Reflection; using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.Internal.Tools; using Microsoft.ML.Runtime.Internal.Utilities; using Newtonsoft.Json.Linq; @@ -67,13 +68,7 @@ public static JObject BuildAllManifests(IExceptionContext ectx, ModuleCatalog ca { var jField = new JObject(); jField[FieldNames.Name] = fieldInfo.Name; - var type = fieldInfo.PropertyType; - // Dive inside Optional. - if (type.IsGenericType && type.GetGenericTypeDefinition() == typeof(Optional<>)) - type = type.GetGenericArguments()[0]; - // Dive inside Nullable. - if (type.IsGenericType && type.GetGenericTypeDefinition() == typeof(Nullable<>)) - type = type.GetGenericArguments()[0]; + var type = CSharpGeneratorUtils.ExtractOptionalOrNullableType(fieldInfo.PropertyType); // Dive inside Var. if (type.IsGenericType && type.GetGenericTypeDefinition() == typeof(Var<>)) type = type.GetGenericArguments()[0]; @@ -308,14 +303,7 @@ private static JToken BuildTypeToken(IExceptionContext ectx, FieldInfo fieldInfo jo[FieldNames.ItemType] = typeString; return jo; } - - // Dive inside Optional. - if (type.IsGenericType && type.GetGenericTypeDefinition() == typeof(Optional<>)) - type = type.GetGenericArguments()[0]; - - // Dive inside Nullable. - if (type.IsGenericType && type.GetGenericTypeDefinition() == typeof(Nullable<>)) - type = type.GetGenericArguments()[0]; + type = CSharpGeneratorUtils.ExtractOptionalOrNullableType(type); // Dive inside Var. if (type.IsGenericType && type.GetGenericTypeDefinition() == typeof(Var<>)) @@ -356,7 +344,7 @@ private static JToken BuildTypeToken(IExceptionContext ectx, FieldInfo fieldInfo case TlcModule.DataKind.Enum: jo = new JObject(); jo[FieldNames.Kind] = typeEnum.ToString(); - var values = Enum.GetNames(type); + var values = Enum.GetNames(type).Where(n => type.GetField(n).GetCustomAttribute() == null); jo[FieldNames.Values] = new JArray(values); return jo; case TlcModule.DataKind.Array: diff --git a/src/Microsoft.ML/Runtime/EntryPoints/MacroUtils.cs b/src/Microsoft.ML/Runtime/EntryPoints/MacroUtils.cs index aa5e8abc16..7e7f6acbac 100644 --- a/src/Microsoft.ML/Runtime/EntryPoints/MacroUtils.cs +++ b/src/Microsoft.ML/Runtime/EntryPoints/MacroUtils.cs @@ -30,9 +30,8 @@ public sealed class EvaluatorSettings { public string LabelColumn { get; set; } public string NameColumn { get; set; } - public string ScoreColumn { get; set; } - public string[] StratColumn { get; set; } public string WeightColumn { get; set; } + public string GroupColumn { get; set; } public string FeatureColumn { get; set; } public EvaluatorSettings() @@ -61,8 +60,6 @@ private static Dictionary { LabelColumn = settings.LabelColumn, NameColumn = settings.NameColumn, - ScoreColumn = settings.ScoreColumn, - StratColumn = settings.StratColumn, WeightColumn = settings.WeightColumn }, EvaluatorOutput = () => new Models.BinaryClassificationEvaluator.Output() @@ -77,8 +74,6 @@ private static Dictionary { LabelColumn = settings.LabelColumn, NameColumn = settings.NameColumn, - ScoreColumn = settings.ScoreColumn, - StratColumn = settings.StratColumn, WeightColumn = settings.WeightColumn }, EvaluatorOutput = () => new Models.ClassificationEvaluator.Output() @@ -93,9 +88,8 @@ private static Dictionary { LabelColumn = settings.LabelColumn, NameColumn = settings.NameColumn, - ScoreColumn = settings.ScoreColumn, - StratColumn = settings.StratColumn, - WeightColumn = settings.WeightColumn + WeightColumn = settings.WeightColumn, + GroupIdColumn = settings.GroupColumn }, EvaluatorOutput = () => new Models.RankerEvaluator.Output() } @@ -109,8 +103,6 @@ private static Dictionary { LabelColumn = settings.LabelColumn, NameColumn = settings.NameColumn, - ScoreColumn = settings.ScoreColumn, - StratColumn = settings.StratColumn, WeightColumn = settings.WeightColumn }, EvaluatorOutput = () => new Models.RegressionEvaluator.Output() @@ -125,8 +117,6 @@ private static Dictionary { LabelColumn = settings.LabelColumn, NameColumn = settings.NameColumn, - ScoreColumn = settings.ScoreColumn, - StratColumn = settings.StratColumn, WeightColumn = settings.WeightColumn, }, EvaluatorOutput = () => new Models.MultiOutputRegressionEvaluator.Output() @@ -141,8 +131,6 @@ private static Dictionary { LabelColumn = settings.LabelColumn, NameColumn = settings.NameColumn, - ScoreColumn = settings.ScoreColumn, - StratColumn = settings.StratColumn, WeightColumn = settings.WeightColumn }, EvaluatorOutput = () => new Models.AnomalyDetectionEvaluator.Output() @@ -157,8 +145,6 @@ private static Dictionary { LabelColumn = settings.LabelColumn, NameColumn = settings.NameColumn, - ScoreColumn = settings.ScoreColumn, - StratColumn = settings.StratColumn, WeightColumn = settings.WeightColumn, FeatureColumn = settings.FeatureColumn }, diff --git a/src/Microsoft.ML/Runtime/EntryPoints/TrainTestMacro.cs b/src/Microsoft.ML/Runtime/EntryPoints/TrainTestMacro.cs index edd4cf6e5b..1029cfd970 100644 --- a/src/Microsoft.ML/Runtime/EntryPoints/TrainTestMacro.cs +++ b/src/Microsoft.ML/Runtime/EntryPoints/TrainTestMacro.cs @@ -26,7 +26,7 @@ public sealed class SubGraphOutput { [Argument(ArgumentType.AtMostOnce, HelpText = "The predictor model", SortOrder = 1)] public Var PredictorModel; - + [Argument(ArgumentType.AtMostOnce, HelpText = "Transform model", SortOrder = 2)] public Var TransformModel; } @@ -62,6 +62,15 @@ public sealed class Arguments [Argument(ArgumentType.AtMostOnce, HelpText = "Indicates whether to include and output training dataset metrics.", SortOrder = 9)] public Boolean IncludeTrainingMetrics = false; + + [Argument(ArgumentType.LastOccurenceWins, HelpText = "Column to use for labels", ShortName = "lab", SortOrder = 10)] + public string LabelColumn = DefaultColumnNames.Label; + + [Argument(ArgumentType.LastOccurenceWins, HelpText = "Column to use for example weight", ShortName = "weight", SortOrder = 11)] + public Optional WeightColumn = Optional.Implicit(DefaultColumnNames.Weight); + + [Argument(ArgumentType.LastOccurenceWins, HelpText = "Column to use for grouping", ShortName = "group", SortOrder = 12)] + public Optional GroupColumn = Optional.Implicit(DefaultColumnNames.GroupId); } public sealed class Output @@ -110,7 +119,8 @@ public static CommonOutputs.MacroOutput TrainTest( // Parse the subgraph. var subGraphRunContext = new RunContext(env); - var subGraphNodes = EntryPointNode.ValidateNodes(env, subGraphRunContext, input.Nodes, node.Catalog); + var subGraphNodes = EntryPointNode.ValidateNodes(env, subGraphRunContext, input.Nodes, node.Catalog, input.LabelColumn, + input.GroupColumn.IsExplicit ? input.GroupColumn.Value : null, input.WeightColumn.IsExplicit ? input.WeightColumn.Value : null); // Change the subgraph to use the training data as input. var varName = input.Inputs.Data.VarName; @@ -130,7 +140,7 @@ public static CommonOutputs.MacroOutput TrainTest( if (!subGraphRunContext.TryGetVariable(varName, out dataVariable)) throw env.Except($"Invalid variable name '{varName}'."); - string outputVarName = input.Outputs.PredictorModel == null ? node.GetOutputVariableName(nameof(Output.TransformModel)) : + string outputVarName = input.Outputs.PredictorModel == null ? node.GetOutputVariableName(nameof(Output.TransformModel)) : node.GetOutputVariableName(nameof(Output.PredictorModel)); foreach (var subGraphNode in subGraphNodes) @@ -206,11 +216,12 @@ public static CommonOutputs.MacroOutput TrainTest( // Do not double-add previous nodes. exp.Reset(); - // REVIEW: we need to extract the proper label column name here to pass to the evaluators. - // This is where you would add code to do it. + // REVIEW: add similar support for NameColumn and FeatureColumn. var settings = new MacroUtils.EvaluatorSettings { - LabelColumn = DefaultColumnNames.Label + LabelColumn = input.LabelColumn, + WeightColumn = input.WeightColumn.IsExplicit ? input.WeightColumn.Value : null, + GroupColumn = input.GroupColumn.IsExplicit ? input.GroupColumn.Value : null }; string outVariableName; @@ -249,7 +260,7 @@ public static CommonOutputs.MacroOutput TrainTest( var evalInputOutputTraining = MacroUtils.GetEvaluatorInputOutput(input.Kind, settings); var evalNodeTraining = evalInputOutputTraining.Item1; var evalOutputTraining = evalInputOutputTraining.Item2; - evalNodeTraining.Data.VarName = input.Outputs.PredictorModel == null ? datasetTransformNodeTrainingOutput.OutputData.VarName : + evalNodeTraining.Data.VarName = input.Outputs.PredictorModel == null ? datasetTransformNodeTrainingOutput.OutputData.VarName : scoreNodeTrainingOutput.ScoredData.VarName; if (node.OutputMap.TryGetValue(nameof(Output.TrainingWarnings), out outVariableName)) diff --git a/src/Microsoft.ML/Runtime/Internal/Tools/CSharpApiGenerator.cs b/src/Microsoft.ML/Runtime/Internal/Tools/CSharpApiGenerator.cs index 7f5114b185..db7c1d490d 100644 --- a/src/Microsoft.ML/Runtime/Internal/Tools/CSharpApiGenerator.cs +++ b/src/Microsoft.ML/Runtime/Internal/Tools/CSharpApiGenerator.cs @@ -3,11 +3,10 @@ // See the LICENSE file in the project root for more information. using System; -using System.CodeDom; using System.Collections.Generic; using System.IO; using System.Linq; -using Microsoft.CSharp; +using System.Reflection; using Microsoft.ML.Runtime; using Microsoft.ML.Runtime.CommandLine; using Microsoft.ML.Runtime.Data; @@ -34,381 +33,13 @@ public sealed class Arguments public string[] Exclude; } - private static class GeneratorUtils - { - public static string GetFullMethodName(ModuleCatalog.EntryPointInfo entryPointInfo) - { - return entryPointInfo.Name; - } - - public static Tuple GetClassAndMethodNames(ModuleCatalog.EntryPointInfo entryPointInfo) - { - var split = entryPointInfo.Name.Split('.'); - Contracts.Assert(split.Length == 2); - return new Tuple(split[0], split[1]); - } - - public static string GetCSharpTypeName(Type type) - { - if (type.IsGenericType && type.GetGenericTypeDefinition() == typeof(Nullable<>)) - return GetCSharpTypeName(type.GetGenericArguments()[0]) + "?"; - - string name; - using (var p = new CSharpCodeProvider()) - name = p.GetTypeOutput(new CodeTypeReference(type)); - return name; - } - - public static string GetOutputType(Type outputType) - { - Contracts.Check(Var.CheckType(outputType)); - - if (outputType.IsArray) - return $"ArrayVar<{GetCSharpTypeName(outputType.GetElementType())}>"; - if (outputType.IsGenericType && outputType.GetGenericTypeDefinition() == typeof(Dictionary<,>) - && outputType.GetGenericTypeArgumentsEx()[0] == typeof(string)) - { - return $"DictionaryVar<{GetCSharpTypeName(outputType.GetGenericTypeArgumentsEx()[1])}>"; - } - - return $"Var<{GetCSharpTypeName(outputType)}>"; - } - - public static string GetInputType(ModuleCatalog catalog, Type inputType, - Dictionary typesSymbolTable, string rootNameSpace = "") - { - if (inputType.IsGenericType && inputType.GetGenericTypeDefinition() == typeof(Var<>)) - return $"Var<{GetCSharpTypeName(inputType.GetGenericTypeArgumentsEx()[0])}>"; - - if (inputType.IsArray && Var.CheckType(inputType.GetElementType())) - return $"ArrayVar<{GetCSharpTypeName(inputType.GetElementType())}>"; - - if (inputType.IsGenericType && inputType.GetGenericTypeDefinition() == typeof(Dictionary<,>) - && inputType.GetGenericTypeArgumentsEx()[0] == typeof(string)) - { - return $"DictionaryVar<{GetCSharpTypeName(inputType.GetGenericTypeArgumentsEx()[1])}>"; - } - - if (Var.CheckType(inputType)) - return $"Var<{GetCSharpTypeName(inputType)}>"; - - bool isNullable = false; - bool isOptional = false; - var type = inputType; - if (type.IsGenericType && type.GetGenericTypeDefinition() == typeof(Nullable<>)) - { - type = type.GetGenericArguments()[0]; - isNullable = true; - } - else if (type.IsGenericType && type.GetGenericTypeDefinition() == typeof(Optional<>)) - { - type = type.GetGenericArguments()[0]; - isOptional = true; - } - - var typeEnum = TlcModule.GetDataType(type); - switch (typeEnum) - { - case TlcModule.DataKind.Float: - case TlcModule.DataKind.Int: - case TlcModule.DataKind.UInt: - case TlcModule.DataKind.Char: - case TlcModule.DataKind.String: - case TlcModule.DataKind.Bool: - case TlcModule.DataKind.DataView: - case TlcModule.DataKind.TransformModel: - case TlcModule.DataKind.PredictorModel: - case TlcModule.DataKind.FileHandle: - return GetCSharpTypeName(inputType); - case TlcModule.DataKind.Array: - return GetInputType(catalog, inputType.GetElementType(), typesSymbolTable) + "[]"; - case TlcModule.DataKind.Component: - string kind; - bool success = catalog.TryGetComponentKind(type, out kind); - Contracts.Assert(success); - return $"{kind}"; - case TlcModule.DataKind.Enum: - var enumName = GetEnumName(type, typesSymbolTable, rootNameSpace); - if (isNullable) - return $"{enumName}?"; - if (isOptional) - return $"Optional<{enumName}>"; - return $"{enumName}"; - default: - if (isNullable) - return rootNameSpace + typesSymbolTable[type.FullName]; - if (isOptional) - return $"Optional<{rootNameSpace + typesSymbolTable[type.FullName]}>"; - if (typesSymbolTable.ContainsKey(type.FullName)) - return rootNameSpace + typesSymbolTable[type.FullName]; - else - return GetSymbolFromType(typesSymbolTable, type, rootNameSpace); - } - } - - public static bool IsComponent(Type inputType) - { - if (inputType.IsArray && Var.CheckType(inputType.GetElementType())) - return false; - - if (inputType.IsGenericType && inputType.GetGenericTypeDefinition() == typeof(Dictionary<,>) - && inputType.GetGenericTypeArgumentsEx()[0] == typeof(string)) - { - return false; - } - - if (Var.CheckType(inputType)) - return false; - - var type = inputType; - if (type.IsGenericType && type.GetGenericTypeDefinition() == typeof(Nullable<>)) - type = type.GetGenericArguments()[0]; - else if (type.IsGenericType && type.GetGenericTypeDefinition() == typeof(Optional<>)) - type = type.GetGenericArguments()[0]; - - var typeEnum = TlcModule.GetDataType(type); - return typeEnum == TlcModule.DataKind.Component; - } - - public static string Capitalize(string s) - { - if (string.IsNullOrEmpty(s)) - return s; - return char.ToUpperInvariant(s[0]) + s.Substring(1); - } - - private static string GetCharAsString(char value) - { - switch (value) - { - case '\t': - return "\\t"; - case '\n': - return "\\n"; - case '\r': - return "\\r"; - case '\\': - return "\\"; - case '\"': - return "\""; - case '\'': - return "\\'"; - case '\0': - return "\\0"; - case '\a': - return "\\a"; - case '\b': - return "\\b"; - case '\f': - return "\\f"; - case '\v': - return "\\v"; - default: - return value.ToString(); - } - } - - public static string GetValue(ModuleCatalog catalog, Type fieldType, object fieldValue, - Dictionary typesSymbolTable, string rootNameSpace = "") - { - if (fieldType.IsGenericType && fieldType.GetGenericTypeDefinition() == typeof(Var<>)) - return $"new Var<{GetCSharpTypeName(fieldType.GetGenericTypeArgumentsEx()[0])}>()"; - - if (fieldType.IsArray && Var.CheckType(fieldType.GetElementType())) - return $"new ArrayVar<{GetCSharpTypeName(fieldType.GetElementType())}>()"; - - if (fieldType.IsGenericType && fieldType.GetGenericTypeDefinition() == typeof(Dictionary<,>) - && fieldType.GetGenericTypeArgumentsEx()[0] == typeof(string)) - { - return $"new DictionaryVar<{GetCSharpTypeName(fieldType.GetGenericTypeArgumentsEx()[1])}>()"; - } - - if (Var.CheckType(fieldType)) - return $"new Var<{GetCSharpTypeName(fieldType)}>()"; - - if (fieldValue == null) - return null; - - if (!fieldType.IsInterface) - { - try - { - var defaultFieldValue = Activator.CreateInstance(fieldType); - if (defaultFieldValue == fieldValue) - return null; - } - catch (MissingMethodException) - { - // No parameterless constructor, ignore. - } - } - - var typeEnum = TlcModule.GetDataType(fieldType); - if (fieldType.IsGenericType && (fieldType.GetGenericTypeDefinition() == typeof(Optional<>) || fieldType.GetGenericTypeDefinition() == typeof(Nullable<>))) - fieldType = fieldType.GetGenericArguments()[0]; - switch (typeEnum) - { - case TlcModule.DataKind.Array: - var arr = fieldValue as Array; - if (arr != null && arr.GetLength(0) > 0) - return $"{{ {string.Join(", ", arr.Cast().Select(item => GetValue(catalog, fieldType.GetElementType(), item, typesSymbolTable)))} }}"; - return null; - case TlcModule.DataKind.String: - var strval = fieldValue as string; - if (strval != null) - return Quote(strval); - return null; - case TlcModule.DataKind.Float: - if (fieldValue is double d) - { - if (double.IsPositiveInfinity(d)) - return "double.PositiveInfinity"; - if (double.IsNegativeInfinity(d)) - return "double.NegativeInfinity"; - if (d != 0) - return d.ToString("R") + "d"; - } - else if (fieldValue is float f) - { - if (float.IsPositiveInfinity(f)) - return "float.PositiveInfinity"; - if (float.IsNegativeInfinity(f)) - return "float.NegativeInfinity"; - if (f != 0) - return f.ToString("R") + "f"; - } - return null; - case TlcModule.DataKind.Int: - if (fieldValue is int i) - { - if (i != 0) - return i.ToString(); - } - else if (fieldValue is long l) - { - if (l != 0) - return l.ToString(); - } - return null; - case TlcModule.DataKind.Bool: - return (bool)fieldValue ? "true" : "false"; - case TlcModule.DataKind.Enum: - return GetEnumName(fieldType, typesSymbolTable, rootNameSpace) + "." + fieldValue; - case TlcModule.DataKind.Char: - return $"'{GetCharAsString((char)fieldValue)}'"; - case TlcModule.DataKind.Component: - var type = fieldValue.GetType(); - ModuleCatalog.ComponentInfo componentInfo; - if (!catalog.TryFindComponent(fieldType, type, out componentInfo)) - return null; - object defaultComponent = null; - try - { - defaultComponent = Activator.CreateInstance(componentInfo.ArgumentType); - } - catch (MissingMethodException) - { - // No parameterless constructor, ignore. - } - var propertyBag = new List(); - if (defaultComponent != null) - { - foreach (var fieldInfo in componentInfo.ArgumentType.GetFields()) - { - var inputAttr = fieldInfo.GetCustomAttributes(typeof(ArgumentAttribute), false).FirstOrDefault() as ArgumentAttribute; - if (inputAttr == null || inputAttr.Visibility == ArgumentAttribute.VisibilityType.CmdLineOnly) - continue; - if (fieldInfo.FieldType == typeof(JArray) || fieldInfo.FieldType == typeof(JObject)) - continue; - - var propertyValue = GetValue(catalog, fieldInfo.FieldType, fieldInfo.GetValue(fieldValue), typesSymbolTable); - var defaultPropertyValue = GetValue(catalog, fieldInfo.FieldType, fieldInfo.GetValue(defaultComponent), typesSymbolTable); - if (propertyValue != defaultPropertyValue) - propertyBag.Add($"{GeneratorUtils.Capitalize(inputAttr.Name ?? fieldInfo.Name)} = {propertyValue}"); - } - } - var properties = propertyBag.Count > 0 ? $" {{ {string.Join(", ", propertyBag)} }}" : ""; - return $"new {GetComponentName(componentInfo)}(){properties}"; - case TlcModule.DataKind.Unknown: - return $"new {rootNameSpace + typesSymbolTable[fieldType.FullName]}()"; - default: - return fieldValue.ToString(); - } - } - - private static string Quote(string src) - { - var dst = src.Replace("\\", @"\\").Replace("\"", "\\\"").Replace("\n", @"\n").Replace("\r", @"\r"); - return "\"" + dst + "\""; - } - - public static string GetComponentName(ModuleCatalog.ComponentInfo component) - { - return $"{Capitalize(component.Name)}{component.Kind}"; - } - - public static string GetEnumName(Type type, Dictionary typesSymbolTable, string rootNamespace = "") - { - if (typesSymbolTable.ContainsKey(type.FullName)) - return rootNamespace + typesSymbolTable[type.FullName]; - else - return GetSymbolFromType(typesSymbolTable, type, rootNamespace); - } - - public static string GetJsonFromField(string fieldName, Type fieldType) - { - if (fieldType.IsArray && Var.CheckType(fieldType.GetElementType())) - return $"{{({fieldName}.IsValue ? {fieldName}.VarName : $\"'${{{fieldName}.VarName}}'\")}}"; - if (fieldType.IsGenericType && fieldType.GetGenericTypeDefinition() == typeof(Dictionary<,>) - && fieldType.GetGenericTypeArgumentsEx()[0] == typeof(string)) - { - return $"'${{{fieldName}.VarName}}'"; - } - if (Var.CheckType(fieldType)) - return $"'${{{fieldName}.VarName}}'"; - - var isNullable = false; - var type = fieldType; - if (type.IsGenericType && type.GetGenericTypeDefinition() == typeof(Nullable<>)) - { - type = type.GetGenericArguments()[0]; - isNullable = true; - } - if (type.IsGenericType && type.GetGenericTypeDefinition() == typeof(Optional<>)) - type = type.GetGenericArguments()[0]; - - var typeEnum = TlcModule.GetDataType(type); - switch (typeEnum) - { - default: - if (isNullable) - return $"{{(!{fieldName}.HasValue ? \"null\" : $\"{{{fieldName}.Value}}\")}}"; - return $"{{{fieldName}}}"; - case TlcModule.DataKind.Enum: - if (isNullable) - return $"{{(!{fieldName}.HasValue ? \"null\" : $\"'{{{fieldName}.Value}}'\")}}"; - return $"'{{{fieldName}}}'"; - case TlcModule.DataKind.String: - return $"{{({fieldName} == null ? \"null\" : $\"'{{{fieldName}}}'\")}}"; - case TlcModule.DataKind.Bool: - if (isNullable) - return $"{{(!{fieldName}.HasValue ? \"null\" : {fieldName}.Value ? \"true\" : \"false\")}}"; - return $"'{{({fieldName} ? \"true\" : \"false\")}}'"; - case TlcModule.DataKind.Component: - case TlcModule.DataKind.Unknown: - return $"{{({fieldName} == null ? \"null\" : {fieldName}.ToJson())}}"; - case TlcModule.DataKind.Array: - return $"[{{({fieldName} == null ? \"\" : string.Join(\",\", {fieldName}.Select(f => $\"{GetJsonFromField("f", type.GetElementType())}\")))}}]"; - } - } - } - private readonly IHost _host; private readonly string _csFilename; private readonly string _regenerate; private readonly HashSet _excludedSet; private const string RegistrationName = "CSharpApiGenerator"; - public Dictionary _typesSymbolTable = new Dictionary(); + private const string _defaultNamespace = "Microsoft.ML."; + private readonly GeneratedClasses _generatedClasses; public CSharpApiGenerator(IHostEnvironment env, Arguments args, string regenerate) { @@ -423,6 +54,7 @@ public CSharpApiGenerator(IHostEnvironment env, Arguments args, string regenerat _csFilename = "CSharpApi.cs"; _regenerate = regenerate; _excludedSet = new HashSet(args.Exclude); + _generatedClasses = new GeneratedClasses(); } public void Generate(IEnumerable infos) @@ -434,17 +66,17 @@ public void Generate(IEnumerable infos) var writer = IndentingTextWriter.Wrap(sw, " "); // Generate header - GenerateHeader(writer); + CSharpGeneratorUtils.GenerateHeader(writer); foreach (var entryPointInfo in catalog.AllEntryPoints().Where(x => !_excludedSet.Contains(x.Name)).OrderBy(x => x.Name)) { // Generate method - GenerateMethod(writer, entryPointInfo, catalog); + CSharpGeneratorUtils.GenerateMethod(writer, entryPointInfo.Name, _defaultNamespace); } // Generate footer - GenerateFooter(writer); - GenerateFooter(writer); + CSharpGeneratorUtils.GenerateFooter(writer); + CSharpGeneratorUtils.GenerateFooter(writer); foreach (var entryPointInfo in catalog.AllEntryPoints().Where(x => !_excludedSet.Contains(x.Name)).OrderBy(x => x.Name)) { @@ -456,68 +88,28 @@ public void Generate(IEnumerable infos) writer.WriteLine("{"); writer.Indent(); - foreach (var kind in catalog.GetAllComponentKinds().OrderBy(x => x)) + foreach (var kind in catalog.GetAllComponentKinds()) { // Generate kind base class GenerateComponentKind(writer, kind); - foreach (var component in catalog.GetAllComponents(kind).OrderBy(x => x.Name)) + foreach (var component in catalog.GetAllComponents(kind)) { // Generate component GenerateComponent(writer, component, catalog); } } - GenerateFooter(writer); - GenerateFooter(writer); + CSharpGeneratorUtils.GenerateFooter(writer); + CSharpGeneratorUtils.GenerateFooter(writer); writer.WriteLine("#pragma warning restore"); } } - private void GenerateHeader(IndentingTextWriter writer) + private void GenerateInputOutput(IndentingTextWriter writer, ModuleCatalog.EntryPointInfo entryPointInfo, ModuleCatalog catalog) { - writer.WriteLine("//------------------------------------------------------------------------------"); - writer.WriteLine("// "); - writer.WriteLine("// This code was generated by a tool."); - writer.WriteLine("//"); - writer.WriteLine("// Changes to this file may cause incorrect behavior and will be lost if"); - writer.WriteLine("// the code is regenerated."); - writer.WriteLine("// "); - writer.WriteLine("//------------------------------------------------------------------------------"); - //writer.WriteLine($"// This file is auto generated. To regenerate it, run: {_regenerate}"); - writer.WriteLine("#pragma warning disable"); - writer.WriteLine("using System.Collections.Generic;"); - writer.WriteLine("using Microsoft.ML.Runtime;"); - writer.WriteLine("using Microsoft.ML.Runtime.Data;"); - writer.WriteLine("using Microsoft.ML.Runtime.EntryPoints;"); - writer.WriteLine("using Newtonsoft.Json;"); - writer.WriteLine("using System;"); - writer.WriteLine("using System.Linq;"); - writer.WriteLine("using Microsoft.ML.Runtime.CommandLine;"); - writer.WriteLine(); - writer.WriteLine("namespace Microsoft.ML"); - writer.WriteLine("{"); - writer.Indent(); - writer.WriteLine("namespace Runtime"); - writer.WriteLine("{"); - writer.Indent(); - writer.WriteLine("public sealed partial class Experiment"); - writer.WriteLine("{"); - writer.Indent(); - } - - private void GenerateFooter(IndentingTextWriter writer) - { - writer.Outdent(); - writer.WriteLine("}"); - } - - private void GenerateInputOutput(IndentingTextWriter writer, - ModuleCatalog.EntryPointInfo entryPointInfo, - ModuleCatalog catalog) - { - var classAndMethod = GeneratorUtils.GetClassAndMethodNames(entryPointInfo); - writer.WriteLine($"namespace {classAndMethod.Item1}"); + var classAndMethod = CSharpGeneratorUtils.GetEntryPointMetadata(entryPointInfo); + writer.WriteLine($"namespace {classAndMethod.Namespace}"); writer.WriteLine("{"); writer.Indent(); GenerateInput(writer, entryPointInfo, catalog); @@ -526,78 +118,6 @@ private void GenerateInputOutput(IndentingTextWriter writer, writer.WriteLine(); } - /// - /// This methods creates a unique name for a class/struct/enum, given a type and a namespace. - /// It generates the name based on the property of the type - /// (see description here https://msdn.microsoft.com/en-us/library/system.type.fullname(v=vs.110).aspx). - /// Example: Assume we have the following structure in namespace X.Y: - /// class A { - /// class B { - /// enum C { - /// Value1, - /// Value2 - /// } - /// } - /// } - /// The full name of C would be X.Y.A+B+C. This method will generate the name "ABC" from it. In case - /// A is generic with one generic type, then the full name of typeof(A<float>.B.C) would be X.Y.A`1+B+C[[System.Single]]. - /// In this case, this method will generate the name "ASingleBC". - /// - /// A dictionary containing the names of the classes already generated. - /// This parameter is only used to ensure that the newly generated name is unique. - /// The type for which to generate the new name. - /// The namespace prefix to the new name. - /// A unique name derived from the given type and namespace. - private static string GetSymbolFromType(Dictionary typesSymbolTable, Type type, string currentNamespace) - { - var fullTypeName = type.FullName; - string name = currentNamespace != "" ? currentNamespace + '.' : ""; - - int bracketIndex = fullTypeName.IndexOf('['); - Type[] genericTypes = null; - if (type.IsGenericType) - genericTypes = type.GetGenericArguments(); - if (bracketIndex > 0) - { - Contracts.AssertValue(genericTypes); - fullTypeName = fullTypeName.Substring(0, bracketIndex); - } - - // When the type is nested, the names of the outer types are concatenated with a '+'. - var nestedNames = fullTypeName.Split('+'); - var baseName = nestedNames[0]; - - // We currently only handle generic types in the outer most class, support for generic inner classes - // can be added if needed. - int backTickIndex = baseName.LastIndexOf('`'); - int dotIndex = baseName.LastIndexOf('.'); - Contracts.Assert(dotIndex >= 0); - if (backTickIndex < 0) - name += baseName.Substring(dotIndex + 1); - else - { - name += baseName.Substring(dotIndex + 1, backTickIndex - dotIndex - 1); - Contracts.AssertValue(genericTypes); - if (genericTypes != null) - { - foreach (var genType in genericTypes) - { - var splitNames = genType.FullName.Split('+'); - if (splitNames[0].LastIndexOf('.') >= 0) - splitNames[0] = splitNames[0].Substring(splitNames[0].LastIndexOf('.') + 1); - name += string.Join("", splitNames); - } - } - } - - for (int i = 1; i < nestedNames.Length; i++) - name += nestedNames[i]; - - Contracts.Assert(typesSymbolTable.Select(kvp => kvp.Value).All(str => string.Compare(str, name) != 0)); - - return name; - } - private void GenerateEnums(IndentingTextWriter writer, Type inputType, string currentNamespace) { foreach (var fieldInfo in inputType.GetFields()) @@ -605,14 +125,8 @@ private void GenerateEnums(IndentingTextWriter writer, Type inputType, string cu var inputAttr = fieldInfo.GetCustomAttributes(typeof(ArgumentAttribute), false).FirstOrDefault() as ArgumentAttribute; if (inputAttr == null || inputAttr.Visibility == ArgumentAttribute.VisibilityType.CmdLineOnly) continue; - - var type = fieldInfo.FieldType; - if (type.IsGenericType && type.GetGenericTypeDefinition() == typeof(Nullable<>)) - type = type.GetGenericArguments()[0]; - if (type.IsGenericType && type.GetGenericTypeDefinition() == typeof(Optional<>)) - type = type.GetGenericArguments()[0]; - - if (_typesSymbolTable.ContainsKey(type.FullName)) + var type = CSharpGeneratorUtils.ExtractOptionalOrNullableType(fieldInfo.FieldType); + if (_generatedClasses.IsGenerated(type.FullName)) continue; if (!type.IsEnum) @@ -625,15 +139,16 @@ private void GenerateEnums(IndentingTextWriter writer, Type inputType, string cu var enumType = Enum.GetUnderlyingType(type); - _typesSymbolTable[type.FullName] = GetSymbolFromType(_typesSymbolTable, type, currentNamespace); + var apiName = _generatedClasses.GetApiName(type, currentNamespace); if (enumType == typeof(int)) - writer.WriteLine($"public enum {_typesSymbolTable[type.FullName].Substring(_typesSymbolTable[type.FullName].LastIndexOf('.') + 1)}"); + writer.WriteLine($"public enum {apiName}"); else { Contracts.Assert(enumType == typeof(byte)); - writer.WriteLine($"public enum {_typesSymbolTable[type.FullName].Substring(_typesSymbolTable[type.FullName].LastIndexOf('.') + 1)} : byte"); + writer.WriteLine($"public enum {apiName} : byte"); } + _generatedClasses.MarkAsGenerated(type.FullName); writer.Write("{"); writer.Indent(); var names = Enum.GetNames(type); @@ -642,6 +157,8 @@ private void GenerateEnums(IndentingTextWriter writer, Type inputType, string cu for (int i = 0; i < names.Length; i++) { var name = names[i]; + if (type.GetField(name).GetCustomAttribute() != null) + continue; var value = values.GetValue(i); writer.WriteLine(prefix); if (enumType == typeof(int)) @@ -660,25 +177,7 @@ private void GenerateEnums(IndentingTextWriter writer, Type inputType, string cu } } - string GetFriendlyTypeName(string currentNameSpace, string typeName) - { - Contracts.Assert(typeName.Length >= currentNameSpace.Length); - - int index = 0; - for (index = 0; index < currentNameSpace.Length && currentNameSpace[index] == typeName[index]; index++) ; - - if (index == 0) - return typeName; - if (typeName[index - 1] == '.') - return typeName.Substring(index); - - return typeName; - } - - private void GenerateStructs(IndentingTextWriter writer, - Type inputType, - ModuleCatalog catalog, - string currentNamespace) + private void GenerateClasses(IndentingTextWriter writer, Type inputType, ModuleCatalog catalog, string currentNamespace) { foreach (var fieldInfo in inputType.GetFields()) { @@ -687,10 +186,7 @@ private void GenerateStructs(IndentingTextWriter writer, continue; var type = fieldInfo.FieldType; - if (type.IsGenericType && type.GetGenericTypeDefinition() == typeof(Nullable<>)) - type = type.GetGenericArguments()[0]; - if (type.IsGenericType && type.GetGenericTypeDefinition() == typeof(Optional<>)) - type = type.GetGenericArguments()[0]; + type = CSharpGeneratorUtils.ExtractOptionalOrNullableType(type); if (type.IsArray) type = type.GetElementType(); if (type == typeof(JArray) || type == typeof(JObject)) @@ -707,265 +203,210 @@ private void GenerateStructs(IndentingTextWriter writer, if (typeEnum != TlcModule.DataKind.Unknown) continue; - if (_typesSymbolTable.ContainsKey(type.FullName)) + if (_generatedClasses.IsGenerated(type.FullName)) continue; + GenerateEnums(writer, type, currentNamespace); + GenerateClasses(writer, type, catalog, currentNamespace); - _typesSymbolTable[type.FullName] = GetSymbolFromType(_typesSymbolTable, type, currentNamespace); + var apiName = _generatedClasses.GetApiName(type, currentNamespace); string classBase = ""; if (type.IsSubclassOf(typeof(OneToOneColumn))) - classBase = $" : OneToOneColumn<{_typesSymbolTable[type.FullName].Substring(_typesSymbolTable[type.FullName].LastIndexOf('.') + 1)}>, IOneToOneColumn"; + classBase = $" : OneToOneColumn<{apiName}>, IOneToOneColumn"; else if (type.IsSubclassOf(typeof(ManyToOneColumn))) - classBase = $" : ManyToOneColumn<{_typesSymbolTable[type.FullName].Substring(_typesSymbolTable[type.FullName].LastIndexOf('.') + 1)}>, IManyToOneColumn"; - writer.WriteLine($"public sealed partial class {_typesSymbolTable[type.FullName].Substring(_typesSymbolTable[type.FullName].LastIndexOf('.') + 1)}{classBase}"); + classBase = $" : ManyToOneColumn<{apiName}>, IManyToOneColumn"; + writer.WriteLine($"public sealed partial class {apiName}{classBase}"); writer.WriteLine("{"); writer.Indent(); - GenerateInputFields(writer, type, catalog, _typesSymbolTable); + _generatedClasses.MarkAsGenerated(type.FullName); + GenerateInputFields(writer, type, catalog, currentNamespace); writer.Outdent(); writer.WriteLine("}"); writer.WriteLine(); - GenerateStructs(writer, type, catalog, currentNamespace); } } - private void GenerateLoaderAddInputMethod(IndentingTextWriter writer, string className) + private void GenerateColumnAddMethods(IndentingTextWriter writer, Type inputType, ModuleCatalog catalog, + string className, out Type columnType) { - //Constructor. - writer.WriteLine("[JsonIgnore]"); - writer.WriteLine("private string _inputFilePath = null;"); - writer.WriteLine($"public {className}(string filePath)"); + columnType = null; + foreach (var fieldInfo in inputType.GetFields()) + { + var inputAttr = fieldInfo.GetCustomAttributes(typeof(ArgumentAttribute), false).FirstOrDefault() as ArgumentAttribute; + if (inputAttr == null || inputAttr.Visibility == ArgumentAttribute.VisibilityType.CmdLineOnly) + continue; + var type = CSharpGeneratorUtils.ExtractOptionalOrNullableType(fieldInfo.FieldType); + var isArray = type.IsArray; + if (isArray) + type = type.GetElementType(); + if (type == typeof(JArray) || type == typeof(JObject)) + continue; + if (type.IsGenericType && type.GetGenericTypeDefinition() == typeof(Var<>)) + continue; + var typeEnum = TlcModule.GetDataType(type); + if (typeEnum != TlcModule.DataKind.Unknown) + continue; + + if (type.IsSubclassOf(typeof(OneToOneColumn))) + columnType = GenerateOneToOneColumn(writer, className, columnType, fieldInfo, inputAttr, type, isArray); + else if (type.IsSubclassOf(typeof(ManyToOneColumn))) + columnType = GenerateManyToOneColumn(writer, className, columnType, fieldInfo, inputAttr, type, isArray); + } + } + + private Type GenerateManyToOneColumn(IndentingTextWriter writer, string className, Type columnType, + System.Reflection.FieldInfo fieldInfo, ArgumentAttribute inputAttr, Type type, bool isArray) + { + var fieldName = CSharpGeneratorUtils.Capitalize(inputAttr.Name ?? fieldInfo.Name); + var apiName = _generatedClasses.GetApiName(type, ""); + writer.WriteLine($"public {className}()"); + writer.WriteLine("{"); + writer.WriteLine("}"); + writer.WriteLine(""); + writer.WriteLine($"public {className}(string output{fieldName}, params string[] input{fieldName}s)"); writer.WriteLine("{"); writer.Indent(); - writer.WriteLine("_inputFilePath = filePath;"); + writer.WriteLine($"Add{fieldName}(output{fieldName}, input{fieldName}s);"); writer.Outdent(); writer.WriteLine("}"); writer.WriteLine(""); - - //SetInput. - writer.WriteLine($"public void SetInput(IHostEnvironment env, Experiment experiment)"); + writer.WriteLine($"public void Add{fieldName}(string name, params string[] source)"); writer.WriteLine("{"); writer.Indent(); - writer.WriteLine("IFileHandle inputFile = new SimpleFileHandle(env, _inputFilePath, false, false);"); - writer.WriteLine("experiment.SetInput(InputFile, inputFile);"); + if (isArray) + { + writer.WriteLine($"var list = {fieldName} == null ? new List<{apiName}>() : new List<{apiName}>({fieldName});"); + writer.WriteLine($"list.Add(ManyToOneColumn<{apiName}>.Create(name, source));"); + writer.WriteLine($"{fieldName} = list.ToArray();"); + } + else + writer.WriteLine($"{fieldName} = ManyToOneColumn<{apiName}>.Create(name, source);"); writer.Outdent(); writer.WriteLine("}"); - writer.WriteLine(""); + writer.WriteLine(); - //GetInputData - writer.WriteLine("public Var GetInputData() => null;"); - writer.WriteLine(""); + Contracts.Assert(columnType == null); - //Apply. - writer.WriteLine($"public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment)"); + columnType = type; + return columnType; + } + + private Type GenerateOneToOneColumn(IndentingTextWriter writer, string className, Type columnType, + System.Reflection.FieldInfo fieldInfo, ArgumentAttribute inputAttr, Type type, bool isArray) + { + var fieldName = CSharpGeneratorUtils.Capitalize(inputAttr.Name ?? fieldInfo.Name); + var generatedType = _generatedClasses.GetApiName(type, ""); + writer.WriteLine($"public {className}()"); writer.WriteLine("{"); - writer.Indent(); - writer.WriteLine("Contracts.Assert(previousStep == null);"); + writer.WriteLine("}"); writer.WriteLine(""); - writer.WriteLine($"return new {className}PipelineStep(experiment.Add(this));"); + writer.WriteLine($"public {className}(params string[] input{fieldName}s)"); + writer.WriteLine("{"); + writer.Indent(); + writer.WriteLine($"if (input{fieldName}s != null)"); + writer.WriteLine("{"); + writer.Indent(); + writer.WriteLine($"foreach (string input in input{fieldName}s)"); + writer.WriteLine("{"); + writer.Indent(); + writer.WriteLine($"Add{fieldName}(input);"); + writer.Outdent(); + writer.WriteLine("}"); + writer.Outdent(); + writer.WriteLine("}"); writer.Outdent(); writer.WriteLine("}"); writer.WriteLine(""); - - //Pipelinestep class. - writer.WriteLine($"private class {className}PipelineStep : ILearningPipelineDataStep"); + writer.WriteLine($"public {className}(params (string inputColumn, string outputColumn)[] inputOutput{fieldName}s)"); + writer.WriteLine("{"); + writer.Indent(); + writer.WriteLine($"if (inputOutput{fieldName}s != null)"); writer.WriteLine("{"); writer.Indent(); - writer.WriteLine($"public {className}PipelineStep (Output output)"); + writer.WriteLine($"foreach (var inputOutput in inputOutput{fieldName}s)"); writer.WriteLine("{"); writer.Indent(); - writer.WriteLine("Data = output.Data;"); - writer.WriteLine("Model = null;"); + writer.WriteLine($"Add{fieldName}(inputOutput.outputColumn, inputOutput.inputColumn);"); writer.Outdent(); writer.WriteLine("}"); - writer.WriteLine(); - writer.WriteLine("public Var Data { get; }"); - writer.WriteLine("public Var Model { get; }"); writer.Outdent(); writer.WriteLine("}"); - } - - private void GenerateColumnAddMethods(IndentingTextWriter writer, - Type inputType, - ModuleCatalog catalog, - string className, - out Type columnType) - { - columnType = null; - foreach (var fieldInfo in inputType.GetFields()) + writer.Outdent(); + writer.WriteLine("}"); + writer.WriteLine(""); + writer.WriteLine($"public void Add{fieldName}(string inputColumn)"); + writer.WriteLine("{"); + writer.Indent(); + if (isArray) { - var inputAttr = fieldInfo.GetCustomAttributes(typeof(ArgumentAttribute), false).FirstOrDefault() as ArgumentAttribute; - if (inputAttr == null || inputAttr.Visibility == ArgumentAttribute.VisibilityType.CmdLineOnly) - continue; - - var type = fieldInfo.FieldType; - if (type.IsGenericType && type.GetGenericTypeDefinition() == typeof(Nullable<>)) - type = type.GetGenericArguments()[0]; - if (type.IsGenericType && type.GetGenericTypeDefinition() == typeof(Optional<>)) - type = type.GetGenericArguments()[0]; - var isArray = type.IsArray; - if (isArray) - type = type.GetElementType(); - if (type == typeof(JArray) || type == typeof(JObject)) - continue; - if (type.IsGenericType && type.GetGenericTypeDefinition() == typeof(Var<>)) - continue; - var typeEnum = TlcModule.GetDataType(type); - if (typeEnum != TlcModule.DataKind.Unknown) - continue; - - if (type.IsSubclassOf(typeof(OneToOneColumn))) - { - var fieldName = GeneratorUtils.Capitalize(inputAttr.Name ?? fieldInfo.Name); - writer.WriteLine($"public {className}()"); - writer.WriteLine("{"); - writer.WriteLine("}"); - writer.WriteLine(""); - writer.WriteLine($"public {className}(params string[] input{fieldName}s)"); - writer.WriteLine("{"); - writer.Indent(); - writer.WriteLine($"if (input{fieldName}s != null)"); - writer.WriteLine("{"); - writer.Indent(); - writer.WriteLine($"foreach (string input in input{fieldName}s)"); - writer.WriteLine("{"); - writer.Indent(); - writer.WriteLine($"Add{fieldName}(input);"); - writer.Outdent(); - writer.WriteLine("}"); - writer.Outdent(); - writer.WriteLine("}"); - writer.Outdent(); - writer.WriteLine("}"); - writer.WriteLine(""); - writer.WriteLine($"public {className}(params ValueTuple[] inputOutput{fieldName}s)"); - writer.WriteLine("{"); - writer.Indent(); - writer.WriteLine($"if (inputOutput{fieldName}s != null)"); - writer.WriteLine("{"); - writer.Indent(); - writer.WriteLine($"foreach (ValueTuple inputOutput in inputOutput{fieldName}s)"); - writer.WriteLine("{"); - writer.Indent(); - writer.WriteLine($"Add{fieldName}(inputOutput.Item2, inputOutput.Item1);"); - writer.Outdent(); - writer.WriteLine("}"); - writer.Outdent(); - writer.WriteLine("}"); - writer.Outdent(); - writer.WriteLine("}"); - writer.WriteLine(""); - writer.WriteLine($"public void Add{fieldName}(string source)"); - writer.WriteLine("{"); - writer.Indent(); - if (isArray) - { - writer.WriteLine($"var list = {fieldName} == null ? new List<{_typesSymbolTable[type.FullName]}>() : new List<{_typesSymbolTable[type.FullName]}>({fieldName});"); - writer.WriteLine($"list.Add(OneToOneColumn<{_typesSymbolTable[type.FullName]}>.Create(source));"); - writer.WriteLine($"{fieldName} = list.ToArray();"); - } - else - writer.WriteLine($"{fieldName} = OneToOneColumn<{_typesSymbolTable[type.FullName]}>.Create(source);"); - writer.Outdent(); - writer.WriteLine("}"); - writer.WriteLine(); - writer.WriteLine($"public void Add{fieldName}(string name, string source)"); - writer.WriteLine("{"); - writer.Indent(); - if (isArray) - { - writer.WriteLine($"var list = {fieldName} == null ? new List<{_typesSymbolTable[type.FullName]}>() : new List<{_typesSymbolTable[type.FullName]}>({fieldName});"); - writer.WriteLine($"list.Add(OneToOneColumn<{_typesSymbolTable[type.FullName]}>.Create(name, source));"); - writer.WriteLine($"{fieldName} = list.ToArray();"); - } - else - writer.WriteLine($"{fieldName} = OneToOneColumn<{_typesSymbolTable[type.FullName]}>.Create(name, source);"); - writer.Outdent(); - writer.WriteLine("}"); - writer.WriteLine(); - - Contracts.Assert(columnType == null); - - columnType = type; - } - else if (type.IsSubclassOf(typeof(ManyToOneColumn))) - { - var fieldName = GeneratorUtils.Capitalize(inputAttr.Name ?? fieldInfo.Name); - writer.WriteLine($"public {className}()"); - writer.WriteLine("{"); - writer.WriteLine("}"); - writer.WriteLine(""); - writer.WriteLine($"public {className}(string output{fieldName}, params string[] input{fieldName}s)"); - writer.WriteLine("{"); - writer.Indent(); - writer.WriteLine($"Add{fieldName}(output{fieldName}, input{fieldName}s);"); - writer.Outdent(); - writer.WriteLine("}"); - writer.WriteLine(""); - writer.WriteLine($"public void Add{fieldName}(string name, params string[] source)"); - writer.WriteLine("{"); - writer.Indent(); - if (isArray) - { - writer.WriteLine($"var list = {fieldName} == null ? new List<{_typesSymbolTable[type.FullName]}>() : new List<{_typesSymbolTable[type.FullName]}>({fieldName});"); - writer.WriteLine($"list.Add(ManyToOneColumn<{_typesSymbolTable[type.FullName]}>.Create(name, source));"); - writer.WriteLine($"{fieldName} = list.ToArray();"); - } - else - writer.WriteLine($"{fieldName} = ManyToOneColumn<{_typesSymbolTable[type.FullName]}>.Create(name, source);"); - writer.Outdent(); - writer.WriteLine("}"); - writer.WriteLine(); + writer.WriteLine($"var list = {fieldName} == null ? new List<{generatedType}>() : new List<{generatedType}>({fieldName});"); + writer.WriteLine($"list.Add(OneToOneColumn<{generatedType}>.Create(inputColumn));"); + writer.WriteLine($"{fieldName} = list.ToArray();"); + } + else + writer.WriteLine($"{fieldName} = OneToOneColumn<{generatedType}>.Create(inputColumn);"); + writer.Outdent(); + writer.WriteLine("}"); + writer.WriteLine(); + writer.WriteLine($"public void Add{fieldName}(string outputColumn, string inputColumn)"); + writer.WriteLine("{"); + writer.Indent(); + if (isArray) + { + writer.WriteLine($"var list = {fieldName} == null ? new List<{generatedType}>() : new List<{generatedType}>({fieldName});"); + writer.WriteLine($"list.Add(OneToOneColumn<{generatedType}>.Create(outputColumn, inputColumn));"); + writer.WriteLine($"{fieldName} = list.ToArray();"); + } + else + writer.WriteLine($"{fieldName} = OneToOneColumn<{generatedType}>.Create(outputColumn, inputColumn);"); + writer.Outdent(); + writer.WriteLine("}"); + writer.WriteLine(); - Contracts.Assert(columnType == null); + Contracts.Assert(columnType == null); - columnType = type; - } - } + columnType = type; + return columnType; } - private void GenerateInput(IndentingTextWriter writer, - ModuleCatalog.EntryPointInfo entryPointInfo, - ModuleCatalog catalog) + private void GenerateInput(IndentingTextWriter writer, ModuleCatalog.EntryPointInfo entryPointInfo, ModuleCatalog catalog) { - var classAndMethod = GeneratorUtils.GetClassAndMethodNames(entryPointInfo); + var entryPointMetadata = CSharpGeneratorUtils.GetEntryPointMetadata(entryPointInfo); string classBase = ""; if (entryPointInfo.InputKinds != null) { - classBase += $" : {string.Join(", ", entryPointInfo.InputKinds.Select(GeneratorUtils.GetCSharpTypeName))}"; + classBase += $" : {string.Join(", ", entryPointInfo.InputKinds.Select(CSharpGeneratorUtils.GetCSharpTypeName))}"; if (entryPointInfo.InputKinds.Any(t => typeof(ITrainerInput).IsAssignableFrom(t) || typeof(ITransformInput).IsAssignableFrom(t))) classBase += ", Microsoft.ML.ILearningPipelineItem"; } - GenerateEnums(writer, entryPointInfo.InputType, classAndMethod.Item1); + GenerateEnums(writer, entryPointInfo.InputType, _defaultNamespace + entryPointMetadata.Namespace); writer.WriteLine(); - GenerateStructs(writer, entryPointInfo.InputType, catalog, classAndMethod.Item1); - writer.WriteLine("/// "); - foreach (var line in entryPointInfo.Description.Split(new[] { Environment.NewLine }, StringSplitOptions.RemoveEmptyEntries)) - writer.WriteLine($"/// {line}"); - writer.WriteLine("/// "); - - if(entryPointInfo.ObsoleteAttribute != null) + GenerateClasses(writer, entryPointInfo.InputType, catalog, _defaultNamespace + entryPointMetadata.Namespace); + CSharpGeneratorUtils.GenerateSummary(writer, entryPointInfo.Description); + + if (entryPointInfo.ObsoleteAttribute != null) writer.WriteLine($"[Obsolete(\"{entryPointInfo.ObsoleteAttribute.Message}\")]"); - - writer.WriteLine($"public sealed partial class {classAndMethod.Item2}{classBase}"); + + writer.WriteLine($"public sealed partial class {entryPointMetadata.ClassName}{classBase}"); writer.WriteLine("{"); writer.Indent(); writer.WriteLine(); if (entryPointInfo.InputKinds != null && entryPointInfo.InputKinds.Any(t => typeof(ILearningPipelineLoader).IsAssignableFrom(t))) - GenerateLoaderAddInputMethod(writer, classAndMethod.Item2); + CSharpGeneratorUtils.GenerateLoaderAddInputMethod(writer, entryPointMetadata.ClassName); - GenerateColumnAddMethods(writer, entryPointInfo.InputType, catalog, classAndMethod.Item2, out Type transformType); + GenerateColumnAddMethods(writer, entryPointInfo.InputType, catalog, entryPointMetadata.ClassName, out Type transformType); writer.WriteLine(); - GenerateInputFields(writer, entryPointInfo.InputType, catalog, _typesSymbolTable); + GenerateInputFields(writer, entryPointInfo.InputType, catalog, _defaultNamespace + entryPointMetadata.Namespace); writer.WriteLine(); GenerateOutput(writer, entryPointInfo, out HashSet outputVariableNames); - GenerateApplyFunction(writer, entryPointInfo, transformType, outputVariableNames, entryPointInfo.InputKinds); + GenerateApplyFunction(writer, entryPointMetadata.ClassName, transformType, outputVariableNames, entryPointInfo.InputKinds); writer.Outdent(); writer.WriteLine("}"); } - private static void GenerateApplyFunction(IndentingTextWriter writer, ModuleCatalog.EntryPointInfo entryPointInfo, - Type type, HashSet outputVariableNames, Type[] inputKinds) + private static void GenerateApplyFunction(IndentingTextWriter writer, string className, Type type, + HashSet outputVariableNames, Type[] inputKinds) { if (inputKinds == null) return; @@ -987,7 +428,6 @@ private static void GenerateApplyFunction(IndentingTextWriter writer, ModuleCata writer.WriteLine("public Var GetInputData() => TrainingData;"); writer.WriteLine(""); - string className = GeneratorUtils.GetClassAndMethodNames(entryPointInfo).Item2; writer.WriteLine("public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment)"); writer.WriteLine("{"); @@ -1057,8 +497,7 @@ private static void GenerateApplyFunction(IndentingTextWriter writer, ModuleCata writer.WriteLine("}"); } - private static void GenerateInputFields(IndentingTextWriter writer, - Type inputType, ModuleCatalog catalog, Dictionary typesSymbolTable, string rootNameSpace = "") + private void GenerateInputFields(IndentingTextWriter writer, Type inputType, ModuleCatalog catalog, string rootNameSpace) { var defaults = Activator.CreateInstance(inputType); foreach (var fieldInfo in inputType.GetFields()) @@ -1070,20 +509,18 @@ private static void GenerateInputFields(IndentingTextWriter writer, if (fieldInfo.FieldType == typeof(JObject)) continue; - writer.WriteLine("/// "); - writer.WriteLine($"/// {inputAttr.HelpText}"); - writer.WriteLine("/// "); + CSharpGeneratorUtils.GenerateSummary(writer, inputAttr.HelpText); if (fieldInfo.FieldType == typeof(JArray)) { - writer.WriteLine($"public Experiment {GeneratorUtils.Capitalize(inputAttr.Name ?? fieldInfo.Name)} {{ get; set; }}"); + writer.WriteLine($"public Experiment {CSharpGeneratorUtils.Capitalize(inputAttr.Name ?? fieldInfo.Name)} {{ get; set; }}"); writer.WriteLine(); continue; } - var inputTypeString = GeneratorUtils.GetInputType(catalog, fieldInfo.FieldType, typesSymbolTable, rootNameSpace); - if (GeneratorUtils.IsComponent(fieldInfo.FieldType)) + var inputTypeString = CSharpGeneratorUtils.GetInputType(catalog, fieldInfo.FieldType, _generatedClasses, rootNameSpace); + if (CSharpGeneratorUtils.IsComponent(fieldInfo.FieldType)) writer.WriteLine("[JsonConverter(typeof(ComponentSerializer))]"); - if (GeneratorUtils.Capitalize(inputAttr.Name ?? fieldInfo.Name) != (inputAttr.Name ?? fieldInfo.Name)) + if (CSharpGeneratorUtils.Capitalize(inputAttr.Name ?? fieldInfo.Name) != (inputAttr.Name ?? fieldInfo.Name)) writer.WriteLine($"[JsonProperty(\"{inputAttr.Name ?? fieldInfo.Name}\")]"); // For range attributes on properties @@ -1105,8 +542,8 @@ private static void GenerateInputFields(IndentingTextWriter writer, writer.WriteLine(sweepableParamAttr.ToString()); } - writer.Write($"public {inputTypeString} {GeneratorUtils.Capitalize(inputAttr.Name ?? fieldInfo.Name)} {{ get; set; }}"); - var defaultValue = GeneratorUtils.GetValue(catalog, fieldInfo.FieldType, fieldInfo.GetValue(defaults), typesSymbolTable, rootNameSpace); + writer.Write($"public {inputTypeString} {CSharpGeneratorUtils.Capitalize(inputAttr.Name ?? fieldInfo.Name)} {{ get; set; }}"); + var defaultValue = CSharpGeneratorUtils.GetValue(catalog, fieldInfo.FieldType, fieldInfo.GetValue(defaults), _generatedClasses, rootNameSpace); if (defaultValue != null) writer.Write($" = {defaultValue};"); writer.WriteLine(); @@ -1114,14 +551,12 @@ private static void GenerateInputFields(IndentingTextWriter writer, } } - private void GenerateOutput(IndentingTextWriter writer, - ModuleCatalog.EntryPointInfo entryPointInfo, - out HashSet outputVariableNames) + private void GenerateOutput(IndentingTextWriter writer, ModuleCatalog.EntryPointInfo entryPointInfo, out HashSet outputVariableNames) { outputVariableNames = new HashSet(); string classBase = ""; if (entryPointInfo.OutputKinds != null) - classBase = $" : {string.Join(", ", entryPointInfo.OutputKinds.Select(GeneratorUtils.GetCSharpTypeName))}"; + classBase = $" : {string.Join(", ", entryPointInfo.OutputKinds.Select(CSharpGeneratorUtils.GetCSharpTypeName))}"; writer.WriteLine($"public sealed class Output{classBase}"); writer.WriteLine("{"); writer.Indent(); @@ -1136,12 +571,10 @@ private void GenerateOutput(IndentingTextWriter writer, if (outputAttr == null) continue; - writer.WriteLine("/// "); - writer.WriteLine($"/// {outputAttr.Desc}"); - writer.WriteLine("/// "); - var outputTypeString = GeneratorUtils.GetOutputType(fieldInfo.FieldType); - outputVariableNames.Add(GeneratorUtils.Capitalize(outputAttr.Name ?? fieldInfo.Name)); - writer.WriteLine($"public {outputTypeString} {GeneratorUtils.Capitalize(outputAttr.Name ?? fieldInfo.Name)} {{ get; set; }} = new {outputTypeString}();"); + CSharpGeneratorUtils.GenerateSummary(writer, outputAttr.Desc); + var outputTypeString = CSharpGeneratorUtils.GetOutputType(fieldInfo.FieldType); + outputVariableNames.Add(CSharpGeneratorUtils.Capitalize(outputAttr.Name ?? fieldInfo.Name)); + writer.WriteLine($"public {outputTypeString} {CSharpGeneratorUtils.Capitalize(outputAttr.Name ?? fieldInfo.Name)} {{ get; set; }} = new {outputTypeString}();"); writer.WriteLine(); } @@ -1149,30 +582,6 @@ private void GenerateOutput(IndentingTextWriter writer, writer.WriteLine("}"); } - private void GenerateMethod(IndentingTextWriter writer, - ModuleCatalog.EntryPointInfo entryPointInfo, - ModuleCatalog catalog) - { - var inputOuputClassName = GeneratorUtils.GetFullMethodName(entryPointInfo); - inputOuputClassName = "Microsoft.ML." + inputOuputClassName; - writer.WriteLine($"public {inputOuputClassName}.Output Add({inputOuputClassName} input)"); - writer.WriteLine("{"); - writer.Indent(); - writer.WriteLine($"var output = new {inputOuputClassName}.Output();"); - writer.WriteLine("Add(input, output);"); - writer.WriteLine("return output;"); - writer.Outdent(); - writer.WriteLine("}"); - writer.WriteLine(); - writer.WriteLine($"public void Add({inputOuputClassName} input, {inputOuputClassName}.Output output)"); - writer.WriteLine("{"); - writer.Indent(); - writer.WriteLine($"_jsonNodes.Add(Serialize(\"{entryPointInfo.Name}\", input, output));"); - writer.Outdent(); - writer.WriteLine("}"); - writer.WriteLine(); - } - private void GenerateComponentKind(IndentingTextWriter writer, string kind) { writer.WriteLine($"public abstract class {kind} : ComponentKind {{}}"); @@ -1183,15 +592,13 @@ private void GenerateComponent(IndentingTextWriter writer, ModuleCatalog.Compone { GenerateEnums(writer, component.ArgumentType, "Runtime"); writer.WriteLine(); - GenerateStructs(writer, component.ArgumentType, catalog, "Runtime"); + GenerateClasses(writer, component.ArgumentType, catalog, "Runtime"); writer.WriteLine(); - writer.WriteLine("/// "); - writer.WriteLine($"/// {component.Description}"); - writer.WriteLine("/// "); - writer.WriteLine($"public sealed class {GeneratorUtils.GetComponentName(component)} : {component.Kind}"); + CSharpGeneratorUtils.GenerateSummary(writer, component.Description); + writer.WriteLine($"public sealed class {CSharpGeneratorUtils.GetComponentName(component)} : {component.Kind}"); writer.WriteLine("{"); writer.Indent(); - GenerateInputFields(writer, component.ArgumentType, catalog, _typesSymbolTable, "Microsoft.ML."); + GenerateInputFields(writer, component.ArgumentType, catalog, "Runtime"); writer.WriteLine($"internal override string ComponentName => \"{component.Name}\";"); writer.Outdent(); writer.WriteLine("}"); diff --git a/src/Microsoft.ML/Runtime/Internal/Tools/CSharpGeneratorUtils.cs b/src/Microsoft.ML/Runtime/Internal/Tools/CSharpGeneratorUtils.cs new file mode 100644 index 0000000000..cca73a21f9 --- /dev/null +++ b/src/Microsoft.ML/Runtime/Internal/Tools/CSharpGeneratorUtils.cs @@ -0,0 +1,476 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.CodeDom; +using System.Collections.Generic; +using System.Linq; +using System.Reflection; +using Microsoft.CSharp; +using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.EntryPoints; +using Microsoft.ML.Runtime.Internal.Utilities; +using Newtonsoft.Json.Linq; + +namespace Microsoft.ML.Runtime.Internal.Tools +{ + internal static class CSharpGeneratorUtils + { + public sealed class EntryPointGenerationMetadata + { + public string Namespace { get; } + public string ClassName { get; } + public EntryPointGenerationMetadata(string classNamespace, string className) + { + Namespace = classNamespace; + ClassName = className; + } + } + + public static EntryPointGenerationMetadata GetEntryPointMetadata(ModuleCatalog.EntryPointInfo entryPointInfo) + { + var split = entryPointInfo.Name.Split('.'); + Contracts.Check(split.Length == 2); + return new EntryPointGenerationMetadata(split[0], split[1]); + } + + public static Type ExtractOptionalOrNullableType(Type type) + { + if (type.IsGenericType && (type.GetGenericTypeDefinition() == typeof(Optional<>) || type.GetGenericTypeDefinition() == typeof(Nullable<>))) + type = type.GetGenericArguments()[0]; + + return type; + } + + public static Type ExtractOptionalOrNullableType(Type type, out bool isNullable, out bool isOptional) + { + isNullable = false; + isOptional = false; + if (type.IsGenericType && type.GetGenericTypeDefinition() == typeof(Nullable<>)) + { + type = type.GetGenericArguments()[0]; + isNullable = true; + } + else if (type.IsGenericType && type.GetGenericTypeDefinition() == typeof(Optional<>)) + { + type = type.GetGenericArguments()[0]; + isOptional = true; + } + return type; + } + + public static string GetCSharpTypeName(Type type) + { + if (type.IsGenericType && type.GetGenericTypeDefinition() == typeof(Nullable<>)) + return GetCSharpTypeName(type.GetGenericArguments()[0]) + "?"; + + using (var p = new CSharpCodeProvider()) + return p.GetTypeOutput(new CodeTypeReference(type)); + } + + public static string GetOutputType(Type outputType) + { + Contracts.Check(Var.CheckType(outputType)); + + if (outputType.IsArray) + return $"ArrayVar<{GetCSharpTypeName(outputType.GetElementType())}>"; + if (outputType.IsGenericType && outputType.GetGenericTypeDefinition() == typeof(Dictionary<,>) + && outputType.GetGenericTypeArgumentsEx()[0] == typeof(string)) + { + return $"DictionaryVar<{GetCSharpTypeName(outputType.GetGenericTypeArgumentsEx()[1])}>"; + } + + return $"Var<{GetCSharpTypeName(outputType)}>"; + } + + public static string GetInputType(ModuleCatalog catalog, Type inputType, GeneratedClasses generatedClasses, string rootNameSpace) + { + if (inputType.IsGenericType && inputType.GetGenericTypeDefinition() == typeof(Var<>)) + return $"Var<{GetCSharpTypeName(inputType.GetGenericTypeArgumentsEx()[0])}>"; + + if (inputType.IsArray && Var.CheckType(inputType.GetElementType())) + return $"ArrayVar<{GetCSharpTypeName(inputType.GetElementType())}>"; + + if (inputType.IsGenericType && inputType.GetGenericTypeDefinition() == typeof(Dictionary<,>) + && inputType.GetGenericTypeArgumentsEx()[0] == typeof(string)) + { + return $"DictionaryVar<{GetCSharpTypeName(inputType.GetGenericTypeArgumentsEx()[1])}>"; + } + + if (Var.CheckType(inputType)) + return $"Var<{GetCSharpTypeName(inputType)}>"; + + var type = ExtractOptionalOrNullableType(inputType, out bool isNullable, out bool isOptional); + var typeEnum = TlcModule.GetDataType(type); + switch (typeEnum) + { + case TlcModule.DataKind.Float: + case TlcModule.DataKind.Int: + case TlcModule.DataKind.UInt: + case TlcModule.DataKind.Char: + case TlcModule.DataKind.String: + case TlcModule.DataKind.Bool: + case TlcModule.DataKind.DataView: + case TlcModule.DataKind.TransformModel: + case TlcModule.DataKind.PredictorModel: + case TlcModule.DataKind.FileHandle: + return GetCSharpTypeName(inputType); + case TlcModule.DataKind.Array: + return GetInputType(catalog, inputType.GetElementType(), generatedClasses, rootNameSpace) + "[]"; + case TlcModule.DataKind.Component: + string kind; + bool success = catalog.TryGetComponentKind(type, out kind); + Contracts.Assert(success); + return $"{kind}"; + case TlcModule.DataKind.Enum: + var enumName = generatedClasses.GetApiName(type, rootNameSpace); + if (isNullable) + return $"{enumName}?"; + if (isOptional) + return $"Optional<{enumName}>"; + return $"{enumName}"; + default: + if (isNullable) + return generatedClasses.GetApiName(type, rootNameSpace) + "?"; + if (isOptional) + return $"Optional<{generatedClasses.GetApiName(type, rootNameSpace)}>"; + return generatedClasses.GetApiName(type, rootNameSpace); + } + } + + public static bool IsComponent(Type inputType) + { + if (inputType.IsArray && Var.CheckType(inputType.GetElementType())) + return false; + + if (inputType.IsGenericType && inputType.GetGenericTypeDefinition() == typeof(Dictionary<,>) + && inputType.GetGenericTypeArgumentsEx()[0] == typeof(string)) + { + return false; + } + + if (Var.CheckType(inputType)) + return false; + + var type = ExtractOptionalOrNullableType(inputType); + var typeEnum = TlcModule.GetDataType(type); + return typeEnum == TlcModule.DataKind.Component; + } + + public static string Capitalize(string s) + { + if (string.IsNullOrEmpty(s)) + return s; + return char.ToUpperInvariant(s[0]) + s.Substring(1); + } + + private static string GetCharAsString(char value) + { + switch (value) + { + case '\t': + return "\\t"; + case '\n': + return "\\n"; + case '\r': + return "\\r"; + case '\\': + return "\\"; + case '\"': + return "\""; + case '\'': + return "\\'"; + case '\0': + return "\\0"; + case '\a': + return "\\a"; + case '\b': + return "\\b"; + case '\f': + return "\\f"; + case '\v': + return "\\v"; + default: + return value.ToString(); + } + } + + public static string GetValue(ModuleCatalog catalog, Type fieldType, object fieldValue, + GeneratedClasses generatedClasses, string rootNameSpace) + { + if (fieldType.IsGenericType && fieldType.GetGenericTypeDefinition() == typeof(Var<>)) + return $"new Var<{GetCSharpTypeName(fieldType.GetGenericTypeArgumentsEx()[0])}>()"; + + if (fieldType.IsArray && Var.CheckType(fieldType.GetElementType())) + return $"new ArrayVar<{GetCSharpTypeName(fieldType.GetElementType())}>()"; + + if (fieldType.IsGenericType && fieldType.GetGenericTypeDefinition() == typeof(Dictionary<,>) + && fieldType.GetGenericTypeArgumentsEx()[0] == typeof(string)) + { + return $"new DictionaryVar<{GetCSharpTypeName(fieldType.GetGenericTypeArgumentsEx()[1])}>()"; + } + + if (Var.CheckType(fieldType)) + return $"new Var<{GetCSharpTypeName(fieldType)}>()"; + + if (fieldValue == null) + return null; + + if (!fieldType.IsInterface) + { + try + { + var defaultFieldValue = Activator.CreateInstance(fieldType); + if (defaultFieldValue == fieldValue) + return null; + } + catch (MissingMethodException) + { + // No parameterless constructor, ignore. + } + } + + var typeEnum = TlcModule.GetDataType(fieldType); + fieldType = ExtractOptionalOrNullableType(fieldType, out bool isNullable, out bool isOptional); + switch (typeEnum) + { + case TlcModule.DataKind.Array: + var arr = fieldValue as Array; + if (arr != null && arr.GetLength(0) > 0) + return $"{{ {string.Join(", ", arr.Cast().Select(item => GetValue(catalog, fieldType.GetElementType(), item, generatedClasses, rootNameSpace)))} }}"; + return null; + case TlcModule.DataKind.String: + var strval = fieldValue as string; + if (strval != null) + return Quote(strval); + return null; + case TlcModule.DataKind.Float: + if (fieldValue is double d) + { + if (double.IsPositiveInfinity(d)) + return "double.PositiveInfinity"; + if (double.IsNegativeInfinity(d)) + return "double.NegativeInfinity"; + if (d != 0) + return d.ToString("R") + "d"; + } + else if (fieldValue is float f) + { + if (float.IsPositiveInfinity(f)) + return "float.PositiveInfinity"; + if (float.IsNegativeInfinity(f)) + return "float.NegativeInfinity"; + if (f != 0) + return f.ToString("R") + "f"; + } + return null; + case TlcModule.DataKind.Int: + if (fieldValue is int i) + { + if (i != 0) + return i.ToString(); + } + else if (fieldValue is long l) + { + if (l != 0) + return l.ToString(); + } + return null; + case TlcModule.DataKind.Bool: + return (bool)fieldValue ? "true" : "false"; + case TlcModule.DataKind.Enum: + string enumAsString = fieldValue.ToString(); + if (fieldType.GetField(enumAsString).GetCustomAttribute() != null) + { + // The default value for the enum has the hiding attribute on it. We will search for + // alternate names. Regrettably I see no way beyond a manual scan. + + string unhiddenName = Enum.GetNames(fieldType).Zip(Enum.GetValues(fieldType).Cast(), (name, val) => (name, val)) + .Where(pair => pair.val.Equals(fieldValue)) + .Where(pair => fieldType.GetField(pair.name).GetCustomAttribute() == null) + .Select(pair => pair.name).FirstOrDefault(); + enumAsString = unhiddenName ?? throw Contracts.Except($"Could not find unhidden alternative for '{fieldValue}' in type '{fieldType}'"); + } + if (generatedClasses.IsGenerated(fieldType.FullName)) + return generatedClasses.GetApiName(fieldType, rootNameSpace) + "." + enumAsString; + else + return generatedClasses.GetApiName(fieldType, "Runtime") + "." + enumAsString; + case TlcModule.DataKind.Char: + return $"'{GetCharAsString((char)fieldValue)}'"; + case TlcModule.DataKind.Component: + var type = fieldValue.GetType(); + ModuleCatalog.ComponentInfo componentInfo; + if (!catalog.TryFindComponent(fieldType, type, out componentInfo)) + return null; + object defaultComponent = null; + try + { + defaultComponent = Activator.CreateInstance(componentInfo.ArgumentType); + } + catch (MissingMethodException) + { + // No parameterless constructor, ignore. + } + var propertyBag = new List(); + if (defaultComponent != null) + { + foreach (var fieldInfo in componentInfo.ArgumentType.GetFields()) + { + var inputAttr = fieldInfo.GetCustomAttributes(typeof(ArgumentAttribute), false).FirstOrDefault() as ArgumentAttribute; + if (inputAttr == null || inputAttr.Visibility == ArgumentAttribute.VisibilityType.CmdLineOnly) + continue; + if (fieldInfo.FieldType == typeof(JArray) || fieldInfo.FieldType == typeof(JObject)) + continue; + + var propertyValue = GetValue(catalog, fieldInfo.FieldType, fieldInfo.GetValue(fieldValue), generatedClasses, rootNameSpace); + var defaultPropertyValue = GetValue(catalog, fieldInfo.FieldType, fieldInfo.GetValue(defaultComponent), generatedClasses, rootNameSpace); + if (propertyValue != defaultPropertyValue) + propertyBag.Add($"{Capitalize(inputAttr.Name ?? fieldInfo.Name)} = {propertyValue}"); + } + } + var properties = propertyBag.Count > 0 ? $" {{ {string.Join(", ", propertyBag)} }}" : ""; + return $"new {GetComponentName(componentInfo)}(){properties}"; + case TlcModule.DataKind.Unknown: + return $"new {generatedClasses.GetApiName(fieldType, rootNameSpace)}()"; + default: + return fieldValue.ToString(); + } + } + + private static string Quote(string src) + { + var dst = src.Replace("\\", @"\\").Replace("\"", "\\\"").Replace("\n", @"\n").Replace("\r", @"\r"); + return "\"" + dst + "\""; + } + + public static string GetComponentName(ModuleCatalog.ComponentInfo component) + { + return $"{Capitalize(component.Name)}{component.Kind}"; + } + + public static void GenerateSummary(IndentingTextWriter writer, string summary) + { + if (string.IsNullOrEmpty(summary)) + return; + writer.WriteLine("/// "); + foreach (var line in summary.Split(new[] { Environment.NewLine }, StringSplitOptions.RemoveEmptyEntries)) + writer.WriteLine($"/// {line}"); + writer.WriteLine("/// "); + } + + public static void GenerateHeader(IndentingTextWriter writer) + { + writer.WriteLine("//------------------------------------------------------------------------------"); + writer.WriteLine("// "); + writer.WriteLine("// This code was generated by a tool."); + writer.WriteLine("//"); + writer.WriteLine("// Changes to this file may cause incorrect behavior and will be lost if"); + writer.WriteLine("// the code is regenerated."); + writer.WriteLine("// "); + writer.WriteLine("//------------------------------------------------------------------------------"); + writer.WriteLine("#pragma warning disable"); + writer.WriteLine("using System.Collections.Generic;"); + writer.WriteLine("using Microsoft.ML.Runtime;"); + writer.WriteLine("using Microsoft.ML.Runtime.Data;"); + writer.WriteLine("using Microsoft.ML.Runtime.EntryPoints;"); + writer.WriteLine("using Newtonsoft.Json;"); + writer.WriteLine("using System;"); + writer.WriteLine("using System.Linq;"); + writer.WriteLine("using Microsoft.ML.Runtime.CommandLine;"); + writer.WriteLine(); + writer.WriteLine("namespace Microsoft.ML"); + writer.WriteLine("{"); + writer.Indent(); + writer.WriteLine("namespace Runtime"); + writer.WriteLine("{"); + writer.Indent(); + writer.WriteLine("public sealed partial class Experiment"); + writer.WriteLine("{"); + writer.Indent(); + } + + public static void GenerateFooter(IndentingTextWriter writer) + { + writer.Outdent(); + writer.WriteLine("}"); + } + + public static void GenerateMethod(IndentingTextWriter writer, string className, string defaultNamespace) + { + var inputOuputClassName = defaultNamespace + className; + writer.WriteLine($"public {inputOuputClassName}.Output Add({inputOuputClassName} input)"); + writer.WriteLine("{"); + writer.Indent(); + writer.WriteLine($"var output = new {inputOuputClassName}.Output();"); + writer.WriteLine("Add(input, output);"); + writer.WriteLine("return output;"); + writer.Outdent(); + writer.WriteLine("}"); + writer.WriteLine(); + writer.WriteLine($"public void Add({inputOuputClassName} input, {inputOuputClassName}.Output output)"); + writer.WriteLine("{"); + writer.Indent(); + writer.WriteLine($"_jsonNodes.Add(Serialize(\"{className}\", input, output));"); + writer.Outdent(); + writer.WriteLine("}"); + writer.WriteLine(); + } + + public static void GenerateLoaderAddInputMethod(IndentingTextWriter writer, string className) + { + //Constructor. + writer.WriteLine("[JsonIgnore]"); + writer.WriteLine("private string _inputFilePath = null;"); + writer.WriteLine($"public {className}(string filePath)"); + writer.WriteLine("{"); + writer.Indent(); + writer.WriteLine("_inputFilePath = filePath;"); + writer.Outdent(); + writer.WriteLine("}"); + writer.WriteLine(""); + + //SetInput. + writer.WriteLine($"public void SetInput(IHostEnvironment env, Experiment experiment)"); + writer.WriteLine("{"); + writer.Indent(); + writer.WriteLine("IFileHandle inputFile = new SimpleFileHandle(env, _inputFilePath, false, false);"); + writer.WriteLine("experiment.SetInput(InputFile, inputFile);"); + writer.Outdent(); + writer.WriteLine("}"); + writer.WriteLine(""); + + //GetInputData + writer.WriteLine("public Var GetInputData() => null;"); + writer.WriteLine(""); + + //Apply. + writer.WriteLine($"public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment)"); + writer.WriteLine("{"); + writer.Indent(); + writer.WriteLine("Contracts.Assert(previousStep == null);"); + writer.WriteLine(""); + writer.WriteLine($"return new {className}PipelineStep(experiment.Add(this));"); + writer.Outdent(); + writer.WriteLine("}"); + writer.WriteLine(""); + + //Pipelinestep class. + writer.WriteLine($"private class {className}PipelineStep : ILearningPipelineDataStep"); + writer.WriteLine("{"); + writer.Indent(); + writer.WriteLine($"public {className}PipelineStep (Output output)"); + writer.WriteLine("{"); + writer.Indent(); + writer.WriteLine("Data = output.Data;"); + writer.WriteLine("Model = null;"); + writer.Outdent(); + writer.WriteLine("}"); + writer.WriteLine(); + writer.WriteLine("public Var Data { get; }"); + writer.WriteLine("public Var Model { get; }"); + writer.Outdent(); + writer.WriteLine("}"); + } + } +} diff --git a/src/Microsoft.ML/Runtime/Internal/Tools/GeneratedClasses.cs b/src/Microsoft.ML/Runtime/Internal/Tools/GeneratedClasses.cs new file mode 100644 index 0000000000..fe8adf35fc --- /dev/null +++ b/src/Microsoft.ML/Runtime/Internal/Tools/GeneratedClasses.cs @@ -0,0 +1,102 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using System; +using System.Collections.Generic; +using System.Linq; + +namespace Microsoft.ML.Runtime.Internal.Tools +{ + internal sealed class GeneratedClasses + { + private sealed class ApiClass + { + public string OriginalName { get; set; } + public string NewName { get; set; } + public bool Generated { get; set; } + } + + private readonly Dictionary _typesSymbolTable; + + public GeneratedClasses() + { + _typesSymbolTable = new Dictionary(); + } + + public string GetApiName(Type type, string rootNamespace) + { + string apiName = ""; + if (!_typesSymbolTable.TryGetValue(type.FullName, out ApiClass apiClass)) + apiName = GenerateIntenalName(type, rootNamespace); + else + apiName = apiClass.NewName; + + if (!string.IsNullOrEmpty(rootNamespace)&& apiName.StartsWith(rootNamespace)) + return apiName.Substring(rootNamespace.Length + 1); + else return apiName; + } + + private string GenerateIntenalName(Type type, string currentNamespace) + { + var fullTypeName = type.FullName; + string name = currentNamespace != "" ? currentNamespace + '.' : ""; + + int bracketIndex = fullTypeName.IndexOf('['); + Type[] genericTypes = null; + if (type.IsGenericType) + genericTypes = type.GetGenericArguments(); + if (bracketIndex > 0) + { + Contracts.AssertValue(genericTypes); + fullTypeName = fullTypeName.Substring(0, bracketIndex); + } + + // When the type is nested, the names of the outer types are concatenated with a '+'. + var nestedNames = fullTypeName.Split('+'); + var baseName = nestedNames[0]; + + // We currently only handle generic types in the outer most class, support for generic inner classes + // can be added if needed. + int backTickIndex = baseName.LastIndexOf('`'); + int dotIndex = baseName.LastIndexOf('.'); + Contracts.Assert(dotIndex >= 0); + if (backTickIndex < 0) + name += baseName.Substring(dotIndex + 1); + else + { + name += baseName.Substring(dotIndex + 1, backTickIndex - dotIndex - 1); + Contracts.AssertValue(genericTypes); + if (genericTypes != null) + { + foreach (var genType in genericTypes) + { + var splitNames = genType.FullName.Split('+'); + if (splitNames[0].LastIndexOf('.') >= 0) + splitNames[0] = splitNames[0].Substring(splitNames[0].LastIndexOf('.') + 1); + name += string.Join("", splitNames); + } + } + } + + for (int i = 1; i < nestedNames.Length; i++) + name += nestedNames[i]; + + Contracts.Assert(_typesSymbolTable.Values.All(apiclass => string.Compare(apiclass.NewName, name) != 0)); + _typesSymbolTable[type.FullName] = new ApiClass { OriginalName = type.FullName, Generated = false, NewName = name }; + return name; + } + + internal bool IsGenerated(string fullName) + { + if (!_typesSymbolTable.ContainsKey(fullName)) + return false; + return _typesSymbolTable[fullName].Generated; + } + + internal void MarkAsGenerated(string fullName) + { + _typesSymbolTable[fullName].Generated = true; + } + } +} diff --git a/src/Native/CMakeLists.txt b/src/Native/CMakeLists.txt index 947a664ab6..767f6151fa 100644 --- a/src/Native/CMakeLists.txt +++ b/src/Native/CMakeLists.txt @@ -179,4 +179,6 @@ function(install_library_and_symbols targetName) endfunction() add_subdirectory(CpuMathNative) -add_subdirectory(FastTreeNative) \ No newline at end of file +add_subdirectory(FastTreeNative) +add_subdirectory(LdaNative) +add_subdirectory(FactorizationMachineNative) diff --git a/src/Native/FactorizationMachineNative/CMakeLists.txt b/src/Native/FactorizationMachineNative/CMakeLists.txt new file mode 100644 index 0000000000..d3d91d04d4 --- /dev/null +++ b/src/Native/FactorizationMachineNative/CMakeLists.txt @@ -0,0 +1,14 @@ +project (FactorizationMachineNative) + +set(SOURCES + FactorizationMachineCore.cpp +) + +if(WIN32) +else() + list(APPEND SOURCES ${VERSION_FILE_PATH}) +endif() + +add_library(FactorizationMachineNative SHARED ${SOURCES} ${RESOURCES}) + +install_library_and_symbols (FactorizationMachineNative) \ No newline at end of file diff --git a/src/Native/FactorizationMachineNative/FactorizationMachineCore.cpp b/src/Native/FactorizationMachineNative/FactorizationMachineCore.cpp new file mode 100644 index 0000000000..982b266683 --- /dev/null +++ b/src/Native/FactorizationMachineNative/FactorizationMachineCore.cpp @@ -0,0 +1,179 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +#include "../Stdafx.h" +#include +#include +#include +#include + +// Compute the output value of the field-aware factorization, as the sum of the linear part and the latent part. +// The linear part is the inner product of linearWeights and featureValues. +// The latent part is the sum of all intra-field interactions in one field f, for all fields possible. +EXPORT_API(void) CalculateIntermediateVariablesNative(int fieldCount, int latentDim, int count, _In_ int * fieldIndices, _In_ int * featureIndices, _In_ float * featureValues, + _In_ float * linearWeights, _In_ float * latentWeights, _Inout_ float * latentSum, _Out_ float * response) +{ + // The number of all possible fields. + const int m = fieldCount; + const int d = latentDim; + const int c = count; + const int * pf = fieldIndices; + const int * pi = featureIndices; + const float * px = featureValues; + const float * pw = linearWeights; + const float * pv = latentWeights; + float * pq = latentSum; + float linearResponse = 0; + float latentResponse = 0; + + memset(pq, 0, sizeof(float) * m * m * d); + __m128 _y = _mm_setzero_ps(); + __m128 _tmp = _mm_setzero_ps(); + + for (int i = 0; i < c; i++) + { + const int f = pf[i]; + const int j = pi[i]; + linearResponse += pw[j] * px[i]; + + const __m128 _x = _mm_load1_ps(px + i); + const __m128 _xx = _mm_mul_ps(_x, _x); + + // tmp -= * x * x + const int vBias = j * m * d + f * d; + + // j-th feature's latent vector in the f-th field hidden space. + const float * vjf = pv + vBias; + + for (int k = 0; k + 4 <= d; k += 4) + { + const __m128 _v = _mm_load_ps(vjf + k); + _tmp = _mm_sub_ps(_tmp, _mm_mul_ps(_mm_mul_ps(_v, _v), _xx)); + } + + for (int fprime = 0; fprime < m; fprime++) + { + const int vBias = j * m * d + fprime * d; + const int qBias = f * m * d + fprime * d; + const float * vjfprime = pv + vBias; + float * qffprime = pq + qBias; + + // q_f,f' += v_j,f' * x + for (int k = 0; k + 4 <= d; k += 4) + { + const __m128 _v = _mm_load_ps(vjfprime + k); + __m128 _q = _mm_load_ps(qffprime + k); + _q = _mm_add_ps(_q, _mm_mul_ps(_v, _x)); + _mm_store_ps(qffprime + k, _q); + } + } + } + + for (int f = 0; f < m; f++) + { + // tmp += + const float * qff = pq + f * m * d + f * d; + for (int k = 0; k + 4 <= d; k += 4) + { + __m128 _qff = _mm_load_ps(qff + k); + + // Intra-field interactions. + _tmp = _mm_add_ps(_tmp, _mm_mul_ps(_qff, _qff)); + } + + // y += , f != f' + // Whis loop handles inter - field interactions because f != f'. + for (int fprime = f + 1; fprime < m; fprime++) + { + const float * qffprime = pq + f * m * d + fprime * d; + const float * qfprimef = pq + fprime * m * d + f * d; + for (int k = 0; k + 4 <= d; k += 4) + { + // Inter-field interaction. + __m128 _qffprime = _mm_load_ps(qffprime + k); + __m128 _qfprimef = _mm_load_ps(qfprimef + k); + _y = _mm_add_ps(_y, _mm_mul_ps(_qffprime, _qfprimef)); + } + } + } + + _y = _mm_add_ps(_y, _mm_mul_ps(_mm_set_ps1(0.5f), _tmp)); + _tmp = _mm_add_ps(_y, _mm_movehl_ps(_y, _y)); + _y = _mm_add_ps(_tmp, _mm_shuffle_ps(_tmp, _tmp, 1)); // The lowest slot is the response value. + _mm_store_ss(&latentResponse, _y); + *response = linearResponse + latentResponse; +} + +// Calculate the stochastic gradient and update the model. +// The /*const*/ comment on the parameters of the function means that their values should not get altered by this function. +EXPORT_API(void) CalculateGradientAndUpdateNative(float lambdaLinear, float lambdaLatent, float learningRate, int fieldCount, int latentDim, float weight, int count, + _In_ int* /*const*/ fieldIndices, _In_ int* /*const*/ featureIndices, _In_ float* /*const*/ featureValues, _In_ float* /*const*/ latentSum, float slope, + _Inout_ float* linearWeights, _Inout_ float* latentWeights, _Inout_ float* linearAccumulatedSquaredGrads, _Inout_ float* latentAccumulatedSquaredGrads) +{ + const int m = fieldCount; + const int d = latentDim; + const int c = count; + const int * pf = fieldIndices; + const int * pi = featureIndices; + const float * px = featureValues; + const float * pq = latentSum; + float * pw = linearWeights; + float * pv = latentWeights; + float * phw = linearAccumulatedSquaredGrads; + float * phv = latentAccumulatedSquaredGrads; + + const __m128 _wei = _mm_set_ps1(weight); + const __m128 _s = _mm_set_ps1(slope); + const __m128 _lr = _mm_set_ps1(learningRate); + const __m128 _lambdav = _mm_set_ps1(lambdaLatent); + + for (int i = 0; i < count; i++) + { + const int f = pf[i]; + const int j = pi[i]; + + // Calculate gradient of linear term w_j. + float g = weight * (lambdaLinear * pw[j] + slope * px[i]); + + // Accumulate the gradient of the linear term. + phw[j] += g * g; + + // Perform ADAGRAD update rule to adjust linear term. + pw[j] -= learningRate / sqrt(phw[j]) * g; + + // Update latent term, v_j,f', f'=1,...,m. + const __m128 _x = _mm_load1_ps(px + i); + for (int fprime = 0; fprime < m; fprime++) + { + float * vjfprime = pv + j * m * d + fprime * d; + float * hvjfprime = phv + j * m * d + fprime * d; + const float * qfprimef = pq + fprime * m * d + f * d; + const __m128 _sx = _mm_mul_ps(_s, _x); + + for (int k = 0; k + 4 <= d; k += 4) + { + __m128 _v = _mm_load_ps(vjfprime + k); + __m128 _q = _mm_load_ps(qfprimef + k); + + // Calculate L2-norm regularization's gradient. + __m128 _g = _mm_mul_ps(_lambdav, _v); + + // Calculate loss function's gradient. + if (fprime != f) + _g = _mm_add_ps(_g, _mm_mul_ps(_sx, _q)); + else + _g = _mm_add_ps(_g, _mm_mul_ps(_sx, _mm_sub_ps(_q, _mm_mul_ps(_v, _x)))); + _g = _mm_mul_ps(_wei, _g); + + // Accumulate the gradient of latent vectors. + const __m128 _h = _mm_add_ps(_mm_load_ps(hvjfprime + k), _mm_mul_ps(_g, _g)); + + // Perform ADAGRAD update rule to adjust latent vector. + _v = _mm_sub_ps(_v, _mm_mul_ps(_lr, _mm_mul_ps(_mm_rsqrt_ps(_h), _g))); + _mm_store_ps(vjfprime + k, _v); + _mm_store_ps(hvjfprime + k, _h); + } + } + } +} diff --git a/src/Native/LdaNative/CMakeLists.txt b/src/Native/LdaNative/CMakeLists.txt new file mode 100644 index 0000000000..f2e1d340de --- /dev/null +++ b/src/Native/LdaNative/CMakeLists.txt @@ -0,0 +1,19 @@ +project (LdaNative) + +set(SOURCES + alias_multinomial_rng_int.cpp + data_block.cpp + hybrid_alias_map.cpp + hybrid_map.cpp + lda_document.cpp + lda_engine.cpp + lda_engine_export.cpp + light_doc_sampler.cpp + light_hash_map.cpp + model_block.cpp + utils.cpp +) + +add_library(LdaNative SHARED ${SOURCES} ${RESOURCES}) + +install_library_and_symbols (LdaNative) diff --git a/src/Native/LdaNative/alias_multinomial_rng_int.cpp b/src/Native/LdaNative/alias_multinomial_rng_int.cpp new file mode 100644 index 0000000000..a945feb6be --- /dev/null +++ b/src/Native/LdaNative/alias_multinomial_rng_int.cpp @@ -0,0 +1,49 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +#include "alias_multinomial_rng_int.hpp" +#include "rand_int_rng.h" +#include +#include +#include +#include + +namespace wood +{ + AliasMultinomialRNGInt::AliasMultinomialRNGInt() + : n_(-1), internal_memory_(nullptr) + { + + } + AliasMultinomialRNGInt::~AliasMultinomialRNGInt() + { + if (internal_memory_ != nullptr) + { + delete[]internal_memory_; + } + } + + int32_t AliasMultinomialRNGInt::Next(xorshift_rng& rng, std::vector& alias_kv) + { + // NOTE: stl uniform_real_distribution generates the highest quality random numbers + // yet, the other two are much faster + auto sample = rng.rand(); + + // NOTE: use std::floor is too slow + // here we guarantee sample * n_ is nonnegative, this makes cast work + int idx = sample / a_int_; + + if (n_ <= idx) + { + idx = n_ - 1; + } + + // the following code is equivalent to + // return sample < V_[idx] ? idx : K_[idx]; + // but faster, see + // http://stackoverflow.com/questions/6754454/speed-difference-between-if-else-and-ternary-operator-in-c + int m = -(sample < alias_kv[idx].v_); + return (idx & m) | (alias_kv[idx].k_ & ~m); + } +} \ No newline at end of file diff --git a/src/Native/LdaNative/alias_multinomial_rng_int.hpp b/src/Native/LdaNative/alias_multinomial_rng_int.hpp new file mode 100644 index 0000000000..e25bc5bc17 --- /dev/null +++ b/src/Native/LdaNative/alias_multinomial_rng_int.hpp @@ -0,0 +1,454 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +#pragma once + +#include +#include +#include +#include +#include +#include +#include "rand_int_rng.h" +#include +#include +/* +Algorithm described in +http://www.jstatsoft.org/v11/i03/paper +George Marsaglia +Fast generation of discrete random variables +*/ +namespace wood +{ + struct alias_k_v + { + int32_t k_; + int32_t v_; + }; + + class AliasMultinomialRNGInt + { + public: + AliasMultinomialRNGInt(); + ~AliasMultinomialRNGInt(); + + void Init(int K) + { + L_.resize(K); + H_.resize(K); + proportion_int_.resize(K); + internal_memory_ = new int32_t[2 * K]; + } + + void SetProportionMass(std::vector &proportion, + float mass, + std::vector &alias_kv, + int32_t *height, + xorshift_rng &rng) + { + n_ = (int32_t)proportion.size(); //proportion number should be kept within 2Billion + + mass_int_ = 0x7fffffff; + a_int_ = mass_int_ / n_; + mass_int_ = a_int_ * n_; + *height = a_int_; + + int64_t mass_sum = 0; //use int64_t to avoid overflowing + for (int i = 0; i < n_; ++i) + { + proportion[i] /= mass; + proportion_int_[i] = (int32_t)(proportion[i] * mass_int_); + mass_sum += proportion_int_[i]; + } + + if (mass_sum > mass_int_) + { + //Todo: is this data type safe? more is int and mass_sum is in64 + int32_t more = (int32_t)(mass_sum - mass_int_); + + int i = 0; + int id = 0; + int r = 0; + while (i < more) + { + if (proportion_int_[id] >= 1) + { + proportion_int_[id]--; + ++i; + } + id = (id + 1) % n_; + } + } + + if (mass_sum < mass_int_) + { + //Todo: is this data type safe? more is int and mass_sum is in64 + int32_t more = (int32_t)(mass_int_ - mass_sum); + + int i = 0; + int id = 0; + while (i < more) + { + proportion_int_[id]++; + id = (id + 1) % n_; + i++; + } + } + + for (int i = 0; i < n_; ++i) + { + alias_kv[i].k_ = i; + alias_kv[i].v_ = (i + 1) * a_int_; + } + + int32_t L_head = 0; + int32_t L_tail = 0; + + int32_t H_head = 0; + int32_t H_tail = 0; + + for (auto i = 0; i < proportion_int_.size(); ++i) + { + auto val = proportion_int_[i]; + if (val < a_int_) + { + L_[L_tail].first = i; + L_[L_tail].second = val; + ++L_tail; + } + else + { + H_[H_tail].first = i; + H_[H_tail].second = val; + ++H_tail; + } + } + + assert(L_tail + H_tail == n_); + + while (L_head != L_tail && H_head != H_tail) + { + auto &i_pi = L_[L_head++]; + auto &h_ph = H_[H_head++]; + + alias_kv[i_pi.first].k_ = h_ph.first; + alias_kv[i_pi.first].v_ = i_pi.first * a_int_ + i_pi.second; + + auto sum = h_ph.second + i_pi.second; + if (sum > 2 * a_int_) + { + H_[H_tail].first = h_ph.first; + H_[H_tail].second = sum - a_int_; + ++H_tail; + } + else + { + L_[L_tail].first = h_ph.first; + L_[L_tail].second = sum - a_int_; + ++L_tail; + } + } + while (L_head != L_tail) + { + auto first = L_[L_head].first; + auto second = L_[L_head].second; + alias_kv[first].k_ = first; + alias_kv[first].v_ = first * a_int_ + second; + ++L_head; + } + while (H_head != H_tail) + { + auto first = H_[H_head].first; + auto second = H_[H_head].second; + alias_kv[first].k_ = first; + alias_kv[first].v_ = first * a_int_ + second; + ++H_head; + } + + } + + inline void SetProportionMass(std::vector &proportion, + float mass, + int32_t* memory, + int32_t *height, + xorshift_rng &rng) + { + n_ = (int32_t)proportion.size(); + + mass_int_ = 0x7fffffff; + a_int_ = mass_int_ / n_; + mass_int_ = a_int_ * n_; + *height = a_int_; + + int64_t mass_sum = 0; + for (int i = 0; i < n_; ++i) + { + proportion[i] /= mass; + proportion_int_[i] = (int32_t)(proportion[i] * mass_int_); + mass_sum += proportion_int_[i]; + } + + if (mass_sum > mass_int_) + { + //Todo: is this data type safe? more is int and mass_sum is in64 + int32_t more = (int32_t)(mass_sum - mass_int_); + int i = 0; + int id = 0; + int r = 0; + while (i < more) + { + if (proportion_int_[id] >= 1) + { + proportion_int_[id]--; + ++i; + } + id = (id + 1) % n_; + } + } + + if (mass_sum < mass_int_) + { + //Todo: is this data type safe? more is int and mass_sum is in64 + int32_t more = (int32_t)(mass_int_ - mass_sum); + int i = 0; + int id = 0; + while (i < more) + { + proportion_int_[id]++; + id = (id + 1) % n_; + i++; + } + } + + for (int i = 0; i < n_; ++i) + { + int32_t *p = internal_memory_ + 2 * i; + *p = i; p++; + *p = (i + 1) * a_int_; + } + + int32_t L_head = 0; + int32_t L_tail = 0; + + int32_t H_head = 0; + int32_t H_tail = 0; + + for (auto i = 0; i < n_; ++i) + { + auto val = proportion_int_[i]; + if (val < a_int_) + { + L_[L_tail].first = i; + L_[L_tail].second = val; + ++L_tail; + } + else + { + H_[H_tail].first = i; + H_[H_tail].second = val; + ++H_tail; + } + } + + assert(L_tail + H_tail == n_); + + while (L_head != L_tail && H_head != H_tail) + { + auto &i_pi = L_[L_head++]; + auto &h_ph = H_[H_head++]; + + int32_t *p = internal_memory_ + 2 * i_pi.first; + *p = h_ph.first; p++; + *p = i_pi.first * a_int_ + i_pi.second; + + auto sum = h_ph.second + i_pi.second; + if (sum > 2 * a_int_) + { + H_[H_tail].first = h_ph.first; + H_[H_tail].second = sum - a_int_; + ++H_tail; + } + else + { + L_[L_tail].first = h_ph.first; + L_[L_tail].second = sum - a_int_; + ++L_tail; + } + } + while (L_head != L_tail) + { + auto first = L_[L_head].first; + auto second = L_[L_head].second; + + int32_t *p = internal_memory_ + 2 * first; + *p = first; p++; + *p = first * a_int_ + second; + ++L_head; + } + while (H_head != H_tail) + { + auto first = H_[H_head].first; + auto second = H_[H_head].second; + + int32_t *p = internal_memory_ + 2 * first; + *p = first; p++; + *p = first * a_int_ + second; + ++H_head; + } + memcpy(memory, internal_memory_, sizeof(int32_t)* 2 * n_); + } + + inline void SetProportionMass(std::vector &proportion, + int32_t size, + float mass, + int32_t* memory, + int32_t *height, + xorshift_rng &rng, + int32_t word_id) + { + n_ = size; + + mass_int_ = 0x7fffffff; + a_int_ = mass_int_ / n_; + mass_int_ = a_int_ * n_; + *height = a_int_; + + int64_t mass_sum = 0; + for (int i = 0; i < n_; ++i) + { + proportion[i] /= mass; + proportion_int_[i] = (int32_t)(proportion[i] * mass_int_); + mass_sum += proportion_int_[i]; + } + + if (mass_sum > mass_int_) + { + //Todo: is this data type safe? more is int and mass_sum is in64 + int32_t more = (int32_t)(mass_sum - mass_int_); + + int i = 0; + int id = 0; + int r = 0; + while (i < more) + { + if (proportion_int_[id] >= 1) + { + proportion_int_[id]--; + ++i; + } + id = (id + 1) % n_; + } + } + + if (mass_sum < mass_int_) + { + //Todo: is this data type safe? more is int and mass_sum is in64 + int32_t more = (int32_t)(mass_int_ - mass_sum); + + int i = 0; + int id = 0; + while (i < more) + { + proportion_int_[id]++; + id = (id + 1) % n_; + i++; + } + } + + int32_t L_head = 0; + int32_t L_tail = 0; + int32_t H_head = 0; + int32_t H_tail = 0; + + for (int i = 0; i < n_; ++i) + { + int32_t *p = memory + 2 * i; + *p = i; p++; + *p = (i + 1) * a_int_; + } + + for (auto i = 0; i < n_; ++i) + { + auto val = proportion_int_[i]; + if (val < a_int_) + { + L_[L_tail].first = i; + L_[L_tail].second = val; + ++L_tail; + } + else + { + H_[H_tail].first = i; + H_[H_tail].second = val; + ++H_tail; + } + } + + assert(L_tail + H_tail == n_); + + while (L_head != L_tail && H_head != H_tail) + { + auto &i_pi = L_[L_head++]; + auto &h_ph = H_[H_head++]; + + int32_t *p = memory + 2 * i_pi.first; + *p = h_ph.first; p++; + *p = i_pi.first * a_int_ + i_pi.second; + + auto sum = h_ph.second + i_pi.second; + if (sum > 2 * a_int_) + { + H_[H_tail].first = h_ph.first; + H_[H_tail].second = sum - a_int_; + ++H_tail; + } + else + { + L_[L_tail].first = h_ph.first; + L_[L_tail].second = sum - a_int_; + ++L_tail; + } + } + while (L_head != L_tail) + { + auto first = L_[L_head].first; + auto second = L_[L_head].second; + int32_t *p = memory + 2 * first; + *p = first; p++; + *p = first * a_int_ + second; + ++L_head; + } + while (H_head != H_tail) + { + auto first = H_[H_head].first; + auto second = H_[H_head].second; + int32_t *p = memory + 2 * first; + + *p = first; p++; + *p = first * a_int_ + second; + ++H_head; + } + } + + // Make sure to call SetProportion or SetProportionMass before calling Next + int32_t Next(xorshift_rng& rng, std::vector& alias_kv); + + private: + void GenerateAliasTable(std::vector& alias_kv); + + public: + AliasMultinomialRNGInt(const AliasMultinomialRNGInt &other) = delete; + AliasMultinomialRNGInt& operator=(const AliasMultinomialRNGInt &other) = delete; + + std::vector proportion_int_; + int32_t *internal_memory_; + + int32_t n_; + int32_t a_int_; + int32_t mass_int_; + + std::vector> L_; + std::vector> H_; + }; +} \ No newline at end of file diff --git a/src/Native/LdaNative/data_block.cpp b/src/Native/LdaNative/data_block.cpp new file mode 100644 index 0000000000..11b56b9ad7 --- /dev/null +++ b/src/Native/LdaNative/data_block.cpp @@ -0,0 +1,117 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +#include +#include "data_block.h" +#include "lda_document.h" + +namespace lda +{ + LDADataBlock::LDADataBlock(int32_t num_threads) : + num_threads_(num_threads), has_read_(false), index_document_(0), documents_buffer_(nullptr), offset_buffer_(nullptr) + { + } + + LDADataBlock::~LDADataBlock() + { + if (has_read_) + { + delete[] offset_buffer_; + delete[] documents_buffer_; + } + } + + void LDADataBlock::Clear() + { + has_read_ = false; + index_document_ = 0; + used_size_ = 0; + + num_documents_ = 0; + corpus_size_ = 0; + + if (offset_buffer_) + { + delete[]offset_buffer_; + offset_buffer_ = nullptr; + } + if (documents_buffer_) + { + delete[]documents_buffer_; + documents_buffer_ = nullptr; + } + } + + void LDADataBlock::Allocate(const int32_t num_document, const int64_t corpus_size) + { + num_documents_ = num_document; + corpus_size_ = corpus_size; + + offset_buffer_ = new int64_t[num_documents_ + 1]; // +1: one for the end of last document, + documents_buffer_ = new int32_t[corpus_size_]; + + index_document_ = 0; + used_size_ = 0; + + offset_buffer_[0] = 0; + } + + + //term_id, term_freq, term_num + int LDADataBlock::Add(int32_t* term_id, int32_t* term_freq, int32_t term_num) + { + int64_t data_length = 1; + + int64_t idx = offset_buffer_[index_document_] + 1; + for (int i = 0; i < term_num; ++i) + { + for (int j = 0; j < term_freq[i]; ++j) + { + documents_buffer_[idx++] = term_id[i]; + documents_buffer_[idx++] = 0; + data_length += 2; + } + } + + index_document_++; + used_size_ += data_length; + + offset_buffer_[index_document_] = used_size_; + has_read_ = true; + + return (int)data_length; + } + + int LDADataBlock::AddDense(int32_t* term_freq, int32_t term_num) + { + int64_t data_length = 1; + + int64_t idx = offset_buffer_[index_document_] + 1; + for (int i = 0; i < term_num; ++i) + { + for (int j = 0; j < term_freq[i]; ++j) + { + documents_buffer_[idx++] = i; + documents_buffer_[idx++] = 0; + data_length += 2; + } + } + + index_document_++; + used_size_ += data_length; + + offset_buffer_[index_document_] = used_size_; + has_read_ = true; + + return (int)data_length; + } + + std::shared_ptr LDADataBlock::GetOneDoc(int32_t index) const + { + std::shared_ptr returned_ptr( + new LDADocument(documents_buffer_ + offset_buffer_[index], + documents_buffer_ + offset_buffer_[index + 1])); + return returned_ptr; + } +} \ No newline at end of file diff --git a/src/Native/LdaNative/data_block.h b/src/Native/LdaNative/data_block.h new file mode 100644 index 0000000000..9f0894a858 --- /dev/null +++ b/src/Native/LdaNative/data_block.h @@ -0,0 +1,70 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +#pragma once + +#include +#include +#include +#include "light_hash_map.h" + +namespace lda +{ + class LDADocument; + class LDADataBlock + { + public: + explicit LDADataBlock(int32_t num_threads); + ~LDADataBlock(); + + void Clear(); + //in data feedin scenario + void Allocate(const int32_t num_document, const int64_t corpus_size); + //port the data from external process, e.g. c# + int AddDense(int32_t* term_freq, int32_t term_num); + int Add(int32_t* term_id, int32_t* term_freq, int32_t term_num); + std::shared_ptr GetOneDoc(int32_t index) const; + + inline int32_t num_documents() const; + // Return the first document for thread thread_id + inline int32_t Begin(int32_t thread_id) const; + // Return the next to last document for thread thread_i + inline int32_t End(int32_t thread_id) const; + + + private: + LDADataBlock(const LDADataBlock& other) = delete; + LDADataBlock& operator=(const LDADataBlock& other) = delete; + + int32_t num_threads_; + bool has_read_; // equal true if LDADataBlock holds memory + + int32_t index_document_; + int64_t used_size_; + + int32_t num_documents_; + int64_t corpus_size_; + + int64_t* offset_buffer_; // offset_buffer_ size = num_document_ + 1 + int32_t* documents_buffer_; // documents_buffer_ size = corpus_size_; + }; + + inline int32_t LDADataBlock::num_documents() const + { + return num_documents_; + } + inline int32_t LDADataBlock::Begin(int32_t thread_id) const + { + int32_t num_of_one_doc = num_documents_ / num_threads_; + return thread_id * num_of_one_doc; + } + + inline int32_t LDADataBlock::End(int32_t thread_id) const + { + if (thread_id == num_threads_ - 1) // last thread + return num_documents_; + int32_t num_of_one_doc = num_documents_ / num_threads_; + return (thread_id + 1) * num_of_one_doc; + } +} \ No newline at end of file diff --git a/src/Native/LdaNative/hybrid_alias_map.cpp b/src/Native/LdaNative/hybrid_alias_map.cpp new file mode 100644 index 0000000000..fcbeee3806 --- /dev/null +++ b/src/Native/LdaNative/hybrid_alias_map.cpp @@ -0,0 +1,198 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +#include +#include +#include +#include "utils.hpp" +#include +#include "hybrid_alias_map.h" + +namespace lda +{ + hybrid_alias_map::hybrid_alias_map() + :memory_(nullptr), + is_dense_(1), + kv_(nullptr), + idx_(nullptr), + capacity_(0), + size_(0), + mass_(0), + n_kw_mass_(0.0), + beta_mass_(0.0) + { + } + hybrid_alias_map::hybrid_alias_map(int32_t *memory, int32_t is_dense, int32_t capacity) + :memory_(memory), + is_dense_(is_dense), + capacity_(capacity), + kv_(nullptr), + idx_(nullptr), + size_(0), + mass_(0), + n_kw_mass_(0.0), + beta_mass_(0.0) + { + if (is_dense_) + { + kv_ = memory_; + idx_ = nullptr; + } + else + { + kv_ = memory_; + idx_ = memory_ + capacity_ * 2; + } + } + + hybrid_alias_map::hybrid_alias_map(const hybrid_alias_map &other) + { + this->memory_ = other.memory_; + this->is_dense_ = other.is_dense_; + this->capacity_ = other.capacity_; + + this->kv_ = other.kv_; + this->idx_ = other.idx_; + this->height_ = other.height_; + this->size_ = other.size_; + + this->mass_ = other.mass_; + this->n_kw_mass_ = other.n_kw_mass_; + this->beta_mass_ = other.beta_mass_; + } + hybrid_alias_map& hybrid_alias_map::operator=(const hybrid_alias_map &other) + { + this->memory_ = other.memory_; + this->is_dense_ = other.is_dense_; + this->capacity_ = other.capacity_; + + this->kv_ = other.kv_; + this->idx_ = other.idx_; + this->height_ = other.height_; + this->size_ = other.size_; + + this->mass_ = other.mass_; + this->n_kw_mass_ = other.n_kw_mass_; + this->beta_mass_ = other.beta_mass_; + + return *this; + } + + void hybrid_alias_map::clear() + { + size_ = 0; + } + + std::string hybrid_alias_map::DebugString() + { + std::string str = ""; + + if (size_ == 0) + { + return str; + } + + str += "is_dense:" + std::to_string(is_dense_) + " height:" + std::to_string(height_) + " mass:" + std::to_string(n_kw_mass_); + if (is_dense_) + { + for (int i = 0; i < capacity_; ++i) + { + str += " " + std::to_string(i) + ":" + std::to_string(*(memory_ + 2 * i)) + ":" + std::to_string(*(memory_ + 2 * i + 1)); + } + } + else + { + for (int i = 0; i < size_; ++i) + { + str += " " + std::to_string(idx_[i]) + ":" + std::to_string(*(memory_ + 2 * i)) + ":" + std::to_string(*(memory_ + 2 * i + 1)); + } + } + + return str; + } + + void hybrid_alias_map::build_table( + wood::AliasMultinomialRNGInt &alias_rng, + const hybrid_map &word_topic_row, + const std::vector &summary_row, + std::vector &q_w_proportion, + float beta, + float beta_sum, + int word_id, + wood::xorshift_rng &rng) + { + if (is_dense_) + { + size_ = capacity_; + mass_ = 0.0; + for (int k = 0; k < capacity_; ++k) + { + int32_t n_kw = word_topic_row[k]; + float prop = (n_kw + beta) / (summary_row[k] + beta_sum); + q_w_proportion[k] = prop; + mass_ += prop; + } + if (size_ == 0) + { + return; + } + alias_rng.SetProportionMass(q_w_proportion, mass_, memory_, &height_, rng); + + } + else + { + if (word_topic_row.is_dense()) + { + size_ = 0; + n_kw_mass_ = 0.0; + for (int k = 0; k < word_topic_row.capacity_; ++k) + { + if (word_topic_row.memory_[k] == 0) continue; + int32_t n_tw = word_topic_row.memory_[k]; + int64_t n_t = summary_row[k]; + q_w_proportion[size_] = n_tw / (n_t + beta_sum); + idx_[size_] = k; + n_kw_mass_ += q_w_proportion[size_]; + ++size_; + } + + if (size_ == 0) + { + // it is possible that, the local tf of a word is zero + return; + } + alias_rng.SetProportionMass(q_w_proportion, size_, n_kw_mass_, memory_, &height_, rng, word_id); + } + else + { + size_ = 0; + n_kw_mass_ = 0.0; + int32_t row_capacity = word_topic_row.capacity_; + for (int k = 0; k < row_capacity; ++k) + { + int32_t key = word_topic_row.key_[k]; + if (key > 0) + { + int32_t n_kw = word_topic_row.value_[k]; + float prop = n_kw / (summary_row[key - 1] + beta_sum); + + + + q_w_proportion[size_] = prop; + idx_[size_] = word_topic_row.key_[k] - 1; // minus one from the the internal key + n_kw_mass_ += prop; + + ++size_; + } + } + if (size_ == 0) + { + // it is possible that, the local tf of a word is zero + return; + } + alias_rng.SetProportionMass(q_w_proportion, size_, n_kw_mass_, memory_, &height_, rng, word_id); + } + } + } +} diff --git a/src/Native/LdaNative/hybrid_alias_map.h b/src/Native/LdaNative/hybrid_alias_map.h new file mode 100644 index 0000000000..f62b1e33af --- /dev/null +++ b/src/Native/LdaNative/hybrid_alias_map.h @@ -0,0 +1,128 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +#pragma once +#include +#include +#include +#include +#include +#include +#include "alias_multinomial_rng_int.hpp" +#include "hybrid_map.h" + +namespace lda +{ + class hybrid_alias_map + { + public: + + hybrid_alias_map(); + hybrid_alias_map(int32_t *memory, int32_t is_dense, int32_t capacity); + hybrid_alias_map(const hybrid_alias_map &other); + hybrid_alias_map& operator=(const hybrid_alias_map &other); + + void clear(); + inline int32_t size() const; + + std::string DebugString(); + void build_table( + wood::AliasMultinomialRNGInt &alias_rng, + const hybrid_map &word_topic_row, + const std::vector &summary_row, + std::vector &q_w_proportion, + float beta, + float beta_sum, + int word_id, + wood::xorshift_rng &rng); + + inline int32_t next(wood::xorshift_rng &rng, int32_t beta_height, float beta_mass, std::vector &beta_k_v, bool debug); + + private: + int32_t *memory_; + int32_t is_dense_; + int32_t *kv_; + int32_t *idx_; + int32_t height_; + int32_t capacity_; + int32_t size_; + + float mass_; + float n_kw_mass_; + float beta_mass_; + }; + + inline int32_t hybrid_alias_map::size() const + { + return size_; + } + + inline int32_t hybrid_alias_map::next(wood::xorshift_rng &rng, int32_t beta_height, float beta_mass, std::vector &beta_k_v, bool debug) + { + //NOTE: here we will set those unseen words' topic to 0. logicall we could set it to random as well. + if (capacity_ == 0) + { + return 0; + } + + if (is_dense_) + { + auto sample = rng.rand(); + int idx = sample / height_; + if (idx >= size_) + { + idx = size_ - 1; + } + + int32_t *p = memory_ + 2 * idx; + int32_t k = *p; + p++; + int32_t v = *p; + int32_t m = -(sample < v); + return (idx & m) | (k & ~m); + } + else + { + float sample = rng.rand_real() * (n_kw_mass_ + beta_mass); + if (sample < n_kw_mass_) + { + auto n_kw_sample = rng.rand(); + int32_t idx = n_kw_sample / height_; + + if (idx >= size_) + { + idx = size_ - 1; + } + + + int32_t *p = memory_ + 2 * idx; + int32_t k = *p; p++; + int32_t v = *p; + int32_t id = idx_[idx]; + int32_t k_id = idx_[k]; + + int32_t m = -(n_kw_sample < v); + return (id & m) | (k_id & ~m); + + } + else + { + auto sampleLocal = rng.rand(); + int idx = sampleLocal / beta_height; + int beta_size = (int)beta_k_v.size(); + + if (idx >= beta_size) + { + idx = beta_size - 1; + } + + int32_t k = beta_k_v[idx].k_; + int32_t v = beta_k_v[idx].v_; + int32_t m = -(sampleLocal < v); + return (idx & m) | (k & ~m); + } + } + } + +} \ No newline at end of file diff --git a/src/Native/LdaNative/hybrid_map.cpp b/src/Native/LdaNative/hybrid_map.cpp new file mode 100644 index 0000000000..e5c8252702 --- /dev/null +++ b/src/Native/LdaNative/hybrid_map.cpp @@ -0,0 +1,142 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +#include +#include +#include +#include "utils.hpp" +#include +#include "hybrid_map.h" + +namespace lda +{ + hybrid_map::hybrid_map() + :memory_(nullptr), + is_dense_(1), + capacity_(0), + empty_key_(0), + deleted_key_(-1), + key_(nullptr), + value_(nullptr), + num_deleted_key_(0), + external_rehash_buf_(nullptr) + { + } + hybrid_map::hybrid_map(int32_t *memory, int32_t is_dense, int32_t capacity, int32_t num_deleted_key + , int32_t *external_rehash_buf_) + : memory_(memory), + is_dense_(is_dense), + capacity_(capacity), + empty_key_(0), + deleted_key_(-1), + key_(nullptr), + value_(nullptr), + num_deleted_key_(num_deleted_key), + external_rehash_buf_(external_rehash_buf_) + { + if (is_dense_ == 0) { + key_ = memory_; + value_ = memory_ + capacity_; + } + } + + hybrid_map::hybrid_map(const hybrid_map &other) + { + this->memory_ = other.memory_; + this->is_dense_ = other.is_dense_; + this->capacity_ = other.capacity_; + empty_key_ = other.empty_key_; + deleted_key_ = other.deleted_key_; + num_deleted_key_ = other.num_deleted_key_; + external_rehash_buf_ = other.external_rehash_buf_; + if (this->is_dense_) + { + this->key_ = nullptr; + this->value_ = nullptr; + } + else + { + this->key_ = this->memory_; + this->value_ = this->memory_ + capacity_; + } + + } + hybrid_map& hybrid_map::operator=(const hybrid_map &other) + { + this->memory_ = other.memory_; + this->is_dense_ = other.is_dense_; + this->capacity_ = other.capacity_; + empty_key_ = other.empty_key_; + deleted_key_ = other.deleted_key_; + num_deleted_key_ = other.num_deleted_key_; + external_rehash_buf_ = other.external_rehash_buf_; + if (this->is_dense_) + { + this->key_ = nullptr; + this->value_ = nullptr; + } + else + { + this->key_ = this->memory_; + this->value_ = this->memory_ + capacity_; + } + return *this; + } + + void hybrid_map::clear() + { + int32_t memory_size = is_dense_ ? capacity_ : 2 * capacity_; + memset(memory_, 0, memory_size * sizeof(int32_t)); + } + + std::string hybrid_map::DumpString() const + { + if (is_dense_) + { + std::string result; + for (int i = 0; i < capacity_; ++i) + { + if (memory_[i] != 0) + { + result += std::to_string(i) + ":" + std::to_string(memory_[i]) + " "; + } + } + return result; + } + else + { + std::string result; + for (int i = 0; i < capacity_; ++i) + { + if (key_[i] > 0) + { + result += std::to_string(key_[i] - 1) + ":" + std::to_string(value_[i]) + " "; + } + } + return result; + } + } + + void hybrid_map::sorted_rehashing() + { + if (!is_dense_) + { + std::map rehash_buffer; + for (int i = 0; i < capacity_; ++i) + { + if (key_[i] > 0) + { + rehash_buffer[key_[i] - 1] = value_[i]; + } + } + memset(memory_, 0, 2 * capacity_ * sizeof(int32_t)); + for (auto it = rehash_buffer.begin(); + it != rehash_buffer.end(); ++it) + { + inc(it->first, it->second); + } + } + } + +} diff --git a/src/Native/LdaNative/hybrid_map.h b/src/Native/LdaNative/hybrid_map.h new file mode 100644 index 0000000000..88bbc82d5b --- /dev/null +++ b/src/Native/LdaNative/hybrid_map.h @@ -0,0 +1,238 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +#pragma once +#include +#include +#include +#include +#include +#include +#include + +// The probing method: +// Linear probing +// #define JUMP_(key, num_probes) ( 1 ) + +// Quadratic probing +#define JUMP_(key, num_probes) ( num_probes ) +#define ILLEGAL_BUCKET -1 + +namespace lda +{ + class hybrid_alias_map; + + class hybrid_map + { + friend class hybrid_alias_map; + public: + hybrid_map(); + hybrid_map(int32_t *memory, int32_t is_dense, int32_t capacity, int32_t num_deleted_key + , int32_t *external_rehash_buf_); + hybrid_map(const hybrid_map &other); + hybrid_map& operator=(const hybrid_map &other); + + + void clear(); + std::string DumpString() const; + void sorted_rehashing(); + + inline int32_t nonzero_num() const; + inline bool is_dense() const; + inline int32_t capacity() const; + inline int32_t *memory() const; + inline int32_t* key() const; + inline int32_t* value() const; + inline void rehashing(); + inline void inc(int32_t key, int32_t delta); + // query the value of |key| + // if |key| is in the table, return the |value| corresonding to |key| + // if not, just return 0 + inline int32_t operator[](int32_t key) const; + + private: + inline std::pair find_position(const int32_t key) const; + + int32_t *memory_; + int32_t is_dense_; + int32_t *key_; + int32_t *value_; + + // if |is_dense_| == true, capactiy_ is the length of an array + // if |is dense_| == false, capacity_ is the size of a light hash table + int32_t capacity_; + int32_t empty_key_; + int32_t deleted_key_; + + int32_t num_deleted_key_; + int32_t* external_rehash_buf_; + }; + + inline int32_t hybrid_map::nonzero_num() const + { + if (is_dense_) + { + int32_t size = 0; + for (int i = 0; i < capacity_; ++i) + { + if (memory_[i] > 0) + { + ++size; + } + } + return size; + } + else + { + int32_t size = 0; + for (int i = 0; i < capacity_; ++i) + { + if (key_[i] > 0) + { + ++size; + } + } + return size; + } + } + + inline bool hybrid_map::is_dense() const + { + return is_dense_ != 0; + } + + inline int32_t hybrid_map::capacity() const + { + return capacity_; + } + + inline int32_t* hybrid_map::memory() const + { + return memory_; + } + inline int32_t* hybrid_map::key() const + { + return key_; + } + inline int32_t* hybrid_map::value() const + { + return value_; + } + inline void hybrid_map::rehashing() + { + if (!is_dense_) + { + memcpy(external_rehash_buf_, memory_, 2 * capacity_ * sizeof(int32_t)); + int32_t *key = external_rehash_buf_; + int32_t *value = external_rehash_buf_ + capacity_; + memset(memory_, 0, 2 * capacity_ * sizeof(int32_t)); + for (int i = 0; i < capacity_; ++i) + { + if (key[i] > 0) + { + inc(key[i] - 1, value[i]); + } + } + num_deleted_key_ = 0; + } + } + inline void hybrid_map::inc(int32_t key, int32_t delta) + { + if (is_dense_) + { + memory_[key] += delta; + } + else + { + int32_t internal_key = key + 1; + std::pair pos = find_position(internal_key); + if (pos.first != ILLEGAL_BUCKET) + { + value_[pos.first] += delta; + if (value_[pos.first] == 0) // the value becomes zero, delete the key + { + key_[pos.first] = deleted_key_; + + ++num_deleted_key_; // num_deleted_key ++ + if (num_deleted_key_ * 20 > capacity_) + { + rehashing(); + } + } + } + else // not found the key, insert it with delta as value + { + key_[pos.second] = internal_key; + value_[pos.second] = delta; + } + } + } + + // query the value of |key| + // if |key| is in the table, return the |value| corresonding to |key| + // if not, just return 0 + inline int32_t hybrid_map::operator[](int32_t key) const + { + if (is_dense_) + { + //return memory_[key]; + if (capacity_ > 0) + { + return memory_[key]; + } + else + { + return 0; + } + } + else + { + int32_t internal_key = key + 1; + std::pair pos = find_position(internal_key); + if (pos.first != ILLEGAL_BUCKET) + { + return value_[pos.first]; + } + else + { + return 0; + } + } + } + inline std::pair hybrid_map::find_position(const int32_t key) const + { + int num_probes = 0; + int32_t capacity_minus_one = capacity_ - 1; + int32_t idx = key % capacity_; + int32_t insert_pos = ILLEGAL_BUCKET; + while (1) // probe until something happens + { + if (key_[idx] == empty_key_) // bucket is empty + { + if (insert_pos == ILLEGAL_BUCKET) // found no prior place to insert + { + return std::pair(ILLEGAL_BUCKET, idx); + } + else // previously, there is a position to insert + { + return std::pair(ILLEGAL_BUCKET, insert_pos); + } + } + else if (key_[idx] == deleted_key_) // keep searching, but makr to insert + { + if (insert_pos == ILLEGAL_BUCKET) + { + insert_pos = idx; + } + } + else if (key_[idx] == key) + { + return std::pair(idx, ILLEGAL_BUCKET); + } + ++num_probes; // we are doing another probe + idx = (idx + JUMP_(key, num_probes) & capacity_minus_one); + assert(num_probes < capacity_); // && "Hashtable is full: an error in key_equal<> or hash<>"); + } + } +} \ No newline at end of file diff --git a/src/Native/LdaNative/lda_document.cpp b/src/Native/LdaNative/lda_document.cpp new file mode 100644 index 0000000000..c2a3371020 --- /dev/null +++ b/src/Native/LdaNative/lda_document.cpp @@ -0,0 +1,29 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +#include "lda_document.h" + +namespace lda +{ + LDADocument::LDADocument(int32_t* memory_begin, int32_t* memory_end) : + memory_begin_(memory_begin), memory_end_(memory_end), cursor_(*memory_begin) {} + + // should be called when sweeped over all the tokens in a document + void LDADocument::ResetCursor() + { + cursor_ = 0; + } + void LDADocument::GetDocTopicCounter(lda::light_hash_map& doc_topic_counter) + { + int32_t* p = memory_begin_ + 2; + int32_t num = 0; + while (p < memory_end_) + { + doc_topic_counter.inc(*p, 1); + ++p; ++p; + if (++num == 512) + return; + } + } +} \ No newline at end of file diff --git a/src/Native/LdaNative/lda_document.h b/src/Native/LdaNative/lda_document.h new file mode 100644 index 0000000000..45df42f06c --- /dev/null +++ b/src/Native/LdaNative/lda_document.h @@ -0,0 +1,60 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +#pragma once + +#include +#include +#include +#include "light_hash_map.h" + +namespace lda +{ + class LDADocument + { + public: + const int32_t kMaxSizeLightHash = 512; // This is for the easy use of LightHashMap + + LDADocument(int32_t* memory_begin, int32_t* memory_end); + + inline int32_t size() const; + inline int32_t& get_cursor(); + inline int32_t Word(int32_t index) const; + inline int32_t Topic(int32_t index) const; + inline void SetTopic(int32_t index, int32_t topic); + + // should be called when sweeped over all the tokens in a document + void ResetCursor(); + void GetDocTopicCounter(lda::light_hash_map& doc_topic_counter); + + private: + LDADocument(const LDADocument &other) = delete; + LDADocument& operator=(const LDADocument &other) = delete; + + int32_t* memory_begin_; + int32_t* memory_end_; + int32_t& cursor_; // cursor_ is reference of *memory_begin_ + }; + + inline int32_t LDADocument::size() const + { + return (std::min)(static_cast((memory_end_ - memory_begin_) / 2), kMaxSizeLightHash); + } + inline int32_t& LDADocument::get_cursor() + { + return cursor_; + } + inline int32_t LDADocument::Word(int32_t index) const + { + return *(memory_begin_ + 1 + index * 2); + } + inline int32_t LDADocument::Topic(int32_t index) const + { + return *(memory_begin_ + 2 + index * 2); + } + inline void LDADocument::SetTopic(int32_t index, int32_t topic) + { + *(memory_begin_ + 2 + index * 2) = topic; + } +} \ No newline at end of file diff --git a/src/Native/LdaNative/lda_engine.cpp b/src/Native/LdaNative/lda_engine.cpp new file mode 100644 index 0000000000..5650ce73b5 --- /dev/null +++ b/src/Native/LdaNative/lda_engine.cpp @@ -0,0 +1,1066 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "timer.h" +#include "rand_int_rng.h" +#include "lda_document.h" +#include "data_block.h" +#include "model_block.h" +#include "lda_engine.hpp" +#include "utils.hpp" +#include "simple_barrier.h" +#include "light_doc_sampler.hpp" + +#ifdef _MSC_VER +#include "windows.h" +#elif defined(__APPLE__) +#include +#include +#include +#else +#include "sched.h" +#endif + +namespace lda { + LdaEngine::LdaEngine(int numTopic, + int numVocab, + float alphaSum, + float beta, + int numIter, + int likelihoodInterval, + int numThread, + int mhstep, + int maxDocToken) + : K_(numTopic), + V_(numVocab), + compute_ll_interval_(likelihoodInterval), + beta_(beta), + num_iterations_(numIter), + mh_step_(mhstep), + alpha_sum_(alphaSum), + maxDocToken_(maxDocToken), + samplers_(nullptr), + document_buffer_(nullptr) + { + if (numThread > 0) + { + num_threads_ = numThread; + } + else + { + unsigned int uNumCPU = std::thread::hardware_concurrency(); + num_threads_ = std::max(1, (int)(uNumCPU - 2)); + } + printf("using %d thread(s) to do train/test\n", num_threads_); + + bAlphaSumMultiplied = false; + atomic_stats_ = new LDAEngineAtomics(); + model_block_ = new LDAModelBlock(); + data_block_ = new LDADataBlock(num_threads_); + process_barrier_ = new SimpleBarrier(num_threads_); + samplerQueue_ = new CBlockedIntQueue(); + + document_buffer_ = new int32_t*[num_threads_]; + for (int i = 0; i < num_threads_; i++) + document_buffer_[i] = new int32_t[maxDocToken_ * 2 + 1]; + + likelihood_in_iter_ = nullptr; + + beta_sum_ = beta_ * V_; + } + + LdaEngine::LdaEngine(int32_t K, int32_t V, int32_t num_threads, int32_t compute_ll_interval, float beta, int32_t num_iterations, int32_t mh_step, float alpha_sum, int maxDocToken) + : K_(K), + V_(V), + compute_ll_interval_(compute_ll_interval), + beta_(beta), + num_iterations_(num_iterations), + mh_step_(mh_step), + alpha_sum_(alpha_sum), + maxDocToken_(maxDocToken), + samplers_(nullptr), + document_buffer_(nullptr) + { + if (num_threads > 0) + { + num_threads_ = num_threads; + } + else + { + unsigned int uNumCPU = std::thread::hardware_concurrency(); + num_threads_ = std::max(1, (int)(uNumCPU - 2)); + } + bAlphaSumMultiplied = false; + process_barrier_ = new SimpleBarrier(num_threads_); + atomic_stats_ = new LDAEngineAtomics(); + data_block_ = new LDADataBlock(num_threads_); + model_block_ = new LDAModelBlock(); + samplerQueue_ = new CBlockedIntQueue(); + + document_buffer_ = new int32_t*[num_threads_]; + for (int i = 0; i < num_threads_; i++) + document_buffer_[i] = new int32_t[maxDocToken_ * 2 + 1]; + + likelihood_in_iter_ = nullptr; + beta_sum_ = beta_ * V_; + } + + + LdaEngine::~LdaEngine() + { + //delete memory space + delete process_barrier_; + process_barrier_ = nullptr; + + delete data_block_; + data_block_ = nullptr; + + delete atomic_stats_; + atomic_stats_ = nullptr; + + delete model_block_; + model_block_ = nullptr; + + delete samplerQueue_; + samplerQueue_ = nullptr; + + for (int i = 0; i < num_threads_; ++i) + { + delete samplers_[i]; + } + delete[] samplers_; + + if (document_buffer_) + { + for (int i = 0; i < num_threads_; ++i) + { + delete[]document_buffer_[i]; + document_buffer_[i] = nullptr; + } + delete[]document_buffer_; + document_buffer_ = nullptr; + } + + if (likelihood_in_iter_) + { + delete[] likelihood_in_iter_; + likelihood_in_iter_ = nullptr; + } + } + + bool LdaEngine::InitializeBeforeTrain() + { + CTimer tmDebug(true); + CheckFunction(0, tmDebug, "enter initializeBeforeTrain", false); + //allocate model memory from the data preloaded + AllocateModelMemory(data_block_); + CheckFunction(0, tmDebug, "allocate model memory", false); + + double alloc_start = lda::get_time(); + global_word_topic_table_.resize(V_); + alias_rng_int_.Init(K_); + beta_k_v_.resize(K_); + global_alias_k_v_.resize(V_); + + for (int i = 0; i < V_; ++i) + { + global_alias_k_v_[i] = model_block_->get_alias_row(i); + } + global_summary_row_.resize(K_); + CheckFunction(0, tmDebug, "initlaizing global tables used in sampling", false); + + word_range_for_each_thread_.resize(num_threads_ + 1); + int32_t word_num_each_thread = V_ / num_threads_; + word_range_for_each_thread_[0] = 0; + for (int32_t i = 0; i < num_threads_ - 1; ++i) + { + word_range_for_each_thread_[i + 1] = word_range_for_each_thread_[i] + word_num_each_thread; + } + word_range_for_each_thread_[num_threads_] = V_; + + //setup sampler + samplers_ = new LightDocSampler*[num_threads_]; + samplerQueue_->clear(); + + for (int i = 0; i < num_threads_; ++i) + { + samplers_[i] = new LightDocSampler( + K_, + V_, + num_threads_, + mh_step_, + beta_, + alpha_sum_, + global_word_topic_table_, + global_summary_row_, + global_alias_k_v_, + beta_height_, + beta_mass_, + beta_k_v_); + + samplerQueue_->push(i); + } + CheckFunction(0, tmDebug, "create samplers", false); + return true; + } + + void LdaEngine::InitializeBeforeTest() + { + // TODO: + // Allocating space for word-topic-table and alias table according to the input data of SetModel interface (done) + // Create multiple thread-specific sampler + // set word_range_for_each_thread_ + // Adjust the alpha_sum_ parameter for each thread-specific sampler + CTimer tmDebug(true); + CheckFunction(0, tmDebug, "enter initializeBeforeTest", false); + + global_word_topic_table_.resize(V_); + alias_rng_int_.Init(K_); + beta_k_v_.resize(K_); + global_alias_k_v_.resize(V_); + + for (int i = 0; i < V_; ++i) + { + global_alias_k_v_[i] = model_block_->get_alias_row(i); + } + CheckFunction(0, tmDebug, "initlaizing global tables used in sampling", false); + + // Set the word range for each thread + word_range_for_each_thread_.resize(num_threads_ + 1); + int32_t word_num_each_thread = V_ / num_threads_; + word_range_for_each_thread_[0] = 0; + for (int32_t i = 0; i < num_threads_ - 1; ++i) + { + word_range_for_each_thread_[i + 1] = word_range_for_each_thread_[i] + word_num_each_thread; + } + word_range_for_each_thread_[num_threads_] = V_; + + //setup sampler + if (samplers_) + { + for (int i = 0; i < num_threads_; ++i) + { + delete samplers_[i]; + } + delete[] samplers_; + } + if (document_buffer_) + { + for (int i = 0; i < num_threads_; ++i) + { + delete[]document_buffer_[i]; + document_buffer_[i] = nullptr; + } + delete[]document_buffer_; + document_buffer_ = nullptr; + } + + samplers_ = new LightDocSampler*[num_threads_]; + document_buffer_ = new int32_t*[num_threads_]; + samplerQueue_->clear(); + + for (int i = 0; i < num_threads_; ++i) + { + samplers_[i] = new LightDocSampler( + K_, + V_, + num_threads_, + mh_step_, + beta_, + alpha_sum_, + global_word_topic_table_, + global_summary_row_, + global_alias_k_v_, + beta_height_, + beta_mass_, + beta_k_v_); + + samplers_[i]->AdaptAlphaSum(false); + document_buffer_[i] = new int32_t[maxDocToken_ * 2 + 1]; + + samplerQueue_->push(i); + } + CheckFunction(0, tmDebug, "create samplers", false); + + // build alias table + // build alias table for the dense term, beta_k_v_, which is shared by all the words + beta_mass_ = 0; + std::vector proportion(K_); + for (int k = 0; k < K_; ++k) + { + proportion[k] = beta_ / (global_summary_row_[k] + beta_sum_); + beta_mass_ += proportion[k]; + } + alias_rng_int_.SetProportionMass(proportion, beta_mass_, beta_k_v_, &beta_height_, samplers_[0]->rng()); + + // build alias table for the sparse term + for (int thread_id = 0; thread_id < num_threads_; ++thread_id) + { + LightDocSampler &sampler = *(samplers_[thread_id]); + sampler.build_alias_table(word_range_for_each_thread_[thread_id], word_range_for_each_thread_[thread_id + 1], thread_id); + } + CheckFunction(0, tmDebug, "build alisa table", false); + } + + void LdaEngine::Train(const char* pTrainOutput) + { + std::vector threads(num_threads_); + atomic_stats_->thread_counter_ = 0; + + for (auto& thr : threads) { + thr = std::thread(&LdaEngine::Training_Thread, this); + } + + printf("started training with %d threads\n", num_threads_); + for (auto& thr : threads) { + thr.join(); + } + + if (pTrainOutput) + { + DumpDocTopicTable(pTrainOutput); + } + } + + void LdaEngine::Test(int32_t burnin_iter, float* pLoglikelihood) + { + std::vector threads(num_threads_); + atomic_stats_->thread_counter_ = 0; + burnin_iterations_ = burnin_iter; + + likelihood_in_iter_ = new float[burnin_iterations_]; + for (int i = 0; i < burnin_iterations_; i++) + { + likelihood_in_iter_[i] = 0.0; + } + + for (auto& thr : threads) { + thr = std::thread(&LdaEngine::Testing_Thread, this); + } + + printf("started testing with %d threads\n", num_threads_); + + for (auto& thr : threads) { + thr.join(); + } + + //get the loglikelihood of each burn in iteration + for (int i = 0; i < burnin_iterations_; i++) + { + pLoglikelihood[i] = likelihood_in_iter_[i]; //just set an arbitary value here for later update + } + } + + void LdaEngine::CheckFunction(int thread_id, CTimer &tmDebug, const char* msg, bool waitBarrier) + { + } + + void LdaEngine::Training_Thread() + { + CTimer tmDebug(true); + + int thread_id = atomic_stats_->thread_counter_++; + std::vector> llcontainer; + // Set core affinity which helps performance improvement +#ifdef _MSC_VER + long long maskLL = 0; + maskLL |= (1LL << (thread_id)); + DWORD_PTR mask = maskLL; + SetThreadAffinityMask(GetCurrentThread(), mask); +#elif defined(__APPLE__) + thread_port_t thread = pthread_mach_thread_np(pthread_self()); + thread_affinity_policy_data_t policy = { thread_id }; + thread_policy_set(thread, THREAD_AFFINITY_POLICY, (thread_policy_t)&policy, 1); +#else + cpu_set_t set; + CPU_ZERO(&set); + CPU_SET(thread_id, &set); + sched_setaffinity(0, sizeof(cpu_set_t), &set); +#endif + + // Each thread builds a portion of word-topic table. We do this way because each word-topic row + // has a thread-specific buffer for rehashing + process_barrier_->wait(); + LightDocSampler &sampler_ = *(samplers_[thread_id]); + sampler_.AdaptAlphaSum(true); + + sampler_.build_word_topic_table(thread_id, num_threads_, *model_block_); + process_barrier_->wait(); + CheckFunction(thread_id, tmDebug, "intialize word_topic_table for sampler - in function train_thread"); + + int32_t token_num = 0; + int32_t doc_start = data_block_->Begin(thread_id); + int32_t doc_end = data_block_->End(thread_id); + + for (int32_t doc_index = doc_start; doc_index != doc_end; ++doc_index) + { + std::shared_ptr doc = data_block_->GetOneDoc(doc_index); + int doc_size = doc->size(); + for (int i = 0; i < doc_size; ++i) + { + int topic = sampler_.rand_k(); + doc->SetTopic(i, topic); + } + int cursor = doc->get_cursor(); + token_num += sampler_.GlobalInit(doc.get()); + } + process_barrier_->wait(); + CheckFunction(thread_id, tmDebug, "intialize token topic before iterations - in function train_thread"); + + for (int i = 0; i < num_threads_; ++i) + { + std::vector& wtd_vec = samplers_[i]->get_word_topic_delta(thread_id); + for (auto& wtd : wtd_vec) + { + global_word_topic_table_[wtd.word].inc(wtd.topic, wtd.delta); + } + } + process_barrier_->wait(); + CheckFunction(thread_id, tmDebug, "intialize word topic model before iterations - in function train_thread"); + + // use thread-private delta table to get global table + { + std::lock_guard lock(atomic_stats_->global_mutex_); + + std::vector &summary = sampler_.get_delta_summary_row(); + for (int i = 0; i < K_; ++i) + { + global_summary_row_[i] += summary[i]; + } + } + process_barrier_->wait(); + CheckFunction(thread_id, tmDebug, "global summary & Complete setup train before iterations - in function train_thread"); + + for (int iter = 0; iter < num_iterations_; ++iter) + { + CheckFunction(thread_id, tmDebug, "----------------------iteration start - in function train_thread---------------------"); + int32_t token_sweeped = 0; + atomic_stats_->num_tokens_clock_ = 0; + // build alias table + // build alias table for the dense term, beta_k_v_, which is shared by all the words + if (thread_id == 0) + { + beta_mass_ = 0; + std::vector proportion(K_); + for (int k = 0; k < K_; ++k) + { + proportion[k] = beta_ / (global_summary_row_[k] + beta_sum_); + beta_mass_ += proportion[k]; + } + + alias_rng_int_.SetProportionMass(proportion, beta_mass_, beta_k_v_, &beta_height_, sampler_.rng()); + } + process_barrier_->wait(); + CheckFunction(thread_id, tmDebug, "built alias table dense - in function train_thread"); + + // build alias table for the sparse term + sampler_.build_alias_table(word_range_for_each_thread_[thread_id], word_range_for_each_thread_[thread_id + 1], thread_id); + process_barrier_->wait(); + CheckFunction(thread_id, tmDebug, "built alias table sparse - in function train_thread"); + + sampler_.EpocInit(); + process_barrier_->wait(); + CheckFunction(thread_id, tmDebug, "EpochInit - in function train_thread"); + + // main part of the training - sampling over documents in this iteration + double iter_start = lda::get_time(); + int32_t doc_start_local = data_block_->Begin(thread_id); + int32_t doc_end_local = data_block_->End(thread_id); + + for (int32_t doc_index = doc_start_local; doc_index != doc_end_local; ++doc_index) + { + std::shared_ptr doc = data_block_->GetOneDoc(doc_index); + token_sweeped += sampler_.SampleOneDoc(doc.get()); + } + atomic_stats_->num_tokens_clock_ += token_sweeped; + + process_barrier_->wait(); + double iter_end = lda::get_time(); + + if (thread_id == 0) + { + double seconds_this_iter = iter_end - iter_start; + + printf("Iter: %04d", iter); + std::cout + << "\tThread = " << thread_id + << "\tTokens: " << atomic_stats_->num_tokens_clock_ + << "\tTook: " << seconds_this_iter << " sec" + << "\tThroughput: " + << static_cast(atomic_stats_->num_tokens_clock_) / (seconds_this_iter) << " token/(thread*sec)" + << std::endl; + } + process_barrier_->wait(); + CheckFunction(thread_id, tmDebug, "train(gibbs sampling) - in function train_thread"); + + // syncup global table + double sync_start = lda::get_time(); + for (int i = 0; i < num_threads_; ++i) + { + std::vector & wtd_vec = samplers_[i]->get_word_topic_delta(thread_id); + for (auto& wtd : wtd_vec) + { + global_word_topic_table_[wtd.word].inc(wtd.topic, wtd.delta); + } + } + + // use thread-private delta table to update global table + { + std::lock_guard lock(atomic_stats_->global_mutex_); + std::vector &summary = sampler_.get_delta_summary_row(); + for (int i = 0; i < K_; ++i) + { + global_summary_row_[i] += summary[i]; + } + } + process_barrier_->wait(); + CheckFunction(thread_id, tmDebug, "syncup global word_topic table - in function train_thread"); + + if (compute_ll_interval_ != -1 && (iter % compute_ll_interval_ == 0 || iter == num_iterations_ - 1)) + { + double ll = EvalLogLikelihood(true, thread_id, iter, sampler_); + llcontainer.push_back(std::pair(iter, ll)); + } + + CheckFunction(thread_id, tmDebug, "----------------------iteration end - in function train_thread---------------------"); + } + + if (thread_id == 0) + { + //output the ll once + for (int i = 0; i < llcontainer.size(); i++) + { + printf("loglikelihood @iter%04d = %f\n", llcontainer[i].first, llcontainer[i].second); + } + } + + process_barrier_->wait(); + + snprintf(tmDebug.m_szMessage, 200, "thread_id = %d, training iterations", thread_id); + tmDebug.InnerTag(); + } + + void LdaEngine::Testing_Thread() + { + int thread_id = atomic_stats_->thread_counter_++; + + // Set core affinity which helps performance improvement +#ifdef _MSC_VER + long long maskLL = 0; + maskLL |= (1LL << (thread_id)); + DWORD_PTR mask = maskLL; + SetThreadAffinityMask(GetCurrentThread(), mask); +#elif defined(__APPLE__) + thread_port_t thread = pthread_mach_thread_np(pthread_self()); + thread_affinity_policy_data_t policy = { thread_id }; + thread_policy_set(thread, THREAD_AFFINITY_POLICY, (thread_policy_t)&policy, 1); +#else + cpu_set_t set; + CPU_ZERO(&set); + CPU_SET(thread_id, &set); + sched_setaffinity(0, sizeof(cpu_set_t), &set); +#endif + process_barrier_->wait(); + + //// Each thread builds a portion of word-topic table. We do this way because each word-topic row + //// has a thread-specific buffer for rehashing + LightDocSampler &sampler_ = *(samplers_[thread_id]); + sampler_.AdaptAlphaSum(false); + + double init_start = lda::get_time(); + int32_t token_num = 0; + int32_t doc_start = data_block_->Begin(thread_id); + int32_t doc_end = data_block_->End(thread_id); + + for (int32_t doc_index = doc_start; doc_index != doc_end; ++doc_index) + { + std::shared_ptr doc = data_block_->GetOneDoc(doc_index); + int doc_size = doc->size(); + for (int i = 0; i < doc_size; ++i) + { + int topic = sampler_.rand_k(); + doc->SetTopic(i, topic); + } + int cursor = doc->get_cursor(); + token_num += sampler_.GlobalInit(doc.get()); + } + + process_barrier_->wait(); + + // build alias table + // build alias table for the dense term, beta_k_v_, which is shared by all the words + if (thread_id == 0) + { + beta_mass_ = 0; + std::vector proportion(K_); + for (int k = 0; k < K_; ++k) + { + proportion[k] = beta_ / (global_summary_row_[k] + beta_sum_); + beta_mass_ += proportion[k]; + } + + alias_rng_int_.SetProportionMass(proportion, beta_mass_, beta_k_v_, &beta_height_, sampler_.rng()); + } + + // build alias table for the sparse term + double alias_start = lda::get_time(); + process_barrier_->wait(); + sampler_.build_alias_table(word_range_for_each_thread_[thread_id], word_range_for_each_thread_[thread_id + 1], thread_id); + process_barrier_->wait(); + + // print the log-likelihood before inference + EvalLogLikelihood(true, thread_id, 0, sampler_); + + double total_start = lda::get_time(); + for (int iter = 0; iter < burnin_iterations_; ++iter) + { + double iter_start = lda::get_time(); + int32_t token_sweeped = 0; + atomic_stats_->num_tokens_clock_ = 0; + int32_t doc_start_local = data_block_->Begin(thread_id); + int32_t doc_end_local = data_block_->End(thread_id); + + for (int32_t doc_index = doc_start_local; doc_index != doc_end_local; ++doc_index) + { + std::shared_ptr doc = data_block_->GetOneDoc(doc_index); + token_sweeped += sampler_.InferOneDoc(doc.get()); + } + atomic_stats_->num_tokens_clock_ += token_sweeped; + + process_barrier_->wait(); + double iter_end = lda::get_time(); + + if (thread_id == 0) + { + double seconds_this_iter = iter_end - iter_start; + + printf("Iter: %04d", iter); + std::cout + << "\tThread = " << thread_id + << "\tTokens: " << atomic_stats_->num_tokens_clock_ + << "\tTook: " << seconds_this_iter << " sec" + << "\tThroughput: " + << static_cast(atomic_stats_->num_tokens_clock_) / (seconds_this_iter) << " token/(thread*sec)" + << std::endl; + + } + + process_barrier_->wait(); + + if (compute_ll_interval_ != -1 && (iter % compute_ll_interval_ == 0 || iter == burnin_iterations_ - 1)) + { + EvalLogLikelihood(false, thread_id, iter, sampler_); + } + } + + double total_end = lda::get_time(); + printf("thread_id = %d, Total time for burnin iterations : %f sec.\n", thread_id, total_end - total_start); + } + + void LdaEngine::AllocateDataMemory(int num_document, int64_t corpus_size) + { + data_block_->Allocate(num_document, corpus_size); + } + + void LdaEngine::AllocateModelMemory(const LDADataBlock* data_block) + { + model_block_->InitFromDataBlock(data_block, V_, K_); + + global_word_topic_table_.resize(V_); + + for (int i = 0; i < V_; ++i) + { + global_word_topic_table_[i] = model_block_->get_row(i, nullptr); + } + } + + void LdaEngine::AllocateModelMemory(int num_vocabs, int num_topics, int64_t nonzero_num) + { + model_block_->Init(num_vocabs, num_topics, nonzero_num); + + global_word_topic_table_.resize(num_vocabs); + + for (int i = 0; i < num_vocabs; ++i) + { + global_word_topic_table_[i] = model_block_->get_row(i, nullptr); + } + } + + void LdaEngine::AllocateModelMemory(int num_vocabs, int num_topics, int64_t mem_block_size, int64_t alias_mem_block_size) + { + model_block_->Init(num_vocabs, num_topics, mem_block_size, alias_mem_block_size); //memory allocated here + + global_word_topic_table_.resize(num_vocabs); + global_summary_row_.resize(K_, 0); + + //each value inside the global_word_topic_table_ will be set while call SetWordTopic() + } + + int LdaEngine::FeedInData(int* term_id, int* term_freq, int32_t term_num, int32_t vocab_size) + { + if (V_ == 0) //number vocab could be set in allocating model memory function + V_ = vocab_size; + + //data_block represent for one doc + return data_block_->Add(term_id, term_freq, term_num); + } + + int LdaEngine::FeedInDataDense(int* term_freq, int32_t term_num, int32_t vocab_size) + { + if (V_ == 0) //number vocab could be set in allocating model memory function + V_ = vocab_size; + + //data_block represent for one doc + return data_block_->AddDense(term_freq, term_num); + } + + void LdaEngine::TestOneDoc(int* term_id, int* term_freq, int32_t term_num, int* pTopics, int* pProbs, int32_t& numTopicsMax, int32_t numBurnIter, bool reset) + { + //numTopicsMax initialy holds the max returned topic number in order to hold the pTopic/pProbs memory in outside function + //when data return, numTopicsMax should contains the real topic number returned. + int sampler_id = 0; + sampler_id = samplerQueue_->pop(); + + LightDocSampler &sampler = *(samplers_[sampler_id]); + int64_t data_length = 1; + for (int i = 0; i < term_num; ++i) + { + for (int j = 0; j < term_freq[i]; ++j) + { + data_length += 2; + } + } + + assert(data_length <= maxDocToken_ * 2 + 1); + + if (reset) + { + // restart the rng seeds, so that we always get consistent result for the same input + rng_.restart(); + sampler.rng_restart(); + } + + // NOTE: in multi-threaded implementation, the dynamic memory allocation + // may cause contention at OS heap lock + int64_t idx = 1; + for (int i = 0; i < term_num; ++i) + { + for (int j = 0; j < term_freq[i]; ++j) + { + document_buffer_[sampler_id][idx++] = term_id[i]; + document_buffer_[sampler_id][idx++] = rng_.rand_k(K_); + } + } + + std::shared_ptr doc(new LDADocument(document_buffer_[sampler_id], document_buffer_[sampler_id] + data_length)); + + for (int iter = 0; iter < numBurnIter; ++iter) + { + sampler.InferOneDoc(doc.get()); + } + sampler.GetDocTopic(doc.get(), pTopics, pProbs, numTopicsMax); + + samplerQueue_->push(sampler_id); + } + + void LdaEngine::TestOneDocDense(int* term_freq, int32_t term_num, int* pTopics, int* pProbs, int32_t& numTopicsMax, int32_t numBurnIter, bool reset) + { + //numTopicsMax initialy holds the max returned topic number in order to hold the pTopic/pProbs memory in outside function + //when data return, numTopicsMax should contains the real topic number returned. + int sampler_id = 0; + sampler_id = samplerQueue_->pop(); + + LightDocSampler &sampler = *(samplers_[sampler_id]); + int64_t data_length = 1; + for (int i = 0; i < term_num; ++i) + { + for (int j = 0; j < term_freq[i]; ++j) + { + data_length += 2; + } + } + + assert(data_length <= maxDocToken_ * 2 + 1); + + if (reset) + { + // restart the rng seeds, so that we always get consistent result for the same input + rng_.restart(); + sampler.rng_restart(); + } + + // NOTE: in multi-threaded implementation, the dynamic memory allocation + // may cause contention at OS heap lock + int64_t idx = 1; + for (int i = 0; i < term_num; ++i) + { + for (int j = 0; j < term_freq[i]; ++j) + { + document_buffer_[sampler_id][idx++] = i; + document_buffer_[sampler_id][idx++] = rng_.rand_k(K_); + } + } + + std::shared_ptr doc(new LDADocument(document_buffer_[sampler_id], document_buffer_[sampler_id] + data_length)); + + for (int iter = 0; iter < numBurnIter; ++iter) + { + sampler.InferOneDoc(doc.get()); + } + sampler.GetDocTopic(doc.get(), pTopics, pProbs, numTopicsMax); + + samplerQueue_->push(sampler_id); + } + + void LdaEngine::GetDocTopic(int docID, int* pTopic, int* pProb, int32_t& numTopicReturn) + { + //get the current topic vector of the document + int thread_id = 0; + LightDocSampler &sampler = *(samplers_[thread_id]); + + sampler.GetDocTopic(data_block_->GetOneDoc(docID).get(), pTopic, pProb, numTopicReturn); + } + + void LdaEngine::SetAlphaSum(float avgDocLength) + { + if (!bAlphaSumMultiplied) + { + alpha_sum_ = alpha_sum_ * avgDocLength; + bAlphaSumMultiplied = true; + } + printf("alpha_sum was set to %f", alpha_sum_); + } + + bool LdaEngine::ClearData() + { + data_block_->Clear(); + return true; + } + + bool LdaEngine::ClearModel() + { + model_block_->Clear(); + return true; + } + + //function to support dumping the topic_model model file + void LdaEngine::GetWordTopic(int32_t wordId, int32_t* pTopic, int32_t* pProb, int32_t& length) + { + //cap the topic number here according to inpassed value of length + int lengthCap = length; + + // NOTE: we MUST check whether the word-topic row is empty before get its value + if (global_word_topic_table_[wordId].capacity() == 0) + { + length = 0; + return; + } + + length = 0; + for (int i = 0; i < K_; ++i) + { + if (global_word_topic_table_[wordId][i] > 0) + { + pTopic[length] = i; + pProb[length] = global_word_topic_table_[wordId][i]; + length++; + + if (length >= lengthCap) + break; + } + } + } + + // Compare by frequencies in descending order. + bool CompareTerms(const std::pair &term1, const std::pair &term2) + { + // REVIEW: consider changing this to impose a total order, since quicksort is not stable. + return term2.second < term1.second; + } + + void LdaEngine::GetTopicSummary(int32_t topicId, int32_t* pWords, float* pProb, int32_t& length) + { + std::vector> allTermsVec; + int sumFreq = 0; + for (int i = 0; i < V_; i++) //for all the terms check the topic distribution + { + if (global_word_topic_table_[i][topicId] > 0) + { + std::pair p; + p.first = i; + p.second = global_word_topic_table_[i][topicId]; + allTermsVec.push_back(p); + sumFreq += global_word_topic_table_[i][topicId]; + } + } + + std::sort(allTermsVec.begin(), allTermsVec.end(), CompareTerms); + + int usedTerm = (int)allTermsVec.size(); + length = std::min(usedTerm, length); + for (int i = 0; i < length; i++) + { + pWords[i] = allTermsVec[i].first; + pProb[i] = (((float)(allTermsVec[i].second)) + beta_) / (sumFreq + beta_ * V_); + } + } + + //function to support loading the topic_model model file + void LdaEngine::SetWordTopic(int32_t wordId, int32_t* pTopic, int32_t* pProb, int32_t length) + { + //NOTE: whether we should really use the "true" here + model_block_->SetWordInfo(wordId, length, true); + global_word_topic_table_[wordId] = model_block_->get_row(wordId, nullptr); + + for (int i = 0; i < length; ++i) + { + global_word_topic_table_[wordId].inc(pTopic[i], pProb[i]); + global_summary_row_[pTopic[i]] += pProb[i]; + } + } + + void LdaEngine::GetModelStat(int64_t &memBlockSize, int64_t &aliasMemBlockSize) + { + //NOTE: get the model's value at the end of training stage. try to save these two numbers to disk file + model_block_->GetModelStat(memBlockSize, aliasMemBlockSize); + } + + double LdaEngine::EvalLogLikelihood(bool is_train, int thread_id, int iter, LightDocSampler &sampler) + { + double doc_ll = 0; + double word_ll = 0; + + if (thread_id == 0) + { + atomic_stats_->doc_ll_ = 0; + atomic_stats_->word_ll_ = 0; + } + process_barrier_->wait(); + + int doc_num = 0; + int32_t doc_start = data_block_->Begin(thread_id); + int32_t doc_end = data_block_->End(thread_id); + for (int32_t doc_index = doc_start; doc_index != doc_end; ++doc_index) + { + std::shared_ptr doc = data_block_->GetOneDoc(doc_index); + doc_ll += sampler.ComputeOneDocLLH(doc.get()); + doc_num++; + } + atomic_stats_->doc_ll_ = atomic_stats_->doc_ll_ + doc_ll; + process_barrier_->wait(); + + word_ll = sampler.ComputeWordLLH(word_range_for_each_thread_[thread_id], word_range_for_each_thread_[thread_id + 1]); + atomic_stats_->word_ll_ = atomic_stats_->word_ll_ + word_ll; + process_barrier_->wait(); + + double total_ll = 0; + if (thread_id == 0) + { + double normalized_ll = sampler.NormalizeWordLLH(); + + total_ll = 0; + total_ll += atomic_stats_->doc_ll_; + total_ll += atomic_stats_->word_ll_; + total_ll += normalized_ll; + + if (!is_train) + { + likelihood_in_iter_[iter] = (float)total_ll; + } + + std::cout << "Total likelihood: " << total_ll << "\t"; + std::cout << "..........[Nomralized word ll: " << normalized_ll << "\t" + << "Word likelihood: " << atomic_stats_->word_ll_ << "\t" + << "Doc likelihood: " << atomic_stats_->doc_ll_ << "]" << std::endl; + } + process_barrier_->wait(); + + return total_ll; + } + + void LdaEngine::DumpDocTopicTable(const std::string& doc_topic_file) + { + std::ofstream dt_stream; + dt_stream.open(doc_topic_file, std::ios::out); + assert(dt_stream.good()); + + int32_t num_documents = data_block_->num_documents(); + int32_t doc_start = 0; + int32_t doc_end = num_documents; + + lda::light_hash_map doc_topic_counter_(1024); + + for (int32_t doc_index = doc_start; doc_index != doc_end; ++doc_index) + { + std::shared_ptr doc = data_block_->GetOneDoc(doc_index); + doc_topic_counter_.clear(); + doc->GetDocTopicCounter(doc_topic_counter_); + + dt_stream << doc_index; + if (doc->size()) + { + int32_t capacity = doc_topic_counter_.capacity(); + int32_t *key = doc_topic_counter_.key(); + int32_t *value = doc_topic_counter_.value(); + int32_t nonzero_num = 0; + + for (int i = 0; i < capacity; ++i) + { + if (key[i] > 0) + { + dt_stream << " " << key[i] - 1 << ":" << value[i]; + } + } + } + dt_stream << std::endl; + } + dt_stream.close(); + } + + void LdaEngine::DumpFullModel(const std::string& word_topic_dump) + { + std::ofstream wt_stream; + wt_stream.open(word_topic_dump, std::ios::out); + assert(wt_stream.good()); + + for (int w = 0; w < V_; ++w) + { + int nonzero_num = global_word_topic_table_[w].nonzero_num(); + if (nonzero_num) + { + wt_stream << w; + for (int t = 0; t < K_; ++t) + { + if (global_word_topic_table_[w][t] > 0) + { + wt_stream << " " << t << ":" << global_word_topic_table_[w][t]; + } + } + wt_stream << std::endl; + } + } + wt_stream.close(); + + std::ofstream summary_stream; + summary_stream.open("summary_row.txt", std::ios::out); + for (int i = 0; i < K_; ++i) + { + summary_stream << global_summary_row_[i] << std::endl; + } + summary_stream.close(); + } +} // namespace lda diff --git a/src/Native/LdaNative/lda_engine.hpp b/src/Native/LdaNative/lda_engine.hpp new file mode 100644 index 0000000000..95a107f355 --- /dev/null +++ b/src/Native/LdaNative/lda_engine.hpp @@ -0,0 +1,144 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +#pragma once + +#include +#include +#include +#include +#include +#include + +#include "lda_document.h" +#include "hybrid_map.h" +#include "hybrid_alias_map.h" + +#include "alias_multinomial_rng_int.hpp" + +#ifdef _MSC_VER +#define EXPORT_API(ret) extern "C" __declspec(dllexport) ret __stdcall +#else +#define EXPORT_API(ret) extern "C" __attribute__((visibility("default"))) ret +#endif + +//ignore all such warnings since our stl class will not used internally in the class as private member +#pragma warning(disable : 4251) +class CTimer; +namespace lda { + + class LDADataBlock; + class LDAModelBlock; + class SimpleBarrier; + struct LDAEngineAtomics; + class LightDocSampler; + class CBlockedIntQueue; + + // Engine takes care of the entire pipeline of LDA, from reading data to + // spawning threads, to recording execution time and loglikelihood. + class LdaEngine { + public: + LdaEngine(); + LdaEngine(int numTopic, + int numVocab, + float alphaSum, + float beta, + int numIter, + int likelihoodInterval, + int numThread, + int mhstep, + int maxDocToken); + + LdaEngine(int32_t K, int32_t V, int32_t num_threads, int32_t compute_ll_interval, float beta, int32_t num_iterations, int32_t mh_step, float alpha_sum, int maxDocToken); + + ~LdaEngine(); + + + void InitializeBeforeTest(); + bool InitializeBeforeTrain(); + void AllocateDataMemory(int num_document, int64_t corpus_size); + void AllocateModelMemory(const LDADataBlock* data_block); //in this case, model memory is allocated according to the datablock; + void AllocateModelMemory(int num_vocabs, int num_topics, int64_t nonzero_num); + void AllocateModelMemory(int num_vocabs, int num_topics, int64_t mem_block_size, int64_t alias_mem_block_size); + void SetAlphaSum(float avgDocLength); //alphasum parameter is set by avgdoclength * alpha + + //IO, data + bool ClearData(); //for clean up training data + bool ClearModel(); //for testing purpose, before calling SetWordTopic, please clear the old model + + int FeedInData(int* term_id, int* term_freq, int32_t term_num, int32_t vocab_size); + int FeedInDataDense(int* term_freq, int32_t term_num, int32_t vocab_size); + + //IO, model + // NOTE: assume pTopic and pProb are allocated outside the function + // the length returned will be capped by the pass-in initial value of length(usually it's the size of preallocated memory for pTopic&pProb + void GetWordTopic(int32_t wordId, int32_t* pTopic, int32_t* pProb, int32_t& length); + void SetWordTopic(int32_t wordId, int32_t* pTopic, int32_t* pProb, int32_t length); + void GetModelStat(int64_t &memBlockSize, int64_t &aliasMemBlockSize); + void GetTopicSummary(int32_t topicId, int32_t* pWords, float* pProb, int32_t& length); + + //mutlithread train/test with the data inside the engine + void Train(const char* pTrainOutput = nullptr); + void Test(int32_t burnin_iter, float* pLoglikelihood); + + //testing on single doc + void TestOneDoc(int* term_id, int* term_freq, int32_t term_num, int* pTopics, int* pProbs, int32_t& numTopicsMax, int32_t numBurnIter, bool reset); + void TestOneDocDense(int* term_freq, int32_t term_num, int* pTopics, int* pProbs, int32_t& numTopicsMax, int32_t numBurnIter, bool reset); + void GetDocTopic(int docID, int* pTopic, int* pProb, int32_t& numTopicReturn); // use this function to get the doc's topic output in batch testing scenario + + //output model(word topic) and doc topic + void DumpFullModel(const std::string& word_topic_dump); + void DumpDocTopicTable(const std::string& doc_topic_file); + + private: + double EvalLogLikelihood(bool is_train, int thread_id, int iter, LightDocSampler &sampler); + + private: // private data + void Training_Thread(); + void Testing_Thread(); + void CheckFunction(int thread_id, CTimer& tmDebug, const char* msg, bool waitBarrier = true); + + // Number of topics + int32_t K_; + // Number of vocabs. + int32_t V_; + + int32_t compute_ll_interval_; + int32_t num_threads_; + int32_t num_iterations_; + int32_t burnin_iterations_; + int32_t mh_step_; + float beta_; + float alpha_sum_; + float beta_sum_; + int maxDocToken_; + bool bAlphaSumMultiplied; //used to check whether alpha_sum_ is real alpha sum but not alpha + std::vector word_range_for_each_thread_; + + LDAEngineAtomics* atomic_stats_; + SimpleBarrier* process_barrier_; // Local barrier across threads. + + LDADataBlock* data_block_; + LDAModelBlock* model_block_; + + std::vector global_word_topic_table_; + std::vector global_alias_k_v_; + std::vector global_summary_row_; + + // for generating alias table of beta term + wood::AliasMultinomialRNGInt alias_rng_int_; + int32_t beta_height_; + float beta_mass_; + std::vector beta_k_v_; + + LightDocSampler **samplers_; + float* likelihood_in_iter_; + + // For TestDocSafe purpose + int32_t **document_buffer_; + + wood::xorshift_rng rng_; + CBlockedIntQueue *samplerQueue_; + }; +} // namespace lda diff --git a/src/Native/LdaNative/lda_engine_export.cpp b/src/Native/LdaNative/lda_engine_export.cpp new file mode 100644 index 0000000000..7f6bc62b70 --- /dev/null +++ b/src/Native/LdaNative/lda_engine_export.cpp @@ -0,0 +1,109 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +#include "lda_engine.hpp" + +/// This file use to expose public API to be consumed by ML.NET. +namespace lda { + + EXPORT_API(LdaEngine*) CreateEngine(int numTopic, int numVocab, float alphaSum, float beta, int numIter, int likelihoodInterval, int numThread, int mhstep, int maxDocToken) + { + return new LdaEngine(numTopic, numVocab, alphaSum, beta, numIter, likelihoodInterval, numThread, mhstep, maxDocToken); + } + + EXPORT_API(void) DestroyEngine(LdaEngine* engine) + { + delete engine; + } + + EXPORT_API(void) AllocateModelMemory(LdaEngine* engine, int numTopic, int numVocab, int64_t tableSize, int64_t aliasTableSize) + { + engine->AllocateModelMemory(numVocab, numTopic, tableSize, aliasTableSize); + } + + EXPORT_API(void) AllocateDataMemory(LdaEngine* engine, int num_document, int64_t corpus_size) + { + engine->AllocateDataMemory(num_document, corpus_size); + } + + EXPORT_API(void) Train(LdaEngine* engine, const char* trainOutput) + { + engine->Train(trainOutput); + } + + EXPORT_API(void) Test(LdaEngine* engine, int32_t burnin_iter, float* pLoglikelihood) + { + engine->Test(burnin_iter, pLoglikelihood); + } + + EXPORT_API(void) CleanData(LdaEngine* engine) + { + engine->ClearData(); + } + + EXPORT_API(void) CleanModel(LdaEngine* engine) + { + engine->ClearModel(); + } + + EXPORT_API(void) GetModelStat(LdaEngine* engine, int64_t &memBlockSize, int64_t &aliasMemBlockSize) + { + engine->GetModelStat(memBlockSize, aliasMemBlockSize); + } + + EXPORT_API(void) GetWordTopic(LdaEngine* engine, int32_t wordId, int32_t* pTopic, int32_t* pProb, int32_t& length) + { + engine->GetWordTopic(wordId, pTopic, pProb, length); + } + + EXPORT_API(void) SetWordTopic(LdaEngine* engine, int32_t wordId, int32_t* pTopic, int32_t* pProb, int32_t length) + { + engine->SetWordTopic(wordId, pTopic, pProb, length); + } + + EXPORT_API(void) GetTopicSummary(LdaEngine* engine, int32_t topicId, int32_t* pWords, float* pProb, int32_t& length) + { + engine->GetTopicSummary(topicId, pWords, pProb, length); + } + + EXPORT_API(void) SetAlphaSum(LdaEngine* engine, float avgDocLength) + { + engine->SetAlphaSum(avgDocLength); + } + + EXPORT_API(int) FeedInData(LdaEngine* engine, int* term_id, int* term_freq, int32_t term_num, int32_t vocab_size) + { + return engine->FeedInData(term_id, term_freq, term_num, vocab_size); + } + + EXPORT_API(int) FeedInDataDense(LdaEngine* engine, int* term_freq, int32_t term_num, int32_t vocab_size) + { + return engine->FeedInDataDense(term_freq, term_num, vocab_size); + } + + EXPORT_API(void) GetDocTopic(LdaEngine* engine, int docID, int* pTopic, int* pProb, int32_t& numTopicReturn) + { + engine->GetDocTopic(docID, pTopic, pProb, numTopicReturn); + } + + EXPORT_API(void) TestOneDoc(LdaEngine* engine, int* term_id, int* term_freq, int32_t term_num, int* pTopics, int* pProbs, int32_t& numTopicsMax, int32_t numBurnIter, bool reset) + { + engine->TestOneDoc(term_id, term_freq, term_num, pTopics, pProbs, numTopicsMax, numBurnIter, reset); + } + + EXPORT_API(void) TestOneDocDense(LdaEngine* engine, int* term_freq, int32_t term_num, int* pTopics, int* pProbs, int32_t& numTopicsMax, int32_t numBurnIter, bool reset) + { + engine->TestOneDocDense(term_freq, term_num, pTopics, pProbs, numTopicsMax, numBurnIter, reset); + } + + EXPORT_API(void) InitializeBeforeTrain(LdaEngine* engine) + { + engine->InitializeBeforeTrain(); + } + + EXPORT_API(void) InitializeBeforeTest(LdaEngine* engine) + { + engine->InitializeBeforeTest(); + } +} \ No newline at end of file diff --git a/src/Native/LdaNative/light_doc_sampler.cpp b/src/Native/LdaNative/light_doc_sampler.cpp new file mode 100644 index 0000000000..ea628d3891 --- /dev/null +++ b/src/Native/LdaNative/light_doc_sampler.cpp @@ -0,0 +1,667 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +#include +#include +#include +#include +#include +#include + +#include "lda_document.h" +#include "light_doc_sampler.hpp" + + +namespace lda +{ + LightDocSampler::LightDocSampler( + int32_t K, + int32_t V, + int32_t num_threads, + int32_t mh_step, + float beta, + float alpha_sum, + std::vector &word_topic_table, + std::vector &summary_row, + std::vector &alias_kv, + int32_t &beta_height, + float& beta_mass, + std::vector &beta_k_v) + : doc_topic_counter_(1024), + word_topic_table_(word_topic_table), summary_row_(summary_row), + alias_k_v_(alias_kv), + beta_height_(beta_height), + beta_mass_(beta_mass), + beta_k_v_(beta_k_v), + K_(K), + V_(V), + num_threads_(num_threads), + mh_step_for_gs_(mh_step), + beta_(beta), + alpha_sum_(alpha_sum) + { + beta_sum_ = beta_ * V_; + alpha_ = alpha_sum_ / K_; + + ll_alpha_ = (lda::real_t)0.01; + ll_alpha_sum_ = ll_alpha_ * K_; + + // Precompute LLH parameters + log_doc_normalizer_ = LogGamma(ll_alpha_ * K_) - K_ * LogGamma(ll_alpha_); + log_topic_normalizer_ = LogGamma(beta_sum_) - V_ * LogGamma(beta_); + + alias_rng_.Init(K_); + + q_w_proportion_.resize(K_); + delta_summary_row_.resize(K_); + word_topic_delta_.resize(num_threads_); + + rehashing_buf_ = new int32_t[K_ * 2]; + } + + LightDocSampler::~LightDocSampler() + { + delete[] rehashing_buf_; + } + + // Initialize word_topic_table and doc_topic_counter for each doc + int32_t LightDocSampler::GlobalInit(LDADocument *doc) + { + int32_t token_num = 0; + int32_t doc_size = doc->size(); + for (int i = 0; i < doc_size; ++i) + { + int32_t w = doc->Word(i); + int32_t t = doc->Topic(i); + + word_topic_delta wtd; + int32_t shard_id = w % num_threads_; + wtd.word = w; + wtd.topic = t; + wtd.delta = 1; + word_topic_delta_[shard_id].push_back(wtd); + + ++delta_summary_row_[t]; + + ++token_num; + } + return token_num; + } + + int32_t LightDocSampler::DocInit(LDADocument *doc) + { + int num_words = doc->size(); + + // compute the doc_topic_counter on the fly + doc_topic_counter_.clear(); + doc->GetDocTopicCounter(doc_topic_counter_); + + doc_size_ = num_words; + n_td_sum_ = (lda::real_t)num_words; + + return 0; + } + + bool CompareFirstElement(const std::pair &p1, const std::pair &p2) + { + return p1.first < p2.first; + } + + void LightDocSampler::GetDocTopic(LDADocument *doc, int* pTopics, int* pProbs, int32_t& numTopicsMax) + { + doc_topic_counter_.clear(); + doc->GetDocTopicCounter(doc_topic_counter_); + + // NOTE: do we have to assume this? + // probably first sort the topic vector according to the probs and keep the first numTopicsMax topics + // We assume the numTopicsMax is not less than the length of current document?? or it should be maxiumly the toipc number + // assert(numTopicsMax >= doc->size()); + + int32_t capacity = doc_topic_counter_.capacity(); + int32_t *key = doc_topic_counter_.key(); + int32_t *value = doc_topic_counter_.value(); + + std::vector> vec; + int32_t idx = 0; + for (int i = 0; i < capacity; ++i) + { + if (key[i] > 0) + { + std::pair pair; + pair.first = key[i] - 1; + pair.second = value[i]; + vec.push_back(pair); + idx++; + + if (idx == numTopicsMax) + break; + } + } + numTopicsMax = idx; + std::sort(vec.begin(), vec.end(), CompareFirstElement); + for (int i = 0; i < idx; i++) + { + pTopics[i] = vec[i].first; + pProbs[i] = vec[i].second; + } + } + + void LightDocSampler::EpocInit() + { + std::fill(delta_summary_row_.begin(), delta_summary_row_.end(), 0); + for (auto &shard : word_topic_delta_) + { + shard.clear(); + } + } + + void LightDocSampler::AdaptAlphaSum(bool is_train) + { + rng_.restart(); //reset the sampler so that we will get deterministic result by different runs, train-test, train-save-test, etc. + + if (is_train) + { + if (alpha_sum_ < 10) + { + alpha_sum_ = 100; + } + } + else + { + if (alpha_sum_ > 10) + { + alpha_sum_ = 1; + } + } + alpha_ = alpha_sum_ / K_; + } + + void LightDocSampler::build_alias_table(int32_t lower, int32_t upper, int thread_id) + { + for (int w = lower; w < upper; ++w) + { + GenerateAliasTableforWord(w); + } + } + void LightDocSampler::build_word_topic_table(int32_t thread_id, int32_t num_threads, lda::LDAModelBlock &model_block) + { + for (int i = 0; i < V_; ++i) + { + if (i % num_threads == thread_id) + { + word_topic_table_[i] = model_block.get_row(i, rehashing_buf_); + } + } + } + + int32_t LightDocSampler::SampleOneDoc(LDADocument *doc) + { + return OldProposalFreshSample(doc); + } + + int32_t LightDocSampler::InferOneDoc(LDADocument *doc) + { + return OldProposalFreshSampleInfer(doc); + } + int32_t LightDocSampler::Sample2WordFirst(LDADocument *doc, int32_t w, int32_t s, int32_t old_topic) + { + int32_t w_t_cnt; + int32_t w_s_cnt; + + real_t n_td_alpha; + real_t n_sd_alpha; + real_t n_tw_beta; + real_t n_sw_beta; + real_t n_s_beta_sum; + real_t n_t_beta_sum; + + real_t proposal_s; + real_t proposal_t; + + real_t nominator; + real_t denominator; + + real_t rejection; + real_t pi; + int m; + + for (int i = 0; i < mh_step_for_gs_; ++i) + { + int32_t t; + + t = alias_k_v_[w].next(rng_, beta_height_, beta_mass_, beta_k_v_, false); + + rejection = rng_.rand_real(); + + n_td_alpha = doc_topic_counter_[t] + alpha_; + n_sd_alpha = doc_topic_counter_[s] + alpha_; + + + w_s_cnt = get_word_topic(w, s); + w_t_cnt = get_word_topic(w, t); + + if (s != old_topic && t != old_topic) + { + n_tw_beta = w_t_cnt + beta_; + n_t_beta_sum = summary_row_[t] + beta_sum_; + + n_sw_beta = w_s_cnt + beta_; + n_s_beta_sum = summary_row_[s] + beta_sum_; + } + else if (s != old_topic && t == old_topic) + { + n_td_alpha -= 1; + + n_tw_beta = w_t_cnt + beta_ - 1; + n_t_beta_sum = summary_row_[t] + beta_sum_ - 1; + + n_sw_beta = w_s_cnt + beta_; + n_s_beta_sum = summary_row_[s] + beta_sum_; + } + else if (s == old_topic && t != old_topic) + { + n_sd_alpha -= 1; + + n_tw_beta = w_t_cnt + beta_; + n_t_beta_sum = summary_row_[t] + beta_sum_; + + n_sw_beta = w_s_cnt + beta_ - 1; + n_s_beta_sum = summary_row_[s] + beta_sum_ - 1; + } + else + { + n_td_alpha -= 1; + n_sd_alpha -= 1; + + n_tw_beta = w_t_cnt + beta_ - 1; + n_t_beta_sum = summary_row_[t] + beta_sum_ - 1; + + n_sw_beta = w_s_cnt + beta_ - 1; + n_s_beta_sum = summary_row_[s] + beta_sum_ - 1; + } + + proposal_s = (w_s_cnt + beta_) / (summary_row_[s] + beta_sum_); + proposal_t = (w_t_cnt + beta_) / (summary_row_[t] + beta_sum_); + + nominator = n_td_alpha + * n_tw_beta + * n_s_beta_sum + * proposal_s; + + denominator = n_sd_alpha + * n_sw_beta + * n_t_beta_sum + * proposal_t; + + + pi = std::min((real_t)1.0, nominator / denominator); + + // s = rejection < pi ? t : s; + m = -(rejection < pi); + s = (t & m) | (s & ~m); + + real_t n_td_or_alpha = rng_.rand_real() * (n_td_sum_ + alpha_sum_); + if (n_td_or_alpha < n_td_sum_) + { + int32_t t_idx = rng_.rand_k(doc_size_); + t = doc->Topic(t_idx); + } + else + { + t = rng_.rand_k(K_); + } + + rejection = rng_.rand_real(); + + n_td_alpha = doc_topic_counter_[t] + alpha_; + n_sd_alpha = doc_topic_counter_[s] + alpha_; + + + if (s != old_topic && t != old_topic) + { + w_t_cnt = get_word_topic(w, t); + n_tw_beta = w_t_cnt + beta_; + n_t_beta_sum = summary_row_[t] + beta_sum_; + + w_s_cnt = get_word_topic(w, s); + n_sw_beta = w_s_cnt + beta_; + n_s_beta_sum = summary_row_[s] + beta_sum_; + } + else if (s != old_topic && t == old_topic) + { + n_td_alpha -= 1; + + w_t_cnt = get_word_topic(w, t) - 1; + n_tw_beta = w_t_cnt + beta_; + n_t_beta_sum = summary_row_[t] + beta_sum_ - 1; + + w_s_cnt = get_word_topic(w, s); + n_sw_beta = w_s_cnt + beta_; + n_s_beta_sum = summary_row_[s] + beta_sum_; + } + else if (s == old_topic && t != old_topic) + { + n_sd_alpha -= 1; + + w_t_cnt = get_word_topic(w, t); + n_tw_beta = w_t_cnt + beta_; + n_t_beta_sum = summary_row_[t] + beta_sum_; + + w_s_cnt = get_word_topic(w, s) - 1; + n_sw_beta = w_s_cnt + beta_; + n_s_beta_sum = summary_row_[s] + beta_sum_ - 1; + } + else + { + n_td_alpha -= 1; + n_sd_alpha -= 1; + + w_t_cnt = get_word_topic(w, t) - 1; + n_tw_beta = w_t_cnt + beta_; + n_t_beta_sum = summary_row_[t] + beta_sum_ - 1; + + w_s_cnt = get_word_topic(w, s) - 1; + n_sw_beta = w_s_cnt + beta_; + n_s_beta_sum = summary_row_[s] + beta_sum_ - 1; + } + + proposal_t = doc_topic_counter_[t] + alpha_; + proposal_s = doc_topic_counter_[s] + alpha_; + + nominator = n_td_alpha + * n_tw_beta + * n_s_beta_sum + * proposal_s; + + denominator = n_sd_alpha + * n_sw_beta + * n_t_beta_sum + * proposal_t; + + + pi = std::min((real_t)1.0, nominator / denominator); + + // s = rejection < pi ? t : s; + m = -(rejection < pi); + s = (t & m) | (s & ~m); + } + int32_t src = s; + return src; + } + + int32_t LightDocSampler::Sample2WordFirstInfer(LDADocument *doc, int32_t w, int32_t s, int32_t old_topic) + { + int32_t w_t_cnt; + int32_t w_s_cnt; + + float n_td_alpha; + float n_sd_alpha; + float n_tw_beta; + float n_sw_beta; + float n_s_beta_sum; + float n_t_beta_sum; + + float nominator; + float denominator; + + float rejection; + float pi; + int m; + + for (int i = 0; i < mh_step_for_gs_; ++i) + { + int32_t t; + t = alias_k_v_[w].next(rng_, beta_height_, beta_mass_, beta_k_v_, false); + + rejection = rng_.rand_real(); + + n_td_alpha = doc_topic_counter_[t] + alpha_; + n_sd_alpha = doc_topic_counter_[s] + alpha_; + + nominator = n_td_alpha; + denominator = n_sd_alpha; + + pi = std::min((float)1.0, nominator / denominator); + + m = -(rejection < pi); + s = (t & m) | (s & ~m); + + float n_td_or_alpha = rng_.rand_real() * (n_td_sum_ + alpha_sum_); + if (n_td_or_alpha < n_td_sum_) + { + int32_t t_idx = rng_.rand_k(doc_size_); + t = doc->Topic(t_idx); + } + else + { + t = rng_.rand_k(K_); + } + + rejection = rng_.rand_real(); + + + w_t_cnt = get_word_topic(w, t); + n_tw_beta = w_t_cnt + beta_; + n_t_beta_sum = summary_row_[t] + beta_sum_; + + w_s_cnt = get_word_topic(w, s); + n_sw_beta = w_s_cnt + beta_; + n_s_beta_sum = summary_row_[s] + beta_sum_; + + nominator = n_tw_beta + * n_s_beta_sum; + + + denominator = n_sw_beta + * n_t_beta_sum; + + pi = std::min((float)1.0, nominator / denominator); + + m = -(rejection < pi); + s = (t & m) | (s & ~m); + } + int32_t src = s; + return src; + } + + int32_t LightDocSampler::OldProposalFreshSample(LDADocument *doc) + { + DocInit(doc); + int num_token = doc->size(); + int32_t &cursor = doc->get_cursor(); + + int32_t token_sweeped = 0; + cursor = 0; + + while (cursor < num_token) + { + ++token_sweeped; + + int32_t w = doc->Word(cursor); + int32_t s = doc->Topic(cursor); // old topic + + int t = Sample2WordFirst(doc, w, s, s); // new topic + + if (s != t) + { + word_topic_delta wtd; + int32_t shard_id = w % num_threads_; + wtd.word = w; + wtd.topic = s; + wtd.delta = -1; + word_topic_delta_[shard_id].push_back(wtd); + + wtd.topic = t; + wtd.delta = +1; + word_topic_delta_[shard_id].push_back(wtd); + + --delta_summary_row_[s]; + ++delta_summary_row_[t]; + + doc->SetTopic(cursor, t); + doc_topic_counter_.inc(s, -1); + doc_topic_counter_.inc(t, 1); + } + cursor++; + } + return token_sweeped; + } + + int32_t LightDocSampler::OldProposalFreshSampleInfer(LDADocument *doc) + { + + DocInit(doc); + int num_token = doc->size(); + int32_t &cursor = doc->get_cursor(); + + int32_t token_sweeped = 0; + cursor = 0; + + while (cursor < num_token) + { + ++token_sweeped; + + int32_t w = doc->Word(cursor); + int32_t s = doc->Topic(cursor); // old topic + + int t = Sample2WordFirstInfer(doc, w, s, s); // new topic + + if (s != t) + { + doc->SetTopic(cursor, t); + doc_topic_counter_.inc(s, -1); + doc_topic_counter_.inc(t, 1); + } + cursor++; + } + return token_sweeped; + } + + double LightDocSampler::NormalizeWordLLH() + { + double word_llh = K_ * log_topic_normalizer_; + for (int k = 0; k < K_; ++k) + { + word_llh -= LogGamma(summary_row_[k] + beta_sum_); + } + return word_llh; + } + + + double LightDocSampler::ComputeOneDocLLH(LDADocument* doc) + { + double doc_ll = 0; + double one_doc_llh = log_doc_normalizer_; + + // Compute doc-topic vector on the fly. + int num_tokens = doc->size(); + + if (num_tokens == 0) + { + return doc_ll; + } + + doc_topic_counter_.clear(); + doc->GetDocTopicCounter(doc_topic_counter_); + + int32_t capacity = doc_topic_counter_.capacity(); + int32_t *key = doc_topic_counter_.key(); + int32_t *value = doc_topic_counter_.value(); + int32_t nonzero_num = 0; + + for (int i = 0; i < capacity; ++i) + { + if (key[i] > 0) + { + one_doc_llh += LogGamma(value[i] + ll_alpha_); + ++nonzero_num; + } + } + one_doc_llh += (K_ - nonzero_num) * LogGamma(ll_alpha_); + one_doc_llh -= LogGamma(num_tokens + ll_alpha_ * K_); + + doc_ll += one_doc_llh; + return doc_ll; + } + + double LightDocSampler::ComputeWordLLH(int32_t lower, int32_t upper) + { + // word_llh is P(w|z). + double word_llh = 0; + double zero_entry_llh = LogGamma(beta_); + + // Since some vocabs are not present in the corpus, use num_words_seen to + // count # of words in corpus. + int num_words_seen = 0; + for (int w = lower; w < upper; ++w) + { + auto word_topic_row = get_word_row(w); + int32_t total_count = 0; + double delta = 0; + if (word_topic_row.is_dense()) + { + int32_t* memory = word_topic_row.memory(); + int32_t capacity = word_topic_row.capacity(); + int32_t count; + for (int i = 0; i < capacity; ++i) + { + count = memory[i]; + total_count += count; + delta += LogGamma(count + beta_); + } + } + else + { + int32_t* key = word_topic_row.key(); + int32_t* value = word_topic_row.value(); + int32_t capacity = word_topic_row.capacity(); + int32_t count; + int32_t nonzero_num = 0; + for (int i = 0; i < capacity; ++i) + { + if (key[i] > 0) + { + count = value[i]; + total_count += count; + delta += LogGamma(count + beta_); + ++nonzero_num; + } + } + delta += (K_ - nonzero_num) * zero_entry_llh; + } + + if (total_count) + { + word_llh += delta; + } + } + + return word_llh; + } + + void LightDocSampler::Dump(const std::string &dump_name, int32_t lower, int32_t upper) + { + std::ofstream wt_stream; + wt_stream.open(dump_name, std::ios::out); + + for (int w = lower; w < upper; ++w) + { + //why not just a serialization of current hybrid_map? do we need to do a search? + int nonzero_num = word_topic_table_[w].nonzero_num(); + if (nonzero_num) + { + wt_stream << w; + for (int t = 0; t < K_; ++t) + { + if (word_topic_table_[w][t] > 0) + { + wt_stream << " " << t << ":" << word_topic_table_[w][t]; + } + } + wt_stream << std::endl; + } + } + wt_stream.close(); + } +} \ No newline at end of file diff --git a/src/Native/LdaNative/light_doc_sampler.hpp b/src/Native/LdaNative/light_doc_sampler.hpp new file mode 100644 index 0000000000..82e37b3bc5 --- /dev/null +++ b/src/Native/LdaNative/light_doc_sampler.hpp @@ -0,0 +1,187 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +#pragma once + +#include "type_common.h" +#include "lda_document.h" +#include "rand_int_rng.h" +#include +#include +#include +#include +#include +#include +#include "alias_multinomial_rng_int.hpp" +#include "light_hash_map.h" +#include "utils.hpp" +#include "hybrid_map.h" +#include "hybrid_alias_map.h" +#include "model_block.h" + +namespace lda +{ + struct word_topic_delta + { + int32_t word; + int32_t topic; + int32_t delta; + }; + + class LightDocSampler + { + public: + LightDocSampler( + int32_t K, + int32_t V, + int32_t num_threads, + int32_t mh_step, + float beta, + float alpha_sum, + std::vector &word_topic_table, + std::vector &summary_row, + std::vector &alias_kv, + int32_t &beta_height, + float &beta_mass, + std::vector &beta_k_v + ); + + ~LightDocSampler(); + + int32_t GlobalInit(LDADocument *doc); + int32_t DocInit(LDADocument *doc); + void EpocInit(); + void AdaptAlphaSum(bool is_train); + void GetDocTopic(LDADocument *doc, int* pTopics, int* pProbs, int32_t& numTopicsMax); + + + int32_t SampleOneDoc(LDADocument *doc); + int32_t InferOneDoc(LDADocument *doc); + + // The i-th complete-llh calculation will use row i in llh_able_. This is + // part of log P(z) in eq.[3]. + double ComputeOneDocLLH(LDADocument* doc); + double ComputeWordLLH(int32_t lower, int32_t upper); + double NormalizeWordLLH(); + + inline void rng_restart() + { + rng_.restart(); + } + + + void Dump(const std::string &dump_name, int32_t lower, int32_t upper); + + void build_alias_table(int32_t lower, int32_t upper, int thread_id); + void build_word_topic_table(int32_t thread_id, int32_t num_threads, lda::LDAModelBlock &model_block); + + inline int32_t rand_k(); + inline wood::xorshift_rng& rng(); + inline lda::hybrid_map& get_word_row(int32_t word); + inline std::vector &get_summary_row(); + inline std::vector& get_word_topic_delta(int32_t thread_id); + inline std::vector& get_delta_summary_row(); + + private: + int32_t Sample2WordFirst(LDADocument *doc, int32_t w, int32_t s, int32_t old_topic); + int32_t Sample2WordFirstInfer(LDADocument *doc, int32_t w, int32_t s, int32_t old_topic); + inline void GenerateAliasTableforWord(int32_t word); + inline int32_t get_word_topic(int32_t word, int32_t topic); + inline void word_topic_dec(int32_t word, int32_t topic); + inline void word_topic_inc(int32_t word, int32_t topic); + int32_t OldProposalFreshSample(LDADocument *doc); + int32_t OldProposalFreshSampleInfer(LDADocument *doc); + + private: + int32_t num_tokens_; + int32_t num_unique_words_; + + int32_t K_; + int32_t V_; + real_t beta_; + real_t beta_sum_; + real_t alpha_; + real_t alpha_sum_; + + real_t ll_alpha_; + real_t ll_alpha_sum_; + + real_t delta_alpha_sum_; + + std::vector q_w_proportion_; + wood::AliasMultinomialRNGInt alias_rng_; + wood::xorshift_rng rng_; + std::vector &alias_k_v_; + + int32_t doc_size_; + + // the number of Metropolis Hastings step + int32_t mh_step_for_gs_; + real_t n_td_sum_; + + // model + std::vector &summary_row_; + std::vector &word_topic_table_; + int32_t *rehashing_buf_; + + int32_t &beta_height_; + float &beta_mass_; + std::vector &beta_k_v_; + + // delta + std::vector delta_summary_row_; + + int32_t num_threads_; + std::vector> word_topic_delta_; + + // ================ Precompute LLH Parameters ================= + // Log of normalization constant (per docoument) from eq.[3]. + double log_doc_normalizer_; + + // Log of normalization constant (per topic) from eq.[2]. + double log_topic_normalizer_; + lda::light_hash_map doc_topic_counter_; + }; + + inline int32_t LightDocSampler::rand_k() + { + return rng_.rand_k(K_); + } + inline wood::xorshift_rng& LightDocSampler::rng() + { + return rng_; + } + inline lda::hybrid_map& LightDocSampler::get_word_row(int32_t word) + { + return word_topic_table_[word]; + } + inline std::vector& LightDocSampler::get_summary_row() + { + return summary_row_; + } + inline std::vector& LightDocSampler::get_word_topic_delta(int32_t thread_id) + { + return word_topic_delta_[thread_id]; + } + inline std::vector& LightDocSampler::get_delta_summary_row() + { + return delta_summary_row_; + } + inline int32_t LightDocSampler::get_word_topic(int32_t word, int32_t topic) + { + return word_topic_table_[word][topic]; + } + inline void LightDocSampler::word_topic_dec(int32_t word, int32_t topic) + { + word_topic_table_[word].inc(topic, -1); + } + inline void LightDocSampler::word_topic_inc(int32_t word, int32_t topic) + { + word_topic_table_[word].inc(topic, 1); + } + inline void LightDocSampler::GenerateAliasTableforWord(int32_t word) + { + alias_k_v_[word].build_table(alias_rng_, word_topic_table_[word], summary_row_, q_w_proportion_, beta_, beta_sum_, word, rng_); + } +} \ No newline at end of file diff --git a/src/Native/LdaNative/light_hash_map.cpp b/src/Native/LdaNative/light_hash_map.cpp new file mode 100644 index 0000000000..ae070c5e1c --- /dev/null +++ b/src/Native/LdaNative/light_hash_map.cpp @@ -0,0 +1,76 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +#include +#include "light_hash_map.h" + +namespace lda +{ + light_hash_map::light_hash_map(int32_t *mem_block, int32_t capacity) : + own_memory_(false), + capacity_(capacity), + mem_block_(mem_block), + empty_key_(0), + deleted_key_(-2) + { + key_ = mem_block_; + value_ = mem_block_ + capacity_; + clear(); + } + + light_hash_map::light_hash_map(int32_t capacity) : + own_memory_(true), + capacity_(capacity), + empty_key_(0), + deleted_key_(-2) + { + mem_block_ = new int32_t[capacity_ * 2]; + key_ = mem_block_; + value_ = mem_block_ + capacity_; + clear(); + } + + // must call set_memory after construction before use + light_hash_map::light_hash_map() : + capacity_(1024), + own_memory_(false), + empty_key_(0), + deleted_key_(-2), + mem_block_(nullptr), + key_(nullptr), + value_(nullptr) + { + } + + light_hash_map::~light_hash_map() + { + capacity_ = 0; + if (own_memory_ && mem_block_ != nullptr) + { + delete[]mem_block_; + } + + mem_block_ = nullptr; + key_ = nullptr; + value_ = nullptr; + } + + void light_hash_map::clear() + { + memset(mem_block_, 0, capacity_ * 2 * sizeof(int32_t)); + } + + void light_hash_map::sort() + { + //key is probablly empty in key_, sort by value_ + //this is just for the output process like getting the topic of document or a topic of term + } + + void light_hash_map::set_memory(int32_t *mem_block) + { + mem_block_ = mem_block; + key_ = mem_block_; + value_ = mem_block_ + capacity_; + } +} \ No newline at end of file diff --git a/src/Native/LdaNative/light_hash_map.h b/src/Native/LdaNative/light_hash_map.h new file mode 100644 index 0000000000..6e07c4ce58 --- /dev/null +++ b/src/Native/LdaNative/light_hash_map.h @@ -0,0 +1,189 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +#pragma once +#include +#include +#include +#include + +/* +A light-weight hash table, borrowing the idea from google::dense_hash_map +0, pair must be +1, It can or can not own memory, +2, It has a fixed capacity, needless to resize or shrink, +3, capacity_ should at lease be twice of the maximum number of inserted items, guaranteeing a low load factor, +4, capacity_ should be an integer power of 2 +5, emptry_key_ is fixed to 0 +6, deleted_key_ is fixed to -2 +*/ + +namespace lda +{ +// The probing method: +// Linear probing +// #define JUMP_(key, num_probes) ( 1 ) + +// Quadratic probing +#define JUMP_(key, num_probes) ( num_probes ) + +#define ILLEGAL_BUCKET -1 + + class light_hash_map + { + public: + + // must call set_memory after construction before use + light_hash_map(); + // NOTE: the size of mem_block_ = 2 * capacity_ + light_hash_map(int32_t *mem_block, int32_t capacity); + light_hash_map(int32_t capacity); + + ~light_hash_map(); + + void clear(); + void set_memory(int32_t *mem_block); + void sort(); + + inline int32_t capacity() const; + inline int32_t size() const; + inline int32_t* key() const; + inline int32_t* value() const; + // whether we can find the |key| in this hash table + inline bool has(int32_t key) const; + + // if |key| is already in table, increase its coresponding |value| with |delta| + // if not, insert |key| into the table and set |delta| as the |value| of |key| + inline void inc(int32_t key, int32_t delta); + + // query the value of |key| + // if |key| is in the table, return the |value| corresonding to |key| + // if not, just return 0 + inline int32_t operator[](int32_t key); + + private: + + light_hash_map(const light_hash_map &other) = delete; + light_hash_map& operator=(const light_hash_map &other) = delete; + + // Returns a pair of positions: 1st where the object is, 2nd where + // it would go if you wanted to insert it. 1st is ILLEGAL_BUCKET + // if object is not found; 2nd is ILLEGAL_BUCKET if it is. + // NOTE: because of deletions where-to-insert is not trivial: it's the + // first deleted bucket we see, as long as we don't find the key later + inline std::pair find_position(const int32_t key) const; + + bool own_memory_; + int32_t capacity_; + int32_t *mem_block_; + int32_t *key_; + int32_t *value_; + + int32_t empty_key_; + int32_t deleted_key_; + }; + + inline int32_t light_hash_map::capacity() const + { + return capacity_; + } + inline int32_t light_hash_map::size() const + { + int32_t size = 0; + for (int i = 0; i < capacity_; ++i) + { + if (key_[i] > 0) + { + ++size; + } + } + return size; + } + + inline int32_t* light_hash_map::key() const + { + return key_; + } + inline int32_t* light_hash_map::value() const + { + return value_; + } + + inline bool light_hash_map::has(int32_t key) const + { + int32_t internal_key = key + 1; + std::pair pos = find_position(internal_key); + return pos.first != ILLEGAL_BUCKET; + } + + inline void light_hash_map::inc(int32_t key, int32_t delta) + { + int32_t internal_key = key + 1; + std::pair pos = find_position(internal_key); + if (pos.first != ILLEGAL_BUCKET) + { + value_[pos.first] += delta; + if (value_[pos.first] == 0) // the value becomes zero, delete the key + { + key_[pos.first] = deleted_key_; + } + } + else // not found the key, insert it with delta as value + { + key_[pos.second] = internal_key; + value_[pos.second] = delta; + } + } + + inline int32_t light_hash_map::operator[](int32_t key) + { + int32_t internal_key = key + 1; + std::pair pos = find_position(internal_key); + if (pos.first != ILLEGAL_BUCKET) + { + return value_[pos.first]; + } + else + { + return 0; + } + } + + inline std::pair light_hash_map::find_position(const int32_t key) const + { + int num_probes = 0; + int32_t capacity_minus_one = capacity_ - 1; + int32_t idx = key % capacity_; + int32_t insert_pos = ILLEGAL_BUCKET; + while (1) // probe until something happens + { + if (key_[idx] == empty_key_) // bucket is empty + { + if (insert_pos == ILLEGAL_BUCKET) // found no prior place to insert + { + return std::pair(ILLEGAL_BUCKET, idx); + } + else // previously, there is a position to insert + { + return std::pair(ILLEGAL_BUCKET, insert_pos); + } + } + else if (key_[idx] == deleted_key_) // keep searching, but makr to insert + { + if (insert_pos == ILLEGAL_BUCKET) + { + insert_pos = idx; + } + } + else if (key_[idx] == key) + { + return std::pair(idx, ILLEGAL_BUCKET); + } + ++num_probes; // we are doing another probe + idx = (idx + JUMP_(key, num_probes) & capacity_minus_one); + assert(num_probes < capacity_ + && "Hashtable is full: an error in key_equal<> or hash<>"); + } + } +} \ No newline at end of file diff --git a/src/Native/LdaNative/model_block.cpp b/src/Native/LdaNative/model_block.cpp new file mode 100644 index 0000000000..ec15834aca --- /dev/null +++ b/src/Native/LdaNative/model_block.cpp @@ -0,0 +1,463 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + + +#include +#include +#include +#include "utils.hpp" +#include +#include "model_block.h" +#include "lda_document.h" + +namespace lda +{ + int64_t upper_bound(int64_t x) + { + if (x == 0) + { + return 0; + } + int64_t shift = 0; + int64_t y = 1; + x--; + while (x) + { + x = x >> 1; + y = y << 1; + ++shift; + } + return y; + } + + int32_t align64(int32_t size) + { + if (size % 64 == 0) + { + return size; + } + else + { + size = 64 * (size / 64) + 64; + return size; + } + } + + + LDAModelBlock::LDAModelBlock() + : dict_(nullptr), + num_vocabs_(0), + mem_block_size_(0), + mem_block_(nullptr), + alias_mem_block_size_(0), + alias_mem_block_(nullptr) + { + } + LDAModelBlock::~LDAModelBlock() + { + Clear(); + } + + void LDAModelBlock::Clear() + { + if (dict_) + { + delete[]dict_; + dict_ = nullptr; + } + if (mem_block_) + { + delete[]mem_block_; + mem_block_ = nullptr; + } + if (alias_mem_block_) + { + delete[]alias_mem_block_; + alias_mem_block_ = nullptr; + } + + num_vocabs_ = -1; + num_topics_ = -1; + + mem_block_size_ = 0; + alias_mem_block_size_ = 0; + } + + void LDAModelBlock::Init(int32_t num_vocabs, int32_t num_topics, int64_t nonzero_num) + { + num_vocabs_ = num_vocabs; + num_topics_ = num_topics; + + dict_ = new WordEntry[num_vocabs_]; + for (int i = 0; i < num_vocabs_; ++i) + { + // This warning is a false positive. Supressing it similar to the existing one on Line 140 below. +#pragma warning(suppress: 6386) + dict_[i].is_dense_ = 0; + dict_[i].is_alias_dense_ = 0; + } + + mem_block_size_ = 2 * upper_bound(load_factor_ * nonzero_num); + alias_mem_block_size_ = nonzero_num * 3; + + mem_block_ = new int32_t[mem_block_size_](); // NOTE: force to initialize the values to be zero + alias_mem_block_ = new int32_t[alias_mem_block_size_](); // NOTE: force to initialize the values to be zero + } + + void LDAModelBlock::Init(int32_t num_vocabs, int32_t num_topics, int64_t mem_block_size, int64_t alias_mem_block_size) + { + num_vocabs_ = num_vocabs; + num_topics_ = num_topics; + + dict_ = new WordEntry[num_vocabs_]; + for (int i = 0; i < num_vocabs_; ++i) + { + // This warning is a false positive. Supressing it similar to the existing one on Line 140 below. +#pragma warning(suppress: 6386) + dict_[i].is_dense_ = 0; + dict_[i].is_alias_dense_ = 0; + } + + mem_block_size_ = mem_block_size; + mem_block_ = new int32_t[mem_block_size_](); // NOTE : force to initialize the values to be zero + + alias_mem_block_size_ = alias_mem_block_size; + alias_mem_block_ = new int32_t[alias_mem_block_size_](); //NOTE: force to initialize the values to be zero + + std::cout << "mem_block_size = " << mem_block_size_ * 4 << std::endl; + std::cout << "alias_mem_block_size = " << alias_mem_block_size_ * 4 << std::endl; + + offset_ = 0; + alias_offset_ = 0; + } + + void LDAModelBlock::Init(int32_t num_vocabs, int32_t num_topics) + { + num_vocabs_ = num_vocabs; + num_topics_ = num_topics; + + dict_ = new WordEntry[num_vocabs_]; + for (int i = 0; i < num_vocabs_; ++i) + { + // This warning is a false positive caused by an old bug in PREfast. It is fixed in VS 2015. +#pragma warning(suppress: 6386) + dict_[i].tf = 0; + dict_[i].is_dense_ = 0; + dict_[i].is_alias_dense_ = 0; + } + } + + void LDAModelBlock::SetWordInfo(int word_id, int32_t nonzero_num, bool fullSparse) + { + dict_[word_id].word_id_ = word_id; + dict_[word_id].tf = nonzero_num; + + int32_t hot_thresh; + if (fullSparse) + { + // use a very large threshold to ensure every row of word-topic-table using a sparse representation + hot_thresh = std::numeric_limits::max(); + } + else + { + hot_thresh = num_topics_ / (2 * load_factor_); //hybrid + } + int32_t alias_hot_thresh; + if (fullSparse) + { + // use a very large threshold to ensure every row of alias table using a sparse representation + alias_hot_thresh = std::numeric_limits::max(); + } + else + { + alias_hot_thresh = (num_topics_ * 2) / 3; + } + + int32_t capacity = 0; + int32_t row_size = 0; + int32_t alias_capacity = 0; + int32_t alias_row_size = 0; + + if (dict_[word_id].tf >= hot_thresh) + { + dict_[word_id].is_dense_ = 1; + capacity = num_topics_; + row_size = capacity; + } + else if (dict_[word_id].tf > 0) + { + dict_[word_id].is_dense_ = 0; + int capacity_lower_bound = load_factor_ * dict_[word_id].tf; + capacity = (int32_t)upper_bound(capacity_lower_bound); + row_size = capacity * 2; + } + else + { + dict_[word_id].is_dense_ = 1; + row_size = 0; + capacity = 0; + } + + dict_[word_id].offset_ = offset_; + dict_[word_id].end_offset_ = offset_ + row_size; + dict_[word_id].capacity_ = capacity; + + offset_ += row_size; + + if (dict_[word_id].tf >= alias_hot_thresh) + { + alias_capacity = num_topics_; + alias_row_size = 2 * num_topics_; + dict_[word_id].is_alias_dense_ = 1; + } + else if (dict_[word_id].tf > 0) + { + alias_capacity = dict_[word_id].tf; + alias_row_size = 3 * dict_[word_id].tf; + dict_[word_id].is_alias_dense_ = 0; + } + else + { + alias_capacity = 0; + alias_row_size = 0; + dict_[word_id].is_alias_dense_ = 1; + } + dict_[word_id].alias_capacity_ = alias_capacity; + dict_[word_id].alias_offset_ = alias_offset_; + dict_[word_id].alias_end_offset_ = alias_offset_ + alias_row_size; + + alias_offset_ += alias_row_size; + } + + // NOTE: sometimes, we use totally sparse representation (in testing phase), fullSparse == true + // in other times, we use hybrid structure (in training phase), fullSparse == false + void LDAModelBlock::InitModelBlockByTFS(bool fullSparse) + { + const int32_t max_tf_thresh = std::numeric_limits::max(); + int32_t hot_thresh; + if (fullSparse) + { + // totally sparse + // use a very large threshold to ensure every row of word-topic-table using a sparse representation + hot_thresh = std::numeric_limits::max(); + } + else + { + // hybrid + hot_thresh = num_topics_ / (2 * load_factor_); + } + int32_t alias_hot_thresh; + if (fullSparse) + { + // use a very large threshold to ensure every row of alias table using a sparse representation + alias_hot_thresh = std::numeric_limits::max(); + } + else + { + alias_hot_thresh = (num_topics_ * 2) / 3; + } + + int32_t word_id; + int32_t capacity = 0; + int32_t row_size = 0; + int32_t alias_capacity = 0; + int32_t alias_row_size = 0; + + int64_t offset = 0; + int64_t alias_offset = 0; + + for (word_id = 0; word_id < num_vocabs_; ++word_id) + { + int32_t tf = dict_[word_id].tf; + + dict_[word_id].word_id_ = word_id; + dict_[word_id].tf = tf; + + if (tf >= hot_thresh) + { + dict_[word_id].is_dense_ = 1; + capacity = num_topics_; + row_size = capacity; + } + else if (tf > 0) + { + dict_[word_id].is_dense_ = 0; + int capacity_lower_bound = load_factor_ * tf; + capacity = (int32_t)upper_bound(capacity_lower_bound); + row_size = capacity * 2; + } + else + { + dict_[word_id].is_dense_ = 1; + capacity = 0; + row_size = 0; + } + + dict_[word_id].offset_ = offset; + dict_[word_id].end_offset_ = offset + row_size; + dict_[word_id].capacity_ = capacity; + + offset += row_size; + + if (tf >= alias_hot_thresh) + { + alias_capacity = num_topics_; + alias_row_size = 2 * num_topics_; + dict_[word_id].is_alias_dense_ = 1; + } + else if (tf > 0) + { + alias_capacity = tf; + alias_row_size = 3 * tf; + dict_[word_id].is_alias_dense_ = 0; + } + else + { + alias_capacity = 0; + alias_row_size = 0; + dict_[word_id].is_alias_dense_ = 1; + } + dict_[word_id].alias_capacity_ = alias_capacity; + dict_[word_id].alias_offset_ = alias_offset; + dict_[word_id].alias_end_offset_ = alias_offset + alias_row_size; + alias_offset += alias_row_size; + } + + mem_block_size_ = dict_[num_vocabs_ - 1].end_offset_; + mem_block_ = new int32_t[mem_block_size_](); // NOTE: force to initialize the values to be zero + + alias_mem_block_size_ = dict_[num_vocabs_ - 1].alias_end_offset_; + alias_mem_block_ = new int32_t[alias_mem_block_size_](); //NOTE: force to initialize the values to be zero + + std::cout << "mem_block_size = " << mem_block_size_ * 4 << std::endl; + std::cout << "alias_mem_block_size = " << alias_mem_block_size_ * 4 << std::endl; + } + + void LDAModelBlock::InitFromDataBlock(const LDADataBlock *data_block, int32_t num_vocabs, int32_t num_topics) + { + num_vocabs_ = num_vocabs; + num_topics_ = num_topics; + + int32_t doc_num = data_block->num_documents(); + dict_ = new WordEntry[num_vocabs_]; + for (int i = 0; i < num_vocabs_; ++i) + { + dict_[i].tf = 0; + } + + for (int i = 0; i < doc_num; ++i) + { + std::shared_ptr doc = data_block->GetOneDoc(i); + int32_t doc_size = doc->size(); + for (int j = 0; j < doc_size; ++j) + { + int32_t w = doc->Word(j); + dict_[w].tf++; + } + } + + InitModelBlockByTFS(false); + } + // Count the number of nonzero values in each row + void LDAModelBlock::CountNonZero(std::vector &tfs) + { + for (int i = 0; i < num_vocabs_; ++i) + { + hybrid_map row(mem_block_ + dict_[i].offset_, + dict_[i].is_dense_, + dict_[i].capacity_, + 0, + nullptr); + tfs[i] = row.nonzero_num(); + } + } + + void LDAModelBlock::GetModelSizeByTFS(bool fullSparse, std::vector &tfs, int64_t &mem_block_size, int64_t &alias_mem_block_size) + { + const int32_t max_tf_thresh = std::numeric_limits::max(); + int32_t hot_thresh; + if (fullSparse) + { + // totally sparse + // use a very large threshold to ensure every row of word-topic-table using a sparse representation + hot_thresh = std::numeric_limits::max(); + } + else + { + // hybrid + hot_thresh = num_topics_ / (2 * load_factor_); + } + // hot_thresh = 0; // totally dense + int32_t alias_hot_thresh; + if (fullSparse) + { + // use a very large threshold to ensure every row of alias table using a sparse representation + alias_hot_thresh = std::numeric_limits::max(); + } + else + { + alias_hot_thresh = (num_topics_ * 2) / 3; + } + + int32_t word_id; + int32_t capacity = 0; + int32_t alias_capacity = 0; + int32_t row_size = 0; + int32_t alias_row_size = 0; + + mem_block_size = 0; + alias_mem_block_size = 0; + + for (word_id = 0; word_id < num_vocabs_; ++word_id) + { + int32_t tf = tfs[word_id]; + + if (tf >= hot_thresh) + { + capacity = num_topics_; + row_size = capacity; + } + else if (tf > 0) + { + int capacity_lower_bound = load_factor_ * tf; + capacity = (int32_t)upper_bound(capacity_lower_bound); + row_size = capacity * 2; + } + else + { + capacity = 0; + row_size = 0; + } + mem_block_size += row_size; + + if (tf >= alias_hot_thresh) + { + alias_capacity = num_topics_; + alias_row_size = 2 * num_topics_; + } + else if (tf > 0) + { + alias_capacity = tf; + alias_row_size = 3 * tf; + } + else + { + alias_capacity = 0; + alias_row_size = 0; + } + alias_mem_block_size += alias_row_size; + } + } + + // NOTE: we can re-use the dict_ variable here, but we deliberately not use it. + // This function should not change the internal state of model_block_ + void LDAModelBlock::GetModelStat(int64_t &mem_block_size, int64_t &alias_mem_block_size) + { + std::vector tfs(num_vocabs_, 0); + CountNonZero(tfs); + + // calculate the mem_block_size, alias_mem_block_size + GetModelSizeByTFS(true, tfs, mem_block_size, alias_mem_block_size); + } +} diff --git a/src/Native/LdaNative/model_block.h b/src/Native/LdaNative/model_block.h new file mode 100644 index 0000000000..2160be1d7f --- /dev/null +++ b/src/Native/LdaNative/model_block.h @@ -0,0 +1,93 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +#pragma once +#include +#include +#include +#include +#include +#include +#include "data_block.h" +#include "hybrid_map.h" +#include "hybrid_alias_map.h" + +namespace lda +{ + struct WordEntry + { + int32_t word_id_; + int64_t offset_; + int64_t end_offset_; + int32_t capacity_; + int32_t is_dense_; + + int32_t tf; + int64_t alias_offset_; + int64_t alias_end_offset_; + int32_t alias_capacity_; + int32_t is_alias_dense_; + }; + + class LDAModelBlock + { + public: + LDAModelBlock(); + ~LDAModelBlock(); + + inline hybrid_map get_row(int word_id, int32_t *external_buf); + inline hybrid_alias_map get_alias_row(int word_id); + void SetWordInfo(int word_id, int32_t nonzero_num, bool fullSparse); + + void Clear(); + void Init(int32_t num_vocabs, int32_t num_topics); + void Init(int32_t num_vocabs, int32_t num_topics, int64_t nonzero_num); + void Init(int32_t num_vocabs, int32_t num_topics, int64_t mem_block_size, int64_t alias_mem_block_size); + + void InitFromDataBlock(const LDADataBlock *data_block, int32_t num_vocabs, int32_t num_topics); + + void GetModelStat(int64_t &mem_block_size, int64_t &alias_mem_block_size); + + private: + + LDAModelBlock(const LDAModelBlock &other) = delete; + LDAModelBlock& operator=(const LDAModelBlock &other) = delete; + + void CountNonZero(std::vector &tfs); + void InitModelBlockByTFS(bool fullSparse); + void GetModelSizeByTFS(bool fullSparse, std::vector &tfs, int64_t &mem_block_size, int64_t &alias_mem_block_size); + + int32_t num_vocabs_; + int32_t num_topics_; + WordEntry *dict_; + int32_t *mem_block_; + int64_t mem_block_size_; + + int32_t *alias_mem_block_; + int64_t alias_mem_block_size_; + + int64_t offset_; + int64_t alias_offset_; + + const int32_t load_factor_ = 2; + const int32_t sparse_factor_ = 5; + }; + inline hybrid_map LDAModelBlock::get_row(int word_id, int32_t *external_buf) + { + hybrid_map row(mem_block_ + dict_[word_id].offset_, + dict_[word_id].is_dense_, + dict_[word_id].capacity_, + 0, + external_buf); + return row; + } + inline hybrid_alias_map LDAModelBlock::get_alias_row(int word_id) + { + hybrid_alias_map row(alias_mem_block_ + dict_[word_id].alias_offset_, + dict_[word_id].is_alias_dense_, + dict_[word_id].alias_capacity_); + return row; + } + +} \ No newline at end of file diff --git a/src/Native/LdaNative/rand_int_rng.h b/src/Native/LdaNative/rand_int_rng.h new file mode 100644 index 0000000000..c51943e11f --- /dev/null +++ b/src/Native/LdaNative/rand_int_rng.h @@ -0,0 +1,45 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +#pragma once +#include +#include + +namespace wood +{ + class xorshift_rng + { + public: + xorshift_rng() + { + jxr = 1234567; + } + ~xorshift_rng() {} + + inline void restart() + { + jxr = 1234567; + } + + inline int32_t rand() + { + jxr ^= (jxr << 13); jxr ^= (jxr >> 17); jxr ^= (jxr << 5); //get random (xorshift) 32-bit integer + return jxr & 0x7fffffff; + } + inline int32_t rand_k(int K) + { + return (int32_t)(rand() * 4.6566125e-10 * K); + } + inline float rand_real() + { + return (float)(rand() * 4.6566125e-10); + } + private: + + xorshift_rng(const xorshift_rng &other) = delete; + xorshift_rng& operator=(const xorshift_rng &other) = delete; + + unsigned int jxr; + }; +} \ No newline at end of file diff --git a/src/Native/LdaNative/simple_barrier.h b/src/Native/LdaNative/simple_barrier.h new file mode 100644 index 0000000000..55f8d601b9 --- /dev/null +++ b/src/Native/LdaNative/simple_barrier.h @@ -0,0 +1,66 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +#ifndef _SIMPLE_BARRIER_H_ +#define _SIMPLE_BARRIER_H_ + +#include +#include +#include +namespace lda +{ + class SimpleBarrier + { + public: + SimpleBarrier(unsigned int n) :barrier_size_(n), num_of_waiting_(0), rounds_(0) + {}; + + void reset() + { + throw "not implemented yet."; + } + + bool wait() + { + std::unique_lock lock(mutex_); + if (num_of_waiting_.fetch_add(1) >= barrier_size_ - 1) + { + cond_.notify_all(); + num_of_waiting_.store(0); + rounds_.fetch_add(1); + return true; + } + else + { + + unsigned int i = rounds_.load(); + cond_.wait(lock, [&]{return i != rounds_.load(); }); + return false; + } + } + + ~SimpleBarrier() + { + num_of_waiting_ = 0; + rounds_ = 0; + } + + + + protected: + const unsigned int barrier_size_; + + std::atomic num_of_waiting_; + std::atomic rounds_; + std::condition_variable cond_; + std::mutex mutex_; + }; +} + + + + + +#endif // _SIMPLE_BARRIER_H_ + diff --git a/src/Native/LdaNative/timer.h b/src/Native/LdaNative/timer.h new file mode 100644 index 0000000000..ac9aff94b2 --- /dev/null +++ b/src/Native/LdaNative/timer.h @@ -0,0 +1,95 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +#pragma once +#include +using namespace std::chrono; +class CTimer +{ + +private: + + steady_clock::time_point startPerfomanceCount; + steady_clock::time_point endPerfomanceCount; + duration totalElapsed; +public: + char m_szMessage[1024]; + +public: + CTimer() + { + Initialize(); + } + + CTimer(bool bStartOnCreate) + { + Initialize(); + + if (bStartOnCreate) + { + Start(); + } + } + + void Initialize() + { + totalElapsed = duration(); + } + + void Start() + { + startPerfomanceCount = std::chrono::steady_clock::now(); + } + + // time unit: seconds + void Tag(const char* pszMsg = NULL) + { + endPerfomanceCount = std::chrono::steady_clock::now(); + totalElapsed += duration_cast> (endPerfomanceCount - startPerfomanceCount); + OutputStatistics(pszMsg); + //start next round + Start(); + } + + // time unit: seconds + void InnerTag() + { + endPerfomanceCount = std::chrono::steady_clock::now(); + totalElapsed += duration_cast> (endPerfomanceCount - startPerfomanceCount); + + OutputStatistics(m_szMessage); + + //start next round + Start(); + } + + float GetTotalElaps() + { + return totalElapsed.count(); + } + float GetTimeSpan() + { + endPerfomanceCount = std::chrono::steady_clock::now(); + totalElapsed += duration_cast> (endPerfomanceCount - startPerfomanceCount); + float timespent = totalElapsed.count(); + + //start next round + Start(); + + return timespent; + } + + float GetTaggedTimeSpan() + { + return duration_cast> (endPerfomanceCount - startPerfomanceCount).count(); + } + + void OutputStatistics(const char* pszMsg = NULL) + { + printf("Time Cost totally: %f, last time span(%s): %f seconds.\n", GetTotalElaps(), pszMsg, GetTaggedTimeSpan()); + } + +private: + CTimer(const CTimer& obj); +}; \ No newline at end of file diff --git a/src/Native/LdaNative/type_common.h b/src/Native/LdaNative/type_common.h new file mode 100644 index 0000000000..a7043b28b1 --- /dev/null +++ b/src/Native/LdaNative/type_common.h @@ -0,0 +1,9 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +#pragma once + +namespace lda { + typedef float real_t; +} \ No newline at end of file diff --git a/src/Native/LdaNative/utils.cpp b/src/Native/LdaNative/utils.cpp new file mode 100644 index 0000000000..c1c5cee076 --- /dev/null +++ b/src/Native/LdaNative/utils.cpp @@ -0,0 +1,62 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +#include "utils.hpp" + +#include "math.h" +#include + +namespace { + const double cof[6] = { 76.18009172947146, -86.50532032941677, + 24.01409824083091, -1.231739572450155, + 0.1208650973866179e-2, -0.5395239384953e-5 + }; +} + +namespace lda { + + double LogGamma(double xx) + { + int j; + double x, y, tmp1, ser; + y = xx; + x = xx; + tmp1 = x + 5.5; + tmp1 -= (x + 0.5)*log(tmp1); + ser = 1.000000000190015; + for (j = 0; j < 6; j++) ser += cof[j] / ++y; + return -tmp1 + log(2.5066282746310005*ser / x); + } + + + double get_time() { + auto start = std::chrono::high_resolution_clock::now(); + auto since_epoch = start.time_since_epoch(); + return std::chrono::duration_cast>>(since_epoch).count(); + } + + void CBlockedIntQueue::clear() + { + std::lock_guard lock(_mutex); + _queue.clear(); + } + + int CBlockedIntQueue::pop() + { + std::unique_lock lock(_mutex); + _condition.wait(lock, [this] { return !_queue.empty(); }); + auto val = _queue.front(); + _queue.pop_front(); + return val; + } + + void CBlockedIntQueue::push(int value) + { + { + std::lock_guard lock(_mutex); + _queue.push_back(value); + } + _condition.notify_one(); + } +} diff --git a/src/Native/LdaNative/utils.hpp b/src/Native/LdaNative/utils.hpp new file mode 100644 index 0000000000..7b71ec67da --- /dev/null +++ b/src/Native/LdaNative/utils.hpp @@ -0,0 +1,49 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +#pragma once +#define NOMINMAX + +#include +#include +#include +#include +#include + + +namespace lda { + + double LogGamma(double xx); + double get_time(); + + struct LDAEngineAtomics + { + LDAEngineAtomics() :doc_ll_(0), word_ll_(0), num_tokens_clock_(0), thread_counter_(0){} + ~LDAEngineAtomics() {} + + std::atomic doc_ll_; + std::atomic word_ll_; + + // # of tokens processed in a Clock() call. + std::atomic num_tokens_clock_; + std::atomic thread_counter_; + + std::mutex global_mutex_; + }; + + class CBlockedIntQueue + { + public: + void clear(); + int pop(); + void push(int value); + + private: + std::mutex _mutex; + std::condition_variable _condition; + std::deque _queue; + }; + + +} diff --git a/src/Native/build.proj b/src/Native/build.proj index 5074517fda..c7f4f2386e 100644 --- a/src/Native/build.proj +++ b/src/Native/build.proj @@ -71,9 +71,12 @@ - + + diff --git a/test/BaselineOutput/Common/EntryPoints/core_ep-list.tsv b/test/BaselineOutput/Common/EntryPoints/core_ep-list.tsv index 61f2604a8d..ce4f2b09fa 100644 --- a/test/BaselineOutput/Common/EntryPoints/core_ep-list.tsv +++ b/test/BaselineOutput/Common/EntryPoints/core_ep-list.tsv @@ -5,36 +5,48 @@ Data.PredictorModelArrayConverter Create an array variable of IPredictorModel Mi Data.TextLoader Import a dataset from a text file Microsoft.ML.Runtime.EntryPoints.ImportTextData TextLoader Microsoft.ML.Runtime.EntryPoints.ImportTextData+LoaderInput Microsoft.ML.Runtime.EntryPoints.ImportTextData+Output Data.TransformModelArrayConverter Create an array variable of ITransformModel Microsoft.ML.Runtime.EntryPoints.CrossValidationBinaryMacro MakeArray Microsoft.ML.Runtime.EntryPoints.CrossValidationBinaryMacro+ArrayITransformModelInput Microsoft.ML.Runtime.EntryPoints.CrossValidationBinaryMacro+ArrayITransformModelOutput Models.AnomalyDetectionEvaluator Evaluates an anomaly detection scored dataset. Microsoft.ML.Runtime.Data.Evaluate AnomalyDetection Microsoft.ML.Runtime.Data.AnomalyDetectionMamlEvaluator+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+CommonEvaluateOutput +Models.AnomalyPipelineEnsemble Combine anomaly detection models into an ensemble Microsoft.ML.Runtime.EntryPoints.EnsembleCreator CreateAnomalyPipelineEnsemble Microsoft.ML.Runtime.EntryPoints.EnsembleCreator+PipelineAnomalyInput Microsoft.ML.Runtime.EntryPoints.CommonOutputs+AnomalyDetectionOutput Models.BinaryClassificationEvaluator Evaluates a binary classification scored dataset. Microsoft.ML.Runtime.Data.Evaluate Binary Microsoft.ML.Runtime.Data.BinaryClassifierMamlEvaluator+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+ClassificationEvaluateOutput Models.BinaryCrossValidator Cross validation for binary classification Microsoft.ML.Runtime.EntryPoints.CrossValidationBinaryMacro CrossValidateBinary Microsoft.ML.Runtime.EntryPoints.CrossValidationBinaryMacro+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+MacroOutput`1[Microsoft.ML.Runtime.EntryPoints.CrossValidationBinaryMacro+Output] +Models.BinaryEnsemble Combine binary classifiers into an ensemble Microsoft.ML.Runtime.EntryPoints.EnsembleCreator CreateBinaryEnsemble Microsoft.ML.Runtime.EntryPoints.EnsembleCreator+ClassifierInput Microsoft.ML.Runtime.EntryPoints.CommonOutputs+BinaryClassificationOutput +Models.BinaryPipelineEnsemble Combine binary classification models into an ensemble Microsoft.ML.Runtime.EntryPoints.EnsembleCreator CreateBinaryPipelineEnsemble Microsoft.ML.Runtime.EntryPoints.EnsembleCreator+PipelineClassifierInput Microsoft.ML.Runtime.EntryPoints.CommonOutputs+BinaryClassificationOutput Models.ClassificationEvaluator Evaluates a multi class classification scored dataset. Microsoft.ML.Runtime.Data.Evaluate MultiClass Microsoft.ML.Runtime.Data.MultiClassMamlEvaluator+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+ClassificationEvaluateOutput Models.ClusterEvaluator Evaluates a clustering scored dataset. Microsoft.ML.Runtime.Data.Evaluate Clustering Microsoft.ML.Runtime.Data.ClusteringMamlEvaluator+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+CommonEvaluateOutput Models.CrossValidationResultsCombiner Combine the metric data views returned from cross validation. Microsoft.ML.Runtime.EntryPoints.CrossValidationMacro CombineMetrics Microsoft.ML.Runtime.EntryPoints.CrossValidationMacro+CombineMetricsInput Microsoft.ML.Runtime.EntryPoints.CrossValidationMacro+CombinedOutput Models.CrossValidator Cross validation for general learning Microsoft.ML.Runtime.EntryPoints.CrossValidationMacro CrossValidate Microsoft.ML.Runtime.EntryPoints.CrossValidationMacro+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+MacroOutput`1[Microsoft.ML.Runtime.EntryPoints.CrossValidationMacro+Output] Models.CrossValidatorDatasetSplitter Split the dataset into the specified number of cross-validation folds (train and test sets) Microsoft.ML.Runtime.EntryPoints.CVSplit Split Microsoft.ML.Runtime.EntryPoints.CVSplit+Input Microsoft.ML.Runtime.EntryPoints.CVSplit+Output Models.DatasetTransformer Applies a TransformModel to a dataset. Microsoft.ML.Runtime.EntryPoints.ModelOperations Apply Microsoft.ML.Runtime.EntryPoints.ModelOperations+ApplyTransformModelInput Microsoft.ML.Runtime.EntryPoints.ModelOperations+ApplyTransformModelOutput +Models.EnsembleSummary Summarize a pipeline ensemble predictor. Microsoft.ML.Runtime.Ensemble.EntryPoints.PipelineEnsemble Summarize Microsoft.ML.Runtime.EntryPoints.SummarizePredictor+Input Microsoft.ML.Runtime.Ensemble.EntryPoints.PipelineEnsemble+SummaryOutput Models.FixedPlattCalibrator Apply a Platt calibrator with a fixed slope and offset to an input model Microsoft.ML.Runtime.Internal.Calibration.Calibrate FixedPlatt Microsoft.ML.Runtime.Internal.Calibration.Calibrate+FixedPlattInput Microsoft.ML.Runtime.EntryPoints.CommonOutputs+CalibratorOutput +Models.MultiClassPipelineEnsemble Combine multiclass classifiers into an ensemble Microsoft.ML.Runtime.EntryPoints.EnsembleCreator CreateMultiClassPipelineEnsemble Microsoft.ML.Runtime.EntryPoints.EnsembleCreator+PipelineClassifierInput Microsoft.ML.Runtime.EntryPoints.CommonOutputs+MulticlassClassificationOutput Models.MultiOutputRegressionEvaluator Evaluates a multi output regression scored dataset. Microsoft.ML.Runtime.Data.Evaluate MultiOutputRegression Microsoft.ML.Runtime.Data.MultiOutputRegressionMamlEvaluator+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+CommonEvaluateOutput Models.NaiveCalibrator Apply a Naive calibrator to an input model Microsoft.ML.Runtime.Internal.Calibration.Calibrate Naive Microsoft.ML.Runtime.Internal.Calibration.Calibrate+NoArgumentsInput Microsoft.ML.Runtime.EntryPoints.CommonOutputs+CalibratorOutput Models.OneVersusAll One-vs-All macro (OVA) Microsoft.ML.Runtime.EntryPoints.OneVersusAllMacro OVA Microsoft.ML.Runtime.EntryPoints.OneVersusAllMacro+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+MacroOutput`1[Microsoft.ML.Runtime.EntryPoints.OneVersusAllMacro+Output] +Models.OnnxConverter Converts the model to ONNX format. Microsoft.ML.Runtime.Model.Onnx.SaveOnnxCommand Apply Microsoft.ML.Runtime.Model.Onnx.SaveOnnxCommand+Arguments Microsoft.ML.Runtime.Model.Onnx.SaveOnnxCommand+Output Models.OvaModelCombiner Combines a sequence of PredictorModels into a single model Microsoft.ML.Runtime.Learners.OvaPredictor CombineOvaModels Microsoft.ML.Runtime.EntryPoints.ModelOperations+CombineOvaPredictorModelsInput Microsoft.ML.Runtime.EntryPoints.ModelOperations+PredictorModelOutput Models.PAVCalibrator Apply a PAV calibrator to an input model Microsoft.ML.Runtime.Internal.Calibration.Calibrate Pav Microsoft.ML.Runtime.Internal.Calibration.Calibrate+NoArgumentsInput Microsoft.ML.Runtime.EntryPoints.CommonOutputs+CalibratorOutput Models.PipelineSweeper AutoML pipeline sweeping optimzation macro. Microsoft.ML.Runtime.EntryPoints.PipelineSweeperMacro PipelineSweep Microsoft.ML.Runtime.EntryPoints.PipelineSweeperMacro+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+MacroOutput`1[Microsoft.ML.Runtime.EntryPoints.PipelineSweeperMacro+Output] Models.PlattCalibrator Apply a Platt calibrator to an input model Microsoft.ML.Runtime.Internal.Calibration.Calibrate Platt Microsoft.ML.Runtime.Internal.Calibration.Calibrate+NoArgumentsInput Microsoft.ML.Runtime.EntryPoints.CommonOutputs+CalibratorOutput Models.QuantileRegressionEvaluator Evaluates a quantile regression scored dataset. Microsoft.ML.Runtime.Data.Evaluate QuantileRegression Microsoft.ML.Runtime.Data.QuantileRegressionMamlEvaluator+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+CommonEvaluateOutput Models.RankerEvaluator Evaluates a ranking scored dataset. Microsoft.ML.Runtime.Data.Evaluate Ranking Microsoft.ML.Runtime.Data.RankerMamlEvaluator+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+CommonEvaluateOutput +Models.RegressionEnsemble Combine regression models into an ensemble Microsoft.ML.Runtime.EntryPoints.EnsembleCreator CreateRegressionEnsemble Microsoft.ML.Runtime.EntryPoints.EnsembleCreator+RegressionInput Microsoft.ML.Runtime.EntryPoints.CommonOutputs+RegressionOutput Models.RegressionEvaluator Evaluates a regression scored dataset. Microsoft.ML.Runtime.Data.Evaluate Regression Microsoft.ML.Runtime.Data.RegressionMamlEvaluator+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+CommonEvaluateOutput +Models.RegressionPipelineEnsemble Combine regression models into an ensemble Microsoft.ML.Runtime.EntryPoints.EnsembleCreator CreateRegressionPipelineEnsemble Microsoft.ML.Runtime.EntryPoints.EnsembleCreator+PipelineRegressionInput Microsoft.ML.Runtime.EntryPoints.CommonOutputs+RegressionOutput Models.Summarizer Summarize a linear regression predictor. Microsoft.ML.Runtime.EntryPoints.SummarizePredictor Summarize Microsoft.ML.Runtime.EntryPoints.SummarizePredictor+Input Microsoft.ML.Runtime.EntryPoints.CommonOutputs+SummaryOutput Models.SweepResultExtractor Extracts the sweep result. Microsoft.ML.Runtime.EntryPoints.PipelineSweeperMacro ExtractSweepResult Microsoft.ML.Runtime.EntryPoints.PipelineSweeperMacro+ResultInput Microsoft.ML.Runtime.EntryPoints.PipelineSweeperMacro+Output Models.TrainTestBinaryEvaluator Train test for binary classification Microsoft.ML.Runtime.EntryPoints.TrainTestBinaryMacro TrainTestBinary Microsoft.ML.Runtime.EntryPoints.TrainTestBinaryMacro+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+MacroOutput`1[Microsoft.ML.Runtime.EntryPoints.TrainTestBinaryMacro+Output] Models.TrainTestEvaluator General train test for any supported evaluator Microsoft.ML.Runtime.EntryPoints.TrainTestMacro TrainTest Microsoft.ML.Runtime.EntryPoints.TrainTestMacro+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+MacroOutput`1[Microsoft.ML.Runtime.EntryPoints.TrainTestMacro+Output] Trainers.AveragedPerceptronBinaryClassifier Train a Average perceptron. Microsoft.ML.Runtime.Learners.AveragedPerceptronTrainer TrainBinary Microsoft.ML.Runtime.Learners.AveragedPerceptronTrainer+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+BinaryClassificationOutput +Trainers.EnsembleBinaryClassifier Train binary ensemble. Microsoft.ML.Ensemble.EntryPoints.Ensemble CreateBinaryEnsemble Microsoft.ML.Runtime.Ensemble.EnsembleTrainer+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+BinaryClassificationOutput +Trainers.EnsembleClassification Train multiclass ensemble. Microsoft.ML.Ensemble.EntryPoints.Ensemble CreateMultiClassEnsemble Microsoft.ML.Runtime.Ensemble.MulticlassDataPartitionEnsembleTrainer+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+MulticlassClassificationOutput +Trainers.EnsembleRegression Train regression ensemble. Microsoft.ML.Ensemble.EntryPoints.Ensemble CreateRegressionEnsemble Microsoft.ML.Runtime.Ensemble.RegressionEnsembleTrainer+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+RegressionOutput Trainers.FastForestBinaryClassifier Uses a random forest learner to perform binary classification. Microsoft.ML.Runtime.FastTree.FastForest TrainBinary Microsoft.ML.Runtime.FastTree.FastForestClassification+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+BinaryClassificationOutput Trainers.FastForestRegressor Trains a random forest to fit target values using least-squares. Microsoft.ML.Runtime.FastTree.FastForest TrainRegression Microsoft.ML.Runtime.FastTree.FastForestRegression+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+RegressionOutput Trainers.FastTreeBinaryClassifier Uses a logit-boost boosted tree learner to perform binary classification. Microsoft.ML.Runtime.FastTree.FastTree TrainBinary Microsoft.ML.Runtime.FastTree.FastTreeBinaryClassificationTrainer+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+BinaryClassificationOutput Trainers.FastTreeRanker Trains gradient boosted decision trees to the LambdaRank quasi-gradient. Microsoft.ML.Runtime.FastTree.FastTree TrainRanking Microsoft.ML.Runtime.FastTree.FastTreeRankingTrainer+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+RankingOutput Trainers.FastTreeRegressor Trains gradient boosted decision trees to fit target values using least-squares. Microsoft.ML.Runtime.FastTree.FastTree TrainRegression Microsoft.ML.Runtime.FastTree.FastTreeRegressionTrainer+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+RegressionOutput Trainers.FastTreeTweedieRegressor Trains gradient boosted decision trees to fit target values using a Tweedie loss function. This learner is a generalization of Poisson, compound Poisson, and gamma regression. Microsoft.ML.Runtime.FastTree.FastTree TrainTweedieRegression Microsoft.ML.Runtime.FastTree.FastTreeTweedieTrainer+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+RegressionOutput +Trainers.FieldAwareFactorizationMachineBinaryClassifier Train a field-aware factorization machine for binary classification Microsoft.ML.Runtime.FactorizationMachine.FieldAwareFactorizationMachineTrainer TrainBinary Microsoft.ML.Runtime.FactorizationMachine.FieldAwareFactorizationMachineTrainer+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+BinaryClassificationOutput Trainers.GeneralizedAdditiveModelBinaryClassifier Trains a gradient boosted stump per feature, on all features simultaneously, to fit target values using least-squares. It mantains no interactions between features. Microsoft.ML.Runtime.FastTree.Gam TrainBinary Microsoft.ML.Runtime.FastTree.BinaryClassificationGamTrainer+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+BinaryClassificationOutput Trainers.GeneralizedAdditiveModelRegressor Trains a gradient boosted stump per feature, on all features simultaneously, to fit target values using least-squares. It mantains no interactions between features. Microsoft.ML.Runtime.FastTree.Gam TrainRegression Microsoft.ML.Runtime.FastTree.RegressionGamTrainer+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+RegressionOutput Trainers.KMeansPlusPlusClusterer K-means is a popular clustering algorithm. With K-means, the data is clustered into a specified number of clusters in order to minimize the within-cluster sum of squares. K-means++ improves upon K-means by using a better method for choosing the initial cluster centers. Microsoft.ML.Runtime.KMeans.KMeansPlusPlusTrainer TrainKMeans Microsoft.ML.Runtime.KMeans.KMeansPlusPlusTrainer+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+ClusteringOutput @@ -43,7 +55,6 @@ Trainers.LogisticRegressionBinaryClassifier Train a logistic regression binary m Trainers.LogisticRegressionClassifier Train a logistic regression multi class model Microsoft.ML.Runtime.Learners.LogisticRegression TrainMultiClass Microsoft.ML.Runtime.Learners.MulticlassLogisticRegression+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+MulticlassClassificationOutput Trainers.NaiveBayesClassifier Train a MultiClassNaiveBayesTrainer. Microsoft.ML.Runtime.Learners.MultiClassNaiveBayesTrainer TrainMultiClassNaiveBayesTrainer Microsoft.ML.Runtime.Learners.MultiClassNaiveBayesTrainer+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+MulticlassClassificationOutput Trainers.OnlineGradientDescentRegressor Train a Online gradient descent perceptron. Microsoft.ML.Runtime.Learners.OnlineGradientDescentTrainer TrainRegression Microsoft.ML.Runtime.Learners.OnlineGradientDescentTrainer+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+RegressionOutput -Trainers.OrdinaryLeastSquaresRegressor Train an OLS regression model. Microsoft.ML.Runtime.Learners.OlsLinearRegressionTrainer TrainRegression Microsoft.ML.Runtime.Learners.OlsLinearRegressionTrainer+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+RegressionOutput Trainers.PcaAnomalyDetector Train an PCA Anomaly model. Microsoft.ML.Runtime.PCA.RandomizedPcaTrainer TrainPcaAnomaly Microsoft.ML.Runtime.PCA.RandomizedPcaTrainer+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+AnomalyDetectionOutput Trainers.PoissonRegressor Train an Poisson regression model. Microsoft.ML.Runtime.Learners.PoissonRegression TrainRegression Microsoft.ML.Runtime.Learners.PoissonRegression+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+RegressionOutput Trainers.StochasticDualCoordinateAscentBinaryClassifier Train an SDCA binary model. Microsoft.ML.Runtime.Learners.Sdca TrainBinary Microsoft.ML.Runtime.Learners.LinearClassificationTrainer+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+BinaryClassificationOutput @@ -76,6 +87,7 @@ Transforms.KeyToTextConverter KeyToValueTransform utilizes KeyValues metadata to Transforms.LabelColumnKeyBooleanConverter Transforms the label to either key or bool (if needed) to make it suitable for classification. Microsoft.ML.Runtime.EntryPoints.FeatureCombiner PrepareClassificationLabel Microsoft.ML.Runtime.EntryPoints.FeatureCombiner+ClassificationLabelInput Microsoft.ML.Runtime.EntryPoints.CommonOutputs+TransformOutput Transforms.LabelIndicator Label remapper used by OVA Microsoft.ML.Runtime.Data.LabelIndicatorTransform LabelIndicator Microsoft.ML.Runtime.Data.LabelIndicatorTransform+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+TransformOutput Transforms.LabelToFloatConverter Transforms the label to float to make it suitable for regression. Microsoft.ML.Runtime.EntryPoints.FeatureCombiner PrepareRegressionLabel Microsoft.ML.Runtime.EntryPoints.FeatureCombiner+RegressionLabelInput Microsoft.ML.Runtime.EntryPoints.CommonOutputs+TransformOutput +Transforms.LightLda The LDA transform implements LightLDA, a state-of-the-art implementation of Latent Dirichlet Allocation. Microsoft.ML.Runtime.Transforms.TextAnalytics LightLda Microsoft.ML.Runtime.TextAnalytics.LdaTransform+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+TransformOutput Transforms.LogMeanVarianceNormalizer Normalizes the data based on the computed mean and variance of the logarithm of the data. Microsoft.ML.Runtime.Data.Normalize LogMeanVar Microsoft.ML.Runtime.Data.NormalizeTransform+LogMeanVarArguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+TransformOutput Transforms.LpNormalizer Normalize vectors (rows) individually by rescaling them to unit norm (L2, L1 or LInf). Performs the following operation on a vector X: Y = (X - M) / D, where M is mean and D is either L2 norm, L1 norm or LInf norm. Microsoft.ML.Runtime.Data.LpNormalization Normalize Microsoft.ML.Runtime.Data.LpNormNormalizerTransform+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+TransformOutput Transforms.ManyHeterogeneousModelCombiner Combines a sequence of TransformModels and a PredictorModel into a single PredictorModel. Microsoft.ML.Runtime.EntryPoints.ModelOperations CombineModels Microsoft.ML.Runtime.EntryPoints.ModelOperations+PredictorModelInput Microsoft.ML.Runtime.EntryPoints.ModelOperations+PredictorModelOutput diff --git a/test/BaselineOutput/Common/EntryPoints/core_manifest.json b/test/BaselineOutput/Common/EntryPoints/core_manifest.json index 7e06c77821..0acb5971b0 100644 --- a/test/BaselineOutput/Common/EntryPoints/core_manifest.json +++ b/test/BaselineOutput/Common/EntryPoints/core_manifest.json @@ -654,6 +654,57 @@ "IEvaluatorOutput" ] }, + { + "Name": "Models.AnomalyPipelineEnsemble", + "Desc": "Combine anomaly detection models into an ensemble", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "Models", + "Type": { + "Kind": "Array", + "ItemType": "PredictorModel" + }, + "Desc": "The models to combine into an ensemble", + "Aliases": [ + "models" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "ModelCombiner", + "Type": { + "Kind": "Enum", + "Values": [ + "Median", + "Average" + ] + }, + "Desc": "The combiner used to combine the scores", + "Aliases": [ + "combiner" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Average" + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "OutputKind": [ + "IAnomalyDetectionOutput", + "ITrainerOutput" + ] + }, { "Name": "Models.BinaryClassificationEvaluator", "Desc": "Evaluates a binary classification scored dataset.", @@ -967,6 +1018,122 @@ } ] }, + { + "Name": "Models.BinaryEnsemble", + "Desc": "Combine binary classifiers into an ensemble", + "FriendlyName": "Parallel Ensemble (bagging, stacking, etc)", + "ShortName": null, + "Inputs": [ + { + "Name": "Models", + "Type": { + "Kind": "Array", + "ItemType": "PredictorModel" + }, + "Desc": "The models to combine into an ensemble", + "Aliases": [ + "models" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "ModelCombiner", + "Type": { + "Kind": "Enum", + "Values": [ + "Median", + "Average", + "Vote" + ] + }, + "Desc": "The combiner used to combine the scores", + "Aliases": [ + "combiner" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Median" + }, + { + "Name": "ValidatePipelines", + "Type": "Bool", + "Desc": "Whether to validate that all the pipelines are identical", + "Aliases": [ + "validate" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": true + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "OutputKind": [ + "IBinaryClassificationOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Models.BinaryPipelineEnsemble", + "Desc": "Combine binary classification models into an ensemble", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "Models", + "Type": { + "Kind": "Array", + "ItemType": "PredictorModel" + }, + "Desc": "The models to combine into an ensemble", + "Aliases": [ + "models" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "ModelCombiner", + "Type": { + "Kind": "Enum", + "Values": [ + "Median", + "Average", + "Vote" + ] + }, + "Desc": "The combiner used to combine the scores", + "Aliases": [ + "combiner" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Median" + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "OutputKind": [ + "IBinaryClassificationOutput", + "ITrainerOutput" + ] + }, { "Name": "Models.ClassificationEvaluator", "Desc": "Evaluates a multi class classification scored dataset.", @@ -1333,6 +1500,18 @@ "IsNullable": false, "Default": "Label" }, + { + "Name": "WeightColumn", + "Type": "String", + "Desc": "Column to use for example weight", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": "Weight" + }, { "Name": "Kind", "Type": { @@ -1352,6 +1531,18 @@ "SortOrder": 6.0, "IsNullable": false, "Default": "SignatureBinaryClassifierTrainer" + }, + { + "Name": "GroupColumn", + "Type": "String", + "Desc": "Column to use for grouping", + "Aliases": [ + "group" + ], + "Required": false, + "SortOrder": 12.0, + "IsNullable": false, + "Default": "GroupId" } ], "Outputs": [ @@ -1504,6 +1695,42 @@ "SortOrder": 8.0, "IsNullable": false, "Default": "SignatureBinaryClassifierTrainer" + }, + { + "Name": "LabelColumn", + "Type": "String", + "Desc": "Column to use for labels", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 10.0, + "IsNullable": false, + "Default": "Label" + }, + { + "Name": "WeightColumn", + "Type": "String", + "Desc": "Column to use for example weight", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 11.0, + "IsNullable": false, + "Default": "Weight" + }, + { + "Name": "GroupColumn", + "Type": "String", + "Desc": "Column to use for grouping", + "Aliases": [ + "group" + ], + "Required": false, + "SortOrder": 12.0, + "IsNullable": false, + "Default": "GroupId" } ], "Outputs": [ @@ -1634,6 +1861,43 @@ "ITransformInput" ] }, + { + "Name": "Models.EnsembleSummary", + "Desc": "Summarize a pipeline ensemble predictor.", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The predictor to summarize", + "Aliases": [ + "predictorModel" + ], + "Required": true, + "SortOrder": 150.0, + "IsNullable": false + } + ], + "Outputs": [ + { + "Name": "Summaries", + "Type": { + "Kind": "Array", + "ItemType": "DataView" + }, + "Desc": "The summaries of the individual predictors" + }, + { + "Name": "Stats", + "Type": { + "Kind": "Array", + "ItemType": "DataView" + }, + "Desc": "The model statistics of the individual predictors" + } + ] + }, { "Name": "Models.FixedPlattCalibrator", "Desc": "Apply a Platt calibrator with a fixed slope and offset to an input model", @@ -1717,51 +1981,103 @@ ] }, { - "Name": "Models.MultiOutputRegressionEvaluator", - "Desc": "Evaluates a multi output regression scored dataset.", + "Name": "Models.MultiClassPipelineEnsemble", + "Desc": "Combine multiclass classifiers into an ensemble", "FriendlyName": null, "ShortName": null, "Inputs": [ { - "Name": "Data", - "Type": "DataView", - "Desc": "The data to be used for evaluation.", + "Name": "Models", + "Type": { + "Kind": "Array", + "ItemType": "PredictorModel" + }, + "Desc": "The models to combine into an ensemble", "Aliases": [ - "data" + "models" ], "Required": true, "SortOrder": 1.0, "IsNullable": false }, { - "Name": "NameColumn", - "Type": "String", - "Desc": "Name column name.", - "Aliases": [ - "name" - ], - "Required": false, - "SortOrder": 2.0, - "IsNullable": false, - "Default": "Name" - }, - { - "Name": "LossFunction", + "Name": "ModelCombiner", "Type": { - "Kind": "Component", - "ComponentKind": "RegressionLossFunction" + "Kind": "Enum", + "Values": [ + "Median", + "Average", + "Vote" + ] }, - "Desc": "Loss function", + "Desc": "The combiner used to combine the scores", "Aliases": [ - "loss" + "combiner" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 2.0, "IsNullable": false, - "Default": { - "Name": "SquaredLoss" - } - }, + "Default": "Median" + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "OutputKind": [ + "IMulticlassClassificationOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Models.MultiOutputRegressionEvaluator", + "Desc": "Evaluates a multi output regression scored dataset.", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "The data to be used for evaluation.", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "NameColumn", + "Type": "String", + "Desc": "Name column name.", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Name" + }, + { + "Name": "LossFunction", + "Type": { + "Kind": "Component", + "ComponentKind": "RegressionLossFunction" + }, + "Desc": "Loss function", + "Aliases": [ + "loss" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": { + "Name": "SquaredLoss" + } + }, { "Name": "SupressScoresAndLabels", "Type": "Bool", @@ -2055,6 +2371,95 @@ "ITrainerInput" ] }, + { + "Name": "Models.OnnxConverter", + "Desc": "Converts the model to ONNX format.", + "FriendlyName": "ONNX Converter.", + "ShortName": null, + "Inputs": [ + { + "Name": "DataFile", + "Type": "String", + "Desc": "The data file", + "Aliases": [ + "data" + ], + "Required": false, + "SortOrder": 0.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Onnx", + "Type": "String", + "Desc": "The path to write the output ONNX to.", + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Json", + "Type": "String", + "Desc": "The path to write the output JSON to.", + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Name", + "Type": "String", + "Desc": "The 'name' property in the output ONNX. By default this will be the ONNX extension-less name.", + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Domain", + "Type": "String", + "Desc": "The 'domain' property in the output ONNX.", + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "InputsToDrop", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "Array of input column names to drop", + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "OutputsToDrop", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "Array of output column names to drop", + "Required": false, + "SortOrder": 8.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Model that needs to be converted to ONNX format.", + "Required": true, + "SortOrder": 10.0, + "IsNullable": false + } + ], + "Outputs": [] + }, { "Name": "Models.OvaModelCombiner", "Desc": "Combines a sequence of PredictorModels into a single model", @@ -2673,6 +3078,69 @@ "IEvaluatorOutput" ] }, + { + "Name": "Models.RegressionEnsemble", + "Desc": "Combine regression models into an ensemble", + "FriendlyName": "Regression Ensemble (bagging, stacking, etc)", + "ShortName": null, + "Inputs": [ + { + "Name": "Models", + "Type": { + "Kind": "Array", + "ItemType": "PredictorModel" + }, + "Desc": "The models to combine into an ensemble", + "Aliases": [ + "models" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "ModelCombiner", + "Type": { + "Kind": "Enum", + "Values": [ + "Median", + "Average" + ] + }, + "Desc": "The combiner used to combine the scores", + "Aliases": [ + "combiner" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Median" + }, + { + "Name": "ValidatePipelines", + "Type": "Bool", + "Desc": "Whether to validate that all the pipelines are identical", + "Aliases": [ + "validate" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": true + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "OutputKind": [ + "IRegressionOutput", + "ITrainerOutput" + ] + }, { "Name": "Models.RegressionEvaluator", "Desc": "Evaluates a regression scored dataset.", @@ -2795,6 +3263,57 @@ "IEvaluatorOutput" ] }, + { + "Name": "Models.RegressionPipelineEnsemble", + "Desc": "Combine regression models into an ensemble", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "Models", + "Type": { + "Kind": "Array", + "ItemType": "PredictorModel" + }, + "Desc": "The models to combine into an ensemble", + "Aliases": [ + "models" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "ModelCombiner", + "Type": { + "Kind": "Enum", + "Values": [ + "Median", + "Average" + ] + }, + "Desc": "The combiner used to combine the scores", + "Aliases": [ + "combiner" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Median" + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "OutputKind": [ + "IRegressionOutput", + "ITrainerOutput" + ] + }, { "Name": "Models.Summarizer", "Desc": "Summarize a linear regression predictor.", @@ -3107,6 +3626,42 @@ "SortOrder": 9.0, "IsNullable": false, "Default": false + }, + { + "Name": "LabelColumn", + "Type": "String", + "Desc": "Column to use for labels", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 10.0, + "IsNullable": false, + "Default": "Label" + }, + { + "Name": "WeightColumn", + "Type": "String", + "Desc": "Column to use for example weight", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 11.0, + "IsNullable": false, + "Default": "Weight" + }, + { + "Name": "GroupColumn", + "Type": "String", + "Desc": "Column to use for grouping", + "Aliases": [ + "group" + ], + "Required": false, + "SortOrder": 12.0, + "IsNullable": false, + "Default": "GroupId" } ], "Outputs": [ @@ -3510,31 +4065,11 @@ ] }, { - "Name": "Trainers.FastForestBinaryClassifier", - "Desc": "Uses a random forest learner to perform binary classification.", - "FriendlyName": "Fast Forest Classification", - "ShortName": "ff", + "Name": "Trainers.EnsembleBinaryClassifier", + "Desc": "Train binary ensemble.", + "FriendlyName": "Parallel Ensemble (bagging, stacking, etc)", + "ShortName": null, "Inputs": [ - { - "Name": "NumTrees", - "Type": "Int", - "Desc": "Number of weak hypotheses in the ensemble", - "Aliases": [ - "iter" - ], - "Required": false, - "SortOrder": 1.0, - "IsNullable": false, - "Default": 100, - "SweepRange": { - "RangeType": "Discrete", - "Values": [ - 20, - 100, - 500 - ] - } - }, { "Name": "TrainingData", "Type": "DataView", @@ -3547,22 +4082,25 @@ "IsNullable": false }, { - "Name": "NumLeaves", - "Type": "Int", - "Desc": "The max number of leaves in each regression tree", + "Name": "SamplingType", + "Type": { + "Kind": "Component", + "ComponentKind": "EnsembleSubsetSelector" + }, + "Desc": "Sampling Type", "Aliases": [ - "nl" + "st" ], "Required": false, "SortOrder": 2.0, "IsNullable": false, - "Default": 20, - "SweepRange": { - "RangeType": "Long", - "Min": 2, - "Max": 128, - "StepSize": 4.0, - "IsLogScale": true + "Default": { + "Name": "BootstrapSelector", + "Settings": { + "FeatureSelector": { + "Name": "AllFeatureSelector" + } + } } }, { @@ -3578,24 +4116,16 @@ "Default": "Features" }, { - "Name": "MinDocumentsInLeafs", + "Name": "NumModels", "Type": "Int", - "Desc": "The minimal number of documents allowed in a leaf of a regression tree, out of the subsampled data", + "Desc": "Number of models per batch. If not specified, will default to 50 if there is only one base predictor, or the number of base predictors otherwise.", "Aliases": [ - "mil" + "nm" ], "Required": false, "SortOrder": 3.0, - "IsNullable": false, - "Default": 10, - "SweepRange": { - "RangeType": "Discrete", - "Values": [ - 1, - 10, - 50 - ] - } + "IsNullable": true, + "Default": null }, { "Name": "LabelColumn", @@ -3610,28 +4140,38 @@ "Default": "Label" }, { - "Name": "WeightColumn", - "Type": "String", - "Desc": "Column to use for example weight", + "Name": "SubModelSelectorType", + "Type": { + "Kind": "Component", + "ComponentKind": "EnsembleBinarySubModelSelector" + }, + "Desc": "Algorithm to prune the base learners for selective Ensemble", "Aliases": [ - "weight" + "pt" ], "Required": false, "SortOrder": 4.0, "IsNullable": false, - "Default": "Weight" + "Default": { + "Name": "AllSelector" + } }, { - "Name": "GroupIdColumn", - "Type": "String", - "Desc": "Column to use for example groupId", + "Name": "OutputCombiner", + "Type": { + "Kind": "Component", + "ComponentKind": "EnsembleBinaryOutputCombiner" + }, + "Desc": "Output combiner", "Aliases": [ - "groupId" + "oc" ], "Required": false, "SortOrder": 5.0, "IsNullable": false, - "Default": "GroupId" + "Default": { + "Name": "Median" + } }, { "Name": "NormalizeFeatures", @@ -3674,547 +4214,517 @@ "Default": "Auto" }, { - "Name": "MaxTreeOutput", - "Type": "Float", - "Desc": "Upper bound on absolute value of single tree output", + "Name": "TrainParallel", + "Type": "Bool", + "Desc": "All the base learners will run asynchronously if the value is true", "Aliases": [ - "mo" + "tp" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 106.0, "IsNullable": false, - "Default": 100.0 + "Default": false }, { - "Name": "Calibrator", + "Name": "BatchSize", + "Type": "Int", + "Desc": "Batch size", + "Aliases": [ + "bs" + ], + "Required": false, + "SortOrder": 107.0, + "IsNullable": false, + "Default": -1 + }, + { + "Name": "ShowMetrics", + "Type": "Bool", + "Desc": "True, if metrics for each model need to be evaluated and shown in comparison table. This is done by using validation set if available or the training set", + "Aliases": [ + "sm" + ], + "Required": false, + "SortOrder": 108.0, + "IsNullable": false, + "Default": false + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ITrainerInputWithLabel", + "ITrainerInput" + ], + "OutputKind": [ + "IBinaryClassificationOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Trainers.EnsembleClassification", + "Desc": "Train multiclass ensemble.", + "FriendlyName": "Parallel Ensemble (bagging, stacking, etc)", + "ShortName": null, + "Inputs": [ + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "SamplingType", "Type": { "Kind": "Component", - "ComponentKind": "CalibratorTrainer" + "ComponentKind": "EnsembleSubsetSelector" }, - "Desc": "The calibrator kind to apply to the predictor. Specify null for no calibration", + "Desc": "Sampling Type", + "Aliases": [ + "st" + ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 2.0, "IsNullable": false, "Default": { - "Name": "PlattCalibrator" + "Name": "BootstrapSelector", + "Settings": { + "FeatureSelector": { + "Name": "AllFeatureSelector" + } + } } }, { - "Name": "MaxCalibrationExamples", - "Type": "Int", - "Desc": "The maximum number of examples to use when training the calibrator", + "Name": "FeatureColumn", + "Type": "String", + "Desc": "Column to use for features", + "Aliases": [ + "feat" + ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 2.0, "IsNullable": false, - "Default": 1000000 + "Default": "Features" }, { - "Name": "QuantileSampleCount", + "Name": "NumModels", "Type": "Int", - "Desc": "Number of labels to be sampled from each leaf to make the distribtuion", + "Desc": "Number of models per batch. If not specified, will default to 50 if there is only one base predictor, or the number of base predictors otherwise.", "Aliases": [ - "qsc" + "nm" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 3.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "LabelColumn", + "Type": "String", + "Desc": "Column to use for labels", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 3.0, "IsNullable": false, - "Default": 100 + "Default": "Label" }, { - "Name": "ParallelTrainer", + "Name": "SubModelSelectorType", "Type": { "Kind": "Component", - "ComponentKind": "ParallelTraining" + "ComponentKind": "EnsembleMulticlassSubModelSelector" }, - "Desc": "Allows to choose Parallel FastTree Learning Algorithm", + "Desc": "Algorithm to prune the base learners for selective Ensemble", "Aliases": [ - "parag" + "pt" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 4.0, "IsNullable": false, "Default": { - "Name": "Single" + "Name": "AllSelectorMultiClass" } }, { - "Name": "NumThreads", - "Type": "Int", - "Desc": "The number of threads to use", + "Name": "OutputCombiner", + "Type": { + "Kind": "Component", + "ComponentKind": "EnsembleMulticlassOutputCombiner" + }, + "Desc": "Output combiner", "Aliases": [ - "t" + "oc" ], "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null + "SortOrder": 5.0, + "IsNullable": false, + "Default": { + "Name": "MultiMedian" + } }, { - "Name": "RngSeed", - "Type": "Int", - "Desc": "The seed of the random number generator", + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", "Aliases": [ - "r1" + "norm" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 5.0, "IsNullable": false, - "Default": 123 + "Default": "Auto" }, { - "Name": "FeatureSelectSeed", - "Type": "Int", - "Desc": "The seed of the active feature selection", + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "Disk", + "None" + ] + }, + "Desc": "Whether learner should cache input training data", "Aliases": [ - "r3" + "cache" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 6.0, "IsNullable": false, - "Default": 123 + "Default": "Auto" }, { - "Name": "EntropyCoefficient", - "Type": "Float", - "Desc": "The entropy (regularization) coefficient between 0 and 1", + "Name": "TrainParallel", + "Type": "Bool", + "Desc": "All the base learners will run asynchronously if the value is true", "Aliases": [ - "e" + "tp" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 106.0, "IsNullable": false, - "Default": 0.0 + "Default": false }, { - "Name": "HistogramPoolSize", + "Name": "BatchSize", "Type": "Int", - "Desc": "The number of histograms in the pool (between 2 and numLeaves)", + "Desc": "Batch size", "Aliases": [ - "ps" + "bs" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 107.0, "IsNullable": false, "Default": -1 }, { - "Name": "DiskTranspose", + "Name": "ShowMetrics", "Type": "Bool", - "Desc": "Whether to utilize the disk or the data's native transposition facilities (where applicable) when performing the transpose", + "Desc": "True, if metrics for each model need to be evaluated and shown in comparison table. This is done by using validation set if available or the training set", "Aliases": [ - "dt" + "sm" ], "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, + "SortOrder": 108.0, + "IsNullable": false, + "Default": false + } + ], + "Outputs": [ { - "Name": "FeatureFlocks", - "Type": "Bool", - "Desc": "Whether to collectivize features during dataset preparation to speed up training", + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ITrainerInputWithLabel", + "ITrainerInput" + ], + "OutputKind": [ + "IMulticlassClassificationOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Trainers.EnsembleRegression", + "Desc": "Train regression ensemble.", + "FriendlyName": "Parallel Ensemble (bagging, stacking, etc)", + "ShortName": null, + "Inputs": [ + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", "Aliases": [ - "flocks" + "data" ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": true + "Required": true, + "SortOrder": 1.0, + "IsNullable": false }, { - "Name": "CategoricalSplit", - "Type": "Bool", - "Desc": "Whether to do split based on multiple categorical feature values.", + "Name": "SamplingType", + "Type": { + "Kind": "Component", + "ComponentKind": "EnsembleSubsetSelector" + }, + "Desc": "Sampling Type", "Aliases": [ - "cat" + "st" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 2.0, "IsNullable": false, - "Default": false + "Default": { + "Name": "BootstrapSelector", + "Settings": { + "FeatureSelector": { + "Name": "AllFeatureSelector" + } + } + } }, { - "Name": "MaxCategoricalGroupsPerNode", - "Type": "Int", - "Desc": "Maximum categorical split groups to consider when splitting on a categorical feature. Split groups are a collection of split points. This is used to reduce overfitting when there many categorical features.", + "Name": "FeatureColumn", + "Type": "String", + "Desc": "Column to use for features", "Aliases": [ - "mcg" + "feat" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 2.0, "IsNullable": false, - "Default": 64 + "Default": "Features" }, { - "Name": "MaxCategoricalSplitPoints", + "Name": "NumModels", "Type": "Int", - "Desc": "Maximum categorical split points to consider when splitting on a categorical feature.", + "Desc": "Number of models per batch. If not specified, will default to 50 if there is only one base predictor, or the number of base predictors otherwise.", "Aliases": [ - "maxcat" + "nm" ], "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 64 + "SortOrder": 3.0, + "IsNullable": true, + "Default": null }, { - "Name": "MinDocsPercentageForCategoricalSplit", - "Type": "Float", - "Desc": "Minimum categorical docs percentage in a bin to consider for a split.", + "Name": "LabelColumn", + "Type": "String", + "Desc": "Column to use for labels", "Aliases": [ - "mdop" + "lab" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 3.0, "IsNullable": false, - "Default": 0.001 + "Default": "Label" }, { - "Name": "MinDocsForCategoricalSplit", - "Type": "Int", - "Desc": "Minimum categorical doc count in a bin to consider for a split.", + "Name": "SubModelSelectorType", + "Type": { + "Kind": "Component", + "ComponentKind": "EnsembleRegressionSubModelSelector" + }, + "Desc": "Algorithm to prune the base learners for selective Ensemble", "Aliases": [ - "mdo" + "pt" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 4.0, "IsNullable": false, - "Default": 100 + "Default": { + "Name": "AllSelector" + } }, { - "Name": "Bias", - "Type": "Float", - "Desc": "Bias for calculating gradient for each feature bin for a categorical feature.", + "Name": "OutputCombiner", + "Type": { + "Kind": "Component", + "ComponentKind": "EnsembleRegressionOutputCombiner" + }, + "Desc": "Output combiner", "Aliases": [ - "bias" + "oc" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 5.0, "IsNullable": false, - "Default": 0.0 + "Default": { + "Name": "Median" + } }, { - "Name": "Bundling", + "Name": "NormalizeFeatures", "Type": { "Kind": "Enum", "Values": [ - "None", - "AggregateLowPopulation", - "Adjacent" + "No", + "Warn", + "Auto", + "Yes" ] }, - "Desc": "Bundle low population bins. Bundle.None(0): no bundling, Bundle.AggregateLowPopulation(1): Bundle low population, Bundle.Adjacent(2): Neighbor low population bundle.", + "Desc": "Normalize option for the feature column", "Aliases": [ - "bundle" + "norm" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 5.0, "IsNullable": false, - "Default": "None" + "Default": "Auto" }, { - "Name": "MaxBins", - "Type": "Int", - "Desc": "Maximum number of distinct values (bins) per feature", + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "Disk", + "None" + ] + }, + "Desc": "Whether learner should cache input training data", "Aliases": [ - "mb" + "cache" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 6.0, "IsNullable": false, - "Default": 255 + "Default": "Auto" }, { - "Name": "SparsifyThreshold", - "Type": "Float", - "Desc": "Sparsity level needed to use sparse feature representation", + "Name": "TrainParallel", + "Type": "Bool", + "Desc": "All the base learners will run asynchronously if the value is true", "Aliases": [ - "sp" + "tp" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 106.0, "IsNullable": false, - "Default": 0.7 + "Default": false }, { - "Name": "FeatureFirstUsePenalty", - "Type": "Float", - "Desc": "The feature first use penalty coefficient", + "Name": "BatchSize", + "Type": "Int", + "Desc": "Batch size", "Aliases": [ - "ffup" + "bs" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 107.0, "IsNullable": false, - "Default": 0.0 + "Default": -1 }, { - "Name": "FeatureReusePenalty", - "Type": "Float", - "Desc": "The feature re-use penalty (regularization) coefficient", + "Name": "ShowMetrics", + "Type": "Bool", + "Desc": "True, if metrics for each model need to be evaluated and shown in comparison table. This is done by using validation set if available or the training set", "Aliases": [ - "frup" + "sm" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 108.0, "IsNullable": false, - "Default": 0.0 - }, + "Default": false + } + ], + "Outputs": [ { - "Name": "GainConfidenceLevel", - "Type": "Float", - "Desc": "Tree fitting gain confidence requirement (should be in the range [0,1) ).", + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ITrainerInputWithLabel", + "ITrainerInput" + ], + "OutputKind": [ + "IRegressionOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Trainers.FastForestBinaryClassifier", + "Desc": "Uses a random forest learner to perform binary classification.", + "FriendlyName": "Fast Forest Classification", + "ShortName": "ff", + "Inputs": [ + { + "Name": "NumTrees", + "Type": "Int", + "Desc": "Total number of decision trees to create in the ensemble", "Aliases": [ - "gainconf" + "iter" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 1.0, "IsNullable": false, - "Default": 0.0 + "Default": 100, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 20, + 100, + 500 + ] + } }, { - "Name": "SoftmaxTemperature", - "Type": "Float", - "Desc": "The temperature of the randomized softmax distribution for choosing the feature", + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", "Aliases": [ - "smtemp" + "data" ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 0.0 + "Required": true, + "SortOrder": 1.0, + "IsNullable": false }, { - "Name": "ExecutionTimes", - "Type": "Bool", - "Desc": "Print execution time breakdown to stdout", + "Name": "NumLeaves", + "Type": "Int", + "Desc": "The max number of leaves in each regression tree", "Aliases": [ - "et" + "nl" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 2.0, "IsNullable": false, - "Default": false - }, - { - "Name": "FeatureFraction", - "Type": "Float", - "Desc": "The fraction of features (chosen randomly) to use on each iteration", - "Aliases": [ - "ff" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 0.7 - }, - { - "Name": "BaggingSize", - "Type": "Int", - "Desc": "Number of trees in each bag (0 for disabling bagging)", - "Aliases": [ - "bag" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 1 - }, - { - "Name": "BaggingTrainFraction", - "Type": "Float", - "Desc": "Percentage of training examples used in each bag", - "Aliases": [ - "bagfrac" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 0.7 - }, - { - "Name": "SplitFraction", - "Type": "Float", - "Desc": "The fraction of features (chosen randomly) to use on each split", - "Aliases": [ - "sf" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 0.7 - }, - { - "Name": "Smoothing", - "Type": "Float", - "Desc": "Smoothing paramter for tree regularization", - "Aliases": [ - "s" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 0.0 - }, - { - "Name": "AllowEmptyTrees", - "Type": "Bool", - "Desc": "When a root split is impossible, allow training to proceed", - "Aliases": [ - "allowempty", - "dummies" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": true - }, - { - "Name": "FeatureCompressionLevel", - "Type": "Int", - "Desc": "The level of feature compression to use", - "Aliases": [ - "fcomp" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 1 - }, - { - "Name": "CompressEnsemble", - "Type": "Bool", - "Desc": "Compress the tree Ensemble", - "Aliases": [ - "cmp" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": false - }, - { - "Name": "MaxTreesAfterCompression", - "Type": "Int", - "Desc": "Maximum Number of trees after compression", - "Aliases": [ - "cmpmax" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": -1 - }, - { - "Name": "PrintTestGraph", - "Type": "Bool", - "Desc": "Print metrics graph for the first test set", - "Aliases": [ - "graph" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": false - }, - { - "Name": "PrintTrainValidGraph", - "Type": "Bool", - "Desc": "Print Train and Validation metrics in graph", - "Aliases": [ - "graphtv" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": false - }, - { - "Name": "TestFrequency", - "Type": "Int", - "Desc": "Calculate metric values for train/valid/test every k rounds", - "Aliases": [ - "tf" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 2147483647 - } - ], - "Outputs": [ - { - "Name": "PredictorModel", - "Type": "PredictorModel", - "Desc": "The trained model" - } - ], - "InputKind": [ - "ITrainerInputWithGroupId", - "ITrainerInputWithWeight", - "ITrainerInputWithLabel", - "ITrainerInput" - ], - "OutputKind": [ - "IBinaryClassificationOutput", - "ITrainerOutput" - ] - }, - { - "Name": "Trainers.FastForestRegressor", - "Desc": "Trains a random forest to fit target values using least-squares.", - "FriendlyName": "FastForestRegression", - "ShortName": "ffr", - "Inputs": [ - { - "Name": "NumTrees", - "Type": "Int", - "Desc": "Number of weak hypotheses in the ensemble", - "Aliases": [ - "iter" - ], - "Required": false, - "SortOrder": 1.0, - "IsNullable": false, - "Default": 100, - "SweepRange": { - "RangeType": "Discrete", - "Values": [ - 20, - 100, - 500 - ] - } - }, - { - "Name": "TrainingData", - "Type": "DataView", - "Desc": "The data to be used for training", - "Aliases": [ - "data" - ], - "Required": true, - "SortOrder": 1.0, - "IsNullable": false - }, - { - "Name": "NumLeaves", - "Type": "Int", - "Desc": "The max number of leaves in each regression tree", - "Aliases": [ - "nl" - ], - "Required": false, - "SortOrder": 2.0, - "IsNullable": false, - "Default": 20, - "SweepRange": { - "RangeType": "Long", - "Min": 2, - "Max": 128, - "StepSize": 4.0, - "IsLogScale": true - } + "Default": 20, + "SweepRange": { + "RangeType": "Long", + "Min": 2, + "Max": 128, + "StepSize": 4.0, + "IsLogScale": true + } }, { "Name": "FeatureColumn", @@ -4325,13 +4835,39 @@ "Default": "Auto" }, { - "Name": "ShuffleLabels", - "Type": "Bool", - "Desc": "Shuffle the labels on every iteration. Useful probably only if using this tree as a tree leaf featurizer for multiclass.", + "Name": "MaxTreeOutput", + "Type": "Float", + "Desc": "Upper bound on absolute value of single tree output", + "Aliases": [ + "mo" + ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": false + "Default": 100.0 + }, + { + "Name": "Calibrator", + "Type": { + "Kind": "Component", + "ComponentKind": "CalibratorTrainer" + }, + "Desc": "The calibrator kind to apply to the predictor. Specify null for no calibration", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": { + "Name": "PlattCalibrator" + } + }, + { + "Name": "MaxCalibrationExamples", + "Type": "Int", + "Desc": "The maximum number of examples to use when training the calibrator", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1000000 }, { "Name": "QuantileSampleCount", @@ -4781,20 +5317,20 @@ "ITrainerInput" ], "OutputKind": [ - "IRegressionOutput", + "IBinaryClassificationOutput", "ITrainerOutput" ] }, { - "Name": "Trainers.FastTreeBinaryClassifier", - "Desc": "Uses a logit-boost boosted tree learner to perform binary classification.", - "FriendlyName": "FastTree (Boosted Trees) Classification", - "ShortName": "ftc", + "Name": "Trainers.FastForestRegressor", + "Desc": "Trains a random forest to fit target values using least-squares.", + "FriendlyName": "FastForestRegression", + "ShortName": "ffr", "Inputs": [ { "Name": "NumTrees", "Type": "Int", - "Desc": "Number of weak hypotheses in the ensemble", + "Desc": "Total number of decision trees to create in the ensemble", "Aliases": [ "iter" ], @@ -4885,24 +5421,6 @@ "IsNullable": false, "Default": "Label" }, - { - "Name": "LearningRates", - "Type": "Float", - "Desc": "The learning rate", - "Aliases": [ - "lr" - ], - "Required": false, - "SortOrder": 4.0, - "IsNullable": false, - "Default": 0.2, - "SweepRange": { - "RangeType": "Float", - "Min": 0.025, - "Max": 0.4, - "IsLogScale": true - } - }, { "Name": "WeightColumn", "Type": "String", @@ -4968,390 +5486,369 @@ "Default": "Auto" }, { - "Name": "UnbalancedSets", + "Name": "ShuffleLabels", "Type": "Bool", - "Desc": "Should we use derivatives optimized for unbalanced sets", - "Aliases": [ - "us" - ], + "Desc": "Shuffle the labels on every iteration. Useful probably only if using this tree as a tree leaf featurizer for multiclass.", "Required": false, "SortOrder": 150.0, "IsNullable": false, "Default": false }, { - "Name": "BestStepRankingRegressionTrees", - "Type": "Bool", - "Desc": "Use best regression step trees?", + "Name": "QuantileSampleCount", + "Type": "Int", + "Desc": "Number of labels to be sampled from each leaf to make the distribtuion", "Aliases": [ - "bsr" + "qsc" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": false + "Default": 100 }, { - "Name": "UseLineSearch", - "Type": "Bool", - "Desc": "Should we use line search for a step size", + "Name": "ParallelTrainer", + "Type": { + "Kind": "Component", + "ComponentKind": "ParallelTraining" + }, + "Desc": "Allows to choose Parallel FastTree Learning Algorithm", "Aliases": [ - "ls" + "parag" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": false + "Default": { + "Name": "Single" + } }, { - "Name": "NumPostBracketSteps", + "Name": "NumThreads", "Type": "Int", - "Desc": "Number of post-bracket line search steps", + "Desc": "The number of threads to use", "Aliases": [ - "lssteps" + "t" ], "Required": false, "SortOrder": 150.0, - "IsNullable": false, - "Default": 0 + "IsNullable": true, + "Default": null }, { - "Name": "MinStepSize", - "Type": "Float", - "Desc": "Minimum line search step size", + "Name": "RngSeed", + "Type": "Int", + "Desc": "The seed of the random number generator", "Aliases": [ - "minstep" + "r1" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0.0 + "Default": 123 }, { - "Name": "OptimizationAlgorithm", - "Type": { - "Kind": "Enum", - "Values": [ - "GradientDescent", - "AcceleratedGradientDescent", - "ConjugateGradientDescent" - ] - }, - "Desc": "Optimization algorithm to be used (GradientDescent, AcceleratedGradientDescent)", + "Name": "FeatureSelectSeed", + "Type": "Int", + "Desc": "The seed of the active feature selection", "Aliases": [ - "oa" + "r3" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": "GradientDescent" + "Default": 123 }, { - "Name": "EarlyStoppingRule", - "Type": { - "Kind": "Component", - "ComponentKind": "EarlyStoppingCriterion" - }, - "Desc": "Early stopping rule. (Validation set (/valid) is required.)", + "Name": "EntropyCoefficient", + "Type": "Float", + "Desc": "The entropy (regularization) coefficient between 0 and 1", "Aliases": [ - "esr" + "e" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": null + "Default": 0.0 }, { - "Name": "EarlyStoppingMetrics", + "Name": "HistogramPoolSize", "Type": "Int", - "Desc": "Early stopping metrics. (For regression, 1: L1, 2:L2; for ranking, 1:NDCG@1, 3:NDCG@3)", + "Desc": "The number of histograms in the pool (between 2 and numLeaves)", "Aliases": [ - "esmt" + "ps" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0 + "Default": -1 }, { - "Name": "EnablePruning", + "Name": "DiskTranspose", "Type": "Bool", - "Desc": "Enable post-training pruning to avoid overfitting. (a validation set is required)", + "Desc": "Whether to utilize the disk or the data's native transposition facilities (where applicable) when performing the transpose", "Aliases": [ - "pruning" + "dt" ], "Required": false, "SortOrder": 150.0, - "IsNullable": false, - "Default": false + "IsNullable": true, + "Default": null }, { - "Name": "UseTolerantPruning", + "Name": "FeatureFlocks", "Type": "Bool", - "Desc": "Use window and tolerance for pruning", + "Desc": "Whether to collectivize features during dataset preparation to speed up training", "Aliases": [ - "prtol" + "flocks" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": false + "Default": true }, { - "Name": "PruningThreshold", - "Type": "Float", - "Desc": "The tolerance threshold for pruning", + "Name": "CategoricalSplit", + "Type": "Bool", + "Desc": "Whether to do split based on multiple categorical feature values.", "Aliases": [ - "prth" + "cat" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0.004 + "Default": false }, { - "Name": "PruningWindowSize", + "Name": "MaxCategoricalGroupsPerNode", "Type": "Int", - "Desc": "The moving window size for pruning", + "Desc": "Maximum categorical split groups to consider when splitting on a categorical feature. Split groups are a collection of split points. This is used to reduce overfitting when there many categorical features.", "Aliases": [ - "prws" + "mcg" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 5 + "Default": 64 }, { - "Name": "Shrinkage", - "Type": "Float", - "Desc": "Shrinkage", + "Name": "MaxCategoricalSplitPoints", + "Type": "Int", + "Desc": "Maximum categorical split points to consider when splitting on a categorical feature.", "Aliases": [ - "shrk" + "maxcat" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 1.0, - "SweepRange": { - "RangeType": "Float", - "Min": 0.025, - "Max": 4.0, - "IsLogScale": true - } + "Default": 64 }, { - "Name": "DropoutRate", + "Name": "MinDocsPercentageForCategoricalSplit", "Type": "Float", - "Desc": "Dropout rate for tree regularization", + "Desc": "Minimum categorical docs percentage in a bin to consider for a split.", "Aliases": [ - "tdrop" + "mdop" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0.0, - "SweepRange": { - "RangeType": "Discrete", - "Values": [ - 0.0, - 1E-09, - 0.05, - 0.1, - 0.2 - ] - } + "Default": 0.001 }, { - "Name": "GetDerivativesSampleRate", + "Name": "MinDocsForCategoricalSplit", "Type": "Int", - "Desc": "Sample each query 1 in k times in the GetDerivatives function", + "Desc": "Minimum categorical doc count in a bin to consider for a split.", "Aliases": [ - "sr" + "mdo" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 1 + "Default": 100 }, { - "Name": "WriteLastEnsemble", - "Type": "Bool", - "Desc": "Write the last ensemble instead of the one determined by early stopping", + "Name": "Bias", + "Type": "Float", + "Desc": "Bias for calculating gradient for each feature bin for a categorical feature.", "Aliases": [ - "hl" + "bias" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": false + "Default": 0.0 }, { - "Name": "MaxTreeOutput", - "Type": "Float", - "Desc": "Upper bound on absolute value of single tree output", + "Name": "Bundling", + "Type": { + "Kind": "Enum", + "Values": [ + "None", + "AggregateLowPopulation", + "Adjacent" + ] + }, + "Desc": "Bundle low population bins. Bundle.None(0): no bundling, Bundle.AggregateLowPopulation(1): Bundle low population, Bundle.Adjacent(2): Neighbor low population bundle.", "Aliases": [ - "mo" + "bundle" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 100.0 + "Default": "None" }, { - "Name": "RandomStart", - "Type": "Bool", - "Desc": "Training starts from random ordering (determined by /r1)", + "Name": "MaxBins", + "Type": "Int", + "Desc": "Maximum number of distinct values (bins) per feature", "Aliases": [ - "rs" + "mb" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": false + "Default": 255 }, { - "Name": "FilterZeroLambdas", - "Type": "Bool", - "Desc": "Filter zero lambdas during training", + "Name": "SparsifyThreshold", + "Type": "Float", + "Desc": "Sparsity level needed to use sparse feature representation", "Aliases": [ - "fzl" + "sp" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": false + "Default": 0.7 }, { - "Name": "BaselineScoresFormula", - "Type": "String", - "Desc": "Freeform defining the scores that should be used as the baseline ranker", + "Name": "FeatureFirstUsePenalty", + "Type": "Float", + "Desc": "The feature first use penalty coefficient", "Aliases": [ - "basescores" + "ffup" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": null + "Default": 0.0 }, { - "Name": "BaselineAlphaRisk", - "Type": "String", - "Desc": "Baseline alpha for tradeoffs of risk (0 is normal training)", + "Name": "FeatureReusePenalty", + "Type": "Float", + "Desc": "The feature re-use penalty (regularization) coefficient", "Aliases": [ - "basealpha" + "frup" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": null + "Default": 0.0 }, { - "Name": "PositionDiscountFreeform", - "Type": "String", - "Desc": "The discount freeform which specifies the per position discounts of documents in a query (uses a single variable P for position where P=0 is first position)", + "Name": "GainConfidenceLevel", + "Type": "Float", + "Desc": "Tree fitting gain confidence requirement (should be in the range [0,1) ).", "Aliases": [ - "pdff" + "gainconf" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": null + "Default": 0.0 }, { - "Name": "ParallelTrainer", - "Type": { - "Kind": "Component", - "ComponentKind": "ParallelTraining" - }, - "Desc": "Allows to choose Parallel FastTree Learning Algorithm", + "Name": "SoftmaxTemperature", + "Type": "Float", + "Desc": "The temperature of the randomized softmax distribution for choosing the feature", "Aliases": [ - "parag" + "smtemp" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": { - "Name": "Single" - } + "Default": 0.0 }, { - "Name": "NumThreads", - "Type": "Int", - "Desc": "The number of threads to use", + "Name": "ExecutionTimes", + "Type": "Bool", + "Desc": "Print execution time breakdown to stdout", "Aliases": [ - "t" + "et" ], "Required": false, "SortOrder": 150.0, - "IsNullable": true, - "Default": null + "IsNullable": false, + "Default": false }, { - "Name": "RngSeed", - "Type": "Int", - "Desc": "The seed of the random number generator", + "Name": "FeatureFraction", + "Type": "Float", + "Desc": "The fraction of features (chosen randomly) to use on each iteration", "Aliases": [ - "r1" + "ff" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 123 + "Default": 0.7 }, { - "Name": "FeatureSelectSeed", + "Name": "BaggingSize", "Type": "Int", - "Desc": "The seed of the active feature selection", + "Desc": "Number of trees in each bag (0 for disabling bagging)", "Aliases": [ - "r3" + "bag" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 123 + "Default": 1 }, { - "Name": "EntropyCoefficient", + "Name": "BaggingTrainFraction", "Type": "Float", - "Desc": "The entropy (regularization) coefficient between 0 and 1", + "Desc": "Percentage of training examples used in each bag", "Aliases": [ - "e" + "bagfrac" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0.0 + "Default": 0.7 }, { - "Name": "HistogramPoolSize", - "Type": "Int", - "Desc": "The number of histograms in the pool (between 2 and numLeaves)", + "Name": "SplitFraction", + "Type": "Float", + "Desc": "The fraction of features (chosen randomly) to use on each split", "Aliases": [ - "ps" + "sf" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": -1 + "Default": 0.7 }, { - "Name": "DiskTranspose", - "Type": "Bool", - "Desc": "Whether to utilize the disk or the data's native transposition facilities (where applicable) when performing the transpose", + "Name": "Smoothing", + "Type": "Float", + "Desc": "Smoothing paramter for tree regularization", "Aliases": [ - "dt" + "s" ], "Required": false, "SortOrder": 150.0, - "IsNullable": true, - "Default": null + "IsNullable": false, + "Default": 0.0 }, { - "Name": "FeatureFlocks", + "Name": "AllowEmptyTrees", "Type": "Bool", - "Desc": "Whether to collectivize features during dataset preparation to speed up training", + "Desc": "When a root split is impossible, allow training to proceed", "Aliases": [ - "flocks" + "allowempty", + "dummies" ], "Required": false, "SortOrder": 150.0, @@ -5359,295 +5856,390 @@ "Default": true }, { - "Name": "CategoricalSplit", - "Type": "Bool", - "Desc": "Whether to do split based on multiple categorical feature values.", + "Name": "FeatureCompressionLevel", + "Type": "Int", + "Desc": "The level of feature compression to use", "Aliases": [ - "cat" + "fcomp" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": false + "Default": 1 }, { - "Name": "MaxCategoricalGroupsPerNode", - "Type": "Int", - "Desc": "Maximum categorical split groups to consider when splitting on a categorical feature. Split groups are a collection of split points. This is used to reduce overfitting when there many categorical features.", + "Name": "CompressEnsemble", + "Type": "Bool", + "Desc": "Compress the tree Ensemble", "Aliases": [ - "mcg" + "cmp" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 64 + "Default": false }, { - "Name": "MaxCategoricalSplitPoints", + "Name": "MaxTreesAfterCompression", "Type": "Int", - "Desc": "Maximum categorical split points to consider when splitting on a categorical feature.", + "Desc": "Maximum Number of trees after compression", "Aliases": [ - "maxcat" + "cmpmax" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 64 + "Default": -1 }, { - "Name": "MinDocsPercentageForCategoricalSplit", - "Type": "Float", - "Desc": "Minimum categorical docs percentage in a bin to consider for a split.", + "Name": "PrintTestGraph", + "Type": "Bool", + "Desc": "Print metrics graph for the first test set", "Aliases": [ - "mdop" + "graph" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0.001 + "Default": false }, { - "Name": "MinDocsForCategoricalSplit", - "Type": "Int", - "Desc": "Minimum categorical doc count in a bin to consider for a split.", + "Name": "PrintTrainValidGraph", + "Type": "Bool", + "Desc": "Print Train and Validation metrics in graph", "Aliases": [ - "mdo" + "graphtv" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 100 + "Default": false }, { - "Name": "Bias", - "Type": "Float", - "Desc": "Bias for calculating gradient for each feature bin for a categorical feature.", + "Name": "TestFrequency", + "Type": "Int", + "Desc": "Calculate metric values for train/valid/test every k rounds", "Aliases": [ - "bias" + "tf" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0.0 - }, + "Default": 2147483647 + } + ], + "Outputs": [ { - "Name": "Bundling", - "Type": { - "Kind": "Enum", - "Values": [ - "None", - "AggregateLowPopulation", - "Adjacent" - ] - }, - "Desc": "Bundle low population bins. Bundle.None(0): no bundling, Bundle.AggregateLowPopulation(1): Bundle low population, Bundle.Adjacent(2): Neighbor low population bundle.", + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ITrainerInputWithGroupId", + "ITrainerInputWithWeight", + "ITrainerInputWithLabel", + "ITrainerInput" + ], + "OutputKind": [ + "IRegressionOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Trainers.FastTreeBinaryClassifier", + "Desc": "Uses a logit-boost boosted tree learner to perform binary classification.", + "FriendlyName": "FastTree (Boosted Trees) Classification", + "ShortName": "ftc", + "Inputs": [ + { + "Name": "NumTrees", + "Type": "Int", + "Desc": "Total number of decision trees to create in the ensemble", "Aliases": [ - "bundle" + "iter" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 1.0, "IsNullable": false, - "Default": "None" + "Default": 100, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 20, + 100, + 500 + ] + } }, { - "Name": "MaxBins", + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "NumLeaves", "Type": "Int", - "Desc": "Maximum number of distinct values (bins) per feature", + "Desc": "The max number of leaves in each regression tree", "Aliases": [ - "mb" + "nl" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 2.0, "IsNullable": false, - "Default": 255 + "Default": 20, + "SweepRange": { + "RangeType": "Long", + "Min": 2, + "Max": 128, + "StepSize": 4.0, + "IsLogScale": true + } }, { - "Name": "SparsifyThreshold", - "Type": "Float", - "Desc": "Sparsity level needed to use sparse feature representation", + "Name": "FeatureColumn", + "Type": "String", + "Desc": "Column to use for features", "Aliases": [ - "sp" + "feat" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 2.0, "IsNullable": false, - "Default": 0.7 + "Default": "Features" }, { - "Name": "FeatureFirstUsePenalty", - "Type": "Float", - "Desc": "The feature first use penalty coefficient", + "Name": "MinDocumentsInLeafs", + "Type": "Int", + "Desc": "The minimal number of documents allowed in a leaf of a regression tree, out of the subsampled data", "Aliases": [ - "ffup" + "mil" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 3.0, "IsNullable": false, - "Default": 0.0 + "Default": 10, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 1, + 10, + 50 + ] + } }, { - "Name": "FeatureReusePenalty", - "Type": "Float", - "Desc": "The feature re-use penalty (regularization) coefficient", + "Name": "LabelColumn", + "Type": "String", + "Desc": "Column to use for labels", "Aliases": [ - "frup" + "lab" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 3.0, "IsNullable": false, - "Default": 0.0 + "Default": "Label" }, { - "Name": "GainConfidenceLevel", + "Name": "LearningRates", "Type": "Float", - "Desc": "Tree fitting gain confidence requirement (should be in the range [0,1) ).", + "Desc": "The learning rate", "Aliases": [ - "gainconf" + "lr" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 4.0, "IsNullable": false, - "Default": 0.0 + "Default": 0.2, + "SweepRange": { + "RangeType": "Float", + "Min": 0.025, + "Max": 0.4, + "IsLogScale": true + } }, { - "Name": "SoftmaxTemperature", - "Type": "Float", - "Desc": "The temperature of the randomized softmax distribution for choosing the feature", + "Name": "WeightColumn", + "Type": "String", + "Desc": "Column to use for example weight", "Aliases": [ - "smtemp" + "weight" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 4.0, "IsNullable": false, - "Default": 0.0 + "Default": "Weight" }, { - "Name": "ExecutionTimes", - "Type": "Bool", - "Desc": "Print execution time breakdown to stdout", + "Name": "GroupIdColumn", + "Type": "String", + "Desc": "Column to use for example groupId", "Aliases": [ - "et" + "groupId" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 5.0, "IsNullable": false, - "Default": false + "Default": "GroupId" }, { - "Name": "FeatureFraction", - "Type": "Float", - "Desc": "The fraction of features (chosen randomly) to use on each iteration", + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", "Aliases": [ - "ff" + "norm" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 5.0, "IsNullable": false, - "Default": 1.0 + "Default": "Auto" }, { - "Name": "BaggingSize", - "Type": "Int", - "Desc": "Number of trees in each bag (0 for disabling bagging)", + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "Disk", + "None" + ] + }, + "Desc": "Whether learner should cache input training data", "Aliases": [ - "bag" + "cache" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "UnbalancedSets", + "Type": "Bool", + "Desc": "Should we use derivatives optimized for unbalanced sets", + "Aliases": [ + "us" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0 + "Default": false }, { - "Name": "BaggingTrainFraction", - "Type": "Float", - "Desc": "Percentage of training examples used in each bag", + "Name": "BestStepRankingRegressionTrees", + "Type": "Bool", + "Desc": "Use best regression step trees?", "Aliases": [ - "bagfrac" + "bsr" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0.7 + "Default": false }, { - "Name": "SplitFraction", - "Type": "Float", - "Desc": "The fraction of features (chosen randomly) to use on each split", + "Name": "UseLineSearch", + "Type": "Bool", + "Desc": "Should we use line search for a step size", "Aliases": [ - "sf" + "ls" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 1.0 + "Default": false }, { - "Name": "Smoothing", - "Type": "Float", - "Desc": "Smoothing paramter for tree regularization", + "Name": "NumPostBracketSteps", + "Type": "Int", + "Desc": "Number of post-bracket line search steps", "Aliases": [ - "s" + "lssteps" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0.0 + "Default": 0 }, { - "Name": "AllowEmptyTrees", - "Type": "Bool", - "Desc": "When a root split is impossible, allow training to proceed", + "Name": "MinStepSize", + "Type": "Float", + "Desc": "Minimum line search step size", "Aliases": [ - "allowempty", - "dummies" + "minstep" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": true + "Default": 0.0 }, { - "Name": "FeatureCompressionLevel", - "Type": "Int", - "Desc": "The level of feature compression to use", + "Name": "OptimizationAlgorithm", + "Type": { + "Kind": "Enum", + "Values": [ + "GradientDescent", + "AcceleratedGradientDescent", + "ConjugateGradientDescent" + ] + }, + "Desc": "Optimization algorithm to be used (GradientDescent, AcceleratedGradientDescent)", "Aliases": [ - "fcomp" + "oa" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 1 + "Default": "GradientDescent" }, { - "Name": "CompressEnsemble", - "Type": "Bool", - "Desc": "Compress the tree Ensemble", + "Name": "EarlyStoppingRule", + "Type": { + "Kind": "Component", + "ComponentKind": "EarlyStoppingCriterion" + }, + "Desc": "Early stopping rule. (Validation set (/valid) is required.)", "Aliases": [ - "cmp" + "esr" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": false + "Default": null }, { - "Name": "MaxTreesAfterCompression", + "Name": "EarlyStoppingMetrics", "Type": "Int", - "Desc": "Maximum Number of trees after compression", + "Desc": "Early stopping metrics. (For regression, 1: L1, 2:L2; for ranking, 1:NDCG@1, 3:NDCG@3)", "Aliases": [ - "cmpmax" + "esmt" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": -1 + "Default": 0 }, { - "Name": "PrintTestGraph", + "Name": "EnablePruning", "Type": "Bool", - "Desc": "Print metrics graph for the first test set", + "Desc": "Enable post-training pruning to avoid overfitting. (a validation set is required)", "Aliases": [ - "graph" + "pruning" ], "Required": false, "SortOrder": 150.0, @@ -5655,11 +6247,11 @@ "Default": false }, { - "Name": "PrintTrainValidGraph", + "Name": "UseTolerantPruning", "Type": "Bool", - "Desc": "Print Train and Validation metrics in graph", + "Desc": "Use window and tolerance for pruning", "Aliases": [ - "graphtv" + "prtol" ], "Required": false, "SortOrder": 150.0, @@ -5667,329 +6259,272 @@ "Default": false }, { - "Name": "TestFrequency", - "Type": "Int", - "Desc": "Calculate metric values for train/valid/test every k rounds", + "Name": "PruningThreshold", + "Type": "Float", + "Desc": "The tolerance threshold for pruning", "Aliases": [ - "tf" + "prth" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 2147483647 - } - ], - "Outputs": [ - { - "Name": "PredictorModel", - "Type": "PredictorModel", - "Desc": "The trained model" - } - ], - "InputKind": [ - "ITrainerInputWithGroupId", - "ITrainerInputWithWeight", - "ITrainerInputWithLabel", - "ITrainerInput" - ], - "OutputKind": [ - "IBinaryClassificationOutput", - "ITrainerOutput" - ] - }, - { - "Name": "Trainers.FastTreeRanker", - "Desc": "Trains gradient boosted decision trees to the LambdaRank quasi-gradient.", - "FriendlyName": "FastTree (Boosted Trees) Ranking", - "ShortName": "ftrank", - "Inputs": [ + "Default": 0.004 + }, { - "Name": "NumTrees", + "Name": "PruningWindowSize", "Type": "Int", - "Desc": "Number of weak hypotheses in the ensemble", + "Desc": "The moving window size for pruning", "Aliases": [ - "iter" + "prws" ], "Required": false, - "SortOrder": 1.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": 100, - "SweepRange": { - "RangeType": "Discrete", - "Values": [ - 20, - 100, - 500 - ] - } - }, - { - "Name": "TrainingData", - "Type": "DataView", - "Desc": "The data to be used for training", - "Aliases": [ - "data" - ], - "Required": true, - "SortOrder": 1.0, - "IsNullable": false + "Default": 5 }, { - "Name": "NumLeaves", - "Type": "Int", - "Desc": "The max number of leaves in each regression tree", + "Name": "Shrinkage", + "Type": "Float", + "Desc": "Shrinkage", "Aliases": [ - "nl" + "shrk" ], "Required": false, - "SortOrder": 2.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": 20, + "Default": 1.0, "SweepRange": { - "RangeType": "Long", - "Min": 2, - "Max": 128, - "StepSize": 4.0, + "RangeType": "Float", + "Min": 0.025, + "Max": 4.0, "IsLogScale": true } }, { - "Name": "FeatureColumn", - "Type": "String", - "Desc": "Column to use for features", + "Name": "DropoutRate", + "Type": "Float", + "Desc": "Dropout rate for tree regularization", "Aliases": [ - "feat" + "tdrop" ], "Required": false, - "SortOrder": 2.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": "Features" + "Default": 0.0, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.0, + 1E-09, + 0.05, + 0.1, + 0.2 + ] + } }, { - "Name": "MinDocumentsInLeafs", + "Name": "GetDerivativesSampleRate", "Type": "Int", - "Desc": "The minimal number of documents allowed in a leaf of a regression tree, out of the subsampled data", + "Desc": "Sample each query 1 in k times in the GetDerivatives function", "Aliases": [ - "mil" + "sr" ], "Required": false, - "SortOrder": 3.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": 10, - "SweepRange": { - "RangeType": "Discrete", - "Values": [ - 1, - 10, - 50 - ] - } + "Default": 1 }, { - "Name": "LabelColumn", - "Type": "String", - "Desc": "Column to use for labels", + "Name": "WriteLastEnsemble", + "Type": "Bool", + "Desc": "Write the last ensemble instead of the one determined by early stopping", "Aliases": [ - "lab" + "hl" ], "Required": false, - "SortOrder": 3.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": "Label" + "Default": false }, { - "Name": "LearningRates", + "Name": "MaxTreeOutput", "Type": "Float", - "Desc": "The learning rate", + "Desc": "Upper bound on absolute value of single tree output", "Aliases": [ - "lr" + "mo" ], "Required": false, - "SortOrder": 4.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": 0.2, - "SweepRange": { - "RangeType": "Float", - "Min": 0.025, - "Max": 0.4, - "IsLogScale": true - } + "Default": 100.0 }, { - "Name": "WeightColumn", - "Type": "String", - "Desc": "Column to use for example weight", + "Name": "RandomStart", + "Type": "Bool", + "Desc": "Training starts from random ordering (determined by /r1)", "Aliases": [ - "weight" + "rs" ], "Required": false, - "SortOrder": 4.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": "Weight" + "Default": false }, { - "Name": "GroupIdColumn", - "Type": "String", - "Desc": "Column to use for example groupId", + "Name": "FilterZeroLambdas", + "Type": "Bool", + "Desc": "Filter zero lambdas during training", "Aliases": [ - "groupId" + "fzl" ], "Required": false, - "SortOrder": 5.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": "GroupId" + "Default": false }, { - "Name": "NormalizeFeatures", - "Type": { - "Kind": "Enum", - "Values": [ - "No", - "Warn", - "Auto", - "Yes" - ] - }, - "Desc": "Normalize option for the feature column", + "Name": "BaselineScoresFormula", + "Type": "String", + "Desc": "Freeform defining the scores that should be used as the baseline ranker", "Aliases": [ - "norm" + "basescores" ], "Required": false, - "SortOrder": 5.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": "Auto" + "Default": null }, { - "Name": "Caching", - "Type": { - "Kind": "Enum", - "Values": [ - "Auto", - "Memory", - "Disk", - "None" - ] - }, - "Desc": "Whether learner should cache input training data", + "Name": "BaselineAlphaRisk", + "Type": "String", + "Desc": "Baseline alpha for tradeoffs of risk (0 is normal training)", "Aliases": [ - "cache" + "basealpha" ], "Required": false, - "SortOrder": 6.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": "Auto" + "Default": null }, { - "Name": "CustomGains", + "Name": "PositionDiscountFreeform", "Type": "String", - "Desc": "Comma seperated list of gains associated to each relevance label.", + "Desc": "The discount freeform which specifies the per position discounts of documents in a query (uses a single variable P for position where P=0 is first position)", "Aliases": [ - "gains" + "pdff" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": "0,3,7,15,31" + "Default": null }, { - "Name": "TrainDcg", - "Type": "Bool", - "Desc": "Train DCG instead of NDCG", + "Name": "ParallelTrainer", + "Type": { + "Kind": "Component", + "ComponentKind": "ParallelTraining" + }, + "Desc": "Allows to choose Parallel FastTree Learning Algorithm", "Aliases": [ - "dcg" + "parag" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": false + "Default": { + "Name": "Single" + } }, { - "Name": "SortingAlgorithm", - "Type": "String", - "Desc": "The sorting algorithm to use for DCG and LambdaMart calculations [DescendingStablePessimistic/DescendingStable/DescendingReverse/DescendingDotNet]", + "Name": "NumThreads", + "Type": "Int", + "Desc": "The number of threads to use", "Aliases": [ - "sort" + "t" ], "Required": false, "SortOrder": 150.0, - "IsNullable": false, - "Default": "DescendingStablePessimistic" + "IsNullable": true, + "Default": null }, { - "Name": "LambdaMartMaxTruncation", + "Name": "RngSeed", "Type": "Int", - "Desc": "max-NDCG truncation to use in the Lambda Mart algorithm", + "Desc": "The seed of the random number generator", "Aliases": [ - "n" + "r1" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 100 + "Default": 123 }, { - "Name": "ShiftedNdcg", - "Type": "Bool", - "Desc": "Use shifted NDCG", + "Name": "FeatureSelectSeed", + "Type": "Int", + "Desc": "The seed of the active feature selection", + "Aliases": [ + "r3" + ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": false + "Default": 123 }, { - "Name": "CostFunctionParam", - "Type": "Char", - "Desc": "Cost function parameter (w/c)", + "Name": "EntropyCoefficient", + "Type": "Float", + "Desc": "The entropy (regularization) coefficient between 0 and 1", "Aliases": [ - "cf" + "e" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": "w" + "Default": 0.0 }, { - "Name": "DistanceWeight2", - "Type": "Bool", - "Desc": "Distance weight 2 adjustment to cost", + "Name": "HistogramPoolSize", + "Type": "Int", + "Desc": "The number of histograms in the pool (between 2 and numLeaves)", "Aliases": [ - "dw" + "ps" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": false + "Default": -1 }, { - "Name": "NormalizeQueryLambdas", + "Name": "DiskTranspose", "Type": "Bool", - "Desc": "Normalize query lambdas", + "Desc": "Whether to utilize the disk or the data's native transposition facilities (where applicable) when performing the transpose", "Aliases": [ - "nql" + "dt" ], "Required": false, "SortOrder": 150.0, - "IsNullable": false, - "Default": false + "IsNullable": true, + "Default": null }, { - "Name": "BestStepRankingRegressionTrees", + "Name": "FeatureFlocks", "Type": "Bool", - "Desc": "Use best regression step trees?", + "Desc": "Whether to collectivize features during dataset preparation to speed up training", "Aliases": [ - "bsr" + "flocks" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": false + "Default": true }, { - "Name": "UseLineSearch", + "Name": "CategoricalSplit", "Type": "Bool", - "Desc": "Should we use line search for a step size", + "Desc": "Whether to do split based on multiple categorical feature values.", "Aliases": [ - "ls" + "cat" ], "Required": false, "SortOrder": 150.0, @@ -5997,181 +6532,162 @@ "Default": false }, { - "Name": "NumPostBracketSteps", + "Name": "MaxCategoricalGroupsPerNode", "Type": "Int", - "Desc": "Number of post-bracket line search steps", + "Desc": "Maximum categorical split groups to consider when splitting on a categorical feature. Split groups are a collection of split points. This is used to reduce overfitting when there many categorical features.", "Aliases": [ - "lssteps" + "mcg" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0 + "Default": 64 }, { - "Name": "MinStepSize", - "Type": "Float", - "Desc": "Minimum line search step size", + "Name": "MaxCategoricalSplitPoints", + "Type": "Int", + "Desc": "Maximum categorical split points to consider when splitting on a categorical feature.", "Aliases": [ - "minstep" + "maxcat" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0.0 + "Default": 64 }, { - "Name": "OptimizationAlgorithm", - "Type": { - "Kind": "Enum", - "Values": [ - "GradientDescent", - "AcceleratedGradientDescent", - "ConjugateGradientDescent" - ] - }, - "Desc": "Optimization algorithm to be used (GradientDescent, AcceleratedGradientDescent)", + "Name": "MinDocsPercentageForCategoricalSplit", + "Type": "Float", + "Desc": "Minimum categorical docs percentage in a bin to consider for a split.", "Aliases": [ - "oa" + "mdop" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": "GradientDescent" + "Default": 0.001 }, { - "Name": "EarlyStoppingRule", - "Type": { - "Kind": "Component", - "ComponentKind": "EarlyStoppingCriterion" - }, - "Desc": "Early stopping rule. (Validation set (/valid) is required.)", + "Name": "MinDocsForCategoricalSplit", + "Type": "Int", + "Desc": "Minimum categorical doc count in a bin to consider for a split.", "Aliases": [ - "esr" + "mdo" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": null + "Default": 100 }, { - "Name": "EarlyStoppingMetrics", - "Type": "Int", - "Desc": "Early stopping metrics. (For regression, 1: L1, 2:L2; for ranking, 1:NDCG@1, 3:NDCG@3)", + "Name": "Bias", + "Type": "Float", + "Desc": "Bias for calculating gradient for each feature bin for a categorical feature.", "Aliases": [ - "esmt" + "bias" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 1 + "Default": 0.0 }, { - "Name": "EnablePruning", - "Type": "Bool", - "Desc": "Enable post-training pruning to avoid overfitting. (a validation set is required)", + "Name": "Bundling", + "Type": { + "Kind": "Enum", + "Values": [ + "None", + "AggregateLowPopulation", + "Adjacent" + ] + }, + "Desc": "Bundle low population bins. Bundle.None(0): no bundling, Bundle.AggregateLowPopulation(1): Bundle low population, Bundle.Adjacent(2): Neighbor low population bundle.", "Aliases": [ - "pruning" + "bundle" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": false + "Default": "None" }, { - "Name": "UseTolerantPruning", - "Type": "Bool", - "Desc": "Use window and tolerance for pruning", + "Name": "MaxBins", + "Type": "Int", + "Desc": "Maximum number of distinct values (bins) per feature", "Aliases": [ - "prtol" + "mb" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": false + "Default": 255 }, { - "Name": "PruningThreshold", + "Name": "SparsifyThreshold", "Type": "Float", - "Desc": "The tolerance threshold for pruning", + "Desc": "Sparsity level needed to use sparse feature representation", "Aliases": [ - "prth" + "sp" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0.004 + "Default": 0.7 }, { - "Name": "PruningWindowSize", - "Type": "Int", - "Desc": "The moving window size for pruning", + "Name": "FeatureFirstUsePenalty", + "Type": "Float", + "Desc": "The feature first use penalty coefficient", "Aliases": [ - "prws" + "ffup" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 5 + "Default": 0.0 }, { - "Name": "Shrinkage", + "Name": "FeatureReusePenalty", "Type": "Float", - "Desc": "Shrinkage", + "Desc": "The feature re-use penalty (regularization) coefficient", "Aliases": [ - "shrk" + "frup" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 1.0, - "SweepRange": { - "RangeType": "Float", - "Min": 0.025, - "Max": 4.0, - "IsLogScale": true - } + "Default": 0.0 }, { - "Name": "DropoutRate", + "Name": "GainConfidenceLevel", "Type": "Float", - "Desc": "Dropout rate for tree regularization", + "Desc": "Tree fitting gain confidence requirement (should be in the range [0,1) ).", "Aliases": [ - "tdrop" + "gainconf" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0.0, - "SweepRange": { - "RangeType": "Discrete", - "Values": [ - 0.0, - 1E-09, - 0.05, - 0.1, - 0.2 - ] - } + "Default": 0.0 }, { - "Name": "GetDerivativesSampleRate", - "Type": "Int", - "Desc": "Sample each query 1 in k times in the GetDerivatives function", + "Name": "SoftmaxTemperature", + "Type": "Float", + "Desc": "The temperature of the randomized softmax distribution for choosing the feature", "Aliases": [ - "sr" + "smtemp" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 1 + "Default": 0.0 }, { - "Name": "WriteLastEnsemble", + "Name": "ExecutionTimes", "Type": "Bool", - "Desc": "Write the last ensemble instead of the one determined by early stopping", + "Desc": "Print execution time breakdown to stdout", "Aliases": [ - "hl" + "et" ], "Required": false, "SortOrder": 150.0, @@ -6179,347 +6695,426 @@ "Default": false }, { - "Name": "MaxTreeOutput", + "Name": "FeatureFraction", "Type": "Float", - "Desc": "Upper bound on absolute value of single tree output", + "Desc": "The fraction of features (chosen randomly) to use on each iteration", "Aliases": [ - "mo" + "ff" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 100.0 + "Default": 1.0 }, { - "Name": "RandomStart", - "Type": "Bool", - "Desc": "Training starts from random ordering (determined by /r1)", + "Name": "BaggingSize", + "Type": "Int", + "Desc": "Number of trees in each bag (0 for disabling bagging)", "Aliases": [ - "rs" + "bag" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": false + "Default": 0 }, { - "Name": "FilterZeroLambdas", - "Type": "Bool", - "Desc": "Filter zero lambdas during training", + "Name": "BaggingTrainFraction", + "Type": "Float", + "Desc": "Percentage of training examples used in each bag", "Aliases": [ - "fzl" + "bagfrac" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": false + "Default": 0.7 }, { - "Name": "BaselineScoresFormula", - "Type": "String", - "Desc": "Freeform defining the scores that should be used as the baseline ranker", + "Name": "SplitFraction", + "Type": "Float", + "Desc": "The fraction of features (chosen randomly) to use on each split", "Aliases": [ - "basescores" + "sf" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": null + "Default": 1.0 }, { - "Name": "BaselineAlphaRisk", - "Type": "String", - "Desc": "Baseline alpha for tradeoffs of risk (0 is normal training)", + "Name": "Smoothing", + "Type": "Float", + "Desc": "Smoothing paramter for tree regularization", "Aliases": [ - "basealpha" + "s" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": null + "Default": 0.0 }, { - "Name": "PositionDiscountFreeform", - "Type": "String", - "Desc": "The discount freeform which specifies the per position discounts of documents in a query (uses a single variable P for position where P=0 is first position)", + "Name": "AllowEmptyTrees", + "Type": "Bool", + "Desc": "When a root split is impossible, allow training to proceed", "Aliases": [ - "pdff" + "allowempty", + "dummies" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": null + "Default": true }, { - "Name": "ParallelTrainer", - "Type": { - "Kind": "Component", - "ComponentKind": "ParallelTraining" - }, - "Desc": "Allows to choose Parallel FastTree Learning Algorithm", + "Name": "FeatureCompressionLevel", + "Type": "Int", + "Desc": "The level of feature compression to use", "Aliases": [ - "parag" + "fcomp" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": { - "Name": "Single" - } + "Default": 1 }, { - "Name": "NumThreads", - "Type": "Int", - "Desc": "The number of threads to use", + "Name": "CompressEnsemble", + "Type": "Bool", + "Desc": "Compress the tree Ensemble", "Aliases": [ - "t" + "cmp" ], "Required": false, "SortOrder": 150.0, - "IsNullable": true, - "Default": null + "IsNullable": false, + "Default": false }, { - "Name": "RngSeed", + "Name": "MaxTreesAfterCompression", "Type": "Int", - "Desc": "The seed of the random number generator", + "Desc": "Maximum Number of trees after compression", "Aliases": [ - "r1" + "cmpmax" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 123 + "Default": -1 }, { - "Name": "FeatureSelectSeed", - "Type": "Int", - "Desc": "The seed of the active feature selection", + "Name": "PrintTestGraph", + "Type": "Bool", + "Desc": "Print metrics graph for the first test set", "Aliases": [ - "r3" + "graph" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 123 + "Default": false }, { - "Name": "EntropyCoefficient", - "Type": "Float", - "Desc": "The entropy (regularization) coefficient between 0 and 1", + "Name": "PrintTrainValidGraph", + "Type": "Bool", + "Desc": "Print Train and Validation metrics in graph", "Aliases": [ - "e" + "graphtv" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0.0 + "Default": false }, { - "Name": "HistogramPoolSize", + "Name": "TestFrequency", "Type": "Int", - "Desc": "The number of histograms in the pool (between 2 and numLeaves)", + "Desc": "Calculate metric values for train/valid/test every k rounds", "Aliases": [ - "ps" + "tf" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": -1 - }, + "Default": 2147483647 + } + ], + "Outputs": [ { - "Name": "DiskTranspose", - "Type": "Bool", - "Desc": "Whether to utilize the disk or the data's native transposition facilities (where applicable) when performing the transpose", + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ITrainerInputWithGroupId", + "ITrainerInputWithWeight", + "ITrainerInputWithLabel", + "ITrainerInput" + ], + "OutputKind": [ + "IBinaryClassificationOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Trainers.FastTreeRanker", + "Desc": "Trains gradient boosted decision trees to the LambdaRank quasi-gradient.", + "FriendlyName": "FastTree (Boosted Trees) Ranking", + "ShortName": "ftrank", + "Inputs": [ + { + "Name": "NumTrees", + "Type": "Int", + "Desc": "Total number of decision trees to create in the ensemble", "Aliases": [ - "dt" + "iter" ], "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null + "SortOrder": 1.0, + "IsNullable": false, + "Default": 100, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 20, + 100, + 500 + ] + } }, { - "Name": "FeatureFlocks", - "Type": "Bool", - "Desc": "Whether to collectivize features during dataset preparation to speed up training", + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", "Aliases": [ - "flocks" + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "NumLeaves", + "Type": "Int", + "Desc": "The max number of leaves in each regression tree", + "Aliases": [ + "nl" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 2.0, "IsNullable": false, - "Default": true + "Default": 20, + "SweepRange": { + "RangeType": "Long", + "Min": 2, + "Max": 128, + "StepSize": 4.0, + "IsLogScale": true + } }, { - "Name": "CategoricalSplit", - "Type": "Bool", - "Desc": "Whether to do split based on multiple categorical feature values.", + "Name": "FeatureColumn", + "Type": "String", + "Desc": "Column to use for features", "Aliases": [ - "cat" + "feat" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 2.0, "IsNullable": false, - "Default": false + "Default": "Features" }, { - "Name": "MaxCategoricalGroupsPerNode", + "Name": "MinDocumentsInLeafs", "Type": "Int", - "Desc": "Maximum categorical split groups to consider when splitting on a categorical feature. Split groups are a collection of split points. This is used to reduce overfitting when there many categorical features.", + "Desc": "The minimal number of documents allowed in a leaf of a regression tree, out of the subsampled data", "Aliases": [ - "mcg" + "mil" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 3.0, "IsNullable": false, - "Default": 64 + "Default": 10, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 1, + 10, + 50 + ] + } }, { - "Name": "MaxCategoricalSplitPoints", - "Type": "Int", - "Desc": "Maximum categorical split points to consider when splitting on a categorical feature.", + "Name": "LabelColumn", + "Type": "String", + "Desc": "Column to use for labels", "Aliases": [ - "maxcat" + "lab" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 3.0, "IsNullable": false, - "Default": 64 + "Default": "Label" }, { - "Name": "MinDocsPercentageForCategoricalSplit", + "Name": "LearningRates", "Type": "Float", - "Desc": "Minimum categorical docs percentage in a bin to consider for a split.", + "Desc": "The learning rate", "Aliases": [ - "mdop" + "lr" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 4.0, "IsNullable": false, - "Default": 0.001 + "Default": 0.2, + "SweepRange": { + "RangeType": "Float", + "Min": 0.025, + "Max": 0.4, + "IsLogScale": true + } }, { - "Name": "MinDocsForCategoricalSplit", - "Type": "Int", - "Desc": "Minimum categorical doc count in a bin to consider for a split.", + "Name": "WeightColumn", + "Type": "String", + "Desc": "Column to use for example weight", "Aliases": [ - "mdo" + "weight" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 4.0, "IsNullable": false, - "Default": 100 + "Default": "Weight" }, { - "Name": "Bias", - "Type": "Float", - "Desc": "Bias for calculating gradient for each feature bin for a categorical feature.", + "Name": "GroupIdColumn", + "Type": "String", + "Desc": "Column to use for example groupId", "Aliases": [ - "bias" + "groupId" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 5.0, "IsNullable": false, - "Default": 0.0 + "Default": "GroupId" }, { - "Name": "Bundling", + "Name": "NormalizeFeatures", "Type": { "Kind": "Enum", "Values": [ - "None", - "AggregateLowPopulation", - "Adjacent" + "No", + "Warn", + "Auto", + "Yes" ] }, - "Desc": "Bundle low population bins. Bundle.None(0): no bundling, Bundle.AggregateLowPopulation(1): Bundle low population, Bundle.Adjacent(2): Neighbor low population bundle.", + "Desc": "Normalize option for the feature column", "Aliases": [ - "bundle" + "norm" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 5.0, "IsNullable": false, - "Default": "None" + "Default": "Auto" }, { - "Name": "MaxBins", - "Type": "Int", - "Desc": "Maximum number of distinct values (bins) per feature", + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "Disk", + "None" + ] + }, + "Desc": "Whether learner should cache input training data", "Aliases": [ - "mb" + "cache" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 6.0, "IsNullable": false, - "Default": 255 + "Default": "Auto" }, { - "Name": "SparsifyThreshold", - "Type": "Float", - "Desc": "Sparsity level needed to use sparse feature representation", + "Name": "CustomGains", + "Type": "String", + "Desc": "Comma seperated list of gains associated to each relevance label.", "Aliases": [ - "sp" + "gains" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0.7 + "Default": "0,3,7,15,31" }, { - "Name": "FeatureFirstUsePenalty", - "Type": "Float", - "Desc": "The feature first use penalty coefficient", + "Name": "TrainDcg", + "Type": "Bool", + "Desc": "Train DCG instead of NDCG", "Aliases": [ - "ffup" + "dcg" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0.0 + "Default": false }, { - "Name": "FeatureReusePenalty", - "Type": "Float", - "Desc": "The feature re-use penalty (regularization) coefficient", + "Name": "SortingAlgorithm", + "Type": "String", + "Desc": "The sorting algorithm to use for DCG and LambdaMart calculations [DescendingStablePessimistic/DescendingStable/DescendingReverse/DescendingDotNet]", "Aliases": [ - "frup" + "sort" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0.0 + "Default": "DescendingStablePessimistic" }, { - "Name": "GainConfidenceLevel", - "Type": "Float", - "Desc": "Tree fitting gain confidence requirement (should be in the range [0,1) ).", + "Name": "LambdaMartMaxTruncation", + "Type": "Int", + "Desc": "max-NDCG truncation to use in the Lambda Mart algorithm", "Aliases": [ - "gainconf" + "n" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0.0 + "Default": 100 }, { - "Name": "SoftmaxTemperature", - "Type": "Float", - "Desc": "The temperature of the randomized softmax distribution for choosing the feature", + "Name": "ShiftedNdcg", + "Type": "Bool", + "Desc": "Use shifted NDCG", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "CostFunctionParam", + "Type": "Char", + "Desc": "Cost function parameter (w/c)", "Aliases": [ - "smtemp" + "cf" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0.0 + "Default": "w" }, { - "Name": "ExecutionTimes", + "Name": "DistanceWeight2", "Type": "Bool", - "Desc": "Print execution time breakdown to stdout", + "Desc": "Distance weight 2 adjustment to cost", "Aliases": [ - "et" + "dw" ], "Required": false, "SortOrder": 150.0, @@ -6527,59 +7122,59 @@ "Default": false }, { - "Name": "FeatureFraction", - "Type": "Float", - "Desc": "The fraction of features (chosen randomly) to use on each iteration", + "Name": "NormalizeQueryLambdas", + "Type": "Bool", + "Desc": "Normalize query lambdas", "Aliases": [ - "ff" + "nql" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 1.0 + "Default": false }, { - "Name": "BaggingSize", - "Type": "Int", - "Desc": "Number of trees in each bag (0 for disabling bagging)", + "Name": "BestStepRankingRegressionTrees", + "Type": "Bool", + "Desc": "Use best regression step trees?", "Aliases": [ - "bag" + "bsr" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0 + "Default": false }, { - "Name": "BaggingTrainFraction", - "Type": "Float", - "Desc": "Percentage of training examples used in each bag", + "Name": "UseLineSearch", + "Type": "Bool", + "Desc": "Should we use line search for a step size", "Aliases": [ - "bagfrac" + "ls" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0.7 + "Default": false }, { - "Name": "SplitFraction", - "Type": "Float", - "Desc": "The fraction of features (chosen randomly) to use on each split", + "Name": "NumPostBracketSteps", + "Type": "Int", + "Desc": "Number of post-bracket line search steps", "Aliases": [ - "sf" + "lssteps" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 1.0 + "Default": 0 }, { - "Name": "Smoothing", + "Name": "MinStepSize", "Type": "Float", - "Desc": "Smoothing paramter for tree regularization", + "Desc": "Minimum line search step size", "Aliases": [ - "s" + "minstep" ], "Required": false, "SortOrder": 150.0, @@ -6587,60 +7182,69 @@ "Default": 0.0 }, { - "Name": "AllowEmptyTrees", - "Type": "Bool", - "Desc": "When a root split is impossible, allow training to proceed", + "Name": "OptimizationAlgorithm", + "Type": { + "Kind": "Enum", + "Values": [ + "GradientDescent", + "AcceleratedGradientDescent", + "ConjugateGradientDescent" + ] + }, + "Desc": "Optimization algorithm to be used (GradientDescent, AcceleratedGradientDescent)", "Aliases": [ - "allowempty", - "dummies" + "oa" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": true + "Default": "GradientDescent" }, { - "Name": "FeatureCompressionLevel", - "Type": "Int", - "Desc": "The level of feature compression to use", + "Name": "EarlyStoppingRule", + "Type": { + "Kind": "Component", + "ComponentKind": "EarlyStoppingCriterion" + }, + "Desc": "Early stopping rule. (Validation set (/valid) is required.)", "Aliases": [ - "fcomp" + "esr" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 1 + "Default": null }, { - "Name": "CompressEnsemble", - "Type": "Bool", - "Desc": "Compress the tree Ensemble", + "Name": "EarlyStoppingMetrics", + "Type": "Int", + "Desc": "Early stopping metrics. (For regression, 1: L1, 2:L2; for ranking, 1:NDCG@1, 3:NDCG@3)", "Aliases": [ - "cmp" + "esmt" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": false + "Default": 1 }, { - "Name": "MaxTreesAfterCompression", - "Type": "Int", - "Desc": "Maximum Number of trees after compression", + "Name": "EnablePruning", + "Type": "Bool", + "Desc": "Enable post-training pruning to avoid overfitting. (a validation set is required)", "Aliases": [ - "cmpmax" + "pruning" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": -1 + "Default": false }, { - "Name": "PrintTestGraph", + "Name": "UseTolerantPruning", "Type": "Bool", - "Desc": "Print metrics graph for the first test set", + "Desc": "Use window and tolerance for pruning", "Aliases": [ - "graph" + "prtol" ], "Required": false, "SortOrder": 150.0, @@ -6648,272 +7252,224 @@ "Default": false }, { - "Name": "PrintTrainValidGraph", - "Type": "Bool", - "Desc": "Print Train and Validation metrics in graph", + "Name": "PruningThreshold", + "Type": "Float", + "Desc": "The tolerance threshold for pruning", "Aliases": [ - "graphtv" + "prth" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": false + "Default": 0.004 }, { - "Name": "TestFrequency", + "Name": "PruningWindowSize", "Type": "Int", - "Desc": "Calculate metric values for train/valid/test every k rounds", + "Desc": "The moving window size for pruning", "Aliases": [ - "tf" + "prws" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 2147483647 - } - ], - "Outputs": [ - { - "Name": "PredictorModel", - "Type": "PredictorModel", - "Desc": "The trained model" - } - ], - "InputKind": [ - "ITrainerInputWithGroupId", - "ITrainerInputWithWeight", - "ITrainerInputWithLabel", - "ITrainerInput" - ], - "OutputKind": [ - "IRankingOutput", - "ITrainerOutput" - ] - }, - { - "Name": "Trainers.FastTreeRegressor", - "Desc": "Trains gradient boosted decision trees to fit target values using least-squares.", - "FriendlyName": "FastTree (Boosted Trees) Regression", - "ShortName": "ftr", - "Inputs": [ + "Default": 5 + }, { - "Name": "NumTrees", - "Type": "Int", - "Desc": "Number of weak hypotheses in the ensemble", + "Name": "Shrinkage", + "Type": "Float", + "Desc": "Shrinkage", "Aliases": [ - "iter" + "shrk" ], "Required": false, - "SortOrder": 1.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": 100, + "Default": 1.0, "SweepRange": { - "RangeType": "Discrete", - "Values": [ - 20, - 100, - 500 - ] + "RangeType": "Float", + "Min": 0.025, + "Max": 4.0, + "IsLogScale": true } }, { - "Name": "TrainingData", - "Type": "DataView", - "Desc": "The data to be used for training", - "Aliases": [ - "data" - ], - "Required": true, - "SortOrder": 1.0, - "IsNullable": false - }, - { - "Name": "NumLeaves", - "Type": "Int", - "Desc": "The max number of leaves in each regression tree", + "Name": "DropoutRate", + "Type": "Float", + "Desc": "Dropout rate for tree regularization", "Aliases": [ - "nl" + "tdrop" ], "Required": false, - "SortOrder": 2.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": 20, + "Default": 0.0, "SweepRange": { - "RangeType": "Long", - "Min": 2, - "Max": 128, - "StepSize": 4.0, - "IsLogScale": true + "RangeType": "Discrete", + "Values": [ + 0.0, + 1E-09, + 0.05, + 0.1, + 0.2 + ] } }, { - "Name": "FeatureColumn", - "Type": "String", - "Desc": "Column to use for features", + "Name": "GetDerivativesSampleRate", + "Type": "Int", + "Desc": "Sample each query 1 in k times in the GetDerivatives function", "Aliases": [ - "feat" + "sr" ], "Required": false, - "SortOrder": 2.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": "Features" + "Default": 1 }, { - "Name": "MinDocumentsInLeafs", - "Type": "Int", - "Desc": "The minimal number of documents allowed in a leaf of a regression tree, out of the subsampled data", + "Name": "WriteLastEnsemble", + "Type": "Bool", + "Desc": "Write the last ensemble instead of the one determined by early stopping", "Aliases": [ - "mil" + "hl" ], "Required": false, - "SortOrder": 3.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": 10, - "SweepRange": { - "RangeType": "Discrete", - "Values": [ - 1, - 10, - 50 - ] - } + "Default": false }, { - "Name": "LabelColumn", - "Type": "String", - "Desc": "Column to use for labels", + "Name": "MaxTreeOutput", + "Type": "Float", + "Desc": "Upper bound on absolute value of single tree output", "Aliases": [ - "lab" + "mo" ], "Required": false, - "SortOrder": 3.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": "Label" + "Default": 100.0 }, { - "Name": "LearningRates", - "Type": "Float", - "Desc": "The learning rate", + "Name": "RandomStart", + "Type": "Bool", + "Desc": "Training starts from random ordering (determined by /r1)", "Aliases": [ - "lr" + "rs" ], "Required": false, - "SortOrder": 4.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": 0.2, - "SweepRange": { - "RangeType": "Float", - "Min": 0.025, - "Max": 0.4, - "IsLogScale": true - } + "Default": false }, { - "Name": "WeightColumn", + "Name": "FilterZeroLambdas", + "Type": "Bool", + "Desc": "Filter zero lambdas during training", + "Aliases": [ + "fzl" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "BaselineScoresFormula", "Type": "String", - "Desc": "Column to use for example weight", + "Desc": "Freeform defining the scores that should be used as the baseline ranker", "Aliases": [ - "weight" + "basescores" ], "Required": false, - "SortOrder": 4.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": "Weight" + "Default": null }, { - "Name": "GroupIdColumn", + "Name": "BaselineAlphaRisk", "Type": "String", - "Desc": "Column to use for example groupId", + "Desc": "Baseline alpha for tradeoffs of risk (0 is normal training)", "Aliases": [ - "groupId" + "basealpha" ], "Required": false, - "SortOrder": 5.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": "GroupId" + "Default": null }, { - "Name": "NormalizeFeatures", - "Type": { - "Kind": "Enum", - "Values": [ - "No", - "Warn", - "Auto", - "Yes" - ] - }, - "Desc": "Normalize option for the feature column", + "Name": "PositionDiscountFreeform", + "Type": "String", + "Desc": "The discount freeform which specifies the per position discounts of documents in a query (uses a single variable P for position where P=0 is first position)", "Aliases": [ - "norm" + "pdff" ], "Required": false, - "SortOrder": 5.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": "Auto" + "Default": null }, { - "Name": "Caching", + "Name": "ParallelTrainer", "Type": { - "Kind": "Enum", - "Values": [ - "Auto", - "Memory", - "Disk", - "None" - ] + "Kind": "Component", + "ComponentKind": "ParallelTraining" }, - "Desc": "Whether learner should cache input training data", + "Desc": "Allows to choose Parallel FastTree Learning Algorithm", "Aliases": [ - "cache" + "parag" ], "Required": false, - "SortOrder": 6.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": "Auto" + "Default": { + "Name": "Single" + } }, { - "Name": "BestStepRankingRegressionTrees", - "Type": "Bool", - "Desc": "Use best regression step trees?", + "Name": "NumThreads", + "Type": "Int", + "Desc": "The number of threads to use", "Aliases": [ - "bsr" + "t" ], "Required": false, "SortOrder": 150.0, - "IsNullable": false, - "Default": false + "IsNullable": true, + "Default": null }, { - "Name": "UseLineSearch", - "Type": "Bool", - "Desc": "Should we use line search for a step size", + "Name": "RngSeed", + "Type": "Int", + "Desc": "The seed of the random number generator", "Aliases": [ - "ls" + "r1" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": false + "Default": 123 }, { - "Name": "NumPostBracketSteps", + "Name": "FeatureSelectSeed", "Type": "Int", - "Desc": "Number of post-bracket line search steps", + "Desc": "The seed of the active feature selection", "Aliases": [ - "lssteps" + "r3" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0 + "Default": 123 }, { - "Name": "MinStepSize", + "Name": "EntropyCoefficient", "Type": "Float", - "Desc": "Minimum line search step size", + "Desc": "The entropy (regularization) coefficient between 0 and 1", "Aliases": [ - "minstep" + "e" ], "Required": false, "SortOrder": 150.0, @@ -6921,57 +7477,47 @@ "Default": 0.0 }, { - "Name": "OptimizationAlgorithm", - "Type": { - "Kind": "Enum", - "Values": [ - "GradientDescent", - "AcceleratedGradientDescent", - "ConjugateGradientDescent" - ] - }, - "Desc": "Optimization algorithm to be used (GradientDescent, AcceleratedGradientDescent)", + "Name": "HistogramPoolSize", + "Type": "Int", + "Desc": "The number of histograms in the pool (between 2 and numLeaves)", "Aliases": [ - "oa" + "ps" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": "GradientDescent" + "Default": -1 }, { - "Name": "EarlyStoppingRule", - "Type": { - "Kind": "Component", - "ComponentKind": "EarlyStoppingCriterion" - }, - "Desc": "Early stopping rule. (Validation set (/valid) is required.)", + "Name": "DiskTranspose", + "Type": "Bool", + "Desc": "Whether to utilize the disk or the data's native transposition facilities (where applicable) when performing the transpose", "Aliases": [ - "esr" + "dt" ], "Required": false, "SortOrder": 150.0, - "IsNullable": false, + "IsNullable": true, "Default": null }, { - "Name": "EarlyStoppingMetrics", - "Type": "Int", - "Desc": "Early stopping metrics. (For regression, 1: L1, 2:L2; for ranking, 1:NDCG@1, 3:NDCG@3)", + "Name": "FeatureFlocks", + "Type": "Bool", + "Desc": "Whether to collectivize features during dataset preparation to speed up training", "Aliases": [ - "esmt" + "flocks" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 1 + "Default": true }, { - "Name": "EnablePruning", + "Name": "CategoricalSplit", "Type": "Bool", - "Desc": "Enable post-training pruning to avoid overfitting. (a validation set is required)", + "Desc": "Whether to do split based on multiple categorical feature values.", "Aliases": [ - "pruning" + "cat" ], "Required": false, "SortOrder": 150.0, @@ -6979,284 +7525,259 @@ "Default": false }, { - "Name": "UseTolerantPruning", - "Type": "Bool", - "Desc": "Use window and tolerance for pruning", + "Name": "MaxCategoricalGroupsPerNode", + "Type": "Int", + "Desc": "Maximum categorical split groups to consider when splitting on a categorical feature. Split groups are a collection of split points. This is used to reduce overfitting when there many categorical features.", "Aliases": [ - "prtol" + "mcg" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": false + "Default": 64 }, { - "Name": "PruningThreshold", - "Type": "Float", - "Desc": "The tolerance threshold for pruning", + "Name": "MaxCategoricalSplitPoints", + "Type": "Int", + "Desc": "Maximum categorical split points to consider when splitting on a categorical feature.", "Aliases": [ - "prth" + "maxcat" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0.004 + "Default": 64 }, { - "Name": "PruningWindowSize", - "Type": "Int", - "Desc": "The moving window size for pruning", + "Name": "MinDocsPercentageForCategoricalSplit", + "Type": "Float", + "Desc": "Minimum categorical docs percentage in a bin to consider for a split.", "Aliases": [ - "prws" + "mdop" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 5 + "Default": 0.001 }, { - "Name": "Shrinkage", - "Type": "Float", - "Desc": "Shrinkage", + "Name": "MinDocsForCategoricalSplit", + "Type": "Int", + "Desc": "Minimum categorical doc count in a bin to consider for a split.", "Aliases": [ - "shrk" + "mdo" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 1.0, - "SweepRange": { - "RangeType": "Float", - "Min": 0.025, - "Max": 4.0, - "IsLogScale": true - } + "Default": 100 }, { - "Name": "DropoutRate", + "Name": "Bias", "Type": "Float", - "Desc": "Dropout rate for tree regularization", + "Desc": "Bias for calculating gradient for each feature bin for a categorical feature.", "Aliases": [ - "tdrop" + "bias" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0.0, - "SweepRange": { - "RangeType": "Discrete", + "Default": 0.0 + }, + { + "Name": "Bundling", + "Type": { + "Kind": "Enum", "Values": [ - 0.0, - 1E-09, - 0.05, - 0.1, - 0.2 + "None", + "AggregateLowPopulation", + "Adjacent" ] - } - }, - { - "Name": "GetDerivativesSampleRate", - "Type": "Int", - "Desc": "Sample each query 1 in k times in the GetDerivatives function", + }, + "Desc": "Bundle low population bins. Bundle.None(0): no bundling, Bundle.AggregateLowPopulation(1): Bundle low population, Bundle.Adjacent(2): Neighbor low population bundle.", "Aliases": [ - "sr" + "bundle" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 1 + "Default": "None" }, { - "Name": "WriteLastEnsemble", - "Type": "Bool", - "Desc": "Write the last ensemble instead of the one determined by early stopping", + "Name": "MaxBins", + "Type": "Int", + "Desc": "Maximum number of distinct values (bins) per feature", "Aliases": [ - "hl" + "mb" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": false + "Default": 255 }, { - "Name": "MaxTreeOutput", + "Name": "SparsifyThreshold", "Type": "Float", - "Desc": "Upper bound on absolute value of single tree output", + "Desc": "Sparsity level needed to use sparse feature representation", "Aliases": [ - "mo" + "sp" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 100.0 + "Default": 0.7 }, { - "Name": "RandomStart", - "Type": "Bool", - "Desc": "Training starts from random ordering (determined by /r1)", + "Name": "FeatureFirstUsePenalty", + "Type": "Float", + "Desc": "The feature first use penalty coefficient", "Aliases": [ - "rs" + "ffup" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": false + "Default": 0.0 }, { - "Name": "FilterZeroLambdas", - "Type": "Bool", - "Desc": "Filter zero lambdas during training", + "Name": "FeatureReusePenalty", + "Type": "Float", + "Desc": "The feature re-use penalty (regularization) coefficient", "Aliases": [ - "fzl" + "frup" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": false + "Default": 0.0 }, { - "Name": "BaselineScoresFormula", - "Type": "String", - "Desc": "Freeform defining the scores that should be used as the baseline ranker", + "Name": "GainConfidenceLevel", + "Type": "Float", + "Desc": "Tree fitting gain confidence requirement (should be in the range [0,1) ).", "Aliases": [ - "basescores" + "gainconf" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": null + "Default": 0.0 }, { - "Name": "BaselineAlphaRisk", - "Type": "String", - "Desc": "Baseline alpha for tradeoffs of risk (0 is normal training)", + "Name": "SoftmaxTemperature", + "Type": "Float", + "Desc": "The temperature of the randomized softmax distribution for choosing the feature", "Aliases": [ - "basealpha" + "smtemp" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": null + "Default": 0.0 }, { - "Name": "PositionDiscountFreeform", - "Type": "String", - "Desc": "The discount freeform which specifies the per position discounts of documents in a query (uses a single variable P for position where P=0 is first position)", + "Name": "ExecutionTimes", + "Type": "Bool", + "Desc": "Print execution time breakdown to stdout", "Aliases": [ - "pdff" + "et" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": null + "Default": false }, { - "Name": "ParallelTrainer", - "Type": { - "Kind": "Component", - "ComponentKind": "ParallelTraining" - }, - "Desc": "Allows to choose Parallel FastTree Learning Algorithm", + "Name": "FeatureFraction", + "Type": "Float", + "Desc": "The fraction of features (chosen randomly) to use on each iteration", "Aliases": [ - "parag" + "ff" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": { - "Name": "Single" - } - }, - { - "Name": "NumThreads", - "Type": "Int", - "Desc": "The number of threads to use", - "Aliases": [ - "t" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null + "Default": 1.0 }, { - "Name": "RngSeed", + "Name": "BaggingSize", "Type": "Int", - "Desc": "The seed of the random number generator", + "Desc": "Number of trees in each bag (0 for disabling bagging)", "Aliases": [ - "r1" + "bag" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 123 + "Default": 0 }, { - "Name": "FeatureSelectSeed", - "Type": "Int", - "Desc": "The seed of the active feature selection", + "Name": "BaggingTrainFraction", + "Type": "Float", + "Desc": "Percentage of training examples used in each bag", "Aliases": [ - "r3" + "bagfrac" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 123 + "Default": 0.7 }, { - "Name": "EntropyCoefficient", + "Name": "SplitFraction", "Type": "Float", - "Desc": "The entropy (regularization) coefficient between 0 and 1", + "Desc": "The fraction of features (chosen randomly) to use on each split", "Aliases": [ - "e" + "sf" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0.0 + "Default": 1.0 }, { - "Name": "HistogramPoolSize", - "Type": "Int", - "Desc": "The number of histograms in the pool (between 2 and numLeaves)", + "Name": "Smoothing", + "Type": "Float", + "Desc": "Smoothing paramter for tree regularization", "Aliases": [ - "ps" + "s" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": -1 + "Default": 0.0 }, { - "Name": "DiskTranspose", + "Name": "AllowEmptyTrees", "Type": "Bool", - "Desc": "Whether to utilize the disk or the data's native transposition facilities (where applicable) when performing the transpose", + "Desc": "When a root split is impossible, allow training to proceed", "Aliases": [ - "dt" + "allowempty", + "dummies" ], "Required": false, "SortOrder": 150.0, - "IsNullable": true, - "Default": null + "IsNullable": false, + "Default": true }, { - "Name": "FeatureFlocks", - "Type": "Bool", - "Desc": "Whether to collectivize features during dataset preparation to speed up training", + "Name": "FeatureCompressionLevel", + "Type": "Int", + "Desc": "The level of feature compression to use", "Aliases": [ - "flocks" + "fcomp" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": true + "Default": 1 }, { - "Name": "CategoricalSplit", + "Name": "CompressEnsemble", "Type": "Bool", - "Desc": "Whether to do split based on multiple categorical feature values.", + "Desc": "Compress the tree Ensemble", "Aliases": [ - "cat" + "cmp" ], "Required": false, "SortOrder": 150.0, @@ -7264,283 +7785,354 @@ "Default": false }, { - "Name": "MaxCategoricalGroupsPerNode", + "Name": "MaxTreesAfterCompression", "Type": "Int", - "Desc": "Maximum categorical split groups to consider when splitting on a categorical feature. Split groups are a collection of split points. This is used to reduce overfitting when there many categorical features.", + "Desc": "Maximum Number of trees after compression", "Aliases": [ - "mcg" + "cmpmax" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 64 + "Default": -1 }, { - "Name": "MaxCategoricalSplitPoints", - "Type": "Int", - "Desc": "Maximum categorical split points to consider when splitting on a categorical feature.", + "Name": "PrintTestGraph", + "Type": "Bool", + "Desc": "Print metrics graph for the first test set", "Aliases": [ - "maxcat" + "graph" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 64 + "Default": false }, { - "Name": "MinDocsPercentageForCategoricalSplit", - "Type": "Float", - "Desc": "Minimum categorical docs percentage in a bin to consider for a split.", + "Name": "PrintTrainValidGraph", + "Type": "Bool", + "Desc": "Print Train and Validation metrics in graph", "Aliases": [ - "mdop" + "graphtv" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0.001 + "Default": false }, { - "Name": "MinDocsForCategoricalSplit", + "Name": "TestFrequency", "Type": "Int", - "Desc": "Minimum categorical doc count in a bin to consider for a split.", + "Desc": "Calculate metric values for train/valid/test every k rounds", "Aliases": [ - "mdo" + "tf" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 100 - }, + "Default": 2147483647 + } + ], + "Outputs": [ { - "Name": "Bias", - "Type": "Float", - "Desc": "Bias for calculating gradient for each feature bin for a categorical feature.", + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ITrainerInputWithGroupId", + "ITrainerInputWithWeight", + "ITrainerInputWithLabel", + "ITrainerInput" + ], + "OutputKind": [ + "IRankingOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Trainers.FastTreeRegressor", + "Desc": "Trains gradient boosted decision trees to fit target values using least-squares.", + "FriendlyName": "FastTree (Boosted Trees) Regression", + "ShortName": "ftr", + "Inputs": [ + { + "Name": "NumTrees", + "Type": "Int", + "Desc": "Total number of decision trees to create in the ensemble", "Aliases": [ - "bias" + "iter" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 1.0, "IsNullable": false, - "Default": 0.0 - }, - { - "Name": "Bundling", - "Type": { - "Kind": "Enum", - "Values": [ - "None", - "AggregateLowPopulation", - "Adjacent" + "Default": 100, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 20, + 100, + 500 ] - }, - "Desc": "Bundle low population bins. Bundle.None(0): no bundling, Bundle.AggregateLowPopulation(1): Bundle low population, Bundle.Adjacent(2): Neighbor low population bundle.", + } + }, + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", "Aliases": [ - "bundle" + "data" ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": "None" + "Required": true, + "SortOrder": 1.0, + "IsNullable": false }, { - "Name": "MaxBins", + "Name": "NumLeaves", "Type": "Int", - "Desc": "Maximum number of distinct values (bins) per feature", + "Desc": "The max number of leaves in each regression tree", "Aliases": [ - "mb" + "nl" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 2.0, "IsNullable": false, - "Default": 255 + "Default": 20, + "SweepRange": { + "RangeType": "Long", + "Min": 2, + "Max": 128, + "StepSize": 4.0, + "IsLogScale": true + } }, { - "Name": "SparsifyThreshold", - "Type": "Float", - "Desc": "Sparsity level needed to use sparse feature representation", + "Name": "FeatureColumn", + "Type": "String", + "Desc": "Column to use for features", "Aliases": [ - "sp" + "feat" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 2.0, "IsNullable": false, - "Default": 0.7 + "Default": "Features" }, { - "Name": "FeatureFirstUsePenalty", - "Type": "Float", - "Desc": "The feature first use penalty coefficient", + "Name": "MinDocumentsInLeafs", + "Type": "Int", + "Desc": "The minimal number of documents allowed in a leaf of a regression tree, out of the subsampled data", "Aliases": [ - "ffup" + "mil" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 3.0, "IsNullable": false, - "Default": 0.0 + "Default": 10, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 1, + 10, + 50 + ] + } }, { - "Name": "FeatureReusePenalty", - "Type": "Float", - "Desc": "The feature re-use penalty (regularization) coefficient", + "Name": "LabelColumn", + "Type": "String", + "Desc": "Column to use for labels", "Aliases": [ - "frup" + "lab" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 3.0, "IsNullable": false, - "Default": 0.0 + "Default": "Label" }, { - "Name": "GainConfidenceLevel", + "Name": "LearningRates", "Type": "Float", - "Desc": "Tree fitting gain confidence requirement (should be in the range [0,1) ).", + "Desc": "The learning rate", "Aliases": [ - "gainconf" + "lr" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 4.0, "IsNullable": false, - "Default": 0.0 + "Default": 0.2, + "SweepRange": { + "RangeType": "Float", + "Min": 0.025, + "Max": 0.4, + "IsLogScale": true + } }, { - "Name": "SoftmaxTemperature", - "Type": "Float", - "Desc": "The temperature of the randomized softmax distribution for choosing the feature", + "Name": "WeightColumn", + "Type": "String", + "Desc": "Column to use for example weight", "Aliases": [ - "smtemp" + "weight" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 4.0, "IsNullable": false, - "Default": 0.0 + "Default": "Weight" }, { - "Name": "ExecutionTimes", - "Type": "Bool", - "Desc": "Print execution time breakdown to stdout", + "Name": "GroupIdColumn", + "Type": "String", + "Desc": "Column to use for example groupId", "Aliases": [ - "et" + "groupId" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 5.0, "IsNullable": false, - "Default": false + "Default": "GroupId" }, { - "Name": "FeatureFraction", - "Type": "Float", - "Desc": "The fraction of features (chosen randomly) to use on each iteration", + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", "Aliases": [ - "ff" + "norm" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 5.0, "IsNullable": false, - "Default": 1.0 + "Default": "Auto" }, { - "Name": "BaggingSize", - "Type": "Int", - "Desc": "Number of trees in each bag (0 for disabling bagging)", + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "Disk", + "None" + ] + }, + "Desc": "Whether learner should cache input training data", "Aliases": [ - "bag" + "cache" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 6.0, "IsNullable": false, - "Default": 0 + "Default": "Auto" }, { - "Name": "BaggingTrainFraction", - "Type": "Float", - "Desc": "Percentage of training examples used in each bag", + "Name": "BestStepRankingRegressionTrees", + "Type": "Bool", + "Desc": "Use best regression step trees?", "Aliases": [ - "bagfrac" + "bsr" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0.7 + "Default": false }, { - "Name": "SplitFraction", - "Type": "Float", - "Desc": "The fraction of features (chosen randomly) to use on each split", + "Name": "UseLineSearch", + "Type": "Bool", + "Desc": "Should we use line search for a step size", "Aliases": [ - "sf" + "ls" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 1.0 + "Default": false }, { - "Name": "Smoothing", - "Type": "Float", - "Desc": "Smoothing paramter for tree regularization", + "Name": "NumPostBracketSteps", + "Type": "Int", + "Desc": "Number of post-bracket line search steps", "Aliases": [ - "s" + "lssteps" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0.0 + "Default": 0 }, { - "Name": "AllowEmptyTrees", - "Type": "Bool", - "Desc": "When a root split is impossible, allow training to proceed", + "Name": "MinStepSize", + "Type": "Float", + "Desc": "Minimum line search step size", "Aliases": [ - "allowempty", - "dummies" + "minstep" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": true + "Default": 0.0 }, { - "Name": "FeatureCompressionLevel", - "Type": "Int", - "Desc": "The level of feature compression to use", + "Name": "OptimizationAlgorithm", + "Type": { + "Kind": "Enum", + "Values": [ + "GradientDescent", + "AcceleratedGradientDescent", + "ConjugateGradientDescent" + ] + }, + "Desc": "Optimization algorithm to be used (GradientDescent, AcceleratedGradientDescent)", "Aliases": [ - "fcomp" + "oa" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 1 + "Default": "GradientDescent" }, { - "Name": "CompressEnsemble", - "Type": "Bool", - "Desc": "Compress the tree Ensemble", + "Name": "EarlyStoppingRule", + "Type": { + "Kind": "Component", + "ComponentKind": "EarlyStoppingCriterion" + }, + "Desc": "Early stopping rule. (Validation set (/valid) is required.)", "Aliases": [ - "cmp" + "esr" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": false + "Default": null }, { - "Name": "MaxTreesAfterCompression", + "Name": "EarlyStoppingMetrics", "Type": "Int", - "Desc": "Maximum Number of trees after compression", + "Desc": "Early stopping metrics. (For regression, 1: L1, 2:L2; for ranking, 1:NDCG@1, 3:NDCG@3)", "Aliases": [ - "cmpmax" + "esmt" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": -1 + "Default": 1 }, { - "Name": "PrintTestGraph", + "Name": "EnablePruning", "Type": "Bool", - "Desc": "Print metrics graph for the first test set", + "Desc": "Enable post-training pruning to avoid overfitting. (a validation set is required)", "Aliases": [ - "graph" + "pruning" ], "Required": false, "SortOrder": 150.0, @@ -7548,11 +8140,11 @@ "Default": false }, { - "Name": "PrintTrainValidGraph", + "Name": "UseTolerantPruning", "Type": "Bool", - "Desc": "Print Train and Validation metrics in graph", + "Desc": "Use window and tolerance for pruning", "Aliases": [ - "graphtv" + "prtol" ], "Required": false, "SortOrder": 150.0, @@ -7560,809 +8152,469 @@ "Default": false }, { - "Name": "TestFrequency", - "Type": "Int", - "Desc": "Calculate metric values for train/valid/test every k rounds", + "Name": "PruningThreshold", + "Type": "Float", + "Desc": "The tolerance threshold for pruning", "Aliases": [ - "tf" + "prth" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 2147483647 - } - ], - "Outputs": [ - { - "Name": "PredictorModel", - "Type": "PredictorModel", - "Desc": "The trained model" - } - ], - "InputKind": [ - "ITrainerInputWithGroupId", - "ITrainerInputWithWeight", - "ITrainerInputWithLabel", - "ITrainerInput" - ], - "OutputKind": [ - "IRegressionOutput", - "ITrainerOutput" - ] - }, - { - "Name": "Trainers.FastTreeTweedieRegressor", - "Desc": "Trains gradient boosted decision trees to fit target values using a Tweedie loss function. This learner is a generalization of Poisson, compound Poisson, and gamma regression.", - "FriendlyName": "FastTree (Boosted Trees) Tweedie Regression", - "ShortName": "fttweedie", - "Inputs": [ + "Default": 0.004 + }, { - "Name": "NumTrees", + "Name": "PruningWindowSize", "Type": "Int", - "Desc": "Number of weak hypotheses in the ensemble", + "Desc": "The moving window size for pruning", "Aliases": [ - "iter" + "prws" ], "Required": false, - "SortOrder": 1.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": 100, - "SweepRange": { - "RangeType": "Discrete", - "Values": [ - 20, - 100, - 500 - ] - } - }, - { - "Name": "TrainingData", - "Type": "DataView", - "Desc": "The data to be used for training", - "Aliases": [ - "data" - ], - "Required": true, - "SortOrder": 1.0, - "IsNullable": false + "Default": 5 }, { - "Name": "NumLeaves", - "Type": "Int", - "Desc": "The max number of leaves in each regression tree", + "Name": "Shrinkage", + "Type": "Float", + "Desc": "Shrinkage", "Aliases": [ - "nl" + "shrk" ], "Required": false, - "SortOrder": 2.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": 20, + "Default": 1.0, "SweepRange": { - "RangeType": "Long", - "Min": 2, - "Max": 128, - "StepSize": 4.0, + "RangeType": "Float", + "Min": 0.025, + "Max": 4.0, "IsLogScale": true } }, { - "Name": "FeatureColumn", - "Type": "String", - "Desc": "Column to use for features", - "Aliases": [ - "feat" - ], - "Required": false, - "SortOrder": 2.0, - "IsNullable": false, - "Default": "Features" - }, - { - "Name": "MinDocumentsInLeafs", - "Type": "Int", - "Desc": "The minimal number of documents allowed in a leaf of a regression tree, out of the subsampled data", + "Name": "DropoutRate", + "Type": "Float", + "Desc": "Dropout rate for tree regularization", "Aliases": [ - "mil" + "tdrop" ], "Required": false, - "SortOrder": 3.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": 10, + "Default": 0.0, "SweepRange": { "RangeType": "Discrete", "Values": [ - 1, - 10, - 50 + 0.0, + 1E-09, + 0.05, + 0.1, + 0.2 ] } }, { - "Name": "LabelColumn", - "Type": "String", - "Desc": "Column to use for labels", + "Name": "GetDerivativesSampleRate", + "Type": "Int", + "Desc": "Sample each query 1 in k times in the GetDerivatives function", "Aliases": [ - "lab" + "sr" ], "Required": false, - "SortOrder": 3.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": "Label" + "Default": 1 }, { - "Name": "LearningRates", - "Type": "Float", - "Desc": "The learning rate", + "Name": "WriteLastEnsemble", + "Type": "Bool", + "Desc": "Write the last ensemble instead of the one determined by early stopping", "Aliases": [ - "lr" + "hl" ], "Required": false, - "SortOrder": 4.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": 0.2, - "SweepRange": { - "RangeType": "Float", - "Min": 0.025, - "Max": 0.4, - "IsLogScale": true - } + "Default": false }, { - "Name": "WeightColumn", - "Type": "String", - "Desc": "Column to use for example weight", + "Name": "MaxTreeOutput", + "Type": "Float", + "Desc": "Upper bound on absolute value of single tree output", "Aliases": [ - "weight" + "mo" ], "Required": false, - "SortOrder": 4.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": "Weight" + "Default": 100.0 }, { - "Name": "GroupIdColumn", - "Type": "String", - "Desc": "Column to use for example groupId", + "Name": "RandomStart", + "Type": "Bool", + "Desc": "Training starts from random ordering (determined by /r1)", "Aliases": [ - "groupId" + "rs" ], "Required": false, - "SortOrder": 5.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": "GroupId" + "Default": false }, { - "Name": "NormalizeFeatures", - "Type": { - "Kind": "Enum", - "Values": [ - "No", - "Warn", - "Auto", - "Yes" - ] - }, - "Desc": "Normalize option for the feature column", + "Name": "FilterZeroLambdas", + "Type": "Bool", + "Desc": "Filter zero lambdas during training", "Aliases": [ - "norm" + "fzl" ], "Required": false, - "SortOrder": 5.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": "Auto" + "Default": false }, { - "Name": "Caching", - "Type": { - "Kind": "Enum", - "Values": [ - "Auto", - "Memory", - "Disk", - "None" - ] - }, - "Desc": "Whether learner should cache input training data", + "Name": "BaselineScoresFormula", + "Type": "String", + "Desc": "Freeform defining the scores that should be used as the baseline ranker", "Aliases": [ - "cache" + "basescores" ], "Required": false, - "SortOrder": 6.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": "Auto" + "Default": null }, { - "Name": "Index", - "Type": "Float", - "Desc": "Index parameter for the Tweedie distribution, in the range [1, 2]. 1 is Poisson loss, 2 is gamma loss, and intermediate values are compound Poisson loss.", + "Name": "BaselineAlphaRisk", + "Type": "String", + "Desc": "Baseline alpha for tradeoffs of risk (0 is normal training)", + "Aliases": [ + "basealpha" + ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 1.5 + "Default": null }, { - "Name": "BestStepRankingRegressionTrees", - "Type": "Bool", - "Desc": "Use best regression step trees?", + "Name": "PositionDiscountFreeform", + "Type": "String", + "Desc": "The discount freeform which specifies the per position discounts of documents in a query (uses a single variable P for position where P=0 is first position)", "Aliases": [ - "bsr" + "pdff" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": false + "Default": null }, { - "Name": "UseLineSearch", - "Type": "Bool", - "Desc": "Should we use line search for a step size", + "Name": "ParallelTrainer", + "Type": { + "Kind": "Component", + "ComponentKind": "ParallelTraining" + }, + "Desc": "Allows to choose Parallel FastTree Learning Algorithm", "Aliases": [ - "ls" + "parag" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": false + "Default": { + "Name": "Single" + } }, { - "Name": "NumPostBracketSteps", + "Name": "NumThreads", "Type": "Int", - "Desc": "Number of post-bracket line search steps", + "Desc": "The number of threads to use", "Aliases": [ - "lssteps" + "t" ], "Required": false, "SortOrder": 150.0, - "IsNullable": false, - "Default": 0 + "IsNullable": true, + "Default": null }, { - "Name": "MinStepSize", - "Type": "Float", - "Desc": "Minimum line search step size", + "Name": "RngSeed", + "Type": "Int", + "Desc": "The seed of the random number generator", "Aliases": [ - "minstep" + "r1" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0.0 + "Default": 123 }, { - "Name": "OptimizationAlgorithm", - "Type": { - "Kind": "Enum", - "Values": [ - "GradientDescent", - "AcceleratedGradientDescent", - "ConjugateGradientDescent" - ] - }, - "Desc": "Optimization algorithm to be used (GradientDescent, AcceleratedGradientDescent)", + "Name": "FeatureSelectSeed", + "Type": "Int", + "Desc": "The seed of the active feature selection", "Aliases": [ - "oa" + "r3" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": "GradientDescent" + "Default": 123 }, { - "Name": "EarlyStoppingRule", - "Type": { - "Kind": "Component", - "ComponentKind": "EarlyStoppingCriterion" - }, - "Desc": "Early stopping rule. (Validation set (/valid) is required.)", + "Name": "EntropyCoefficient", + "Type": "Float", + "Desc": "The entropy (regularization) coefficient between 0 and 1", "Aliases": [ - "esr" + "e" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": null + "Default": 0.0 }, { - "Name": "EarlyStoppingMetrics", + "Name": "HistogramPoolSize", "Type": "Int", - "Desc": "Early stopping metrics. (For regression, 1: L1, 2:L2; for ranking, 1:NDCG@1, 3:NDCG@3)", + "Desc": "The number of histograms in the pool (between 2 and numLeaves)", "Aliases": [ - "esmt" + "ps" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0 + "Default": -1 }, { - "Name": "EnablePruning", + "Name": "DiskTranspose", "Type": "Bool", - "Desc": "Enable post-training pruning to avoid overfitting. (a validation set is required)", + "Desc": "Whether to utilize the disk or the data's native transposition facilities (where applicable) when performing the transpose", "Aliases": [ - "pruning" + "dt" ], "Required": false, "SortOrder": 150.0, - "IsNullable": false, - "Default": false + "IsNullable": true, + "Default": null }, { - "Name": "UseTolerantPruning", + "Name": "FeatureFlocks", "Type": "Bool", - "Desc": "Use window and tolerance for pruning", + "Desc": "Whether to collectivize features during dataset preparation to speed up training", "Aliases": [ - "prtol" + "flocks" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": false + "Default": true }, { - "Name": "PruningThreshold", - "Type": "Float", - "Desc": "The tolerance threshold for pruning", + "Name": "CategoricalSplit", + "Type": "Bool", + "Desc": "Whether to do split based on multiple categorical feature values.", "Aliases": [ - "prth" + "cat" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0.004 + "Default": false }, { - "Name": "PruningWindowSize", + "Name": "MaxCategoricalGroupsPerNode", "Type": "Int", - "Desc": "The moving window size for pruning", + "Desc": "Maximum categorical split groups to consider when splitting on a categorical feature. Split groups are a collection of split points. This is used to reduce overfitting when there many categorical features.", "Aliases": [ - "prws" + "mcg" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 5 + "Default": 64 }, { - "Name": "Shrinkage", - "Type": "Float", - "Desc": "Shrinkage", + "Name": "MaxCategoricalSplitPoints", + "Type": "Int", + "Desc": "Maximum categorical split points to consider when splitting on a categorical feature.", "Aliases": [ - "shrk" + "maxcat" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 1.0, - "SweepRange": { - "RangeType": "Float", - "Min": 0.025, - "Max": 4.0, - "IsLogScale": true - } + "Default": 64 }, { - "Name": "DropoutRate", + "Name": "MinDocsPercentageForCategoricalSplit", "Type": "Float", - "Desc": "Dropout rate for tree regularization", + "Desc": "Minimum categorical docs percentage in a bin to consider for a split.", "Aliases": [ - "tdrop" + "mdop" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0.0, - "SweepRange": { - "RangeType": "Discrete", - "Values": [ - 0.0, - 1E-09, - 0.05, - 0.1, - 0.2 - ] - } + "Default": 0.001 }, { - "Name": "GetDerivativesSampleRate", + "Name": "MinDocsForCategoricalSplit", "Type": "Int", - "Desc": "Sample each query 1 in k times in the GetDerivatives function", + "Desc": "Minimum categorical doc count in a bin to consider for a split.", "Aliases": [ - "sr" + "mdo" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 1 + "Default": 100 }, { - "Name": "WriteLastEnsemble", - "Type": "Bool", - "Desc": "Write the last ensemble instead of the one determined by early stopping", + "Name": "Bias", + "Type": "Float", + "Desc": "Bias for calculating gradient for each feature bin for a categorical feature.", "Aliases": [ - "hl" + "bias" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": false + "Default": 0.0 }, { - "Name": "MaxTreeOutput", - "Type": "Float", - "Desc": "Upper bound on absolute value of single tree output", + "Name": "Bundling", + "Type": { + "Kind": "Enum", + "Values": [ + "None", + "AggregateLowPopulation", + "Adjacent" + ] + }, + "Desc": "Bundle low population bins. Bundle.None(0): no bundling, Bundle.AggregateLowPopulation(1): Bundle low population, Bundle.Adjacent(2): Neighbor low population bundle.", "Aliases": [ - "mo" + "bundle" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 100.0 + "Default": "None" }, { - "Name": "RandomStart", - "Type": "Bool", - "Desc": "Training starts from random ordering (determined by /r1)", + "Name": "MaxBins", + "Type": "Int", + "Desc": "Maximum number of distinct values (bins) per feature", "Aliases": [ - "rs" + "mb" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": false + "Default": 255 }, { - "Name": "FilterZeroLambdas", - "Type": "Bool", - "Desc": "Filter zero lambdas during training", + "Name": "SparsifyThreshold", + "Type": "Float", + "Desc": "Sparsity level needed to use sparse feature representation", "Aliases": [ - "fzl" + "sp" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": false + "Default": 0.7 }, { - "Name": "BaselineScoresFormula", - "Type": "String", - "Desc": "Freeform defining the scores that should be used as the baseline ranker", + "Name": "FeatureFirstUsePenalty", + "Type": "Float", + "Desc": "The feature first use penalty coefficient", "Aliases": [ - "basescores" + "ffup" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": null + "Default": 0.0 }, { - "Name": "BaselineAlphaRisk", - "Type": "String", - "Desc": "Baseline alpha for tradeoffs of risk (0 is normal training)", + "Name": "FeatureReusePenalty", + "Type": "Float", + "Desc": "The feature re-use penalty (regularization) coefficient", "Aliases": [ - "basealpha" + "frup" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": null + "Default": 0.0 }, { - "Name": "PositionDiscountFreeform", - "Type": "String", - "Desc": "The discount freeform which specifies the per position discounts of documents in a query (uses a single variable P for position where P=0 is first position)", + "Name": "GainConfidenceLevel", + "Type": "Float", + "Desc": "Tree fitting gain confidence requirement (should be in the range [0,1) ).", "Aliases": [ - "pdff" + "gainconf" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": null + "Default": 0.0 }, { - "Name": "ParallelTrainer", - "Type": { - "Kind": "Component", - "ComponentKind": "ParallelTraining" - }, - "Desc": "Allows to choose Parallel FastTree Learning Algorithm", + "Name": "SoftmaxTemperature", + "Type": "Float", + "Desc": "The temperature of the randomized softmax distribution for choosing the feature", "Aliases": [ - "parag" + "smtemp" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": { - "Name": "Single" - } + "Default": 0.0 }, { - "Name": "NumThreads", - "Type": "Int", - "Desc": "The number of threads to use", + "Name": "ExecutionTimes", + "Type": "Bool", + "Desc": "Print execution time breakdown to stdout", "Aliases": [ - "t" + "et" ], "Required": false, "SortOrder": 150.0, - "IsNullable": true, - "Default": null + "IsNullable": false, + "Default": false }, { - "Name": "RngSeed", - "Type": "Int", - "Desc": "The seed of the random number generator", + "Name": "FeatureFraction", + "Type": "Float", + "Desc": "The fraction of features (chosen randomly) to use on each iteration", "Aliases": [ - "r1" + "ff" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 123 + "Default": 1.0 }, { - "Name": "FeatureSelectSeed", + "Name": "BaggingSize", "Type": "Int", - "Desc": "The seed of the active feature selection", + "Desc": "Number of trees in each bag (0 for disabling bagging)", "Aliases": [ - "r3" + "bag" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 123 + "Default": 0 }, { - "Name": "EntropyCoefficient", + "Name": "BaggingTrainFraction", "Type": "Float", - "Desc": "The entropy (regularization) coefficient between 0 and 1", - "Aliases": [ - "e" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 0.0 - }, - { - "Name": "HistogramPoolSize", - "Type": "Int", - "Desc": "The number of histograms in the pool (between 2 and numLeaves)", - "Aliases": [ - "ps" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": -1 - }, - { - "Name": "DiskTranspose", - "Type": "Bool", - "Desc": "Whether to utilize the disk or the data's native transposition facilities (where applicable) when performing the transpose", - "Aliases": [ - "dt" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "FeatureFlocks", - "Type": "Bool", - "Desc": "Whether to collectivize features during dataset preparation to speed up training", - "Aliases": [ - "flocks" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": true - }, - { - "Name": "CategoricalSplit", - "Type": "Bool", - "Desc": "Whether to do split based on multiple categorical feature values.", - "Aliases": [ - "cat" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": false - }, - { - "Name": "MaxCategoricalGroupsPerNode", - "Type": "Int", - "Desc": "Maximum categorical split groups to consider when splitting on a categorical feature. Split groups are a collection of split points. This is used to reduce overfitting when there many categorical features.", - "Aliases": [ - "mcg" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 64 - }, - { - "Name": "MaxCategoricalSplitPoints", - "Type": "Int", - "Desc": "Maximum categorical split points to consider when splitting on a categorical feature.", - "Aliases": [ - "maxcat" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 64 - }, - { - "Name": "MinDocsPercentageForCategoricalSplit", - "Type": "Float", - "Desc": "Minimum categorical docs percentage in a bin to consider for a split.", - "Aliases": [ - "mdop" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 0.001 - }, - { - "Name": "MinDocsForCategoricalSplit", - "Type": "Int", - "Desc": "Minimum categorical doc count in a bin to consider for a split.", - "Aliases": [ - "mdo" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 100 - }, - { - "Name": "Bias", - "Type": "Float", - "Desc": "Bias for calculating gradient for each feature bin for a categorical feature.", - "Aliases": [ - "bias" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 0.0 - }, - { - "Name": "Bundling", - "Type": { - "Kind": "Enum", - "Values": [ - "None", - "AggregateLowPopulation", - "Adjacent" - ] - }, - "Desc": "Bundle low population bins. Bundle.None(0): no bundling, Bundle.AggregateLowPopulation(1): Bundle low population, Bundle.Adjacent(2): Neighbor low population bundle.", - "Aliases": [ - "bundle" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": "None" - }, - { - "Name": "MaxBins", - "Type": "Int", - "Desc": "Maximum number of distinct values (bins) per feature", - "Aliases": [ - "mb" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 255 - }, - { - "Name": "SparsifyThreshold", - "Type": "Float", - "Desc": "Sparsity level needed to use sparse feature representation", - "Aliases": [ - "sp" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 0.7 - }, - { - "Name": "FeatureFirstUsePenalty", - "Type": "Float", - "Desc": "The feature first use penalty coefficient", - "Aliases": [ - "ffup" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 0.0 - }, - { - "Name": "FeatureReusePenalty", - "Type": "Float", - "Desc": "The feature re-use penalty (regularization) coefficient", - "Aliases": [ - "frup" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 0.0 - }, - { - "Name": "GainConfidenceLevel", - "Type": "Float", - "Desc": "Tree fitting gain confidence requirement (should be in the range [0,1) ).", - "Aliases": [ - "gainconf" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 0.0 - }, - { - "Name": "SoftmaxTemperature", - "Type": "Float", - "Desc": "The temperature of the randomized softmax distribution for choosing the feature", - "Aliases": [ - "smtemp" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 0.0 - }, - { - "Name": "ExecutionTimes", - "Type": "Bool", - "Desc": "Print execution time breakdown to stdout", - "Aliases": [ - "et" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": false - }, - { - "Name": "FeatureFraction", - "Type": "Float", - "Desc": "The fraction of features (chosen randomly) to use on each iteration", - "Aliases": [ - "ff" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 1.0 - }, - { - "Name": "BaggingSize", - "Type": "Int", - "Desc": "Number of trees in each bag (0 for disabling bagging)", - "Aliases": [ - "bag" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 0 - }, - { - "Name": "BaggingTrainFraction", - "Type": "Float", - "Desc": "Percentage of training examples used in each bag", + "Desc": "Percentage of training examples used in each bag", "Aliases": [ "bagfrac" ], @@ -8500,28 +8752,28 @@ ] }, { - "Name": "Trainers.GeneralizedAdditiveModelBinaryClassifier", - "Desc": "Trains a gradient boosted stump per feature, on all features simultaneously, to fit target values using least-squares. It mantains no interactions between features.", - "FriendlyName": "Generalized Additive Model for Binary Classification", - "ShortName": "gam", + "Name": "Trainers.FastTreeTweedieRegressor", + "Desc": "Trains gradient boosted decision trees to fit target values using a Tweedie loss function. This learner is a generalization of Poisson, compound Poisson, and gamma regression.", + "FriendlyName": "FastTree (Boosted Trees) Tweedie Regression", + "ShortName": "fttweedie", "Inputs": [ { - "Name": "NumIterations", + "Name": "NumTrees", "Type": "Int", - "Desc": "Total number of iterations over all features", + "Desc": "Total number of decision trees to create in the ensemble", "Aliases": [ "iter" ], "Required": false, "SortOrder": 1.0, "IsNullable": false, - "Default": 9500, + "Default": 100, "SweepRange": { "RangeType": "Discrete", "Values": [ - 200, - 1500, - 9500 + 20, + 100, + 500 ] } }, @@ -8536,6 +8788,25 @@ "SortOrder": 1.0, "IsNullable": false }, + { + "Name": "NumLeaves", + "Type": "Int", + "Desc": "The max number of leaves in each regression tree", + "Aliases": [ + "nl" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": 20, + "SweepRange": { + "RangeType": "Long", + "Min": 2, + "Max": 128, + "StepSize": 4.0, + "IsLogScale": true + } + }, { "Name": "FeatureColumn", "Type": "String", @@ -8549,11 +8820,11 @@ "Default": "Features" }, { - "Name": "MinDocuments", + "Name": "MinDocumentsInLeafs", "Type": "Int", - "Desc": "Minimum number of training instances required to form a partition", + "Desc": "The minimal number of documents allowed in a leaf of a regression tree, out of the subsampled data", "Aliases": [ - "mi" + "mil" ], "Required": false, "SortOrder": 3.0, @@ -8590,11 +8861,11 @@ "Required": false, "SortOrder": 4.0, "IsNullable": false, - "Default": 0.002, + "Default": 0.2, "SweepRange": { "RangeType": "Float", - "Min": 0.001, - "Max": 0.1, + "Min": 0.025, + "Max": 0.4, "IsLogScale": true } }, @@ -8610,6 +8881,18 @@ "IsNullable": false, "Default": "Weight" }, + { + "Name": "GroupIdColumn", + "Type": "String", + "Desc": "Column to use for example groupId", + "Aliases": [ + "groupId" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "GroupId" + }, { "Name": "NormalizeFeatures", "Type": { @@ -8651,11 +8934,20 @@ "Default": "Auto" }, { - "Name": "UnbalancedSets", + "Name": "Index", + "Type": "Float", + "Desc": "Index parameter for the Tweedie distribution, in the range [1, 2]. 1 is Poisson loss, 2 is gamma loss, and intermediate values are compound Poisson loss.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.5 + }, + { + "Name": "BestStepRankingRegressionTrees", "Type": "Bool", - "Desc": "Should we use derivatives optimized for unbalanced sets", + "Desc": "Use best regression step trees?", "Aliases": [ - "us" + "bsr" ], "Required": false, "SortOrder": 150.0, @@ -8663,34 +8955,35 @@ "Default": false }, { - "Name": "Calibrator", - "Type": { - "Kind": "Component", - "ComponentKind": "CalibratorTrainer" - }, - "Desc": "The calibrator kind to apply to the predictor. Specify null for no calibration", + "Name": "UseLineSearch", + "Type": "Bool", + "Desc": "Should we use line search for a step size", + "Aliases": [ + "ls" + ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": { - "Name": "PlattCalibrator" - } + "Default": false }, { - "Name": "MaxCalibrationExamples", + "Name": "NumPostBracketSteps", "Type": "Int", - "Desc": "The maximum number of examples to use when training the calibrator", + "Desc": "Number of post-bracket line search steps", + "Aliases": [ + "lssteps" + ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 1000000 + "Default": 0 }, { - "Name": "EntropyCoefficient", + "Name": "MinStepSize", "Type": "Float", - "Desc": "The entropy (regularization) coefficient between 0 and 1", + "Desc": "Minimum line search step size", "Aliases": [ - "e" + "minstep" ], "Required": false, "SortOrder": 150.0, @@ -8698,293 +8991,251 @@ "Default": 0.0 }, { - "Name": "GainConfidenceLevel", - "Type": "Int", - "Desc": "Tree fitting gain confidence requirement (should be in the range [0,1) ).", + "Name": "OptimizationAlgorithm", + "Type": { + "Kind": "Enum", + "Values": [ + "GradientDescent", + "AcceleratedGradientDescent", + "ConjugateGradientDescent" + ] + }, + "Desc": "Optimization algorithm to be used (GradientDescent, AcceleratedGradientDescent)", "Aliases": [ - "gainconf" + "oa" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0 + "Default": "GradientDescent" }, { - "Name": "NumThreads", - "Type": "Int", - "Desc": "The number of threads to use", + "Name": "EarlyStoppingRule", + "Type": { + "Kind": "Component", + "ComponentKind": "EarlyStoppingCriterion" + }, + "Desc": "Early stopping rule. (Validation set (/valid) is required.)", "Aliases": [ - "t" + "esr" ], "Required": false, "SortOrder": 150.0, - "IsNullable": true, + "IsNullable": false, "Default": null }, { - "Name": "DiskTranspose", - "Type": "Bool", - "Desc": "Whether to utilize the disk or the data's native transposition facilities (where applicable) when performing the transpose", + "Name": "EarlyStoppingMetrics", + "Type": "Int", + "Desc": "Early stopping metrics. (For regression, 1: L1, 2:L2; for ranking, 1:NDCG@1, 3:NDCG@3)", "Aliases": [ - "dt" + "esmt" ], "Required": false, "SortOrder": 150.0, - "IsNullable": true, - "Default": null + "IsNullable": false, + "Default": 0 }, { - "Name": "MaxBins", - "Type": "Int", - "Desc": "Maximum number of distinct values (bins) per feature", + "Name": "EnablePruning", + "Type": "Bool", + "Desc": "Enable post-training pruning to avoid overfitting. (a validation set is required)", "Aliases": [ - "mb" + "pruning" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 255 + "Default": false }, { - "Name": "MaxOutput", - "Type": "Float", - "Desc": "Upper bound on absolute value of single output", + "Name": "UseTolerantPruning", + "Type": "Bool", + "Desc": "Use window and tolerance for pruning", "Aliases": [ - "mo" + "prtol" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": "Infinity" + "Default": false }, { - "Name": "GetDerivativesSampleRate", - "Type": "Int", - "Desc": "Sample each query 1 in k times in the GetDerivatives function", + "Name": "PruningThreshold", + "Type": "Float", + "Desc": "The tolerance threshold for pruning", "Aliases": [ - "sr" + "prth" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 1 + "Default": 0.004 }, { - "Name": "RngSeed", + "Name": "PruningWindowSize", "Type": "Int", - "Desc": "The seed of the random number generator", + "Desc": "The moving window size for pruning", "Aliases": [ - "r1" + "prws" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 123 + "Default": 5 }, { - "Name": "FeatureFlocks", - "Type": "Bool", - "Desc": "Whether to collectivize features during dataset preparation to speed up training", + "Name": "Shrinkage", + "Type": "Float", + "Desc": "Shrinkage", "Aliases": [ - "flocks" + "shrk" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": true - } - ], - "Outputs": [ - { - "Name": "PredictorModel", - "Type": "PredictorModel", - "Desc": "The trained model" - } - ], - "InputKind": [ - "ITrainerInputWithWeight", - "ITrainerInputWithLabel", - "ITrainerInput" - ], - "OutputKind": [ - "IBinaryClassificationOutput", - "ITrainerOutput" - ] - }, - { - "Name": "Trainers.GeneralizedAdditiveModelRegressor", - "Desc": "Trains a gradient boosted stump per feature, on all features simultaneously, to fit target values using least-squares. It mantains no interactions between features.", - "FriendlyName": "Generalized Additive Model for Regression", - "ShortName": "gamr", - "Inputs": [ + "Default": 1.0, + "SweepRange": { + "RangeType": "Float", + "Min": 0.025, + "Max": 4.0, + "IsLogScale": true + } + }, { - "Name": "NumIterations", - "Type": "Int", - "Desc": "Total number of iterations over all features", + "Name": "DropoutRate", + "Type": "Float", + "Desc": "Dropout rate for tree regularization", "Aliases": [ - "iter" + "tdrop" ], "Required": false, - "SortOrder": 1.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": 9500, + "Default": 0.0, "SweepRange": { "RangeType": "Discrete", "Values": [ - 200, - 1500, - 9500 + 0.0, + 1E-09, + 0.05, + 0.1, + 0.2 ] } }, { - "Name": "TrainingData", - "Type": "DataView", - "Desc": "The data to be used for training", + "Name": "GetDerivativesSampleRate", + "Type": "Int", + "Desc": "Sample each query 1 in k times in the GetDerivatives function", "Aliases": [ - "data" + "sr" ], - "Required": true, - "SortOrder": 1.0, - "IsNullable": false + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1 }, { - "Name": "FeatureColumn", - "Type": "String", - "Desc": "Column to use for features", + "Name": "WriteLastEnsemble", + "Type": "Bool", + "Desc": "Write the last ensemble instead of the one determined by early stopping", "Aliases": [ - "feat" + "hl" ], "Required": false, - "SortOrder": 2.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": "Features" + "Default": false }, { - "Name": "MinDocuments", - "Type": "Int", - "Desc": "Minimum number of training instances required to form a partition", + "Name": "MaxTreeOutput", + "Type": "Float", + "Desc": "Upper bound on absolute value of single tree output", "Aliases": [ - "mi" + "mo" ], "Required": false, - "SortOrder": 3.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": 10, - "SweepRange": { - "RangeType": "Discrete", - "Values": [ - 1, - 10, - 50 - ] - } + "Default": 100.0 }, { - "Name": "LabelColumn", - "Type": "String", - "Desc": "Column to use for labels", + "Name": "RandomStart", + "Type": "Bool", + "Desc": "Training starts from random ordering (determined by /r1)", "Aliases": [ - "lab" + "rs" ], "Required": false, - "SortOrder": 3.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": "Label" + "Default": false }, { - "Name": "LearningRates", - "Type": "Float", - "Desc": "The learning rate", + "Name": "FilterZeroLambdas", + "Type": "Bool", + "Desc": "Filter zero lambdas during training", "Aliases": [ - "lr" + "fzl" ], "Required": false, - "SortOrder": 4.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": 0.002, - "SweepRange": { - "RangeType": "Float", - "Min": 0.001, - "Max": 0.1, - "IsLogScale": true - } + "Default": false }, { - "Name": "WeightColumn", + "Name": "BaselineScoresFormula", "Type": "String", - "Desc": "Column to use for example weight", - "Aliases": [ - "weight" - ], - "Required": false, - "SortOrder": 4.0, - "IsNullable": false, - "Default": "Weight" - }, - { - "Name": "NormalizeFeatures", - "Type": { - "Kind": "Enum", - "Values": [ - "No", - "Warn", - "Auto", - "Yes" - ] - }, - "Desc": "Normalize option for the feature column", + "Desc": "Freeform defining the scores that should be used as the baseline ranker", "Aliases": [ - "norm" + "basescores" ], "Required": false, - "SortOrder": 5.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": "Auto" + "Default": null }, { - "Name": "Caching", - "Type": { - "Kind": "Enum", - "Values": [ - "Auto", - "Memory", - "Disk", - "None" - ] - }, - "Desc": "Whether learner should cache input training data", + "Name": "BaselineAlphaRisk", + "Type": "String", + "Desc": "Baseline alpha for tradeoffs of risk (0 is normal training)", "Aliases": [ - "cache" + "basealpha" ], "Required": false, - "SortOrder": 6.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": "Auto" + "Default": null }, { - "Name": "EntropyCoefficient", - "Type": "Float", - "Desc": "The entropy (regularization) coefficient between 0 and 1", + "Name": "PositionDiscountFreeform", + "Type": "String", + "Desc": "The discount freeform which specifies the per position discounts of documents in a query (uses a single variable P for position where P=0 is first position)", "Aliases": [ - "e" + "pdff" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0.0 + "Default": null }, { - "Name": "GainConfidenceLevel", - "Type": "Int", - "Desc": "Tree fitting gain confidence requirement (should be in the range [0,1) ).", + "Name": "ParallelTrainer", + "Type": { + "Kind": "Component", + "ComponentKind": "ParallelTraining" + }, + "Desc": "Allows to choose Parallel FastTree Learning Algorithm", "Aliases": [ - "gainconf" + "parag" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0 + "Default": { + "Name": "Single" + } }, { "Name": "NumThreads", @@ -8999,64 +9250,64 @@ "Default": null }, { - "Name": "DiskTranspose", - "Type": "Bool", - "Desc": "Whether to utilize the disk or the data's native transposition facilities (where applicable) when performing the transpose", + "Name": "RngSeed", + "Type": "Int", + "Desc": "The seed of the random number generator", "Aliases": [ - "dt" + "r1" ], "Required": false, "SortOrder": 150.0, - "IsNullable": true, - "Default": null + "IsNullable": false, + "Default": 123 }, { - "Name": "MaxBins", + "Name": "FeatureSelectSeed", "Type": "Int", - "Desc": "Maximum number of distinct values (bins) per feature", + "Desc": "The seed of the active feature selection", "Aliases": [ - "mb" + "r3" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 255 + "Default": 123 }, { - "Name": "MaxOutput", + "Name": "EntropyCoefficient", "Type": "Float", - "Desc": "Upper bound on absolute value of single output", + "Desc": "The entropy (regularization) coefficient between 0 and 1", "Aliases": [ - "mo" + "e" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": "Infinity" + "Default": 0.0 }, { - "Name": "GetDerivativesSampleRate", + "Name": "HistogramPoolSize", "Type": "Int", - "Desc": "Sample each query 1 in k times in the GetDerivatives function", + "Desc": "The number of histograms in the pool (between 2 and numLeaves)", "Aliases": [ - "sr" + "ps" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 1 + "Default": -1 }, { - "Name": "RngSeed", - "Type": "Int", - "Desc": "The seed of the random number generator", + "Name": "DiskTranspose", + "Type": "Bool", + "Desc": "Whether to utilize the disk or the data's native transposition facilities (where applicable) when performing the transpose", "Aliases": [ - "r1" + "dt" ], "Required": false, "SortOrder": 150.0, - "IsNullable": false, - "Default": 123 + "IsNullable": true, + "Default": null }, { "Name": "FeatureFlocks", @@ -9069,459 +9320,326 @@ "SortOrder": 150.0, "IsNullable": false, "Default": true - } - ], - "Outputs": [ - { - "Name": "PredictorModel", - "Type": "PredictorModel", - "Desc": "The trained model" - } - ], - "InputKind": [ - "ITrainerInputWithWeight", - "ITrainerInputWithLabel", - "ITrainerInput" - ], - "OutputKind": [ - "IRegressionOutput", - "ITrainerOutput" - ] - }, - { - "Name": "Trainers.KMeansPlusPlusClusterer", - "Desc": "K-means is a popular clustering algorithm. With K-means, the data is clustered into a specified number of clusters in order to minimize the within-cluster sum of squares. K-means++ improves upon K-means by using a better method for choosing the initial cluster centers.", - "FriendlyName": "KMeans++ Clustering", - "ShortName": "KM", - "Inputs": [ + }, { - "Name": "TrainingData", - "Type": "DataView", - "Desc": "The data to be used for training", + "Name": "CategoricalSplit", + "Type": "Bool", + "Desc": "Whether to do split based on multiple categorical feature values.", "Aliases": [ - "data" + "cat" ], - "Required": true, - "SortOrder": 1.0, - "IsNullable": false + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false }, { - "Name": "FeatureColumn", - "Type": "String", - "Desc": "Column to use for features", + "Name": "MaxCategoricalGroupsPerNode", + "Type": "Int", + "Desc": "Maximum categorical split groups to consider when splitting on a categorical feature. Split groups are a collection of split points. This is used to reduce overfitting when there many categorical features.", "Aliases": [ - "feat" + "mcg" ], "Required": false, - "SortOrder": 2.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": "Features" + "Default": 64 }, { - "Name": "WeightColumn", - "Type": "String", - "Desc": "Column to use for example weight", + "Name": "MaxCategoricalSplitPoints", + "Type": "Int", + "Desc": "Maximum categorical split points to consider when splitting on a categorical feature.", "Aliases": [ - "weight" + "maxcat" ], "Required": false, - "SortOrder": 4.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": "Weight" + "Default": 64 }, { - "Name": "NormalizeFeatures", - "Type": { - "Kind": "Enum", - "Values": [ - "No", - "Warn", - "Auto", - "Yes" - ] - }, - "Desc": "Normalize option for the feature column", + "Name": "MinDocsPercentageForCategoricalSplit", + "Type": "Float", + "Desc": "Minimum categorical docs percentage in a bin to consider for a split.", "Aliases": [ - "norm" + "mdop" ], "Required": false, - "SortOrder": 5.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": "Auto" + "Default": 0.001 }, { - "Name": "Caching", - "Type": { - "Kind": "Enum", - "Values": [ - "Auto", - "Memory", - "Disk", - "None" - ] - }, - "Desc": "Whether learner should cache input training data", + "Name": "MinDocsForCategoricalSplit", + "Type": "Int", + "Desc": "Minimum categorical doc count in a bin to consider for a split.", "Aliases": [ - "cache" + "mdo" ], "Required": false, - "SortOrder": 6.0, - "IsNullable": false, - "Default": "Auto" - }, - { - "Name": "K", - "Type": "Int", - "Desc": "The number of clusters", - "Required": false, - "SortOrder": 50.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": 5, - "SweepRange": { - "RangeType": "Discrete", - "Values": [ - 5, - 10, - 20, - 40 - ] - } + "Default": 100 }, { - "Name": "NumThreads", - "Type": "Int", - "Desc": "Degree of lock-free parallelism. Defaults to automatic. Determinism not guaranteed.", + "Name": "Bias", + "Type": "Float", + "Desc": "Bias for calculating gradient for each feature bin for a categorical feature.", "Aliases": [ - "nt", - "t", - "threads" + "bias" ], "Required": false, - "SortOrder": 50.0, - "IsNullable": true, - "Default": null + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 }, { - "Name": "InitAlgorithm", + "Name": "Bundling", "Type": { "Kind": "Enum", "Values": [ - "KMeansPlusPlus", - "Random", - "KMeansParallel" + "None", + "AggregateLowPopulation", + "Adjacent" ] }, - "Desc": "Cluster initialization algorithm", + "Desc": "Bundle low population bins. Bundle.None(0): no bundling, Bundle.AggregateLowPopulation(1): Bundle low population, Bundle.Adjacent(2): Neighbor low population bundle.", "Aliases": [ - "init" + "bundle" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": "KMeansParallel" + "Default": "None" }, { - "Name": "OptTol", - "Type": "Float", - "Desc": "Tolerance parameter for trainer convergence. Lower = slower, more accurate", + "Name": "MaxBins", + "Type": "Int", + "Desc": "Maximum number of distinct values (bins) per feature", "Aliases": [ - "ot" + "mb" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 1E-07 + "Default": 255 }, { - "Name": "MaxIterations", - "Type": "Int", - "Desc": "Maximum number of iterations.", + "Name": "SparsifyThreshold", + "Type": "Float", + "Desc": "Sparsity level needed to use sparse feature representation", "Aliases": [ - "maxiter" + "sp" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 1000 + "Default": 0.7 }, { - "Name": "AccelMemBudgetMb", - "Type": "Int", - "Desc": "Memory budget (in MBs) to use for KMeans acceleration", + "Name": "FeatureFirstUsePenalty", + "Type": "Float", + "Desc": "The feature first use penalty coefficient", "Aliases": [ - "accelMemBudgetMb" + "ffup" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 4096 - } - ], - "Outputs": [ - { - "Name": "PredictorModel", - "Type": "PredictorModel", - "Desc": "The trained model" - } - ], - "InputKind": [ - "IUnsupervisedTrainerWithWeight", - "ITrainerInput" - ], - "OutputKind": [ - "IClusteringOutput", - "ITrainerOutput" - ] - }, - { - "Name": "Trainers.LinearSvmBinaryClassifier", - "Desc": "Train a linear SVM.", - "FriendlyName": "SVM (Pegasos-Linear)", - "ShortName": "svm", - "Inputs": [ - { - "Name": "TrainingData", - "Type": "DataView", - "Desc": "The data to be used for training", - "Aliases": [ - "data" - ], - "Required": true, - "SortOrder": 1.0, - "IsNullable": false + "Default": 0.0 }, { - "Name": "FeatureColumn", - "Type": "String", - "Desc": "Column to use for features", + "Name": "FeatureReusePenalty", + "Type": "Float", + "Desc": "The feature re-use penalty (regularization) coefficient", "Aliases": [ - "feat" + "frup" ], "Required": false, - "SortOrder": 2.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": "Features" + "Default": 0.0 }, { - "Name": "LabelColumn", - "Type": "String", - "Desc": "Column to use for labels", + "Name": "GainConfidenceLevel", + "Type": "Float", + "Desc": "Tree fitting gain confidence requirement (should be in the range [0,1) ).", "Aliases": [ - "lab" + "gainconf" ], "Required": false, - "SortOrder": 3.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": "Label" + "Default": 0.0 }, { - "Name": "NormalizeFeatures", - "Type": { - "Kind": "Enum", - "Values": [ - "No", - "Warn", - "Auto", - "Yes" - ] - }, - "Desc": "Normalize option for the feature column", + "Name": "SoftmaxTemperature", + "Type": "Float", + "Desc": "The temperature of the randomized softmax distribution for choosing the feature", "Aliases": [ - "norm" + "smtemp" ], "Required": false, - "SortOrder": 5.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": "Auto" + "Default": 0.0 }, { - "Name": "Caching", - "Type": { - "Kind": "Enum", - "Values": [ - "Auto", - "Memory", - "Disk", - "None" - ] - }, - "Desc": "Whether learner should cache input training data", + "Name": "ExecutionTimes", + "Type": "Bool", + "Desc": "Print execution time breakdown to stdout", "Aliases": [ - "cache" + "et" ], "Required": false, - "SortOrder": 6.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": "Auto" + "Default": false }, { - "Name": "Lambda", + "Name": "FeatureFraction", "Type": "Float", - "Desc": "Regularizer constant", + "Desc": "The fraction of features (chosen randomly) to use on each iteration", "Aliases": [ - "lambda" + "ff" ], "Required": false, - "SortOrder": 50.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": 0.001, - "SweepRange": { - "RangeType": "Float", - "Min": 1E-05, - "Max": 0.1, - "StepSize": 10.0, - "IsLogScale": true - } + "Default": 1.0 }, { - "Name": "PerformProjection", - "Type": "Bool", - "Desc": "Perform projection to unit-ball? Typically used with batch size > 1.", + "Name": "BaggingSize", + "Type": "Int", + "Desc": "Number of trees in each bag (0 for disabling bagging)", "Aliases": [ - "project" + "bag" ], "Required": false, - "SortOrder": 50.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": false, - "SweepRange": { - "RangeType": "Discrete", - "Values": [ - false, - true - ] - } + "Default": 0 }, { - "Name": "NumIterations", - "Type": "Int", - "Desc": "Number of iterations", + "Name": "BaggingTrainFraction", + "Type": "Float", + "Desc": "Percentage of training examples used in each bag", "Aliases": [ - "iter" + "bagfrac" ], "Required": false, - "SortOrder": 50.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": 1, - "SweepRange": { - "RangeType": "Long", - "Min": 1, - "Max": 100, - "StepSize": 10.0, - "IsLogScale": true - } + "Default": 0.7 }, { - "Name": "InitWtsDiameter", + "Name": "SplitFraction", "Type": "Float", - "Desc": "Init weights diameter", + "Desc": "The fraction of features (chosen randomly) to use on each split", "Aliases": [ - "initwts" + "sf" ], "Required": false, - "SortOrder": 140.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": 0.0, - "SweepRange": { - "RangeType": "Float", - "Min": 0.0, - "Max": 1.0, - "NumSteps": 5 - } + "Default": 1.0 }, { - "Name": "NoBias", - "Type": "Bool", - "Desc": "No bias", + "Name": "Smoothing", + "Type": "Float", + "Desc": "Smoothing paramter for tree regularization", + "Aliases": [ + "s" + ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": false, - "SweepRange": { - "RangeType": "Discrete", - "Values": [ - false, - true - ] - } + "Default": 0.0 }, { - "Name": "Calibrator", - "Type": { - "Kind": "Component", - "ComponentKind": "CalibratorTrainer" - }, - "Desc": "The calibrator kind to apply to the predictor. Specify null for no calibration", + "Name": "AllowEmptyTrees", + "Type": "Bool", + "Desc": "When a root split is impossible, allow training to proceed", + "Aliases": [ + "allowempty", + "dummies" + ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": { - "Name": "PlattCalibrator" - } + "Default": true }, { - "Name": "MaxCalibrationExamples", + "Name": "FeatureCompressionLevel", "Type": "Int", - "Desc": "The maximum number of examples to use when training the calibrator", + "Desc": "The level of feature compression to use", + "Aliases": [ + "fcomp" + ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 1000000 + "Default": 1 }, { - "Name": "InitialWeights", - "Type": "String", - "Desc": "Initial Weights and bias, comma-separated", + "Name": "CompressEnsemble", + "Type": "Bool", + "Desc": "Compress the tree Ensemble", "Aliases": [ - "initweights" + "cmp" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": null + "Default": false }, { - "Name": "Shuffle", + "Name": "MaxTreesAfterCompression", + "Type": "Int", + "Desc": "Maximum Number of trees after compression", + "Aliases": [ + "cmpmax" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": -1 + }, + { + "Name": "PrintTestGraph", "Type": "Bool", - "Desc": "Whether to shuffle for each training iteration", + "Desc": "Print metrics graph for the first test set", "Aliases": [ - "shuf" + "graph" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": true, - "SweepRange": { - "RangeType": "Discrete", - "Values": [ - false, - true - ] - } + "Default": false }, { - "Name": "StreamingCacheSize", - "Type": "Int", - "Desc": "Size of cache when trained in Scope", + "Name": "PrintTrainValidGraph", + "Type": "Bool", + "Desc": "Print Train and Validation metrics in graph", "Aliases": [ - "cache" + "graphtv" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 1000000 + "Default": false }, { - "Name": "BatchSize", + "Name": "TestFrequency", "Type": "Int", - "Desc": "Batch size", + "Desc": "Calculate metric values for train/valid/test every k rounds", "Aliases": [ - "batch" + "tf" ], "Required": false, - "SortOrder": 190.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": 1 + "Default": 2147483647 } ], "Outputs": [ @@ -9532,20 +9650,40 @@ } ], "InputKind": [ + "ITrainerInputWithGroupId", + "ITrainerInputWithWeight", "ITrainerInputWithLabel", "ITrainerInput" ], "OutputKind": [ - "IBinaryClassificationOutput", + "IRegressionOutput", "ITrainerOutput" ] }, { - "Name": "Trainers.LogisticRegressionBinaryClassifier", - "Desc": "Train a logistic regression binary model", - "FriendlyName": "Logistic Regression", - "ShortName": "lr", + "Name": "Trainers.FieldAwareFactorizationMachineBinaryClassifier", + "Desc": "Train a field-aware factorization machine for binary classification", + "FriendlyName": "Field-aware Factorization Machine", + "ShortName": "ffm", "Inputs": [ + { + "Name": "LearningRate", + "Type": "Float", + "Desc": "Initial learning rate", + "Aliases": [ + "lr" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": 0.1, + "SweepRange": { + "RangeType": "Float", + "Min": 0.001, + "Max": 1.0, + "IsLogScale": true + } + }, { "Name": "TrainingData", "Type": "DataView", @@ -9557,6 +9695,23 @@ "SortOrder": 1.0, "IsNullable": false }, + { + "Name": "Iters", + "Type": "Int", + "Desc": "Number of training iterations", + "Aliases": [ + "iter" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": 5, + "SweepRange": { + "RangeType": "Long", + "Min": 1, + "Max": 100 + } + }, { "Name": "FeatureColumn", "Type": "String", @@ -9569,6 +9724,23 @@ "IsNullable": false, "Default": "Features" }, + { + "Name": "LatentDim", + "Type": "Int", + "Desc": "Latent space dimension", + "Aliases": [ + "d" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": 20, + "SweepRange": { + "RangeType": "Long", + "Min": 4, + "Max": 100 + } + }, { "Name": "LabelColumn", "Type": "String", @@ -9582,16 +9754,40 @@ "Default": "Label" }, { - "Name": "WeightColumn", - "Type": "String", - "Desc": "Column to use for example weight", + "Name": "LambdaLinear", + "Type": "Float", + "Desc": "Regularization coefficient of linear weights", "Aliases": [ - "weight" + "lambdaLinear" ], "Required": false, "SortOrder": 4.0, "IsNullable": false, - "Default": "Weight" + "Default": 0.0001, + "SweepRange": { + "RangeType": "Float", + "Min": 1E-08, + "Max": 1.0, + "IsLogScale": true + } + }, + { + "Name": "LambdaLatent", + "Type": "Float", + "Desc": "Regularization coefficient of latent weights", + "Aliases": [ + "lambdaLatent" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": 0.0001, + "SweepRange": { + "RangeType": "Float", + "Min": 1E-08, + "Max": 1.0, + "IsLogScale": true + } }, { "Name": "NormalizeFeatures", @@ -9613,6 +9809,18 @@ "IsNullable": false, "Default": "Auto" }, + { + "Name": "Norm", + "Type": "Bool", + "Desc": "Whether to normalize the input vectors so that the concatenation of all fields' feature vectors is unit-length", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": true + }, { "Name": "Caching", "Type": { @@ -9634,208 +9842,48 @@ "Default": "Auto" }, { - "Name": "ShowTrainingStats", + "Name": "Shuffle", "Type": "Bool", - "Desc": "Show statistics of training examples.", + "Desc": "Whether to shuffle for each training iteration", "Aliases": [ - "stat" + "shuf" ], "Required": false, - "SortOrder": 50.0, + "SortOrder": 90.0, "IsNullable": false, - "Default": false + "Default": true }, { - "Name": "L2Weight", - "Type": "Float", - "Desc": "L2 regularization weight", + "Name": "Verbose", + "Type": "Bool", + "Desc": "Report traning progress or not", "Aliases": [ - "l2" + "verbose" ], "Required": false, - "SortOrder": 50.0, + "SortOrder": 91.0, "IsNullable": false, - "Default": 1.0, - "SweepRange": { - "RangeType": "Float", - "Min": 0.0, - "Max": 1.0, - "NumSteps": 4 - } + "Default": true }, { - "Name": "L1Weight", + "Name": "Radius", "Type": "Float", - "Desc": "L1 regularization weight", + "Desc": "Radius of initial latent factors", "Aliases": [ - "l1" + "rad" ], "Required": false, - "SortOrder": 50.0, + "SortOrder": 110.0, "IsNullable": false, - "Default": 1.0, + "Default": 0.5, "SweepRange": { "RangeType": "Float", - "Min": 0.0, - "Max": 1.0, - "NumSteps": 4 + "Min": 0.1, + "Max": 1.0 } - }, - { - "Name": "OptTol", - "Type": "Float", - "Desc": "Tolerance parameter for optimization convergence. Lower = slower, more accurate", - "Aliases": [ - "ot" - ], - "Required": false, - "SortOrder": 50.0, - "IsNullable": false, - "Default": 1E-07, - "SweepRange": { - "RangeType": "Discrete", - "Values": [ - 0.0001, - 1E-07 - ] - } - }, - { - "Name": "MemorySize", - "Type": "Int", - "Desc": "Memory size for L-BFGS. Lower=faster, less accurate", - "Aliases": [ - "m" - ], - "Required": false, - "SortOrder": 50.0, - "IsNullable": false, - "Default": 20, - "SweepRange": { - "RangeType": "Discrete", - "Values": [ - 5, - 20, - 50 - ] - } - }, - { - "Name": "EnforceNonNegativity", - "Type": "Bool", - "Desc": "Enforce non-negative weights", - "Aliases": [ - "nn" - ], - "Required": false, - "SortOrder": 90.0, - "IsNullable": false, - "Default": false - }, - { - "Name": "InitWtsDiameter", - "Type": "Float", - "Desc": "Init weights diameter", - "Aliases": [ - "initwts" - ], - "Required": false, - "SortOrder": 140.0, - "IsNullable": false, - "Default": 0.0, - "SweepRange": { - "RangeType": "Float", - "Min": 0.0, - "Max": 1.0, - "NumSteps": 5 - } - }, - { - "Name": "MaxIterations", - "Type": "Int", - "Desc": "Maximum iterations.", - "Aliases": [ - "maxiter" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 2147483647, - "SweepRange": { - "RangeType": "Long", - "Min": 1, - "Max": 2147483647 - } - }, - { - "Name": "SgdInitializationTolerance", - "Type": "Float", - "Desc": "Run SGD to initialize LR weights, converging to this tolerance", - "Aliases": [ - "sgd" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 0.0 - }, - { - "Name": "Quiet", - "Type": "Bool", - "Desc": "If set to true, produce no output during training.", - "Aliases": [ - "q" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": false - }, - { - "Name": "UseThreads", - "Type": "Bool", - "Desc": "Whether or not to use threads. Default is true", - "Aliases": [ - "t" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": true - }, - { - "Name": "NumThreads", - "Type": "Int", - "Desc": "Number of threads", - "Aliases": [ - "nt" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "DenseOptimizer", - "Type": "Bool", - "Desc": "Force densification of the internal optimization vectors", - "Aliases": [ - "do" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": false, - "SweepRange": { - "RangeType": "Discrete", - "Values": [ - false, - true - ] - } - } - ], - "Outputs": [ + } + ], + "Outputs": [ { "Name": "PredictorModel", "Type": "PredictorModel", @@ -9843,7 +9891,6 @@ } ], "InputKind": [ - "ITrainerInputWithWeight", "ITrainerInputWithLabel", "ITrainerInput" ], @@ -9853,11 +9900,31 @@ ] }, { - "Name": "Trainers.LogisticRegressionClassifier", - "Desc": "Train a logistic regression multi class model", - "FriendlyName": "Multi-class Logistic Regression", - "ShortName": "mlr", + "Name": "Trainers.GeneralizedAdditiveModelBinaryClassifier", + "Desc": "Trains a gradient boosted stump per feature, on all features simultaneously, to fit target values using least-squares. It mantains no interactions between features.", + "FriendlyName": "Generalized Additive Model for Binary Classification", + "ShortName": "gam", "Inputs": [ + { + "Name": "NumIterations", + "Type": "Int", + "Desc": "Total number of iterations over all features", + "Aliases": [ + "iter" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": 9500, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 200, + 1500, + 9500 + ] + } + }, { "Name": "TrainingData", "Type": "DataView", @@ -9881,6 +9948,26 @@ "IsNullable": false, "Default": "Features" }, + { + "Name": "MinDocuments", + "Type": "Int", + "Desc": "Minimum number of training instances required to form a partition", + "Aliases": [ + "mi" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": 10, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 1, + 10, + 50 + ] + } + }, { "Name": "LabelColumn", "Type": "String", @@ -9893,6 +9980,24 @@ "IsNullable": false, "Default": "Label" }, + { + "Name": "LearningRates", + "Type": "Float", + "Desc": "The learning rate", + "Aliases": [ + "lr" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": 0.002, + "SweepRange": { + "RangeType": "Float", + "Min": 0.001, + "Max": 0.1, + "IsLogScale": true + } + }, { "Name": "WeightColumn", "Type": "String", @@ -9946,205 +10051,147 @@ "Default": "Auto" }, { - "Name": "ShowTrainingStats", + "Name": "UnbalancedSets", "Type": "Bool", - "Desc": "Show statistics of training examples.", + "Desc": "Should we use derivatives optimized for unbalanced sets", "Aliases": [ - "stat" + "us" ], "Required": false, - "SortOrder": 50.0, + "SortOrder": 150.0, "IsNullable": false, "Default": false }, { - "Name": "L2Weight", - "Type": "Float", - "Desc": "L2 regularization weight", - "Aliases": [ - "l2" - ], + "Name": "Calibrator", + "Type": { + "Kind": "Component", + "ComponentKind": "CalibratorTrainer" + }, + "Desc": "The calibrator kind to apply to the predictor. Specify null for no calibration", "Required": false, - "SortOrder": 50.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": 1.0, - "SweepRange": { - "RangeType": "Float", - "Min": 0.0, - "Max": 1.0, - "NumSteps": 4 + "Default": { + "Name": "PlattCalibrator" } }, { - "Name": "L1Weight", - "Type": "Float", - "Desc": "L1 regularization weight", - "Aliases": [ - "l1" - ], + "Name": "MaxCalibrationExamples", + "Type": "Int", + "Desc": "The maximum number of examples to use when training the calibrator", "Required": false, - "SortOrder": 50.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": 1.0, - "SweepRange": { - "RangeType": "Float", - "Min": 0.0, - "Max": 1.0, - "NumSteps": 4 - } + "Default": 1000000 }, { - "Name": "OptTol", + "Name": "EntropyCoefficient", "Type": "Float", - "Desc": "Tolerance parameter for optimization convergence. Lower = slower, more accurate", + "Desc": "The entropy (regularization) coefficient between 0 and 1", "Aliases": [ - "ot" + "e" ], "Required": false, - "SortOrder": 50.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": 1E-07, - "SweepRange": { - "RangeType": "Discrete", - "Values": [ - 0.0001, - 1E-07 - ] - } + "Default": 0.0 }, { - "Name": "MemorySize", + "Name": "GainConfidenceLevel", "Type": "Int", - "Desc": "Memory size for L-BFGS. Lower=faster, less accurate", + "Desc": "Tree fitting gain confidence requirement (should be in the range [0,1) ).", "Aliases": [ - "m" + "gainconf" ], "Required": false, - "SortOrder": 50.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": 20, - "SweepRange": { - "RangeType": "Discrete", - "Values": [ - 5, - 20, - 50 - ] - } + "Default": 0 }, { - "Name": "EnforceNonNegativity", - "Type": "Bool", - "Desc": "Enforce non-negative weights", + "Name": "NumThreads", + "Type": "Int", + "Desc": "The number of threads to use", "Aliases": [ - "nn" + "t" ], "Required": false, - "SortOrder": 90.0, - "IsNullable": false, - "Default": false + "SortOrder": 150.0, + "IsNullable": true, + "Default": null }, { - "Name": "InitWtsDiameter", - "Type": "Float", - "Desc": "Init weights diameter", + "Name": "DiskTranspose", + "Type": "Bool", + "Desc": "Whether to utilize the disk or the data's native transposition facilities (where applicable) when performing the transpose", "Aliases": [ - "initwts" + "dt" ], "Required": false, - "SortOrder": 140.0, - "IsNullable": false, - "Default": 0.0, - "SweepRange": { - "RangeType": "Float", - "Min": 0.0, - "Max": 1.0, - "NumSteps": 5 - } + "SortOrder": 150.0, + "IsNullable": true, + "Default": null }, { - "Name": "MaxIterations", + "Name": "MaxBins", "Type": "Int", - "Desc": "Maximum iterations.", + "Desc": "Maximum number of distinct values (bins) per feature", "Aliases": [ - "maxiter" + "mb" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 2147483647, - "SweepRange": { - "RangeType": "Long", - "Min": 1, - "Max": 2147483647 - } + "Default": 255 }, { - "Name": "SgdInitializationTolerance", + "Name": "MaxOutput", "Type": "Float", - "Desc": "Run SGD to initialize LR weights, converging to this tolerance", - "Aliases": [ - "sgd" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 0.0 - }, - { - "Name": "Quiet", - "Type": "Bool", - "Desc": "If set to true, produce no output during training.", + "Desc": "Upper bound on absolute value of single output", "Aliases": [ - "q" + "mo" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": false + "Default": "Infinity" }, { - "Name": "UseThreads", - "Type": "Bool", - "Desc": "Whether or not to use threads. Default is true", + "Name": "GetDerivativesSampleRate", + "Type": "Int", + "Desc": "Sample each query 1 in k times in the GetDerivatives function", "Aliases": [ - "t" + "sr" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": true + "Default": 1 }, { - "Name": "NumThreads", + "Name": "RngSeed", "Type": "Int", - "Desc": "Number of threads", + "Desc": "The seed of the random number generator", "Aliases": [ - "nt" + "r1" ], "Required": false, "SortOrder": 150.0, - "IsNullable": true, - "Default": null + "IsNullable": false, + "Default": 123 }, { - "Name": "DenseOptimizer", + "Name": "FeatureFlocks", "Type": "Bool", - "Desc": "Force densification of the internal optimization vectors", + "Desc": "Whether to collectivize features during dataset preparation to speed up training", "Aliases": [ - "do" + "flocks" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": false, - "SweepRange": { - "RangeType": "Discrete", - "Values": [ - false, - true - ] - } + "Default": true } ], "Outputs": [ @@ -10160,16 +10207,36 @@ "ITrainerInput" ], "OutputKind": [ - "IMulticlassClassificationOutput", + "IBinaryClassificationOutput", "ITrainerOutput" ] }, { - "Name": "Trainers.NaiveBayesClassifier", - "Desc": "Train a MultiClassNaiveBayesTrainer.", - "FriendlyName": "Multiclass Naive Bayes", - "ShortName": "MNB", + "Name": "Trainers.GeneralizedAdditiveModelRegressor", + "Desc": "Trains a gradient boosted stump per feature, on all features simultaneously, to fit target values using least-squares. It mantains no interactions between features.", + "FriendlyName": "Generalized Additive Model for Regression", + "ShortName": "gamr", "Inputs": [ + { + "Name": "NumIterations", + "Type": "Int", + "Desc": "Total number of iterations over all features", + "Aliases": [ + "iter" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": 9500, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 200, + 1500, + 9500 + ] + } + }, { "Name": "TrainingData", "Type": "DataView", @@ -10193,6 +10260,26 @@ "IsNullable": false, "Default": "Features" }, + { + "Name": "MinDocuments", + "Type": "Int", + "Desc": "Minimum number of training instances required to form a partition", + "Aliases": [ + "mi" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": 10, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 1, + 10, + 50 + ] + } + }, { "Name": "LabelColumn", "Type": "String", @@ -10205,6 +10292,36 @@ "IsNullable": false, "Default": "Label" }, + { + "Name": "LearningRates", + "Type": "Float", + "Desc": "The learning rate", + "Aliases": [ + "lr" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": 0.002, + "SweepRange": { + "RangeType": "Float", + "Min": 0.001, + "Max": 0.1, + "IsLogScale": true + } + }, + { + "Name": "WeightColumn", + "Type": "String", + "Desc": "Column to use for example weight", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": "Weight" + }, { "Name": "NormalizeFeatures", "Type": { @@ -10244,36 +10361,145 @@ "SortOrder": 6.0, "IsNullable": false, "Default": "Auto" - } - ], - "Outputs": [ - { - "Name": "PredictorModel", - "Type": "PredictorModel", - "Desc": "The trained model" - } - ], - "InputKind": [ - "ITrainerInputWithLabel", - "ITrainerInput" - ], - "OutputKind": [ - "IMulticlassClassificationOutput", - "ITrainerOutput" - ] - }, - { - "Name": "Trainers.OnlineGradientDescentRegressor", - "Desc": "Train a Online gradient descent perceptron.", - "FriendlyName": "Stochastic Gradient Descent (Regression)", - "ShortName": "ogd", - "Inputs": [ + }, { - "Name": "TrainingData", - "Type": "DataView", - "Desc": "The data to be used for training", + "Name": "EntropyCoefficient", + "Type": "Float", + "Desc": "The entropy (regularization) coefficient between 0 and 1", "Aliases": [ - "data" + "e" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0 + }, + { + "Name": "GainConfidenceLevel", + "Type": "Int", + "Desc": "Tree fitting gain confidence requirement (should be in the range [0,1) ).", + "Aliases": [ + "gainconf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0 + }, + { + "Name": "NumThreads", + "Type": "Int", + "Desc": "The number of threads to use", + "Aliases": [ + "t" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "DiskTranspose", + "Type": "Bool", + "Desc": "Whether to utilize the disk or the data's native transposition facilities (where applicable) when performing the transpose", + "Aliases": [ + "dt" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "MaxBins", + "Type": "Int", + "Desc": "Maximum number of distinct values (bins) per feature", + "Aliases": [ + "mb" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 255 + }, + { + "Name": "MaxOutput", + "Type": "Float", + "Desc": "Upper bound on absolute value of single output", + "Aliases": [ + "mo" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "Infinity" + }, + { + "Name": "GetDerivativesSampleRate", + "Type": "Int", + "Desc": "Sample each query 1 in k times in the GetDerivatives function", + "Aliases": [ + "sr" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1 + }, + { + "Name": "RngSeed", + "Type": "Int", + "Desc": "The seed of the random number generator", + "Aliases": [ + "r1" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 123 + }, + { + "Name": "FeatureFlocks", + "Type": "Bool", + "Desc": "Whether to collectivize features during dataset preparation to speed up training", + "Aliases": [ + "flocks" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ITrainerInputWithWeight", + "ITrainerInputWithLabel", + "ITrainerInput" + ], + "OutputKind": [ + "IRegressionOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Trainers.KMeansPlusPlusClusterer", + "Desc": "K-means is a popular clustering algorithm. With K-means, the data is clustered into a specified number of clusters in order to minimize the within-cluster sum of squares. K-means++ improves upon K-means by using a better method for choosing the initial cluster centers.", + "FriendlyName": "KMeans++ Clustering", + "ShortName": "KM", + "Inputs": [ + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" ], "Required": true, "SortOrder": 1.0, @@ -10292,16 +10518,16 @@ "Default": "Features" }, { - "Name": "LabelColumn", + "Name": "WeightColumn", "Type": "String", - "Desc": "Column to use for labels", + "Desc": "Column to use for example weight", "Aliases": [ - "lab" + "weight" ], "Required": false, - "SortOrder": 3.0, + "SortOrder": 4.0, "IsNullable": false, - "Default": "Label" + "Default": "Weight" }, { "Name": "NormalizeFeatures", @@ -10344,187 +10570,303 @@ "Default": "Auto" }, { - "Name": "LossFunction", - "Type": { - "Kind": "Component", - "ComponentKind": "RegressionLossFunction" - }, - "Desc": "Loss Function", - "Aliases": [ - "loss" - ], - "Required": false, - "SortOrder": 50.0, - "IsNullable": false, - "Default": { - "Name": "SquaredLoss" - } - }, - { - "Name": "LearningRate", - "Type": "Float", - "Desc": "Learning rate", - "Aliases": [ - "lr" - ], + "Name": "K", + "Type": "Int", + "Desc": "The number of clusters", "Required": false, "SortOrder": 50.0, "IsNullable": false, - "Default": 0.1, + "Default": 5, "SweepRange": { "RangeType": "Discrete", "Values": [ - 0.01, - 0.1, - 0.5, - 1.0 + 5, + 10, + 20, + 40 ] } }, { - "Name": "DecreaseLearningRate", - "Type": "Bool", - "Desc": "Decrease learning rate", + "Name": "NumThreads", + "Type": "Int", + "Desc": "Degree of lock-free parallelism. Defaults to automatic. Determinism not guaranteed.", "Aliases": [ - "decreaselr" + "nt", + "t", + "threads" ], "Required": false, "SortOrder": 50.0, - "IsNullable": false, - "Default": true, - "SweepRange": { - "RangeType": "Discrete", + "IsNullable": true, + "Default": null + }, + { + "Name": "InitAlgorithm", + "Type": { + "Kind": "Enum", "Values": [ - false, - true + "KMeansPlusPlus", + "Random", + "KMeansParallel" ] - } + }, + "Desc": "Cluster initialization algorithm", + "Aliases": [ + "init" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "KMeansParallel" }, { - "Name": "L2RegularizerWeight", + "Name": "OptTol", "Type": "Float", - "Desc": "L2 Regularization Weight", + "Desc": "Tolerance parameter for trainer convergence. Lower = slower, more accurate", "Aliases": [ - "reg" + "ot" ], "Required": false, - "SortOrder": 50.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": 0.0, - "SweepRange": { - "RangeType": "Float", - "Min": 0.0, - "Max": 0.5 - } + "Default": 1E-07 }, { - "Name": "NumIterations", + "Name": "MaxIterations", "Type": "Int", - "Desc": "Number of iterations", + "Desc": "Maximum number of iterations.", "Aliases": [ - "iter" + "maxiter" ], "Required": false, - "SortOrder": 50.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": 1, - "SweepRange": { - "RangeType": "Long", - "Min": 1, - "Max": 100, - "StepSize": 10.0, - "IsLogScale": true - } + "Default": 1000 }, { - "Name": "InitWtsDiameter", - "Type": "Float", - "Desc": "Init weights diameter", + "Name": "AccelMemBudgetMb", + "Type": "Int", + "Desc": "Memory budget (in MBs) to use for KMeans acceleration", "Aliases": [ - "initwts" + "accelMemBudgetMb" ], "Required": false, - "SortOrder": 140.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": 0.0, - "SweepRange": { - "RangeType": "Float", - "Min": 0.0, - "Max": 1.0, - "NumSteps": 5 - } + "Default": 4096 + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "IUnsupervisedTrainerWithWeight", + "ITrainerInput" + ], + "OutputKind": [ + "IClusteringOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Trainers.LinearSvmBinaryClassifier", + "Desc": "Train a linear SVM.", + "FriendlyName": "SVM (Pegasos-Linear)", + "ShortName": "svm", + "Inputs": [ + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false }, { - "Name": "ResetWeightsAfterXExamples", - "Type": "Int", - "Desc": "Number of examples after which weights will be reset to the current average", + "Name": "FeatureColumn", + "Type": "String", + "Desc": "Column to use for features", "Aliases": [ - "numreset" + "feat" ], "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Features" }, { - "Name": "DoLazyUpdates", - "Type": "Bool", - "Desc": "Instead of updating averaged weights on every example, only update when loss is nonzero", + "Name": "LabelColumn", + "Type": "String", + "Desc": "Column to use for labels", "Aliases": [ - "lazy" + "lab" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 3.0, "IsNullable": false, - "Default": true + "Default": "Label" }, { - "Name": "RecencyGain", + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "Disk", + "None" + ] + }, + "Desc": "Whether learner should cache input training data", + "Aliases": [ + "cache" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "Lambda", "Type": "Float", - "Desc": "Extra weight given to more recent updates", + "Desc": "Regularizer constant", "Aliases": [ - "rg" + "lambda" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 50.0, "IsNullable": false, - "Default": 0.0 + "Default": 0.001, + "SweepRange": { + "RangeType": "Float", + "Min": 1E-05, + "Max": 0.1, + "StepSize": 10.0, + "IsLogScale": true + } }, { - "Name": "RecencyGainMulti", + "Name": "PerformProjection", "Type": "Bool", - "Desc": "Whether Recency Gain is multiplicative (vs. additive)", + "Desc": "Perform projection to unit-ball? Typically used with batch size > 1.", "Aliases": [ - "rgm" + "project" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 50.0, "IsNullable": false, - "Default": false + "Default": false, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + false, + true + ] + } }, { - "Name": "Averaged", - "Type": "Bool", - "Desc": "Do averaging?", + "Name": "NumIterations", + "Type": "Int", + "Desc": "Number of iterations", "Aliases": [ - "avg" + "iter" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 50.0, "IsNullable": false, - "Default": true + "Default": 1, + "SweepRange": { + "RangeType": "Long", + "Min": 1, + "Max": 100, + "StepSize": 10.0, + "IsLogScale": true + } }, { - "Name": "AveragedTolerance", + "Name": "InitWtsDiameter", "Type": "Float", - "Desc": "The inexactness tolerance for averaging", + "Desc": "Init weights diameter", "Aliases": [ - "avgtol" + "initwts" ], "Required": false, + "SortOrder": 140.0, + "IsNullable": false, + "Default": 0.0, + "SweepRange": { + "RangeType": "Float", + "Min": 0.0, + "Max": 1.0, + "NumSteps": 5 + } + }, + { + "Name": "NoBias", + "Type": "Bool", + "Desc": "No bias", + "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0.01 + "Default": false, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + false, + true + ] + } + }, + { + "Name": "Calibrator", + "Type": { + "Kind": "Component", + "ComponentKind": "CalibratorTrainer" + }, + "Desc": "The calibrator kind to apply to the predictor. Specify null for no calibration", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": { + "Name": "PlattCalibrator" + } + }, + { + "Name": "MaxCalibrationExamples", + "Type": "Int", + "Desc": "The maximum number of examples to use when training the calibrator", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1000000 }, { "Name": "InitialWeights", @@ -10568,6 +10910,18 @@ "SortOrder": 150.0, "IsNullable": false, "Default": 1000000 + }, + { + "Name": "BatchSize", + "Type": "Int", + "Desc": "Batch size", + "Aliases": [ + "batch" + ], + "Required": false, + "SortOrder": 190.0, + "IsNullable": false, + "Default": 1 } ], "Outputs": [ @@ -10582,15 +10936,15 @@ "ITrainerInput" ], "OutputKind": [ - "IRegressionOutput", + "IBinaryClassificationOutput", "ITrainerOutput" ] }, { - "Name": "Trainers.OrdinaryLeastSquaresRegressor", - "Desc": "Train an OLS regression model.", - "FriendlyName": "Ordinary Least Squares (Regression)", - "ShortName": "ols", + "Name": "Trainers.LogisticRegressionBinaryClassifier", + "Desc": "Train a logistic regression binary model", + "FriendlyName": "Logistic Regression", + "ShortName": "lr", "Inputs": [ { "Name": "TrainingData", @@ -10680,204 +11034,205 @@ "Default": "Auto" }, { - "Name": "L2Weight", - "Type": "Float", - "Desc": "L2 regularization weight", + "Name": "ShowTrainingStats", + "Type": "Bool", + "Desc": "Show statistics of training examples.", "Aliases": [ - "l2" + "stat" ], "Required": false, "SortOrder": 50.0, "IsNullable": false, - "Default": 1E-06, - "SweepRange": { - "RangeType": "Discrete", - "Values": [ - 1E-06, - 0.1, - 1.0 - ] - } + "Default": false }, { - "Name": "PerParameterSignificance", - "Type": "Bool", - "Desc": "Whether to calculate per parameter significance statistics", + "Name": "L2Weight", + "Type": "Float", + "Desc": "L2 regularization weight", "Aliases": [ - "sig" + "l2" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 50.0, "IsNullable": false, - "Default": true - } - ], - "Outputs": [ - { - "Name": "PredictorModel", - "Type": "PredictorModel", - "Desc": "The trained model" - } - ], - "InputKind": [ - "ITrainerInputWithWeight", - "ITrainerInputWithLabel", - "ITrainerInput" - ], - "OutputKind": [ - "IRegressionOutput", - "ITrainerOutput" - ] - }, - { - "Name": "Trainers.PcaAnomalyDetector", - "Desc": "Train an PCA Anomaly model.", - "FriendlyName": "PCA Anomaly Detector", - "ShortName": "pcaAnom", - "Inputs": [ + "Default": 1.0, + "SweepRange": { + "RangeType": "Float", + "Min": 0.0, + "Max": 1.0, + "NumSteps": 4 + } + }, { - "Name": "TrainingData", - "Type": "DataView", - "Desc": "The data to be used for training", + "Name": "L1Weight", + "Type": "Float", + "Desc": "L1 regularization weight", "Aliases": [ - "data" + "l1" ], - "Required": true, - "SortOrder": 1.0, - "IsNullable": false + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 1.0, + "SweepRange": { + "RangeType": "Float", + "Min": 0.0, + "Max": 1.0, + "NumSteps": 4 + } }, { - "Name": "FeatureColumn", - "Type": "String", - "Desc": "Column to use for features", + "Name": "OptTol", + "Type": "Float", + "Desc": "Tolerance parameter for optimization convergence. Lower = slower, more accurate", "Aliases": [ - "feat" + "ot" ], "Required": false, - "SortOrder": 2.0, + "SortOrder": 50.0, "IsNullable": false, - "Default": "Features" + "Default": 1E-07, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.0001, + 1E-07 + ] + } }, { - "Name": "WeightColumn", - "Type": "String", - "Desc": "Column to use for example weight", + "Name": "MemorySize", + "Type": "Int", + "Desc": "Memory size for L-BFGS. Lower=faster, less accurate", "Aliases": [ - "weight" + "m" ], "Required": false, - "SortOrder": 4.0, + "SortOrder": 50.0, "IsNullable": false, - "Default": "Weight" - }, - { - "Name": "NormalizeFeatures", - "Type": { - "Kind": "Enum", + "Default": 20, + "SweepRange": { + "RangeType": "Discrete", "Values": [ - "No", - "Warn", - "Auto", - "Yes" + 5, + 20, + 50 ] - }, - "Desc": "Normalize option for the feature column", + } + }, + { + "Name": "EnforceNonNegativity", + "Type": "Bool", + "Desc": "Enforce non-negative weights", "Aliases": [ - "norm" + "nn" ], "Required": false, - "SortOrder": 5.0, + "SortOrder": 90.0, "IsNullable": false, - "Default": "Auto" + "Default": false }, { - "Name": "Caching", - "Type": { - "Kind": "Enum", - "Values": [ - "Auto", - "Memory", - "Disk", - "None" - ] - }, - "Desc": "Whether learner should cache input training data", + "Name": "InitWtsDiameter", + "Type": "Float", + "Desc": "Init weights diameter", "Aliases": [ - "cache" + "initwts" ], "Required": false, - "SortOrder": 6.0, + "SortOrder": 140.0, "IsNullable": false, - "Default": "Auto" + "Default": 0.0, + "SweepRange": { + "RangeType": "Float", + "Min": 0.0, + "Max": 1.0, + "NumSteps": 5 + } }, { - "Name": "Rank", + "Name": "MaxIterations", "Type": "Int", - "Desc": "The number of components in the PCA", + "Desc": "Maximum iterations.", "Aliases": [ - "k" + "maxiter" ], "Required": false, - "SortOrder": 50.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": 20, + "Default": 2147483647, "SweepRange": { - "RangeType": "Discrete", - "Values": [ - 10, - 20, - 40, - 80 - ] + "RangeType": "Long", + "Min": 1, + "Max": 2147483647 } }, { - "Name": "Oversampling", - "Type": "Int", - "Desc": "Oversampling parameter for randomized PCA training", + "Name": "SgdInitializationTolerance", + "Type": "Float", + "Desc": "Run SGD to initialize LR weights, converging to this tolerance", + "Aliases": [ + "sgd" + ], "Required": false, - "SortOrder": 50.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": 20, - "SweepRange": { - "RangeType": "Discrete", - "Values": [ - 10, - 20, - 40 - ] - } + "Default": 0.0 }, { - "Name": "Center", + "Name": "Quiet", "Type": "Bool", - "Desc": "If enabled, data is centered to be zero mean", + "Desc": "If set to true, produce no output during training.", "Aliases": [ - "center" + "q" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": true, - "SweepRange": { - "RangeType": "Discrete", - "Values": [ - false, - true - ] - } + "Default": false }, { - "Name": "Seed", + "Name": "UseThreads", + "Type": "Bool", + "Desc": "Whether or not to use threads. Default is true", + "Aliases": [ + "t" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "NumThreads", "Type": "Int", - "Desc": "The seed for random number generation", + "Desc": "Number of threads", "Aliases": [ - "seed" + "nt" ], "Required": false, "SortOrder": 150.0, "IsNullable": true, "Default": null + }, + { + "Name": "DenseOptimizer", + "Type": "Bool", + "Desc": "Force densification of the internal optimization vectors", + "Aliases": [ + "do" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + false, + true + ] + } } ], "Outputs": [ @@ -10888,19 +11243,20 @@ } ], "InputKind": [ - "IUnsupervisedTrainerWithWeight", + "ITrainerInputWithWeight", + "ITrainerInputWithLabel", "ITrainerInput" ], "OutputKind": [ - "IAnomalyDetectionOutput", + "IBinaryClassificationOutput", "ITrainerOutput" ] }, { - "Name": "Trainers.PoissonRegressor", - "Desc": "Train an Poisson regression model.", - "FriendlyName": "Poisson Regression", - "ShortName": "PR", + "Name": "Trainers.LogisticRegressionClassifier", + "Desc": "Train a logistic regression multi class model", + "FriendlyName": "Multi-class Logistic Regression", + "ShortName": "mlr", "Inputs": [ { "Name": "TrainingData", @@ -10990,11 +11346,23 @@ "Default": "Auto" }, { - "Name": "L2Weight", - "Type": "Float", - "Desc": "L2 regularization weight", + "Name": "ShowTrainingStats", + "Type": "Bool", + "Desc": "Show statistics of training examples.", "Aliases": [ - "l2" + "stat" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "L2Weight", + "Type": "Float", + "Desc": "L2 regularization weight", + "Aliases": [ + "l2" ], "Required": false, "SortOrder": 50.0, @@ -11192,40 +11560,16 @@ "ITrainerInput" ], "OutputKind": [ - "IRegressionOutput", + "IMulticlassClassificationOutput", "ITrainerOutput" ] }, { - "Name": "Trainers.StochasticDualCoordinateAscentBinaryClassifier", - "Desc": "Train an SDCA binary model.", - "FriendlyName": "Fast Linear (SA-SDCA)", - "ShortName": "SDCA", + "Name": "Trainers.NaiveBayesClassifier", + "Desc": "Train a MultiClassNaiveBayesTrainer.", + "FriendlyName": "Multiclass Naive Bayes", + "ShortName": "MNB", "Inputs": [ - { - "Name": "L2Const", - "Type": "Float", - "Desc": "L2 regularizer constant. By default the l2 constant is automatically inferred based on data set.", - "Aliases": [ - "l2" - ], - "Required": false, - "SortOrder": 1.0, - "IsNullable": true, - "Default": null, - "SweepRange": { - "RangeType": "Discrete", - "Values": [ - "", - 1E-07, - 1E-06, - 1E-05, - 0.0001, - 0.001, - 0.01 - ] - } - }, { "Name": "TrainingData", "Type": "DataView", @@ -11237,29 +11581,6 @@ "SortOrder": 1.0, "IsNullable": false }, - { - "Name": "L1Threshold", - "Type": "Float", - "Desc": "L1 soft threshold (L1/L2). Note that it is easier to control and sweep using the threshold parameter than the raw L1-regularizer constant. By default the l1 threshold is automatically inferred based on data set.", - "Aliases": [ - "l1" - ], - "Required": false, - "SortOrder": 2.0, - "IsNullable": true, - "Default": null, - "SweepRange": { - "RangeType": "Discrete", - "Values": [ - "", - 0.0, - 0.25, - 0.5, - 0.75, - 1.0 - ] - } - }, { "Name": "FeatureColumn", "Type": "String", @@ -11323,166 +11644,330 @@ "SortOrder": 6.0, "IsNullable": false, "Default": "Auto" + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" + } + ], + "InputKind": [ + "ITrainerInputWithLabel", + "ITrainerInput" + ], + "OutputKind": [ + "IMulticlassClassificationOutput", + "ITrainerOutput" + ] + }, + { + "Name": "Trainers.OnlineGradientDescentRegressor", + "Desc": "Train a Online gradient descent perceptron.", + "FriendlyName": "Stochastic Gradient Descent (Regression)", + "ShortName": "ogd", + "Inputs": [ + { + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false }, { - "Name": "LossFunction", - "Type": { - "Kind": "Component", - "ComponentKind": "SDCAClassificationLossFunction" - }, - "Desc": "Loss Function", + "Name": "FeatureColumn", + "Type": "String", + "Desc": "Column to use for features", "Aliases": [ - "loss" + "feat" ], "Required": false, - "SortOrder": 50.0, + "SortOrder": 2.0, "IsNullable": false, - "Default": { - "Name": "LogLoss" - } + "Default": "Features" }, { - "Name": "NumThreads", - "Type": "Int", - "Desc": "Degree of lock-free parallelism. Defaults to automatic. Determinism not guaranteed.", + "Name": "LabelColumn", + "Type": "String", + "Desc": "Column to use for labels", "Aliases": [ - "nt", - "t", - "threads" + "lab" ], "Required": false, - "SortOrder": 50.0, - "IsNullable": true, - "Default": null + "SortOrder": 3.0, + "IsNullable": false, + "Default": "Label" }, { - "Name": "PositiveInstanceWeight", - "Type": "Float", - "Desc": "Apply weight to the positive class, for imbalanced data", + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", "Aliases": [ - "piw" + "norm" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 5.0, "IsNullable": false, - "Default": 1.0 + "Default": "Auto" }, { - "Name": "Calibrator", + "Name": "Caching", "Type": { - "Kind": "Component", - "ComponentKind": "CalibratorTrainer" + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "Disk", + "None" + ] }, - "Desc": "The calibrator kind to apply to the predictor. Specify null for no calibration", + "Desc": "Whether learner should cache input training data", + "Aliases": [ + "cache" + ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 6.0, "IsNullable": false, - "Default": { - "Name": "PlattCalibrator" - } + "Default": "Auto" }, { - "Name": "MaxCalibrationExamples", - "Type": "Int", - "Desc": "The maximum number of examples to use when training the calibrator", + "Name": "LossFunction", + "Type": { + "Kind": "Component", + "ComponentKind": "RegressionLossFunction" + }, + "Desc": "Loss Function", + "Aliases": [ + "loss" + ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 50.0, "IsNullable": false, - "Default": 1000000 + "Default": { + "Name": "SquaredLoss" + } }, { - "Name": "ConvergenceTolerance", + "Name": "LearningRate", "Type": "Float", - "Desc": "The tolerance for the ratio between duality gap and primal loss for convergence checking.", + "Desc": "Learning rate", "Aliases": [ - "tol" + "lr" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 50.0, "IsNullable": false, "Default": 0.1, "SweepRange": { "RangeType": "Discrete", "Values": [ - 0.001, 0.01, 0.1, - 0.2 + 0.5, + 1.0 ] } }, { - "Name": "MaxIterations", - "Type": "Int", - "Desc": "Maximum number of iterations; set to 1 to simulate online learning. Defaults to automatic.", + "Name": "DecreaseLearningRate", + "Type": "Bool", + "Desc": "Decrease learning rate", "Aliases": [ - "iter" + "decreaselr" ], "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null, + "SortOrder": 50.0, + "IsNullable": false, + "Default": true, "SweepRange": { "RangeType": "Discrete", "Values": [ - "", - 10, - 20, - 100 + false, + true ] } }, { - "Name": "Shuffle", - "Type": "Bool", - "Desc": "Shuffle data every epoch?", + "Name": "L2RegularizerWeight", + "Type": "Float", + "Desc": "L2 Regularization Weight", "Aliases": [ - "shuf" + "reg" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 50.0, "IsNullable": false, - "Default": true, + "Default": 0.0, "SweepRange": { - "RangeType": "Discrete", - "Values": [ - false, - true - ] + "RangeType": "Float", + "Min": 0.0, + "Max": 0.5 } }, { - "Name": "CheckFrequency", + "Name": "NumIterations", "Type": "Int", - "Desc": "Convergence check frequency (in terms of number of iterations). Set as negative or zero for not checking at all. If left blank, it defaults to check after every 'numThreads' iterations.", + "Desc": "Number of iterations", "Aliases": [ - "checkFreq" + "iter" ], "Required": false, - "SortOrder": 150.0, - "IsNullable": true, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 1, + "SweepRange": { + "RangeType": "Long", + "Min": 1, + "Max": 100, + "StepSize": 10.0, + "IsLogScale": true + } + }, + { + "Name": "InitWtsDiameter", + "Type": "Float", + "Desc": "Init weights diameter", + "Aliases": [ + "initwts" + ], + "Required": false, + "SortOrder": 140.0, + "IsNullable": false, + "Default": 0.0, + "SweepRange": { + "RangeType": "Float", + "Min": 0.0, + "Max": 1.0, + "NumSteps": 5 + } + }, + { + "Name": "ResetWeightsAfterXExamples", + "Type": "Int", + "Desc": "Number of examples after which weights will be reset to the current average", + "Aliases": [ + "numreset" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, "Default": null }, { - "Name": "BiasLearningRate", + "Name": "DoLazyUpdates", + "Type": "Bool", + "Desc": "Instead of updating averaged weights on every example, only update when loss is nonzero", + "Aliases": [ + "lazy" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "RecencyGain", "Type": "Float", - "Desc": "The learning rate for adjusting bias from being regularized.", + "Desc": "Extra weight given to more recent updates", "Aliases": [ - "blr" + "rg" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0.0, + "Default": 0.0 + }, + { + "Name": "RecencyGainMulti", + "Type": "Bool", + "Desc": "Whether Recency Gain is multiplicative (vs. additive)", + "Aliases": [ + "rgm" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "Averaged", + "Type": "Bool", + "Desc": "Do averaging?", + "Aliases": [ + "avg" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "AveragedTolerance", + "Type": "Float", + "Desc": "The inexactness tolerance for averaging", + "Aliases": [ + "avgtol" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.01 + }, + { + "Name": "InitialWeights", + "Type": "String", + "Desc": "Initial Weights and bias, comma-separated", + "Aliases": [ + "initweights" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Shuffle", + "Type": "Bool", + "Desc": "Whether to shuffle for each training iteration", + "Aliases": [ + "shuf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true, "SweepRange": { "RangeType": "Discrete", "Values": [ - 0.0, - 0.01, - 0.1, - 1.0 + false, + true ] } + }, + { + "Name": "StreamingCacheSize", + "Type": "Int", + "Desc": "Size of cache when trained in Scope", + "Aliases": [ + "cache" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1000000 } ], "Outputs": [ @@ -11497,40 +11982,16 @@ "ITrainerInput" ], "OutputKind": [ - "IBinaryClassificationOutput", + "IRegressionOutput", "ITrainerOutput" ] }, { - "Name": "Trainers.StochasticDualCoordinateAscentClassifier", - "Desc": "Train an SDCA multi class model", - "FriendlyName": "Fast Linear Multi-class Classification (SA-SDCA)", - "ShortName": "sasdcamc", + "Name": "Trainers.PcaAnomalyDetector", + "Desc": "Train an PCA Anomaly model.", + "FriendlyName": "PCA Anomaly Detector", + "ShortName": "pcaAnom", "Inputs": [ - { - "Name": "L2Const", - "Type": "Float", - "Desc": "L2 regularizer constant. By default the l2 constant is automatically inferred based on data set.", - "Aliases": [ - "l2" - ], - "Required": false, - "SortOrder": 1.0, - "IsNullable": true, - "Default": null, - "SweepRange": { - "RangeType": "Discrete", - "Values": [ - "", - 1E-07, - 1E-06, - 1E-05, - 0.0001, - 0.001, - 0.01 - ] - } - }, { "Name": "TrainingData", "Type": "DataView", @@ -11542,29 +12003,6 @@ "SortOrder": 1.0, "IsNullable": false }, - { - "Name": "L1Threshold", - "Type": "Float", - "Desc": "L1 soft threshold (L1/L2). Note that it is easier to control and sweep using the threshold parameter than the raw L1-regularizer constant. By default the l1 threshold is automatically inferred based on data set.", - "Aliases": [ - "l1" - ], - "Required": false, - "SortOrder": 2.0, - "IsNullable": true, - "Default": null, - "SweepRange": { - "RangeType": "Discrete", - "Values": [ - "", - 0.0, - 0.25, - 0.5, - 0.75, - 1.0 - ] - } - }, { "Name": "FeatureColumn", "Type": "String", @@ -11578,16 +12016,16 @@ "Default": "Features" }, { - "Name": "LabelColumn", + "Name": "WeightColumn", "Type": "String", - "Desc": "Column to use for labels", + "Desc": "Column to use for example weight", "Aliases": [ - "lab" + "weight" ], "Required": false, - "SortOrder": 3.0, + "SortOrder": 4.0, "IsNullable": false, - "Default": "Label" + "Default": "Weight" }, { "Name": "NormalizeFeatures", @@ -11630,84 +12068,49 @@ "Default": "Auto" }, { - "Name": "LossFunction", - "Type": { - "Kind": "Component", - "ComponentKind": "SDCAClassificationLossFunction" - }, - "Desc": "Loss Function", + "Name": "Rank", + "Type": "Int", + "Desc": "The number of components in the PCA", "Aliases": [ - "loss" + "k" ], "Required": false, "SortOrder": 50.0, "IsNullable": false, - "Default": { - "Name": "LogLoss" + "Default": 20, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 10, + 20, + 40, + 80 + ] } }, { - "Name": "NumThreads", + "Name": "Oversampling", "Type": "Int", - "Desc": "Degree of lock-free parallelism. Defaults to automatic. Determinism not guaranteed.", - "Aliases": [ - "nt", - "t", - "threads" - ], + "Desc": "Oversampling parameter for randomized PCA training", "Required": false, "SortOrder": 50.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "ConvergenceTolerance", - "Type": "Float", - "Desc": "The tolerance for the ratio between duality gap and primal loss for convergence checking.", - "Aliases": [ - "tol" - ], - "Required": false, - "SortOrder": 150.0, "IsNullable": false, - "Default": 0.1, + "Default": 20, "SweepRange": { "RangeType": "Discrete", "Values": [ - 0.001, - 0.01, - 0.1, - 0.2 + 10, + 20, + 40 ] } }, { - "Name": "MaxIterations", - "Type": "Int", - "Desc": "Maximum number of iterations; set to 1 to simulate online learning. Defaults to automatic.", - "Aliases": [ - "iter" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null, - "SweepRange": { - "RangeType": "Discrete", - "Values": [ - "", - 10, - 20, - 100 - ] - } - }, - { - "Name": "Shuffle", + "Name": "Center", "Type": "Bool", - "Desc": "Shuffle data every epoch?", + "Desc": "If enabled, data is centered to be zero mean", "Aliases": [ - "shuf" + "center" ], "Required": false, "SortOrder": 150.0, @@ -11722,37 +12125,16 @@ } }, { - "Name": "CheckFrequency", + "Name": "Seed", "Type": "Int", - "Desc": "Convergence check frequency (in terms of number of iterations). Set as negative or zero for not checking at all. If left blank, it defaults to check after every 'numThreads' iterations.", + "Desc": "The seed for random number generation", "Aliases": [ - "checkFreq" + "seed" ], "Required": false, "SortOrder": 150.0, "IsNullable": true, "Default": null - }, - { - "Name": "BiasLearningRate", - "Type": "Float", - "Desc": "The learning rate for adjusting bias from being regularized.", - "Aliases": [ - "blr" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 0.0, - "SweepRange": { - "RangeType": "Discrete", - "Values": [ - 0.0, - 0.01, - 0.1, - 1.0 - ] - } } ], "Outputs": [ @@ -11763,44 +12145,20 @@ } ], "InputKind": [ - "ITrainerInputWithLabel", + "IUnsupervisedTrainerWithWeight", "ITrainerInput" ], "OutputKind": [ - "IMulticlassClassificationOutput", + "IAnomalyDetectionOutput", "ITrainerOutput" ] }, { - "Name": "Trainers.StochasticDualCoordinateAscentRegressor", - "Desc": "Train an SDCA regression model", - "FriendlyName": "Fast Linear Regression (SA-SDCA)", - "ShortName": "sasdcar", + "Name": "Trainers.PoissonRegressor", + "Desc": "Train an Poisson regression model.", + "FriendlyName": "Poisson Regression", + "ShortName": "PR", "Inputs": [ - { - "Name": "L2Const", - "Type": "Float", - "Desc": "L2 regularizer constant. By default the l2 constant is automatically inferred based on data set.", - "Aliases": [ - "l2" - ], - "Required": false, - "SortOrder": 1.0, - "IsNullable": true, - "Default": null, - "SweepRange": { - "RangeType": "Discrete", - "Values": [ - "", - 1E-07, - 1E-06, - 1E-05, - 0.0001, - 0.001, - 0.01 - ] - } - }, { "Name": "TrainingData", "Type": "DataView", @@ -11812,29 +12170,6 @@ "SortOrder": 1.0, "IsNullable": false }, - { - "Name": "L1Threshold", - "Type": "Float", - "Desc": "L1 soft threshold (L1/L2). Note that it is easier to control and sweep using the threshold parameter than the raw L1-regularizer constant. By default the l1 threshold is automatically inferred based on data set.", - "Aliases": [ - "l1" - ], - "Required": false, - "SortOrder": 2.0, - "IsNullable": true, - "Default": null, - "SweepRange": { - "RangeType": "Discrete", - "Values": [ - "", - 0.0, - 0.25, - 0.5, - 0.75, - 1.0 - ] - } - }, { "Name": "FeatureColumn", "Type": "String", @@ -11859,6 +12194,18 @@ "IsNullable": false, "Default": "Label" }, + { + "Name": "WeightColumn", + "Type": "String", + "Desc": "Column to use for example weight", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": "Weight" + }, { "Name": "NormalizeFeatures", "Type": { @@ -11900,127 +12247,191 @@ "Default": "Auto" }, { - "Name": "LossFunction", - "Type": { - "Kind": "Component", - "ComponentKind": "SDCARegressionLossFunction" - }, - "Desc": "Loss Function", + "Name": "L2Weight", + "Type": "Float", + "Desc": "L2 regularization weight", "Aliases": [ - "loss" + "l2" ], "Required": false, "SortOrder": 50.0, "IsNullable": false, - "Default": { - "Name": "SquaredLoss" + "Default": 1.0, + "SweepRange": { + "RangeType": "Float", + "Min": 0.0, + "Max": 1.0, + "NumSteps": 4 } }, { - "Name": "NumThreads", - "Type": "Int", - "Desc": "Degree of lock-free parallelism. Defaults to automatic. Determinism not guaranteed.", + "Name": "L1Weight", + "Type": "Float", + "Desc": "L1 regularization weight", "Aliases": [ - "nt", - "t", - "threads" + "l1" ], "Required": false, "SortOrder": 50.0, - "IsNullable": true, - "Default": null + "IsNullable": false, + "Default": 1.0, + "SweepRange": { + "RangeType": "Float", + "Min": 0.0, + "Max": 1.0, + "NumSteps": 4 + } }, { - "Name": "ConvergenceTolerance", + "Name": "OptTol", "Type": "Float", - "Desc": "The tolerance for the ratio between duality gap and primal loss for convergence checking.", + "Desc": "Tolerance parameter for optimization convergence. Lower = slower, more accurate", "Aliases": [ - "tol" + "ot" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 50.0, "IsNullable": false, - "Default": 0.01, + "Default": 1E-07, "SweepRange": { "RangeType": "Discrete", "Values": [ - 0.001, - 0.01, - 0.1, - 0.2 + 0.0001, + 1E-07 ] } }, { - "Name": "MaxIterations", + "Name": "MemorySize", "Type": "Int", - "Desc": "Maximum number of iterations; set to 1 to simulate online learning. Defaults to automatic.", + "Desc": "Memory size for L-BFGS. Lower=faster, less accurate", "Aliases": [ - "iter" + "m" ], "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 20, "SweepRange": { "RangeType": "Discrete", "Values": [ - "", - 10, + 5, 20, - 100 + 50 ] } }, { - "Name": "Shuffle", + "Name": "EnforceNonNegativity", "Type": "Bool", - "Desc": "Shuffle data every epoch?", + "Desc": "Enforce non-negative weights", "Aliases": [ - "shuf" + "nn" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 90.0, "IsNullable": false, - "Default": true, + "Default": false + }, + { + "Name": "InitWtsDiameter", + "Type": "Float", + "Desc": "Init weights diameter", + "Aliases": [ + "initwts" + ], + "Required": false, + "SortOrder": 140.0, + "IsNullable": false, + "Default": 0.0, "SweepRange": { - "RangeType": "Discrete", - "Values": [ - false, - true - ] + "RangeType": "Float", + "Min": 0.0, + "Max": 1.0, + "NumSteps": 5 } }, { - "Name": "CheckFrequency", + "Name": "MaxIterations", "Type": "Int", - "Desc": "Convergence check frequency (in terms of number of iterations). Set as negative or zero for not checking at all. If left blank, it defaults to check after every 'numThreads' iterations.", + "Desc": "Maximum iterations.", "Aliases": [ - "checkFreq" + "maxiter" ], "Required": false, "SortOrder": 150.0, - "IsNullable": true, - "Default": null + "IsNullable": false, + "Default": 2147483647, + "SweepRange": { + "RangeType": "Long", + "Min": 1, + "Max": 2147483647 + } }, { - "Name": "BiasLearningRate", + "Name": "SgdInitializationTolerance", "Type": "Float", - "Desc": "The learning rate for adjusting bias from being regularized.", + "Desc": "Run SGD to initialize LR weights, converging to this tolerance", "Aliases": [ - "blr" + "sgd" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 1.0, - "SweepRange": { - "RangeType": "Discrete", - "Values": [ - 0.0, - 0.01, - 0.1, - 1.0 + "Default": 0.0 + }, + { + "Name": "Quiet", + "Type": "Bool", + "Desc": "If set to true, produce no output during training.", + "Aliases": [ + "q" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "UseThreads", + "Type": "Bool", + "Desc": "Whether or not to use threads. Default is true", + "Aliases": [ + "t" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "NumThreads", + "Type": "Int", + "Desc": "Number of threads", + "Aliases": [ + "nt" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "DenseOptimizer", + "Type": "Bool", + "Desc": "Force densification of the internal optimization vectors", + "Aliases": [ + "do" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + false, + true ] } } @@ -12033,6 +12444,7 @@ } ], "InputKind": [ + "ITrainerInputWithWeight", "ITrainerInputWithLabel", "ITrainerInput" ], @@ -12042,11 +12454,35 @@ ] }, { - "Name": "Trainers.StochasticGradientDescentBinaryClassifier", - "Desc": "Train an Hogwild SGD binary model.", - "FriendlyName": "Hogwild SGD (binary)", - "ShortName": "HogwildSGD", + "Name": "Trainers.StochasticDualCoordinateAscentBinaryClassifier", + "Desc": "Train an SDCA binary model.", + "FriendlyName": "Fast Linear (SA-SDCA)", + "ShortName": "SDCA", "Inputs": [ + { + "Name": "L2Const", + "Type": "Float", + "Desc": "L2 regularizer constant. By default the l2 constant is automatically inferred based on data set.", + "Aliases": [ + "l2" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": true, + "Default": null, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + "", + 1E-07, + 1E-06, + 1E-05, + 0.0001, + 0.001, + 0.01 + ] + } + }, { "Name": "TrainingData", "Type": "DataView", @@ -12058,6 +12494,29 @@ "SortOrder": 1.0, "IsNullable": false }, + { + "Name": "L1Threshold", + "Type": "Float", + "Desc": "L1 soft threshold (L1/L2). Note that it is easier to control and sweep using the threshold parameter than the raw L1-regularizer constant. By default the l1 threshold is automatically inferred based on data set.", + "Aliases": [ + "l1" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": true, + "Default": null, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + "", + 0.0, + 0.25, + 0.5, + 0.75, + 1.0 + ] + } + }, { "Name": "FeatureColumn", "Type": "String", @@ -12082,18 +12541,6 @@ "IsNullable": false, "Default": "Label" }, - { - "Name": "WeightColumn", - "Type": "String", - "Desc": "Column to use for example weight", - "Aliases": [ - "weight" - ], - "Required": false, - "SortOrder": 4.0, - "IsNullable": false, - "Default": "Weight" - }, { "Name": "NormalizeFeatures", "Type": { @@ -12138,7 +12585,7 @@ "Name": "LossFunction", "Type": { "Kind": "Component", - "ComponentKind": "ClassificationLossFunction" + "ComponentKind": "SDCAClassificationLossFunction" }, "Desc": "Loss Function", "Aliases": [ @@ -12151,32 +12598,10 @@ "Name": "LogLoss" } }, - { - "Name": "L2Const", - "Type": "Float", - "Desc": "L2 regularizer constant", - "Aliases": [ - "l2" - ], - "Required": false, - "SortOrder": 50.0, - "IsNullable": false, - "Default": 1E-06, - "SweepRange": { - "RangeType": "Discrete", - "Values": [ - 1E-07, - 5E-07, - 1E-06, - 5E-06, - 1E-05 - ] - } - }, { "Name": "NumThreads", "Type": "Int", - "Desc": "Degree of lock-free parallelism. Defaults to automatic depending on data sparseness. Determinism not guaranteed.", + "Desc": "Degree of lock-free parallelism. Defaults to automatic. Determinism not guaranteed.", "Aliases": [ "nt", "t", @@ -12187,61 +12612,83 @@ "IsNullable": true, "Default": null }, + { + "Name": "PositiveInstanceWeight", + "Type": "Float", + "Desc": "Apply weight to the positive class, for imbalanced data", + "Aliases": [ + "piw" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.0 + }, + { + "Name": "Calibrator", + "Type": { + "Kind": "Component", + "ComponentKind": "CalibratorTrainer" + }, + "Desc": "The calibrator kind to apply to the predictor. Specify null for no calibration", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": { + "Name": "PlattCalibrator" + } + }, + { + "Name": "MaxCalibrationExamples", + "Type": "Int", + "Desc": "The maximum number of examples to use when training the calibrator", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1000000 + }, { "Name": "ConvergenceTolerance", "Type": "Float", - "Desc": "Exponential moving averaged improvement tolerance for convergence", + "Desc": "The tolerance for the ratio between duality gap and primal loss for convergence checking.", "Aliases": [ "tol" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0.0001, + "Default": 0.1, "SweepRange": { "RangeType": "Discrete", "Values": [ - 0.01, 0.001, - 0.0001, - 1E-05 + 0.01, + 0.1, + 0.2 ] } }, { "Name": "MaxIterations", "Type": "Int", - "Desc": "Maximum number of iterations; set to 1 to simulate online learning.", + "Desc": "Maximum number of iterations; set to 1 to simulate online learning. Defaults to automatic.", "Aliases": [ "iter" ], "Required": false, "SortOrder": 150.0, - "IsNullable": false, - "Default": 20, + "IsNullable": true, + "Default": null, "SweepRange": { "RangeType": "Discrete", "Values": [ - 1, - 5, + "", 10, - 20 + 20, + 100 ] } }, - { - "Name": "InitLearningRate", - "Type": "Float", - "Desc": "Initial learning rate (only used by SGD)", - "Aliases": [ - "ilr", - "lr" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 0.01 - }, { "Name": "Shuffle", "Type": "Bool", @@ -12261,22 +12708,10 @@ ] } }, - { - "Name": "PositiveInstanceWeight", - "Type": "Float", - "Desc": "Apply weight to the positive class, for imbalanced data", - "Aliases": [ - "piw" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 1.0 - }, { "Name": "CheckFrequency", "Type": "Int", - "Desc": "Convergence check frequency (in terms of number of iterations). Default equals number of threads", + "Desc": "Convergence check frequency (in terms of number of iterations). Set as negative or zero for not checking at all. If left blank, it defaults to check after every 'numThreads' iterations.", "Aliases": [ "checkFreq" ], @@ -12286,27 +12721,25 @@ "Default": null }, { - "Name": "Calibrator", - "Type": { - "Kind": "Component", - "ComponentKind": "CalibratorTrainer" - }, - "Desc": "The calibrator kind to apply to the predictor. Specify null for no calibration", + "Name": "BiasLearningRate", + "Type": "Float", + "Desc": "The learning rate for adjusting bias from being regularized.", + "Aliases": [ + "blr" + ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": { - "Name": "PlattCalibrator" + "Default": 0.0, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.0, + 0.01, + 0.1, + 1.0 + ] } - }, - { - "Name": "MaxCalibrationExamples", - "Type": "Int", - "Desc": "The maximum number of examples to use when training the calibrator", - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 1000000 } ], "Outputs": [ @@ -12317,7 +12750,6 @@ } ], "InputKind": [ - "ITrainerInputWithWeight", "ITrainerInputWithLabel", "ITrainerInput" ], @@ -12327,747 +12759,836 @@ ] }, { - "Name": "Transforms.ApproximateBootstrapSampler", - "Desc": "Approximate bootstrap sampling.", - "FriendlyName": "Bootstrap Sample Transform", - "ShortName": "BootstrapSample", + "Name": "Trainers.StochasticDualCoordinateAscentClassifier", + "Desc": "Train an SDCA multi class model", + "FriendlyName": "Fast Linear Multi-class Classification (SA-SDCA)", + "ShortName": "sasdcamc", "Inputs": [ { - "Name": "Data", + "Name": "L2Const", + "Type": "Float", + "Desc": "L2 regularizer constant. By default the l2 constant is automatically inferred based on data set.", + "Aliases": [ + "l2" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": true, + "Default": null, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + "", + 1E-07, + 1E-06, + 1E-05, + 0.0001, + 0.001, + 0.01 + ] + } + }, + { + "Name": "TrainingData", "Type": "DataView", - "Desc": "Input dataset", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], "Required": true, "SortOrder": 1.0, "IsNullable": false }, { - "Name": "Complement", - "Type": "Bool", - "Desc": "Whether this is the out-of-bag sample, that is, all those rows that are not selected by the transform.", + "Name": "L1Threshold", + "Type": "Float", + "Desc": "L1 soft threshold (L1/L2). Note that it is easier to control and sweep using the threshold parameter than the raw L1-regularizer constant. By default the l1 threshold is automatically inferred based on data set.", "Aliases": [ - "comp" + "l1" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": true, + "Default": null, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + "", + 0.0, + 0.25, + 0.5, + 0.75, + 1.0 + ] + } + }, + { + "Name": "FeatureColumn", + "Type": "String", + "Desc": "Column to use for features", + "Aliases": [ + "feat" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Features" + }, + { + "Name": "LabelColumn", + "Type": "String", + "Desc": "Column to use for labels", + "Aliases": [ + "lab" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": "Label" + }, + { + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "Disk", + "None" + ] + }, + "Desc": "Whether learner should cache input training data", + "Aliases": [ + "cache" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": "Auto" + }, + { + "Name": "LossFunction", + "Type": { + "Kind": "Component", + "ComponentKind": "SDCAClassificationLossFunction" + }, + "Desc": "Loss Function", + "Aliases": [ + "loss" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": { + "Name": "LogLoss" + } + }, + { + "Name": "NumThreads", + "Type": "Int", + "Desc": "Degree of lock-free parallelism. Defaults to automatic. Determinism not guaranteed.", + "Aliases": [ + "nt", + "t", + "threads" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "ConvergenceTolerance", + "Type": "Float", + "Desc": "The tolerance for the ratio between duality gap and primal loss for convergence checking.", + "Aliases": [ + "tol" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": false + "Default": 0.1, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.001, + 0.01, + 0.1, + 0.2 + ] + } }, { - "Name": "Seed", - "Type": "UInt", - "Desc": "The random seed. If unspecified random state will be instead derived from the environment.", + "Name": "MaxIterations", + "Type": "Int", + "Desc": "Maximum number of iterations; set to 1 to simulate online learning. Defaults to automatic.", + "Aliases": [ + "iter" + ], "Required": false, "SortOrder": 150.0, "IsNullable": true, - "Default": null + "Default": null, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + "", + 10, + 20, + 100 + ] + } }, { - "Name": "ShuffleInput", + "Name": "Shuffle", "Type": "Bool", - "Desc": "Whether we should attempt to shuffle the source data. By default on, but can be turned off for efficiency.", + "Desc": "Shuffle data every epoch?", "Aliases": [ - "si" + "shuf" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": true + "Default": true, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + false, + true + ] + } }, { - "Name": "PoolSize", + "Name": "CheckFrequency", "Type": "Int", - "Desc": "When shuffling the output, the number of output rows to keep in that pool. Note that shuffling of output is completely distinct from shuffling of input.", + "Desc": "Convergence check frequency (in terms of number of iterations). Set as negative or zero for not checking at all. If left blank, it defaults to check after every 'numThreads' iterations.", "Aliases": [ - "pool" + "checkFreq" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "BiasLearningRate", + "Type": "Float", + "Desc": "The learning rate for adjusting bias from being regularized.", + "Aliases": [ + "blr" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 1000 + "Default": 0.0, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.0, + 0.01, + 0.1, + 1.0 + ] + } } ], "Outputs": [ - { - "Name": "OutputData", - "Type": "DataView", - "Desc": "Transformed dataset" - }, - { - "Name": "Model", - "Type": "TransformModel", - "Desc": "Transform model" - } - ], - "InputKind": [ - "ITransformInput" - ], - "OutputKind": [ - "ITransformOutput" - ] - }, - { - "Name": "Transforms.BinaryPredictionScoreColumnsRenamer", - "Desc": "For binary prediction, it renames the PredictedLabel and Score columns to include the name of the positive class.", - "FriendlyName": "Rename Binary Prediction Score Columns", - "ShortName": null, - "Inputs": [ - { - "Name": "Data", - "Type": "DataView", - "Desc": "Input dataset", - "Required": true, - "SortOrder": 1.0, - "IsNullable": false - }, { "Name": "PredictorModel", "Type": "PredictorModel", - "Desc": "The predictor model used in scoring", - "Required": true, - "SortOrder": 2.0, - "IsNullable": false - } - ], - "Outputs": [ - { - "Name": "OutputData", - "Type": "DataView", - "Desc": "Transformed dataset" - }, - { - "Name": "Model", - "Type": "TransformModel", - "Desc": "Transform model" + "Desc": "The trained model" } ], "InputKind": [ - "ITransformInput" + "ITrainerInputWithLabel", + "ITrainerInput" ], "OutputKind": [ - "ITransformOutput" + "IMulticlassClassificationOutput", + "ITrainerOutput" ] }, { - "Name": "Transforms.BinNormalizer", - "Desc": "The values are assigned into equidensity bins and a value is mapped to its bin_number/number_of_bins.", - "FriendlyName": "Binning Normalizer", - "ShortName": "Bin", + "Name": "Trainers.StochasticDualCoordinateAscentRegressor", + "Desc": "Train an SDCA regression model", + "FriendlyName": "Fast Linear Regression (SA-SDCA)", + "ShortName": "sasdcar", "Inputs": [ { - "Name": "Column", - "Type": { - "Kind": "Array", - "ItemType": { - "Kind": "Struct", - "Fields": [ - { - "Name": "NumBins", - "Type": "Int", - "Desc": "Max number of bins, power of 2 recommended", - "Aliases": [ - "bins" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "FixZero", - "Type": "Bool", - "Desc": "Whether to map zero to zero, preserving sparsity", - "Aliases": [ - "zero" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "MaxTrainingExamples", - "Type": "Int", - "Desc": "Max number of examples used to train the normalizer", - "Aliases": [ - "maxtrain" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "Name", - "Type": "String", - "Desc": "Name of the new column", - "Aliases": [ - "name" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": null - }, - { - "Name": "Source", - "Type": "String", - "Desc": "Name of the source column", - "Aliases": [ - "src" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": null - } - ] - } - }, - "Desc": "New column definition(s) (optional form: name:src)", + "Name": "L2Const", + "Type": "Float", + "Desc": "L2 regularizer constant. By default the l2 constant is automatically inferred based on data set.", "Aliases": [ - "col" + "l2" ], "Required": false, "SortOrder": 1.0, - "IsNullable": false, - "Default": null + "IsNullable": true, + "Default": null, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + "", + 1E-07, + 1E-06, + 1E-05, + 0.0001, + 0.001, + 0.01 + ] + } }, { - "Name": "Data", + "Name": "TrainingData", "Type": "DataView", - "Desc": "Input dataset", + "Desc": "The data to be used for training", + "Aliases": [ + "data" + ], "Required": true, "SortOrder": 1.0, "IsNullable": false }, { - "Name": "NumBins", - "Type": "Int", - "Desc": "Max number of bins, power of 2 recommended", + "Name": "L1Threshold", + "Type": "Float", + "Desc": "L1 soft threshold (L1/L2). Note that it is easier to control and sweep using the threshold parameter than the raw L1-regularizer constant. By default the l1 threshold is automatically inferred based on data set.", "Aliases": [ - "bins" + "l1" ], "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 1024 + "SortOrder": 2.0, + "IsNullable": true, + "Default": null, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + "", + 0.0, + 0.25, + 0.5, + 0.75, + 1.0 + ] + } }, { - "Name": "FixZero", - "Type": "Bool", - "Desc": "Whether to map zero to zero, preserving sparsity", + "Name": "FeatureColumn", + "Type": "String", + "Desc": "Column to use for features", "Aliases": [ - "zero" + "feat" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 2.0, "IsNullable": false, - "Default": true + "Default": "Features" }, { - "Name": "MaxTrainingExamples", - "Type": "Int", - "Desc": "Max number of examples used to train the normalizer", + "Name": "LabelColumn", + "Type": "String", + "Desc": "Column to use for labels", "Aliases": [ - "maxtrain" + "lab" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 3.0, "IsNullable": false, - "Default": 1000000000 - } - ], - "Outputs": [ - { - "Name": "OutputData", - "Type": "DataView", - "Desc": "Transformed dataset" + "Default": "Label" }, { - "Name": "Model", - "Type": "TransformModel", - "Desc": "Transform model" - } - ], - "InputKind": [ - "ITransformInput" - ], - "OutputKind": [ - "ITransformOutput" - ] - }, - { - "Name": "Transforms.CategoricalHashOneHotVectorizer", - "Desc": "Encodes the categorical variable with hash-based encoding", - "FriendlyName": "Categorical Hash Transform", - "ShortName": null, - "Inputs": [ + "Name": "NormalizeFeatures", + "Type": { + "Kind": "Enum", + "Values": [ + "No", + "Warn", + "Auto", + "Yes" + ] + }, + "Desc": "Normalize option for the feature column", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "Auto" + }, { - "Name": "Column", + "Name": "Caching", "Type": { - "Kind": "Array", - "ItemType": { - "Kind": "Struct", - "Fields": [ - { - "Name": "OutputKind", - "Type": { - "Kind": "Enum", - "Values": [ - "Bag", - "Ind", - "Key", - "Bin" - ] - }, - "Desc": "Output kind: Bag (multi-set vector), Ind (indicator vector), or Key (index)", - "Aliases": [ - "kind" - ], - "Required": false, - "SortOrder": 102.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "HashBits", - "Type": "Int", - "Desc": "The number of bits to hash into. Must be between 1 and 30, inclusive.", - "Aliases": [ - "bits" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "Seed", - "Type": "UInt", - "Desc": "Hashing seed", - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "Ordered", - "Type": "Bool", - "Desc": "Whether the position of each term should be included in the hash", - "Aliases": [ - "ord" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "InvertHash", - "Type": "Int", - "Desc": "Limit the number of keys used to generate the slot name to this many. 0 means no invert hashing, -1 means no limit.", - "Aliases": [ - "ih" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "Name", - "Type": "String", - "Desc": "Name of the new column", - "Aliases": [ - "name" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": null - }, - { - "Name": "Source", - "Type": "String", - "Desc": "Name of the source column", - "Aliases": [ - "src" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": null - } - ] - } + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "Disk", + "None" + ] }, - "Desc": "New column definition(s) (optional form: name:hashBits:src)", + "Desc": "Whether learner should cache input training data", "Aliases": [ - "col" + "cache" ], - "Required": true, - "SortOrder": 1.0, - "IsNullable": false + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": "Auto" }, { - "Name": "Data", - "Type": "DataView", - "Desc": "Input dataset", - "Required": true, - "SortOrder": 1.0, - "IsNullable": false + "Name": "LossFunction", + "Type": { + "Kind": "Component", + "ComponentKind": "SDCARegressionLossFunction" + }, + "Desc": "Loss Function", + "Aliases": [ + "loss" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": { + "Name": "SquaredLoss" + } }, { - "Name": "HashBits", + "Name": "NumThreads", "Type": "Int", - "Desc": "Number of bits to hash into. Must be between 1 and 30, inclusive.", + "Desc": "Degree of lock-free parallelism. Defaults to automatic. Determinism not guaranteed.", "Aliases": [ - "bits" + "nt", + "t", + "threads" ], "Required": false, - "SortOrder": 2.0, - "IsNullable": false, - "Default": 16 + "SortOrder": 50.0, + "IsNullable": true, + "Default": null }, { - "Name": "OutputKind", - "Type": { - "Kind": "Enum", - "Values": [ - "Bag", - "Ind", - "Key", - "Bin" - ] - }, - "Desc": "Output kind: Bag (multi-set vector), Ind (indicator vector), or Key (index)", + "Name": "ConvergenceTolerance", + "Type": "Float", + "Desc": "The tolerance for the ratio between duality gap and primal loss for convergence checking.", "Aliases": [ - "kind" + "tol" ], "Required": false, - "SortOrder": 102.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": "Bag" + "Default": 0.01, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.001, + 0.01, + 0.1, + 0.2 + ] + } }, { - "Name": "Seed", - "Type": "UInt", - "Desc": "Hashing seed", + "Name": "MaxIterations", + "Type": "Int", + "Desc": "Maximum number of iterations; set to 1 to simulate online learning. Defaults to automatic.", + "Aliases": [ + "iter" + ], "Required": false, "SortOrder": 150.0, - "IsNullable": false, - "Default": 314489979 + "IsNullable": true, + "Default": null, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + "", + 10, + 20, + 100 + ] + } }, { - "Name": "Ordered", + "Name": "Shuffle", "Type": "Bool", - "Desc": "Whether the position of each term should be included in the hash", + "Desc": "Shuffle data every epoch?", "Aliases": [ - "ord" + "shuf" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": true + "Default": true, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + false, + true + ] + } }, { - "Name": "InvertHash", + "Name": "CheckFrequency", "Type": "Int", - "Desc": "Limit the number of keys used to generate the slot name to this many. 0 means no invert hashing, -1 means no limit.", + "Desc": "Convergence check frequency (in terms of number of iterations). Set as negative or zero for not checking at all. If left blank, it defaults to check after every 'numThreads' iterations.", "Aliases": [ - "ih" + "checkFreq" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "BiasLearningRate", + "Type": "Float", + "Desc": "The learning rate for adjusting bias from being regularized.", + "Aliases": [ + "blr" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 0 + "Default": 1.0, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.0, + 0.01, + 0.1, + 1.0 + ] + } } ], "Outputs": [ { - "Name": "OutputData", - "Type": "DataView", - "Desc": "Transformed dataset" - }, - { - "Name": "Model", - "Type": "TransformModel", - "Desc": "Transform model" + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" } ], "InputKind": [ - "ITransformInput" + "ITrainerInputWithLabel", + "ITrainerInput" ], "OutputKind": [ - "ITransformOutput" + "IRegressionOutput", + "ITrainerOutput" ] }, { - "Name": "Transforms.CategoricalOneHotVectorizer", - "Desc": "Encodes the categorical variable with one-hot encoding based on term dictionary", - "FriendlyName": "Categorical Transform", - "ShortName": null, + "Name": "Trainers.StochasticGradientDescentBinaryClassifier", + "Desc": "Train an Hogwild SGD binary model.", + "FriendlyName": "Hogwild SGD (binary)", + "ShortName": "HogwildSGD", "Inputs": [ { - "Name": "Column", - "Type": { - "Kind": "Array", - "ItemType": { - "Kind": "Struct", - "Fields": [ - { - "Name": "OutputKind", - "Type": { - "Kind": "Enum", - "Values": [ - "Bag", - "Ind", - "Key", - "Bin" - ] - }, - "Desc": "Output kind: Bag (multi-set vector), Ind (indicator vector), Key (index), or Binary encoded indicator vector", - "Aliases": [ - "kind" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "MaxNumTerms", - "Type": "Int", - "Desc": "Maximum number of terms to keep when auto-training", - "Aliases": [ - "max" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "Term", - "Type": { - "Kind": "Array", - "ItemType": "String" - }, - "Desc": "List of terms", - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": null - }, - { - "Name": "Sort", - "Type": { - "Kind": "Enum", - "Values": [ - "Occurrence", - "Value" - ] - }, - "Desc": "How items should be ordered when vectorized. By default, they will be in the order encountered. If by value items are sorted according to their default comparison, e.g., text sorting will be case sensitive (e.g., 'A' then 'Z' then 'a').", - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "TextKeyValues", - "Type": "Bool", - "Desc": "Whether key value metadata should be text, regardless of the actual input type", - "Aliases": [ - "textkv" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "Name", - "Type": "String", - "Desc": "Name of the new column", - "Aliases": [ - "name" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": null - }, - { - "Name": "Source", - "Type": "String", - "Desc": "Name of the source column", - "Aliases": [ - "src" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": null - } - ] - } - }, - "Desc": "New column definition(s) (optional form: name:src)", + "Name": "TrainingData", + "Type": "DataView", + "Desc": "The data to be used for training", "Aliases": [ - "col" + "data" ], "Required": true, "SortOrder": 1.0, "IsNullable": false }, { - "Name": "Data", - "Type": "DataView", - "Desc": "Input dataset", - "Required": true, - "SortOrder": 1.0, - "IsNullable": false + "Name": "FeatureColumn", + "Type": "String", + "Desc": "Column to use for features", + "Aliases": [ + "feat" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Features" }, { - "Name": "MaxNumTerms", - "Type": "Int", - "Desc": "Maximum number of terms to keep per column when auto-training", + "Name": "LabelColumn", + "Type": "String", + "Desc": "Column to use for labels", "Aliases": [ - "max" + "lab" ], "Required": false, - "SortOrder": 5.0, + "SortOrder": 3.0, "IsNullable": false, - "Default": 1000000 + "Default": "Label" }, { - "Name": "OutputKind", + "Name": "WeightColumn", + "Type": "String", + "Desc": "Column to use for example weight", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": "Weight" + }, + { + "Name": "NormalizeFeatures", "Type": { "Kind": "Enum", "Values": [ - "Bag", - "Ind", - "Key", - "Bin" + "No", + "Warn", + "Auto", + "Yes" ] }, - "Desc": "Output kind: Bag (multi-set vector), Ind (indicator vector), or Key (index)", + "Desc": "Normalize option for the feature column", "Aliases": [ - "kind" + "norm" ], "Required": false, - "SortOrder": 102.0, + "SortOrder": 5.0, "IsNullable": false, - "Default": "Ind" + "Default": "Auto" }, { - "Name": "Term", + "Name": "Caching", "Type": { - "Kind": "Array", - "ItemType": "String" + "Kind": "Enum", + "Values": [ + "Auto", + "Memory", + "Disk", + "None" + ] }, - "Desc": "List of terms", + "Desc": "Whether learner should cache input training data", + "Aliases": [ + "cache" + ], "Required": false, - "SortOrder": 106.0, + "SortOrder": 6.0, "IsNullable": false, - "Default": null + "Default": "Auto" }, { - "Name": "Sort", + "Name": "LossFunction", "Type": { - "Kind": "Enum", - "Values": [ - "Occurrence", - "Value" - ] + "Kind": "Component", + "ComponentKind": "ClassificationLossFunction" }, - "Desc": "How items should be ordered when vectorized. By default, they will be in the order encountered. If by value items are sorted according to their default comparison, e.g., text sorting will be case sensitive (e.g., 'A' then 'Z' then 'a').", + "Desc": "Loss Function", + "Aliases": [ + "loss" + ], "Required": false, - "SortOrder": 113.0, + "SortOrder": 50.0, "IsNullable": false, - "Default": "Occurrence" + "Default": { + "Name": "LogLoss" + } }, { - "Name": "TextKeyValues", - "Type": "Bool", - "Desc": "Whether key value metadata should be text, regardless of the actual input type", + "Name": "L2Const", + "Type": "Float", + "Desc": "L2 regularizer constant", "Aliases": [ - "textkv" + "l2" ], "Required": false, - "SortOrder": 114.0, + "SortOrder": 50.0, "IsNullable": false, - "Default": true - } - ], - "Outputs": [ + "Default": 1E-06, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 1E-07, + 5E-07, + 1E-06, + 5E-06, + 1E-05 + ] + } + }, { - "Name": "OutputData", - "Type": "DataView", - "Desc": "Transformed dataset" + "Name": "NumThreads", + "Type": "Int", + "Desc": "Degree of lock-free parallelism. Defaults to automatic depending on data sparseness. Determinism not guaranteed.", + "Aliases": [ + "nt", + "t", + "threads" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": true, + "Default": null }, { - "Name": "Model", - "Type": "TransformModel", - "Desc": "Transform model" + "Name": "ConvergenceTolerance", + "Type": "Float", + "Desc": "Exponential moving averaged improvement tolerance for convergence", + "Aliases": [ + "tol" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.0001, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.01, + 0.001, + 0.0001, + 1E-05 + ] + } + }, + { + "Name": "MaxIterations", + "Type": "Int", + "Desc": "Maximum number of iterations; set to 1 to simulate online learning.", + "Aliases": [ + "iter" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 20, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 1, + 5, + 10, + 20 + ] + } + }, + { + "Name": "InitLearningRate", + "Type": "Float", + "Desc": "Initial learning rate (only used by SGD)", + "Aliases": [ + "ilr", + "lr" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.01 + }, + { + "Name": "Shuffle", + "Type": "Bool", + "Desc": "Shuffle data every epoch?", + "Aliases": [ + "shuf" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + false, + true + ] + } + }, + { + "Name": "PositiveInstanceWeight", + "Type": "Float", + "Desc": "Apply weight to the positive class, for imbalanced data", + "Aliases": [ + "piw" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.0 + }, + { + "Name": "CheckFrequency", + "Type": "Int", + "Desc": "Convergence check frequency (in terms of number of iterations). Default equals number of threads", + "Aliases": [ + "checkFreq" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Calibrator", + "Type": { + "Kind": "Component", + "ComponentKind": "CalibratorTrainer" + }, + "Desc": "The calibrator kind to apply to the predictor. Specify null for no calibration", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": { + "Name": "PlattCalibrator" + } + }, + { + "Name": "MaxCalibrationExamples", + "Type": "Int", + "Desc": "The maximum number of examples to use when training the calibrator", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1000000 + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The trained model" } ], "InputKind": [ - "ITransformInput" + "ITrainerInputWithWeight", + "ITrainerInputWithLabel", + "ITrainerInput" ], "OutputKind": [ - "ITransformOutput" + "IBinaryClassificationOutput", + "ITrainerOutput" ] }, { - "Name": "Transforms.CharacterTokenizer", - "Desc": "Character-oriented tokenizer where text is considered a sequence of characters.", - "FriendlyName": "Character Tokenizer Transform", - "ShortName": "CharToken", + "Name": "Transforms.ApproximateBootstrapSampler", + "Desc": "Approximate bootstrap sampling.", + "FriendlyName": "Bootstrap Sample Transform", + "ShortName": "BootstrapSample", "Inputs": [ - { - "Name": "Column", - "Type": { - "Kind": "Array", - "ItemType": { - "Kind": "Struct", - "Fields": [ - { - "Name": "Name", - "Type": "String", - "Desc": "Name of the new column", - "Aliases": [ - "name" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": null - }, - { - "Name": "Source", - "Type": "String", - "Desc": "Name of the source column", - "Aliases": [ - "src" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": null - } - ] - } - }, - "Desc": "New column definition(s) (optional form: name:src)", - "Aliases": [ - "col" - ], - "Required": true, - "SortOrder": 1.0, - "IsNullable": false - }, { "Name": "Data", "Type": "DataView", @@ -13077,16 +13598,49 @@ "IsNullable": false }, { - "Name": "UseMarkerChars", + "Name": "Complement", "Type": "Bool", - "Desc": "Whether to mark the beginning/end of each row/slot with start of text character (0x02)/end of text character (0x03)", + "Desc": "Whether this is the out-of-bag sample, that is, all those rows that are not selected by the transform.", "Aliases": [ - "mark" + "comp" ], "Required": false, - "SortOrder": 2.0, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "Seed", + "Type": "UInt", + "Desc": "The random seed. If unspecified random state will be instead derived from the environment.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "ShuffleInput", + "Type": "Bool", + "Desc": "Whether we should attempt to shuffle the source data. By default on, but can be turned off for efficiency.", + "Aliases": [ + "si" + ], + "Required": false, + "SortOrder": 150.0, "IsNullable": false, "Default": true + }, + { + "Name": "PoolSize", + "Type": "Int", + "Desc": "When shuffling the output, the number of output rows to keep in that pool. Note that shuffling of output is completely distinct from shuffling of input.", + "Aliases": [ + "pool" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1000 } ], "Outputs": [ @@ -13109,62 +13663,25 @@ ] }, { - "Name": "Transforms.ColumnConcatenator", - "Desc": "Concatenates two columns of the same item type.", - "FriendlyName": "Concat Transform", - "ShortName": "Concat", + "Name": "Transforms.BinaryPredictionScoreColumnsRenamer", + "Desc": "For binary prediction, it renames the PredictedLabel and Score columns to include the name of the positive class.", + "FriendlyName": "Rename Binary Prediction Score Columns", + "ShortName": null, "Inputs": [ { - "Name": "Column", - "Type": { - "Kind": "Array", - "ItemType": { - "Kind": "Struct", - "Fields": [ - { - "Name": "Name", - "Type": "String", - "Desc": "Name of the new column", - "Aliases": [ - "name" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": null - }, - { - "Name": "Source", - "Type": { - "Kind": "Array", - "ItemType": "String" - }, - "Desc": "Name of the source column", - "Aliases": [ - "src" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": null - } - ] - } - }, - "Desc": "New column definition(s) (optional form: name:srcs)", - "Aliases": [ - "col" - ], + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", "Required": true, "SortOrder": 1.0, "IsNullable": false }, { - "Name": "Data", - "Type": "DataView", - "Desc": "Input dataset", + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The predictor model used in scoring", "Required": true, - "SortOrder": 1.0, + "SortOrder": 2.0, "IsNullable": false } ], @@ -13188,10 +13705,10 @@ ] }, { - "Name": "Transforms.ColumnCopier", - "Desc": "Duplicates columns from the dataset", - "FriendlyName": "Copy Columns Transform", - "ShortName": "Copy", + "Name": "Transforms.BinNormalizer", + "Desc": "The values are assigned into equidensity bins and a value is mapped to its bin_number/number_of_bins.", + "FriendlyName": "Binning Normalizer", + "ShortName": "Bin", "Inputs": [ { "Name": "Column", @@ -13200,6 +13717,42 @@ "ItemType": { "Kind": "Struct", "Fields": [ + { + "Name": "NumBins", + "Type": "Int", + "Desc": "Max number of bins, power of 2 recommended", + "Aliases": [ + "bins" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "FixZero", + "Type": "Bool", + "Desc": "Whether to map zero to zero, preserving sparsity", + "Aliases": [ + "zero" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "MaxTrainingExamples", + "Type": "Int", + "Desc": "Max number of examples used to train the normalizer", + "Aliases": [ + "maxtrain" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, { "Name": "Name", "Type": "String", @@ -13231,9 +13784,10 @@ "Aliases": [ "col" ], - "Required": true, + "Required": false, "SortOrder": 1.0, - "IsNullable": false + "IsNullable": false, + "Default": null }, { "Name": "Data", @@ -13242,103 +13796,42 @@ "Required": true, "SortOrder": 1.0, "IsNullable": false - } - ], - "Outputs": [ - { - "Name": "OutputData", - "Type": "DataView", - "Desc": "Transformed dataset" }, { - "Name": "Model", - "Type": "TransformModel", - "Desc": "Transform model" - } - ], - "InputKind": [ - "ITransformInput" - ], - "OutputKind": [ - "ITransformOutput" - ] - }, - { - "Name": "Transforms.ColumnDropper", - "Desc": "Drops columns from the dataset", - "FriendlyName": "Drop Columns Transform", - "ShortName": "Drop", - "Inputs": [ - { - "Name": "Column", - "Type": { - "Kind": "Array", - "ItemType": "String" - }, - "Desc": "Column name to drop", + "Name": "NumBins", + "Type": "Int", + "Desc": "Max number of bins, power of 2 recommended", "Aliases": [ - "col" + "bins" ], - "Required": true, - "SortOrder": 1.0, - "IsNullable": false - }, - { - "Name": "Data", - "Type": "DataView", - "Desc": "Input dataset", - "Required": true, - "SortOrder": 1.0, - "IsNullable": false - } - ], - "Outputs": [ - { - "Name": "OutputData", - "Type": "DataView", - "Desc": "Transformed dataset" + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1024 }, { - "Name": "Model", - "Type": "TransformModel", - "Desc": "Transform model" - } - ], - "InputKind": [ - "ITransformInput" - ], - "OutputKind": [ - "ITransformOutput" - ] - }, - { - "Name": "Transforms.ColumnSelector", - "Desc": "Selects a set of columns, dropping all others", - "FriendlyName": "Select Columns", - "ShortName": null, - "Inputs": [ - { - "Name": "Column", - "Type": { - "Kind": "Array", - "ItemType": "String" - }, - "Desc": "Column name to keep", + "Name": "FixZero", + "Type": "Bool", + "Desc": "Whether to map zero to zero, preserving sparsity", "Aliases": [ - "col" + "zero" ], "Required": false, - "SortOrder": 1.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": null + "Default": true }, { - "Name": "Data", - "Type": "DataView", - "Desc": "Input dataset", - "Required": true, - "SortOrder": 1.0, - "IsNullable": false + "Name": "MaxTrainingExamples", + "Type": "Int", + "Desc": "Max number of examples used to train the normalizer", + "Aliases": [ + "maxtrain" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1000000000 } ], "Outputs": [ @@ -13361,10 +13854,10 @@ ] }, { - "Name": "Transforms.ColumnTypeConverter", - "Desc": "Converts a column to a different type, using standard conversions.", - "FriendlyName": "Convert Transform", - "ShortName": "Convert", + "Name": "Transforms.CategoricalHashOneHotVectorizer", + "Desc": "Encodes the categorical variable with hash-based encoding", + "FriendlyName": "Categorical Hash Transform", + "ShortName": null, "Inputs": [ { "Name": "Column", @@ -13374,74 +13867,87 @@ "Kind": "Struct", "Fields": [ { - "Name": "ResultType", + "Name": "OutputKind", "Type": { "Kind": "Enum", "Values": [ - "I1", - "U1", - "I2", - "U2", - "I4", - "U4", - "I8", - "U8", - "R4", - "Num", - "R8", - "TX", - "Text", - "TXT", - "BL", - "Bool", - "TimeSpan", - "TS", - "DT", - "DateTime", - "DZ", - "DateTimeZone", - "UG", - "U16" + "Bag", + "Ind", + "Key", + "Bin" ] }, - "Desc": "The result type", + "Desc": "Output kind: Bag (multi-set vector), Ind (indicator vector), or Key (index)", "Aliases": [ - "type" + "kind" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 102.0, "IsNullable": true, "Default": null }, { - "Name": "Range", - "Type": "String", - "Desc": "For a key column, this defines the range of values", + "Name": "HashBits", + "Type": "Int", + "Desc": "The number of bits to hash into. Must be between 1 and 30, inclusive.", "Aliases": [ - "key" + "bits" ], "Required": false, "SortOrder": 150.0, - "IsNullable": false, + "IsNullable": true, "Default": null }, { - "Name": "Name", - "Type": "String", - "Desc": "Name of the new column", - "Aliases": [ - "name" - ], + "Name": "Seed", + "Type": "UInt", + "Desc": "Hashing seed", "Required": false, "SortOrder": 150.0, - "IsNullable": false, + "IsNullable": true, "Default": null }, { - "Name": "Source", - "Type": "String", - "Desc": "Name of the source column", - "Aliases": [ + "Name": "Ordered", + "Type": "Bool", + "Desc": "Whether the position of each term should be included in the hash", + "Aliases": [ + "ord" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "InvertHash", + "Type": "Int", + "Desc": "Limit the number of keys used to generate the slot name to this many. 0 means no invert hashing, -1 means no limit.", + "Aliases": [ + "ih" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ "src" ], "Required": false, @@ -13452,7 +13958,7 @@ ] } }, - "Desc": "New column definition(s) (optional form: name:type:src)", + "Desc": "New column definition(s) (optional form: name:hashBits:src)", "Aliases": [ "col" ], @@ -13469,119 +13975,69 @@ "IsNullable": false }, { - "Name": "ResultType", + "Name": "HashBits", + "Type": "Int", + "Desc": "Number of bits to hash into. Must be between 1 and 30, inclusive.", + "Aliases": [ + "bits" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": 16 + }, + { + "Name": "OutputKind", "Type": { "Kind": "Enum", "Values": [ - "I1", - "U1", - "I2", - "U2", - "I4", - "U4", - "I8", - "U8", - "R4", - "Num", - "R8", - "TX", - "Text", - "TXT", - "BL", - "Bool", - "TimeSpan", - "TS", - "DT", - "DateTime", - "DZ", - "DateTimeZone", - "UG", - "U16" + "Bag", + "Ind", + "Key", + "Bin" ] }, - "Desc": "The result type", + "Desc": "Output kind: Bag (multi-set vector), Ind (indicator vector), or Key (index)", "Aliases": [ - "type" + "kind" ], "Required": false, - "SortOrder": 2.0, - "IsNullable": true, - "Default": null + "SortOrder": 102.0, + "IsNullable": false, + "Default": "Bag" }, { - "Name": "Range", - "Type": "String", - "Desc": "For a key column, this defines the range of values", - "Aliases": [ - "key" - ], + "Name": "Seed", + "Type": "UInt", + "Desc": "Hashing seed", "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": null - } - ], - "Outputs": [ - { - "Name": "OutputData", - "Type": "DataView", - "Desc": "Transformed dataset" + "Default": 314489979 }, { - "Name": "Model", - "Type": "TransformModel", - "Desc": "Transform model" - } - ], - "InputKind": [ - "ITransformInput" - ], - "OutputKind": [ - "ITransformOutput" - ] - }, - { - "Name": "Transforms.CombinerByContiguousGroupId", - "Desc": "Groups values of a scalar column into a vector, by a contiguous group ID", - "FriendlyName": "Group Transform", - "ShortName": "Group", - "Inputs": [ - { - "Name": "GroupKey", - "Type": { - "Kind": "Array", - "ItemType": "String" - }, - "Desc": "Columns to group by", + "Name": "Ordered", + "Type": "Bool", + "Desc": "Whether the position of each term should be included in the hash", "Aliases": [ - "g" + "ord" ], "Required": false, - "SortOrder": 1.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": null - }, - { - "Name": "Data", - "Type": "DataView", - "Desc": "Input dataset", - "Required": true, - "SortOrder": 1.0, - "IsNullable": false + "Default": true }, { - "Name": "Column", - "Type": { - "Kind": "Array", - "ItemType": "String" - }, - "Desc": "Columns to group together", + "Name": "InvertHash", + "Type": "Int", + "Desc": "Limit the number of keys used to generate the slot name to this many. 0 means no invert hashing, -1 means no limit.", "Aliases": [ - "col" + "ih" ], - "Required": true, - "SortOrder": 2.0, - "IsNullable": false + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0 } ], "Outputs": [ @@ -13604,9 +14060,9 @@ ] }, { - "Name": "Transforms.ConditionalNormalizer", - "Desc": "Normalize the columns only if needed", - "FriendlyName": "Normalize If Needed", + "Name": "Transforms.CategoricalOneHotVectorizer", + "Desc": "Encodes the categorical variable with one-hot encoding based on term dictionary", + "FriendlyName": "Categorical Transform", "ShortName": null, "Inputs": [ { @@ -13617,11 +14073,19 @@ "Kind": "Struct", "Fields": [ { - "Name": "FixZero", - "Type": "Bool", - "Desc": "Whether to map zero to zero, preserving sparsity", + "Name": "OutputKind", + "Type": { + "Kind": "Enum", + "Values": [ + "Bag", + "Ind", + "Key", + "Bin" + ] + }, + "Desc": "Output kind: Bag (multi-set vector), Ind (indicator vector), Key (index), or Binary encoded indicator vector", "Aliases": [ - "zero" + "kind" ], "Required": false, "SortOrder": 150.0, @@ -13629,11 +14093,50 @@ "Default": null }, { - "Name": "MaxTrainingExamples", + "Name": "MaxNumTerms", "Type": "Int", - "Desc": "Max number of examples used to train the normalizer", + "Desc": "Maximum number of terms to keep when auto-training", "Aliases": [ - "maxtrain" + "max" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Term", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "List of terms", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Sort", + "Type": { + "Kind": "Enum", + "Values": [ + "Occurrence", + "Value" + ] + }, + "Desc": "How items should be ordered when vectorized. By default, they will be in the order encountered. If by value items are sorted according to their default comparison, e.g., text sorting will be case sensitive (e.g., 'A' then 'Z' then 'a').", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "TextKeyValues", + "Type": "Bool", + "Desc": "Whether key value metadata should be text, regardless of the actual input type", + "Aliases": [ + "textkv" ], "Required": false, "SortOrder": 150.0, @@ -13684,173 +14187,101 @@ "IsNullable": false }, { - "Name": "FixZero", - "Type": "Bool", - "Desc": "Whether to map zero to zero, preserving sparsity", + "Name": "MaxNumTerms", + "Type": "Int", + "Desc": "Maximum number of terms to keep per column when auto-training", "Aliases": [ - "zero" + "max" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 5.0, "IsNullable": false, - "Default": true + "Default": 1000000 }, { - "Name": "MaxTrainingExamples", - "Type": "Int", - "Desc": "Max number of examples used to train the normalizer", - "Aliases": [ - "maxtrain" + "Name": "OutputKind", + "Type": { + "Kind": "Enum", + "Values": [ + "Bag", + "Ind", + "Key", + "Bin" + ] + }, + "Desc": "Output kind: Bag (multi-set vector), Ind (indicator vector), or Key (index)", + "Aliases": [ + "kind" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 102.0, "IsNullable": false, - "Default": 1000000000 - } - ], - "Outputs": [ - { - "Name": "OutputData", - "Type": "DataView", - "Desc": "Transformed dataset" + "Default": "Ind" }, { - "Name": "Model", - "Type": "TransformModel", - "Desc": "Transform model" - } - ], - "InputKind": [ - "ITransformInput" - ] - }, - { - "Name": "Transforms.DataCache", - "Desc": "Caches using the specified cache option.", - "FriendlyName": "Cache Data", - "ShortName": null, - "Inputs": [ - { - "Name": "Data", - "Type": "DataView", - "Desc": "Input dataset", - "Required": true, - "SortOrder": 1.0, - "IsNullable": false + "Name": "Term", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "List of terms", + "Required": false, + "SortOrder": 106.0, + "IsNullable": false, + "Default": null }, { - "Name": "Caching", + "Name": "Sort", "Type": { "Kind": "Enum", "Values": [ - "Memory", - "Disk" + "Occurrence", + "Value" ] }, - "Desc": "Caching strategy", - "Required": true, - "SortOrder": 2.0, + "Desc": "How items should be ordered when vectorized. By default, they will be in the order encountered. If by value items are sorted according to their default comparison, e.g., text sorting will be case sensitive (e.g., 'A' then 'Z' then 'a').", + "Required": false, + "SortOrder": 113.0, "IsNullable": false, - "Default": "Memory" - } - ], - "Outputs": [ - { - "Name": "OutputData", - "Type": "DataView", - "Desc": "Dataset" - } - ], - "InputKind": [ - "ITransformInput" - ] - }, - { - "Name": "Transforms.DatasetScorer", - "Desc": "Score a dataset with a predictor model", - "FriendlyName": null, - "ShortName": null, - "Inputs": [ - { - "Name": "Data", - "Type": "DataView", - "Desc": "The dataset to be scored", - "Required": true, - "SortOrder": 1.0, - "IsNullable": false - }, - { - "Name": "PredictorModel", - "Type": "PredictorModel", - "Desc": "The predictor model to apply to data", - "Required": true, - "SortOrder": 2.0, - "IsNullable": false + "Default": "Occurrence" }, { - "Name": "Suffix", - "Type": "String", - "Desc": "Suffix to append to the score columns", + "Name": "TextKeyValues", + "Type": "Bool", + "Desc": "Whether key value metadata should be text, regardless of the actual input type", + "Aliases": [ + "textkv" + ], "Required": false, - "SortOrder": 3.0, + "SortOrder": 114.0, "IsNullable": false, - "Default": null + "Default": true } ], "Outputs": [ { - "Name": "ScoredData", - "Type": "DataView", - "Desc": "The scored dataset" - }, - { - "Name": "ScoringTransform", - "Type": "TransformModel", - "Desc": "The scoring transform" - } - ] - }, - { - "Name": "Transforms.DatasetTransformScorer", - "Desc": "Score a dataset with a transform model", - "FriendlyName": null, - "ShortName": null, - "Inputs": [ - { - "Name": "Data", + "Name": "OutputData", "Type": "DataView", - "Desc": "The dataset to be scored", - "Required": true, - "SortOrder": 1.0, - "IsNullable": false + "Desc": "Transformed dataset" }, { - "Name": "TransformModel", + "Name": "Model", "Type": "TransformModel", - "Desc": "The transform model to apply to data", - "Required": true, - "SortOrder": 2.0, - "IsNullable": false + "Desc": "Transform model" } ], - "Outputs": [ - { - "Name": "ScoredData", - "Type": "DataView", - "Desc": "The scored dataset" - }, - { - "Name": "ScoringTransform", - "Type": "TransformModel", - "Desc": "The scoring transform" - } + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" ] }, { - "Name": "Transforms.Dictionarizer", - "Desc": "Converts input values (words, numbers, etc.) to index in a dictionary.", - "FriendlyName": "Term Transform", - "ShortName": "TermTransform", + "Name": "Transforms.CharacterTokenizer", + "Desc": "Character-oriented tokenizer where text is considered a sequence of characters.", + "FriendlyName": "Character Tokenizer Transform", + "ShortName": "CharToken", "Inputs": [ { "Name": "Column", @@ -13859,57 +14290,6 @@ "ItemType": { "Kind": "Struct", "Fields": [ - { - "Name": "MaxNumTerms", - "Type": "Int", - "Desc": "Maximum number of terms to keep when auto-training", - "Aliases": [ - "max" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "Term", - "Type": { - "Kind": "Array", - "ItemType": "String" - }, - "Desc": "List of terms", - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": null - }, - { - "Name": "Sort", - "Type": { - "Kind": "Enum", - "Values": [ - "Occurrence", - "Value" - ] - }, - "Desc": "How items should be ordered when vectorized. By default, they will be in the order encountered. If by value items are sorted according to their default comparison, e.g., text sorting will be case sensitive (e.g., 'A' then 'Z' then 'a').", - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "TextKeyValues", - "Type": "Bool", - "Desc": "Whether key value metadata should be text, regardless of the actual input type", - "Aliases": [ - "textkv" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, { "Name": "Name", "Type": "String", @@ -13941,10 +14321,9 @@ "Aliases": [ "col" ], - "Required": false, + "Required": true, "SortOrder": 1.0, - "IsNullable": false, - "Default": null + "IsNullable": false }, { "Name": "Data", @@ -13955,55 +14334,95 @@ "IsNullable": false }, { - "Name": "MaxNumTerms", - "Type": "Int", - "Desc": "Maximum number of terms to keep per column when auto-training", + "Name": "UseMarkerChars", + "Type": "Bool", + "Desc": "Whether to mark the beginning/end of each row/slot with start of text character (0x02)/end of text character (0x03)", "Aliases": [ - "max" + "mark" ], "Required": false, - "SortOrder": 5.0, + "SortOrder": 2.0, "IsNullable": false, - "Default": 1000000 + "Default": true + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" }, { - "Name": "Term", - "Type": { - "Kind": "Array", - "ItemType": "String" - }, - "Desc": "List of terms", - "Required": false, - "SortOrder": 106.0, - "IsNullable": false, - "Default": null - }, + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.ColumnConcatenator", + "Desc": "Concatenates two columns of the same item type.", + "FriendlyName": "Concat Transform", + "ShortName": "Concat", + "Inputs": [ { - "Name": "Sort", + "Name": "Column", "Type": { - "Kind": "Enum", - "Values": [ - "Occurrence", - "Value" - ] + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } }, - "Desc": "How items should be ordered when vectorized. By default, they will be in the order encountered. If by value items are sorted according to their default comparison, e.g., text sorting will be case sensitive (e.g., 'A' then 'Z' then 'a').", - "Required": false, - "SortOrder": 113.0, - "IsNullable": false, - "Default": "Occurrence" - }, - { - "Name": "TextKeyValues", - "Type": "Bool", - "Desc": "Whether key value metadata should be text, regardless of the actual input type", + "Desc": "New column definition(s) (optional form: name:srcs)", "Aliases": [ - "textkv" + "col" ], - "Required": false, - "SortOrder": 114.0, - "IsNullable": false, - "Default": false + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false } ], "Outputs": [ @@ -14026,11 +14445,53 @@ ] }, { - "Name": "Transforms.FeatureCombiner", - "Desc": "Combines all the features into one feature column.", - "FriendlyName": "Feature Combiner", - "ShortName": "fc", + "Name": "Transforms.ColumnCopier", + "Desc": "Duplicates columns from the dataset", + "FriendlyName": "Copy Columns Transform", + "ShortName": "Copy", "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:src)", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, { "Name": "Data", "Type": "DataView", @@ -14038,18 +14499,6 @@ "Required": true, "SortOrder": 1.0, "IsNullable": false - }, - { - "Name": "Features", - "Type": { - "Kind": "Array", - "ItemType": "String" - }, - "Desc": "Features", - "Required": false, - "SortOrder": 2.0, - "IsNullable": false, - "Default": null } ], "Outputs": [ @@ -14072,10 +14521,10 @@ ] }, { - "Name": "Transforms.FeatureSelectorByCount", - "Desc": "Selects the slots for which the count of non-default values is greater than or equal to a threshold.", - "FriendlyName": "Count Feature Selection Transform", - "ShortName": null, + "Name": "Transforms.ColumnDropper", + "Desc": "Drops columns from the dataset", + "FriendlyName": "Drop Columns Transform", + "ShortName": "Drop", "Inputs": [ { "Name": "Column", @@ -14083,7 +14532,7 @@ "Kind": "Array", "ItemType": "String" }, - "Desc": "Columns to use for feature selection", + "Desc": "Column name to drop", "Aliases": [ "col" ], @@ -14091,18 +14540,6 @@ "SortOrder": 1.0, "IsNullable": false }, - { - "Name": "Count", - "Type": "Int", - "Desc": "If the count of non-default values for a slot is greater than or equal to this threshold, the slot is preserved", - "Aliases": [ - "c" - ], - "Required": true, - "SortOrder": 1.0, - "IsNullable": false, - "Default": 1 - }, { "Name": "Data", "Type": "DataView", @@ -14132,10 +14569,10 @@ ] }, { - "Name": "Transforms.FeatureSelectorByMutualInformation", - "Desc": "Selects the top k slots across all specified columns ordered by their mutual information with the label column.", - "FriendlyName": "Mutual Information Feature Selection Transform", - "ShortName": "MIFeatureSelection", + "Name": "Transforms.ColumnSelector", + "Desc": "Selects a set of columns, dropping all others", + "FriendlyName": "Select Columns", + "ShortName": null, "Inputs": [ { "Name": "Column", @@ -14143,26 +14580,14 @@ "Kind": "Array", "ItemType": "String" }, - "Desc": "Columns to use for feature selection", + "Desc": "Column name to keep", "Aliases": [ "col" ], - "Required": true, - "SortOrder": 1.0, - "IsNullable": false - }, - { - "Name": "SlotsInOutput", - "Type": "Int", - "Desc": "The maximum number of slots to preserve in output", - "Aliases": [ - "topk", - "numSlotsToKeep" - ], "Required": false, "SortOrder": 1.0, "IsNullable": false, - "Default": 1000 + "Default": null }, { "Name": "Data", @@ -14171,30 +14596,6 @@ "Required": true, "SortOrder": 1.0, "IsNullable": false - }, - { - "Name": "LabelColumn", - "Type": "String", - "Desc": "Column to use for labels", - "Aliases": [ - "lab" - ], - "Required": false, - "SortOrder": 4.0, - "IsNullable": false, - "Default": "Label" - }, - { - "Name": "NumBins", - "Type": "Int", - "Desc": "Max number of bins for R4/R8 columns, power of 2 recommended", - "Aliases": [ - "bins" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 256 } ], "Outputs": [ @@ -14217,10 +14618,10 @@ ] }, { - "Name": "Transforms.GlobalContrastNormalizer", - "Desc": "Performs a global contrast normalization on input values: Y = (s * X - M) / D, where s is a scale, M is mean and D is either L2 norm or standard deviation.", - "FriendlyName": "Global Contrast Normalization Transform", - "ShortName": "Gcn", + "Name": "Transforms.ColumnTypeConverter", + "Desc": "Converts a column to a different type, using standard conversions.", + "FriendlyName": "Convert Transform", + "ShortName": "Convert", "Inputs": [ { "Name": "Column", @@ -14230,33 +14631,58 @@ "Kind": "Struct", "Fields": [ { - "Name": "UseStdDev", - "Type": "Bool", - "Desc": "Normalize by standard deviation rather than L2 norm", - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "Scale", - "Type": "Float", - "Desc": "Scale features by this value", - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "SubMean", - "Type": "Bool", - "Desc": "Subtract mean from each value before normalizing", - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { + "Name": "ResultType", + "Type": { + "Kind": "Enum", + "Values": [ + "I1", + "U1", + "I2", + "U2", + "I4", + "U4", + "I8", + "U8", + "R4", + "Num", + "R8", + "TX", + "Text", + "TXT", + "BL", + "Bool", + "TimeSpan", + "TS", + "DT", + "DateTime", + "DZ", + "DateTimeZone", + "UG", + "U16" + ] + }, + "Desc": "The result type", + "Aliases": [ + "type" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Range", + "Type": "String", + "Desc": "For a key column, this defines the range of values", + "Aliases": [ + "key" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { "Name": "Name", "Type": "String", "Desc": "Name of the new column", @@ -14283,23 +14709,13 @@ ] } }, - "Desc": "New column definition(s) (optional form: name:src)", + "Desc": "New column definition(s) (optional form: name:type:src)", "Aliases": [ "col" ], - "Required": false, - "SortOrder": 1.0, - "IsNullable": false, - "Default": null - }, - { - "Name": "SubMean", - "Type": "Bool", - "Desc": "Subtract mean from each value before normalizing", - "Required": false, + "Required": true, "SortOrder": 1.0, - "IsNullable": false, - "Default": true + "IsNullable": false }, { "Name": "Data", @@ -14310,25 +14726,119 @@ "IsNullable": false }, { - "Name": "UseStdDev", - "Type": "Bool", - "Desc": "Normalize by standard deviation rather than L2 norm", + "Name": "ResultType", + "Type": { + "Kind": "Enum", + "Values": [ + "I1", + "U1", + "I2", + "U2", + "I4", + "U4", + "I8", + "U8", + "R4", + "Num", + "R8", + "TX", + "Text", + "TXT", + "BL", + "Bool", + "TimeSpan", + "TS", + "DT", + "DateTime", + "DZ", + "DateTimeZone", + "UG", + "U16" + ] + }, + "Desc": "The result type", "Aliases": [ - "useStd" + "type" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Range", + "Type": "String", + "Desc": "For a key column, this defines the range of values", + "Aliases": [ + "key" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": false + "Default": null + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" }, { - "Name": "Scale", - "Type": "Float", - "Desc": "Scale features by this value", + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.CombinerByContiguousGroupId", + "Desc": "Groups values of a scalar column into a vector, by a contiguous group ID", + "FriendlyName": "Group Transform", + "ShortName": "Group", + "Inputs": [ + { + "Name": "GroupKey", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "Columns to group by", + "Aliases": [ + "g" + ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 1.0, "IsNullable": false, - "Default": 1.0 + "Default": null + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "Columns to group together", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 2.0, + "IsNullable": false } ], "Outputs": [ @@ -14351,10 +14861,10 @@ ] }, { - "Name": "Transforms.HashConverter", - "Desc": "Converts column values into hashes. This transform accepts both numeric and text inputs, both single and vector-valued columns. This is a part of the Dracula transform.", - "FriendlyName": "Hash Join Transform", - "ShortName": "HashJoin", + "Name": "Transforms.ConditionalNormalizer", + "Desc": "Normalize the columns only if needed", + "FriendlyName": "Normalize If Needed", + "ShortName": null, "Inputs": [ { "Name": "Column", @@ -14364,29 +14874,11 @@ "Kind": "Struct", "Fields": [ { - "Name": "Join", + "Name": "FixZero", "Type": "Bool", - "Desc": "Whether the values need to be combined for a single hash", - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "CustomSlotMap", - "Type": "String", - "Desc": "Which slots should be combined together. Example: 0,3,5;0,1;3;2,1,0. Overrides 'join'.", - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": null - }, - { - "Name": "HashBits", - "Type": "Int", - "Desc": "Number of bits to hash into. Must be between 1 and 31, inclusive.", + "Desc": "Whether to map zero to zero, preserving sparsity", "Aliases": [ - "bits" + "zero" ], "Required": false, "SortOrder": 150.0, @@ -14394,20 +14886,11 @@ "Default": null }, { - "Name": "Seed", - "Type": "UInt", - "Desc": "Hashing seed", - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "Ordered", - "Type": "Bool", - "Desc": "Whether the position of each term should be included in the hash", + "Name": "MaxTrainingExamples", + "Type": "Int", + "Desc": "Max number of examples used to train the normalizer", "Aliases": [ - "ord" + "maxtrain" ], "Required": false, "SortOrder": 150.0, @@ -14458,46 +14941,28 @@ "IsNullable": false }, { - "Name": "HashBits", - "Type": "Int", - "Desc": "Number of bits to hash into. Must be between 1 and 31, inclusive.", + "Name": "FixZero", + "Type": "Bool", + "Desc": "Whether to map zero to zero, preserving sparsity", "Aliases": [ - "bits" + "zero" ], "Required": false, - "SortOrder": 2.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": 31 + "Default": true }, { - "Name": "Join", - "Type": "Bool", - "Desc": "Whether the values need to be combined for a single hash", + "Name": "MaxTrainingExamples", + "Type": "Int", + "Desc": "Max number of examples used to train the normalizer", + "Aliases": [ + "maxtrain" + ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": true - }, - { - "Name": "Seed", - "Type": "UInt", - "Desc": "Hashing seed", - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 314489979 - }, - { - "Name": "Ordered", - "Type": "Bool", - "Desc": "Whether the position of each term should be included in the hash", - "Aliases": [ - "ord" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": true + "Default": 1000000000 } ], "Outputs": [ @@ -14514,59 +14979,14 @@ ], "InputKind": [ "ITransformInput" - ], - "OutputKind": [ - "ITransformOutput" ] }, { - "Name": "Transforms.KeyToTextConverter", - "Desc": "KeyToValueTransform utilizes KeyValues metadata to map key indices to the corresponding values in the KeyValues metadata.", - "FriendlyName": "Key To Value Transform", + "Name": "Transforms.DataCache", + "Desc": "Caches using the specified cache option.", + "FriendlyName": "Cache Data", "ShortName": null, "Inputs": [ - { - "Name": "Column", - "Type": { - "Kind": "Array", - "ItemType": { - "Kind": "Struct", - "Fields": [ - { - "Name": "Name", - "Type": "String", - "Desc": "Name of the new column", - "Aliases": [ - "name" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": null - }, - { - "Name": "Source", - "Type": "String", - "Desc": "Name of the source column", - "Aliases": [ - "src" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": null - } - ] - } - }, - "Desc": "New column definition(s) (optional form: name:src)", - "Aliases": [ - "col" - ], - "Required": true, - "SortOrder": 1.0, - "IsNullable": false - }, { "Name": "Data", "Type": "DataView", @@ -14574,83 +14994,120 @@ "Required": true, "SortOrder": 1.0, "IsNullable": false + }, + { + "Name": "Caching", + "Type": { + "Kind": "Enum", + "Values": [ + "Memory", + "Disk" + ] + }, + "Desc": "Caching strategy", + "Required": true, + "SortOrder": 2.0, + "IsNullable": false, + "Default": "Memory" } ], "Outputs": [ { "Name": "OutputData", "Type": "DataView", - "Desc": "Transformed dataset" - }, - { - "Name": "Model", - "Type": "TransformModel", - "Desc": "Transform model" + "Desc": "Dataset" } ], "InputKind": [ "ITransformInput" - ], - "OutputKind": [ - "ITransformOutput" ] }, { - "Name": "Transforms.LabelColumnKeyBooleanConverter", - "Desc": "Transforms the label to either key or bool (if needed) to make it suitable for classification.", - "FriendlyName": "Prepare Classification Label", + "Name": "Transforms.DatasetScorer", + "Desc": "Score a dataset with a predictor model", + "FriendlyName": null, "ShortName": null, "Inputs": [ { "Name": "Data", "Type": "DataView", - "Desc": "Input dataset", + "Desc": "The dataset to be scored", "Required": true, "SortOrder": 1.0, "IsNullable": false }, { - "Name": "LabelColumn", - "Type": "String", - "Desc": "The label column", + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The predictor model to apply to data", "Required": true, "SortOrder": 2.0, "IsNullable": false }, { - "Name": "TextKeyValues", - "Type": "Bool", - "Desc": "Convert the key values to text", + "Name": "Suffix", + "Type": "String", + "Desc": "Suffix to append to the score columns", "Required": false, "SortOrder": 3.0, "IsNullable": false, - "Default": true + "Default": null } ], "Outputs": [ { - "Name": "OutputData", + "Name": "ScoredData", "Type": "DataView", - "Desc": "Transformed dataset" + "Desc": "The scored dataset" }, { - "Name": "Model", + "Name": "ScoringTransform", "Type": "TransformModel", - "Desc": "Transform model" + "Desc": "The scoring transform" + } + ] + }, + { + "Name": "Transforms.DatasetTransformScorer", + "Desc": "Score a dataset with a transform model", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "The dataset to be scored", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "TransformModel", + "Type": "TransformModel", + "Desc": "The transform model to apply to data", + "Required": true, + "SortOrder": 2.0, + "IsNullable": false } ], - "InputKind": [ - "ITransformInput" - ], - "OutputKind": [ - "ITransformOutput" + "Outputs": [ + { + "Name": "ScoredData", + "Type": "DataView", + "Desc": "The scored dataset" + }, + { + "Name": "ScoringTransform", + "Type": "TransformModel", + "Desc": "The scoring transform" + } ] }, { - "Name": "Transforms.LabelIndicator", - "Desc": "Label remapper used by OVA", - "FriendlyName": "LabelIndicator", - "ShortName": "LabelIndictator", + "Name": "Transforms.Dictionarizer", + "Desc": "Converts input values (words, numbers, etc.) to index in a dictionary.", + "FriendlyName": "Term Transform", + "ShortName": "TermTransform", "Inputs": [ { "Name": "Column", @@ -14660,11 +15117,50 @@ "Kind": "Struct", "Fields": [ { - "Name": "ClassIndex", + "Name": "MaxNumTerms", "Type": "Int", - "Desc": "The positive example class for binary classification.", + "Desc": "Maximum number of terms to keep when auto-training", "Aliases": [ - "index" + "max" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Term", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "List of terms", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Sort", + "Type": { + "Kind": "Enum", + "Values": [ + "Occurrence", + "Value" + ] + }, + "Desc": "How items should be ordered when vectorized. By default, they will be in the order encountered. If by value items are sorted according to their default comparison, e.g., text sorting will be case sensitive (e.g., 'A' then 'Z' then 'a').", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "TextKeyValues", + "Type": "Bool", + "Desc": "Whether key value metadata should be text, regardless of the actual input type", + "Aliases": [ + "textkv" ], "Required": false, "SortOrder": 150.0, @@ -14716,42 +15212,81 @@ "IsNullable": false }, { - "Name": "ClassIndex", + "Name": "MaxNumTerms", "Type": "Int", - "Desc": "Label of the positive class.", + "Desc": "Maximum number of terms to keep per column when auto-training", "Aliases": [ - "index" + "max" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 5.0, "IsNullable": false, - "Default": 0 - } - ], - "Outputs": [ + "Default": 1000000 + }, { - "Name": "OutputData", - "Type": "DataView", - "Desc": "Transformed dataset" + "Name": "Term", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "List of terms", + "Required": false, + "SortOrder": 106.0, + "IsNullable": false, + "Default": null }, { - "Name": "Model", - "Type": "TransformModel", - "Desc": "Transform model" - } - ], - "InputKind": [ - "ITransformInput" - ], - "OutputKind": [ - "ITransformOutput" - ] + "Name": "Sort", + "Type": { + "Kind": "Enum", + "Values": [ + "Occurrence", + "Value" + ] + }, + "Desc": "How items should be ordered when vectorized. By default, they will be in the order encountered. If by value items are sorted according to their default comparison, e.g., text sorting will be case sensitive (e.g., 'A' then 'Z' then 'a').", + "Required": false, + "SortOrder": 113.0, + "IsNullable": false, + "Default": "Occurrence" + }, + { + "Name": "TextKeyValues", + "Type": "Bool", + "Desc": "Whether key value metadata should be text, regardless of the actual input type", + "Aliases": [ + "textkv" + ], + "Required": false, + "SortOrder": 114.0, + "IsNullable": false, + "Default": false + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] }, { - "Name": "Transforms.LabelToFloatConverter", - "Desc": "Transforms the label to float to make it suitable for regression.", - "FriendlyName": "Prepare Regression Label", - "ShortName": null, + "Name": "Transforms.FeatureCombiner", + "Desc": "Combines all the features into one feature column.", + "FriendlyName": "Feature Combiner", + "ShortName": "fc", "Inputs": [ { "Name": "Data", @@ -14762,11 +15297,75 @@ "IsNullable": false }, { - "Name": "LabelColumn", - "Type": "String", - "Desc": "The label column", - "Required": true, + "Name": "Features", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "Features", + "Required": false, "SortOrder": 2.0, + "IsNullable": false, + "Default": null + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.FeatureSelectorByCount", + "Desc": "Selects the slots for which the count of non-default values is greater than or equal to a threshold.", + "FriendlyName": "Count Feature Selection Transform", + "ShortName": null, + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "Columns to use for feature selection", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Count", + "Type": "Int", + "Desc": "If the count of non-default values for a slot is greater than or equal to this threshold, the slot is preserved", + "Aliases": [ + "c" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false, + "Default": 1 + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, "IsNullable": false } ], @@ -14790,65 +15389,37 @@ ] }, { - "Name": "Transforms.LogMeanVarianceNormalizer", - "Desc": "Normalizes the data based on the computed mean and variance of the logarithm of the data.", - "FriendlyName": "LogMeanVar Normalizer", - "ShortName": "LogMeanVar", + "Name": "Transforms.FeatureSelectorByMutualInformation", + "Desc": "Selects the top k slots across all specified columns ordered by their mutual information with the label column.", + "FriendlyName": "Mutual Information Feature Selection Transform", + "ShortName": "MIFeatureSelection", "Inputs": [ { "Name": "Column", "Type": { "Kind": "Array", - "ItemType": { - "Kind": "Struct", - "Fields": [ - { - "Name": "MaxTrainingExamples", - "Type": "Int", - "Desc": "Max number of examples used to train the normalizer", - "Aliases": [ - "maxtrain" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "Name", - "Type": "String", - "Desc": "Name of the new column", - "Aliases": [ - "name" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": null - }, - { - "Name": "Source", - "Type": "String", - "Desc": "Name of the source column", - "Aliases": [ - "src" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": null - } - ] - } + "ItemType": "String" }, - "Desc": "New column definition(s) (optional form: name:src)", + "Desc": "Columns to use for feature selection", "Aliases": [ "col" ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "SlotsInOutput", + "Type": "Int", + "Desc": "The maximum number of slots to preserve in output", + "Aliases": [ + "topk", + "numSlotsToKeep" + ], "Required": false, "SortOrder": 1.0, "IsNullable": false, - "Default": null + "Default": 1000 }, { "Name": "Data", @@ -14859,28 +15430,28 @@ "IsNullable": false }, { - "Name": "UseCdf", - "Type": "Bool", - "Desc": "Whether to use CDF as the output", + "Name": "LabelColumn", + "Type": "String", + "Desc": "Column to use for labels", "Aliases": [ - "cdf" + "lab" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 4.0, "IsNullable": false, - "Default": true + "Default": "Label" }, { - "Name": "MaxTrainingExamples", + "Name": "NumBins", "Type": "Int", - "Desc": "Max number of examples used to train the normalizer", + "Desc": "Max number of bins for R4/R8 columns, power of 2 recommended", "Aliases": [ - "maxtrain" + "bins" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 1000000000 + "Default": 256 } ], "Outputs": [ @@ -14903,10 +15474,10 @@ ] }, { - "Name": "Transforms.LpNormalizer", - "Desc": "Normalize vectors (rows) individually by rescaling them to unit norm (L2, L1 or LInf). Performs the following operation on a vector X: Y = (X - M) / D, where M is mean and D is either L2 norm, L1 norm or LInf norm.", - "FriendlyName": "Lp-Norm Normalizer", - "ShortName": "lpnorm", + "Name": "Transforms.GlobalContrastNormalizer", + "Desc": "Performs a global contrast normalization on input values: Y = (s * X - M) / D, where s is a scale, M is mean and D is either L2 norm or standard deviation.", + "FriendlyName": "Global Contrast Normalization Transform", + "ShortName": "Gcn", "Inputs": [ { "Name": "Column", @@ -14916,22 +15487,20 @@ "Kind": "Struct", "Fields": [ { - "Name": "NormKind", - "Type": { - "Kind": "Enum", - "Values": [ - "L2Norm", - "StdDev", - "L1Norm", - "LInf" - ] - }, - "Desc": "The norm to use to normalize each sample", - "Aliases": [ - "norm" - ], + "Name": "UseStdDev", + "Type": "Bool", + "Desc": "Normalize by standard deviation rather than L2 norm", "Required": false, - "SortOrder": 1.0, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Scale", + "Type": "Float", + "Desc": "Scale features by this value", + "Required": false, + "SortOrder": 150.0, "IsNullable": true, "Default": null }, @@ -14975,29 +15544,19 @@ "Aliases": [ "col" ], - "Required": true, + "Required": false, "SortOrder": 1.0, - "IsNullable": false + "IsNullable": false, + "Default": null }, { - "Name": "NormKind", - "Type": { - "Kind": "Enum", - "Values": [ - "L2Norm", - "StdDev", - "L1Norm", - "LInf" - ] - }, - "Desc": "The norm to use to normalize each sample", - "Aliases": [ - "norm" - ], + "Name": "SubMean", + "Type": "Bool", + "Desc": "Subtract mean from each value before normalizing", "Required": false, "SortOrder": 1.0, "IsNullable": false, - "Default": "L2Norm" + "Default": true }, { "Name": "Data", @@ -15008,13 +15567,25 @@ "IsNullable": false }, { - "Name": "SubMean", + "Name": "UseStdDev", "Type": "Bool", - "Desc": "Subtract mean from each value before normalizing", + "Desc": "Normalize by standard deviation rather than L2 norm", + "Aliases": [ + "useStd" + ], "Required": false, - "SortOrder": 2.0, + "SortOrder": 150.0, "IsNullable": false, "Default": false + }, + { + "Name": "Scale", + "Type": "Float", + "Desc": "Scale features by this value", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1.0 } ], "Outputs": [ @@ -15037,44 +15608,10 @@ ] }, { - "Name": "Transforms.ManyHeterogeneousModelCombiner", - "Desc": "Combines a sequence of TransformModels and a PredictorModel into a single PredictorModel.", - "FriendlyName": null, - "ShortName": null, - "Inputs": [ - { - "Name": "TransformModels", - "Type": { - "Kind": "Array", - "ItemType": "TransformModel" - }, - "Desc": "Transform model", - "Required": true, - "SortOrder": 1.0, - "IsNullable": false - }, - { - "Name": "PredictorModel", - "Type": "PredictorModel", - "Desc": "Predictor model", - "Required": true, - "SortOrder": 2.0, - "IsNullable": false - } - ], - "Outputs": [ - { - "Name": "PredictorModel", - "Type": "PredictorModel", - "Desc": "Predictor model" - } - ] - }, - { - "Name": "Transforms.MeanVarianceNormalizer", - "Desc": "Normalizes the data based on the computed mean and variance of the data.", - "FriendlyName": "MeanVar Normalizer", - "ShortName": "MeanVar", + "Name": "Transforms.HashConverter", + "Desc": "Converts column values into hashes. This transform accepts both numeric and text inputs, both single and vector-valued columns. This is a part of the Dracula transform.", + "FriendlyName": "Hash Join Transform", + "ShortName": "HashJoin", "Inputs": [ { "Name": "Column", @@ -15084,11 +15621,29 @@ "Kind": "Struct", "Fields": [ { - "Name": "FixZero", + "Name": "Join", "Type": "Bool", - "Desc": "Whether to map zero to zero, preserving sparsity", + "Desc": "Whether the values need to be combined for a single hash", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "CustomSlotMap", + "Type": "String", + "Desc": "Which slots should be combined together. Example: 0,3,5;0,1;3;2,1,0. Overrides 'join'.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "HashBits", + "Type": "Int", + "Desc": "Number of bits to hash into. Must be between 1 and 31, inclusive.", "Aliases": [ - "zero" + "bits" ], "Required": false, "SortOrder": 150.0, @@ -15096,11 +15651,20 @@ "Default": null }, { - "Name": "MaxTrainingExamples", - "Type": "Int", - "Desc": "Max number of examples used to train the normalizer", + "Name": "Seed", + "Type": "UInt", + "Desc": "Hashing seed", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Ordered", + "Type": "Bool", + "Desc": "Whether the position of each term should be included in the hash", "Aliases": [ - "maxtrain" + "ord" ], "Required": false, "SortOrder": 150.0, @@ -15151,40 +15715,46 @@ "IsNullable": false }, { - "Name": "UseCdf", - "Type": "Bool", - "Desc": "Whether to use CDF as the output", + "Name": "HashBits", + "Type": "Int", + "Desc": "Number of bits to hash into. Must be between 1 and 31, inclusive.", "Aliases": [ - "cdf" + "bits" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 2.0, "IsNullable": false, - "Default": false + "Default": 31 }, { - "Name": "FixZero", + "Name": "Join", "Type": "Bool", - "Desc": "Whether to map zero to zero, preserving sparsity", - "Aliases": [ - "zero" - ], + "Desc": "Whether the values need to be combined for a single hash", "Required": false, "SortOrder": 150.0, "IsNullable": false, "Default": true }, { - "Name": "MaxTrainingExamples", - "Type": "Int", - "Desc": "Max number of examples used to train the normalizer", + "Name": "Seed", + "Type": "UInt", + "Desc": "Hashing seed", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 314489979 + }, + { + "Name": "Ordered", + "Type": "Bool", + "Desc": "Whether the position of each term should be included in the hash", "Aliases": [ - "maxtrain" + "ord" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 1000000000 + "Default": true } ], "Outputs": [ @@ -15207,10 +15777,10 @@ ] }, { - "Name": "Transforms.MinMaxNormalizer", - "Desc": "Normalizes the data based on the observed minimum and maximum values of the data.", - "FriendlyName": "Min-Max Normalizer", - "ShortName": "MinMax", + "Name": "Transforms.KeyToTextConverter", + "Desc": "KeyToValueTransform utilizes KeyValues metadata to map key indices to the corresponding values in the KeyValues metadata.", + "FriendlyName": "Key To Value Transform", + "ShortName": null, "Inputs": [ { "Name": "Column", @@ -15219,30 +15789,6 @@ "ItemType": { "Kind": "Struct", "Fields": [ - { - "Name": "FixZero", - "Type": "Bool", - "Desc": "Whether to map zero to zero, preserving sparsity", - "Aliases": [ - "zero" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "MaxTrainingExamples", - "Type": "Int", - "Desc": "Max number of examples used to train the normalizer", - "Aliases": [ - "maxtrain" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, { "Name": "Name", "Type": "String", @@ -15278,6 +15824,40 @@ "SortOrder": 1.0, "IsNullable": false }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.LabelColumnKeyBooleanConverter", + "Desc": "Transforms the label to either key or bool (if needed) to make it suitable for classification.", + "FriendlyName": "Prepare Classification Label", + "ShortName": null, + "Inputs": [ { "Name": "Data", "Type": "DataView", @@ -15287,28 +15867,21 @@ "IsNullable": false }, { - "Name": "FixZero", - "Type": "Bool", - "Desc": "Whether to map zero to zero, preserving sparsity", - "Aliases": [ - "zero" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": true + "Name": "LabelColumn", + "Type": "String", + "Desc": "The label column", + "Required": true, + "SortOrder": 2.0, + "IsNullable": false }, { - "Name": "MaxTrainingExamples", - "Type": "Int", - "Desc": "Max number of examples used to train the normalizer", - "Aliases": [ - "maxtrain" - ], + "Name": "TextKeyValues", + "Type": "Bool", + "Desc": "Convert the key values to text", "Required": false, - "SortOrder": 150.0, + "SortOrder": 3.0, "IsNullable": false, - "Default": 1000000000 + "Default": true } ], "Outputs": [ @@ -15331,57 +15904,24 @@ ] }, { - "Name": "Transforms.MissingValueHandler", - "Desc": "Handle missing values by replacing them with either the default value or the mean/min/max value (for non-text columns only). An indicator column can optionally be concatenated, if theinput column type is numeric.", - "FriendlyName": "NA Handle Transform", - "ShortName": "NAHandle", - "Inputs": [ - { - "Name": "Column", + "Name": "Transforms.LabelIndicator", + "Desc": "Label remapper used by OVA", + "FriendlyName": "LabelIndicator", + "ShortName": "LabelIndictator", + "Inputs": [ + { + "Name": "Column", "Type": { "Kind": "Array", "ItemType": { "Kind": "Struct", "Fields": [ { - "Name": "Kind", - "Type": { - "Kind": "Enum", - "Values": [ - "Default", - "Def", - "DefaultValue", - "Mean", - "Minimum", - "Min", - "Maximum", - "Max" - ] - }, - "Desc": "The replacement method to utilize", - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "ImputeBySlot", - "Type": "Bool", - "Desc": "Whether to impute values by slot", - "Aliases": [ - "slot" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "ConcatIndicator", - "Type": "Bool", - "Desc": "Whether or not to concatenate an indicator vector column to the value column", + "Name": "ClassIndex", + "Type": "Int", + "Desc": "The positive example class for binary classification.", "Aliases": [ - "ind" + "index" ], "Required": false, "SortOrder": 150.0, @@ -15415,13 +15955,14 @@ ] } }, - "Desc": "New column definition(s) (optional form: name:rep:src)", + "Desc": "New column definition(s) (optional form: name:src)", "Aliases": [ "col" ], - "Required": true, + "Required": false, "SortOrder": 1.0, - "IsNullable": false + "IsNullable": false, + "Default": null }, { "Name": "Data", @@ -15432,52 +15973,16 @@ "IsNullable": false }, { - "Name": "ReplaceWith", - "Type": { - "Kind": "Enum", - "Values": [ - "Default", - "Def", - "DefaultValue", - "Mean", - "Minimum", - "Min", - "Maximum", - "Max" - ] - }, - "Desc": "The replacement method to utilize", - "Aliases": [ - "kind" - ], - "Required": false, - "SortOrder": 2.0, - "IsNullable": false, - "Default": "Def" - }, - { - "Name": "ImputeBySlot", - "Type": "Bool", - "Desc": "Whether to impute values by slot", - "Aliases": [ - "slot" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": true - }, - { - "Name": "Concat", - "Type": "Bool", - "Desc": "Whether or not to concatenate an indicator vector column to the value column", + "Name": "ClassIndex", + "Type": "Int", + "Desc": "Label of the positive class.", "Aliases": [ - "ind" + "index" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": true + "Default": 0 } ], "Outputs": [ @@ -15500,59 +16005,25 @@ ] }, { - "Name": "Transforms.MissingValueIndicator", - "Desc": "Create a boolean output column with the same number of slots as the input column, where the output value is true if the value in the input column is missing.", - "FriendlyName": "NA Indicator Transform", - "ShortName": "NAInd", + "Name": "Transforms.LabelToFloatConverter", + "Desc": "Transforms the label to float to make it suitable for regression.", + "FriendlyName": "Prepare Regression Label", + "ShortName": null, "Inputs": [ { - "Name": "Column", - "Type": { - "Kind": "Array", - "ItemType": { - "Kind": "Struct", - "Fields": [ - { - "Name": "Name", - "Type": "String", - "Desc": "Name of the new column", - "Aliases": [ - "name" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": null - }, - { - "Name": "Source", - "Type": "String", - "Desc": "Name of the source column", - "Aliases": [ - "src" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": null - } - ] - } - }, - "Desc": "New column definition(s) (optional form: name:src)", - "Aliases": [ - "col" - ], + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", "Required": true, "SortOrder": 1.0, "IsNullable": false }, { - "Name": "Data", - "Type": "DataView", - "Desc": "Input dataset", + "Name": "LabelColumn", + "Type": "String", + "Desc": "The label column", "Required": true, - "SortOrder": 1.0, + "SortOrder": 2.0, "IsNullable": false } ], @@ -15576,11 +16047,19 @@ ] }, { - "Name": "Transforms.MissingValuesDropper", - "Desc": "Removes NAs from vector columns.", - "FriendlyName": "NA Drop Transform", - "ShortName": "NADrop", + "Name": "Transforms.LightLda", + "Desc": "The LDA transform implements LightLDA, a state-of-the-art implementation of Latent Dirichlet Allocation.", + "FriendlyName": "Latent Dirichlet Allocation Transform", + "ShortName": "LightLda", "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, { "Name": "Column", "Type": { @@ -15588,6 +16067,126 @@ "ItemType": { "Kind": "Struct", "Fields": [ + { + "Name": "NumTopic", + "Type": "Int", + "Desc": "The number of topics in the LDA", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "AlphaSum", + "Type": "Float", + "Desc": "Dirichlet prior on document-topic vectors", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Beta", + "Type": "Float", + "Desc": "Dirichlet prior on vocab-topic vectors", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Mhstep", + "Type": "Int", + "Desc": "Number of Metropolis Hasting step", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "NumIterations", + "Type": "Int", + "Desc": "Number of iterations", + "Aliases": [ + "iter" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "LikelihoodInterval", + "Type": "Int", + "Desc": "Compute log likelihood over local dataset on this iteration interval", + "Aliases": [ + "llInterval" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "NumThreads", + "Type": "Int", + "Desc": "The number of training threads", + "Aliases": [ + "t" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "NumMaxDocToken", + "Type": "Int", + "Desc": "The threshold of maximum count of tokens per doc", + "Aliases": [ + "maxNumToken" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "NumSummaryTermPerTopic", + "Type": "Int", + "Desc": "The number of words to summarize the topic", + "Aliases": [ + "ns" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "NumBurninIterations", + "Type": "Int", + "Desc": "The number of burn-in iterations", + "Aliases": [ + "burninIter" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": 10 + }, + { + "Name": "ResetRandomGenerator", + "Type": "Bool", + "Desc": "Reset the random number generator for each document", + "Aliases": [ + "reset" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, { "Name": "Name", "Type": "String", @@ -15615,74 +16214,195 @@ ] } }, - "Desc": "Columns to drop the NAs for", + "Desc": "New column definition(s) (optional form: name:srcs)", "Aliases": [ "col" ], "Required": true, - "SortOrder": 1.0, + "SortOrder": 49.0, "IsNullable": false }, { - "Name": "Data", - "Type": "DataView", - "Desc": "Input dataset", - "Required": true, - "SortOrder": 1.0, - "IsNullable": false - } - ], - "Outputs": [ + "Name": "NumTopic", + "Type": "Int", + "Desc": "The number of topics in the LDA", + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 100, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 20, + 40, + 100, + 200 + ] + } + }, { - "Name": "OutputData", - "Type": "DataView", - "Desc": "Transformed dataset" + "Name": "NumMaxDocToken", + "Type": "Int", + "Desc": "The threshold of maximum count of tokens per doc", + "Aliases": [ + "maxNumToken" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": false, + "Default": 512 }, { - "Name": "Model", - "Type": "TransformModel", - "Desc": "Transform model" - } - ], - "InputKind": [ - "ITransformInput" - ], - "OutputKind": [ - "ITransformOutput" - ] - }, - { - "Name": "Transforms.MissingValuesRowDropper", - "Desc": "Filters out rows that contain missing values.", - "FriendlyName": "NA Filter", - "ShortName": "NAFilter", - "Inputs": [ + "Name": "NumThreads", + "Type": "Int", + "Desc": "The number of training threads. Default value depends on number of logical processors.", + "Aliases": [ + "t" + ], + "Required": false, + "SortOrder": 50.0, + "IsNullable": true, + "Default": null + }, { - "Name": "Column", - "Type": { - "Kind": "Array", - "ItemType": "String" - }, - "Desc": "Column", + "Name": "AlphaSum", + "Type": "Float", + "Desc": "Dirichlet prior on document-topic vectors", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 100.0, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 1, + 10, + 100, + 200 + ] + } + }, + { + "Name": "Beta", + "Type": "Float", + "Desc": "Dirichlet prior on vocab-topic vectors", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0.01, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 0.01, + 0.015, + 0.07, + 0.02 + ] + } + }, + { + "Name": "Mhstep", + "Type": "Int", + "Desc": "Number of Metropolis Hasting step", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 4, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 2, + 4, + 8, + 16 + ] + } + }, + { + "Name": "NumIterations", + "Type": "Int", + "Desc": "Number of iterations", "Aliases": [ - "col" + "iter" ], - "Required": true, - "SortOrder": 1.0, - "IsNullable": false + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 200, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 100, + 200, + 300, + 400 + ] + } }, { - "Name": "Data", - "Type": "DataView", - "Desc": "Input dataset", - "Required": true, - "SortOrder": 1.0, - "IsNullable": false + "Name": "LikelihoodInterval", + "Type": "Int", + "Desc": "Compute log likelihood over local dataset on this iteration interval", + "Aliases": [ + "llInterval" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 5 }, { - "Name": "Complement", + "Name": "NumSummaryTermPerTopic", + "Type": "Int", + "Desc": "The number of words to summarize the topic", + "Aliases": [ + "ns" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 10 + }, + { + "Name": "NumBurninIterations", + "Type": "Int", + "Desc": "The number of burn-in iterations", + "Aliases": [ + "burninIter" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 10, + "SweepRange": { + "RangeType": "Discrete", + "Values": [ + 10, + 20, + 30, + 40 + ] + } + }, + { + "Name": "ResetRandomGenerator", "Type": "Bool", - "Desc": "If true, keep only rows that contain NA values, and filter the rest.", + "Desc": "Reset the random number generator for each document", + "Aliases": [ + "reset" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "OutputTopicWordSummary", + "Type": "Bool", + "Desc": "Whether to output the topic-word summary in text format", + "Aliases": [ + "summary" + ], "Required": false, "SortOrder": 150.0, "IsNullable": false, @@ -15709,10 +16429,10 @@ ] }, { - "Name": "Transforms.MissingValueSubstitutor", - "Desc": "Create an output column of the same type and size of the input column, where missing values are replaced with either the default value or the mean/min/max value (for non-text columns only).", - "FriendlyName": "NA Replace Transform", - "ShortName": "NARep", + "Name": "Transforms.LogMeanVarianceNormalizer", + "Desc": "Normalizes the data based on the computed mean and variance of the logarithm of the data.", + "FriendlyName": "LogMeanVar Normalizer", + "ShortName": "LogMeanVar", "Inputs": [ { "Name": "Column", @@ -15722,47 +16442,14 @@ "Kind": "Struct", "Fields": [ { - "Name": "ReplacementString", - "Type": "String", - "Desc": "Replacement value for NAs (uses default value if not given)", + "Name": "MaxTrainingExamples", + "Type": "Int", + "Desc": "Max number of examples used to train the normalizer", "Aliases": [ - "rep" + "maxtrain" ], "Required": false, "SortOrder": 150.0, - "IsNullable": false, - "Default": null - }, - { - "Name": "Kind", - "Type": { - "Kind": "Enum", - "Values": [ - "Default", - "DefaultValue", - "Def", - "Mean", - "Min", - "Minimum", - "Max", - "Maximum", - "SpecifiedValue", - "Val", - "Value" - ] - }, - "Desc": "The replacement method to utilize", - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "Slot", - "Type": "Bool", - "Desc": "Whether to impute values by slot", - "Required": false, - "SortOrder": 150.0, "IsNullable": true, "Default": null }, @@ -15793,13 +16480,14 @@ ] } }, - "Desc": "New column definition(s) (optional form: name:rep:src)", + "Desc": "New column definition(s) (optional form: name:src)", "Aliases": [ "col" ], - "Required": true, + "Required": false, "SortOrder": 1.0, - "IsNullable": false + "IsNullable": false, + "Default": null }, { "Name": "Data", @@ -15810,43 +16498,28 @@ "IsNullable": false }, { - "Name": "ReplacementKind", - "Type": { - "Kind": "Enum", - "Values": [ - "Default", - "DefaultValue", - "Def", - "Mean", - "Min", - "Minimum", - "Max", - "Maximum", - "SpecifiedValue", - "Val", - "Value" - ] - }, - "Desc": "The replacement method to utilize", + "Name": "UseCdf", + "Type": "Bool", + "Desc": "Whether to use CDF as the output", "Aliases": [ - "kind" + "cdf" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": "Def" + "Default": true }, { - "Name": "ImputeBySlot", - "Type": "Bool", - "Desc": "Whether to impute values by slot", + "Name": "MaxTrainingExamples", + "Type": "Int", + "Desc": "Max number of examples used to train the normalizer", "Aliases": [ - "slot" + "maxtrain" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": true + "Default": 1000000000 } ], "Outputs": [ @@ -15869,37 +16542,10 @@ ] }, { - "Name": "Transforms.ModelCombiner", - "Desc": "Combines a sequence of TransformModels into a single model", - "FriendlyName": null, - "ShortName": null, - "Inputs": [ - { - "Name": "Models", - "Type": { - "Kind": "Array", - "ItemType": "TransformModel" - }, - "Desc": "Input models", - "Required": false, - "SortOrder": 1.0, - "IsNullable": false, - "Default": null - } - ], - "Outputs": [ - { - "Name": "OutputModel", - "Type": "TransformModel", - "Desc": "Combined model" - } - ] - }, - { - "Name": "Transforms.NGramTranslator", - "Desc": "Produces a bag of counts of ngrams (sequences of consecutive values of length 1-n) in a given vector of keys. It does so by building a dictionary of ngrams and using the id in the dictionary as the index in the bag.", - "FriendlyName": "NGram Transform", - "ShortName": "NgramTransform", + "Name": "Transforms.LpNormalizer", + "Desc": "Normalize vectors (rows) individually by rescaling them to unit norm (L2, L1 or LInf). Performs the following operation on a vector X: Y = (X - M) / D, where M is mean and D is either L2 norm, L1 norm or LInf norm.", + "FriendlyName": "Lp-Norm Normalizer", + "ShortName": "lpnorm", "Inputs": [ { "Name": "Column", @@ -15909,67 +16555,29 @@ "Kind": "Struct", "Fields": [ { - "Name": "NgramLength", - "Type": "Int", - "Desc": "Maximum ngram length", - "Aliases": [ - "ngram" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "AllLengths", - "Type": "Bool", - "Desc": "Whether to include all ngram lengths up to NgramLength or only NgramLength", - "Aliases": [ - "all" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "SkipLength", - "Type": "Int", - "Desc": "Maximum number of tokens to skip when constructing an ngram", - "Aliases": [ - "skips" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "MaxNumTerms", + "Name": "NormKind", "Type": { - "Kind": "Array", - "ItemType": "Int" + "Kind": "Enum", + "Values": [ + "L2Norm", + "StdDev", + "L1Norm", + "LInf" + ] }, - "Desc": "Maximum number of ngrams to store in the dictionary", + "Desc": "The norm to use to normalize each sample", "Aliases": [ - "max" + "norm" ], "Required": false, - "SortOrder": 150.0, - "IsNullable": false, + "SortOrder": 1.0, + "IsNullable": true, "Default": null }, { - "Name": "Weighting", - "Type": { - "Kind": "Enum", - "Values": [ - "Tf", - "Idf", - "TfIdf" - ] - }, - "Desc": "Statistical measure used to evaluate how important a word is to a document in a corpus", + "Name": "SubMean", + "Type": "Bool", + "Desc": "Subtract mean from each value before normalizing", "Required": false, "SortOrder": 150.0, "IsNullable": true, @@ -16006,114 +16614,30 @@ "Aliases": [ "col" ], - "Required": false, - "SortOrder": 1.0, - "IsNullable": false, - "Default": null - }, - { - "Name": "Data", - "Type": "DataView", - "Desc": "Input dataset", "Required": true, "SortOrder": 1.0, "IsNullable": false }, { - "Name": "NgramLength", - "Type": "Int", - "Desc": "Maximum ngram length", - "Aliases": [ - "ngram" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 2 - }, - { - "Name": "AllLengths", - "Type": "Bool", - "Desc": "Whether to store all ngram lengths up to ngramLength, or only ngramLength", - "Aliases": [ - "all" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": true - }, - { - "Name": "SkipLength", - "Type": "Int", - "Desc": "Maximum number of tokens to skip when constructing an ngram", - "Aliases": [ - "skips" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 0 - }, - { - "Name": "MaxNumTerms", - "Type": { - "Kind": "Array", - "ItemType": "Int" - }, - "Desc": "Maximum number of ngrams to store in the dictionary", - "Aliases": [ - "max" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": [ - 10000000 - ] - }, - { - "Name": "Weighting", + "Name": "NormKind", "Type": { "Kind": "Enum", "Values": [ - "Tf", - "Idf", - "TfIdf" + "L2Norm", + "StdDev", + "L1Norm", + "LInf" ] }, - "Desc": "The weighting criteria", + "Desc": "The norm to use to normalize each sample", + "Aliases": [ + "norm" + ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 1.0, "IsNullable": false, - "Default": "Tf" - } - ], - "Outputs": [ - { - "Name": "OutputData", - "Type": "DataView", - "Desc": "Transformed dataset" + "Default": "L2Norm" }, - { - "Name": "Model", - "Type": "TransformModel", - "Desc": "Transform model" - } - ], - "InputKind": [ - "ITransformInput" - ], - "OutputKind": [ - "ITransformOutput" - ] - }, - { - "Name": "Transforms.NoOperation", - "Desc": "Does nothing.", - "FriendlyName": "No Op", - "ShortName": "Nop", - "Inputs": [ { "Name": "Data", "Type": "DataView", @@ -16121,6 +16645,15 @@ "Required": true, "SortOrder": 1.0, "IsNullable": false + }, + { + "Name": "SubMean", + "Type": "Bool", + "Desc": "Subtract mean from each value before normalizing", + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": false } ], "Outputs": [ @@ -16143,58 +16676,44 @@ ] }, { - "Name": "Transforms.OptionalColumnCreator", - "Desc": "If the source column does not exist after deserialization, create a column with the right type and default values.", - "FriendlyName": "Optional Column Transform", - "ShortName": "optional", + "Name": "Transforms.ManyHeterogeneousModelCombiner", + "Desc": "Combines a sequence of TransformModels and a PredictorModel into a single PredictorModel.", + "FriendlyName": null, + "ShortName": null, "Inputs": [ { - "Name": "Column", + "Name": "TransformModels", "Type": { "Kind": "Array", - "ItemType": "String" + "ItemType": "TransformModel" }, - "Desc": "New column definition(s)", - "Aliases": [ - "col" - ], + "Desc": "Transform model", "Required": true, "SortOrder": 1.0, "IsNullable": false }, { - "Name": "Data", - "Type": "DataView", - "Desc": "Input dataset", + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "Predictor model", "Required": true, - "SortOrder": 1.0, + "SortOrder": 2.0, "IsNullable": false } ], "Outputs": [ { - "Name": "OutputData", - "Type": "DataView", - "Desc": "Transformed dataset" - }, - { - "Name": "Model", - "Type": "TransformModel", - "Desc": "Transform model" + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "Predictor model" } - ], - "InputKind": [ - "ITransformInput" - ], - "OutputKind": [ - "ITransformOutput" ] }, { - "Name": "Transforms.PcaCalculator", - "Desc": "Train an PCA Anomaly model.", - "FriendlyName": "Principal Component Analysis Transform", - "ShortName": "Pca", + "Name": "Transforms.MeanVarianceNormalizer", + "Desc": "Normalizes the data based on the computed mean and variance of the data.", + "FriendlyName": "MeanVar Normalizer", + "ShortName": "MeanVar", "Inputs": [ { "Name": "Column", @@ -16204,47 +16723,11 @@ "Kind": "Struct", "Fields": [ { - "Name": "WeightColumn", - "Type": "String", - "Desc": "The name of the weight column", - "Aliases": [ - "weight" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": null - }, - { - "Name": "Rank", - "Type": "Int", - "Desc": "The number of components in the PCA", - "Aliases": [ - "k" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "Oversampling", - "Type": "Int", - "Desc": "Oversampling parameter for randomized PCA training", - "Aliases": [ - "over" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "Center", + "Name": "FixZero", "Type": "Bool", - "Desc": "If enabled, data is centered to be zero mean", + "Desc": "Whether to map zero to zero, preserving sparsity", "Aliases": [ - "center" + "zero" ], "Required": false, "SortOrder": 150.0, @@ -16252,11 +16735,11 @@ "Default": null }, { - "Name": "Seed", + "Name": "MaxTrainingExamples", "Type": "Int", - "Desc": "The seed for random number generation", + "Desc": "Max number of examples used to train the normalizer", "Aliases": [ - "seed" + "maxtrain" ], "Required": false, "SortOrder": 150.0, @@ -16307,100 +16790,40 @@ "IsNullable": false }, { - "Name": "WeightColumn", - "Type": "String", - "Desc": "The name of the weight column", + "Name": "UseCdf", + "Type": "Bool", + "Desc": "Whether to use CDF as the output", "Aliases": [ - "weight" + "cdf" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": null + "Default": false }, { - "Name": "Rank", - "Type": "Int", - "Desc": "The number of components in the PCA", + "Name": "FixZero", + "Type": "Bool", + "Desc": "Whether to map zero to zero, preserving sparsity", "Aliases": [ - "k" + "zero" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 20 + "Default": true }, { - "Name": "Oversampling", + "Name": "MaxTrainingExamples", "Type": "Int", - "Desc": "Oversampling parameter for randomized PCA training", + "Desc": "Max number of examples used to train the normalizer", "Aliases": [ - "over" + "maxtrain" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 20 - }, - { - "Name": "Center", - "Type": "Bool", - "Desc": "If enabled, data is centered to be zero mean", - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": true - }, - { - "Name": "Seed", - "Type": "Int", - "Desc": "The seed for random number generation", - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 0 - } - ], - "Outputs": [ - { - "Name": "OutputData", - "Type": "DataView", - "Desc": "Transformed dataset" - }, - { - "Name": "Model", - "Type": "TransformModel", - "Desc": "Transform model" - } - ], - "InputKind": [ - "ITransformInput" - ], - "OutputKind": [ - "ITransformOutput" - ] - }, - { - "Name": "Transforms.PredictedLabelColumnOriginalValueConverter", - "Desc": "Transforms a predicted label column to its original values, unless it is of type bool.", - "FriendlyName": "Convert Predicted Label", - "ShortName": null, - "Inputs": [ - { - "Name": "Data", - "Type": "DataView", - "Desc": "Input dataset", - "Required": true, - "SortOrder": 1.0, - "IsNullable": false - }, - { - "Name": "PredictedLabelColumn", - "Type": "String", - "Desc": "The predicted label column", - "Required": true, - "SortOrder": 2.0, - "IsNullable": false + "Default": 1000000000 } ], "Outputs": [ @@ -16423,10 +16846,10 @@ ] }, { - "Name": "Transforms.RandomNumberGenerator", - "Desc": "Adds a column with a generated number sequence.", - "FriendlyName": "Generate Number Transform", - "ShortName": "Generate", + "Name": "Transforms.MinMaxNormalizer", + "Desc": "Normalizes the data based on the observed minimum and maximum values of the data.", + "FriendlyName": "Min-Max Normalizer", + "ShortName": "MinMax", "Inputs": [ { "Name": "Column", @@ -16436,23 +16859,23 @@ "Kind": "Struct", "Fields": [ { - "Name": "Name", - "Type": "String", - "Desc": "Name of the new column", + "Name": "FixZero", + "Type": "Bool", + "Desc": "Whether to map zero to zero, preserving sparsity", "Aliases": [ - "name" + "zero" ], "Required": false, "SortOrder": 150.0, - "IsNullable": false, + "IsNullable": true, "Default": null }, { - "Name": "UseCounter", - "Type": "Bool", - "Desc": "Use an auto-incremented integer starting at zero instead of a random number", + "Name": "MaxTrainingExamples", + "Type": "Int", + "Desc": "Max number of examples used to train the normalizer", "Aliases": [ - "cnt" + "maxtrain" ], "Required": false, "SortOrder": 150.0, @@ -16460,18 +16883,33 @@ "Default": null }, { - "Name": "Seed", - "Type": "UInt", - "Desc": "The random seed", + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], "Required": false, "SortOrder": 150.0, - "IsNullable": true, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, "Default": null } ] } }, - "Desc": "New column definition(s) (optional form: name:seed)", + "Desc": "New column definition(s) (optional form: name:src)", "Aliases": [ "col" ], @@ -16488,25 +16926,28 @@ "IsNullable": false }, { - "Name": "UseCounter", + "Name": "FixZero", "Type": "Bool", - "Desc": "Use an auto-incremented integer starting at zero instead of a random number", + "Desc": "Whether to map zero to zero, preserving sparsity", "Aliases": [ - "cnt" + "zero" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": false + "Default": true }, { - "Name": "Seed", - "Type": "UInt", - "Desc": "The random seed", + "Name": "MaxTrainingExamples", + "Type": "Int", + "Desc": "Max number of examples used to train the normalizer", + "Aliases": [ + "maxtrain" + ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 42 + "Default": 1000000000 } ], "Outputs": [ @@ -16529,74 +16970,145 @@ ] }, { - "Name": "Transforms.RowRangeFilter", - "Desc": "Filters a dataview on a column of type Single, Double or Key (contiguous). Keeps the values that are in the specified min/max range. NaNs are always filtered out. If the input is a Key type, the min/max are considered percentages of the number of values.", - "FriendlyName": "Range Filter", - "ShortName": "RangeFilter", + "Name": "Transforms.MissingValueHandler", + "Desc": "Handle missing values by replacing them with either the default value or the mean/min/max value (for non-text columns only). An indicator column can optionally be concatenated, if theinput column type is numeric.", + "FriendlyName": "NA Handle Transform", + "ShortName": "NAHandle", "Inputs": [ { "Name": "Column", - "Type": "String", - "Desc": "Column", - "Aliases": [ - "col" - ], - "Required": true, - "SortOrder": 1.0, - "IsNullable": false - }, - { - "Name": "Data", - "Type": "DataView", - "Desc": "Input dataset", - "Required": true, - "SortOrder": 1.0, - "IsNullable": false - }, - { - "Name": "Min", - "Type": "Float", - "Desc": "Minimum value (0 to 1 for key types)", - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, - { - "Name": "Max", - "Type": "Float", - "Desc": "Maximum value (0 to 1 for key types)", - "Required": false, - "SortOrder": 150.0, - "IsNullable": true, - "Default": null - }, + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "Kind", + "Type": { + "Kind": "Enum", + "Values": [ + "DefaultValue", + "Mean", + "Minimum", + "Maximum" + ] + }, + "Desc": "The replacement method to utilize", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "ImputeBySlot", + "Type": "Bool", + "Desc": "Whether to impute values by slot", + "Aliases": [ + "slot" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "ConcatIndicator", + "Type": "Bool", + "Desc": "Whether or not to concatenate an indicator vector column to the value column", + "Aliases": [ + "ind" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:rep:src)", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, { - "Name": "Complement", - "Type": "Bool", - "Desc": "If true, keep the values that fall outside the range.", + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "ReplaceWith", + "Type": { + "Kind": "Enum", + "Values": [ + "DefaultValue", + "Mean", + "Minimum", + "Maximum" + ] + }, + "Desc": "The replacement method to utilize", + "Aliases": [ + "kind" + ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 2.0, "IsNullable": false, - "Default": false + "Default": "Def" }, { - "Name": "IncludeMin", + "Name": "ImputeBySlot", "Type": "Bool", - "Desc": "If true, include in the range the values that are equal to min.", + "Desc": "Whether to impute values by slot", + "Aliases": [ + "slot" + ], "Required": false, "SortOrder": 150.0, "IsNullable": false, "Default": true }, { - "Name": "IncludeMax", + "Name": "Concat", "Type": "Bool", - "Desc": "If true, include in the range the values that are equal to max.", + "Desc": "Whether or not to concatenate an indicator vector column to the value column", + "Aliases": [ + "ind" + ], "Required": false, "SortOrder": 150.0, - "IsNullable": true, - "Default": null + "IsNullable": false, + "Default": true } ], "Outputs": [ @@ -16619,22 +17131,52 @@ ] }, { - "Name": "Transforms.RowSkipAndTakeFilter", - "Desc": "Allows limiting input to a subset of rows at an optional offset. Can be used to implement data paging.", - "FriendlyName": "Skip and Take Filter", - "ShortName": "SkipTake", + "Name": "Transforms.MissingValueIndicator", + "Desc": "Create a boolean output column with the same number of slots as the input column, where the output value is true if the value in the input column is missing.", + "FriendlyName": "NA Indicator Transform", + "ShortName": "NAInd", "Inputs": [ { - "Name": "Skip", - "Type": "Int", - "Desc": "Number of items to skip", + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:src)", "Aliases": [ - "s" + "col" ], - "Required": false, + "Required": true, "SortOrder": 1.0, - "IsNullable": true, - "Default": null + "IsNullable": false }, { "Name": "Data", @@ -16643,18 +17185,6 @@ "Required": true, "SortOrder": 1.0, "IsNullable": false - }, - { - "Name": "Take", - "Type": "Int", - "Desc": "Number of items to take", - "Aliases": [ - "t" - ], - "Required": false, - "SortOrder": 2.0, - "IsNullable": true, - "Default": null } ], "Outputs": [ @@ -16677,24 +17207,52 @@ ] }, { - "Name": "Transforms.RowSkipFilter", - "Desc": "Allows limiting input to a subset of rows by skipping a number of rows.", - "FriendlyName": "Skip Filter", - "ShortName": "Skip", + "Name": "Transforms.MissingValuesDropper", + "Desc": "Removes NAs from vector columns.", + "FriendlyName": "NA Drop Transform", + "ShortName": "NADrop", "Inputs": [ { - "Name": "Count", - "Type": "Int", - "Desc": "Number of items to skip", + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "Columns to drop the NAs for", "Aliases": [ - "c", - "n", - "s" + "col" ], "Required": true, "SortOrder": 1.0, - "IsNullable": false, - "Default": 0 + "IsNullable": false }, { "Name": "Data", @@ -16725,201 +17283,18 @@ ] }, { - "Name": "Transforms.RowTakeFilter", - "Desc": "Allows limiting input to a subset of rows by taking N first rows.", - "FriendlyName": "Take Filter", - "ShortName": "Take", + "Name": "Transforms.MissingValuesRowDropper", + "Desc": "Filters out rows that contain missing values.", + "FriendlyName": "NA Filter", + "ShortName": "NAFilter", "Inputs": [ - { - "Name": "Count", - "Type": "Int", - "Desc": "Number of items to take", - "Aliases": [ - "c", - "n", - "t" - ], - "Required": true, - "SortOrder": 1.0, - "IsNullable": false, - "Default": 9223372036854775807 - }, - { - "Name": "Data", - "Type": "DataView", - "Desc": "Input dataset", - "Required": true, - "SortOrder": 1.0, - "IsNullable": false - } - ], - "Outputs": [ - { - "Name": "OutputData", - "Type": "DataView", - "Desc": "Transformed dataset" - }, - { - "Name": "Model", - "Type": "TransformModel", - "Desc": "Transform model" - } - ], - "InputKind": [ - "ITransformInput" - ], - "OutputKind": [ - "ITransformOutput" - ] - }, - { - "Name": "Transforms.ScoreColumnSelector", - "Desc": "Selects only the last score columns and the extra columns specified in the arguments.", - "FriendlyName": "Choose Columns By Index", - "ShortName": null, - "Inputs": [ - { - "Name": "Data", - "Type": "DataView", - "Desc": "Input dataset", - "Required": true, - "SortOrder": 1.0, - "IsNullable": false - }, - { - "Name": "ExtraColumns", - "Type": { - "Kind": "Array", - "ItemType": "String" - }, - "Desc": "Extra columns to write", - "Required": false, - "SortOrder": 2.0, - "IsNullable": false, - "Default": null - } - ], - "Outputs": [ - { - "Name": "OutputData", - "Type": "DataView", - "Desc": "Transformed dataset" - }, - { - "Name": "Model", - "Type": "TransformModel", - "Desc": "Transform model" - } - ], - "InputKind": [ - "ITransformInput" - ], - "OutputKind": [ - "ITransformOutput" - ] - }, - { - "Name": "Transforms.Scorer", - "Desc": "Turn the predictor model into a transform model", - "FriendlyName": null, - "ShortName": null, - "Inputs": [ - { - "Name": "PredictorModel", - "Type": "PredictorModel", - "Desc": "The predictor model to turn into a transform", - "Required": true, - "SortOrder": 1.0, - "IsNullable": false - } - ], - "Outputs": [ - { - "Name": "ScoredData", - "Type": "DataView", - "Desc": "The scored dataset" - }, - { - "Name": "ScoringTransform", - "Type": "TransformModel", - "Desc": "The scoring transform" - } - ] - }, - { - "Name": "Transforms.Segregator", - "Desc": "Un-groups vector columns into sequences of rows, inverse of Group transform", - "FriendlyName": "Un-group Transform", - "ShortName": "Ungroup", - "Inputs": [ - { - "Name": "Data", - "Type": "DataView", - "Desc": "Input dataset", - "Required": true, - "SortOrder": 1.0, - "IsNullable": false - }, { "Name": "Column", "Type": { "Kind": "Array", "ItemType": "String" }, - "Desc": "Columns to unroll, or 'pivot'", - "Aliases": [ - "col" - ], - "Required": true, - "SortOrder": 150.0, - "IsNullable": false - }, - { - "Name": "Mode", - "Type": { - "Kind": "Enum", - "Values": [ - "Inner", - "Outer", - "First" - ] - }, - "Desc": "Specifies how to unroll multiple pivot columns of different size.", - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": "Inner" - } - ], - "Outputs": [ - { - "Name": "OutputData", - "Type": "DataView", - "Desc": "Transformed dataset" - }, - { - "Name": "Model", - "Type": "TransformModel", - "Desc": "Transform model" - } - ], - "InputKind": [ - "ITransformInput" - ], - "OutputKind": [ - "ITransformOutput" - ] - }, - { - "Name": "Transforms.SentimentAnalyzer", - "Desc": "Uses a pretrained sentiment model to score input strings", - "FriendlyName": "Sentiment Analyzing Transform", - "ShortName": "Senti", - "Inputs": [ - { - "Name": "Source", - "Type": "String", - "Desc": "Name of the source column.", + "Desc": "Column", "Aliases": [ "col" ], @@ -16936,16 +17311,13 @@ "IsNullable": false }, { - "Name": "Name", - "Type": "String", - "Desc": "Name of the new column.", - "Aliases": [ - "dst" - ], + "Name": "Complement", + "Type": "Bool", + "Desc": "If true, keep only rows that contain NA values, and filter the rest.", "Required": false, - "SortOrder": 2.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": null + "Default": false } ], "Outputs": [ @@ -16968,10 +17340,10 @@ ] }, { - "Name": "Transforms.SupervisedBinNormalizer", - "Desc": "Similar to BinNormalizer, but calculates bins based on correlation with the label column, not equi-density. The new value is bin_number / number_of_bins.", - "FriendlyName": "Supervised Binning Normalizer", - "ShortName": "SupBin", + "Name": "Transforms.MissingValueSubstitutor", + "Desc": "Create an output column of the same type and size of the input column, where missing values are replaced with either the default value or the mean/min/max value (for non-text columns only).", + "FriendlyName": "NA Replace Transform", + "ShortName": "NARep", "Inputs": [ { "Name": "Column", @@ -16981,36 +17353,39 @@ "Kind": "Struct", "Fields": [ { - "Name": "NumBins", - "Type": "Int", - "Desc": "Max number of bins, power of 2 recommended", + "Name": "ReplacementString", + "Type": "String", + "Desc": "Replacement value for NAs (uses default value if not given)", "Aliases": [ - "bins" + "rep" ], "Required": false, "SortOrder": 150.0, - "IsNullable": true, + "IsNullable": false, "Default": null }, { - "Name": "FixZero", - "Type": "Bool", - "Desc": "Whether to map zero to zero, preserving sparsity", - "Aliases": [ - "zero" - ], + "Name": "Kind", + "Type": { + "Kind": "Enum", + "Values": [ + "DefaultValue", + "Mean", + "Minimum", + "Maximum", + "SpecifiedValue" + ] + }, + "Desc": "The replacement method to utilize", "Required": false, "SortOrder": 150.0, "IsNullable": true, "Default": null }, { - "Name": "MaxTrainingExamples", - "Type": "Int", - "Desc": "Max number of examples used to train the normalizer", - "Aliases": [ - "maxtrain" - ], + "Name": "Slot", + "Type": "Bool", + "Desc": "Whether to impute values by slot", "Required": false, "SortOrder": 150.0, "IsNullable": true, @@ -17043,14 +17418,13 @@ ] } }, - "Desc": "New column definition(s) (optional form: name:src)", + "Desc": "New column definition(s) (optional form: name:rep:src)", "Aliases": [ "col" ], - "Required": false, + "Required": true, "SortOrder": 1.0, - "IsNullable": false, - "Default": null + "IsNullable": false }, { "Name": "Data", @@ -17061,61 +17435,37 @@ "IsNullable": false }, { - "Name": "LabelColumn", - "Type": "String", - "Desc": "Label column for supervised binning", - "Aliases": [ - "label", - "lab" - ], - "Required": true, - "SortOrder": 150.0, - "IsNullable": false - }, - { - "Name": "MinBinSize", - "Type": "Int", - "Desc": "Minimum number of examples per bin", - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 10 - }, - { - "Name": "NumBins", - "Type": "Int", - "Desc": "Max number of bins, power of 2 recommended", + "Name": "ReplacementKind", + "Type": { + "Kind": "Enum", + "Values": [ + "DefaultValue", + "Mean", + "Minimum", + "Maximum", + "SpecifiedValue" + ] + }, + "Desc": "The replacement method to utilize", "Aliases": [ - "bins" + "kind" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, - "Default": 1024 + "Default": "Default" }, { - "Name": "FixZero", + "Name": "ImputeBySlot", "Type": "Bool", - "Desc": "Whether to map zero to zero, preserving sparsity", + "Desc": "Whether to impute values by slot", "Aliases": [ - "zero" + "slot" ], "Required": false, "SortOrder": 150.0, "IsNullable": false, "Default": true - }, - { - "Name": "MaxTrainingExamples", - "Type": "Int", - "Desc": "Max number of examples used to train the normalizer", - "Aliases": [ - "maxtrain" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": 1000000000 } ], "Outputs": [ @@ -17138,52 +17488,147 @@ ] }, { - "Name": "Transforms.TextFeaturizer", - "Desc": "A transform that turns a collection of text documents into numerical feature vectors. The feature vectors are normalized counts of (word and/or character) ngrams in a given tokenized text.", - "FriendlyName": "Text Transform", - "ShortName": "Text", + "Name": "Transforms.ModelCombiner", + "Desc": "Combines a sequence of TransformModels into a single model", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "Models", + "Type": { + "Kind": "Array", + "ItemType": "TransformModel" + }, + "Desc": "Input models", + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": null + } + ], + "Outputs": [ + { + "Name": "OutputModel", + "Type": "TransformModel", + "Desc": "Combined model" + } + ] + }, + { + "Name": "Transforms.NGramTranslator", + "Desc": "Produces a bag of counts of ngrams (sequences of consecutive values of length 1-n) in a given vector of keys. It does so by building a dictionary of ngrams and using the id in the dictionary as the index in the bag.", + "FriendlyName": "NGram Transform", + "ShortName": "NgramTransform", "Inputs": [ { "Name": "Column", "Type": { - "Kind": "Struct", - "Fields": [ - { - "Name": "Name", - "Type": "String", - "Desc": "Name of the new column", - "Aliases": [ - "name" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": null - }, - { - "Name": "Source", - "Type": { - "Kind": "Array", - "ItemType": "String" + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "NgramLength", + "Type": "Int", + "Desc": "Maximum ngram length", + "Aliases": [ + "ngram" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null }, - "Desc": "Name of the source column", - "Aliases": [ - "src" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": null - } - ] + { + "Name": "AllLengths", + "Type": "Bool", + "Desc": "Whether to include all ngram lengths up to NgramLength or only NgramLength", + "Aliases": [ + "all" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "SkipLength", + "Type": "Int", + "Desc": "Maximum number of tokens to skip when constructing an ngram", + "Aliases": [ + "skips" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "MaxNumTerms", + "Type": { + "Kind": "Array", + "ItemType": "Int" + }, + "Desc": "Maximum number of ngrams to store in the dictionary", + "Aliases": [ + "max" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Weighting", + "Type": { + "Kind": "Enum", + "Values": [ + "Tf", + "Idf", + "TfIdf" + ] + }, + "Desc": "Statistical measure used to evaluate how important a word is to a document in a corpus", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } }, - "Desc": "New column definition (optional form: name:srcs).", + "Desc": "New column definition(s) (optional form: name:src)", "Aliases": [ "col" ], - "Required": true, + "Required": false, "SortOrder": 1.0, - "IsNullable": false + "IsNullable": false, + "Default": null }, { "Name": "Data", @@ -17194,232 +17639,155 @@ "IsNullable": false }, { - "Name": "Language", - "Type": { - "Kind": "Enum", - "Values": [ - "English", - "French", - "German", - "Dutch", - "Italian", - "Spanish", - "Japanese" - ] - }, - "Desc": "Dataset language or 'AutoDetect' to detect language per row.", + "Name": "NgramLength", + "Type": "Int", + "Desc": "Maximum ngram length", "Aliases": [ - "lang" + "ngram" ], "Required": false, - "SortOrder": 3.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": "English" + "Default": 2 }, { - "Name": "StopWordsRemover", - "Type": { - "Kind": "Component", - "ComponentKind": "StopWordsRemover" - }, - "Desc": "Stopwords remover.", + "Name": "AllLengths", + "Type": "Bool", + "Desc": "Whether to store all ngram lengths up to ngramLength, or only ngramLength", "Aliases": [ - "remover" + "all" ], "Required": false, - "SortOrder": 4.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": null + "Default": true }, { - "Name": "TextCase", - "Type": { - "Kind": "Enum", - "Values": [ - "Lower", - "Upper", - "None" - ] - }, - "Desc": "Casing text using the rules of the invariant culture.", - "Aliases": [ - "case" - ], - "Required": false, - "SortOrder": 5.0, - "IsNullable": false, - "Default": "Lower" - }, - { - "Name": "KeepDiacritics", - "Type": "Bool", - "Desc": "Whether to keep diacritical marks or remove them.", - "Aliases": [ - "diac" - ], - "Required": false, - "SortOrder": 6.0, - "IsNullable": false, - "Default": false - }, - { - "Name": "KeepPunctuations", - "Type": "Bool", - "Desc": "Whether to keep punctuation marks or remove them.", - "Aliases": [ - "punc" - ], - "Required": false, - "SortOrder": 7.0, - "IsNullable": false, - "Default": true - }, - { - "Name": "KeepNumbers", - "Type": "Bool", - "Desc": "Whether to keep numbers or remove them.", + "Name": "SkipLength", + "Type": "Int", + "Desc": "Maximum number of tokens to skip when constructing an ngram", "Aliases": [ - "num" + "skips" ], "Required": false, - "SortOrder": 8.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": true + "Default": 0 }, { - "Name": "OutputTokens", - "Type": "Bool", - "Desc": "Whether to output the transformed text tokens as an additional column.", + "Name": "MaxNumTerms", + "Type": { + "Kind": "Array", + "ItemType": "Int" + }, + "Desc": "Maximum number of ngrams to store in the dictionary", "Aliases": [ - "tokens", - "showtext", - "showTransformedText" + "max" ], "Required": false, - "SortOrder": 9.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": false + "Default": [ + 10000000 + ] }, { - "Name": "Dictionary", + "Name": "Weighting", "Type": { - "Kind": "Struct", - "Fields": [ - { - "Name": "Term", - "Type": { - "Kind": "Array", - "ItemType": "String" - }, - "Desc": "List of terms", - "Required": false, - "SortOrder": 1.0, - "IsNullable": false, - "Default": null - }, - { - "Name": "Sort", - "Type": { - "Kind": "Enum", - "Values": [ - "Occurrence", - "Value" - ] - }, - "Desc": "How items should be ordered when vectorized. By default, they will be in the order encountered. If by value items are sorted according to their default comparison, e.g., text sorting will be case sensitive (e.g., 'A' then 'Z' then 'a').", - "Required": false, - "SortOrder": 5.0, - "IsNullable": false, - "Default": "Occurrence" - }, - { - "Name": "DropUnknowns", - "Type": "Bool", - "Desc": "Drop unknown terms instead of mapping them to NA term.", - "Aliases": [ - "dropna" - ], - "Required": false, - "SortOrder": 6.0, - "IsNullable": false, - "Default": false - } + "Kind": "Enum", + "Values": [ + "Tf", + "Idf", + "TfIdf" ] }, - "Desc": "A dictionary of whitelisted terms.", - "Aliases": [ - "dict" - ], + "Desc": "The weighting criteria", "Required": false, - "SortOrder": 10.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": null + "Default": "Tf" + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" }, { - "Name": "WordFeatureExtractor", - "Type": { - "Kind": "Component", - "ComponentKind": "NgramExtractor" - }, - "Desc": "Ngram feature extractor to use for words (WordBag/WordHashBag).", - "Aliases": [ - "wordExtractor" - ], - "Required": false, - "SortOrder": 11.0, - "IsNullable": false, - "Default": { - "Name": "NGram", - "Settings": { - "MaxNumTerms": [ - 10000000 - ] - } - } + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.NoOperation", + "Desc": "Does nothing.", + "FriendlyName": "No Op", + "ShortName": "Nop", + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" }, { - "Name": "CharFeatureExtractor", + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.OptionalColumnCreator", + "Desc": "If the source column does not exist after deserialization, create a column with the right type and default values.", + "FriendlyName": "Optional Column Transform", + "ShortName": "optional", + "Inputs": [ + { + "Name": "Column", "Type": { - "Kind": "Component", - "ComponentKind": "NgramExtractor" + "Kind": "Array", + "ItemType": "String" }, - "Desc": "Ngram feature extractor to use for characters (WordBag/WordHashBag).", + "Desc": "New column definition(s)", "Aliases": [ - "charExtractor" + "col" ], - "Required": false, - "SortOrder": 12.0, - "IsNullable": false, - "Default": { - "Name": "NGram", - "Settings": { - "NgramLength": 3, - "AllLengths": false, - "MaxNumTerms": [ - 10000000 - ] - } - } + "Required": true, + "SortOrder": 1.0, + "IsNullable": false }, { - "Name": "VectorNormalizer", - "Type": { - "Kind": "Enum", - "Values": [ - "None", - "L1", - "L2", - "LInf" - ] - }, - "Desc": "Normalize vectors (rows) individually by rescaling them to unit norm.", - "Aliases": [ - "norm" - ], - "Required": false, - "SortOrder": 13.0, - "IsNullable": false, - "Default": "L2" + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false } ], "Outputs": [ @@ -17442,11 +17810,11 @@ ] }, { - "Name": "Transforms.TextToKeyConverter", - "Desc": "Converts input values (words, numbers, etc.) to index in a dictionary.", - "FriendlyName": "Term Transform", - "ShortName": null, - "Inputs": [ + "Name": "Transforms.PcaCalculator", + "Desc": "Train an PCA Anomaly model.", + "FriendlyName": "Principal Component Analysis Transform", + "ShortName": "Pca", + "Inputs": [ { "Name": "Column", "Type": { @@ -17455,11 +17823,23 @@ "Kind": "Struct", "Fields": [ { - "Name": "MaxNumTerms", + "Name": "WeightColumn", + "Type": "String", + "Desc": "The name of the weight column", + "Aliases": [ + "weight" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Rank", "Type": "Int", - "Desc": "Maximum number of terms to keep when auto-training", + "Desc": "The number of components in the PCA", "Aliases": [ - "max" + "k" ], "Required": false, "SortOrder": 150.0, @@ -17467,38 +17847,35 @@ "Default": null }, { - "Name": "Term", - "Type": { - "Kind": "Array", - "ItemType": "String" - }, - "Desc": "List of terms", + "Name": "Oversampling", + "Type": "Int", + "Desc": "Oversampling parameter for randomized PCA training", + "Aliases": [ + "over" + ], "Required": false, "SortOrder": 150.0, - "IsNullable": false, + "IsNullable": true, "Default": null }, { - "Name": "Sort", - "Type": { - "Kind": "Enum", - "Values": [ - "Occurrence", - "Value" - ] - }, - "Desc": "How items should be ordered when vectorized. By default, they will be in the order encountered. If by value items are sorted according to their default comparison, e.g., text sorting will be case sensitive (e.g., 'A' then 'Z' then 'a').", + "Name": "Center", + "Type": "Bool", + "Desc": "If enabled, data is centered to be zero mean", + "Aliases": [ + "center" + ], "Required": false, "SortOrder": 150.0, "IsNullable": true, "Default": null }, { - "Name": "TextKeyValues", - "Type": "Bool", - "Desc": "Whether key value metadata should be text, regardless of the actual input type", + "Name": "Seed", + "Type": "Int", + "Desc": "The seed for random number generation", "Aliases": [ - "textkv" + "seed" ], "Required": false, "SortOrder": 150.0, @@ -17536,10 +17913,9 @@ "Aliases": [ "col" ], - "Required": false, + "Required": true, "SortOrder": 1.0, - "IsNullable": false, - "Default": null + "IsNullable": false }, { "Name": "Data", @@ -17550,55 +17926,58 @@ "IsNullable": false }, { - "Name": "MaxNumTerms", - "Type": "Int", - "Desc": "Maximum number of terms to keep per column when auto-training", + "Name": "WeightColumn", + "Type": "String", + "Desc": "The name of the weight column", "Aliases": [ - "max" + "weight" ], "Required": false, - "SortOrder": 5.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": 1000000 + "Default": null }, { - "Name": "Term", - "Type": { - "Kind": "Array", - "ItemType": "String" - }, - "Desc": "List of terms", + "Name": "Rank", + "Type": "Int", + "Desc": "The number of components in the PCA", + "Aliases": [ + "k" + ], "Required": false, - "SortOrder": 106.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": null + "Default": 20 }, { - "Name": "Sort", - "Type": { - "Kind": "Enum", - "Values": [ - "Occurrence", - "Value" - ] - }, - "Desc": "How items should be ordered when vectorized. By default, they will be in the order encountered. If by value items are sorted according to their default comparison, e.g., text sorting will be case sensitive (e.g., 'A' then 'Z' then 'a').", + "Name": "Oversampling", + "Type": "Int", + "Desc": "Oversampling parameter for randomized PCA training", + "Aliases": [ + "over" + ], "Required": false, - "SortOrder": 113.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": "Occurrence" + "Default": 20 }, { - "Name": "TextKeyValues", + "Name": "Center", "Type": "Bool", - "Desc": "Whether key value metadata should be text, regardless of the actual input type", - "Aliases": [ - "textkv" - ], + "Desc": "If enabled, data is centered to be zero mean", "Required": false, - "SortOrder": 114.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": false + "Default": true + }, + { + "Name": "Seed", + "Type": "Int", + "Desc": "The seed for random number generation", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0 } ], "Outputs": [ @@ -17621,9 +18000,9 @@ ] }, { - "Name": "Transforms.TrainTestDatasetSplitter", - "Desc": "Split the dataset into train and test sets", - "FriendlyName": "Dataset Train-Test Split", + "Name": "Transforms.PredictedLabelColumnOriginalValueConverter", + "Desc": "Transforms a predicted label column to its original values, unless it is of type bool.", + "FriendlyName": "Convert Predicted Label", "ShortName": null, "Inputs": [ { @@ -17635,85 +18014,118 @@ "IsNullable": false }, { - "Name": "Fraction", - "Type": "Float", - "Desc": "Fraction of training data", - "Required": false, - "SortOrder": 2.0, - "IsNullable": false, - "Default": 0.8 - }, - { - "Name": "StratificationColumn", + "Name": "PredictedLabelColumn", "Type": "String", - "Desc": "Stratification column", - "Aliases": [ - "strat" - ], - "Required": false, - "SortOrder": 3.0, - "IsNullable": false, - "Default": null + "Desc": "The predicted label column", + "Required": true, + "SortOrder": 2.0, + "IsNullable": false } ], "Outputs": [ { - "Name": "TrainData", + "Name": "OutputData", "Type": "DataView", - "Desc": "Training data" + "Desc": "Transformed dataset" }, { - "Name": "TestData", - "Type": "DataView", - "Desc": "Testing data" + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" ] }, { - "Name": "Transforms.TreeLeafFeaturizer", - "Desc": "Trains a tree ensemble, or loads it from a file, then maps a numeric feature vector to three outputs: 1. A vector containing the individual tree outputs of the tree ensemble. 2. A vector indicating the leaves that the feature vector falls on in the tree ensemble. 3. A vector indicating the paths that the feature vector falls on in the tree ensemble. If a both a model file and a trainer are specified - will use the model file. If neither are specified, will train a default FastTree model. This can handle key labels by training a regression model towards their optionally permuted indices.", - "FriendlyName": "Tree Ensemble Featurization Transform", - "ShortName": "TreeFeat", + "Name": "Transforms.RandomNumberGenerator", + "Desc": "Adds a column with a generated number sequence.", + "FriendlyName": "Generate Number Transform", + "ShortName": "Generate", "Inputs": [ { - "Name": "Data", - "Type": "DataView", - "Desc": "Input dataset", + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "UseCounter", + "Type": "Bool", + "Desc": "Use an auto-incremented integer starting at zero instead of a random number", + "Aliases": [ + "cnt" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Seed", + "Type": "UInt", + "Desc": "The random seed", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:seed)", + "Aliases": [ + "col" + ], "Required": true, "SortOrder": 1.0, "IsNullable": false }, { - "Name": "PredictorModel", - "Type": "PredictorModel", - "Desc": "Trainer to use", + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", "Required": true, - "SortOrder": 10.0, + "SortOrder": 1.0, "IsNullable": false }, { - "Name": "Suffix", - "Type": "String", - "Desc": "Output column: The suffix to append to the default column names", + "Name": "UseCounter", + "Type": "Bool", + "Desc": "Use an auto-incremented integer starting at zero instead of a random number", "Aliases": [ - "ex" + "cnt" ], "Required": false, - "SortOrder": 101.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": null + "Default": false }, { - "Name": "LabelPermutationSeed", - "Type": "Int", - "Desc": "If specified, determines the permutation seed for applying this featurizer to a multiclass problem.", - "Aliases": [ - "lps" - ], + "Name": "Seed", + "Type": "UInt", + "Desc": "The random seed", "Required": false, - "SortOrder": 102.0, + "SortOrder": 150.0, "IsNullable": false, - "Default": 0 + "Default": 42 } ], "Outputs": [ @@ -17729,7 +18141,6 @@ } ], "InputKind": [ - "IFeaturizerInput", "ITransformInput" ], "OutputKind": [ @@ -17737,95 +18148,111 @@ ] }, { - "Name": "Transforms.TwoHeterogeneousModelCombiner", - "Desc": "Combines a TransformModel and a PredictorModel into a single PredictorModel.", - "FriendlyName": null, - "ShortName": null, + "Name": "Transforms.RowRangeFilter", + "Desc": "Filters a dataview on a column of type Single, Double or Key (contiguous). Keeps the values that are in the specified min/max range. NaNs are always filtered out. If the input is a Key type, the min/max are considered percentages of the number of values.", + "FriendlyName": "Range Filter", + "ShortName": "RangeFilter", "Inputs": [ { - "Name": "TransformModel", - "Type": "TransformModel", - "Desc": "Transform model", + "Name": "Column", + "Type": "String", + "Desc": "Column", + "Aliases": [ + "col" + ], "Required": true, "SortOrder": 1.0, "IsNullable": false }, { - "Name": "PredictorModel", - "Type": "PredictorModel", - "Desc": "Predictor model", + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", "Required": true, - "SortOrder": 2.0, + "SortOrder": 1.0, "IsNullable": false + }, + { + "Name": "Min", + "Type": "Float", + "Desc": "Minimum value (0 to 1 for key types)", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Max", + "Type": "Float", + "Desc": "Maximum value (0 to 1 for key types)", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Complement", + "Type": "Bool", + "Desc": "If true, keep the values that fall outside the range.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "IncludeMin", + "Type": "Bool", + "Desc": "If true, include in the range the values that are equal to min.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "IncludeMax", + "Type": "Bool", + "Desc": "If true, include in the range the values that are equal to max.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null } ], "Outputs": [ { - "Name": "PredictorModel", - "Type": "PredictorModel", - "Desc": "Predictor model" + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" ] }, { - "Name": "Transforms.WordTokenizer", - "Desc": "The input to this transform is text, and the output is a vector of text containing the words (tokens) in the original text. The separator is space, but can be specified as any other character (or multiple characters) if needed.", - "FriendlyName": "Tokenize Text Transform", - "ShortName": "TokenizeTextTransform", + "Name": "Transforms.RowSkipAndTakeFilter", + "Desc": "Allows limiting input to a subset of rows at an optional offset. Can be used to implement data paging.", + "FriendlyName": "Skip and Take Filter", + "ShortName": "SkipTake", "Inputs": [ { - "Name": "Column", - "Type": { - "Kind": "Array", - "ItemType": { - "Kind": "Struct", - "Fields": [ - { - "Name": "TermSeparators", - "Type": "String", - "Desc": "Comma separated set of term separator(s). Commonly: 'space', 'comma', 'semicolon' or other single character.", - "Aliases": [ - "sep" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": null - }, - { - "Name": "Name", - "Type": "String", - "Desc": "Name of the new column", - "Aliases": [ - "name" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": null - }, - { - "Name": "Source", - "Type": "String", - "Desc": "Name of the source column", - "Aliases": [ - "src" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": null - } - ] - } - }, - "Desc": "New column definition(s)", + "Name": "Skip", + "Type": "Int", + "Desc": "Number of items to skip", "Aliases": [ - "col" + "s" ], "Required": false, "SortOrder": 1.0, - "IsNullable": false, + "IsNullable": true, "Default": null }, { @@ -17837,16 +18264,64 @@ "IsNullable": false }, { - "Name": "TermSeparators", - "Type": "String", - "Desc": "Comma separated set of term separator(s). Commonly: 'space', 'comma', 'semicolon' or other single character.", + "Name": "Take", + "Type": "Int", + "Desc": "Number of items to take", "Aliases": [ - "sep" + "t" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 2.0, + "IsNullable": true, + "Default": null + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.RowSkipFilter", + "Desc": "Allows limiting input to a subset of rows by skipping a number of rows.", + "FriendlyName": "Skip Filter", + "ShortName": "Skip", + "Inputs": [ + { + "Name": "Count", + "Type": "Int", + "Desc": "Number of items to skip", + "Aliases": [ + "c", + "n", + "s" + ], + "Required": true, + "SortOrder": 1.0, "IsNullable": false, - "Default": "space" + "Default": 0 + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false } ], "Outputs": [ @@ -17867,494 +18342,2324 @@ "OutputKind": [ "ITransformOutput" ] - } - ], - "Components": [ + }, { - "Kind": "AutoMlEngine", - "Components": [ + "Name": "Transforms.RowTakeFilter", + "Desc": "Allows limiting input to a subset of rows by taking N first rows.", + "FriendlyName": "Take Filter", + "ShortName": "Take", + "Inputs": [ { - "Name": "Defaults", - "Desc": "AutoML engine that returns learners with default settings.", - "FriendlyName": "Defaults Engine", - "Settings": [] + "Name": "Count", + "Type": "Int", + "Desc": "Number of items to take", + "Aliases": [ + "c", + "n", + "t" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false, + "Default": 9223372036854775807 }, { - "Name": "Rocket", - "Desc": "AutoML engine that consists of distinct, hierarchical stages of operation.", - "FriendlyName": "Rocket Engine", - "Settings": [ - { - "Name": "TopKLearners", - "Type": "Int", - "Desc": "Number of learners to retain for second stage.", - "Aliases": [ + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.ScoreColumnSelector", + "Desc": "Selects only the last score columns and the extra columns specified in the arguments.", + "FriendlyName": "Choose Columns By Index", + "ShortName": null, + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "ExtraColumns", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "Extra columns to write", + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": null + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.Scorer", + "Desc": "Turn the predictor model into a transform model", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "The predictor model to turn into a transform", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + } + ], + "Outputs": [ + { + "Name": "ScoredData", + "Type": "DataView", + "Desc": "The scored dataset" + }, + { + "Name": "ScoringTransform", + "Type": "TransformModel", + "Desc": "The scoring transform" + } + ] + }, + { + "Name": "Transforms.Segregator", + "Desc": "Un-groups vector columns into sequences of rows, inverse of Group transform", + "FriendlyName": "Un-group Transform", + "ShortName": "Ungroup", + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "Columns to unroll, or 'pivot'", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 150.0, + "IsNullable": false + }, + { + "Name": "Mode", + "Type": { + "Kind": "Enum", + "Values": [ + "Inner", + "Outer", + "First" + ] + }, + "Desc": "Specifies how to unroll multiple pivot columns of different size.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "Inner" + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.SentimentAnalyzer", + "Desc": "Uses a pretrained sentiment model to score input strings", + "FriendlyName": "Sentiment Analyzing Transform", + "ShortName": "Senti", + "Inputs": [ + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column.", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column.", + "Aliases": [ + "dst" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": null + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.SupervisedBinNormalizer", + "Desc": "Similar to BinNormalizer, but calculates bins based on correlation with the label column, not equi-density. The new value is bin_number / number_of_bins.", + "FriendlyName": "Supervised Binning Normalizer", + "ShortName": "SupBin", + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "NumBins", + "Type": "Int", + "Desc": "Max number of bins, power of 2 recommended", + "Aliases": [ + "bins" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "FixZero", + "Type": "Bool", + "Desc": "Whether to map zero to zero, preserving sparsity", + "Aliases": [ + "zero" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "MaxTrainingExamples", + "Type": "Int", + "Desc": "Max number of examples used to train the normalizer", + "Aliases": [ + "maxtrain" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:src)", + "Aliases": [ + "col" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "LabelColumn", + "Type": "String", + "Desc": "Label column for supervised binning", + "Aliases": [ + "label", + "lab" + ], + "Required": true, + "SortOrder": 150.0, + "IsNullable": false + }, + { + "Name": "MinBinSize", + "Type": "Int", + "Desc": "Minimum number of examples per bin", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 10 + }, + { + "Name": "NumBins", + "Type": "Int", + "Desc": "Max number of bins, power of 2 recommended", + "Aliases": [ + "bins" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1024 + }, + { + "Name": "FixZero", + "Type": "Bool", + "Desc": "Whether to map zero to zero, preserving sparsity", + "Aliases": [ + "zero" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "MaxTrainingExamples", + "Type": "Int", + "Desc": "Max number of examples used to train the normalizer", + "Aliases": [ + "maxtrain" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 1000000000 + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.TextFeaturizer", + "Desc": "A transform that turns a collection of text documents into numerical feature vectors. The feature vectors are normalized counts of (word and/or character) ngrams in a given tokenized text.", + "FriendlyName": "Text Transform", + "ShortName": "Text", + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Struct", + "Fields": [ + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + }, + "Desc": "New column definition (optional form: name:srcs).", + "Aliases": [ + "col" + ], + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Language", + "Type": { + "Kind": "Enum", + "Values": [ + "English", + "French", + "German", + "Dutch", + "Italian", + "Spanish", + "Japanese" + ] + }, + "Desc": "Dataset language or 'AutoDetect' to detect language per row.", + "Aliases": [ + "lang" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": "English" + }, + { + "Name": "StopWordsRemover", + "Type": { + "Kind": "Component", + "ComponentKind": "StopWordsRemover" + }, + "Desc": "Stopwords remover.", + "Aliases": [ + "remover" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "TextCase", + "Type": { + "Kind": "Enum", + "Values": [ + "Lower", + "Upper", + "None" + ] + }, + "Desc": "Casing text using the rules of the invariant culture.", + "Aliases": [ + "case" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "Lower" + }, + { + "Name": "KeepDiacritics", + "Type": "Bool", + "Desc": "Whether to keep diacritical marks or remove them.", + "Aliases": [ + "diac" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "KeepPunctuations", + "Type": "Bool", + "Desc": "Whether to keep punctuation marks or remove them.", + "Aliases": [ + "punc" + ], + "Required": false, + "SortOrder": 7.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "KeepNumbers", + "Type": "Bool", + "Desc": "Whether to keep numbers or remove them.", + "Aliases": [ + "num" + ], + "Required": false, + "SortOrder": 8.0, + "IsNullable": false, + "Default": true + }, + { + "Name": "OutputTokens", + "Type": "Bool", + "Desc": "Whether to output the transformed text tokens as an additional column.", + "Aliases": [ + "tokens", + "showtext", + "showTransformedText" + ], + "Required": false, + "SortOrder": 9.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "Dictionary", + "Type": { + "Kind": "Struct", + "Fields": [ + { + "Name": "Term", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "List of terms", + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Sort", + "Type": { + "Kind": "Enum", + "Values": [ + "Occurrence", + "Value" + ] + }, + "Desc": "How items should be ordered when vectorized. By default, they will be in the order encountered. If by value items are sorted according to their default comparison, e.g., text sorting will be case sensitive (e.g., 'A' then 'Z' then 'a').", + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": "Occurrence" + }, + { + "Name": "DropUnknowns", + "Type": "Bool", + "Desc": "Drop unknown terms instead of mapping them to NA term.", + "Aliases": [ + "dropna" + ], + "Required": false, + "SortOrder": 6.0, + "IsNullable": false, + "Default": false + } + ] + }, + "Desc": "A dictionary of whitelisted terms.", + "Aliases": [ + "dict" + ], + "Required": false, + "SortOrder": 10.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "WordFeatureExtractor", + "Type": { + "Kind": "Component", + "ComponentKind": "NgramExtractor" + }, + "Desc": "Ngram feature extractor to use for words (WordBag/WordHashBag).", + "Aliases": [ + "wordExtractor" + ], + "Required": false, + "SortOrder": 11.0, + "IsNullable": false, + "Default": { + "Name": "NGram", + "Settings": { + "MaxNumTerms": [ + 10000000 + ] + } + } + }, + { + "Name": "CharFeatureExtractor", + "Type": { + "Kind": "Component", + "ComponentKind": "NgramExtractor" + }, + "Desc": "Ngram feature extractor to use for characters (WordBag/WordHashBag).", + "Aliases": [ + "charExtractor" + ], + "Required": false, + "SortOrder": 12.0, + "IsNullable": false, + "Default": { + "Name": "NGram", + "Settings": { + "NgramLength": 3, + "AllLengths": false, + "MaxNumTerms": [ + 10000000 + ] + } + } + }, + { + "Name": "VectorNormalizer", + "Type": { + "Kind": "Enum", + "Values": [ + "None", + "L1", + "L2", + "LInf" + ] + }, + "Desc": "Normalize vectors (rows) individually by rescaling them to unit norm.", + "Aliases": [ + "norm" + ], + "Required": false, + "SortOrder": 13.0, + "IsNullable": false, + "Default": "L2" + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.TextToKeyConverter", + "Desc": "Converts input values (words, numbers, etc.) to index in a dictionary.", + "FriendlyName": "Term Transform", + "ShortName": null, + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "MaxNumTerms", + "Type": "Int", + "Desc": "Maximum number of terms to keep when auto-training", + "Aliases": [ + "max" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Term", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "List of terms", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Sort", + "Type": { + "Kind": "Enum", + "Values": [ + "Occurrence", + "Value" + ] + }, + "Desc": "How items should be ordered when vectorized. By default, they will be in the order encountered. If by value items are sorted according to their default comparison, e.g., text sorting will be case sensitive (e.g., 'A' then 'Z' then 'a').", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "TextKeyValues", + "Type": "Bool", + "Desc": "Whether key value metadata should be text, regardless of the actual input type", + "Aliases": [ + "textkv" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s) (optional form: name:src)", + "Aliases": [ + "col" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "MaxNumTerms", + "Type": "Int", + "Desc": "Maximum number of terms to keep per column when auto-training", + "Aliases": [ + "max" + ], + "Required": false, + "SortOrder": 5.0, + "IsNullable": false, + "Default": 1000000 + }, + { + "Name": "Term", + "Type": { + "Kind": "Array", + "ItemType": "String" + }, + "Desc": "List of terms", + "Required": false, + "SortOrder": 106.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Sort", + "Type": { + "Kind": "Enum", + "Values": [ + "Occurrence", + "Value" + ] + }, + "Desc": "How items should be ordered when vectorized. By default, they will be in the order encountered. If by value items are sorted according to their default comparison, e.g., text sorting will be case sensitive (e.g., 'A' then 'Z' then 'a').", + "Required": false, + "SortOrder": 113.0, + "IsNullable": false, + "Default": "Occurrence" + }, + { + "Name": "TextKeyValues", + "Type": "Bool", + "Desc": "Whether key value metadata should be text, regardless of the actual input type", + "Aliases": [ + "textkv" + ], + "Required": false, + "SortOrder": 114.0, + "IsNullable": false, + "Default": false + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.TrainTestDatasetSplitter", + "Desc": "Split the dataset into train and test sets", + "FriendlyName": "Dataset Train-Test Split", + "ShortName": null, + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "Fraction", + "Type": "Float", + "Desc": "Fraction of training data", + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": 0.8 + }, + { + "Name": "StratificationColumn", + "Type": "String", + "Desc": "Stratification column", + "Aliases": [ + "strat" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": null + } + ], + "Outputs": [ + { + "Name": "TrainData", + "Type": "DataView", + "Desc": "Training data" + }, + { + "Name": "TestData", + "Type": "DataView", + "Desc": "Testing data" + } + ] + }, + { + "Name": "Transforms.TreeLeafFeaturizer", + "Desc": "Trains a tree ensemble, or loads it from a file, then maps a numeric feature vector to three outputs: 1. A vector containing the individual tree outputs of the tree ensemble. 2. A vector indicating the leaves that the feature vector falls on in the tree ensemble. 3. A vector indicating the paths that the feature vector falls on in the tree ensemble. If a both a model file and a trainer are specified - will use the model file. If neither are specified, will train a default FastTree model. This can handle key labels by training a regression model towards their optionally permuted indices.", + "FriendlyName": "Tree Ensemble Featurization Transform", + "ShortName": "TreeFeat", + "Inputs": [ + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "Trainer to use", + "Required": true, + "SortOrder": 10.0, + "IsNullable": false + }, + { + "Name": "Suffix", + "Type": "String", + "Desc": "Output column: The suffix to append to the default column names", + "Aliases": [ + "ex" + ], + "Required": false, + "SortOrder": 101.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "LabelPermutationSeed", + "Type": "Int", + "Desc": "If specified, determines the permutation seed for applying this featurizer to a multiclass problem.", + "Aliases": [ + "lps" + ], + "Required": false, + "SortOrder": 102.0, + "IsNullable": false, + "Default": 0 + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "IFeaturizerInput", + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + }, + { + "Name": "Transforms.TwoHeterogeneousModelCombiner", + "Desc": "Combines a TransformModel and a PredictorModel into a single PredictorModel.", + "FriendlyName": null, + "ShortName": null, + "Inputs": [ + { + "Name": "TransformModel", + "Type": "TransformModel", + "Desc": "Transform model", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "Predictor model", + "Required": true, + "SortOrder": 2.0, + "IsNullable": false + } + ], + "Outputs": [ + { + "Name": "PredictorModel", + "Type": "PredictorModel", + "Desc": "Predictor model" + } + ] + }, + { + "Name": "Transforms.WordTokenizer", + "Desc": "The input to this transform is text, and the output is a vector of text containing the words (tokens) in the original text. The separator is space, but can be specified as any other character (or multiple characters) if needed.", + "FriendlyName": "Tokenize Text Transform", + "ShortName": "TokenizeTextTransform", + "Inputs": [ + { + "Name": "Column", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "TermSeparators", + "Type": "String", + "Desc": "Comma separated set of term separator(s). Commonly: 'space', 'comma', 'semicolon' or other single character.", + "Aliases": [ + "sep" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the new column", + "Aliases": [ + "name" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Source", + "Type": "String", + "Desc": "Name of the source column", + "Aliases": [ + "src" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": null + } + ] + } + }, + "Desc": "New column definition(s)", + "Aliases": [ + "col" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Data", + "Type": "DataView", + "Desc": "Input dataset", + "Required": true, + "SortOrder": 1.0, + "IsNullable": false + }, + { + "Name": "TermSeparators", + "Type": "String", + "Desc": "Comma separated set of term separator(s). Commonly: 'space', 'comma', 'semicolon' or other single character.", + "Aliases": [ + "sep" + ], + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "space" + } + ], + "Outputs": [ + { + "Name": "OutputData", + "Type": "DataView", + "Desc": "Transformed dataset" + }, + { + "Name": "Model", + "Type": "TransformModel", + "Desc": "Transform model" + } + ], + "InputKind": [ + "ITransformInput" + ], + "OutputKind": [ + "ITransformOutput" + ] + } + ], + "Components": [ + { + "Kind": "AutoMlEngine", + "Components": [ + { + "Name": "Defaults", + "Desc": "AutoML engine that returns learners with default settings.", + "FriendlyName": "Defaults Engine", + "Settings": [] + }, + { + "Name": "Rocket", + "Desc": "AutoML engine that consists of distinct, hierarchical stages of operation.", + "FriendlyName": "Rocket Engine", + "Settings": [ + { + "Name": "TopKLearners", + "Type": "Int", + "Desc": "Number of learners to retain for second stage.", + "Aliases": [ "topk" ], "Required": false, - "SortOrder": 1.0, + "SortOrder": 1.0, + "IsNullable": false, + "Default": 2 + }, + { + "Name": "SecondRoundTrialsPerLearner", + "Type": "Int", + "Desc": "Number of trials for retained second stage learners.", + "Aliases": [ + "stage2num" + ], + "Required": false, + "SortOrder": 2.0, + "IsNullable": false, + "Default": 5 + }, + { + "Name": "RandomInitialization", + "Type": "Bool", + "Desc": "Use random initialization only.", + "Aliases": [ + "randinit" + ], + "Required": false, + "SortOrder": 3.0, + "IsNullable": false, + "Default": false + }, + { + "Name": "NumInitializationPipelines", + "Type": "Int", + "Desc": "Number of initilization pipelines, used for random initialization only.", + "Aliases": [ + "numinitseeds" + ], + "Required": false, + "SortOrder": 4.0, + "IsNullable": false, + "Default": 20 + } + ] + }, + { + "Name": "UniformRandom", + "Desc": "AutoML engine using uniform random sampling.", + "FriendlyName": "Uniform Random Engine", + "Settings": [] + } + ] + }, + { + "Kind": "AutoMlStateBase", + "Components": [ + { + "Name": "AutoMlState", + "Desc": "State of an AutoML search and search space.", + "FriendlyName": "AutoML State", + "Aliases": [ + "automlst" + ], + "Settings": [ + { + "Name": "Metric", + "Type": { + "Kind": "Enum", + "Values": [ + "Auc", + "AccuracyMicro", + "AccuracyMacro", + "L2", + "F1", + "AuPrc", + "TopKAccuracy", + "Rms", + "LossFn", + "RSquared", + "LogLoss", + "LogLossReduction", + "Ndcg", + "Dcg", + "PositivePrecision", + "PositiveRecall", + "NegativePrecision", + "NegativeRecall", + "DrAtK", + "DrAtPFpr", + "DrAtNumPos", + "NumAnomalies", + "ThreshAtK", + "ThreshAtP", + "ThreshAtNumPos", + "Nmi", + "AvgMinScore", + "Dbi" + ] + }, + "Desc": "Supported metric for evaluator.", + "Aliases": [ + "metric" + ], + "Required": true, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "Auc" + }, + { + "Name": "Engine", + "Type": { + "Kind": "Component", + "ComponentKind": "AutoMlEngine" + }, + "Desc": "AutoML engine (pipeline optimizer) that generates next candidates.", + "Aliases": [ + "engine" + ], + "Required": true, + "SortOrder": 150.0, + "IsNullable": false + }, + { + "Name": "TrainerKind", + "Type": { + "Kind": "Enum", + "Values": [ + "SignatureBinaryClassifierTrainer", + "SignatureMultiClassClassifierTrainer", + "SignatureRankerTrainer", + "SignatureRegressorTrainer", + "SignatureMultiOutputRegressorTrainer", + "SignatureAnomalyDetectorTrainer", + "SignatureClusteringTrainer" + ] + }, + "Desc": "Kind of trainer for task, such as binary classification trainer, multiclass trainer, etc.", + "Aliases": [ + "tk" + ], + "Required": true, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "SignatureBinaryClassifierTrainer" + }, + { + "Name": "TerminatorArgs", + "Type": { + "Kind": "Component", + "ComponentKind": "SearchTerminator" + }, + "Desc": "Arguments for creating terminator, which determines when to stop search.", + "Aliases": [ + "term" + ], + "Required": true, + "SortOrder": 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(Non negative value)", + "Name": "FeatureSelector", + "Type": { + "Kind": "Component", + "ComponentKind": "EnsembleFeatureSelector" + }, + "Desc": "The Feature selector", "Aliases": [ - "th" + "fs" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 1.0, "IsNullable": false, - "Default": 0.01, - "Range": { - "Min": 0.0 + "Default": { + "Name": "AllFeatureSelector" } } ] }, { - "Name": "UP", - "Desc": "Stops in case of consecutive loss in generality.", - "FriendlyName": "Consecutive Loss in Generality (UP)", + "Name": "RandomPartitionSelector", + "Desc": null, + "FriendlyName": "Random Partition Selector", "Settings": [ { - "Name": "WindowSize", - "Type": "Int", - "Desc": "The window size.", + "Name": "FeatureSelector", + "Type": { + "Kind": "Component", + "ComponentKind": "EnsembleFeatureSelector" + }, + "Desc": "The Feature selector", "Aliases": [ - "w" + "fs" ], "Required": false, - "SortOrder": 150.0, + "SortOrder": 1.0, "IsNullable": false, - "Default": 5, - "Range": { - "Inf": 0 + "Default": { + "Name": "AllFeatureSelector" } } ] @@ -18372,7 +20677,7 @@ { "Name": "NumTrees", "Type": "Int", - "Desc": "Number of weak hypotheses in the ensemble", + "Desc": "Total number of decision trees to create in the ensemble", "Aliases": [ "iter" ], @@ -19266,7 +21571,7 @@ { "Name": "NumTrees", "Type": "Int", - "Desc": "Number of weak hypotheses in the ensemble", + "Desc": "Total number of decision trees to create in the ensemble", "Aliases": [ "iter" ], @@ -20241,7 +22546,7 @@ { "Name": "NumTrees", "Type": "Int", - "Desc": "Number of weak hypotheses in the ensemble", + "Desc": "Total number of decision trees to create in the ensemble", "Aliases": [ "iter" ], @@ -21123,7 +23428,7 @@ { "Name": "NumTrees", "Type": "Int", - "Desc": "Number of weak hypotheses in the ensemble", + "Desc": "Total number of decision trees to create in the ensemble", "Aliases": [ "iter" ], @@ -22196,6 +24501,140 @@ } ] }, + { + "Kind": "PartitionedPathParser", + "Components": [ + { + "Name": "ParquetPathParser", + "Desc": "Extract name/value pairs from Parquet formatted directory names. Example path: Year=2018/Month=12/data1.parquet", + "FriendlyName": "Parquet Partitioned Path Parser", + "Aliases": [ + "ParqPP" + ], + "Settings": [] + }, + { + "Name": "SimplePathParser", + "Desc": "A simple parser that extracts directory names as column values. Column names are defined as arguments.", + "FriendlyName": "Simple Partitioned Path Parser", + "Aliases": [ + "SmplPP" + ], + "Settings": [ + { + "Name": "Columns", + "Type": { + "Kind": "Array", + "ItemType": { + "Kind": "Struct", + "Fields": [ + { + "Name": "Name", + "Type": "String", + "Desc": "Name of the column.", + "Required": true, + "SortOrder": 150.0, + "IsNullable": false + }, + { + "Name": "Type", + "Type": { + "Kind": "Enum", + "Values": [ + "I1", + "U1", + "I2", + "U2", + "I4", + "U4", + "I8", + "U8", + "R4", + "Num", + "R8", + "TX", + "Text", + "TXT", + "BL", + "Bool", + "TimeSpan", + "TS", + "DT", + "DateTime", + "DZ", + "DateTimeZone", + "UG", + "U16" + ] + }, + "Desc": "Data type of the column.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": true, + "Default": null + }, + { + "Name": "Source", + "Type": "Int", + "Desc": "Index of the directory representing this column.", + "Required": true, + "SortOrder": 150.0, + "IsNullable": false, + "Default": 0 + } + ] + } + }, + "Desc": "Column definitions used to override the Partitioned Path Parser. Expected with the format name:type:numeric-source, e.g. col=MyFeature:R4:1", + "Aliases": [ + "col" + ], + "Required": false, + "SortOrder": 1.0, + "IsNullable": false, + "Default": null + }, + { + "Name": "Type", + "Type": { + "Kind": "Enum", + "Values": [ + "I1", + "U1", + "I2", + "U2", + "I4", + "U4", + "I8", + "U8", + "R4", + "Num", + "R8", + "TX", + "Text", + "TXT", + "BL", + "Bool", + "TimeSpan", + "TS", + "DT", + "DateTime", + "DZ", + "DateTimeZone", + "UG", + "U16" + ] + }, + "Desc": "Data type of each column.", + "Required": false, + "SortOrder": 150.0, + "IsNullable": false, + "Default": "TX" + } + ] + } + ] + }, { "Kind": "RegressionLossFunction", "Components": [ diff --git a/test/BaselineOutput/Common/EntryPoints/lr-weights.txt b/test/BaselineOutput/Common/EntryPoints/lr-weights.txt new file mode 100644 index 0000000000..ed489c6ce6 --- /dev/null +++ b/test/BaselineOutput/Common/EntryPoints/lr-weights.txt @@ -0,0 +1,8 @@ +#@ TextLoader{ +#@ header+ +#@ sep=tab +#@ col=Bias:R4:0 +#@ col=Weights:R4:1-9 +#@ } +Bias thickness uniform_size uniform_shape adhesion epit_size bare_nuclei bland_chromatin normal_nucleoli mitoses +-6.186806 2.65800762 1.68089855 1.944068 1.42514718 0.8536965 2.9325006 1.74816787 1.58165014 0.595681 diff --git a/test/BaselineOutput/Common/EntryPoints/mc-lr-stats.txt b/test/BaselineOutput/Common/EntryPoints/mc-lr-stats.txt new file mode 100644 index 0000000000..3f451e8f26 --- /dev/null +++ b/test/BaselineOutput/Common/EntryPoints/mc-lr-stats.txt @@ -0,0 +1,10 @@ +#@ TextLoader{ +#@ header+ +#@ sep=tab +#@ col={name={Count of training examples} type=I8 src=0} +#@ col={name={Residual Deviance} type=R4 src=1} +#@ col={name={Null Deviance} type=R4 src=2} +#@ col=AIC:R4:3 +#@ } +Count of training examples Residual Deviance Null Deviance AIC +683 119.098892 884.3502 159.098892 diff --git a/test/BaselineOutput/Common/EntryPoints/mc-lr-weights.txt b/test/BaselineOutput/Common/EntryPoints/mc-lr-weights.txt new file mode 100644 index 0000000000..4276ad9c53 --- /dev/null +++ b/test/BaselineOutput/Common/EntryPoints/mc-lr-weights.txt @@ -0,0 +1,10 @@ +#@ TextLoader{ +#@ header+ +#@ sep=tab +#@ col=Bias:R4:0 +#@ col=Weights:R4:1-9 +#@ col=ClassNames:TX:10 +#@ } +Bias thickness uniform_size uniform_shape adhesion epit_size bare_nuclei bland_chromatin normal_nucleoli mitoses ClassNames +3.36404228 -1.579712 -0.8266232 -1.051891 -0.79305464 -0.386733949 -1.59106934 -1.01550019 -0.8356989 -0.332574666 Class_0 +-3.36404562 1.57971311 0.826623559 1.051891 0.7930542 0.386735022 1.59107041 1.015499 0.8356983 0.332574 Class_1 diff --git a/test/BaselineOutput/Common/EntryPoints/pca-weights.txt b/test/BaselineOutput/Common/EntryPoints/pca-weights.txt new file mode 100644 index 0000000000..cf1e0bf302 --- /dev/null +++ b/test/BaselineOutput/Common/EntryPoints/pca-weights.txt @@ -0,0 +1,28 @@ +#@ TextLoader{ +#@ header+ +#@ sep=tab +#@ col=VectorName:TX:0 +#@ col=VectorData:R4:1-784 +#@ } +VectorName 784 0:"" +EigenVector0 0 0 0 0 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file mode 100644 index 0000000000..3608e165c9 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/Command/CommandCrossValidationKeyLabelWithFloatKeyValues-out.txt @@ -0,0 +1,44 @@ +maml.exe CV tr=FastRankRanking{t=1} strat=Strat threads=- norm=Warn prexf=rangefilter{col=Label min=20 max=25} prexf=term{col=Strat:Label} dout=%Output% loader=text{col=Features:R4:10-14 col=Label:R4:9 col=GroupId:TX:1 header+} data=%Data% out=%Output% xf=term{col=Label} xf=hash{col=GroupId} +Not adding a normalizer. +Making per-feature arrays +Changing data from row-wise to column-wise +Processed 40 instances +Binning and forming Feature objects +Reserved memory for tree learner: 10764 bytes +Starting to train ... +Not training a calibrator because it is not needed. +Not adding a normalizer. +Making per-feature arrays +Changing data from row-wise to column-wise +Processed 32 instances +Binning and forming Feature objects +Reserved memory for tree learner: 6396 bytes +Starting to train ... +Not training a calibrator because it is not needed. +NDCG@1: 0.000000 +NDCG@2: 0.000000 +NDCG@3: 0.000000 +DCG@1: 0.000000 +DCG@2: 0.000000 +DCG@3: 0.000000 +NDCG@1: 0.000000 +NDCG@2: 0.000000 +NDCG@3: 0.000000 +DCG@1: 0.000000 +DCG@2: 0.000000 +DCG@3: 0.000000 + +OVERALL RESULTS +--------------------------------------- +NDCG@1: 0.000000 (0.0000) +NDCG@2: 0.000000 (0.0000) +NDCG@3: 0.000000 (0.0000) +DCG@1: 0.000000 (0.0000) +DCG@2: 0.000000 (0.0000) +DCG@3: 0.000000 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/SingleDebug/FieldAwareFactorizationMachine/FieldAwareFactorizationMachine-CV-breast-cancer-out.txt b/test/BaselineOutput/SingleDebug/FieldAwareFactorizationMachine/FieldAwareFactorizationMachine-CV-breast-cancer-out.txt new file mode 100644 index 0000000000..d05c1b0b1c --- /dev/null +++ b/test/BaselineOutput/SingleDebug/FieldAwareFactorizationMachine/FieldAwareFactorizationMachine-CV-breast-cancer-out.txt @@ -0,0 +1,77 @@ +maml.exe CV tr=FieldAwareFactorizationMachine{d=5 shuf- norm-} col[Feature]=DupFeatures threads=- norm=No dout=%Output% data=%Data% seed=1 xf=Copy{col=DupFeatures:Features} xf=MinMax{col=Features col=DupFeatures} +Not adding a normalizer. +Warning: Skipped 8 examples with bad label/weight/features in training set +Not training a calibrator because it is not needed. +Not adding a normalizer. +Warning: Skipped 8 examples with bad label/weight/features in training set +Not training a calibrator because it is not needed. +Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. +TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 122 | 12 | 0.9104 + negative || 4 | 216 | 0.9818 + ||====================== +Precision || 0.9683 | 0.9474 | +OVERALL 0/1 ACCURACY: 0.954802 +LOG LOSS/instance: 0.259660 +Test-set entropy (prior Log-Loss/instance): 0.956998 +LOG-LOSS REDUCTION (RIG): 72.867233 +AUC: 0.984973 +Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. +TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 92 | 13 | 0.8762 + negative || 2 | 222 | 0.9911 + ||====================== +Precision || 0.9787 | 0.9447 | +OVERALL 0/1 ACCURACY: 0.954407 +LOG LOSS/instance: 0.260480 +Test-set entropy (prior Log-Loss/instance): 0.903454 +LOG-LOSS REDUCTION (RIG): 71.168362 +AUC: 0.967049 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.976011 (0.0090) +Accuracy: 0.954605 (0.0002) +Positive precision: 0.973489 (0.0052) +Positive recall: 0.893319 (0.0171) +Negative precision: 0.946025 (0.0013) +Negative recall: 0.986445 (0.0046) +Log-loss: 0.260070 (0.0004) +Log-loss reduction: 72.017798 (0.8494) +F1 Score: 0.931542 (0.0069) +AUPRC: 0.974115 (0.0054) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Normalize' started. +[1] (%Time%) 337 examples +[1] 'Normalize' finished in %Time%. +[2] 'Training' started. +[2] (%Time%) 1 iterations, 329 examples Training-loss: 0.371414389819699 +[2] (%Time%) 2 iterations, 329 examples Training-loss: 0.225137821503565 +[2] (%Time%) 3 iterations, 329 examples Training-loss: 0.197323119398265 +[2] (%Time%) 4 iterations, 329 examples Training-loss: 0.183649426646222 +[2] (%Time%) 5 iterations, 329 examples Training-loss: 0.174400635825405 +[2] 'Training' finished in %Time%. +[3] 'Normalize #2' started. +[3] (%Time%) 362 examples +[3] 'Normalize #2' finished in %Time%. +[4] 'Training #2' started. +[4] (%Time%) 1 iterations, 354 examples Training-loss: 0.35872800705401 +[4] (%Time%) 2 iterations, 354 examples Training-loss: 0.239609312114266 +[4] (%Time%) 3 iterations, 354 examples Training-loss: 0.210775498912242 +[4] (%Time%) 4 iterations, 354 examples Training-loss: 0.19625903089058 +[4] (%Time%) 5 iterations, 354 examples Training-loss: 0.187121580244397 +[4] 'Training #2' finished in %Time%. diff --git a/test/BaselineOutput/SingleDebug/FieldAwareFactorizationMachine/FieldAwareFactorizationMachine-CV-breast-cancer-rp.txt b/test/BaselineOutput/SingleDebug/FieldAwareFactorizationMachine/FieldAwareFactorizationMachine-CV-breast-cancer-rp.txt new file mode 100644 index 0000000000..b826c5ae0d --- /dev/null +++ b/test/BaselineOutput/SingleDebug/FieldAwareFactorizationMachine/FieldAwareFactorizationMachine-CV-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +FieldAwareFactorizationMachine +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /d /norm /shuf Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.976011 0.954605 0.973489 0.893319 0.946025 0.986445 0.26007 72.0178 0.931542 0.974115 5 - - 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0.148859844 0.23253137628148873 0 diff --git a/test/BaselineOutput/SingleDebug/FieldAwareFactorizationMachine/FieldAwareFactorizationMachine-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/SingleDebug/FieldAwareFactorizationMachine/FieldAwareFactorizationMachine-TrainTest-breast-cancer-out.txt new file mode 100644 index 0000000000..3805af6e7c --- /dev/null +++ b/test/BaselineOutput/SingleDebug/FieldAwareFactorizationMachine/FieldAwareFactorizationMachine-TrainTest-breast-cancer-out.txt @@ -0,0 +1,51 @@ +maml.exe TrainTest test=%Data% tr=FieldAwareFactorizationMachine{d=5 shuf- norm-} col[Feature]=DupFeatures norm=No dout=%Output% data=%Data% out=%Output% seed=1 xf=Copy{col=DupFeatures:Features} xf=MinMax{col=Features col=DupFeatures} +Not adding a normalizer. +Warning: Skipped 16 examples with bad label/weight/features in training set +Not training a calibrator because it is not needed. +Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. +TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 215 | 24 | 0.8996 + negative || 7 | 437 | 0.9842 + ||====================== +Precision || 0.9685 | 0.9479 | +OVERALL 0/1 ACCURACY: 0.954612 +LOG LOSS/instance: 0.228754 +Test-set entropy (prior Log-Loss/instance): 0.934003 +LOG-LOSS REDUCTION (RIG): 75.508177 +AUC: 0.982029 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.982029 (0.0000) +Accuracy: 0.954612 (0.0000) +Positive precision: 0.968468 (0.0000) +Positive recall: 0.899582 (0.0000) +Negative precision: 0.947939 (0.0000) +Negative recall: 0.984234 (0.0000) +Log-loss: 0.228754 (0.0000) +Log-loss reduction: 75.508177 (0.0000) +F1 Score: 0.932755 (0.0000) +AUPRC: 0.980228 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Normalize' started. +[1] (%Time%) 699 examples +[1] 'Normalize' finished in %Time%. +[2] 'Training' started. +[2] (%Time%) 1 iterations, 683 examples Training-loss: 0.306117119945184 +[2] (%Time%) 2 iterations, 683 examples Training-loss: 0.193084570883075 +[2] (%Time%) 3 iterations, 683 examples Training-loss: 0.173782368769797 +[2] (%Time%) 4 iterations, 683 examples Training-loss: 0.163879262610855 +[2] (%Time%) 5 iterations, 683 examples Training-loss: 0.157117446501075 +[2] 'Training' finished in %Time%. +[3] 'Saving model' started. +[3] 'Saving model' finished in %Time%. diff --git a/test/BaselineOutput/SingleDebug/FieldAwareFactorizationMachine/FieldAwareFactorizationMachine-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/SingleDebug/FieldAwareFactorizationMachine/FieldAwareFactorizationMachine-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..4a01e926c6 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/FieldAwareFactorizationMachine/FieldAwareFactorizationMachine-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +FieldAwareFactorizationMachine +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /d /norm /shuf Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.982029 0.954612 0.968468 0.899582 0.947939 0.984234 0.228754 75.50818 0.932755 0.980228 5 - - FieldAwareFactorizationMachine %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=FieldAwareFactorizationMachine{d=5 shuf- norm-} col[Feature]=DupFeatures norm=No dout=%Output% data=%Data% out=%Output% seed=1 xf=Copy{col=DupFeatures:Features} xf=MinMax{col=Features col=DupFeatures} /d:5;/norm:-;/shuf:- + diff --git 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-2.135698 0.105675265 0.16112931691390375 0 +691 1 5.792588 0.9969592 0.0043936168831631463 1 +692 0 -2.253997 0.095005244 0.14401866240282399 0 +693 0 -2.65598679 0.06562097 0.097920204018736551 0 +694 0 -2.47353578 0.0777343661 0.11674575467283718 0 +695 0 -2.11052465 0.108078077 0.16501067045825779 0 +696 1 7.72577858 0.999558866 0.00063656221445328599 1 +697 1 3.85642529 0.97929436 0.030185519608588427 1 +698 1 5.01899147 0.993432164 0.009506637452477578 1 diff --git a/test/BaselineOutput/SingleDebug/LightGBM/LightGBMDart-CV-breast-cancer.dart-out.txt b/test/BaselineOutput/SingleDebug/LightGBM/LightGBMDart-CV-breast-cancer.dart-out.txt new file mode 100644 index 0000000000..44635aa70a --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBM/LightGBMDart-CV-breast-cancer.dart-out.txt @@ -0,0 +1,65 @@ +maml.exe CV tr=LightGBM{nt=1 iter=10 booster=dart lr=0.2 mil=10 nl=20} threads=- cache=- dout=%Output% loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} data=%Data% seed=1 +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +LightGBM objective=binary +Not training a calibrator because it is not needed. +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +LightGBM objective=binary +Not training a calibrator because it is not needed. +TEST POSITIVE RATIO: 0.3702 (134.0/(134.0+228.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 128 | 6 | 0.9552 + negative || 10 | 218 | 0.9561 + ||====================== +Precision || 0.9275 | 0.9732 | +OVERALL 0/1 ACCURACY: 0.955801 +LOG LOSS/instance: 0.301908 +Test-set entropy (prior Log-Loss/instance): 0.950799 +LOG-LOSS REDUCTION (RIG): 68.246963 +AUC: 0.982473 +TEST POSITIVE RATIO: 0.3175 (107.0/(107.0+230.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 100 | 7 | 0.9346 + negative || 8 | 222 | 0.9652 + ||====================== +Precision || 0.9259 | 0.9694 | +OVERALL 0/1 ACCURACY: 0.955490 +LOG LOSS/instance: 0.290926 +Test-set entropy (prior Log-Loss/instance): 0.901650 +LOG-LOSS REDUCTION (RIG): 67.734017 +AUC: 0.992076 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.987274 (0.0048) +Accuracy: 0.955645 (0.0002) +Positive precision: 0.926731 (0.0008) +Positive recall: 0.944902 (0.0103) +Negative precision: 0.971323 (0.0019) +Negative recall: 0.960679 (0.0045) +Log-loss: 0.296417 (0.0055) +Log-loss reduction: 67.990490 (0.2565) +F1 Score: 0.935705 (0.0055) +AUPRC: 0.969894 (0.0121) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Loading data for LightGBM' started. +[1] 'Loading data for LightGBM' finished in %Time%. +[2] 'Training with LightGBM' started. +[2] 'Training with LightGBM' finished in %Time%. +[3] 'Loading data for LightGBM #2' started. +[3] 'Loading data for LightGBM #2' finished in %Time%. +[4] 'Training with LightGBM #2' started. +[4] 'Training with LightGBM #2' finished in %Time%. diff --git a/test/BaselineOutput/SingleDebug/LightGBM/LightGBMDart-CV-breast-cancer.dart-rp.txt b/test/BaselineOutput/SingleDebug/LightGBM/LightGBMDart-CV-breast-cancer.dart-rp.txt new file mode 100644 index 0000000000..e91ca11d6f --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBM/LightGBMDart-CV-breast-cancer.dart-rp.txt @@ -0,0 +1,4 @@ +LightGBM +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /iter /lr /nl /mil /booster /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.987274 0.955645 0.926731 0.944902 0.971323 0.960679 0.296417 67.99049 0.935705 0.969894 10 0.2 20 10 dart 1 LightGBM %Data% %Output% 99 0 0 maml.exe CV tr=LightGBM{nt=1 iter=10 booster=dart lr=0.2 mil=10 nl=20} threads=- cache=- dout=%Output% loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} data=%Data% seed=1 /iter:10;/lr:0.2;/nl:20;/mil:10;/booster:dart;/nt:1 + diff --git a/test/BaselineOutput/SingleDebug/LightGBM/LightGBMDart-CV-breast-cancer.dart.txt b/test/BaselineOutput/SingleDebug/LightGBM/LightGBMDart-CV-breast-cancer.dart.txt new file mode 100644 index 0000000000..d69c5c6cb2 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBM/LightGBMDart-CV-breast-cancer.dart.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +5 1 3.77653313 0.868557751 0.20330631668376986 1 +6 0 -2.74509573 0.202208519 0.32591637677515645 0 +8 0 -4.4950676 0.0955624059 0.14490713407046754 0 +9 0 -4.4950676 0.0955624059 0.14490713407046754 0 +10 0 -3.95260167 0.12171372 0.18723682820261706 0 +11 0 -4.4950676 0.0955624059 0.14490713407046754 0 +18 1 3.28788471 0.8380707 0.25485615558954922 1 +20 1 -0.3835451 0.452203244 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a/test/BaselineOutput/SingleDebug/LightGBM/LightGBMDart-TrainTest-breast-cancer.dart-out.txt b/test/BaselineOutput/SingleDebug/LightGBM/LightGBMDart-TrainTest-breast-cancer.dart-out.txt new file mode 100644 index 0000000000..232f6326d4 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBM/LightGBMDart-TrainTest-breast-cancer.dart-out.txt @@ -0,0 +1,45 @@ +maml.exe TrainTest test=%Data% tr=LightGBM{nt=1 iter=10 booster=dart lr=0.2 mil=10 nl=20} cache=- dout=%Output% loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} data=%Data% out=%Output% seed=1 +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +LightGBM objective=binary +Not training a calibrator because it is not needed. +TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 239 | 2 | 0.9917 + negative || 12 | 446 | 0.9738 + ||====================== +Precision || 0.9522 | 0.9955 | +OVERALL 0/1 ACCURACY: 0.979971 +LOG LOSS/instance: 0.198312 +Test-set entropy (prior Log-Loss/instance): 0.929318 +LOG-LOSS REDUCTION (RIG): 78.660434 +AUC: 0.995996 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.995996 (0.0000) +Accuracy: 0.979971 (0.0000) +Positive precision: 0.952191 (0.0000) +Positive recall: 0.991701 (0.0000) +Negative precision: 0.995536 (0.0000) +Negative recall: 0.973799 (0.0000) +Log-loss: 0.198312 (0.0000) +Log-loss reduction: 78.660434 (0.0000) +F1 Score: 0.971545 (0.0000) +AUPRC: 0.983584 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Loading data for LightGBM' started. +[1] 'Loading data for LightGBM' finished in %Time%. +[2] 'Training with LightGBM' started. +[2] 'Training with LightGBM' finished in %Time%. +[3] 'Saving model' started. +[3] 'Saving model' finished in %Time%. diff --git a/test/BaselineOutput/SingleDebug/LightGBM/LightGBMDart-TrainTest-breast-cancer.dart-rp.txt b/test/BaselineOutput/SingleDebug/LightGBM/LightGBMDart-TrainTest-breast-cancer.dart-rp.txt new file mode 100644 index 0000000000..a29ae4cd9c --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBM/LightGBMDart-TrainTest-breast-cancer.dart-rp.txt @@ -0,0 +1,4 @@ +LightGBM +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /iter /lr /nl /mil /booster /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.995996 0.979971 0.952191 0.991701 0.995536 0.973799 0.198312 78.66043 0.971545 0.983584 10 0.2 20 10 dart 1 LightGBM %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LightGBM{nt=1 iter=10 booster=dart lr=0.2 mil=10 nl=20} cache=- dout=%Output% loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} data=%Data% out=%Output% seed=1 /iter:10;/lr:0.2;/nl:20;/mil:10;/booster:dart;/nt:1 + diff --git a/test/BaselineOutput/SingleDebug/LightGBM/LightGBMDart-TrainTest-breast-cancer.dart.txt b/test/BaselineOutput/SingleDebug/LightGBM/LightGBMDart-TrainTest-breast-cancer.dart.txt new file mode 100644 index 0000000000..80e77323e6 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBM/LightGBMDart-TrainTest-breast-cancer.dart.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -5.06709051 0.0735397339 0.11019899139311135 0 +1 0 0.9656145 0.6184105 1.3899067256376549 1 +2 0 -5.06709051 0.0735397339 0.11019899139311135 0 +3 0 0.586611152 0.5728052 1.2270340961595763 1 +4 0 -4.53108168 0.09401735 0.14244467134955621 0 +5 1 4.762709 0.9153944 0.12753458870756793 1 +6 0 -2.32187271 0.238497183 0.39307872073754024 0 +7 0 -5.06709051 0.0735397339 0.11019899139311135 0 +8 0 -4.47418737 0.09646857 0.14635330428919666 0 +9 0 -5.06709051 0.0735397339 0.11019899139311135 0 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b/test/BaselineOutput/SingleDebug/LightGBM/LightGBMGoss-CV-breast-cancer.goss-out.txt new file mode 100644 index 0000000000..8bd89002c1 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBM/LightGBMGoss-CV-breast-cancer.goss-out.txt @@ -0,0 +1,65 @@ +maml.exe CV tr=LightGBM{nt=1 iter=10 v=+ booster=goss lr=0.2 mil=10 nl=20} threads=- cache=- dout=%Output% loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} data=%Data% seed=1 +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +LightGBM objective=binary +Not training a calibrator because it is not needed. +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +LightGBM objective=binary +Not training a calibrator because it is not needed. +TEST POSITIVE RATIO: 0.3702 (134.0/(134.0+228.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 127 | 7 | 0.9478 + negative || 10 | 218 | 0.9561 + ||====================== +Precision || 0.9270 | 0.9689 | +OVERALL 0/1 ACCURACY: 0.953039 +LOG LOSS/instance: 0.240776 +Test-set entropy (prior Log-Loss/instance): 0.950799 +LOG-LOSS REDUCTION (RIG): 74.676422 +AUC: 0.982031 +TEST POSITIVE RATIO: 0.3175 (107.0/(107.0+230.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 100 | 7 | 0.9346 + negative || 5 | 225 | 0.9783 + ||====================== +Precision || 0.9524 | 0.9698 | +OVERALL 0/1 ACCURACY: 0.964392 +LOG LOSS/instance: 0.205801 +Test-set entropy (prior Log-Loss/instance): 0.901650 +LOG-LOSS REDUCTION (RIG): 77.175051 +AUC: 0.994718 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.988374 (0.0063) +Accuracy: 0.958715 (0.0057) +Positive precision: 0.939694 (0.0127) +Positive recall: 0.941170 (0.0066) +Negative precision: 0.969358 (0.0005) +Negative recall: 0.967201 (0.0111) +Log-loss: 0.223289 (0.0175) +Log-loss reduction: 75.925737 (1.2493) +F1 Score: 0.940333 (0.0031) +AUPRC: 0.970067 (0.0183) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Loading data for LightGBM' started. +[1] 'Loading data for LightGBM' finished in %Time%. +[2] 'Training with LightGBM' started. +[2] 'Training with LightGBM' finished in %Time%. +[3] 'Loading data for LightGBM #2' started. +[3] 'Loading data for LightGBM #2' finished in %Time%. +[4] 'Training with LightGBM #2' started. +[4] 'Training with LightGBM #2' finished in %Time%. diff --git a/test/BaselineOutput/SingleDebug/LightGBM/LightGBMGoss-CV-breast-cancer.goss-rp.txt b/test/BaselineOutput/SingleDebug/LightGBM/LightGBMGoss-CV-breast-cancer.goss-rp.txt new file mode 100644 index 0000000000..d3c7ce0fd0 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBM/LightGBMGoss-CV-breast-cancer.goss-rp.txt @@ -0,0 +1,4 @@ +LightGBM +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /iter /lr /nl /mil /booster /v /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.988374 0.958715 0.939694 0.94117 0.969358 0.967201 0.223289 75.92574 0.940333 0.970067 10 0.2 20 10 goss + 1 LightGBM %Data% %Output% 99 0 0 maml.exe CV tr=LightGBM{nt=1 iter=10 v=+ booster=goss lr=0.2 mil=10 nl=20} threads=- cache=- dout=%Output% loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} data=%Data% seed=1 /iter:10;/lr:0.2;/nl:20;/mil:10;/booster:goss;/v:+;/nt:1 + diff --git a/test/BaselineOutput/SingleDebug/LightGBM/LightGBMGoss-CV-breast-cancer.goss.txt b/test/BaselineOutput/SingleDebug/LightGBM/LightGBMGoss-CV-breast-cancer.goss.txt new file mode 100644 index 0000000000..13f90c4658 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBM/LightGBMGoss-CV-breast-cancer.goss.txt @@ 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0.070973273947168333 0 +687 0 -5.16711855 0.07020404 0.10501394052891096 0 +690 0 -5.974533 0.0480044559 0.070973273947168333 0 +695 0 -5.974533 0.0480044559 0.070973273947168333 0 diff --git a/test/BaselineOutput/SingleDebug/LightGBM/LightGBMGoss-TrainTest-breast-cancer.goss-out.txt b/test/BaselineOutput/SingleDebug/LightGBM/LightGBMGoss-TrainTest-breast-cancer.goss-out.txt new file mode 100644 index 0000000000..ba50420a1d --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBM/LightGBMGoss-TrainTest-breast-cancer.goss-out.txt @@ -0,0 +1,45 @@ +maml.exe TrainTest test=%Data% tr=LightGBM{nt=1 iter=10 v=+ booster=goss lr=0.2 mil=10 nl=20} cache=- dout=%Output% loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} data=%Data% out=%Output% seed=1 +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +LightGBM objective=binary +Not training a calibrator because it is not needed. +TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 240 | 1 | 0.9959 + negative || 8 | 450 | 0.9825 + ||====================== +Precision || 0.9677 | 0.9978 | +OVERALL 0/1 ACCURACY: 0.987124 +LOG LOSS/instance: 0.167803 +Test-set entropy (prior Log-Loss/instance): 0.929318 +LOG-LOSS REDUCTION (RIG): 81.943403 +AUC: 0.995765 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.995765 (0.0000) +Accuracy: 0.987124 (0.0000) +Positive precision: 0.967742 (0.0000) +Positive recall: 0.995851 (0.0000) +Negative precision: 0.997783 (0.0000) +Negative recall: 0.982533 (0.0000) +Log-loss: 0.167803 (0.0000) +Log-loss reduction: 81.943403 (0.0000) +F1 Score: 0.981595 (0.0000) +AUPRC: 0.986193 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Loading data for LightGBM' started. +[1] 'Loading data for LightGBM' finished in %Time%. +[2] 'Training with LightGBM' started. +[2] 'Training with LightGBM' finished in %Time%. +[3] 'Saving model' started. +[3] 'Saving model' finished in %Time%. diff --git a/test/BaselineOutput/SingleDebug/LightGBM/LightGBMGoss-TrainTest-breast-cancer.goss-rp.txt b/test/BaselineOutput/SingleDebug/LightGBM/LightGBMGoss-TrainTest-breast-cancer.goss-rp.txt new file mode 100644 index 0000000000..77718de528 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBM/LightGBMGoss-TrainTest-breast-cancer.goss-rp.txt @@ -0,0 +1,4 @@ +LightGBM +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /iter /lr /nl /mil /booster /v /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.995765 0.987124 0.967742 0.995851 0.997783 0.982533 0.167803 81.94341 0.981595 0.986193 10 0.2 20 10 goss + 1 LightGBM %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LightGBM{nt=1 iter=10 v=+ booster=goss lr=0.2 mil=10 nl=20} cache=- dout=%Output% loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} data=%Data% out=%Output% seed=1 /iter:10;/lr:0.2;/nl:20;/mil:10;/booster:goss;/v:+;/nt:1 + diff --git a/test/BaselineOutput/SingleDebug/LightGBM/LightGBMGoss-TrainTest-breast-cancer.goss.txt b/test/BaselineOutput/SingleDebug/LightGBM/LightGBMGoss-TrainTest-breast-cancer.goss.txt new file mode 100644 index 0000000000..c5643a9c46 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBM/LightGBMGoss-TrainTest-breast-cancer.goss.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -6.12573862 0.04466511 0.065921539368278048 0 +1 0 0.7871392 0.5971417 1.3116555550127289 1 +2 0 -5.88718367 0.0500402562 0.074061716914150894 0 +3 0 -0.0538624227 0.4932676 0.98070400644517874 0 +4 0 -5.88278341 0.0501449443 0.074220714532658688 0 +5 1 4.913594 0.921057045 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a/test/BaselineOutput/SingleDebug/LightGBMBinary/LightGBM-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/SingleDebug/LightGBMBinary/LightGBM-TrainTest-breast-cancer-out.txt new file mode 100644 index 0000000000..2496917e04 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBMBinary/LightGBM-TrainTest-breast-cancer-out.txt @@ -0,0 +1,45 @@ +maml.exe TrainTest test=%Data% tr=LightGBMBinary{nt=1 nl=5 mil=5 lr=0.25 iter=20 mb=255} cache=- dout=%Output% loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} data=%Data% out=%Output% seed=1 +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +LightGBM objective=binary +Not training a calibrator because it is not needed. +TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 240 | 1 | 0.9959 + negative || 11 | 447 | 0.9760 + ||====================== +Precision || 0.9562 | 0.9978 | +OVERALL 0/1 ACCURACY: 0.982833 +LOG LOSS/instance: 0.086938 +Test-set entropy (prior Log-Loss/instance): 0.929318 +LOG-LOSS REDUCTION (RIG): 90.644963 +AUC: 0.996313 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.996313 (0.0000) +Accuracy: 0.982833 (0.0000) +Positive precision: 0.956175 (0.0000) +Positive recall: 0.995851 (0.0000) +Negative precision: 0.997768 (0.0000) +Negative recall: 0.975983 (0.0000) +Log-loss: 0.086938 (0.0000) +Log-loss reduction: 90.644963 (0.0000) +F1 Score: 0.975610 (0.0000) +AUPRC: 0.987034 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Loading data for LightGBM' started. +[1] 'Loading data for LightGBM' finished in %Time%. +[2] 'Training with LightGBM' started. +[2] 'Training with LightGBM' finished in %Time%. +[3] 'Saving model' started. +[3] 'Saving model' finished in %Time%. diff --git a/test/BaselineOutput/SingleDebug/LightGBMBinary/LightGBM-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/SingleDebug/LightGBMBinary/LightGBM-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..0a2dcff3a5 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBMBinary/LightGBM-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +LightGBMBinary +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /iter /lr /nl /mil /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.996313 0.982833 0.956175 0.995851 0.997768 0.975983 0.086938 90.64497 0.97561 0.987034 20 0.25 5 5 1 LightGBMBinary %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LightGBMBinary{nt=1 nl=5 mil=5 lr=0.25 iter=20 mb=255} cache=- dout=%Output% loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} data=%Data% out=%Output% seed=1 /iter:20;/lr:0.25;/nl:5;/mil:5;/nt:1 + diff --git a/test/BaselineOutput/SingleDebug/LightGBMBinary/LightGBM-TrainTest-breast-cancer.txt b/test/BaselineOutput/SingleDebug/LightGBMBinary/LightGBM-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..8180e64edd --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBMBinary/LightGBM-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -11.1408987 0.003794317 0.0054844536464761871 0 +1 0 0.191842213 0.5239619 1.0708510547998533 1 +2 0 -10.352807 0.00561656 0.0081258244335933715 0 +3 0 -0.745744765 0.407847136 0.75595844049774485 0 +4 0 -10.6034384 0.00495831249 0.0071711259144706186 0 +5 1 9.274134 0.9904069 0.013906771757572052 1 +6 0 -2.67131853 0.2082248 0.33683722078474054 0 +7 0 -11.1408987 0.003794317 0.0054844536464761871 0 +8 0 -9.129003 0.0103077162 0.014948064293186902 0 +9 0 -10.793726 0.00451033656 0.0065217589263549926 0 +10 0 -10.0781832 0.00643791631 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+689 0 -9.129003 0.0103077162 0.014948064293186902 0 +690 0 -10.6034384 0.00495831249 0.0071711259144706186 0 +691 1 3.86220169 0.873371243 0.19533306500151071 1 +692 0 -11.1408987 0.003794317 0.0054844536464761871 0 +693 0 -11.1408987 0.003794317 0.0054844536464761871 0 +694 0 -10.352807 0.00561656 0.0081258244335933715 0 +695 0 -11.1408987 0.003794317 0.0054844536464761871 0 +696 1 9.067044 0.9893714 0.015415871487885204 1 +697 1 4.20529032 0.89116 0.16624359830210719 1 +698 1 4.20529032 0.89116 0.16624359830210719 1 diff --git a/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-CV-iris.key-out.txt b/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-CV-iris.key-out.txt new file mode 100644 index 0000000000..558f21ef1f --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-CV-iris.key-out.txt @@ -0,0 +1,67 @@ +maml.exe CV tr=LightGBMMC{nt=1 iter=10 v=- lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:TX:0 col=Features:1-*} data=%Data% seed=1 xf=Term{col=Label} +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +Auto-tuning parameters: UseSoftmax = False +LightGBM objective=multiclassova +Not training a calibrator because it is not needed. +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +Auto-tuning parameters: UseSoftmax = False +LightGBM objective=multiclassova +Not training a calibrator because it is not needed. + +Confusion table + ||======================== +PREDICTED || 0 | 1 | 2 | Recall +TRUTH ||======================== + 0 || 21 | 0 | 0 | 1.0000 + 1 || 0 | 27 | 3 | 0.9000 + 2 || 0 | 2 | 26 | 0.9286 + ||======================== +Precision ||1.0000 |0.9310 |0.8966 | +Accuracy(micro-avg): 0.936709 +Accuracy(macro-avg): 0.942857 +Log-loss: 0.312681 +Log-loss reduction: 71.248182 + +Confusion table + ||======================== +PREDICTED || 0 | 1 | 2 | Recall +TRUTH ||======================== + 0 || 29 | 0 | 0 | 1.0000 + 1 || 0 | 19 | 1 | 0.9500 + 2 || 0 | 2 | 20 | 0.9091 + ||======================== +Precision ||1.0000 |0.9048 |0.9524 | +Accuracy(micro-avg): 0.957746 +Accuracy(macro-avg): 0.953030 +Log-loss: 0.193389 +Log-loss reduction: 82.186751 + +OVERALL RESULTS +--------------------------------------- +Accuracy(micro-avg): 0.947228 (0.0105) +Accuracy(macro-avg): 0.947944 (0.0051) +Log-loss: 0.253035 (0.0596) +Log-loss reduction: 76.717466 (5.4693) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Building term dictionary' started. +[1] (%Time%) 71 examples Total Terms: 3 +[1] 'Building term dictionary' finished in %Time%. +[2] 'Loading data for LightGBM' started. +[2] 'Loading data for LightGBM' finished in %Time%. +[3] 'Training with LightGBM' started. +[3] 'Training with LightGBM' finished in %Time%. +[4] 'Building term dictionary #2' started. +[4] (%Time%) 79 examples Total Terms: 3 +[4] 'Building term dictionary #2' finished in %Time%. +[5] 'Loading data for LightGBM #2' started. +[5] 'Loading data for LightGBM #2' finished in %Time%. +[6] 'Training with LightGBM #2' started. +[6] 'Training with LightGBM #2' finished in %Time%. diff --git a/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-CV-iris.key-rp.txt b/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-CV-iris.key-rp.txt new file mode 100644 index 0000000000..fe2fcf392a --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-CV-iris.key-rp.txt @@ -0,0 +1,4 @@ +LightGBMMC +Accuracy(micro-avg) Accuracy(macro-avg) Log-loss Log-loss reduction /iter /lr /nl /mil /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.947228 0.947944 0.253035 76.71747 10 0.2 20 10 1 LightGBMMC %Data% %Output% 99 0 0 maml.exe CV tr=LightGBMMC{nt=1 iter=10 v=- lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:TX:0 col=Features:1-*} data=%Data% seed=1 xf=Term{col=Label} /iter:10;/lr:0.2;/nl:20;/mil:10;/nt:1 + diff --git a/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-CV-iris.key.txt b/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-CV-iris.key.txt new file mode 100644 index 0000000000..9b303c86aa --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-CV-iris.key.txt @@ -0,0 +1,151 @@ +Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class +5 0 1 0.25328578422472414 0.776246 0.1675262 0.0562277846 0 1 2 +6 0 1 0.12225769559664824 0.8849203 0.0591422766 0.05593741 0 2 1 +8 0 1 0.13903052119099127 0.870201468 0.07016017 0.05963834 0 1 2 +9 0 1 0.13970851121881944 0.8696117 0.07047898 0.0599093363 0 1 2 +10 0 1 0.12269405525649343 0.88453424 0.0593406856 0.0561250672 0 2 1 +11 0 1 0.12269405525649343 0.88453424 0.0593406856 0.0561250672 0 2 1 +18 0 1 0.25328578422472414 0.776246 0.1675262 0.0562277846 0 1 2 +20 0 1 0.25328578422472414 0.776246 0.1675262 0.0562277846 0 1 2 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0.05696099 2 1 0 +130 2 0.12489127544479019 0.882592857 0.06075944 0.05664773 2 0 1 +134 2 2 0.85550144650046023 0.5024456 0.42507 0.07248444 1 2 0 +135 2 0.12489127544479019 0.882592857 0.06075944 0.05664773 2 0 1 +136 2 0.1485323137279633 0.861972153 0.0780924 0.05993546 2 1 0 +139 2 0.16469395959924102 0.848153234 0.09065638 0.0611904152 2 1 0 +140 2 0.1540180682859322 0.857256532 0.08267425 0.0600692481 2 1 0 +142 2 0.1903299513274587 0.8266863 0.116503216 0.05681048 2 1 0 +143 2 0.15317496105764933 0.8579796 0.08057635 0.0614440367 2 1 0 +146 2 0.1903299513274587 0.8266863 0.116503216 0.05681048 2 1 0 +148 2 0.15751162588437911 0.8542669 0.08508965 0.0606434755 2 1 0 +149 2 0.22273115731729765 0.80033 0.142754182 0.05691586 2 1 0 diff --git a/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-CV-iris.keyU404-out.txt b/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-CV-iris.keyU404-out.txt new file mode 100644 index 0000000000..e45fbbb6c2 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-CV-iris.keyU404-out.txt @@ -0,0 +1,65 @@ +maml.exe CV tr=LightGBMMC{nt=1 iter=10 v=- lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:U4[0-4]:0 col=Features:1-4} data=%Data% seed=1 +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +Auto-tuning parameters: UseSoftmax = False +LightGBM objective=multiclassova +Not training a calibrator because it is not needed. +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +Auto-tuning parameters: UseSoftmax = False +LightGBM objective=multiclassova +Not training a calibrator because it is not needed. + +Confusion table + ||======================================== +PREDICTED || 0 | 1 | 2 | 3 | 4 | Recall +TRUTH ||======================================== + 0 || 21 | 0 | 0 | 0 | 0 | 1.0000 + 1 || 0 | 27 | 3 | 0 | 0 | 0.9000 + 2 || 0 | 2 | 26 | 0 | 0 | 0.9286 + 3 || 0 | 0 | 0 | 0 | 0 | 0.0000 + 4 || 0 | 0 | 0 | 0 | 0 | 0.0000 + ||======================================== +Precision ||1.0000 |0.9310 |0.8966 |0.0000 |0.0000 | +Accuracy(micro-avg): 0.936709 +Accuracy(macro-avg): 0.942857 +Log-loss: 0.312681 +Log-loss reduction: 71.248176 + +Confusion table + ||======================================== +PREDICTED || 0 | 1 | 2 | 3 | 4 | Recall +TRUTH ||======================================== + 0 || 29 | 0 | 0 | 0 | 0 | 1.0000 + 1 || 0 | 19 | 1 | 0 | 0 | 0.9500 + 2 || 0 | 2 | 20 | 0 | 0 | 0.9091 + 3 || 0 | 0 | 0 | 0 | 0 | 0.0000 + 4 || 0 | 0 | 0 | 0 | 0 | 0.0000 + ||======================================== +Precision ||1.0000 |0.9048 |0.9524 |0.0000 |0.0000 | +Accuracy(micro-avg): 0.957746 +Accuracy(macro-avg): 0.953030 +Log-loss: 0.193390 +Log-loss reduction: 82.186746 + +OVERALL RESULTS +--------------------------------------- +Accuracy(micro-avg): 0.947228 (0.0105) +Accuracy(macro-avg): 0.947944 (0.0051) +Log-loss: 0.253035 (0.0596) +Log-loss reduction: 76.717461 (5.4693) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Loading data for LightGBM' started. +[1] 'Loading data for LightGBM' finished in %Time%. +[2] 'Training with LightGBM' started. +[2] 'Training with LightGBM' finished in %Time%. +[3] 'Loading data for LightGBM #2' started. +[3] 'Loading data for LightGBM #2' finished in %Time%. +[4] 'Training with LightGBM #2' started. +[4] 'Training with LightGBM #2' finished in %Time%. diff --git a/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-CV-iris.keyU404-rp.txt b/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-CV-iris.keyU404-rp.txt new file mode 100644 index 0000000000..f803e379e2 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-CV-iris.keyU404-rp.txt @@ -0,0 +1,4 @@ +LightGBMMC +Accuracy(micro-avg) Accuracy(macro-avg) Log-loss Log-loss reduction /iter /lr /nl /mil /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.947228 0.947944 0.253035 76.71746 10 0.2 20 10 1 LightGBMMC %Data% %Output% 99 0 0 maml.exe CV tr=LightGBMMC{nt=1 iter=10 v=- lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:U4[0-4]:0 col=Features:1-4} data=%Data% seed=1 /iter:10;/lr:0.2;/nl:20;/mil:10;/nt:1 + diff --git a/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-CV-iris.keyU404.txt b/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-CV-iris.keyU404.txt new file mode 100644 index 0000000000..cb0d9331ce --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-CV-iris.keyU404.txt @@ -0,0 +1,151 @@ +Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class +5 0 1 0.25328586101049333 0.776245952 0.167526186 0.05622778 0 1 2 +6 0 1 0.12225776295259752 0.884920239 0.0591422729 0.0559374057 0 2 1 +8 0 1 0.13903058968621793 0.8702014 0.07016017 0.0596383363 0 1 2 +9 0 1 0.13970857976050094 0.8696116 0.0704789758 0.0599093325 0 1 2 +10 0 1 0.12269412264184056 0.8845342 0.0593406819 0.0561250634 0 2 1 +11 0 1 0.12269412264184056 0.8845342 0.0593406819 0.0561250634 0 2 1 +18 0 1 0.25328586101049333 0.776245952 0.167526186 0.05622778 0 1 2 +20 0 1 0.25328586101049333 0.776245952 0.167526186 0.05622778 0 1 2 +21 0 1 0.12269412264184056 0.8845342 0.0593406819 0.0561250634 0 2 1 +25 0 1 0.13970857976050094 0.8696116 0.0704789758 0.0599093325 0 1 2 +28 0 1 0.12225776295259752 0.884920239 0.0591422729 0.0559374057 0 2 1 +31 0 1 0.12269412264184056 0.8845342 0.0593406819 0.0561250634 0 2 1 +32 0 1 0.12269412264184056 0.8845342 0.0593406819 0.0561250634 0 2 1 +35 0 1 0.13903058968621793 0.8702014 0.07016017 0.0596383363 0 1 2 +37 0 1 0.13970857976050094 0.8696116 0.0704789758 0.0599093325 0 1 2 +40 0 1 0.12225776295259752 0.884920239 0.0591422729 0.0559374057 0 2 1 +41 0 1 0.17550963613619172 0.8390293 0.09255581 0.06841481 0 1 2 +44 0 1 0.25328586101049333 0.776245952 0.167526186 0.05622778 0 1 2 +45 0 1 0.13903058968621793 0.8702014 0.07016017 0.0596383363 0 1 2 +46 0 1 0.12269412264184056 0.8845342 0.0593406819 0.0561250634 0 2 1 +48 0 1 0.12269412264184056 0.8845342 0.0593406819 0.0561250634 0 2 1 +50 1 2 0.48031316690941278 0.61858964 0.293158859 0.08825144 1 2 0 +51 1 2 0.18552274772100014 0.83066994 0.09896273 0.0703672841 1 0 2 +52 1 3 1.8686139310181762 0.745523036 0.154337436 0.1001395 2 1 0 +54 1 2 0.45819824450238866 0.6324221 0.314907759 0.0526700951 1 2 0 +56 1 2 0.58631345356437459 0.5563746 0.357763946 0.08586141 1 2 0 +60 1 2 0.54904634529954011 0.5775003 0.363432616 0.05906704 1 0 2 +63 1 2 0.44288817880347908 0.642179 0.304338247 0.05348268 1 2 0 +64 1 2 0.14288662456903103 0.866852343 0.06818299 0.06496459 1 2 0 +66 1 2 0.13927192749760864 0.8699914 0.06910439 0.0609041043 1 2 0 +68 1 2 0.1475586146516118 0.862811863 0.08110717 0.05608093 1 2 0 +69 1 2 0.13690032984210998 0.87205714 0.07104707 0.0568957143 1 2 0 +70 1 2 0.58631345356437459 0.5563746 0.357763946 0.08586141 1 2 0 +71 1 2 0.15194434625076791 0.8590361 0.07716796 0.06379592 1 2 0 +72 1 3 1.4639004514496772 0.7123725 0.231332213 0.05629522 2 1 0 +73 1 2 0.45819824450238866 0.6324221 0.314907759 0.0526700951 1 2 0 +74 1 2 0.13796626095978629 0.8711281 0.06828712 0.0605847239 1 2 0 +76 1 2 0.45819824450238866 0.6324221 0.314907759 0.0526700951 1 2 0 +77 1 3 2.0734221020246566 0.8150108 0.1257547 0.0592344366 2 1 0 +79 1 2 0.54904634529954011 0.5775003 0.363432616 0.05906704 1 0 2 +82 1 2 0.13641926529166934 0.872476757 0.07081407 0.05670913 1 2 0 +88 1 2 0.13407540741246499 0.8745241 0.06425437 0.06122142 1 2 0 +90 1 2 0.1425660487370225 0.8671303 0.07629539 0.05657427 1 2 0 +91 1 2 0.44288817880347908 0.642179 0.304338247 0.05348268 1 2 0 +92 1 2 0.13641926529166934 0.872476757 0.07081407 0.05670913 1 2 0 +93 1 2 0.54904634529954011 0.5775003 0.363432616 0.05906704 1 0 2 +95 1 2 0.13407540741246499 0.8745241 0.06425437 0.06122142 1 2 0 +96 1 2 0.13407540741246499 0.8745241 0.06425437 0.06122142 1 2 0 +97 1 2 0.13796626095978629 0.8711281 0.06828712 0.0605847239 1 2 0 +98 1 2 0.54904634529954011 0.5775003 0.363432616 0.05906704 1 0 2 +99 1 2 0.13879081663734727 0.8704101 0.06865642 0.0609334148 1 2 0 +100 2 3 0.16223557410388861 0.8502409 0.09284732 0.05691175 2 0 1 +102 2 3 0.1228227021544628 0.884420455 0.0591995977 0.0563798733 2 1 0 +104 2 3 0.1228227021544628 0.884420455 0.0591995977 0.0563798733 2 1 0 +105 2 3 0.1228227021544628 0.884420455 0.0591995977 0.0563798733 2 1 0 +106 2 2 2.3434393651921286 0.847623646 0.0959969 0.0563793853 1 2 0 +108 2 3 0.22657601711176881 0.797258735 0.1479769 0.05476432 2 1 0 +109 2 3 0.16223557410388861 0.8502409 0.09284732 0.05691175 2 0 1 +111 2 3 0.17784894692703557 0.837068856 0.108788565 0.0541424938 2 1 0 +112 2 3 0.13281462271870764 0.8756274 0.0683108345 0.0560617223 2 1 0 +113 2 3 0.19621674447868781 0.8218341 0.122739322 0.0554265566 2 1 0 +115 2 3 0.17200944752597333 0.8419712 0.0923435241 0.0656852 2 0 1 +117 2 3 0.16223557410388861 0.8502409 0.09284732 0.05691175 2 0 1 +120 2 3 0.16223557410388861 0.8502409 0.09284732 0.05691175 2 0 1 +121 2 3 0.16411849615386448 0.848641455 0.09412395 0.0572345145 2 1 0 +122 2 3 0.13716679158045109 0.8718248 0.07206305 0.05611211 2 1 0 +123 2 3 0.28256679418751329 0.7538463 0.189867079 0.056286566 2 1 0 +125 2 3 0.20564897455362954 0.8141188 0.09413585 0.09174528 2 0 1 +128 2 3 0.13716679158045109 0.8718248 0.07206305 0.05611211 2 1 0 +129 2 3 0.16567802369146914 0.847319 0.0954867 0.0571942143 2 1 0 +131 2 3 0.16223557410388861 0.8502409 0.09284732 0.05691175 2 0 1 +132 2 3 0.13716679158045109 0.8718248 0.07206305 0.05611211 2 1 0 +133 2 3 0.29113045769451545 0.747418165 0.191831976 0.06074977 2 1 0 +137 2 3 0.22116869967287578 0.801581442 0.104995131 0.09342335 2 1 0 +138 2 2 0.99148913716896714 0.5769956 0.371023774 0.0519805774 1 2 0 +141 2 3 0.18520126566083658 0.830937 0.09454043 0.07452247 2 0 1 +144 2 3 0.16223557410388861 0.8502409 0.09284732 0.05691175 2 0 1 +145 2 3 0.14505504697273677 0.8649747 0.07757514 0.0574500971 2 1 0 +147 2 3 0.14505504697273677 0.8649747 0.07757514 0.0574500971 2 1 0 +0 0 1 0.12805244799353907 0.879807234 0.0614267029 0.05876603 0 1 2 +1 0 1 0.13227381045206946 0.8761011 0.0642271042 0.059671782 0 1 2 +2 0 1 0.12805244799353907 0.879807234 0.0614267029 0.05876603 0 1 2 +3 0 1 0.13111987178887785 0.8771126 0.06430127 0.0585860461 0 1 2 +4 0 1 0.12805244799353907 0.879807234 0.0614267029 0.05876603 0 1 2 +7 0 1 0.12805244799353907 0.879807234 0.0614267029 0.05876603 0 1 2 +12 0 1 0.13227381045206946 0.8761011 0.0642271042 0.059671782 0 1 2 +13 0 1 0.13227381045206946 0.8761011 0.0642271042 0.059671782 0 1 2 +14 0 1 0.18823445128170324 0.82842046 0.120264135 0.0513153374 0 1 2 +15 0 1 0.18823445128170324 0.82842046 0.120264135 0.0513153374 0 1 2 +16 0 1 0.12377570311405263 0.883578 0.0616899729 0.05473199 0 1 2 +17 0 1 0.12805244799353907 0.879807234 0.0614267029 0.05876603 0 1 2 +19 0 1 0.12805244799353907 0.879807234 0.0614267029 0.05876603 0 1 2 +22 0 1 0.12805244799353907 0.879807234 0.0614267029 0.05876603 0 1 2 +23 0 1 0.13145134579195075 0.876821935 0.0646113753 0.05856663 0 1 2 +24 0 1 0.13145134579195075 0.876821935 0.0646113753 0.05856663 0 1 2 +26 0 1 0.13145134579195075 0.876821935 0.0646113753 0.05856663 0 1 2 +27 0 1 0.12805244799353907 0.879807234 0.0614267029 0.05876603 0 1 2 +29 0 1 0.13145134579195075 0.876821935 0.0646113753 0.05856663 0 1 2 +30 0 1 0.13466438623099261 0.8740092 0.06761201 0.0583787523 0 1 2 +33 0 1 0.18823445128170324 0.82842046 0.120264135 0.0513153374 0 1 2 +34 0 1 0.13111987178887785 0.8771126 0.06430127 0.0585860461 0 1 2 +36 0 1 0.18823445128170324 0.82842046 0.120264135 0.0513153374 0 1 2 +38 0 1 0.13227381045206946 0.8761011 0.0642271042 0.059671782 0 1 2 +39 0 1 0.12805244799353907 0.879807234 0.0614267029 0.05876603 0 1 2 +42 0 1 0.12805244799353907 0.879807234 0.0614267029 0.05876603 0 1 2 +43 0 1 0.13145134579195075 0.876821935 0.0646113753 0.05856663 0 1 2 +47 0 1 0.12805244799353907 0.879807234 0.0614267029 0.05876603 0 1 2 +49 0 1 0.12805244799353907 0.879807234 0.0614267029 0.05876603 0 1 2 +53 1 2 0.13238620890661379 0.8760026 0.0634366348 0.06056068 1 2 0 +55 1 2 0.13788484165848175 0.871199 0.0693822056 0.0594187342 1 2 0 +57 1 2 0.14180966894555355 0.8677864 0.06764004 0.06457351 1 2 0 +58 1 2 0.1459902363533046 0.864166141 0.07662086 0.059212923 1 2 0 +59 1 2 0.1580238898720478 0.8538294 0.08263559 0.0635349452 1 2 0 +61 1 2 0.24588109562120131 0.7820152 0.155458942 0.06252578 1 2 0 +62 1 2 0.13238620890661379 0.8760026 0.0634366348 0.06056068 1 2 0 +65 1 2 0.13132980842568853 0.8769285 0.063261956 0.0598095059 1 2 0 +67 1 2 0.13238620890661379 0.8760026 0.0634366348 0.06056068 1 2 0 +75 1 2 0.16325820211530925 0.84937185 0.09269803 0.05793005 1 2 0 +78 1 2 0.25856072585963152 0.772162139 0.166928485 0.0609093234 1 2 0 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0.870110333 0.06958966 0.06029994 2 1 0 +118 2 3 0.12489134297836053 0.8825928 0.0607594363 0.0566477254 2 0 1 +119 2 2 0.82777753715835345 0.5009504 0.437019467 0.06203004 1 2 0 +124 2 3 0.1531750305285868 0.857979536 0.0805763453 0.06144403 2 1 0 +126 2 3 0.41542573035225555 0.6600592 0.280402571 0.0595381558 2 1 0 +127 2 3 0.39337764883077281 0.6747739 0.268265069 0.0569609851 2 1 0 +130 2 3 0.12489134297836053 0.8825928 0.0607594363 0.0566477254 2 0 1 +134 2 2 0.85550151661202845 0.5024455 0.425069958 0.07248443 1 2 0 +135 2 3 0.12489134297836053 0.8825928 0.0607594363 0.0566477254 2 0 1 +136 2 3 0.14853238287711923 0.8619721 0.0780924 0.05993546 2 1 0 +139 2 3 0.16469402987504086 0.8481532 0.09065638 0.06119041 2 1 0 +140 2 3 0.15401813781546583 0.8572565 0.08267424 0.0600692444 2 1 0 +142 2 3 0.19033002342813979 0.826686263 0.116503209 0.0568104759 2 1 0 +143 2 3 0.1531750305285868 0.857979536 0.0805763453 0.06144403 2 1 0 +146 2 3 0.19033002342813979 0.826686263 0.116503209 0.0568104759 2 1 0 +148 2 3 0.15751169565724296 0.8542668 0.08508964 0.06064347 2 1 0 +149 2 3 0.22273123179238696 0.8003299 0.142754182 0.0569158569 2 1 0 diff --git a/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-TrainTest-iris.key-out.txt b/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-TrainTest-iris.key-out.txt new file mode 100644 index 0000000000..64a3bc287d --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-TrainTest-iris.key-out.txt @@ -0,0 +1,43 @@ +maml.exe TrainTest test=%Data% tr=LightGBMMC{nt=1 iter=10 v=- lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:TX:0 col=Features:1-*} data=%Data% out=%Output% seed=1 xf=Term{col=Label} +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +Auto-tuning parameters: UseSoftmax = False +LightGBM objective=multiclassova +Not training a calibrator because it is not needed. + +Confusion table + ||======================== +PREDICTED || 0 | 1 | 2 | Recall +TRUTH ||======================== + 0 || 50 | 0 | 0 | 1.0000 + 1 || 0 | 47 | 3 | 0.9400 + 2 || 0 | 1 | 49 | 0.9800 + ||======================== +Precision ||1.0000 |0.9792 |0.9423 | +Accuracy(micro-avg): 0.973333 +Accuracy(macro-avg): 0.973333 +Log-loss: 0.196674 +Log-loss reduction: 82.097919 + +OVERALL RESULTS +--------------------------------------- +Accuracy(micro-avg): 0.973333 (0.0000) +Accuracy(macro-avg): 0.973333 (0.0000) +Log-loss: 0.196674 (0.0000) +Log-loss reduction: 82.097919 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Building term dictionary' started. +[1] (%Time%) 150 examples Total Terms: 3 +[1] 'Building term dictionary' finished in %Time%. +[2] 'Loading data for LightGBM' started. +[2] 'Loading data for LightGBM' finished in %Time%. +[3] 'Training with LightGBM' started. +[3] 'Training with LightGBM' finished in %Time%. +[4] 'Saving model' started. +[4] 'Saving model' finished in %Time%. diff --git a/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-TrainTest-iris.key-rp.txt b/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-TrainTest-iris.key-rp.txt new file mode 100644 index 0000000000..23dc3f1a86 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-TrainTest-iris.key-rp.txt @@ -0,0 +1,4 @@ +LightGBMMC +Accuracy(micro-avg) Accuracy(macro-avg) Log-loss Log-loss reduction /iter /lr /nl /mil /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.973333 0.973333 0.196674 82.09792 10 0.2 20 10 1 LightGBMMC %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LightGBMMC{nt=1 iter=10 v=- lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:TX:0 col=Features:1-*} data=%Data% out=%Output% seed=1 xf=Term{col=Label} /iter:10;/lr:0.2;/nl:20;/mil:10;/nt:1 + diff --git a/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-TrainTest-iris.key.txt b/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-TrainTest-iris.key.txt new file mode 100644 index 0000000000..4fc92f1b19 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-TrainTest-iris.key.txt @@ -0,0 +1,151 @@ +Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class +0 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +1 0 0 0.13247487107987427 0.875924945 0.06492095 0.05915408 0 1 2 +2 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +3 0 0 0.13247487107987427 0.875924945 0.06492095 0.05915408 0 1 2 +4 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +5 0 0 0.12456001063410244 0.8828853 0.0595866032 0.057528127 0 2 1 +6 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +7 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +8 0 0 0.13247487107987427 0.875924945 0.06492095 0.05915408 0 1 2 +9 0 0 0.13247487107987427 0.875924945 0.06492095 0.05915408 0 1 2 +10 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +11 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +12 0 0 0.13247487107987427 0.875924945 0.06492095 0.05915408 0 1 2 +13 0 0 0.13247487107987427 0.875924945 0.06492095 0.05915408 0 1 2 +14 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +15 0 0 0.12456001063410244 0.8828853 0.0595866032 0.057528127 0 2 1 +16 0 0 0.12456001063410244 0.8828853 0.0595866032 0.057528127 0 2 1 +17 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +18 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +19 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +20 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +21 0 0 0.12456001063410244 0.8828853 0.0595866032 0.057528127 0 2 1 +22 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +23 0 0 0.33277494936956031 0.7169315 0.227236316 0.0558321662 0 1 2 +24 0 0 0.32796044820794762 0.7203915 0.224743724 0.054864794 0 1 2 +25 0 0 0.13247487107987427 0.875924945 0.06492095 0.05915408 0 1 2 +26 0 0 0.12456001063410244 0.8828853 0.0595866032 0.057528127 0 2 1 +27 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +28 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +29 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +30 0 0 0.13247487107987427 0.875924945 0.06492095 0.05915408 0 1 2 +31 0 0 0.12456001063410244 0.8828853 0.0595866032 0.057528127 0 2 1 +32 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +33 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +34 0 0 0.13247487107987427 0.875924945 0.06492095 0.05915408 0 1 2 +35 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +36 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +37 0 0 0.13247487107987427 0.875924945 0.06492095 0.05915408 0 1 2 +38 0 0 0.13247487107987427 0.875924945 0.06492095 0.05915408 0 1 2 +39 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +40 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +41 0 0 0.13811810171792147 0.8709958 0.0645556152 0.06444856 0 1 2 +42 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +43 0 0 0.33277494936956031 0.7169315 0.227236316 0.0558321662 0 1 2 +44 0 0 0.33634513183392067 0.7143765 0.229578465 0.05604506 0 1 2 +45 0 0 0.13247487107987427 0.875924945 0.06492095 0.05915408 0 1 2 +46 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +47 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +48 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +49 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +50 1 1 0.23100889631685528 0.7937324 0.149178371 0.0570892133 1 2 0 +51 1 1 0.15607289835381286 0.8554968 0.0860009938 0.0585021749 1 2 0 +52 1 1 0.50877014563522738 0.601234555 0.345829129 0.052936323 1 2 0 +53 1 1 0.13306515458964616 0.875408053 0.06540278 0.0591891445 1 2 0 +54 1 1 0.15325749593441318 0.8579088 0.08456552 0.05752569 1 2 0 +55 1 1 0.12725977902560126 0.8805049 0.06000033 0.0594947673 1 2 0 +56 1 1 0.33773914875897953 0.71338135 0.194789842 0.0918288156 1 2 0 +57 1 1 0.13547311823330146 0.873302639 0.066508 0.06018936 1 2 0 +58 1 1 0.12416960207104245 0.883230031 0.05863201 0.05813798 1 2 0 +59 1 1 0.1273381037166878 0.880435944 0.0600349456 0.0595290959 1 2 0 +60 1 1 0.13547311823330146 0.873302639 0.066508 0.06018936 1 2 0 +61 1 1 0.1609832630763833 0.8513063 0.0884949937 0.0601987168 1 2 0 +62 1 1 0.13286234147782883 0.8755856 0.0653095841 0.0591047965 1 2 0 +63 1 1 0.23582975138698256 0.789915144 0.151939109 0.05814572 1 2 0 +64 1 1 0.13095815072854303 0.8772545 0.0616324246 0.06111311 1 2 0 +65 1 1 0.12594102775547797 0.881666839 0.0594169162 0.05891627 1 2 0 +66 1 1 0.1609832630763833 0.8513063 0.0884949937 0.0601987168 1 2 0 +67 1 1 0.12566071119127656 0.881914 0.0592928044 0.058793202 1 2 0 +68 1 1 0.16292738185521705 0.8496529 0.09265414 0.0576929823 1 2 0 +69 1 1 0.13131716612503977 0.8769396 0.06459884 0.05846158 1 2 0 +70 1 2 0.95035919356690834 0.554423034 0.386602134 0.0589748733 2 1 0 +71 1 1 0.12529959921655817 0.882232547 0.0591328628 0.05863461 1 2 0 +72 1 1 0.50877014563522738 0.601234555 0.345829129 0.052936323 1 2 0 +73 1 1 0.23582975138698256 0.789915144 0.151939109 0.05814572 1 2 0 +74 1 1 0.12416960207104245 0.883230031 0.05863201 0.05813798 1 2 0 +75 1 1 0.12594102775547797 0.881666839 0.0594169162 0.05891627 1 2 0 +76 1 1 0.26095097058717287 0.7703187 0.174276143 0.0554051772 1 2 0 +77 1 2 1.6029370592055334 0.738739431 0.2013044 0.0599561781 2 1 0 +78 1 1 0.15976734311668664 0.852342069 0.08787854 0.0597793758 1 2 0 +79 1 1 0.12955227996567303 0.87848866 0.0610127263 0.0604986325 1 2 0 +80 1 1 0.13547311823330146 0.873302639 0.066508 0.06018936 1 2 0 +81 1 1 0.13547311823330146 0.873302639 0.066508 0.06018936 1 2 0 +82 1 1 0.12725977902560126 0.8805049 0.06000033 0.0594947673 1 2 0 +83 1 2 0.80815870423273684 0.493023425 0.445677936 0.06129863 2 1 0 +84 1 1 0.16454027823998496 0.8482836 0.09029396 0.0614224635 1 2 0 +85 1 1 0.2070731159429981 0.8129602 0.100639634 0.08640016 1 0 2 +86 1 1 0.2987897056212489 0.7417154 0.20252648 0.0557581745 1 2 0 +87 1 1 0.12976317713440702 0.8783034 0.06388293 0.0578136854 1 2 0 +88 1 1 0.12903506453208818 0.878943145 0.06078452 0.06027235 1 2 0 +89 1 1 0.13306515458964616 0.875408053 0.06540278 0.0591891445 1 2 0 +90 1 1 0.1273381037166878 0.880435944 0.0600349456 0.0595290959 1 2 0 +91 1 1 0.12737263080959327 0.880405545 0.0600502156 0.0595442355 1 2 0 +92 1 1 0.12725977902560126 0.8805049 0.06000033 0.0594947673 1 2 0 +93 1 1 0.13547311823330146 0.873302639 0.066508 0.06018936 1 2 0 +94 1 1 0.12725977902560126 0.8805049 0.06000033 0.0594947673 1 2 0 +95 1 1 0.12903506453208818 0.878943145 0.06078452 0.06027235 1 2 0 +96 1 1 0.12661229526028364 0.8810752 0.05971398 0.05921083 1 2 0 +97 1 1 0.12529959921655817 0.882232547 0.0591328628 0.05863461 1 2 0 +98 1 1 0.44858136712049312 0.638533354 0.30528307 0.0561835542 1 0 2 +99 1 1 0.12725977902560126 0.8805049 0.06000033 0.0594947673 1 2 0 +100 2 2 0.16260754098734898 0.8499247 0.09625085 0.0538244657 2 0 1 +101 2 2 0.16184034002313213 0.850577 0.0913400352 0.0580829456 2 1 0 +102 2 2 0.12174369977157315 0.885375261 0.0585552119 0.0560695 2 0 1 +103 2 2 0.12568524504717099 0.8818924 0.0596662238 0.05844138 2 1 0 +104 2 2 0.12174369977157315 0.885375261 0.0585552119 0.0560695 2 0 1 +105 2 2 0.12174369977157315 0.885375261 0.0585552119 0.0560695 2 0 1 +106 2 2 0.44758539287303228 0.639169633 0.301120639 0.0597097427 2 1 0 +107 2 2 0.12568524504717099 0.8818924 0.0596662238 0.05844138 2 1 0 +108 2 2 0.12568524504717099 0.8818924 0.0596662238 0.05844138 2 1 0 +109 2 2 0.16260754098734898 0.8499247 0.09625085 0.0538244657 2 0 1 +110 2 2 0.19147015233338821 0.8257443 0.114775449 0.0594803 2 1 0 +111 2 2 0.12895206364866429 0.8790161 0.06284211 0.05814181 2 1 0 +112 2 2 0.12174369977157315 0.885375261 0.0585552119 0.0560695 2 0 1 +113 2 2 0.23003014798568333 0.794509649 0.147670493 0.0578198843 2 1 0 +114 2 2 0.16184034002313213 0.850577 0.0913400352 0.0580829456 2 1 0 +115 2 2 0.14622594690253263 0.8639625 0.0778152943 0.0582222156 2 1 0 +116 2 2 0.13019277678014771 0.8779262 0.06272268 0.0593511574 2 1 0 +117 2 2 0.16260754098734898 0.8499247 0.09625085 0.0538244657 2 0 1 +118 2 2 0.11933918199486065 0.8875067 0.0584422871 0.054051 2 0 1 +119 2 1 0.85092928263060119 0.5151953 0.427017927 0.05778675 1 2 0 +120 2 2 0.12174369977157315 0.885375261 0.0585552119 0.0560695 2 0 1 +121 2 2 0.27313894704628855 0.760987043 0.18166633 0.0573466346 2 1 0 +122 2 2 0.11933918199486065 0.8875067 0.0584422871 0.054051 2 0 1 +123 2 2 0.29377982293137428 0.7454406 0.197279379 0.0572799928 2 1 0 +124 2 2 0.16260754098734898 0.8499247 0.09625085 0.0538244657 2 0 1 +125 2 2 0.13019277678014771 0.8779262 0.06272268 0.0593511574 2 1 0 +126 2 2 0.43516329539237014 0.647159 0.2948179 0.0580231175 2 1 0 +127 2 2 0.36323660387324913 0.6954219 0.244927272 0.0596508458 2 1 0 +128 2 2 0.11933918199486065 0.8875067 0.0584422871 0.054051 2 0 1 +129 2 2 0.47537462515590923 0.6216521 0.316322356 0.0620255135 2 1 0 +130 2 2 0.11933918199486065 0.8875067 0.0584422871 0.054051 2 0 1 +131 2 2 0.16468208306001242 0.8481633 0.09738051 0.0544561855 2 0 1 +132 2 2 0.11933918199486065 0.8875067 0.0584422871 0.054051 2 0 1 +133 2 2 0.7071985901643314 0.493023425 0.445677936 0.06129863 2 1 0 +134 2 2 0.47537462515590923 0.6216521 0.316322356 0.0620255135 2 1 0 +135 2 2 0.12174369977157315 0.885375261 0.0585552119 0.0560695 2 0 1 +136 2 2 0.16260754098734898 0.8499247 0.09625085 0.0538244657 2 0 1 +137 2 2 0.13019277678014771 0.8779262 0.06272268 0.0593511574 2 1 0 +138 2 2 0.55105996253236122 0.5763386 0.362355322 0.06130607 2 1 0 +139 2 2 0.12174369977157315 0.885375261 0.0585552119 0.0560695 2 0 1 +140 2 2 0.12174369977157315 0.885375261 0.0585552119 0.0560695 2 0 1 +141 2 2 0.18701313089818 0.829432845 0.112345912 0.0582212359 2 1 0 +142 2 2 0.16184034002313213 0.850577 0.0913400352 0.0580829456 2 1 0 +143 2 2 0.12174369977157315 0.885375261 0.0585552119 0.0560695 2 0 1 +144 2 2 0.16260754098734898 0.8499247 0.09625085 0.0538244657 2 0 1 +145 2 2 0.14622594690253263 0.8639625 0.0778152943 0.0582222156 2 1 0 +146 2 2 0.23003014798568333 0.794509649 0.147670493 0.0578198843 2 1 0 +147 2 2 0.14864613971263602 0.861874044 0.07900992 0.0591160469 2 1 0 +148 2 2 0.16260754098734898 0.8499247 0.09625085 0.0538244657 2 0 1 +149 2 2 0.20528992585094685 0.814411163 0.1260209 0.05956797 2 1 0 diff --git a/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-TrainTest-iris.keyU404-out.txt b/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-TrainTest-iris.keyU404-out.txt new file mode 100644 index 0000000000..0263d06d71 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-TrainTest-iris.keyU404-out.txt @@ -0,0 +1,42 @@ +maml.exe TrainTest test=%Data% tr=LightGBMMC{nt=1 iter=10 v=- lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:U4[0-4]:0 col=Features:1-4} data=%Data% out=%Output% seed=1 +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +Auto-tuning parameters: UseSoftmax = False +LightGBM objective=multiclassova +Not training a calibrator because it is not needed. + +Confusion table + ||======================================== +PREDICTED || 0 | 1 | 2 | 3 | 4 | Recall +TRUTH ||======================================== + 0 || 50 | 0 | 0 | 0 | 0 | 1.0000 + 1 || 0 | 47 | 3 | 0 | 0 | 0.9400 + 2 || 0 | 1 | 49 | 0 | 0 | 0.9800 + 3 || 0 | 0 | 0 | 0 | 0 | 0.0000 + 4 || 0 | 0 | 0 | 0 | 0 | 0.0000 + ||======================================== +Precision ||1.0000 |0.9792 |0.9423 |0.0000 |0.0000 | +Accuracy(micro-avg): 0.973333 +Accuracy(macro-avg): 0.973333 +Log-loss: 0.196675 +Log-loss reduction: 82.097914 + +OVERALL RESULTS +--------------------------------------- +Accuracy(micro-avg): 0.973333 (0.0000) +Accuracy(macro-avg): 0.973333 (0.0000) +Log-loss: 0.196675 (0.0000) +Log-loss reduction: 82.097914 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Loading data for LightGBM' started. +[1] 'Loading data for LightGBM' finished in %Time%. +[2] 'Training with LightGBM' started. +[2] 'Training with LightGBM' finished in %Time%. +[3] 'Saving model' started. +[3] 'Saving model' finished in %Time%. diff --git a/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-TrainTest-iris.keyU404-rp.txt b/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-TrainTest-iris.keyU404-rp.txt new file mode 100644 index 0000000000..0842df2191 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-TrainTest-iris.keyU404-rp.txt @@ -0,0 +1,4 @@ +LightGBMMC +Accuracy(micro-avg) Accuracy(macro-avg) Log-loss Log-loss reduction /iter /lr /nl /mil /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.973333 0.973333 0.196675 82.09792 10 0.2 20 10 1 LightGBMMC %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LightGBMMC{nt=1 iter=10 v=- lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:U4[0-4]:0 col=Features:1-4} data=%Data% out=%Output% seed=1 /iter:10;/lr:0.2;/nl:20;/mil:10;/nt:1 + diff --git a/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-TrainTest-iris.keyU404.txt b/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-TrainTest-iris.keyU404.txt new file mode 100644 index 0000000000..41d4c75d8b --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBMMC/LightGBMMC-TrainTest-iris.keyU404.txt @@ -0,0 +1,151 @@ +Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class +0 0 0 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2 2 0.16260761111667693 0.8499246 0.09625085 0.053824462 2 0 1 +145 2 2 0.14622601589238901 0.8639624 0.0778152943 0.0582222119 2 1 0 +146 2 2 0.23003022300635434 0.7945096 0.147670478 0.05781988 2 1 0 +147 2 2 0.14864620886966337 0.861874 0.07900992 0.0591160432 2 1 0 +148 2 2 0.16260761111667693 0.8499246 0.09625085 0.053824462 2 0 1 +149 2 2 0.20528999903836082 0.8144111 0.126020879 0.0595679656 2 1 0 diff --git a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-wine-out.txt b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-wine-out.txt new file mode 100644 index 0000000000..9631870541 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-wine-out.txt @@ -0,0 +1,44 @@ +maml.exe CV tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +LightGBM objective=regression +Not training a calibrator because it is not needed. +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +LightGBM objective=regression +Not training a calibrator because it is not needed. +L1(avg): 0.524348 +L2(avg): 0.466735 +RMS(avg): 0.683180 +Loss-fn(avg): 0.466735 +R Squared: 0.400415 +L1(avg): 0.517508 +L2(avg): 0.458039 +RMS(avg): 0.676786 +Loss-fn(avg): 0.458039 +R Squared: 0.420159 + +OVERALL RESULTS +--------------------------------------- +L1(avg): 0.520928 (0.0034) +L2(avg): 0.462387 (0.0043) +RMS(avg): 0.679983 (0.0032) +Loss-fn(avg): 0.462387 (0.0043) +R Squared: 0.410287 (0.0099) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Loading data for LightGBM' started. +[1] 'Loading data for LightGBM' finished in %Time%. +[2] 'Training with LightGBM' started. +[2] (%Time%) Iteration: 50 Training-l2: 0.189697165394939 +[2] 'Training with LightGBM' finished in %Time%. +[3] 'Loading data for LightGBM #2' started. +[3] 'Loading data for LightGBM #2' finished in %Time%. +[4] 'Training with LightGBM #2' started. +[4] (%Time%) Iteration: 50 Training-l2: 0.204982247876212 +[4] 'Training with LightGBM #2' finished in %Time%. diff --git a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-wine-rp.txt b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-wine-rp.txt new file mode 100644 index 0000000000..ede416ff06 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-wine-rp.txt @@ -0,0 +1,4 @@ +LightGBMR +L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared /iter /lr /nl /mil /booster /v /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.520928 0.462387 0.679983 0.462387 0.410287 50 0.2 20 10 gbdt{l2=0.2 l1=0.2} + 1 LightGBMR %Data% %Output% 99 0 0 maml.exe CV tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 /iter:50;/lr:0.2;/nl:20;/mil:10;/booster:gbdt{l2=0.2 l1=0.2};/v:+;/nt:1 + diff --git a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-wine.txt b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-wine.txt new file mode 100644 index 0000000000..7bc6a0ec0b --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-CV-wine.txt @@ -0,0 +1,4899 @@ +Instance Label Score L1-loss L2-loss +5 6 5.80828 0.19172000885009766 0.036756561793481524 +6 6 5.206482 0.79351806640625 0.62967092171311378 +8 6 5.50727 0.49273014068603516 0.24278299154048 +9 6 5.99071741 0.0092825889587402344 8.6166457776926109E-05 +10 5 5.78352642 0.78352642059326172 0.61391365176768886 +11 5 5.3672 0.36719989776611328 0.13483576491944405 +18 6 5.8091054 0.19089460372924805 0.036440749732946642 +20 8 7.235471 0.76452922821044922 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5.90926361 0.09073638916015625 0.0082330923178233206 +4894 5 5.370122 0.37012195587158203 0.13699026221820532 +4896 7 6.225442 0.77455806732177734 0.59994019965324696 diff --git a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-TrainTest-wine-out.txt b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-TrainTest-wine-out.txt new file mode 100644 index 0000000000..56988214d4 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-TrainTest-wine-out.txt @@ -0,0 +1,32 @@ +maml.exe TrainTest test=%Data% tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +LightGBM objective=regression +Not training a calibrator because it is not needed. +L1(avg): 0.402080 +L2(avg): 0.272274 +RMS(avg): 0.521799 +Loss-fn(avg): 0.272274 +R Squared: 0.652798 + +OVERALL RESULTS +--------------------------------------- +L1(avg): 0.402080 (0.0000) +L2(avg): 0.272274 (0.0000) +RMS(avg): 0.521799 (0.0000) +Loss-fn(avg): 0.272274 (0.0000) +R Squared: 0.652798 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Loading data for LightGBM' started. +[1] 'Loading data for LightGBM' finished in %Time%. +[2] 'Training with LightGBM' started. +[2] (%Time%) Iteration: 50 Training-l2: 0.272273893168108 +[2] 'Training with LightGBM' finished in %Time%. +[3] 'Saving model' started. +[3] 'Saving model' finished in %Time%. diff --git a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-TrainTest-wine-rp.txt b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-TrainTest-wine-rp.txt new file mode 100644 index 0000000000..981a680d8e --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-TrainTest-wine-rp.txt @@ -0,0 +1,4 @@ +LightGBMR +L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared /iter /lr /nl /mil /booster /v /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.40208 0.272274 0.521799 0.272274 0.652798 50 0.2 20 10 gbdt{l2=0.2 l1=0.2} + 1 LightGBMR %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 /iter:50;/lr:0.2;/nl:20;/mil:10;/booster:gbdt{l2=0.2 l1=0.2};/v:+;/nt:1 + diff --git a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-TrainTest-wine.txt b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-TrainTest-wine.txt new file mode 100644 index 0000000000..1bbc59f006 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMReg-TrainTest-wine.txt @@ -0,0 +1,4899 @@ +Instance Label Score L1-loss L2-loss +0 6 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0.19656626694109036 +4895 6 5.4413476 0.55865240097045898 0.31209250511005848 +4896 7 7.037857 0.0378570556640625 0.0014331566635519266 +4897 6 5.9355073 0.064492702484130859 0.0041593086737066187 diff --git a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-CV-wine.MAE-out.txt b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-CV-wine.MAE-out.txt new file mode 100644 index 0000000000..0209b5a363 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-CV-wine.MAE-out.txt @@ -0,0 +1,44 @@ +maml.exe CV tr=LightGBMR{nt=1 iter=50 em=mae v=+ lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +LightGBM objective=regression +Not training a calibrator because it is not needed. +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +LightGBM objective=regression +Not training a calibrator because it is not needed. +L1(avg): 0.529227 +L2(avg): 0.479877 +RMS(avg): 0.692731 +Loss-fn(avg): 0.479877 +R Squared: 0.383532 +L1(avg): 0.519622 +L2(avg): 0.463327 +RMS(avg): 0.680681 +Loss-fn(avg): 0.463327 +R Squared: 0.413465 + +OVERALL RESULTS +--------------------------------------- +L1(avg): 0.524425 (0.0048) +L2(avg): 0.471602 (0.0083) +RMS(avg): 0.686706 (0.0060) +Loss-fn(avg): 0.471602 (0.0083) +R Squared: 0.398499 (0.0150) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Loading data for LightGBM' started. +[1] 'Loading data for LightGBM' finished in %Time%. +[2] 'Training with LightGBM' started. +[2] (%Time%) Iteration: 50 Training-l1: 0.344282018934789 +[2] 'Training with LightGBM' finished in %Time%. +[3] 'Loading data for LightGBM #2' started. +[3] 'Loading data for LightGBM #2' finished in %Time%. +[4] 'Training with LightGBM #2' started. +[4] (%Time%) Iteration: 50 Training-l1: 0.345211959030736 +[4] 'Training with LightGBM #2' finished in %Time%. diff --git a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-CV-wine.MAE-rp.txt b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-CV-wine.MAE-rp.txt new file mode 100644 index 0000000000..e2cc16093d --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-CV-wine.MAE-rp.txt @@ -0,0 +1,4 @@ +LightGBMR +L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared /iter /lr /nl /mil /v /nt /em Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.524425 0.471602 0.686706 0.471602 0.398499 50 0.2 20 10 + 1 Mae LightGBMR %Data% %Output% 99 0 0 maml.exe CV tr=LightGBMR{nt=1 iter=50 em=mae v=+ lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 /iter:50;/lr:0.2;/nl:20;/mil:10;/v:+;/nt:1;/em:Mae + diff --git a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-CV-wine.MAE.txt b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-CV-wine.MAE.txt new file mode 100644 index 0000000000..23cd505851 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-CV-wine.MAE.txt @@ -0,0 +1,4899 @@ +Instance Label Score L1-loss L2-loss +5 6 5.80925941 0.19074058532714844 0.036381970890943194 +6 6 5.25261068 0.74738931655883789 0.55859079050628679 +8 6 5.54791927 0.45208072662353516 0.20437698338446353 +9 6 5.8812604 0.11873960494995117 0.014099093783670469 +10 5 5.785368 0.78536796569824219 0.61680284154499532 +11 5 5.39364529 0.39364528656005859 0.15495661163095065 +18 6 6.00869131 0.0086913108825683594 7.5538884857451194E-05 +20 8 7.3507967 0.64920330047607422 0.42146492534902791 +21 7 5.944835 1.0551648139953613 1.1133727846938655 +25 6 5.694817 0.30518293380737305 0.093136623087275439 +28 6 5.61064863 0.38935136795043945 0.15159448772487849 +31 6 5.437836 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1.0083649315276944 +4889 6 5.75216627 0.2478337287902832 0.061421557126095649 +4894 5 5.44779968 0.4477996826171875 0.20052455575205386 +4896 7 6.65991926 0.34008073806762695 0.11565490840462189 diff --git a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-TrainTest-wine.MAE-out.txt b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-TrainTest-wine.MAE-out.txt new file mode 100644 index 0000000000..4792a52b30 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-TrainTest-wine.MAE-out.txt @@ -0,0 +1,32 @@ +maml.exe TrainTest test=%Data% tr=LightGBMR{nt=1 iter=50 em=mae v=+ lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +LightGBM objective=regression +Not training a calibrator because it is not needed. +L1(avg): 0.407028 +L2(avg): 0.274963 +RMS(avg): 0.524369 +Loss-fn(avg): 0.274963 +R Squared: 0.649369 + +OVERALL RESULTS +--------------------------------------- +L1(avg): 0.407028 (0.0000) +L2(avg): 0.274963 (0.0000) +RMS(avg): 0.524369 (0.0000) +Loss-fn(avg): 0.274963 (0.0000) +R Squared: 0.649369 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Loading data for LightGBM' started. +[1] 'Loading data for LightGBM' finished in %Time%. +[2] 'Training with LightGBM' started. +[2] (%Time%) Iteration: 50 Training-l1: 0.407028426136092 +[2] 'Training with LightGBM' finished in %Time%. +[3] 'Saving model' started. +[3] 'Saving model' finished in %Time%. diff --git a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-TrainTest-wine.MAE-rp.txt b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-TrainTest-wine.MAE-rp.txt new file mode 100644 index 0000000000..cf5f4d432b --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-TrainTest-wine.MAE-rp.txt @@ -0,0 +1,4 @@ +LightGBMR +L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared /iter /lr /nl /mil /v /nt /em Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.407028 0.274963 0.524369 0.274963 0.649369 50 0.2 20 10 + 1 Mae LightGBMR %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LightGBMR{nt=1 iter=50 em=mae v=+ lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 /iter:50;/lr:0.2;/nl:20;/mil:10;/v:+;/nt:1;/em:Mae + diff --git a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-TrainTest-wine.MAE.txt b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-TrainTest-wine.MAE.txt new file mode 100644 index 0000000000..c0618c3738 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegMae-TrainTest-wine.MAE.txt @@ -0,0 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0.69805526733398438 0.48728115625272039 +4896 7 6.68631124 0.31368875503540039 0.098400635035659434 +4897 6 6.04107332 0.041073322296142578 0.001687017804442803 diff --git a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-CV-wine.RMSE-out.txt b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-CV-wine.RMSE-out.txt new file mode 100644 index 0000000000..6483a88e76 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-CV-wine.RMSE-out.txt @@ -0,0 +1,44 @@ +maml.exe CV tr=LightGBMR{nt=1 iter=50 em=rmse v=+ lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +LightGBM objective=regression +Not training a calibrator because it is not needed. +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +LightGBM objective=regression +Not training a calibrator because it is not needed. +L1(avg): 0.529227 +L2(avg): 0.479877 +RMS(avg): 0.692731 +Loss-fn(avg): 0.479877 +R Squared: 0.383532 +L1(avg): 0.519622 +L2(avg): 0.463327 +RMS(avg): 0.680681 +Loss-fn(avg): 0.463327 +R Squared: 0.413465 + +OVERALL RESULTS +--------------------------------------- +L1(avg): 0.524425 (0.0048) +L2(avg): 0.471602 (0.0083) +RMS(avg): 0.686706 (0.0060) +Loss-fn(avg): 0.471602 (0.0083) +R Squared: 0.398499 (0.0150) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Loading data for LightGBM' started. +[1] 'Loading data for LightGBM' finished in %Time%. +[2] 'Training with LightGBM' started. +[2] (%Time%) Iteration: 50 Training-rmse: 0.444161678699535 +[2] 'Training with LightGBM' finished in %Time%. +[3] 'Loading data for LightGBM #2' started. +[3] 'Loading data for LightGBM #2' finished in %Time%. +[4] 'Training with LightGBM #2' started. +[4] (%Time%) Iteration: 50 Training-rmse: 0.447777922357938 +[4] 'Training with LightGBM #2' finished in %Time%. diff --git a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-CV-wine.RMSE-rp.txt b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-CV-wine.RMSE-rp.txt new file mode 100644 index 0000000000..56cb0fac01 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-CV-wine.RMSE-rp.txt @@ -0,0 +1,4 @@ +LightGBMR +L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared /iter /lr /nl /mil /v /nt /em Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.524425 0.471602 0.686706 0.471602 0.398499 50 0.2 20 10 + 1 Rmse LightGBMR %Data% %Output% 99 0 0 maml.exe CV tr=LightGBMR{nt=1 iter=50 em=rmse v=+ lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 /iter:50;/lr:0.2;/nl:20;/mil:10;/v:+;/nt:1;/em:Rmse + diff --git a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-CV-wine.RMSE.txt b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-CV-wine.RMSE.txt new file mode 100644 index 0000000000..23cd505851 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-CV-wine.RMSE.txt @@ -0,0 +1,4899 @@ +Instance Label Score L1-loss L2-loss +5 6 5.80925941 0.19074058532714844 0.036381970890943194 +6 6 5.25261068 0.74738931655883789 0.55859079050628679 +8 6 5.54791927 0.45208072662353516 0.20437698338446353 +9 6 5.8812604 0.11873960494995117 0.014099093783670469 +10 5 5.785368 0.78536796569824219 0.61680284154499532 +11 5 5.39364529 0.39364528656005859 0.15495661163095065 +18 6 6.00869131 0.0086913108825683594 7.5538884857451194E-05 +20 8 7.3507967 0.64920330047607422 0.42146492534902791 +21 7 5.944835 1.0551648139953613 1.1133727846938655 +25 6 5.694817 0.30518293380737305 0.093136623087275439 +28 6 5.61064863 0.38935136795043945 0.15159448772487849 +31 6 5.437836 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1.0083649315276944 +4889 6 5.75216627 0.2478337287902832 0.061421557126095649 +4894 5 5.44779968 0.4477996826171875 0.20052455575205386 +4896 7 6.65991926 0.34008073806762695 0.11565490840462189 diff --git a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-TrainTest-wine.RMSE-out.txt b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-TrainTest-wine.RMSE-out.txt new file mode 100644 index 0000000000..d01de377a4 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-TrainTest-wine.RMSE-out.txt @@ -0,0 +1,32 @@ +maml.exe TrainTest test=%Data% tr=LightGBMR{nt=1 iter=50 em=rmse v=+ lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +LightGBM objective=regression +Not training a calibrator because it is not needed. +L1(avg): 0.407028 +L2(avg): 0.274963 +RMS(avg): 0.524369 +Loss-fn(avg): 0.274963 +R Squared: 0.649369 + +OVERALL RESULTS +--------------------------------------- +L1(avg): 0.407028 (0.0000) +L2(avg): 0.274963 (0.0000) +RMS(avg): 0.524369 (0.0000) +Loss-fn(avg): 0.274963 (0.0000) +R Squared: 0.649369 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Loading data for LightGBM' started. +[1] 'Loading data for LightGBM' finished in %Time%. +[2] 'Training with LightGBM' started. +[2] (%Time%) Iteration: 50 Training-rmse: 0.524369259872456 +[2] 'Training with LightGBM' finished in %Time%. +[3] 'Saving model' started. +[3] 'Saving model' finished in %Time%. diff --git a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-TrainTest-wine.RMSE-rp.txt b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-TrainTest-wine.RMSE-rp.txt new file mode 100644 index 0000000000..2c813d3277 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-TrainTest-wine.RMSE-rp.txt @@ -0,0 +1,4 @@ +LightGBMR +L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared /iter /lr /nl /mil /v /nt /em Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.407028 0.274963 0.524369 0.274963 0.649369 50 0.2 20 10 + 1 Rmse LightGBMR %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LightGBMR{nt=1 iter=50 em=rmse v=+ lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 /iter:50;/lr:0.2;/nl:20;/mil:10;/v:+;/nt:1;/em:Rmse + diff --git a/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-TrainTest-wine.RMSE.txt b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-TrainTest-wine.RMSE.txt new file mode 100644 index 0000000000..c0618c3738 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/LightGBMR/LightGBMRegRmse-TrainTest-wine.RMSE.txt 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b/test/BaselineOutput/SingleDebug/SavePipe/PartitionedNamedDirectories-Schema.txt @@ -0,0 +1,5 @@ +---- PartitionedFileLoader ---- +3 columns: + L0: Text + Year: Text + Month: Text diff --git a/test/BaselineOutput/SingleDebug/SavePipe/PartitionedUnnamedDirectories-Data.txt b/test/BaselineOutput/SingleDebug/SavePipe/PartitionedUnnamedDirectories-Data.txt new file mode 100644 index 0000000000..4c7f650844 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/SavePipe/PartitionedUnnamedDirectories-Data.txt @@ -0,0 +1,16 @@ +#@ TextLoader{ +#@ header+ +#@ sep=tab +#@ col=L0:I4:0 +#@ col=Month:I4:1 +#@ col=Path:TX:2 +#@ } +L0 Month Path +1 1 2017/01/data1.csv +5 1 2017/01/data2.csv +7 1 2017/01/data2.csv +0 1 2017/01/dataBadSchema.csv +0 1 2017/01/dataBadSchema.csv +22 2 2017/02/data1.csv +24 2 2017/02/data1.csv +26 2 2017/02/data1.csv diff --git a/test/BaselineOutput/SingleDebug/SavePipe/PartitionedUnnamedDirectories-Schema.txt b/test/BaselineOutput/SingleDebug/SavePipe/PartitionedUnnamedDirectories-Schema.txt new file mode 100644 index 0000000000..5eeac7698b --- /dev/null +++ b/test/BaselineOutput/SingleDebug/SavePipe/PartitionedUnnamedDirectories-Schema.txt @@ -0,0 +1,5 @@ +---- PartitionedFileLoader ---- +3 columns: + L0: I4 + Month: I4 + Path: Text diff --git a/test/BaselineOutput/SingleDebug/pcaAnomaly/Default-TrainTest-azureCounterUnlabeled-out.txt b/test/BaselineOutput/SingleDebug/pcaAnomaly/Default-TrainTest-azureCounterUnlabeled-out.txt new file mode 100644 index 0000000000..d0abf79d6f --- /dev/null +++ b/test/BaselineOutput/SingleDebug/pcaAnomaly/Default-TrainTest-azureCounterUnlabeled-out.txt @@ -0,0 +1,24 @@ +maml.exe TrainTest test=%Data%\azure-test-unlabeled.txt tr=pcaAnomaly dout=%Output%\pcaAnomaly\Default-TrainTest-azureCounterUnlabeled.txt loader=Text{sep=space col=Name:TX:0 col=Features:~} data=%Data%\azure-train-unlabeled.txt out=%Output%\pcaAnomaly\Default-TrainTest-azureCounterUnlabeled-model.zip seed=1 xf=Expr{col=Label:Name expr={x : na(float(0))}} +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Not training a calibrator because it is not needed. +Warning: Encountered 6590 unlabeled instances during testing. +Detection rate at 10 false positives: NaN +Detection rate at 0.01 false positive rate: NaN +Detection rate at 0 positive predictions: NaN +Threshold @K FP: NaN +Threshold @P FPR: NaN +Threshold @NumPos: NaN +AUC: NaN + +OVERALL RESULTS +--------------------------------------- +AUC: NaN (0.0000) +DR @K FP: NaN (0.0000) +DR @P FPR: NaN (0.0000) +DR @NumPos: NaN (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/SingleDebug/pcaAnomaly/Default-TrainTest-azureCounterUnlabeled-rp.txt b/test/BaselineOutput/SingleDebug/pcaAnomaly/Default-TrainTest-azureCounterUnlabeled-rp.txt new file mode 100644 index 0000000000..8dc0fad5a7 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/pcaAnomaly/Default-TrainTest-azureCounterUnlabeled-rp.txt @@ -0,0 +1,4 @@ +pcaAnomaly +AUC DR @K FP DR @P FPR DR @NumPos Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +NaN NaN NaN NaN pcaAnomaly %Data%\azure-train-unlabeled.txt %Data%\azure-test-unlabeled.txt %Output%\pcaAnomaly\Default-TrainTest-azureCounterUnlabeled-out.txt 99 0 0 maml.exe TrainTest test=%Data%\azure-test-unlabeled.txt tr=pcaAnomaly dout=%Output%\pcaAnomaly\Default-TrainTest-azureCounterUnlabeled.txt loader=Text{sep=space col=Name:TX:0 col=Features:~} data=%Data%\azure-train-unlabeled.txt out=%Output%\pcaAnomaly\Default-TrainTest-azureCounterUnlabeled-model.zip seed=1 xf=Expr{col=Label:Name expr={x : na(float(0))}} + diff --git a/test/BaselineOutput/SingleDebug/pcaAnomaly/Default-TrainTest-azureCounterUnlabeled-summary.txt b/test/BaselineOutput/SingleDebug/pcaAnomaly/Default-TrainTest-azureCounterUnlabeled-summary.txt new file mode 100644 index 0000000000..52e03c7595 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/pcaAnomaly/Default-TrainTest-azureCounterUnlabeled-summary.txt @@ -0,0 +1,25 @@ +Dimension: 50 +Rank: 20 +Mean vector: 0.788325 0.788341 0.7883744 0.7883848 0.7883936 0.7883905 0.788416 0.7884151 0.7884451 0.7884336 0.7884297 0.7884075 0.7883975 0.7883836 0.7883822 0.7883797 0.7883725 0.7883604 0.7883703 0.7883449 0.7883869 0.7883851 0.7883923 0.788395 0.7883939 0.7883962 0.7884332 0.7884462 0.7884642 0.7884554 0.7884371 0.7883986 0.7883911 0.7883792 0.7883636 0.7883496 0.788327 0.7883144 0.7883062 0.7882917 0.7882761 0.7882612 0.7882473 0.788215 0.7881997 0.7881925 0.7881818 0.788156 0.7881312 0.7880983 +Projected mean vector: -5.572146 -0.0009027272 -8.744746E-05 0.1541603 -0.0005702823 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+2014-07-11_17_55_00.0000000___2014-07-11_22_00_00.0000000 0.05277346 +2014-07-11_18_00_00.0000000___2014-07-11_22_05_00.0000000 0.05909107 +2014-07-11_18_05_00.0000000___2014-07-11_22_10_00.0000000 0.0608733743 +2014-07-11_18_10_00.0000000___2014-07-11_22_15_00.0000000 0.0568289869 +2014-07-11_18_15_00.0000000___2014-07-11_22_20_00.0000000 0.05434453 +2014-07-11_18_20_00.0000000___2014-07-11_22_25_00.0000000 0.05590492 +2014-07-11_18_25_00.0000000___2014-07-11_22_30_00.0000000 0.0602416545 +2014-07-11_18_30_00.0000000___2014-07-11_22_35_00.0000000 0.06299596 +2014-07-11_18_35_00.0000000___2014-07-11_22_40_00.0000000 0.0630844459 +2014-07-11_18_40_00.0000000___2014-07-11_22_45_00.0000000 0.06260439 diff --git a/test/BaselineOutput/SingleDebug/pcaAnomaly/Default-TrainTest-mnistOneClass-out.txt b/test/BaselineOutput/SingleDebug/pcaAnomaly/Default-TrainTest-mnistOneClass-out.txt new file mode 100644 index 0000000000..260d9fb858 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/pcaAnomaly/Default-TrainTest-mnistOneClass-out.txt @@ -0,0 +1,76 @@ +maml.exe TrainTest test=%Data% tr=pcaAnomaly dout=%Output% data=%Data% out=%Output% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Not training a calibrator because it is not needed. +50 Top-scored Results +================================================= +Instance Anomaly Score Labeled +66 0.9938 0 +48 0.9909 5 +25 0.9909 0 +72 0.9708 7 +37 0.9144 0 +69 0.8545 5 +70 0.776 1 +5 0.7011 0 +17 0.6974 0 +22 0.6871 9 +74 0.6836 4 +87 0.6795 1 +61 0.6758 6 +102 0.6643 1 +46 0.6519 0 +111 0.648 0 +76 0.6162 0 +12 0.5826 0 +109 0.5744 0 +60 0.57 0 +21 0.5687 0 +83 0.5668 6 +10 0.5591 0 +44 0.5571 0 +35 0.5547 0 +29 0.5546 0 +54 0.5536 0 +27 0.5526 0 +6 0.5507 0 +52 0.5501 0 +30 0.5477 0 +113 0.5465 0 +94 0.5448 0 +40 0.5444 0 +79 0.5365 0 +103 0.531 0 +58 0.5304 0 +64 0.5297 0 +9 0.5292 0 +3 0.5262 0 +78 0.5254 0 +47 0.5241 0 +23 0.5238 0 +110 0.5206 0 +114 0.52 0 +82 0.519 0 +101 0.5127 0 +32 0.5113 0 +88 0.5107 0 +24 0.5096 0 +Detection rate at 10 false positives: 0.900000 +Detection rate at 0.01 false positive rate: 0.100000 +Detection rate at 10 positive predictions: 0.500000 +Threshold @K FP: 0.574407 +Threshold @P FPR: 0.990874 +Threshold @NumPos: 0.687074 +AUC: 0.956190 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.956190 (0.0000) +DR @K FP: 0.900000 (0.0000) +DR @P FPR: 0.100000 (0.0000) +DR @NumPos: 0.500000 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/SingleDebug/pcaAnomaly/Default-TrainTest-mnistOneClass-rp.txt b/test/BaselineOutput/SingleDebug/pcaAnomaly/Default-TrainTest-mnistOneClass-rp.txt new file mode 100644 index 0000000000..4948ad2f4e --- /dev/null +++ b/test/BaselineOutput/SingleDebug/pcaAnomaly/Default-TrainTest-mnistOneClass-rp.txt @@ -0,0 +1,4 @@ +pcaAnomaly +AUC DR @K FP DR @P FPR DR @NumPos Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.95619 0.9 0.1 0.5 pcaAnomaly %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=pcaAnomaly dout=%Output% data=%Data% out=%Output% seed=1 + diff --git a/test/BaselineOutput/SingleDebug/pcaAnomaly/Default-TrainTest-mnistOneClass.txt b/test/BaselineOutput/SingleDebug/pcaAnomaly/Default-TrainTest-mnistOneClass.txt new file mode 100644 index 0000000000..8667e96c93 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/pcaAnomaly/Default-TrainTest-mnistOneClass.txt @@ -0,0 +1,116 @@ +Instance Label Score +0 0 0.495893419 +1 0 0.450051278 +2 0 0.461110771 +3 0 0.5262271 +4 0 0.508700669 +5 0 0.7010657 +6 0 0.550729036 +7 0 0.45529598 +8 0 0.432054043 +9 0 0.529151142 +10 0 0.559123456 +11 0 0.485286921 +12 0 0.5826422 +13 0 0.420975477 +14 0 0.490322858 +15 0 0.5079249 +16 0 0.468320668 +17 0 0.6973921 +18 0 0.490462124 +19 0 0.469306976 +20 0 0.434285462 +21 0 0.5687201 +22 9 0.6870739 +23 0 0.5237843 +24 0 0.509592056 +25 0 0.9908741 +26 0 0.501218855 +27 0 0.5525645 +28 0 0.47950533 +29 0 0.554561734 +30 0 0.547651231 +31 0 0.4881632 +32 0 0.511275351 +33 0 0.3691424 +34 0 0.4903799 +35 0 0.5547312 +36 0 0.47143057 +37 0 0.914354265 +38 0 0.5050705 +39 0 0.432126135 +40 0 0.54437536 +41 0 0.435738117 +42 0 0.394098163 +43 0 0.449369878 +44 0 0.557093143 +45 0 0.4455139 +46 0 0.6519489 +47 0 0.524138033 +48 5 0.990943134 +49 0 0.443518072 +50 0 0.463696748 +51 0 0.398020357 +52 0 0.5500833 +53 0 0.4488295 +54 0 0.5536161 +55 0 0.418236881 +56 0 0.409066468 +57 0 0.5022479 +58 0 0.5304069 +59 0 0.446812063 +60 0 0.5700437 +61 6 0.6758252 +62 0 0.457501948 +63 0 0.436834127 +64 0 0.5297206 +65 0 0.500145555 +66 0 0.9938066 +67 0 0.4682963 +68 0 0.422779649 +69 5 0.854525864 +70 1 0.7760329 +71 0 0.5095027 +72 7 0.9708281 +73 0 0.5008225 +74 4 0.6835667 +75 0 0.436794817 +76 0 0.6161679 +77 0 0.4936375 +78 0 0.5253543 +79 0 0.536463261 +80 0 0.467982769 +81 0 0.416288167 +82 0 0.518951237 +83 6 0.5667783 +84 0 0.4754018 +85 0 0.451952815 +86 0 0.4157417 +87 1 0.679460764 +88 0 0.510666132 +89 0 0.4798051 +90 0 0.474666148 +91 0 0.503672063 +92 0 0.483663946 +93 0 0.426248729 +94 0 0.544837534 +95 0 0.454061627 +96 0 0.4768858 +97 0 0.475224346 +98 0 0.4648013 +99 0 0.449731052 +100 0 0.374409467 +101 0 0.512731969 +102 1 0.664328635 +103 0 0.5310194 +104 0 0.449136227 +105 0 0.5033788 +106 0 0.4501728 +107 0 0.499612123 +108 0 0.467777759 +109 0 0.574407458 +110 0 0.520628 +111 0 0.648003161 +112 0 0.3824868 +113 0 0.5464604 +114 0 0.519995868 diff --git a/test/BaselineOutput/SingleDebug/pcaAnomaly/NoNorm-TrainTest-mnistOneClass-out.txt b/test/BaselineOutput/SingleDebug/pcaAnomaly/NoNorm-TrainTest-mnistOneClass-out.txt new file mode 100644 index 0000000000..6ba35663ca --- /dev/null +++ b/test/BaselineOutput/SingleDebug/pcaAnomaly/NoNorm-TrainTest-mnistOneClass-out.txt @@ -0,0 +1,76 @@ +maml.exe TrainTest test=%Data% tr=pcaAnomaly norm=No dout=%Output% data=%Data% out=%Output% seed=1 +Not adding a normalizer. +Not training a calibrator because it is not needed. +50 Top-scored Results +================================================= +Instance Anomaly Score Labeled +69 0.8341 5 +70 0.7756 1 +48 0.7316 5 +72 0.7184 7 +74 0.6831 4 +87 0.6784 1 +61 0.6754 6 +17 0.6731 0 +22 0.6632 9 +102 0.6629 1 +46 0.6559 0 +37 0.6169 0 +76 0.6153 0 +12 0.5829 0 +109 0.5751 0 +60 0.5704 0 +25 0.5697 0 +21 0.5682 0 +83 0.5649 6 +10 0.5587 0 +44 0.556 0 +29 0.5546 0 +35 0.5544 0 +66 0.5534 0 +54 0.5529 0 +27 0.5521 0 +6 0.5503 0 +52 0.5492 0 +30 0.5469 0 +113 0.5458 0 +94 0.5442 0 +40 0.5439 0 +79 0.5364 0 +103 0.5301 0 +64 0.5299 0 +58 0.5296 0 +9 0.5289 0 +3 0.5263 0 +78 0.5259 0 +5 0.5245 0 +47 0.5233 0 +23 0.5228 0 +114 0.5201 0 +110 0.5201 0 +82 0.5188 0 +111 0.5146 0 +101 0.5131 0 +32 0.5108 0 +88 0.5106 0 +24 0.5095 0 +Detection rate at 10 false positives: 1.000000 +Detection rate at 0.01 false positive rate: 0.900000 +Detection rate at 10 positive predictions: 0.900000 +Threshold @K FP: 0.558688 +Threshold @P FPR: 0.655883 +Threshold @NumPos: 0.662917 +AUC: 0.989524 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.989524 (0.0000) +DR @K FP: 1.000000 (0.0000) +DR @P FPR: 0.900000 (0.0000) +DR @NumPos: 0.900000 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/SingleDebug/pcaAnomaly/NoNorm-TrainTest-mnistOneClass-rp.txt b/test/BaselineOutput/SingleDebug/pcaAnomaly/NoNorm-TrainTest-mnistOneClass-rp.txt new file mode 100644 index 0000000000..a80438aae1 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/pcaAnomaly/NoNorm-TrainTest-mnistOneClass-rp.txt @@ -0,0 +1,4 @@ +pcaAnomaly +AUC DR @K FP DR @P FPR DR @NumPos Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.989524 1 0.9 0.9 pcaAnomaly %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=pcaAnomaly norm=No dout=%Output% data=%Data% out=%Output% seed=1 + diff --git a/test/BaselineOutput/SingleDebug/pcaAnomaly/NoNorm-TrainTest-mnistOneClass.txt b/test/BaselineOutput/SingleDebug/pcaAnomaly/NoNorm-TrainTest-mnistOneClass.txt new file mode 100644 index 0000000000..45a1104275 --- /dev/null +++ b/test/BaselineOutput/SingleDebug/pcaAnomaly/NoNorm-TrainTest-mnistOneClass.txt @@ -0,0 +1,116 @@ +Instance Label Score +0 0 0.4951306 +1 0 0.449331224 +2 0 0.461190224 +3 0 0.526296139 +4 0 0.508475244 +5 0 0.5244605 +6 0 0.550277948 +7 0 0.455368549 +8 0 0.431095362 +9 0 0.528869 +10 0 0.558688045 +11 0 0.4851844 +12 0 0.582877636 +13 0 0.420640141 +14 0 0.489568383 +15 0 0.5076097 +16 0 0.4680955 +17 0 0.6731393 +18 0 0.490120977 +19 0 0.468934834 +20 0 0.4334778 +21 0 0.5682155 +22 9 0.6632366 +23 0 0.5228237 +24 0 0.5094715 +25 0 0.5697219 +26 0 0.501028359 +27 0 0.5520762 +28 0 0.478860557 +29 0 0.5545906 +30 0 0.546905637 +31 0 0.4884955 +32 0 0.5108325 +33 0 0.368852228 +34 0 0.4903543 +35 0 0.554350555 +36 0 0.471144259 +37 0 0.61691314 +38 0 0.5044613 +39 0 0.432231337 +40 0 0.543896258 +41 0 0.4354026 +42 0 0.393868625 +43 0 0.4491824 +44 0 0.5560113 +45 0 0.44493416 +46 0 0.6558832 +47 0 0.523334861 +48 5 0.731561244 +49 0 0.44316262 +50 0 0.463617116 +51 0 0.397496134 +52 0 0.549204767 +53 0 0.449091375 +54 0 0.5528623 +55 0 0.417973459 +56 0 0.409094721 +57 0 0.501895368 +58 0 0.5295681 +59 0 0.446579576 +60 0 0.5703988 +61 6 0.6754471 +62 0 0.4571285 +63 0 0.436919063 +64 0 0.529872537 +65 0 0.499967635 +66 0 0.553377748 +67 0 0.467494756 +68 0 0.422788024 +69 5 0.8340921 +70 1 0.7755893 +71 0 0.509331048 +72 7 0.7183694 +73 0 0.501034141 +74 4 0.6831258 +75 0 0.4366773 +76 0 0.615255535 +77 0 0.493479639 +78 0 0.5258877 +79 0 0.5363522 +80 0 0.467402875 +81 0 0.416329473 +82 0 0.518832862 +83 6 0.564920068 +84 0 0.475099474 +85 0 0.45117566 +86 0 0.4157634 +87 1 0.678395748 +88 0 0.510561645 +89 0 0.480021447 +90 0 0.474439472 +91 0 0.5036237 +92 0 0.483466953 +93 0 0.426080644 +94 0 0.5442002 +95 0 0.453564852 +96 0 0.4767387 +97 0 0.475078881 +98 0 0.4640796 +99 0 0.4493989 +100 0 0.374264151 +101 0 0.5131056 +102 1 0.66291666 +103 0 0.530139863 +104 0 0.449070573 +105 0 0.5036392 +106 0 0.450024515 +107 0 0.499411643 +108 0 0.467788219 +109 0 0.575076 +110 0 0.520050168 +111 0 0.514583051 +112 0 0.38196522 +113 0 0.5457588 +114 0 0.520133436 diff --git a/test/BaselineOutput/SingleRelease/Command/CommandCrossValidationKeyLabelWithFloatKeyValues-out.txt b/test/BaselineOutput/SingleRelease/Command/CommandCrossValidationKeyLabelWithFloatKeyValues-out.txt new file mode 100644 index 0000000000..3608e165c9 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/Command/CommandCrossValidationKeyLabelWithFloatKeyValues-out.txt @@ -0,0 +1,44 @@ +maml.exe CV tr=FastRankRanking{t=1} strat=Strat threads=- norm=Warn prexf=rangefilter{col=Label min=20 max=25} prexf=term{col=Strat:Label} dout=%Output% loader=text{col=Features:R4:10-14 col=Label:R4:9 col=GroupId:TX:1 header+} data=%Data% out=%Output% xf=term{col=Label} xf=hash{col=GroupId} +Not adding a normalizer. +Making per-feature arrays +Changing data from row-wise to column-wise +Processed 40 instances +Binning and forming Feature objects +Reserved memory for tree learner: 10764 bytes +Starting to train ... +Not training a calibrator because it is not needed. +Not adding a normalizer. +Making per-feature arrays +Changing data from row-wise to column-wise +Processed 32 instances +Binning and forming Feature objects +Reserved memory for tree learner: 6396 bytes +Starting to train ... +Not training a calibrator because it is not needed. +NDCG@1: 0.000000 +NDCG@2: 0.000000 +NDCG@3: 0.000000 +DCG@1: 0.000000 +DCG@2: 0.000000 +DCG@3: 0.000000 +NDCG@1: 0.000000 +NDCG@2: 0.000000 +NDCG@3: 0.000000 +DCG@1: 0.000000 +DCG@2: 0.000000 +DCG@3: 0.000000 + +OVERALL RESULTS +--------------------------------------- +NDCG@1: 0.000000 (0.0000) +NDCG@2: 0.000000 (0.0000) +NDCG@3: 0.000000 (0.0000) +DCG@1: 0.000000 (0.0000) +DCG@2: 0.000000 (0.0000) +DCG@3: 0.000000 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/SingleRelease/FieldAwareFactorizationMachine/FieldAwareFactorizationMachine-CV-breast-cancer-out.txt b/test/BaselineOutput/SingleRelease/FieldAwareFactorizationMachine/FieldAwareFactorizationMachine-CV-breast-cancer-out.txt new file mode 100644 index 0000000000..d05c1b0b1c --- /dev/null +++ b/test/BaselineOutput/SingleRelease/FieldAwareFactorizationMachine/FieldAwareFactorizationMachine-CV-breast-cancer-out.txt @@ -0,0 +1,77 @@ +maml.exe CV tr=FieldAwareFactorizationMachine{d=5 shuf- norm-} col[Feature]=DupFeatures threads=- norm=No dout=%Output% data=%Data% seed=1 xf=Copy{col=DupFeatures:Features} xf=MinMax{col=Features col=DupFeatures} +Not adding a normalizer. +Warning: Skipped 8 examples with bad label/weight/features in training set +Not training a calibrator because it is not needed. +Not adding a normalizer. +Warning: Skipped 8 examples with bad label/weight/features in training set +Not training a calibrator because it is not needed. +Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. +TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 122 | 12 | 0.9104 + negative || 4 | 216 | 0.9818 + ||====================== +Precision || 0.9683 | 0.9474 | +OVERALL 0/1 ACCURACY: 0.954802 +LOG LOSS/instance: 0.259660 +Test-set entropy (prior Log-Loss/instance): 0.956998 +LOG-LOSS REDUCTION (RIG): 72.867233 +AUC: 0.984973 +Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. +TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 92 | 13 | 0.8762 + negative || 2 | 222 | 0.9911 + ||====================== +Precision || 0.9787 | 0.9447 | +OVERALL 0/1 ACCURACY: 0.954407 +LOG LOSS/instance: 0.260480 +Test-set entropy (prior Log-Loss/instance): 0.903454 +LOG-LOSS REDUCTION (RIG): 71.168362 +AUC: 0.967049 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.976011 (0.0090) +Accuracy: 0.954605 (0.0002) +Positive precision: 0.973489 (0.0052) +Positive recall: 0.893319 (0.0171) +Negative precision: 0.946025 (0.0013) +Negative recall: 0.986445 (0.0046) +Log-loss: 0.260070 (0.0004) +Log-loss reduction: 72.017798 (0.8494) +F1 Score: 0.931542 (0.0069) +AUPRC: 0.974115 (0.0054) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Normalize' started. +[1] (%Time%) 337 examples +[1] 'Normalize' finished in %Time%. +[2] 'Training' started. +[2] (%Time%) 1 iterations, 329 examples Training-loss: 0.371414389819699 +[2] (%Time%) 2 iterations, 329 examples Training-loss: 0.225137821503565 +[2] (%Time%) 3 iterations, 329 examples Training-loss: 0.197323119398265 +[2] (%Time%) 4 iterations, 329 examples Training-loss: 0.183649426646222 +[2] (%Time%) 5 iterations, 329 examples Training-loss: 0.174400635825405 +[2] 'Training' finished in %Time%. +[3] 'Normalize #2' started. +[3] (%Time%) 362 examples +[3] 'Normalize #2' finished in %Time%. +[4] 'Training #2' started. +[4] (%Time%) 1 iterations, 354 examples Training-loss: 0.35872800705401 +[4] (%Time%) 2 iterations, 354 examples Training-loss: 0.239609312114266 +[4] (%Time%) 3 iterations, 354 examples Training-loss: 0.210775498912242 +[4] (%Time%) 4 iterations, 354 examples Training-loss: 0.19625903089058 +[4] (%Time%) 5 iterations, 354 examples Training-loss: 0.187121580244397 +[4] 'Training #2' finished in %Time%. diff --git a/test/BaselineOutput/SingleRelease/FieldAwareFactorizationMachine/FieldAwareFactorizationMachine-CV-breast-cancer-rp.txt b/test/BaselineOutput/SingleRelease/FieldAwareFactorizationMachine/FieldAwareFactorizationMachine-CV-breast-cancer-rp.txt new file mode 100644 index 0000000000..b826c5ae0d --- /dev/null +++ b/test/BaselineOutput/SingleRelease/FieldAwareFactorizationMachine/FieldAwareFactorizationMachine-CV-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +FieldAwareFactorizationMachine +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /d /norm /shuf Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.976011 0.954605 0.973489 0.893319 0.946025 0.986445 0.26007 72.0178 0.931542 0.974115 5 - - FieldAwareFactorizationMachine %Data% %Output% 99 0 0 maml.exe CV tr=FieldAwareFactorizationMachine{d=5 shuf- norm-} col[Feature]=DupFeatures threads=- norm=No dout=%Output% data=%Data% seed=1 xf=Copy{col=DupFeatures:Features} xf=MinMax{col=Features col=DupFeatures} /d:5;/norm:-;/shuf:- + diff --git a/test/BaselineOutput/SingleRelease/FieldAwareFactorizationMachine/FieldAwareFactorizationMachine-CV-breast-cancer.txt b/test/BaselineOutput/SingleRelease/FieldAwareFactorizationMachine/FieldAwareFactorizationMachine-CV-breast-cancer.txt new file mode 100644 index 0000000000..c1977a346f --- /dev/null +++ b/test/BaselineOutput/SingleRelease/FieldAwareFactorizationMachine/FieldAwareFactorizationMachine-CV-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +5 1 14.8638926 0.999999642 5.1594804484713121E-07 1 +6 0 -0.8998656 0.289078116 0.49223705031518167 0 +8 0 -2.48059678 0.0772296637 0.11595646754997578 0 +9 0 -2.373167 0.08524186 0.12853774899383463 0 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0.148859844 0.23253137628148873 0 diff --git a/test/BaselineOutput/SingleRelease/FieldAwareFactorizationMachine/FieldAwareFactorizationMachine-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/SingleRelease/FieldAwareFactorizationMachine/FieldAwareFactorizationMachine-TrainTest-breast-cancer-out.txt new file mode 100644 index 0000000000..3805af6e7c --- /dev/null +++ b/test/BaselineOutput/SingleRelease/FieldAwareFactorizationMachine/FieldAwareFactorizationMachine-TrainTest-breast-cancer-out.txt @@ -0,0 +1,51 @@ +maml.exe TrainTest test=%Data% tr=FieldAwareFactorizationMachine{d=5 shuf- norm-} col[Feature]=DupFeatures norm=No dout=%Output% data=%Data% out=%Output% seed=1 xf=Copy{col=DupFeatures:Features} xf=MinMax{col=Features col=DupFeatures} +Not adding a normalizer. +Warning: Skipped 16 examples with bad label/weight/features in training set +Not training a calibrator because it is not needed. +Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. +TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 215 | 24 | 0.8996 + negative || 7 | 437 | 0.9842 + ||====================== +Precision || 0.9685 | 0.9479 | +OVERALL 0/1 ACCURACY: 0.954612 +LOG LOSS/instance: 0.228754 +Test-set entropy (prior Log-Loss/instance): 0.934003 +LOG-LOSS REDUCTION (RIG): 75.508177 +AUC: 0.982029 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.982029 (0.0000) +Accuracy: 0.954612 (0.0000) +Positive precision: 0.968468 (0.0000) +Positive recall: 0.899582 (0.0000) +Negative precision: 0.947939 (0.0000) +Negative recall: 0.984234 (0.0000) +Log-loss: 0.228754 (0.0000) +Log-loss reduction: 75.508177 (0.0000) +F1 Score: 0.932755 (0.0000) +AUPRC: 0.980228 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Normalize' started. +[1] (%Time%) 699 examples +[1] 'Normalize' finished in %Time%. +[2] 'Training' started. +[2] (%Time%) 1 iterations, 683 examples Training-loss: 0.306117119945184 +[2] (%Time%) 2 iterations, 683 examples Training-loss: 0.193084570883075 +[2] (%Time%) 3 iterations, 683 examples Training-loss: 0.173782368769797 +[2] (%Time%) 4 iterations, 683 examples Training-loss: 0.163879262610855 +[2] (%Time%) 5 iterations, 683 examples Training-loss: 0.157117446501075 +[2] 'Training' finished in %Time%. +[3] 'Saving model' started. +[3] 'Saving model' finished in %Time%. diff --git a/test/BaselineOutput/SingleRelease/FieldAwareFactorizationMachine/FieldAwareFactorizationMachine-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/SingleRelease/FieldAwareFactorizationMachine/FieldAwareFactorizationMachine-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..4a01e926c6 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/FieldAwareFactorizationMachine/FieldAwareFactorizationMachine-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +FieldAwareFactorizationMachine +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /d /norm /shuf Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.982029 0.954612 0.968468 0.899582 0.947939 0.984234 0.228754 75.50818 0.932755 0.980228 5 - - FieldAwareFactorizationMachine %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=FieldAwareFactorizationMachine{d=5 shuf- norm-} col[Feature]=DupFeatures norm=No dout=%Output% data=%Data% out=%Output% seed=1 xf=Copy{col=DupFeatures:Features} xf=MinMax{col=Features col=DupFeatures} /d:5;/norm:-;/shuf:- + diff --git 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loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} data=%Data% seed=1 +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +LightGBM objective=binary +Not training a calibrator because it is not needed. +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +LightGBM objective=binary +Not training a calibrator because it is not needed. +TEST POSITIVE RATIO: 0.3702 (134.0/(134.0+228.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 128 | 6 | 0.9552 + negative || 10 | 218 | 0.9561 + ||====================== +Precision || 0.9275 | 0.9732 | +OVERALL 0/1 ACCURACY: 0.955801 +LOG LOSS/instance: 0.301908 +Test-set entropy (prior Log-Loss/instance): 0.950799 +LOG-LOSS REDUCTION (RIG): 68.246963 +AUC: 0.982473 +TEST POSITIVE RATIO: 0.3175 (107.0/(107.0+230.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 100 | 7 | 0.9346 + negative || 8 | 222 | 0.9652 + ||====================== +Precision || 0.9259 | 0.9694 | +OVERALL 0/1 ACCURACY: 0.955490 +LOG LOSS/instance: 0.290926 +Test-set entropy (prior Log-Loss/instance): 0.901650 +LOG-LOSS REDUCTION (RIG): 67.734017 +AUC: 0.992076 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.987274 (0.0048) +Accuracy: 0.955645 (0.0002) +Positive precision: 0.926731 (0.0008) +Positive recall: 0.944902 (0.0103) +Negative precision: 0.971323 (0.0019) +Negative recall: 0.960679 (0.0045) +Log-loss: 0.296417 (0.0055) +Log-loss reduction: 67.990490 (0.2565) +F1 Score: 0.935705 (0.0055) +AUPRC: 0.969894 (0.0121) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Loading data for LightGBM' started. +[1] 'Loading data for LightGBM' finished in %Time%. +[2] 'Training with LightGBM' started. +[2] 'Training with LightGBM' finished in %Time%. +[3] 'Loading data for LightGBM #2' started. +[3] 'Loading data for LightGBM #2' finished in %Time%. +[4] 'Training with LightGBM #2' started. +[4] 'Training with LightGBM #2' finished in %Time%. diff --git a/test/BaselineOutput/SingleRelease/LightGBM/LightGBMDart-CV-breast-cancer.dart-rp.txt b/test/BaselineOutput/SingleRelease/LightGBM/LightGBMDart-CV-breast-cancer.dart-rp.txt new file mode 100644 index 0000000000..e91ca11d6f --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBM/LightGBMDart-CV-breast-cancer.dart-rp.txt @@ -0,0 +1,4 @@ +LightGBM +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /iter /lr /nl /mil /booster /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.987274 0.955645 0.926731 0.944902 0.971323 0.960679 0.296417 67.99049 0.935705 0.969894 10 0.2 20 10 dart 1 LightGBM %Data% %Output% 99 0 0 maml.exe CV tr=LightGBM{nt=1 iter=10 booster=dart lr=0.2 mil=10 nl=20} threads=- cache=- dout=%Output% loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} data=%Data% seed=1 /iter:10;/lr:0.2;/nl:20;/mil:10;/booster:dart;/nt:1 + diff --git a/test/BaselineOutput/SingleRelease/LightGBM/LightGBMDart-CV-breast-cancer.dart.txt b/test/BaselineOutput/SingleRelease/LightGBM/LightGBMDart-CV-breast-cancer.dart.txt new file mode 100644 index 0000000000..d69c5c6cb2 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBM/LightGBMDart-CV-breast-cancer.dart.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +5 1 3.77653313 0.868557751 0.20330631668376986 1 +6 0 -2.74509573 0.202208519 0.32591637677515645 0 +8 0 -4.4950676 0.0955624059 0.14490713407046754 0 +9 0 -4.4950676 0.0955624059 0.14490713407046754 0 +10 0 -3.95260167 0.12171372 0.18723682820261706 0 +11 0 -4.4950676 0.0955624059 0.14490713407046754 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a/test/BaselineOutput/SingleRelease/LightGBM/LightGBMDart-TrainTest-breast-cancer.dart-out.txt b/test/BaselineOutput/SingleRelease/LightGBM/LightGBMDart-TrainTest-breast-cancer.dart-out.txt new file mode 100644 index 0000000000..232f6326d4 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBM/LightGBMDart-TrainTest-breast-cancer.dart-out.txt @@ -0,0 +1,45 @@ +maml.exe TrainTest test=%Data% tr=LightGBM{nt=1 iter=10 booster=dart lr=0.2 mil=10 nl=20} cache=- dout=%Output% loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} data=%Data% out=%Output% seed=1 +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +LightGBM objective=binary +Not training a calibrator because it is not needed. +TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 239 | 2 | 0.9917 + negative || 12 | 446 | 0.9738 + ||====================== +Precision || 0.9522 | 0.9955 | +OVERALL 0/1 ACCURACY: 0.979971 +LOG LOSS/instance: 0.198312 +Test-set entropy (prior Log-Loss/instance): 0.929318 +LOG-LOSS REDUCTION (RIG): 78.660434 +AUC: 0.995996 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.995996 (0.0000) +Accuracy: 0.979971 (0.0000) +Positive precision: 0.952191 (0.0000) +Positive recall: 0.991701 (0.0000) +Negative precision: 0.995536 (0.0000) +Negative recall: 0.973799 (0.0000) +Log-loss: 0.198312 (0.0000) +Log-loss reduction: 78.660434 (0.0000) +F1 Score: 0.971545 (0.0000) +AUPRC: 0.983584 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Loading data for LightGBM' started. +[1] 'Loading data for LightGBM' finished in %Time%. +[2] 'Training with LightGBM' started. +[2] 'Training with LightGBM' finished in %Time%. +[3] 'Saving model' started. +[3] 'Saving model' finished in %Time%. diff --git a/test/BaselineOutput/SingleRelease/LightGBM/LightGBMDart-TrainTest-breast-cancer.dart-rp.txt b/test/BaselineOutput/SingleRelease/LightGBM/LightGBMDart-TrainTest-breast-cancer.dart-rp.txt new file mode 100644 index 0000000000..a29ae4cd9c --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBM/LightGBMDart-TrainTest-breast-cancer.dart-rp.txt @@ -0,0 +1,4 @@ +LightGBM +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /iter /lr /nl /mil /booster /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.995996 0.979971 0.952191 0.991701 0.995536 0.973799 0.198312 78.66043 0.971545 0.983584 10 0.2 20 10 dart 1 LightGBM %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LightGBM{nt=1 iter=10 booster=dart lr=0.2 mil=10 nl=20} cache=- dout=%Output% loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} data=%Data% out=%Output% seed=1 /iter:10;/lr:0.2;/nl:20;/mil:10;/booster:dart;/nt:1 + diff --git a/test/BaselineOutput/SingleRelease/LightGBM/LightGBMDart-TrainTest-breast-cancer.dart.txt b/test/BaselineOutput/SingleRelease/LightGBM/LightGBMDart-TrainTest-breast-cancer.dart.txt new file mode 100644 index 0000000000..80e77323e6 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBM/LightGBMDart-TrainTest-breast-cancer.dart.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -5.06709051 0.0735397339 0.11019899139311135 0 +1 0 0.9656145 0.6184105 1.3899067256376549 1 +2 0 -5.06709051 0.0735397339 0.11019899139311135 0 +3 0 0.586611152 0.5728052 1.2270340961595763 1 +4 0 -4.53108168 0.09401735 0.14244467134955621 0 +5 1 4.762709 0.9153944 0.12753458870756793 1 +6 0 -2.32187271 0.238497183 0.39307872073754024 0 +7 0 -5.06709051 0.0735397339 0.11019899139311135 0 +8 0 -4.47418737 0.09646857 0.14635330428919666 0 +9 0 -5.06709051 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a/test/BaselineOutput/SingleRelease/LightGBM/LightGBMGoss-CV-breast-cancer.goss-out.txt b/test/BaselineOutput/SingleRelease/LightGBM/LightGBMGoss-CV-breast-cancer.goss-out.txt new file mode 100644 index 0000000000..8bd89002c1 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBM/LightGBMGoss-CV-breast-cancer.goss-out.txt @@ -0,0 +1,65 @@ +maml.exe CV tr=LightGBM{nt=1 iter=10 v=+ booster=goss lr=0.2 mil=10 nl=20} threads=- cache=- dout=%Output% loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} data=%Data% seed=1 +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +LightGBM objective=binary +Not training a calibrator because it is not needed. +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +LightGBM objective=binary +Not training a calibrator because it is not needed. +TEST POSITIVE RATIO: 0.3702 (134.0/(134.0+228.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 127 | 7 | 0.9478 + negative || 10 | 218 | 0.9561 + ||====================== +Precision || 0.9270 | 0.9689 | +OVERALL 0/1 ACCURACY: 0.953039 +LOG LOSS/instance: 0.240776 +Test-set entropy (prior Log-Loss/instance): 0.950799 +LOG-LOSS REDUCTION (RIG): 74.676422 +AUC: 0.982031 +TEST POSITIVE RATIO: 0.3175 (107.0/(107.0+230.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 100 | 7 | 0.9346 + negative || 5 | 225 | 0.9783 + ||====================== +Precision || 0.9524 | 0.9698 | +OVERALL 0/1 ACCURACY: 0.964392 +LOG LOSS/instance: 0.205801 +Test-set entropy (prior Log-Loss/instance): 0.901650 +LOG-LOSS REDUCTION (RIG): 77.175051 +AUC: 0.994718 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.988374 (0.0063) +Accuracy: 0.958715 (0.0057) +Positive precision: 0.939694 (0.0127) +Positive recall: 0.941170 (0.0066) +Negative precision: 0.969358 (0.0005) +Negative recall: 0.967201 (0.0111) +Log-loss: 0.223289 (0.0175) +Log-loss reduction: 75.925737 (1.2493) +F1 Score: 0.940333 (0.0031) +AUPRC: 0.970067 (0.0183) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Loading data for LightGBM' started. +[1] 'Loading data for LightGBM' finished in %Time%. +[2] 'Training with LightGBM' started. +[2] 'Training with LightGBM' finished in %Time%. +[3] 'Loading data for LightGBM #2' started. +[3] 'Loading data for LightGBM #2' finished in %Time%. +[4] 'Training with LightGBM #2' started. +[4] 'Training with LightGBM #2' finished in %Time%. diff --git a/test/BaselineOutput/SingleRelease/LightGBM/LightGBMGoss-CV-breast-cancer.goss-rp.txt b/test/BaselineOutput/SingleRelease/LightGBM/LightGBMGoss-CV-breast-cancer.goss-rp.txt new file mode 100644 index 0000000000..d3c7ce0fd0 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBM/LightGBMGoss-CV-breast-cancer.goss-rp.txt @@ -0,0 +1,4 @@ +LightGBM +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /iter /lr /nl /mil /booster /v /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.988374 0.958715 0.939694 0.94117 0.969358 0.967201 0.223289 75.92574 0.940333 0.970067 10 0.2 20 10 goss + 1 LightGBM %Data% %Output% 99 0 0 maml.exe CV tr=LightGBM{nt=1 iter=10 v=+ booster=goss lr=0.2 mil=10 nl=20} threads=- cache=- dout=%Output% loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} data=%Data% seed=1 /iter:10;/lr:0.2;/nl:20;/mil:10;/booster:goss;/v:+;/nt:1 + diff --git a/test/BaselineOutput/SingleRelease/LightGBM/LightGBMGoss-CV-breast-cancer.goss.txt b/test/BaselineOutput/SingleRelease/LightGBM/LightGBMGoss-CV-breast-cancer.goss.txt new file mode 100644 index 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-5.974533 0.0480044559 0.070973273947168333 0 +684 0 -5.974533 0.0480044559 0.070973273947168333 0 +686 0 -5.974533 0.0480044559 0.070973273947168333 0 +687 0 -5.16711855 0.07020404 0.10501394052891096 0 +690 0 -5.974533 0.0480044559 0.070973273947168333 0 +695 0 -5.974533 0.0480044559 0.070973273947168333 0 diff --git a/test/BaselineOutput/SingleRelease/LightGBM/LightGBMGoss-TrainTest-breast-cancer.goss-out.txt b/test/BaselineOutput/SingleRelease/LightGBM/LightGBMGoss-TrainTest-breast-cancer.goss-out.txt new file mode 100644 index 0000000000..ba50420a1d --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBM/LightGBMGoss-TrainTest-breast-cancer.goss-out.txt @@ -0,0 +1,45 @@ +maml.exe TrainTest test=%Data% tr=LightGBM{nt=1 iter=10 v=+ booster=goss lr=0.2 mil=10 nl=20} cache=- dout=%Output% loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} data=%Data% out=%Output% seed=1 +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +LightGBM objective=binary +Not training a calibrator because it is not needed. +TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 240 | 1 | 0.9959 + negative || 8 | 450 | 0.9825 + ||====================== +Precision || 0.9677 | 0.9978 | +OVERALL 0/1 ACCURACY: 0.987124 +LOG LOSS/instance: 0.167803 +Test-set entropy (prior Log-Loss/instance): 0.929318 +LOG-LOSS REDUCTION (RIG): 81.943403 +AUC: 0.995765 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.995765 (0.0000) +Accuracy: 0.987124 (0.0000) +Positive precision: 0.967742 (0.0000) +Positive recall: 0.995851 (0.0000) +Negative precision: 0.997783 (0.0000) +Negative recall: 0.982533 (0.0000) +Log-loss: 0.167803 (0.0000) +Log-loss reduction: 81.943403 (0.0000) +F1 Score: 0.981595 (0.0000) +AUPRC: 0.986193 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Loading data for LightGBM' started. +[1] 'Loading data for LightGBM' finished in %Time%. +[2] 'Training with LightGBM' started. +[2] 'Training with LightGBM' finished in %Time%. +[3] 'Saving model' started. +[3] 'Saving model' finished in %Time%. diff --git a/test/BaselineOutput/SingleRelease/LightGBM/LightGBMGoss-TrainTest-breast-cancer.goss-rp.txt b/test/BaselineOutput/SingleRelease/LightGBM/LightGBMGoss-TrainTest-breast-cancer.goss-rp.txt new file mode 100644 index 0000000000..77718de528 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBM/LightGBMGoss-TrainTest-breast-cancer.goss-rp.txt @@ -0,0 +1,4 @@ +LightGBM +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /iter /lr /nl /mil /booster /v /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.995765 0.987124 0.967742 0.995851 0.997783 0.982533 0.167803 81.94341 0.981595 0.986193 10 0.2 20 10 goss + 1 LightGBM %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LightGBM{nt=1 iter=10 v=+ booster=goss lr=0.2 mil=10 nl=20} cache=- dout=%Output% loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} data=%Data% out=%Output% seed=1 /iter:10;/lr:0.2;/nl:20;/mil:10;/booster:goss;/v:+;/nt:1 + diff --git a/test/BaselineOutput/SingleRelease/LightGBM/LightGBMGoss-TrainTest-breast-cancer.goss.txt b/test/BaselineOutput/SingleRelease/LightGBM/LightGBMGoss-TrainTest-breast-cancer.goss.txt new file mode 100644 index 0000000000..c5643a9c46 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBM/LightGBMGoss-TrainTest-breast-cancer.goss.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -6.12573862 0.04466511 0.065921539368278048 0 +1 0 0.7871392 0.5971417 1.3116555550127289 1 +2 0 -5.88718367 0.0500402562 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0.25679456140192347 1 +698 1 3.72445273 0.86555624 0.20830053219774505 1 diff --git a/test/BaselineOutput/SingleRelease/LightGBMBinary/LightGBM-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/SingleRelease/LightGBMBinary/LightGBM-TrainTest-breast-cancer-out.txt new file mode 100644 index 0000000000..2496917e04 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBMBinary/LightGBM-TrainTest-breast-cancer-out.txt @@ -0,0 +1,45 @@ +maml.exe TrainTest test=%Data% tr=LightGBMBinary{nt=1 nl=5 mil=5 lr=0.25 iter=20 mb=255} cache=- dout=%Output% loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} data=%Data% out=%Output% seed=1 +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +LightGBM objective=binary +Not training a calibrator because it is not needed. +TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) +Confusion table + ||====================== +PREDICTED || positive | negative | Recall +TRUTH ||====================== + positive || 240 | 1 | 0.9959 + negative || 11 | 447 | 0.9760 + ||====================== +Precision || 0.9562 | 0.9978 | +OVERALL 0/1 ACCURACY: 0.982833 +LOG LOSS/instance: 0.086938 +Test-set entropy (prior Log-Loss/instance): 0.929318 +LOG-LOSS REDUCTION (RIG): 90.644963 +AUC: 0.996313 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.996313 (0.0000) +Accuracy: 0.982833 (0.0000) +Positive precision: 0.956175 (0.0000) +Positive recall: 0.995851 (0.0000) +Negative precision: 0.997768 (0.0000) +Negative recall: 0.975983 (0.0000) +Log-loss: 0.086938 (0.0000) +Log-loss reduction: 90.644963 (0.0000) +F1 Score: 0.975610 (0.0000) +AUPRC: 0.987034 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Loading data for LightGBM' started. +[1] 'Loading data for LightGBM' finished in %Time%. +[2] 'Training with LightGBM' started. +[2] 'Training with LightGBM' finished in %Time%. +[3] 'Saving model' started. +[3] 'Saving model' finished in %Time%. diff --git a/test/BaselineOutput/SingleRelease/LightGBMBinary/LightGBM-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/SingleRelease/LightGBMBinary/LightGBM-TrainTest-breast-cancer-rp.txt new file mode 100644 index 0000000000..0a2dcff3a5 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBMBinary/LightGBM-TrainTest-breast-cancer-rp.txt @@ -0,0 +1,4 @@ +LightGBMBinary +AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /iter /lr /nl /mil /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.996313 0.982833 0.956175 0.995851 0.997768 0.975983 0.086938 90.64497 0.97561 0.987034 20 0.25 5 5 1 LightGBMBinary %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LightGBMBinary{nt=1 nl=5 mil=5 lr=0.25 iter=20 mb=255} cache=- dout=%Output% loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} data=%Data% out=%Output% seed=1 /iter:20;/lr:0.25;/nl:5;/mil:5;/nt:1 + diff --git a/test/BaselineOutput/SingleRelease/LightGBMBinary/LightGBM-TrainTest-breast-cancer.txt b/test/BaselineOutput/SingleRelease/LightGBMBinary/LightGBM-TrainTest-breast-cancer.txt new file mode 100644 index 0000000000..8180e64edd --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBMBinary/LightGBM-TrainTest-breast-cancer.txt @@ -0,0 +1,700 @@ +Instance Label Score Probability Log-loss Assigned +0 0 -11.1408987 0.003794317 0.0054844536464761871 0 +1 0 0.191842213 0.5239619 1.0708510547998533 1 +2 0 -10.352807 0.00561656 0.0081258244335933715 0 +3 0 -0.745744765 0.407847136 0.75595844049774485 0 +4 0 -10.6034384 0.00495831249 0.0071711259144706186 0 +5 1 9.274134 0.9904069 0.013906771757572052 1 +6 0 -2.67131853 0.2082248 0.33683722078474054 0 +7 0 -11.1408987 0.003794317 0.0054844536464761871 0 +8 0 -9.129003 0.0103077162 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threads=- dout=%Output% loader=Text{col=Label:TX:0 col=Features:1-*} data=%Data% seed=1 xf=Term{col=Label} +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +Auto-tuning parameters: UseSoftmax = False +LightGBM objective=multiclassova +Not training a calibrator because it is not needed. +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +Auto-tuning parameters: UseSoftmax = False +LightGBM objective=multiclassova +Not training a calibrator because it is not needed. + +Confusion table + ||======================== +PREDICTED || 0 | 1 | 2 | Recall +TRUTH ||======================== + 0 || 21 | 0 | 0 | 1.0000 + 1 || 0 | 27 | 3 | 0.9000 + 2 || 0 | 2 | 26 | 0.9286 + ||======================== +Precision ||1.0000 |0.9310 |0.8966 | +Accuracy(micro-avg): 0.936709 +Accuracy(macro-avg): 0.942857 +Log-loss: 0.312681 +Log-loss reduction: 71.248182 + +Confusion table + ||======================== +PREDICTED || 0 | 1 | 2 | Recall +TRUTH ||======================== + 0 || 29 | 0 | 0 | 1.0000 + 1 || 0 | 19 | 1 | 0.9500 + 2 || 0 | 2 | 20 | 0.9091 + ||======================== +Precision ||1.0000 |0.9048 |0.9524 | +Accuracy(micro-avg): 0.957746 +Accuracy(macro-avg): 0.953030 +Log-loss: 0.193389 +Log-loss reduction: 82.186751 + +OVERALL RESULTS +--------------------------------------- +Accuracy(micro-avg): 0.947228 (0.0105) +Accuracy(macro-avg): 0.947944 (0.0051) +Log-loss: 0.253035 (0.0596) +Log-loss reduction: 76.717466 (5.4693) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Building term dictionary' started. +[1] (%Time%) 71 examples Total Terms: 3 +[1] 'Building term dictionary' finished in %Time%. +[2] 'Loading data for LightGBM' started. +[2] 'Loading data for LightGBM' finished in %Time%. +[3] 'Training with LightGBM' started. +[3] 'Training with LightGBM' finished in %Time%. +[4] 'Building term dictionary #2' started. +[4] (%Time%) 79 examples Total Terms: 3 +[4] 'Building term dictionary #2' finished in %Time%. +[5] 'Loading data for LightGBM #2' started. +[5] 'Loading data for LightGBM #2' finished in %Time%. +[6] 'Training with LightGBM #2' started. +[6] 'Training with LightGBM #2' finished in %Time%. diff --git a/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-CV-iris.key-rp.txt b/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-CV-iris.key-rp.txt new file mode 100644 index 0000000000..fe2fcf392a --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-CV-iris.key-rp.txt @@ -0,0 +1,4 @@ +LightGBMMC +Accuracy(micro-avg) Accuracy(macro-avg) Log-loss Log-loss reduction /iter /lr /nl /mil /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.947228 0.947944 0.253035 76.71747 10 0.2 20 10 1 LightGBMMC %Data% %Output% 99 0 0 maml.exe CV tr=LightGBMMC{nt=1 iter=10 v=- lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:TX:0 col=Features:1-*} data=%Data% seed=1 xf=Term{col=Label} /iter:10;/lr:0.2;/nl:20;/mil:10;/nt:1 + diff --git a/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-CV-iris.key.txt b/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-CV-iris.key.txt new file mode 100644 index 0000000000..9b303c86aa --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-CV-iris.key.txt @@ -0,0 +1,151 @@ +Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class +5 0 1 0.25328578422472414 0.776246 0.1675262 0.0562277846 0 1 2 +6 0 1 0.12225769559664824 0.8849203 0.0591422766 0.05593741 0 2 1 +8 0 1 0.13903052119099127 0.870201468 0.07016017 0.05963834 0 1 2 +9 0 1 0.13970851121881944 0.8696117 0.07047898 0.0599093363 0 1 2 +10 0 1 0.12269405525649343 0.88453424 0.0593406856 0.0561250672 0 2 1 +11 0 1 0.12269405525649343 0.88453424 0.0593406856 0.0561250672 0 2 1 +18 0 1 0.25328578422472414 0.776246 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2 1 0 +127 2 0.39337756049800737 0.674773932 0.268265069 0.05696099 2 1 0 +130 2 0.12489127544479019 0.882592857 0.06075944 0.05664773 2 0 1 +134 2 2 0.85550144650046023 0.5024456 0.42507 0.07248444 1 2 0 +135 2 0.12489127544479019 0.882592857 0.06075944 0.05664773 2 0 1 +136 2 0.1485323137279633 0.861972153 0.0780924 0.05993546 2 1 0 +139 2 0.16469395959924102 0.848153234 0.09065638 0.0611904152 2 1 0 +140 2 0.1540180682859322 0.857256532 0.08267425 0.0600692481 2 1 0 +142 2 0.1903299513274587 0.8266863 0.116503216 0.05681048 2 1 0 +143 2 0.15317496105764933 0.8579796 0.08057635 0.0614440367 2 1 0 +146 2 0.1903299513274587 0.8266863 0.116503216 0.05681048 2 1 0 +148 2 0.15751162588437911 0.8542669 0.08508965 0.0606434755 2 1 0 +149 2 0.22273115731729765 0.80033 0.142754182 0.05691586 2 1 0 diff --git a/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-CV-iris.keyU404-out.txt b/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-CV-iris.keyU404-out.txt new file mode 100644 index 0000000000..e45fbbb6c2 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-CV-iris.keyU404-out.txt @@ -0,0 +1,65 @@ +maml.exe CV tr=LightGBMMC{nt=1 iter=10 v=- lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:U4[0-4]:0 col=Features:1-4} data=%Data% seed=1 +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +Auto-tuning parameters: UseSoftmax = False +LightGBM objective=multiclassova +Not training a calibrator because it is not needed. +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +Auto-tuning parameters: UseSoftmax = False +LightGBM objective=multiclassova +Not training a calibrator because it is not needed. + +Confusion table + ||======================================== +PREDICTED || 0 | 1 | 2 | 3 | 4 | Recall +TRUTH ||======================================== + 0 || 21 | 0 | 0 | 0 | 0 | 1.0000 + 1 || 0 | 27 | 3 | 0 | 0 | 0.9000 + 2 || 0 | 2 | 26 | 0 | 0 | 0.9286 + 3 || 0 | 0 | 0 | 0 | 0 | 0.0000 + 4 || 0 | 0 | 0 | 0 | 0 | 0.0000 + ||======================================== +Precision ||1.0000 |0.9310 |0.8966 |0.0000 |0.0000 | +Accuracy(micro-avg): 0.936709 +Accuracy(macro-avg): 0.942857 +Log-loss: 0.312681 +Log-loss reduction: 71.248176 + +Confusion table + ||======================================== +PREDICTED || 0 | 1 | 2 | 3 | 4 | Recall +TRUTH ||======================================== + 0 || 29 | 0 | 0 | 0 | 0 | 1.0000 + 1 || 0 | 19 | 1 | 0 | 0 | 0.9500 + 2 || 0 | 2 | 20 | 0 | 0 | 0.9091 + 3 || 0 | 0 | 0 | 0 | 0 | 0.0000 + 4 || 0 | 0 | 0 | 0 | 0 | 0.0000 + ||======================================== +Precision ||1.0000 |0.9048 |0.9524 |0.0000 |0.0000 | +Accuracy(micro-avg): 0.957746 +Accuracy(macro-avg): 0.953030 +Log-loss: 0.193390 +Log-loss reduction: 82.186746 + +OVERALL RESULTS +--------------------------------------- +Accuracy(micro-avg): 0.947228 (0.0105) +Accuracy(macro-avg): 0.947944 (0.0051) +Log-loss: 0.253035 (0.0596) +Log-loss reduction: 76.717461 (5.4693) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Loading data for LightGBM' started. +[1] 'Loading data for LightGBM' finished in %Time%. +[2] 'Training with LightGBM' started. +[2] 'Training with LightGBM' finished in %Time%. +[3] 'Loading data for LightGBM #2' started. +[3] 'Loading data for LightGBM #2' finished in %Time%. +[4] 'Training with LightGBM #2' started. +[4] 'Training with LightGBM #2' finished in %Time%. diff --git a/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-CV-iris.keyU404-rp.txt b/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-CV-iris.keyU404-rp.txt new file mode 100644 index 0000000000..f803e379e2 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-CV-iris.keyU404-rp.txt @@ -0,0 +1,4 @@ +LightGBMMC +Accuracy(micro-avg) Accuracy(macro-avg) Log-loss Log-loss reduction /iter /lr /nl /mil /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.947228 0.947944 0.253035 76.71746 10 0.2 20 10 1 LightGBMMC %Data% %Output% 99 0 0 maml.exe CV tr=LightGBMMC{nt=1 iter=10 v=- lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:U4[0-4]:0 col=Features:1-4} data=%Data% seed=1 /iter:10;/lr:0.2;/nl:20;/mil:10;/nt:1 + diff --git a/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-CV-iris.keyU404.txt b/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-CV-iris.keyU404.txt new file mode 100644 index 0000000000..cb0d9331ce --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-CV-iris.keyU404.txt @@ -0,0 +1,151 @@ +Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class +5 0 1 0.25328586101049333 0.776245952 0.167526186 0.05622778 0 1 2 +6 0 1 0.12225776295259752 0.884920239 0.0591422729 0.0559374057 0 2 1 +8 0 1 0.13903058968621793 0.8702014 0.07016017 0.0596383363 0 1 2 +9 0 1 0.13970857976050094 0.8696116 0.0704789758 0.0599093325 0 1 2 +10 0 1 0.12269412264184056 0.8845342 0.0593406819 0.0561250634 0 2 1 +11 0 1 0.12269412264184056 0.8845342 0.0593406819 0.0561250634 0 2 1 +18 0 1 0.25328586101049333 0.776245952 0.167526186 0.05622778 0 1 2 +20 0 1 0.25328586101049333 0.776245952 0.167526186 0.05622778 0 1 2 +21 0 1 0.12269412264184056 0.8845342 0.0593406819 0.0561250634 0 2 1 +25 0 1 0.13970857976050094 0.8696116 0.0704789758 0.0599093325 0 1 2 +28 0 1 0.12225776295259752 0.884920239 0.0591422729 0.0559374057 0 2 1 +31 0 1 0.12269412264184056 0.8845342 0.0593406819 0.0561250634 0 2 1 +32 0 1 0.12269412264184056 0.8845342 0.0593406819 0.0561250634 0 2 1 +35 0 1 0.13903058968621793 0.8702014 0.07016017 0.0596383363 0 1 2 +37 0 1 0.13970857976050094 0.8696116 0.0704789758 0.0599093325 0 1 2 +40 0 1 0.12225776295259752 0.884920239 0.0591422729 0.0559374057 0 2 1 +41 0 1 0.17550963613619172 0.8390293 0.09255581 0.06841481 0 1 2 +44 0 1 0.25328586101049333 0.776245952 0.167526186 0.05622778 0 1 2 +45 0 1 0.13903058968621793 0.8702014 0.07016017 0.0596383363 0 1 2 +46 0 1 0.12269412264184056 0.8845342 0.0593406819 0.0561250634 0 2 1 +48 0 1 0.12269412264184056 0.8845342 0.0593406819 0.0561250634 0 2 1 +50 1 2 0.48031316690941278 0.61858964 0.293158859 0.08825144 1 2 0 +51 1 2 0.18552274772100014 0.83066994 0.09896273 0.0703672841 1 0 2 +52 1 3 1.8686139310181762 0.745523036 0.154337436 0.1001395 2 1 0 +54 1 2 0.45819824450238866 0.6324221 0.314907759 0.0526700951 1 2 0 +56 1 2 0.58631345356437459 0.5563746 0.357763946 0.08586141 1 2 0 +60 1 2 0.54904634529954011 0.5775003 0.363432616 0.05906704 1 0 2 +63 1 2 0.44288817880347908 0.642179 0.304338247 0.05348268 1 2 0 +64 1 2 0.14288662456903103 0.866852343 0.06818299 0.06496459 1 2 0 +66 1 2 0.13927192749760864 0.8699914 0.06910439 0.0609041043 1 2 0 +68 1 2 0.1475586146516118 0.862811863 0.08110717 0.05608093 1 2 0 +69 1 2 0.13690032984210998 0.87205714 0.07104707 0.0568957143 1 2 0 +70 1 2 0.58631345356437459 0.5563746 0.357763946 0.08586141 1 2 0 +71 1 2 0.15194434625076791 0.8590361 0.07716796 0.06379592 1 2 0 +72 1 3 1.4639004514496772 0.7123725 0.231332213 0.05629522 2 1 0 +73 1 2 0.45819824450238866 0.6324221 0.314907759 0.0526700951 1 2 0 +74 1 2 0.13796626095978629 0.8711281 0.06828712 0.0605847239 1 2 0 +76 1 2 0.45819824450238866 0.6324221 0.314907759 0.0526700951 1 2 0 +77 1 3 2.0734221020246566 0.8150108 0.1257547 0.0592344366 2 1 0 +79 1 2 0.54904634529954011 0.5775003 0.363432616 0.05906704 1 0 2 +82 1 2 0.13641926529166934 0.872476757 0.07081407 0.05670913 1 2 0 +88 1 2 0.13407540741246499 0.8745241 0.06425437 0.06122142 1 2 0 +90 1 2 0.1425660487370225 0.8671303 0.07629539 0.05657427 1 2 0 +91 1 2 0.44288817880347908 0.642179 0.304338247 0.05348268 1 2 0 +92 1 2 0.13641926529166934 0.872476757 0.07081407 0.05670913 1 2 0 +93 1 2 0.54904634529954011 0.5775003 0.363432616 0.05906704 1 0 2 +95 1 2 0.13407540741246499 0.8745241 0.06425437 0.06122142 1 2 0 +96 1 2 0.13407540741246499 0.8745241 0.06425437 0.06122142 1 2 0 +97 1 2 0.13796626095978629 0.8711281 0.06828712 0.0605847239 1 2 0 +98 1 2 0.54904634529954011 0.5775003 0.363432616 0.05906704 1 0 2 +99 1 2 0.13879081663734727 0.8704101 0.06865642 0.0609334148 1 2 0 +100 2 3 0.16223557410388861 0.8502409 0.09284732 0.05691175 2 0 1 +102 2 3 0.1228227021544628 0.884420455 0.0591995977 0.0563798733 2 1 0 +104 2 3 0.1228227021544628 0.884420455 0.0591995977 0.0563798733 2 1 0 +105 2 3 0.1228227021544628 0.884420455 0.0591995977 0.0563798733 2 1 0 +106 2 2 2.3434393651921286 0.847623646 0.0959969 0.0563793853 1 2 0 +108 2 3 0.22657601711176881 0.797258735 0.1479769 0.05476432 2 1 0 +109 2 3 0.16223557410388861 0.8502409 0.09284732 0.05691175 2 0 1 +111 2 3 0.17784894692703557 0.837068856 0.108788565 0.0541424938 2 1 0 +112 2 3 0.13281462271870764 0.8756274 0.0683108345 0.0560617223 2 1 0 +113 2 3 0.19621674447868781 0.8218341 0.122739322 0.0554265566 2 1 0 +115 2 3 0.17200944752597333 0.8419712 0.0923435241 0.0656852 2 0 1 +117 2 3 0.16223557410388861 0.8502409 0.09284732 0.05691175 2 0 1 +120 2 3 0.16223557410388861 0.8502409 0.09284732 0.05691175 2 0 1 +121 2 3 0.16411849615386448 0.848641455 0.09412395 0.0572345145 2 1 0 +122 2 3 0.13716679158045109 0.8718248 0.07206305 0.05611211 2 1 0 +123 2 3 0.28256679418751329 0.7538463 0.189867079 0.056286566 2 1 0 +125 2 3 0.20564897455362954 0.8141188 0.09413585 0.09174528 2 0 1 +128 2 3 0.13716679158045109 0.8718248 0.07206305 0.05611211 2 1 0 +129 2 3 0.16567802369146914 0.847319 0.0954867 0.0571942143 2 1 0 +131 2 3 0.16223557410388861 0.8502409 0.09284732 0.05691175 2 0 1 +132 2 3 0.13716679158045109 0.8718248 0.07206305 0.05611211 2 1 0 +133 2 3 0.29113045769451545 0.747418165 0.191831976 0.06074977 2 1 0 +137 2 3 0.22116869967287578 0.801581442 0.104995131 0.09342335 2 1 0 +138 2 2 0.99148913716896714 0.5769956 0.371023774 0.0519805774 1 2 0 +141 2 3 0.18520126566083658 0.830937 0.09454043 0.07452247 2 0 1 +144 2 3 0.16223557410388861 0.8502409 0.09284732 0.05691175 2 0 1 +145 2 3 0.14505504697273677 0.8649747 0.07757514 0.0574500971 2 1 0 +147 2 3 0.14505504697273677 0.8649747 0.07757514 0.0574500971 2 1 0 +0 0 1 0.12805244799353907 0.879807234 0.0614267029 0.05876603 0 1 2 +1 0 1 0.13227381045206946 0.8761011 0.0642271042 0.059671782 0 1 2 +2 0 1 0.12805244799353907 0.879807234 0.0614267029 0.05876603 0 1 2 +3 0 1 0.13111987178887785 0.8771126 0.06430127 0.0585860461 0 1 2 +4 0 1 0.12805244799353907 0.879807234 0.0614267029 0.05876603 0 1 2 +7 0 1 0.12805244799353907 0.879807234 0.0614267029 0.05876603 0 1 2 +12 0 1 0.13227381045206946 0.8761011 0.0642271042 0.059671782 0 1 2 +13 0 1 0.13227381045206946 0.8761011 0.0642271042 0.059671782 0 1 2 +14 0 1 0.18823445128170324 0.82842046 0.120264135 0.0513153374 0 1 2 +15 0 1 0.18823445128170324 0.82842046 0.120264135 0.0513153374 0 1 2 +16 0 1 0.12377570311405263 0.883578 0.0616899729 0.05473199 0 1 2 +17 0 1 0.12805244799353907 0.879807234 0.0614267029 0.05876603 0 1 2 +19 0 1 0.12805244799353907 0.879807234 0.0614267029 0.05876603 0 1 2 +22 0 1 0.12805244799353907 0.879807234 0.0614267029 0.05876603 0 1 2 +23 0 1 0.13145134579195075 0.876821935 0.0646113753 0.05856663 0 1 2 +24 0 1 0.13145134579195075 0.876821935 0.0646113753 0.05856663 0 1 2 +26 0 1 0.13145134579195075 0.876821935 0.0646113753 0.05856663 0 1 2 +27 0 1 0.12805244799353907 0.879807234 0.0614267029 0.05876603 0 1 2 +29 0 1 0.13145134579195075 0.876821935 0.0646113753 0.05856663 0 1 2 +30 0 1 0.13466438623099261 0.8740092 0.06761201 0.0583787523 0 1 2 +33 0 1 0.18823445128170324 0.82842046 0.120264135 0.0513153374 0 1 2 +34 0 1 0.13111987178887785 0.8771126 0.06430127 0.0585860461 0 1 2 +36 0 1 0.18823445128170324 0.82842046 0.120264135 0.0513153374 0 1 2 +38 0 1 0.13227381045206946 0.8761011 0.0642271042 0.059671782 0 1 2 +39 0 1 0.12805244799353907 0.879807234 0.0614267029 0.05876603 0 1 2 +42 0 1 0.12805244799353907 0.879807234 0.0614267029 0.05876603 0 1 2 +43 0 1 0.13145134579195075 0.876821935 0.0646113753 0.05856663 0 1 2 +47 0 1 0.12805244799353907 0.879807234 0.0614267029 0.05876603 0 1 2 +49 0 1 0.12805244799353907 0.879807234 0.0614267029 0.05876603 0 1 2 +53 1 2 0.13238620890661379 0.8760026 0.0634366348 0.06056068 1 2 0 +55 1 2 0.13788484165848175 0.871199 0.0693822056 0.0594187342 1 2 0 +57 1 2 0.14180966894555355 0.8677864 0.06764004 0.06457351 1 2 0 +58 1 2 0.1459902363533046 0.864166141 0.07662086 0.059212923 1 2 0 +59 1 2 0.1580238898720478 0.8538294 0.08263559 0.0635349452 1 2 0 +61 1 2 0.24588109562120131 0.7820152 0.155458942 0.06252578 1 2 0 +62 1 2 0.13238620890661379 0.8760026 0.0634366348 0.06056068 1 2 0 +65 1 2 0.13132980842568853 0.8769285 0.063261956 0.0598095059 1 2 0 +67 1 2 0.13238620890661379 0.8760026 0.0634366348 0.06056068 1 2 0 +75 1 2 0.16325820211530925 0.84937185 0.09269803 0.05793005 1 2 0 +78 1 2 0.25856072585963152 0.772162139 0.166928485 0.0609093234 1 2 0 +80 1 2 0.13238620890661379 0.8760026 0.0634366348 0.06056068 1 2 0 +81 1 2 0.13238620890661379 0.8760026 0.0634366348 0.06056068 1 2 0 +83 1 3 0.93589303759413867 0.559196055 0.392235428 0.04856844 2 1 0 +84 1 2 0.30739863434463804 0.7353574 0.193893969 0.07074856 1 2 0 +85 1 2 0.36387328935790564 0.69497925 0.250199646 0.05482102 1 2 0 +86 1 2 0.2248240850683334 0.7986567 0.1374587 0.06388454 1 2 0 +87 1 2 0.15039000144304782 0.860372365 0.0804473162 0.0591802672 1 2 0 +89 1 2 0.13238620890661379 0.8760026 0.0634366348 0.06056068 1 2 0 +94 1 2 0.13238620890661379 0.8760026 0.0634366348 0.06056068 1 2 0 +101 2 3 0.19033002342813979 0.826686263 0.116503209 0.0568104759 2 1 0 +103 2 3 0.13913525587344278 0.870110333 0.06958966 0.06029994 2 1 0 +107 2 3 0.14333565848826485 0.8664632 0.07176019 0.0617765337 2 1 0 +110 2 3 0.23871440437506003 0.7876398 0.156446442 0.0559136942 2 1 0 +114 2 3 0.19033002342813979 0.826686263 0.116503209 0.0568104759 2 1 0 +116 2 3 0.13913525587344278 0.870110333 0.06958966 0.06029994 2 1 0 +118 2 3 0.12489134297836053 0.8825928 0.0607594363 0.0566477254 2 0 1 +119 2 2 0.82777753715835345 0.5009504 0.437019467 0.06203004 1 2 0 +124 2 3 0.1531750305285868 0.857979536 0.0805763453 0.06144403 2 1 0 +126 2 3 0.41542573035225555 0.6600592 0.280402571 0.0595381558 2 1 0 +127 2 3 0.39337764883077281 0.6747739 0.268265069 0.0569609851 2 1 0 +130 2 3 0.12489134297836053 0.8825928 0.0607594363 0.0566477254 2 0 1 +134 2 2 0.85550151661202845 0.5024455 0.425069958 0.07248443 1 2 0 +135 2 3 0.12489134297836053 0.8825928 0.0607594363 0.0566477254 2 0 1 +136 2 3 0.14853238287711923 0.8619721 0.0780924 0.05993546 2 1 0 +139 2 3 0.16469402987504086 0.8481532 0.09065638 0.06119041 2 1 0 +140 2 3 0.15401813781546583 0.8572565 0.08267424 0.0600692444 2 1 0 +142 2 3 0.19033002342813979 0.826686263 0.116503209 0.0568104759 2 1 0 +143 2 3 0.1531750305285868 0.857979536 0.0805763453 0.06144403 2 1 0 +146 2 3 0.19033002342813979 0.826686263 0.116503209 0.0568104759 2 1 0 +148 2 3 0.15751169565724296 0.8542668 0.08508964 0.06064347 2 1 0 +149 2 3 0.22273123179238696 0.8003299 0.142754182 0.0569158569 2 1 0 diff --git a/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-TrainTest-iris.key-out.txt b/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-TrainTest-iris.key-out.txt new file mode 100644 index 0000000000..64a3bc287d --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-TrainTest-iris.key-out.txt @@ -0,0 +1,43 @@ +maml.exe TrainTest test=%Data% tr=LightGBMMC{nt=1 iter=10 v=- lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:TX:0 col=Features:1-*} data=%Data% out=%Output% seed=1 xf=Term{col=Label} +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +Auto-tuning parameters: UseSoftmax = False +LightGBM objective=multiclassova +Not training a calibrator because it is not needed. + +Confusion table + ||======================== +PREDICTED || 0 | 1 | 2 | Recall +TRUTH ||======================== + 0 || 50 | 0 | 0 | 1.0000 + 1 || 0 | 47 | 3 | 0.9400 + 2 || 0 | 1 | 49 | 0.9800 + ||======================== +Precision ||1.0000 |0.9792 |0.9423 | +Accuracy(micro-avg): 0.973333 +Accuracy(macro-avg): 0.973333 +Log-loss: 0.196674 +Log-loss reduction: 82.097919 + +OVERALL RESULTS +--------------------------------------- +Accuracy(micro-avg): 0.973333 (0.0000) +Accuracy(macro-avg): 0.973333 (0.0000) +Log-loss: 0.196674 (0.0000) +Log-loss reduction: 82.097919 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Building term dictionary' started. +[1] (%Time%) 150 examples Total Terms: 3 +[1] 'Building term dictionary' finished in %Time%. +[2] 'Loading data for LightGBM' started. +[2] 'Loading data for LightGBM' finished in %Time%. +[3] 'Training with LightGBM' started. +[3] 'Training with LightGBM' finished in %Time%. +[4] 'Saving model' started. +[4] 'Saving model' finished in %Time%. diff --git a/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-TrainTest-iris.key-rp.txt b/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-TrainTest-iris.key-rp.txt new file mode 100644 index 0000000000..23dc3f1a86 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-TrainTest-iris.key-rp.txt @@ -0,0 +1,4 @@ +LightGBMMC +Accuracy(micro-avg) Accuracy(macro-avg) Log-loss Log-loss reduction /iter /lr /nl /mil /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.973333 0.973333 0.196674 82.09792 10 0.2 20 10 1 LightGBMMC %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LightGBMMC{nt=1 iter=10 v=- lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:TX:0 col=Features:1-*} data=%Data% out=%Output% seed=1 xf=Term{col=Label} /iter:10;/lr:0.2;/nl:20;/mil:10;/nt:1 + diff --git a/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-TrainTest-iris.key.txt b/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-TrainTest-iris.key.txt new file mode 100644 index 0000000000..4fc92f1b19 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-TrainTest-iris.key.txt @@ -0,0 +1,151 @@ +Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class +0 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +1 0 0 0.13247487107987427 0.875924945 0.06492095 0.05915408 0 1 2 +2 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +3 0 0 0.13247487107987427 0.875924945 0.06492095 0.05915408 0 1 2 +4 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +5 0 0 0.12456001063410244 0.8828853 0.0595866032 0.057528127 0 2 1 +6 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +7 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +8 0 0 0.13247487107987427 0.875924945 0.06492095 0.05915408 0 1 2 +9 0 0 0.13247487107987427 0.875924945 0.06492095 0.05915408 0 1 2 +10 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +11 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +12 0 0 0.13247487107987427 0.875924945 0.06492095 0.05915408 0 1 2 +13 0 0 0.13247487107987427 0.875924945 0.06492095 0.05915408 0 1 2 +14 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +15 0 0 0.12456001063410244 0.8828853 0.0595866032 0.057528127 0 2 1 +16 0 0 0.12456001063410244 0.8828853 0.0595866032 0.057528127 0 2 1 +17 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +18 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +19 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +20 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +21 0 0 0.12456001063410244 0.8828853 0.0595866032 0.057528127 0 2 1 +22 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +23 0 0 0.33277494936956031 0.7169315 0.227236316 0.0558321662 0 1 2 +24 0 0 0.32796044820794762 0.7203915 0.224743724 0.054864794 0 1 2 +25 0 0 0.13247487107987427 0.875924945 0.06492095 0.05915408 0 1 2 +26 0 0 0.12456001063410244 0.8828853 0.0595866032 0.057528127 0 2 1 +27 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +28 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +29 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +30 0 0 0.13247487107987427 0.875924945 0.06492095 0.05915408 0 1 2 +31 0 0 0.12456001063410244 0.8828853 0.0595866032 0.057528127 0 2 1 +32 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +33 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +34 0 0 0.13247487107987427 0.875924945 0.06492095 0.05915408 0 1 2 +35 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +36 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +37 0 0 0.13247487107987427 0.875924945 0.06492095 0.05915408 0 1 2 +38 0 0 0.13247487107987427 0.875924945 0.06492095 0.05915408 0 1 2 +39 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +40 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +41 0 0 0.13811810171792147 0.8709958 0.0645556152 0.06444856 0 1 2 +42 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +43 0 0 0.33277494936956031 0.7169315 0.227236316 0.0558321662 0 1 2 +44 0 0 0.33634513183392067 0.7143765 0.229578465 0.05604506 0 1 2 +45 0 0 0.13247487107987427 0.875924945 0.06492095 0.05915408 0 1 2 +46 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +47 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +48 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +49 0 0 0.12333860159928871 0.8839643 0.05903762 0.05699811 0 2 1 +50 1 1 0.23100889631685528 0.7937324 0.149178371 0.0570892133 1 2 0 +51 1 1 0.15607289835381286 0.8554968 0.0860009938 0.0585021749 1 2 0 +52 1 1 0.50877014563522738 0.601234555 0.345829129 0.052936323 1 2 0 +53 1 1 0.13306515458964616 0.875408053 0.06540278 0.0591891445 1 2 0 +54 1 1 0.15325749593441318 0.8579088 0.08456552 0.05752569 1 2 0 +55 1 1 0.12725977902560126 0.8805049 0.06000033 0.0594947673 1 2 0 +56 1 1 0.33773914875897953 0.71338135 0.194789842 0.0918288156 1 2 0 +57 1 1 0.13547311823330146 0.873302639 0.066508 0.06018936 1 2 0 +58 1 1 0.12416960207104245 0.883230031 0.05863201 0.05813798 1 2 0 +59 1 1 0.1273381037166878 0.880435944 0.0600349456 0.0595290959 1 2 0 +60 1 1 0.13547311823330146 0.873302639 0.066508 0.06018936 1 2 0 +61 1 1 0.1609832630763833 0.8513063 0.0884949937 0.0601987168 1 2 0 +62 1 1 0.13286234147782883 0.8755856 0.0653095841 0.0591047965 1 2 0 +63 1 1 0.23582975138698256 0.789915144 0.151939109 0.05814572 1 2 0 +64 1 1 0.13095815072854303 0.8772545 0.0616324246 0.06111311 1 2 0 +65 1 1 0.12594102775547797 0.881666839 0.0594169162 0.05891627 1 2 0 +66 1 1 0.1609832630763833 0.8513063 0.0884949937 0.0601987168 1 2 0 +67 1 1 0.12566071119127656 0.881914 0.0592928044 0.058793202 1 2 0 +68 1 1 0.16292738185521705 0.8496529 0.09265414 0.0576929823 1 2 0 +69 1 1 0.13131716612503977 0.8769396 0.06459884 0.05846158 1 2 0 +70 1 2 0.95035919356690834 0.554423034 0.386602134 0.0589748733 2 1 0 +71 1 1 0.12529959921655817 0.882232547 0.0591328628 0.05863461 1 2 0 +72 1 1 0.50877014563522738 0.601234555 0.345829129 0.052936323 1 2 0 +73 1 1 0.23582975138698256 0.789915144 0.151939109 0.05814572 1 2 0 +74 1 1 0.12416960207104245 0.883230031 0.05863201 0.05813798 1 2 0 +75 1 1 0.12594102775547797 0.881666839 0.0594169162 0.05891627 1 2 0 +76 1 1 0.26095097058717287 0.7703187 0.174276143 0.0554051772 1 2 0 +77 1 2 1.6029370592055334 0.738739431 0.2013044 0.0599561781 2 1 0 +78 1 1 0.15976734311668664 0.852342069 0.08787854 0.0597793758 1 2 0 +79 1 1 0.12955227996567303 0.87848866 0.0610127263 0.0604986325 1 2 0 +80 1 1 0.13547311823330146 0.873302639 0.066508 0.06018936 1 2 0 +81 1 1 0.13547311823330146 0.873302639 0.066508 0.06018936 1 2 0 +82 1 1 0.12725977902560126 0.8805049 0.06000033 0.0594947673 1 2 0 +83 1 2 0.80815870423273684 0.493023425 0.445677936 0.06129863 2 1 0 +84 1 1 0.16454027823998496 0.8482836 0.09029396 0.0614224635 1 2 0 +85 1 1 0.2070731159429981 0.8129602 0.100639634 0.08640016 1 0 2 +86 1 1 0.2987897056212489 0.7417154 0.20252648 0.0557581745 1 2 0 +87 1 1 0.12976317713440702 0.8783034 0.06388293 0.0578136854 1 2 0 +88 1 1 0.12903506453208818 0.878943145 0.06078452 0.06027235 1 2 0 +89 1 1 0.13306515458964616 0.875408053 0.06540278 0.0591891445 1 2 0 +90 1 1 0.1273381037166878 0.880435944 0.0600349456 0.0595290959 1 2 0 +91 1 1 0.12737263080959327 0.880405545 0.0600502156 0.0595442355 1 2 0 +92 1 1 0.12725977902560126 0.8805049 0.06000033 0.0594947673 1 2 0 +93 1 1 0.13547311823330146 0.873302639 0.066508 0.06018936 1 2 0 +94 1 1 0.12725977902560126 0.8805049 0.06000033 0.0594947673 1 2 0 +95 1 1 0.12903506453208818 0.878943145 0.06078452 0.06027235 1 2 0 +96 1 1 0.12661229526028364 0.8810752 0.05971398 0.05921083 1 2 0 +97 1 1 0.12529959921655817 0.882232547 0.0591328628 0.05863461 1 2 0 +98 1 1 0.44858136712049312 0.638533354 0.30528307 0.0561835542 1 0 2 +99 1 1 0.12725977902560126 0.8805049 0.06000033 0.0594947673 1 2 0 +100 2 2 0.16260754098734898 0.8499247 0.09625085 0.0538244657 2 0 1 +101 2 2 0.16184034002313213 0.850577 0.0913400352 0.0580829456 2 1 0 +102 2 2 0.12174369977157315 0.885375261 0.0585552119 0.0560695 2 0 1 +103 2 2 0.12568524504717099 0.8818924 0.0596662238 0.05844138 2 1 0 +104 2 2 0.12174369977157315 0.885375261 0.0585552119 0.0560695 2 0 1 +105 2 2 0.12174369977157315 0.885375261 0.0585552119 0.0560695 2 0 1 +106 2 2 0.44758539287303228 0.639169633 0.301120639 0.0597097427 2 1 0 +107 2 2 0.12568524504717099 0.8818924 0.0596662238 0.05844138 2 1 0 +108 2 2 0.12568524504717099 0.8818924 0.0596662238 0.05844138 2 1 0 +109 2 2 0.16260754098734898 0.8499247 0.09625085 0.0538244657 2 0 1 +110 2 2 0.19147015233338821 0.8257443 0.114775449 0.0594803 2 1 0 +111 2 2 0.12895206364866429 0.8790161 0.06284211 0.05814181 2 1 0 +112 2 2 0.12174369977157315 0.885375261 0.0585552119 0.0560695 2 0 1 +113 2 2 0.23003014798568333 0.794509649 0.147670493 0.0578198843 2 1 0 +114 2 2 0.16184034002313213 0.850577 0.0913400352 0.0580829456 2 1 0 +115 2 2 0.14622594690253263 0.8639625 0.0778152943 0.0582222156 2 1 0 +116 2 2 0.13019277678014771 0.8779262 0.06272268 0.0593511574 2 1 0 +117 2 2 0.16260754098734898 0.8499247 0.09625085 0.0538244657 2 0 1 +118 2 2 0.11933918199486065 0.8875067 0.0584422871 0.054051 2 0 1 +119 2 1 0.85092928263060119 0.5151953 0.427017927 0.05778675 1 2 0 +120 2 2 0.12174369977157315 0.885375261 0.0585552119 0.0560695 2 0 1 +121 2 2 0.27313894704628855 0.760987043 0.18166633 0.0573466346 2 1 0 +122 2 2 0.11933918199486065 0.8875067 0.0584422871 0.054051 2 0 1 +123 2 2 0.29377982293137428 0.7454406 0.197279379 0.0572799928 2 1 0 +124 2 2 0.16260754098734898 0.8499247 0.09625085 0.0538244657 2 0 1 +125 2 2 0.13019277678014771 0.8779262 0.06272268 0.0593511574 2 1 0 +126 2 2 0.43516329539237014 0.647159 0.2948179 0.0580231175 2 1 0 +127 2 2 0.36323660387324913 0.6954219 0.244927272 0.0596508458 2 1 0 +128 2 2 0.11933918199486065 0.8875067 0.0584422871 0.054051 2 0 1 +129 2 2 0.47537462515590923 0.6216521 0.316322356 0.0620255135 2 1 0 +130 2 2 0.11933918199486065 0.8875067 0.0584422871 0.054051 2 0 1 +131 2 2 0.16468208306001242 0.8481633 0.09738051 0.0544561855 2 0 1 +132 2 2 0.11933918199486065 0.8875067 0.0584422871 0.054051 2 0 1 +133 2 2 0.7071985901643314 0.493023425 0.445677936 0.06129863 2 1 0 +134 2 2 0.47537462515590923 0.6216521 0.316322356 0.0620255135 2 1 0 +135 2 2 0.12174369977157315 0.885375261 0.0585552119 0.0560695 2 0 1 +136 2 2 0.16260754098734898 0.8499247 0.09625085 0.0538244657 2 0 1 +137 2 2 0.13019277678014771 0.8779262 0.06272268 0.0593511574 2 1 0 +138 2 2 0.55105996253236122 0.5763386 0.362355322 0.06130607 2 1 0 +139 2 2 0.12174369977157315 0.885375261 0.0585552119 0.0560695 2 0 1 +140 2 2 0.12174369977157315 0.885375261 0.0585552119 0.0560695 2 0 1 +141 2 2 0.18701313089818 0.829432845 0.112345912 0.0582212359 2 1 0 +142 2 2 0.16184034002313213 0.850577 0.0913400352 0.0580829456 2 1 0 +143 2 2 0.12174369977157315 0.885375261 0.0585552119 0.0560695 2 0 1 +144 2 2 0.16260754098734898 0.8499247 0.09625085 0.0538244657 2 0 1 +145 2 2 0.14622594690253263 0.8639625 0.0778152943 0.0582222156 2 1 0 +146 2 2 0.23003014798568333 0.794509649 0.147670493 0.0578198843 2 1 0 +147 2 2 0.14864613971263602 0.861874044 0.07900992 0.0591160469 2 1 0 +148 2 2 0.16260754098734898 0.8499247 0.09625085 0.0538244657 2 0 1 +149 2 2 0.20528992585094685 0.814411163 0.1260209 0.05956797 2 1 0 diff --git a/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-TrainTest-iris.keyU404-out.txt b/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-TrainTest-iris.keyU404-out.txt new file mode 100644 index 0000000000..0263d06d71 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-TrainTest-iris.keyU404-out.txt @@ -0,0 +1,42 @@ +maml.exe TrainTest test=%Data% tr=LightGBMMC{nt=1 iter=10 v=- lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:U4[0-4]:0 col=Features:1-4} data=%Data% out=%Output% seed=1 +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +Auto-tuning parameters: UseSoftmax = False +LightGBM objective=multiclassova +Not training a calibrator because it is not needed. + +Confusion table + ||======================================== +PREDICTED || 0 | 1 | 2 | 3 | 4 | Recall +TRUTH ||======================================== + 0 || 50 | 0 | 0 | 0 | 0 | 1.0000 + 1 || 0 | 47 | 3 | 0 | 0 | 0.9400 + 2 || 0 | 1 | 49 | 0 | 0 | 0.9800 + 3 || 0 | 0 | 0 | 0 | 0 | 0.0000 + 4 || 0 | 0 | 0 | 0 | 0 | 0.0000 + ||======================================== +Precision ||1.0000 |0.9792 |0.9423 |0.0000 |0.0000 | +Accuracy(micro-avg): 0.973333 +Accuracy(macro-avg): 0.973333 +Log-loss: 0.196675 +Log-loss reduction: 82.097914 + +OVERALL RESULTS +--------------------------------------- +Accuracy(micro-avg): 0.973333 (0.0000) +Accuracy(macro-avg): 0.973333 (0.0000) +Log-loss: 0.196675 (0.0000) +Log-loss reduction: 82.097914 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Loading data for LightGBM' started. +[1] 'Loading data for LightGBM' finished in %Time%. +[2] 'Training with LightGBM' started. +[2] 'Training with LightGBM' finished in %Time%. +[3] 'Saving model' started. +[3] 'Saving model' finished in %Time%. diff --git a/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-TrainTest-iris.keyU404-rp.txt b/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-TrainTest-iris.keyU404-rp.txt new file mode 100644 index 0000000000..0842df2191 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-TrainTest-iris.keyU404-rp.txt @@ -0,0 +1,4 @@ +LightGBMMC +Accuracy(micro-avg) Accuracy(macro-avg) Log-loss Log-loss reduction /iter /lr /nl /mil /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.973333 0.973333 0.196675 82.09792 10 0.2 20 10 1 LightGBMMC %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LightGBMMC{nt=1 iter=10 v=- lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:U4[0-4]:0 col=Features:1-4} data=%Data% out=%Output% seed=1 /iter:10;/lr:0.2;/nl:20;/mil:10;/nt:1 + diff --git a/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-TrainTest-iris.keyU404.txt b/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-TrainTest-iris.keyU404.txt new file mode 100644 index 0000000000..41d4c75d8b --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBMMC/LightGBMMC-TrainTest-iris.keyU404.txt @@ -0,0 +1,151 @@ +Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class +0 0 0 0.1233386690280828 0.883964241 0.0590376146 0.0569981076 0 2 1 +1 0 0 0.13247487107987427 0.875924945 0.06492095 0.0591540746 0 1 2 +2 0 0 0.1233386690280828 0.883964241 0.0590376146 0.0569981076 0 2 1 +3 0 0 0.13247487107987427 0.875924945 0.06492095 0.0591540746 0 1 2 +4 0 0 0.1233386690280828 0.883964241 0.0590376146 0.0569981076 0 2 1 +5 0 0 0.12456007814530498 0.8828852 0.0595866 0.0575281233 0 2 1 +6 0 0 0.1233386690280828 0.883964241 0.0590376146 0.0569981076 0 2 1 +7 0 0 0.1233386690280828 0.883964241 0.0590376146 0.0569981076 0 2 1 +8 0 0 0.13247487107987427 0.875924945 0.06492095 0.0591540746 0 1 2 +9 0 0 0.13247487107987427 0.875924945 0.06492095 0.0591540746 0 1 2 +10 0 0 0.1233386690280828 0.883964241 0.0590376146 0.0569981076 0 2 1 +11 0 0 0.1233386690280828 0.883964241 0.0590376146 0.0569981076 0 2 1 +12 0 0 0.13247487107987427 0.875924945 0.06492095 0.0591540746 0 1 2 +13 0 0 0.13247487107987427 0.875924945 0.06492095 0.0591540746 0 1 2 +14 0 0 0.1233386690280828 0.883964241 0.0590376146 0.0569981076 0 2 1 +15 0 0 0.12456007814530498 0.8828852 0.0595866 0.0575281233 0 2 1 +16 0 0 0.12456007814530498 0.8828852 0.0595866 0.0575281233 0 2 1 +17 0 0 0.1233386690280828 0.883964241 0.0590376146 0.0569981076 0 2 1 +18 0 0 0.1233386690280828 0.883964241 0.0590376146 0.0569981076 0 2 1 +19 0 0 0.1233386690280828 0.883964241 0.0590376146 0.0569981076 0 2 1 +20 0 0 0.1233386690280828 0.883964241 0.0590376146 0.0569981076 0 2 1 +21 0 0 0.12456007814530498 0.8828852 0.0595866 0.0575281233 0 2 1 +22 0 0 0.1233386690280828 0.883964241 0.0590376146 0.0569981076 0 2 1 +23 0 0 0.33277503250811041 0.716931462 0.2272363 0.0558321625 0 1 2 +24 0 0 0.32796053094718908 0.720391452 0.224743709 0.05486479 0 1 2 +25 0 0 0.13247487107987427 0.875924945 0.06492095 0.0591540746 0 1 2 +26 0 0 0.12456007814530498 0.8828852 0.0595866 0.0575281233 0 2 1 +27 0 0 0.1233386690280828 0.883964241 0.0590376146 0.0569981076 0 2 1 +28 0 0 0.1233386690280828 0.883964241 0.0590376146 0.0569981076 0 2 1 +29 0 0 0.1233386690280828 0.883964241 0.0590376146 0.0569981076 0 2 1 +30 0 0 0.13247487107987427 0.875924945 0.06492095 0.0591540746 0 1 2 +31 0 0 0.12456007814530498 0.8828852 0.0595866 0.0575281233 0 2 1 +32 0 0 0.1233386690280828 0.883964241 0.0590376146 0.0569981076 0 2 1 +33 0 0 0.1233386690280828 0.883964241 0.0590376146 0.0569981076 0 2 1 +34 0 0 0.13247487107987427 0.875924945 0.06492095 0.0591540746 0 1 2 +35 0 0 0.1233386690280828 0.883964241 0.0590376146 0.0569981076 0 2 1 +36 0 0 0.1233386690280828 0.883964241 0.0590376146 0.0569981076 0 2 1 +37 0 0 0.13247487107987427 0.875924945 0.06492095 0.0591540746 0 1 2 +38 0 0 0.13247487107987427 0.875924945 0.06492095 0.0591540746 0 1 2 +39 0 0 0.1233386690280828 0.883964241 0.0590376146 0.0569981076 0 2 1 +40 0 0 0.1233386690280828 0.883964241 0.0590376146 0.0569981076 0 2 1 +41 0 0 0.13811817015068023 0.87099576 0.06455561 0.06444855 0 1 2 +42 0 0 0.1233386690280828 0.883964241 0.0590376146 0.0569981076 0 2 1 +43 0 0 0.33277503250811041 0.716931462 0.2272363 0.0558321625 0 1 2 +44 0 0 0.33634521526982103 0.71437645 0.22957845 0.0560450554 0 1 2 +45 0 0 0.13247487107987427 0.875924945 0.06492095 0.0591540746 0 1 2 +46 0 0 0.1233386690280828 0.883964241 0.0590376146 0.0569981076 0 2 1 +47 0 0 0.1233386690280828 0.883964241 0.0590376146 0.0569981076 0 2 1 +48 0 0 0.1233386690280828 0.883964241 0.0590376146 0.0569981076 0 2 1 +49 0 0 0.1233386690280828 0.883964241 0.0590376146 0.0569981076 0 2 1 +50 1 1 0.23100897141098861 0.793732345 0.149178356 0.05708921 1 2 0 +51 1 1 0.15607296802636475 0.855496764 0.0860009938 0.05850217 1 2 0 +52 1 1 0.50877024477232313 0.6012345 0.3458291 0.052936323 1 2 0 +53 1 1 0.13306522267748996 0.875408 0.06540278 0.05918914 1 2 0 +54 1 1 0.15325756541108468 0.8579087 0.08456551 0.0575256869 1 2 0 +55 1 1 0.1272598467193147 0.880504847 0.0600003265 0.0594947636 1 2 0 +56 1 1 0.33773923231127212 0.7133813 0.194789827 0.09182881 1 2 0 +57 1 1 0.13547318648529588 0.8733026 0.0665079951 0.0601893552 1 2 0 +58 1 1 0.12416966955589319 0.88323 0.058632005 0.0581379756 1 2 0 +59 1 1 0.12733817141570353 0.8804359 0.0600349456 0.0595290922 1 2 0 +60 1 1 0.13547318648529588 0.8733026 0.0665079951 0.0601893552 1 2 0 +61 1 1 0.16098333309189417 0.85130626 0.08849499 0.0601987131 1 2 0 +62 1 1 0.1328624095518649 0.875585556 0.06530958 0.0591047928 1 2 0 +63 1 1 0.23582975138698256 0.789915144 0.151939109 0.0581457168 1 2 0 +64 1 1 0.13095821867307653 0.8772544 0.06163242 0.0611131079 1 2 0 +65 1 1 0.12594109535997908 0.8816668 0.0594169125 0.058916267 1 2 0 +66 1 1 0.16098333309189417 0.85130626 0.08849499 0.0601987131 1 2 0 +67 1 1 0.12566077877682966 0.88191396 0.0592928 0.0587932 1 2 0 +68 1 1 0.16292745200697881 0.8496528 0.09265413 0.05769298 1 2 0 +69 1 1 0.13131723409397075 0.876939535 0.06459884 0.0584615767 1 2 0 +70 1 2 0.95035927065474868 0.554423 0.3866021 0.05897487 2 1 0 +71 1 1 0.12529966677770971 0.8822325 0.05913286 0.0586346053 1 2 0 +72 1 1 0.50877024477232313 0.6012345 0.3458291 0.052936323 1 2 0 +73 1 1 0.23582975138698256 0.789915144 0.151939109 0.0581457168 1 2 0 +74 1 1 0.12416966955589319 0.88323 0.058632005 0.0581379756 1 2 0 +75 1 1 0.12594109535997908 0.8816668 0.0594169125 0.058916267 1 2 0 +76 1 1 0.26095104796378088 0.7703186 0.174276143 0.0554051735 1 2 0 +77 1 2 1.6029370592055334 0.7387394 0.2013044 0.0599561743 2 1 0 +78 1 1 0.159767413047116 0.852342 0.08787853 0.0597793721 1 2 0 +79 1 1 0.12955234781475239 0.8784886 0.0610127226 0.06049863 1 2 0 +80 1 1 0.13547318648529588 0.8733026 0.0665079951 0.0601893552 1 2 0 +81 1 1 0.13547318648529588 0.8733026 0.0665079951 0.0601893552 1 2 0 +82 1 1 0.1272598467193147 0.880504847 0.0600003265 0.0594947636 1 2 0 +83 1 2 0.80815877110237733 0.4930234 0.4456779 0.0612986274 2 1 0 +84 1 1 0.16454034850498556 0.8482835 0.09029395 0.06142246 1 2 0 +85 1 1 0.2070731892610356 0.812960148 0.100639626 0.08640015 1 0 2 +86 1 1 0.29878978598178807 0.7417153 0.202526465 0.05575817 1 2 0 +87 1 1 0.12976324499779707 0.878303349 0.06388293 0.05781368 1 2 0 +88 1 1 0.12903513234608402 0.8789431 0.060784515 0.0602723472 1 2 0 +89 1 1 0.13306522267748996 0.875408 0.06540278 0.05918914 1 2 0 +90 1 1 0.12733817141570353 0.8804359 0.0600349456 0.0595290922 1 2 0 +91 1 1 0.12737269851094649 0.8804055 0.06005021 0.05954423 1 2 0 +92 1 1 0.1272598467193147 0.880504847 0.0600003265 0.0594947636 1 2 0 +93 1 1 0.13547318648529588 0.8733026 0.0665079951 0.0601893552 1 2 0 +94 1 1 0.1272598467193147 0.880504847 0.0600003265 0.0594947636 1 2 0 +95 1 1 0.12903513234608402 0.8789431 0.060784515 0.0602723472 1 2 0 +96 1 1 0.12661236291018069 0.881075144 0.0597139746 0.0592108257 1 2 0 +97 1 1 0.12529966677770971 0.8822325 0.05913286 0.0586346053 1 2 0 +98 1 1 0.44858136712049312 0.638533354 0.30528307 0.05618355 1 0 2 +99 1 1 0.1272598467193147 0.880504847 0.0600003265 0.0594947636 1 2 0 +100 2 2 0.16260761111667693 0.8499246 0.09625085 0.053824462 2 0 1 +101 2 2 0.16184034002313213 0.850577 0.09134003 0.0580829419 2 1 0 +102 2 2 0.12174369977157315 0.885375261 0.05855521 0.056069497 2 0 1 +103 2 2 0.12568531263438223 0.8818923 0.05966622 0.0584413745 2 1 0 +104 2 2 0.12174369977157315 0.885375261 0.05855521 0.056069497 2 0 1 +105 2 2 0.12174369977157315 0.885375261 0.05855521 0.056069497 2 0 1 +106 2 2 0.44758548612628529 0.6391696 0.3011206 0.05970974 2 1 0 +107 2 2 0.12568531263438223 0.8818923 0.05966622 0.0584413745 2 1 0 +108 2 2 0.12568531263438223 0.8818923 0.05966622 0.0584413745 2 1 0 +109 2 2 0.16260761111667693 0.8499246 0.09625085 0.053824462 2 0 1 +110 2 2 0.19147022451632548 0.8257442 0.114775442 0.0594802946 2 1 0 +111 2 2 0.12895213145703174 0.879016042 0.06284211 0.0581418052 2 1 0 +112 2 2 0.12174369977157315 0.885375261 0.05855521 0.056069497 2 0 1 +113 2 2 0.23003022300635434 0.7945096 0.147670478 0.05781988 2 1 0 +114 2 2 0.16184034002313213 0.850577 0.09134003 0.0580829419 2 1 0 +115 2 2 0.14622601589238901 0.8639624 0.0778152943 0.0582222119 2 1 0 +116 2 2 0.13019284467269809 0.8779261 0.06272268 0.0593511537 2 1 0 +117 2 2 0.16260761111667693 0.8499246 0.09625085 0.053824462 2 0 1 +118 2 2 0.11933924915451727 0.887506664 0.0584422834 0.0540509969 2 0 1 +119 2 1 0.85092928263060119 0.515195251 0.427017927 0.05778675 1 2 0 +120 2 2 0.12174369977157315 0.885375261 0.05855521 0.056069497 2 0 1 +121 2 2 0.27313902537173135 0.760987 0.181666315 0.05734663 2 1 0 +122 2 2 0.11933924915451727 0.887506664 0.0584422834 0.0540509969 2 0 1 +123 2 2 0.29377982293137428 0.7454406 0.197279364 0.05727999 2 1 0 +124 2 2 0.16260761111667693 0.8499246 0.09625085 0.053824462 2 0 1 +125 2 2 0.13019284467269809 0.8779261 0.06272268 0.0593511537 2 1 0 +126 2 2 0.43516338749438732 0.6471589 0.294817865 0.0580231138 2 1 0 +127 2 2 0.36323668958330446 0.6954218 0.244927257 0.0596508421 2 1 0 +128 2 2 0.11933924915451727 0.887506664 0.0584422834 0.0540509969 2 0 1 +129 2 2 0.47537472103694156 0.621652067 0.316322356 0.06202551 2 1 0 +130 2 2 0.11933924915451727 0.887506664 0.0584422834 0.0540509969 2 0 1 +131 2 2 0.16468215333497763 0.848163247 0.09738051 0.05445618 2 0 1 +132 2 2 0.11933924915451727 0.887506664 0.0584422834 0.0540509969 2 0 1 +133 2 2 0.70719865061241916 0.4930234 0.4456779 0.0612986274 2 1 0 +134 2 2 0.47537472103694156 0.621652067 0.316322356 0.06202551 2 1 0 +135 2 2 0.12174369977157315 0.885375261 0.05855521 0.056069497 2 0 1 +136 2 2 0.16260761111667693 0.8499246 0.09625085 0.053824462 2 0 1 +137 2 2 0.13019284467269809 0.8779261 0.06272268 0.0593511537 2 1 0 +138 2 2 0.55105996253236122 0.5763386 0.362355322 0.0613060668 2 1 0 +139 2 2 0.12174369977157315 0.885375261 0.05855521 0.056069497 2 0 1 +140 2 2 0.12174369977157315 0.885375261 0.05855521 0.056069497 2 0 1 +141 2 2 0.18701320276011227 0.8294328 0.112345912 0.0582212321 2 1 0 +142 2 2 0.16184034002313213 0.850577 0.09134003 0.0580829419 2 1 0 +143 2 2 0.12174369977157315 0.885375261 0.05855521 0.056069497 2 0 1 +144 2 2 0.16260761111667693 0.8499246 0.09625085 0.053824462 2 0 1 +145 2 2 0.14622601589238901 0.8639624 0.0778152943 0.0582222119 2 1 0 +146 2 2 0.23003022300635434 0.7945096 0.147670478 0.05781988 2 1 0 +147 2 2 0.14864620886966337 0.861874 0.07900992 0.0591160432 2 1 0 +148 2 2 0.16260761111667693 0.8499246 0.09625085 0.053824462 2 0 1 +149 2 2 0.20528999903836082 0.8144111 0.126020879 0.0595679656 2 1 0 diff --git a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-CV-wine-out.txt b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-CV-wine-out.txt new file mode 100644 index 0000000000..9631870541 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-CV-wine-out.txt @@ -0,0 +1,44 @@ +maml.exe CV tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +LightGBM objective=regression +Not training a calibrator because it is not needed. +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +LightGBM objective=regression +Not training a calibrator because it is not needed. +L1(avg): 0.524348 +L2(avg): 0.466735 +RMS(avg): 0.683180 +Loss-fn(avg): 0.466735 +R Squared: 0.400415 +L1(avg): 0.517508 +L2(avg): 0.458039 +RMS(avg): 0.676786 +Loss-fn(avg): 0.458039 +R Squared: 0.420159 + +OVERALL RESULTS +--------------------------------------- +L1(avg): 0.520928 (0.0034) +L2(avg): 0.462387 (0.0043) +RMS(avg): 0.679983 (0.0032) +Loss-fn(avg): 0.462387 (0.0043) +R Squared: 0.410287 (0.0099) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Loading data for LightGBM' started. +[1] 'Loading data for LightGBM' finished in %Time%. +[2] 'Training with LightGBM' started. +[2] (%Time%) Iteration: 50 Training-l2: 0.189697165394939 +[2] 'Training with LightGBM' finished in %Time%. +[3] 'Loading data for LightGBM #2' started. +[3] 'Loading data for LightGBM #2' finished in %Time%. +[4] 'Training with LightGBM #2' started. +[4] (%Time%) Iteration: 50 Training-l2: 0.204982247876212 +[4] 'Training with LightGBM #2' finished in %Time%. diff --git a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-CV-wine-rp.txt b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-CV-wine-rp.txt new file mode 100644 index 0000000000..ede416ff06 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-CV-wine-rp.txt @@ -0,0 +1,4 @@ +LightGBMR +L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared /iter /lr /nl /mil /booster /v /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.520928 0.462387 0.679983 0.462387 0.410287 50 0.2 20 10 gbdt{l2=0.2 l1=0.2} + 1 LightGBMR %Data% %Output% 99 0 0 maml.exe CV tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 /iter:50;/lr:0.2;/nl:20;/mil:10;/booster:gbdt{l2=0.2 l1=0.2};/v:+;/nt:1 + diff --git a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-CV-wine.txt b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-CV-wine.txt new file mode 100644 index 0000000000..7bc6a0ec0b --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-CV-wine.txt @@ -0,0 +1,4899 @@ +Instance Label Score L1-loss L2-loss +5 6 5.80828 0.19172000885009766 0.036756561793481524 +6 6 5.206482 0.79351806640625 0.62967092171311378 +8 6 5.50727 0.49273014068603516 0.24278299154048 +9 6 5.99071741 0.0092825889587402344 8.6166457776926109E-05 +10 5 5.78352642 0.78352642059326172 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2.1267070770263672 4.5228829914740345 +4888 5 5.89248848 0.89248847961425781 0.79653568624416948 +4889 6 5.90926361 0.09073638916015625 0.0082330923178233206 +4894 5 5.370122 0.37012195587158203 0.13699026221820532 +4896 7 6.225442 0.77455806732177734 0.59994019965324696 diff --git a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-TrainTest-wine-out.txt b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-TrainTest-wine-out.txt new file mode 100644 index 0000000000..56988214d4 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-TrainTest-wine-out.txt @@ -0,0 +1,32 @@ +maml.exe TrainTest test=%Data% tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +LightGBM objective=regression +Not training a calibrator because it is not needed. +L1(avg): 0.402080 +L2(avg): 0.272274 +RMS(avg): 0.521799 +Loss-fn(avg): 0.272274 +R Squared: 0.652798 + +OVERALL RESULTS +--------------------------------------- +L1(avg): 0.402080 (0.0000) +L2(avg): 0.272274 (0.0000) +RMS(avg): 0.521799 (0.0000) +Loss-fn(avg): 0.272274 (0.0000) +R Squared: 0.652798 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Loading data for LightGBM' started. +[1] 'Loading data for LightGBM' finished in %Time%. +[2] 'Training with LightGBM' started. +[2] (%Time%) Iteration: 50 Training-l2: 0.272273893168108 +[2] 'Training with LightGBM' finished in %Time%. +[3] 'Saving model' started. +[3] 'Saving model' finished in %Time%. diff --git a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-TrainTest-wine-rp.txt b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-TrainTest-wine-rp.txt new file mode 100644 index 0000000000..981a680d8e --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-TrainTest-wine-rp.txt @@ -0,0 +1,4 @@ +LightGBMR +L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared /iter /lr /nl /mil /booster /v /nt Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.40208 0.272274 0.521799 0.272274 0.652798 50 0.2 20 10 gbdt{l2=0.2 l1=0.2} + 1 LightGBMR %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LightGBMR{nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 /iter:50;/lr:0.2;/nl:20;/mil:10;/booster:gbdt{l2=0.2 l1=0.2};/v:+;/nt:1 + diff --git a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-TrainTest-wine.txt b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMReg-TrainTest-wine.txt new file mode 100644 index 0000000000..1bbc59f006 --- /dev/null +++ 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0.19656626694109036 +4895 6 5.4413476 0.55865240097045898 0.31209250511005848 +4896 7 7.037857 0.0378570556640625 0.0014331566635519266 +4897 6 5.9355073 0.064492702484130859 0.0041593086737066187 diff --git a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-CV-wine.MAE-out.txt b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-CV-wine.MAE-out.txt new file mode 100644 index 0000000000..0209b5a363 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-CV-wine.MAE-out.txt @@ -0,0 +1,44 @@ +maml.exe CV tr=LightGBMR{nt=1 iter=50 em=mae v=+ lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +LightGBM objective=regression +Not training a calibrator because it is not needed. +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +LightGBM objective=regression +Not training a calibrator because it is not needed. +L1(avg): 0.529227 +L2(avg): 0.479877 +RMS(avg): 0.692731 +Loss-fn(avg): 0.479877 +R Squared: 0.383532 +L1(avg): 0.519622 +L2(avg): 0.463327 +RMS(avg): 0.680681 +Loss-fn(avg): 0.463327 +R Squared: 0.413465 + +OVERALL RESULTS +--------------------------------------- +L1(avg): 0.524425 (0.0048) +L2(avg): 0.471602 (0.0083) +RMS(avg): 0.686706 (0.0060) +Loss-fn(avg): 0.471602 (0.0083) +R Squared: 0.398499 (0.0150) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Loading data for LightGBM' started. +[1] 'Loading data for LightGBM' finished in %Time%. +[2] 'Training with LightGBM' started. +[2] (%Time%) Iteration: 50 Training-l1: 0.344282018934789 +[2] 'Training with LightGBM' finished in %Time%. +[3] 'Loading data for LightGBM #2' started. +[3] 'Loading data for LightGBM #2' finished in %Time%. +[4] 'Training with LightGBM #2' started. +[4] (%Time%) Iteration: 50 Training-l1: 0.345211959030736 +[4] 'Training with LightGBM #2' finished in %Time%. diff --git a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-CV-wine.MAE-rp.txt b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-CV-wine.MAE-rp.txt new file mode 100644 index 0000000000..e2cc16093d --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-CV-wine.MAE-rp.txt @@ -0,0 +1,4 @@ +LightGBMR +L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared /iter /lr /nl /mil /v /nt /em Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.524425 0.471602 0.686706 0.471602 0.398499 50 0.2 20 10 + 1 Mae LightGBMR %Data% %Output% 99 0 0 maml.exe CV tr=LightGBMR{nt=1 iter=50 em=mae v=+ lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 /iter:50;/lr:0.2;/nl:20;/mil:10;/v:+;/nt:1;/em:Mae + diff --git a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-CV-wine.MAE.txt b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-CV-wine.MAE.txt new file mode 100644 index 0000000000..23cd505851 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-CV-wine.MAE.txt @@ -0,0 +1,4899 @@ +Instance Label Score L1-loss L2-loss +5 6 5.80925941 0.19074058532714844 0.036381970890943194 +6 6 5.25261068 0.74738931655883789 0.55859079050628679 +8 6 5.54791927 0.45208072662353516 0.20437698338446353 +9 6 5.8812604 0.11873960494995117 0.014099093783670469 +10 5 5.785368 0.78536796569824219 0.61680284154499532 +11 5 5.39364529 0.39364528656005859 0.15495661163095065 +18 6 6.00869131 0.0086913108825683594 7.5538884857451194E-05 +20 8 7.3507967 0.64920330047607422 0.42146492534902791 +21 7 5.944835 1.0551648139953613 1.1133727846938655 +25 6 5.694817 0.30518293380737305 0.093136623087275439 +28 6 5.61064863 0.38935136795043945 0.15159448772487849 +31 6 5.437836 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1.0083649315276944 +4889 6 5.75216627 0.2478337287902832 0.061421557126095649 +4894 5 5.44779968 0.4477996826171875 0.20052455575205386 +4896 7 6.65991926 0.34008073806762695 0.11565490840462189 diff --git a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-TrainTest-wine.MAE-out.txt b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-TrainTest-wine.MAE-out.txt new file mode 100644 index 0000000000..4792a52b30 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-TrainTest-wine.MAE-out.txt @@ -0,0 +1,32 @@ +maml.exe TrainTest test=%Data% tr=LightGBMR{nt=1 iter=50 em=mae v=+ lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +LightGBM objective=regression +Not training a calibrator because it is not needed. +L1(avg): 0.407028 +L2(avg): 0.274963 +RMS(avg): 0.524369 +Loss-fn(avg): 0.274963 +R Squared: 0.649369 + +OVERALL RESULTS +--------------------------------------- +L1(avg): 0.407028 (0.0000) +L2(avg): 0.274963 (0.0000) +RMS(avg): 0.524369 (0.0000) +Loss-fn(avg): 0.274963 (0.0000) +R Squared: 0.649369 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Loading data for LightGBM' started. +[1] 'Loading data for LightGBM' finished in %Time%. +[2] 'Training with LightGBM' started. +[2] (%Time%) Iteration: 50 Training-l1: 0.407028426136092 +[2] 'Training with LightGBM' finished in %Time%. +[3] 'Saving model' started. +[3] 'Saving model' finished in %Time%. diff --git a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-TrainTest-wine.MAE-rp.txt b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-TrainTest-wine.MAE-rp.txt new file mode 100644 index 0000000000..cf5f4d432b --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-TrainTest-wine.MAE-rp.txt @@ -0,0 +1,4 @@ +LightGBMR +L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared /iter /lr /nl /mil /v /nt /em Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.407028 0.274963 0.524369 0.274963 0.649369 50 0.2 20 10 + 1 Mae LightGBMR %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LightGBMR{nt=1 iter=50 em=mae v=+ lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 /iter:50;/lr:0.2;/nl:20;/mil:10;/v:+;/nt:1;/em:Mae + diff --git a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-TrainTest-wine.MAE.txt b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-TrainTest-wine.MAE.txt new file mode 100644 index 0000000000..c0618c3738 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegMae-TrainTest-wine.MAE.txt @@ 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0.69805526733398438 0.48728115625272039 +4896 7 6.68631124 0.31368875503540039 0.098400635035659434 +4897 6 6.04107332 0.041073322296142578 0.001687017804442803 diff --git a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-CV-wine.RMSE-out.txt b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-CV-wine.RMSE-out.txt new file mode 100644 index 0000000000..6483a88e76 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-CV-wine.RMSE-out.txt @@ -0,0 +1,44 @@ +maml.exe CV tr=LightGBMR{nt=1 iter=50 em=rmse v=+ lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +LightGBM objective=regression +Not training a calibrator because it is not needed. +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +LightGBM objective=regression +Not training a calibrator because it is not needed. +L1(avg): 0.529227 +L2(avg): 0.479877 +RMS(avg): 0.692731 +Loss-fn(avg): 0.479877 +R Squared: 0.383532 +L1(avg): 0.519622 +L2(avg): 0.463327 +RMS(avg): 0.680681 +Loss-fn(avg): 0.463327 +R Squared: 0.413465 + +OVERALL RESULTS +--------------------------------------- +L1(avg): 0.524425 (0.0048) +L2(avg): 0.471602 (0.0083) +RMS(avg): 0.686706 (0.0060) +Loss-fn(avg): 0.471602 (0.0083) +R Squared: 0.398499 (0.0150) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Loading data for LightGBM' started. +[1] 'Loading data for LightGBM' finished in %Time%. +[2] 'Training with LightGBM' started. +[2] (%Time%) Iteration: 50 Training-rmse: 0.444161678699535 +[2] 'Training with LightGBM' finished in %Time%. +[3] 'Loading data for LightGBM #2' started. +[3] 'Loading data for LightGBM #2' finished in %Time%. +[4] 'Training with LightGBM #2' started. +[4] (%Time%) Iteration: 50 Training-rmse: 0.447777922357938 +[4] 'Training with LightGBM #2' finished in %Time%. diff --git a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-CV-wine.RMSE-rp.txt b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-CV-wine.RMSE-rp.txt new file mode 100644 index 0000000000..56cb0fac01 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-CV-wine.RMSE-rp.txt @@ -0,0 +1,4 @@ +LightGBMR +L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared /iter /lr /nl /mil /v /nt /em Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.524425 0.471602 0.686706 0.471602 0.398499 50 0.2 20 10 + 1 Rmse LightGBMR %Data% %Output% 99 0 0 maml.exe CV tr=LightGBMR{nt=1 iter=50 em=rmse v=+ lr=0.2 mil=10 nl=20} threads=- dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% seed=1 /iter:50;/lr:0.2;/nl:20;/mil:10;/v:+;/nt:1;/em:Rmse + diff --git a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-CV-wine.RMSE.txt b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-CV-wine.RMSE.txt new file mode 100644 index 0000000000..23cd505851 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-CV-wine.RMSE.txt @@ -0,0 +1,4899 @@ +Instance Label Score L1-loss L2-loss +5 6 5.80925941 0.19074058532714844 0.036381970890943194 +6 6 5.25261068 0.74738931655883789 0.55859079050628679 +8 6 5.54791927 0.45208072662353516 0.20437698338446353 +9 6 5.8812604 0.11873960494995117 0.014099093783670469 +10 5 5.785368 0.78536796569824219 0.61680284154499532 +11 5 5.39364529 0.39364528656005859 0.15495661163095065 +18 6 6.00869131 0.0086913108825683594 7.5538884857451194E-05 +20 8 7.3507967 0.64920330047607422 0.42146492534902791 +21 7 5.944835 1.0551648139953613 1.1133727846938655 +25 6 5.694817 0.30518293380737305 0.093136623087275439 +28 6 5.61064863 0.38935136795043945 0.15159448772487849 +31 6 5.437836 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1.0083649315276944 +4889 6 5.75216627 0.2478337287902832 0.061421557126095649 +4894 5 5.44779968 0.4477996826171875 0.20052455575205386 +4896 7 6.65991926 0.34008073806762695 0.11565490840462189 diff --git a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-TrainTest-wine.RMSE-out.txt b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-TrainTest-wine.RMSE-out.txt new file mode 100644 index 0000000000..d01de377a4 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-TrainTest-wine.RMSE-out.txt @@ -0,0 +1,32 @@ +maml.exe TrainTest test=%Data% tr=LightGBMR{nt=1 iter=50 em=rmse v=+ lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 +Not adding a normalizer. +Auto-tuning parameters: UseCat = False +LightGBM objective=regression +Not training a calibrator because it is not needed. +L1(avg): 0.407028 +L2(avg): 0.274963 +RMS(avg): 0.524369 +Loss-fn(avg): 0.274963 +R Squared: 0.649369 + +OVERALL RESULTS +--------------------------------------- +L1(avg): 0.407028 (0.0000) +L2(avg): 0.274963 (0.0000) +RMS(avg): 0.524369 (0.0000) +Loss-fn(avg): 0.274963 (0.0000) +R Squared: 0.649369 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + +--- Progress log --- +[1] 'Loading data for LightGBM' started. +[1] 'Loading data for LightGBM' finished in %Time%. +[2] 'Training with LightGBM' started. +[2] (%Time%) Iteration: 50 Training-rmse: 0.524369259872456 +[2] 'Training with LightGBM' finished in %Time%. +[3] 'Saving model' started. +[3] 'Saving model' finished in %Time%. diff --git a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-TrainTest-wine.RMSE-rp.txt b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-TrainTest-wine.RMSE-rp.txt new file mode 100644 index 0000000000..2c813d3277 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-TrainTest-wine.RMSE-rp.txt @@ -0,0 +1,4 @@ +LightGBMR +L1(avg) L2(avg) RMS(avg) Loss-fn(avg) R Squared /iter /lr /nl /mil /v /nt /em Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.407028 0.274963 0.524369 0.274963 0.649369 50 0.2 20 10 + 1 Rmse LightGBMR %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LightGBMR{nt=1 iter=50 em=rmse v=+ lr=0.2 mil=10 nl=20} dout=%Output% loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=%Data% out=%Output% seed=1 /iter:50;/lr:0.2;/nl:20;/mil:10;/v:+;/nt:1;/em:Rmse + diff --git a/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-TrainTest-wine.RMSE.txt b/test/BaselineOutput/SingleRelease/LightGBMR/LightGBMRegRmse-TrainTest-wine.RMSE.txt new file mode 100644 index 0000000000..c0618c3738 --- /dev/null +++ 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b/test/BaselineOutput/SingleRelease/SavePipe/PartitionedNamedDirectories-Schema.txt @@ -0,0 +1,5 @@ +---- PartitionedFileLoader ---- +3 columns: + L0: Text + Year: Text + Month: Text diff --git a/test/BaselineOutput/SingleRelease/SavePipe/PartitionedUnnamedDirectories-Data.txt b/test/BaselineOutput/SingleRelease/SavePipe/PartitionedUnnamedDirectories-Data.txt new file mode 100644 index 0000000000..4c7f650844 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/SavePipe/PartitionedUnnamedDirectories-Data.txt @@ -0,0 +1,16 @@ +#@ TextLoader{ +#@ header+ +#@ sep=tab +#@ col=L0:I4:0 +#@ col=Month:I4:1 +#@ col=Path:TX:2 +#@ } +L0 Month Path +1 1 2017/01/data1.csv +5 1 2017/01/data2.csv +7 1 2017/01/data2.csv +0 1 2017/01/dataBadSchema.csv +0 1 2017/01/dataBadSchema.csv +22 2 2017/02/data1.csv +24 2 2017/02/data1.csv +26 2 2017/02/data1.csv diff --git a/test/BaselineOutput/SingleRelease/SavePipe/PartitionedUnnamedDirectories-Schema.txt b/test/BaselineOutput/SingleRelease/SavePipe/PartitionedUnnamedDirectories-Schema.txt new file mode 100644 index 0000000000..5eeac7698b --- /dev/null +++ b/test/BaselineOutput/SingleRelease/SavePipe/PartitionedUnnamedDirectories-Schema.txt @@ -0,0 +1,5 @@ +---- PartitionedFileLoader ---- +3 columns: + L0: I4 + Month: I4 + Path: Text diff --git a/test/BaselineOutput/SingleRelease/pcaAnomaly/Default-TrainTest-azureCounterUnlabeled-out.txt b/test/BaselineOutput/SingleRelease/pcaAnomaly/Default-TrainTest-azureCounterUnlabeled-out.txt new file mode 100644 index 0000000000..d0abf79d6f --- /dev/null +++ b/test/BaselineOutput/SingleRelease/pcaAnomaly/Default-TrainTest-azureCounterUnlabeled-out.txt @@ -0,0 +1,24 @@ +maml.exe TrainTest test=%Data%\azure-test-unlabeled.txt tr=pcaAnomaly dout=%Output%\pcaAnomaly\Default-TrainTest-azureCounterUnlabeled.txt loader=Text{sep=space col=Name:TX:0 col=Features:~} data=%Data%\azure-train-unlabeled.txt out=%Output%\pcaAnomaly\Default-TrainTest-azureCounterUnlabeled-model.zip seed=1 xf=Expr{col=Label:Name expr={x : na(float(0))}} +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Not training a calibrator because it is not needed. +Warning: Encountered 6590 unlabeled instances during testing. +Detection rate at 10 false positives: NaN +Detection rate at 0.01 false positive rate: NaN +Detection rate at 0 positive predictions: NaN +Threshold @K FP: NaN +Threshold @P FPR: NaN +Threshold @NumPos: NaN +AUC: NaN + +OVERALL RESULTS +--------------------------------------- +AUC: NaN (0.0000) +DR @K FP: NaN (0.0000) +DR @P FPR: NaN (0.0000) +DR @NumPos: NaN (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/SingleRelease/pcaAnomaly/Default-TrainTest-azureCounterUnlabeled-rp.txt b/test/BaselineOutput/SingleRelease/pcaAnomaly/Default-TrainTest-azureCounterUnlabeled-rp.txt new file mode 100644 index 0000000000..8dc0fad5a7 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/pcaAnomaly/Default-TrainTest-azureCounterUnlabeled-rp.txt @@ -0,0 +1,4 @@ +pcaAnomaly +AUC DR @K FP DR @P FPR DR @NumPos Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +NaN NaN NaN NaN pcaAnomaly %Data%\azure-train-unlabeled.txt %Data%\azure-test-unlabeled.txt %Output%\pcaAnomaly\Default-TrainTest-azureCounterUnlabeled-out.txt 99 0 0 maml.exe TrainTest test=%Data%\azure-test-unlabeled.txt tr=pcaAnomaly dout=%Output%\pcaAnomaly\Default-TrainTest-azureCounterUnlabeled.txt loader=Text{sep=space col=Name:TX:0 col=Features:~} data=%Data%\azure-train-unlabeled.txt out=%Output%\pcaAnomaly\Default-TrainTest-azureCounterUnlabeled-model.zip seed=1 xf=Expr{col=Label:Name expr={x : na(float(0))}} + diff --git a/test/BaselineOutput/SingleRelease/pcaAnomaly/Default-TrainTest-azureCounterUnlabeled-summary.txt b/test/BaselineOutput/SingleRelease/pcaAnomaly/Default-TrainTest-azureCounterUnlabeled-summary.txt new file mode 100644 index 0000000000..52e03c7595 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/pcaAnomaly/Default-TrainTest-azureCounterUnlabeled-summary.txt @@ -0,0 +1,25 @@ +Dimension: 50 +Rank: 20 +Mean vector: 0.788325 0.788341 0.7883744 0.7883848 0.7883936 0.7883905 0.788416 0.7884151 0.7884451 0.7884336 0.7884297 0.7884075 0.7883975 0.7883836 0.7883822 0.7883797 0.7883725 0.7883604 0.7883703 0.7883449 0.7883869 0.7883851 0.7883923 0.788395 0.7883939 0.7883962 0.7884332 0.7884462 0.7884642 0.7884554 0.7884371 0.7883986 0.7883911 0.7883792 0.7883636 0.7883496 0.788327 0.7883144 0.7883062 0.7882917 0.7882761 0.7882612 0.7882473 0.788215 0.7881997 0.7881925 0.7881818 0.788156 0.7881312 0.7880983 +Projected mean vector: -5.572146 -0.0009027272 -8.744746E-05 0.1541603 -0.0005702823 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+2014-07-11_17_55_00.0000000___2014-07-11_22_00_00.0000000 0.05277346 +2014-07-11_18_00_00.0000000___2014-07-11_22_05_00.0000000 0.05909107 +2014-07-11_18_05_00.0000000___2014-07-11_22_10_00.0000000 0.0608733743 +2014-07-11_18_10_00.0000000___2014-07-11_22_15_00.0000000 0.0568289869 +2014-07-11_18_15_00.0000000___2014-07-11_22_20_00.0000000 0.05434453 +2014-07-11_18_20_00.0000000___2014-07-11_22_25_00.0000000 0.05590492 +2014-07-11_18_25_00.0000000___2014-07-11_22_30_00.0000000 0.0602416545 +2014-07-11_18_30_00.0000000___2014-07-11_22_35_00.0000000 0.06299596 +2014-07-11_18_35_00.0000000___2014-07-11_22_40_00.0000000 0.0630844459 +2014-07-11_18_40_00.0000000___2014-07-11_22_45_00.0000000 0.06260439 diff --git a/test/BaselineOutput/SingleRelease/pcaAnomaly/Default-TrainTest-mnistOneClass-out.txt b/test/BaselineOutput/SingleRelease/pcaAnomaly/Default-TrainTest-mnistOneClass-out.txt new file mode 100644 index 0000000000..260d9fb858 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/pcaAnomaly/Default-TrainTest-mnistOneClass-out.txt @@ -0,0 +1,76 @@ +maml.exe TrainTest test=%Data% tr=pcaAnomaly dout=%Output% data=%Data% out=%Output% seed=1 +Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. +Not training a calibrator because it is not needed. +50 Top-scored Results +================================================= +Instance Anomaly Score Labeled +66 0.9938 0 +48 0.9909 5 +25 0.9909 0 +72 0.9708 7 +37 0.9144 0 +69 0.8545 5 +70 0.776 1 +5 0.7011 0 +17 0.6974 0 +22 0.6871 9 +74 0.6836 4 +87 0.6795 1 +61 0.6758 6 +102 0.6643 1 +46 0.6519 0 +111 0.648 0 +76 0.6162 0 +12 0.5826 0 +109 0.5744 0 +60 0.57 0 +21 0.5687 0 +83 0.5668 6 +10 0.5591 0 +44 0.5571 0 +35 0.5547 0 +29 0.5546 0 +54 0.5536 0 +27 0.5526 0 +6 0.5507 0 +52 0.5501 0 +30 0.5477 0 +113 0.5465 0 +94 0.5448 0 +40 0.5444 0 +79 0.5365 0 +103 0.531 0 +58 0.5304 0 +64 0.5297 0 +9 0.5292 0 +3 0.5262 0 +78 0.5254 0 +47 0.5241 0 +23 0.5238 0 +110 0.5206 0 +114 0.52 0 +82 0.519 0 +101 0.5127 0 +32 0.5113 0 +88 0.5107 0 +24 0.5096 0 +Detection rate at 10 false positives: 0.900000 +Detection rate at 0.01 false positive rate: 0.100000 +Detection rate at 10 positive predictions: 0.500000 +Threshold @K FP: 0.574407 +Threshold @P FPR: 0.990874 +Threshold @NumPos: 0.687074 +AUC: 0.956190 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.956190 (0.0000) +DR @K FP: 0.900000 (0.0000) +DR @P FPR: 0.100000 (0.0000) +DR @NumPos: 0.500000 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/SingleRelease/pcaAnomaly/Default-TrainTest-mnistOneClass-rp.txt b/test/BaselineOutput/SingleRelease/pcaAnomaly/Default-TrainTest-mnistOneClass-rp.txt new file mode 100644 index 0000000000..4948ad2f4e --- /dev/null +++ b/test/BaselineOutput/SingleRelease/pcaAnomaly/Default-TrainTest-mnistOneClass-rp.txt @@ -0,0 +1,4 @@ +pcaAnomaly +AUC DR @K FP DR @P FPR DR @NumPos Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.95619 0.9 0.1 0.5 pcaAnomaly %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=pcaAnomaly dout=%Output% data=%Data% out=%Output% seed=1 + diff --git a/test/BaselineOutput/SingleRelease/pcaAnomaly/Default-TrainTest-mnistOneClass.txt b/test/BaselineOutput/SingleRelease/pcaAnomaly/Default-TrainTest-mnistOneClass.txt new file mode 100644 index 0000000000..8667e96c93 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/pcaAnomaly/Default-TrainTest-mnistOneClass.txt @@ -0,0 +1,116 @@ +Instance Label Score +0 0 0.495893419 +1 0 0.450051278 +2 0 0.461110771 +3 0 0.5262271 +4 0 0.508700669 +5 0 0.7010657 +6 0 0.550729036 +7 0 0.45529598 +8 0 0.432054043 +9 0 0.529151142 +10 0 0.559123456 +11 0 0.485286921 +12 0 0.5826422 +13 0 0.420975477 +14 0 0.490322858 +15 0 0.5079249 +16 0 0.468320668 +17 0 0.6973921 +18 0 0.490462124 +19 0 0.469306976 +20 0 0.434285462 +21 0 0.5687201 +22 9 0.6870739 +23 0 0.5237843 +24 0 0.509592056 +25 0 0.9908741 +26 0 0.501218855 +27 0 0.5525645 +28 0 0.47950533 +29 0 0.554561734 +30 0 0.547651231 +31 0 0.4881632 +32 0 0.511275351 +33 0 0.3691424 +34 0 0.4903799 +35 0 0.5547312 +36 0 0.47143057 +37 0 0.914354265 +38 0 0.5050705 +39 0 0.432126135 +40 0 0.54437536 +41 0 0.435738117 +42 0 0.394098163 +43 0 0.449369878 +44 0 0.557093143 +45 0 0.4455139 +46 0 0.6519489 +47 0 0.524138033 +48 5 0.990943134 +49 0 0.443518072 +50 0 0.463696748 +51 0 0.398020357 +52 0 0.5500833 +53 0 0.4488295 +54 0 0.5536161 +55 0 0.418236881 +56 0 0.409066468 +57 0 0.5022479 +58 0 0.5304069 +59 0 0.446812063 +60 0 0.5700437 +61 6 0.6758252 +62 0 0.457501948 +63 0 0.436834127 +64 0 0.5297206 +65 0 0.500145555 +66 0 0.9938066 +67 0 0.4682963 +68 0 0.422779649 +69 5 0.854525864 +70 1 0.7760329 +71 0 0.5095027 +72 7 0.9708281 +73 0 0.5008225 +74 4 0.6835667 +75 0 0.436794817 +76 0 0.6161679 +77 0 0.4936375 +78 0 0.5253543 +79 0 0.536463261 +80 0 0.467982769 +81 0 0.416288167 +82 0 0.518951237 +83 6 0.5667783 +84 0 0.4754018 +85 0 0.451952815 +86 0 0.4157417 +87 1 0.679460764 +88 0 0.510666132 +89 0 0.4798051 +90 0 0.474666148 +91 0 0.503672063 +92 0 0.483663946 +93 0 0.426248729 +94 0 0.544837534 +95 0 0.454061627 +96 0 0.4768858 +97 0 0.475224346 +98 0 0.4648013 +99 0 0.449731052 +100 0 0.374409467 +101 0 0.512731969 +102 1 0.664328635 +103 0 0.5310194 +104 0 0.449136227 +105 0 0.5033788 +106 0 0.4501728 +107 0 0.499612123 +108 0 0.467777759 +109 0 0.574407458 +110 0 0.520628 +111 0 0.648003161 +112 0 0.3824868 +113 0 0.5464604 +114 0 0.519995868 diff --git a/test/BaselineOutput/SingleRelease/pcaAnomaly/NoNorm-TrainTest-mnistOneClass-out.txt b/test/BaselineOutput/SingleRelease/pcaAnomaly/NoNorm-TrainTest-mnistOneClass-out.txt new file mode 100644 index 0000000000..6ba35663ca --- /dev/null +++ b/test/BaselineOutput/SingleRelease/pcaAnomaly/NoNorm-TrainTest-mnistOneClass-out.txt @@ -0,0 +1,76 @@ +maml.exe TrainTest test=%Data% tr=pcaAnomaly norm=No dout=%Output% data=%Data% out=%Output% seed=1 +Not adding a normalizer. +Not training a calibrator because it is not needed. +50 Top-scored Results +================================================= +Instance Anomaly Score Labeled +69 0.8341 5 +70 0.7756 1 +48 0.7316 5 +72 0.7184 7 +74 0.6831 4 +87 0.6784 1 +61 0.6754 6 +17 0.6731 0 +22 0.6632 9 +102 0.6629 1 +46 0.6559 0 +37 0.6169 0 +76 0.6153 0 +12 0.5829 0 +109 0.5751 0 +60 0.5704 0 +25 0.5697 0 +21 0.5682 0 +83 0.5649 6 +10 0.5587 0 +44 0.556 0 +29 0.5546 0 +35 0.5544 0 +66 0.5534 0 +54 0.5529 0 +27 0.5521 0 +6 0.5503 0 +52 0.5492 0 +30 0.5469 0 +113 0.5458 0 +94 0.5442 0 +40 0.5439 0 +79 0.5364 0 +103 0.5301 0 +64 0.5299 0 +58 0.5296 0 +9 0.5289 0 +3 0.5263 0 +78 0.5259 0 +5 0.5245 0 +47 0.5233 0 +23 0.5228 0 +114 0.5201 0 +110 0.5201 0 +82 0.5188 0 +111 0.5146 0 +101 0.5131 0 +32 0.5108 0 +88 0.5106 0 +24 0.5095 0 +Detection rate at 10 false positives: 1.000000 +Detection rate at 0.01 false positive rate: 0.900000 +Detection rate at 10 positive predictions: 0.900000 +Threshold @K FP: 0.558688 +Threshold @P FPR: 0.655883 +Threshold @NumPos: 0.662917 +AUC: 0.989524 + +OVERALL RESULTS +--------------------------------------- +AUC: 0.989524 (0.0000) +DR @K FP: 1.000000 (0.0000) +DR @P FPR: 0.900000 (0.0000) +DR @NumPos: 0.900000 (0.0000) + +--------------------------------------- +Physical memory usage(MB): %Number% +Virtual memory usage(MB): %Number% +%DateTime% Time elapsed(s): %Number% + diff --git a/test/BaselineOutput/SingleRelease/pcaAnomaly/NoNorm-TrainTest-mnistOneClass-rp.txt b/test/BaselineOutput/SingleRelease/pcaAnomaly/NoNorm-TrainTest-mnistOneClass-rp.txt new file mode 100644 index 0000000000..a80438aae1 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/pcaAnomaly/NoNorm-TrainTest-mnistOneClass-rp.txt @@ -0,0 +1,4 @@ +pcaAnomaly +AUC DR @K FP DR @P FPR DR @NumPos Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings +0.989524 1 0.9 0.9 pcaAnomaly %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=pcaAnomaly norm=No dout=%Output% data=%Data% out=%Output% seed=1 + diff --git a/test/BaselineOutput/SingleRelease/pcaAnomaly/NoNorm-TrainTest-mnistOneClass.txt b/test/BaselineOutput/SingleRelease/pcaAnomaly/NoNorm-TrainTest-mnistOneClass.txt new file mode 100644 index 0000000000..45a1104275 --- /dev/null +++ b/test/BaselineOutput/SingleRelease/pcaAnomaly/NoNorm-TrainTest-mnistOneClass.txt @@ -0,0 +1,116 @@ +Instance Label Score +0 0 0.4951306 +1 0 0.449331224 +2 0 0.461190224 +3 0 0.526296139 +4 0 0.508475244 +5 0 0.5244605 +6 0 0.550277948 +7 0 0.455368549 +8 0 0.431095362 +9 0 0.528869 +10 0 0.558688045 +11 0 0.4851844 +12 0 0.582877636 +13 0 0.420640141 +14 0 0.489568383 +15 0 0.5076097 +16 0 0.4680955 +17 0 0.6731393 +18 0 0.490120977 +19 0 0.468934834 +20 0 0.4334778 +21 0 0.5682155 +22 9 0.6632366 +23 0 0.5228237 +24 0 0.5094715 +25 0 0.5697219 +26 0 0.501028359 +27 0 0.5520762 +28 0 0.478860557 +29 0 0.5545906 +30 0 0.546905637 +31 0 0.4884955 +32 0 0.5108325 +33 0 0.368852228 +34 0 0.4903543 +35 0 0.554350555 +36 0 0.471144259 +37 0 0.61691314 +38 0 0.5044613 +39 0 0.432231337 +40 0 0.543896258 +41 0 0.4354026 +42 0 0.393868625 +43 0 0.4491824 +44 0 0.5560113 +45 0 0.44493416 +46 0 0.6558832 +47 0 0.523334861 +48 5 0.731561244 +49 0 0.44316262 +50 0 0.463617116 +51 0 0.397496134 +52 0 0.549204767 +53 0 0.449091375 +54 0 0.5528623 +55 0 0.417973459 +56 0 0.409094721 +57 0 0.501895368 +58 0 0.5295681 +59 0 0.446579576 +60 0 0.5703988 +61 6 0.6754471 +62 0 0.4571285 +63 0 0.436919063 +64 0 0.529872537 +65 0 0.499967635 +66 0 0.553377748 +67 0 0.467494756 +68 0 0.422788024 +69 5 0.8340921 +70 1 0.7755893 +71 0 0.509331048 +72 7 0.7183694 +73 0 0.501034141 +74 4 0.6831258 +75 0 0.4366773 +76 0 0.615255535 +77 0 0.493479639 +78 0 0.5258877 +79 0 0.5363522 +80 0 0.467402875 +81 0 0.416329473 +82 0 0.518832862 +83 6 0.564920068 +84 0 0.475099474 +85 0 0.45117566 +86 0 0.4157634 +87 1 0.678395748 +88 0 0.510561645 +89 0 0.480021447 +90 0 0.474439472 +91 0 0.5036237 +92 0 0.483466953 +93 0 0.426080644 +94 0 0.5442002 +95 0 0.453564852 +96 0 0.4767387 +97 0 0.475078881 +98 0 0.4640796 +99 0 0.4493989 +100 0 0.374264151 +101 0 0.5131056 +102 1 0.66291666 +103 0 0.530139863 +104 0 0.449070573 +105 0 0.5036392 +106 0 0.450024515 +107 0 0.499411643 +108 0 0.467788219 +109 0 0.575076 +110 0 0.520050168 +111 0 0.514583051 +112 0 0.38196522 +113 0 0.5457588 +114 0 0.520133436 diff --git a/test/Microsoft.ML.Core.Tests/Microsoft.ML.Core.Tests.csproj b/test/Microsoft.ML.Core.Tests/Microsoft.ML.Core.Tests.csproj index 9f38858721..3cf404f981 100644 --- a/test/Microsoft.ML.Core.Tests/Microsoft.ML.Core.Tests.csproj +++ b/test/Microsoft.ML.Core.Tests/Microsoft.ML.Core.Tests.csproj @@ -8,6 +8,7 @@ + @@ -20,6 +21,7 @@ + \ No newline at end of file diff --git a/test/Microsoft.ML.Core.Tests/UnitTests/TestCSharpApi.cs b/test/Microsoft.ML.Core.Tests/UnitTests/TestCSharpApi.cs index 4c1969c2d9..42943eda70 100644 --- a/test/Microsoft.ML.Core.Tests/UnitTests/TestCSharpApi.cs +++ b/test/Microsoft.ML.Core.Tests/UnitTests/TestCSharpApi.cs @@ -262,7 +262,7 @@ public void TestCrossValidationBinaryMacro() [Fact] public void TestCrossValidationMacro() { - var dataPath = GetDataPath(TestDatasets.winequality.trainFilename); + var dataPath = GetDataPath(TestDatasets.winequalitymacro.trainFilename); using (var env = new TlcEnvironment(42)) { var subGraph = env.CreateExperiment(); @@ -270,16 +270,22 @@ public void TestCrossValidationMacro() var nop = new ML.Transforms.NoOperation(); var nopOutput = subGraph.Add(nop); - var learnerInput = new ML.Trainers.StochasticDualCoordinateAscentRegressor + var generate = new ML.Transforms.RandomNumberGenerator(); + generate.Column = new[] { new ML.Transforms.GenerateNumberTransformColumn() { Name = "Weight1" } }; + generate.Data = nopOutput.OutputData; + var generateOutput = subGraph.Add(generate); + + var learnerInput = new ML.Trainers.PoissonRegressor { - TrainingData = nopOutput.OutputData, - NumThreads = 1 + TrainingData = generateOutput.OutputData, + NumThreads = 1, + WeightColumn = "Weight1" }; var learnerOutput = subGraph.Add(learnerInput); var modelCombine = new ML.Transforms.ManyHeterogeneousModelCombiner { - TransformModels = new ArrayVar(nopOutput.Model), + TransformModels = new ArrayVar(nopOutput.Model, generateOutput.Model), PredictorModel = learnerOutput.PredictorModel }; var modelCombineOutput = subGraph.Add(modelCombine); @@ -297,14 +303,14 @@ public void TestCrossValidationMacro() { Name = "Label", Source = new [] { new TextLoaderRange(11) }, - Type = DataKind.Num + Type = ML.Data.DataKind.Num }, new TextLoaderColumn() { Name = "Features", Source = new [] { new TextLoaderRange(0,10) }, - Type = DataKind.Num + Type = ML.Data.DataKind.Num } } } @@ -316,7 +322,8 @@ public void TestCrossValidationMacro() Data = importOutput.Data, Nodes = subGraph, Kind = ML.Models.MacroUtilsTrainerKinds.SignatureRegressorTrainer, - TransformModel = null + TransformModel = null, + WeightColumn = "Weight1" }; crossValidate.Inputs.Data = nop.Data; crossValidate.Outputs.PredictorModel = modelCombineOutput.PredictorModel; @@ -332,40 +339,66 @@ public void TestCrossValidationMacro() Assert.True(b); b = schema.TryGetColumnIndex("Fold Index", out int foldCol); Assert.True(b); - using (var cursor = data.GetRowCursor(col => col == metricCol || col == foldCol)) + b = schema.TryGetColumnIndex("IsWeighted", out int isWeightedCol); + using (var cursor = data.GetRowCursor(col => col == metricCol || col == foldCol || col == isWeightedCol)) { var getter = cursor.GetGetter(metricCol); var foldGetter = cursor.GetGetter(foldCol); + var isWeightedGetter = cursor.GetGetter(isWeightedCol); DvText fold = default; + DvBool isWeighted = default; - // Get the verage. - b = cursor.MoveNext(); - Assert.True(b); double avg = 0; - getter(ref avg); - foldGetter(ref fold); - Assert.True(fold.EqualsStr("Average")); - - // Get the standard deviation. - b = cursor.MoveNext(); - Assert.True(b); - double stdev = 0; - getter(ref stdev); - foldGetter(ref fold); - Assert.True(fold.EqualsStr("Standard Deviation")); - Assert.Equal(0.0013, stdev, 4); - - double sum = 0; - double val = 0; - for (int f = 0; f < 2; f++) + double weightedAvg = 0; + for (int w = 0; w < 2; w++) { + // Get the average. b = cursor.MoveNext(); Assert.True(b); - getter(ref val); + if (w == 1) + getter(ref weightedAvg); + else + getter(ref avg); foldGetter(ref fold); - sum += val; - Assert.True(fold.EqualsStr("Fold " + f)); + Assert.True(fold.EqualsStr("Average")); + isWeightedGetter(ref isWeighted); + Assert.True(isWeighted.IsTrue == (w == 1)); + + // Get the standard deviation. + b = cursor.MoveNext(); + Assert.True(b); + double stdev = 0; + getter(ref stdev); + foldGetter(ref fold); + Assert.True(fold.EqualsStr("Standard Deviation")); + if (w == 1) + Assert.Equal(0.002827, stdev, 6); + else + Assert.Equal(0.002376, stdev, 6); + isWeightedGetter(ref isWeighted); + Assert.True(isWeighted.IsTrue == (w == 1)); + } + double sum = 0; + double weightedSum = 0; + for (int f = 0; f < 2; f++) + { + for (int w = 0; w < 2; w++) + { + b = cursor.MoveNext(); + Assert.True(b); + double val = 0; + getter(ref val); + foldGetter(ref fold); + if (w == 1) + weightedSum += val; + else + sum += val; + Assert.True(fold.EqualsStr("Fold " + f)); + isWeightedGetter(ref isWeighted); + Assert.True(isWeighted.IsTrue == (w == 1)); + } } + Assert.Equal(weightedAvg, weightedSum / 2); Assert.Equal(avg, sum / 2); b = cursor.MoveNext(); Assert.False(b); @@ -495,6 +528,89 @@ public void TestCrossValidationMacroWithMultiClass() } Assert.Equal(0, rowCount); } + + var warnings = experiment.GetOutput(crossValidateOutput.Warnings); + using (var cursor = warnings.GetRowCursor(col => true)) + Assert.False(cursor.MoveNext()); + } + } + + [Fact] + public void TestCrossValidationMacroMultiClassWithWarnings() + { + var dataPath = GetDataPath(@"Train-Tiny-28x28.txt"); + using (var env = new TlcEnvironment(42)) + { + var subGraph = env.CreateExperiment(); + + var nop = new ML.Transforms.NoOperation(); + var nopOutput = subGraph.Add(nop); + + var learnerInput = new ML.Trainers.LogisticRegressionClassifier + { + TrainingData = nopOutput.OutputData, + NumThreads = 1 + }; + var learnerOutput = subGraph.Add(learnerInput); + + var experiment = env.CreateExperiment(); + var importInput = new ML.Data.TextLoader(dataPath); + var importOutput = experiment.Add(importInput); + + var filter = new ML.Transforms.RowRangeFilter(); + filter.Data = importOutput.Data; + filter.Column = "Label"; + filter.Min = 0; + filter.Max = 5; + var filterOutput = experiment.Add(filter); + + var term = new ML.Transforms.TextToKeyConverter(); + term.Column = new[] + { + new ML.Transforms.TermTransformColumn() + { + Source = "Label", Name = "Strat", Sort = ML.Transforms.TermTransformSortOrder.Value + } + }; + term.Data = filterOutput.OutputData; + var termOutput = experiment.Add(term); + + var crossValidate = new ML.Models.CrossValidator + { + Data = termOutput.OutputData, + Nodes = subGraph, + Kind = ML.Models.MacroUtilsTrainerKinds.SignatureMultiClassClassifierTrainer, + TransformModel = null, + StratificationColumn = "Strat" + }; + crossValidate.Inputs.Data = nop.Data; + crossValidate.Outputs.PredictorModel = learnerOutput.PredictorModel; + var crossValidateOutput = experiment.Add(crossValidate); + + experiment.Compile(); + importInput.SetInput(env, experiment); + experiment.Run(); + var warnings = experiment.GetOutput(crossValidateOutput.Warnings); + + var schema = warnings.Schema; + var b = schema.TryGetColumnIndex("WarningText", out int warningCol); + Assert.True(b); + using (var cursor = warnings.GetRowCursor(col => col == warningCol)) + { + var getter = cursor.GetGetter(warningCol); + + b = cursor.MoveNext(); + Assert.True(b); + var warning = default(DvText); + getter(ref warning); + Assert.Contains("test instances with class values not seen in the training set.", warning.ToString()); + b = cursor.MoveNext(); + Assert.True(b); + getter(ref warning); + Assert.Contains("Detected columns of variable length: SortedScores, SortedClasses", warning.ToString()); + b = cursor.MoveNext(); + Assert.False(b); + } } } @@ -593,5 +709,236 @@ public void TestCrossValidationMacroWithStratification() } } } + + [Fact] + public void TestCrossValidationMacroWithNonDefaultNames() + { + string dataPath = GetDataPath(@"adult.tiny.with-schema.txt"); + using (var env = new TlcEnvironment(42)) + { + var subGraph = env.CreateExperiment(); + + var textToKey = new ML.Transforms.TextToKeyConverter(); + textToKey.Column = new[] { new ML.Transforms.TermTransformColumn() { Name = "Label1", Source = "Label" } }; + var textToKeyOutput = subGraph.Add(textToKey); + + var hash = new ML.Transforms.HashConverter(); + hash.Column = new[] { new ML.Transforms.HashJoinTransformColumn() { Name = "GroupId1", Source = "Workclass" } }; + hash.Data = textToKeyOutput.OutputData; + var hashOutput = subGraph.Add(hash); + + var learnerInput = new Trainers.FastTreeRanker + { + TrainingData = hashOutput.OutputData, + NumThreads = 1, + LabelColumn = "Label1", + GroupIdColumn = "GroupId1" + }; + var learnerOutput = subGraph.Add(learnerInput); + + var modelCombine = new ML.Transforms.ManyHeterogeneousModelCombiner + { + TransformModels = new ArrayVar(textToKeyOutput.Model, hashOutput.Model), + PredictorModel = learnerOutput.PredictorModel + }; + var modelCombineOutput = subGraph.Add(modelCombine); + + var experiment = env.CreateExperiment(); + var importInput = new ML.Data.TextLoader(dataPath); + importInput.Arguments.HasHeader = true; + importInput.Arguments.Column = new TextLoaderColumn[] + { + new TextLoaderColumn { Name = "Label", Source = new[] { new TextLoaderRange(0) } }, + new TextLoaderColumn { Name = "Workclass", Source = new[] { new TextLoaderRange(1) }, Type = ML.Data.DataKind.Text }, + new TextLoaderColumn { Name = "Features", Source = new[] { new TextLoaderRange(9, 14) } } + }; + var importOutput = experiment.Add(importInput); + + var crossValidate = new Models.CrossValidator + { + Data = importOutput.Data, + Nodes = subGraph, + TransformModel = null, + LabelColumn = "Label1", + GroupColumn = "GroupId1", + Kind = Models.MacroUtilsTrainerKinds.SignatureRankerTrainer + }; + crossValidate.Inputs.Data = textToKey.Data; + crossValidate.Outputs.PredictorModel = modelCombineOutput.PredictorModel; + var crossValidateOutput = experiment.Add(crossValidate); + experiment.Compile(); + experiment.SetInput(importInput.InputFile, new SimpleFileHandle(env, dataPath, false, false)); + experiment.Run(); + var data = experiment.GetOutput(crossValidateOutput.OverallMetrics); + + var schema = data.Schema; + var b = schema.TryGetColumnIndex("NDCG", out int metricCol); + Assert.True(b); + b = schema.TryGetColumnIndex("Fold Index", out int foldCol); + Assert.True(b); + using (var cursor = data.GetRowCursor(col => col == metricCol || col == foldCol)) + { + var getter = cursor.GetGetter>(metricCol); + var foldGetter = cursor.GetGetter(foldCol); + DvText fold = default; + + // Get the verage. + b = cursor.MoveNext(); + Assert.True(b); + var avg = default(VBuffer); + getter(ref avg); + foldGetter(ref fold); + Assert.True(fold.EqualsStr("Average")); + + // Get the standard deviation. + b = cursor.MoveNext(); + Assert.True(b); + var stdev = default(VBuffer); + getter(ref stdev); + foldGetter(ref fold); + Assert.True(fold.EqualsStr("Standard Deviation")); + Assert.Equal(5.247, stdev.Values[0], 3); + Assert.Equal(4.703, stdev.Values[1], 3); + Assert.Equal(3.844, stdev.Values[2], 3); + + var sumBldr = new BufferBuilder(R8Adder.Instance); + sumBldr.Reset(avg.Length, true); + var val = default(VBuffer); + for (int f = 0; f < 2; f++) + { + b = cursor.MoveNext(); + Assert.True(b); + getter(ref val); + foldGetter(ref fold); + sumBldr.AddFeatures(0, ref val); + Assert.True(fold.EqualsStr("Fold " + f)); + } + var sum = default(VBuffer); + sumBldr.GetResult(ref sum); + for (int i = 0; i < avg.Length; i++) + Assert.Equal(avg.Values[i], sum.Values[i] / 2); + b = cursor.MoveNext(); + Assert.False(b); + } + } + } + + [Fact] + public void TestOvaMacro() + { + var dataPath = GetDataPath(@"iris.txt"); + using (var env = new TlcEnvironment(42)) + { + // Specify subgraph for OVA + var subGraph = env.CreateExperiment(); + var learnerInput = new Trainers.StochasticDualCoordinateAscentBinaryClassifier { NumThreads = 1 }; + var learnerOutput = subGraph.Add(learnerInput); + // Create pipeline with OVA and multiclass scoring. + var experiment = env.CreateExperiment(); + var importInput = new ML.Data.TextLoader(dataPath); + importInput.Arguments.Column = new TextLoaderColumn[] + { + new TextLoaderColumn { Name = "Label", Source = new[] { new TextLoaderRange(0) } }, + new TextLoaderColumn { Name = "Features", Source = new[] { new TextLoaderRange(1,4) } } + }; + var importOutput = experiment.Add(importInput); + var oneVersusAll = new Models.OneVersusAll + { + TrainingData = importOutput.Data, + Nodes = subGraph, + UseProbabilities = true, + }; + var ovaOutput = experiment.Add(oneVersusAll); + var scoreInput = new ML.Transforms.DatasetScorer + { + Data = importOutput.Data, + PredictorModel = ovaOutput.PredictorModel + }; + var scoreOutput = experiment.Add(scoreInput); + var evalInput = new ML.Models.ClassificationEvaluator + { + Data = scoreOutput.ScoredData + }; + var evalOutput = experiment.Add(evalInput); + experiment.Compile(); + experiment.SetInput(importInput.InputFile, new SimpleFileHandle(env, dataPath, false, false)); + experiment.Run(); + + var data = experiment.GetOutput(evalOutput.OverallMetrics); + var schema = data.Schema; + var b = schema.TryGetColumnIndex(MultiClassClassifierEvaluator.AccuracyMacro, out int accCol); + Assert.True(b); + using (var cursor = data.GetRowCursor(col => col == accCol)) + { + var getter = cursor.GetGetter(accCol); + b = cursor.MoveNext(); + Assert.True(b); + double acc = 0; + getter(ref acc); + Assert.Equal(0.96, acc, 2); + b = cursor.MoveNext(); + Assert.False(b); + } + } + } + + [Fact] + public void TestOvaMacroWithUncalibratedLearner() + { + var dataPath = GetDataPath(@"iris.txt"); + using (var env = new TlcEnvironment(42)) + { + // Specify subgraph for OVA + var subGraph = env.CreateExperiment(); + var learnerInput = new Trainers.AveragedPerceptronBinaryClassifier { Shuffle = false }; + var learnerOutput = subGraph.Add(learnerInput); + // Create pipeline with OVA and multiclass scoring. + var experiment = env.CreateExperiment(); + var importInput = new ML.Data.TextLoader(dataPath); + importInput.Arguments.Column = new TextLoaderColumn[] + { + new TextLoaderColumn { Name = "Label", Source = new[] { new TextLoaderRange(0) } }, + new TextLoaderColumn { Name = "Features", Source = new[] { new TextLoaderRange(1,4) } } + }; + var importOutput = experiment.Add(importInput); + var oneVersusAll = new Models.OneVersusAll + { + TrainingData = importOutput.Data, + Nodes = subGraph, + UseProbabilities = true, + }; + var ovaOutput = experiment.Add(oneVersusAll); + var scoreInput = new ML.Transforms.DatasetScorer + { + Data = importOutput.Data, + PredictorModel = ovaOutput.PredictorModel + }; + var scoreOutput = experiment.Add(scoreInput); + var evalInput = new ML.Models.ClassificationEvaluator + { + Data = scoreOutput.ScoredData + }; + var evalOutput = experiment.Add(evalInput); + experiment.Compile(); + experiment.SetInput(importInput.InputFile, new SimpleFileHandle(env, dataPath, false, false)); + experiment.Run(); + + var data = experiment.GetOutput(evalOutput.OverallMetrics); + var schema = data.Schema; + var b = schema.TryGetColumnIndex(MultiClassClassifierEvaluator.AccuracyMacro, out int accCol); + Assert.True(b); + using (var cursor = data.GetRowCursor(col => col == accCol)) + { + var getter = cursor.GetGetter(accCol); + b = cursor.MoveNext(); + Assert.True(b); + double acc = 0; + getter(ref acc); + Assert.Equal(0.71, acc, 2); + b = cursor.MoveNext(); + Assert.False(b); + } + } + } } } diff --git a/test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs b/test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs index 5249d59ace..ded78d11de 100644 --- a/test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs +++ b/test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs @@ -10,11 +10,15 @@ using Microsoft.ML.Runtime.Core.Tests.UnitTests; using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.Data.IO; +using Microsoft.ML.Runtime.Ensemble.OutputCombiners; using Microsoft.ML.Runtime.EntryPoints; using Microsoft.ML.Runtime.EntryPoints.JsonUtils; using Microsoft.ML.Runtime.FastTree; +using Microsoft.ML.Runtime.Internal.Calibration; using Microsoft.ML.Runtime.Internal.Utilities; using Microsoft.ML.Runtime.Learners; +using Microsoft.ML.Runtime.PCA; +using Microsoft.ML.Runtime.TextAnalytics; using Newtonsoft.Json; using Newtonsoft.Json.Linq; using Xunit; @@ -28,69 +32,41 @@ public TestEntryPoints(ITestOutputHelper output) : base(output) { } - [Fact] - public void EntryPointTrainTestSplit() + private IDataView GetBreastCancerDataView() { var dataPath = GetDataPath("breast-cancer.txt"); var inputFile = new SimpleFileHandle(Env, dataPath, false, false); - /*var dataView = ImportTextData.ImportText(Env, new ImportTextData.Input - { InputFile = inputFile, CustomSchema = "col=Label:0 col=Features:TX:1-9" }).Data;*/ - - var dataView = ImportTextData.TextLoader(Env, new ImportTextData.LoaderInput() + return ImportTextData.TextLoader(Env, new ImportTextData.LoaderInput() { Arguments = { - SeparatorChars = new []{',' }, - HasHeader = true, Column = new[] { new TextLoader.Column() { Name = "Label", Source = new [] { new TextLoader.Range() { Min = 0, Max = 0} }, - Type = Runtime.Data.DataKind.Text + Type = Runtime.Data.DataKind.R4 }, new TextLoader.Column() { Name = "Features", Source = new [] { new TextLoader.Range() { Min = 1, Max = 9} }, - Type = Runtime.Data.DataKind.Text + Type = Runtime.Data.DataKind.R4 } } }, InputFile = inputFile }).Data; - - var splitOutput = TrainTestSplit.Split(Env, new TrainTestSplit.Input { Data = dataView, Fraction = 0.9f }); - - int totalRows = CountRows(dataView); - int trainRows = CountRows(splitOutput.TrainData); - int testRows = CountRows(splitOutput.TestData); - - Assert.Equal(totalRows, trainRows + testRows); - Assert.Equal(0.9, (double)trainRows / totalRows, 1); - } - - private static int CountRows(IDataView dataView) - { - int totalRows = 0; - using (var cursor = dataView.GetRowCursor(col => false)) - { - while (cursor.MoveNext()) - totalRows++; - } - - return totalRows; } - [Fact()] - public void EntryPointFeatureCombiner() + private IDataView GetBreastCancerDataviewWithTextColumns() { var dataPath = GetDataPath("breast-cancer.txt"); var inputFile = new SimpleFileHandle(Env, dataPath, false, false); - var dataView = ImportTextData.TextLoader(Env, new ImportTextData.LoaderInput() + return ImportTextData.TextLoader(Env, new ImportTextData.LoaderInput() { Arguments = { @@ -127,6 +103,39 @@ public void EntryPointFeatureCombiner() InputFile = inputFile }).Data; + } + + + [Fact] + public void EntryPointTrainTestSplit() + { + var dataView = GetBreastCancerDataView(); + var splitOutput = TrainTestSplit.Split(Env, new TrainTestSplit.Input { Data = dataView, Fraction = 0.9f }); + + int totalRows = CountRows(dataView); + int trainRows = CountRows(splitOutput.TrainData); + int testRows = CountRows(splitOutput.TestData); + + Assert.Equal(totalRows, trainRows + testRows); + Assert.Equal(0.9, (double)trainRows / totalRows, 1); + } + + private static int CountRows(IDataView dataView) + { + int totalRows = 0; + using (var cursor = dataView.GetRowCursor(col => false)) + { + while (cursor.MoveNext()) + totalRows++; + } + + return totalRows; + } + + [Fact()] + public void EntryPointFeatureCombiner() + { + var dataView = GetBreastCancerDataviewWithTextColumns(); dataView = Env.CreateTransform("Term{col=F1}", dataView); var result = FeatureCombiner.PrepareFeatures(Env, new FeatureCombiner.FeatureCombinerInput() { Data = dataView, Features = new[] { "F1", "F2", "Rest" } }).OutputData; @@ -145,48 +154,8 @@ public void EntryPointFeatureCombiner() [Fact] public void EntryPointScoring() { - var dataPath = GetDataPath("breast-cancer.txt"); - var inputFile = new SimpleFileHandle(Env, dataPath, false, false); - var dataView = ImportTextData.TextLoader(Env, new ImportTextData.LoaderInput() - { - Arguments = - { - HasHeader = true, - Column = new[] - { - new TextLoader.Column() - { - Name = "Label", - Source = new [] { new TextLoader.Range() { Min = 0, Max = 0} } - }, - - new TextLoader.Column() - { - Name = "F1", - Source = new [] { new TextLoader.Range() { Min = 1, Max = 1} }, - Type = Runtime.Data.DataKind.Text - }, - - new TextLoader.Column() - { - Name = "F2", - Source = new [] { new TextLoader.Range() { Min = 2, Max = 2} }, - Type = Runtime.Data.DataKind.I4 - }, - - new TextLoader.Column() - { - Name = "Rest", - Source = new [] { new TextLoader.Range() { Min = 3, Max = 9} } - } - } - }, - - InputFile = inputFile - }).Data; - + var dataView = GetBreastCancerDataviewWithTextColumns(); dataView = Env.CreateTransform("Term{col=F1}", dataView); - var trainData = FeatureCombiner.PrepareFeatures(Env, new FeatureCombiner.FeatureCombinerInput() { Data = dataView, Features = new[] { "F1", "F2", "Rest" } }); var lrModel = LogisticRegression.TrainBinary(Env, new LogisticRegression.Arguments { TrainingData = trainData.OutputData }).PredictorModel; var model = ModelOperations.CombineTwoModels(Env, new ModelOperations.SimplePredictorModelInput() { TransformModel = trainData.Model, PredictorModel = lrModel }).PredictorModel; @@ -205,45 +174,7 @@ public void EntryPointScoring() [Fact] public void EntryPointApplyModel() { - var dataPath = GetDataPath("breast-cancer.txt"); - var inputFile = new SimpleFileHandle(Env, dataPath, false, false); - var dataView = ImportTextData.TextLoader(Env, new ImportTextData.LoaderInput() - { - Arguments = - { - HasHeader = true, - Column = new[] - { - new TextLoader.Column() - { - Name = "Label", - Source = new [] { new TextLoader.Range() { Min = 0, Max = 0} }, - }, - - new TextLoader.Column() - { - Name = "F1", - Source = new [] { new TextLoader.Range() { Min = 1, Max = 1} }, - Type = Runtime.Data.DataKind.Text - }, - - new TextLoader.Column() - { - Name = "F2", - Source = new [] { new TextLoader.Range() { Min = 2, Max = 2} }, - Type = Runtime.Data.DataKind.I4 - }, - - new TextLoader.Column() - { - Name = "Rest", - Source = new [] { new TextLoader.Range() { Min = 3, Max = 9} } - } - } - }, - - InputFile = inputFile - }).Data; + var dataView = GetBreastCancerDataviewWithTextColumns(); dataView = Env.CreateTransform("Term{col=F1}", dataView); @@ -257,50 +188,7 @@ public void EntryPointApplyModel() [Fact] public void EntryPointCaching() { - var dataPath = GetDataPath("breast-cancer.txt"); - var inputFile = new SimpleFileHandle(Env, dataPath, false, false); - /*var dataView = ImportTextData.ImportText(Env, new ImportTextData.Input { InputFile = inputFile, - CustomSchema = "col=Label:0 col=F1:TX:1 col=F2:I4:2 col=Rest:3-9" }).Data; - */ - - var dataView = ImportTextData.TextLoader(Env, new ImportTextData.LoaderInput() - { - Arguments = - { - SeparatorChars = new []{',' }, - HasHeader = true, - Column = new[] - { - new TextLoader.Column() - { - Name = "Label", - Source = new [] { new TextLoader.Range() { Min = 0, Max = 0} } - }, - - new TextLoader.Column() - { - Name = "F1", - Source = new [] { new TextLoader.Range() { Min = 1, Max = 1} }, - Type = Runtime.Data.DataKind.Text - }, - - new TextLoader.Column() - { - Name = "F2", - Source = new [] { new TextLoader.Range() { Min = 2, Max = 2} }, - Type = Runtime.Data.DataKind.I4 - }, - - new TextLoader.Column() - { - Name = "Rest", - Source = new [] { new TextLoader.Range() { Min = 3, Max = 9} } - } - } - }, - - InputFile = inputFile - }).Data; + var dataView = GetBreastCancerDataviewWithTextColumns(); dataView = Env.CreateTransform("Term{col=F1}", dataView); @@ -479,6 +367,128 @@ public void EntryPointInputArgsChecks() EntryPointUtils.CheckInputArgs(Env, input); } + [Fact] + public void EntryPointCreateEnsemble() + { + var dataView = GetBreastCancerDataView(); + const int nModels = 5; + var splitOutput = CVSplit.Split(Env, new CVSplit.Input { Data = dataView, NumFolds = nModels + 1 }); + var predictorModels = new IPredictorModel[nModels]; + var individualScores = new IDataView[nModels]; + for (int i = 0; i < nModels; i++) + { + var data = splitOutput.TrainData[i]; + var lrInput = new LogisticRegression.Arguments + { + TrainingData = data, + L1Weight = (Single)0.1 * i, + L2Weight = (Single)0.01 * (1 + i), + NormalizeFeatures = NormalizeOption.No + }; + predictorModels[i] = LogisticRegression.TrainBinary(Env, lrInput).PredictorModel; + individualScores[i] = + ScoreModel.Score(Env, + new ScoreModel.Input { Data = splitOutput.TestData[nModels], PredictorModel = predictorModels[i] }) + .ScoredData; + + individualScores[i] = new CopyColumnsTransform(Env, + new CopyColumnsTransform.Arguments() + { + Column = new[] + { + new CopyColumnsTransform.Column() + { + Name = MetadataUtils.Const.ScoreValueKind.Score + i, + Source = MetadataUtils.Const.ScoreValueKind.Score + }, + } + }, individualScores[i]); + individualScores[i] = new DropColumnsTransform(Env, + new DropColumnsTransform.Arguments() { Column = new[] { MetadataUtils.Const.ScoreValueKind.Score } }, + individualScores[i]); + } + + var avgEnsembleInput = new EnsembleCreator.ClassifierInput { Models = predictorModels, ModelCombiner = EnsembleCreator.ClassifierCombiner.Average }; + var avgEnsemble = EnsembleCreator.CreateBinaryEnsemble(Env, avgEnsembleInput).PredictorModel; + var avgScored = + ScoreModel.Score(Env, + new ScoreModel.Input { Data = splitOutput.TestData[nModels], PredictorModel = avgEnsemble }).ScoredData; + + var medEnsembleInput = new EnsembleCreator.ClassifierInput { Models = predictorModels }; + var medEnsemble = EnsembleCreator.CreateBinaryEnsemble(Env, medEnsembleInput).PredictorModel; + var medScored = + ScoreModel.Score(Env, + new ScoreModel.Input { Data = splitOutput.TestData[nModels], PredictorModel = medEnsemble }).ScoredData; + + var regEnsembleInput = new EnsembleCreator.RegressionInput() { Models = predictorModels }; + var regEnsemble = EnsembleCreator.CreateRegressionEnsemble(Env, regEnsembleInput).PredictorModel; + var regScored = + ScoreModel.Score(Env, + new ScoreModel.Input { Data = splitOutput.TestData[nModels], PredictorModel = regEnsemble }).ScoredData; + + var zippedScores = ZipDataView.Create(Env, individualScores); + + var avgComb = new Average(Env).GetCombiner(); + var medComb = new Median(Env).GetCombiner(); + using (var curs1 = avgScored.GetRowCursor(col => true)) + using (var curs2 = medScored.GetRowCursor(col => true)) + using (var curs3 = regScored.GetRowCursor(col => true)) + using (var curs4 = zippedScores.GetRowCursor(col => true)) + { + var found = curs1.Schema.TryGetColumnIndex(MetadataUtils.Const.ScoreValueKind.Score, out int scoreCol); + Assert.True(found); + var avgScoreGetter = curs1.GetGetter(scoreCol); + + found = curs2.Schema.TryGetColumnIndex(MetadataUtils.Const.ScoreValueKind.Score, out scoreCol); + Assert.True(found); + var medScoreGetter = curs2.GetGetter(scoreCol); + + found = curs3.Schema.TryGetColumnIndex(MetadataUtils.Const.ScoreValueKind.Score, out scoreCol); + Assert.True(found); + var regScoreGetter = curs3.GetGetter(scoreCol); + + var individualScoreGetters = new ValueGetter[nModels]; + for (int i = 0; i < nModels; i++) + { + curs4.Schema.TryGetColumnIndex(MetadataUtils.Const.ScoreValueKind.Score + i, out scoreCol); + individualScoreGetters[i] = curs4.GetGetter(scoreCol); + } + + var scoreBuffer = new Single[nModels]; + while (curs1.MoveNext()) + { + var move = curs2.MoveNext(); + Assert.True(move); + move = curs3.MoveNext(); + Assert.True(move); + move = curs4.MoveNext(); + Assert.True(move); + + Single score = 0; + avgScoreGetter(ref score); + for (int i = 0; i < nModels; i++) + individualScoreGetters[i](ref scoreBuffer[i]); + Single avgScore = 0; + avgComb(ref avgScore, scoreBuffer, null); + Assert.Equal(score, avgScore); + + medScoreGetter(ref score); + Single medScore = 0; + medComb(ref medScore, scoreBuffer, null); + Assert.Equal(score, medScore); + + regScoreGetter(ref score); + Assert.Equal(score, medScore); + } + var moved = curs2.MoveNext(); + Assert.False(moved); + moved = curs3.MoveNext(); + Assert.False(moved); + moved = curs4.MoveNext(); + Assert.False(moved); + } + } + [Fact] public void EntryPointOptionalParams() { @@ -628,46 +638,681 @@ public void EntryPointExecGraphCommand() // Assert.NotNull(model); //} - //[Fact] - //public void EntryPointCalibrate() - //{ - // var dataPath = GetDataPath("breast-cancer.txt"); - // var inputFile = new SimpleFileHandle(Env, dataPath, false, false); - // var dataView = ImportTextData.ImportText(Env, new ImportTextData.Input { InputFile = inputFile, CustomSchema = "col=Label:0 col=Features:1-9" }).Data; - - // var splitOutput = CVSplit.Split(Env, new CVSplit.Input { Data = dataView, NumFolds = 3 }); - - // var lrModel = LogisticRegression.TrainBinary(Env, new LogisticRegression.Arguments { TrainingData = splitOutput.TestData[0] }).PredictorModel; - // var calibratedLrModel = Calibrate.FixedPlatt(Env, - // new Calibrate.FixedPlattInput { Data = splitOutput.TestData[1], UncalibratedPredictorModel = lrModel }).PredictorModel; - - // var scored1 = ScoreModel.Score(Env, new ScoreModel.Input() { Data = splitOutput.TestData[2], PredictorModel = lrModel }).ScoredData; - // scored1 = ScoreModel.SelectColumns(Env, new ScoreModel.ScoreColumnSelectorInput() { Data = scored1, ExtraColumns = new[] { "Label" } }).OutputData; - - // var scored2 = ScoreModel.Score(Env, new ScoreModel.Input() { Data = splitOutput.TestData[2], PredictorModel = calibratedLrModel }).ScoredData; - // scored2 = ScoreModel.SelectColumns(Env, new ScoreModel.ScoreColumnSelectorInput() { Data = scored2, ExtraColumns = new[] { "Label" } }).OutputData; - - // Assert.Equal(4, scored1.Schema.ColumnCount); - // CheckSameValues(scored1, scored2); - - // var input = new Calibrate.NoArgumentsInput() { Data = splitOutput.TestData[1], UncalibratedPredictorModel = lrModel }; - // calibratedLrModel = Calibrate.Platt(Env, input).PredictorModel; - // calibratedLrModel = Calibrate.Naive(Env, input).PredictorModel; - // calibratedLrModel = Calibrate.Pav(Env, input).PredictorModel; - - // // This tests that the SchemaBindableCalibratedPredictor doesn't get confused if its sub-predictor is already calibrated. - // var fastForest = new FastForestClassification(Env, new FastForestClassification.Arguments()); - // var rmd = RoleMappedData.Create(splitOutput.TrainData[0], - // RoleMappedSchema.CreatePair(RoleMappedSchema.ColumnRole.Feature, "Features"), - // RoleMappedSchema.CreatePair(RoleMappedSchema.ColumnRole.Label, "Label")); - // fastForest.Train(rmd); - // var ffModel = new PredictorModel(Env, rmd, splitOutput.TrainData[0], fastForest.CreatePredictor()); - // var calibratedFfModel = Calibrate.Platt(Env, - // new Calibrate.NoArgumentsInput() { Data = splitOutput.TestData[0], UncalibratedPredictorModel = ffModel }).PredictorModel; - // var twiceCalibratedFfModel = Calibrate.Platt(Env, - // new Calibrate.NoArgumentsInput() { Data = splitOutput.TestData[0], UncalibratedPredictorModel = calibratedFfModel }).PredictorModel; - // var scoredFf = ScoreModel.Score(Env, new ScoreModel.Input() { Data = splitOutput.TestData[2], PredictorModel = twiceCalibratedFfModel }).ScoredData; - //} + [Fact] + public void EntryPointCalibrate() + { + var dataView = GetBreastCancerDataView(); + + var splitOutput = CVSplit.Split(Env, new CVSplit.Input { Data = dataView, NumFolds = 3 }); + + var lrModel = LogisticRegression.TrainBinary(Env, new LogisticRegression.Arguments { TrainingData = splitOutput.TestData[0] }).PredictorModel; + var calibratedLrModel = Calibrate.FixedPlatt(Env, + new Calibrate.FixedPlattInput { Data = splitOutput.TestData[1], UncalibratedPredictorModel = lrModel }).PredictorModel; + + var scored1 = ScoreModel.Score(Env, new ScoreModel.Input() { Data = splitOutput.TestData[2], PredictorModel = lrModel }).ScoredData; + scored1 = ScoreModel.SelectColumns(Env, new ScoreModel.ScoreColumnSelectorInput() { Data = scored1, ExtraColumns = new[] { "Label" } }).OutputData; + + var scored2 = ScoreModel.Score(Env, new ScoreModel.Input() { Data = splitOutput.TestData[2], PredictorModel = calibratedLrModel }).ScoredData; + scored2 = ScoreModel.SelectColumns(Env, new ScoreModel.ScoreColumnSelectorInput() { Data = scored2, ExtraColumns = new[] { "Label" } }).OutputData; + + Assert.Equal(4, scored1.Schema.ColumnCount); + CheckSameValues(scored1, scored2); + + var input = new Calibrate.NoArgumentsInput() { Data = splitOutput.TestData[1], UncalibratedPredictorModel = lrModel }; + calibratedLrModel = Calibrate.Platt(Env, input).PredictorModel; + calibratedLrModel = Calibrate.Naive(Env, input).PredictorModel; + calibratedLrModel = Calibrate.Pav(Env, input).PredictorModel; + + // This tests that the SchemaBindableCalibratedPredictor doesn't get confused if its sub-predictor is already calibrated. + var fastForest = new FastForestClassification(Env, new FastForestClassification.Arguments()); + var rmd = RoleMappedData.Create(splitOutput.TrainData[0], + RoleMappedSchema.CreatePair(RoleMappedSchema.ColumnRole.Feature, "Features"), + RoleMappedSchema.CreatePair(RoleMappedSchema.ColumnRole.Label, "Label")); + fastForest.Train(rmd); + var ffModel = new PredictorModel(Env, rmd, splitOutput.TrainData[0], fastForest.CreatePredictor()); + var calibratedFfModel = Calibrate.Platt(Env, + new Calibrate.NoArgumentsInput() { Data = splitOutput.TestData[0], UncalibratedPredictorModel = ffModel }).PredictorModel; + var twiceCalibratedFfModel = Calibrate.Platt(Env, + new Calibrate.NoArgumentsInput() { Data = splitOutput.TestData[0], UncalibratedPredictorModel = calibratedFfModel }).PredictorModel; + var scoredFf = ScoreModel.Score(Env, new ScoreModel.Input() { Data = splitOutput.TestData[2], PredictorModel = twiceCalibratedFfModel }).ScoredData; + } + + + [Fact] + public void EntryPointPipelineEnsemble() + { + var dataView = GetBreastCancerDataView(); + const int nModels = 5; + var splitOutput = CVSplit.Split(Env, new CVSplit.Input { Data = dataView, NumFolds = nModels + 1 }); + var predictorModels = new IPredictorModel[nModels]; + var individualScores = new IDataView[nModels]; + for (int i = 0; i < nModels; i++) + { + var data = splitOutput.TrainData[i]; + data = new RffTransform(Env, new RffTransform.Arguments() + { + Column = new[] + { + new RffTransform.Column() {Name = "Features1", Source = "Features"}, + new RffTransform.Column() {Name = "Features2", Source = "Features"}, + }, + NewDim = 10, + UseSin = false + }, data); + data = new ConcatTransform(Env, new ConcatTransform.Arguments() + { + Column = new[] { new ConcatTransform.Column() { Name = "Features", Source = new[] { "Features1", "Features2" } } } + }, data); + + data = new TermTransform(Env, new TermTransform.Arguments() + { + Column = new[] + { + new TermTransform.Column() + { + Name = "Label", + Source = "Label", + Sort = TermTransform.SortOrder.Value + } + } + }, data); + + var lrInput = new LogisticRegression.Arguments + { + TrainingData = data, + L1Weight = (Single)0.1 * i, + L2Weight = (Single)0.01 * (1 + i), + NormalizeFeatures = NormalizeOption.Yes + }; + predictorModels[i] = LogisticRegression.TrainBinary(Env, lrInput).PredictorModel; + var transformModel = new TransformModel(Env, data, splitOutput.TrainData[i]); + + predictorModels[i] = ModelOperations.CombineTwoModels(Env, + new ModelOperations.SimplePredictorModelInput() + { PredictorModel = predictorModels[i], TransformModel = transformModel }).PredictorModel; + + individualScores[i] = + ScoreModel.Score(Env, + new ScoreModel.Input { Data = splitOutput.TestData[nModels], PredictorModel = predictorModels[i] }) + .ScoredData; + } + + var binaryEnsembleModel = EnsembleCreator.CreateBinaryPipelineEnsemble(Env, + new EnsembleCreator.PipelineClassifierInput() + { + ModelCombiner = EntryPoints.EnsembleCreator.ClassifierCombiner.Average, + Models = predictorModels + }).PredictorModel; + var binaryEnsembleCalibrated = Calibrate.Platt(Env, + new Calibrate.NoArgumentsInput() + { + Data = splitOutput.TestData[nModels], + UncalibratedPredictorModel = binaryEnsembleModel + }).PredictorModel; + var binaryScored = ScoreModel.Score(Env, + new ScoreModel.Input + { + Data = splitOutput.TestData[nModels], + PredictorModel = binaryEnsembleModel + }).ScoredData; + var binaryScoredCalibrated = ScoreModel.Score(Env, + new ScoreModel.Input + { + Data = splitOutput.TestData[nModels], + PredictorModel = binaryEnsembleCalibrated + }).ScoredData; + + var regressionEnsembleModel = EntryPoints.EnsembleCreator.CreateRegressionPipelineEnsemble(Env, + new EntryPoints.EnsembleCreator.PipelineRegressionInput() + { + ModelCombiner = EntryPoints.EnsembleCreator.ScoreCombiner.Average, + Models = predictorModels + }).PredictorModel; + var regressionScored = ScoreModel.Score(Env, + new ScoreModel.Input + { + Data = splitOutput.TestData[nModels], + PredictorModel = regressionEnsembleModel + }).ScoredData; + + var anomalyEnsembleModel = EntryPoints.EnsembleCreator.CreateAnomalyPipelineEnsemble(Env, + new EntryPoints.EnsembleCreator.PipelineAnomalyInput() + { + ModelCombiner = EnsembleCreator.ScoreCombiner.Average, + Models = predictorModels + }).PredictorModel; + var anomalyScored = ScoreModel.Score(Env, + new ScoreModel.Input + { + Data = splitOutput.TestData[nModels], + PredictorModel = anomalyEnsembleModel + }).ScoredData; + + // Make sure the scorers have the correct types. + var hasScoreCol = binaryScored.Schema.TryGetColumnIndex(MetadataUtils.Const.ScoreValueKind.Score, out int scoreIndex); + Assert.True(hasScoreCol, "Data scored with binary ensemble does not have a score column"); + var type = binaryScored.Schema.GetMetadataTypeOrNull(MetadataUtils.Kinds.ScoreColumnKind, scoreIndex); + Assert.True(type != null && type.IsText, "Binary ensemble scored data does not have correct type of metadata."); + var kind = default(DvText); + binaryScored.Schema.GetMetadata(MetadataUtils.Kinds.ScoreColumnKind, scoreIndex, ref kind); + Assert.True(kind.EqualsStr(MetadataUtils.Const.ScoreColumnKind.BinaryClassification), + $"Binary ensemble scored data column type should be '{MetadataUtils.Const.ScoreColumnKind.BinaryClassification}', but is instead '{kind}'"); + + hasScoreCol = regressionScored.Schema.TryGetColumnIndex(MetadataUtils.Const.ScoreValueKind.Score, out scoreIndex); + Assert.True(hasScoreCol, "Data scored with regression ensemble does not have a score column"); + type = regressionScored.Schema.GetMetadataTypeOrNull(MetadataUtils.Kinds.ScoreColumnKind, scoreIndex); + Assert.True(type != null && type.IsText, "Regression ensemble scored data does not have correct type of metadata."); + regressionScored.Schema.GetMetadata(MetadataUtils.Kinds.ScoreColumnKind, scoreIndex, ref kind); + Assert.True(kind.EqualsStr(MetadataUtils.Const.ScoreColumnKind.Regression), + $"Regression ensemble scored data column type should be '{MetadataUtils.Const.ScoreColumnKind.Regression}', but is instead '{kind}'"); + + hasScoreCol = anomalyScored.Schema.TryGetColumnIndex(MetadataUtils.Const.ScoreValueKind.Score, out scoreIndex); + Assert.True(hasScoreCol, "Data scored with anomaly detection ensemble does not have a score column"); + type = anomalyScored.Schema.GetMetadataTypeOrNull(MetadataUtils.Kinds.ScoreColumnKind, scoreIndex); + Assert.True(type != null && type.IsText, "Anomaly detection ensemble scored data does not have correct type of metadata."); + anomalyScored.Schema.GetMetadata(MetadataUtils.Kinds.ScoreColumnKind, scoreIndex, ref kind); + Assert.True(kind.EqualsStr(MetadataUtils.Const.ScoreColumnKind.AnomalyDetection), + $"Anomaly detection ensemble scored data column type should be '{MetadataUtils.Const.ScoreColumnKind.AnomalyDetection}', but is instead '{kind}'"); + + var modelPath = DeleteOutputPath("SavePipe", "PipelineEnsembleModel.zip"); + using (var file = Env.CreateOutputFile(modelPath)) + using (var strm = file.CreateWriteStream()) + regressionEnsembleModel.Save(Env, strm); + + IPredictorModel loadedFromSaved; + using (var file = Env.OpenInputFile(modelPath)) + using (var strm = file.OpenReadStream()) + loadedFromSaved = new PredictorModel(Env, strm); + + var scoredFromSaved = ScoreModel.Score(Env, + new ScoreModel.Input() + { + Data = splitOutput.TestData[nModels], + PredictorModel = loadedFromSaved + }).ScoredData; + + using (var cursReg = regressionScored.GetRowCursor(col => true)) + using (var cursBin = binaryScored.GetRowCursor(col => true)) + using (var cursBinCali = binaryScoredCalibrated.GetRowCursor(col => true)) + using (var cursAnom = anomalyScored.GetRowCursor(col => true)) + using (var curs0 = individualScores[0].GetRowCursor(col => true)) + using (var curs1 = individualScores[1].GetRowCursor(col => true)) + using (var curs2 = individualScores[2].GetRowCursor(col => true)) + using (var curs3 = individualScores[3].GetRowCursor(col => true)) + using (var curs4 = individualScores[4].GetRowCursor(col => true)) + using (var cursSaved = scoredFromSaved.GetRowCursor(col => true)) + { + var good = curs0.Schema.TryGetColumnIndex(MetadataUtils.Const.ScoreValueKind.Score, out int col); + Assert.True(good); + var getter0 = curs0.GetGetter(col); + good = curs1.Schema.TryGetColumnIndex(MetadataUtils.Const.ScoreValueKind.Score, out col); + Assert.True(good); + var getter1 = curs1.GetGetter(col); + good = curs2.Schema.TryGetColumnIndex(MetadataUtils.Const.ScoreValueKind.Score, out col); + Assert.True(good); + var getter2 = curs2.GetGetter(col); + good = curs3.Schema.TryGetColumnIndex(MetadataUtils.Const.ScoreValueKind.Score, out col); + Assert.True(good); + var getter3 = curs3.GetGetter(col); + good = curs4.Schema.TryGetColumnIndex(MetadataUtils.Const.ScoreValueKind.Score, out col); + Assert.True(good); + var getter4 = curs4.GetGetter(col); + good = cursReg.Schema.TryGetColumnIndex(MetadataUtils.Const.ScoreValueKind.Score, out col); + Assert.True(good); + var getterReg = cursReg.GetGetter(col); + good = cursBin.Schema.TryGetColumnIndex(MetadataUtils.Const.ScoreValueKind.Score, out col); + Assert.True(good); + var getterBin = cursBin.GetGetter(col); + good = cursBinCali.Schema.TryGetColumnIndex(MetadataUtils.Const.ScoreValueKind.Score, out col); + Assert.True(good); + var getterBinCali = cursBinCali.GetGetter(col); + good = cursSaved.Schema.TryGetColumnIndex(MetadataUtils.Const.ScoreValueKind.Score, out col); + Assert.True(good); + var getterSaved = cursSaved.GetGetter(col); + good = cursAnom.Schema.TryGetColumnIndex(MetadataUtils.Const.ScoreValueKind.Score, out col); + Assert.True(good); + var getterAnom = cursAnom.GetGetter(col); + + var c = new Average(Env).GetCombiner(); + while (cursReg.MoveNext()) + { + Single score = 0; + getterReg(ref score); + Assert.True(curs0.MoveNext()); + Assert.True(curs1.MoveNext()); + Assert.True(curs2.MoveNext()); + Assert.True(curs3.MoveNext()); + Assert.True(curs4.MoveNext()); + Assert.True(cursBin.MoveNext()); + Assert.True(cursBinCali.MoveNext()); + Assert.True(cursSaved.MoveNext()); + Assert.True(cursAnom.MoveNext()); + Single[] score0 = new Single[5]; + getter0(ref score0[0]); + getter1(ref score0[1]); + getter2(ref score0[2]); + getter3(ref score0[3]); + getter4(ref score0[4]); + Single scoreBin = 0; + Single scoreBinCali = 0; + Single scoreSaved = 0; + Single scoreAnom = 0; + getterBin(ref scoreBin); + getterBinCali(ref scoreBinCali); + getterSaved(ref scoreSaved); + getterAnom(ref scoreAnom); + Assert.True(Single.IsNaN(scoreBin) && Single.IsNaN(score) || scoreBin == score); + Assert.True(Single.IsNaN(scoreBinCali) && Single.IsNaN(score) || scoreBinCali == score); + Assert.True(Single.IsNaN(scoreSaved) && Single.IsNaN(score) || scoreSaved == score); + Assert.True(Single.IsNaN(scoreAnom) && Single.IsNaN(score) || scoreAnom == score); + + Single avg = 0; + c(ref avg, score0, null); + Assert.True(Single.IsNaN(avg) && Single.IsNaN(score) || avg == score); + } + Assert.False(curs0.MoveNext()); + Assert.False(curs1.MoveNext()); + Assert.False(curs2.MoveNext()); + Assert.False(curs3.MoveNext()); + Assert.False(curs4.MoveNext()); + Assert.False(cursBin.MoveNext()); + Assert.False(cursBinCali.MoveNext()); + Assert.False(cursSaved.MoveNext()); + Assert.False(cursAnom.MoveNext()); + } + } + + + [Fact] + public void EntryPointPipelineEnsembleText() + { + var dataPath = GetDataPath("lm.sample.txt"); + var inputFile = new SimpleFileHandle(Env, dataPath, false, false); + var dataView = ImportTextData.TextLoader(Env, new ImportTextData.LoaderInput() + { + Arguments = + { + HasHeader = true, + Column = new[] + { + new TextLoader.Column() + { + Name = "Label", + Source = new [] { new TextLoader.Range() { Min = 0, Max = 0} }, + Type = Runtime.Data.DataKind.TX + }, + + new TextLoader.Column() + { + Name = "Text", + Source = new [] { new TextLoader.Range() { Min = 3, Max = 3} }, + Type = Runtime.Data.DataKind.TX + } + } + }, + + InputFile = inputFile + }).Data; + + ValueMapper labelToBinary = + (ref DvText src, ref DvBool dst) => + { + if (src.EqualsStr("Sport")) + dst = DvBool.True; + else + dst = DvBool.False; + }; + dataView = LambdaColumnMapper.Create(Env, "TextToBinaryLabel", dataView, "Label", "Label", + TextType.Instance, BoolType.Instance, labelToBinary); + + const int nModels = 5; + var splitOutput = CVSplit.Split(Env, new CVSplit.Input { Data = dataView, NumFolds = nModels + 1 }); + var predictorModels = new IPredictorModel[nModels]; + var individualScores = new IDataView[nModels]; + for (int i = 0; i < nModels; i++) + { + var data = splitOutput.TrainData[i]; + if (i % 2 == 0) + { + data = TextTransform.Create(Env, + new TextTransform.Arguments() + { + Column = new TextTransform.Column() { Name = "Features", Source = new[] { "Text" } }, + StopWordsRemover = new PredefinedStopWordsRemoverFactory() + }, data); + } + else + { + data = WordHashBagTransform.Create(Env, + new WordHashBagTransform.Arguments() + { + Column = + new[] { new WordHashBagTransform.Column() { Name = "Features", Source = new[] { "Text" } }, } + }, + data); + } + var lrInput = new LogisticRegression.Arguments + { + TrainingData = data, + L1Weight = (Single)0.1 * i, + L2Weight = (Single)0.01 * (1 + i), + NormalizeFeatures = NormalizeOption.Yes + }; + predictorModels[i] = LogisticRegression.TrainBinary(Env, lrInput).PredictorModel; + var transformModel = new TransformModel(Env, data, splitOutput.TrainData[i]); + + predictorModels[i] = ModelOperations.CombineTwoModels(Env, + new ModelOperations.SimplePredictorModelInput() + { PredictorModel = predictorModels[i], TransformModel = transformModel }).PredictorModel; + + individualScores[i] = + ScoreModel.Score(Env, + new ScoreModel.Input { Data = splitOutput.TestData[nModels], PredictorModel = predictorModels[i] }) + .ScoredData; + } + + var binaryEnsembleModel = EnsembleCreator.CreateBinaryPipelineEnsemble(Env, + new EnsembleCreator.PipelineClassifierInput() + { + ModelCombiner = EnsembleCreator.ClassifierCombiner.Average, + Models = predictorModels + }).PredictorModel; + var binaryEnsembleCalibrated = Calibrate.Platt(Env, + new Calibrate.NoArgumentsInput() + { + Data = splitOutput.TestData[nModels], + UncalibratedPredictorModel = binaryEnsembleModel + }).PredictorModel; + var binaryScored = ScoreModel.Score(Env, + new ScoreModel.Input + { + Data = splitOutput.TestData[nModels], + PredictorModel = binaryEnsembleModel + }).ScoredData; + var binaryScoredCalibrated = ScoreModel.Score(Env, + new ScoreModel.Input + { + Data = splitOutput.TestData[nModels], + PredictorModel = binaryEnsembleCalibrated + }).ScoredData; + + var regressionEnsembleModel = EnsembleCreator.CreateRegressionPipelineEnsemble(Env, + new EnsembleCreator.PipelineRegressionInput() + { + ModelCombiner = EnsembleCreator.ScoreCombiner.Average, + Models = predictorModels + }).PredictorModel; + var regressionScored = ScoreModel.Score(Env, + new ScoreModel.Input + { + Data = splitOutput.TestData[nModels], + PredictorModel = regressionEnsembleModel + }).ScoredData; + + var modelPath = DeleteOutputPath("SavePipe", "PipelineEnsembleModel.zip"); + using (var file = Env.CreateOutputFile(modelPath)) + using (var strm = file.CreateWriteStream()) + regressionEnsembleModel.Save(Env, strm); + + IPredictorModel loadedFromSaved; + using (var file = Env.OpenInputFile(modelPath)) + using (var strm = file.OpenReadStream()) + loadedFromSaved = new PredictorModel(Env, strm); + + var scoredFromSaved = ScoreModel.Score(Env, + new ScoreModel.Input() + { + Data = splitOutput.TestData[nModels], + PredictorModel = loadedFromSaved + }).ScoredData; + + using (var cursReg = regressionScored.GetRowCursor(col => true)) + using (var cursBin = binaryScored.GetRowCursor(col => true)) + using (var cursBinCali = binaryScoredCalibrated.GetRowCursor(col => true)) + using (var curs0 = individualScores[0].GetRowCursor(col => true)) + using (var curs1 = individualScores[1].GetRowCursor(col => true)) + using (var curs2 = individualScores[2].GetRowCursor(col => true)) + using (var curs3 = individualScores[3].GetRowCursor(col => true)) + using (var curs4 = individualScores[4].GetRowCursor(col => true)) + using (var cursSaved = scoredFromSaved.GetRowCursor(col => true)) + { + var good = curs0.Schema.TryGetColumnIndex(MetadataUtils.Const.ScoreValueKind.Score, out int col); + Assert.True(good); + var getter0 = curs0.GetGetter(col); + good = curs1.Schema.TryGetColumnIndex(MetadataUtils.Const.ScoreValueKind.Score, out col); + Assert.True(good); + var getter1 = curs1.GetGetter(col); + good = curs2.Schema.TryGetColumnIndex(MetadataUtils.Const.ScoreValueKind.Score, out col); + Assert.True(good); + var getter2 = curs2.GetGetter(col); + good = curs3.Schema.TryGetColumnIndex(MetadataUtils.Const.ScoreValueKind.Score, out col); + Assert.True(good); + var getter3 = curs3.GetGetter(col); + good = curs4.Schema.TryGetColumnIndex(MetadataUtils.Const.ScoreValueKind.Score, out col); + Assert.True(good); + var getter4 = curs4.GetGetter(col); + good = cursReg.Schema.TryGetColumnIndex(MetadataUtils.Const.ScoreValueKind.Score, out col); + Assert.True(good); + var getterReg = cursReg.GetGetter(col); + good = cursBin.Schema.TryGetColumnIndex(MetadataUtils.Const.ScoreValueKind.Score, out col); + Assert.True(good); + var getterBin = cursBin.GetGetter(col); + good = cursBinCali.Schema.TryGetColumnIndex(MetadataUtils.Const.ScoreValueKind.Score, out col); + Assert.True(good); + var getterBinCali = cursBinCali.GetGetter(col); + good = cursSaved.Schema.TryGetColumnIndex(MetadataUtils.Const.ScoreValueKind.Score, out col); + Assert.True(good); + var getterSaved = cursSaved.GetGetter(col); + + var c = new Average(Env).GetCombiner(); + while (cursReg.MoveNext()) + { + Single score = 0; + getterReg(ref score); + Assert.True(curs0.MoveNext()); + Assert.True(curs1.MoveNext()); + Assert.True(curs2.MoveNext()); + Assert.True(curs3.MoveNext()); + Assert.True(curs4.MoveNext()); + Assert.True(cursBin.MoveNext()); + Assert.True(cursBinCali.MoveNext()); + Assert.True(cursSaved.MoveNext()); + Single[] score0 = new Single[5]; + getter0(ref score0[0]); + getter1(ref score0[1]); + getter2(ref score0[2]); + getter3(ref score0[3]); + getter4(ref score0[4]); + Single scoreBin = 0; + Single scoreBinCali = 0; + Single scoreSaved = 0; + getterBin(ref scoreBin); + getterBinCali(ref scoreBinCali); + getterSaved(ref scoreSaved); + Assert.True(Single.IsNaN(scoreBin) && Single.IsNaN(score) || scoreBin == score); + Assert.True(Single.IsNaN(scoreBinCali) && Single.IsNaN(score) || scoreBinCali == score); + Assert.True(Single.IsNaN(scoreSaved) && Single.IsNaN(score) || scoreSaved == score); + + Single avg = 0; + c(ref avg, score0, null); + Assert.True(Single.IsNaN(avg) && Single.IsNaN(score) || avg == score); + } + Assert.False(curs0.MoveNext()); + Assert.False(curs1.MoveNext()); + Assert.False(curs2.MoveNext()); + Assert.False(curs3.MoveNext()); + Assert.False(curs4.MoveNext()); + Assert.False(cursBin.MoveNext()); + Assert.False(cursBinCali.MoveNext()); + Assert.False(cursSaved.MoveNext()); + } + } + + [Fact] + public void EntryPointMulticlassPipelineEnsemble() + { + var dataPath = GetDataPath("iris.txt"); + var inputFile = new SimpleFileHandle(Env, dataPath, false, false); + var dataView = ImportTextData.TextLoader(Env, new ImportTextData.LoaderInput() + { + Arguments = + { + Column = new[] + { + new TextLoader.Column() + { + Name = "Label", + Source = new [] { new TextLoader.Range() { Min = 0, Max = 0} }, + Type = Runtime.Data.DataKind.R4 + }, + + new TextLoader.Column() + { + Name = "Features", + Source = new [] { new TextLoader.Range() { Min = 1, Max = 4} }, + Type = Runtime.Data.DataKind.R4 + } + } + }, + + InputFile = inputFile + }).Data; + + const int nModels = 5; + var splitOutput = CVSplit.Split(Env, new CVSplit.Input { Data = dataView, NumFolds = nModels + 1 }); + var predictorModels = new IPredictorModel[nModels]; + var individualScores = new IDataView[nModels]; + for (int i = 0; i < nModels; i++) + { + var data = splitOutput.TrainData[i]; + data = new RffTransform(Env, new RffTransform.Arguments() + { + Column = new[] + { + new RffTransform.Column() {Name = "Features1", Source = "Features"}, + new RffTransform.Column() {Name = "Features2", Source = "Features"}, + }, + NewDim = 10, + UseSin = false + }, data); + data = new ConcatTransform(Env, new ConcatTransform.Arguments() + { + Column = new[] { new ConcatTransform.Column() { Name = "Features", Source = new[] { "Features1", "Features2" } } } + }, data); + + var mlr = new MulticlassLogisticRegression(Env, new MulticlassLogisticRegression.Arguments()); + RoleMappedData rmd = RoleMappedData.Create(data, + RoleMappedSchema.CreatePair(RoleMappedSchema.ColumnRole.Feature, "Features"), + RoleMappedSchema.CreatePair(RoleMappedSchema.ColumnRole.Label, "Label")); + mlr.Train(rmd); + + predictorModels[i] = new PredictorModel(Env, rmd, data, mlr.CreatePredictor()); + var transformModel = new TransformModel(Env, data, splitOutput.TrainData[i]); + + predictorModels[i] = ModelOperations.CombineTwoModels(Env, + new ModelOperations.SimplePredictorModelInput() + { PredictorModel = predictorModels[i], TransformModel = transformModel }).PredictorModel; + + individualScores[i] = + ScoreModel.Score(Env, + new ScoreModel.Input { Data = splitOutput.TestData[nModels], PredictorModel = predictorModels[i] }) + .ScoredData; + } + + var mcEnsembleModel = EnsembleCreator.CreateMultiClassPipelineEnsemble(Env, + new EnsembleCreator.PipelineClassifierInput() + { + ModelCombiner = EnsembleCreator.ClassifierCombiner.Average, + Models = predictorModels + }).PredictorModel; + var mcScored = ScoreModel.Score(Env, + new ScoreModel.Input + { + Data = splitOutput.TestData[nModels], + PredictorModel = mcEnsembleModel + }).ScoredData; + + var modelPath = DeleteOutputPath("SavePipe", "PipelineEnsembleModel.zip"); + using (var file = Env.CreateOutputFile(modelPath)) + using (var strm = file.CreateWriteStream()) + mcEnsembleModel.Save(Env, strm); + + IPredictorModel loadedFromSaved; + using (var file = Env.OpenInputFile(modelPath)) + using (var strm = file.OpenReadStream()) + loadedFromSaved = new PredictorModel(Env, strm); + + var scoredFromSaved = ScoreModel.Score(Env, + new ScoreModel.Input() + { + Data = splitOutput.TestData[nModels], + PredictorModel = loadedFromSaved + }).ScoredData; + + using (var curs = mcScored.GetRowCursor(col => true)) + using (var cursSaved = scoredFromSaved.GetRowCursor(col => true)) + using (var curs0 = individualScores[0].GetRowCursor(col => true)) + using (var curs1 = individualScores[1].GetRowCursor(col => true)) + using (var curs2 = individualScores[2].GetRowCursor(col => true)) + using (var curs3 = individualScores[3].GetRowCursor(col => true)) + using (var curs4 = individualScores[4].GetRowCursor(col => true)) + { + var good = curs0.Schema.TryGetColumnIndex(MetadataUtils.Const.ScoreValueKind.Score, out int col); + Assert.True(good); + var getter0 = curs0.GetGetter>(col); + good = curs1.Schema.TryGetColumnIndex(MetadataUtils.Const.ScoreValueKind.Score, out col); + Assert.True(good); + var getter1 = curs1.GetGetter>(col); + good = curs2.Schema.TryGetColumnIndex(MetadataUtils.Const.ScoreValueKind.Score, out col); + Assert.True(good); + var getter2 = curs2.GetGetter>(col); + good = curs3.Schema.TryGetColumnIndex(MetadataUtils.Const.ScoreValueKind.Score, out col); + Assert.True(good); + var getter3 = curs3.GetGetter>(col); + good = curs4.Schema.TryGetColumnIndex(MetadataUtils.Const.ScoreValueKind.Score, out col); + Assert.True(good); + var getter4 = curs4.GetGetter>(col); + good = curs.Schema.TryGetColumnIndex(MetadataUtils.Const.ScoreValueKind.Score, out col); + Assert.True(good); + var getter = curs.GetGetter>(col); + good = cursSaved.Schema.TryGetColumnIndex(MetadataUtils.Const.ScoreValueKind.Score, out col); + Assert.True(good); + var getterSaved = cursSaved.GetGetter>(col); + + var c = new MultiAverage(Env, new MultiAverage.Arguments()).GetCombiner(); + VBuffer score = default(VBuffer); + VBuffer[] score0 = new VBuffer[5]; + VBuffer scoreSaved = default(VBuffer); + VBuffer avg = default(VBuffer); + VBuffer dense1 = default(VBuffer); + VBuffer dense2 = default(VBuffer); + while (curs.MoveNext()) + { + getter(ref score); + Assert.True(curs0.MoveNext()); + Assert.True(curs1.MoveNext()); + Assert.True(curs2.MoveNext()); + Assert.True(curs3.MoveNext()); + Assert.True(curs4.MoveNext()); + Assert.True(cursSaved.MoveNext()); + getter0(ref score0[0]); + getter1(ref score0[1]); + getter2(ref score0[2]); + getter3(ref score0[3]); + getter4(ref score0[4]); + getterSaved(ref scoreSaved); + Assert.True(CompareVBuffers(ref scoreSaved, ref score, ref dense1, ref dense2)); + c(ref avg, score0, null); + Assert.True(CompareVBuffers(ref avg, ref score, ref dense1, ref dense2)); + } + Assert.False(curs0.MoveNext()); + Assert.False(curs1.MoveNext()); + Assert.False(curs2.MoveNext()); + Assert.False(curs3.MoveNext()); + Assert.False(curs4.MoveNext()); + Assert.False(cursSaved.MoveNext()); + } + } private static bool CompareVBuffers(ref VBuffer v1, ref VBuffer v2, ref VBuffer dense1, ref VBuffer dense2) { @@ -910,25 +1555,33 @@ public void EntryPointTextToKeyToText() } private void RunTrainScoreEvaluate(string learner, string evaluator, string dataPath, string warningsPath, string overallMetricsPath, - string instanceMetricsPath, string confusionMatrixPath = null, string loader = null) + string instanceMetricsPath, string confusionMatrixPath = null, string loader = null, string transforms = null, + string splitterInput = "AllData") { - string inputGraph = string.Format(@" + if (string.IsNullOrEmpty(transforms)) + transforms = ""; + loader = string.IsNullOrWhiteSpace(loader) ? "" : string.Format(",'CustomSchema': '{0}'", loader); + var confusionMatrixVar = confusionMatrixPath != null ? ", 'ConfusionMatrix': '$ConfusionMatrix'" : ""; + confusionMatrixPath = confusionMatrixPath != null ? string.Format(", 'ConfusionMatrix' : '{0}'", EscapePath(confusionMatrixPath)) : ""; + var scorerModel = string.IsNullOrEmpty(transforms) ? "Model" : "CombinedModel"; + string inputGraph = $@" {{ 'Nodes': [ {{ 'Name': 'Data.CustomTextLoader', 'Inputs': {{ 'InputFile': '$file' - {8} + {loader} }}, 'Outputs': {{ 'Data': '$AllData' }} }}, + {transforms} {{ 'Name': 'Transforms.TrainTestDatasetSplitter', 'Inputs': {{ - 'Data': '$AllData', + 'Data': '${splitterInput}', 'Fraction': 0.8 }}, 'Outputs': {{ @@ -937,7 +1590,7 @@ private void RunTrainScoreEvaluate(string learner, string evaluator, string data }} }}, {{ - 'Name': '{0}', + 'Name': '{learner}', 'Inputs': {{ 'TrainingData': '$TrainData' }}, @@ -956,7 +1609,7 @@ private void RunTrainScoreEvaluate(string learner, string evaluator, string data }} }}, {{ - 'Name': '{1}', + 'Name': '{evaluator}', 'Inputs': {{ 'Data': '$ScoredData' }}, @@ -964,23 +1617,20 @@ private void RunTrainScoreEvaluate(string learner, string evaluator, string data 'Warnings': '$Warnings', 'OverallMetrics': '$OverallMetrics', 'PerInstanceMetrics': '$PerInstanceMetrics' - {6} + {confusionMatrixVar} }} }} ], 'Inputs' : {{ - 'file' : '{2}' + 'file' : '{EscapePath(dataPath)}' }}, 'Outputs' : {{ - 'Warnings' : '{3}', - 'OverallMetrics' : '{4}', - 'PerInstanceMetrics' : '{5}' - {7} + 'Warnings' : '{EscapePath(warningsPath)}', + 'OverallMetrics' : '{EscapePath(overallMetricsPath)}', + 'PerInstanceMetrics' : '{EscapePath(instanceMetricsPath)}' + {confusionMatrixPath} }} - }}", learner, evaluator, EscapePath(dataPath), EscapePath(warningsPath), EscapePath(overallMetricsPath), EscapePath(instanceMetricsPath), - confusionMatrixPath != null ? ", 'ConfusionMatrix': '$ConfusionMatrix'" : "", - confusionMatrixPath != null ? string.Format(", 'ConfusionMatrix' : '{0}'", EscapePath(confusionMatrixPath)) : "", - string.IsNullOrWhiteSpace(loader) ? "" : string.Format(",'CustomSchema': '{0}'", loader)); + }}"; var jsonPath = DeleteOutputPath("graph.json"); File.WriteAllLines(jsonPath, new[] { inputGraph }); @@ -1041,13 +1691,13 @@ public void EntryPointEvaluateMultiClass() [Fact] public void EntryPointEvaluateRegression() { - var dataPath = GetDataPath(TestDatasets.winequality.trainFilename); + var dataPath = GetDataPath(TestDatasets.winequalitymacro.trainFilename); var warningsPath = DeleteOutputPath("warnings.idv"); var overallMetricsPath = DeleteOutputPath("overall.idv"); var instanceMetricsPath = DeleteOutputPath("instance.idv"); RunTrainScoreEvaluate("Trainers.StochasticDualCoordinateAscentRegressor", "Models.RegressionEvaluator", - dataPath, warningsPath, overallMetricsPath, instanceMetricsPath, loader: TestDatasets.winequality.loaderSettings); + dataPath, warningsPath, overallMetricsPath, instanceMetricsPath, loader: TestDatasets.winequalitymacro.loaderSettings); using (var loader = new BinaryLoader(Env, new BinaryLoader.Arguments(), warningsPath)) Assert.Equal(0, CountRows(loader)); @@ -1059,6 +1709,81 @@ public void EntryPointEvaluateRegression() Assert.Equal(975, CountRows(loader)); } + [Fact] + public void EntryPointEvaluateRanking() + { + var dataPath = GetDataPath(@"adult.tiny.with-schema.txt"); + var warningsPath = DeleteOutputPath("warnings.idv"); + var overallMetricsPath = DeleteOutputPath("overall.idv"); + var instanceMetricsPath = DeleteOutputPath("instance.idv"); + + var transforms = @" + { + 'Inputs': { + 'Column': [ + { + 'Name': 'GroupId', + 'Source': 'Workclass' + } + ], + 'Data': '$AllData', + 'MaxNumTerms': 1000000, + 'Sort': 'Occurrence', + 'TextKeyValues': false + }, + 'Name': 'Transforms.TextToKeyConverter', + 'Outputs': { + 'Model': '$output_model1', + 'OutputData': '$output_data1' + } + }, + { + 'Name': 'Transforms.LabelColumnKeyBooleanConverter', + 'Inputs': { + 'Data': '$output_data1', + 'LabelColumn': 'Label', + 'TextKeyValues': false + }, + 'Outputs': { + 'Model': '$output_model2', + 'OutputData': '$output_data2' + } + }, + { + 'Name': 'Transforms.ColumnCopier', + 'Inputs': { + 'Column': [ + { + 'Name': 'Features', + 'Source': 'NumericFeatures' + } + ], + 'Data': '$output_data2' + }, + 'Outputs': { + 'Model': '$output_model3', + 'OutputData': '$output_data3' + } + },"; + + RunTrainScoreEvaluate("Trainers.FastTreeRanker", "Models.RankerEvaluator", + dataPath, warningsPath, overallMetricsPath, instanceMetricsPath, + splitterInput: "output_data3", transforms: transforms); + + using (var loader = new BinaryLoader(Env, new BinaryLoader.Arguments(), warningsPath)) + Assert.Equal(0, CountRows(loader)); + + using (var loader = new BinaryLoader(Env, new BinaryLoader.Arguments(), overallMetricsPath)) + Assert.Equal(1, CountRows(loader)); + + using (var loader = new BinaryLoader(Env, new BinaryLoader.Arguments(), instanceMetricsPath)) + { + Assert.Equal(103, CountRows(loader)); + Assert.True(loader.Schema.TryGetColumnIndex("GroupId", out var groupCol)); + Assert.True(loader.Schema.TryGetColumnIndex("Label", out var labelCol)); + } + } + [Fact] public void EntryPointSdcaBinary() { @@ -1074,7 +1799,7 @@ public void EntryPointSDCAMultiClass() [Fact()] public void EntryPointSDCARegression() { - TestEntryPointRoutine(TestDatasets.winequality.trainFilename, "Trainers.StochasticDualCoordinateAscentRegressor", loader: TestDatasets.winequality.loaderSettings); + TestEntryPointRoutine(TestDatasets.winequalitymacro.trainFilename, "Trainers.StochasticDualCoordinateAscentRegressor", loader: TestDatasets.winequalitymacro.loaderSettings); } [Fact] @@ -1115,6 +1840,39 @@ public void EntryPointPcaTransform() }); } + [Fact] + public void EntryPointLightLdaTransform() + { + string dataFile = DeleteOutputPath("SavePipe", "SavePipeTextLightLda-SampleText.txt"); + File.WriteAllLines(dataFile, new[] { + "The quick brown fox jumps over the lazy dog.", + "The five boxing wizards jump quickly." + }); + + TestEntryPointPipelineRoutine(dataFile, "sep={ } col=T:TX:0-**", + new[] + { + "Transforms.TextFeaturizer", + "Transforms.LightLda" + }, + new[] + { + @"'Column': { + 'Name': 'T', + 'Source': [ + 'T' + ] + + }, + 'VectorNormalizer': 'None'", + @"'Column': [ + { + 'Name': 'T', + 'Source': 'T' + }]" + }); + } + [Fact] public void EntryPointAveragePerceptron() { @@ -1133,6 +1891,24 @@ public void EntryPointLinearSVM() TestEntryPointRoutine("iris.txt", "Trainers.LinearSvmBinaryClassifier"); } + [Fact] + public void EntryPointBinaryEnsemble() + { + TestEntryPointRoutine("iris.txt", "Trainers.EnsembleBinaryClassifier"); + } + + [Fact] + public void EntryPointClassificationEnsemble() + { + TestEntryPointRoutine("iris.txt", "Trainers.EnsembleClassification"); + } + + [Fact] + public void EntryPointRegressionEnsemble() + { + TestEntryPointRoutine(TestDatasets.winequalitymacro.trainFilename, "Trainers.EnsembleRegression", loader: TestDatasets.winequalitymacro.loaderSettings); + } + [Fact] public void EntryPointNaiveBayesMultiClass() { @@ -1145,10 +1921,10 @@ public void EntryPointHogwildSGD() TestEntryPointRoutine("breast-cancer.txt", "Trainers.StochasticGradientDescentBinaryClassifier"); } - [Fact()] + [Fact] public void EntryPointPoissonRegression() { - TestEntryPointRoutine(TestDatasets.winequality.trainFilename, "Trainers.PoissonRegressor", loader: TestDatasets.winequality.loaderSettings); + TestEntryPointRoutine(TestDatasets.winequalitymacro.trainFilename, "Trainers.PoissonRegressor", loader: TestDatasets.winequalitymacro.loaderSettings); } [Fact] @@ -2612,6 +3388,145 @@ public void EntryPointNodeSchedulingFields() } } + [Fact] + public void EntryPointLinearPredictorSummary() + { + var dataPath = GetDataPath("breast-cancer-withheader.txt"); + var inputFile = new SimpleFileHandle(Env, dataPath, false, false); + + /*var dataView = ImportTextData.ImportText(Env, new ImportTextData.Input + { InputFile = inputFile, CustomSchema = "header+ col=Label:0 col=Features:Num:1-9"*/ + var dataView = ImportTextData.TextLoader(Env, new ImportTextData.LoaderInput() + { + Arguments = + { + SeparatorChars = new []{'\t' }, + HasHeader = true, + Column = new[] + { + new TextLoader.Column() + { + Name = "Label", + Source = new [] { new TextLoader.Range() { Min = 0, Max = 0} }, + }, + + new TextLoader.Column() + { + Name = "Features", + Source = new [] { new TextLoader.Range() { Min = 1, Max = 9} }, + Type = Runtime.Data.DataKind.Num + } + } + }, + + InputFile = inputFile, + }).Data; + + var lrInput = new LogisticRegression.Arguments + { + TrainingData = dataView, + NormalizeFeatures = NormalizeOption.Yes, + NumThreads = 1, + // REVIEW: this depends on MKL library which is not available + ShowTrainingStats = false + }; + var model = LogisticRegression.TrainBinary(Env, lrInput).PredictorModel; + + var mcLrInput = new MulticlassLogisticRegression.Arguments + { + TrainingData = dataView, + NormalizeFeatures = NormalizeOption.Yes, + NumThreads = 1, + ShowTrainingStats = true + }; + var mcModel = LogisticRegression.TrainMultiClass(Env, mcLrInput).PredictorModel; + + var output = SummarizePredictor.Summarize(Env, + new SummarizePredictor.Input() { PredictorModel = model }); + + var mcOutput = SummarizePredictor.Summarize(Env, + new SummarizePredictor.Input() { PredictorModel = mcModel }); + + using (var ch = Env.Register("LinearPredictorSummary").Start("Save Data Views")) + { + var weights = DeleteOutputPath(@"../Common/EntryPoints", "lr-weights.txt"); + var saver = Env.CreateSaver("Text"); + using (var file = Env.CreateOutputFile(weights)) + DataSaverUtils.SaveDataView(ch, saver, output.Summary, file); + + // REVIEW: enable this once MKL library is available + // var stats = DeleteOutputPath(@"../Common/EntryPoints", "lr-stats.txt"); + // using (var file = Env.CreateOutputFile(stats)) + // DataSaverUtils.SaveDataView(ch, saver, output.Stats, file); + + weights = DeleteOutputPath(@"../Common/EntryPoints", "mc-lr-weights.txt"); + using (var file = Env.CreateOutputFile(weights)) + DataSaverUtils.SaveDataView(ch, saver, mcOutput.Summary, file); + + var stats = DeleteOutputPath(@"../Common/EntryPoints", "mc-lr-stats.txt"); + using (var file = Env.CreateOutputFile(stats)) + DataSaverUtils.SaveDataView(ch, saver, mcOutput.Stats, file); + + ch.Done(); + } + + CheckEquality(@"../Common/EntryPoints", "lr-weights.txt"); + // CheckEquality(@"../Common/EntryPoints", "lr-stats.txt"); + CheckEquality(@"../Common/EntryPoints", "mc-lr-weights.txt"); + CheckEquality(@"../Common/EntryPoints", "mc-lr-stats.txt"); + Done(); + } + + [Fact] + public void EntryPointPcaPredictorSummary() + { + var dataPath = GetDataPath("MNIST.Train.0-class.tiny.txt"); + using (var inputFile = new SimpleFileHandle(Env, dataPath, false, false)) + { + var dataView = ImportTextData.TextLoader(Env, new ImportTextData.LoaderInput() + { + Arguments = + { + SeparatorChars = new []{'\t' }, + HasHeader = false, + Column = new[] + { + new TextLoader.Column() + { + Name = "Features", + Source = new [] { new TextLoader.Range() { Min = 1, Max = 784} }, + Type = Runtime.Data.DataKind.R4 + } + } + }, + + InputFile = inputFile, + }).Data; + + var pcaInput = new RandomizedPcaTrainer.Arguments + { + TrainingData = dataView, + }; + var model = RandomizedPcaTrainer.TrainPcaAnomaly(Env, pcaInput).PredictorModel; + + var output = SummarizePredictor.Summarize(Env, + new SummarizePredictor.Input() { PredictorModel = model }); + + using (var ch = Env.Register("PcaPredictorSummary").Start("Save Data Views")) + { + var weights = DeleteOutputPath(@"../Common/EntryPoints", "pca-weights.txt"); + var saver = Env.CreateSaver("Text"); + using (var file = Env.CreateOutputFile(weights)) + DataSaverUtils.SaveDataView(ch, saver, output.Summary, file); + + ch.Done(); + } + + CheckEquality(@"../Common/EntryPoints", "pca-weights.txt"); + Done(); + } + } + [Fact] public void EntryPointPrepareLabelConvertPredictedLabel() { diff --git a/test/Microsoft.ML.Predictor.Tests/Microsoft.ML.Predictor.Tests.csproj b/test/Microsoft.ML.Predictor.Tests/Microsoft.ML.Predictor.Tests.csproj index 2329491e08..7f0a8d05f9 100644 --- a/test/Microsoft.ML.Predictor.Tests/Microsoft.ML.Predictor.Tests.csproj +++ b/test/Microsoft.ML.Predictor.Tests/Microsoft.ML.Predictor.Tests.csproj @@ -13,6 +13,7 @@ + @@ -25,6 +26,7 @@ + diff --git a/test/Microsoft.ML.Predictor.Tests/TestAutoInference.cs b/test/Microsoft.ML.Predictor.Tests/TestAutoInference.cs index 07f6a70295..77697eea9f 100644 --- a/test/Microsoft.ML.Predictor.Tests/TestAutoInference.cs +++ b/test/Microsoft.ML.Predictor.Tests/TestAutoInference.cs @@ -2,12 +2,14 @@ // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. -using System.Linq; -using Newtonsoft.Json.Linq; using Microsoft.ML.Runtime.Data; using Microsoft.ML.Runtime.EntryPoints; using Microsoft.ML.Runtime.EntryPoints.JsonUtils; using Microsoft.ML.Runtime.PipelineInference; +using Newtonsoft.Json.Linq; +using System.Collections.Generic; +using System.Linq; +using System.Runtime.InteropServices; using Xunit; using Xunit.Abstractions; @@ -411,6 +413,27 @@ public void TestPipelineNodeCloning() } } + [Fact] + public void TestSupportedMetricsByName() + { + var names = new List() + { + AutoInference.SupportedMetric.AccuracyMacro.Name, + AutoInference.SupportedMetric.AccuracyMicro.Name, + AutoInference.SupportedMetric.Auc.Name, + AutoInference.SupportedMetric.AuPrc.Name, + AutoInference.SupportedMetric.Dbi.Name, + AutoInference.SupportedMetric.F1.Name, + AutoInference.SupportedMetric.LogLossReduction.Name + }; + + foreach (var name in names) + { + var metric = AutoInference.SupportedMetric.ByName(name); + Assert.Equal(metric.Name, name); + } + } + [Fact] public void TestHyperparameterFreezing() { diff --git a/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs b/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs index 09440d843f..26ba5d118e 100644 --- a/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs +++ b/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs @@ -11,6 +11,12 @@ namespace Microsoft.ML.Runtime.RunTests { + using Microsoft.ML.Runtime.Data; + using Microsoft.ML.Runtime.EntryPoints; + using Microsoft.ML.Runtime.FastTree; + using Microsoft.ML.Runtime.FastTree.Internal; + using System.Linq; + using System.Runtime.InteropServices; using Xunit; using Xunit.Abstractions; using TestLearners = TestLearnersBase; @@ -382,7 +388,7 @@ public void WeightingFastForestRegressionPredictorsTest() }); Done(); } - + [Fact] [TestCategory("Binary")] [TestCategory("FastTree")] @@ -392,7 +398,7 @@ public void FastTreeBinaryClassificationTest() { var learners = new[] { TestLearners.FastTreeClassfier, TestLearners.FastTreeDropoutClassfier, TestLearners.FastTreeBsrClassfier, TestLearners.FastTreeClassfierDisk }; - var binaryClassificationDatasets = new List { TestDatasets.breastCancerPipe}; + var binaryClassificationDatasets = new List { TestDatasets.breastCancerPipe }; foreach (var learner in learners) { foreach (TestDataset dataset in binaryClassificationDatasets) @@ -401,6 +407,259 @@ public void FastTreeBinaryClassificationTest() }); Done(); } + + [Fact] + [TestCategory("Binary")] + [TestCategory("LightGBM")] + public void LightGBMClassificationTest() + { + var learners = new[] { TestLearners.LightGBMClassifier }; + var binaryClassificationDatasets = new List { TestDatasets.breastCancerPipe }; + foreach (var learner in learners) + { + foreach (TestDataset dataset in binaryClassificationDatasets) + Run_TrainTest(learner, dataset); + } + + Done(); + } + + [Fact] + [TestCategory("Binary"), TestCategory("LightGBM")] + public void GossLightGBMTest() + { + var binaryPredictors = new[] { TestLearners.LightGBMGoss }; + var binaryClassificationDatasets = new List { TestDatasets.breastCancerPipe }; + RunAllTests(binaryPredictors, binaryClassificationDatasets, extraTag: "goss"); + Done(); + } + + [Fact] + [TestCategory("Binary")] + [TestCategory("LightGBM")] + public void DartLightGBMTest() + { + var binaryPredictors = new[] { TestLearners.LightGBMDart }; + var binaryClassificationDatasets = new List { TestDatasets.breastCancerPipe }; + RunAllTests(binaryPredictors, binaryClassificationDatasets, extraTag: "dart"); + Done(); + } + + /// + /// A test for multi class classifiers. + /// + [Fact] + [TestCategory("Multiclass")] + [TestCategory("LightGBM")] + public void MultiClassifierLightGBMKeyLabelTest() + { + var multiPredictors = new[] { TestLearners.LightGBMMC }; + var multiClassificationDatasets = new[] { TestDatasets.irisLoader }; + RunAllTests(multiPredictors, multiClassificationDatasets, extraTag: "key"); + Done(); + } + + /// + /// A test for multi class classifiers. + /// + [Fact] + [TestCategory("Multiclass")] + [TestCategory("LightGBM")] + public void MultiClassifierLightGBMKeyLabelU404Test() + { + var multiPredictors = new[] { TestLearners.LightGBMMC }; + var multiClassificationDatasets = new[] { TestDatasets.irisLoaderU404 }; + RunAllTests(multiPredictors, multiClassificationDatasets, extraTag: "keyU404"); + Done(); + } + + /// + /// A test for regression. + /// + [Fact] + [TestCategory("Regression")] + [TestCategory("LightGBM")] + public void RegressorLightGBMTest() + { + var regPredictors = new[] { TestLearners.LightGBMReg }; + var regDatasets = new[] { TestDatasets.winequality }; + RunAllTests(regPredictors, regDatasets); + Done(); + } + + /// + /// A test for regression. + /// + [Fact] + [TestCategory("Regression")] + [TestCategory("LightGBM")] + public void RegressorLightGBMMAETest() + { + var regPredictors = new[] { TestLearners.LightGBMRegMae }; + var regDatasets = new[] { TestDatasets.winequality }; + RunAllTests(regPredictors, regDatasets, extraTag: "MAE"); + Done(); + } + + /// + /// A test for regression. + /// + [Fact] + [TestCategory("Regression")] + [TestCategory("LightGBM")] + public void RegressorLightGBMRMSETest() + { + var regPredictors = new[] { TestLearners.LightGBMRegRmse }; + var regDatasets = new[] { TestDatasets.winequality }; + RunAllTests(regPredictors, regDatasets, extraTag: "RMSE"); + Done(); + } + + /// + /// A test for ranking. The training does not seem to be accurate. + /// The evaluation is still based on nDCG which is not really convenient pair-wise ranking. + /// + [Fact(Skip = "Need to find ranking dataset.")] + [TestCategory("Ranking")] + [TestCategory("LightGBM")] + public void RankingLightGBMTest() + { + var args = new PredictorAndArgs + { + Trainer = new SubComponent("LightGBMRank", + "nt=1 iter=20 v=+ mil=20 nl=20 lr=0.2") + }; + + var rankPredictors = new[] { args }; + var rankDatasets = new[] { TestDatasets.MQ2008 }; + RunAllTests(rankPredictors, rankDatasets); + Done(); + } + + [Fact] + public void TestTreeEnsembleCombiner() + { + var dataPath = GetDataPath("breast-cancer.txt"); + var inputFile = new SimpleFileHandle(Env, dataPath, false, false); +#pragma warning disable 0618 + var dataView = ImportTextData.ImportText(Env, new ImportTextData.Input { InputFile = inputFile }).Data; +#pragma warning restore 0618 + + var fastTrees = new IPredictorModel[3]; + for (int i = 0; i < 3; i++) + { + fastTrees[i] = FastTree.TrainBinary(Env, new FastTreeBinaryClassificationTrainer.Arguments + { + FeatureColumn = "Features", + NumTrees = 5, + NumLeaves = 4, + LabelColumn = DefaultColumnNames.Label, + TrainingData = dataView + }).PredictorModel; + } + CombineAndTestTreeEnsembles(dataView, fastTrees); + } + + [Fact] + public void TestTreeEnsembleCombinerWithCategoricalSplits() + { + var dataPath = GetDataPath("adult.tiny.with-schema.txt"); + var inputFile = new SimpleFileHandle(Env, dataPath, false, false); +#pragma warning disable 0618 + var dataView = ImportTextData.ImportText(Env, new ImportTextData.Input { InputFile = inputFile }).Data; +#pragma warning restore 0618 + + var cat = CategoricalTransform.Create(Env, + new CategoricalTransform.Arguments() + { + Column = new[] + { + new CategoricalTransform.Column() { Name = "Features", Source = "Categories" } + } + }, dataView); + var fastTrees = new IPredictorModel[3]; + for (int i = 0; i < 3; i++) + { + fastTrees[i] = FastTree.TrainBinary(Env, new FastTreeBinaryClassificationTrainer.Arguments + { + FeatureColumn = "Features", + NumTrees = 5, + NumLeaves = 4, + CategoricalSplit = true, + LabelColumn = DefaultColumnNames.Label, + TrainingData = cat + }).PredictorModel; + } + CombineAndTestTreeEnsembles(cat, fastTrees); + } + + private void CombineAndTestTreeEnsembles(IDataView idv, IPredictorModel[] fastTrees) + { + var combiner = new TreeEnsembleCombiner(Env, PredictionKind.BinaryClassification); + + var fastTree = combiner.CombineModels(fastTrees.Select(pm => pm.Predictor as IPredictorProducing)); + + var data = RoleMappedData.Create(idv, RoleMappedSchema.CreatePair(RoleMappedSchema.ColumnRole.Feature, "Features")); + var scored = ScoreModel.Score(Env, new ScoreModel.Input() { Data = idv, PredictorModel = new PredictorModel(Env, data, idv, fastTree) }).ScoredData; + Assert.True(scored.Schema.TryGetColumnIndex("Score", out int scoreCol)); + Assert.True(scored.Schema.TryGetColumnIndex("Probability", out int probCol)); + Assert.True(scored.Schema.TryGetColumnIndex("PredictedLabel", out int predCol)); + + var scoredArray = new IDataView[3]; + var scoreColArray = new int[3]; + var probColArray = new int[3]; + var predColArray = new int[3]; + for (int i = 0; i < 3; i++) + { + scoredArray[i] = ScoreModel.Score(Env, new ScoreModel.Input() { Data = idv, PredictorModel = fastTrees[i] }).ScoredData; + Assert.True(scoredArray[i].Schema.TryGetColumnIndex("Score", out scoreColArray[i])); + Assert.True(scoredArray[i].Schema.TryGetColumnIndex("Probability", out probColArray[i])); + Assert.True(scoredArray[i].Schema.TryGetColumnIndex("PredictedLabel", out predColArray[i])); + } + + var cursors = new IRowCursor[3]; + using (var curs = scored.GetRowCursor(c => c == scoreCol || c == probCol || c == predCol)) + using (cursors[0] = scoredArray[0].GetRowCursor(c => c == scoreColArray[0] || c == probColArray[0] || c == predColArray[0])) + using (cursors[1] = scoredArray[1].GetRowCursor(c => c == scoreColArray[1] || c == probColArray[1] || c == predColArray[1])) + using (cursors[2] = scoredArray[2].GetRowCursor(c => c == scoreColArray[2] || c == probColArray[2] || c == predColArray[2])) + { + var scoreGetter = curs.GetGetter(scoreCol); + var probGetter = curs.GetGetter(probCol); + var predGetter = curs.GetGetter(predCol); + var scoreGetters = new ValueGetter[3]; + var probGetters = new ValueGetter[3]; + var predGetters = new ValueGetter[3]; + for (int i = 0; i < 3; i++) + { + scoreGetters[i] = cursors[i].GetGetter(scoreColArray[i]); + probGetters[i] = cursors[i].GetGetter(probColArray[i]); + predGetters[i] = cursors[i].GetGetter(predColArray[i]); + } + + float score = 0; + float prob = 0; + var pred = default(DvBool); + var scores = new float[3]; + var probs = new float[3]; + var preds = new DvBool[3]; + while (curs.MoveNext()) + { + scoreGetter(ref score); + probGetter(ref prob); + predGetter(ref pred); + for (int i = 0; i < 3; i++) + { + Assert.True(cursors[i].MoveNext()); + scoreGetters[i](ref scores[i]); + probGetters[i](ref probs[i]); + predGetters[i](ref preds[i]); + } + Assert.Equal(score, 0.4 * scores.Sum() / 3, 5); + Assert.Equal(prob, 1 / (1 + Math.Exp(-score)), 6); + Assert.True(pred.IsTrue == score > 0); + } + } + } [Fact] [TestCategory("Binary")] @@ -651,7 +910,7 @@ public void RegressorSdcaTest() Done(); } -#region "Regressor" + #region "Regressor" #if OLD_TESTS // REVIEW: Port these tests? /// @@ -962,7 +1221,7 @@ public void RegressorSyntheticDuplicatedOlsTest() } #endif -#endregion + #endregion /// ///A test for FR ranker @@ -1042,7 +1301,7 @@ public IList GetDatasetsForCalibratorTest() public void DefaultCalibratorPerceptronTest() { var datasets = GetDatasetsForCalibratorTest(); - RunAllTests( new[] { TestLearners.perceptronDefault }, datasets, new string[] { "cali={}" }, "nocalibration"); + RunAllTests(new[] { TestLearners.perceptronDefault }, datasets, new string[] { "cali={}" }, "nocalibration"); Done(); } @@ -1054,7 +1313,7 @@ public void DefaultCalibratorPerceptronTest() public void PAVCalibratorPerceptronTest() { var datasets = GetDatasetsForCalibratorTest(); - RunAllTests( new[] { TestLearners.perceptronDefault }, datasets, new[] { "cali=PAV" }, "PAVcalibration"); + RunAllTests(new[] { TestLearners.perceptronDefault }, datasets, new[] { "cali=PAV" }, "PAVcalibration"); Done(); } @@ -1066,7 +1325,7 @@ public void PAVCalibratorPerceptronTest() public void RandomCalibratorPerceptronTest() { var datasets = GetDatasetsForCalibratorTest(); - RunAllTests( new[] { TestLearners.perceptronDefault }, datasets, new string[] { "numcali=200" }, "calibrateRandom"); + RunAllTests(new[] { TestLearners.perceptronDefault }, datasets, new string[] { "numcali=200" }, "calibrateRandom"); Done(); } @@ -1078,7 +1337,7 @@ public void RandomCalibratorPerceptronTest() public void NoCalibratorLinearSvmTest() { var datasets = GetDatasetsForCalibratorTest(); - RunAllTests( new[] { TestLearners.linearSVM }, datasets, new string[] { "cali={}" }, "nocalibration"); + RunAllTests(new[] { TestLearners.linearSVM }, datasets, new string[] { "cali={}" }, "nocalibration"); Done(); } @@ -1090,7 +1349,7 @@ public void NoCalibratorLinearSvmTest() public void PAVCalibratorLinearSvmTest() { var datasets = GetDatasetsForCalibratorTest(); - RunAllTests( new[] { TestLearners.linearSVM }, datasets, new string[] { "cali=PAV" }, "PAVcalibration"); + RunAllTests(new[] { TestLearners.linearSVM }, datasets, new string[] { "cali=PAV" }, "PAVcalibration"); Done(); } @@ -1215,8 +1474,7 @@ public void NnConfigTests() Done(); } -#if !CORECLR - [Fact(Skip = "Need CoreTLC specific baseline update")] + [Fact] [TestCategory("Anomaly")] public void PcaAnomalyTest() { @@ -1225,11 +1483,12 @@ public void PcaAnomalyTest() // REVIEW: This next test was misbehaving in a strange way that seems to have gone away // mysteriously (bad build?). - Run_TrainTest(TestLearners.PCAAnomalyDefault, TestDatasets.azureCounterUnlabeled, summary: true); + // REVIEW: enable this test afte Expr transform is available. Currently maml breaks on xf=Expr setting + // Run_TrainTest(TestLearners.PCAAnomalyDefault, TestDatasets.azureCounterUnlabeled, summary: true); Done(); } -#endif + /// ///A test for one-class svm (libsvm wrapper) /// diff --git a/test/Microsoft.ML.Sweeper.Tests/Microsoft.ML.Sweeper.Tests.csproj b/test/Microsoft.ML.Sweeper.Tests/Microsoft.ML.Sweeper.Tests.csproj new file mode 100644 index 0000000000..3364b79fda --- /dev/null +++ b/test/Microsoft.ML.Sweeper.Tests/Microsoft.ML.Sweeper.Tests.csproj @@ -0,0 +1,12 @@ + + + + netcoreapp2.0 + CORECLR + false + + + + + + diff --git a/test/Microsoft.ML.Sweeper.Tests/SweeperTest.cs b/test/Microsoft.ML.Sweeper.Tests/SweeperTest.cs new file mode 100644 index 0000000000..4bcb69874f --- /dev/null +++ b/test/Microsoft.ML.Sweeper.Tests/SweeperTest.cs @@ -0,0 +1,69 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Runtime; +using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.RunTests; +using Microsoft.ML.Runtime.Sweeper; +using System; +using System.IO; +using Xunit; + +namespace Microsoft.ML.Sweeper.Tests +{ + public class SweeperTest + { + [Fact] + public void UniformRandomSweeperReturnsDistinctValuesWhenProposeSweep() + { + DiscreteValueGenerator valueGenerator = CreateDiscreteValueGenerator(); + + using (var writer = new StreamWriter(new MemoryStream())) + using (var env = new TlcEnvironment(42, outWriter: writer, errWriter: writer)) + { + var sweeper = new UniformRandomSweeper(env, + new SweeperBase.ArgumentsBase(), + new[] { valueGenerator }); + + var results = sweeper.ProposeSweeps(3); + Assert.NotNull(results); + + int length = results.Length; + Assert.Equal(2, length); + } + } + + [Fact] + public void RandomGridSweeperReturnsDistinctValuesWhenProposeSweep() + { + DiscreteValueGenerator valueGenerator = CreateDiscreteValueGenerator(); + + using (var writer = new StreamWriter(new MemoryStream())) + using (var env = new TlcEnvironment(42, outWriter: writer, errWriter: writer)) + { + var sweeper = new RandomGridSweeper(env, + new RandomGridSweeper.Arguments(), + new[] { valueGenerator }); + + var results = sweeper.ProposeSweeps(3); + Assert.NotNull(results); + + int length = results.Length; + Assert.Equal(2, length); + } + } + + private static DiscreteValueGenerator CreateDiscreteValueGenerator() + { + var args = new DiscreteParamArguments() + { + Name = "TestParam", + Values = new string[] { "one", "two" } + }; + + return new DiscreteValueGenerator(args); + } + } +} diff --git a/test/Microsoft.ML.TestFramework/BaseTestClass.cs b/test/Microsoft.ML.TestFramework/BaseTestClass.cs index cb5781f25c..b5d7a23db3 100644 --- a/test/Microsoft.ML.TestFramework/BaseTestClass.cs +++ b/test/Microsoft.ML.TestFramework/BaseTestClass.cs @@ -3,7 +3,9 @@ // See the LICENSE file in the project root for more information. using Microsoft.ML.Runtime.Internal.Internallearn.Test; +using System.Globalization; using System.IO; +using System.Threading; using Xunit.Abstractions; namespace Microsoft.ML.TestFramework @@ -18,6 +20,11 @@ public class BaseTestClass public BaseTestClass(ITestOutputHelper output) { + //This locale is currently set for tests only so that the produced output + //files can be compared on systems with other locales to give set of known + //correct results that are on en-US locale. + Thread.CurrentThread.CurrentCulture = new CultureInfo("en-US"); + var currentAssemblyLocation = new FileInfo(typeof(BaseTestClass).Assembly.Location); _rootDir = currentAssemblyLocation.Directory.Parent.Parent.Parent.Parent.FullName; _outDir = Path.Combine(currentAssemblyLocation.Directory.FullName, "TestOutput"); diff --git a/test/Microsoft.ML.TestFramework/DataPipe/TestDataPipe.cs b/test/Microsoft.ML.TestFramework/DataPipe/TestDataPipe.cs new file mode 100644 index 0000000000..c598879795 --- /dev/null +++ b/test/Microsoft.ML.TestFramework/DataPipe/TestDataPipe.cs @@ -0,0 +1,148 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Float = System.Single; + +using System; +using System.Collections.Generic; +using System.IO; +using Microsoft.ML.Runtime.CommandLine; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.Data.IO; +using Microsoft.ML.Runtime.Internal.Utilities; +using Microsoft.ML.Runtime.Model; +using Microsoft.ML.Runtime.TextAnalytics; +using Xunit; + +namespace Microsoft.ML.Runtime.RunTests +{ + /// + /// A class for non-baseline data pipe tests. + /// + public sealed partial class TestDataPipeNoBaseline : TestDataViewBase + { + [Fact] + public void TestLDATransform() + { + var builder = new ArrayDataViewBuilder(Env); + var data = new[] + { + new[] { (Float)1.0, (Float)0.0, (Float)0.0 }, + new[] { (Float)0.0, (Float)1.0, (Float)0.0 }, + new[] { (Float)0.0, (Float)0.0, (Float)1.0 }, + }; + + builder.AddColumn("F1V", NumberType.Float, data); + + var srcView = builder.GetDataView(); + + LdaTransform.Column col = new LdaTransform.Column(); + col.Source = "F1V"; + col.NumTopic = 20; + col.NumTopic = 3; + col.NumSummaryTermPerTopic = 3; + col.AlphaSum = 3; + col.NumThreads = 1; + col.ResetRandomGenerator = true; + LdaTransform.Arguments args = new LdaTransform.Arguments(); + args.Column = new LdaTransform.Column[] { col }; + + LdaTransform ldaTransform = new LdaTransform(Env, args, srcView); + + using (var cursor = ldaTransform.GetRowCursor(c => true)) + { + var resultGetter = cursor.GetGetter>(1); + VBuffer resultFirstRow = new VBuffer(); + VBuffer resultSecondRow = new VBuffer(); + VBuffer resultThirdRow = new VBuffer(); + + Assert.True(cursor.MoveNext()); + resultGetter(ref resultFirstRow); + Assert.True(cursor.MoveNext()); + resultGetter(ref resultSecondRow); + Assert.True(cursor.MoveNext()); + resultGetter(ref resultThirdRow); + Assert.False(cursor.MoveNext()); + + Assert.True(resultFirstRow.Length == 3); + Assert.True(resultFirstRow.GetItemOrDefault(0) == 0); + Assert.True(resultFirstRow.GetItemOrDefault(2) == 0); + Assert.True(resultFirstRow.GetItemOrDefault(1) == 1.0); + Assert.True(resultSecondRow.Length == 3); + Assert.True(resultSecondRow.GetItemOrDefault(0) == 0); + Assert.True(resultSecondRow.GetItemOrDefault(2) == 0); + Assert.True(resultSecondRow.GetItemOrDefault(1) == 1.0); + Assert.True(resultThirdRow.Length == 3); + Assert.True(resultThirdRow.GetItemOrDefault(0) == 0); + Assert.True(resultThirdRow.GetItemOrDefault(1) == 0); + Assert.True(resultThirdRow.GetItemOrDefault(2) == 1.0); + } + + using (var cursor = ldaTransform.GetRowCursor(c => true)) + { + var resultGetter = cursor.GetGetter>(1); + VBuffer resultFirstRow = new VBuffer(); + VBuffer resultSecondRow = new VBuffer(); + VBuffer resultThirdRow = new VBuffer(); + + Assert.True(cursor.MoveNext()); + resultGetter(ref resultFirstRow); + Assert.True(cursor.MoveNext()); + resultGetter(ref resultSecondRow); + Assert.True(cursor.MoveNext()); + resultGetter(ref resultThirdRow); + Assert.False(cursor.MoveNext()); + + Assert.True(resultFirstRow.Length == 3); + Assert.True(resultFirstRow.GetItemOrDefault(0) == 0); + Assert.True(resultFirstRow.GetItemOrDefault(2) == 0); + Assert.True(resultFirstRow.GetItemOrDefault(1) == 1.0); + Assert.True(resultSecondRow.Length == 3); + Assert.True(resultSecondRow.GetItemOrDefault(0) == 0); + Assert.True(resultSecondRow.GetItemOrDefault(2) == 0); + Assert.True(resultSecondRow.GetItemOrDefault(1) == 1.0); + Assert.True(resultThirdRow.Length == 3); + Assert.True(resultThirdRow.GetItemOrDefault(0) == 0); + Assert.True(resultThirdRow.GetItemOrDefault(1) == 0); + Assert.True(resultThirdRow.GetItemOrDefault(2) == 1.0); + } + } + + [Fact] + public void TestLdaTransformEmptyDocumentException() + { + var builder = new ArrayDataViewBuilder(Env); + var data = new[] + { + new[] { (Float)0.0, (Float)0.0, (Float)0.0 }, + new[] { (Float)0.0, (Float)0.0, (Float)0.0 }, + new[] { (Float)0.0, (Float)0.0, (Float)0.0 }, + }; + + builder.AddColumn("Zeros", NumberType.Float, data); + + var srcView = builder.GetDataView(); + var col = new LdaTransform.Column() + { + Source = "Zeros" + }; + var args = new LdaTransform.Arguments() + { + Column = new[] { col } + }; + + try + { + var lda = new LdaTransform(Env, args, srcView); + } + catch (InvalidOperationException ex) + { + Assert.Equal(ex.Message, string.Format("The specified documents are all empty in column '{0}'.", col.Source)); + return; + } + + Assert.True(false, "The LDA transform does not throw expected error on empty documents."); + } + } +} diff --git a/test/Microsoft.ML.TestFramework/Datasets.cs b/test/Microsoft.ML.TestFramework/Datasets.cs index c65bd3a444..272780021b 100644 --- a/test/Microsoft.ML.TestFramework/Datasets.cs +++ b/test/Microsoft.ML.TestFramework/Datasets.cs @@ -152,7 +152,7 @@ public static class TestDatasets testFilename = "housing.txt" }; - public static TestDataset winequality = new TestDataset + public static TestDataset winequalitymacro = new TestDataset { name = "wine", trainFilename = "external/winequality-white.csv", @@ -160,6 +160,14 @@ public static class TestDatasets loaderSettings = "col=Label:R4:11 col=Features:R4:0-10 sep=; header+" }; + public static TestDataset winequality = new TestDataset + { + name = "wine", + trainFilename = "external/winequality-white.csv", + testFilename = "external/winequality-white.csv", + loaderSettings = "loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+}" + }; + public static TestDataset msm = new TestDataset { // REVIEW: Why is the MSM train set smaller than the test set? Reverse these! @@ -593,8 +601,8 @@ public static class TestDatasets public static TestDataset mnistOneClass = new TestDataset() { name = "mnistOneClass", - trainFilename = @"..\OneClass\MNIST.Train.0-class.tiny.txt", - testFilename = @"..\OneClass\MNIST.Test.tiny.txt", + trainFilename = @"MNIST.Train.0-class.tiny.txt", + testFilename = @"MNIST.Test.tiny.txt", settings = "" }; diff --git a/test/Microsoft.ML.TestFramework/Learners.cs b/test/Microsoft.ML.TestFramework/Learners.cs index 0fcced53e5..6ac7a3a212 100644 --- a/test/Microsoft.ML.TestFramework/Learners.cs +++ b/test/Microsoft.ML.TestFramework/Learners.cs @@ -267,6 +267,55 @@ static TestLearnersBase() BaselineProgress = true, }; + public static PredictorAndArgs LightGBMClassifier = new PredictorAndArgs + { + Trainer = new SubComponent("LightGBMBinary", "nt=1 nl=5 mil=5 lr=0.25 iter=20 mb=255"), + Tag = "LightGBM", + BaselineProgress = true, + }; + + public static PredictorAndArgs LightGBMGoss = new PredictorAndArgs + { + Trainer = new SubComponent("LightGBM", "nt=1 iter=10 v=+ booster=goss lr=0.2 mil=10 nl=20"), + Tag = "LightGBMGoss", + BaselineProgress = true, + }; + + public static PredictorAndArgs LightGBMDart = new PredictorAndArgs + { + Trainer = new SubComponent("LightGBM", "nt=1 iter=10 booster=dart lr=0.2 mil=10 nl=20"), + Tag = "LightGBMDart", + BaselineProgress = true, + }; + + public static PredictorAndArgs LightGBMMC = new PredictorAndArgs + { + Trainer = new SubComponent("LightGBMMC", "nt=1 iter=10 v=- lr=0.2 mil=10 nl=20"), + Tag = "LightGBMMC", + BaselineProgress = true, + }; + + public static PredictorAndArgs LightGBMReg = new PredictorAndArgs + { + Trainer = new SubComponent("LightGBMR", "nt=1 iter=50 v=+ booster=gbdt{l1=0.2 l2=0.2} lr=0.2 mil=10 nl=20"), + Tag = "LightGBMReg", + BaselineProgress = true, + }; + + public static PredictorAndArgs LightGBMRegMae = new PredictorAndArgs + { + Trainer = new SubComponent("LightGBMR", "nt=1 iter=50 em=mae v=+ lr=0.2 mil=10 nl=20"), + Tag = "LightGBMRegMae", + BaselineProgress = true, + }; + + public static PredictorAndArgs LightGBMRegRmse = new PredictorAndArgs + { + Trainer = new SubComponent("LightGBMR", "nt=1 iter=50 em=rmse v=+ lr=0.2 mil=10 nl=20"), + Tag = "LightGBMRegRmse", + BaselineProgress = true, + }; + public static PredictorAndArgs FastTreeWithCategoricalClassfier = new PredictorAndArgs { Trainer = new SubComponent("FastTreeBinaryClassification", "cat=+ nl=5 mil=5 lr=0.25 iter=20 mb=255"), @@ -589,19 +638,18 @@ public static PredictorAndArgs DssmDefault(int qryFeaturesCount, int docFeatures Trainer = new SubComponent("OneClassSVM", "ker=PolynomialKernel {b=1}"), }; - /* public static PredictorAndArgs PCAAnomalyDefault = new PredictorAndArgs { - Trainer = new SubComponent(RandomizedPcaTrainer.LoadNameValue), + Trainer = new SubComponent("pcaAnomaly"), Tag = "Default" }; public static PredictorAndArgs PCAAnomalyNoNorm = new PredictorAndArgs { - Trainer = new SubComponent(RandomizedPcaTrainer.LoadNameValue), + Trainer = new SubComponent("pcaAnomaly"), MamlArgs = new[] { "norm=no" }, Tag = "NoNorm" - };*/ + }; public static PredictorAndArgs LDSVMDefault = new PredictorAndArgs { diff --git a/test/Microsoft.ML.TestFramework/Microsoft.ML.TestFramework.csproj b/test/Microsoft.ML.TestFramework/Microsoft.ML.TestFramework.csproj index d9cf8a2f29..2eb04a1437 100644 --- a/test/Microsoft.ML.TestFramework/Microsoft.ML.TestFramework.csproj +++ b/test/Microsoft.ML.TestFramework/Microsoft.ML.TestFramework.csproj @@ -13,4 +13,9 @@ + + + + + \ No newline at end of file diff --git a/test/Microsoft.ML.TestFramework/ModelHelper.cs b/test/Microsoft.ML.TestFramework/ModelHelper.cs index edf4408bcb..42c684e51a 100644 --- a/test/Microsoft.ML.TestFramework/ModelHelper.cs +++ b/test/Microsoft.ML.TestFramework/ModelHelper.cs @@ -70,147 +70,147 @@ private static ITransformModel CreateKcHousePricePredictorModel(string dataPath) { Name = "Id", Source = new [] { new TextLoaderRange(0) }, - Type = Runtime.Data.DataKind.Text + Type = Data.DataKind.Text }, new TextLoaderColumn() { Name = "Date", Source = new [] { new TextLoaderRange(1) }, - Type = Runtime.Data.DataKind.Text + Type = Data.DataKind.Text }, new TextLoaderColumn() { Name = "Label", Source = new [] { new TextLoaderRange(2) }, - Type = Runtime.Data.DataKind.Num + Type = Data.DataKind.Num }, new TextLoaderColumn() { Name = "Bedrooms", Source = new [] { new TextLoaderRange(3) }, - Type = Runtime.Data.DataKind.Num + Type = Data.DataKind.Num }, new TextLoaderColumn() { Name = "Bathrooms", Source = new [] { new TextLoaderRange(4) }, - Type = Runtime.Data.DataKind.Num + Type = Data.DataKind.Num }, new TextLoaderColumn() { Name = "SqftLiving", Source = new [] { new TextLoaderRange(5) }, - Type = Runtime.Data.DataKind.Num + Type = Data.DataKind.Num }, new TextLoaderColumn() { Name = "SqftLot", Source = new [] { new TextLoaderRange(6) }, - Type = Runtime.Data.DataKind.Num + Type = Data.DataKind.Num }, new TextLoaderColumn() { Name = "Floors", Source = new [] { new TextLoaderRange(7) }, - Type = Runtime.Data.DataKind.Num + Type = Data.DataKind.Num }, new TextLoaderColumn() { Name = "Waterfront", Source = new [] { new TextLoaderRange(8) }, - Type = Runtime.Data.DataKind.Num + Type = Data.DataKind.Num }, new TextLoaderColumn() { Name = "View", Source = new [] { new TextLoaderRange(9) }, - Type = Runtime.Data.DataKind.Num + Type = Data.DataKind.Num }, new TextLoaderColumn() { Name = "Condition", Source = new [] { new TextLoaderRange(10) }, - Type = Runtime.Data.DataKind.Num + Type = Data.DataKind.Num }, new TextLoaderColumn() { Name = "Grade", Source = new [] { new TextLoaderRange(11) }, - Type = Runtime.Data.DataKind.Num + Type = Data.DataKind.Num }, new TextLoaderColumn() { Name = "SqftAbove", Source = new [] { new TextLoaderRange(12) }, - Type = Runtime.Data.DataKind.Num + Type = Data.DataKind.Num }, new TextLoaderColumn() { Name = "SqftBasement", Source = new [] { new TextLoaderRange(13) }, - Type = Runtime.Data.DataKind.Num + Type = Data.DataKind.Num }, new TextLoaderColumn() { Name = "YearBuilt", Source = new [] { new TextLoaderRange(14) }, - Type = Runtime.Data.DataKind.Num + Type = Data.DataKind.Num }, new TextLoaderColumn() { Name = "YearRenovated", Source = new [] { new TextLoaderRange(15) }, - Type = Runtime.Data.DataKind.Num + Type = Data.DataKind.Num }, new TextLoaderColumn() { Name = "Zipcode", Source = new [] { new TextLoaderRange(16) }, - Type = Runtime.Data.DataKind.Num + Type = Data.DataKind.Num }, new TextLoaderColumn() { Name = "Lat", Source = new [] { new TextLoaderRange(17) }, - Type = Runtime.Data.DataKind.Num + Type = Data.DataKind.Num }, new TextLoaderColumn() { Name = "Long", Source = new [] { new TextLoaderRange(18) }, - Type = Runtime.Data.DataKind.Num + Type = Data.DataKind.Num }, new TextLoaderColumn() { Name = "SqftLiving15", Source = new [] { new TextLoaderRange(19) }, - Type = Runtime.Data.DataKind.Num + Type = Data.DataKind.Num }, new TextLoaderColumn() { Name = "SqftLot15", Source = new [] { new TextLoaderRange(20) }, - Type = Runtime.Data.DataKind.Num + Type = Data.DataKind.Num }, } } diff --git a/test/Microsoft.ML.TestFramework/TestCommandBase.cs b/test/Microsoft.ML.TestFramework/TestCommandBase.cs index 708778a48a..d634a250e2 100644 --- a/test/Microsoft.ML.TestFramework/TestCommandBase.cs +++ b/test/Microsoft.ML.TestFramework/TestCommandBase.cs @@ -294,7 +294,7 @@ protected bool TestCore(RunContextBase ctx, string cmdName, string args, params if (!ctx.NoComparisons) { all &= outputPath.CheckEqualityNormalized(); - if(toCompare != null) + if (toCompare != null) foreach (var c in toCompare) all &= c.CheckEquality(); } @@ -504,7 +504,7 @@ protected void TestPipeFromModel(string dataPath, OutputPath model) public abstract class TestSteppedDmCommandBase : TestDmCommandBase { - protected TestSteppedDmCommandBase(ITestOutputHelper helper): base(helper) + protected TestSteppedDmCommandBase(ITestOutputHelper helper) : base(helper) { _step = 0; _paramsStep = -1; @@ -813,6 +813,21 @@ public void CommandCrossValidation() Done(); } + [Fact] + public void CommandCrossValidationKeyLabelWithFloatKeyValues() + { + RunMTAThread(() => + { + string pathData = GetDataPath(@"adult.tiny.with-schema.txt"); + var perInstFile = CreateOutputPath("perinst.txt"); + // Create a copy of the label column and use it for stratification, in order to create different label counts in the different folds. + string extraArgs = $"tr=FastRankRanking{{t=1}} strat=Strat prexf=rangefilter{{col=Label min=20 max=25}} prexf=term{{col=Strat:Label}} xf=term{{col=Label}} xf=hash{{col=GroupId}} threads- norm=Warn dout={{{perInstFile.Path}}}"; + string loaderArgs = "loader=text{col=Features:R4:10-14 col=Label:R4:9 col=GroupId:TX:1 header+}"; + TestCore("cv", pathData, loaderArgs, extraArgs); + }); + Done(); + } + [TestCategory(Cat)] [Fact(Skip = "Need CoreTLC specific baseline update")] public void CommandCrossValidationVectorNoNames() @@ -1528,7 +1543,7 @@ public void CommandTrainScoreEvaluateRegression() Done(); } - [Fact(Skip = "Need CoreTLC specific baseline update")] + [Fact(Skip = "Need CoreTLC specific baseline update")] [TestCategory("SDCAR")] public void CommandTrainScoreWTFSdcaR() { diff --git a/test/Microsoft.ML.Tests/CSharpCodeGen.cs b/test/Microsoft.ML.Tests/CSharpCodeGen.cs index c647110702..678edac461 100644 --- a/test/Microsoft.ML.Tests/CSharpCodeGen.cs +++ b/test/Microsoft.ML.Tests/CSharpCodeGen.cs @@ -2,6 +2,7 @@ // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. +using Microsoft.ML.Runtime.RunTests; using Microsoft.ML.TestFramework; using System.IO; using Xunit; @@ -9,17 +10,39 @@ namespace Microsoft.ML.Tests { - public class CSharpCodeGen : BaseTestClass + public class CSharpCodeGen : BaseTestBaseline { public CSharpCodeGen(ITestOutputHelper output) : base(output) { } - [Fact(Skip = "Temporary solution(Windows ONLY) to regenerate codegenerated CSharpAPI.cs")] - public void GenerateCSharpAPI() + [Fact(Skip = "Execute this test if you want to regenerate CSharpApi file")] + public void RegenerateCSharpApi() { - var cSharpAPIPath = Path.Combine(RootDir, @"src\\Microsoft.ML\\CSharpApi.cs"); - Runtime.Tools.Maml.Main(new[] { $"? generator=cs{{csFilename={cSharpAPIPath}}}" }); + var basePath = GetDataPath("../../src/Microsoft.ML/CSharpApi.cs"); + Runtime.Tools.Maml.Main(new[] { $"? generator=cs{{csFilename={basePath}}}" }); + } + + [Fact] + public void TestGeneratedCSharpAPI() + { + var dataPath = GetOutputPath("Api.cs"); + Runtime.Tools.Maml.Main(new[] { $"? generator=cs{{csFilename={dataPath}}}" }); + + var basePath = GetDataPath("../../src/Microsoft.ML/CSharpApi.cs"); + using (StreamReader baseline = OpenReader(basePath)) + using (StreamReader result = OpenReader(dataPath)) + { + for (; ; ) + { + string line1 = baseline.ReadLine(); + string line2 = result.ReadLine(); + + if (line1 == null && line2 == null) + break; + Assert.Equal(line1, line2); + } + } } } } diff --git a/test/Microsoft.ML.Tests/CollectionDataSourceTests.cs b/test/Microsoft.ML.Tests/CollectionDataSourceTests.cs index 923d4eb375..42b85ae20f 100644 --- a/test/Microsoft.ML.Tests/CollectionDataSourceTests.cs +++ b/test/Microsoft.ML.Tests/CollectionDataSourceTests.cs @@ -134,9 +134,9 @@ public void CanTrain() { var pipeline = new LearningPipeline(); var data = new List() { - new IrisData { SepalLength = 1f, SepalWidth = 1f ,PetalLength=0.3f, PetalWidth=5.1f, Label=1}, - new IrisData { SepalLength = 1f, SepalWidth = 1f ,PetalLength=0.3f, PetalWidth=5.1f, Label=1}, - new IrisData { SepalLength = 1.2f, SepalWidth = 0.5f ,PetalLength=0.3f, PetalWidth=5.1f, Label=0} + new IrisData { SepalLength = 1f, SepalWidth = 1f, PetalLength=0.3f, PetalWidth=5.1f, Label=1}, + new IrisData { SepalLength = 1f, SepalWidth = 1f, PetalLength=0.3f, PetalWidth=5.1f, Label=1}, + new IrisData { SepalLength = 1.2f, SepalWidth = 0.5f, PetalLength=0.3f, PetalWidth=5.1f, Label=0} }; var collection = CollectionDataSource.Create(data); diff --git a/test/Microsoft.ML.Tests/LearningPipelineTests.cs b/test/Microsoft.ML.Tests/LearningPipelineTests.cs index 3ccc36255f..165b8d8fd2 100644 --- a/test/Microsoft.ML.Tests/LearningPipelineTests.cs +++ b/test/Microsoft.ML.Tests/LearningPipelineTests.cs @@ -2,7 +2,6 @@ // The .NET Foundation licenses this file to you under the MIT license. // See the LICENSE file in the project root for more information. -using Microsoft.ML; using Microsoft.ML.Data; using Microsoft.ML.Runtime.Api; using Microsoft.ML.Runtime.Data; @@ -34,9 +33,9 @@ public void CanAddAndRemoveFromPipeline() { var pipeline = new LearningPipeline() { - new Transforms.CategoricalOneHotVectorizer("String1", "String2"), - new Transforms.ColumnConcatenator(outputColumn: "Features", "String1", "String2", "Number1", "Number2"), - new Trainers.StochasticDualCoordinateAscentRegressor() + new CategoricalOneHotVectorizer("String1", "String2"), + new ColumnConcatenator(outputColumn: "Features", "String1", "String2", "Number1", "Number2"), + new StochasticDualCoordinateAscentRegressor() }; Assert.NotNull(pipeline); Assert.Equal(3, pipeline.Count); @@ -66,7 +65,7 @@ private class TransformedData public void TransformOnlyPipeline() { const string _dataPath = @"..\..\Data\breast-cancer.txt"; - var pipeline = new LearningPipeline(); + var pipeline = new LearningPipeline(seed: 1, conc: 1); pipeline.Add(new ML.Data.TextLoader(_dataPath).CreateFrom(useHeader: false)); pipeline.Add(new CategoricalHashOneHotVectorizer("F1") { HashBits = 10, Seed = 314489979, OutputKind = CategoricalTransformOutputKind.Bag }); var model = pipeline.Train(); @@ -103,9 +102,11 @@ public class Prediction public void NoTransformPipeline() { var data = new Data[1]; - data[0] = new Data(); - data[0].Features = new float[] { 0.0f, 1.0f }; - data[0].Label = 0f; + data[0] = new Data + { + Features = new float[] { 0.0f, 1.0f }, + Label = 0f + }; var pipeline = new LearningPipeline(); pipeline.Add(CollectionDataSource.Create(data)); pipeline.Add(new FastForestBinaryClassifier()); @@ -126,9 +127,11 @@ public class BooleanLabelData public void BooleanLabelPipeline() { var data = new BooleanLabelData[1]; - data[0] = new BooleanLabelData(); - data[0].Features = new float[] { 0.0f, 1.0f }; - data[0].Label = false; + data[0] = new BooleanLabelData + { + Features = new float[] { 0.0f, 1.0f }, + Label = false + }; var pipeline = new LearningPipeline(); pipeline.Add(CollectionDataSource.Create(data)); pipeline.Add(new FastForestBinaryClassifier()); @@ -149,16 +152,37 @@ public class NullableBooleanLabelData public void NullableBooleanLabelPipeline() { var data = new NullableBooleanLabelData[2]; - data[0] = new NullableBooleanLabelData(); - data[0].Features = new float[] { 0.0f, 1.0f }; - data[0].Label = null; - data[1] = new NullableBooleanLabelData(); - data[1].Features = new float[] { 1.0f, 0.0f }; - data[1].Label = false; + data[0] = new NullableBooleanLabelData + { + Features = new float[] { 0.0f, 1.0f }, + Label = null + }; + data[1] = new NullableBooleanLabelData + { + Features = new float[] { 1.0f, 0.0f }, + Label = false + }; var pipeline = new LearningPipeline(); pipeline.Add(CollectionDataSource.Create(data)); pipeline.Add(new FastForestBinaryClassifier()); var model = pipeline.Train(); } + + [Fact] + public void AppendPipeline() + { + var pipeline = new LearningPipeline(); + pipeline.Append(new CategoricalOneHotVectorizer("String1", "String2")) + .Append(new ColumnConcatenator(outputColumn: "Features", "String1", "String2", "Number1", "Number2")) + .Append(new StochasticDualCoordinateAscentRegressor()); + Assert.NotNull(pipeline); + Assert.Equal(3, pipeline.Count); + + pipeline.Remove(pipeline.ElementAt(2)); + Assert.Equal(2, pipeline.Count); + + pipeline.Append(new StochasticDualCoordinateAscentRegressor()); + Assert.Equal(3, pipeline.Count); + } } } diff --git a/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj b/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj index 733c669ffc..8635fdf798 100644 --- a/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj +++ b/test/Microsoft.ML.Tests/Microsoft.ML.Tests.csproj @@ -4,17 +4,21 @@ + + + + \ No newline at end of file diff --git a/test/Microsoft.ML.Tests/OnnxTests.cs b/test/Microsoft.ML.Tests/OnnxTests.cs new file mode 100644 index 0000000000..477a9c6fa6 --- /dev/null +++ b/test/Microsoft.ML.Tests/OnnxTests.cs @@ -0,0 +1,93 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Data; +using Microsoft.ML.Models; +using Microsoft.ML.Runtime.Api; +using Microsoft.ML.Runtime.Data; +using Microsoft.ML.Runtime.RunTests; +using Microsoft.ML.Trainers; +using System.IO; +using Xunit; +using Xunit.Abstractions; + +namespace Microsoft.ML.Tests +{ + public class OnnxTests : BaseTestBaseline + { + public OnnxTests(ITestOutputHelper output) : base(output) + { + } + + public class BreastCancerData + { + public float Label; + + [VectorType(9)] + public float[] Features; + } + + public class BreastCancerPrediction + { + [ColumnName("PredictedLabel")] + public DvBool Cancerous; + } + + [Fact] + public void BinaryClassificationSaveModelToOnnxTest() + { + string dataPath = GetDataPath(@"breast-cancer.txt"); + var pipeline = new LearningPipeline(); + + pipeline.Add(new Data.TextLoader(dataPath) + { + Arguments = new TextLoaderArguments + { + Separator = new[] { '\t' }, + HasHeader = true, + Column = new[] + { + new TextLoaderColumn() + { + Name = "Label", + Source = new [] { new TextLoaderRange(0) }, + Type = Data.DataKind.Num + }, + + new TextLoaderColumn() + { + Name = "Features", + Source = new [] { new TextLoaderRange(1, 9) }, + Type = Data.DataKind.Num + } + } + } + }); + + pipeline.Add(new FastTreeBinaryClassifier() { NumLeaves = 5, NumTrees = 5, MinDocumentsInLeafs = 2 }); + + var model = pipeline.Train(); + var subDir = Path.Combine("..", "..", "BaselineOutput", "Common", "Onnx", "BinaryClassification", "BreastCancer"); + var onnxPath = GetOutputPath(subDir, "SaveModelToOnnxTest.pb"); + DeleteOutputPath(onnxPath); + + var onnxAsJsonPath = GetOutputPath(subDir, "SaveModelToOnnxTest.json"); + DeleteOutputPath(onnxAsJsonPath); + + OnnxConverter converter = new OnnxConverter() + { + InputsToDrop = new[] { "Label" }, + OutputsToDrop = new[] { "Label", "Features" }, + Onnx = onnxPath, + Json = onnxAsJsonPath, + Domain = "Onnx" + }; + + converter.Convert(model); + + CheckEquality(subDir, "SaveModelToOnnxTest.json"); + Done(); + } + } +} diff --git a/test/Microsoft.ML.Tests/PartitionedFileLoaderTests.cs b/test/Microsoft.ML.Tests/PartitionedFileLoaderTests.cs new file mode 100644 index 0000000000..4b5371a98b --- /dev/null +++ b/test/Microsoft.ML.Tests/PartitionedFileLoaderTests.cs @@ -0,0 +1,54 @@ +// Licensed to the .NET Foundation under one or more agreements. +// The .NET Foundation licenses this file to you under the MIT license. +// See the LICENSE file in the project root for more information. + +using Microsoft.ML.Runtime.RunTests; +using System.IO; +using Xunit; +using Xunit.Abstractions; + +namespace Microsoft.ML.Tests +{ + public class PartitionedFileLoaderTests : TestDataPipeBase + { + public PartitionedFileLoaderTests(ITestOutputHelper output) + : base(output) + { + + } + + [Fact] + public void PartitionedNamedDirectories() + { + string basePath = GetDataPath("Partitioned", "Named"); + string pathData = Path.Combine(basePath, "...", "*.csv"); + + TestCore(pathData, false, + new[] { + "loader=Part{bp=" + basePath + " loader=Text{header+ sep=comma col=L0:TX:0}}" + }); + + Done(); + } + + [Fact] + public void PartitionedUnnamedDirectories() + { + string basePath = GetDataPath("Partitioned", "Unnamed"); ; + string pathData = Path.Combine(basePath, "...", "*.csv"); + + TestCore(pathData, false, + new[] { + "loader=Part{parser=SmplPP{col=Month:I4:1} path+ bp=" + basePath + " loader=Text{header+ sep=comma col=L0:I4:1}}" + }); + + // Test again with global parser data type. + TestCore(pathData, false, + new[] { + "loader=Part{parser=SmplPP{type=I4 col=Month:1} path+ bp=" + basePath + " loader=Text{header+ sep=comma col=L0:I4:1}}" + }); + + Done(); + } + } +} diff --git a/test/Microsoft.ML.Tests/Scenarios/ClusteringTests.cs b/test/Microsoft.ML.Tests/Scenarios/ClusteringTests.cs index b9c6713af7..ad1027bb1a 100644 --- a/test/Microsoft.ML.Tests/Scenarios/ClusteringTests.cs +++ b/test/Microsoft.ML.Tests/Scenarios/ClusteringTests.cs @@ -1,4 +1,5 @@ using Microsoft.ML.Data; +using Microsoft.ML.Models; using Microsoft.ML.Runtime; using Microsoft.ML.Runtime.Api; using Microsoft.ML.Trainers; @@ -16,8 +17,8 @@ public void PredictNewsCluster() { string dataPath = GetDataPath(@"external/20newsgroups.txt"); - var pipeline = new LearningPipeline(); - pipeline.Add(new TextLoader(dataPath).CreateFrom(useHeader: false, allowQuotedStrings:true, supportSparse:false)); + var pipeline = new LearningPipeline(seed: 1, conc: 1); + pipeline.Add(new TextLoader(dataPath).CreateFrom(useHeader: false, allowQuotedStrings: true, supportSparse: false)); pipeline.Add(new ColumnConcatenator("AllText", "Subject", "Content")); pipeline.Add(new TextFeaturizer("Features", "AllText") { @@ -81,8 +82,8 @@ public class ClusteringData public void PredictClusters() { int n = 1000; - int k = 5; - var rand = new Random(); + int k = 4; + var rand = new Random(1); var clusters = new ClusteringData[k]; var data = new ClusteringData[n]; for (int i = 0; i < k; i++) @@ -94,7 +95,7 @@ public void PredictClusters() for (int i = 0; i < n; i++) { var index = rand.Next(0, k); - var shift = (rand.NextDouble() - 0.5) / k; + var shift = (rand.NextDouble() - 0.5) / 10; data[i] = new ClusteringData { Points = new float[2] @@ -104,7 +105,7 @@ public void PredictClusters() } }; } - var pipeline = new LearningPipeline(); + var pipeline = new LearningPipeline(seed: 1, conc: 1); pipeline.Add(CollectionDataSource.Create(data)); pipeline.Add(new KMeansPlusPlusClusterer() { K = k }); var model = pipeline.Train(); @@ -116,6 +117,16 @@ public void PredictClusters() Assert.True(!labels.Contains(scores.SelectedClusterId)); labels.Add(scores.SelectedClusterId); } + + var evaluator = new ClusterEvaluator(); + var testData = CollectionDataSource.Create(clusters); + ClusterMetrics metrics = evaluator.Evaluate(model, testData); + + //Label is not specified, so NMI would be equal to NaN + Assert.Equal(metrics.Nmi, double.NaN); + //Calculate dbi is false by default so Dbi would be 0 + Assert.Equal(metrics.Dbi, (double)0.0); + Assert.Equal(metrics.AvgMinScore, (double)0.0, 5); } } } diff --git a/test/Microsoft.ML.Tests/Scenarios/HousePricePredictionTests.cs b/test/Microsoft.ML.Tests/Scenarios/HousePricePredictionTests.cs index 81a2d950b5..85e4a13eac 100644 --- a/test/Microsoft.ML.Tests/Scenarios/HousePricePredictionTests.cs +++ b/test/Microsoft.ML.Tests/Scenarios/HousePricePredictionTests.cs @@ -3,6 +3,7 @@ // See the LICENSE file in the project root for more information. using Microsoft.ML.Runtime.Api; +using Microsoft.ML.Runtime.RunTests; using Microsoft.ML.TestFramework; using Xunit; using Xunit.Abstractions; diff --git a/test/Microsoft.ML.Tests/Scenarios/IrisPlantClassificationTests.cs b/test/Microsoft.ML.Tests/Scenarios/IrisPlantClassificationTests.cs index 5dcbf3a588..696fb9e92d 100644 --- a/test/Microsoft.ML.Tests/Scenarios/IrisPlantClassificationTests.cs +++ b/test/Microsoft.ML.Tests/Scenarios/IrisPlantClassificationTests.cs @@ -18,7 +18,7 @@ public void TrainAndPredictIrisModelTest() { string dataPath = GetDataPath("iris.txt"); - var pipeline = new LearningPipeline(); + var pipeline = new LearningPipeline(seed: 1, conc: 1); pipeline.Add(new TextLoader(dataPath).CreateFrom(useHeader: false)); pipeline.Add(new ColumnConcatenator(outputColumn: "Features", @@ -33,7 +33,7 @@ public void TrainAndPredictIrisModelTest() SepalLength = 3.3f, SepalWidth = 1.6f, PetalLength = 0.2f, - PetalWidth= 5.1f, + PetalWidth = 5.1f, }); Assert.Equal(1, prediction.PredictedLabels[0], 2); @@ -136,6 +136,37 @@ public class IrisPrediction [ColumnName("Score")] public float[] PredictedLabels; } + + [Fact] + public void TrainOva() + { + string dataPath = GetDataPath("iris.txt"); + + var pipeline = new LearningPipeline(seed: 1, conc: 1); + pipeline.Add(new TextLoader(dataPath).CreateFrom(useHeader: false)); + pipeline.Add(new ColumnConcatenator(outputColumn: "Features", + "SepalLength", "SepalWidth", "PetalLength", "PetalWidth")); + + pipeline.Add(OneVersusAll.With(new StochasticDualCoordinateAscentBinaryClassifier())); + + var model = pipeline.Train(); + + var testData = new TextLoader(dataPath).CreateFrom(useHeader: false); + var evaluator = new ClassificationEvaluator(); + ClassificationMetrics metrics = evaluator.Evaluate(model, testData); + CheckMetrics(metrics); + + var trainTest = new TrainTestEvaluator() { Kind = MacroUtilsTrainerKinds.SignatureMultiClassClassifierTrainer }.TrainTestEvaluate(pipeline, testData); + CheckMetrics(trainTest.ClassificationMetrics); + } + + private void CheckMetrics(ClassificationMetrics metrics) + { + Assert.Equal(.96, metrics.AccuracyMacro, 2); + Assert.Equal(.96, metrics.AccuracyMicro, 2); + Assert.Equal(.19, metrics.LogLoss, 1); + Assert.InRange(metrics.LogLossReduction, 80, 84); + } } } diff --git a/test/Microsoft.ML.Tests/Scenarios/SentimentPredictionTests.cs b/test/Microsoft.ML.Tests/Scenarios/SentimentPredictionTests.cs index 701864c577..1ebc2489ec 100644 --- a/test/Microsoft.ML.Tests/Scenarios/SentimentPredictionTests.cs +++ b/test/Microsoft.ML.Tests/Scenarios/SentimentPredictionTests.cs @@ -27,7 +27,7 @@ public void TrainAndPredictSentimentModelTest() var model = pipeline.Train(); var testData = PrepareTextLoaderTestData(); var evaluator = new BinaryClassificationEvaluator(); - BinaryClassificationMetrics metrics = evaluator.Evaluate(model, testData); + var metrics = evaluator.Evaluate(model, testData); ValidateExamples(model); ValidateBinaryMetrics(metrics); } @@ -36,7 +36,6 @@ public void TrainAndPredictSentimentModelTest() public void TrainTestPredictSentimentModelTest() { var pipeline = PreparePipeline(); - PredictionModel model = pipeline.Train(); var testData = PrepareTextLoaderTestData(); var tt = new TrainTestEvaluator().TrainTestEvaluate(pipeline, testData); @@ -212,14 +211,14 @@ private LearningPipeline PreparePipeline() { Name = "Label", Source = new [] { new TextLoaderRange(0) }, - Type = Runtime.Data.DataKind.Num + Type = Data.DataKind.Num }, new TextLoaderColumn() { Name = "SentimentText", Source = new [] { new TextLoaderRange(1) }, - Type = Runtime.Data.DataKind.Text + Type = Data.DataKind.Text } } } @@ -266,14 +265,14 @@ private Data.TextLoader PrepareTextLoaderTestData() { Name = "Label", Source = new [] { new TextLoaderRange(0) }, - Type = Runtime.Data.DataKind.Num + Type = Data.DataKind.Num }, new TextLoaderColumn() { Name = "SentimentText", Source = new [] { new TextLoaderRange(1) }, - Type = Runtime.Data.DataKind.Text + Type = Data.DataKind.Text } } } diff --git a/test/Microsoft.ML.Tests/TextLoaderTests.cs b/test/Microsoft.ML.Tests/TextLoaderTests.cs index 40c0b6525f..61a1744dfb 100644 --- a/test/Microsoft.ML.Tests/TextLoaderTests.cs +++ b/test/Microsoft.ML.Tests/TextLoaderTests.cs @@ -32,8 +32,11 @@ public void ConstructorDoesntThrow() Assert.NotNull(new Data.TextLoader("fakeFile.txt").CreateFrom(useHeader: false, supportSparse: false, trimWhitespace: false)); Assert.NotNull(new Data.TextLoader("fakeFile.txt").CreateFrom(useHeader: false, supportSparse: false)); Assert.NotNull(new Data.TextLoader("fakeFile.txt").CreateFrom(useHeader: false, allowQuotedStrings: false)); + + Assert.NotNull(new Data.TextLoader("fakeFile.txt").CreateFrom()); } + [Fact] public void CanSuccessfullyApplyATransform() { @@ -264,6 +267,15 @@ public class Input public float Number1; } + public class InputWithUnderscore + { + [Column("0")] + public string String_1; + + [Column("1")] + public float Number_1; + } + public class ModelWithoutColumnAttribute { public string String1; diff --git a/test/data/MNIST.Test.tiny.txt b/test/data/MNIST.Test.tiny.txt new file mode 100644 index 0000000000..7cc99f71d1 --- /dev/null +++ b/test/data/MNIST.Test.tiny.txt @@ -0,0 +1,115 @@ +0 784 153:38 154:195 155:228 156:64 157:82 158:156 159:21 180:41 181:224 182:253 183:254 184:249 185:212 186:253 187:208 188:20 207:137 208:203 209:253 210:227 211:208 212:253 213:112 214:213 215:253 216:205 217:9 234:135 235:249 236:253 237:167 238:41 239:151 240:182 241:21 242:103 243:246 244:254 245:63 261:98 262:254 263:253 264:207 265:35 270:6 271:79 272:189 273:237 274:58 288:45 289:254 290:255 291:254 292:36 300:106 301:254 302:185 315:21 316:224 317:253 318:254 319:130 320:3 328:5 329:152 330:224 331:12 343:37 344:253 345:253 346:159 347:9 357:68 358:247 359:46 371:76 372:253 373:253 385:61 386:232 387:24 399:159 400:253 401:253 413:113 414:240 415:35 427:186 428:254 429:228 441:55 442:243 443:39 454:51 455:250 456:253 457:78 468:42 469:233 470:216 482:55 483:253 484:253 485:72 496:119 497:253 498:171 510:32 511:231 512:253 513:85 523:40 524:234 525:253 526:55 539:62 540:253 541:253 550:40 551:181 552:254 553:181 554:11 567:27 568:212 569:254 570:170 571:5 578:167 579:254 580:234 581:13 596:109 597:253 598:254 599:101 600:8 604:51 605:158 606:249 607:233 608:110 624:22 625:139 626:254 627:253 628:222 629:178 630:128 631:218 632:238 633:253 634:231 635:49 653:20 654:144 655:228 656:250 657:253 658:253 659:254 660:249 661:189 662:83 684:62 685:72 686:169 687:105 688:57 +0 784 125:19 126:130 127:255 128:143 129:30 150:41 151:184 152:101 153:209 154:253 155:253 156:253 157:237 158:68 177:2 178:214 179:253 180:253 181:248 182:178 183:179 184:253 185:253 186:200 187:7 205:68 206:249 207:253 208:216 209:12 212:96 213:184 214:253 215:141 216:14 234:227 235:253 236:188 237:6 241:41 242:238 243:253 244:81 261:74 262:251 263:253 264:117 270:201 271:253 272:81 289:82 290:253 291:223 292:23 298:98 299:253 300:127 317:164 318:253 319:167 326:98 327:253 328:163 344:40 345:248 346:253 347:97 354:8 355:244 356:188 372:44 373:253 374:253 375:97 383:244 384:188 400:44 401:253 402:245 403:21 411:244 412:188 428:44 429:253 430:243 439:244 440:179 456:44 457:253 458:243 466:65 467:250 468:81 484:44 485:253 486:243 494:139 495:253 496:81 512:44 513:253 514:243 521:69 522:252 523:239 524:38 540:44 541:253 542:250 543:67 548:43 549:226 550:253 551:152 568:44 569:253 570:253 571:140 575:38 576:180 577:253 578:253 579:28 596:1 597:130 598:253 599:253 600:129 601:40 602:141 603:228 604:253 605:246 606:92 607:1 625:24 626:205 627:253 628:253 629:253 630:253 631:253 632:253 633:164 654:18 655:171 656:253 657:253 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605:43 620:156 621:254 622:175 626:19 627:108 628:190 629:249 630:255 631:213 632:43 648:156 649:254 650:236 651:106 652:99 653:159 654:222 655:254 656:254 657:254 658:133 659:9 +0 784 131:8 132:43 133:39 156:43 157:85 158:173 159:201 160:252 161:247 162:169 163:18 182:22 183:180 184:242 185:252 186:252 187:253 188:245 189:242 190:247 191:83 209:45 210:253 211:252 212:252 213:252 214:252 215:84 216:56 217:101 218:252 219:95 237:177 238:253 239:252 240:252 241:155 242:42 245:110 246:252 247:164 263:39 264:69 265:236 266:254 267:253 268:214 273:39 274:253 275:129 290:64 291:247 292:252 293:252 294:253 295:252 296:82 301:171 302:226 303:24 317:93 318:239 319:252 320:226 321:86 322:232 323:231 324:27 328:43 329:234 330:199 331:7 343:16 344:108 345:218 346:252 347:221 348:119 349:14 356:155 357:252 358:210 359:14 370:9 371:186 372:252 373:253 374:217 375:29 383:36 384:242 385:252 386:101 398:101 399:253 400:253 401:247 402:97 410:18 411:210 412:253 413:214 426:188 427:252 428:252 429:131 438:185 439:253 440:252 441:91 453:123 454:251 455:226 456:129 457:9 463:8 464:128 465:232 466:249 467:179 468:91 469:2 480:11 481:171 482:252 483:210 484:14 490:38 491:171 492:252 493:252 494:199 495:35 508:130 509:252 510:244 511:66 516:27 517:194 518:236 519:252 520:252 521:182 522:24 536:193 537:253 538:127 541:8 542:43 543:139 544:218 545:253 546:255 547:142 548:53 564:148 565:252 566:212 567:85 568:173 569:201 570:252 571:252 572:252 573:182 574:107 575:16 592:139 593:252 594:252 595:252 596:252 597:253 598:245 599:178 600:100 601:4 620:14 621:184 622:252 623:252 624:226 625:199 626:56 649:14 650:42 651:42 652:24 653:18 diff --git a/test/data/Partitioned/Named/Year=2017/Month=01/data1.csv b/test/data/Partitioned/Named/Year=2017/Month=01/data1.csv new file mode 100644 index 0000000000..c69b170df5 --- /dev/null +++ b/test/data/Partitioned/Named/Year=2017/Month=01/data1.csv @@ -0,0 +1,2 @@ +col1, col2 +0, 1 diff --git a/test/data/Partitioned/Named/Year=2017/Month=01/data2.csv b/test/data/Partitioned/Named/Year=2017/Month=01/data2.csv new file mode 100644 index 0000000000..23cb4a7b19 --- /dev/null +++ b/test/data/Partitioned/Named/Year=2017/Month=01/data2.csv @@ -0,0 +1,3 @@ +col1, col2 +4, 5 +6, 7 \ No newline at end of file diff --git a/test/data/Partitioned/Named/Year=2017/Month=01/dataEmpty.csv b/test/data/Partitioned/Named/Year=2017/Month=01/dataEmpty.csv new file mode 100644 index 0000000000..d55d30cf89 --- /dev/null +++ b/test/data/Partitioned/Named/Year=2017/Month=01/dataEmpty.csv @@ -0,0 +1 @@ +col1, col2 \ No newline at end of file diff --git a/test/data/Partitioned/Named/Year=2017/Month=02/data1.csv b/test/data/Partitioned/Named/Year=2017/Month=02/data1.csv new file mode 100644 index 0000000000..6d80a7f679 --- /dev/null +++ b/test/data/Partitioned/Named/Year=2017/Month=02/data1.csv @@ -0,0 +1,4 @@ +col1, col2 +21, 22 +23, 24 +25, 26 diff --git a/test/data/Partitioned/Named/Year=2017/TestBadDir/data1.csv b/test/data/Partitioned/Named/Year=2017/TestBadDir/data1.csv new file mode 100644 index 0000000000..6d80a7f679 --- /dev/null +++ b/test/data/Partitioned/Named/Year=2017/TestBadDir/data1.csv @@ -0,0 +1,4 @@ +col1, col2 +21, 22 +23, 24 +25, 26 diff --git a/test/data/Partitioned/Unnamed/2017/01/data1.csv b/test/data/Partitioned/Unnamed/2017/01/data1.csv new file mode 100644 index 0000000000..c69b170df5 --- /dev/null +++ b/test/data/Partitioned/Unnamed/2017/01/data1.csv @@ -0,0 +1,2 @@ +col1, col2 +0, 1 diff --git a/test/data/Partitioned/Unnamed/2017/01/data2.csv b/test/data/Partitioned/Unnamed/2017/01/data2.csv new file mode 100644 index 0000000000..23cb4a7b19 --- /dev/null +++ b/test/data/Partitioned/Unnamed/2017/01/data2.csv @@ -0,0 +1,3 @@ +col1, col2 +4, 5 +6, 7 \ No newline at end of file diff --git a/test/data/Partitioned/Unnamed/2017/01/dataBadSchema.csv b/test/data/Partitioned/Unnamed/2017/01/dataBadSchema.csv new file mode 100644 index 0000000000..43dbd9e3f6 --- /dev/null +++ b/test/data/Partitioned/Unnamed/2017/01/dataBadSchema.csv @@ -0,0 +1,3 @@ +col1 +11 +12 diff --git a/test/data/Partitioned/Unnamed/2017/02/data1.csv b/test/data/Partitioned/Unnamed/2017/02/data1.csv new file mode 100644 index 0000000000..6d80a7f679 --- /dev/null +++ b/test/data/Partitioned/Unnamed/2017/02/data1.csv @@ -0,0 +1,4 @@ +col1, col2 +21, 22 +23, 24 +25, 26 diff --git a/test/data/breast-cancer-withheader.txt b/test/data/breast-cancer-withheader.txt new file mode 100644 index 0000000000..fab10fb7da --- /dev/null +++ b/test/data/breast-cancer-withheader.txt @@ -0,0 +1,701 @@ +// Number of Instances: 699 (as of 15 July 1992) +// Number of Attributes: 10 plus the class attribute +// Attribute Information: +// +// # Attribute Domain +// -- ----------------------------------------- +// 1. Label 0=benign, 1=malignant +// 2. Clump Thickness 1 - 10 +// 3. Uniformity of Cell Size 1 - 10 +// 4. Uniformity of Cell Shape 1 - 10 +// 5. Marginal Adhesion 1 - 10 +// 6. Single Epithelial Cell Size 1 - 10 +// 7. Bare Nuclei 1 - 10 +// 8. Bland Chromatin 1 - 10 +// 9. Normal Nucleoli 1 - 10 +// 10. Mitoses 1 - 10 + +label thickness uniform_size uniform_shape adhesion epit_size bare_nuclei bland_chromatin normal_nucleoli mitoses +0 5 1 1 1 2 1 3 1 1 +0 5 4 4 5 7 10 3 2 1 +0 3 1 1 1 2 2 3 1 1 +0 6 8 8 1 3 4 3 7 1 +0 4 1 1 3 2 1 3 1 1 +1 8 10 10 8 7 10 9 7 1 +0 1 1 1 1 2 10 3 1 1 +0 2 1 2 1 2 1 3 1 1 +0 2 1 1 1 2 1 1 1 5 +0 4 2 1 1 2 1 2 1 1 +0 1 1 1 1 1 1 3 1 1 +0 2 1 1 1 2 1 2 1 1 +1 5 3 3 3 2 3 4 4 1 +0 1 1 1 1 2 3 3 1 1 +1 8 7 5 10 7 9 5 5 4 +1 7 4 6 4 6 1 4 3 1 +0 4 1 1 1 2 1 2 1 1 +0 4 1 1 1 2 1 3 1 1 +1 10 7 7 6 4 10 4 1 2 +0 6 1 1 1 2 1 3 1 1 +1 7 3 2 10 5 10 5 4 4 +1 10 5 5 3 6 7 7 10 1 +0 3 1 1 1 2 1 2 1 1 +0 1 1 1 1 2 1 3 1 1 +1 5 2 3 4 2 7 3 6 1 +0 3 2 1 1 1 1 2 1 1 +0 5 1 1 1 2 1 2 1 1 +0 2 1 1 1 2 1 2 1 1 +0 1 1 3 1 2 1 1 1 1 +0 3 1 1 1 1 1 2 1 1 +0 2 1 1 1 2 1 3 1 1 +1 10 7 7 3 8 5 7 4 3 +0 2 1 1 2 2 1 3 1 1 +0 3 1 2 1 2 1 2 1 1 +0 2 1 1 1 2 1 2 1 1 +1 10 10 10 8 6 1 8 9 1 +0 6 2 1 1 1 1 7 1 1 +1 5 4 4 9 2 10 5 6 1 +1 2 5 3 3 6 7 7 5 1 +1 10 4 3 1 3 3 6 5 2 +1 6 10 10 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a/test/data/lm.sample.txt b/test/data/lm.sample.txt new file mode 100644 index 0000000000..4aa28ab03f --- /dev/null +++ b/test/data/lm.sample.txt @@ -0,0 +1,120 @@ +Wirtschaft de-DE url-pathpart-wirtschaft url-pathpart-soziales url-domainname-www.spiegel.de url-domainprefix-www url-domainprefix-www.spiegel url-domaintype-de url-domainsuffix-spiegel.de url-firstpartpagename-0,1518,793077,00 url-pagepartsplitname-0,1518,793077,00 url-lastpartpagename-html#ref=rss weg fuer milliardenhilfe frei vor dem parlamentsgebaeude toben strassenkaempfe zwischen demonstranten drinnen haben die griechischen abgeordneten das drastische sparpaket am abend endgueltig beschlossen die entscheidung ist eine wichtige voraussetzung fuer die auszahlung von weiteren acht milliarden euro hilfsgeldern athen das griechische parlament hat einem umfassenden sparpaket endgueltig zugestimmt +Wirtschaft de-DE url-pathpart-finanzen url-pathpart-versicherer url-pathpart-460206 url-domainname-www.wiwo.de url-domainprefix-www url-domainprefix-www.wiwo url-domaintype-de url-domainsuffix-wiwo.de versicherer lediglich air worldwide ein spezialist fuer risikomodelle wagte sich schnell mit einer ersten schaetzung vor +Wirtschaft de-DE url-pathpart-artikel url-domainname-www.neues-deutschland.de url-domainprefix-www url-domainprefix-www.neues-deutschland url-domaintype-de url-domainsuffix-neues-deutschland.de url-firstpartpagename-198684 url-pagepartsplitname-198684 url-lastpartpagename-html neue todesfaelle durch ehec hannover berlin dpa nd in niedersachsen hat der ehec erreger erneut ein todesopfer gefordert eine ueber 70 jahre alte frau aus cuxhaven sei am freitagmorgen in einem bremer krankenhaus an hus dem haemolytisch uraemischen syndrom gestorben sagte ein sprecher des niedersaechsischen gesundheitsministeriums am freitag in hannover die infektion sei bei der frau bereits labordiagnostisch nachgewiesen zudem habe sich der tod einer 41 jaehrigen vom vergangenen mittwoch auf ehec zurueckfuehren lassen die frau stammte ebenfalls aus cuxhaven damit ist die zahl der bestaetigten todesfaelle durch den gefaehrlichen darmkeim ehec in deutschland auf fuenf gestiegen nach angaben des robert koch instituts rki sind von donnerstag auf freitag etwa 60 neue hus faelle gemeldet worden die gesundheitsbehoerden in kopenhagen und stockholm teilten mit in daenemark und schweden seien bisher 32 ehec faelle nachgewiesen worden alle betroffenen seien zuvor in deutschland auf reisen gewesen das aggressive bakterium ehec treibt seit mitte mai in deutschland sein unwesen das hus ist eine schwere verlaufsform bei der giftige stoffwechselprodukte des bakteriums zu nierenschaeden fuehren koennen auf der suche nach einer quelle fuer den erreger waren experten am donnerstag bei gurken aus spanien fuendig geworden +Wirtschaft de-DE url-pathpart-artikel url-domainname-www.neues-deutschland.de url-domainprefix-www url-domainprefix-www.neues-deutschland url-domaintype-de url-domainsuffix-neues-deutschland.de url-firstpartpagename-204736 url-pagepartsplitname-204736 url-lastpartpagename-html italiens schattenwirtschaft blueht in rom beginnt in diesen tagen die parlamentarische diskussion ueber das sparpaket das vergangene woche verabschiedet wurde darin werden in erster linie die aermeren zur kasse gebeten ein wichtiger wirtschaftszweig bleibt vollkommen ungeschoren +Wirtschaft de-DE url-pathpart-wirtschaft url-domainname-www.spiegel.de url-domainprefix-www url-domainprefix-www.spiegel url-domaintype-de url-domainsuffix-spiegel.de url-firstpartpagename-0,1518,785498,00 url-pagepartsplitname-0,1518,785498,00 url-lastpartpagename-html#ref=rss krise in griechenland die wirtschaftsaussichten fuer griechenland sind offenbar noch schlimmer als bislang angenommen +Wirtschaft de-DE url-pathpart-artikel url-domainname-www.neues-deutschland.de url-domainprefix-www url-domainprefix-www.neues-deutschland url-domaintype-de url-domainsuffix-neues-deutschland.de url-firstpartpagename-206132 url-pagepartsplitname-206132 url-lastpartpagename-html auf wackligen fuessen die sogenannte laenderklausel im gesetzentwurf zur abscheidung und speicherung von co2 bietet nach einem gutachten keine ausreichende rechtssicherheit auf der zielgeraden des bereits im bundestag verabschiedeten gesetzentwurfes zur speicherung und lagerung von kohlendioxid co2 haben sich am dienstag in kiel noch einmal die umweltverbaende von greenpeace und bund zu wort gemeldet und die sogenannte laenderklausel als ein womoeglich unsicheres mittel zur verhinderung der co2 verpressung bezeichnet diese biete juristische angriffsflaechen zudem gebe es ausserhalb der zwoelf seemeilen umfassenden aussenwirtschaftszone in der nordsee ohnehin keinen zugriff fuer eine laenderhoheit beide organisationen stellten ein entsprechendes rechtsgutachten der anwaeltin roda verheyen aus hamburg vor in dessen schlussfolgerung steht die forderung speziell an die beiden schwarz gelben landesregierungen von schleswig holstein und niedersachsen am 23 september im bundesrat gegen das ausgearbeitete ccs carbon capture and storage gesetz zu stimmen weil es den einzelnen laendern nur eine truegerische sicherheit biete dass es nicht doch zur erprobung oder anwendung der speichertechnologie komme greenpeace energieexpertin anike peters spricht in diesem zusammenhang von einem faulen deal die bund klimaexpertin tina loeffelsend nennt es eine beruhigungspille die der bevoelkerung verabreicht werde bereits 2017 erfahre das gesetz eine evaluierung und damit wahrscheinlich eine novellierung prophezeit die anwaeltin verheyen tamara zieschang staatssekretaerin aus dem schleswig holsteinischen wirtschaftsministerium hatte kuerzlich auf einer versammlung der buergerinitiative gegen co2 speicherung in nordfriesland bekraeftigt dass es mit der laenderklausel gelingen werde die im hohen norden auf breite ablehnung stossenden plaene zu verhindern insbesondere sorgen sich landwirte und trinkwasserverbaende die sich von vornherein nicht auf moegliche gefahren einlassen wollen und mit den zwischen und endlagerunzulaenglichkeiten rund um den atommuell in der asse und gorleben genuegend anschauungsunterricht geliefert bekommen abgesehen von der sicherheitsphilosophie steht das gesetz und hier besonders die laenderklausel aus sicht der ccs gegner auch rechtlich auf wackeligen fuessen es handele sich geradezu um eine arbeitsbeschaffungsmassnahme fuer juristen so verheyen die sich nicht vorstellen kann dass energiekonzerne sich gaenzlich von ihren ccs vorhaben abbringen lassen erst recht nicht vor dem hintergrund von 21 geplanten oder noch in bau befindlichen kohlekraftwerken und der wende in der energiepolitik dass bundesrechtliche kompetenz in solch einem politikfeld nun ausgerechnet auf die laenderebene heruntergereicht werde stelle ein novum dar betont verheyen eine gesetzliche verankerung des sankt florians prinzips und damit eine ungleichgewichtung einer lastenteilung sei unter umstaenden verfassungsrechtlich angreifbar fuehrt verheyen aus da mit der laenderklausel weder co2 pipelines und transportwege noch die abscheidung selbst verhindert werden sei die vermeintliche teilblockade die womoeglich gerichtlich wieder kassiert werden koenne irrefuehrend klueger sei zur vorgeschriebenen umsetzung der eu richtlinie ein verbotsgesetz wie es oesterreich bereits verabschiedet hat versichert loeffelsend um das zu erreichen kuendigte schleswig holsteins bund landesvorsitzende sybille macht baumgarten an der oertliche protest in form von montagsdemonstrationen werde nicht nachlassen ccs carbon capture and storage steht fuer die technologie der co2 abscheidung und speicherung im juli hatte der bundestag einen gesetzentwurf verabschiedet der diese technologie zu forschungszwecken erlaubt das gesetz geht auf eine eu richtlinie zurueck +Wirtschaft de-DE url-pathpart-finanzen url-pathpart-strengerer url-pathpart-stresstest url-pathpart-fuer url-pathpart-banken url-pathpart-459120 url-domainname-www.wiwo.de url-domainprefix-www url-domainprefix-www.wiwo url-domaintype-de url-domainsuffix-wiwo.de strengerer stresstest fuer banken die neue eu bankenaufsicht eba will die finanzinstitute strengeren stresstests unterwerfen die kriterien des verschaerften verfahrens sind bislang offen doch immerhin steht jetzt der zeitplan fest fuer den 11 maerz hat die bundesbank die wichtigen deutschen banken vorgeladen um ueber die details des wieder bevorstehenden stresstests zu sprechen das erwaehnte wolfgang kirsch vorstandsvorsitzender der genossenschaftlich organisierten dz bank anlaesslich der bilanzpressekonferenz seines hauses in frankfurt kirsch vermutet dass die neue europaeische bankenaufsichtsbehoerde eba die anforderungen an die institute hochschrauben koennte laut eba chef andrea enria sieht es tatsaechlich danach aus wir muessen aus der vergangenheit lernen und zu einer strengeren und serioeseren ueberpruefung kommen sagte enria der nachrichtenagentur reuters mit der vergangenheit spielt der bankenaufseher auf den ersten europaeischen stresstest an der im vergangenen jahr stattfand damals waren nur sieben der 91 getesteten institute durchgefallen darunter die verstaatlichte deutsche immobilienbank hypo real estate irische banken dagegen die mit irland den zweiten eu mitgliedstaat nach griechenland in die krise gestuerzt hatten bestanden den test dieses manko will die seit januar 2011 fuer die europaeische bankenaufsicht zustaendige eba beim nun bevorstehenden zweiten testlauf beseitigen aufgabe der eba ist in kooperation mit nationalen kontrollbehoerden wie der bundesbank oder der bundesanstalt fuer finanzdienstleistungsaufsicht bafin fuer stabile finanzmaerkte zu sorgen die eba will ausserdem die finanzmarktregeln in der europaeischen union harmonisieren um gleiche bedingungen fuer alle europaeischen institute zu schaffen wie bereits im vergangenen jahr simulieren die kontrolleure im rahmen des stresstests ein wirtschaftliches negativszenario das die europaeische zentralbank entwickelt hat die simulation soll zeigen ob europas wichtigste banken fit genug sind um finanzschocks wie fallende immobilienpreise oder explodierende zinsen fuer staatsanleihen zu ueberstehen ziel ist zu beurteilen wie krisenresistent der finanzsektor ist erst am 18 maerz wird bekannt gegeben wie viele und welche finanzinstitute ueberprueft werden auch will die eba erst im april bekannt geben wie die testbedingungen genau aussehen doch schon morgen wollen die aufseher laut enria diese informationen an die fuer den stresstest ausgewaehlten banken weiterleiten das soll den betroffenen instituten die moeglichkeit fuer ein kritisches feedback geben im juni will die eba das ergebnis des stresstests bekannt geben strittig ist in welcher form die oeffentlichkeit und die spieler an den finanzmaerkten darueber zu informieren sind wie einzelne institute abgeschnitten haben der genossenschaftsbanker wolfgang kirsch bevorzugt eine regulierung im stillen denn schlechte testergebnisse duerften zur folge haben dass bedrohte banken keine privaten kapitalgeber mehr finden +Wirtschaft de-DE url-pathpart-artikel url-domainname-www.neues-deutschland.de url-domainprefix-www url-domainprefix-www.neues-deutschland url-domaintype-de url-domainsuffix-neues-deutschland.de url-firstpartpagename-207623 url-pagepartsplitname-207623 url-lastpartpagename-html boeings albtraum ist ausgeliefert irgendwie ist man geneigt die maschine mit einem trabant zu vergleichen erstens weil das ddr auto auch zum gutteil aus verbundwerkstoffen bestand und daher sehr leicht war zweitens weil kunden ewig und drei tage auf die auslieferung ihres traums warten mussten doch nun wird der erste der 55 von ana bestellten 787 langstreckenjets richtung japan abheben mit rund dreieinhalbjaehriger verspaetung im november wird die 787 zunaechst auf ana inlandslinien eingesetzt im dezember folgt dann die verbindung tokio peking bevor ana dann ab januar mit der 787 zwischen tokio haneda und frankfurt am main pendelt insgesamt sind derzeit 820 dreamliner deren rumpf und tragflaechen zum grossteil aus kohlefaserwerkstoff bestehen in boeings orderbuechern vermerkt das ist ein stattlicher auftrag die airlines erhoffen sich einen effektivitaetsschub von der maschine dass der typ so spaet kommt liegt unter anderem daran dass die produktion zum grossen teil ins ausland verlagert wurde gut ein drittel der bauteile des modells kommt aus japan von den zulieferfirmen mitsubishi kawasaki und juji heavy industries das boeing management war nicht in der lage die herstellung zu koordinieren und dabei technische schwierigkeiten die sich bei tests herausgestellt haben zu beheben die ersten drei exemplare musste der hersteller wegen unverkaeuflichkeit abschreiben so wurden aus veranschlagten sechs milliarden dollar entwicklungskosten rund 15 milliarden dollar und der dreamliner zum teuersten zivilflugzeugprogramm aller zeiten also zum albtraum der konzernbosse boeing dementiert nicht dass man erst um die tausend jets verkaufen muss bevor man in die gewinnzone fliegen kann auch bei der modernisierung ihres jumbos 747 hat boeing bislang nicht viel glueck die us frachtflug gesellschaft air atlas hat drei ihrer urspruenglich zwoelf georderten maschinen abbestellt als gruende werden mangelnde termintreue sowie leistungserwaegungen geltend gemacht unlaengst hatte die cargolux gesellschaft boeing duepiert weil sie die uebergabezeremonie platzen liess wegen technischer probleme hatte sich die erstauslieferung des neuen jumbos immer wieder verschoben man liegt rund zwei jahre hinter dem urspruenglichen zeitplan zurueck was die zuverlaessigkeit des 787 traum typs betrifft so kann lufthansa einiges dazu beitragen der technik betrieb der deutsche airline uebernimmt fuer einen weiteren japanischen 787 kunden die japan airlines wartung und service +Gesundheit de-DE url-pathpart-c url-pathpart-32201 url-pathpart-f url-pathpart-423986 url-pathpart-s 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url-pathpart-443314 url-pathpart-s url-pathpart-144f4210 url-pathpart-l url-pathpart-0L0Sfocus0Bde0Cgesundheit0Cratgeber0Csexualitaet0Cnews0Cattraktivitaet0Ewie0Emaenner0Efrauen0Eum0Eden0Efinger0Ewickeln0Iaid0I6198990Bhtml url-domainname-rss2.focus.de url-domainprefix-rss2 url-domainprefix-rss2.focus url-domaintype-de url-domainsuffix-focus.de url-firstpartpagename-story01 url-pagepartsplitname-story01 url-lastpartpagename-htm attraktivitaet wie maenner frauen um den finger wickeln das laengenverhaeltnis von zeige und ringfinger eines mannes ist aussagekraeftig +Gesundheit de-DE url-pathpart-c url-pathpart-32201 url-pathpart-f url-pathpart-423986 url-pathpart-s url-pathpart-144c9d77 url-pathpart-l url-pathpart-0L0Saerztezeitung0Bde0Cpraxis0Iwirtschaft0Cpersonalfuehurng0Carticle0C650A740A0Cdeutsche0Ekein0Eproblem0Eteilzeit0Echefinnen0Bhtml url-domainname-rss.feedsportal.com url-domainprefix-rss url-domainprefix-rss.feedsportal url-domaintype-com url-domainsuffix-feedsportal.com url-firstpartpagename-story01 url-pagepartsplitname-story01 url-lastpartpagename-htm deutsche haben kein problem mit teilzeit chefinnen muenchen reh gut drei viertel der deutschen koennen sich vorstellen unter einer vorgesetzten mit teilzeitvertrag zu arbeiten das zeigt eine repraesentative online umfrage unter 1000 buergern der unternehmensberatung rochus mummert die angst vieler unternehmen dass teilzeit chefinnen autoritaetsprobleme drohten sei also unbegruendet so das beratungsunternehmen noch sei aber nicht einmal jede vierte fuehrungsposition im mittleren management mit einer frau besetzt dabei haetten frauen als leitende teilzeitangestellte die chance nicht nur beruf und familie sondern auch karriere und familie miteinander zu verbinden das fachliche und persoenliche wissen eine fuehrungsposition zu uebernehmen trauen die bundesbuerger den frauen laut der umfrage allemal zu fuer zwei von drei befragten macht es keinen unterschied ob sie von einer frau oder einem mann gefuehrt werden unternehmen die fuer qualifizierte frauen als arbeitgeber attraktiv sein moechten sollten sich beeilen fuehrungskultur und fuehrungsprozesse auf teilzeitmodelle auszurichten sagt bernhard walter senior berater bei rochus mummert dabei geht es nicht um die erfuellung wie und von wem auch immer berechneter frauenquoten sondern um das ureigene interesse der firmen im kampf um die besten koepfe auch auf alle koepfe zugreifen zu koennen die definition von arbeitgeberattraktivitaet hoert in diesem zusammenhang eben nicht beim betriebskindergarten auf sondern umfasst genauso das aufzeigen klarer karriereperspektiven +Gesundheit de-DE url-pathpart-news url-domainname-www.n24.de url-domainprefix-www url-domainprefix-www.n24 url-domaintype-de url-domainsuffix-n24.de url-firstpartpagename-newsitem_7180047 url-pagepartsplitname-newsitem url-pagepartsplitname-7180047 url-lastpartpagename-html fernsehen als einschlafhilfe wer unter schlafstoerungen leidet laesst kaum etwas unversucht um abends besser zur ruhe zu kommen wie eine repraesentative umfrage im auftrag des gesundheitsmagazins apotheken umschau herausfand ist die gaengigste methode der betroffenen das schlichte abwarten +Gesundheit de-DE url-pathpart-politik url-pathpart-gesellschaft url-pathpart-berufspolitik url-pathpart-article url-pathpart-672701 url-domainname-www.aerztezeitung.de url-domainprefix-www url-domainprefix-www.aerztezeitung url-domaintype-de url-domainsuffix-aerztezeitung.de url-firstpartpagename-kbv-chef-koehler-fuerchtet-blockade-spezialaerztlichen-versorgung url-pagepartsplitname-kbv url-pagepartsplitname-chef url-pagepartsplitname-koehler url-pagepartsplitname-fuerchtet url-pagepartsplitname-blockade url-pagepartsplitname-spezialaerztlichen url-pagepartsplitname-versorgung url-lastpartpagename-html kbv chef koehler fuerchtet blockade der spezialaerztlichen versorgung das versorgungsgesetz eroeffnet kven viele handlungsmoeglichkeiten eindringlich mahnt kbv chef dr andreas koehler jetzt zu liefern sein vorstandskollege dr carl heinz mueller fordert erneut die abschaffung der richtgroessen regresse berlin im vorfeld der parlamentarischen beratungen des gkv versorgungsstrukturgesetzes hat kbv chef andreas koehler am freitag bei der internen vertreterversammlung seiner organisation die kven ermahnt die chancen dieses gesetzes zu nutzen diese regierung beschert uns ein liberales gesetz es schafft neue instrumente aber wir muessen sie auch nutzen wollen sagte koehler bereits fuer oktober kuendigte er ein neues gesamtkonzept fuer die bedarfsplanung an hier liegt der ball klar im feld der aerzteschaft die freiheiten die die kven bei der bedarfsplanung erhalten bedeuteten auch gewohnheiten auf den pruefstand zu stellen die ermittlung von verhaeltniszahlen je landkreis oder kreisfreier stadt auf der grundlage der bedarfsverteilung von 1990 sei nicht mehr zeitgemaess je nach fachgruppe muessten differenzierte bedarfsplanungsregionen jenseits von land und stadtkreisen gefunden werden entschieden werden muesse die frage ob auch die ausschliesslich auftragnehmenden fachaerzte in die bedarfsplanung einbezogen werden sollen ein besonderes problem fuer die bedarfsplanung koennten die psychotherapeuten werden angesichts der demografischen entwicklung muesse die zahl der notwendigen sitze dramatisch erhoeht werden das werde aber nur funktionieren wenn die psychotherapie ausserhalb der morbiditaetsbedingten gesamtverguetung finanziert werde diese fachgruppe sei ein gutes beispiel dafuer dass eine am tatsaechlichen versorgungsbedarf orientierte planung sinnvoll sei um wartezeiten auf einen therapieplatz zu verkuerzen koehler sorgt sich um realisierung der spezialaerztlichen versorgung mit sorge sieht koehler dass aufgrund der intervention der laender die spezialaerztliche versorgung nicht realisiert wird zwar seien erhebliche korrekturen am entwurf von paragraf 116 b noetig +Gesundheit de-DE url-pathpart-medizin url-pathpart-krankheiten url-pathpart-demenz url-pathpart-article url-pathpart-673819 url-domainname-www.aerztezeitung.de url-domainprefix-www url-domainprefix-www.aerztezeitung url-domaintype-de url-domainsuffix-aerztezeitung.de url-firstpartpagename-wenn-menschen-teuren-anzug-stundenlang-duschen url-pagepartsplitname-wenn url-pagepartsplitname-menschen url-pagepartsplitname-teuren url-pagepartsplitname-anzug url-pagepartsplitname-stundenlang url-pagepartsplitname-duschen url-lastpartpagename-html wenn menschen im teuren anzug stundenlang duschen ein mann in anzug unter der dusche +Gesundheit de-DE url-pathpart-politik url-pathpart-gesellschaft url-pathpart-article url-pathpart-673507 url-domainname-www.aerztezeitung.de url-domainprefix-www url-domainprefix-www.aerztezeitung url-domaintype-de url-domainsuffix-aerztezeitung.de url-firstpartpagename-spd-setzt-haeusliche-pflege-rund-uhr url-pagepartsplitname-spd url-pagepartsplitname-setzt url-pagepartsplitname-haeusliche url-pagepartsplitname-pflege url-pagepartsplitname-rund url-pagepartsplitname-uhr url-lastpartpagename-html spd setzt auf haeusliche pflege rund um die uhr die spd bundestagsfraktion hat eine pflege wunschliste erarbeitet +Gesundheit de-DE url-pathpart-medizin url-pathpart-krankheiten url-pathpart-neuro url-pathpart-psychiatrische url-pathpart-krankheiten url-pathpart-schlafstoerungen url-pathpart-article url-pathpart-672538 url-domainname-www.aerztezeitung.de url-domainprefix-www url-domainprefix-www.aerztezeitung url-domaintype-de url-domainsuffix-aerztezeitung.de url-firstpartpagename-dopamin-agonisten-bringen-quaelend-rastlose-beine-ruhe url-pagepartsplitname-dopamin url-pagepartsplitname-agonisten url-pagepartsplitname-bringen url-pagepartsplitname-quaelend url-pagepartsplitname-rastlose url-pagepartsplitname-beine url-pagepartsplitname-ruhe url-lastpartpagename-html dopamin agonisten bringen quaelend rastlose beine zur ruhe das restless legs syndrom rls laesst sich mit dopaminergen arzneien inzwischen gut behandeln auch eine augmentation ist weitgehend vermeidbar allerdings wird es immer noch zu selten erkannt wiesbaden es zieht kribbelt und brennt in den beinen man findet kaum noch schlaf +Deutschland de-DE url-pathpart-c url-pathpart-33356 url-pathpart-f url-pathpart-566375 url-pathpart-s url-pathpart-1782004b url-pathpart-l url-pathpart-0L0Stagesspiegel0Bde0Cberlin0Cpolizei0Ejustiz0Ckinderwagen0Ebrandstiftungen0Everdaechtiger0Efestgenommen0C451520A80Bhtml url-domainname-tagesspiegel.feedsportal.com url-domainprefix-tagesspiegel url-domainprefix-tagesspiegel.feedsportal url-domaintype-com url-domainsuffix-feedsportal.com url-firstpartpagename-story01 url-pagepartsplitname-story01 url-lastpartpagename-htm kinderwagen brandstiftungen update in prenzlauer berg hat die polizei am fruehen freitagmorgen einen 29 jaehrigen festgenommen er soll in einem wohnhaus in der winsstrasse zwei kinderwagen in brand gesteckt haben wenn sie diese felder durch einen klick aktivieren werden informationen an facebook twitter oder google in die usa uebertragen und unter umstaenden auch dort gespeichert bei dem festgenommenen handele es sich um einen zeitungsaustraeger aus neukoelln teilte die polizei mit der 29 jaehrige hatte bereits die aufmerksamkeit der ermittler auf sich gezogen und wurde deshalb in der vergangenen nacht beobachtet nachdem er seine zeitungslieferung entgegengenommen hatte laut polizeiangaben folgten ihm zivilpolizisten waehrend er seine route durch den kiez absolvierte jeder hausflur wurde von den zivilpolizisten kontrolliert in einem haus in der winsstrasse hielt sich der beobachtete laengere zeit auf nachdem er das gebaeude wieder verlassen hatte und in die heinrich roller strasse abgebogen war entdeckten die beamten die brennenden kinderwagen informierten die kollegen und versuchten mit feuerloeschern den brand zu ersticken in der heinrich roller strasse klickten dann kurz darauf die handschellen der festgenommene hatte ein einwegfeuerzeug dabei er wurde nach einer erkennungsdienstlichen behandlung den ermittlern des zustaendigen brandkommissariates des landeskriminalamtes ueberstellt inwieweit der mann auch fuer brandlegungen an kinderwagen in der vergangenheit in frage kommt ist gegenstand der laufenden ermittlungen am nachmittag lief noch die vernehmung des mannes durch die ermittler vom lka spaeter soll entschieden werden ob ein haftbefehl beantragt wird fuer wie viele braende der 29 jaehrige insgesamt im verdacht steht wollte die polizei am freitag noch nicht sagen als zeitungszusteller hatte der mann zu dutzenden haeusern im bezirk zugang in der gegend hatte es immer wieder trotz verschlossener tueren gebrannt wie die ermittler auf den verdaechtigen aufmerksam wurden wollten sie aus ermittlungstaktischen gruenden nicht verraten sicher ist nur dass es die erste nacht war in der zivilpolizisten den verdaechtigen auf schritt und tritt ueberwacht und auch gleich ertappt haben haeufig verraten brandstifter sich selbst indem sie am tatort bleiben und vorgeben beim loeschen zu helfen oder sie kehren zurueck um sich ihr werk anzusehen die polizei ist daher speziell geschult auf verdaechtige schaulustige und besonders engagierte helfer zu achten inwieweit der mann auch fuer brandlegungen an kinderwagen in der vergangenheit in frage kommt ist gegenstand der laufenden ermittlungen in berlin kommt es seit monaten regelmaessig zu braenden in hausfluren nach polizeiangaben der polizei gab es zwischen januar und mai bereits 163 braende in hausfluren bei denen zum teil mieter rauchvergiftungen erlitten und hoher sachschaden entstand im vergleichszeitraum des vorjahres waren es insgesamt 144 brandstiftungen wenn sie diese felder durch einen klick aktivieren werden informationen an facebook twitter oder google in die usa uebertragen und unter umstaenden auch dort gespeichert +Deutschland de-DE url-pathpart-c url-pathpart-33356 url-pathpart-f url-pathpart-566375 url-pathpart-s url-pathpart-175ec8be url-pathpart-l url-pathpart-0L0Stagesspiegel0Bde0Cberlin0Cpolizei0Ejustiz0C580Ejaehriger0Edurch0Esprengsatz0Ein0Epark0Everletzt0C4498170A0Bhtml url-domainname-tagesspiegel.feedsportal.com url-domainprefix-tagesspiegel url-domainprefix-tagesspiegel.feedsportal url-domaintype-com url-domainsuffix-feedsportal.com url-firstpartpagename-story01 url-pagepartsplitname-story01 url-lastpartpagename-htm 58 jaehriger durch sprengsatz in park verletzt update bei der explosion eines sprengsatzes in einem park im wedding ist am sonntag ein 58 jaehriger verletzt worden im wedding wurden schon zuvor rohrbomben gefunden wenn sie diese felder durch einen klick aktivieren werden informationen an facebook twitter oder google in die usa uebertragen und unter umstaenden auch dort gespeichert ein spaziergaenger ist am sonntagnachmittag in wedding von einer bombe schwer verletzt worden die in einer plastiktuete versteckt war der 58 jaehrige aus dem gleichen bezirk hatte seinen hund im schillerpark nahe der edinburger strasse ausgefuehrt als er eine neben einer parkbank herumliegende plastiktuete aufheben wollte kam es zur explosion der mann erlitt schwere verletzungen im gesicht und an den beinen ersten erkenntnissen zufolge handelte es sich bei dem sprengsatz um eine rohrbombe ein aehnlicher fall bei dem aber niemand verletzt wurde ereignete sich bereits im mai dieses jahres gleichfalls in wedding die polizei sperrte den schillerpark ueber stunden komplett ab und durchsuchte ihn dabei wurde jedoch nichts gefaehrliches mehr gefunden erschwert war die spurensicherung durch starke regenfaelle der schwerverletzte wurde von einem notarztwagen ins weddinger virchowklinikum gebracht der mann schwebt nicht in lebensgefahr bei der such nach dem bombenbauer tappen die ermittler weiterhin im dunkeln hiess es am montag von einem polizeisprecher die polizei warnte am sonntagabend davor verdaechtige gegenstaende vor allem in tueten aufzuheben im verdachtsfalle solle die polizei alarmiert werden am sonntagabend suchten sprengstoffspuerhunde im schillerpark nach gegenstaenden die moeglicherweise mit in der plastiktuete lagen und durch die explosion weggeschleudert worden waren der fuer sprengstoffdelikte zustaendige staatsschutz bei der polizei hat die ermittlungen uebernommen die experten sehen einen zusammenhang mit einem aehnlichen vorfall im vergangenen mai am 26 mai war ebenfalls in wedding eine rohrbombe in einer plastiktuete gefunden worden eine passantin hatte den verdaechtigen und mit kabeln versehenen gegenstand an der boeschung des nordufers entdeckt und die polizei gerufen die beamten konnten die bombe damals entschaerfen am tag danach hatte das praesidium mitgeteilt dass die eigenkonstruktion sprengfaehig gewesen sei die explosionswirkung habe jedoch nur fuer die unmittelbare umgebung ausgereicht also fuer denjenigen der die selbstbaubombe hochhebt fuer die auf der anderen seite des spandauer schifffahrtskanals gelegene auslaenderbehoerde habe keine gefahr bestanden hiess es die in der linken szene umstrittene behoerde war damals als moegliches ziel genannt worden spaetestens seit dem anschlag im schillerpark ist zu vermuten dass die rohrbomben von einem taeter wahllos abgelegt werden denn in oder am schillerpark ist laut polizei kein politisches motiv zu erkennen offensichtlich hat der bombenbastler sogar seine faehigkeiten weiterentwickelt und perfektioniert auch in den jahren davor hatte es bereits mehrere rohrbombenalarme in wedding gegeben im juni 2007 und juni 2008 waren jeweils in gruenanlagen am dohnagestell reste von rohrbomben gefunden worden diese waren bereits zur explosion gebracht worden durch wen und wieso konnte die polizei nie ermitteln auch diese beiden fundorte liegen ganz in der naehe von jenen in diesem jahr ermittler des lka pruefen nun ob es sich moeglicherweise in all diesen faellen um den gleichen taeter handelt erleichtert wird dies weil die nicht detonierte bastelbombe vom nordufer quasi als vergleichsstueck dienen kann im november 2002 hatten zwei lehrlinge eine selbstgebaute rohrbombe auf einen bvg bus geworfen niemand wurde verletzt bei ihrer festnahme gestanden sie den sprengsatz in ihrer ausbildungswerkstatt hergestellt zu haben ein zusammenhang mit den jetzigen faellen gilt aber als unwahrscheinlich die beiden lehrlinge erhielten bewaehrungsstrafen von sechs und acht monaten wenn sie diese felder durch einen klick aktivieren werden informationen an facebook twitter oder google in die usa uebertragen und unter umstaenden auch dort gespeichert +Ausland de-DE url-pathpart-politik url-pathpart-ausland url-domainname-www.spiegel.de url-domainprefix-www url-domainprefix-www.spiegel url-domaintype-de url-domainsuffix-spiegel.de url-firstpartpagename-0,1518,780177,00 url-pagepartsplitname-0,1518,780177,00 url-lastpartpagename-html#ref=rss militaer chinas erster flugzeugtraeger hat seinen heimathafen dalian im nordosten des landes wieder erreicht nach fuenf tagen hat er die testfahrt im pazifik abschlossen der betrieb des in der ukraine gekauften 300 meter langen schiffs beunruhigt die nachbarlaender und die usa peking der erste chinesische flugzeugtraeger hat am sonntag eine fuenftaegige testfahrt abgeschlossen wie die amtliche nachrichtenagentur xinhua berichtete kehrte die warjag in die hafenstadt dalian im nordosten des landes zurueck ein kleiner teil des seegebiets vor dalian war bis zum abend fuer andere schiffe gesperrt die regierung in peking hatte 1998 der ukraine das stillgelegte schiff ohne motoren sowie waffen und navigationssysteme abgekauft und fuer eine generalueberholung in den hafen von dalian geschleppt seither wurde es umgeruestet nach chinesischen angaben soll der flugzeugtraeger forschungs und uebungszwecken dienen nachbarlaender beobachteten die testfahrt jedoch angesichts chinesischer seemachtsambitionen mit sorge +Ausland de-DE url-pathpart-US url-pathpart-Schuldenkrise url-pathpart-!75381 url-domainname-www.taz.de url-domainprefix-www url-domainprefix-www.taz url-domaintype-de url-domainsuffix-taz.de us schuldenkrise die republikaner verschieben eine abstimmung und die linke protestiert doch ueber obama sprechen nur wenige seine art zu verhandeln missfaellt vielen von dorothea hahn hoert auf die weisheit des heiligen wortes ruft welton gaddy am donnerstagmittag in eine runde aus linken demokratinnen gewerkschafterinnen und frauengruppen sie haben sich direkt vor dem kapitol in washington versammelt um im allerletzten moment zu verhindern dass die sozialversicherung und die beiden staatlichen krankenversicherungen fuer beduerftige gekuerzt werden auf ihren transparenten steht +Unterhaltung de-DE url-pathpart-vermischtes url-pathpart-article1797547 url-domainname-www.morgenpost.de url-domainprefix-www url-domainprefix-www.morgenpost url-domaintype-de url-domainsuffix-morgenpost.de url-firstpartpagename-Kerner-will-nicht-mehr-Kerner-machen url-pagepartsplitname-Kerner url-pagepartsplitname-will url-pagepartsplitname-nicht url-pagepartsplitname-mehr url-pagepartsplitname-Kerner url-pagepartsplitname-machen url-lastpartpagename-html sat 1 zuerst hat er getraeumt dann hat er gebangt schliesslich wieder gehofft doch nun hat johannes b kerner seinen mitarbeitern am dienstag verkuendet dass er zum jahresende sein magazin kerner bei sat 1 beenden werde ja es stimmt johannes b kerner wird am 15 dezember in form eines jahresrueckblicks zum letzten mal sein magazin praesentieren sagte eine sprecherin des muenchner privatsenders mit grossen erwartungen war der damals 44 jaehrige hoffnungstraeger der frueher schon bei sat 1 aktiv war im jahr 2009 vom zdf zu seinem alten sender gewechselt mit ihm wuerden auch die vielen stamm zuschauer die vorher sein mehrfach die woche ausgestrahltes magazin im zdf gesehen hatten abwandern hatte der gebuertige bonner insgeheim gedacht doch die spekulationen erfuellten sich nicht der zdf zuschauer wechselt nicht einfach zu sat 1 auch nicht wegen kerner nach seinem start am montagabend fuhr die sendung quoten im einstelligen marktanteilsbereich ein und das beim fuer sat 1 so wichtigen publikum zwischen 14 und 49 jahren zu wenig fuer einen kommerziell arbeitenden programmveranstalter so bekam kerners magazin am donnerstag eine neue chance denn der moderator war ein zu wertvolles gesamtgut fuer den sender um ihn sofort aus dem programm zu verbannen als tv produzent und fussballexperte mit vielen kontakten in die sportbranche spielt er im fernsehen eine gewichtige rolle daran aenderte auch der geschaeftsfuehrerwechsel bei sat 1 nichts guido bolten der kerner 2009 noch verpflichtet hatte wurde anfang 2010 von andreas bartl abgeloest auf dem neuen sendeplatz donnerstagabends meist um 22 15 uhr erholte sich die sendung allmaehlich die marktanteile stiessen in den unteren zweistelligen marktanteilsbereich doch vor wenigen wochen verkuendete der moderator im branchendienst dwdl de vorsichtshalber schon mal +Unterhaltung de-DE url-pathpart-aktuelles url-pathpart-stars url-portnumber-80 url-domainname-www.tz-online.de url-domainprefix-www url-domainprefix-www.tz-online url-domaintype-de url-domainsuffix-tz-online.de url-hasarguments- url-firstpartpagename-aura-dione-nackt-neuem-albumcover-1432280 url-pagepartsplitname-aura url-pagepartsplitname-dione url-pagepartsplitname-nackt url-pagepartsplitname-neuem url-pagepartsplitname-albumcover url-pagepartsplitname-1432280 url-lastpartpagename-html url-argname-cmp url-argvalue-defrss url-argvaluesplit-defrss aura dione nuernberg so hatte sich monika gruber ihren ersten auftritt bei wetten dass sicher nicht vorgestellt die kabarettistin wurde bei der samstagabend show opfer einer technsichen panne das war passiert +Unterhaltung de-DE url-pathpart-kultur url-pathpart-article1777949 url-domainname-www.morgenpost.de url-domainprefix-www url-domainprefix-www.morgenpost url-domaintype-de url-domainsuffix-morgenpost.de url-firstpartpagename-Roman-Polanski-fuer-Lebenswerk-geehrt url-pagepartsplitname-Roman url-pagepartsplitname-Polanski url-pagepartsplitname-fuer url-pagepartsplitname-Lebenswerk url-pagepartsplitname-geehrt url-lastpartpagename-html filmfestival kult regisseur roman polanski hat beim zurich film festival den preis fuer sein lebenswerk entgegengenommen zwei jahre spaeter als geplant zwei jahre spaeter als geplant hat der polnisch franzoesische starregisseur roman polanski am dienstagabend in zuerich den preis fuer sein lebenswerk entgegengenommen die auszeichnung war ihm vom zurich film festival bereits 2009 zugesprochen worden doch als er vor zwei jahren am 26 september in die schweiz einreisen wollte wurde er in zuerich verhaftet grund war ein internationaler haftbefehl der usa diese forderten die auslieferung polanskis weil der regisseur 1977 eine minderjaehrige missbraucht haben soll polanski wurde in seinem chalet in gstaad unter hausarrest gestellt im juli 2010 wurde er freigelassen die schweiz lehnte seine auslieferung ab die behoerden hatten zweifel an der darstellung des sachverhalts durch die usa die us behoerden legen dem polnisch franzoesischen oscarpreistraeger zur last 1977 eine 13 jaehrige vergewaltigt zu haben polanski hatte seinerzeit eingeraeumt das maedchen mit champagner und drogen verfuehrt zu haben als ihm aber seiner ansicht nach absprachewidrig eine laengere haftstrafe drohte floh er 1978 aus den usa und kehrte seither nie wieder zurueck mit zwei jahren verspaetung sollte der 78 jaehrige nun am dienstagabend das goldene auge des zuercher filmfestivals entgegennehmen besser spaet als nie sagte er in einem interview mit dem franzoesischsprachigen schweizer fernsehen er sei nach wie vor sehr gerne in der schweiz die festivalleitung hatte sich ueber die zusage polanskis sehr gefreut wir sind sehr stolz und geehrt roman polanski nun endlich in zuerich empfangen zu koennen teilte sie mit auch den oscar mit dem er 2003 fuer der pianist als bester regisseur ausgezeichnet wurde wagte er aus angst vor verhaftung nicht selbst entgegenzunehmen als sein film der ghostwriter auf der berlinale 2010 uraufgefuehrt wurde sass er noch in gstaad im hausarrest zur premiere von der gott des gemetzels in venedig reiste er nicht an an seinem wohnsitz paris war er als franzoesischer staatsbuerger vor einer auslieferung sicher polanski schuf bis zu seinem 35 lebensjahr bereits vier filme die ihm allesamt hoechste internationale anerkennung einbrachten und klassiker der leinwand sind danach ueberwand er berufliche wie auch private tiefschlaege und errang 2003 mit dem oscar fuer das holocaust drama der pianist seinen groessten triumph es war der spaete doch laengst ueberfaellige hoehepunkt einer karriere die polanski einen herausragenden platz in der filmgeschichte sichert +Unterhaltung de-DE url-pathpart-nachrichten url-pathpart-stars url-portnumber-80 url-domainname-www.tz-online.de url-domainprefix-www url-domainprefix-www.tz-online url-domaintype-de url-domainsuffix-tz-online.de url-hasarguments- url-firstpartpagename-kurt-kroemer-darum-brauche-keinen-fuehrerschein-1456312 url-pagepartsplitname-kurt url-pagepartsplitname-kroemer url-pagepartsplitname-darum url-pagepartsplitname-brauche url-pagepartsplitname-keinen url-pagepartsplitname-fuehrerschein url-pagepartsplitname-1456312 url-lastpartpagename-html url-argname-cmp url-argvalue-defrss url-argvaluesplit-defrss kurt kroemer der komiker kurt kroemer hat keine lust auto zu fahren kurt kroemer hat keinen fuehrerschein sagte der 36 jaehrige der nachrichtenagentur dpa ich vermisse ihn auch nicht wird er bei freunden und bekannten im auto mitgenommen werde fast immer geflucht immer da wo man gerade auto fahren will ist stau sagte kroemer da laufe ich lieber vor seinen auftritten gehe er in der stadt in der gastiere ausgiebig spazieren da sehe und erlebe ich die tollsten sachen sagte kroemer diese kommen dann auf die buehne die geschichten von der strasse sind meist die groessten brueller sagte er vor dem start seiner deutschlandtournee am freitag in magdeburg +Unterhaltung de-DE url-pathpart-nachrichten url-pathpart-stars url-portnumber-80 url-domainname-www.tz-online.de url-domainprefix-www url-domainprefix-www.tz-online url-domaintype-de url-domainsuffix-tz-online.de url-hasarguments- url-firstpartpagename-keine-strafe-skandal-rocker-pete-doherty-1430824 url-pagepartsplitname-keine url-pagepartsplitname-strafe url-pagepartsplitname-skandal url-pagepartsplitname-rocker url-pagepartsplitname-pete url-pagepartsplitname-doherty url-pagepartsplitname-1430824 url-lastpartpagename-html url-argname-cmp url-argvalue-defrss url-argvaluesplit-defrss keine strafe fuer skandal rocker pete doherty nuernberg so hatte sich monika gruber ihren ersten auftritt bei wetten dass sicher nicht vorgestellt die kabarettistin wurde bei der samstagabend show opfer einer technsichen panne das war passiert +Sport de-DE url-pathpart-artikel url-domainname-www.neues-deutschland.de url-domainprefix-www url-domainprefix-www.neues-deutschland url-domaintype-de url-domainsuffix-neues-deutschland.de url-firstpartpagename-204208 url-pagepartsplitname-204208 url-lastpartpagename-html baldriantropfen fuer den calcio nicht der gesamte italienische fussball ist korrupt es handelt sich nur um einzelne schwarze schafe in der kickerherde zu dieser besaenftigenden schlussfolgerung verleitet der urteilsspruch der ersten instanz der disziplinarkommission des italienischen fussballverbandes figc sie bestaetigte am dienstag weitgehend die strafen die der anklaeger stefano palazzi zuvor gefordert hatte zur debatte stand allerdings nur ein kleiner teil der zwischenzeitlich von staatsanwaelten wettanbietern und medien als verdaechtig erachteten spiele danach wurden die wichtigsten drahtzieher der spielabsprachen und wettbetruegereien zu fuenf jahren sperre und einem folgenden lebenslangen betaetigungsverbot in allen der figc angeschlossenen ligen verurteilt dies betrifft u a den ex nationalspieler giuseppe signori den die staatsanwaelte aus cremona fuer den kopf der sogenannten bologneser gruppe halten zur gleichen strafe wurden der ex profi antonio bellavista der sportdirektor des drittligisten ravenna calcio giorgio buffone und der wettbuerobetreiber massimo erodiani verurteilt von noch aktiven profis traf es den zweitligaspieler vincenzo sommese ascoli sowie die drittliga akteure carlo gervasoni cremona und marco paoloni cremona und benevento deren machenschaften loesten den anfangsverdacht der staatsanwaltschaft cremona aus in der serie a wurden nur der kapitaen von aufsteiger atalanta bergamo cristiano doni 3 jahre 6 monate und dessen mannschaftsgefaehrte thomas manfredini 3 jahre belangt beide waren von ihrem verein bereits bei einem freundschaftsspiel am wochenende nicht mehr beruecksichtigt worden nicht deshalb sondern wegen einer urspruenglich fehlerhaften addierung wurde atalanta vom gericht mit einem minuspunkt weniger belegt statt mit geforderten minus sieben startet der aufsteiger nun mit minus sechs punkten in die am 27 august beginnende saison figc anklaeger palazzi vergab zwei minuspunkte pro anklagepunkt fuer vereinsangehoerige und einen zusatzpunkt bei besonderer schwere des vergehens andere vereine konnten sich ueber eine noch gewichtigere strafverkuerzung freuen drittligist benevento geht nur mit neun statt 14 minuspunkten in die neue saison cremoneses handicap betraegt sechs statt neun punkte bei zweitligist ascoli blieb es bei den geforderten sechs punkten abzug die drittligisten alessandria und ravenna muessen zwangsweise absteigen die urteile sind allerdings noch nicht endgueltig fuer kommenden dienstag ist die verhandlung der zweiten instanz geplant giuseppe signori kuendigte bereits berufung an ebenso die anwaelte von atalanta bergamo und us cremonese prozessbeobachter gehen von einem aehnlichen szenario wie bei der aufarbeitung des schiedsrichterbestechungsskandals im jahre 2006 aus dort wurden die strafen von instanz zu instanz milder einsicht in schuldhaftes verhalten ist im italienischen fussball nicht sonderlich verbreitet wie schon 2006 beklagten auch die aktuellen richter eine omerta ein schweigegeluebde im fussballmilieu das eine aufklaerung erschwere dass der gesamte sumpf nicht trockengelegt wurde zeigt sich indes daran dass die disziplinarkammer nur ueber jene 18 spiele befand die die staatsanwaltschaft cremona in einer ersten phase im visier hatte bei mindestens 30 spielen der italienischen ligen hatten wettanbieter jedoch unregelmaessigkeiten festgestellt das verfahren dient damit eher der beruhigung anstatt der aufklaerung +Sport de-DE url-pathpart-artikel url-domainname-www.neues-deutschland.de url-domainprefix-www url-domainprefix-www.neues-deutschland url-domaintype-de url-domainsuffix-neues-deutschland.de url-firstpartpagename-199706 url-pagepartsplitname-199706 url-lastpartpagename-html fussball pur aber professioneller vor rund zwei wochen hat thomas bastian beim finanziell krisengeschuettelten fussballklub sv babelsberg 03 die nachfolge des zurueckgetretenen praesidenten rainer sperr uebernommen ueber die immer noch angespannte situation und die schwierige aufgabe den verein zu stabilisieren sprach mit dem 51 jaehrigen kino inhaber der schon seit 2003 als vertreter der fans im aufsichtsrat sass peer wilhelms nd +Sport de-DE url-pathpart-sport url-pathpart-wintersport url-domainname-www.focus.de url-domainprefix-www url-domainprefix-www.focus url-domaintype-de url-domainsuffix-focus.de url-firstpartpagename-eishockey-del-del-mannheim-und-eisbaeren-mit-muehevollen-siegen_aid_677052 url-pagepartsplitname-eishockey url-pagepartsplitname-del url-pagepartsplitname-del url-pagepartsplitname-mannheim url-pagepartsplitname-und url-pagepartsplitname-eisbaeren url-pagepartsplitname-mit url-pagepartsplitname-muehevollen url-pagepartsplitname-siegen url-pagepartsplitname-aid url-pagepartsplitname-677052 url-lastpartpagename-html eishockey del del spitzenreiter adler mannheim hat am 12 spieltag der deutschen eishockey liga die tabellenfuehrung durch ein muehevolles 4 +Sport de-DE url-pathpart-sport url-pathpart-fussball url-domainname-www.focus.de url-domainprefix-www url-domainprefix-www.focus url-domaintype-de url-domainsuffix-focus.de url-firstpartpagename-dfl-bierwerbung-rauball-contra-zwanziger_aid_676706 url-pagepartsplitname-dfl url-pagepartsplitname-bierwerbung url-pagepartsplitname-rauball url-pagepartsplitname-contra url-pagepartsplitname-zwanziger url-pagepartsplitname-aid url-pagepartsplitname-676706 url-lastpartpagename-html dfl bierwerbung wenn der dfb beschliesst keine werbevertraege mit brauereien mehr abzuschliessen werden wir die dfl einen anderen weg gehen sagte rauball bei einem sport empfang der spd buergerschaftsfraktion im festsaal des hamburger rathauses zwanziger hatte die verhandlungen ueber eine vertragsverlaengerung mit dem nationalmannschaftssponsor bitburger ueber die euro 2012 hinaus gestoppt da der deutsche fussball bund dfb die neue aktion alkoholfrei sport geniessen des deutschen olympischen sportbundes dosb und der bundeszentrale fuer gesundheitliche aufklaerung bzga unterstuetzt hat zwanziger vorlaeufig sein veto gegen eine vertragsverlaengerung eingelegt wenn der verband zeitgleich mit seinem partner bitburger ueber eine vertragsverlaengerung verhandelt dann muss sehr genau ausgelotet werden ob und wie sich diese aktivitaet noch mit einer bierwerbung vertraegt sagte zwanziger leidtragende eines generellen alkoholwerbeverbots waere vor allem die bundesliga der rund 300 millionen euro verloren gingen wir muessen nicht paepstlicher sein als der papst sagte rauball am donnerstag +Sport de-DE url-pathpart-c url-pathpart-32191 url-pathpart-f url-pathpart-443319 url-pathpart-s url-pathpart-18d3c775 url-pathpart-l url-pathpart-0L0Sfocus0Bde0Csport0Cfussball0Cbundesliga10Cbundesliga0Ehsv0Eboss0Estevens0Everpflichtung0Enoch0Enicht0Efix0Iaid0I6685620Bhtml url-domainname-rss2.focus.de url-domainprefix-rss2 url-domainprefix-rss2.focus url-domaintype-de url-domainsuffix-focus.de url-firstpartpagename-story01 url-pagepartsplitname-story01 url-lastpartpagename-htm bundesliga hsv boss die chefetage des hsv will anfang kommender woche einen neuen trainer praesentieren hat aber eine bereits feststehende verpflichtung des niederlaenders huub stevens dementiert die chefetage des fussball bundesligisten hamburger sv will anfang kommender woche einen nachfolger fuer den entlassenen trainer michael oenning praesentieren hat aber eine bereits feststehende verpflichtung des niederlaenders huub stevens dementiert es gibt noch nichts zu vermelden es ist noch nichts fix sagte der hamburger vorstandsvorsitzende carl edgar jarchow nach dem ersten saisonsieg am freitagabend beim vfb stuttgart 2 +Sport de-DE url-pathpart-artikel url-domainname-www.neues-deutschland.de url-domainprefix-www url-domainprefix-www.neues-deutschland url-domaintype-de url-domainsuffix-neues-deutschland.de url-firstpartpagename-205901 url-pagepartsplitname-205901 url-lastpartpagename-html breite flotte duenne spitze fuer manche deutsche medienvertreter war die bis sonntag dauernde ruder wm im slowenischen bled schon am donnerstag vorbei da gewann der bedeutungsschwanger benamte deutschland achter sein 30 rennen und den dritten titel in serie das grossboot gern als flaggschiff des deutschen rudersports tituliert verdiente sich diese beim olympia debakel 2008 mit rang acht in peking verloren gegangene bezeichnung erst bei dieser wm wieder seit der olympiapleite hat das vom dortmunder ralf holtmeyer betreute prestigeobjekt kein rennen mehr verloren in der oeffentlichkeit wird der achter als synonym fuer den rudersport betrachtet und uebersehen dass er nur ein mosaikstein einer vielfaeltigen sportart ist in bled stehen 27 wettbewerbe auf dem programm 14 olympisch acht nichtolympisch und fuenf sogenannte adaptive events fuer behinderte von den 14 wiederum interessieren neben dem achter eventuell noch der einer marcel hacker und die doppelvierer natuerlich hat der achter jeden respekt verdient art und weise wie er unter regie des in wriezen geborenen steuermanns martin sauer die weltelite dominiert ist faszinierend gleichwohl stehen dem andere kaum nach beispiel +Sport de-DE url-pathpart-c url-pathpart-32191 url-pathpart-f url-pathpart-443319 url-pathpart-s url-pathpart-1835b8d7 url-pathpart-l url-pathpart-0L0Sfocus0Bde0Csport0Cmehrsport0Cmoderner0Efuenfkampf0Ewm0Efuenfkampf0Ewm0Edeutsche0Emaenner0Eim0Efinale0Echancenlos0Iaid0I6642680Bhtml url-domainname-rss2.focus.de url-domainprefix-rss2 url-domainprefix-rss2.focus url-domaintype-de url-domainsuffix-focus.de url-firstpartpagename-story01 url-pagepartsplitname-story01 url-lastpartpagename-htm moderner fuenfkampf wm fuenfkampf wm andrei moissejew hat sich bei der heim wm der modernen fuenfkaempfer in moskau die krone aufgesetzt das deutsche herren trio hatte hingegen mit der entscheidung nichts zu tun doppel olympiasieger delf borrmann berlin stefan koellner potsdam und alexander nobis berlin verpassten als 17 19 und 25 die top ten deutlich silber hinter dem ueberlegenen moissejew der 2004 in athen und 2008 in peking olympisches gold geholt hatte gewann sein russischer landsmann alexander lesun vor dem ungarn adam marosi weltmeister von 2009 +Sport de-DE url-pathpart-c url-pathpart-32191 url-pathpart-f url-pathpart-443319 url-pathpart-s url-pathpart-1782d53b url-pathpart-l url-pathpart-0L0Sfocus0Bde0Csport0Cfussball0Cbundesliga10Cbundesliga0Eschalke0Eentscheidung0Eim0Efall0Eral0Ebis0Ezum0Esonntag0Iaid0I6569510Bhtml url-domainname-rss2.focus.de url-domainprefix-rss2 url-domainprefix-rss2.focus url-domaintype-de url-domainsuffix-focus.de url-firstpartpagename-story01 url-pagepartsplitname-story01 url-lastpartpagename-htm bundesliga schalke ein klaerendes gespraech mit manager horst heldt werde noch vor dem spiel gegen mainz stattfinden sagte trainer ralf rangnick am freitag nach dem 0 +Sport de-DE url-pathpart-2011 url-pathpart-40 url-domainname-www.zeit.de url-domainprefix-www url-domainprefix-www.zeit url-domaintype-de url-domainsuffix-zeit.de url-firstpartpagename-Deutschlandkarte-Fussball url-pagepartsplitname-Deutschlandkarte url-pagepartsplitname-Fussball deutschlandkarte wer will kann belegen dass das interesse am frauenfussball seit der weltmeisterschaft im sommer enorm ja rasant gestiegen ist in der neuen bundesliga saison kamen zu den spielen des weiblichen fc bayern muenchen im schnitt 615 zuschauer statt 473 im vorigen jahr was einer steigerung von mehr als 30 prozent entspricht um die karte zu vergroessern klicken sie bitte hier andererseits verzeihung 615 zuschauer das schaffen die bayern maenner beim training der vergleich ist aber deswegen unfair weil die bayern frauen in der bundesliga nur mittelmass sind siebte von zwoelfen sind sie gerade die traditionell erfolgreichen frankfurterinnen duisburgerinnen und potsdamerinnen ziehen schon mehr zuschauer an es scheint zu helfen wenn es keine konkurrierenden erstliga maennermannschaft in der stadt gibt von einer sogwirkung durch die maenner ist also eher nicht auszugehen eine ausnahme ist der vfl wolfsburg vielleicht liegt es an dem was man dort am wochenende sonst so tun kann vielleicht ist es aber auch die emanzipierteste stadt des landes +Sport de-DE url-pathpart-sport url-pathpart-formel1 url-domainname-www.focus.de url-domainprefix-www url-domainprefix-www.focus url-domaintype-de url-domainsuffix-focus.de url-firstpartpagename-formel-1-hamilton-schnappt-vettel-pole-position-weg_aid_674833 url-pagepartsplitname-formel url-pagepartsplitname-1 url-pagepartsplitname-hamilton url-pagepartsplitname-schnappt url-pagepartsplitname-vettel url-pagepartsplitname-pole url-pagepartsplitname-position url-pagepartsplitname-weg url-pagepartsplitname-aid url-pagepartsplitname-674833 url-lastpartpagename-html formel 1 hamilton schnappt vettel pole position weg mclaren pilot lewis hamilton hat sebastian vettel die pole position fuer den gp von suedkorea weggeschnappt vettel fehlten auf rang zwei etwas mehr als zwei zehntelsekunden jede serie endet einmal auch fuer sebastian vettel und sein red bull team das in suedkorea beim 16 rennen des jahres erstmals nicht die pole position holte in einem wieder einmal erneut spannenden qualifying stoppte der brite lewis hamilton im mclaren mercedes den bullen express und verwies den alten und neuen weltmeister vettel um etwas mehr als zwei zehntelsekunden auf platz zwei worueber sich der champion dennoch freute ich bin sehr gluecklich gerade weil es anfangs hier nicht so gut aussah mclaren war sehr stark aber auch wir waren im qualifying auf den punkt da sagte vettel und fuegte hinzu +Technik & Wissen de-DE url-pathpart-artikel url-domainname-www.neues-deutschland.de url-domainprefix-www url-domainprefix-www.neues-deutschland url-domaintype-de url-domainsuffix-neues-deutschland.de url-firstpartpagename-171875 url-pagepartsplitname-171875 url-lastpartpagename-html held mit schattenseiten kein zweifel er war sehr gefragt und so schrieb er +Technik & Wissen de-DE url-pathpart-wirtschaft url-pathpart-soziales url-domainname-www.spiegel.de url-domainprefix-www url-domainprefix-www.spiegel url-domaintype-de url-domainsuffix-spiegel.de url-firstpartpagename-0,1518,768732,00 url-pagepartsplitname-0,1518,768732,00 url-lastpartpagename-html#ref=rss solarfoerderung die solarbranche kann vorerst aufatmen +Technik & Wissen de-DE url-pathpart-artikel url-domainname-www.neues-deutschland.de url-domainprefix-www url-domainprefix-www.neues-deutschland url-domaintype-de url-domainsuffix-neues-deutschland.de url-firstpartpagename-172364 url-pagepartsplitname-172364 url-lastpartpagename-html traumatische erlebnisse brennen sich ein nd +Technik & Wissen de-DE url-pathpart-artikel url-domainname-www.neues-deutschland.de url-domainprefix-www url-domainprefix-www.neues-deutschland url-domaintype-de url-domainsuffix-neues-deutschland.de url-firstpartpagename-169252 url-pagepartsplitname-169252 url-lastpartpagename-html ist da draussen jemand eine frage bewegt die menschheit seit langem +Technik & Wissen de-DE url-pathpart-wissenschaft url-pathpart-weltall url-domainname-www.spiegel.de url-domainprefix-www url-domainprefix-www.spiegel url-domaintype-de url-domainsuffix-spiegel.de url-firstpartpagename-0,1518,747514,00 url-pagepartsplitname-0,1518,747514,00 url-lastpartpagename-html#ref=rss space shuttle 40 000 menschen schauten vor ort zu 1100 journalisten waren versammelt +Technik & Wissen de-DE url-pathpart-wissenschaft url-pathpart-technik url-domainname-www.spiegel.de url-domainprefix-www url-domainprefix-www.spiegel url-domaintype-de url-domainsuffix-spiegel.de url-firstpartpagename-0,1518,740808,00 url-pagepartsplitname-0,1518,740808,00 url-lastpartpagename-html#ref=rss neckarwestheim i das atomkraftwerk neckarwestheim i ist der erste meiler der seinen weiterbetrieb der von schwarz gelb beschlossenen laufzeitverlaengerung verdankt doch ein neues gutachten moniert dass wichtige sicherheitsnachruestungen jahrelang verschleppt wurden lange sah es so aus als wuerde der erste block des kernkraftwerks neckarwestheim in diesen tagen zum technikmuseum werden nach dem von rot gruen beschlossenen atomausstieg waere der uralt meiler offizielle abkuerzung +Technik & Wissen de-DE url-pathpart-wissenschaft url-pathpart-technik url-domainname-www.spiegel.de url-domainprefix-www url-domainprefix-www.spiegel url-domaintype-de url-domainsuffix-spiegel.de url-firstpartpagename-0,1518,751970,00 url-pagepartsplitname-0,1518,751970,00 url-lastpartpagename-html#ref=rss akw katastrophe in japan zum ersten mal gibt es bilder die den zustand des akw fukushima aus naechster naehe zeigen +Technik & Wissen de-DE url-pathpart-~r url-pathpart-rp url-pathpart-online url-pathpart-rss url-pathpart-wissen url-pathpart-~3 url-pathpart-kAfWJpo4WuM url-domainname-feeds.rp-online.de url-domainprefix-feeds url-domainprefix-feeds.rp-online url-domaintype-de url-domainsuffix-rp-online.de url-firstpartpagename-1019185 url-pagepartsplitname-1019185 studie arten fliehen schneller vor klimawandel washington rpo viele tiere und pflanzen fliehen sehr viel schneller als gedacht vor dem klimawandel +Technik & Wissen de-DE url-pathpart-~r url-pathpart-rp url-pathpart-online url-pathpart-rss url-pathpart-wissen url-pathpart-~3 url-pathpart-PTSlL1qolho url-domainname-feeds.rp-online.de url-domainprefix-feeds url-domainprefix-feeds.rp-online url-domaintype-de url-domainsuffix-rp-online.de url-firstpartpagename-story01 url-pagepartsplitname-story01 url-lastpartpagename-htm was man wissen kann aber nicht muss warum man sich nicht selbst kitzeln kann duesseldorf rpo es gibt sachen die kann man wissen muss man aber nicht man koennte auch ohne sie leben interessant sind sie aber dennoch und als gespraechsstoff fuer die naechste party halten sie allemal her wir haben eine menge davon fuer sie gesammelt zum beispiel diese +Technik & Wissen de-DE url-pathpart-wissen url-domainname-www.sueddeutsche.de url-domainprefix-www url-domainprefix-www.sueddeutsche url-domaintype-de url-domainsuffix-sueddeutsche.de url-firstpartpagename-packeeis-schrumpft-auf-neues-minimum-gefaehrliches-tauwetter-im-ewigen-eis-1 url-pagepartsplitname-packeeis url-pagepartsplitname-schrumpft url-pagepartsplitname-auf url-pagepartsplitname-neues url-pagepartsplitname-minimum url-pagepartsplitname-gefaehrliches url-pagepartsplitname-tauwetter url-pagepartsplitname-im url-pagepartsplitname-ewigen url-pagepartsplitname-eis url-pagepartsplitname-1 url-lastpartpagename-1141322 packeis schrumpft auf neues minimum das meereis in der arktis wird in diesem sommer auf ein neues minimum zusammenschmelzen das aktuelle tempo des eisrueckgangs laesst darauf schliessen dass der bisherige negativrekord aus dem jahr 2007 unterboten wird teilte die universitaet bremen am freitag mit demnach ist die meereisflaeche am donnerstag auf 4 24 millionen quadratkilometer geschrumpft und lag damit unter dem entsprechenden tageswert des jahres 2007 damals waren 4 27 millionen quadratkilometer gemessen worden dass in diesem jahr ein negativrekord zu erwarten ist hatte sich bereits am anfang der woche abgezeichnet die eisdecke ist derzeit an den raendern so stark aufgebrochen dass die sonneneinstrahlung die oberste wasserschicht erwaermen kann und noch viele schollen schmelzen werden sagte ruediger gerdes meereisphysiker am bremerhavener alfred wegener institut am montag der rueckgang des sommerlichen eises betraegt seit 1972 bereits 50 prozent warnt der bremer umweltphysiker georg heygster dabei hat die packeisflaeche in diesem jahr offenbar eine andere form als in den vergangenen eisarmen perioden in diesem sommer gab es auffallend grosse eisfreie flaechen innerhalb der packeis zone zum beispiel in der laptev see im norden russlands dieses loch erweckt den eindruck als sei das eis hier von unten geschmolzen es tat sich anfang august auf und hat inzwischen die groesse hollands erreicht erklaerte der meereisforscher lars kaleschke vom klimacampus der universitaet hamburg vor wenigen tagen auch wissenschaftler des alfred wegener instituts die vor kurzem an bord des forschungseisbrechers polarstern bis zum nordpol vorgedrungen waren bestaetigten die geringe dicke des meereises messungen hatten einen durchschnittswert von 90 zentimetern ergeben im jahr 2001 war das meereis noch durchschnittlich zwei meter dick die arktis gilt unter klimaforschern als aeusserst sensible region die dortigen eisflaechen reagieren nicht nur empfindlich auf die erderwaermung sie bestimmen auch massgeblich deren voranschreiten so wie sich ein weisses auto unter sonnenstrahlung weniger aufheizt als ein dunkles fahrzeug reflektiert die eisflaeche der arktis mehr sonnenstrahlung zurueck in den weltraum als das vergleichsweise dunkle meerwasser schmilzt das packeis der arktis kurbelt das den klimawandel zusaetzlich an +Technik & Wissen de-DE url-pathpart-wissenschaft url-pathpart-weltall url-domainname-www.spiegel.de url-domainprefix-www url-domainprefix-www.spiegel url-domaintype-de url-domainsuffix-spiegel.de url-firstpartpagename-0,1518,792822,00 url-pagepartsplitname-0,1518,792822,00 url-lastpartpagename-html#ref=rss start des galileo satelliten es soll eine doppelpremiere werden erstmals hebt eine russische sojus rakete von europas weltraumbahnhof kourou ab und +Technik & Wissen de-DE url-pathpart-spiegel url-domainname-www.spiegel.de url-domainprefix-www url-domainprefix-www.spiegel url-domaintype-de url-domainsuffix-spiegel.de url-firstpartpagename-0,1518,791040,00 url-pagepartsplitname-0,1518,791040,00 url-lastpartpagename-html#ref=rss fliegen wie ein vogel kann der mensch bald fliegen wie ein vogel tueftler jagen dem menschheitstraum des schwingenflugs mit beweglichen fluegeln nach als die sonne blutrot ueber dem morgennebel in die hoehe steigt fliegt todd reichert davon emporgetragen wird er vom auf und ab maechtiger fluegel kein motor treibt sein vehikel an mit blosser kraft der beine die er in die pedale stemmt hebt er ab sein strampeln wird durch ein seilsystem auf die fluegel uebertragen monatelang hat der kanadische ingenieurstudent fuer diesen moment trainiert er nahm acht kilo ab und erhielt unterricht von einem kickboxer zusammen mit kommilitonen der university of toronto hatte er vier jahre lang das einzigartige superleichtflugzeug zusammengeschraubt der snowbird besteht grossenteils aus carbonstangen balsaholz und plastikfolie und wiegt nur 44 kilogramm hat aber eine spannweite von 32 metern fast so viel wie eine boeing 737 reicherts flug dauerte nur 19 3 sekunden nach 145 metern verliessen ihn die kraefte und er musste landen mit hilfe des kinderwagenrads das er unter den rumpf geschraubt hatte fuer den piloten war der huepfer ein triumph ein uralter traum vom fliegen ist wahr geworden sagt reichert die vision sich aus eigener kraft in die luefte zu erheben beschwor bereits der antike mythos von ikarus mit schwingen aus federn und wachs erhob dieser sich in die luft bis er der sonne zu nahe kam +Technik & Wissen de-DE url-pathpart-~r url-pathpart-rp url-pathpart-online url-pathpart-rss url-pathpart-wissen url-pathpart-~3 url-pathpart-ukOgqmbUdqU url-domainname-feeds.rp-online.de url-domainprefix-feeds url-domainprefix-feeds.rp-online url-domaintype-de url-domainsuffix-rp-online.de url-firstpartpagename-story01 url-pagepartsplitname-story01 url-lastpartpagename-htm bau gefaehrdet amazonas ureinwohner brasilianisches gericht stoppt riesenstaudamm sao paulo rpo ein brasilianisches gericht hat einen baustopp fuer den drittgroessten staudamm der welt im amazonas gebiet angeordnet der bau des umstrittenen belo monte staudamms gefaehrde den fischfang der ureinwohner hiess es am mittwoch zur begruendung das baukonsortium duerfe keine infrastrukturmassnahmen vornehmen welche den natuerlichen strom des xingu flusses und damit den fischbestand beeintraechtigten das bundesgericht des nordbrasilianischen staates para untersagte dem baukonsortium norte energia das flussbett des xingu eines zuflusses des amazonas durch den bau eines hafens oder von deichen durch sprengungen oder das graben von kanaelen zu veraendern bautaetigkeiten die keine auswirkungen auf die oertliche fischerei haben duerfen dem gerichtsurteil zufolge fortgesetzt werden wenn das baukonsortium gegen das urteil verstoesst muss es pro tag 200 000 real etwa 80 000 euro strafe zahlen gegen das elf milliarden dollar 8 1 milliarden euro teure projekt laufen indianische ureinwohner der region und umweltschuetzer seit langem sturm sie verweisen darauf dass den berechnungen zufolge eine flaeche von 500 quadratkilometern geflutet werden wird und 16 000 menschen umgesiedelt werden muessen in ihrem protest werden die ureinwohner von der katholischen kirche aber auch von prominenten wie dem kanadischen starregisseur james cameron avatar oder dem britischen rockmusiker sting unterstuetzt +Technik & Wissen de-DE url-pathpart-wissenschaft url-pathpart-technik url-domainname-www.spiegel.de url-domainprefix-www url-domainprefix-www.spiegel url-domaintype-de url-domainsuffix-spiegel.de url-firstpartpagename-0,1518,785831,00 url-pagepartsplitname-0,1518,785831,00 url-lastpartpagename-html#ref=rss atomunfall in suedfrankreich ein ofen fuer radioaktive abfaelle ist explodiert ein arbeiter gestorben doch die ursache fuer das unglueck in der atomanlage marcoule bleibt unklar trotzdem gaben die behoerden schnell entwarnung die franzoesischen medien berichten sehr gelassen nur die anwohner reagieren empoert paris die meldung schockierte die boersen und weckte erinnerungen an das unglueck in japan +Technik & Wissen de-DE url-pathpart-wissenschaft url-pathpart-weltall url-domainname-www.spiegel.de url-domainprefix-www url-domainprefix-www.spiegel url-domaintype-de url-domainsuffix-spiegel.de url-firstpartpagename-0,1518,783022,00 url-pagepartsplitname-0,1518,783022,00 url-lastpartpagename-html#ref=rss absturz eines raumtransporters der absturz eines russischen raumschiffes hat folgen fuer die internationale raumstation +Technik & Wissen de-DE url-pathpart-wissenschaft url-pathpart-technik url-domainname-www.spiegel.de url-domainprefix-www url-domainprefix-www.spiegel url-domaintype-de url-domainsuffix-spiegel.de url-firstpartpagename-0,1518,785765,00 url-pagepartsplitname-0,1518,785765,00 url-lastpartpagename-html#ref=rss kernenergie auf dem gelaende der atomanlage marcoule in suedfrankreich hat sich eine explosion ereignet ein mensch ist nach behoerdenangaben ums leben gekommen vier weitere wurden verletzt radioaktivitaet soll nicht in die umwelt gelangt sein paris wien in der suedfranzoesischen atomanlage marcoule gab es am montag eine explosion es habe einen toten gegeben teilte die franzoesische atomaufsicht asn mit vier weitere personen seien verletzt worden eine davon schwer strahlung ist laut asn und der oertlichen polizei nicht ausgetreten die behoerden richteten dennoch eine sicherheitszone ein falls noch radioaktivitaet entweichen sollte nach angaben der asn ist bei dem unfall ein verbrennungsofen fuer schwach radioaktive abfaelle explodiert dazu zaehlen unter anderem kleidung von arbeitern sowie metalle und beton die ursache des ungluecks war zunaechst unklar es handelt sich um einen industrieunfall nicht um einen atomunfall sagte ein sprecher des staatlichen stromkonzerns edf dessen tochterunternehmen socodei die anlage betreibt der bei dem unfall gestorbene arbeiter sei in sekundenschnelle verbrannt sagte ein edf sprecher zu spiegel online der mann habe in einem raum neben dem verbrennungsofen gearbeitet als dieser explodiert sei die asn hat den unfall am montagnachmittag offiziell fuer beendet erklaert dieser unfall bedeutet keine radioaktivitaet und keine massnahmen zum schutz der bevoelkerung teilte die asn mit die ihren krisenstab wieder aufloeste die explosion in einem verbrennungsofen habe ein feuer entfacht das gegen 13 uhr unter kontrolle gewesen sei das gebaeude in dem der ofen stand sei nicht beschaedigt worden die vier verletzten von denen einer schwere verbrennungen erlitt seien nicht verstrahlt worden auch ausserhalb des gebaeudes sei keine radioaktivitaet gemessen worden nun solle untersucht werden wie es zu dem unfall kam auch das unabhaengige franzoesische atomforschungsinstitut criirad hat entwarnung gegeben +Technik & Wissen de-DE url-pathpart-wissenschaft url-pathpart-article13595532 url-domainname-www.welt.de url-domainprefix-www url-domainprefix-www.welt url-domaintype-de url-domainsuffix-welt.de url-firstpartpagename-Arktis-Eis-schmilzt-auf-Rekordminimum-zusammen url-pagepartsplitname-Arktis url-pagepartsplitname-Eis url-pagepartsplitname-schmilzt url-pagepartsplitname-auf url-pagepartsplitname-Rekordminimum url-pagepartsplitname-zusammen url-lastpartpagename-html klimawandel das eis im nordpolarmeer erreicht ein rekordminimum der tiefstand ist laut experten nicht mehr durch die natuerliche variabilitaet zu erklaeren das meereis ist in diesem sommer auf ein neues rekordminimum geschmolzen das selbst die bisher geringste eisausdehnung im nordpolarmeer aus dem jahr 2007 unterschreitet mit nur noch 4 24 millionen quadratkilometer ist das sommerliche eis seit 1971 um 50 prozent zurueckgegangen georg heygster vom institut fuer umweltphysik an der universitaet bremen zufolge ist diese negativmarke nicht mehr durch die natuerliche variabilitaet von jahr zu jahr zu erklaeren sondern zeigt nur umso deutlicher was fuer gravierende auswirkungen der klimawandel auf die arktis hat schon vor wochen hatten wissenschaftler diesen september mit dem tiefstand der eisbedeckung gerechnet jetzt wurden die zahlen von der universitaet in bremen vorgelegt die eisausdehnung schwankt nach heygsters angaben im jahresverlauf zwischen etwa 15 millionen quadratkilometern im maerz und 5 quadratkilometern im september der aktuelle wert laege allerdings diesen september um 0 6 prozent unter dem minimum von 2007 fuer kleinlebewesen die an der unterseite des eises leben und gleichzeitig ausgangspunkt der nahrungskette auch fuer uns menschen sind bleibt immer weniger lebensraum erlaeuterte der umweltphysiker der universitaet bremen weiter hinzu kommt dass auch in diesem jahr die nordost und nordwestpassagen wieder gleichzeitig eisfrei seien was erstmalig 2008 beobachtet wurde heygst aussagen zufolge zeigen mehrjaehrige beobachtungen dass die mittlere eisdecke abnimmt es bleibt abzuwarten wie sich die situation weiter entwickelt da der wert der eisausdehnung in den naechsten wochen weiter abnehmen kann +Technik & Wissen de-DE url-pathpart-wissenschaft url-pathpart-weltall url-domainname-www.spiegel.de url-domainprefix-www url-domainprefix-www.spiegel url-domaintype-de url-domainsuffix-spiegel.de url-firstpartpagename-0,1518,786411,00 url-pagepartsplitname-0,1518,786411,00 url-lastpartpagename-html#ref=rss space launch system die us raumfahrtbehoerde sucht den shuttle nachfolger nun ist die nasa fuendig geworden die neue riesenrakete space launch system soll astronauten kuenftig sogar bis zum mars bringen doch das kraftpaket kostet viel geld deswegen setzen die ingenieure auch auf technik von gestern berlin es soll ein kraftvoller befreiungsschlag werden +Technik & Wissen de-DE url-pathpart-wissenschaft url-pathpart-article13575103 url-domainname-www.welt.de url-domainprefix-www url-domainprefix-www.welt url-domaintype-de url-domainsuffix-welt.de url-firstpartpagename-9000-Saeuglinge-sterben-taeglich-Versorgung-fehlt url-pagepartsplitname-9000 url-pagepartsplitname-Saeuglinge url-pagepartsplitname-sterben url-pagepartsplitname-taeglich url-pagepartsplitname-Versorgung url-pagepartsplitname-fehlt url-lastpartpagename-html un schaetzungen rund 40 prozent aller todesfaelle von kindern unter fuenf jahren seien in den ersten 28 tagen nach der geburt zu verzeichnen erlaeuterte die who dabei ist die kritischste zeit die erste woche hauptursache fuer die saeuglingssterblichkeit sind unterernaehrung infektionen und sauerstoffmangel auch zu frueh geborene kinder haben in entwicklungslaendern geringere ueberlebenschancen weil es an medizinischer betreuung und geeigneten geraeten wie brutkaesten mangelt allerdings sank demnach die zahl der neugeborenen die in ihren ersten vier lebenswochen sterben zwischen 1990 und 2009 von schaetzungsweise 4 6 millionen auf 3 3 millionen als grund sieht die who die gestiegenen investitionen im gesundheitswesen grosse fortschritte gebe es in china in afrika sehe es dagegen schlecht aus in indien treten nach who angaben jaehrlich rund 900 000 todesfaelle bei neugeborenen auf das seien 28 prozent der gesamttodesfaelle in diesem alter weltweit es folgen nigeria und pakistan china liegt auf platz vier mit blick auf die gesamtzahl der todesfaelle bei neugeborenen dort ist der anteil der todesfaelle von 23 pro 1000 lebendgeborene im jahr 1990 auf 11 pro 1000 lebendgeborene im jahr 2009 zurueckgegangen tschechien und andere eu staaten haben sich deutlich verbessert da in afrika nur ein rueckgang von jaehrlich einem prozent zu verzeichnen sei duerfte es rein statistisch gesehen mehr als 150 jahre dauern bis es den level der usa oder grossbritanniens bei der neugeborenensterblichkeit erreicht habe heisst es in der mitteilung weiter von den zehn laendern die die sterberate bei neugeborenen um jeweils mindestens zwei drittel in zwei dekaden gesenkt haben gehoeren unter anderem zypern tschechien estland griechenland und luxemburg der kampf gegen die kindersterblichkeit gehoert zu den acht millenniumsentwicklungszielen der un verglichen mit 1990 soll die kindersterblichkeit bis 2015 um zwei drittel sinken die un millenniumsziele zu denen auch die halbierung der armut zaehlt wurden im jahr 2000 von den staats und regierungschefs beschlossen +Technik & Wissen de-DE url-pathpart-c url-pathpart-795 url-pathpart-f url-pathpart-448250 url-pathpart-s url-pathpart-143ff78b url-pathpart-l url-pathpart-0L0Ssueddeutsche0Bde0Cwissen0Carktis0Etauender0Epermafrostboden0Ewie0Eein0Eschweizer0Ekaese0E10B10A85930A url-domainname-rssfeed.sueddeutsche.de url-domainprefix-rssfeed url-domainprefix-rssfeed.sueddeutsche url-domaintype-de url-domainsuffix-sueddeutsche.de url-firstpartpagename-story01 url-pagepartsplitname-story01 url-lastpartpagename-htm arktis hans wolfgang hubberten ist leiter der aussenstelle potsdam des alfred wegener instituts fuer polar und meeresforschung awi seit fast 20 jahren erforscht er die dauerfrostboeden sibiriens hubberten +Technik & Wissen de-DE url-pathpart-wissenschaft url-pathpart-weltall url-domainname-www.spiegel.de url-domainprefix-www url-domainprefix-www.spiegel url-domaintype-de url-domainsuffix-spiegel.de url-firstpartpagename-0,1518,748765,00 url-pagepartsplitname-0,1518,748765,00 url-lastpartpagename-html#ref=rss letzter ausseneinsatz an der iss zwei astronauten der us raumfaehre discovery haben ihren zweiten ausseneinsatz an der iss erfolgreich abgeschlossen damit ging eine aera zu ende +Technik & Wissen de-DE url-pathpart-wissenschaft url-pathpart-weltall url-domainname-www.spiegel.de url-domainprefix-www url-domainprefix-www.spiegel url-domaintype-de url-domainsuffix-spiegel.de url-firstpartpagename-0,1518,764936,00 url-pagepartsplitname-0,1518,764936,00 url-lastpartpagename-html#ref=rss europas neuer astronautenchef strahlemann auf schwieriger mission +Technik & Wissen de-DE url-pathpart-wissenschaft url-pathpart-technik url-domainname-www.spiegel.de url-domainprefix-www url-domainprefix-www.spiegel url-domaintype-de url-domainsuffix-spiegel.de url-firstpartpagename-0,1518,656774,00 url-pagepartsplitname-0,1518,656774,00 url-lastpartpagename-html#ref=rss raketen fliegen mit eis und aluminium ein neuartiger raketen treibstoff koennte die raumfahrt revolutionieren das ueberraschend simple gemisch aus wasser und kleinsten aluminium partikeln soll umweltfreundlicher sicherer und ebenso leistungsstark wie herkoemmlicher sprit sein zudem koennte es den weg zum mars ebnen es ist jahrzehnte her seit die ersten raketen in den himmel gestiegen sind doch waehrend sich die flugkoerper selbst rasant weiterentwickelt haben sind die treibstoffe heute weitgehend dieselben wie noch vor mehr als 50 jahren jetzt aber koennte eine mixtur aus metall und eis einen entscheidenden fortschritt bringen er soll nach vorstellungen seiner entwickler raketenstarts nicht nur sauberer machen sondern koennte auch das nachtanken an weit entfernten zielen wie etwa dem mars ermoeglichen der treibstoff namens alice kurz fuer aluminium ice soll seine energie aus der chemischen reaktion zwischen wasser und aluminium erhalten zudem koennte der dabei entstehende wasserstoff auch noch fuer andere dinge gut sein etwa fuer den betrieb von brennstoffzellen auf langzeit raumfluegen forscher der purdue university in west lafayette us bundesstaat indiana haben im august bereits eine rund drei meter lange rakete mit hilfe des neuartigen treibstoffs in den himmel geschossen der flugkoerper erreichte nach angaben der universitaet eine hoehe von immerhin rund 400 metern damit haben wir bewiesen dass das konzept prinzipiell funktioniert sagte purdue professor steven son ihre ergebnisse haben die wissenschaftler unter anderem im fachblatt proceedings of the combustion institute veroeffentlicht der schluessel zur kraft von alice ist die geringe groesse der aluminium partikel solche teilchen werden bereits in treibstoffen eingesetzt etwa in den feststoff booster raketen des space shuttles oder in den neuen ares raketen der nasa doch die partikel sind meist wesentlich groesser als jene die in alice zum einsatz kommen die sind im durchmesser lediglich rund 80 nanometer klein verbrennen deshalb schneller und ermoeglichen eine bessere kontrolle ueber den rueckstoss so die purdue forscher die alu partikel werden dafuer mit wasser zu einer zaehen paste vermischt sie wird in einen zylinder mit einem stab in der mitte gefuellt und eingefroren ist die masse hart wird der stab entfernt so dass eine runde aushoehlung zurueckbleibt der feststoff wird dann mit hilfe eines kleinen raketentriebwerks an der spitze des zylinders gezuendet +Gesundheit de-DE url-pathpart-~r url-pathpart-rp url-pathpart-online url-pathpart-rss url-pathpart-gesundheit url-pathpart-~3 url-pathpart-SkdtvYgtg5c url-domainname-feeds.rp-online.de url-domainprefix-feeds url-domainprefix-feeds.rp-online url-domaintype-de url-domainsuffix-rp-online.de url-firstpartpagename-story01 url-pagepartsplitname-story01 url-lastpartpagename-htm brustschmerzen durch verspannten ruecken was haltungsschaeden ausloesen koennen frankfurt main rpo die sorge ist gross wenn es ploetzlich im brustkorb sticht viele betroffene denken sofort an einen herzinfarkt oder auch an brustkrebs mit haltungsschaeden und muskelverspannungen wird das stechen nicht in verbindung gebracht nicht selten aber ist das bruststechen die folge von haltungsschaeden durch zu vieles sitzen und zu wenig bewegung bei brustschmerzen muss natuerlich als erstes ein arzt andere ursachen wie herzinfarkt rippenbrueche oder erkrankungen der lunge ausschliessen sagt peter ivanits facharzt fuer orthopaedie aus frankfurt am main aber bei vielen patienten ruehre das stechen in der brust das meist im sitzen oder anderen ruhephasen auftrete von einer verspannten rueckenmuskulatur mit massagen und krankengymnastik bekommen wir die akuten beschwerden schnell in der griff sagt ivanits aber ohne vorbeugung koennten die brustschmerzen regelmaessig wiederkehren die beste praevention ist regelmaessiger sport sagt ivanits im alltag koennten aber auch ein paar einfache regeln schon helfen +Gesundheit de-DE url-pathpart-~r url-pathpart-rp url-pathpart-online url-pathpart-rss url-pathpart-gesundheit url-pathpart-~3 url-pathpart-nsxWjaU67aA url-domainname-feeds.rp-online.de url-domainprefix-feeds url-domainprefix-feeds.rp-online url-domaintype-de url-domainsuffix-rp-online.de url-firstpartpagename-story01 url-pagepartsplitname-story01 url-lastpartpagename-htm steve jobs seltene krebskrankheit sieben jahre ueberleben bedeuten einen sieg rp unter medizinern wurde der fall steve jobs seit langem kontrovers diskutiert auf der einen seite +Ausland de-DE url-pathpart-politik url-pathpart-article2055267 url-domainname-www.abendblatt.de url-domainprefix-www url-domainprefix-www.abendblatt url-domaintype-de url-domainsuffix-abendblatt.de url-firstpartpagename-Menschenjagd-auf-Kairos-Strassen-nach-Christen-Protesten url-pagepartsplitname-Menschenjagd url-pagepartsplitname-auf url-pagepartsplitname-Kairos url-pagepartsplitname-Strassen url-pagepartsplitname-nach url-pagepartsplitname-Christen url-pagepartsplitname-Protesten url-lastpartpagename-html menschenjagd auf kairos strassen nach christen protesten hamburg kairo als tausende christliche kopten gemeinsam mit vielen muslimen in kairo auf die strasse gingen um gegen die wachsende diskriminierung der kopten zu demonstrieren sollte dies ein zeichen friedlicher solidaritaet setzen doch das chaos das dann daraus erwuchs gefaehrdet die gesamte gesellschaftliche und politische entwicklung im bevoelkerungsreichsten arabischen staat ploetzlich flogen steine dann fielen schuesse schliesslich marschierte die armee auf ein militaerfahrzeug raste vor und zurueck durch die aufgebrachte menge und ueberrollte mehrere menschen am ende waren nach offiziellen angaben mindestens 26 menschen tot und weit mehr als 200 verletzt inoffizielle beobachter sprachen gar von 36 toten und mehr als 300 verletzten es waren die schlimmsten unruhen seit den wirren im zusammenhang mit dem sturz des langjaehrigen despoten husni mubarak im februar die bundesregierung in berlin forderte die fuehrung in kairo auf fuer ein klima religioeser toleranz zu sorgen und die vorgaenge so schnell wie moeglich aufzuklaeren wie regierungssprecher steffen seibert sagte bundesaussenminister guido westerwelle fdp verurteilte die gewalt am rande des eu ministerrats in luxemburg scharf wer als christ seinen glauben praktizieren moechte muss das frei tun koennen ohne dass er koerperlich bedroht wird oder um sein leben fuerchten muss sagte westerwelle die eu aussenbeauftragte catherine ashton sagte die europaeische union erwarte dass aegypten menschen jeder glaubensrichtung schuetze 26 tote sagte ashton entsetzt niemand auf der welt gibt menschen das recht einen religionskampf zu fuehren der in kairo herrschende militaerrat verstaerkte die sicherheitsmassnahmen verhaengte ueber teile kairos eine naechtliche ausgangssperre und zog zusaetzliche truppen vor dem parlamentsgebaeude und anderen zentralen einrichtungen zusammen die unruhen hatten am gebaeude des staatlichen fernsehens begonnen und sich dann rasch auf andere teile der hauptstadt inklusive des tahrir platzes ausgeweitet dort hatte die revolte gegen mubarak ihren ausgang genommen ausloeser der juengsten koptischen proteste waren ein angriff radikaler muslime auf die kirche des dorfes mari nab bei edfu die teilweise zerstoert wurde und der konflikt um eine schule in der provinz minia gewesen in der christliche maedchen gezwungen worden waren mit kopftuechern zu erscheinen die kopten werfen der neuen regierung in kairo vor sie nicht vor uebergriffen zu schuetzen und fordern den ruecktritt des gouverneurs der provinz assuan der die angriffe auf die kirche gerechtfertigt habe gouverneur mustafa al sajjid hatte behauptet das gotteshaus sei ohne genehmigung errichtet worden es war nicht die erste koptische kirche die in flammen aufging die radikalislamische salafistenbewegung wies jede verantwortung fuer die unruhen zurueck nach dem sturz mubaraks haben radikale islamisten in aegypten starken auftrieb erhalten rund 1000 soldaten und polizisten griffen in die sich rasch entwickelnden handfesten auseinandersetzungen zwischen kopten und militanten muslimen ein der staatliche fernsehsender nil tv wurde unter anderem von der internationalen gesellschaft fuer menschenrechte igfm beschuldigt sehr einseitig zulasten der kopten berichtet zu haben beobachter sprachen von hetze so meldete das staatliche programm channel 1 die kopten haetten zwei soldaten erschossen obwohl deren todesumstaende voellig ungeklaert waren und rief zur unterstuetzung der muslime auf diese stroemten dann herbei und attackierten die kopten ein rasender mob machte regelrecht jagd auf autos in denen koptische christen vermutet wurden die fahrzeuge und ihre insassen wurden angegriffen wie augenzeugen berichteten griffen armee und polizei nicht dagegen ein die groesste aegyptische zeitung al ahram berichtete anders als das fernsehen und schrieb der friedliche prokoptische protestzug sei von provokateuren radikalen muslimen und soldaten mit steinen und schuessen angegriffen worden ministerpraesident essam scharaf rief die aegypter zur ruhe auf es handle sich keineswegs um religionsunruhen sondern um eine verschwoerung der premier fuegte hinzu +Ausland de-DE url-pathpart-politik url-pathpart-article2006955 url-domainname-www.abendblatt.de url-domainprefix-www url-domainprefix-www.abendblatt url-domaintype-de url-domainsuffix-abendblatt.de url-firstpartpagename-28-Menschen-sterben-bei-Attentat-auf-Moschee url-pagepartsplitname-28 url-pagepartsplitname-Menschen url-pagepartsplitname-sterben url-pagepartsplitname-bei url-pagepartsplitname-Attentat url-pagepartsplitname-auf url-pagepartsplitname-Moschee url-lastpartpagename-html 28 menschen sterben bei attentat auf moschee bagdad dubai bei einem selbstmordanschlag sind in der irakischen hauptstadt bagdad mindestens 28 menschen getoetet worden 37 weitere personen wurden verletzt wie der arabische fernsehsender al arabija am sonntag unter berufung auf das irakische innenministerium weiter mitteilte der attentaeter habe sich nach dem abendgebet in einer sunnitischen moschee im westen bagdads in die luft gesprengt unter den toten war den angaben zufolge auch ein parlamentsabgeordneter die um al kura moschee im stadtviertel al dschamiaah ist das groesste sunnitische gotteshaus in der irakischen hauptstadt die bluttat weckte erinnerungen an einen anschlag auf einen schiitischen schrein in der sunnitischen stadt samarra im jahr 2006 der eine welle sektiererisch motivierte gewalt im irak ausloeste dpa dapd +Ausland de-DE url-pathpart-politik url-pathpart-ausland url-pathpart-krise url-pathpart-in url-pathpart-der url-pathpart-arabischen url-pathpart-welt url-domainname-www.focus.de url-domainprefix-www url-domainprefix-www.focus url-domaintype-de url-domainsuffix-focus.de url-firstpartpagename-libyen-rebellen-sind-gaddafi-auf-der-spur_aid_659357 url-pagepartsplitname-libyen url-pagepartsplitname-rebellen url-pagepartsplitname-sind url-pagepartsplitname-gaddafi url-pagepartsplitname-auf url-pagepartsplitname-der url-pagepartsplitname-spur url-pagepartsplitname-aid url-pagepartsplitname-659357 url-lastpartpagename-html libyen rebellen sind gaddafi auf der spur nach der eroberung von tripolis durch libysche rebellen stehen jetzt auch muammar el gaddafis letzte machtbastionen im land vor dem fall weiter unklar ist wo der diktator untergetaucht ist angeblich verfolgen die rebellen einen verdaechtigen lkw konvoi die kaempfe zwischen aufstaendischen und gaddafi treuen truppen konzentrierten sich am freitag auf die beiden verbliebenen hochburgen des alten regimes gaddafis heimatstadt sirte sowie die wuestenstadt sebha im zentrum des landes dagegen flauten die kaempfe in tripolis nach fernsehberichten deutlich ab weiter unklar ist wo der langjaehrige diktator untergetaucht ist nach einem medienbericht sollen die aufstaendischen einem lastwagen konvoi verfolgen in dem sie gaddafi vermuten sie hofften die fahrzeuge etwa 40 bis 50 kilometer vor tripolis abfangen zu koennen sagte sicherheitsberater abdul karim basama vom uebergangsrat der maltesischen zeitung the times of malta es solle verhindert werden dass gaddafi nach sirte oder sebha durchkomme derweil beklagte bundesverteidigungsminister thomas de maiziere cdu fehler der internationalen staatengemeinschaft bei der einschaetzung der lage in libyen wir haben uns mindestens dreimal geirrt nicht nur wir deutschen der ganze westen sagte er in der bundesakademie fuer sicherheitspolitik in berlin gaddafi sei erst massiv unterschaetzt und dann massiv ueberschaetzt worden dann haben wir gesagt es wird lange anhalten und es gibt ueberhaupt keine veraenderungen und in einer woche war tripolis erobert der chef der libyschen uebergangsregierung mahmud dschibril erklaerte die aufstaendischen haetten inzwischen fast im ganzen land die oberhand nur sebha sirte sowie das suedoestlich von tripolis gelegene bani walid seien noch nicht unter kontrolle sagte er nach angaben der tuerkischen nachrichtenagentur anadolu in ankara ziel sei es die staedte ohne blutvergiessen einzunehmen britische kampfflugzeuge feuerten in der nacht zum freitag raketen auf eine kommando und kontrollzentrale in sirte ab gaddafi gegner riefen die einwohner auf die stadt kampflos zu uebergeben im gegenzug sollten nur aus sirte stammende kaempfer in die kuestenstadt einruecken hiess es die aufstaendischen sammelten unterdessen ihre einheiten nahe sirte wo sich einheiten und anhaenger gaddafis verschanzt haben in der garnisonsstadt sebha lieferten sich anhaenger und gegner gaddafis heftige kaempfe dutzende rebellen seien getoetet worden als sie das hauptquartier des militaergeheimdienstes gestuermt haetten teilten die aufstaendischen mit sie haetten zwei stadtviertel eingenommen es gebe kein wasser und keinen strom in tripolis sagten die neuen machthaber ein auf dem zentralen gruenen platz geplantes freitagsgebet aus sicherheitsgruenden ab die glaeubigen versammelten sich in der nahe gelegenen el kabir moschee der prediger scheich ahmed milad gaddur rief die libyer auf keine rache zu ueben unterdessen kommen immer mehr grausamkeiten der vergangenen tage ans licht reporter der fernsehsender el dschasira und bbc berichteten von graeueltaten auf beiden seiten el dschasira zeigte bilder von leichen in gruenanalagen in dem noch am donnerstag besonders heftig umkaempften stadtteil von tripolis abu salim ein reporter beschrieb die lage in einem nahe gelegenen krankenhaus als katastrophal dort stapelten sich die leichen nach informationen der menschenrechtsorganisation amnesty international haben gaddafi anhaenger womoeglich mehr als 100 gefangene in militaercamps nahe tripolis brutal getoetet die gaddafi treuen truppen haetten granaten geworfen und mit schusswaffen auf die gefangenen gefeuert +Ausland de-DE url-pathpart-politik url-pathpart-ausland url-pathpart-krise url-pathpart-in url-pathpart-der url-pathpart-arabischen url-pathpart-welt url-domainname-www.focus.de url-domainprefix-www url-domainprefix-www.focus url-domaintype-de url-domainsuffix-focus.de url-firstpartpagename-libyen-china-bot-gaddafi-grosse-mengen-waffen-an_aid_662179 url-pagepartsplitname-libyen url-pagepartsplitname-china url-pagepartsplitname-bot url-pagepartsplitname-gaddafi url-pagepartsplitname-grosse url-pagepartsplitname-mengen url-pagepartsplitname-waffen url-pagepartsplitname-an url-pagepartsplitname-aid url-pagepartsplitname-662179 url-lastpartpagename-html libyen china bot gaddafi grosse mengen waffen an china hat sich offenbar ueber un sanktionen hinweg gesetzt und dem gaddafi regime vor seinem zerfall grosse mengen an waffen und munition angeboten die chinesischen waffenhaendler wollten bei bedarf sogar auf bestellung liefern chinesische firmen haetten den truppen von libyens langjaehrigem machthaber muammar gaddafi im juli den verkauf von raketenwerfern panzerabwehrraketen und anderen waffen im wert von rund 200 millionen dollar angeboten berichtete die new york times am montag zuvor hatte auch die kanadische the globe and mail davon berichtet wir haben hinweise dass es geschaeftsbeziehungen zwischen china und gaddafi gegeben hat sagte rebellensprecher abdulrahman busin der new york times und wir haben alle dokumente die dies beweisen demnach reiste eine libysche delegation mitte juli nach china und sprach mit vertretern verschiedener ruestungskonzerne die konzerne haetten angeboten ihre gesamten lagerbestaende zu verkaufen und bei bedarf auf bestellung zu produzieren die lieferungen sollten demnach ueber die drittstaaten algerien und suedafrika erfolgen die chinesische seite habe darauf hingewiesen dass ein teil der waffen bereits in algerien gelagert sei und schnell ueber die grenze nach libyen gebracht werden koenne die gastgeber haetten sich bei den libyern fuer deren diskretion bedankt und auf die noetige vertraulichkeit hingewiesen ob es zu lieferungen gekommen sei gehe aus den dokumenten nicht hervor vertreter der neuen libyschen fuehrung in tripolis sagten die dokumente bestaetigten den verdacht dass es eine enge zusammenarbeit der gaddafi fuehrung mit china algerien und suedafrika gegeben habe der militaerbeauftragte des nationalen uebergangsrates omar hariri sagte er sei sich ziemlich sicher dass waffen in libyen eingetroffen und gegen das libysche volk verwendet worden seien die dokumente wurden auf einem muellhaufen in einem stadtteil von tripolis entdeckt in dem viele mitglieder der gaddafi fuehrung lebten china ist das einzige staendige mitglied im un sicherheitsrat das den uebergangsrat bislang nicht als legitimen vertreter libyens anerkannt hat eine bestaetigung fuer die berichte oder die echtheit der dokumente gab es nicht ein nato und ein un vertreter bezeichneten sie der new york times zufolge jedoch als hoechst unwahrscheinlich den vereinten nationen sei nichts von waffengeschaeften mit china bekannt sagte das mitglied einer un kommission zur ueberwachung des libyen embargos das chinesische aussenministerium raeumte ein dass mitglieder von gaddafis regierung auf chinesische ruestungsfirmen zugegangen seien dies sei aber ohne wissen der chinesischen regierung passiert sagte ministeriumssprecherin jian yu die chinesischen firmen haben keine entsprechenden vertraege unterschrieben noch haben sie militaergueter nach libyen ausgeliefert nachdem der un sicherheitsrat die resolution verabschiedet habe seien die entsprechenden regierungsstellen angewiesen worden sie strengstens einzuhalten +Ausland de-DE url-pathpart-politik url-pathpart-ausland url-domainname-www.focus.de url-domainprefix-www url-domainprefix-www.focus url-domaintype-de url-domainsuffix-focus.de url-firstpartpagename-afghanistan-auswaertiges-amt-bestaetigt-tod-eines-deutschen-urlaubers_aid_668931 url-pagepartsplitname-afghanistan url-pagepartsplitname-auswaertiges url-pagepartsplitname-amt url-pagepartsplitname-bestaetigt url-pagepartsplitname-tod url-pagepartsplitname-eines url-pagepartsplitname-deutschen url-pagepartsplitname-urlaubers url-pagepartsplitname-aid url-pagepartsplitname-668931 url-lastpartpagename-html afghanistan auswaertiges amt bestaetigt tod eines deutschen urlaubers ein ministeriumssprecher sagte am sonntag in berlin die untersuchungen haetten ergeben dass es sich bei einer der beiden in der westafghanischen provinz ghor getoeteten personen um einen deutschen staatsangehoerigen handele nach angaben der afghanischen behoerden hatten bewaffnete angreifer den deutschen touristen sowie seinen afghanischen begleiter am samstag in der als relativ sicher geltenden region erschossen die provinz ghor gilt normalerweise als verhaeltnismaessig ruhig der tourist sei aber von den behoerden gewarnt worden dass seine sicherheit nicht gewaehrleistet werden koenne sagte vize polizeichef abdul raschid die leiche des deutschen sei in die hauptstadt kabul gebracht worden +Ausland de-DE url-pathpart-politik url-pathpart-ausland url-domainname-www.focus.de url-domainprefix-www url-domainprefix-www.focus url-domaintype-de url-domainsuffix-focus.de url-firstpartpagename-somalia-kalaschnikows-fuer-gute-koranschueler_aid_667250 url-pagepartsplitname-somalia url-pagepartsplitname-kalaschnikows url-pagepartsplitname-fuer url-pagepartsplitname-gute url-pagepartsplitname-koranschueler url-pagepartsplitname-aid url-pagepartsplitname-667250 url-lastpartpagename-html somalia kalaschnikows fuer gute koranschueler ein von der radikalislamischen el schabaab miliz betriebener radiosender in somalia hat einen rezitierwettbewerb fuer kinder veranstaltet als hauptpreis gab es nicht nur geld zu gewinnen sondern auch jede menge schwere waffen der gewinner ging mit einem ak 47 kalaschnikow sturmgewehr und einem geldpreis in hoehe von umgerechnet 500 euro nach hause wie der britische sender bbc am dienstag berichtete der zweiplatzierte erhielt ebenfalls eine kalaschnikow und eine summe von 350 euro waehrend der drittplatzierte den wettstreit mit zwei handgranaten und etwa 300 euro verliess zudem erhielten die teilnehmer religioese buecher hiess es kinder sollten eine hand fuer ihre erziehung benutzen und in der anderen eine waffe halten um den islam zu beschuetzen sagte el schabaab sprecher mukhtar robow bei der preisverleihung auch raketenwerfer gab es schon als hautpgewinn der wettbewerb des radiosenders andulus wurde im fastenmonat ramadan in elasha 20 kilometer von der hauptstadt mogadischu entfernt ausgetragen zur teilnahme aufgerufen waren kinder und jugendliche zwischen zehn und 17 jahren in den vergangenen zwei jahren hatten kinder bei aehnlichen wettbewerben bereits um einen raketenwerfer als hauptgewinn gerungen die rebellen der el schabaab kontrollieren grosse teile des suedlichen und zentralen somalias wo derzeit eine verheerende duerre herrscht die miliz bekaempft die uebergangsregierung in mogadischu und will am horn von afrika einen islamischen gottesstaat einrichten der sich an einem weltweiten dschihad beteiligt +Ausland de-DE url-pathpart-politik url-pathpart-ausland url-domainname-www.focus.de url-domainprefix-www url-domainprefix-www.focus url-domaintype-de url-domainsuffix-focus.de url-firstpartpagename-drohung-von-el-kaida-100-anschlaege-als-rache-fuer-bin-laden_aid_657300 url-pagepartsplitname-drohung url-pagepartsplitname-von url-pagepartsplitname-el url-pagepartsplitname-kaida url-pagepartsplitname-100 url-pagepartsplitname-anschlaege url-pagepartsplitname-als url-pagepartsplitname-rache url-pagepartsplitname-fuer url-pagepartsplitname-bin url-pagepartsplitname-laden url-pagepartsplitname-aid url-pagepartsplitname-657300 url-lastpartpagename-html drohung von el kaida 100 anschlaege als rache fuer bin laden dem irak droht eine neue welle der gewalt +Ausland de-DE url-pathpart-politik url-pathpart-ausland url-pathpart-article2014661 url-domainname-www.abendblatt.de url-domainprefix-www url-domainprefix-www.abendblatt url-domaintype-de url-domainsuffix-abendblatt.de url-firstpartpagename-Gaddafi-verhoerte-Terrorverdaechtige-fuer-die-CIA url-pagepartsplitname-Gaddafi url-pagepartsplitname-verhoerte url-pagepartsplitname-Terrorverdaechtige url-pagepartsplitname-fuer url-pagepartsplitname-die url-pagepartsplitname-CIA url-lastpartpagename-html gaddafi verhoerte terrorverdaechtige fuer die cia tripolis die zentrale des libyschen geheimdienstes lis liegt mitten in tripolis beiderseits der strasse sind rebellen postiert vor dem eingangstor stehen zwei gelaendefahrzeuge mit schweren geschuetzen auf der ladeflaeche schwere bewachung kein wunder +Unterhaltung de-DE url-pathpart-nachrichten url-pathpart-stars url-portnumber-80 url-domainname-www.tz-online.de url-domainprefix-www url-domainprefix-www.tz-online url-domaintype-de url-domainsuffix-tz-online.de url-hasarguments- url-firstpartpagename-roland-emmerich-macht-actionfilme-liebe-1436652 url-pagepartsplitname-roland url-pagepartsplitname-emmerich url-pagepartsplitname-macht url-pagepartsplitname-actionfilme url-pagepartsplitname-liebe url-pagepartsplitname-1436652 url-lastpartpagename-html url-argname-cmp url-argvalue-defrss url-argvaluesplit-defrss roland emmerich macht actionfilme mit liebe nuernberg so hatte sich monika gruber ihren ersten auftritt bei wetten dass sicher nicht vorgestellt die kabarettistin wurde bei der samstagabend show opfer einer technsichen panne das war passiert +Unterhaltung de-DE url-pathpart-nachrichten url-pathpart-stars url-portnumber-80 url-domainname-www.tz-online.de url-domainprefix-www url-domainprefix-www.tz-online url-domaintype-de url-domainsuffix-tz-online.de url-hasarguments- url-firstpartpagename-pete-doherty-kann-seine-strafe-nicht-bezahlen-1453565 url-pagepartsplitname-pete url-pagepartsplitname-doherty url-pagepartsplitname-kann url-pagepartsplitname-seine url-pagepartsplitname-strafe url-pagepartsplitname-nicht url-pagepartsplitname-bezahlen url-pagepartsplitname-1453565 url-lastpartpagename-html url-argname-cmp url-argvalue-defrss url-argvaluesplit-defrss pete doherty rocker pete doherty 32 will seine schulden bei der berliner justiz in raten abstottern der musiker habe ein entsprechendes gesuch gestellt teilte staatsanwaltschaftssprecher martin steltner am mittwoch mit das muss geprueft werden der musiker hatte sich einen strafbefehl von 30 000 euro eingehandelt weil er im dezember 2009 in kreuzberg bei einer kneipentour ein auto demoliert haben soll noch gebe es keinen haftbefehl aber doherty muesse damit rechnen wenn er seine strafe nicht zahle erklaerte steltner zu einem bericht der zeitung b z wenn der musiker die summe begleiche sei die angelegenheit erledigt und dem musiker bleibe eine ersatzfreiheitsstrafe von 30 tagen hinter gittern erspart nuernberg so hatte sich monika gruber ihren ersten auftritt bei wetten dass sicher nicht vorgestellt die kabarettistin wurde bei der samstagabend show opfer einer technsichen panne das war passiert +Unterhaltung de-DE url-pathpart-kultur url-pathpart-kino url-pathpart-tv url-domainname-www.focus.de url-domainprefix-www url-domainprefix-www.focus url-domaintype-de url-domainsuffix-focus.de url-firstpartpagename-late-night-show-ohne-zuschauer-harald-schmidts-magere-quoten_aid_674601 url-pagepartsplitname-late url-pagepartsplitname-night url-pagepartsplitname-show url-pagepartsplitname-ohne url-pagepartsplitname-zuschauer url-pagepartsplitname-harald url-pagepartsplitname-schmidts url-pagepartsplitname-magere url-pagepartsplitname-quoten url-pagepartsplitname-aid url-pagepartsplitname-674601 url-lastpartpagename-html late night show ohne zuschauer harald schmidts magere quoten die harald schmidt show hat noch immer wenig zuschauer bei den 14 bis 49 jaehrigen schauen nur 9 1 prozent die late night show schmidts sender sat 1 betrachtet die quoten ganz entspannt harald schmidts quoten sind weiterhin schlecht nach angabens seines senders sat 1 erreicht der moderator mit seiner late night show durchschnittlich derzeit nur 9 1 prozent in der werberelevanten zielgruppe der 14 bis 49 jaehrigen die zahl der zuschauer liegt bei 510 000 beim gesamtpublikum sind es im schnitt 830 000 und damit 6 9 prozent der sender sat 1 sieht die bisher mageren quoten seines neuen late night talkers dennoch gelassen wir betrachten die quoten ganz entspannt sagte sat 1 sprecherin diana schardt auf anfrage uns ist bewusst dass es etwas zeit benoetigt bis sich die zuschauer an die regelmaessige sendezeit um 23 15 uhr an zwei aufeinanderfolgenden tagen gewoehnen die harald schmidt show war am 13 september gestartet schmidt der bereits zwischen 1995 bis 2003 fuer sat 1 gearbeitet hatte moderierte zuvor eine late night show in der ard sein neuer vertrag mit dem privatsender enthaelt nach aussagen des moderators explizit keine quotenvorgaben +Sport de-DE url-pathpart-c url-pathpart-32191 url-pathpart-f url-pathpart-443319 url-pathpart-s url-pathpart-17f65946 url-pathpart-l url-pathpart-0L0Sfocus0Bde0Csport0Cmehrsport0Cbasketball0Ewm0Ebasketball0Eem0Eerster0Esieg0Efuer0Eisrael0Iaid0I6620A570Bhtml url-domainname-rss2.focus.de url-domainprefix-rss2 url-domainprefix-rss2.focus url-domaintype-de url-domainsuffix-focus.de url-firstpartpagename-story01 url-pagepartsplitname-story01 url-lastpartpagename-htm basketball wm basketball em israel ist bei der basketball em der erste sieg gelungen der bisherige tabellenletzte der deutschen gruppe b setzte sich mit 91 +Sport de-DE url-pathpart-sport url-pathpart-fussball url-domainname-www.focus.de url-domainprefix-www url-domainprefix-www.focus url-domaintype-de url-domainsuffix-focus.de url-firstpartpagename-junioren-u17-auswahl-startet-mit-sieg-in-em-qualifikation_aid_674504 url-pagepartsplitname-junioren url-pagepartsplitname-u17 url-pagepartsplitname-auswahl url-pagepartsplitname-startet url-pagepartsplitname-mit url-pagepartsplitname-sieg url-pagepartsplitname-in url-pagepartsplitname-em url-pagepartsplitname-qualifikation url-pagepartsplitname-aid url-pagepartsplitname-674504 url-lastpartpagename-html junioren u17 auswahl startet mit sieg in em qualifikation die u17 auswahl des deutschen fussball bundes ist mit einem klaren sieg in die erste qualifikationsrunde zur europameisterschaft 2012 gestartet die u17 auswahl des deutschen fussball bundes dfb ist mit einem klaren sieg in die erste qualifikationsrunde zur europameisterschaft 2012 gestartet die mannschaft von dfb trainer stefan boeger setzte sich in tallinn gegen gastgeber estland mit 5 +Technik & Wissen de-DE url-pathpart-wissenschaft url-pathpart-article13608441 url-domainname-www.welt.de url-domainprefix-www url-domainprefix-www.welt url-domaintype-de url-domainsuffix-welt.de url-firstpartpagename-Pflanzen-legen-Wasservorrat-im-Boden-an url-pagepartsplitname-Pflanzen url-pagepartsplitname-legen url-pagepartsplitname-Wasservorrat url-pagepartsplitname-im url-pagepartsplitname-Boden url-pagepartsplitname-an url-lastpartpagename-html vorratswirtschaft pflanzen sondern im wurzelbereich eine wabbelige substanz ab die hilft ihnen wasser im boden speichern zu koennen so koennen sie trockenphasen ueberbruecken pflanzen koennen mit wasser im boden eine art vorratswirtschaft betreiben zumindest koennen sie nach neuen forschungsergebnissen in der naehe ihrer wurzeln so viel wasser speichern dass sie damit kuerzere trockenperioden ueberstehen koennen das haben wissenschaftler der universitaet goettingen zusammen mit kollegen aus potsdam leipzig und den usa herausgefunden foto +Technik & Wissen de-DE url-pathpart-wissenschaft url-pathpart-article13608359 url-domainname-www.welt.de url-domainprefix-www url-domainprefix-www.welt url-domaintype-de url-domainsuffix-welt.de url-firstpartpagename-Bitte-wer-Das-ist-Albert-Szent-Gyoergyi url-pagepartsplitname-Bitte url-pagepartsplitname-wer url-pagepartsplitname-Das url-pagepartsplitname-ist url-pagepartsplitname-Albert url-pagepartsplitname-Szent url-pagepartsplitname-Gyoergyi url-lastpartpagename-html google doodle die suchmaschine ueberrascht wieder einmal auf ihrer startseite was mag sich hinter diesem etikett einer obstkiste verstecken es ist albert szent gyoergyi albert szent wer frisches obst auf der startseite von google das google doodle ein gekritzel aus besonderem anlass das das unternehmslogo von google verfremdet darstellt hatte zuletzt mit einer hommage an freddy mercury den 1991 verstorbenen saenger der rockband queen ueberrascht foto +Technik & Wissen de-DE url-pathpart-wissen url-domainname-www.stern.de url-domainprefix-www url-domainprefix-www.stern url-domaintype-de url-domainsuffix-stern.de url-firstpartpagename-qumran-rollen-museum-stellt-uralte-bibelschriften-online-1732537 url-pagepartsplitname-qumran url-pagepartsplitname-rollen url-pagepartsplitname-museum url-pagepartsplitname-stellt url-pagepartsplitname-uralte url-pagepartsplitname-bibelschriften url-pagepartsplitname-online url-pagepartsplitname-1732537 url-lastpartpagename-html#utm_source=standard&utm_medium=rssfeed&utm_campaign=wissen video die 2000 jahre alten qumran schriften sind jetzt online zugaenglich das israelische nationalmuseum hat die schriftrollen digitalisiert und im internet auf einer eigens von google entwickelten datenbank veroeffentlicht +Technik & Wissen de-DE url-pathpart-2011 url-pathpart-16 url-domainname-www.zeit.de url-domainprefix-www url-domainprefix-www.zeit url-domaintype-de url-domainsuffix-zeit.de url-firstpartpagename-Strahlung url-pagepartsplitname-Strahlung geschichte der radioaktivitaet frau roentgen ist verstimmt sie hat bereits dreimal nach ihrem mann geschickt um ihn zum abendessen zu holen erst kommt er gar nicht dann sitzt er schweigend am tisch isst nur ein paar bissen und verschwindet auch schon wieder im labor in jenen novembertagen 1895 arbeitet der wuerzburger physiker wilhelm conrad roentgen wie besessen an einer interessanten entdeckung am 28 dezember macht er sie mit seiner studie ueber eine neue art von strahlen oeffentlich und einen monat spaeter berichtet er in einer vorlesung erstmals ueber die geheimnisvollen x strahlen er belaesst es aber nicht bei worten sondern holt den schweizer anatomie professor rudolf albert von koelliker aus dem auditorium nach vorn koelliker muss seine hand auf eine belichtungsplatte legen dann jagt roentgen strom durch eine seltsam geschwaerzte roehre anschliessend haelt er den gebannten zuhoerern die belichtete aufnahme vor die nase sie zeigt deutlich erkennbar die handknochen des kollegen roentgens entdeckung ist ebenso spektakulaer wie leicht verstaendlich der blick durch die aeussere materie ins innenleben von menschen und dingen loest einen taumel der begeisterung aus und revolutioniert die medizin in rasendem tempo die new york sun spricht von einem triumph der wissenschaft +Technik & Wissen de-DE url-pathpart-artikel url-domainname-www.neues-deutschland.de url-domainprefix-www url-domainprefix-www.neues-deutschland url-domaintype-de url-domainsuffix-neues-deutschland.de url-firstpartpagename-174431 url-pagepartsplitname-174431 url-lastpartpagename-html viel zoff um einen stoff ein erbitterter streit geht in die naechste runde gegenstand des konflikts ist eine farblose kristalline chemikalie sie ist unverzichtbar fuer die herstellung von polycarbonat kunststoffen und einigen kunstharzen +Wirtschaft de-DE url-pathpart-finanzen url-pathpart-news url-domainname-www.focus.de url-domainprefix-www url-domainprefix-www.focus url-domaintype-de url-domainsuffix-focus.de url-firstpartpagename-illegaler-insider-handel-wall-street-mogul-muss-elf-jahre-hinter-gitter_aid_674503 url-pagepartsplitname-illegaler url-pagepartsplitname-insider url-pagepartsplitname-handel url-pagepartsplitname-wall url-pagepartsplitname-street url-pagepartsplitname-mogul url-pagepartsplitname-muss url-pagepartsplitname-elf url-pagepartsplitname-jahre url-pagepartsplitname-hinter url-pagepartsplitname-gitter url-pagepartsplitname-aid url-pagepartsplitname-674503 url-lastpartpagename-html illegaler insider handel wall street mogul muss elf jahre hinter gitter nach einem spektakulaeren prozess hat ein new yorker gericht den wall street mogul raj rajaratnam wegen insider handels zu elf jahren gefaengnis verurteilt weil der haendler krank ist und eifrig spendet fiel die strafe nicht noch haerter aus das gericht setzte damit am donnerstag ein geringeres strafmass gegen den 53 jaehrigen milliardaer fest als von der staatsanwaltschaft gefordert rajaratnam der den galleon hedgefonds gegruendet hatte muss ab ende november in haft und soll zuvor ein bussgeld in hoehe von zehn millionen dollar knapp 7 3 millionen euro zahlen er wurde am 11 mai schuldig gesprochen zwischen 2003 und 2009 millionenschwere insidergeschaefte verantwortet zu haben insgesamt soll er damit rund 72 millionen dollar etwa 52 5 millionen euro erwirtschaftet haben die staatsanwaltschaft hatte eine haftstrafe von mindestens neunzehneinhalb jahren gefordert richter richard holwell begruendete das mildere strafmass mit gesundheitlichen problemen rajaratnams der aus sri lanka stammende finanzmogul habe diabetes in fortgeschrittenem stadium was zu einer nierenstoerung fuehre sagte holwell als weiteren grund nannte der richter rajaratnams grosszuegige spendentaetigkeit die weit ueber das uebliche mass hinausreiche ueberdies seien insidergeschaefte weniger gefaehrlich als betrugsmodelle nach dem schneeballsystem wie sie zum beispiel von dem 2009 verurteilten milliardenbetrueger bernard madoff angewendet wurden das gegen rajaratnam festgesetzte strafmass ist laut wall street journal das hoechste das in den vergangenen 20 jahren in den usa wegen insidergeschaeften verhaengt wurde die verteidigung hatte auf maximal sechseinhalb jahre haft plaediert da der von rajaratnam mit den insidergeschaeften erwirtschaftete gewinn weniger als acht millionen dollar 5 8 millionen euro betrage +Gesundheit de-DE url-pathpart-ratgeber url-pathpart-gesundheit url-pathpart-article1971240 url-domainname-www.abendblatt.de url-domainprefix-www url-domainprefix-www.abendblatt url-domaintype-de url-domainsuffix-abendblatt.de url-firstpartpagename-Testosteron-schuetzt-vor-Entzuendungen-und-Allergien url-pagepartsplitname-Testosteron url-pagepartsplitname-schuetzt url-pagepartsplitname-vor url-pagepartsplitname-Entzuendungen url-pagepartsplitname-und url-pagepartsplitname-Allergien url-lastpartpagename-html testosteron schuetzt vor entzuendungen und allergien jena das maennliche erscheinungsbild wird in grossem masse vom geschlechtshormon testosteron gesteuert es laesst haare und muskeln wachsen hinzu kommt offenbar noch eine abwehrstaerkende wirkung des hormons deshalb leiden maenner seltener unter entzuendungen und allergien als frauen das teilte die universitaet jena am dienstag mit bestimmte zellen erzeugen demnach im weiblichen koerper fast doppelt so viele stoffe die eine entzuendung foerdern wie im maennlichen organismus daher wuerden vor allem frauen durch arthritis oder asthma geplagt doch der wirkung maennlicher hormone koennen sich auch die weiblichen immunzellen nicht entziehen im reagenzglas habe sich gezeigt dass testosteron in den zellen beider geschlechter die enzyme hemmt die fuer eine entzuendliche reaktion sorgen die jenaer wissenschaftler carlo pergola und oliver werz fordern deshalb massgeschneiderte therapien fuer maenner und frauen +Wirtschaft de-DE url-pathpart-wirtschaft url-pathpart-soziales url-domainname-www.spiegel.de url-domainprefix-www url-domainprefix-www.spiegel url-domaintype-de url-domainsuffix-spiegel.de url-firstpartpagename-0,1518,792948,00 url-pagepartsplitname-0,1518,792948,00 url-lastpartpagename-html#ref=rss konjunkturprognose die bundesregierung rechnet fuer 2012 mit einer spuerbar schwaecheren konjunktur doch die buerger sollen das nicht spueren im gegenteil +Deutschland de-DE url-pathpart-politik url-pathpart-deutschland url-domainname-www.focus.de url-domainprefix-www url-domainprefix-www.focus url-domaintype-de url-domainsuffix-focus.de url-firstpartpagename-bundespraesident-in-tokio-wulff-reist-ins-japanische-katastrophengebiet_aid_677623 url-pagepartsplitname-bundespraesident url-pagepartsplitname-in url-pagepartsplitname-tokio url-pagepartsplitname-wulff url-pagepartsplitname-reist url-pagepartsplitname-ins url-pagepartsplitname-japanische url-pagepartsplitname-katastrophengebiet url-pagepartsplitname-aid url-pagepartsplitname-677623 url-lastpartpagename-html bundespraesident in tokio wulff reist ins japanische katastrophengebiet nach seiner ankunft in japan trifft bundespraesident wulff den kaiser japans regierung ergreift jetzt die initiative und fordert den betreiber tepco auf 24 milliarden euro einzusparen sonst gibt es kein geld vom staat am zweiten tag seines besuchs in japan ist bundespraesident christian wulff in tokio mit kaiser akihito und mit hochrangigen japanischen unternehmern zusammengetroffen akihito begruesste den deutschen staatsgast am montag im kaiserlichen palast in der innenstadt von tokio offizieller anlass des fuenftaegigen besuchs ist der 150 jahrestag der aufnahme diplomatischer beziehungen zwischen tokio und berlin bei dem treffen mit dem kaiser und einem anschliessenden mittagessen fuer die delegation duerfte es aber auch um die folgen der reaktorkatastrophe von fukushima gegangen sein bereits nach seiner ankunft am sonntag hatte wulff dem japanischen volk weiter solidaritaet und unterstuetzung aus deutschland zugesichert am montagabend trifft wulff auch mit regierungschef yoshihiko noda und anderen fuehrenden politikern des landes zusammen am dienstag will sich der bundespraesident im katastrophengebiet selbst ein bild von der lage machen und mit menschen sprechen die immer noch in behelfsunterkuenften leben auch sieben monate nach erdbeben tsunami und atomunfall sind noch zehntausende ohne obdach auch mit vorstandsvorsitzenden japanischer konzerne eroerterte wulff am montag konsequenzen aus dem atomunfall von fukushima ueber die zukunft der kernkraft gab es unterschiedliche positionen die nach angaben aus delegationskreisen vor dem bundespraesidenten offen diskutiert wurden wulff sprach mit den unternehmern auch ueber bestehende barrieren fuer deutsche unternehmen etwa bei investitionen in den bereichen energie und umwelttechnik in japan er hob die bedeutung des standortes hervor und sagte +Wirtschaft de-DE url-pathpart-finanzen url-pathpart-boerse url-domainname-www.manager-magazin.de url-domainprefix-www url-domainprefix-www.manager-magazin url-domaintype-de url-domainsuffix-manager-magazin.de url-firstpartpagename-0,2828,792885,00 url-pagepartsplitname-0,2828,792885,00 url-lastpartpagename-html#ref=rss langfristige aktien investments guenstige bewertungen locken derzeit so genannte value investoren an die boerse gute voraussetzungen fuer erfolgreiches langfristiges investieren bringen wissenschaftlern zufolge gerade deutsche anleger mit aber bestehen sie damit auch den praxistest hamburg an der boerse schlaegt gegenwaertig die stunde der value investoren das sind jene anleger die per fundamentalanalyse also durch moeglichst praezise ermittlung des unternehmenswertes sowie vergleich dessen mit der boersenbewertung guenstige papiere identifizieren und dann kaufen getreu einem populaeren rat des einstigen boersengurus andre kostolany wonach sich anleger aktien ins depot legen und dann ein paar jahre schlafen sollen werden die papiere in der folge eisern gehalten damit sie langfristig die erhoffte rendite bringen so zumindest die theorie experten zufolge koennte der zeitpunkt fuer ein value investment zurzeit besser kaum sein das ist ganz offensichtlich sagt etwa hendrik leber chef des value spezialisten acatis zu manager magazin online momentan bekommt man am aktienmarkt viel gute qualitaet fuer wenig geld leber haelt vor allem viele firmen der oel gas und energiebranche fuer attraktiv wie etwa die deutschen versorger eon und rwe oder die tschechische cez gleiches gilt fuer den industriezweig medizintechnik beispielsweise mit der firma medtronic die unter anderem implantate wie herzschrittmacher herstellt als prominentester und wohl auch erfolgreichster value investor weltweit gilt der us amerikanische multimilliardaer warren buffett chef der anlagefirma berkshire hathaway und einst schueler von benjamin graham dem urvater dieser anlagestrategie beide wussten +Wirtschaft de-DE url-pathpart-wirtschaft url-pathpart-news url-domainname-www.stern.de url-domainprefix-www url-domainprefix-www.stern url-domaintype-de url-domainsuffix-stern.de url-firstpartpagename-herabstufung-der-kreditwuerdigkeit-moodys-droht-frankreich-1739890 url-pagepartsplitname-herabstufung url-pagepartsplitname-der url-pagepartsplitname-kreditwuerdigkeit url-pagepartsplitname-moodys url-pagepartsplitname-droht url-pagepartsplitname-frankreich url-pagepartsplitname-1739890 url-lastpartpagename-html#utm_source=standard&utm_medium=rssfeed&utm_campaign=wirtschaft herabstufung der kreditwuerdigkeit die ratingagentur moody s prueft die herabstufung von frankreichs kreditwuerdigkeit innerhalb der kommenden drei monate werde moody s bewerten inwieweit die franzoesische regierung die angekuendigten massnahmen zur reduzierung des staatsdefizits umsetze erklaerte die ratingagentur am montag anhand dessen solle entschieden werden ob frankreich das derzeit mit der bestnote aaa bewertet wird noch die bedingungen fuer die perspektive stabil erfuelle neben moody s bewerten derzeit auch die beiden anderen grossen ratingagenturen standard poor s und fitch frankreich mit der bestnote diese ermoeglicht es dem land auf den internationalen finanzmaerkten unter guenstigen bedingungen kredite aufzunehmen sollte moody s zu dem ergebnis kommen dass die note mit einer negativen perspektive einhergeht waere frankreich nach den usa das zweite grosse land dessen kreditwuerdigkeit herabgestuft wird moody s betonte dass es sich bei der studie um eine routinemaessig einmal jaehrlich stattfindene ueberpruefung der finanzlage frankreichs handle die agentur ist aber die erste der drei grossen konkurrenten die die hoechstbewertung fuer frankreich infrage stellt im zusammenhang mit den massiven finanzproblemen der franzoesisch belgischen grossbank dexia hatte die ratingagentur anfang des monats noch die stabilitaet frankreichs betont waehrend sie belgien vor einer herabstufung seiner kreditwuerdigkeit gewarnt hatte +Wirtschaft de-DE url-pathpart-print url-pathpart-die url-pathpart-welt url-pathpart-finanzen url-pathpart-article13668414 url-domainname-www.welt.de url-domainprefix-www url-domainprefix-www.welt url-domaintype-de url-domainsuffix-welt.de url-firstpartpagename-Finanzen-Kompakt url-pagepartsplitname-Finanzen url-pagepartsplitname-Kompakt url-lastpartpagename-html kompakt millionen gesetzlich versicherte sparen im kommenden jahr geld +Wirtschaft de-DE url-pathpart-finanzen url-pathpart-der url-pathpart-staat url-pathpart-spekuliert url-pathpart-gegen url-pathpart-seine url-pathpart-buerger url-pathpart-485903 url-domainname-www.wiwo.de url-domainprefix-www url-domainprefix-www.wiwo url-domaintype-de url-domainsuffix-wiwo.de der staat spekuliert gegen seine buerger jeden tag ein neuer vorschlag zur rettung der welt zumindest aber der eu und des euros brot und spiele im alten rom zur befriedung der massen eingesetzt funktioniert auch im neuen deutschland es gibt viel zu verteilen inzwischen vergeht kein tag ohne neue vorschlaege zur rettung der welt vor dem finanziellen kollaps danach bemuehen die medien sich ganz schnell sie zu interpretieren bevor politiker und banker wieder etwas neues vorschlagen erkenntniswert tendenz gegen null worum es wirklich geht hat der philosoph peter sloterdijk schon vor einem jahr so formuliert +Wirtschaft de-DE url-pathpart-finanzen url-pathpart-article13666843 url-domainname-www.welt.de url-domainprefix-www url-domainprefix-www.welt url-domaintype-de url-domainsuffix-welt.de url-firstpartpagename-Die-zweifelhaften-Klima-Bilanzen-deutscher-Firmen url-pagepartsplitname-Die url-pagepartsplitname-zweifelhaften url-pagepartsplitname-Klima url-pagepartsplitname-Bilanzen url-pagepartsplitname-deutscher url-pagepartsplitname-Firmen url-lastpartpagename-html co2 emissionen mehr als 100 deutsche konzerne berichten ueber ihre co 2 emissionen daraus wird ein index fuer investoren erstellt doch der sinn des ganzen ist fraglich es ist ein unglaublicher aufwand +Unterhaltung de-DE url-pathpart-nachrichten url-pathpart-stars url-portnumber-80 url-domainname-www.tz-online.de url-domainprefix-www url-domainprefix-www.tz-online url-domaintype-de url-domainsuffix-tz-online.de url-hasarguments- url-firstpartpagename-carreys-tochter-reicht-scheidung-1463234 url-pagepartsplitname-carreys url-pagepartsplitname-tochter url-pagepartsplitname-reicht url-pagepartsplitname-scheidung url-pagepartsplitname-1463234 url-lastpartpagename-html url-argname-cmp url-argvalue-defrss url-argvaluesplit-defrss jim carreys tochter reicht die scheidung ein nuernberg so hatte sich monika gruber ihren ersten auftritt bei wetten dass sicher nicht vorgestellt die kabarettistin wurde bei der samstagabend show opfer einer technsichen panne das war passiert +Deutschland de-DE url-pathpart-politik url-pathpart-deutschland url-pathpart-article2063264 url-domainname-www.abendblatt.de url-domainprefix-www url-domainprefix-www.abendblatt url-domaintype-de url-domainsuffix-abendblatt.de url-firstpartpagename-Weniger-Faelle-von-sexuellem-Kindesmissbrauch url-pagepartsplitname-Weniger url-pagepartsplitname-Faelle url-pagepartsplitname-von url-pagepartsplitname-sexuellem url-pagepartsplitname-Kindesmissbrauch url-lastpartpagename-html weniger faelle von sexuellem kindesmissbrauch trotz der juengsten skandale ist die zahl der opfer zurueckgegangen offensichtlich zeigen missbrauchsopfer ihre peiniger haeufiger an berlin anders als die juengsten skandale nahe legen sind faelle von sexuellem kindesmissbrauch in den vergangenen jahren weniger geworden in einer repraesentativen befragung des kriminologischen forschungsinstituts niedersachsen leitung +Gesundheit de-DE url-pathpart-gesundheit url-pathpart-ratgeber url-pathpart-zukunftsmedizin url-pathpart-therapie url-pathpart-tid url-pathpart-9595 url-domainname-www.focus.de url-domainprefix-www url-domainprefix-www.focus url-domaintype-de url-domainsuffix-focus.de url-firstpartpagename-ips-der-durchbruch-von-yamanaka_aid_295044 url-pagepartsplitname-ips url-pagepartsplitname-der url-pagepartsplitname-durchbruch url-pagepartsplitname-von url-pagepartsplitname-yamanaka url-pagepartsplitname-aid url-pagepartsplitname-295044 url-lastpartpagename-html risiken nebenwirkung das menschliche erbgut ist wie eine bibliothek die jeder einzelnen koerperzelle zur verfuegung steht doch die meisten koerperzellen lesen nur einzelne kapitel aus dem buch das zu ihrem organ oder gewebe gehoert ihr entwicklungspotenzial ist daher begrenzt biologisch hat das einen sinn denn je laenger eine zelle lebt desto eher haeuft sich fehlerhafter buchstabensalat an damit steigt zum beispiel das risiko fuer krebs embryonale stammzellen hingegen haben noch keine festgelegten lesevorlieben +Gesundheit de-DE url-pathpart-ratgeber url-pathpart-gesundheit url-pathpart-article1977416 url-domainname-www.abendblatt.de url-domainprefix-www url-domainprefix-www.abendblatt url-domaintype-de url-domainsuffix-abendblatt.de url-firstpartpagename-Bio-Bandscheiben-sollen-Rueckenschmerzen-lindern url-pagepartsplitname-Bio url-pagepartsplitname-Bandscheiben url-pagepartsplitname-sollen url-pagepartsplitname-Rueckenschmerzen url-pagepartsplitname-lindern url-lastpartpagename-html bio bandscheiben sollen rueckenschmerzen lindern washington kaputte bandscheiben wollen us forscher in zukunft mit biologischen implantaten ersetzen erste tests mit bio bandscheiben die aus zellen von schafen aufgebaut worden waren seien in versuchen mit ratten erfolgreich verlaufen die implantate ermoeglichten den tieren volle beweglichkeit und hatten sich nach sechs monaten fast wie natuerliche bandscheiben in die wirbelsaeule integriert schreiben die experten in den proceedings der us akademie der wissenschaften probleme mit den bandscheiben sind die hauptursache fuer schmerzen im ruecken und nackenbereich und verursachen enorme kosten schreiben die forscher in den meisten faelle wuerden die beschwerden konservativ mit physiotherapie und medikamenten behandelt auch operationen seien moeglich bei denen kaputte bandscheiben unter anderem durch mechanische implantate ersetzt werden koennen der medizinische nutzen solcher implantate sei unter fachleuten umstritten unabhaengig davon versagten aber viele implantate ihren dienst weil sie sich lockerten verrutschten oder einfach im laufe der zeit abnutzten die forscher um robby bowles von der cornell university in ithaca us staat new york testeten nun rein biologische bandscheiben implantate dazu erstellten sie zunaechst auf grundlage von computertomographie bildern eine art gussmodell der zu ersetzenden bandscheibe dieses modell nutzten sie dann um mit hilfe von zellen aus der bandscheibe von schafen ein implantat aufzubauen bandscheiben bestehen aus einem inneren wasserreichen gallertkern und einem aeusseren faserring diesen aufbau bildeten die wissenschaftler mit hilfe der unterschiedlichen zelltypen nach sie verpflanzten die bio bandscheibe dann in die schwanzwirbelsaeule von ratten nachdem den nagern dort die betreffende bandscheibe entfernt worden war das implantat liess sich gut in die luecke zwischen den wirbeln einpassen berichten die forscher nach sechs monaten stellen sie fest dass die hoehe der bio bandscheibe erhalten gelieben war und diese sich in die umliegenden wirbelkoerper integriert hatte die zellen hatten eine so genannte extrazellulaere matrix gebildet die sich in der biochemischen zusammensetzung von der natuerlicher bandscheiben kaum unterschied schreiben die forscher weiter auch die mechanischen eigenschaften des implantats also etwa die reaktion auf druckbelastungen aehnelten denen natuerlicher bandscheiben bevor solche bio bandscheiben beim menschen getestet werden koennen muessten viele fragen geklaert werden menschliche bandscheiben seien sehr viel groesser und die mechanischen anforderungen an ein implantat anders als im rattenschwanz ausserdem sei unklar wie das ersatzgewebe reagiere wenn es in ein entzuendetes umfeld implantiert werde die bandscheiben der ratten seien vor der implantation gesund gewesen bei patienten die eine neue bandscheibe benoetigten sei das nicht der fall zudem eigneten sich fuer anwendungen beim menschen keine schafszellen zur herstellung des implantats dpa +Deutschland de-DE url-pathpart-politik url-pathpart-deutschland url-domainname-www.spiegel.de url-domainprefix-www url-domainprefix-www.spiegel url-domaintype-de url-domainsuffix-spiegel.de url-firstpartpagename-0,1518,794069,00 url-pagepartsplitname-0,1518,794069,00 url-lastpartpagename-html#ref=rss minutenprotokoll angela merkel reist mit voller rueckendeckung des bundestages zum eu gipfel nach bruessel bei der abstimmung ueber die staerkung des euro rettungsschirms erreichte sie 503 stimmen und die kanzlermehrheit lesen sie im minutenprotokoll nach wie der tag im bundestag lief fuer kanzlerin angela merkel war es ein erfolgreicher tag im bundestag die erste huerde vor ihrer reise zum eu gipfel nach bruessel schaffte sie mit bravour nach einer turbulenten debatte erhielt der entschliessungsantrag der fraktionen von union fdp spd und gruenen 503 stimmen darunter 311 aus der regierungskoalition die kanzlermehrheit alle fraktionen bis auf die linke gaben damit ihre zustimmung fuer verhandlungen der euro laender ueber eine hoehere schlagkraft des rettungsfonds efsf damit ist merkel in bruessel voll verhandlungsfaehig am mittag hatte merkel mit einer leidenschaftlichen rede um unterstuetzung geworben +Wirtschaft de-DE url-pathpart-print url-pathpart-die url-pathpart-welt url-pathpart-finanzen url-pathpart-article13674828 url-domainname-www.welt.de url-domainprefix-www url-domainprefix-www.welt url-domaintype-de url-domainsuffix-welt.de url-firstpartpagename-Dax-mit-Kurssprung-ins-Wochenende url-pagepartsplitname-Dax url-pagepartsplitname-mit url-pagepartsplitname-Kurssprung url-pagepartsplitname-ins url-pagepartsplitname-Wochenende url-lastpartpagename-html dax mit kurssprung ins wochenende die juengsten aussagen der ratingagentur fitch haben dem dax am freitag noch einmal einen kraeftigen schub gegeben der deutsche leitindex stand zum handelsende mit einem plus von 3 6 prozent bei 5970 punkten den vormittag ueber hatte er noch eher moderate gewinne verzeichnet auch mdax und tecdax zogen an der index mittelgrosser werte gewann 2 9 prozent auf 8956 punkte der technologieindex tecdax drehte zurueck ins plus und arbeitete sich um 1 6 prozent auf 690 punkte vor haendler verwiesen auf aussagen der ratingagentur fitch wonach sie keine rating aenderungen wegen des eu gipfels erwartet zudem wuerde eine starke eu loesung italien und spanien vor einer herabstufung schuetzen deutsche staatsanleihen gaben trotz schwindender erwartungen an den eu gipfel zur schuldenkrise nach der richtungweisende bund future sank bis zum abend um 60 ticks auf 134 63 punkte die rendite der zehnjaehrigen bundesanleihe kletterte auf 2109 prozent der kurs des euro erholte sich nach der veroeffentlichung des besser als erwartet ausgefallenen ifo geschaeftsklimas von anfaenglichen verlusten die europaeische gemeinschaftswaehrung wurde am abend mit 1 3882 us dollar gehandelt zuvor war der euro noch bis auf 1 3773 dollar gefallen favoriten im dax waren nach den kraeftigen vortagsverlusten die banktitel an der dax spitze verteuerten sich commerzbank um 7 4 prozent deutsche bank gewannen 6 9 prozent sie hatten zuvor am staerksten unter der unsicherheit mit blick auf den eu gipfel gelitten man verteuerten sich um 3 7 prozent laut einem handelsblatt bericht sollen sich mit vw man und ipic die eigner der essener ferrostaal am wochenende in paris zu einem gespraech ueber die loesung des konflikts treffen man will sich restlos von ferrostaal trennen an dem es noch 30 prozent haelt schwaecher als der markt entwickelten sich merck die von dem verzicht auf eine weiterentwicklung des parkinson medikaments safinamid belastet wurden die aktie gewann 0 9 prozent eine etwas schlechter als erwartet ausgefallene quartalsbilanz des franzoesischen triebwerkbauers safran belastete den deutschen triebwerksherstellers mtu aero engines dessen papiere gaben um 1 1 prozent nach +Unterhaltung de-DE url-pathpart-aktuelles url-pathpart-stars url-portnumber-80 url-domainname-www.tz-online.de url-domainprefix-www url-domainprefix-www.tz-online url-domaintype-de url-domainsuffix-tz-online.de url-hasarguments- url-firstpartpagename-marion-cotillard-oben-ohne-bad-meer-tz-1446425 url-pagepartsplitname-marion url-pagepartsplitname-cotillard url-pagepartsplitname-oben url-pagepartsplitname-ohne url-pagepartsplitname-bad url-pagepartsplitname-meer url-pagepartsplitname-tz url-pagepartsplitname-1446425 url-lastpartpagename-html url-argname-cmp url-argvalue-defrss url-argvaluesplit-defrss marion cotillard nuernberg so hatte sich monika gruber ihren ersten auftritt bei wetten dass sicher nicht vorgestellt die kabarettistin wurde bei der samstagabend show opfer einer technsichen panne das war passiert +Wirtschaft de-DE url-pathpart-finanzen url-pathpart-geldanlage url-pathpart-article13666921 url-domainname-www.welt.de url-domainprefix-www url-domainprefix-www.welt url-domaintype-de url-domainsuffix-welt.de url-firstpartpagename-Neue-Regeln-zur-Honorarberatung-ziehen-sich-hin url-pagepartsplitname-Neue url-pagepartsplitname-Regeln url-pagepartsplitname-zur url-pagepartsplitname-Honorarberatung url-pagepartsplitname-ziehen url-pagepartsplitname-sich url-pagepartsplitname-hin url-lastpartpagename-html verbraucherschutz fruehestens 2012 sollen neue regeln fuer die honorarberatung kommen derzeit wird das eckpunkte papier von ministerin ilse aigner heiss diskutiert seit rund drei jahren setzt sich verbraucherschutzministerin ilse aigner csu mit ihrer qualitaetsoffensive verbraucherfinanzen fuer bessere informationen fuer bank und versicherungskunden ein im zuge dieser nach der lehman pleite gestarteten initiative sind etwa beratungsprotokolle und informationsblaetter eingefuehrt worden dennoch gibt es im anlegerschutz nach wie vor baustellen und man kann sich des eindrucks nicht erwehren dass den kunden nicht immer die besten produkte empfohlen werden sagt gitta connemann cdu in den vom verbraucherministerium im spaetsommer vorgelegten eckpunkten zur honorarberatung sieht sie den fehlenden baustein in einem grossen mosaik connemann ist mitglied des zustaendigen ausschusses fuer verbraucherschutz mit den neuen regeln zur honorarberatung will das verbraucherministerium den interessenskonflikt zwischen der kostenpflichtigen beratung im interesse des kunden und der vermeintlich kostenlosen provisionsgetrieben beratung entschaerfen so soll zum beispiel die steuerliche behandlung von honoraren mit der von provisionen gleichgestellt werden heute koennen beispielsweise bei lebens policen eingerechnete provisionen steuerlich geltend gemacht werden honorare aber nicht zudem sollen sich nur solche fachleute finanzberater nennen duerfen die eine ausreichende qualifikation nachweisen koennen sie muessen darlegen dass sie einen umfassenden marktueberblick haben und somit ueber kredite policen und anlageprodukte gewissenhaft beraten koennen das eckpunkte papier ist laut verbraucherschutzministerium derzeit in den beratungen zum weiteren zeitplan will man nichts sagen ich gehe davon aus dass da in diesem jahr nichts mehr kommt sagt hingegen lars gatschke vom verbraucherzentrale bundesverband vzbv das papier werde derzeit noch heiss diskutiert fuer die branche koennte es grosse veraenderungen mit sich bringen so werden produktanbieter kuenftig gezwungen sein nettotarife anzubieten oder die im produkt enthaltenen abschlusskosten offen zu legen sagt professor hans peter schwintowski der versicherungsexperte lehrt an der berliner humboldt universitaet schwintowski rechnet damit dass die neuen regeln zur honorarberatung den wettbewerbsdruck enorm steigern und kunden von deutlich guenstigeren tarifen profitieren wuerden +Wirtschaft de-DE url-pathpart-finanzen url-pathpart-banken url-domainname-www.focus.de url-domainprefix-www url-domainprefix-www.focus url-domaintype-de url-domainsuffix-focus.de url-firstpartpagename-sundp-ratingagentur-knoepft-sich-italienische-banken-vor-wachstumsprognose-gekuerzt_aid_667781 url-pagepartsplitname-sundp url-pagepartsplitname-ratingagentur url-pagepartsplitname-knoepft url-pagepartsplitname-sich url-pagepartsplitname-italienische url-pagepartsplitname-banken url-pagepartsplitname-vor url-pagepartsplitname-wachstumsprognose url-pagepartsplitname-gekuerzt url-pagepartsplitname-aid url-pagepartsplitname-667781 url-lastpartpagename-html s p ratingagentur knoepft sich italienische banken vor wachstumsprognose gekuerzt die lage fuer die italienische wirtschaft wird immer verfahrener +Gesundheit de-DE url-pathpart-gesundheit url-pathpart-ratgeber url-pathpart-gehirn url-pathpart-news url-domainname-www.focus.de url-domainprefix-www url-domainprefix-www.focus url-domaintype-de url-domainsuffix-focus.de url-firstpartpagename-psoriasis-und-multiple-sklerose-heilungschancen-fuer-autoimmunkrankheiten_aid_676425 url-pagepartsplitname-psoriasis url-pagepartsplitname-und url-pagepartsplitname-multiple url-pagepartsplitname-sklerose url-pagepartsplitname-heilungschancen url-pagepartsplitname-fuer url-pagepartsplitname-autoimmunkrankheiten url-pagepartsplitname-aid url-pagepartsplitname-676425 url-lastpartpagename-html psoriasis und multiple sklerose heilungschancen fuer autoimmunkrankheiten es war wie verhext +Deutschland de-DE url-pathpart-politik url-pathpart-deutschland url-domainname-www.spiegel.de url-domainprefix-www url-domainprefix-www.spiegel url-domaintype-de url-domainsuffix-spiegel.de url-firstpartpagename-0,1518,791359,00 url-pagepartsplitname-0,1518,791359,00 url-lastpartpagename-html#ref=rss slowakisches nein zum efsf das slowakische nein zum rettungsschirm efsf bringt bruessel in die bredouille die zukunft der euro zone liegt nun in den haenden der opposition in bratislava die europaeischen sozialisten wollen ihren parteifreunden ein ja fuer die zweite abstimmung abringen die zeit draengt berlin eigentlich haette laengst alles im lot sein sollen mit dem ja zum efsf waere die slowakei das 17 und letzte land der euro zone gewesen das gruenes licht fuer den erweiterten rettungsschirm efsf gegeben haette doch nun steckt sand im getriebe was die schwarz gelbe koalition in berlin vor zwei wochen im groessten und wirtschaftlich staerksten land der euro zone zustande brachte scheiterte in der kleinen slowakei in bratislava zerbrach in der nacht die christlich liberale koalition an der abstimmung ueber den efsf die euro kritische partei freiheit und liberalitaet sas verweigerte die zustimmung ministerpraesidentin iveta radicova muss wohl bald bei neuwahlen antreten selbst die vertrauensfrage hatte der resoluten politikerin nichts genuetzt nun blicken radicova und die anderen 16 regierungs und staatschefs der euro zone auf die opposition sie soll helfen die verfahrene lage doch noch zu loesen die gespraeche zwischen der amtierenden regierung und der sozialdemokratischen smer sollen noch am mittwoch beginnen am donnerstag koennte in bratislava die zustimmung zum efsf dann stehen dabei sollen so der plan drei der vier bisherigen regierungsparteien gemeinsam mit den sozialdemokraten fuer den efsf stimmen der fraktionschef der sozialisten im europaparlament martin schulz ist optimistisch er haelt kontakt zu robert fico dem vorsitzenden der slowakischen sozialdemokraten fico habe ihm zugesagt so schulz dem schirm bei der kommenden abstimmung sein ja zu geben die zeit draengt denn am 23 oktober ist der ohnehin schon einmal verschobene eu gipfel angesetzt auf dem die staats und regierungschefs eine reihe weiterer wichtiger entscheidungen zum euro treffen wollen bei dieser gelegenheit will man auch die zustimmung aller mitglieder der euro gruppe zum efsf vorweisen durchkreuzt bratislava den zeitplan steht die eu vor einer tiefen krise abwarten nur keine panik das ist die tonlage der bundeskanzlerin angela merkel gab sich auf ihrer asienreise optimistisch ich bin sehr gewiss dass wir bis zum 23 oktober alle unterschriften aller mitgliedsstaaten unter diesem efsf haben werden sagte die cdu politikerin in ho chi minh stadt ihr vizekanzler philipp roesler sagte +Gesundheit de-DE url-pathpart-~r url-pathpart-rp url-pathpart-online url-pathpart-rss url-pathpart-gesundheit url-pathpart-~3 url-pathpart-ZmonvsoEvHM url-domainname-feeds.rp-online.de url-domainprefix-feeds url-domainprefix-feeds.rp-online url-domaintype-de url-domainsuffix-rp-online.de url-firstpartpagename-story01 url-pagepartsplitname-story01 url-lastpartpagename-htm hormonelle verhuetungsmethoden im ueberblick langfristige verhuetung mit hormonen hormonelle verhuetungsmethoden enthalten das hormon gestagen das den eisprung hemmt und den schleimpfropf der sich am gebaermutterhals befindet verdickt durch diese verdickung ist es fuer die spermien fast unmoeglich in die gebaermutter zu gelangen und eine schwangerschaft auszuloesen fuer frauen die es leid sind taeglich die pille einzunehmen um zu verhueten sind hormonelle methoden deshalb praktisch +Wirtschaft de-DE url-pathpart-wirtschaft url-pathpart-versicherung url-domainname-www.stern.de url-domainprefix-www url-domainprefix-www.stern url-domaintype-de url-domainsuffix-stern.de url-firstpartpagename-gerichtsurteil-zu-provisionen-weg-frei-fuer-discount-versicherungen-1743503 url-pagepartsplitname-gerichtsurteil url-pagepartsplitname-zu url-pagepartsplitname-provisionen url-pagepartsplitname-weg url-pagepartsplitname-frei url-pagepartsplitname-fuer url-pagepartsplitname-discount url-pagepartsplitname-versicherungen url-pagepartsplitname-1743503 url-lastpartpagename-html#utm_source=standard&utm_medium=rssfeed&utm_campaign=wirtschaft gerichtsurteil zu provisionen dieses gerichtsurteil koennte das gaengige geschaeftsmodell fuer lebensversicherungen ins wanken bringen +Wirtschaft de-DE url-pathpart-wirtschaft url-pathpart-unternehmen url-domainname-www.spiegel.de url-domainprefix-www url-domainprefix-www.spiegel url-domaintype-de url-domainsuffix-spiegel.de url-firstpartpagename-0,1518,792387,00 url-pagepartsplitname-0,1518,792387,00 url-lastpartpagename-html#ref=rss rohstoffspekulation die verbraucherorganisation foodwatch attackiert die banken +Deutschland de-DE url-pathpart-politik url-pathpart-deutschland url-pathpart-article13661288 url-domainname-www.welt.de url-domainprefix-www url-domainprefix-www.welt url-domaintype-de url-domainsuffix-welt.de url-firstpartpagename-Machtkampf-um-den-braunen-Thron-der-NPD url-pagepartsplitname-Machtkampf url-pagepartsplitname-um url-pagepartsplitname-den url-pagepartsplitname-braunen url-pagepartsplitname-Thron url-pagepartsplitname-der url-pagepartsplitname-NPD url-lastpartpagename-html parteitag auf dem parteitag der npd wird es zu einer kampfkandidatur um den vorsitz kommen doch der richtungsstreit unter den rechtsextremen ist nur gezeter ueber die fassade frueher wollte er gefuerchtet werden als holger apfel sich 2004 erstmals anschickte bundesweit gehoer zu finden bellte er in die mikros der fraktionschef der gerade in den saechsischen landtag eingezogenen npd redete sich in rage seine stimme ueberschlug sich rasch und seine haut fleckte das war einmal heute will holger apfel ganz nach oben dafuer gibt er sich nun dezent zahlreiche parteifreunde aus vielen landesverbaenden haetten ihn zur kandidatur gedraengt liess er vor drei wochen verbreiten der 40 jaehrige will seinen politischen ziehvater udo voigt auf dem anstehenden bundesparteitag vom chefposten vertreiben wo und wann das halten die parteimanager geheim und der geborene hildesheimer macht auf laessig heute schuetzt er seine stimme leise vor den salti die rechte hand in der hosentasche laechelt er viel serioes will er wirken die npd hort der unverbesserlichen der nsdap juenger und hitler verehrer will er an den erfolg rechtsnationaler parteien rund um deutschland heranfuehren dafuer muesse sie sich als eine zukunftsgewandte nationale partei mit serioeser radikalitaet im politischen spektrum einordnen hat er formuliert serioese radikalitaet bedeutet konstruktiv gegenwartsbezogen und volksnah zu sein unpolitische nostalgiepflege und ziellosen verbalradikalismus will er von der agenda streichen die zukunft der npd sieht er als anti euro partei als magnet konservativer globalisierungsgegner auf dem bundesparteitag wird es daher eine echte kampfkandidatur geben udo voigt seit 15 jahren unangefochten im amt muss den fraktionschef apfel fuerchten zumal der mit udo pastoers fraktionschef der npd im schweriner landtag eine achse geschmiedet hat voigt seinerseits ist bei den westdeutschen landesverbaenden gut vernetzt der 59 jaehrige wird um seinen posten kaempfen pattex udo nennen sie ihn in der partei schon seit langem was es indes auf dem parteitag nicht geben wird ist ein machtkampf der programme apfels serioese radikalitaet ist der spagat eine neonazi partei fuer die oeffentlichkeit in freundlicheren farben zu zeichnen doch der braunton wird weiter durchschimmern inhaltlich trennt voigt und apfel kein blatt voneinander beide haben die partei fuer soziale themen und die neonazistischen kameradschaften geoeffnet beide sind seit ihrer jugend fuer die npd aktiv fuer beide war es nicht die frage ob sie parteichef werden sondern wann eine moderne rechtspartei wird die npd nie apfels buergerliches auftreten ist wunschdenken und kalkulierte fassade eine moderne rechtspartei wird die npd nie ihr fehlt die inhaltliche flexibilitaet populistischer parteien in den nachbarlaendern ihre dehnbarkeit dafuer hat die npd jede menge ideologie ein besuch in ihrer zentrale in berlin koepenick belegt dies mit jedem schritt duester ist es dort die waende haben seit jahren keine neue farbe gesehen die knarzende holztreppe hinauf laesst man die kapuzenpullitraeger hinter sich die im erdgeschoss verschlissene wahlplakate stapeln ab dem ersten stock traegt man hemd und stoffhose mit buegelfalte schliesslich sehen sich die leute von der npd im kampf um sein oder nichtsein des deutschen volkes wir stehen an der spitze dieser erneuerung schreiben sie im programm kein gespraech in der parteizentrale ohne ueberfremdung und verauslaenderung florian stein sitzt am computer in seinem buero gegenueber der kueche das frontfenster ist vergittert der 28 jaehrige ist fuer die mitgliederverwaltung zustaendig er sagt unvermittelt +Wirtschaft de-DE url-pathpart-finanzen url-pathpart-news url-pathpart-konjunktur url-domainname-www.focus.de url-domainprefix-www url-domainprefix-www.focus url-domaintype-de url-domainsuffix-focus.de url-firstpartpagename-deutsche-konjunktur-bundesregierung-erwartet-nur-noch-zwergen-wachstum_aid_676479 url-pagepartsplitname-deutsche url-pagepartsplitname-konjunktur url-pagepartsplitname-bundesregierung url-pagepartsplitname-erwartet url-pagepartsplitname-nur url-pagepartsplitname-noch url-pagepartsplitname-zwergen url-pagepartsplitname-wachstum url-pagepartsplitname-aid url-pagepartsplitname-676479 url-lastpartpagename-html deutsche konjunktur bundesregierung erwartet nur noch zwergen wachstum der boom der deutschen wirtschaft geht dem ende zu die regierung rechnet fuer kommendes jahr nur noch mit einem plus von 1 0 prozent bislang hatte sie fast doppelt so viel vorausgesagt doch es gibt auch eine gute nachricht die bundesregierung erwartet angesichts zunehmender weltweiter risiken einen daempfer fuer den aufschwung in deutschland fuer das kommende jahr wird statt bisher 1 8 prozent ein wachstum von 1 0 prozent prognostiziert wie bundeswirtschaftsminister philipp roesler fdp am donnerstag in berlin mitteilte fuer dieses jahr geht die regierung noch von einem staerkeren wachstum von 2 9 prozent aus auch wenn sich das expansionstempo nun wie erwartet verlangsamt habe bleibe deutschland stabilitaetsanker und wachstumsmotor fuer europa erklaerte roesler tragender pfeiler der entwicklung in deutschland werde zusehends die binnennachfrage der export schwaeche sich ab roesler sagte +Wirtschaft de-DE url-pathpart-wirtschaft url-pathpart-news url-domainname-www.stern.de url-domainprefix-www url-domainprefix-www.stern url-domaintype-de url-domainsuffix-stern.de url-firstpartpagename-eu-gipfel-zur-euro-krise-mittwoch-banken-solls-was-geben-1742231 url-pagepartsplitname-eu url-pagepartsplitname-gipfel url-pagepartsplitname-zur url-pagepartsplitname-euro url-pagepartsplitname-krise url-pagepartsplitname-mittwoch url-pagepartsplitname-banken url-pagepartsplitname-solls url-pagepartsplitname-was url-pagepartsplitname-geben url-pagepartsplitname-1742231 url-lastpartpagename-html#utm_source=standard&utm_medium=rssfeed&utm_campaign=wirtschaft eu gipfel zur euro krise es war eine demonstration angela merkel und nicolas sarkozy traten gemeinsam vor die journalisten in bruessel dicke luft herrschte dort schon deswegen weil die reporter fast zwei stunden lang in erwartung des deutsch franzoesischen duos zusammengepfercht waren die atemnot entlud sich in verbittertem beifall fuer den praesidenten und die kanzlerin die gemeinsam mit den anderen 25 eu regierenden erst um 16 28 das mittagessen beendeten fuer merkel und sarkozy ging es darum zu zeigen dass man keineswegs so zerstritten sei wie nun schon seit tagen berichtet wird ich werde ihnen nicht erlauben zu schreiben dass die sache gescheitert ist sagte sarkozy den journalisten angesichts eines akuten mangels an entscheidungen und auch merkel bat eher um nachsicht +Gesundheit de-DE url-pathpart-ratgeber url-pathpart-article2038045 url-domainname-www.abendblatt.de url-domainprefix-www url-domainprefix-www.abendblatt url-domaintype-de url-domainsuffix-abendblatt.de url-firstpartpagename-Ab-35-regelmaessig-zum-TUeV-eine-Bestandsaufnahme url-pagepartsplitname-Ab url-pagepartsplitname-35 url-pagepartsplitname-regelmaessig url-pagepartsplitname-zum url-pagepartsplitname-TUeV url-pagepartsplitname-eine url-pagepartsplitname-Bestandsaufnahme url-lastpartpagename-html ab 35 regelmaessig zum tuev eine bestandsaufnahme hamburg kerngesunde menschen mitte 30 denken nicht an den arzt sagt stephan hofmeister allgemeinmediziner und hausarzt aus hamburg dabei haben alle gesetzlich versicherten ab dem alter von 35 jahren anspruch zu einer frueherkennungsuntersuchung dem check up 35 und das bevor gesundheitliche beschwerden auftreten alle zwei jahre kann dieser gesundheits tuev wiederholt werden vergleichsweise junge leute kommen aber in der regel nur zu diesem check up wenn sie kinder bekommen oder einen hohen kredit fuer einen hausbau aufnehmen wollen und maenner kommen oft weil ihre partnerin sie schickt berichtet hofmeister aus der praxis zahlen aus niedersachsen zeigen dass nur 26 prozent der berechtigten frauen und 27 prozent der maenner das angebot nutzen in hamburg duerfte die zahl in etwa genauso liegen doch was verbirgt sich hinter diesem check up 35 der zu den sogenannten vorsorge oder frueherkennungsuntersuchungen gehoert die untersuchung ist genau definiert sagt hofmeister stellvertretender vorsitzender der vertreterversammlung der kassenaerztlichen vereinigung hamburg neben einem ausfuehrlichen gespraech ueber vorerkrankungen lebensfuehrung und krankheiten in der familie gehoert eine koerperliche untersuchung mit dem abhorchen von lunge und herz dazu der arzt tastet auch ob der patient vergroesserte lymphknoten eine zu grosse schilddruese oder vergroesserte organe im bauchraum hat auch das gewicht wird ermittelt und der blutdruck gemessen eine blutprobe gehoert ebenfalls dazu zwei werte werden bestimmt +Unterhaltung de-DE url-pathpart-nachrichten url-pathpart-stars url-portnumber-80 url-domainname-www.tz-online.de url-domainprefix-www url-domainprefix-www.tz-online url-domaintype-de url-domainsuffix-tz-online.de url-hasarguments- url-firstpartpagename-lena-geschockt-ihrem-ersten-steuerbescheid-zr-1458344 url-pagepartsplitname-lena url-pagepartsplitname-geschockt url-pagepartsplitname-ihrem url-pagepartsplitname-ersten url-pagepartsplitname-steuerbescheid url-pagepartsplitname-zr url-pagepartsplitname-1458344 url-lastpartpagename-html url-argname-cmp url-argvalue-defrss url-argvaluesplit-defrss lena war geschockt von ihrem ersten steuerbescheid lena meyer landrut mag keine behoerdengaenge und beschaeftigt sich nicht gerne mit steuerbescheiden und versicherungen solche pflichtgaenge finde ich unfassbar anstrengend und belastend vor allem weil das sachen sind die konsequenzen haben wenn ich sie nicht erledige sagte die 20 jaehrige gewinnerin des eurovision song contest 2010 dem berliner tagesspiegel als sie ihren ersten steuerbescheid bekommen habe sei sie geschockt gewesen ich habe abzuege von etwa 43 prozent oh doch so viel habe ich gedacht und dann gleich die frist verpasst da musste ich mahngebuehren zahlen +Wirtschaft de-DE url-pathpart-finanzen url-pathpart-news url-pathpart-unternehmen url-domainname-www.focus.de url-domainprefix-www url-domainprefix-www.focus url-domaintype-de url-domainsuffix-focus.de url-firstpartpagename-bankbranche-citigroup-trotzt-der-schuldenkrise_aid_675473 url-pagepartsplitname-bankbranche url-pagepartsplitname-citigroup url-pagepartsplitname-trotzt url-pagepartsplitname-der url-pagepartsplitname-schuldenkrise url-pagepartsplitname-aid url-pagepartsplitname-675473 url-lastpartpagename-html bankbranche citigroup trotzt der schuldenkrise der umbau der us bank citigroup nach der beinahe pleite in der finanzkrise macht sich offenbar bezahlt +Deutschland de-DE url-pathpart-politik url-pathpart-deutschland url-pathpart-tid url-pathpart-23842 url-domainname-www.focus.de url-domainprefix-www url-domainprefix-www.focus url-domaintype-de url-domainsuffix-focus.de url-firstpartpagename-nach-brandanschlag-in-berlin-polizei-hat-angst-vor-einer-neuen-raf_aid_673445 url-pagepartsplitname-nach url-pagepartsplitname-brandanschlag url-pagepartsplitname-in url-pagepartsplitname-berlin url-pagepartsplitname-polizei url-pagepartsplitname-hat url-pagepartsplitname-angst url-pagepartsplitname-vor url-pagepartsplitname-einer url-pagepartsplitname-neuen url-pagepartsplitname-raf url-pagepartsplitname-aid url-pagepartsplitname-673445 url-lastpartpagename-html nach brandanschlag in berlin polizei hat angst vor einer neuen raf zu den anschlaegen auf die bahn bekennt sich eine gruppe von linksextremisten bisher ist die gruppe komplett unbekannt doch beide polizei gewerkschaften nehmen das bekennerschreiben zum anlass fuer drastische warnungen nach den brandanschlaegen in berlin waehlt der chef der polizeigewerkschaft rainer wendt einen drastischen historischen vergleich wir erleben eine renaissance der rote armee fraktion sagte er gegenueber bild de es wird neue strukturen geben dezentral sehr viel lockerer mit einzelnen kleine gruppen der linke terror wird sich noch steigern unterstuetzung erhaelt wendt von seinem kollegen der gewerkschaft der polizei bernhard witthaut er fordert mehr personal fuer verfassungsschutz und polizeilichen staatsschutz die politik muesse die warnungen des verfassungsschutzes ernst nehmen auch der raf terror hat mit der verharmlosenden sogenannten gewalt gegen sachen begonnen spaeter wurden menschen ermordet mit brandsaetzen in berlin und brandenburg hatten mutmasslich linksextreme am montag den zugverkehr rund um die hauptstadt stark beeintraechtigt am berliner hauptbahnhof waere es um ein haar zu einem womoeglich folgenschweren anschlag gekommen ein mitarbeiter entdeckte die sieben brandsaetze in einem tunnel bevor sie explodieren konnten auf freier strecke nordwestlich von berlin zuendete jedoch ein brandsatz in einem schacht fuer signalkabel und kappte so die wichtige verbindung nach hamburg tausende reisende und pendler werden deshalb auch am dienstag von verspaetungen und zugausfaellen betroffen sein in einem bekennerschreiben protestiert eine linksextreme gruppe im internet gegen den bundeswehreinsatz in afghanistan das landeskriminalamt lka brandenburg haelt das bekennerschreiben nach einer vorlaeufigen bewertung fuer authentisch bundesverkehrsminister peter ramsauer csu verurteilte die brandanschlaege das leben unbeteiligter pendler familien und kinder zu gefaehrden ist keine meinungsaeusserung sondern ein anschlag auf unsere gesellschaft sagte ramsauer der tageszeitung die welt vom dienstag die verantwortlichen wuerden mit aller haerte verfolgt und zur rechenschaft gezogen verletzt wurde bei den anschlaegen niemand moeglicherweise auch weil die bahn ihr personal nach dem anschlag in brandenburg am fruehen morgen auf zusaetzliche kontrollgaenge schickte an der noerdlichen tunneleinfahrt zum hauptbahnhof entdeckte ein mitarbeiter hinter einem trafo haus am mittag sieben brandsaetze in einem kabelschacht spezialisten machten die behaelter mit brennbaren fluessigkeiten rechtzeitig unschaedlich doch die haetten extrem gefaehrlich sein koennen die sprengsaetze haetten enormen schaden anrichten koennen die tunneleinfahrt am hauptbahnhof liegt gut 200 meter vom bahnhof selbst entfernt der nicht gesperrt wurde +Wirtschaft de-DE url-pathpart-artikel url-domainname-www.neues-deutschland.de url-domainprefix-www url-domainprefix-www.neues-deutschland url-domaintype-de url-domainsuffix-neues-deutschland.de url-firstpartpagename-208813 url-pagepartsplitname-208813 url-lastpartpagename-html freund freund wirtschaftsmacht russlands premier wladimir putin nannte den vorsitzenden der volksrepublik china einen teuren freund und der betonte dass sein gast ein grosser freund des chinesischen volkes sei doch nicht nur um artigkeiten geht es bei dem arbeitsbesuch des ersten mannes russlands in peking zwischen januar und august 2011 war china der wichtigste aussenhandelspartner russlands doch um den usa und europa auf wissenschaftlichem und wirtschaftlichen gebiet ein ernster konkurrent zu sein reicht das und beider reichtum an bodenschaetzen nicht aus man will die wirtschaftliche und wissenschaftliche zusammenarbeit auf eine neue ebene die der schluesseltechnologien heben die vorstellungen ueber gemeinsame projekte in der luft und raumfahrt sowie beim schiffbau sind weit gediehen bislang so putin habe man sich beim kauf neuer zivilflugzeuge immer an us amerikanische und westeuropaeische hersteller gehalten aber russland und china seien durchaus in der lage eigene produktionslinien aufzubauen angesprochen wurde der schon laenger anvisierte bau eines gemeinsamen transportflugzeuges die beiderseits geschaetzte russische il 76 ist dabei ein gutes ausgangsmodell putin hatte in den vergangenen jahren bereits eine neuordnung des russischen flugzeugbaus verlangt auch um aus diesem bereich impulse fuer die entwicklung anderer industriebereiche zu erhalten nun versucht er dieses vorhaben bilateral auszubauen und hat einen ueber russland und china hinausgehenden gigantischen absatzmarkt vor augen was moeglich ist zeigt die zusammenarbeit auf militaerischem gebiet der austausch in diesem jahr liegt derzeit geschaetzt bei rund einer milliarde us dollar als schluessel fuer gemeinsamen fortschritt wird auch der energiebereich betrachtet gestuetzt auf gemeinsame erfahrungen beim bau des chinesischen atomkraftwerks tianwan werde man eine engere kooperation bei der nukleartechnik ermoeglichen auf der grundlage modernster technologien und unter beruecksichtigung der eventuellen risiken die praktisch auf den nullpunkt gebracht werden muessen so putin gleichfalls im programm ist der ausbau von stromerzeugung aus wasserkraft und sogenannten alternativen quellen auch auf dem agrarsektor will man kuenftig enger kooperieren um bestehende handels und investitionshindernisse abzubauen verstaendigten sich beide seiten auf eine aktivere nutzung von yuan und rubel bei den bilateralen verrechnungen +Unterhaltung de-DE url-pathpart-nachrichten url-pathpart-stars url-portnumber-80 url-domainname-www.tz-online.de url-domainprefix-www url-domainprefix-www.tz-online url-domaintype-de url-domainsuffix-tz-online.de url-hasarguments- url-firstpartpagename-nicolas-cage-sachsen-anhalt-kamera-1452360 url-pagepartsplitname-nicolas url-pagepartsplitname-cage url-pagepartsplitname-sachsen url-pagepartsplitname-anhalt url-pagepartsplitname-kamera url-pagepartsplitname-1452360 url-lastpartpagename-html url-argname-cmp url-argvalue-defrss url-argvaluesplit-defrss nicolas cage in sachsen anhalt vor kamera nuernberg so hatte sich monika gruber ihren ersten auftritt bei wetten dass sicher nicht vorgestellt die kabarettistin wurde bei der samstagabend show opfer einer technsichen panne das war passiert +Gesundheit de-DE url-pathpart-allergie url-pathpart-aktuelles url-domainname-www.stern.de url-domainprefix-www url-domainprefix-www.stern url-domaintype-de url-domainsuffix-stern.de url-firstpartpagename-lebensmittelallergie-forscher-tricksen-immunsystem-aus-1738501 url-pagepartsplitname-lebensmittelallergie url-pagepartsplitname-forscher url-pagepartsplitname-tricksen url-pagepartsplitname-immunsystem url-pagepartsplitname-aus url-pagepartsplitname-1738501 url-lastpartpagename-html#utm_source=standard&utm_medium=rssfeed&utm_campaign=gesundheit lebensmittelallergie bei einer nahrungsmittelallergie loesen auch kleinste bestandteile bestimmter lebensmitteln heftige auch lebensgefaehrliche immunreaktionen aus jetzt konnten amerikanische mediziner in versuchen mit maeusen mithilfe eines neuartigen verfahrens solche allergischen reaktionen auf erdnuesse abschalten sie koppelten erdnussproteine an weisse blutkoerperchen die den tieren anschliessend injiziert wurden nach nur zweimaliger behandlung hatte das immunsystem der maeuse gelernt mit der nahrung aufgenommene erdnuesse zu tolerieren wie die forscher an einem anderen beispiel zeigen konnten eignet sich die technik auch zur therapie anderer allergieformen schreiben sie im fachblatt journal of immunology wir glauben dass wir einen weg gefunden haben um die allergischen reaktionen bei nahrungsmittelallergien sicher und schnell abzuschalten sagt paul bryce von der northwestern university feinberg school of medicine in chicago der zusammen mit stephen miller das forscherteam leitete fuer ihre experimente hatten die wissenschaftler maeuse mit einer starken erdnussallergie erzeugt der verzehr der nuesse loeste ueberschiessende immunreaktionen aus die einen oft toedlich verlaufenden anaphylaktischen schock zur folge hatten die forscher entnahmen dem blut der maeuse weisse blutkoerperchen sogenannte leukozyten und hefteten daran erdnussproteine die fuer die allergische reaktion verantwortlich sind die so veraenderten leukozyten wurden den maeusen dann wieder injiziert nach einer erneuten behandlung auf dieselbe weise erhielten die tiere erdnussextrakt mit der nahrung der anaphylaktische schock blieb nun aus das immunsystem tolerierte das erdnussprotein weil das protein zuvor bereits von den weissen blutkoerperchen praesentiert wurde sagt bryce er haelt es fuer moeglich mehr als nur eine art von proteinen an die leukozyten zu koppeln so dass auch therapien gegen mehrfache nahrungsmittelallergien denkbar waeren durch die behandlung so bryce wuerde das gestoerte gleichgewicht zwischen zwei typen von immunzellen den aggressiven th2 t zellen und den daempfenden regulatorischen t zellen wiederhergestellt das verfahren erwies sich auch bei einer anderen form der allergie als wirksam bei der ein protein aus eiern asthmaartige anfaelle ausloest in weiteren experimenten pruefen die forscher nun ob sich die leukozyten durch kuenstliche mikropartikel ersetzen lassen solche partikel als traeger von allergie ausloesenden proteinen liessen sich leichter auf standardisierte weise herstellen +Deutschland de-DE url-pathpart-politik url-pathpart-deutschland url-pathpart-article2059094 url-domainname-www.abendblatt.de url-domainprefix-www url-domainprefix-www.abendblatt url-domaintype-de url-domainsuffix-abendblatt.de url-firstpartpagename-Medien-Fahnder-planen-Aktion-gegen-Steuerfluechtlinge url-pagepartsplitname-Medien url-pagepartsplitname-Fahnder url-pagepartsplitname-planen url-pagepartsplitname-Aktion url-pagepartsplitname-gegen url-pagepartsplitname-Steuerfluechtlinge url-lastpartpagename-html medien derzeit sollen die daten von 3000 steuersuendern ueberprueft werden es handelt sich dabei um eine cd die das land nrw 2010 gekauft hatte hamburg bochum deutschen steuerfluechtlingen mit schwarzgeld in luxemburg droht aerger denn steuerfahnder und staatsanwaltschaften aus dem gesamten bundesgebiet bereiten angebliche eine gross angelegte aktion gegen mutmassliche steuersuender vor der spiegel berichtete am donnerstag vorab die ermittler untersuchten daten von einer cd aus luxemburg die das land nrw vor einem jahr fuer vier millionen euro angekauft habe wuppertaler steuerfahnder und die schwerpunktstaatsanwaltschaft fuer wirtschaftskriminalitaet in bochum sollen derzeit daten von etwa 3 000 steuersuendern pruefen in den fokus der ermittler seien kunden einer tochter der britischen grossbank hsbc in luxemburg geraten die bochumer staatsanwaltschaft gab auf nachfrage keine stellungnahme ab die financial times deutschland bezifferte den kaufpreis fuer die cd auf knapp drei millionen euro dafuer enthalte der datentraeger genaue informationen ueber schwarzkonten in luxemburg die eindeutig deutschen hsbc kunden zuzuordnen seien dem bericht zufolge ist fuer november eine razzia geplant die zeitung berichtete unter berufung auf behoerdenkreise der datensatz sei ausserordentlich gross das material erstklassig dadurch dass die ermittlungen im gang seien haetten die steuersuender auch keine chance mehr der strafe durch eine selbstanzeige zu entgehen es wird erwartet dass die cd etliche millionen euro in die staatskasse spuelt dapd +Wirtschaft de-DE url-pathpart-finanzen url-pathpart-news url-domainname-www.focus.de url-domainprefix-www url-domainprefix-www.focus url-domaintype-de url-domainsuffix-focus.de url-firstpartpagename-anti-banken-proteste-polizei-raeumt-zeltlager-von-demonstranten_aid_678189 url-pagepartsplitname-anti url-pagepartsplitname-banken url-pagepartsplitname-proteste url-pagepartsplitname-polizei url-pagepartsplitname-raeumt url-pagepartsplitname-zeltlager url-pagepartsplitname-von url-pagepartsplitname-demonstranten url-pagepartsplitname-aid url-pagepartsplitname-678189 url-lastpartpagename-html anti banken proteste polizei raeumt zeltlager von demonstranten zeugen beklagen ein skandaloeses vorgehen der kalifornischen polizei in der innenstadt von oakland der bewegung occupy wall street zufolge sind bei der raeumung eines zeltlagers 70 menschen festgenommen worden bei den anti banken protesten im kalifornischen oakland sind am dienstag dutzende menschen festgenommen worden nach us medienberichten liess die polizei ein zeltlager in der innenstadt raeumen die demonstranten die mehr als zwei wochen vor dem rathaus campierten hatten ein ultimatum zur raeumung des platzes ignoriert die gruppe occupy oakland sprach in einer twitter mitteilung am dienstag von 70 festnahmen ich zelte nicht ich demonstriere was hier heute passiert ist skandaloes sagte die protestlerin mindy stone nach ihrer festnahme dem san francisco chronicle die stadtverwaltung hatte sich zuvor ueber sanitaere probleme angeblichen drogenmissbrauch und ausschreitungen in dem zeltlager beschwert die bewegung occupy wall street besetzt die wall street hatte im september in new york ihren ausgang genommen und sich auf andere staedte ausgeweitet die menschen wollen die macht der banken brechen verlangen hoehere steuern fuer reiche und verbesserungen im sozialsystem +Unterhaltung de-DE url-pathpart-kultur url-pathpart-kunst url-pathpart-2011 url-pathpart-10 url-domainname-www.zeit.de url-domainprefix-www url-domainprefix-www.zeit url-domaintype-de url-domainsuffix-zeit.de url-firstpartpagename-lehrjahre-eines-dissidenten url-pagepartsplitname-lehrjahre url-pagepartsplitname-eines url-pagepartsplitname-dissidenten ai weiwei ausstellung der berliner martin gropius bau zeigt fotografien des chinesischen kuenstlers ai weiwei aus dessen zeit in new york sie dokumentieren die anfaenge einer grossen karriere er ist frei und ist es nicht seit ai weiwei chinas bekanntester kuenstler am 22 juni aus 80 taegiger haft entlassen wurde darf er sich nicht politisch aeussern darf peking nicht verlassen und schon gar nicht ins ausland reisen der fall ai weiwei ist neben dem inhaftierten friedensnobelpreistraeger liu xiaobo das aktuell gravierendste beispiel der drangsalierung von chinesischen dissidenten museen koennen daran kaum etwas aendern aber sie koennen erinnern und mahnen indem sie ais werke zeigen die nach ihrer premiere in peking 2009 nun aus new york in den gropius bau wandernde ausstellung mit ueber 220 fotografien aus ai weiweis new yorker zeit von 1983 bis 1993 hat der kuenstler selbst kuratiert und aus 10 000 fotos eine auswahl getroffen der bei seiner ankunft 25 jaehrige bewohnte eine winzige bleibe im east village er wollte eigentlich studieren trieb sich aber auf der strasse und in der avantgarde szene herum er schrieb gleichsam tagebuch mit der kamera fing schnappschuesse vom aufbruch ein dokumentierte die eigenen kuenstlerischen anfaenge in echtzeit +Deutschland de-DE url-pathpart-politik url-pathpart-article2052122 url-domainname-www.abendblatt.de url-domainprefix-www url-domainprefix-www.abendblatt url-domaintype-de url-domainsuffix-abendblatt.de url-firstpartpagename-Buschkowsky-Halte-Entwicklung-fuer-komfortabler url-pagepartsplitname-Buschkowsky url-pagepartsplitname-Halte url-pagepartsplitname-Entwicklung url-pagepartsplitname-fuer url-pagepartsplitname-komfortabler url-lastpartpagename-html buschkowsky berlin der neukoellner bezirksbuergermeister heinz buschkowsky spd hat die angekuendigten koalitionsgespraeche seiner partei mit der cdu begruesst zur begruendung sagte der spd politiker am freitag im rbb sender radioeins mit der union gebe es nicht wie mit den gruenen von vornherein konfliktpunkte wie die verlaengerung der a 100 er mache keinen hehl daraus dass er die entwicklung jetzt fuer komfortabler halte auch mit blick auf die politische mehrheit ueber einen zeitraum von fuenf jahren mit der cdu sieht er in weiteren wesentlichen punkten wie der s bahn problematik und der erweiterung des flughafens berlin brandenburg uebereinstimmungen unterschiedliche sichtweisen koennte es nach buschkowskys einschaetzung in der schulpolitik beim rueckkauf der wasserbetriebe und bei der frage geben wie viele staedtische wohnungen berlin sich zulegen sollte um mietsteigerungen zu verhindern berlins spd und cdu beginnen kommenden mittwoch koalitionsgespraeche spd und cdu in berlin wollen naechsten mittwoch mit ihren verhandlungen zur bildung einer grossen koalition beginnen der cdu fraktions und landesparteivorsitzende frank henkel sagte am donnerstag in der rbb abendschau dies habe er mit seinem spd kollegen michael mueller und dem regierenden buergermeister klaus wowereit spd am abend bei einem treffen im abgeordnetenhaus vereinbart wowereit spd sieht in der grossen koalition kein hindernis fuer ein rot gruenes buendnis auf bundesebene nach der bundestagswahl 2013 die koalitionsverhandlungen zwischen spd und gruenen waren am mittwoch im streit ueber die verlaengerung der autobahn 100 gescheitert die cdu die das projekt mittraegt war aus der abgeordnetenhauswahl mitte september nach der spd als zweitstaerkste kraft vor den gruenen hervorgegangen am mittwochabend bot der spd vorstand der cdu koalitionsverhandlungen an henkel sagte das gespraech mit mueller und wowereit am donnerstag sei sehr angenehm und konstruktiv verlaufen es gibt ein klima der vertrauensvollen zusammenarbeit sagte er unter anderem haben man einen zeitplan fuer die koalitionsverhandlungen und die bildung von arbeitsgruppen vereinbart auf moegliche strittige punkte wollte henkel nicht eingehen er betonte lediglich +Wirtschaft de-DE url-pathpart-wirtschaft url-pathpart-news url-domainname-www.stern.de url-domainprefix-www url-domainprefix-www.stern url-domaintype-de url-domainsuffix-stern.de url-firstpartpagename-euro-treffen-in-bruessel-gipfel-erlaesst-griechenland-haelfte-der-schulden-1743959 url-pagepartsplitname-euro url-pagepartsplitname-treffen url-pagepartsplitname-in url-pagepartsplitname-bruessel url-pagepartsplitname-gipfel url-pagepartsplitname-erlaesst url-pagepartsplitname-griechenland url-pagepartsplitname-haelfte url-pagepartsplitname-der url-pagepartsplitname-schulden url-pagepartsplitname-1743959 url-lastpartpagename-html#utm_source=standard&utm_medium=rssfeed&utm_campaign=wirtschaft euro treffen in bruessel auf dem weg zum gipfel +Deutschland de-DE url-pathpart-politik url-pathpart-deutschland url-pathpart-2011 url-pathpart-10 url-domainname-www.zeit.de url-domainprefix-www url-domainprefix-www.zeit url-domaintype-de url-domainsuffix-zeit.de url-firstpartpagename-trojaner-software-diskussion url-pagepartsplitname-trojaner url-pagepartsplitname-software url-pagepartsplitname-diskussion ueberwachungssoftware fuenf faelle aus bayern sind bisher bekannt das vorgehen war immer gleich die ermittler wollten skype gespraeche von verdaechtigen abhoeren sie setzten dafuer aber eine ueberwachungssoftware ein die deutlich mehr kann +Wirtschaft de-DE url-pathpart-finanzen url-pathpart-news url-pathpart-unternehmen url-domainname-www.focus.de url-domainprefix-www url-domainprefix-www.focus url-domaintype-de url-domainsuffix-focus.de url-firstpartpagename-finanzbranche-american-express-profitiert-vom-plastikgeld-boom_aid_676384 url-pagepartsplitname-finanzbranche url-pagepartsplitname-american url-pagepartsplitname-express url-pagepartsplitname-profitiert url-pagepartsplitname-vom url-pagepartsplitname-plastikgeld url-pagepartsplitname-boom url-pagepartsplitname-aid url-pagepartsplitname-676384 url-lastpartpagename-html finanzbranche american express profitiert vom plastikgeld boom die steigende beliebtheit der kreditkarte spuelt dem anbieter american express viel geld in die kasse im dritten quartal stieg der gewinn im vergleich zum vorjahr um 13 prozent auf mehr als eine milliarde dollar american express erzielte im dritten geschaeftsquartal einen gewinn von 1 2 milliarden dollar 873 millionen euro die nutzer haben in diesem quartal viel mit der kreditkarte gezahlt sagte firmenchef kenneth chenault am mittwochabend ortszeit in new york er sprach von einem rekordniveau zudem kam dem finanzkonzern zugute dass die nutzer ihre ausstehenden rechnungen zuverlaessiger bezahlen fuer ausfaelle musste american express noch 249 millionen dollar zuruecklegen nach 373 millionen dollar vor jahresfrist saeumige schuldner hatten sich waehrend der wirtschaftskrise zu einem riesenproblem fuer american express entwickelt anders als die wettbewerber visa und mastercard muss das unternehmen platzende rechnungen selbst verkraften bei den zwei rivalen liegt das risiko bei den partnern welche die karten ausgeben und kredit gewaehren das sind zumeist banken aber auch fluggesellschaften oder autoverleiher trotz der geschaeftszahlen die ueber den analystenerwartungen lagen fiel die aktie in den usa nachboerslich leicht unter den anlegern herrscht die sorge dass die zahlungsausfaelle mit fortschreiten der euro schuldenkrise wieder ansteigen koennten und sich die konsumenten mit einkaeufen wieder mehr zurueckhalten zuletzt waren die ertraege die einnahmen von american express um neun prozent auf 7 6 milliarden dollar gestiegen auch visa und mastercard hatten zuletzt ueber gute geschaefte berichtet ihre aktuellen zwischenbilanzen stehen noch aus alle drei anbieter stellen ihr geschaeft derzeit auf eine breitere basis american express hat fuer eine halbe milliarde euro das bonusprogramm payback uebernommen bei payback koennen verbraucher in teilnehmenden geschaeften und onlineshops punkte fuer jeden einkauf sammeln und diese dann in praemien eintauschen +Wirtschaft de-DE url-pathpart-print url-pathpart-wams url-pathpart-finanzen url-pathpart-article13675924 url-domainname-www.welt.de url-domainprefix-www url-domainprefix-www.welt url-domaintype-de url-domainsuffix-welt.de url-firstpartpagename-Wie-Frauen-wirklich-ticken url-pagepartsplitname-Wie url-pagepartsplitname-Frauen url-pagepartsplitname-wirklich url-pagepartsplitname-ticken url-lastpartpagename-html wie frauen wirklich ticken auch bei der geldanlage geht es zum betraechtlichen teil um psychologie die journalistin anja kuehner hat an einem finanzseminar fuer frauen teilgenommen und ihre beobachtungen in eine charakteristik weiblicher ratsuchender in sachen geldanlage umgesetzt muessen frauen sich bei der beratung zwischen mehreren produkten entscheiden verlaeuft dieser prozess eher schleifenartig sie sind in der lage neue erfahrungen in ihre ueberlegungen einzubauen und bereits getroffene entscheidungen auch wieder zu revidieren ganz anders die maenner +Deutschland de-DE url-pathpart-politik url-pathpart-deutschland url-pathpart-article2072382 url-domainname-www.abendblatt.de url-domainprefix-www url-domainprefix-www.abendblatt url-domaintype-de url-domainsuffix-abendblatt.de url-firstpartpagename-Klares-Ja-zum-Hebel-Merkel-erreicht-Kanzlermehrheit url-pagepartsplitname-Klares url-pagepartsplitname-Ja url-pagepartsplitname-zum url-pagepartsplitname-Hebel url-pagepartsplitname-Merkel url-pagepartsplitname-erreicht url-pagepartsplitname-Kanzlermehrheit url-lastpartpagename-html klares ja zum hebel merkel erreicht kanzlermehrheit 503 von 596 abgeordneten haben fuer den antrag von union fdp spd und gruenen zur ausweitung des euro rettungsschirms gestimmt berlin der bundestag hat sein einverstaendnis zu einer moeglichen ausweitung des euro rettungsschirms efsf gegeben die mehrheit der abgeordneten stimmte am mittwoch fuer einen gemeinsamen antrag von union fdp spd und gruenen der den hebel mechanismus grundsaetzlich freigibt das parlament gab kanzlerin angela merkel damit ein verhandlungsmandat fuer den eu gipfel am abend in bruessel merkel wertete die ausweitung der efsf kapazitaet in ihrer regierungserklaerung als alternativlos die opposition warf schwarz gelb einen chaotischen kurs in der euro politik vor fuer den fraktionsuebergreifenden antrag stimmten 503 von 596 anwesenden abgeordneten 89 parlamentarierer votierten dagegen vier enthielten sich regierungschefin angela merkel cdu hat sogar die kanzlermehrheit erreicht