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add estimator extensions / catalog; add conversion from external to internal pipeline; transform clean-up; add back in test proj and fix build; refactor trainer ext name mappings (dotnet#15)
* Make validation data param mandatory; remove GetFirstPipeline sample * remove deprecated todo * add estimator extensions / catalog; add ability to go from external to internal pipeline; a lot of transform clean-up; add back in test proj and get it building; refactor trainer ext name mappings
1 parent de7ac15 commit 40e8e58

18 files changed

+492
-511
lines changed

‎AutoML.sln

+24-20
Original file line numberDiff line numberDiff line change
@@ -7,50 +7,54 @@ Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "AutoML", "src\AutoML\AutoML
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EndProject
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Samples", "src\Samples\Samples.csproj", "{64A7294E-A2C7-4499-8F0B-4BB074047C6B}"
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EndProject
10-
#Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "InternalClient", "src\InternalClient\InternalClient.csproj", "{8D564A01-DCA9-443A-9995-A5A67BE4C2CD}"
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#EndProject
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#Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "Test", "src\Test\Test.csproj", "{6DA91D40-302C-495C-B1DA-20701CDA49C6}"
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#EndProject
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Test", "src\Test\Test.csproj", "{55ACB7E2-053D-43BB-88E8-0E102FBD62F0}"
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EndProject
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EndGlobalSection
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GlobalSection(SolutionProperties) = preSolution
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HideSolutionNode = FALSE

‎src/AutoML/API/Pipeline.cs

+14-2
Original file line numberDiff line numberDiff line change
@@ -22,13 +22,25 @@ public class PipelineNode
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public PipelineNode(string name, PipelineNodeType elementType,
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string[] inColumns, string[] outColumns,
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IDictionary<string, object> properties)
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IDictionary<string, object> properties = null)
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{
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Name = name;
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ElementType = elementType;
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InColumns = inColumns;
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OutColumns = outColumns;
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Properties = properties;
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Properties = properties ?? new Dictionary<string, object>();
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}
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public PipelineNode(string name, PipelineNodeType elementType,
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string inColumn, string outColumn, IDictionary<string, object> properties = null) :
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this(name, elementType, new string[] { inColumn }, new string[] { outColumn }, properties)
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{
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}
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public PipelineNode(string name, PipelineNodeType elementType,
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string[] inColumns, string outColumn, IDictionary<string, object> properties = null) :
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this(name, elementType, inColumns, new string[] { outColumn }, properties)
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{
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}
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}
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‎src/AutoML/Assembly.cs

+2-2
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@@ -1,4 +1,4 @@
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using System.Runtime.CompilerServices;
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3-
// [assembly: InternalsVisibleTo("InternalClient")]
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// [assembly: InternalsVisibleTo("Test")]
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//[assembly: InternalsVisibleTo("InternalClient")]
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[assembly: InternalsVisibleTo("Test, PublicKey=00240000048000009400000006020000002400005253413100040000010001004b86c4cb78549b34bab61a3b1800e23bfeb5b3ec390074041536a7e3cbd97f5f04cf0f857155a8928eaa29ebfd11cfbbad3ba70efea7bda3226c6a8d370a4cd303f714486b6ebc225985a638471e6ef571cc92a4613c00b8fa65d61ccee0cbe5f36330c9a01f4183559f1bef24cc2917c6d913e3a541333a1d05d9bed22b38cb")]

‎src/AutoML/AutoFitter/InferredPipeline.cs

+40-13
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@@ -2,11 +2,11 @@
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// The .NET Foundation licenses this file to you under the MIT license.
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// See the LICENSE file in the project root for more information.
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5+
using System;
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using System.Collections.Generic;
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using System.Linq;
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using Microsoft.ML.Core.Data;
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using Microsoft.ML.Data;
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using static Microsoft.ML.Auto.TransformInference.ColumnRoutingStructure;
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namespace Microsoft.ML.Auto
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{
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2323
public InferredPipeline(IEnumerable<SuggestedTransform> transforms,
2424
SuggestedTrainer trainer,
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MLContext context = null)
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MLContext context = null,
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bool autoNormalize = true)
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{
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Transforms = transforms.Select(t => t.Clone()).ToList();
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Trainer = trainer.Clone();
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_context = context ?? new MLContext();
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AddNormalizationTransforms();
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if(autoNormalize)
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{
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AddNormalizationTransforms();
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}
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}
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public override string ToString() => $"{Trainer}+{string.Join("+", Transforms.Select(t => t.ToString()))}";
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var pipelineElements = new List<PipelineNode>();
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foreach(var transform in Transforms)
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{
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pipelineElements.Add(transform.ToPipelineNode());
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pipelineElements.Add(transform.PipelineNode);
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}
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pipelineElements.Add(Trainer.ToPipelineNode());
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return new Pipeline(pipelineElements.ToArray());
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}
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public static InferredPipeline FromPipeline(Pipeline pipeline)
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{
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var context = new MLContext();
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var transforms = new List<SuggestedTransform>();
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SuggestedTrainer trainer = null;
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foreach(var pipelineNode in pipeline.Elements)
74+
{
75+
if(pipelineNode.ElementType == PipelineNodeType.Trainer)
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{
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var trainerName = (TrainerName)Enum.Parse(typeof(TrainerName), pipelineNode.Name);
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var trainerExtension = TrainerExtensionCatalog.GetTrainerExtension(trainerName);
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var stringParamVals = pipelineNode.Properties.Select(prop => new StringParameterValue(prop.Key, prop.Value.ToString()));
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var hyperParamSet = new ParameterSet(stringParamVals);
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trainer = new SuggestedTrainer(context, trainerExtension, hyperParamSet);
82+
}
83+
else if (pipelineNode.ElementType == PipelineNodeType.Transform)
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{
85+
var estimatorName = (EstimatorName)Enum.Parse(typeof(EstimatorName), pipelineNode.Name);
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var estimatorExtension = EstimatorExtensionCatalog.GetExtension(estimatorName);
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var estimator = estimatorExtension.CreateInstance(new MLContext(), pipelineNode);
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var transform = new SuggestedTransform(pipelineNode, estimator);
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transforms.Add(transform);
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}
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}
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93+
return new InferredPipeline(transforms, trainer, null, false);
94+
}
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public ITransformer TrainTransformer(IDataView trainData)
6297
{
6398
IEstimator<ITransformer> pipeline = new EstimatorChain<ITransformer>();
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91126
return;
92127
}
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94-
var estimator = _context.Transforms.Normalize(DefaultColumnNames.Features);
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var annotatedColNames = new[] { new AnnotatedName() { Name = DefaultColumnNames.Features, IsNumeric = true } };
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var routingStructure = new TransformInference.ColumnRoutingStructure(annotatedColNames, annotatedColNames);
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var properties = new Dictionary<string, string>()
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{
99-
{ "mode", "MinMax" }
100-
};
101-
var transform = new SuggestedTransform(estimator,
102-
routingStructure: routingStructure, properties: properties);
129+
var transform = NormalizingExtension.CreateSuggestedTransform(_context, DefaultColumnNames.Features, DefaultColumnNames.Features);
103130
Transforms.Add(transform);
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}
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}

‎src/AutoML/AutoFitter/SuggestedTrainer.cs

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_mlContext = mlContext;
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_trainerExtension = trainerExtension;
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SweepParams = _trainerExtension.GetHyperparamSweepRanges();
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TrainerName = _trainerExtension.GetTrainerName();
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TrainerName = TrainerExtensionCatalog.GetTrainerName(_trainerExtension);
2323
SetHyperparamValues(hyperParamSet);
2424
}
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‎src/AutoML/EstimatorExtensions/EstimatorExtensionCatalog.cs

+41
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1+
using System;
2+
using System.Collections.Generic;
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4+
namespace Microsoft.ML.Auto
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{
6+
public enum EstimatorName
7+
{
8+
ColumnConcatenating,
9+
ColumnCopying,
10+
MissingValueIndicator,
11+
Normalizing,
12+
OneHotEncoding,
13+
OneHotHashEncoding,
14+
TextFeaturizing,
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TypeConverting,
16+
ValueToKeyMapping
17+
}
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19+
internal class EstimatorExtensionCatalog
20+
{
21+
private static readonly IDictionary<EstimatorName, Type> _namesToExtensionTypes = new
22+
Dictionary<EstimatorName, Type>()
23+
{
24+
{ EstimatorName.ColumnConcatenating, typeof(ColumnConcatenatingExtension) },
25+
{ EstimatorName.ColumnCopying, typeof(ColumnCopyingExtension) },
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{ EstimatorName.MissingValueIndicator, typeof(MissingValueIndicatorExtension) },
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{ EstimatorName.Normalizing, typeof(NormalizingExtension) },
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{ EstimatorName.OneHotEncoding, typeof(OneHotEncodingExtension) },
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{ EstimatorName.OneHotHashEncoding, typeof(OneHotHashEncodingExtension) },
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{ EstimatorName.TextFeaturizing, typeof(TextFeaturizingExtension) },
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{ EstimatorName.TypeConverting, typeof(TypeConvertingExtension) },
32+
{ EstimatorName.ValueToKeyMapping, typeof(ValueToKeyMappingExtension) },
33+
};
34+
35+
public static IEstimatorExtension GetExtension(EstimatorName estimatorName)
36+
{
37+
var extType = _namesToExtensionTypes[estimatorName];
38+
return (IEstimatorExtension)Activator.CreateInstance(extType);
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}
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}
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}

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