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4ab6eee
Adding the Samples.StaticPipe project.
sfilipi 6d271ea
Adding a sample for SDCA Regression
sfilipi 2236dbe
Splitting the dataset creation to another file,
sfilipi b5f9592
Merge branch 'master' into docsSamples
sfilipi c758415
Renaming the file
sfilipi af43f81
addressing most of eerhardt's comments
sfilipi 1e6b160
Eric's comments: referencing the example only from one class, since i…
sfilipi 24917ae
merge from master + renaming the folder.
sfilipi 512af14
removing the dead project. Adding back timeseries test.
sfilipi 92343cc
switching to the LocalEnvironemnt per Eric's suggestion.
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -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. | ||
|
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| using System; | ||
| using System.IO; | ||
| using System.Text; | ||
|
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| namespace Microsoft.ML.Samples.StaticPipe | ||
| { | ||
| public static class DatasetCreator | ||
| { | ||
| public static (string trainPath, string testPath) CreateRegressionDataset() | ||
| { | ||
| // creating a small sample dataset, and writting it to file | ||
| string trainDataPath = @"RegressionTrainDataset.txt"; | ||
| string testDataPath = @"RegressionTestDataset.txt"; | ||
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| string header = "feature_a, feature_b, target"; | ||
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| int a = 0; | ||
| int b = 0; | ||
| int target = 0; | ||
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| var csvTrain = new StringBuilder().AppendLine(header); | ||
| var csvTest = new StringBuilder().AppendLine(header); | ||
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| Random rnd = new Random(); | ||
| for (int i = 0; i < 1000; i++) | ||
| { | ||
| a = rnd.Next(i - 5, i + 5); | ||
| b = rnd.Next(0, 10); | ||
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| target = 2*a + b; | ||
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| if (i % 15 == 0) | ||
| csvTest.AppendLine($"{a}, {b}, {target}"); | ||
| else | ||
| csvTrain.AppendLine($"{a}, {b} , {target}"); | ||
| } | ||
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| if (!File.Exists(trainDataPath)) | ||
| File.WriteAllText(trainDataPath, csvTrain.ToString()); | ||
| else | ||
| { | ||
| new Exception("Train dataset file already exists"); | ||
| } | ||
|
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| if (!File.Exists(testDataPath)) | ||
| File.WriteAllText(testDataPath, csvTest.ToString()); | ||
| else | ||
| { | ||
| new Exception("Test dataset file already exists"); | ||
| } | ||
|
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| return (trainDataPath, testDataPath); | ||
| } | ||
| } | ||
| } |
20 changes: 20 additions & 0 deletions
20
docs/samples/Microsoft.ML.Samples/Microsoft.ML.Samples.csproj
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,20 @@ | ||
| <Project Sdk="Microsoft.NET.Sdk"> | ||
|
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| <PropertyGroup> | ||
| <TargetFramework>netcoreapp2.1</TargetFramework> | ||
| <OutputType>Exe</OutputType> | ||
| </PropertyGroup> | ||
|
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| <ItemGroup> | ||
| <ProjectReference Include="..\..\..\src\Microsoft.ML.StandardLearners\Microsoft.ML.StandardLearners.csproj" /> | ||
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| <NativeAssemblyReference Include="CpuMathNative" /> | ||
|
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| <ProjectReference Include="..\..\..\src\Microsoft.ML.Analyzer\Microsoft.ML.Analyzer.csproj"> | ||
| <ReferenceOutputAssembly>false</ReferenceOutputAssembly> | ||
| <OutputItemType>Analyzer</OutputItemType> | ||
| </ProjectReference> | ||
|
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| </ItemGroup> | ||
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| </Project> |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,16 @@ | ||
| // 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. | ||
|
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| using Microsoft.ML.Samples.StaticPipe; | ||
|
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| namespace Microsoft.ML.Samples | ||
| { | ||
| internal static class Program | ||
| { | ||
| static void Main(string[] args) | ||
| { | ||
| Trainers.SdcaRegression(); | ||
| } | ||
| } | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -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. | ||
|
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| using Microsoft.ML.Runtime.Data; | ||
| using Microsoft.ML.Runtime.Learners; | ||
| using Microsoft.ML.StaticPipe; | ||
| using System; | ||
|
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| namespace Microsoft.ML.Samples.StaticPipe | ||
| { | ||
| public static class Trainers | ||
| { | ||
| public static void SdcaRegression() | ||
| { | ||
| var (trainDataPath, testDataPath) = DatasetCreator.CreateRegressionDataset(); | ||
|
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| //creating the ML.Net IHostEnvironment object, needed for the pipeline | ||
| var env = new ConsoleEnvironment(seed: 0); | ||
|
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| // creating the ML context, based on the task performed. | ||
| var regressionContext = new RegressionContext(env); | ||
|
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| // Creating a data reader, based on the format of the data | ||
| var reader = TextLoader.CreateReader(env, c => ( | ||
| label: c.LoadFloat(2), | ||
| features: c.LoadFloat(0, 1) | ||
| ), | ||
| separator: ',', hasHeader: true); | ||
|
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| // Read the data | ||
| var trainData = reader.Read(new MultiFileSource(trainDataPath)); | ||
|
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| // The predictor that gets produced out of training | ||
| LinearRegressionPredictor pred = null; | ||
|
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| // Create the estimator | ||
| var learningPipeline = reader.MakeNewEstimator() | ||
| .Append(r => (r.label, score: regressionContext.Trainers.Sdca( | ||
| r.label, | ||
| r.features, | ||
| l1Threshold: 0f, | ||
| maxIterations: 100, | ||
| onFit: p => pred = p) | ||
| ) | ||
| ); | ||
|
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| // fit this pipeline to the training data | ||
| var model = learningPipeline.Fit(trainData); | ||
|
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| // check the weights that the model learned | ||
| VBuffer<float> weights = default; | ||
| pred.GetFeatureWeights(ref weights); | ||
|
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| Console.WriteLine($"weight 0 - {weights.Values[0]}"); | ||
| Console.WriteLine($"weight 1 - {weights.Values[1]}"); | ||
|
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| // test the model we just trained, using the test file. | ||
| var testData = reader.Read(new MultiFileSource(testDataPath)); | ||
| var data = model.Transform(testData); | ||
|
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| //Evaluate how the model is doing on the test data | ||
| var metrics = regressionContext.Evaluate(data, r => r.label, r => r.score); | ||
|
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| Console.WriteLine($"L1 - {metrics.L1}"); | ||
| Console.WriteLine($"L2 - {metrics.L2}"); | ||
| Console.WriteLine($"LossFunction - {metrics.LossFn}"); | ||
| Console.WriteLine($"RMS - {metrics.Rms}"); | ||
| Console.WriteLine($"RSquared - {metrics.RSquared}"); | ||
| } | ||
| } | ||
| } |
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fix #Resolved