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[AutoML] Add AutoML XML documentation to all public members; migrate AutoML projects & tests into ML.NET solution; AutoML test fixes (#3351)
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‎Microsoft.ML.AutoML.sln‎

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‎Microsoft.ML.sln‎

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@@ -274,6 +274,14 @@ Project("{2150E333-8FDC-42A3-9474-1A3956D46DE8}") = "Microsoft.ML.FastTree", "Mi
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pkg\Microsoft.ML.FastTree\Microsoft.ML.FastTree.symbols.nupkgproj = pkg\Microsoft.ML.FastTree\Microsoft.ML.FastTree.symbols.nupkgproj
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.Auto", "src\Microsoft.ML.Auto\Microsoft.ML.Auto.csproj", "{D57A04E8-1A2B-4A3E-A96A-229C746C2FBE}"
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EndProject
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Microsoft.ML.AutoML.Tests", "test\Microsoft.ML.AutoML.Tests\Microsoft.ML.AutoML.Tests.csproj", "{D48126A1-5334-4575-BC91-4CDAA754C8C8}"
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EndProject
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "mlnet", "src\mlnet\mlnet.csproj", "{C2F953F9-9825-48AB-88D8-D4538268F017}"
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EndProject
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "mlnet.Tests", "test\mlnet.Tests\mlnet.Tests.csproj", "{73D4685B-94D2-4C28-A434-16ED6CA39BDE}"
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EndGlobalSection
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GlobalSection(SolutionProperties) = preSolution
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HideSolutionNode = FALSE
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{73D4685B-94D2-4C28-A434-16ED6CA39BDE} = {AED9C836-31E3-4F3F-8ABC-929555D3F3C4}
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GlobalSection(ExtensibilityGlobals) = postSolution
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SolutionGuid = {41165AF1-35BB-4832-A189-73060F82B01D}

‎build.proj‎

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<ItemGroup>
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<Project Include="Microsoft.ML.sln" />
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<Project Include="Microsoft.ML.AutoML.sln" />
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</ItemGroup>
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<Import Project="dir.traversal.targets" />

‎docs/samples/Microsoft.ML.Samples/AutoML/BinaryClassificationExperiment.cs‎

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using Microsoft.ML;
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using Microsoft.ML.Auto;
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namespace Samples.AutoML
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{
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public static class BinaryClassificationExperiment
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{
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public static void Example()
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{
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// Create a new context for ML.NET operations. It can be used for exception tracking and logging,
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// as a catalog of available operations and as the source of randomness.
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var mlContext = new MLContext();
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// Download and featurize the dataset.
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var dataView = Microsoft.ML.SamplesUtils.DatasetUtils.LoadFeaturizedAdultDataset(mlContext);
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// Run an AutoML experiment
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var experimentResult = mlContext.Auto()
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.CreateBinaryClassificationExperiment(60)
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.Execute(dataView, "IsOver50K");
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}
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}
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}

‎docs/samples/Microsoft.ML.Samples/AutoML/BinaryClassificationExperimentWithExperimentSettings.cs‎

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using Microsoft.ML;
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using Microsoft.ML.Auto;
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namespace Samples.AutoML
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{
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public static class BinaryClassificationExperimentWithExperimentSettings
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{
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public static void Example()
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{
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// Create a new context for ML.NET operations. It can be used for exception tracking and logging,
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// as a catalog of available operations and as the source of randomness.
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var mlContext = new MLContext();
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// Download and featurize the dataset.
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var dataView = Microsoft.ML.SamplesUtils.DatasetUtils.LoadFeaturizedAdultDataset(mlContext);
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// Run an AutoML experiment
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var experimentSettings = new BinaryExperimentSettings()
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{
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MaxExperimentTimeInSeconds = 60
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};
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var experimentResult = mlContext.Auto()
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.CreateBinaryClassificationExperiment(experimentSettings)
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.Execute(dataView, "IsOver50K");
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}
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}
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}

‎docs/samples/Microsoft.ML.Samples/AutoML/MulticlassClassificationExperiment.cs‎

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using Microsoft.ML;
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using Microsoft.ML.Auto;
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using Microsoft.ML.SamplesUtils;
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namespace Samples.AutoML
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{
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public static class MulticlassClassificationExperiment
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{
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public static void Example()
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{
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// Create a new context for ML.NET operations. It can be used for exception tracking and logging,
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// as a catalog of available operations and as the source of randomness.
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var mlContext = new MLContext();
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// Download and featurize the dataset.
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// Create a list of data examples.
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var examples = DatasetUtils.GenerateRandomMulticlassClassificationExamples(1000);
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// Convert the examples list to an IDataView object, which is consumable by ML.NET API.
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var dataView = mlContext.Data.LoadFromEnumerable(examples);
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// Run an AutoML experiment
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var experimentResult = mlContext.Auto()
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.CreateMulticlassClassificationExperiment(60)
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.Execute(dataView);
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}
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}
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}

‎docs/samples/Microsoft.ML.Samples/AutoML/MulticlassClassificationExperimentWithExperimentSettings.cs‎

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using Microsoft.ML;
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using Microsoft.ML.Auto;
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using Microsoft.ML.SamplesUtils;
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namespace Samples.AutoML
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{
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public static class MulticlassClassificationExperimentWithExperimentSettings
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{
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public static void Example()
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{
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// Create a new context for ML.NET operations. It can be used for exception tracking and logging,
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// as a catalog of available operations and as the source of randomness.
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var mlContext = new MLContext();
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// Download and featurize the dataset.
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// Create a list of data examples.
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var examples = DatasetUtils.GenerateRandomMulticlassClassificationExamples(1000);
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// Convert the examples list to an IDataView object, which is consumable by ML.NET API.
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var dataView = mlContext.Data.LoadFromEnumerable(examples);
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// Run an AutoML experiment
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var experimentSettings = new MulticlassExperimentSettings()
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{
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MaxExperimentTimeInSeconds = 60
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};
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var experimentResult = mlContext.Auto()
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.CreateMulticlassClassificationExperiment(experimentSettings)
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.Execute(dataView);
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}
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}
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}

‎docs/samples/Microsoft.ML.Samples/AutoML/RegressionExperiment.cs‎

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using Microsoft.ML;
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using Microsoft.ML.Auto;
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using Microsoft.ML.Data;
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namespace Samples.AutoML
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{
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public static class RegressionExperiment
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{
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public static void Example()
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{
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// Downloading a regression dataset from github.com/dotnet/machinelearning
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string dataFile = Microsoft.ML.SamplesUtils.DatasetUtils.DownloadHousingRegressionDataset();
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// Create a new context for ML.NET operations. It can be used for exception tracking and logging,
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// as a catalog of available operations and as the source of randomness.
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var mlContext = new MLContext();
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// Creating a data loader, based on the format of the data
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// The data is tab separated with all numeric columns.
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// The first column being the label and rest are numeric features
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// Here only seven numeric columns are used as features
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var dataView = mlContext.Data.LoadFromTextFile(dataFile, new TextLoader.Options
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{
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Separators = new[] { '\t' },
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HasHeader = true,
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Columns = new[]
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{
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new TextLoader.Column("Label", DataKind.Single, 0),
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new TextLoader.Column("Features", DataKind.Single, 1, 6)
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}
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});
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// Run an AutoML experiment
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var experimentResult = mlContext.Auto()
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.CreateRegressionExperiment(60)
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.Execute(dataView);
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}
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}
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}

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