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fixes dotnet#40 
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-48
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5 files changed

+62
-48
lines changed

‎src/Microsoft.ML.OneDal/OneDalCatalog.cs‎

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// Licensed to the .NET Foundation under one or more agreements.
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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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using Microsoft.ML.Data;
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using Microsoft.ML.Runtime;
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using Microsoft.ML.Trainers;

‎src/Microsoft.ML.OneDal/Properties/AssemblyInfo.cs‎

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// Licensed to the .NET Foundation under one or more agreements.
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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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using System.Runtime.CompilerServices;
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using Microsoft.ML;
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‎test/Microsoft.ML.AutoML.Tests/UserInputValidationTests.cs‎

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@@ -407,7 +407,7 @@ public void ValidateTrainDataColumnTestMultipleMismatchLessThan5()
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ex.Message);
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}
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}
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[Fact]
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public void ValidateTrainDataColumnTestMultipleMismatchMoreThan5()
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{
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ex.Message);
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}
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}
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}
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}

‎test/Microsoft.ML.TestFramework/GlobalBase.cs‎

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@@ -76,7 +76,9 @@ private static void AssertHandler(string msg, IExceptionContext ectx)
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}
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else
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#endif
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{
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Assert.True(false, $"Assert failed: {msg}");
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}
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}
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public static void AssertHandlerTest()

‎test/Microsoft.ML.Tests/TrainerEstimators/OneDALComponentsTests.cs‎

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// Licensed to the .NET Foundation under one or more agreements.
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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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using System.Collections.Generic;
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using System.IO;
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using Microsoft.ML;
@@ -10,52 +14,52 @@ namespace Microsoft.ML.Tests.TrainerEstimators
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{
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public partial class TrainerEstimators
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{
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internal class DataPoint1
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{
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public float Label { get; set; }
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[VectorType(1)]
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public float[] Features { get; set; }
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}
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internal class ScorePoint
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{
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public float Score { get; set; }
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}
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// [NativeDependencyFact("OneDalImports")]
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[Fact]
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public void TestEstimatorOneDalLinReg()
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{
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List<DataPoint1> literalData = new List<DataPoint1>
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{
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new DataPoint1 { Features = new float[]{1}, Label= 39000 },
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new DataPoint1 { Features = new float[]{1.3F}, Label= 46200 },
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new DataPoint1 { Features = new float[]{1.5F}, Label= 37700 },
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new DataPoint1 { Features = new float[]{2}, Label= 43500 },
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new DataPoint1 { Features = new float[]{2.2F}, Label= 40000 },
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new DataPoint1 { Features = new float[]{2.9F}, Label= 56000 }
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};
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var dataView = ML.Data.LoadFromEnumerable(literalData);
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// WL Merge Note: The LinReg is removed. Comment out this test for now.
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//var trainer = ML.Regression.Trainers.LinReg(labelColumnName: "Label", featureColumnName: "Features");
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// // TestEstimatorCore(trainer, dataView);
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//var model = trainer.Fit(dataView);
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//var modelParameters = ((ISingleFeaturePredictionTransformer<object>)model).Model as LinearRegressionModelParameters;
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// // Assert.True(model.Model.HasStatistics);
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// // Assert.NotEmpty(model.Model.StandardErrors);
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// // Assert.NotEmpty(model.Model.PValues);
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// // Assert.NotEmpty(model.Model.TValues);
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//var transferredModel = ML.Regression.Trainers.OnlineGradientDescent(numberOfIterations: 1).Fit(dataView, modelParameters);
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//var predictionEngine = ML.Model.CreatePredictionEngine<DataPoint1, ScorePoint>(transferredModel);
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//var result = predictionEngine.Predict(new DataPoint1 { Features = new float[]{1.3F} });
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// //Assert.True(File.Exists("libOneDalNative.so"));
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//Assert.False(trainer.Info.WantCaching);
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Done();
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internal class DataPoint1
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{
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public float Label { get; set; }
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[VectorType(1)]
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public float[] Features { get; set; }
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}
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internal class ScorePoint
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{
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public float Score { get; set; }
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}
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// [NativeDependencyFact("OneDalImports")]
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[Fact]
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public void TestEstimatorOneDalLinReg()
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{
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List<DataPoint1> literalData = new List<DataPoint1>
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{
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new DataPoint1 { Features = new float[]{1}, Label= 39000 },
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new DataPoint1 { Features = new float[]{1.3F}, Label= 46200 },
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new DataPoint1 { Features = new float[]{1.5F}, Label= 37700 },
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new DataPoint1 { Features = new float[]{2}, Label= 43500 },
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new DataPoint1 { Features = new float[]{2.2F}, Label= 40000 },
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new DataPoint1 { Features = new float[]{2.9F}, Label= 56000 }
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};
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var dataView = ML.Data.LoadFromEnumerable(literalData);
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// WL Merge Note: The LinReg is removed. Comment out this test for now.
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//var trainer = ML.Regression.Trainers.LinReg(labelColumnName: "Label", featureColumnName: "Features");
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// // TestEstimatorCore(trainer, dataView);
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//var model = trainer.Fit(dataView);
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//var modelParameters = ((ISingleFeaturePredictionTransformer<object>)model).Model as LinearRegressionModelParameters;
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// // Assert.True(model.Model.HasStatistics);
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// // Assert.NotEmpty(model.Model.StandardErrors);
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// // Assert.NotEmpty(model.Model.PValues);
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// // Assert.NotEmpty(model.Model.TValues);
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//var transferredModel = ML.Regression.Trainers.OnlineGradientDescent(numberOfIterations: 1).Fit(dataView, modelParameters);
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//var predictionEngine = ML.Model.CreatePredictionEngine<DataPoint1, ScorePoint>(transferredModel);
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//var result = predictionEngine.Predict(new DataPoint1 { Features = new float[]{1.3F} });
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// //Assert.True(File.Exists("libOneDalNative.so"));
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//Assert.False(trainer.Info.WantCaching);
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Done();
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}
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}
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}

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