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dotnet · Mar 3, 2019 · b106fa1 · b106fa1
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diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/PermutationFeatureImportance/PFIHelper.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/PermutationFeatureImportance/PFIHelper.cs
@@ -3,7 +3,6 @@
 using Microsoft.Data.DataView;
 using Microsoft.ML.Trainers;
 using Microsoft.ML.SamplesUtils;
-using Microsoft.ML.Trainers.Mkl;
 
 namespace Microsoft.ML.Samples.Dynamic.PermutationFeatureImportance
 {

diff --git a/...les/Dynamic/Trainers/BinaryClassification/SymbolicStochasticGradientDescentWithOptions.cs b/...les/Dynamic/Trainers/BinaryClassification/SymbolicStochasticGradientDescentWithOptions.cs
@@ -20,7 +20,7 @@ public static void Example()
             var split = mlContext.BinaryClassification.TrainTestSplit(data, testFraction: 0.1);
             // Create data training pipeline
             var pipeline = mlContext.BinaryClassification.Trainers.SymbolicStochasticGradientDescent(
-                    new ML.Trainers.Mkl.SymSgdClassificationTrainer.Options()
+                    new ML.Trainers.SymSgdClassificationTrainer.Options()
                     {
                         LearningRate = 0.2f,
                         NumberOfIterations = 10,

diff --git a/...mples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/OrdinaryLeastSquaresWithOptions.cs b/...mples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/OrdinaryLeastSquaresWithOptions.cs
@@ -1,7 +1,7 @@
 using System;
 using Microsoft.ML.Data;
 using Microsoft.ML.SamplesUtils;
-using Microsoft.ML.Trainers.Mkl;
+using Microsoft.ML.Trainers;
 
 namespace Microsoft.ML.Samples.Dynamic.Trainers.Regression
 {

diff --git a/src/Microsoft.ML.Mkl.Components/ComputeLRTrainingStdThroughHal.cs b/src/Microsoft.ML.Mkl.Components/ComputeLRTrainingStdThroughHal.cs
@@ -5,7 +5,7 @@
 using System;
 using Microsoft.ML.Data;
 using Microsoft.ML.Internal.Utilities;
-using Microsoft.ML.Trainers.Mkl;
+using Microsoft.ML.Trainers;
 
 namespace Microsoft.ML.Trainers
 {

diff --git a/src/Microsoft.ML.Mkl.Components/MklComponentsCatalog.cs b/src/Microsoft.ML.Mkl.Components/MklComponentsCatalog.cs
@@ -3,7 +3,7 @@
 // See the LICENSE file in the project root for more information.
 
 using Microsoft.ML.Data;
-using Microsoft.ML.Trainers.Mkl;
+using Microsoft.ML.Trainers;
 using Microsoft.ML.Transforms;
 
 namespace Microsoft.ML

diff --git a/src/Microsoft.ML.Mkl.Components/OlsLinearRegression.cs b/src/Microsoft.ML.Mkl.Components/OlsLinearRegression.cs
@@ -15,7 +15,7 @@
 using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.Internal.Utilities;
 using Microsoft.ML.Model;
-using Microsoft.ML.Trainers.Mkl;
+using Microsoft.ML.Trainers;
 
 [assembly: LoadableClass(OlsLinearRegressionTrainer.Summary, typeof(OlsLinearRegressionTrainer), typeof(OlsLinearRegressionTrainer.Options),
     new[] { typeof(SignatureRegressorTrainer), typeof(SignatureTrainer), typeof(SignatureFeatureScorerTrainer) },
@@ -29,7 +29,7 @@
 
 [assembly: LoadableClass(typeof(void), typeof(OlsLinearRegressionTrainer), null, typeof(SignatureEntryPointModule), OlsLinearRegressionTrainer.LoadNameValue)]
 
-namespace Microsoft.ML.Trainers.Mkl
+namespace Microsoft.ML.Trainers
 {
     /// <include file='doc.xml' path='doc/members/member[@name="OLS"]/*' />
     public sealed class OlsLinearRegressionTrainer : TrainerEstimatorBase<RegressionPredictionTransformer<OlsLinearRegressionModelParameters>, OlsLinearRegressionModelParameters>

diff --git a/src/Microsoft.ML.Mkl.Components/SymSgdClassificationTrainer.cs b/src/Microsoft.ML.Mkl.Components/SymSgdClassificationTrainer.cs
@@ -16,7 +16,7 @@
 using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.Internal.Utilities;
 using Microsoft.ML.Model;
-using Microsoft.ML.Trainers.Mkl;
+using Microsoft.ML.Trainers;
 using Microsoft.ML.Transforms;
 
 [assembly: LoadableClass(typeof(SymSgdClassificationTrainer), typeof(SymSgdClassificationTrainer.Options),
@@ -27,7 +27,7 @@
 
 [assembly: LoadableClass(typeof(void), typeof(SymSgdClassificationTrainer), null, typeof(SignatureEntryPointModule), SymSgdClassificationTrainer.LoadNameValue)]
 
-namespace Microsoft.ML.Trainers.Mkl
+namespace Microsoft.ML.Trainers
 {
     using TPredictor = CalibratedModelParametersBase<LinearBinaryModelParameters, PlattCalibrator>;
 

diff --git a/test/BaselineOutput/Common/EntryPoints/core_ep-list.tsv b/test/BaselineOutput/Common/EntryPoints/core_ep-list.tsv
@@ -62,14 +62,14 @@ Trainers.LogisticRegressionBinaryClassifier	Logistic Regression is a method in s
 Trainers.LogisticRegressionClassifier	Logistic Regression is a method in statistics used to predict the probability of occurrence of an event and can be used as a classification algorithm. The algorithm predicts the probability of occurrence of an event by fitting data to a logistical function.	Microsoft.ML.Trainers.LogisticRegression	TrainMultiClass	Microsoft.ML.Trainers.MulticlassLogisticRegression+Options	Microsoft.ML.EntryPoints.CommonOutputs+MulticlassClassificationOutput
 Trainers.NaiveBayesClassifier	Train a MultiClassNaiveBayesTrainer.	Microsoft.ML.Trainers.MultiClassNaiveBayesTrainer	TrainMultiClassNaiveBayesTrainer	Microsoft.ML.Trainers.MultiClassNaiveBayesTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+MulticlassClassificationOutput
 Trainers.OnlineGradientDescentRegressor	Train a Online gradient descent perceptron.	Microsoft.ML.Trainers.OnlineGradientDescentTrainer	TrainRegression	Microsoft.ML.Trainers.OnlineGradientDescentTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+RegressionOutput
-Trainers.OrdinaryLeastSquaresRegressor	Train an OLS regression model.	Microsoft.ML.Trainers.Mkl.OlsLinearRegressionTrainer	TrainRegression	Microsoft.ML.Trainers.Mkl.OlsLinearRegressionTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+RegressionOutput
+Trainers.OrdinaryLeastSquaresRegressor	Train an OLS regression model.	Microsoft.ML.Trainers.OlsLinearRegressionTrainer	TrainRegression	Microsoft.ML.Trainers.OlsLinearRegressionTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+RegressionOutput
 Trainers.PcaAnomalyDetector	Train an PCA Anomaly model.	Microsoft.ML.Trainers.RandomizedPcaTrainer	TrainPcaAnomaly	Microsoft.ML.Trainers.RandomizedPcaTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+AnomalyDetectionOutput
 Trainers.PoissonRegressor	Train an Poisson regression model.	Microsoft.ML.Trainers.PoissonRegression	TrainRegression	Microsoft.ML.Trainers.PoissonRegression+Options	Microsoft.ML.EntryPoints.CommonOutputs+RegressionOutput
 Trainers.StochasticDualCoordinateAscentBinaryClassifier	Train an SDCA binary model.	Microsoft.ML.Trainers.Sdca	TrainBinary	Microsoft.ML.Trainers.LegacySdcaBinaryTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+BinaryClassificationOutput
 Trainers.StochasticDualCoordinateAscentClassifier	The SDCA linear multi-class classification trainer.	Microsoft.ML.Trainers.Sdca	TrainMultiClass	Microsoft.ML.Trainers.SdcaMultiClassTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+MulticlassClassificationOutput
 Trainers.StochasticDualCoordinateAscentRegressor	The SDCA linear regression trainer.	Microsoft.ML.Trainers.Sdca	TrainRegression	Microsoft.ML.Trainers.SdcaRegressionTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+RegressionOutput
 Trainers.StochasticGradientDescentBinaryClassifier	Train an Hogwild SGD binary model.	Microsoft.ML.Trainers.LegacySgdBinaryTrainer	TrainBinary	Microsoft.ML.Trainers.LegacySgdBinaryTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+BinaryClassificationOutput
-Trainers.SymSgdBinaryClassifier	Train a symbolic SGD.	Microsoft.ML.Trainers.Mkl.SymSgdClassificationTrainer	TrainSymSgd	Microsoft.ML.Trainers.Mkl.SymSgdClassificationTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+BinaryClassificationOutput
+Trainers.SymSgdBinaryClassifier	Train a symbolic SGD.	Microsoft.ML.Trainers.SymSgdClassificationTrainer	TrainSymSgd	Microsoft.ML.Trainers.SymSgdClassificationTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+BinaryClassificationOutput
 Transforms.ApproximateBootstrapSampler	Approximate bootstrap sampling.	Microsoft.ML.Transforms.BootstrapSample	GetSample	Microsoft.ML.Transforms.BootstrapSamplingTransformer+Options	Microsoft.ML.EntryPoints.CommonOutputs+TransformOutput
 Transforms.BinaryPredictionScoreColumnsRenamer	For binary prediction, it renames the PredictedLabel and Score columns to include the name of the positive class.	Microsoft.ML.EntryPoints.ScoreModel	RenameBinaryPredictionScoreColumns	Microsoft.ML.EntryPoints.ScoreModel+RenameBinaryPredictionScoreColumnsInput	Microsoft.ML.EntryPoints.CommonOutputs+TransformOutput
 Transforms.BinNormalizer	The values are assigned into equidensity bins and a value is mapped to its bin_number/number_of_bins.	Microsoft.ML.Data.Normalize	Bin	Microsoft.ML.Transforms.NormalizeTransform+BinArguments	Microsoft.ML.EntryPoints.CommonOutputs+TransformOutput

diff --git a/test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs b/test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs
@@ -22,7 +22,6 @@
 using Microsoft.ML.Trainers;
 using Microsoft.ML.Trainers.Ensemble;
 using Microsoft.ML.Trainers.FastTree;
-using Microsoft.ML.Trainers.Mkl;
 using Microsoft.ML.Transforms;
 using Microsoft.ML.Transforms.Text;
 using Microsoft.ML.Transforms.TimeSeries;

diff --git a/test/Microsoft.ML.Functional.Tests/Validation.cs b/test/Microsoft.ML.Functional.Tests/Validation.cs
@@ -7,7 +7,7 @@
 using Microsoft.ML.RunTests;
 using Microsoft.ML.TestFramework;
 using Microsoft.ML.Trainers.FastTree;
-using Microsoft.ML.Trainers.Mkl;
+using Microsoft.ML.Trainers;
 using Xunit;
 
 namespace Microsoft.ML.Functional.Tests

diff --git a/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs b/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs
@@ -20,7 +20,6 @@ namespace Microsoft.ML.RunTests
     using Microsoft.ML.TestFramework;
     using Microsoft.ML.Trainers;
     using Microsoft.ML.Trainers.FastTree;
-    using Microsoft.ML.Trainers.Mkl;
     using Xunit;
     using Xunit.Abstractions;
     using TestLearners = TestLearnersBase;

diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/SimpleTrainAndPredict.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/SimpleTrainAndPredict.cs
@@ -6,7 +6,6 @@
 using Microsoft.ML.Data;
 using Microsoft.ML.RunTests;
 using Microsoft.ML.Trainers;
-using Microsoft.ML.Trainers.Mkl;
 using Xunit;
 
 namespace Microsoft.ML.Tests.Scenarios.Api

diff --git a/test/Microsoft.ML.Tests/TrainerEstimators/OlsLinearRegressionTests.cs b/test/Microsoft.ML.Tests/TrainerEstimators/OlsLinearRegressionTests.cs
@@ -3,7 +3,7 @@
 // See the LICENSE file in the project root for more information.
 
 using System.Collections.Generic;
-using Microsoft.ML.Trainers.Mkl;
+using Microsoft.ML.Trainers;
 using Xunit;
 
 namespace Microsoft.ML.Tests.TrainerEstimators

diff --git a/test/Microsoft.ML.Tests/TrainerEstimators/SymSgdClassificationTests.cs b/test/Microsoft.ML.Tests/TrainerEstimators/SymSgdClassificationTests.cs
@@ -4,7 +4,7 @@
 
 using System.Linq;
 using Microsoft.ML.Data;
-using Microsoft.ML.Trainers.Mkl;
+using Microsoft.ML.Trainers;
 using Xunit;
 
 namespace Microsoft.ML.Tests.TrainerEstimators