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Author: codemzs

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
 {

docs/samples/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
 {

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
 {

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

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(OrdinaryLeastSquaresRegressionTrainer.Summary, typeof(OrdinaryLeastSquaresRegressionTrainer), typeof(OrdinaryLeastSquaresRegressionTrainer.Options),
     new[] { typeof(SignatureRegressorTrainer), typeof(SignatureTrainer), typeof(SignatureFeatureScorerTrainer) },
@@ -29,7 +29,7 @@
 
 [assembly: LoadableClass(typeof(void), typeof(OrdinaryLeastSquaresRegressionTrainer), null, typeof(SignatureEntryPointModule), OrdinaryLeastSquaresRegressionTrainer.LoadNameValue)]
 
-namespace Microsoft.ML.Trainers.Mkl
+namespace Microsoft.ML.Trainers
 {
     /// <include file='doc.xml' path='doc/members/member[@name="OLS"]/*' />
     public sealed class OrdinaryLeastSquaresRegressionTrainer : TrainerEstimatorBase<RegressionPredictionTransformer<OlsLinearRegressionModelParameters>, OlsLinearRegressionModelParameters>

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(SymbolicStochasticGradientDescentClassificationTrainer), typeof(SymbolicStochasticGradientDescentClassificationTrainer.Options),
@@ -27,7 +27,7 @@
 
 [assembly: LoadableClass(typeof(void), typeof(SymbolicStochasticGradientDescentClassificationTrainer), null, typeof(SignatureEntryPointModule), SymbolicStochasticGradientDescentClassificationTrainer.LoadNameValue)]
 
-namespace Microsoft.ML.Trainers.Mkl
+namespace Microsoft.ML.Trainers
 {
     using TPredictor = CalibratedModelParametersBase<LinearBinaryModelParameters, PlattCalibrator>;
 

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

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;

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

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;

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

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

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