Microsoft.ML.Internal.Internallearn should be hidden/moved/renamed

Author: codemzs

src/Microsoft.ML.Data/Dirty/ModelParametersBase.cs

@@ -3,9 +3,8 @@
 // See the LICENSE file in the project root for more information.
 
 using System;
-using Microsoft.ML.Model;
 
-namespace Microsoft.ML.Internal.Internallearn
+namespace Microsoft.ML.Model
 {
     /// <summary>
     /// A base class for predictors producing <typeparamref name="TOutput"/>.

src/Microsoft.ML.Data/Dirty/PredictorInterfaces.cs

@@ -9,7 +9,7 @@
 using Microsoft.ML.Calibrator;
 using Microsoft.ML.Data;
 
-namespace Microsoft.ML.Internal.Internallearn
+namespace Microsoft.ML.Model
 {
     /// <summary>
     /// A generic interface for models that can average parameters from multiple instance of self

src/Microsoft.ML.Data/EntryPoints/SummarizePredictor.cs

@@ -5,12 +5,11 @@
 using System.IO;
 using System.Text;
 using Microsoft.Data.DataView;
-using Microsoft.ML.Calibrator;
 using Microsoft.ML.CommandLine;
 using Microsoft.ML.Data;
 using Microsoft.ML.EntryPoints;
 using Microsoft.ML.Internal.Calibration;
-using Microsoft.ML.Internal.Internallearn;
+using Microsoft.ML.Model;
 
 [assembly: EntryPointModule(typeof(SummarizePredictor))]
 

src/Microsoft.ML.Data/Scorers/QuantileRegressionScorer.cs

@@ -8,7 +8,7 @@
 using Microsoft.ML;
 using Microsoft.ML.CommandLine;
 using Microsoft.ML.Data;
-using Microsoft.ML.Internal.Internallearn;
+using Microsoft.ML.Model;
 
 [assembly: LoadableClass(typeof(IDataScorerTransform), typeof(QuantileRegressionScorerTransform), typeof(QuantileRegressionScorerTransform.Arguments),
     typeof(SignatureDataScorer), "Quantile Regression Scorer", "QuantileRegressionScorer", MetadataUtils.Const.ScoreColumnKind.QuantileRegression)]

src/Microsoft.ML.Data/Training/EarlyStoppingCriteria.cs

@@ -6,7 +6,7 @@
 using Microsoft.ML;
 using Microsoft.ML.CommandLine;
 using Microsoft.ML.EntryPoints;
-using Microsoft.ML.Internal.Internallearn;
+using Microsoft.ML.Trainers.FastTree;
 using Float = System.Single;
 
 [assembly: LoadableClass(typeof(TolerantEarlyStoppingCriterion), typeof(TolerantEarlyStoppingCriterion.Options), typeof(SignatureEarlyStoppingCriterion), "Tolerant (TR)", "tr")]
@@ -21,7 +21,7 @@
 [assembly: EntryPointModule(typeof(PQEarlyStoppingCriterion))]
 [assembly: EntryPointModule(typeof(UPEarlyStoppingCriterion))]
 
-namespace Microsoft.ML.Internal.Internallearn
+namespace Microsoft.ML.Trainers.FastTree
 {
     internal delegate void SignatureEarlyStoppingCriterion(bool lowerIsBetter);
 

src/Microsoft.ML.Data/Transforms/ExplainabilityCatalog.cs

@@ -3,7 +3,7 @@
 // See the LICENSE file in the project root for more information.
 
 using Microsoft.ML.Data;
-using Microsoft.ML.Internal.Internallearn;
+using Microsoft.ML.Model;
 
 namespace Microsoft.ML
 {

src/Microsoft.ML.HalLearners/SymSgdClassificationTrainer.cs

@@ -15,6 +15,7 @@
 using Microsoft.ML.Internal.Calibration;
 using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.Internal.Utilities;
+using Microsoft.ML.Model;
 using Microsoft.ML.Trainers.HalLearners;
 using Microsoft.ML.Transforms;
 

src/Microsoft.ML.StandardLearners/Standard/LogisticRegression/LogisticRegression.cs

@@ -10,8 +10,8 @@
 using Microsoft.ML.Data;
 using Microsoft.ML.EntryPoints;
 using Microsoft.ML.Internal.Calibration;
-using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.Internal.Utilities;
+using Microsoft.ML.Model;
 using Microsoft.ML.Numeric;
 using Microsoft.ML.Trainers;
 

src/Microsoft.ML.StandardLearners/Standard/Online/AveragedPerceptron.cs

@@ -9,7 +9,7 @@
 using Microsoft.ML.Data;
 using Microsoft.ML.EntryPoints;
 using Microsoft.ML.Internal.Calibration;
-using Microsoft.ML.Internal.Internallearn;
+using Microsoft.ML.Model;
 using Microsoft.ML.Numeric;
 using Microsoft.ML.Trainers.Online;
 

src/Microsoft.ML.StandardLearners/Standard/Online/LinearSvm.cs

@@ -11,6 +11,7 @@
 using Microsoft.ML.Internal.Calibration;
 using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.Internal.Utilities;
+using Microsoft.ML.Model;
 using Microsoft.ML.Numeric;
 using Microsoft.ML.Trainers.Online;
 

src/Microsoft.ML.StandardLearners/Standard/Online/OnlineGradientDescent.cs

@@ -8,6 +8,7 @@
 using Microsoft.ML.Data;
 using Microsoft.ML.EntryPoints;
 using Microsoft.ML.Internal.Internallearn;
+using Microsoft.ML.Model;
 using Microsoft.ML.Numeric;
 using Microsoft.ML.Trainers.Online;
 

src/Microsoft.ML.StandardLearners/Standard/PoissonRegression/PoissonRegression.cs

@@ -7,8 +7,8 @@
 using Microsoft.ML;
 using Microsoft.ML.Data;
 using Microsoft.ML.EntryPoints;
-using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.Internal.Utilities;
+using Microsoft.ML.Model;
 using Microsoft.ML.Numeric;
 using Microsoft.ML.Trainers;
 

src/Microsoft.ML.StandardLearners/Standard/SdcaBinary.cs

@@ -17,6 +17,7 @@
 using Microsoft.ML.Internal.CpuMath;
 using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.Internal.Utilities;
+using Microsoft.ML.Model;
 using Microsoft.ML.Numeric;
 using Microsoft.ML.Trainers;
 using Microsoft.ML.Transforms;

src/Microsoft.ML.StandardLearners/Standard/SdcaMultiClass.cs

@@ -11,8 +11,8 @@
 using Microsoft.ML.Data;
 using Microsoft.ML.EntryPoints;
 using Microsoft.ML.Internal.CpuMath;
-using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.Internal.Utilities;
+using Microsoft.ML.Model;
 using Microsoft.ML.Numeric;
 using Microsoft.ML.Trainers;
 using Float = System.Single;

src/Microsoft.ML.StandardLearners/Standard/SdcaRegression.cs

@@ -9,8 +9,8 @@
 using Microsoft.ML.Data;
 using Microsoft.ML.Data.Conversion;
 using Microsoft.ML.EntryPoints;
-using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.Internal.Utilities;
+using Microsoft.ML.Model;
 using Microsoft.ML.Trainers;
 
 [assembly: LoadableClass(SdcaRegressionTrainer.Summary, typeof(SdcaRegressionTrainer), typeof(SdcaRegressionTrainer.Options),

src/Microsoft.ML.Transforms/LearnerFeatureSelection.cs

@@ -8,8 +8,8 @@
 using Microsoft.ML;
 using Microsoft.ML.CommandLine;
 using Microsoft.ML.Data;
-using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.Internal.Utilities;
+using Microsoft.ML.Model;
 using Microsoft.ML.Transforms;
 using Microsoft.ML.Transforms.FeatureSelection;
 

src/Microsoft.ML.Transforms/PermutationFeatureImportance.cs

@@ -11,6 +11,7 @@
 using Microsoft.ML.Data;
 using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.Internal.Utilities;
+using Microsoft.ML.Model;
 
 namespace Microsoft.ML.Transforms
 {

test/Microsoft.ML.Core.Tests/UnitTests/TestEarlyStoppingCriteria.cs

@@ -2,8 +2,8 @@
 // The .NET Foundation licenses this file to you under the MIT license.
 // See the LICENSE file in the project root for more information.
 
-using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.TestFramework;
+using Microsoft.ML.Trainers.FastTree;
 using Xunit;
 
 namespace Microsoft.ML.RunTests

test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs

@@ -18,6 +18,7 @@
 using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.Internal.Utilities;
 using Microsoft.ML.LightGBM;
+using Microsoft.ML.Model;
 using Microsoft.ML.Model.OnnxConverter;
 using Microsoft.ML.TestFramework.Attributes;
 using Microsoft.ML.Trainers;

test/Microsoft.ML.Functional.Tests/Validation.cs

@@ -4,9 +4,9 @@
 
 using Microsoft.Data.DataView;
 using Microsoft.ML.Data;
-using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.RunTests;
 using Microsoft.ML.TestFramework;
+using Microsoft.ML.Trainers.FastTree;
 using Microsoft.ML.Trainers.HalLearners;
 using Xunit;
 

test/Microsoft.ML.Predictor.Tests/TestIniModels.cs

@@ -9,6 +9,7 @@
 using Microsoft.ML.Internal.Internallearn;
 using Xunit;
 using Xunit.Abstractions;
+using Microsoft.ML.Model;
 
 namespace Microsoft.ML.RunTests
 {

test/Microsoft.ML.StaticPipelineTesting/Training.cs

@@ -12,6 +12,7 @@
 using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.LightGBM;
 using Microsoft.ML.LightGBM.StaticPipe;
+using Microsoft.ML.Model;
 using Microsoft.ML.RunTests;
 using Microsoft.ML.StaticPipe;
 using Microsoft.ML.TestFramework.Attributes;

test/Microsoft.ML.Tests/FeatureContributionTests.cs

@@ -9,6 +9,7 @@
 using Microsoft.ML.Data.IO;
 using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.Internal.Utilities;
+using Microsoft.ML.Model;
 using Microsoft.ML.RunTests;
 using Microsoft.ML.TestFramework.Attributes;
 using Microsoft.ML.Trainers;