Towards #2326 - removing some namespaces (#2442)

* removing the Microsoft.ML.Learners and the Microsoft.ML.Trainers.SymSGD namespaces.

* removing the Microsoft.ML.Internal.Internallearn.ResultProcessor and the Microsoft.ML.Trainers.FastTree.Internal namespaces.

* regenerating the catalog

Author: sfilipi

docs/samples/Microsoft.ML.Samples/Dynamic/PermutationFeatureImportance/PFIHelper.cs

@@ -1,7 +1,8 @@
 using System;
 using System.Linq;
 using Microsoft.Data.DataView;
-using Microsoft.ML.Learners;
+using Microsoft.ML.Data;
+using Microsoft.ML.Trainers;
 using Microsoft.ML.SamplesUtils;
 using Microsoft.ML.Trainers.HalLearners;
 

docs/samples/Microsoft.ML.Samples/Dynamic/PermutationFeatureImportance/PfiBinaryClassificationExample.cs

@@ -1,6 +1,6 @@
 using System;
 using System.Linq;
-using Microsoft.ML.Learners;
+using Microsoft.ML.Trainers;
 
 namespace Microsoft.ML.Samples.Dynamic.PermutationFeatureImportance
 {

docs/samples/Microsoft.ML.Samples/Static/SDCARegression.cs

@@ -1,7 +1,7 @@
 using System;
 using Microsoft.ML.Data;
-using Microsoft.ML.Learners;
 using Microsoft.ML.StaticPipe;
+using Microsoft.ML.Trainers;
 
 namespace Microsoft.ML.Samples.Static
 {

src/Microsoft.ML.Ensemble/EntryPoints/CreateEnsemble.cs

@@ -11,14 +11,13 @@
 using Microsoft.ML.CommandLine;
 using Microsoft.ML.Data;
 using Microsoft.ML.Ensemble;
-using Microsoft.ML.Ensemble.EntryPoints;
 using Microsoft.ML.Ensemble.OutputCombiners;
 using Microsoft.ML.EntryPoints;
 using Microsoft.ML.Internal.Utilities;
 
 [assembly: LoadableClass(typeof(void), typeof(EnsembleCreator), null, typeof(SignatureEntryPointModule), "CreateEnsemble")]
 
-namespace Microsoft.ML.EntryPoints
+namespace Microsoft.ML.Ensemble
 {
     /// <summary>
     /// A component to combine given models into an ensemble model.

src/Microsoft.ML.Ensemble/EntryPoints/DiversityMeasure.cs

@@ -2,7 +2,7 @@
 // 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.Ensemble.EntryPoints;
+using Microsoft.ML.Ensemble;
 using Microsoft.ML.Ensemble.Selector;
 using Microsoft.ML.Ensemble.Selector.DiversityMeasure;
 using Microsoft.ML.EntryPoints;
@@ -11,7 +11,7 @@
 [assembly: EntryPointModule(typeof(RegressionDisagreementDiversityFactory))]
 [assembly: EntryPointModule(typeof(MultiDisagreementDiversityFactory))]
 
-namespace Microsoft.ML.Ensemble.EntryPoints
+namespace Microsoft.ML.Ensemble
 {
     [TlcModule.Component(Name = DisagreementDiversityMeasure.LoadName, FriendlyName = DisagreementDiversityMeasure.UserName)]
     internal sealed class DisagreementDiversityFactory : ISupportBinaryDiversityMeasureFactory

src/Microsoft.ML.Ensemble/EntryPoints/Ensemble.cs

@@ -3,12 +3,12 @@
 // See the LICENSE file in the project root for more information.
 
 using Microsoft.ML;
-using Microsoft.ML.Ensemble.EntryPoints;
+using Microsoft.ML.Ensemble;
 using Microsoft.ML.EntryPoints;
 
 [assembly: LoadableClass(typeof(void), typeof(Ensemble), null, typeof(SignatureEntryPointModule), "TrainEnsemble")]
 
-namespace Microsoft.ML.Ensemble.EntryPoints
+namespace Microsoft.ML.Ensemble
 {
     internal static class Ensemble
     {

src/Microsoft.ML.Ensemble/EntryPoints/FeatureSelector.cs

@@ -2,15 +2,15 @@
 // 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.Ensemble.EntryPoints;
+using Microsoft.ML.Ensemble;
 using Microsoft.ML.Ensemble.Selector;
 using Microsoft.ML.Ensemble.Selector.FeatureSelector;
 using Microsoft.ML.EntryPoints;
 
 [assembly: EntryPointModule(typeof(AllFeatureSelectorFactory))]
 [assembly: EntryPointModule(typeof(RandomFeatureSelector))]
 
-namespace Microsoft.ML.Ensemble.EntryPoints
+namespace Microsoft.ML.Ensemble
 {
     [TlcModule.Component(Name = AllFeatureSelector.LoadName, FriendlyName = AllFeatureSelector.UserName)]
     public sealed class AllFeatureSelectorFactory : ISupportFeatureSelectorFactory

src/Microsoft.ML.Ensemble/EntryPoints/OutputCombiner.cs

@@ -2,7 +2,7 @@
 // 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.Ensemble.EntryPoints;
+using Microsoft.ML.Ensemble;
 using Microsoft.ML.Ensemble.OutputCombiners;
 using Microsoft.ML.EntryPoints;
 
@@ -18,7 +18,7 @@
 [assembly: EntryPointModule(typeof(VotingFactory))]
 [assembly: EntryPointModule(typeof(WeightedAverage))]
 
-namespace Microsoft.ML.Ensemble.EntryPoints
+namespace Microsoft.ML.Ensemble
 {
     [TlcModule.Component(Name = Average.LoadName, FriendlyName = Average.UserName)]
     public sealed class AverageFactory : ISupportBinaryOutputCombinerFactory, ISupportRegressionOutputCombinerFactory

src/Microsoft.ML.Ensemble/EntryPoints/PipelineEnsemble.cs

@@ -4,13 +4,13 @@
 
 using Microsoft.Data.DataView;
 using Microsoft.ML.Data;
-using Microsoft.ML.Ensemble.EntryPoints;
+using Microsoft.ML.Ensemble;
 using Microsoft.ML.EntryPoints;
 using Microsoft.ML.Internal.Calibration;
 
 [assembly: EntryPointModule(typeof(PipelineEnsemble))]
 
-namespace Microsoft.ML.Ensemble.EntryPoints
+namespace Microsoft.ML.Ensemble
 {
     internal static class PipelineEnsemble
     {

src/Microsoft.ML.Ensemble/EntryPoints/SubModelSelector.cs

@@ -2,7 +2,7 @@
 // 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.Ensemble.EntryPoints;
+using Microsoft.ML.Ensemble;
 using Microsoft.ML.Ensemble.Selector;
 using Microsoft.ML.Ensemble.Selector.SubModelSelector;
 using Microsoft.ML.EntryPoints;
@@ -16,7 +16,7 @@
 [assembly: EntryPointModule(typeof(BestPerformanceSelector))]
 [assembly: EntryPointModule(typeof(BestPerformanceSelectorMultiClass))]
 
-namespace Microsoft.ML.Ensemble.EntryPoints
+namespace Microsoft.ML.Ensemble
 {
     [TlcModule.Component(Name = AllSelector.LoadName, FriendlyName = AllSelector.UserName)]
     public sealed class AllSelectorFactory : ISupportBinarySubModelSelectorFactory, ISupportRegressionSubModelSelectorFactory

src/Microsoft.ML.Ensemble/Selector/SubModelSelector/BestDiverseSelectorBinary.cs

@@ -7,7 +7,6 @@
 using System.Collections.Generic;
 using Microsoft.ML;
 using Microsoft.ML.CommandLine;
-using Microsoft.ML.Ensemble.EntryPoints;
 using Microsoft.ML.Ensemble.Selector;
 using Microsoft.ML.Ensemble.Selector.DiversityMeasure;
 using Microsoft.ML.Ensemble.Selector.SubModelSelector;
@@ -19,8 +18,6 @@
 
 namespace Microsoft.ML.Ensemble.Selector.SubModelSelector
 {
-    using TScalarPredictor = IPredictorProducing<Single>;
-
     internal sealed class BestDiverseSelectorBinary : BaseDiverseSelector<Single, DisagreementDiversityMeasure>, IBinarySubModelSelector
     {
         public const string UserName = "Best Diverse Selector";

src/Microsoft.ML.Ensemble/Selector/SubModelSelector/BestDiverseSelectorMultiClass.cs

@@ -8,7 +8,6 @@
 using Microsoft.ML;
 using Microsoft.ML.CommandLine;
 using Microsoft.ML.Data;
-using Microsoft.ML.Ensemble.EntryPoints;
 using Microsoft.ML.Ensemble.Selector;
 using Microsoft.ML.Ensemble.Selector.DiversityMeasure;
 using Microsoft.ML.Ensemble.Selector.SubModelSelector;
@@ -20,8 +19,6 @@
 
 namespace Microsoft.ML.Ensemble.Selector.SubModelSelector
 {
-    using TVectorPredictor = IPredictorProducing<VBuffer<Single>>;
-
     internal sealed class BestDiverseSelectorMultiClass : BaseDiverseSelector<VBuffer<Single>, IDiversityMeasure<VBuffer<Single>>>, IMulticlassSubModelSelector
     {
         public const string UserName = "Best Diverse Selector";

src/Microsoft.ML.Ensemble/Selector/SubModelSelector/BestDiverseSelectorRegression.cs

@@ -7,7 +7,6 @@
 using System.Collections.Generic;
 using Microsoft.ML;
 using Microsoft.ML.CommandLine;
-using Microsoft.ML.Ensemble.EntryPoints;
 using Microsoft.ML.Ensemble.Selector;
 using Microsoft.ML.Ensemble.Selector.DiversityMeasure;
 using Microsoft.ML.Ensemble.Selector.SubModelSelector;
@@ -19,8 +18,6 @@
 
 namespace Microsoft.ML.Ensemble.Selector.SubModelSelector
 {
-    using TScalarPredictor = IPredictorProducing<Single>;
-
     internal sealed class BestDiverseSelectorRegression : BaseDiverseSelector<Single, RegressionDisagreementDiversityMeasure>, IRegressionSubModelSelector
     {
         public const string UserName = "Best Diverse Selector";

src/Microsoft.ML.Ensemble/Selector/SubsetSelector/BaseSubsetSelector.cs

@@ -6,7 +6,6 @@
 using System.Collections.Generic;
 using Microsoft.ML.CommandLine;
 using Microsoft.ML.Data;
-using Microsoft.ML.Ensemble.EntryPoints;
 using Microsoft.ML.Transforms;
 
 namespace Microsoft.ML.Ensemble.Selector.SubsetSelector

src/Microsoft.ML.Ensemble/Trainer/Binary/EnsembleTrainer.cs

@@ -8,11 +8,10 @@
 using Microsoft.ML;
 using Microsoft.ML.CommandLine;
 using Microsoft.ML.Ensemble;
-using Microsoft.ML.Ensemble.EntryPoints;
 using Microsoft.ML.Ensemble.OutputCombiners;
 using Microsoft.ML.Ensemble.Selector;
 using Microsoft.ML.Internal.Internallearn;
-using Microsoft.ML.Learners;
+using Microsoft.ML.Trainers;
 using Microsoft.ML.Trainers.Online;
 using Microsoft.ML.Training;
 

src/Microsoft.ML.Ensemble/Trainer/Multiclass/MulticlassDataPartitionEnsembleTrainer.cs

@@ -9,11 +9,10 @@
 using Microsoft.ML.CommandLine;
 using Microsoft.ML.Data;
 using Microsoft.ML.Ensemble;
-using Microsoft.ML.Ensemble.EntryPoints;
 using Microsoft.ML.Ensemble.OutputCombiners;
 using Microsoft.ML.Ensemble.Selector;
 using Microsoft.ML.Internal.Internallearn;
-using Microsoft.ML.Learners;
+using Microsoft.ML.Trainers;
 using Microsoft.ML.Training;
 
 [assembly: LoadableClass(MulticlassDataPartitionEnsembleTrainer.Summary, typeof(MulticlassDataPartitionEnsembleTrainer),

src/Microsoft.ML.Ensemble/Trainer/Regression/RegressionEnsembleTrainer.cs

@@ -7,13 +7,11 @@
 using System.Linq;
 using Microsoft.ML;
 using Microsoft.ML.CommandLine;
-using Microsoft.ML.Data;
 using Microsoft.ML.Ensemble;
-using Microsoft.ML.Ensemble.EntryPoints;
 using Microsoft.ML.Ensemble.OutputCombiners;
 using Microsoft.ML.Ensemble.Selector;
 using Microsoft.ML.Internal.Internallearn;
-using Microsoft.ML.Learners;
+using Microsoft.ML.Trainers;
 using Microsoft.ML.Trainers.Online;
 using Microsoft.ML.Training;
 

src/Microsoft.ML.FastTree/BinFile/BinFinder.cs

@@ -7,7 +7,7 @@
 using Microsoft.ML.Data;
 using Microsoft.ML.Internal.Utilities;
 
-namespace Microsoft.ML.Trainers.FastTree.Internal
+namespace Microsoft.ML.Trainers.FastTree
 {
     /// <summary>
     /// A class that bins vectors of doubles into a specified number of equal mass bins.

src/Microsoft.ML.FastTree/BinFile/IniFileParserInterface.cs

@@ -7,7 +7,7 @@
 using System.Runtime.InteropServices;
 using System.Text;
 
-namespace Microsoft.ML.Trainers.FastTree.Internal
+namespace Microsoft.ML.Trainers.FastTree
 {
     internal sealed class IniFileParserInterface
     {

src/Microsoft.ML.FastTree/BoostingFastTree.cs

@@ -6,7 +6,6 @@
 using System.Linq;
 using Microsoft.ML.Core.Data;
 using Microsoft.ML.Internal.Internallearn;
-using Microsoft.ML.Trainers.FastTree.Internal;
 using Float = System.Single;
 
 namespace Microsoft.ML.Trainers.FastTree

src/Microsoft.ML.FastTree/Dataset/Dataset.cs

@@ -8,7 +8,7 @@
 using System.Threading.Tasks;
 using Microsoft.ML.Internal.Utilities;
 
-namespace Microsoft.ML.Trainers.FastTree.Internal
+namespace Microsoft.ML.Trainers.FastTree
 {
     /// <summary>
     /// A dataset of features.

src/Microsoft.ML.FastTree/Dataset/DatasetUtils.cs

@@ -5,7 +5,7 @@
 using System.Collections.Generic;
 using System.Linq;
 
-namespace Microsoft.ML.Trainers.FastTree.Internal
+namespace Microsoft.ML.Trainers.FastTree
 {
     /// <summary>
     /// Loads training/validation/test sets from file

src/Microsoft.ML.FastTree/Dataset/DenseIntArray.cs

@@ -8,7 +8,7 @@
 using System.Runtime.InteropServices;
 using System.Security;
 
-namespace Microsoft.ML.Trainers.FastTree.Internal
+namespace Microsoft.ML.Trainers.FastTree
 {
 #if USE_SINGLE_PRECISION
     using FloatType = System.Single;

src/Microsoft.ML.FastTree/Dataset/Feature.cs

@@ -4,7 +4,7 @@
 
 using System.Linq;
 
-namespace Microsoft.ML.Trainers.FastTree.Internal
+namespace Microsoft.ML.Trainers.FastTree
 {
 
     /// <summary>

src/Microsoft.ML.FastTree/Dataset/FeatureFlock.cs

@@ -15,7 +15,7 @@
 using Microsoft.ML.Internal.CpuMath;
 using Microsoft.ML.Internal.Utilities;
 
-namespace Microsoft.ML.Trainers.FastTree.Internal
+namespace Microsoft.ML.Trainers.FastTree
 {
     /// <summary>
     /// Holds statistics per bin value for a feature. These are yielded by <see cref="SufficientStatsBase.GetBinStats"/>

src/Microsoft.ML.FastTree/Dataset/FeatureHistogram.cs

@@ -5,7 +5,7 @@
 using System;
 using System.Linq;
 
-namespace Microsoft.ML.Trainers.FastTree.Internal
+namespace Microsoft.ML.Trainers.FastTree
 {
 #if USE_SINGLE_PRECISION
     using FloatType = System.Single;

src/Microsoft.ML.FastTree/Dataset/FileObjectStore.cs

@@ -2,7 +2,7 @@
 // The .NET Foundation licenses this file to you under the MIT license.
 // See the LICENSE file in the project root for more information.
 
-namespace Microsoft.ML.Trainers.FastTree.Internal
+namespace Microsoft.ML.Trainers.FastTree
 {
 #if !NO_STORE
     /// <summary>

src/Microsoft.ML.FastTree/Dataset/IntArray.cs

@@ -6,7 +6,7 @@
 using System.Collections.Generic;
 using System.Linq;
 
-namespace Microsoft.ML.Trainers.FastTree.Internal
+namespace Microsoft.ML.Trainers.FastTree
 {
 #if USE_SINGLE_PRECISION
     using FloatType = System.Single;

src/Microsoft.ML.FastTree/Dataset/NHotFeatureFlock.cs

@@ -2,7 +2,7 @@
 // The .NET Foundation licenses this file to you under the MIT license.
 // See the LICENSE file in the project root for more information.
 
-namespace Microsoft.ML.Trainers.FastTree.Internal
+namespace Microsoft.ML.Trainers.FastTree
 {
     /// <summary>
     /// This is a feature flock that misuses a property of <see cref="DeltaSparseIntArray.Sumup"/>

src/Microsoft.ML.FastTree/Dataset/OneHotFeatureFlock.cs

@@ -4,7 +4,7 @@
 
 using System.Linq;
 
-namespace Microsoft.ML.Trainers.FastTree.Internal
+namespace Microsoft.ML.Trainers.FastTree
 {
     /// <summary>
     /// A feature flock for a set of features where per example at most one of the features has a

src/Microsoft.ML.FastTree/Dataset/RepeatIntArray.cs

@@ -6,7 +6,7 @@
 using System.Collections.Generic;
 using System.Linq;
 
-namespace Microsoft.ML.Trainers.FastTree.Internal
+namespace Microsoft.ML.Trainers.FastTree
 {
 #if USE_SINGLE_PRECISION
     using FloatType = Single;

src/Microsoft.ML.FastTree/Dataset/SegmentIntArray.cs

@@ -7,7 +7,7 @@
 using System.Runtime.InteropServices;
 using System.Security;
 
-namespace Microsoft.ML.Trainers.FastTree.Internal
+namespace Microsoft.ML.Trainers.FastTree
 {
 #if USE_SINGLE_PRECISION
     using FloatType = System.Single;

src/Microsoft.ML.FastTree/Dataset/SingletonFeatureFlock.cs

@@ -5,7 +5,7 @@
 using System.Linq;
 using Microsoft.ML.Internal.Utilities;
 
-namespace Microsoft.ML.Trainers.FastTree.Internal
+namespace Microsoft.ML.Trainers.FastTree
 {
     /// <summary>
     /// The singleton feature flock is the simplest possible sort of flock, that is, a flock

src/Microsoft.ML.FastTree/Dataset/SparseIntArray.cs

@@ -7,7 +7,7 @@
 using System.Runtime.InteropServices;
 using System.Security;
 
-namespace Microsoft.ML.Trainers.FastTree.Internal
+namespace Microsoft.ML.Trainers.FastTree
 {
 #if USE_SINGLE_PRECISION
     using FloatType = System.Single;