transform and trainer namespaces don't need to follow the catalogs (#2755)

* folding:

Microsoft.Ml.Transforms.Normalizers
Microsoft.Ml.Transforms.Categoricals
Microsoft.Ml.Transforms.Conversions
Microsoft.Ml.Transforms.Projections

into Microsoft.Ml.Transforms

* folding:

Microsoft.ML.Trainers.KMeans
Microsoft.ML.Trainers.PCA
Microsoft.ML.Trainers.OnlineLearners
Microsoft.ML.Trainers.FactorizationMachine

into Microsoft.ML.Trainers

* regenerating the catalog

* rebase, than regenerate the catalog.

Author: sfilipi

docs/samples/Microsoft.ML.Samples/Dynamic/KeyToValueValueToKey.cs

@@ -1,7 +1,7 @@
 using System;
 using System.Collections.Generic;
 using Microsoft.ML.Data;
-using Microsoft.ML.Transforms.Conversions;
+using Microsoft.ML.Transforms;
 
 namespace Microsoft.ML.Samples.Dynamic
 {

docs/samples/Microsoft.ML.Samples/Dynamic/Normalizer.cs

@@ -2,7 +2,7 @@
 using System.Collections.Generic;
 using System.Linq;
 using Microsoft.ML.Data;
-using Microsoft.ML.Transforms.Normalizers;
+using Microsoft.ML.Transforms;
 
 namespace Microsoft.ML.Samples.Dynamic
 {

docs/samples/Microsoft.ML.Samples/Dynamic/ProjectionTransforms.cs

@@ -55,7 +55,7 @@ public static void Example()
             //0.165 0.117 -0.547  0.014
 
             // A pipeline to project Features column into L-p normalized vector.
-            var lpNormalizePipeline = ml.Transforms.Projection.LpNormalize(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), normKind: Transforms.Projections.LpNormalizingEstimatorBase.NormalizerKind.L1Norm);
+            var lpNormalizePipeline = ml.Transforms.Projection.LpNormalize(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), normKind: Transforms.LpNormalizingEstimatorBase.NormalizerKind.L1Norm);
             // The transformed (projected) data.
             transformedData = lpNormalizePipeline.Fit(trainData).Transform(trainData);
             // Getting the data of the newly created column, so we can preview it.

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/AveragedPerceptronWithOptions.cs

@@ -1,5 +1,4 @@
-using Microsoft.ML;
-using Microsoft.ML.Trainers.Online;
+using Microsoft.ML.Trainers;
 
 namespace Microsoft.ML.Samples.Dynamic.Trainers.BinaryClassification
 {

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FieldAwareFactorizationMachinewWithOptions.cs

@@ -1,7 +1,7 @@
 using System;
 using System.Linq;
 using Microsoft.ML.Data;
-using Microsoft.ML.Trainers.FactorizationMachine;
+using Microsoft.ML.Trainers;
 
 namespace Microsoft.ML.Samples.Dynamic
 {

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/LightGbm.cs

@@ -1,4 +1,4 @@
-using Microsoft.ML.Transforms.Categorical;
+using Microsoft.ML.Transforms;
 
 namespace Microsoft.ML.Samples.Dynamic.Trainers.BinaryClassification
 {

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Projection/VectorWhiten.cs

@@ -41,7 +41,7 @@ public static void Example()
 
             // A pipeline to project Features column into white noise vector.
             var whiteningPipeline = ml.Transforms.Projection.VectorWhiten(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features),
-                kind: Transforms.Projections.WhiteningKind.Zca);
+                kind: Transforms.WhiteningKind.Zca);
             // The transformed (projected) data.
             var transformedData = whiteningPipeline.Fit(trainData).Transform(trainData);
             // Getting the data of the newly created column, so we can preview it.

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Projection/VectorWhitenWithColumnOptions.cs

@@ -39,8 +39,8 @@ public static void Example()
 
 
             // A pipeline to project Features column into white noise vector.
-            var whiteningPipeline = ml.Transforms.Projection.VectorWhiten(new Transforms.Projections.VectorWhiteningEstimator.ColumnOptions(
-                nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), kind: Transforms.Projections.WhiteningKind.Pca, pcaNum: 4));
+            var whiteningPipeline = ml.Transforms.Projection.VectorWhiten(new Transforms.VectorWhiteningEstimator.ColumnOptions(
+                nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), kind: Transforms.WhiteningKind.Pca, pcaNum: 4));
             // The transformed (projected) data.
             var transformedData = whiteningPipeline.Fit(trainData).Transform(trainData);
             // Getting the data of the newly created column, so we can preview it.

docs/samples/Microsoft.ML.Samples/Dynamic/ValueMappingStringToArray.cs

@@ -1,8 +1,6 @@
 using System;
 using System.Collections.Generic;
 using Microsoft.Data.DataView;
-using Microsoft.ML.Data;
-using Microsoft.ML.Transforms.Conversions;
 
 namespace Microsoft.ML.Samples.Dynamic
 {

docs/samples/Microsoft.ML.Samples/Dynamic/ValueMappingStringToKeyType.cs

@@ -1,8 +1,6 @@
 using System;
 using System.Collections.Generic;
 using Microsoft.Data.DataView;
-using Microsoft.ML.Data;
-using Microsoft.ML.Transforms.Conversions;
 
 namespace Microsoft.ML.Samples.Dynamic
 {

src/Microsoft.ML.Data/Commands/CrossValidationCommand.cs

@@ -15,7 +15,6 @@
 using Microsoft.ML.Data;
 using Microsoft.ML.Internal.Utilities;
 using Microsoft.ML.Transforms;
-using Microsoft.ML.Transforms.Conversions;
 
 [assembly: LoadableClass(typeof(CrossValidationCommand), typeof(CrossValidationCommand.Arguments), typeof(SignatureCommand),
     "Cross Validation", CrossValidationCommand.LoadName)]

src/Microsoft.ML.Data/Commands/TrainCommand.cs

@@ -15,7 +15,7 @@
 using Microsoft.ML.Data.IO;
 using Microsoft.ML.Internal.Utilities;
 using Microsoft.ML.Model;
-using Microsoft.ML.Transforms.Normalizers;
+using Microsoft.ML.Transforms;
 
 [assembly: LoadableClass(TrainCommand.Summary, typeof(TrainCommand), typeof(TrainCommand.Arguments), typeof(SignatureCommand),
     "Train Predictor", "Train")]

src/Microsoft.ML.Data/Evaluators/ClusteringEvaluator.cs

@@ -11,9 +11,8 @@
 using Microsoft.ML.Data;
 using Microsoft.ML.EntryPoints;
 using Microsoft.ML.Internal.Utilities;
-using Microsoft.ML.Model;
 using Microsoft.ML.Numeric;
-using Microsoft.ML.Transforms.FeatureSelection;
+using Microsoft.ML.Transforms;
 
 [assembly: LoadableClass(typeof(ClusteringEvaluator), typeof(ClusteringEvaluator), typeof(ClusteringEvaluator.Arguments), typeof(SignatureEvaluator),
     "Clustering Evaluator", ClusteringEvaluator.LoadName, "Clustering")]

src/Microsoft.ML.Data/Evaluators/EvaluatorUtils.cs

@@ -12,7 +12,6 @@
 using Microsoft.ML.Data.IO;
 using Microsoft.ML.Internal.Utilities;
 using Microsoft.ML.Transforms;
-using Microsoft.ML.Transforms.Conversions;
 
 namespace Microsoft.ML.Data
 {

src/Microsoft.ML.Data/Evaluators/MultiClassClassifierEvaluator.cs

@@ -12,9 +12,7 @@
 using Microsoft.ML.Data;
 using Microsoft.ML.EntryPoints;
 using Microsoft.ML.Internal.Utilities;
-using Microsoft.ML.Model;
 using Microsoft.ML.Transforms;
-using Microsoft.ML.Transforms.FeatureSelection;
 
 [assembly: LoadableClass(typeof(MultiClassClassifierEvaluator), typeof(MultiClassClassifierEvaluator), typeof(MultiClassClassifierEvaluator.Arguments), typeof(SignatureEvaluator),
     "Multi-Class Classifier Evaluator", MultiClassClassifierEvaluator.LoadName, "MultiClassClassifier", "MultiClass")]

src/Microsoft.ML.Data/TrainCatalog.cs

@@ -7,7 +7,6 @@
 using Microsoft.Data.DataView;
 using Microsoft.ML.Data;
 using Microsoft.ML.Transforms;
-using Microsoft.ML.Transforms.Conversions;
 
 namespace Microsoft.ML
 {

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

@@ -10,7 +10,7 @@
 using Microsoft.Data.DataView;
 using Microsoft.ML.CommandLine;
 using Microsoft.ML.Internal.Utilities;
-using Microsoft.ML.Transforms.Conversions;
+using Microsoft.ML.Transforms;
 
 namespace Microsoft.ML.Data
 {

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

@@ -5,7 +5,7 @@
 using System.Collections.Generic;
 using Microsoft.Data.DataView;
 using Microsoft.ML.Data;
-using Microsoft.ML.Transforms.Conversions;
+using Microsoft.ML.Transforms;
 
 namespace Microsoft.ML
 {

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

@@ -4,6 +4,7 @@
 
 using Microsoft.ML.Data;
 using Microsoft.ML.Model;
+using Microsoft.ML.Transforms;
 
 namespace Microsoft.ML
 {

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

@@ -10,9 +10,9 @@
 using Microsoft.ML.CommandLine;
 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.Transforms;
 
 [assembly: LoadableClass(FeatureContributionCalculatingTransformer.Summary, typeof(FeatureContributionCalculatingTransformer), null, typeof(SignatureLoadModel),
     FeatureContributionCalculatingTransformer.FriendlyName, FeatureContributionCalculatingTransformer.LoaderSignature)]
@@ -22,7 +22,7 @@
 
 [assembly: LoadableClass(typeof(void), typeof(FeatureContributionEntryPoint), null, typeof(SignatureEntryPointModule), FeatureContributionCalculatingTransformer.LoaderSignature)]
 
-namespace Microsoft.ML.Data
+namespace Microsoft.ML.Transforms
 {
     /// <summary>
     /// The FeatureContributionCalculationTransformer computes model-specific per-feature contributions to the score of each example.

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

@@ -12,8 +12,7 @@
 using Microsoft.ML.CommandLine;
 using Microsoft.ML.Data;
 using Microsoft.ML.Internal.Utilities;
-using Microsoft.ML.Model;
-using Microsoft.ML.Transforms.Conversions;
+using Microsoft.ML.Transforms;
 
 [assembly: LoadableClass(HashingTransformer.Summary, typeof(IDataTransform), typeof(HashingTransformer), typeof(HashingTransformer.Options), typeof(SignatureDataTransform),
     "Hash Transform", "HashTransform", "Hash", DocName = "transform/HashTransform.md")]
@@ -27,7 +26,7 @@
 [assembly: LoadableClass(typeof(IRowMapper), typeof(HashingTransformer), null, typeof(SignatureLoadRowMapper),
    "Hash Transform", HashingTransformer.LoaderSignature)]
 
-namespace Microsoft.ML.Transforms.Conversions
+namespace Microsoft.ML.Transforms
 {
     /// <summary>
     /// This transformer can hash either single valued columns or vector columns. For vector columns,

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

@@ -9,7 +9,6 @@
 using Microsoft.Data.DataView;
 using Microsoft.ML.Data.IO;
 using Microsoft.ML.Internal.Utilities;
-using Microsoft.ML.Model;
 
 namespace Microsoft.ML.Data
 {

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

@@ -13,9 +13,8 @@
 using Microsoft.ML.Data;
 using Microsoft.ML.EntryPoints;
 using Microsoft.ML.Internal.Utilities;
-using Microsoft.ML.Model;
 using Microsoft.ML.Model.Pfa;
-using Microsoft.ML.Transforms.Conversions;
+using Microsoft.ML.Transforms;
 using Newtonsoft.Json.Linq;
 
 [assembly: LoadableClass(typeof(IDataTransform), typeof(KeyToValueMappingTransformer), typeof(KeyToValueMappingTransformer.Options), typeof(SignatureDataTransform),
@@ -30,7 +29,7 @@
 [assembly: LoadableClass(typeof(IRowMapper), typeof(KeyToValueMappingTransformer), null, typeof(SignatureLoadRowMapper),
     KeyToValueMappingTransformer.UserName, KeyToValueMappingTransformer.LoaderSignature)]
 
-namespace Microsoft.ML.Transforms.Conversions
+namespace Microsoft.ML.Transforms
 {
     /// <summary>
     /// KeyToValueTransform utilizes KeyValues metadata to map key indices to the corresponding values in the KeyValues metadata.

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

@@ -11,10 +11,9 @@
 using Microsoft.ML.CommandLine;
 using Microsoft.ML.Data;
 using Microsoft.ML.Internal.Utilities;
-using Microsoft.ML.Model;
 using Microsoft.ML.Model.OnnxConverter;
 using Microsoft.ML.Model.Pfa;
-using Microsoft.ML.Transforms.Conversions;
+using Microsoft.ML.Transforms;
 using Newtonsoft.Json.Linq;
 
 [assembly: LoadableClass(KeyToVectorMappingTransformer.Summary, typeof(IDataTransform), typeof(KeyToVectorMappingTransformer), typeof(KeyToVectorMappingTransformer.Options), typeof(SignatureDataTransform),
@@ -29,7 +28,7 @@
 [assembly: LoadableClass(typeof(IRowMapper), typeof(KeyToVectorMappingTransformer), null, typeof(SignatureLoadRowMapper),
    KeyToVectorMappingTransformer.UserName, KeyToVectorMappingTransformer.LoaderSignature)]
 
-namespace Microsoft.ML.Transforms.Conversions
+namespace Microsoft.ML.Transforms
 {
     /// <summary>
     /// Converts the key types back to their original vectors.

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

@@ -13,10 +13,9 @@
 using Microsoft.ML.Data;
 using Microsoft.ML.EntryPoints;
 using Microsoft.ML.Internal.Internallearn;
-using Microsoft.ML.Model;
 using Microsoft.ML.Model.OnnxConverter;
 using Microsoft.ML.Model.Pfa;
-using Microsoft.ML.Transforms.Normalizers;
+using Microsoft.ML.Transforms;
 using Newtonsoft.Json.Linq;
 
 [assembly: LoadableClass(NormalizeTransform.MinMaxNormalizerSummary, typeof(IDataTransform), typeof(NormalizeTransform), typeof(NormalizeTransform.MinMaxArguments), typeof(SignatureDataTransform),
@@ -40,7 +39,7 @@
 [assembly: LoadableClass(NormalizeTransform.BinNormalizerSummary, typeof(NormalizeTransform.BinColumnFunction), null, typeof(SignatureLoadColumnFunction),
     "Bin Normalizer", NormalizeTransform.BinColumnFunction.LoaderSignature)]
 
-namespace Microsoft.ML.Transforms.Normalizers
+namespace Microsoft.ML.Transforms
 {
     /// <summary>
     /// The normalize transform for support of normalization via the <see cref="IDataTransform"/> mechanism.

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

@@ -10,12 +10,11 @@
 using Microsoft.Data.DataView;
 using Microsoft.ML.Data;
 using Microsoft.ML.Internal.Utilities;
-using Microsoft.ML.Model;
 using Microsoft.ML.Model.OnnxConverter;
 using Microsoft.ML.Model.Pfa;
 using Newtonsoft.Json.Linq;
 
-namespace Microsoft.ML.Transforms.Normalizers
+namespace Microsoft.ML.Transforms
 {
     // !!! WARNING !!!
     // This file contains the Double version for normalizers and is almost identical with NormalizeColumnSng.cs

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

@@ -10,12 +10,11 @@
 using Microsoft.Data.DataView;
 using Microsoft.ML.Data;
 using Microsoft.ML.Internal.Utilities;
-using Microsoft.ML.Model;
 using Microsoft.ML.Model.OnnxConverter;
 using Microsoft.ML.Model.Pfa;
 using Newtonsoft.Json.Linq;
 
-namespace Microsoft.ML.Transforms.Normalizers
+namespace Microsoft.ML.Transforms
 {
     // !!! WARNING !!!
     // This file contains the Single version for normalizers and is almost identical with NormalizeColumnDbl.cs

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

@@ -8,10 +8,9 @@
 using Microsoft.ML;
 using Microsoft.ML.Data;
 using Microsoft.ML.EntryPoints;
-using Microsoft.ML.Model;
 using Microsoft.ML.Model.OnnxConverter;
 using Microsoft.ML.Model.Pfa;
-using Microsoft.ML.Transforms.Normalizers;
+using Microsoft.ML.Transforms;
 using Newtonsoft.Json.Linq;
 
 [assembly: LoadableClass(typeof(void), typeof(Normalize), null, typeof(SignatureEntryPointModule), "Normalize")]

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

@@ -10,10 +10,9 @@
 using Microsoft.Data.DataView;
 using Microsoft.ML;
 using Microsoft.ML.Data;
-using Microsoft.ML.Model;
 using Microsoft.ML.Model.OnnxConverter;
 using Microsoft.ML.Model.Pfa;
-using Microsoft.ML.Transforms.Normalizers;
+using Microsoft.ML.Transforms;
 using Newtonsoft.Json.Linq;
 
 [assembly: LoadableClass(typeof(NormalizingTransformer), null, typeof(SignatureLoadModel),
@@ -25,7 +24,7 @@
 [assembly: LoadableClass(typeof(IDataTransform), typeof(NormalizingTransformer), null, typeof(SignatureLoadDataTransform),
     "", NormalizingTransformer.LoaderSignature, "NormalizeTransform")]
 
-namespace Microsoft.ML.Transforms.Normalizers
+namespace Microsoft.ML.Transforms
 {
     public sealed class NormalizingEstimator : IEstimator<NormalizingTransformer>
     {

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

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

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

@@ -5,7 +5,6 @@
 using System;
 using System.Collections.Generic;
 using Microsoft.Data.DataView;
-using Microsoft.ML.Model;
 
 namespace Microsoft.ML.Data
 {

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

@@ -6,7 +6,6 @@
 using System.Collections.Generic;
 using System.Linq;
 using Microsoft.Data.DataView;
-using Microsoft.ML.Model;
 
 namespace Microsoft.ML.Data
 {

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

@@ -13,8 +13,7 @@
 using Microsoft.ML.Data;
 using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.Internal.Utilities;
-using Microsoft.ML.Model;
-using Microsoft.ML.Transforms.FeatureSelection;
+using Microsoft.ML.Transforms;
 
 [assembly: LoadableClass(SlotsDroppingTransformer.Summary, typeof(IDataTransform), typeof(SlotsDroppingTransformer), typeof(SlotsDroppingTransformer.Options), typeof(SignatureDataTransform),
     SlotsDroppingTransformer.FriendlyName, SlotsDroppingTransformer.LoaderSignature, "DropSlots")]
@@ -28,7 +27,7 @@
 [assembly: LoadableClass(typeof(IRowMapper), typeof(SlotsDroppingTransformer), null, typeof(SignatureLoadRowMapper),
    SlotsDroppingTransformer.FriendlyName, SlotsDroppingTransformer.LoaderSignature)]
 
-namespace Microsoft.ML.Transforms.FeatureSelection
+namespace Microsoft.ML.Transforms
 {
     /// <summary>
     /// Transform to drop slots from columns. If the column is scalar, the only slot that can be dropped is slot 0.

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

@@ -8,7 +8,6 @@
 using System.Reflection;
 using Microsoft.Data.DataView;
 using Microsoft.ML.Internal.Utilities;
-using Microsoft.ML.Model;
 using Microsoft.ML.Model.OnnxConverter;
 using Microsoft.ML.Model.Pfa;
 using Newtonsoft.Json.Linq;