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
 {