Address comments. Everything touched is under TransformsCatalog now.

Author: wschin

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

@@ -28,7 +28,7 @@ public static void Example()
             // 35   1       6-11yrs     1         3         32            5  ...
 
             // A pipeline for normalizing the Induced column.
-            var pipeline = ml.Transforms.Normalization.Normalize("Induced");
+            var pipeline = ml.Transforms.Normalize("Induced");
             // The transformed (normalized according to Normalizer.NormalizerMode.MinMax) data.
             var transformer = pipeline.Fit(trainData);
 
@@ -66,7 +66,7 @@ public static void Example()
 
             // Composing a different pipeline if we wanted to normalize more than one column at a time. 
             // Using log scale as the normalization mode. 
-            var multiColPipeline = ml.Transforms.Normalization.Normalize(NormalizingEstimator.NormalizerMode.LogMeanVariance, new ColumnOptions[] { ("LogInduced", "Induced"), ("LogSpontaneous", "Spontaneous") });
+            var multiColPipeline = ml.Transforms.Normalize(NormalizingEstimator.NormalizerMode.LogMeanVariance, new ColumnOptions[] { ("LogInduced", "Induced"), ("LogSpontaneous", "Spontaneous") });
             // The transformed data.
             var multiColtransformer = multiColPipeline.Fit(trainData);
             var multiColtransformedData = multiColtransformer.Transform(trainData);

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

@@ -19,7 +19,7 @@ public static void Example()
             // Normalize the data set so that for each feature, its maximum value is 1 while its minimum value is 0.
             // Then append a linear regression trainer.
             var pipeline = mlContext.Transforms.Concatenate("Features", featureNames)
-                    .Append(mlContext.Transforms.Normalization.Normalize("Features"))
+                    .Append(mlContext.Transforms.Normalize("Features"))
                     .Append(mlContext.Regression.Trainers.OrdinaryLeastSquares(
                         labelColumnName: labelName, featureColumnName: "Features"));
             var model = pipeline.Fit(data);

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

@@ -21,7 +21,7 @@ public static void Example()
             // Normalize the data set so that for each feature, its maximum value is 1 while its minimum value is 0.
             // Then append a logistic regression trainer.
             var pipeline = mlContext.Transforms.Concatenate("Features", featureNames)
-                    .Append(mlContext.Transforms.Normalization.Normalize("Features"))
+                    .Append(mlContext.Transforms.Normalize("Features"))
                     .Append(mlContext.BinaryClassification.Trainers.LogisticRegression(
                         labelColumnName: labelName, featureColumnName: "Features"));
             var model = pipeline.Fit(data);

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

@@ -37,7 +37,7 @@ public static void Example()
             };
 
             // A pipeline to project Features column into Random fourier space.
-            var rffPipeline = ml.Transforms.KernelExpansion.RandomFourierExpand(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), dimension: 4);
+            var rffPipeline = ml.Transforms.RandomFourierExpand(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), dimension: 4);
             // The transformed (projected) data.
             var transformedData = rffPipeline.Fit(trainData).Transform(trainData);
             // Getting the data of the newly created column, so we can preview it.
@@ -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.Normalization.LpNormalize(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), normKind: Transforms.LpNormalizingEstimatorBase.NormKind.L1Norm);
+            var lpNormalizePipeline = ml.Transforms.LpNormalize(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), normKind: Transforms.LpNormalizingEstimatorBase.NormKind.L1Norm);
             // The transformed (projected) data.
             transformedData = lpNormalizePipeline.Fit(trainData).Transform(trainData);
             // Getting the data of the newly created column, so we can preview it.
@@ -73,7 +73,7 @@ public static void Example()
             // 0.133 0.156 0.178 0.200 0.000 0.022 0.044 0.067 0.089 0.111
 
             // A pipeline to project Features column into L-p normalized vector.
-            var gcNormalizePipeline = ml.Transforms.Normalization.GlobalContrastNormalize(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), ensureZeroMean:false);
+            var gcNormalizePipeline = ml.Transforms.GlobalContrastNormalize(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), ensureZeroMean:false);
             // The transformed (projected) data.
             transformedData = gcNormalizePipeline.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/VectorWhiten.cs

@@ -40,7 +40,7 @@ public static void Example()
 
 
             // A pipeline to project Features column into white noise vector.
-            var whiteningPipeline = ml.Transforms.DimensionReduction.VectorWhiten(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features),
+            var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features),
                 kind: Transforms.WhiteningKind.Zca);
             // The transformed (projected) data.
             var transformedData = whiteningPipeline.Fit(trainData).Transform(trainData);

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

@@ -39,7 +39,7 @@ public static void Example()
 
 
             // A pipeline to project Features column into white noise vector.
-            var whiteningPipeline = ml.Transforms.DimensionReduction.VectorWhiten(new Transforms.VectorWhiteningEstimator.ColumnOptions(
+            var whiteningPipeline = ml.Transforms.VectorWhiten(new Transforms.VectorWhiteningEstimator.ColumnOptions(
                 nameof(SamplesUtils.DatasetUtils.SampleVectorOfNumbersData.Features), kind: Transforms.WhiteningKind.Pca, rank: 4));
             // The transformed (projected) data.
             var transformedData = whiteningPipeline.Fit(trainData).Transform(trainData);

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

@@ -30,21 +30,6 @@ public sealed class TransformsCatalog : IInternalCatalog
         /// </summary>
         public TextTransforms Text { get; }
 
-        /// <summary>
-        /// The list of operations for applying kernel methods.
-        /// </summary>
-        public KernelExpansionTransforms KernelExpansion { get; }
-
-        /// <summary>
-        /// The list of operations for data normalization.
-        /// </summary>
-        public NormalizationTransforms Normalization { get; }
-
-        /// <summary>
-        /// The list of operations for dimension reduction.
-        /// </summary>
-        public DimensionReductionTransforms DimensionReduction { get; }
-
         /// <summary>
         /// The list of operations for selecting features based on some criteria.
         /// </summary>
@@ -58,10 +43,7 @@ internal TransformsCatalog(IHostEnvironment env)
             Categorical = new CategoricalTransforms(this);
             Conversion = new ConversionTransforms(this);
             Text = new TextTransforms(this);
-            KernelExpansion = new KernelExpansionTransforms(this);
             FeatureSelection = new FeatureSelectionTransforms(this);
-            Normalization = new NormalizationTransforms(this);
-            DimensionReduction = new DimensionReductionTransforms(this);
         }
 
         /// <summary>
@@ -106,20 +88,6 @@ internal TextTransforms(TransformsCatalog owner)
             }
         }
 
-        /// <summary>
-        /// The catalog of kernel methods.
-        /// </summary>
-        public sealed class KernelExpansionTransforms : IInternalCatalog
-        {
-            IHostEnvironment IInternalCatalog.Environment => _env;
-            private readonly IHostEnvironment _env;
-
-            internal KernelExpansionTransforms(TransformsCatalog owner)
-            {
-                _env = owner.GetEnvironment();
-            }
-        }
-
         /// <summary>
         /// The catalog of feature selection operations.
         /// </summary>
@@ -133,33 +101,5 @@ internal FeatureSelectionTransforms(TransformsCatalog owner)
                 _env = owner.GetEnvironment();
             }
         }
-
-        /// <summary>
-        /// The catalog of normalization.
-        /// </summary>
-        public sealed class NormalizationTransforms : IInternalCatalog
-        {
-            IHostEnvironment IInternalCatalog.Environment => _env;
-            private readonly IHostEnvironment _env;
-
-            internal NormalizationTransforms(TransformsCatalog owner)
-            {
-                _env = owner.GetEnvironment();
-            }
-        }
-
-        /// <summary>
-        /// The catalog of dimension reduction methods.
-        /// </summary>
-        public sealed class DimensionReductionTransforms : IInternalCatalog
-        {
-            IHostEnvironment IInternalCatalog.Environment => _env;
-            private readonly IHostEnvironment _env;
-
-            internal DimensionReductionTransforms(TransformsCatalog owner)
-            {
-                _env = owner.GetEnvironment();
-            }
-        }
     }
 }

src/Microsoft.ML.Mkl.Components/MklComponentsCatalog.cs

@@ -138,7 +138,7 @@ public static SymbolicStochasticGradientDescentClassificationTrainer SymbolicSto
         /// ]]>
         /// </format>
         /// </example>
-        public static VectorWhiteningEstimator VectorWhiten(this TransformsCatalog.DimensionReductionTransforms catalog, string outputColumnName, string inputColumnName = null,
+        public static VectorWhiteningEstimator VectorWhiten(this TransformsCatalog catalog, string outputColumnName, string inputColumnName = null,
             WhiteningKind kind = VectorWhiteningEstimator.Defaults.Kind,
             float epsilon = VectorWhiteningEstimator.Defaults.Epsilon,
             int maximumNumberOfRows = VectorWhiteningEstimator.Defaults.MaximumNumberOfRows,
@@ -158,7 +158,7 @@ public static VectorWhiteningEstimator VectorWhiten(this TransformsCatalog.Dimen
         /// ]]>
         /// </format>
         /// </example>
-        public static VectorWhiteningEstimator VectorWhiten(this TransformsCatalog.DimensionReductionTransforms catalog, params VectorWhiteningEstimator.ColumnOptions[] columns)
+        public static VectorWhiteningEstimator VectorWhiten(this TransformsCatalog catalog, params VectorWhiteningEstimator.ColumnOptions[] columns)
             => new VectorWhiteningEstimator(CatalogUtils.GetEnvironment(catalog), columns);
 
     }

src/Microsoft.ML.PCA/PCACatalog.cs

@@ -20,7 +20,7 @@ public static class PcaCatalog
         /// <param name="overSampling">Oversampling parameter for randomized PrincipalComponentAnalysis training.</param>
         /// <param name="ensureZeroMean">If enabled, data is centered to be zero mean.</param>
         /// <param name="seed">The seed for random number generation.</param>
-        public static PrincipalComponentAnalysisEstimator ProjectToPrincipalComponents(this TransformsCatalog.DimensionReductionTransforms catalog,
+        public static PrincipalComponentAnalysisEstimator ProjectToPrincipalComponents(this TransformsCatalog catalog,
             string outputColumnName,
             string inputColumnName = null,
             string exampleWeightColumnName = null,
@@ -34,7 +34,7 @@ public static PrincipalComponentAnalysisEstimator ProjectToPrincipalComponents(t
         /// <summary>Initializes a new instance of <see cref="PrincipalComponentAnalysisEstimator"/>.</summary>
         /// <param name="catalog">The transform's catalog.</param>
         /// <param name="columns">Input columns to apply PrincipalComponentAnalysis on.</param>
-        public static PrincipalComponentAnalysisEstimator ProjectToPrincipalComponents(this TransformsCatalog.DimensionReductionTransforms catalog, params PrincipalComponentAnalysisEstimator.ColumnOptions[] columns)
+        public static PrincipalComponentAnalysisEstimator ProjectToPrincipalComponents(this TransformsCatalog catalog, params PrincipalComponentAnalysisEstimator.ColumnOptions[] columns)
             => new PrincipalComponentAnalysisEstimator(CatalogUtils.GetEnvironment(catalog), columns);
 
         /// <summary>

src/Microsoft.ML.SamplesUtils/SamplesDatasetUtils.cs

@@ -172,7 +172,7 @@ public static IDataView LoadFeaturizedAdultDataset(MLContext mlContext)
                     "occupation", "relationship", "ethnicity", "native-country", "age", "education-num",
                     "capital-gain", "capital-loss", "hours-per-week"))
                 // Min-max normalize all the features
-                .Append(mlContext.Transforms.Normalization.Normalize("Features"));
+                .Append(mlContext.Transforms.Normalize("Features"));
 
             var data = loader.Load(dataFile);
             var featurizedData = pipeline.Fit(data).Transform(data);

src/Microsoft.ML.Transforms/KernelCatalog.cs

@@ -19,15 +19,16 @@ public static class KernelExpansionCatalog
         /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
         /// <param name="inputColumnName">Name of column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
         /// <param name="dimension">The number of random Fourier features to create.</param>
-        /// <param name="useCosAndSinBases">If <see langword="true"/>, use both of cos and sin basis functions to create two features for every random Fourier frequency.  /// Otherwise, only cos bases would be used.</param>
+        /// <param name="useCosAndSinBases">If <see langword="true"/>, use both of cos and sin basis functions to create two features for every random Fourier frequency.
+        /// Otherwise, only cos bases would be used.</param>
         /// <example>
         /// <format type="text/markdown">
         /// <![CDATA[
         /// [!code-csharp[CreateRandomFourierFeatures](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/ProjectionTransforms.cs?range=1-6,12-112)]
         /// ]]>
         /// </format>
         /// </example>
-        public static RandomFourierExpansionEstimator RandomFourierExpand(this TransformsCatalog.KernelExpansionTransforms catalog,
+        public static RandomFourierExpansionEstimator RandomFourierExpand(this TransformsCatalog catalog,
             string outputColumnName,
             string inputColumnName = null,
             int dimension = RandomFourierExpansionEstimator.Defaults.Dimension,
@@ -39,7 +40,7 @@ public static RandomFourierExpansionEstimator RandomFourierExpand(this Transform
         /// </summary>
         /// <param name="catalog">The transform's catalog.</param>
         /// <param name="columns">The input columns to use for the transformation.</param>
-        public static RandomFourierExpansionEstimator RandomFourierExpand(this TransformsCatalog.KernelExpansionTransforms catalog, params RandomFourierExpansionEstimator.ColumnOptions[] columns)
+        public static RandomFourierExpansionEstimator RandomFourierExpand(this TransformsCatalog catalog, params RandomFourierExpansionEstimator.ColumnOptions[] columns)
             => new RandomFourierExpansionEstimator(CatalogUtils.GetEnvironment(catalog), columns);
     }
 }