Remove generic normalizer estimator catalog methods.

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

@@ -1,8 +1,7 @@
 using System;
 using System.Collections.Generic;
-using System.Linq;
 using Microsoft.ML.Data;
-using Microsoft.ML.Transforms;
+using Microsoft.ML.Experimental;
 
 namespace Microsoft.ML.Samples.Dynamic
 {
@@ -28,7 +27,7 @@ public static void Example()
             // 35   1       6-11yrs     1         3         32            5  ...
 
             // A pipeline for normalizing the Induced column.
-            var pipeline = ml.Transforms.Normalize("Induced");
+            var pipeline = ml.Transforms.NormalizeMinMax("Induced");
             // The transformed (normalized according to Normalizer.NormalizerMode.MinMax) data.
             var transformer = pipeline.Fit(trainData);
 
@@ -58,8 +57,8 @@ 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.Normalize("LogInduced", "Induced", NormalizingEstimator.NormalizationMode.LogMeanVariance)
-                .Append(ml.Transforms.Normalize("LogSpontaneous", "Spontaneous", NormalizingEstimator.NormalizationMode.LogMeanVariance));
+            var multiColPipeline = ml.Transforms.NormalizeMinMax("LogInduced", "Induced")
+                .Append(ml.Transforms.NormalizeMinMax("LogSpontaneous", "Spontaneous"));
             // The transformed data.
             var multiColtransformer = multiColPipeline.Fit(trainData);
             var multiColtransformedData = multiColtransformer.Transform(trainData);

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

@@ -1,5 +1,6 @@
 using System;
 using System.Linq;
+using Microsoft.ML.Experimental;
 
 namespace Microsoft.ML.Samples.Dynamic.PermutationFeatureImportance
 {
@@ -19,7 +20,7 @@ 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.Normalize("Features"))
+                    .Append(mlContext.Transforms.NormalizeMinMax("Features"))
                     .Append(mlContext.Regression.Trainers.Ols(
                         labelColumnName: labelName, featureColumnName: "Features"));
             var model = pipeline.Fit(data);

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

@@ -1,5 +1,6 @@
 using System;
 using System.Linq;
+using Microsoft.ML.Experimental;
 using Microsoft.ML.Trainers;
 
 namespace Microsoft.ML.Samples.Dynamic.PermutationFeatureImportance
@@ -21,7 +22,7 @@ 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.Normalize("Features"))
+                    .Append(mlContext.Transforms.NormalizeMinMax("Features"))
                     .Append(mlContext.BinaryClassification.Trainers.LbfgsLogisticRegression(
                         labelColumnName: labelName, featureColumnName: "Features"));
             var model = pipeline.Fit(data);

docs/samples/Microsoft.ML.Samples/Microsoft.ML.Samples.csproj

@@ -6,6 +6,7 @@
   </PropertyGroup>
 
   <ItemGroup>
+    <ProjectReference Include="..\..\..\src\Microsoft.ML.Experimental\Microsoft.ML.Experimental.csproj" />
     <ProjectReference Include="..\..\..\src\Microsoft.ML.LightGbm.StaticPipe\Microsoft.ML.LightGbm.StaticPipe.csproj" />
     <ProjectReference Include="..\..\..\src\Microsoft.ML.LightGbm\Microsoft.ML.LightGbm.csproj" />
     <ProjectReference Include="..\..\..\src\Microsoft.ML.Mkl.Components\Microsoft.ML.Mkl.Components.csproj" />

src/Microsoft.ML.SamplesUtils/Microsoft.ML.SamplesUtils.csproj

@@ -8,6 +8,7 @@
   <ItemGroup>
     <ProjectReference Include="..\Microsoft.ML.Core\Microsoft.ML.Core.csproj" />
     <ProjectReference Include="..\Microsoft.ML.Data\Microsoft.ML.Data.csproj" />
+    <ProjectReference Include="..\Microsoft.ML.Experimental\Microsoft.ML.Experimental.csproj" />
     <ProjectReference Include="..\Microsoft.ML.Transforms\Microsoft.ML.Transforms.csproj" />
   </ItemGroup>
 

src/Microsoft.ML.SamplesUtils/SamplesDatasetUtils.cs

@@ -7,6 +7,7 @@
 using System.IO;
 using System.Net;
 using Microsoft.ML.Data;
+using Microsoft.ML.Experimental;
 
 namespace Microsoft.ML.SamplesUtils
 {
@@ -171,7 +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.Normalize("Features"));
+                .Append(mlContext.Transforms.NormalizeMinMax("Features"));
 
             var data = loader.Load(dataFile);
             var featurizedData = pipeline.Fit(data).Transform(data);

src/Microsoft.ML.Transforms/Microsoft.ML.Transforms.csproj

@@ -48,6 +48,7 @@
   <ItemGroup>
     <ProjectReference Include="..\Microsoft.ML.Core\Microsoft.ML.Core.csproj" />
     <ProjectReference Include="..\Microsoft.ML.Data\Microsoft.ML.Data.csproj" />
+    <ProjectReference Include="..\Microsoft.ML.Experimental\Microsoft.ML.Experimental.csproj" />
   </ItemGroup>
 
   <ItemGroup>

src/Microsoft.ML.Transforms/NormalizerCatalog.cs

@@ -9,48 +9,6 @@ namespace Microsoft.ML
     /// </summary>
     public static class NormalizationCatalog
     {
-        /// <summary>
-        /// Normalize (rescale) the column according to the specified <paramref name="mode"/>.
-        /// </summary>
-        /// <param name="catalog">The transform catalog</param>
-        /// <param name="outputColumnName">Name of the column resulting from the transformation of <paramref name="inputColumnName"/>.</param>
-        /// <param name="inputColumnName">Name of the column to transform. If set to <see langword="null"/>, the value of the <paramref name="outputColumnName"/> will be used as source.</param>
-        /// <param name="mode">The <see cref="NormalizingEstimator.NormalizationMode"/> used to map the old values in the new scale. </param>
-        /// <example>
-        /// <format type="text/markdown">
-        /// <![CDATA[
-        /// [!code-csharp[Normalize](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Normalizer.cs)]
-        /// ]]>
-        /// </format>
-        /// </example>
-        public static NormalizingEstimator Normalize(this TransformsCatalog catalog,
-           string outputColumnName, string inputColumnName = null,
-            NormalizingEstimator.NormalizationMode mode = NormalizingEstimator.NormalizationMode.MinMax)
-            => new NormalizingEstimator(CatalogUtils.GetEnvironment(catalog), outputColumnName, inputColumnName ?? outputColumnName, mode);
-
-        /// <summary>
-        /// Normalize (rescale) several columns according to the specified <paramref name="mode"/>.
-        /// </summary>
-        /// <param name="catalog">The transform catalog</param>
-        /// <param name="mode">The <see cref="NormalizingEstimator.NormalizationMode"/> used to map the old values to the new ones. </param>
-        /// <param name="columns">The pairs of input and output columns.</param>
-        /// <example>
-        /// <format type="text/markdown">
-        /// <![CDATA[
-        /// [!code-csharp[Normalize](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Normalizer.cs)]
-        /// ]]>
-        /// </format>
-        /// </example>
-        [BestFriend]
-        internal static NormalizingEstimator Normalize(this TransformsCatalog catalog,
-            NormalizingEstimator.NormalizationMode mode,
-            params InputOutputColumnPair[] columns)
-        {
-            var env = CatalogUtils.GetEnvironment(catalog);
-            env.CheckValue(columns, nameof(columns));
-            return new NormalizingEstimator(env, mode, InputOutputColumnPair.ConvertToValueTuples(columns));
-        }
-
         /// <summary>
         /// Normalize (rescale) columns according to specified custom parameters.
         /// </summary>

test/Microsoft.ML.Benchmarks/KMeansAndLogisticRegressionBench.cs

@@ -6,6 +6,7 @@
 using Microsoft.ML.Benchmarks.Harness;
 using Microsoft.ML.Calibrators;
 using Microsoft.ML.Data;
+using Microsoft.ML.Experimental;
 using Microsoft.ML.TestFramework;
 using Microsoft.ML.Trainers;
 
@@ -35,7 +36,7 @@ public CalibratedModelParametersBase<LinearBinaryModelParameters, PlattCalibrato
             }, hasHeader: true);
 
             var estimatorPipeline = ml.Transforms.Categorical.OneHotEncoding("CatFeatures")
-                .Append(ml.Transforms.Normalize("NumFeatures"))
+                .Append(ml.Transforms.NormalizeMinMax("NumFeatures"))
                 .Append(ml.Transforms.Concatenate("Features", "NumFeatures", "CatFeatures"))
                 .Append(ml.Clustering.Trainers.KMeans("Features"))
                 .Append(ml.Transforms.Concatenate("Features", "Features", "Score"))

test/Microsoft.ML.Benchmarks/Microsoft.ML.Benchmarks.csproj

@@ -15,6 +15,7 @@
   </ItemGroup>
   <ItemGroup>
     <ProjectReference Include="..\..\src\Microsoft.ML.Core\Microsoft.ML.Core.csproj" />
+    <ProjectReference Include="..\..\src\Microsoft.ML.Experimental\Microsoft.ML.Experimental.csproj" />
     <ProjectReference Include="..\..\src\Microsoft.ML.KMeansClustering\Microsoft.ML.KMeansClustering.csproj" />
     <ProjectReference Include="..\..\src\Microsoft.ML.LightGbm\Microsoft.ML.LightGbm.csproj" />
     <ProjectReference Include="..\..\src\Microsoft.ML.StandardTrainers\Microsoft.ML.StandardTrainers.csproj" />
@@ -24,6 +25,6 @@
     <NativeAssemblyReference Include="CpuMathNative" />
     <NativeAssemblyReference Include="FastTreeNative" />
     <NativeAssemblyReference Include="MklImports" />
-    <NativeAssemblyReference Condition="'$(OS)' == 'Windows_NT'" Include="libiomp5md"/>
+    <NativeAssemblyReference Condition="'$(OS)' == 'Windows_NT'" Include="libiomp5md" />
   </ItemGroup>
 </Project>

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

@@ -3,11 +3,11 @@
 // See the LICENSE file in the project root for more information.
 
 using System;
+using Microsoft.ML.Experimental;
 using Microsoft.ML.Functional.Tests.Datasets;
 using Microsoft.ML.RunTests;
 using Microsoft.ML.TestFramework;
 using Microsoft.ML.Trainers;
-using Microsoft.ML.Transforms;
 using Microsoft.ML.Transforms.Text;
 using Xunit;
 using Xunit.Abstractions;
@@ -174,7 +174,7 @@ void ExtensibilityNormalizeColumns()
 
             // Compose the transformation.
             var pipeline = mlContext.Transforms.Concatenate("Features", Iris.Features)
-                .Append(mlContext.Transforms.Normalize("Features", mode: NormalizingEstimator.NormalizationMode.MinMax));
+                .Append(mlContext.Transforms.NormalizeMinMax("Features"));
 
             // Transform the data.
             var transformedData = pipeline.Fit(data).Transform(data);

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

@@ -7,6 +7,7 @@
 using System.Collections.Immutable;
 using System.Linq;
 using Microsoft.ML.Data;
+using Microsoft.ML.Experimental;
 using Microsoft.ML.Functional.Tests.Datasets;
 using Microsoft.ML.RunTests;
 using Microsoft.ML.TestFramework;
@@ -254,7 +255,7 @@ void IntrospectNormalization()
 
             // Compose the transformation.
             var pipeline = mlContext.Transforms.Concatenate("Features", Iris.Features)
-                .Append(mlContext.Transforms.Normalize("Features", mode: NormalizingEstimator.NormalizationMode.MinMax));
+                .Append(mlContext.Transforms.NormalizeMinMax("Features"));
 
             // Fit the pipeline.
             var model = pipeline.Fit(data);

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

@@ -8,6 +8,7 @@
 using System.Linq;
 using Microsoft.ML.Calibrators;
 using Microsoft.ML.Data;
+using Microsoft.ML.Experimental;
 using Microsoft.ML.Functional.Tests.Datasets;
 using Microsoft.ML.RunTests;
 using Microsoft.ML.Trainers.FastTree;
@@ -275,7 +276,7 @@ public void LoadSchemaAndCreateNewData()
             var data = loader.Load(file);
 
             // Pipeline.
-            var pipeline = ML.Transforms.Normalize("Features");
+            var pipeline = ML.Transforms.NormalizeMinMax("Features");
 
             // Train.
             var model = pipeline.Fit(data);
@@ -330,7 +331,7 @@ public void SaveCompositeLoaderAndLoad()
         {
             var file = new MultiFileSource(GetDataPath(TestDatasets.adult.trainFilename));
             var loader = ML.Data.CreateTextLoader<InputData>(hasHeader: true, dataSample: file);
-            var composite = loader.Append(ML.Transforms.Normalize("Features"));
+            var composite = loader.Append(ML.Transforms.NormalizeMinMax("Features"));
             var loaderWithEmbeddedModel = composite.Fit(file);
 
             string modelPath = GetOutputPath(FullTestName + "-model.zip");
@@ -368,7 +369,7 @@ public void SaveLoaderAndTransformerAndLoad()
         {
             var file = new MultiFileSource(GetDataPath(TestDatasets.adult.trainFilename));
             var loader = ML.Data.CreateTextLoader<InputData>(hasHeader: true, dataSample: file);
-            var estimator = ML.Transforms.Normalize("Features");
+            var estimator = ML.Transforms.NormalizeMinMax("Features");
             var data = loader.Load(file);
             var model = estimator.Fit(data);
 
@@ -401,7 +402,7 @@ public void SaveTransformerAndSchemaAndLoad()
         {
             var file = new MultiFileSource(GetDataPath(TestDatasets.adult.trainFilename));
             var loader = ML.Data.CreateTextLoader<InputData>(hasHeader: true, dataSample: file);
-            var estimator = ML.Transforms.Normalize("Features");
+            var estimator = ML.Transforms.NormalizeMinMax("Features");
             var model = estimator.Fit(loader.Load(file));
 
             string modelPath = GetOutputPath(FullTestName + "-model.zip");

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

@@ -3,6 +3,7 @@
 // See the LICENSE file in the project root for more information.
 
 using System.IO;
+using Microsoft.ML.Experimental;
 using Microsoft.ML.Functional.Tests.Datasets;
 using Microsoft.ML.RunTests;
 using Microsoft.ML.TestFramework;
@@ -33,7 +34,7 @@ public void SaveOnnxModelLoadAndScoreFastTree()
 
             // Create a pipeline to train on the housing data.
             var pipeline = mlContext.Transforms.Concatenate("Features", HousingRegression.Features)
-                .Append(mlContext.Transforms.Normalize("Features"))
+                .Append(mlContext.Transforms.NormalizeMinMax("Features"))
                 .AppendCacheCheckpoint(mlContext)
                 .Append(mlContext.Regression.Trainers.FastTree(
                     new FastTreeRegressionTrainer.Options { NumberOfThreads = 1, NumberOfTrees = 10 }));
@@ -85,7 +86,7 @@ public void SaveOnnxModelLoadAndScoreKMeans()
 
             // Create a pipeline to train on the housing data.
             var pipeline = mlContext.Transforms.Concatenate("Features", HousingRegression.Features)
-                .Append(mlContext.Transforms.Normalize("Features"))
+                .Append(mlContext.Transforms.NormalizeMinMax("Features"))
                 .AppendCacheCheckpoint(mlContext)
                 .Append(mlContext.Clustering.Trainers.KMeans(
                     new KMeansTrainer.Options { NumberOfThreads = 1, MaximumNumberOfIterations = 10 }));
@@ -137,7 +138,7 @@ public void SaveOnnxModelLoadAndScoreSDCA()
 
             // Create a pipeline to train on the housing data.
             var pipeline = mlContext.Transforms.Concatenate("Features", HousingRegression.Features)
-                .Append(mlContext.Transforms.Normalize("Features"))
+                .Append(mlContext.Transforms.NormalizeMinMax("Features"))
                 .AppendCacheCheckpoint(mlContext)
                 .Append(mlContext.Regression.Trainers.Sdca(
                     new SdcaRegressionTrainer.Options { NumberOfThreads = 1, MaximumNumberOfIterations = 10 }));

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

@@ -5,6 +5,7 @@
 using System;
 using System.Linq;
 using Microsoft.ML.Data;
+using Microsoft.ML.Experimental;
 using Microsoft.ML.Functional.Tests.Datasets;
 using Microsoft.ML.RunTests;
 using Microsoft.ML.TestFramework;
@@ -316,7 +317,7 @@ public void ContinueTrainingOnlineGradientDescent()
 
             // Create a transformation pipeline.
             var featurizationPipeline = mlContext.Transforms.Concatenate("Features", HousingRegression.Features)
-                .Append(mlContext.Transforms.Normalize("Features"))
+                .Append(mlContext.Transforms.NormalizeMinMax("Features"))
                 .AppendCacheCheckpoint(mlContext);
 
             var trainer = mlContext.Regression.Trainers.OnlineGradientDescent(
@@ -360,7 +361,7 @@ public void ContinueTrainingPoissonRegression()
 
             // Create a transformation pipeline.
             var featurizationPipeline = mlContext.Transforms.Concatenate("Features", HousingRegression.Features)
-                .Append(mlContext.Transforms.Normalize("Features"))
+                .Append(mlContext.Transforms.NormalizeMinMax("Features"))
                 .AppendCacheCheckpoint(mlContext);
 
             var trainer = mlContext.Regression.Trainers.LbfgsPoissonRegression(

test/Microsoft.ML.Tests/CachingTests.cs

@@ -5,6 +5,7 @@
 using System.Linq;
 using System.Threading;
 using Microsoft.ML.Data;
+using Microsoft.ML.Experimental;
 using Microsoft.ML.RunTests;
 using Microsoft.ML.StaticPipe;
 using Xunit;
@@ -43,8 +44,8 @@ public void CacheCheckpointTest()
             var trainData = Enumerable.Range(0, 100).Select(c => new MyData()).ToArray();
 
             var pipe = ML.Transforms.CopyColumns("F1", "Features")
-                .Append(ML.Transforms.Normalize("Norm1", "F1"))
-                .Append(ML.Transforms.Normalize("Norm2", "F1", Transforms.NormalizingEstimator.NormalizationMode.MeanVariance));
+                .Append(ML.Transforms.NormalizeMinMax("Norm1", "F1"))
+                .Append(ML.Transforms.NormalizeMeanVariance("Norm2", "F1"));
 
             pipe.Fit(ML.Data.LoadFromEnumerable(trainData));
 
@@ -53,8 +54,8 @@ public void CacheCheckpointTest()
             trainData = Enumerable.Range(0, 100).Select(c => new MyData()).ToArray();
             pipe = ML.Transforms.CopyColumns("F1", "Features")
                 .AppendCacheCheckpoint(ML)
-                .Append(ML.Transforms.Normalize("Norm1", "F1"))
-                .Append(ML.Transforms.Normalize("Norm2", "F1", Transforms.NormalizingEstimator.NormalizationMode.MeanVariance));
+                .Append(ML.Transforms.NormalizeMinMax("Norm1", "F1"))
+                .Append(ML.Transforms.NormalizeMeanVariance("Norm2", "F1"));
 
             pipe.Fit(ML.Data.LoadFromEnumerable(trainData));
 

test/Microsoft.ML.Tests/FeatureContributionTests.cs

@@ -6,13 +6,12 @@
 using System.IO;
 using Microsoft.ML.Calibrators;
 using Microsoft.ML.Data;
+using Microsoft.ML.Experimental;
 using Microsoft.ML.Data.IO;
 using Microsoft.ML.Internal.Utilities;
-using Microsoft.ML.Model;
 using Microsoft.ML.RunTests;
 using Microsoft.ML.TestFramework.Attributes;
 using Microsoft.ML.Trainers;
-using Microsoft.ML.Transforms;
 using Xunit;
 using Xunit.Abstractions;
 
@@ -306,7 +305,7 @@ private IDataView GetSparseDataset(TaskType task = TaskType.Regression, int numb
             var srcDV = bldr.GetDataView();
 
             var pipeline = ML.Transforms.Concatenate("Features", "X1", "X2VBuffer", "X3Important")
-                .Append(ML.Transforms.Normalize("Features"));
+                .Append(ML.Transforms.NormalizeMinMax("Features"));
 
             if (task == TaskType.BinaryClassification)
                 return pipeline.Append(ML.Transforms.Conversion.ConvertType("Label", outputKind: DataKind.Boolean))

test/Microsoft.ML.Tests/OnnxConversionTest.cs

@@ -10,6 +10,7 @@
 using System.Text.RegularExpressions;
 using Google.Protobuf;
 using Microsoft.ML.Data;
+using Microsoft.ML.Experimental;
 using Microsoft.ML.Model.OnnxConverter;
 using Microsoft.ML.RunTests;
 using Microsoft.ML.Runtime;
@@ -57,7 +58,7 @@ public void SimpleEndToEndOnnxConversionTest()
                 hasHeader: true);
             var cachedTrainData = mlContext.Data.Cache(data);
             var dynamicPipeline =
-                mlContext.Transforms.Normalize("FeatureVector")
+                mlContext.Transforms.NormalizeMinMax("FeatureVector")
                 .AppendCacheCheckpoint(mlContext)
                 .Append(mlContext.Regression.Trainers.Sdca(new SdcaRegressionTrainer.Options() {
                     LabelColumnName = "Target",
@@ -137,7 +138,7 @@ public void KmeansOnnxConversionTest()
                 separatorChar: '\t',
                 hasHeader: true);
 
-            var pipeline = mlContext.Transforms.Normalize("Features").
+            var pipeline = mlContext.Transforms.NormalizeMinMax("Features").
                 Append(mlContext.Clustering.Trainers.KMeans(new Trainers.KMeansTrainer.Options
                 {
                     FeatureColumnName = DefaultColumnNames.Features,
@@ -315,7 +316,7 @@ public void LogisticRegressionOnnxConversionTest()
                 hasHeader: true);
             var cachedTrainData = mlContext.Data.Cache(data);
             var dynamicPipeline =
-                mlContext.Transforms.Normalize("FeatureVector")
+                mlContext.Transforms.NormalizeMinMax("FeatureVector")
                 .AppendCacheCheckpoint(mlContext)
                 .Append(mlContext.Regression.Trainers.Sdca(new SdcaRegressionTrainer.Options() {
                     LabelColumnName = "Target",
@@ -352,7 +353,7 @@ public void LightGbmBinaryClassificationOnnxConversionTest()
                 hasHeader: true);
             var cachedTrainData = mlContext.Data.Cache(data);
             var dynamicPipeline =
-                mlContext.Transforms.Normalize("FeatureVector")
+                mlContext.Transforms.NormalizeMinMax("FeatureVector")
                 .AppendCacheCheckpoint(mlContext)
                 .Append(mlContext.Regression.Trainers.LightGbm(labelColumnName: "Target", featureColumnName: "FeatureVector", numberOfIterations: 3, numberOfLeaves: 16, minimumExampleCountPerLeaf: 100));
             var model = dynamicPipeline.Fit(data);
@@ -383,7 +384,7 @@ public void MulticlassLogisticRegressionOnnxConversionTest()
                 separatorChar: '\t',
                 hasHeader: true);
 
-            var pipeline = mlContext.Transforms.Normalize("Features").
+            var pipeline = mlContext.Transforms.NormalizeMinMax("Features").
                 Append(mlContext.Transforms.Conversion.MapValueToKey("Label")).
                 Append(mlContext.MulticlassClassification.Trainers.LbfgsMaximumEntropy(new LbfgsMaximumEntropyMulticlassTrainer.Options() { NumberOfThreads = 1 }));
 
@@ -416,7 +417,7 @@ public void RemoveVariablesInPipelineTest()
             var pipeline = mlContext.Transforms.Categorical.OneHotEncoding("F2", "F2", Transforms.OneHotEncodingEstimator.OutputKind.Bag)
             .Append(mlContext.Transforms.ReplaceMissingValues(new MissingValueReplacingEstimator.ColumnOptions("F2")))
             .Append(mlContext.Transforms.Concatenate("Features", "F1", "F2"))
-            .Append(mlContext.Transforms.Normalize("Features"))
+            .Append(mlContext.Transforms.NormalizeMinMax("Features"))
             .Append(mlContext.BinaryClassification.Trainers.FastTree(labelColumnName: "Label", featureColumnName: "Features", numberOfLeaves: 2, numberOfTrees: 1, minimumExampleCountPerLeaf: 2));
 
             var model = pipeline.Fit(data);

test/Microsoft.ML.Tests/PermutationFeatureImportanceTests.cs

@@ -6,6 +6,7 @@
 using System.Collections.Immutable;
 using System.Linq;
 using Microsoft.ML.Data;
+using Microsoft.ML.Experimental;
 using Microsoft.ML.Internal.Utilities;
 using Microsoft.ML.RunTests;
 using Microsoft.ML.Trainers;
@@ -421,7 +422,7 @@ private IDataView GetDenseDataset(TaskType task = TaskType.Regression)
             var srcDV = bldr.GetDataView();
 
             var pipeline = ML.Transforms.Concatenate("Features", "X1", "X2Important", "X3", "X4Rand")
-                .Append(ML.Transforms.Normalize("Features"));
+                .Append(ML.Transforms.NormalizeMinMax("Features"));
             if (task == TaskType.BinaryClassification)
                 return pipeline.Append(ML.Transforms.Conversion.ConvertType("Label", outputKind: DataKind.Boolean))
                     .Fit(srcDV).Transform(srcDV);
@@ -501,7 +502,7 @@ private IDataView GetSparseDataset(TaskType task = TaskType.Regression)
             var srcDV = bldr.GetDataView();
 
             var pipeline = ML.Transforms.Concatenate("Features", "X1", "X2VBuffer", "X3Important")
-                .Append(ML.Transforms.Normalize("Features"));
+                .Append(ML.Transforms.NormalizeMinMax("Features"));
             if (task == TaskType.BinaryClassification)
             {
                 return pipeline.Append(ML.Transforms.Conversion.ConvertType("Label", outputKind: DataKind.Boolean))

test/Microsoft.ML.Tests/Scenarios/Api/CookbookSamples/CookbookSamplesDynamicApi.cs

@@ -7,6 +7,7 @@
 using System.IO;
 using System.Linq;
 using Microsoft.ML.Data;
+using Microsoft.ML.Experimental;
 using Microsoft.ML.RunTests;
 using Microsoft.ML.TestFramework;
 using Microsoft.ML.Trainers;
@@ -93,7 +94,7 @@ private void TrainRegression(string trainDataPath, string testDataPath, string m
             var pipeline =
                 // First 'normalize' the data (rescale to be
                 // between -1 and 1 for all examples), and then train the model.
-                mlContext.Transforms.Normalize("FeatureVector")
+                mlContext.Transforms.NormalizeMinMax("FeatureVector")
                 // We add a step for caching data in memory so that the downstream iterative training
                 // algorithm can efficiently scan through the data multiple times. Otherwise, the following
                 // trainer will read data from disk multiple times. The caching mechanism uses an on-demand strategy.

test/Microsoft.ML.Tests/Scenarios/IrisPlantClassificationTests.cs

@@ -3,6 +3,7 @@
 // See the LICENSE file in the project root for more information.
 
 using Microsoft.ML.Data;
+using Microsoft.ML.Experimental;
 using Microsoft.ML.RunTests;
 using Microsoft.ML.TestFramework;
 using Microsoft.ML.Trainers;
@@ -29,7 +30,7 @@ public void TrainAndPredictIrisModelTest()
             );
 
             var pipe = mlContext.Transforms.Concatenate("Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth")
-                            .Append(mlContext.Transforms.Normalize("Features"))
+                            .Append(mlContext.Transforms.NormalizeMinMax("Features"))
                             .Append(mlContext.Transforms.Conversion.MapValueToKey("Label"))
                             .AppendCacheCheckpoint(mlContext)
                             .Append(mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy(

test/Microsoft.ML.Tests/Scenarios/IrisPlantClassificationWithStringLabelTests.cs

@@ -3,6 +3,7 @@
 // See the LICENSE file in the project root for more information.
 
 using Microsoft.ML.Data;
+using Microsoft.ML.Experimental;
 using Microsoft.ML.Trainers;
 using Xunit;
 
@@ -34,7 +35,7 @@ public void TrainAndPredictIrisModelWithStringLabelTest()
 
             // Create Estimator
             var pipe = mlContext.Transforms.Concatenate("Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth")
-                .Append(mlContext.Transforms.Normalize("Features"))
+                .Append(mlContext.Transforms.NormalizeMinMax("Features"))
                 .Append(mlContext.Transforms.Conversion.MapValueToKey("Label", "IrisPlantType"), TransformerScope.TrainTest)
                 .AppendCacheCheckpoint(mlContext)
                 .Append(mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy(

test/Microsoft.ML.Tests/ScenariosWithDirectInstantiation/IrisPlantClassificationTests.cs

@@ -3,6 +3,7 @@
 // See the LICENSE file in the project root for more information.
 
 using Microsoft.ML.Data;
+using Microsoft.ML.Experimental;
 using Microsoft.ML.RunTests;
 using Microsoft.ML.Trainers;
 using Xunit;
@@ -27,7 +28,7 @@ public void TrainAndPredictIrisModelUsingDirectInstantiationTest()
             );
 
             var pipe = mlContext.Transforms.Concatenate("Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth")
-                .Append(mlContext.Transforms.Normalize("Features"))
+                .Append(mlContext.Transforms.NormalizeMinMax("Features"))
                 .Append(mlContext.Transforms.Conversion.MapValueToKey("Label"))
                 .AppendCacheCheckpoint(mlContext)
                 .Append(mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy(

test/Microsoft.ML.Tests/Transformers/NormalizerTests.cs

@@ -225,8 +225,8 @@ public void SimpleConstructorsAndExtensions()
             var est1 = new NormalizingEstimator(Env, "float4");
             var est2 = new NormalizingEstimator(Env, NormalizingEstimator.NormalizationMode.MinMax, ("float4", "float4"));
             var est3 = new NormalizingEstimator(Env, new NormalizingEstimator.MinMaxColumnOptions("float4"));
-            var est4 = ML.Transforms.Normalize("float4", "float4", NormalizingEstimator.NormalizationMode.MinMax);
-            var est5 = ML.Transforms.Normalize("float4");
+            var est4 = ML.Transforms.NormalizeMinMax("float4", "float4");
+            var est5 = ML.Transforms.NormalizeMinMax("float4");
 
             var data1 = est1.Fit(data).Transform(data);
             var data2 = est2.Fit(data).Transform(data);
@@ -246,7 +246,7 @@ public void SimpleConstructorsAndExtensions()
             // Tests for MeanVariance
             var est6 = new NormalizingEstimator(Env, NormalizingEstimator.NormalizationMode.MeanVariance, ("float4", "float4"));
             var est7 = new NormalizingEstimator(Env, new NormalizingEstimator.MeanVarianceColumnOptions("float4"));
-            var est8 = ML.Transforms.Normalize("float4", "float4", NormalizingEstimator.NormalizationMode.MeanVariance);
+            var est8 = ML.Transforms.NormalizeMeanVariance("float4", "float4");
 
             var data6 = est6.Fit(data).Transform(data);
             var data7 = est7.Fit(data).Transform(data);
@@ -259,7 +259,7 @@ public void SimpleConstructorsAndExtensions()
             // Tests for LogMeanVariance
             var est9 = new NormalizingEstimator(Env, NormalizingEstimator.NormalizationMode.LogMeanVariance, ("float4", "float4"));
             var est10 = new NormalizingEstimator(Env, new NormalizingEstimator.LogMeanVarianceColumnOptions("float4"));
-            var est11 = ML.Transforms.Normalize("float4", "float4", NormalizingEstimator.NormalizationMode.LogMeanVariance);
+            var est11 = ML.Transforms.NormalizeLogMeanVariance("float4", "float4");
 
             var data9 = est9.Fit(data).Transform(data);
             var data10 = est10.Fit(data).Transform(data);
@@ -272,7 +272,7 @@ public void SimpleConstructorsAndExtensions()
             // Tests for Binning
             var est12 = new NormalizingEstimator(Env, NormalizingEstimator.NormalizationMode.Binning, ("float4", "float4"));
             var est13 = new NormalizingEstimator(Env, new NormalizingEstimator.BinningColumnOptions("float4"));
-            var est14 = ML.Transforms.Normalize("float4", "float4", NormalizingEstimator.NormalizationMode.Binning);
+            var est14 = ML.Transforms.NormalizeBinning("float4", "float4");
 
             var data12 = est12.Fit(data).Transform(data);
             var data13 = est13.Fit(data).Transform(data);
@@ -285,7 +285,7 @@ public void SimpleConstructorsAndExtensions()
             // Tests for SupervisedBinning
             var est15 = new NormalizingEstimator(Env, NormalizingEstimator.NormalizationMode.SupervisedBinning, ("float4", "float4"));
             var est16 = new NormalizingEstimator(Env, new NormalizingEstimator.SupervisedBinningColumOptions("float4"));
-            var est17 = ML.Transforms.Normalize("float4", "float4", NormalizingEstimator.NormalizationMode.SupervisedBinning);
+            var est17 = ML.Transforms.NormalizeSupervisedBinning("float4", "float4");
 
             var data15 = est15.Fit(data).Transform(data);
             var data16 = est16.Fit(data).Transform(data);
@@ -314,11 +314,11 @@ public void NormalizerExperimentalExtensions()
             var data = loader.Load(dataPath);
 
             // Normalizer Extensions
-            var est1 = ML.Transforms.Normalize("float4", "float4", NormalizingEstimator.NormalizationMode.MinMax);
-            var est2 = ML.Transforms.Normalize("float4", "float4", NormalizingEstimator.NormalizationMode.MeanVariance);
-            var est3 = ML.Transforms.Normalize("float4", "float4", NormalizingEstimator.NormalizationMode.LogMeanVariance); 
-            var est4 = ML.Transforms.Normalize("float4", "float4", NormalizingEstimator.NormalizationMode.Binning); 
-            var est5 = ML.Transforms.Normalize("float4", "float4", NormalizingEstimator.NormalizationMode.SupervisedBinning);
+            var est1 = ML.Transforms.NormalizeMinMax("float4", "float4");
+            var est2 = ML.Transforms.NormalizeMeanVariance("float4", "float4");
+            var est3 = ML.Transforms.NormalizeLogMeanVariance("float4", "float4"); 
+            var est4 = ML.Transforms.NormalizeBinning("float4", "float4"); 
+            var est5 = ML.Transforms.NormalizeSupervisedBinning("float4", "float4");
 
             // Normalizer Extensions (Experimental)
             var est6 = ML.Transforms.NormalizeMinMax("float4", "float4");
@@ -370,7 +370,7 @@ public void NormalizerExperimentalExtensionGetColumnPairs()
             });
 
             var data = loader.Load(dataPath);
-            var est = ML.Transforms.Normalize("output", "input", NormalizingEstimator.NormalizationMode.MinMax);
+            var est = ML.Transforms.NormalizeMinMax("output", "input");
             var t = est.Fit(data);
 
             Assert.Single(t.GetColumnPairs());