Move Normalizer extension method from experimental to stable nuget and remove Normalizer generic APIs

docs/code/MlNetCookBook.md

@@ -344,7 +344,7 @@ var cachedTrainData = mlContext.Data.Cache(trainData);
 var pipeline =
     // First 'normalize' the data (rescale to be
     // between -1 and 1 for all examples)
-    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 load data from disk multiple times. The caching mechanism uses an on-demand strategy.
@@ -625,18 +625,15 @@ var trainData = mlContext.Data.LoadFromTextFile<IrisInputAllFeatures>(dataPath,
     separatorChar: ','
 );
 
-// Apply all kinds of standard ML.NET normalization to the raw features.
+// Apply MinMax normalization to the raw features.
 var pipeline =
-    mlContext.Transforms.Normalize(
-        new NormalizingEstimator.MinMaxColumnOptions("MinMaxNormalized", "Features", fixZero: true),
-        new NormalizingEstimator.MeanVarianceColumnOptions("MeanVarNormalized", "Features", fixZero: true),
-        new NormalizingEstimator.BinningColumnOptions("BinNormalized", "Features", maximumBinCount: 256));
+    mlContext.Transforms.NormalizeMinMax("MinMaxNormalized", "Features");
 
 // Let's train our pipeline of normalizers, and then apply it to the same data.
 var normalizedData = pipeline.Fit(trainData).Transform(trainData);
 
 // Inspect one column of the resulting dataset.
-var meanVarValues = normalizedData.GetColumn<float[]>(normalizedData.Schema["MeanVarNormalized"]).ToArray();
+var meanVarValues = normalizedData.GetColumn<float[]>(normalizedData.Schema["MinMaxNormalized"]).ToArray();
 ```
 
 ## How do I train my model on categorical data?

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

@@ -1,8 +1,6 @@
 using System;
 using System.Collections.Generic;
-using System.Linq;
 using Microsoft.ML.Data;
-using Microsoft.ML.Transforms;
 
 namespace Microsoft.ML.Samples.Dynamic
 {
@@ -28,7 +26,7 @@ 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 +56,8 @@ 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

@@ -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.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

@@ -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.Normalize("Features"))
+                    .Append(mlContext.Transforms.NormalizeMinMax("Features"))
                     .Append(mlContext.BinaryClassification.Trainers.LbfgsLogisticRegression(
                         labelColumnName: labelName, featureColumnName: "Features"));
             var model = pipeline.Fit(data);

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

@@ -39,7 +39,8 @@ internal static class Defaults
             public const long MaximumExampleCount = 1000000000;
         }
 
-        public enum NormalizationMode
+        [BestFriend]
+        internal enum NormalizationMode
         {
             /// <summary>
             /// Linear rescale such that minimum and maximum values are mapped between -1 and 1.

src/Microsoft.ML.SamplesUtils/SamplesDatasetUtils.cs

@@ -171,7 +171,7 @@ 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);