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Remove generic normalizer estimator catalog methods. #3116

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27ce192
Cleanup unused method in Normalizer Estimator.
codemzs Mar 27, 2019
0c283e0
remove normalizer estimator catalog methods that take enum as parameter.
codemzs Apr 1, 2019
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9 changes: 4 additions & 5 deletions docs/samples/Microsoft.ML.Samples/Dynamic/Normalizer.cs
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@@ -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;

@wschinwschinApr 1, 2019

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Is it just experimental? We are removing the generic estimator, so these new methods must be supported officially and therefore deserve a namespace like ML.Transform. Is my understanding correct?

@codemzscodemzsApr 1, 2019

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#3118


namespace Microsoft.ML.Samples.Dynamic
{
Expand All@@ -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);

Expand DownExpand Up@@ -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);
Expand Down
3 changes: 2 additions & 1 deletion ...samples/Microsoft.ML.Samples/Dynamic/PermutationFeatureImportance/PFIRegressionExample.cs
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@@ -1,5 +1,6 @@
using System;
using System.Linq;
using Microsoft.ML.Experimental;

namespace Microsoft.ML.Samples.Dynamic.PermutationFeatureImportance
{
Expand All@@ -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);
Expand Down
3 changes: 2 additions & 1 deletion ...crosoft.ML.Samples/Dynamic/PermutationFeatureImportance/PfiBinaryClassificationExample.cs
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@@ -1,5 +1,6 @@
using System;
using System.Linq;
using Microsoft.ML.Experimental;
using Microsoft.ML.Trainers;

namespace Microsoft.ML.Samples.Dynamic.PermutationFeatureImportance
Expand All@@ -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);
Expand Down
1 change: 1 addition & 0 deletions docs/samples/Microsoft.ML.Samples/Microsoft.ML.Samples.csproj
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Expand Up@@ -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" />
Expand Down
1 change: 1 addition & 0 deletions src/Microsoft.ML.SamplesUtils/Microsoft.ML.SamplesUtils.csproj
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Expand Up@@ -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>

Expand Down
3 changes: 2 additions & 1 deletion src/Microsoft.ML.SamplesUtils/SamplesDatasetUtils.cs
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Expand Up@@ -7,6 +7,7 @@
using System.IO;
using System.Net;
using Microsoft.ML.Data;
using Microsoft.ML.Experimental;

namespace Microsoft.ML.SamplesUtils
{
Expand DownExpand Up@@ -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);
Expand Down
1 change: 1 addition & 0 deletions src/Microsoft.ML.Transforms/Microsoft.ML.Transforms.csproj
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Expand Up@@ -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>
Expand Down
42 changes: 0 additions & 42 deletions src/Microsoft.ML.Transforms/NormalizerCatalog.cs
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Expand Up@@ -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,
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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>
Expand Down
3 changes: 2 additions & 1 deletion test/Microsoft.ML.Benchmarks/KMeansAndLogisticRegressionBench.cs
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Expand Up@@ -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;

Expand DownExpand Up@@ -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"))
Expand Down
3 changes: 2 additions & 1 deletion test/Microsoft.ML.Benchmarks/Microsoft.ML.Benchmarks.csproj
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Expand Up@@ -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" />
Expand All@@ -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>
4 changes: 2 additions & 2 deletions test/Microsoft.ML.Functional.Tests/DataTransformation.cs
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Expand Up@@ -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;
Expand DownExpand Up@@ -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);
Expand Down
3 changes: 2 additions & 1 deletion test/Microsoft.ML.Functional.Tests/IntrospectiveTraining.cs
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Expand Up@@ -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;
Expand DownExpand Up@@ -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);
Expand Down
9 changes: 5 additions & 4 deletions test/Microsoft.ML.Functional.Tests/ModelFiles.cs
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Expand Up@@ -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;
Expand DownExpand Up@@ -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);
Expand DownExpand Up@@ -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");
Expand DownExpand Up@@ -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);

Expand DownExpand Up@@ -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");
Expand Down
7 changes: 4 additions & 3 deletions test/Microsoft.ML.Functional.Tests/ONNX.cs
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Expand Up@@ -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;
Expand DownExpand Up@@ -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 }));
Expand DownExpand Up@@ -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 }));
Expand DownExpand Up@@ -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 }));
Expand Down
5 changes: 3 additions & 2 deletions test/Microsoft.ML.Functional.Tests/Training.cs
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Expand Up@@ -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;
Expand DownExpand Up@@ -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(
Expand DownExpand Up@@ -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(
Expand Down
9 changes: 5 additions & 4 deletions test/Microsoft.ML.Tests/CachingTests.cs
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Expand Up@@ -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;
Expand DownExpand Up@@ -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));

Expand All@@ -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));

Expand Down
5 changes: 2 additions & 3 deletions test/Microsoft.ML.Tests/FeatureContributionTests.cs
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Expand Up@@ -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;

Expand DownExpand Up@@ -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))
Expand Down
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