Update release for 0.11

docs/release-notes/0.11/release-0.11.md

@@ -0,0 +1,40 @@
+# ML.NET 0.11 Release Notes
+
+[ML.NET](https://aka.ms/mlnet) 0.11 will be the last preview release before we reach `Release Candidate` for v1. We continue to push for creating a coherent and clean API surface for [ML.NET](https://aka.ms/mlnet) users. This release includes several bug fixes as well as extensive work on reducing the public API surface. The work on the API is tracked via [this project](https://github.com/dotnet/machinelearning/projects/13). In the upcoming 0.12 (RC1) releases before we reach 1.0, we will continue on refining the API and improving documentation.
+
+### Installation
+
+ML.NET supports Windows, MacOS, and Linux. See [supported OS versions of .NET Core 2.0](https://github.com/dotnet/core/blob/master/release-notes/2.0/2.0-supported-os.md) for more details.
+
+You can install ML.NET NuGet from the CLI using:
+```
+dotnet add package Microsoft.ML
+```
+
+From package manager:
+```
+Install-Package Microsoft.ML
+```
+
+### Release Notes
+
+Below are a few of the highlights from this release. There are many other improvements in the API.
+
+* Creation of components through MLContext: advanced options and other feedback. ([#1798](https://github.coalsom/dotnet/machinelearning/issues/1798))
+* Several issues closed on internalizing public surface. ([19 issues](https://github.com/dotnet/machinelearning/issues?q=is%3Aissue+lockdown+is%3Aclosed))
+* Stop using MEF as part of the public API. ([#2422](https://github.com/dotnet/machinelearning/issues/2422))
+* Several `NameSpace` changes. ([#2326](https://github.com/dotnet/machinelearning/issues/2326))
+* `ONNX` is now `ONNXConverter`. ([#2625](https://github.com/dotnet/machinelearning/pull/2625))
+* `ONNXTransform` is now `ONNXTransformer`. ([#2544](https://github.com/dotnet/machinelearning/pull/2544))
+* `FastTree` has it's own package now. ([#2752](https://github.com/dotnet/machinelearning/issues/2752))
+* `Ensemble` has been moved out of `Microsoft.ML` package. ([#2717](https://github.com/dotnet/machinelearning/issues/2717))
+* Add support for string types in TensorFlowTransformer. ([#2545](https://github.com/dotnet/machinelearning/issues/2545))
+* Make FastTree/LightGBM learned model suitable for public consumption. ([#1960](https://github.com/dotnet/machinelearning/issues/1960))
+
+
+### Acknowledgements
+
+Shoutout to [PaulTFreedman](https://github.com/PaulTFreedman),
+[kant2002](https://github.com/kant2002),
+[jwood803](https://github.com/jwood803), [mareklinka](https://github.com/mareklinka), [elbruno](https://github.com/elbruno) and the [ML.NET](https://aka.ms/mlnet) team for their
+contributions as part of this release!

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/Conversion/ConvertType.cs

@@ -0,0 +1,55 @@
+using System;
+using Microsoft.ML.Data;
+
+namespace Microsoft.ML.Samples.Dynamic
+{
+    public static class ConvertType
+    {
+        private sealed class InputData
+        {
+            public bool Survived;
+        }
+
+        private sealed class TransformedData
+        {
+            public bool Survived { get; set; }
+
+            public Int32 SurvivedInt32 { get; set; }
+        }
+
+        public static void Example()
+        {
+            var mlContext = new MLContext(seed: 1, conc: 1);
+            var rawData = new[] {
+                new InputData() { Survived = true },
+                new InputData() { Survived = false },
+                new InputData() { Survived = true },
+                new InputData() { Survived = false },
+                new InputData() { Survived = false },
+            };
+
+            var data = mlContext.Data.LoadFromEnumerable(rawData);
+
+            // Construct the pipeline.
+            var pipeline = mlContext.Transforms.Conversion.ConvertType("SurvivedInt32", "Survived", DataKind.Int32);
+
+            // Let's train our pipeline, and then apply it to the same data.
+            var transformer = pipeline.Fit(data);
+            var transformedData = transformer.Transform(data);
+
+            // Display original column 'Survived' (boolean) and converted column 'SurvivedInt32' (Int32)
+            var convertedData = mlContext.Data.CreateEnumerable<TransformedData>(transformedData, true);
+            foreach (var item in convertedData)
+            {
+                Console.WriteLine("A:{0,-10}  Aconv:{1}", item.Survived, item.SurvivedInt32);
+            }
+
+            // Output
+            // A: True     Aconv:1
+            // A: False    Aconv:0
+            // A: True     Aconv:1
+            // A: False    Aconv:0
+            // A: False    Aconv:0
+        }
+    }
+}

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
 {

src/Microsoft.ML.Data/Commands/CrossValidationCommand.cs

@@ -15,7 +15,6 @@
 using Microsoft.ML.Data;
 using Microsoft.ML.Internal.Utilities;
 using Microsoft.ML.Transforms;
-using Microsoft.ML.Transforms.Conversions;
 
 [assembly: LoadableClass(typeof(CrossValidationCommand), typeof(CrossValidationCommand.Arguments), typeof(SignatureCommand),
     "Cross Validation", CrossValidationCommand.LoadName)]

src/Microsoft.ML.Data/Commands/TrainCommand.cs

@@ -15,7 +15,7 @@
 using Microsoft.ML.Data.IO;
 using Microsoft.ML.Internal.Utilities;
 using Microsoft.ML.Model;
-using Microsoft.ML.Transforms.Normalizers;
+using Microsoft.ML.Transforms;
 
 [assembly: LoadableClass(TrainCommand.Summary, typeof(TrainCommand), typeof(TrainCommand.Arguments), typeof(SignatureCommand),
     "Train Predictor", "Train")]

src/Microsoft.ML.Data/Data/Conversion.cs

@@ -1642,10 +1642,10 @@ public void Convert(in TX src, ref SB dst)
         #endregion FromTX
 
         #region FromBL
-        public void Convert(in BL src, ref I1 dst) => dst = (I1)(object)src;
-        public void Convert(in BL src, ref I2 dst) => dst = (I2)(object)src;
-        public void Convert(in BL src, ref I4 dst) => dst = (I4)(object)src;
-        public void Convert(in BL src, ref I8 dst) => dst = (I8)(object)src;
+        public void Convert(in BL src, ref I1 dst) => dst = System.Convert.ToSByte(src);
+        public void Convert(in BL src, ref I2 dst) => dst = System.Convert.ToInt16(src);
+        public void Convert(in BL src, ref I4 dst) => dst = System.Convert.ToInt32(src);
+        public void Convert(in BL src, ref I8 dst) => dst = System.Convert.ToInt64(src);
         public void Convert(in BL src, ref R4 dst) => dst = System.Convert.ToSingle(src);
         public void Convert(in BL src, ref R8 dst) => dst = System.Convert.ToDouble(src);
         public void Convert(in BL src, ref BL dst) => dst = src;

src/Microsoft.ML.Data/Evaluators/ClusteringEvaluator.cs

@@ -11,9 +11,8 @@
 using Microsoft.ML.Data;
 using Microsoft.ML.EntryPoints;
 using Microsoft.ML.Internal.Utilities;
-using Microsoft.ML.Model;
 using Microsoft.ML.Numeric;
-using Microsoft.ML.Transforms.FeatureSelection;
+using Microsoft.ML.Transforms;
 
 [assembly: LoadableClass(typeof(ClusteringEvaluator), typeof(ClusteringEvaluator), typeof(ClusteringEvaluator.Arguments), typeof(SignatureEvaluator),
     "Clustering Evaluator", ClusteringEvaluator.LoadName, "Clustering")]

src/Microsoft.ML.Data/Evaluators/EvaluatorUtils.cs

@@ -12,7 +12,6 @@
 using Microsoft.ML.Data.IO;
 using Microsoft.ML.Internal.Utilities;
 using Microsoft.ML.Transforms;
-using Microsoft.ML.Transforms.Conversions;
 
 namespace Microsoft.ML.Data
 {

src/Microsoft.ML.Data/Evaluators/MultiClassClassifierEvaluator.cs

@@ -12,9 +12,7 @@
 using Microsoft.ML.Data;
 using Microsoft.ML.EntryPoints;
 using Microsoft.ML.Internal.Utilities;
-using Microsoft.ML.Model;
 using Microsoft.ML.Transforms;
-using Microsoft.ML.Transforms.FeatureSelection;
 
 [assembly: LoadableClass(typeof(MultiClassClassifierEvaluator), typeof(MultiClassClassifierEvaluator), typeof(MultiClassClassifierEvaluator.Arguments), typeof(SignatureEvaluator),
     "Multi-Class Classifier Evaluator", MultiClassClassifierEvaluator.LoadName, "MultiClassClassifier", "MultiClass")]

src/Microsoft.ML.Data/TrainCatalog.cs

@@ -7,7 +7,6 @@
 using Microsoft.Data.DataView;
 using Microsoft.ML.Data;
 using Microsoft.ML.Transforms;
-using Microsoft.ML.Transforms.Conversions;
 
 namespace Microsoft.ML
 {

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

@@ -10,7 +10,7 @@
 using Microsoft.Data.DataView;
 using Microsoft.ML.CommandLine;
 using Microsoft.ML.Internal.Utilities;
-using Microsoft.ML.Transforms.Conversions;
+using Microsoft.ML.Transforms;
 
 namespace Microsoft.ML.Data
 {

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

@@ -5,7 +5,7 @@
 using System.Collections.Generic;
 using Microsoft.Data.DataView;
 using Microsoft.ML.Data;
-using Microsoft.ML.Transforms.Conversions;
+using Microsoft.ML.Transforms;
 
 namespace Microsoft.ML
 {
@@ -47,6 +47,12 @@ public static HashingEstimator Hash(this TransformsCatalog.ConversionTransforms
         /// <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="outputKind">The expected kind of the output column.</param>
+        /// <example>
+        /// <format type="text/markdown">
+        /// <![CDATA[
+        ///  [!code-csharp[ConvertType](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Conversion/ConvertType.cs)]
+        /// ]]></format>
+        /// </example>
         public static TypeConvertingEstimator ConvertType(this TransformsCatalog.ConversionTransforms catalog, string outputColumnName, string inputColumnName = null,
             DataKind outputKind = ConvertDefaults.DefaultOutputKind)
             => new TypeConvertingEstimator(CatalogUtils.GetEnvironment(catalog), outputColumnName, inputColumnName, outputKind);

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

@@ -4,6 +4,7 @@
 
 using Microsoft.ML.Data;
 using Microsoft.ML.Model;
+using Microsoft.ML.Transforms;
 
 namespace Microsoft.ML
 {

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

@@ -10,9 +10,9 @@
 using Microsoft.ML.CommandLine;
 using Microsoft.ML.Data;
 using Microsoft.ML.EntryPoints;
-using Microsoft.ML.Internal.Internallearn;
 using Microsoft.ML.Internal.Utilities;
 using Microsoft.ML.Model;
+using Microsoft.ML.Transforms;
 
 [assembly: LoadableClass(FeatureContributionCalculatingTransformer.Summary, typeof(FeatureContributionCalculatingTransformer), null, typeof(SignatureLoadModel),
     FeatureContributionCalculatingTransformer.FriendlyName, FeatureContributionCalculatingTransformer.LoaderSignature)]
@@ -22,7 +22,7 @@
 
 [assembly: LoadableClass(typeof(void), typeof(FeatureContributionEntryPoint), null, typeof(SignatureEntryPointModule), FeatureContributionCalculatingTransformer.LoaderSignature)]
 
-namespace Microsoft.ML.Data
+namespace Microsoft.ML.Transforms
 {
     /// <summary>
     /// The FeatureContributionCalculationTransformer computes model-specific per-feature contributions to the score of each example.

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

@@ -12,8 +12,7 @@
 using Microsoft.ML.CommandLine;
 using Microsoft.ML.Data;
 using Microsoft.ML.Internal.Utilities;
-using Microsoft.ML.Model;
-using Microsoft.ML.Transforms.Conversions;
+using Microsoft.ML.Transforms;
 
 [assembly: LoadableClass(HashingTransformer.Summary, typeof(IDataTransform), typeof(HashingTransformer), typeof(HashingTransformer.Options), typeof(SignatureDataTransform),
     "Hash Transform", "HashTransform", "Hash", DocName = "transform/HashTransform.md")]
@@ -27,7 +26,7 @@
 [assembly: LoadableClass(typeof(IRowMapper), typeof(HashingTransformer), null, typeof(SignatureLoadRowMapper),
    "Hash Transform", HashingTransformer.LoaderSignature)]
 
-namespace Microsoft.ML.Transforms.Conversions
+namespace Microsoft.ML.Transforms
 {
     /// <summary>
     /// This transformer can hash either single valued columns or vector columns. For vector columns,

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

@@ -9,7 +9,6 @@
 using Microsoft.Data.DataView;
 using Microsoft.ML.Data.IO;
 using Microsoft.ML.Internal.Utilities;
-using Microsoft.ML.Model;
 
 namespace Microsoft.ML.Data
 {

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

@@ -13,9 +13,8 @@
 using Microsoft.ML.Data;
 using Microsoft.ML.EntryPoints;
 using Microsoft.ML.Internal.Utilities;
-using Microsoft.ML.Model;
 using Microsoft.ML.Model.Pfa;
-using Microsoft.ML.Transforms.Conversions;
+using Microsoft.ML.Transforms;
 using Newtonsoft.Json.Linq;
 
 [assembly: LoadableClass(typeof(IDataTransform), typeof(KeyToValueMappingTransformer), typeof(KeyToValueMappingTransformer.Options), typeof(SignatureDataTransform),
@@ -30,7 +29,7 @@
 [assembly: LoadableClass(typeof(IRowMapper), typeof(KeyToValueMappingTransformer), null, typeof(SignatureLoadRowMapper),
     KeyToValueMappingTransformer.UserName, KeyToValueMappingTransformer.LoaderSignature)]
 
-namespace Microsoft.ML.Transforms.Conversions
+namespace Microsoft.ML.Transforms
 {
     /// <summary>
     /// KeyToValueTransform utilizes KeyValues metadata to map key indices to the corresponding values in the KeyValues metadata.

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

@@ -11,10 +11,9 @@
 using Microsoft.ML.CommandLine;
 using Microsoft.ML.Data;
 using Microsoft.ML.Internal.Utilities;
-using Microsoft.ML.Model;
 using Microsoft.ML.Model.OnnxConverter;
 using Microsoft.ML.Model.Pfa;
-using Microsoft.ML.Transforms.Conversions;
+using Microsoft.ML.Transforms;
 using Newtonsoft.Json.Linq;
 
 [assembly: LoadableClass(KeyToVectorMappingTransformer.Summary, typeof(IDataTransform), typeof(KeyToVectorMappingTransformer), typeof(KeyToVectorMappingTransformer.Options), typeof(SignatureDataTransform),
@@ -29,7 +28,7 @@
 [assembly: LoadableClass(typeof(IRowMapper), typeof(KeyToVectorMappingTransformer), null, typeof(SignatureLoadRowMapper),
    KeyToVectorMappingTransformer.UserName, KeyToVectorMappingTransformer.LoaderSignature)]
 
-namespace Microsoft.ML.Transforms.Conversions
+namespace Microsoft.ML.Transforms
 {
     /// <summary>
     /// Converts the key types back to their original vectors.