Merge pull request #2861 from shauheen/release/v11final

Update release for 0.11

Author: shauheen

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.