Typed SDCA binary trainers

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

@@ -49,7 +49,7 @@ public static void Calibration()
             // Then append the StochasticDualCoordinateAscentBinary binary classifier, setting the "Label" column as the label of the dataset, and 
             // the "Features" column produced by FeaturizeText as the features column. 
             var pipeline = mlContext.Transforms.Text.FeaturizeText("SentimentText", "Features")
-                    .Append(mlContext.BinaryClassification.Trainers.StochasticDualCoordinateAscent(
+                    .Append(mlContext.BinaryClassification.Trainers.StochasticDualCoordinateAscentNonCalibrated(
                         labelColumn: "Sentiment", 
                         featureColumn: "Features", 
                         l2Const: 0.001f, 

docs/samples/Microsoft.ML.Samples/Dynamic/SDCA.cs → docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/SDCALogisticRegression.cs

@@ -5,9 +5,9 @@
 
 namespace Microsoft.ML.Samples.Dynamic
 {
-    public class SDCA_BinaryClassificationExample
+    public class SDCALogisticRegression
     {
-        public static void SDCA_BinaryClassification()
+        public static void Example()
         {
             // Downloading the dataset from github.com/dotnet/machinelearning.
             // This will create a sentiment.tsv file in the filesystem.

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/SDCASupportVectorMachine.cs

@@ -0,0 +1,61 @@
+using System;
+using System.Linq;
+using Microsoft.ML.Data;
+
+namespace Microsoft.ML.Samples.Dynamic
+{
+    public class SDCASupportVectorMachine
+    {
+        public static void Example()
+        {
+            // Generate IEnumerable<BinaryLabelFloatFeatureVectorSample> as training examples.
+            var rawData = SamplesUtils.DatasetUtils.GenerateBinaryLabelFloatFeatureVectorSamples(100);
+
+            // Information in first example.
+            // Label: true
+            Console.WriteLine("First example's label is {0}", rawData.First().Label);
+            // Features is a 10-element float[]:
+            //   [0]	1.0173254	float
+            //   [1]	0.9680227	float
+            //   [2]	0.7581612	float
+            //   [3]	0.406033158	float
+            //   [4]	0.7588848	float
+            //   [5]	1.10602713	float
+            //   [6]	0.6421779	float
+            //   [7]	1.17754972	float
+            //   [8]	0.473704457	float
+            //   [9]	0.4919063	float
+            Console.WriteLine("First example's feature vector is {0}", rawData.First().Features);
+
+            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
+            // as a catalog of available operations and as the source of randomness.
+            var mlContext = new MLContext();
+
+            // Step 1: Read the data as an IDataView.
+            var data = mlContext.Data.ReadFromEnumerable(rawData);
+
+            // ML.NET doesn't cache data set by default. Caching is always recommended when using the
+            // StochasticDualCoordinateAscent algorithm because it may incur multiple data passes.
+            data = mlContext.Data.Cache(data);
+
+            // Step 2: Create a binary classifier. This trainer may produce a logistic regression model.
+            // We set the "Label" column as the label of the dataset, and the "Features" column as the features column.
+            var pipeline = mlContext.BinaryClassification.Trainers.StochasticDualCoordinateAscentNonCalibrated(
+                labelColumn: "Label", featureColumn: "Features", loss: new HingeLoss(), l2Const: 0.001f);
+
+            // Step 3: Train the pipeline created.
+            var model = pipeline.Fit(data);
+
+            // Step 4: Make prediction and evaluate its quality (on training set).
+            var prediction = model.Transform(data);
+
+            var rawPrediction = mlContext.CreateEnumerable<SamplesUtils.DatasetUtils.NonCalibratedBinaryClassifierOutput>(prediction, false);
+
+            // Step 5: Inspect the prediction of the first example.
+            // Note that positive/negative label may be associated with positive/negative score
+            var first = rawPrediction.First();
+            Console.WriteLine("The first example actual label is {0}. The trained model assigns it a score {1}.",
+                first.Label /*true*/, first.Score /*around 3*/);
+        }
+    }
+}

docs/samples/Microsoft.ML.Samples/Static/SDCABinaryClassification.cs

@@ -1,5 +1,4 @@
 using System;
-using Microsoft.ML.Data;
 using Microsoft.ML.StaticPipe;
 
 namespace Microsoft.ML.Samples.Static

src/Microsoft.ML.HalLearners/SymSgdClassificationTrainer.cs

@@ -30,7 +30,7 @@
 
 namespace Microsoft.ML.Trainers.HalLearners
 {
-    using TPredictor = CalibratedModelParametersBase<LinearBinaryModelParameters,PlattCalibrator>;
+    using TPredictor = CalibratedModelParametersBase<LinearBinaryModelParameters, PlattCalibrator>;
 
     /// <include file='doc.xml' path='doc/members/member[@name="SymSGD"]/*' />
     public sealed class SymSgdClassificationTrainer : TrainerEstimatorBase<BinaryPredictionTransformer<TPredictor>, TPredictor>
@@ -207,7 +207,7 @@ private TPredictor CreatePredictor(VBuffer<float> weights, float bias)
             VBufferUtils.CreateMaybeSparseCopy(in weights, ref maybeSparseWeights,
                 Conversions.Instance.GetIsDefaultPredicate<float>(NumberType.R4));
             var predictor = new LinearBinaryModelParameters(Host, in maybeSparseWeights, bias);
-            return new ParameterMixingCalibratedModelParameters<LinearBinaryModelParameters,PlattCalibrator>(Host, predictor, new PlattCalibrator(Host, -1, 0));
+            return new ParameterMixingCalibratedModelParameters<LinearBinaryModelParameters, PlattCalibrator>(Host, predictor, new PlattCalibrator(Host, -1, 0));
         }
 
         protected override BinaryPredictionTransformer<TPredictor> MakeTransformer(TPredictor model, Schema trainSchema)

src/Microsoft.ML.SamplesUtils/SamplesDatasetUtils.cs

@@ -371,6 +371,9 @@ public static IEnumerable<SampleVectorOfNumbersData> GetVectorOfNumbersData()
 
         private const int _simpleBinaryClassSampleFeatureLength = 10;
 
+        /// <summary>
+        /// Example with one binary label and 10 feature values.
+        /// </summary>
         public class BinaryLabelFloatFeatureVectorSample
         {
             public bool Label;
@@ -379,6 +382,27 @@ public class BinaryLabelFloatFeatureVectorSample
             public float[] Features;
         }
 
+        /// <summary>
+        /// Class used to capture prediction of <see cref="BinaryLabelFloatFeatureVectorSample"/> when
+        /// calling <see cref="CursoringUtils.CreateEnumerable"/> via on <see cref="MLContext"/>.
+        /// </summary>
+        public class CalibratedBinaryClassifierOutput
+        {
+            public bool Label;
+            public float Score;
+            public float Probability;
+        }
+
+        /// <summary>
+        /// Class used to capture prediction of <see cref="BinaryLabelFloatFeatureVectorSample"/> when
+        /// calling <see cref="CursoringUtils.CreateEnumerable"/> via on <see cref="MLContext"/>.
+        /// </summary>
+        public class NonCalibratedBinaryClassifierOutput
+        {
+            public bool Label;
+            public float Score;
+        }
+
         public static IEnumerable<BinaryLabelFloatFeatureVectorSample> GenerateBinaryLabelFloatFeatureVectorSamples(int exampleCount)
         {
             var rnd = new Random(0);