Cleaning and Fixing public API for set of learners.

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

@@ -7,22 +7,22 @@ public class PriorTrainer
     {
         public static void Example()
         {
-            // Downloading the dataset from github.com/dotnet/machinelearning.
-            // This will create a sentiment.tsv file in the filesystem.
-            // You can open this file, if you want to see the data. 
-            string dataFile = SamplesUtils.DatasetUtils.DownloadSentimentDataset()[0];
+            // 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();
+
+            // Download and featurize the dataset.
+            var dataFiles = SamplesUtils.DatasetUtils.DownloadSentimentDataset();
+            var trainFile = dataFiles[0];
+            var testFile = dataFiles[1];
 
             // A preview of the data. 
             // Sentiment	SentimentText
             //      0	    " :Erm, thank you. "
             //      1	    ==You're cool==
 
-            // 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: Load the data as an IDataView.
-            // First, we define the loader: specify the data columns and where to find them in the text file.
+            // Step 1: Read the data as an IDataView.
+            // First, we define the reader: specify the data columns and where to find them in the text file.
             var loader = mlContext.Data.CreateTextLoader(
                 columns: new[]
                     {
@@ -31,12 +31,9 @@ public static void Example()
                     },
                 hasHeader: true
             );
-
-            // Load the data
-            var data = loader.Load(dataFile);
 
-            // Split it between training and test data
-            var trainTestData = mlContext.BinaryClassification.TrainTestSplit(data);
+            // Load the data
+            var trainData = loader.Load(trainFile);
 
             // Step 2: Pipeline 
             // Featurize the text column through the FeaturizeText API. 
@@ -47,19 +44,27 @@ public static void Example()
                     .Append(mlContext.BinaryClassification.Trainers.Prior(labelColumnName: "Sentiment"));
 
             // Step 3: Train the pipeline
-            var trainedPipeline = pipeline.Fit(trainTestData.TrainSet);
+            var trainedPipeline = pipeline.Fit(trainData);
 
             // Step 4: Evaluate on the test set
-            var transformedData = trainedPipeline.Transform(trainTestData.TestSet);
+            var transformedData = trainedPipeline.Transform(loader.Load(testFile));
             var evalMetrics = mlContext.BinaryClassification.Evaluate(transformedData, label: "Sentiment");
-
-            // Step 5: Inspect the output
-            Console.WriteLine("Accuracy: " + evalMetrics.Accuracy);
+            SamplesUtils.ConsoleUtils.PrintMetrics(evalMetrics);
 
             // The Prior trainer outputs the proportion of a label in the dataset as the probability of that label.
-            // In this case it means that there is a split of around 64%-36% of positive and negative labels in the dataset.
+            // In this case 'Accuracy: 0.50' means that there is a split of around 50%-50% of positive and negative labels in the test dataset.
             // Expected output:
-            // Accuracy: 0.647058823529412
+
+            //  Accuracy: 0.50
+            //  AUC: 0.50
+            //  F1 Score: 0.67
+            //  Negative Precision: 0.00
+            //  Negative Recall: 0.00
+            //  Positive Precision: 0.50
+            //  Positive Recall: 1.00
+            //  LogLoss: 1.05
+            //  LogLossReduction: -4.89
+            //  Entropy: 1.00
         }
     }
 }

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

@@ -7,59 +7,64 @@ public static class RandomTrainer
     {
         public static void Example()
         {
-            // Downloading the dataset from github.com/dotnet/machinelearning.
-            // This will create a sentiment.tsv file in the filesystem.
-            // You can open this file, if you want to see the data. 
-            string dataFile = SamplesUtils.DatasetUtils.DownloadSentimentDataset()[0];
+            // 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(seed: 1);
+
+            // Download and featurize the dataset.
+            var dataFiles = SamplesUtils.DatasetUtils.DownloadSentimentDataset();
+            var trainFile = dataFiles[0];
+            var testFile = dataFiles[1];
 
             // A preview of the data. 
             // Sentiment	SentimentText
             //      0	    " :Erm, thank you. "
             //      1	    ==You're cool==
 
-            // 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(seed: 1);
-
-            // Step 1: Load the data as an IDataView.
-            // First, we define the loader: specify the data columns and where to find them in the text file.
-            var loader = mlContext.Data.CreateTextLoader(
+            // Step 1: Read the data as an IDataView.
+            // First, we define the reader: specify the data columns and where to find them in the text file.
+            var reader = mlContext.Data.CreateTextLoader(
                 columns: new[]
                     {
                         new TextLoader.Column("Sentiment", DataKind.Single, 0),
                         new TextLoader.Column("SentimentText", DataKind.String, 1)
                     },
                 hasHeader: true
             );
-
-            // Load the data
-            var data = loader.Load(dataFile);
 
-            // Split it between training and test data
-            var trainTestData = mlContext.BinaryClassification.TrainTestSplit(data);
+            // Read the data
+            var trainData = reader.Load(trainFile);
 
             // Step 2: Pipeline 
             // Featurize the text column through the FeaturizeText API. 
             // Then append a 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("Features", "SentimentText")
-                    .AppendCacheCheckpoint(mlContext) // Add a data-cache step within a pipeline.
+                    .AppendCacheCheckpoint(mlContext)
                     .Append(mlContext.BinaryClassification.Trainers.Random());
 
             // Step 3: Train the pipeline
-            var trainedPipeline = pipeline.Fit(trainTestData.TrainSet);
+            var trainedPipeline = pipeline.Fit(trainData);
 
             // Step 4: Evaluate on the test set
-            var transformedData = trainedPipeline.Transform(trainTestData.TestSet);
+            var transformedData = trainedPipeline.Transform(reader.Load(testFile));
             var evalMetrics = mlContext.BinaryClassification.Evaluate(transformedData, label: "Sentiment");
-
-            // Step 5: Inspect the output
-            Console.WriteLine("Accuracy: " + evalMetrics.Accuracy);
+            SamplesUtils.ConsoleUtils.PrintMetrics(evalMetrics);
 
             // We expect an output probability closet to 0.5 as the Random trainer outputs a random prediction.
             // Regardless of the input features, the trainer will predict either positive or negative label with equal probability.
-            // Expected output (close to 0.5):
-            // Accuracy: 0.588235294117647
+            // Expected output: (close to 0.5):
+
+            //  Accuracy: 0.56
+            //  AUC: 0.57
+            //  F1 Score: 0.60
+            //  Negative Precision: 0.57
+            //  Negative Recall: 0.44
+            //  Positive Precision: 0.55
+            //  Positive Recall: 0.67
+            //  LogLoss: 1.53
+            //  LogLossReduction: -53.37
+            //  Entropy: 1.00
         }
     }
 }

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

@@ -20,7 +20,7 @@ public static void Example()
             var split = mlContext.BinaryClassification.TrainTestSplit(data, testFraction: 0.1);
             // Create data training pipeline
             var pipeline = mlContext.BinaryClassification.Trainers.SymbolicStochasticGradientDescent(
-                    new ML.Trainers.HalLearners.SymSgdClassificationTrainer.Options()
+                    new ML.Trainers.HalLearners.SymbolicStochasticGradientDescentClassificationTrainer.Options()
                     {
                         LearningRate = 0.2f,
                         NumberOfIterations = 10,

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/OrdinaryLeastSquaresWithOptions.cs

@@ -44,7 +44,7 @@ public static void Example()
 
             // Create the estimator, here we only need OrdinaryLeastSquares trainer 
             // as data is already processed in a form consumable by the trainer
-            var pipeline = mlContext.Regression.Trainers.OrdinaryLeastSquares(new OlsLinearRegressionTrainer.Options()
+            var pipeline = mlContext.Regression.Trainers.OrdinaryLeastSquares(new OrdinaryLeastSquaresRegressionTrainer.Options()
             {
                 L2Weight = 0.1f,
                 PerParameterSignificance = false

src/Microsoft.ML.HalLearners/ComputeLRTrainingStdThroughHal.cs

@@ -9,7 +9,7 @@
 
 namespace Microsoft.ML.Trainers
 {
-    using Mkl = OlsLinearRegressionTrainer.Mkl;
+    using Mkl = OrdinaryLeastSquaresRegressionTrainer.Mkl;
 
     public sealed class ComputeLRTrainingStdThroughHal : ComputeLRTrainingStd
     {

src/Microsoft.ML.HalLearners/HalLearnersCatalog.cs

@@ -9,12 +9,12 @@
 namespace Microsoft.ML
 {
     /// <summary>
-    /// The trainer catalog extensions for the <see cref="OlsLinearRegressionTrainer"/> and <see cref="SymSgdClassificationTrainer"/>.
+    /// The trainer catalog extensions for the <see cref="OrdinaryLeastSquaresRegressionTrainer"/> and <see cref="SymbolicStochasticGradientDescentClassificationTrainer"/>.
     /// </summary>
     public static class HalLearnersCatalog
     {
         /// <summary>
-        /// Predict a target using a linear regression model trained with the <see cref="OlsLinearRegressionTrainer"/>.
+        /// Predict a target using a linear regression model trained with the <see cref="OrdinaryLeastSquaresRegressionTrainer"/>.
         /// </summary>
         /// <param name="catalog">The <see cref="RegressionCatalog"/>.</param>
         /// <param name="labelColumnName">The name of the label column.</param>
@@ -27,48 +27,48 @@ public static class HalLearnersCatalog
         /// ]]>
         /// </format>
         /// </example>
-        public static OlsLinearRegressionTrainer OrdinaryLeastSquares(this RegressionCatalog.RegressionTrainers catalog,
+        public static OrdinaryLeastSquaresRegressionTrainer OrdinaryLeastSquares(this RegressionCatalog.RegressionTrainers catalog,
             string labelColumnName = DefaultColumnNames.Label,
             string featureColumnName = DefaultColumnNames.Features,
             string exampleWeightColumnName = null)
         {
             Contracts.CheckValue(catalog, nameof(catalog));
             var env = CatalogUtils.GetEnvironment(catalog);
-            var options = new OlsLinearRegressionTrainer.Options
+            var options = new OrdinaryLeastSquaresRegressionTrainer.Options
             {
                 LabelColumnName = labelColumnName,
                 FeatureColumnName = featureColumnName,
                 ExampleWeightColumnName = exampleWeightColumnName
             };
 
-            return new OlsLinearRegressionTrainer(env, options);
+            return new OrdinaryLeastSquaresRegressionTrainer(env, options);
         }
 
         /// <summary>
-        /// Predict a target using a linear regression model trained with the <see cref="OlsLinearRegressionTrainer"/>.
+        /// Predict a target using a linear regression model trained with the <see cref="OrdinaryLeastSquaresRegressionTrainer"/>.
         /// </summary>
         /// <param name="catalog">The <see cref="RegressionCatalog"/>.</param>
-        /// <param name="options">Algorithm advanced options. See <see cref="OlsLinearRegressionTrainer.Options"/>.</param>
+        /// <param name="options">Algorithm advanced options. See <see cref="OrdinaryLeastSquaresRegressionTrainer.Options"/>.</param>
         /// <example>
         /// <format type="text/markdown">
         /// <![CDATA[
         /// [!code-csharp[OrdinaryLeastSquares](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/OrdinaryLeastSquaresWithOptions.cs)]
         /// ]]>
         /// </format>
         /// </example>
-        public static OlsLinearRegressionTrainer OrdinaryLeastSquares(
+        public static OrdinaryLeastSquaresRegressionTrainer OrdinaryLeastSquares(
             this RegressionCatalog.RegressionTrainers catalog,
-            OlsLinearRegressionTrainer.Options options)
+            OrdinaryLeastSquaresRegressionTrainer.Options options)
         {
             Contracts.CheckValue(catalog, nameof(catalog));
             Contracts.CheckValue(options, nameof(options));
 
             var env = CatalogUtils.GetEnvironment(catalog);
-            return new OlsLinearRegressionTrainer(env, options);
+            return new OrdinaryLeastSquaresRegressionTrainer(env, options);
         }
 
         /// <summary>
-        ///  Predict a target using a linear binary classification model trained with the <see cref="SymSgdClassificationTrainer"/>.
+        ///  Predict a target using a linear binary classification model trained with the <see cref="SymbolicStochasticGradientDescentClassificationTrainer"/>.
         /// </summary>
         /// <param name="catalog">The <see cref="BinaryClassificationCatalog"/>.</param>
         /// <param name="labelColumnName">The name of the label column.</param>
@@ -81,43 +81,43 @@ public static OlsLinearRegressionTrainer OrdinaryLeastSquares(
         /// ]]>
         /// </format>
         /// </example>
-        public static SymSgdClassificationTrainer SymbolicStochasticGradientDescent(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
+        public static SymbolicStochasticGradientDescentClassificationTrainer SymbolicStochasticGradientDescent(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
             string labelColumnName = DefaultColumnNames.Label,
             string featureColumnName = DefaultColumnNames.Features,
-            int numberOfIterations = SymSgdClassificationTrainer.Defaults.NumberOfIterations)
+            int numberOfIterations = SymbolicStochasticGradientDescentClassificationTrainer.Defaults.NumberOfIterations)
         {
             Contracts.CheckValue(catalog, nameof(catalog));
             var env = CatalogUtils.GetEnvironment(catalog);
 
-            var options = new SymSgdClassificationTrainer.Options
+            var options = new SymbolicStochasticGradientDescentClassificationTrainer.Options
             {
                 LabelColumnName = labelColumnName,
                 FeatureColumnName = featureColumnName,
             };
 
-            return new SymSgdClassificationTrainer(env, options);
+            return new SymbolicStochasticGradientDescentClassificationTrainer(env, options);
         }
 
         /// <summary>
-        ///  Predict a target using a linear binary classification model trained with the <see cref="SymSgdClassificationTrainer"/>.
+        ///  Predict a target using a linear binary classification model trained with the <see cref="SymbolicStochasticGradientDescentClassificationTrainer"/>.
         /// </summary>
         /// <param name="catalog">The <see cref="BinaryClassificationCatalog"/>.</param>
-        /// <param name="options">Algorithm advanced options. See <see cref="SymSgdClassificationTrainer.Options"/>.</param>
+        /// <param name="options">Algorithm advanced options. See <see cref="SymbolicStochasticGradientDescentClassificationTrainer.Options"/>.</param>
         /// <example>
         /// <format type="text/markdown">
         /// <![CDATA[
         /// [!code-csharp[SymbolicStochasticGradientDescent](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/SymbolicStochasticGradientDescentWithOptions.cs)]
         /// ]]>
         /// </format>
         /// </example>
-        public static SymSgdClassificationTrainer SymbolicStochasticGradientDescent(
+        public static SymbolicStochasticGradientDescentClassificationTrainer SymbolicStochasticGradientDescent(
             this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
-            SymSgdClassificationTrainer.Options options)
+            SymbolicStochasticGradientDescentClassificationTrainer.Options options)
         {
             Contracts.CheckValue(catalog, nameof(catalog));
             Contracts.CheckValue(options, nameof(options));
             var env = CatalogUtils.GetEnvironment(catalog);
-            return new SymSgdClassificationTrainer(env, options);
+            return new SymbolicStochasticGradientDescentClassificationTrainer(env, options);
         }
 
         /// <summary>