Better names to calibreated linear classification models

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

@@ -63,7 +63,7 @@ public static void Example()
                 .Append(ml.Transforms.Text.FeaturizeText("TextFeatures", "Text"))
                 .Append(ml.Transforms.Concatenate("Features", "TextFeatures", "age", "fnlwgt",
                     "education-num", "capital-gain", "capital-loss", "hours-per-week"))
-                .Append(ml.BinaryClassification.Trainers.LogisticRegression());
+                .Append(ml.BinaryClassification.Trainers.LbfgsLogisticRegression());
 
             var model = pipeline.Fit(split.TrainSet);
 

docs/samples/Microsoft.ML.Samples/Dynamic/PermutationFeatureImportance/PfiBinaryClassificationExample.cs

@@ -22,7 +22,7 @@ public static void Example()
             // Then append a logistic regression trainer.
             var pipeline = mlContext.Transforms.Concatenate("Features", featureNames)
                     .Append(mlContext.Transforms.Normalize("Features"))
-                    .Append(mlContext.BinaryClassification.Trainers.LogisticRegression(
+                    .Append(mlContext.BinaryClassification.Trainers.LbfgsLogisticRegression(
                         labelColumnName: labelName, featureColumnName: "Features"));
             var model = pipeline.Fit(data);
 

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

@@ -60,8 +60,8 @@ public static void Example()
             // If we wanted to specify more advanced parameters for the algorithm, 
             // we could do so by tweaking the 'advancedSetting'.
             var advancedPipeline = mlContext.Transforms.Text.FeaturizeText("SentimentText", "Features")
-                                  .Append(mlContext.BinaryClassification.Trainers.SdcaCalibrated(
-                                      new SdcaCalibratedBinaryTrainer.Options { 
+                                  .Append(mlContext.BinaryClassification.Trainers.SdcaLogisticRegression(
+                                      new SdcaLogisticRegressionBinaryTrainer.Options { 
                                         LabelColumnName = "Sentiment",
                                         FeatureColumnName = "Features",
                                         ConvergenceTolerance = 0.01f,  // The learning rate for adjusting bias from being regularized

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

@@ -22,7 +22,7 @@ public static void Example()
             var trainTestData = mlContext.Data.TrainTestSplit(data, testFraction: 0.1);
 
             // Define the trainer options.
-            var options = new SdcaCalibratedBinaryTrainer.Options()
+            var options = new SdcaLogisticRegressionBinaryTrainer.Options()
             {
                 // Make the convergence tolerance tighter.
                 ConvergenceTolerance = 0.05f,
@@ -33,7 +33,7 @@ public static void Example()
             };
 
             // Create data training pipeline.
-            var pipeline = mlContext.BinaryClassification.Trainers.SdcaCalibrated(options);
+            var pipeline = mlContext.BinaryClassification.Trainers.SdcaLogisticRegression(options);
 
             // Fit this pipeline to the training data.
             var model = pipeline.Fit(trainTestData.TrainSet);

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

@@ -19,7 +19,7 @@ public static void Example()
             // Leave out 10% of data for testing.
             var split = mlContext.Data.TrainTestSplit(data, testFraction: 0.1);
             // Create data training pipeline.
-            var pipeline = mlContext.BinaryClassification.Trainers.SymbolicSgd(labelColumnName: "IsOver50K", numberOfIterations: 25);
+            var pipeline = mlContext.BinaryClassification.Trainers.SymbolicSgdLogisticRegression(labelColumnName: "IsOver50K", numberOfIterations: 25);
             var model = pipeline.Fit(split.TrainSet);
 
             // Evaluate how the model is doing on the test data.

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

@@ -19,8 +19,8 @@ public static void Example()
             // Leave out 10% of data for testing.
             var split = mlContext.Data.TrainTestSplit(data, testFraction: 0.1);
             // Create data training pipeline
-            var pipeline = mlContext.BinaryClassification.Trainers.SymbolicSgd(
-                    new ML.Trainers.SymbolicSgdTrainer.Options()
+            var pipeline = mlContext.BinaryClassification.Trainers.SymbolicSgdLogisticRegression(
+                    new ML.Trainers.SymbolicSgdLogisticRegressionBinaryTrainer.Options()
                     {
                         LearningRate = 0.2f,
                         NumberOfIterations = 10,

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/StochasticDualCoordinateAscent.cs

@@ -30,7 +30,7 @@ public static void Example()
                     // Convert the string labels into key types.
                     mlContext.Transforms.Conversion.MapValueToKey("Label")
                     // Apply StochasticDualCoordinateAscent multiclass trainer.
-                    .Append(mlContext.MulticlassClassification.Trainers.SdcaCalibrated());
+                    .Append(mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy());
 
             // Split the data into training and test sets. Only training set is used in fitting
             // the created pipeline. Metrics are computed on the test.

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

@@ -21,7 +21,7 @@ public static void Example()
             var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
 
             // Define the trainer.
-            var pipeline = mlContext.Regression.Trainers.PoissonRegression();
+            var pipeline = mlContext.Regression.Trainers.LbfgsPoissonRegression();
 
             // Train the model.
             var model = pipeline.Fit(trainingData);

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

@@ -22,7 +22,7 @@ public static void Example()
             var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
 
             // Define trainer options.
-            var options = new PoissonRegressionTrainer.Options
+            var options = new LbfgsPoissonRegressionTrainer.Options
             {
                 // Reduce optimization tolerance to speed up training at the cost of accuracy.
                 OptmizationTolerance = 1e-4f,
@@ -33,7 +33,7 @@ public static void Example()
             };
 
             // Define the trainer.
-            var pipeline = mlContext.Regression.Trainers.PoissonRegression(options);
+            var pipeline = mlContext.Regression.Trainers.LbfgsPoissonRegression(options);
 
             // Train the model.
             var model = pipeline.Fit(trainingData);

src/Microsoft.ML.Ensemble/Trainer/Multiclass/MulticlassDataPartitionEnsembleTrainer.cs

@@ -64,8 +64,8 @@ public Arguments()
                             // non-default column names. Unfortuantely no method of resolving this temporary strikes me as being any
                             // less laborious than the proper fix, which is that this "meta" component should itself be a trainer
                             // estimator, as opposed to a regular trainer.
-                            var trainerEstimator = new LbfgsMaximumEntropyTrainer(env, LabelColumnName, FeatureColumnName);
-                            return TrainerUtils.MapTrainerEstimatorToTrainer<LbfgsMaximumEntropyTrainer,
+                            var trainerEstimator = new LbfgsMaximumEntropyMulticlassTrainer(env, LabelColumnName, FeatureColumnName);
+                            return TrainerUtils.MapTrainerEstimatorToTrainer<LbfgsMaximumEntropyMulticlassTrainer,
                                 MaximumEntropyModelParameters, MaximumEntropyModelParameters>(env, trainerEstimator);
                         })
                 };

src/Microsoft.ML.Mkl.Components/MklComponentsCatalog.cs

@@ -10,7 +10,7 @@
 namespace Microsoft.ML
 {
     /// <summary>
-    /// The trainer catalog extensions for the <see cref="OlsTrainer"/> and <see cref="SymbolicSgdTrainer"/>.
+    /// The trainer catalog extensions for the <see cref="OlsTrainer"/> and <see cref="SymbolicSgdLogisticRegressionBinaryTrainer"/>.
     /// </summary>
     public static class MklComponentsCatalog
     {
@@ -69,9 +69,9 @@ public static OlsTrainer Ols(
         }
 
         /// <summary>
-        /// Predict a target using a linear binary classification model trained with the <see cref="SymbolicSgdTrainer"/>.
+        /// Predict a target using a linear binary classification model trained with the <see cref="SymbolicSgdLogisticRegressionBinaryTrainer"/>.
         /// Stochastic gradient descent (SGD) is an iterative algorithm that optimizes a differentiable objective function.
-        /// The <see cref="SymbolicSgdTrainer"/> parallelizes SGD using <a href="https://www.microsoft.com/en-us/research/project/project-parade/#!symbolic-execution">symbolic execution</a>.
+        /// The <see cref="SymbolicSgdLogisticRegressionBinaryTrainer"/> parallelizes SGD using <a href="https://www.microsoft.com/en-us/research/project/project-parade/#!symbolic-execution">symbolic execution</a>.
         /// </summary>
         /// <param name="catalog">The <see cref="BinaryClassificationCatalog"/>.</param>
         /// <param name="labelColumnName">The name of the label column.</param>
@@ -84,45 +84,45 @@ public static OlsTrainer Ols(
         /// ]]>
         /// </format>
         /// </example>
-        public static SymbolicSgdTrainer SymbolicSgd(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
+        public static SymbolicSgdLogisticRegressionBinaryTrainer SymbolicSgdLogisticRegression(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
             string labelColumnName = DefaultColumnNames.Label,
             string featureColumnName = DefaultColumnNames.Features,
-            int numberOfIterations = SymbolicSgdTrainer.Defaults.NumberOfIterations)
+            int numberOfIterations = SymbolicSgdLogisticRegressionBinaryTrainer.Defaults.NumberOfIterations)
         {
             Contracts.CheckValue(catalog, nameof(catalog));
             var env = CatalogUtils.GetEnvironment(catalog);
 
-            var options = new SymbolicSgdTrainer.Options
+            var options = new SymbolicSgdLogisticRegressionBinaryTrainer.Options
             {
                 LabelColumnName = labelColumnName,
                 FeatureColumnName = featureColumnName,
             };
 
-            return new SymbolicSgdTrainer(env, options);
+            return new SymbolicSgdLogisticRegressionBinaryTrainer(env, options);
         }
 
         /// <summary>
-        ///  Predict a target using a linear binary classification model trained with the <see cref="SymbolicSgdTrainer"/>.
+        ///  Predict a target using a linear binary classification model trained with the <see cref="SymbolicSgdLogisticRegressionBinaryTrainer"/>.
         /// Stochastic gradient descent (SGD) is an iterative algorithm that optimizes a differentiable objective function.
-        /// The <see cref="SymbolicSgdTrainer"/> parallelizes SGD using <a href="https://www.microsoft.com/en-us/research/project/project-parade/#!symbolic-execution">symbolic execution</a>.
+        /// The <see cref="SymbolicSgdLogisticRegressionBinaryTrainer"/> parallelizes SGD using <a href="https://www.microsoft.com/en-us/research/project/project-parade/#!symbolic-execution">symbolic execution</a>.
         /// </summary>
         /// <param name="catalog">The <see cref="BinaryClassificationCatalog"/>.</param>
-        /// <param name="options">Algorithm advanced options. See <see cref="SymbolicSgdTrainer.Options"/>.</param>
+        /// <param name="options">Algorithm advanced options. See <see cref="SymbolicSgdLogisticRegressionBinaryTrainer.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 SymbolicSgdTrainer SymbolicSgd(
+        public static SymbolicSgdLogisticRegressionBinaryTrainer SymbolicSgdLogisticRegression(
             this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
-            SymbolicSgdTrainer.Options options)
+            SymbolicSgdLogisticRegressionBinaryTrainer.Options options)
         {
             Contracts.CheckValue(catalog, nameof(catalog));
             Contracts.CheckValue(options, nameof(options));
             var env = CatalogUtils.GetEnvironment(catalog);
-            return new SymbolicSgdTrainer(env, options);
+            return new SymbolicSgdLogisticRegressionBinaryTrainer(env, options);
         }
 
         /// <summary>

src/Microsoft.ML.Mkl.Components/SymSgdClassificationTrainer.cs

@@ -19,20 +19,20 @@
 using Microsoft.ML.Trainers;
 using Microsoft.ML.Transforms;
 
-[assembly: LoadableClass(typeof(SymbolicSgdTrainer), typeof(SymbolicSgdTrainer.Options),
+[assembly: LoadableClass(typeof(SymbolicSgdLogisticRegressionBinaryTrainer), typeof(SymbolicSgdLogisticRegressionBinaryTrainer.Options),
     new[] { typeof(SignatureBinaryClassifierTrainer), typeof(SignatureTrainer), typeof(SignatureFeatureScorerTrainer) },
-    SymbolicSgdTrainer.UserNameValue,
-    SymbolicSgdTrainer.LoadNameValue,
-    SymbolicSgdTrainer.ShortName)]
+    SymbolicSgdLogisticRegressionBinaryTrainer.UserNameValue,
+    SymbolicSgdLogisticRegressionBinaryTrainer.LoadNameValue,
+    SymbolicSgdLogisticRegressionBinaryTrainer.ShortName)]
 
-[assembly: LoadableClass(typeof(void), typeof(SymbolicSgdTrainer), null, typeof(SignatureEntryPointModule), SymbolicSgdTrainer.LoadNameValue)]
+[assembly: LoadableClass(typeof(void), typeof(SymbolicSgdLogisticRegressionBinaryTrainer), null, typeof(SignatureEntryPointModule), SymbolicSgdLogisticRegressionBinaryTrainer.LoadNameValue)]
 
 namespace Microsoft.ML.Trainers
 {
     using TPredictor = CalibratedModelParametersBase<LinearBinaryModelParameters, PlattCalibrator>;
 
     /// <include file='doc.xml' path='doc/members/member[@name="SymSGD"]/*' />
-    public sealed class SymbolicSgdTrainer : TrainerEstimatorBase<BinaryPredictionTransformer<TPredictor>, TPredictor>
+    public sealed class SymbolicSgdLogisticRegressionBinaryTrainer : TrainerEstimatorBase<BinaryPredictionTransformer<TPredictor>, TPredictor>
     {
         internal const string LoadNameValue = "SymbolicSGD";
         internal const string UserNameValue = "Symbolic SGD (binary)";
@@ -195,9 +195,9 @@ private protected override TPredictor TrainModelCore(TrainContext context)
         private protected override PredictionKind PredictionKind => PredictionKind.BinaryClassification;
 
         /// <summary>
-        /// Initializes a new instance of <see cref="SymbolicSgdTrainer"/>
+        /// Initializes a new instance of <see cref="SymbolicSgdLogisticRegressionBinaryTrainer"/>
         /// </summary>
-        internal SymbolicSgdTrainer(IHostEnvironment env, Options options)
+        internal SymbolicSgdLogisticRegressionBinaryTrainer(IHostEnvironment env, Options options)
             : base(Contracts.CheckRef(env, nameof(env)).Register(LoadNameValue), TrainerUtils.MakeR4VecFeature(options.FeatureColumnName),
                   TrainerUtils.MakeBoolScalarLabel(options.LabelColumnName))
         {
@@ -223,7 +223,7 @@ private protected override BinaryPredictionTransformer<TPredictor> MakeTransform
              => new BinaryPredictionTransformer<TPredictor>(Host, model, trainSchema, FeatureColumn.Name);
 
         /// <summary>
-        /// Continues the training of <see cref="SymbolicSgdTrainer"/> using an already trained <paramref name="modelParameters"/>
+        /// Continues the training of <see cref="SymbolicSgdLogisticRegressionBinaryTrainer"/> using an already trained <paramref name="modelParameters"/>
         /// a <see cref="BinaryPredictionTransformer"/>.
         /// </summary>
         public BinaryPredictionTransformer<TPredictor> Fit(IDataView trainData, LinearModelParameters modelParameters)
@@ -241,8 +241,8 @@ private protected override SchemaShape.Column[] GetOutputColumnsCore(SchemaShape
 
         [TlcModule.EntryPoint(Name = "Trainers.SymSgdBinaryClassifier",
             Desc = "Train a symbolic SGD.",
-            UserName = SymbolicSgdTrainer.UserNameValue,
-            ShortName = SymbolicSgdTrainer.ShortName)]
+            UserName = SymbolicSgdLogisticRegressionBinaryTrainer.UserNameValue,
+            ShortName = SymbolicSgdLogisticRegressionBinaryTrainer.ShortName)]
         internal static CommonOutputs.BinaryClassificationOutput TrainSymSgd(IHostEnvironment env, Options options)
         {
             Contracts.CheckValue(env, nameof(env));
@@ -251,7 +251,7 @@ internal static CommonOutputs.BinaryClassificationOutput TrainSymSgd(IHostEnviro
             EntryPointUtils.CheckInputArgs(host, options);
 
             return TrainerEntryPointsUtils.Train<Options, CommonOutputs.BinaryClassificationOutput>(host, options,
-                () => new SymbolicSgdTrainer(host, options),
+                () => new SymbolicSgdLogisticRegressionBinaryTrainer(host, options),
                 () => TrainerEntryPointsUtils.FindColumn(host, options.TrainingData.Schema, options.LabelColumnName));
         }
 
@@ -324,7 +324,7 @@ public void Free()
             // giving an array, we are at _storage[_storageIndex][_indexInCurArray].
             private int _indexInCurArray;
             // This is used to access AccelMemBudget, AccelChunkSize and UsedMemory
-            private readonly SymbolicSgdTrainer _trainer;
+            private readonly SymbolicSgdLogisticRegressionBinaryTrainer _trainer;
 
             private readonly IChannel _ch;
 
@@ -336,7 +336,7 @@ public void Free()
             /// </summary>
             /// <param name="trainer"></param>
             /// <param name="ch"></param>
-            public ArrayManager(SymbolicSgdTrainer trainer, IChannel ch)
+            public ArrayManager(SymbolicSgdLogisticRegressionBinaryTrainer trainer, IChannel ch)
             {
                 _storage = new List<VeryLongArray>();
                 // Setting the default value to 2^17.
@@ -500,7 +500,7 @@ private sealed class InputDataManager : IDisposable
             // This is the index to go over the instances in instanceProperties
             private int _instanceIndex;
             // This is used to access AccelMemBudget, AccelChunkSize and UsedMemory
-            private readonly SymbolicSgdTrainer _trainer;
+            private readonly SymbolicSgdLogisticRegressionBinaryTrainer _trainer;
             private readonly IChannel _ch;
 
             // Whether memorySize was big enough to load the entire instances into the buffer
@@ -511,7 +511,7 @@ private sealed class InputDataManager : IDisposable
             // Tells if we have gone through the dataset entirely.
             public bool FinishedTheLoad => !_cursorMoveNext;
 
-            public InputDataManager(SymbolicSgdTrainer trainer, FloatLabelCursor.Factory cursorFactory, IChannel ch)
+            public InputDataManager(SymbolicSgdLogisticRegressionBinaryTrainer trainer, FloatLabelCursor.Factory cursorFactory, IChannel ch)
             {
                 _instIndices = new ArrayManager<int>(trainer, ch);
                 _instValues = new ArrayManager<float>(trainer, ch);