Sync names in logistic regression and maximum entropy trainers based on L-BFGS

Author: wschin

src/Microsoft.ML.StandardTrainers/Standard/LogisticRegression/LogisticRegression.cs

@@ -15,21 +15,21 @@
 using Microsoft.ML.Runtime;
 using Microsoft.ML.Trainers;
 
-[assembly: LoadableClass(LogisticRegressionBinaryTrainer.Summary, typeof(LogisticRegressionBinaryTrainer), typeof(LogisticRegressionBinaryTrainer.Options),
+[assembly: LoadableClass(LbfgsLogisticRegressionTrainer.Summary, typeof(LbfgsLogisticRegressionTrainer), typeof(LbfgsLogisticRegressionTrainer.Options),
     new[] { typeof(SignatureBinaryClassifierTrainer), typeof(SignatureTrainer), typeof(SignatureFeatureScorerTrainer) },
-    LogisticRegressionBinaryTrainer.UserNameValue,
-    LogisticRegressionBinaryTrainer.LoadNameValue,
-    LogisticRegressionBinaryTrainer.ShortName,
+    LbfgsLogisticRegressionTrainer.UserNameValue,
+    LbfgsLogisticRegressionTrainer.LoadNameValue,
+    LbfgsLogisticRegressionTrainer.ShortName,
     "logisticregressionwrapper")]
 
-[assembly: LoadableClass(typeof(void), typeof(LogisticRegressionBinaryTrainer), null, typeof(SignatureEntryPointModule), LogisticRegressionBinaryTrainer.LoadNameValue)]
+[assembly: LoadableClass(typeof(void), typeof(LbfgsLogisticRegressionTrainer), null, typeof(SignatureEntryPointModule), LbfgsLogisticRegressionTrainer.LoadNameValue)]
 
 namespace Microsoft.ML.Trainers
 {
 
     /// <include file='doc.xml' path='doc/members/member[@name="LBFGS"]/*' />
     /// <include file='doc.xml' path='docs/members/example[@name="LogisticRegressionBinaryClassifier"]/*' />
-    public sealed partial class LogisticRegressionBinaryTrainer : LbfgsTrainerBase<LogisticRegressionBinaryTrainer.Options,
+    public sealed partial class LbfgsLogisticRegressionTrainer : LbfgsTrainerBase<LbfgsLogisticRegressionTrainer.Options,
         BinaryPredictionTransformer<CalibratedModelParametersBase<LinearBinaryModelParameters, PlattCalibrator>>,
         CalibratedModelParametersBase<LinearBinaryModelParameters, PlattCalibrator>>
     {
@@ -54,7 +54,7 @@ public sealed class Options : OptionsBase
             /// <summary>
             /// The instance of <see cref="ComputeLogisticRegressionStandardDeviation"/> that computes the std of the training statistics, at the end of training.
             /// The calculations are not part of Microsoft.ML package, due to the size of MKL.
-            /// If you need these calculations, add the Microsoft.ML.Mkl.Components package, and initialize <see cref="LogisticRegressionBinaryTrainer.Options.ComputeStandardDeviation"/>.
+            /// If you need these calculations, add the Microsoft.ML.Mkl.Components package, and initialize <see cref="LbfgsLogisticRegressionTrainer.Options.ComputeStandardDeviation"/>.
             /// to the <see cref="ComputeLogisticRegressionStandardDeviation"/> implementation in the Microsoft.ML.Mkl.Components package.
             /// </summary>
             public ComputeLogisticRegressionStandardDeviation ComputeStandardDeviation;
@@ -64,7 +64,7 @@ public sealed class Options : OptionsBase
         private ModelStatisticsBase _stats;
 
         /// <summary>
-        /// Initializes a new instance of <see cref="LogisticRegressionBinaryTrainer"/>
+        /// Initializes a new instance of <see cref="LbfgsLogisticRegressionTrainer"/>
         /// </summary>
         /// <param name="env">The environment to use.</param>
         /// <param name="labelColumn">The name of the label column.</param>
@@ -73,9 +73,9 @@ public sealed class Options : OptionsBase
         /// <param name="enforceNoNegativity">Enforce non-negative weights.</param>
         /// <param name="l1Weight">Weight of L1 regularizer term.</param>
         /// <param name="l2Weight">Weight of L2 regularizer term.</param>
-        /// <param name="memorySize">Memory size for <see cref="LogisticRegressionBinaryTrainer"/>. Low=faster, less accurate.</param>
+        /// <param name="memorySize">Memory size for <see cref="LbfgsLogisticRegressionTrainer"/>. Low=faster, less accurate.</param>
         /// <param name="optimizationTolerance">Threshold for optimizer convergence.</param>
-        internal LogisticRegressionBinaryTrainer(IHostEnvironment env,
+        internal LbfgsLogisticRegressionTrainer(IHostEnvironment env,
             string labelColumn = DefaultColumnNames.Label,
             string featureColumn = DefaultColumnNames.Features,
             string weights = null,
@@ -95,9 +95,9 @@ internal LogisticRegressionBinaryTrainer(IHostEnvironment env,
         }
 
         /// <summary>
-        /// Initializes a new instance of <see cref="LogisticRegressionBinaryTrainer"/>
+        /// Initializes a new instance of <see cref="LbfgsLogisticRegressionTrainer"/>
         /// </summary>
-        internal LogisticRegressionBinaryTrainer(IHostEnvironment env, Options options)
+        internal LbfgsLogisticRegressionTrainer(IHostEnvironment env, Options options)
             : base(env, options, TrainerUtils.MakeBoolScalarLabel(options.LabelColumnName))
         {
             _posWeight = 0;
@@ -127,7 +127,7 @@ private protected override BinaryPredictionTransformer<CalibratedModelParameters
             => new BinaryPredictionTransformer<CalibratedModelParametersBase<LinearBinaryModelParameters, PlattCalibrator>>(Host, model, trainSchema, FeatureColumn.Name);
 
         /// <summary>
-        /// Continues the training of a <see cref="LogisticRegressionBinaryTrainer"/> using an already trained <paramref name="modelParameters"/> and returns
+        /// Continues the training of a <see cref="LbfgsLogisticRegressionTrainer"/> using an already trained <paramref name="modelParameters"/> and returns
         /// a <see cref="BinaryPredictionTransformer{CalibratedModelParametersBase}"/>.
         /// </summary>
         public BinaryPredictionTransformer<CalibratedModelParametersBase<LinearBinaryModelParameters, PlattCalibrator>> Fit(IDataView trainData, LinearModelParameters modelParameters)
@@ -417,7 +417,7 @@ internal static CommonOutputs.BinaryClassificationOutput TrainBinary(IHostEnviro
             EntryPointUtils.CheckInputArgs(host, input);
 
             return TrainerEntryPointsUtils.Train<Options, CommonOutputs.BinaryClassificationOutput>(host, input,
-                () => new LogisticRegressionBinaryTrainer(host, input),
+                () => new LbfgsLogisticRegressionTrainer(host, input),
                 () => TrainerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.LabelColumnName),
                 () => TrainerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.ExampleWeightColumnName));
         }
@@ -436,7 +436,7 @@ public abstract class ComputeLogisticRegressionStandardDeviation
         /// Computes the standard deviation matrix of each of the non-zero training weights, needed to calculate further the standard deviation,
         /// p-value and z-Score.
         /// The calculations are not part of Microsoft.ML package, due to the size of MKL.
-        /// If you need these calculations, add the Microsoft.ML.Mkl.Components package, and initialize <see cref="LogisticRegressionBinaryTrainer.Options.ComputeStandardDeviation"/>
+        /// If you need these calculations, add the Microsoft.ML.Mkl.Components package, and initialize <see cref="LbfgsLogisticRegressionTrainer.Options.ComputeStandardDeviation"/>
         /// to the <see cref="ComputeLogisticRegressionStandardDeviation"/> implementation in the Microsoft.ML.Mkl.Components package.
         /// Due to the existence of regularization, an approximation is used to compute the variances of the trained linear coefficients.
         /// </summary>

src/Microsoft.ML.StandardTrainers/Standard/LogisticRegression/MulticlassLogisticRegression.cs

@@ -83,7 +83,7 @@ public sealed class Options : OptionsBase
         /// <param name="enforceNoNegativity">Enforce non-negative weights.</param>
         /// <param name="l1Weight">Weight of L1 regularizer term.</param>
         /// <param name="l2Weight">Weight of L2 regularizer term.</param>
-        /// <param name="memorySize">Memory size for <see cref="LogisticRegressionBinaryTrainer"/>. Low=faster, less accurate.</param>
+        /// <param name="memorySize">Memory size for <see cref="LbfgsLogisticRegressionTrainer"/>. Low=faster, less accurate.</param>
         /// <param name="optimizationTolerance">Threshold for optimizer convergence.</param>
         internal LbfgsMaximumEntropyTrainer(IHostEnvironment env,
             string labelColumn = DefaultColumnNames.Label,

src/Microsoft.ML.StandardTrainers/Standard/PoissonRegression/PoissonRegression.cs

@@ -48,7 +48,7 @@ public sealed class Options : OptionsBase
         /// <param name="l1Weight">Weight of L1 regularizer term.</param>
         /// <param name="l2Weight">Weight of L2 regularizer term.</param>
         /// <param name="optimizationTolerance">Threshold for optimizer convergence.</param>
-        /// <param name="memorySize">Memory size for <see cref="LogisticRegressionBinaryTrainer"/>. Low=faster, less accurate.</param>
+        /// <param name="memorySize">Memory size for <see cref="LbfgsLogisticRegressionTrainer"/>. Low=faster, less accurate.</param>
         /// <param name="enforceNoNegativity">Enforce non-negative weights.</param>
         internal PoissonRegressionTrainer(IHostEnvironment env,
             string labelColumn = DefaultColumnNames.Label,

src/Microsoft.ML.StandardTrainers/StandardTrainersCatalog.cs

@@ -9,7 +9,7 @@
 
 namespace Microsoft.ML
 {
-    using LROptions = LogisticRegressionBinaryTrainer.Options;
+    using LROptions = LbfgsLogisticRegressionTrainer.Options;
 
     /// <summary>
     /// TrainerEstimator extension methods.
@@ -498,7 +498,7 @@ public static OnlineGradientDescentTrainer OnlineGradientDescent(this Regression
         }
 
         /// <summary>
-        ///  Predict a target using a linear binary classification model trained with the <see cref="Trainers.LogisticRegressionBinaryTrainer"/> trainer.
+        ///  Predict a target using a linear binary classification model trained with the <see cref="Trainers.LbfgsLogisticRegressionTrainer"/> trainer.
         /// </summary>
         /// <param name="catalog">The binary classification catalog trainer object.</param>
         /// <param name="labelColumnName">The name of the label column.</param>
@@ -507,7 +507,7 @@ public static OnlineGradientDescentTrainer OnlineGradientDescent(this Regression
         /// <param name="enforceNonNegativity">Enforce non-negative weights.</param>
         /// <param name="l1Regularization">Weight of L1 regularization term.</param>
         /// <param name="l2Regularization">Weight of L2 regularization term.</param>
-        /// <param name="historySize">Memory size for <see cref="Trainers.LogisticRegressionBinaryTrainer"/>. Low=faster, less accurate.</param>
+        /// <param name="historySize">Memory size for <see cref="Trainers.LbfgsLogisticRegressionTrainer"/>. Low=faster, less accurate.</param>
         /// <param name="optimizationTolerance">Threshold for optimizer convergence.</param>
         /// <example>
         /// <format type="text/markdown">
@@ -516,7 +516,7 @@ public static OnlineGradientDescentTrainer OnlineGradientDescent(this Regression
         /// ]]>
         /// </format>
         /// </example>
-        public static LogisticRegressionBinaryTrainer LogisticRegression(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
+        public static LbfgsLogisticRegressionTrainer LogisticRegression(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog,
             string labelColumnName = DefaultColumnNames.Label,
             string featureColumnName = DefaultColumnNames.Features,
             string exampleWeightColumnName = null,
@@ -528,21 +528,21 @@ public static LogisticRegressionBinaryTrainer LogisticRegression(this BinaryClas
         {
             Contracts.CheckValue(catalog, nameof(catalog));
             var env = CatalogUtils.GetEnvironment(catalog);
-            return new LogisticRegressionBinaryTrainer(env, labelColumnName, featureColumnName, exampleWeightColumnName, l1Regularization, l2Regularization, optimizationTolerance, historySize, enforceNonNegativity);
+            return new LbfgsLogisticRegressionTrainer(env, labelColumnName, featureColumnName, exampleWeightColumnName, l1Regularization, l2Regularization, optimizationTolerance, historySize, enforceNonNegativity);
         }
 
         /// <summary>
-        ///  Predict a target using a linear binary classification model trained with the <see cref="Trainers.LogisticRegressionBinaryTrainer"/> trainer.
+        ///  Predict a target using a linear binary classification model trained with the <see cref="Trainers.LbfgsLogisticRegressionTrainer"/> trainer.
         /// </summary>
         /// <param name="catalog">The binary classification catalog trainer object.</param>
         /// <param name="options">Advanced arguments to the algorithm.</param>
-        public static LogisticRegressionBinaryTrainer LogisticRegression(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog, LROptions options)
+        public static LbfgsLogisticRegressionTrainer LogisticRegression(this BinaryClassificationCatalog.BinaryClassificationTrainers catalog, LROptions options)
         {
             Contracts.CheckValue(catalog, nameof(catalog));
             Contracts.CheckValue(options, nameof(options));
 
             var env = CatalogUtils.GetEnvironment(catalog);
-            return new LogisticRegressionBinaryTrainer(env, options);
+            return new LbfgsLogisticRegressionTrainer(env, options);
         }
 
         /// <summary>

src/Microsoft.ML.StaticPipe/LbfgsStatic.cs

@@ -9,15 +9,15 @@
 
 namespace Microsoft.ML.StaticPipe
 {
-    using Options = LogisticRegressionBinaryTrainer.Options;
+    using Options = LbfgsLogisticRegressionTrainer.Options;
 
     /// <summary>
     /// Binary Classification trainer estimators.
     /// </summary>
-    public static class LbfgsBinaryClassificationStaticExtensions
+    public static class LbfgsBinaryExtensions
     {
         /// <summary>
-        ///  Predict a target using a linear binary classification model trained with the <see cref="Microsoft.ML.Trainers.LogisticRegressionBinaryTrainer"/> trainer.
+        ///  Predict a target using a linear binary classification model trained with the <see cref="Microsoft.ML.Trainers.LbfgsLogisticRegressionTrainer"/> trainer.
         /// </summary>
         /// <param name="catalog">The binary classification catalog trainer object.</param>
         /// <param name="label">The label, or dependent variable.</param>
@@ -26,7 +26,7 @@ public static class LbfgsBinaryClassificationStaticExtensions
         /// <param name="enforceNonNegativity">Enforce non-negative weights.</param>
         /// <param name="l1Regularization">Weight of L1 regularization term.</param>
         /// <param name="l2Regularization">Weight of L2 regularization term.</param>
-        /// <param name="historySize">Memory size for <see cref="Microsoft.ML.Trainers.LogisticRegressionBinaryTrainer"/>. Low=faster, less accurate.</param>
+        /// <param name="historySize">Memory size for <see cref="Microsoft.ML.Trainers.LbfgsLogisticRegressionTrainer"/>. Low=faster, less accurate.</param>
         /// <param name="optimizationTolerance">Threshold for optimizer convergence.</param>
         /// <param name="onFit">A delegate that is called every time the
         /// <see cref="Estimator{TInShape, TOutShape, TTransformer}.Fit(DataView{TInShape})"/> method is called on the
@@ -50,7 +50,7 @@ public static (Scalar<float> score, Scalar<float> probability, Scalar<bool> pred
             var rec = new TrainerEstimatorReconciler.BinaryClassifier(
                 (env, labelName, featuresName, weightsName) =>
                 {
-                    var trainer = new LogisticRegressionBinaryTrainer(env, labelName, featuresName, weightsName,
+                    var trainer = new LbfgsLogisticRegressionTrainer(env, labelName, featuresName, weightsName,
                         l1Regularization, l2Regularization, optimizationTolerance, historySize, enforceNonNegativity);
 
                     if (onFit != null)
@@ -63,7 +63,7 @@ public static (Scalar<float> score, Scalar<float> probability, Scalar<bool> pred
         }
 
         /// <summary>
-        ///  Predict a target using a linear binary classification model trained with the <see cref="Microsoft.ML.Trainers.LogisticRegressionBinaryTrainer"/> trainer.
+        ///  Predict a target using a linear binary classification model trained with the <see cref="Microsoft.ML.Trainers.LbfgsLogisticRegressionTrainer"/> trainer.
         /// </summary>
         /// <param name="catalog">The binary classification catalog trainer object.</param>
         /// <param name="label">The label, or dependent variable.</param>
@@ -95,7 +95,7 @@ public static (Scalar<float> score, Scalar<float> probability, Scalar<bool> pred
                     options.FeatureColumnName = featuresName;
                     options.ExampleWeightColumnName = weightsName;
 
-                    var trainer = new LogisticRegressionBinaryTrainer(env, options);
+                    var trainer = new LbfgsLogisticRegressionTrainer(env, options);
 
                     if (onFit != null)
                         return trainer.WithOnFitDelegate(trans => onFit(trans.Model));
@@ -113,7 +113,7 @@ public static (Scalar<float> score, Scalar<float> probability, Scalar<bool> pred
     public static class LbfgsRegressionExtensions
     {
         /// <summary>
-        /// Predict a target using a linear regression model trained with the <see cref="Microsoft.ML.Trainers.LogisticRegressionBinaryTrainer"/> trainer.
+        /// Predict a target using a linear regression model trained with the <see cref="Microsoft.ML.Trainers.LbfgsLogisticRegressionTrainer"/> trainer.
         /// </summary>
         /// <param name="catalog">The regression catalog trainer object.</param>
         /// <param name="label">The label, or dependent variable.</param>
@@ -122,7 +122,7 @@ public static class LbfgsRegressionExtensions
         /// <param name="enforceNonNegativity">Enforce non-negative weights.</param>
         /// <param name="l1Regularization">Weight of L1 regularization term.</param>
         /// <param name="l2Regularization">Weight of L2 regularization term.</param>
-        /// <param name="historySize">Memory size for <see cref="Microsoft.ML.Trainers.LogisticRegressionBinaryTrainer"/>. Low=faster, less accurate.</param>
+        /// <param name="historySize">Memory size for <see cref="Microsoft.ML.Trainers.LbfgsLogisticRegressionTrainer"/>. Low=faster, less accurate.</param>
         /// <param name="optimizationTolerance">Threshold for optimizer convergence.</param>
         /// <param name="onFit">A delegate that is called every time the
         /// <see cref="Estimator{TInShape, TOutShape, TTransformer}.Fit(DataView{TInShape})"/> method is called on the
@@ -159,7 +159,7 @@ public static Scalar<float> PoissonRegression(this RegressionCatalog.RegressionT
         }
 
         /// <summary>
-        /// Predict a target using a linear regression model trained with the <see cref="Microsoft.ML.Trainers.LogisticRegressionBinaryTrainer"/> trainer.
+        /// Predict a target using a linear regression model trained with the <see cref="Microsoft.ML.Trainers.LbfgsLogisticRegressionTrainer"/> trainer.
         /// </summary>
         /// <param name="catalog">The regression catalog trainer object.</param>
         /// <param name="label">The label, or dependent variable.</param>
@@ -218,7 +218,7 @@ public static class LbfgsMulticlassExtensions
         /// <param name="enforceNonNegativity">Enforce non-negative weights.</param>
         /// <param name="l1Regularization">Weight of L1 regularization term.</param>
         /// <param name="l2Regularization">Weight of L2 regularization term.</param>
-        /// <param name="historySize">Memory size for <see cref="Microsoft.ML.Trainers.LogisticRegressionBinaryTrainer"/>. Low=faster, less accurate.</param>
+        /// <param name="historySize">Memory size for <see cref="Microsoft.ML.Trainers.LbfgsLogisticRegressionTrainer"/>. Low=faster, less accurate.</param>
         /// <param name="optimizationTolerance">Threshold for optimizer convergence.</param>
         /// <param name="onFit">A delegate that is called every time the
         /// <see cref="Estimator{TInShape, TOutShape, TTransformer}.Fit(DataView{TInShape})"/> method is called on the