Sdca entry points also strongly-typed based on trained models.

1. Remove arguments to set up calibrator in all SDCA trainer
2. Clean up entry point base on argument's changes.
3. Update test files

Author: wschin

src/Microsoft.ML.StandardLearners/Standard/SdcaBinary.cs

@@ -1399,30 +1399,9 @@ public void Add(Double summand)
         }
     }
 
-    public abstract class SdcaBinaryTrainerBase<TModelParameters> : SdcaTrainerBase<SdcaBinaryTrainerBase<TModelParameters>.Options, BinaryPredictionTransformer<TModelParameters>, TModelParameters>
+    public abstract class SdcaBinaryTrainerBase<TModelParameters> : SdcaTrainerBase<SdcaBinaryTrainerBase<TModelParameters>.BinaryArgumentBase, BinaryPredictionTransformer<TModelParameters>, TModelParameters>
         where TModelParameters : class, IPredictorProducing<float>
     {
-        public sealed class Options : ArgumentsBase
-        {
-            [Argument(ArgumentType.Multiple, HelpText = "Loss Function", ShortName = "loss", SortOrder = 50)]
-            public ISupportSdcaClassificationLossFactory LossFunction = new LogLossFactory();
-
-            [Argument(ArgumentType.AtMostOnce, HelpText = "Apply weight to the positive class, for imbalanced data", ShortName = "piw")]
-            public float PositiveInstanceWeight = 1;
-
-            [Argument(ArgumentType.AtMostOnce, HelpText = "The calibrator kind to apply to the predictor. Specify null for no calibration", Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)]
-            public ICalibratorTrainerFactory Calibrator = null;
-
-            [Argument(ArgumentType.AtMostOnce, HelpText = "The maximum number of examples to use when training the calibrator", Visibility = ArgumentAttribute.VisibilityType.EntryPointsOnly)]
-            public int MaxCalibrationExamples = 1000000;
-
-            internal override void Check(IHostEnvironment env)
-            {
-                base.Check(env);
-                env.CheckUserArg(PositiveInstanceWeight > 0, nameof(PositiveInstanceWeight), "Weight for positive instances must be positive");
-            }
-        }
-
         private readonly ISupportSdcaClassificationLoss _loss;
         private readonly float _positiveInstanceWeight;
 
@@ -1436,6 +1415,18 @@ internal override void Check(IHostEnvironment env)
 
         public override TrainerInfo Info { get; }
 
+        public class BinaryArgumentBase : ArgumentsBase
+        {
+            [Argument(ArgumentType.AtMostOnce, HelpText = "Apply weight to the positive class, for imbalanced data", ShortName = "piw")]
+            public float PositiveInstanceWeight = 1;
+
+            internal override void Check(IHostEnvironment env)
+            {
+                base.Check(env);
+                env.CheckUserArg(PositiveInstanceWeight > 0, nameof(PositiveInstanceWeight), "Weight for positive instances must be positive");
+            }
+        }
+
         /// <summary>
         /// Initializes a new instance of <see cref="SdcaBinaryTrainerBase{TModelParameters}"/>
         /// </summary>
@@ -1460,19 +1451,19 @@ protected SdcaBinaryTrainerBase(IHostEnvironment env,
         {
             Host.CheckNonEmpty(featureColumn, nameof(featureColumn));
             Host.CheckNonEmpty(labelColumn, nameof(labelColumn));
-            _loss = loss ?? Args.LossFunction.CreateComponent(env);
+            _loss = loss ?? new LogLossFactory().CreateComponent(env);
             Loss = _loss;
-            Info = new TrainerInfo(calibration: !(_loss is LogLoss));
+            Info = new TrainerInfo(calibration: false);
             _positiveInstanceWeight = Args.PositiveInstanceWeight;
             _outputColumns = ComputeSdcaBinaryClassifierSchemaShape();
         }
 
-        protected SdcaBinaryTrainerBase(IHostEnvironment env, Options options)
+        protected SdcaBinaryTrainerBase(IHostEnvironment env, BinaryArgumentBase options, ISupportSdcaClassificationLoss loss = null)
             : base(env, options, TrainerUtils.MakeBoolScalarLabel(options.LabelColumn))
         {
-            _loss = options.LossFunction.CreateComponent(env);
+            _loss = loss ?? new LogLossFactory().CreateComponent(env);
             Loss = _loss;
-            Info = new TrainerInfo(calibration: !(_loss is LogLoss));
+            Info = new TrainerInfo(calibration: false);
             _positiveInstanceWeight = Args.PositiveInstanceWeight;
             _outputColumns = ComputeSdcaBinaryClassifierSchemaShape();
         }
@@ -1528,6 +1519,13 @@ public sealed class SdcaCalibratedBinaryTrainer :
         internal const string LoadNameValue = "SDCALR";
         internal const string UserNameValue = "Fast Linear (SA-SDCA) Logistic Regression";
 
+        /// <summary>
+        /// Configuration to training logistic regression using SDCA.
+        /// </summary>
+        public sealed class Options : BinaryArgumentBase
+        {
+        }
+
         internal SdcaCalibratedBinaryTrainer(IHostEnvironment env,
             string labelColumn = DefaultColumnNames.Label,
             string featureColumn = DefaultColumnNames.Features,
@@ -1540,7 +1538,7 @@ internal SdcaCalibratedBinaryTrainer(IHostEnvironment env,
         }
 
         internal SdcaCalibratedBinaryTrainer(IHostEnvironment env, Options options)
-            : base(env, options)
+            : base(env, options, new LogLoss())
         {
         }
 
@@ -1583,6 +1581,15 @@ public sealed class SdcaBinaryTrainer : SdcaBinaryTrainerBase<LinearBinaryModelP
         internal const string LoadNameValue = "SDCA";
         internal const string UserNameValue = "Fast Linear (SA-SDCA)";
 
+        /// <summary>
+        /// General Configuration to training linear model using SDCA.
+        /// </summary>
+        public sealed class Options : BinaryArgumentBase
+        {
+            [Argument(ArgumentType.Multiple, HelpText = "Loss Function", ShortName = "loss", SortOrder = 50)]
+            public ISupportSdcaClassificationLossFactory LossFunction = new LogLossFactory();
+        }
+
         internal SdcaBinaryTrainer(IHostEnvironment env,
             string labelColumn = DefaultColumnNames.Label,
             string featureColumn = DefaultColumnNames.Features,
@@ -1596,7 +1603,7 @@ internal SdcaBinaryTrainer(IHostEnvironment env,
         }
 
         internal SdcaBinaryTrainer(IHostEnvironment env, Options options)
-            : base(env, options)
+            : base(env, options, options.LossFunction.CreateComponent(env))
         {
         }
 
@@ -2025,8 +2032,23 @@ internal static CommonOutputs.BinaryClassificationOutput TrainBinary(IHostEnviro
 
             return LearnerEntryPointsUtils.Train<SdcaBinaryTrainer.Options, CommonOutputs.BinaryClassificationOutput>(host, input,
                 () => new SdcaBinaryTrainer(host, input),
-                () => LearnerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.LabelColumn),
-                maxCalibrationExamples: input.MaxCalibrationExamples);
+                () => LearnerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.LabelColumn));
+        }
+
+        [TlcModule.EntryPoint(Name = "Trainers.StochasticDualCoordinateAscentCalibratedBinaryClassifier",
+            Desc = "Train logistic regression using SDCA.",
+            UserName = SdcaCalibratedBinaryTrainer.UserNameValue,
+            ShortName = SdcaCalibratedBinaryTrainer.LoadNameValue)]
+        internal static CommonOutputs.BinaryClassificationOutput TrainBinary(IHostEnvironment env, SdcaCalibratedBinaryTrainer.Options input)
+        {
+            Contracts.CheckValue(env, nameof(env));
+            var host = env.Register("TrainSDCA");
+            host.CheckValue(input, nameof(input));
+            EntryPointUtils.CheckInputArgs(host, input);
+
+            return LearnerEntryPointsUtils.Train<SdcaCalibratedBinaryTrainer.Options, CommonOutputs.BinaryClassificationOutput>(host, input,
+                () => new SdcaCalibratedBinaryTrainer(host, input),
+                () => LearnerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.LabelColumn));
         }
     }
 }

test/BaselineOutput/Common/EntryPoints/core_ep-list.tsv

@@ -65,7 +65,8 @@ Trainers.OnlineGradientDescentRegressor	Train a Online gradient descent perceptr
 Trainers.OrdinaryLeastSquaresRegressor	Train an OLS regression model.	Microsoft.ML.Trainers.HalLearners.OlsLinearRegressionTrainer	TrainRegression	Microsoft.ML.Trainers.HalLearners.OlsLinearRegressionTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+RegressionOutput
 Trainers.PcaAnomalyDetector	Train an PCA Anomaly model.	Microsoft.ML.Trainers.PCA.RandomizedPcaTrainer	TrainPcaAnomaly	Microsoft.ML.Trainers.PCA.RandomizedPcaTrainer+Arguments	Microsoft.ML.EntryPoints.CommonOutputs+AnomalyDetectionOutput
 Trainers.PoissonRegressor	Train an Poisson regression model.	Microsoft.ML.Trainers.PoissonRegression	TrainRegression	Microsoft.ML.Trainers.PoissonRegression+Options	Microsoft.ML.EntryPoints.CommonOutputs+RegressionOutput
-Trainers.StochasticDualCoordinateAscentBinaryClassifier	Train an SDCA binary model.	Microsoft.ML.Trainers.Sdca	TrainBinary	Microsoft.ML.Trainers.SdcaBinaryTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+BinaryClassificationOutput
+Trainers.StochasticDualCoordinateAscentBinaryClassifier	Train a linear model to binary classification using SDCA.	Microsoft.ML.Trainers.Sdca	TrainBinary	Microsoft.ML.Trainers.SdcaBinaryTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+BinaryClassificationOutput
+Trainers.StochasticDualCoordinateAscentCalibratedBinaryClassifier	Train logistic regression using SDCA.	Microsoft.ML.Trainers.Sdca	TrainBinary	Microsoft.ML.Trainers.SdcaCalibratedBinaryTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+BinaryClassificationOutput
 Trainers.StochasticDualCoordinateAscentClassifier	The SDCA linear multi-class classification trainer.	Microsoft.ML.Trainers.Sdca	TrainMultiClass	Microsoft.ML.Trainers.SdcaMultiClassTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+MulticlassClassificationOutput
 Trainers.StochasticDualCoordinateAscentRegressor	The SDCA linear regression trainer.	Microsoft.ML.Trainers.Sdca	TrainRegression	Microsoft.ML.Trainers.SdcaRegressionTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+RegressionOutput
 Trainers.StochasticGradientDescentBinaryClassifier	Train an Hogwild SGD binary model.	Microsoft.ML.Trainers.StochasticGradientDescentClassificationTrainer	TrainBinary	Microsoft.ML.Trainers.StochasticGradientDescentClassificationTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+BinaryClassificationOutput