entrypoint catalog and some fixes after bulk renaming

Author: artidoro

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

@@ -8,15 +8,15 @@ Models.AnomalyPipelineEnsemble	Combine anomaly detection models into an ensemble
 Models.BinaryClassificationEvaluator	Evaluates a binary classification scored dataset.	Microsoft.ML.Data.Evaluate	Binary	Microsoft.ML.Data.BinaryClassifierMamlEvaluator+Arguments	Microsoft.ML.EntryPoints.CommonOutputs+ClassificationEvaluateOutput
 Models.BinaryEnsemble	Combine binary classifiers into an ensemble	Microsoft.ML.Trainers.Ensemble.EnsembleCreator	CreateBinaryEnsemble	Microsoft.ML.Trainers.Ensemble.EnsembleCreator+ClassifierInput	Microsoft.ML.EntryPoints.CommonOutputs+BinaryClassificationOutput
 Models.BinaryPipelineEnsemble	Combine binary classification models into an ensemble	Microsoft.ML.Trainers.Ensemble.EnsembleCreator	CreateBinaryPipelineEnsemble	Microsoft.ML.Trainers.Ensemble.EnsembleCreator+PipelineClassifierInput	Microsoft.ML.EntryPoints.CommonOutputs+BinaryClassificationOutput
-Models.ClassificationEvaluator	Evaluates a multi class classification scored dataset.	Microsoft.ML.Data.Evaluate	MultiClass	Microsoft.ML.Data.MultiClassMamlEvaluator+Arguments	Microsoft.ML.EntryPoints.CommonOutputs+ClassificationEvaluateOutput
+Models.ClassificationEvaluator	Evaluates a multi class classification scored dataset.	Microsoft.ML.Data.Evaluate	Multiclass	Microsoft.ML.Data.MulticlassClassificationMamlEvaluator+Arguments	Microsoft.ML.EntryPoints.CommonOutputs+ClassificationEvaluateOutput
 Models.ClusterEvaluator	Evaluates a clustering scored dataset.	Microsoft.ML.Data.Evaluate	Clustering	Microsoft.ML.Data.ClusteringMamlEvaluator+Arguments	Microsoft.ML.EntryPoints.CommonOutputs+CommonEvaluateOutput
 Models.CrossValidationResultsCombiner	Combine the metric data views returned from cross validation.	Microsoft.ML.EntryPoints.CrossValidationMacro	CombineMetrics	Microsoft.ML.EntryPoints.CrossValidationMacro+CombineMetricsInput	Microsoft.ML.EntryPoints.CrossValidationMacro+CombinedOutput
 Models.CrossValidator	Cross validation for general learning	Microsoft.ML.EntryPoints.CrossValidationMacro	CrossValidate	Microsoft.ML.EntryPoints.CrossValidationMacro+Arguments	Microsoft.ML.EntryPoints.CommonOutputs+MacroOutput`1[Microsoft.ML.EntryPoints.CrossValidationMacro+Output]
 Models.CrossValidatorDatasetSplitter	Split the dataset into the specified number of cross-validation folds (train and test sets)	Microsoft.ML.EntryPoints.CVSplit	Split	Microsoft.ML.EntryPoints.CVSplit+Input	Microsoft.ML.EntryPoints.CVSplit+Output
 Models.DatasetTransformer	Applies a TransformModel to a dataset.	Microsoft.ML.EntryPoints.ModelOperations	Apply	Microsoft.ML.EntryPoints.ModelOperations+ApplyTransformModelInput	Microsoft.ML.EntryPoints.ModelOperations+ApplyTransformModelOutput
 Models.EnsembleSummary	Summarize a pipeline ensemble predictor.	Microsoft.ML.Trainers.Ensemble.PipelineEnsemble	Summarize	Microsoft.ML.EntryPoints.SummarizePredictor+Input	Microsoft.ML.Trainers.Ensemble.PipelineEnsemble+SummaryOutput
 Models.FixedPlattCalibrator	Apply a Platt calibrator with a fixed slope and offset to an input model	Microsoft.ML.Calibrators.Calibrate	FixedPlatt	Microsoft.ML.Calibrators.Calibrate+FixedPlattInput	Microsoft.ML.EntryPoints.CommonOutputs+CalibratorOutput
-Models.MultiClassPipelineEnsemble	Combine multiclass classifiers into an ensemble	Microsoft.ML.Trainers.Ensemble.EnsembleCreator	CreateMultiClassPipelineEnsemble	Microsoft.ML.Trainers.Ensemble.EnsembleCreator+PipelineClassifierInput	Microsoft.ML.EntryPoints.CommonOutputs+MulticlassClassificationOutput
+Models.MultiClassPipelineEnsemble	Combine multiclass classifiers into an ensemble	Microsoft.ML.Trainers.Ensemble.EnsembleCreator	CreateMulticlassPipelineEnsemble	Microsoft.ML.Trainers.Ensemble.EnsembleCreator+PipelineClassifierInput	Microsoft.ML.EntryPoints.CommonOutputs+MulticlassClassificationOutput
 Models.MultiOutputRegressionEvaluator	Evaluates a multi output regression scored dataset.	Microsoft.ML.Data.Evaluate	MultiOutputRegression	Microsoft.ML.Data.MultiOutputRegressionMamlEvaluator+Arguments	Microsoft.ML.EntryPoints.CommonOutputs+CommonEvaluateOutput
 Models.NaiveCalibrator	Apply a Naive calibrator to an input model	Microsoft.ML.Calibrators.Calibrate	Naive	Microsoft.ML.Calibrators.Calibrate+NoArgumentsInput	Microsoft.ML.EntryPoints.CommonOutputs+CalibratorOutput
 Models.OneVersusAll	One-vs-All macro (OVA)	Microsoft.ML.EntryPoints.OneVersusAllMacro	OneVersusAll	Microsoft.ML.EntryPoints.OneVersusAllMacro+Arguments	Microsoft.ML.EntryPoints.CommonOutputs+MacroOutput`1[Microsoft.ML.EntryPoints.OneVersusAllMacro+Output]
@@ -41,7 +41,7 @@ TimeSeriesProcessingEntryPoints.SsaChangePointDetector	This transform detects th
 TimeSeriesProcessingEntryPoints.SsaSpikeDetector	This transform detects the spikes in a seasonal time-series using Singular Spectrum Analysis (SSA).	Microsoft.ML.Transforms.TimeSeries.TimeSeriesProcessingEntryPoints	SsaSpikeDetector	Microsoft.ML.Transforms.TimeSeries.SsaSpikeDetector+Options	Microsoft.ML.EntryPoints.CommonOutputs+TransformOutput
 Trainers.AveragedPerceptronBinaryClassifier	Averaged Perceptron Binary Classifier.	Microsoft.ML.Trainers.AveragedPerceptronTrainer	TrainBinary	Microsoft.ML.Trainers.AveragedPerceptronTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+BinaryClassificationOutput
 Trainers.EnsembleBinaryClassifier	Train binary ensemble.	Microsoft.ML.Trainers.Ensemble.Ensemble	CreateBinaryEnsemble	Microsoft.ML.Trainers.Ensemble.EnsembleTrainer+Arguments	Microsoft.ML.EntryPoints.CommonOutputs+BinaryClassificationOutput
-Trainers.EnsembleClassification	Train multiclass ensemble.	Microsoft.ML.Trainers.Ensemble.Ensemble	CreateMultiClassEnsemble	Microsoft.ML.Trainers.Ensemble.MulticlassDataPartitionEnsembleTrainer+Arguments	Microsoft.ML.EntryPoints.CommonOutputs+MulticlassClassificationOutput
+Trainers.EnsembleClassification	Train multiclass ensemble.	Microsoft.ML.Trainers.Ensemble.Ensemble	CreateMulticlassEnsemble	Microsoft.ML.Trainers.Ensemble.MulticlassDataPartitionEnsembleTrainer+Arguments	Microsoft.ML.EntryPoints.CommonOutputs+MulticlassClassificationOutput
 Trainers.EnsembleRegression	Train regression ensemble.	Microsoft.ML.Trainers.Ensemble.Ensemble	CreateRegressionEnsemble	Microsoft.ML.Trainers.Ensemble.RegressionEnsembleTrainer+Arguments	Microsoft.ML.EntryPoints.CommonOutputs+RegressionOutput
 Trainers.FastForestBinaryClassifier	Uses a random forest learner to perform binary classification.	Microsoft.ML.Trainers.FastTree.FastForest	TrainBinary	Microsoft.ML.Trainers.FastTree.FastForestClassification+Options	Microsoft.ML.EntryPoints.CommonOutputs+BinaryClassificationOutput
 Trainers.FastForestRegressor	Trains a random forest to fit target values using least-squares.	Microsoft.ML.Trainers.FastTree.FastForest	TrainRegression	Microsoft.ML.Trainers.FastTree.FastForestRegression+Options	Microsoft.ML.EntryPoints.CommonOutputs+RegressionOutput
@@ -54,19 +54,19 @@ Trainers.GeneralizedAdditiveModelBinaryClassifier	Trains a gradient boosted stum
 Trainers.GeneralizedAdditiveModelRegressor	Trains a gradient boosted stump per feature, on all features simultaneously, to fit target values using least-squares. It mantains no interactions between features.	Microsoft.ML.Trainers.FastTree.Gam	TrainRegression	Microsoft.ML.Trainers.FastTree.RegressionGamTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+RegressionOutput
 Trainers.KMeansPlusPlusClusterer	K-means is a popular clustering algorithm. With K-means, the data is clustered into a specified number of clusters in order to minimize the within-cluster sum of squares. K-means++ improves upon K-means by using a better method for choosing the initial cluster centers.	Microsoft.ML.Trainers.KMeansPlusPlusTrainer	TrainKMeans	Microsoft.ML.Trainers.KMeansPlusPlusTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+ClusteringOutput
 Trainers.LightGbmBinaryClassifier	Train a LightGBM binary classification model.	Microsoft.ML.LightGBM.LightGbm	TrainBinary	Microsoft.ML.LightGBM.Options	Microsoft.ML.EntryPoints.CommonOutputs+BinaryClassificationOutput
-Trainers.LightGbmClassifier	Train a LightGBM multi class model.	Microsoft.ML.LightGBM.LightGbm	TrainMultiClass	Microsoft.ML.LightGBM.Options	Microsoft.ML.EntryPoints.CommonOutputs+MulticlassClassificationOutput
+Trainers.LightGbmClassifier	Train a LightGBM multi class model.	Microsoft.ML.LightGBM.LightGbm	TrainMulticlass	Microsoft.ML.LightGBM.Options	Microsoft.ML.EntryPoints.CommonOutputs+MulticlassClassificationOutput
 Trainers.LightGbmRanker	Train a LightGBM ranking model.	Microsoft.ML.LightGBM.LightGbm	TrainRanking	Microsoft.ML.LightGBM.Options	Microsoft.ML.EntryPoints.CommonOutputs+RankingOutput
 Trainers.LightGbmRegressor	LightGBM Regression	Microsoft.ML.LightGBM.LightGbm	TrainRegression	Microsoft.ML.LightGBM.Options	Microsoft.ML.EntryPoints.CommonOutputs+RegressionOutput
 Trainers.LinearSvmBinaryClassifier	Train a linear SVM.	Microsoft.ML.Trainers.LinearSvmTrainer	TrainLinearSvm	Microsoft.ML.Trainers.LinearSvmTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+BinaryClassificationOutput
 Trainers.LogisticRegressionBinaryClassifier	Logistic Regression is a method in statistics used to predict the probability of occurrence of an event and can be used as a classification algorithm. The algorithm predicts the probability of occurrence of an event by fitting data to a logistical function.	Microsoft.ML.Trainers.LogisticRegression	TrainBinary	Microsoft.ML.Trainers.LogisticRegression+Options	Microsoft.ML.EntryPoints.CommonOutputs+BinaryClassificationOutput
-Trainers.LogisticRegressionClassifier	Logistic Regression is a method in statistics used to predict the probability of occurrence of an event and can be used as a classification algorithm. The algorithm predicts the probability of occurrence of an event by fitting data to a logistical function.	Microsoft.ML.Trainers.LogisticRegression	TrainMultiClass	Microsoft.ML.Trainers.MulticlassLogisticRegression+Options	Microsoft.ML.EntryPoints.CommonOutputs+MulticlassClassificationOutput
-Trainers.NaiveBayesClassifier	Train a MultiClassNaiveBayesTrainer.	Microsoft.ML.Trainers.MultiClassNaiveBayesTrainer	TrainMultiClassNaiveBayesTrainer	Microsoft.ML.Trainers.MultiClassNaiveBayesTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+MulticlassClassificationOutput
+Trainers.LogisticRegressionClassifier	Logistic Regression is a method in statistics used to predict the probability of occurrence of an event and can be used as a classification algorithm. The algorithm predicts the probability of occurrence of an event by fitting data to a logistical function.	Microsoft.ML.Trainers.LogisticRegression	TrainMulticlass	Microsoft.ML.Trainers.MulticlassLogisticRegression+Options	Microsoft.ML.EntryPoints.CommonOutputs+MulticlassClassificationOutput
+Trainers.NaiveBayesClassifier	Train a MulticlassNaiveBayesTrainer.	Microsoft.ML.Trainers.MulticlassNaiveBayesTrainer	TrainMulticlassNaiveBayesTrainer	Microsoft.ML.Trainers.MulticlassNaiveBayesTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+MulticlassClassificationOutput
 Trainers.OnlineGradientDescentRegressor	Train a Online gradient descent perceptron.	Microsoft.ML.Trainers.OnlineGradientDescentTrainer	TrainRegression	Microsoft.ML.Trainers.OnlineGradientDescentTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+RegressionOutput
 Trainers.OrdinaryLeastSquaresRegressor	Train an OLS regression model.	Microsoft.ML.Trainers.OrdinaryLeastSquaresRegressionTrainer	TrainRegression	Microsoft.ML.Trainers.OrdinaryLeastSquaresRegressionTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+RegressionOutput
 Trainers.PcaAnomalyDetector	Train an PCA Anomaly model.	Microsoft.ML.Trainers.RandomizedPrincipalComponentAnalyzer	TrainPcaAnomaly	Microsoft.ML.Trainers.RandomizedPrincipalComponentAnalyzer+Options	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.LegacySdcaBinaryTrainer+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.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.LegacySgdBinaryTrainer	TrainBinary	Microsoft.ML.Trainers.LegacySgdBinaryTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+BinaryClassificationOutput
 Trainers.SymSgdBinaryClassifier	Train a symbolic SGD.	Microsoft.ML.Trainers.SymbolicStochasticGradientDescentClassificationTrainer	TrainSymSgd	Microsoft.ML.Trainers.SymbolicStochasticGradientDescentClassificationTrainer+Options	Microsoft.ML.EntryPoints.CommonOutputs+BinaryClassificationOutput

test/BaselineOutput/Common/EntryPoints/core_manifest.json

@@ -1311,7 +1311,7 @@
             "Kind": "Enum",
             "Values": [
               "SignatureBinaryClassifierTrainer",
-              "SignatureMultiClassClassifierTrainer",
+              "SignatureMulticlassClassifierTrainer",
               "SignatureRankerTrainer",
               "SignatureRegressorTrainer",
               "SignatureMultiOutputRegressorTrainer",
@@ -1502,7 +1502,7 @@
             "Kind": "Enum",
             "Values": [
               "SignatureBinaryClassifierTrainer",
-              "SignatureMultiClassClassifierTrainer",
+              "SignatureMulticlassClassifierTrainer",
               "SignatureRankerTrainer",
               "SignatureRegressorTrainer",
               "SignatureMultiOutputRegressorTrainer",
@@ -3177,7 +3177,7 @@
             "Kind": "Enum",
             "Values": [
               "SignatureBinaryClassifierTrainer",
-              "SignatureMultiClassClassifierTrainer",
+              "SignatureMulticlassClassifierTrainer",
               "SignatureRankerTrainer",
               "SignatureRegressorTrainer",
               "SignatureMultiOutputRegressorTrainer",
@@ -13981,7 +13981,7 @@
     },
     {
       "Name": "Trainers.NaiveBayesClassifier",
-      "Desc": "Train a MultiClassNaiveBayesTrainer.",
+      "Desc": "Train a MulticlassNaiveBayesTrainer.",
       "FriendlyName": "Multiclass Naive Bayes",
       "ShortName": "MNB",
       "Inputs": [

test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs

@@ -4396,7 +4396,7 @@ public void TestCrossValidationMacroWithMulticlass()
                             },
                             'StratificationColumn': null,
                             'NumFolds': 2,
-                            'Kind': 'SignatureMultiClassClassifierTrainer',
+                            'Kind': 'SignatureMulticlassClassifierTrainer',
                             'LabelColumn': 'Label',
                             'WeightColumn': null,
                             'GroupColumn': null,
@@ -4623,7 +4623,7 @@ public void TestCrossValidationMacroMulticlassWithWarnings()
                             },
                             'StratificationColumn': 'Strat',
                             'NumFolds': 2,
-                            'Kind': 'SignatureMultiClassClassifierTrainer',
+                            'Kind': 'SignatureMulticlassClassifierTrainer',
                             'LabelColumn': 'Label',
                             'WeightColumn': null,
                             'GroupColumn': null,

test/Microsoft.ML.TestFramework/TestCommandBase.cs

@@ -1190,7 +1190,7 @@ public void CommandTrainMlrWithStats()
             const string loaderCmdline = @"loader=TextLoader{col=Label:TX:4 col=Features:R4:0-3 sep=,}";
             string pathTrain = GetDataPath("iris.data");
             OutputPath trainModel = ModelPath();
-            const string trainArgs = "tr=MulticlassLogisticRegression{maxiter=100 t=- stat=+} xf=Term{col=Label} seed=1";
+            const string trainArgs = "tr=MultiClassLogisticRegression{maxiter=100 t=- stat=+} xf=Term{col=Label} seed=1";
             TestCore("train", pathTrain, loaderCmdline, trainArgs);
 
             _step++;