diff --git a/src/Microsoft.ML.StandardLearners/Standard/OlsLinearRegression.cs b/src/Microsoft.ML.StandardLearners/Standard/OlsLinearRegression.cs index c9ff35a8eb..d927ba0a43 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/OlsLinearRegression.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/OlsLinearRegression.cs @@ -491,20 +491,6 @@ public static void Pptri(Layout layout, UpLo uplo, int n, Double[] ap) } } } - - [TlcModule.EntryPoint(Name= "Trainers.OrdinaryLeastSquaresRegressor", Desc = "Train an OLS regression model.", UserName = UserNameValue, ShortName = ShortName)] - public static CommonOutputs.RegressionOutput TrainRegression(IHostEnvironment env, Arguments input) - { - Contracts.CheckValue(env, nameof(env)); - var host = env.Register("TrainOLS"); - host.CheckValue(input, nameof(input)); - EntryPointUtils.CheckInputArgs(host, input); - - return LearnerEntryPointsUtils.Train(host, input, - () => new OlsLinearRegressionTrainer(host, input), - () => LearnerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.LabelColumn), - () => LearnerEntryPointsUtils.FindColumn(host, input.TrainingData.Schema, input.WeightColumn)); - } } /// diff --git a/src/Microsoft.ML/CSharpApi.cs b/src/Microsoft.ML/CSharpApi.cs index 4d0809350b..c103a96475 100644 --- a/src/Microsoft.ML/CSharpApi.cs +++ b/src/Microsoft.ML/CSharpApi.cs @@ -574,18 +574,6 @@ public void Add(Microsoft.ML.Trainers.OnlineGradientDescentRegressor input, Micr _jsonNodes.Add(Serialize("Trainers.OnlineGradientDescentRegressor", input, output)); } - public Microsoft.ML.Trainers.OrdinaryLeastSquaresRegressor.Output Add(Microsoft.ML.Trainers.OrdinaryLeastSquaresRegressor input) - { - var output = new Microsoft.ML.Trainers.OrdinaryLeastSquaresRegressor.Output(); - Add(input, output); - return output; - } - - public void Add(Microsoft.ML.Trainers.OrdinaryLeastSquaresRegressor input, Microsoft.ML.Trainers.OrdinaryLeastSquaresRegressor.Output output) - { - _jsonNodes.Add(Serialize("Trainers.OrdinaryLeastSquaresRegressor", input, output)); - } - public Microsoft.ML.Trainers.PcaAnomalyDetector.Output Add(Microsoft.ML.Trainers.PcaAnomalyDetector input) { var output = new Microsoft.ML.Trainers.PcaAnomalyDetector.Output(); @@ -1652,7 +1640,7 @@ public TextLoaderPipelineStep (Output output) /// /// Arguments /// - public Data.TextLoaderArguments Arguments { get; set; } = new Data.TextLoaderArguments(); + public Microsoft.ML.Data.TextLoaderArguments Arguments { get; set; } = new Microsoft.ML.Data.TextLoaderArguments(); public sealed class Output @@ -2190,7 +2178,7 @@ public sealed partial class CrossValidationResultsCombiner /// /// Specifies the trainer kind, which determines the evaluator to be used. /// - public Models.MacroUtilsTrainerKinds Kind { get; set; } = Models.MacroUtilsTrainerKinds.SignatureBinaryClassifierTrainer; + public Microsoft.ML.Models.MacroUtilsTrainerKinds Kind { get; set; } = Microsoft.ML.Models.MacroUtilsTrainerKinds.SignatureBinaryClassifierTrainer; public sealed class Output @@ -6295,7 +6283,7 @@ public sealed partial class KMeansPlusPlusClusterer : Microsoft.ML.Runtime.Entry /// /// Cluster initialization algorithm /// - public Trainers.KMeansPlusPlusTrainerInitAlgorithm InitAlgorithm { get; set; } = Trainers.KMeansPlusPlusTrainerInitAlgorithm.KMeansParallel; + public Microsoft.ML.Trainers.KMeansPlusPlusTrainerInitAlgorithm InitAlgorithm { get; set; } = Microsoft.ML.Trainers.KMeansPlusPlusTrainerInitAlgorithm.KMeansParallel; /// /// Tolerance parameter for trainer convergence. Lower = slower, more accurate @@ -6335,12 +6323,12 @@ public sealed partial class KMeansPlusPlusClusterer : Microsoft.ML.Runtime.Entry /// /// Normalize option for the feature column /// - public Models.NormalizeOption NormalizeFeatures { get; set; } = Models.NormalizeOption.Auto; + public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; /// /// Whether learner should cache input training data /// - public Models.CachingOptions Caching { get; set; } = Models.CachingOptions.Auto; + public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IClusteringOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput @@ -6620,12 +6608,12 @@ public sealed partial class LogisticRegressionBinaryClassifier : Microsoft.ML.Ru /// /// Normalize option for the feature column /// - public Models.NormalizeOption NormalizeFeatures { get; set; } = Models.NormalizeOption.Auto; + public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; /// /// Whether learner should cache input training data /// - public Models.CachingOptions Caching { get; set; } = Models.CachingOptions.Auto; + public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IBinaryClassificationOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput @@ -7043,95 +7031,6 @@ public OnlineGradientDescentRegressorPipelineStep(Output output) } } - namespace Trainers - { - - /// - /// Train an OLS regression model. - /// - public sealed partial class OrdinaryLeastSquaresRegressor : Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithWeight, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInputWithLabel, Microsoft.ML.Runtime.EntryPoints.CommonInputs.ITrainerInput, Microsoft.ML.ILearningPipelineItem - { - - - /// - /// L2 regularization weight - /// - [TlcModule.SweepableDiscreteParamAttribute("L2Weight", new object[]{1E-06f, 0.1f, 1f})] - public float L2Weight { get; set; } = 1E-06f; - - /// - /// Whether to calculate per parameter significance statistics - /// - public bool PerParameterSignificance { get; set; } = true; - - /// - /// Column to use for example weight - /// - public Microsoft.ML.Runtime.EntryPoints.Optional WeightColumn { get; set; } - - /// - /// Column to use for labels - /// - public string LabelColumn { get; set; } = "Label"; - - /// - /// The data to be used for training - /// - public Var TrainingData { get; set; } = new Var(); - - /// - /// Column to use for features - /// - public string FeatureColumn { get; set; } = "Features"; - - /// - /// Normalize option for the feature column - /// - public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; - - /// - /// Whether learner should cache input training data - /// - public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; - - - public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IRegressionOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput - { - /// - /// The trained model - /// - public Var PredictorModel { get; set; } = new Var(); - - } - public Var GetInputData() => TrainingData; - - public ILearningPipelineStep ApplyStep(ILearningPipelineStep previousStep, Experiment experiment) - { - if (previousStep != null) - { - if (!(previousStep is ILearningPipelineDataStep dataStep)) - { - throw new InvalidOperationException($"{ nameof(OrdinaryLeastSquaresRegressor)} only supports an { nameof(ILearningPipelineDataStep)} as an input."); - } - - TrainingData = dataStep.Data; - } - Output output = experiment.Add(this); - return new OrdinaryLeastSquaresRegressorPipelineStep(output); - } - - private class OrdinaryLeastSquaresRegressorPipelineStep : ILearningPipelinePredictorStep - { - public OrdinaryLeastSquaresRegressorPipelineStep(Output output) - { - Model = output.PredictorModel; - } - - public Var Model { get; } - } - } - } - namespace Trainers { @@ -7183,12 +7082,12 @@ public sealed partial class PcaAnomalyDetector : Microsoft.ML.Runtime.EntryPoint /// /// Normalize option for the feature column /// - public Models.NormalizeOption NormalizeFeatures { get; set; } = Models.NormalizeOption.Auto; + public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; /// /// Whether learner should cache input training data /// - public Models.CachingOptions Caching { get; set; } = Models.CachingOptions.Auto; + public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; public sealed class Output : Microsoft.ML.Runtime.EntryPoints.CommonOutputs.IAnomalyDetectionOutput, Microsoft.ML.Runtime.EntryPoints.CommonOutputs.ITrainerOutput @@ -12187,15 +12086,15 @@ public PcaCalculator(params ValueTuple[] inputOutputColumns) public void AddColumn(string source) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(source)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(source)); Column = list.ToArray(); } public void AddColumn(string name, string source) { - var list = Column == null ? new List() : new List(Column); - list.Add(OneToOneColumn.Create(name, source)); + var list = Column == null ? new List() : new List(Column); + list.Add(OneToOneColumn.Create(name, source)); Column = list.ToArray(); } @@ -12203,7 +12102,7 @@ public void AddColumn(string name, string source) /// /// New column definition(s) (optional form: name:src) /// - public Transforms.PcaTransformColumn[] Column { get; set; } + public Microsoft.ML.Transforms.PcaTransformColumn[] Column { get; set; } /// /// The name of the weight column @@ -13800,7 +13699,7 @@ public sealed class AutoMlStateAutoMlStateBase : AutoMlStateBase /// /// Supported metric for evaluator. /// - public Microsoft.ML.Runtime.AutoInferenceAutoMlMlStateArgumentsMetrics Metric { get; set; } = Microsoft.ML.Runtime.AutoInferenceAutoMlMlStateArgumentsMetrics.Auc; + public Microsoft.ML.Runtime.AutoInferenceAutoMlMlStateArgumentsMetrics Metric { get; set; } = Microsoft.ML.Runtime.AutoInferenceAutoMlMlStateArgumentsMetrics.Auc; /// /// AutoML engine (pipeline optimizer) that generates next candidates. @@ -13811,7 +13710,7 @@ public sealed class AutoMlStateAutoMlStateBase : AutoMlStateBase /// /// Kind of trainer for task, such as binary classification trainer, multiclass trainer, etc. /// - public Microsoft.ML.Models.MacroUtilsTrainerKinds TrainerKind { get; set; } = Microsoft.ML.Models.MacroUtilsTrainerKinds.SignatureBinaryClassifierTrainer; + public Microsoft.ML.Models.MacroUtilsTrainerKinds TrainerKind { get; set; } = Microsoft.ML.Models.MacroUtilsTrainerKinds.SignatureBinaryClassifierTrainer; /// /// Arguments for creating terminator, which determines when to stop search. @@ -14067,7 +13966,7 @@ public sealed class FastTreeBinaryClassificationFastTreeTrainer : FastTreeTraine /// /// Optimization algorithm to be used (GradientDescent, AcceleratedGradientDescent) /// - public Microsoft.ML.Trainers.BoostedTreeArgsOptimizationAlgorithmType OptimizationAlgorithm { get; set; } = Microsoft.ML.Trainers.BoostedTreeArgsOptimizationAlgorithmType.GradientDescent; + public Microsoft.ML.Trainers.BoostedTreeArgsOptimizationAlgorithmType OptimizationAlgorithm { get; set; } = Microsoft.ML.Trainers.BoostedTreeArgsOptimizationAlgorithmType.GradientDescent; /// /// Early stopping rule. (Validation set (/valid) is required.) @@ -14232,7 +14131,7 @@ public sealed class FastTreeBinaryClassificationFastTreeTrainer : FastTreeTraine /// /// Bundle low population bins. Bundle.None(0): no bundling, Bundle.AggregateLowPopulation(1): Bundle low population, Bundle.Adjacent(2): Neighbor low population bundle. /// - public Microsoft.ML.Trainers.Bundle Bundling { get; set; } = Microsoft.ML.Trainers.Bundle.None; + public Microsoft.ML.Trainers.Bundle Bundling { get; set; } = Microsoft.ML.Trainers.Bundle.None; /// /// Maximum number of distinct values (bins) per feature @@ -14375,12 +14274,12 @@ public sealed class FastTreeBinaryClassificationFastTreeTrainer : FastTreeTraine /// /// Normalize option for the feature column /// - public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; + public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; /// /// Whether learner should cache input training data /// - public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; + public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; internal override string ComponentName => "FastTreeBinaryClassification"; } @@ -14455,7 +14354,7 @@ public sealed class FastTreeRankingFastTreeTrainer : FastTreeTrainer /// /// Optimization algorithm to be used (GradientDescent, AcceleratedGradientDescent) /// - public Microsoft.ML.Trainers.BoostedTreeArgsOptimizationAlgorithmType OptimizationAlgorithm { get; set; } = Microsoft.ML.Trainers.BoostedTreeArgsOptimizationAlgorithmType.GradientDescent; + public Microsoft.ML.Trainers.BoostedTreeArgsOptimizationAlgorithmType OptimizationAlgorithm { get; set; } = Microsoft.ML.Trainers.BoostedTreeArgsOptimizationAlgorithmType.GradientDescent; /// /// Early stopping rule. (Validation set (/valid) is required.) @@ -14620,7 +14519,7 @@ public sealed class FastTreeRankingFastTreeTrainer : FastTreeTrainer /// /// Bundle low population bins. Bundle.None(0): no bundling, Bundle.AggregateLowPopulation(1): Bundle low population, Bundle.Adjacent(2): Neighbor low population bundle. /// - public Microsoft.ML.Trainers.Bundle Bundling { get; set; } = Microsoft.ML.Trainers.Bundle.None; + public Microsoft.ML.Trainers.Bundle Bundling { get; set; } = Microsoft.ML.Trainers.Bundle.None; /// /// Maximum number of distinct values (bins) per feature @@ -14763,12 +14662,12 @@ public sealed class FastTreeRankingFastTreeTrainer : FastTreeTrainer /// /// Normalize option for the feature column /// - public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; + public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; /// /// Whether learner should cache input training data /// - public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; + public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; internal override string ComponentName => "FastTreeRanking"; } @@ -14803,7 +14702,7 @@ public sealed class FastTreeRegressionFastTreeTrainer : FastTreeTrainer /// /// Optimization algorithm to be used (GradientDescent, AcceleratedGradientDescent) /// - public Microsoft.ML.Trainers.BoostedTreeArgsOptimizationAlgorithmType OptimizationAlgorithm { get; set; } = Microsoft.ML.Trainers.BoostedTreeArgsOptimizationAlgorithmType.GradientDescent; + public Microsoft.ML.Trainers.BoostedTreeArgsOptimizationAlgorithmType OptimizationAlgorithm { get; set; } = Microsoft.ML.Trainers.BoostedTreeArgsOptimizationAlgorithmType.GradientDescent; /// /// Early stopping rule. (Validation set (/valid) is required.) @@ -14968,7 +14867,7 @@ public sealed class FastTreeRegressionFastTreeTrainer : FastTreeTrainer /// /// Bundle low population bins. Bundle.None(0): no bundling, Bundle.AggregateLowPopulation(1): Bundle low population, Bundle.Adjacent(2): Neighbor low population bundle. /// - public Microsoft.ML.Trainers.Bundle Bundling { get; set; } = Microsoft.ML.Trainers.Bundle.None; + public Microsoft.ML.Trainers.Bundle Bundling { get; set; } = Microsoft.ML.Trainers.Bundle.None; /// /// Maximum number of distinct values (bins) per feature @@ -15111,12 +15010,12 @@ public sealed class FastTreeRegressionFastTreeTrainer : FastTreeTrainer /// /// Normalize option for the feature column /// - public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; + public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; /// /// Whether learner should cache input training data /// - public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; + public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; internal override string ComponentName => "FastTreeRegression"; } @@ -15156,7 +15055,7 @@ public sealed class FastTreeTweedieRegressionFastTreeTrainer : FastTreeTrainer /// /// Optimization algorithm to be used (GradientDescent, AcceleratedGradientDescent) /// - public Microsoft.ML.Trainers.BoostedTreeArgsOptimizationAlgorithmType OptimizationAlgorithm { get; set; } = Microsoft.ML.Trainers.BoostedTreeArgsOptimizationAlgorithmType.GradientDescent; + public Microsoft.ML.Trainers.BoostedTreeArgsOptimizationAlgorithmType OptimizationAlgorithm { get; set; } = Microsoft.ML.Trainers.BoostedTreeArgsOptimizationAlgorithmType.GradientDescent; /// /// Early stopping rule. (Validation set (/valid) is required.) @@ -15321,7 +15220,7 @@ public sealed class FastTreeTweedieRegressionFastTreeTrainer : FastTreeTrainer /// /// Bundle low population bins. Bundle.None(0): no bundling, Bundle.AggregateLowPopulation(1): Bundle low population, Bundle.Adjacent(2): Neighbor low population bundle. /// - public Microsoft.ML.Trainers.Bundle Bundling { get; set; } = Microsoft.ML.Trainers.Bundle.None; + public Microsoft.ML.Trainers.Bundle Bundling { get; set; } = Microsoft.ML.Trainers.Bundle.None; /// /// Maximum number of distinct values (bins) per feature @@ -15464,12 +15363,12 @@ public sealed class FastTreeTweedieRegressionFastTreeTrainer : FastTreeTrainer /// /// Normalize option for the feature column /// - public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; + public Microsoft.ML.Models.NormalizeOption NormalizeFeatures { get; set; } = Microsoft.ML.Models.NormalizeOption.Auto; /// /// Whether learner should cache input training data /// - public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; + public Microsoft.ML.Models.CachingOptions Caching { get; set; } = Microsoft.ML.Models.CachingOptions.Auto; internal override string ComponentName => "FastTreeTweedieRegression"; } @@ -15506,7 +15405,7 @@ public sealed class NGramNgramExtractor : NgramExtractor /// /// The weighting criteria /// - public Microsoft.ML.Transforms.NgramTransformWeightingCriteria Weighting { get; set; } = Microsoft.ML.Transforms.NgramTransformWeightingCriteria.Tf; + public Microsoft.ML.Transforms.NgramTransformWeightingCriteria Weighting { get; set; } = Microsoft.ML.Transforms.NgramTransformWeightingCriteria.Tf; internal override string ComponentName => "NGram"; } @@ -15581,7 +15480,7 @@ public sealed class ParquetPathParserPartitionedPathParser : PartitionedPathPars } - public sealed class PartitionedFileLoaderColumn + public sealed partial class PartitionedFileLoaderColumn { /// /// Name of the column. @@ -15594,7 +15493,7 @@ public sealed class PartitionedFileLoaderColumn public Microsoft.ML.Transforms.DataKind? Type { get; set; } /// - /// Source index of the column. + /// Index of the directory representing this column. /// public int Source { get; set; } @@ -15614,7 +15513,7 @@ public sealed class SimplePathParserPartitionedPathParser : PartitionedPathParse /// /// Data type of each column. /// - public Microsoft.ML.Transforms.DataKind Type { get; set; } = Microsoft.ML.Transforms.DataKind.TX; + public Microsoft.ML.Transforms.DataKind Type { get; set; } = Microsoft.ML.Transforms.DataKind.TX; internal override string ComponentName => "SimplePathParser"; } diff --git a/test/BaselineOutput/Common/EntryPoints/core_ep-list.tsv b/test/BaselineOutput/Common/EntryPoints/core_ep-list.tsv index f6888637ab..a6d1f50668 100644 --- a/test/BaselineOutput/Common/EntryPoints/core_ep-list.tsv +++ b/test/BaselineOutput/Common/EntryPoints/core_ep-list.tsv @@ -44,7 +44,6 @@ Trainers.LogisticRegressionBinaryClassifier Train a logistic regression binary m Trainers.LogisticRegressionClassifier Train a logistic regression multi class model Microsoft.ML.Runtime.Learners.LogisticRegression TrainMultiClass Microsoft.ML.Runtime.Learners.MulticlassLogisticRegression+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+MulticlassClassificationOutput Trainers.NaiveBayesClassifier Train a MultiClassNaiveBayesTrainer. Microsoft.ML.Runtime.Learners.MultiClassNaiveBayesTrainer TrainMultiClassNaiveBayesTrainer Microsoft.ML.Runtime.Learners.MultiClassNaiveBayesTrainer+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+MulticlassClassificationOutput Trainers.OnlineGradientDescentRegressor Train a Online gradient descent perceptron. Microsoft.ML.Runtime.Learners.OnlineGradientDescentTrainer TrainRegression Microsoft.ML.Runtime.Learners.OnlineGradientDescentTrainer+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+RegressionOutput -Trainers.OrdinaryLeastSquaresRegressor Train an OLS regression model. Microsoft.ML.Runtime.Learners.OlsLinearRegressionTrainer TrainRegression Microsoft.ML.Runtime.Learners.OlsLinearRegressionTrainer+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+RegressionOutput Trainers.PcaAnomalyDetector Train an PCA Anomaly model. Microsoft.ML.Runtime.PCA.RandomizedPcaTrainer TrainPcaAnomaly Microsoft.ML.Runtime.PCA.RandomizedPcaTrainer+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+AnomalyDetectionOutput Trainers.PoissonRegressor Train an Poisson regression model. Microsoft.ML.Runtime.Learners.PoissonRegression TrainRegression Microsoft.ML.Runtime.Learners.PoissonRegression+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+RegressionOutput Trainers.StochasticDualCoordinateAscentBinaryClassifier Train an SDCA binary model. Microsoft.ML.Runtime.Learners.Sdca TrainBinary Microsoft.ML.Runtime.Learners.LinearClassificationTrainer+Arguments Microsoft.ML.Runtime.EntryPoints.CommonOutputs+BinaryClassificationOutput diff --git a/test/BaselineOutput/Common/EntryPoints/core_manifest.json b/test/BaselineOutput/Common/EntryPoints/core_manifest.json index 46aeed032c..ea1a86a2e8 100644 --- a/test/BaselineOutput/Common/EntryPoints/core_manifest.json +++ b/test/BaselineOutput/Common/EntryPoints/core_manifest.json @@ -10771,149 +10771,6 @@ "ITrainerOutput" ] }, - { - "Name": "Trainers.OrdinaryLeastSquaresRegressor", - "Desc": "Train an OLS regression model.", - "FriendlyName": "Ordinary Least Squares (Regression)", - "ShortName": "ols", - "Inputs": [ - { - "Name": "TrainingData", - "Type": "DataView", - "Desc": "The data to be used for training", - "Aliases": [ - "data" - ], - "Required": true, - "SortOrder": 1.0, - "IsNullable": false - }, - { - "Name": "FeatureColumn", - "Type": "String", - "Desc": "Column to use for features", - "Aliases": [ - "feat" - ], - "Required": false, - "SortOrder": 2.0, - "IsNullable": false, - "Default": "Features" - }, - { - "Name": "LabelColumn", - "Type": "String", - "Desc": "Column to use for labels", - "Aliases": [ - "lab" - ], - "Required": false, - "SortOrder": 3.0, - "IsNullable": false, - "Default": "Label" - }, - { - "Name": "WeightColumn", - "Type": "String", - "Desc": "Column to use for example weight", - "Aliases": [ - "weight" - ], - "Required": false, - "SortOrder": 4.0, - "IsNullable": false, - "Default": "Weight" - }, - { - "Name": "NormalizeFeatures", - "Type": { - "Kind": "Enum", - "Values": [ - "No", - "Warn", - "Auto", - "Yes" - ] - }, - "Desc": "Normalize option for the feature column", - "Aliases": [ - "norm" - ], - "Required": false, - "SortOrder": 5.0, - "IsNullable": false, - "Default": "Auto" - }, - { - "Name": "Caching", - "Type": { - "Kind": "Enum", - "Values": [ - "Auto", - "Memory", - "Disk", - "None" - ] - }, - "Desc": "Whether learner should cache input training data", - "Aliases": [ - "cache" - ], - "Required": false, - "SortOrder": 6.0, - "IsNullable": false, - "Default": "Auto" - }, - { - "Name": "L2Weight", - "Type": "Float", - "Desc": "L2 regularization weight", - "Aliases": [ - "l2" - ], - "Required": false, - "SortOrder": 50.0, - "IsNullable": false, - "Default": 1E-06, - "SweepRange": { - "RangeType": "Discrete", - "Values": [ - 1E-06, - 0.1, - 1.0 - ] - } - }, - { - "Name": "PerParameterSignificance", - "Type": "Bool", - "Desc": "Whether to calculate per parameter significance statistics", - "Aliases": [ - "sig" - ], - "Required": false, - "SortOrder": 150.0, - "IsNullable": false, - "Default": true - } - ], - "Outputs": [ - { - "Name": "PredictorModel", - "Type": "PredictorModel", - "Desc": "The trained model" - } - ], - "InputKind": [ - "ITrainerInputWithWeight", - "ITrainerInputWithLabel", - "ITrainerInput" - ], - "OutputKind": [ - "IRegressionOutput", - "ITrainerOutput" - ] - }, { "Name": "Trainers.PcaAnomalyDetector", "Desc": "Train an PCA Anomaly model.",