diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/StochasticDualCoordinateAscentWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/StochasticDualCoordinateAscentWithOptions.cs index c8cb69676d..97475cf45f 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/StochasticDualCoordinateAscentWithOptions.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/StochasticDualCoordinateAscentWithOptions.cs @@ -27,7 +27,7 @@ public static void Example() // Make the convergence tolerance tighter. ConvergenceTolerance = 0.05f, // Increase the maximum number of passes over training data. - NumberOfIterations = 30, + MaximumNumberOfIterations = 30, // Give the instances of the positive class slightly more weight. PositiveInstanceWeight = 1.2f, }; diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Clustering/KMeans.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Clustering/KMeans.cs index 545e65f61a..fb9c867e95 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Clustering/KMeans.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Clustering/KMeans.cs @@ -27,7 +27,7 @@ public static void Example() // A pipeline for concatenating the age, parity and induced columns together in the Features column and training a KMeans model on them. string outputColumnName = "Features"; var pipeline = ml.Transforms.Concatenate(outputColumnName, new[] { "Age", "Parity", "Induced" }) - .Append(ml.Clustering.Trainers.KMeans(outputColumnName, clustersCount: 2)); + .Append(ml.Clustering.Trainers.KMeans(outputColumnName, numberOfClusters: 2)); var model = pipeline.Fit(trainData); diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Clustering/KMeansWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Clustering/KMeansWithOptions.cs index c1cc97d7f8..2ffca06920 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Clustering/KMeansWithOptions.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Clustering/KMeansWithOptions.cs @@ -33,7 +33,7 @@ public static void Example() { FeatureColumnName = outputColumnName, NumberOfClusters = 2, - NumberOfIterations = 100, + MaximumNumberOfIterations = 100, OptimizationTolerance = 1e-6f, NumberOfThreads = 1 } diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/StochasticDualCoordinateAscentWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/StochasticDualCoordinateAscentWithOptions.cs index 600d642365..d803137ce4 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/StochasticDualCoordinateAscentWithOptions.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/StochasticDualCoordinateAscentWithOptions.cs @@ -33,7 +33,7 @@ public static void Example() // Make the convergence tolerance tighter. ConvergenceTolerance = 0.05f, // Increase the maximum number of passes over training data. - NumberOfIterations = 30, + MaximumNumberOfIterations = 30, }; // Create a pipeline. diff --git a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/StochasticDualCoordinateAscentWithOptions.cs b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/StochasticDualCoordinateAscentWithOptions.cs index fc743d8f41..5cb29da11c 100644 --- a/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/StochasticDualCoordinateAscentWithOptions.cs +++ b/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/StochasticDualCoordinateAscentWithOptions.cs @@ -26,7 +26,7 @@ public static void Example() // Make the convergence tolerance tighter. ConvergenceTolerance = 0.02f, // Increase the maximum number of passes over training data. - NumberOfIterations = 30, + MaximumNumberOfIterations = 30, // Increase learning rate for bias BiasLearningRate = 0.1f }; diff --git a/src/Microsoft.ML.KMeansClustering/KMeansCatalog.cs b/src/Microsoft.ML.KMeansClustering/KMeansCatalog.cs index df6d5091e9..2e2c532357 100644 --- a/src/Microsoft.ML.KMeansClustering/KMeansCatalog.cs +++ b/src/Microsoft.ML.KMeansClustering/KMeansCatalog.cs @@ -20,7 +20,7 @@ public static class KMeansClusteringExtensions /// The clustering catalog trainer object. /// The name of the feature column. /// The name of the example weight column (optional). - /// The number of clusters to use for KMeans. + /// The number of clusters to use for KMeans. /// /// /// [Argument(ArgumentType.AtMostOnce, HelpText = "Maximum number of iterations.", ShortName = "maxiter")] [TGUI(Label = "Max Number of Iterations")] - public int NumberOfIterations = 1000; + public int MaximumNumberOfIterations = 1000; /// /// Memory budget (in MBs) to use for KMeans acceleration. @@ -125,8 +125,8 @@ internal KMeansPlusPlusTrainer(IHostEnvironment env, Options options) _k = options.NumberOfClusters; - Host.CheckUserArg(options.NumberOfIterations > 0, nameof(options.NumberOfIterations), "Must be positive"); - _maxIterations = options.NumberOfIterations; + Host.CheckUserArg(options.MaximumNumberOfIterations > 0, nameof(options.MaximumNumberOfIterations), "Must be positive"); + _maxIterations = options.MaximumNumberOfIterations; Host.CheckUserArg(options.OptimizationTolerance > 0, nameof(options.OptimizationTolerance), "Tolerance must be positive"); _convergenceThreshold = options.OptimizationTolerance; diff --git a/src/Microsoft.ML.StandardLearners/Standard/SdcaBinary.cs b/src/Microsoft.ML.StandardLearners/Standard/SdcaBinary.cs index 4dccfa7526..be9d1d35db 100644 --- a/src/Microsoft.ML.StandardLearners/Standard/SdcaBinary.cs +++ b/src/Microsoft.ML.StandardLearners/Standard/SdcaBinary.cs @@ -200,7 +200,7 @@ public abstract class OptionsBase : TrainerInputBaseWithLabel [Argument(ArgumentType.AtMostOnce, HelpText = "Maximum number of iterations; set to 1 to simulate online learning. Defaults to automatic.", NullName = "", ShortName = "iter, MaxIterations")] [TGUI(Label = "Max number of iterations", SuggestedSweeps = ",10,20,100")] [TlcModule.SweepableDiscreteParam("MaxIterations", new object[] { "", 10, 20, 100 })] - public int? NumberOfIterations; + public int? MaximumNumberOfIterations; /// /// Determines whether to shuffle data for each training iteration. @@ -235,7 +235,7 @@ internal virtual void Check(IHostEnvironment env) Contracts.AssertValue(env); env.CheckUserArg(L2Regularization == null || L2Regularization >= 0, nameof(L2Regularization), "L2 constant must be non-negative."); env.CheckUserArg(L1Threshold == null || L1Threshold >= 0, nameof(L1Threshold), "L1 threshold must be non-negative."); - env.CheckUserArg(NumberOfIterations == null || NumberOfIterations > 0, nameof(NumberOfIterations), "Max number of iterations must be positive."); + env.CheckUserArg(MaximumNumberOfIterations == null || MaximumNumberOfIterations > 0, nameof(MaximumNumberOfIterations), "Max number of iterations must be positive."); env.CheckUserArg(ConvergenceTolerance > 0 && ConvergenceTolerance <= 1, nameof(ConvergenceTolerance), "Convergence tolerance must be positive and no larger than 1."); if (L2Regularization < L2LowerBound) @@ -303,7 +303,7 @@ internal SdcaTrainerBase(IHostEnvironment env, TOptions options, SchemaShape.Col SdcaTrainerOptions = options; SdcaTrainerOptions.L2Regularization = l2Const ?? options.L2Regularization; SdcaTrainerOptions.L1Threshold = l1Threshold ?? options.L1Threshold; - SdcaTrainerOptions.NumberOfIterations = maxIterations ?? options.NumberOfIterations; + SdcaTrainerOptions.MaximumNumberOfIterations = maxIterations ?? options.MaximumNumberOfIterations; SdcaTrainerOptions.Check(env); } @@ -442,12 +442,12 @@ private protected sealed override TModel TrainCore(IChannel ch, RoleMappedData d ch.Check(count > 0, "Training set has 0 instances, aborting training."); // Tune the default hyperparameters based on dataset size. - if (SdcaTrainerOptions.NumberOfIterations == null) - SdcaTrainerOptions.NumberOfIterations = TuneDefaultMaxIterations(ch, count, numThreads); + if (SdcaTrainerOptions.MaximumNumberOfIterations == null) + SdcaTrainerOptions.MaximumNumberOfIterations = TuneDefaultMaxIterations(ch, count, numThreads); - Contracts.Assert(SdcaTrainerOptions.NumberOfIterations.HasValue); + Contracts.Assert(SdcaTrainerOptions.MaximumNumberOfIterations.HasValue); if (SdcaTrainerOptions.L2Regularization == null) - SdcaTrainerOptions.L2Regularization = TuneDefaultL2(ch, SdcaTrainerOptions.NumberOfIterations.Value, count, numThreads); + SdcaTrainerOptions.L2Regularization = TuneDefaultL2(ch, SdcaTrainerOptions.MaximumNumberOfIterations.Value, count, numThreads); Contracts.Assert(SdcaTrainerOptions.L2Regularization.HasValue); if (SdcaTrainerOptions.L1Threshold == null) @@ -547,8 +547,8 @@ private protected sealed override TModel TrainCore(IChannel ch, RoleMappedData d ch.AssertValue(metricNames); ch.AssertValue(metrics); ch.Assert(metricNames.Length == metrics.Length); - ch.Assert(SdcaTrainerOptions.NumberOfIterations.HasValue); - var maxIterations = SdcaTrainerOptions.NumberOfIterations.Value; + ch.Assert(SdcaTrainerOptions.MaximumNumberOfIterations.HasValue); + var maxIterations = SdcaTrainerOptions.MaximumNumberOfIterations.Value; var rands = new Random[maxIterations]; for (int i = 0; i < maxIterations; i++) diff --git a/src/Microsoft.ML.StandardLearners/StandardLearnersCatalog.cs b/src/Microsoft.ML.StandardLearners/StandardLearnersCatalog.cs index f32c45ff85..934956274a 100644 --- a/src/Microsoft.ML.StandardLearners/StandardLearnersCatalog.cs +++ b/src/Microsoft.ML.StandardLearners/StandardLearnersCatalog.cs @@ -132,7 +132,7 @@ public static SgdNonCalibratedBinaryTrainer StochasticGradientDescentNonCalibrat /// The name of the example weight column (optional). /// The L2 regularization hyperparameter. /// The L1 regularization hyperparameter. Higher values will tend to lead to more sparse model. - /// The maximum number of passes to perform over the data. + /// The maximum number of passes to perform over the data. /// The custom loss, if unspecified will be . /// /// @@ -147,11 +147,11 @@ public static SdcaRegressionTrainer StochasticDualCoordinateAscent(this Regressi ISupportSdcaRegressionLoss loss = null, float? l2Regularization = null, float? l1Threshold = null, - int? numberOfIterations = null) + int? maximumNumberOfIterations = null) { Contracts.CheckValue(catalog, nameof(catalog)); var env = CatalogUtils.GetEnvironment(catalog); - return new SdcaRegressionTrainer(env, labelColumnName, featureColumnName, exampleWeightColumnName, loss, l2Regularization, l1Threshold, numberOfIterations); + return new SdcaRegressionTrainer(env, labelColumnName, featureColumnName, exampleWeightColumnName, loss, l2Regularization, l1Threshold, maximumNumberOfIterations); } /// @@ -184,7 +184,7 @@ public static SdcaRegressionTrainer StochasticDualCoordinateAscent(this Regressi /// The name of the example weight column (optional). /// The L2 regularization hyperparameter. /// The L1 regularization hyperparameter. Higher values will tend to lead to more sparse model. - /// The maximum number of passes to perform over the data. + /// The maximum number of passes to perform over the data. /// /// /// @@ -237,7 +237,7 @@ public static SdcaBinaryTrainer StochasticDualCoordinateAscent( /// The custom loss. Defaults to if not specified. /// The L2 regularization hyperparameter. /// The L1 regularization hyperparameter. Higher values will tend to lead to more sparse model. - /// The maximum number of passes to perform over the data. + /// The maximum number of passes to perform over the data. /// /// /// @@ -285,7 +285,7 @@ public static SdcaNonCalibratedBinaryTrainer StochasticDualCoordinateAscentNonCa /// The custom loss. Defaults to if not specified. /// The L2 regularization hyperparameter. /// The L1 regularization hyperparameter. Higher values will tend to lead to more sparse model. - /// The maximum number of passes to perform over the data. + /// The maximum number of passes to perform over the data. /// /// /// diff --git a/test/BaselineOutput/Common/EntryPoints/core_manifest.json b/test/BaselineOutput/Common/EntryPoints/core_manifest.json index 6caab60446..d11ed1fadc 100644 --- a/test/BaselineOutput/Common/EntryPoints/core_manifest.json +++ b/test/BaselineOutput/Common/EntryPoints/core_manifest.json @@ -11019,7 +11019,7 @@ "Default": 1E-07 }, { - "Name": "NumberOfIterations", + "Name": "MaximumNumberOfIterations", "Type": "Int", "Desc": "Maximum number of iterations.", "Aliases": [ @@ -15221,7 +15221,7 @@ } }, { - "Name": "NumberOfIterations", + "Name": "MaximumNumberOfIterations", "Type": "Int", "Desc": "Maximum number of iterations; set to 1 to simulate online learning. Defaults to automatic.", "Aliases": [ @@ -15494,7 +15494,7 @@ } }, { - "Name": "NumberOfIterations", + "Name": "MaximumNumberOfIterations", "Type": "Int", "Desc": "Maximum number of iterations; set to 1 to simulate online learning. Defaults to automatic.", "Aliases": [ @@ -15767,7 +15767,7 @@ } }, { - "Name": "NumberOfIterations", + "Name": "MaximumNumberOfIterations", "Type": "Int", "Desc": "Maximum number of iterations; set to 1 to simulate online learning. Defaults to automatic.", "Aliases": [ diff --git a/test/Microsoft.ML.Functional.Tests/IntrospectiveTraining.cs b/test/Microsoft.ML.Functional.Tests/IntrospectiveTraining.cs index 69d8773fc9..894a2e6568 100644 --- a/test/Microsoft.ML.Functional.Tests/IntrospectiveTraining.cs +++ b/test/Microsoft.ML.Functional.Tests/IntrospectiveTraining.cs @@ -425,7 +425,7 @@ private IEstimator (r.label, score: catalog.Trainers.Sdca(r.label, r.features, null, - new SdcaRegressionTrainer.Options() { NumberOfIterations = 2, NumberOfThreads = 1 }, + new SdcaRegressionTrainer.Options() { MaximumNumberOfIterations = 2, NumberOfThreads = 1 }, onFit: p => pred = p))); var pipe = reader.Append(est); @@ -87,7 +87,7 @@ public void SdcaRegressionNameCollision() var est = reader.MakeNewEstimator() .Append(r => (r.label, r.Score, score: catalog.Trainers.Sdca(r.label, r.features, null, - new SdcaRegressionTrainer.Options() { NumberOfIterations = 2, NumberOfThreads = 1 }))); + new SdcaRegressionTrainer.Options() { MaximumNumberOfIterations = 2, NumberOfThreads = 1 }))); var pipe = reader.Append(est); @@ -118,7 +118,7 @@ public void SdcaBinaryClassification() var est = reader.MakeNewEstimator() .Append(r => (r.label, preds: catalog.Trainers.Sdca(r.label, r.features, null, - new SdcaBinaryTrainer.Options { NumberOfIterations = 2, NumberOfThreads = 1 }, + new SdcaBinaryTrainer.Options { MaximumNumberOfIterations = 2, NumberOfThreads = 1 }, onFit: (p) => { pred = p; }))); var pipe = reader.Append(est); @@ -198,7 +198,7 @@ public void SdcaBinaryClassificationNoCalibration() // With a custom loss function we no longer get calibrated predictions. var est = reader.MakeNewEstimator() .Append(r => (r.label, preds: catalog.Trainers.SdcaNonCalibrated(r.label, r.features, null, loss, - new SdcaNonCalibratedBinaryTrainer.Options { NumberOfIterations = 2, NumberOfThreads = 1 }, + new SdcaNonCalibratedBinaryTrainer.Options { MaximumNumberOfIterations = 2, NumberOfThreads = 1 }, onFit: p => pred = p))); var pipe = reader.Append(est); diff --git a/test/Microsoft.ML.Tests/OnnxConversionTest.cs b/test/Microsoft.ML.Tests/OnnxConversionTest.cs index 7fd4a76a71..4705d1f2e8 100644 --- a/test/Microsoft.ML.Tests/OnnxConversionTest.cs +++ b/test/Microsoft.ML.Tests/OnnxConversionTest.cs @@ -140,7 +140,7 @@ public void KmeansOnnxConversionTest() Append(mlContext.Clustering.Trainers.KMeans(new Trainers.KMeansPlusPlusTrainer.Options { FeatureColumnName = DefaultColumnNames.Features, - NumberOfIterations = 1, + MaximumNumberOfIterations = 1, NumberOfClusters = 4, NumberOfThreads = 1, InitializationAlgorithm = Trainers.KMeansPlusPlusTrainer.InitializationAlgorithm.Random diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/DecomposableTrainAndPredict.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/DecomposableTrainAndPredict.cs index 154a9f36b1..b4e82c1f09 100644 --- a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/DecomposableTrainAndPredict.cs +++ b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/DecomposableTrainAndPredict.cs @@ -32,7 +32,7 @@ void DecomposableTrainAndPredict() var pipeline = new ColumnConcatenatingEstimator (ml, "Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth") .Append(new ValueToKeyMappingEstimator(ml, "Label"), TransformerScope.TrainTest) .Append(ml.MulticlassClassification.Trainers.StochasticDualCoordinateAscent( - new SdcaMultiClassTrainer.Options { NumberOfIterations = 100, Shuffle = true, NumberOfThreads = 1, })) + new SdcaMultiClassTrainer.Options { MaximumNumberOfIterations = 100, Shuffle = true, NumberOfThreads = 1, })) .Append(new KeyToValueMappingEstimator(ml, "PredictedLabel")); var model = pipeline.Fit(data).GetModelFor(TransformerScope.Scoring); diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Extensibility.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Extensibility.cs index f96665d08a..9517cd45d6 100644 --- a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Extensibility.cs +++ b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Extensibility.cs @@ -41,7 +41,7 @@ void Extensibility() .Append(new CustomMappingEstimator(ml, action, null), TransformerScope.TrainTest) .Append(new ValueToKeyMappingEstimator(ml, "Label"), TransformerScope.TrainTest) .Append(ml.MulticlassClassification.Trainers.StochasticDualCoordinateAscent( - new SdcaMultiClassTrainer.Options { NumberOfIterations = 100, Shuffle = true, NumberOfThreads = 1 })) + new SdcaMultiClassTrainer.Options { MaximumNumberOfIterations = 100, Shuffle = true, NumberOfThreads = 1 })) .Append(new KeyToValueMappingEstimator(ml, "PredictedLabel")); var model = pipeline.Fit(data).GetModelFor(TransformerScope.Scoring); diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Metacomponents.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Metacomponents.cs index 63264e6b85..6f909cef69 100644 --- a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Metacomponents.cs +++ b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/Metacomponents.cs @@ -24,7 +24,7 @@ public void Metacomponents() var data = ml.Data.LoadFromTextFile(GetDataPath(TestDatasets.irisData.trainFilename), separatorChar: ','); var sdcaTrainer = ml.BinaryClassification.Trainers.StochasticDualCoordinateAscentNonCalibrated( - new SdcaNonCalibratedBinaryTrainer.Options { NumberOfIterations = 100, Shuffle = true, NumberOfThreads = 1, }); + new SdcaNonCalibratedBinaryTrainer.Options { MaximumNumberOfIterations = 100, Shuffle = true, NumberOfThreads = 1, }); var pipeline = new ColumnConcatenatingEstimator (ml, "Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth") .Append(ml.Transforms.Conversion.MapValueToKey("Label"), TransformerScope.TrainTest) diff --git a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/PredictAndMetadata.cs b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/PredictAndMetadata.cs index 6937df3644..7c166f81f3 100644 --- a/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/PredictAndMetadata.cs +++ b/test/Microsoft.ML.Tests/Scenarios/Api/Estimators/PredictAndMetadata.cs @@ -30,7 +30,7 @@ void PredictAndMetadata() var pipeline = ml.Transforms.Concatenate("Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth") .Append(ml.Transforms.Conversion.MapValueToKey("Label"), TransformerScope.TrainTest) .Append(ml.MulticlassClassification.Trainers.StochasticDualCoordinateAscent( - new SdcaMultiClassTrainer.Options { NumberOfIterations = 100, Shuffle = true, NumberOfThreads = 1, })); + new SdcaMultiClassTrainer.Options { MaximumNumberOfIterations = 100, Shuffle = true, NumberOfThreads = 1, })); var model = pipeline.Fit(data).GetModelFor(TransformerScope.Scoring); var engine = model.CreatePredictionEngine(ml); diff --git a/test/Microsoft.ML.Tests/Scenarios/ClusteringTests.cs b/test/Microsoft.ML.Tests/Scenarios/ClusteringTests.cs index bc8bb8c404..ef95c95b45 100644 --- a/test/Microsoft.ML.Tests/Scenarios/ClusteringTests.cs +++ b/test/Microsoft.ML.Tests/Scenarios/ClusteringTests.cs @@ -63,7 +63,7 @@ public void PredictClusters() var testData = mlContext.Data.LoadFromEnumerable(clusters); // Create Estimator - var pipe = mlContext.Clustering.Trainers.KMeans("Features", clustersCount: k); + var pipe = mlContext.Clustering.Trainers.KMeans("Features", numberOfClusters: k); // Train the pipeline var trainedModel = pipe.Fit(trainData); diff --git a/test/Microsoft.ML.Tests/TrainerEstimators/MetalinearEstimators.cs b/test/Microsoft.ML.Tests/TrainerEstimators/MetalinearEstimators.cs index 614ae4397c..51df6e7189 100644 --- a/test/Microsoft.ML.Tests/TrainerEstimators/MetalinearEstimators.cs +++ b/test/Microsoft.ML.Tests/TrainerEstimators/MetalinearEstimators.cs @@ -42,7 +42,7 @@ public void OVAUncalibrated() { var (pipeline, data) = GetMultiClassPipeline(); var sdcaTrainer = ML.BinaryClassification.Trainers.StochasticDualCoordinateAscentNonCalibrated( - new SdcaNonCalibratedBinaryTrainer.Options { NumberOfIterations = 100, Shuffle = true, NumberOfThreads = 1 }); + new SdcaNonCalibratedBinaryTrainer.Options { MaximumNumberOfIterations = 100, Shuffle = true, NumberOfThreads = 1 }); pipeline = pipeline.Append(ML.MulticlassClassification.Trainers.OneVersusAll(sdcaTrainer, useProbabilities: false)) .Append(new KeyToValueMappingEstimator(Env, "PredictedLabel")); @@ -60,7 +60,7 @@ public void PairwiseCouplingTrainer() var (pipeline, data) = GetMultiClassPipeline(); var sdcaTrainer = ML.BinaryClassification.Trainers.StochasticDualCoordinateAscentNonCalibrated( - new SdcaNonCalibratedBinaryTrainer.Options { NumberOfIterations = 100, Shuffle = true, NumberOfThreads = 1 }); + new SdcaNonCalibratedBinaryTrainer.Options { MaximumNumberOfIterations = 100, Shuffle = true, NumberOfThreads = 1 }); pipeline = pipeline.Append(ML.MulticlassClassification.Trainers.PairwiseCoupling(sdcaTrainer)) .Append(ML.Transforms.Conversion.MapKeyToValue("PredictedLabel")); @@ -86,7 +86,7 @@ public void MetacomponentsFeaturesRenamed() new SdcaNonCalibratedBinaryTrainer.Options { LabelColumnName = "Label", FeatureColumnName = "Vars", - NumberOfIterations = 100, + MaximumNumberOfIterations = 100, Shuffle = true, NumberOfThreads = 1, });