public ITrainerEstimator CreateInstance(MLContext mlContext, IEnumerable <SweepableParam> sweepParams, ColumnInformation columnInfo, IDataView validationSet) { var options = TrainerExtensionUtil.CreateOptions <Options>(null, columnInfo.LabelColumnName); return(mlContext.MulticlassClassification.Trainers.ImageClassification(options)); }
public ITrainerEstimator CreateInstance(MLContext mlContext, IEnumerable <SweepableParam> sweepParams, ColumnInformation columnInfo) { var options = TrainerExtensionUtil.CreateOptions <LinearSvmTrainer.Options>(sweepParams, columnInfo.LabelColumnName); return(mlContext.BinaryClassification.Trainers.LinearSvm(options)); }
public ITrainerEstimator CreateInstance(MLContext mlContext, IEnumerable <SweepableParam> sweepParams, ColumnInformation columnInfo, IDataView validationSet) { var options = TrainerExtensionUtil.CreateOptions <SymbolicSgdLogisticRegressionBinaryTrainer.Options>(sweepParams, columnInfo.LabelColumnName); return(mlContext.BinaryClassification.Trainers.SymbolicSgdLogisticRegression(options)); }
public ITrainerEstimator CreateInstance(MLContext mlContext, IEnumerable <SweepableParam> sweepParams, ColumnInformation columnInfo) { var options = TrainerExtensionUtil.CreateOptions <SdcaRegressionTrainer.Options>(sweepParams, columnInfo.LabelColumnName); return(mlContext.Regression.Trainers.Sdca(options)); }
public ITrainerEstimator CreateInstance(MLContext mlContext, IEnumerable <SweepableParam> sweepParams, ColumnInformation columnInfo, IDataView validationSet) { var options = TrainerExtensionUtil.CreateOptions <OnlineGradientDescentTrainer.Options>(sweepParams, columnInfo.LabelColumnName); return(mlContext.Regression.Trainers.OnlineGradientDescent(options)); }
public ITrainerEstimator CreateInstance(MLContext mlContext, IEnumerable <SweepableParam> sweepParams, ColumnInformation columnInfo) { var options = TrainerExtensionUtil.CreateOptions <SdcaMaximumEntropyMulticlassTrainer.Options>(sweepParams, columnInfo.LabelColumnName); return(mlContext.MulticlassClassification.Trainers.SdcaMaximumEntropy(options)); }
public ITrainerEstimator CreateInstance(MLContext mlContext, IEnumerable <SweepableParam> sweepParams, ColumnInformation columnInfo, IDataView validationSet) { var options = TrainerExtensionUtil.CreateOptions <FastTreeRankingTrainer.Options>(sweepParams, columnInfo.LabelColumnName); options.RowGroupColumnName = columnInfo.GroupIdColumnName; return(mlContext.Ranking.Trainers.FastTree(options)); }
public ITrainerEstimator CreateInstance(MLContext mlContext, IEnumerable <SweepableParam> sweepParams, ColumnInformation columnInfo, IDataView validationSet) { var options = TrainerExtensionUtil.CreateOptions <LbfgsMaximumEntropyMulticlassTrainer.Options>(sweepParams, columnInfo.LabelColumnName); options.ExampleWeightColumnName = columnInfo.ExampleWeightColumnName; return(mlContext.MulticlassClassification.Trainers.LbfgsMaximumEntropy(options)); }
public ITrainerEstimator CreateInstance(MLContext mlContext, IEnumerable <SweepableParam> sweepParams, ColumnInformation columnInfo, IDataView validationSet) { var options = TrainerExtensionUtil.CreateOptions <FastTreeBinaryTrainer.Options>(sweepParams, columnInfo.LabelColumnName); options.ExampleWeightColumnName = columnInfo.ExampleWeightColumnName; return(mlContext.BinaryClassification.Trainers.FastTree(options)); }
public ITrainerEstimator CreateInstance(MLContext mlContext, IEnumerable <SweepableParam> sweepParams, ColumnInformation columnInfo) { var options = TrainerExtensionUtil.CreateOptions <FastTreeTweedieTrainer.Options>(sweepParams, columnInfo.LabelColumnName); options.ExampleWeightColumnName = columnInfo.ExampleWeightColumnName; return(mlContext.Regression.Trainers.FastTreeTweedie(options)); }
public ITrainerEstimator CreateInstance(MLContext mlContext, IEnumerable <SweepableParam> sweepParams, ColumnInformation columnInfo) { var options = TrainerExtensionUtil.CreateOptions <SgdCalibratedTrainer.Options>(sweepParams, columnInfo.LabelColumnName); options.ExampleWeightColumnName = columnInfo.ExampleWeightColumnName; return(mlContext.BinaryClassification.Trainers.SgdCalibrated(options)); }
public ITrainerEstimator CreateInstance(MLContext mlContext, IEnumerable <SweepableParam> sweepParams, ColumnInformation columnInfo, IDataView validationSet) { var options = TrainerExtensionUtil.CreateOptions <LbfgsPoissonRegressionTrainer.Options>(sweepParams, columnInfo.LabelColumnName); options.ExampleWeightColumnName = columnInfo.ExampleWeightColumnName; return(mlContext.Regression.Trainers.LbfgsPoissonRegression(options)); }
public ITrainerEsitmator CreateInstance(MLContext mlContext, IEnumerable <SweepableParam> sweepParams, ColumnInformation columnInfo) { var options = TrainerExtensionUtil.CreateOptions <MatrixFactorizationTrainer.Options>(sweepParams); options.LabelColumnName = columnInfo.LabelColumnName; options.MatrixColumnIndexColumnName = columnInfo.UserIdColumnName; options.MatrixRowIndexColumnName = columnInfo.ItemIdColumnName; return(mlContext.Recommendation().Trainers.MatrixFactorization(options)); }
public ITrainerEstimator CreateInstance(MLContext mlContext, IEnumerable <SweepableParam> sweepParams, ColumnInformation columnInfo, IDataView validationSet) { var options = TrainerExtensionUtil.CreateOptions <Options>(null, columnInfo.LabelColumnName); options.ValidationSet = validationSet; var logger = ((IChannelProvider)mlContext).Start(nameof(ImageClassificationExtension)); options.MetricsCallback = (ImageClassificationMetrics metric) => { logger.Trace(metric.ToString()); }; return(mlContext.MulticlassClassification.Trainers.ImageClassification(options)); }
public ITrainerEstimator CreateInstance(MLContext mlContext, IEnumerable <SweepableParam> sweepParams, ColumnInformation columnInfo, IDataView validationSet) { AveragedPerceptronTrainer.Options options = null; if (sweepParams == null || !sweepParams.Any()) { options = new AveragedPerceptronTrainer.Options(); options.NumberOfIterations = DefaultNumIterations; options.LabelColumnName = columnInfo.LabelColumnName; } else { options = TrainerExtensionUtil.CreateOptions <AveragedPerceptronTrainer.Options>(sweepParams, columnInfo.LabelColumnName); if (!sweepParams.Any(p => p.Name == "NumberOfIterations")) { options.NumberOfIterations = DefaultNumIterations; } } return(mlContext.BinaryClassification.Trainers.AveragedPerceptron(options)); }