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));
}
