// This method will be called for each input received from the pipeline to this cmdlet; if no input is received, this method is not called
protected override void ProcessRecord()
{
var context = new MLContext();
var columnInferenceResults = InferColumnsHelper.InferColumns(context, this.Path, new ColumnInformation()
{
LabelColumnName = this.Label
}, null, null, null, false, false, true);
var textLoader = context.Data.CreateTextLoader(columnInferenceResults.TextLoaderOptions, null);
var trainDataset = textLoader.Load(new MultiFileSource(this.Path));
IProgress <RunDetail <MulticlassClassificationMetrics> > progressHandler = new ProgressToCallback <RunDetail <MulticlassClassificationMetrics> >((metric) =>
{
WriteObject(metric);
});
var results = context.Auto()
.CreateMulticlassClassificationExperiment(10)
.Execute(trainDataset, columnInferenceResults.ColumnInformation, null, progressHandler);
}
// This method will be called for each input received from the pipeline to this cmdlet; if no input is received, this method is not called
protected override void ProcessRecord()
{
var context = new MLContext();
var columnInferenceResults = InferColumnsHelper.InferColumns(context, this.Path, new ColumnInformation()
{
LabelColumnName = this.Label, UserIdColumnName = this.User, ItemIdColumnName = this.Item
}, null, null, null, false, false, true);
var textLoader = context.Data.CreateTextLoader(columnInferenceResults.TextLoaderOptions, null);
var trainDataset = textLoader.Load(new MultiFileSource(this.Path));
IProgress <RunDetail <RegressionMetrics> > progressHandler = new ProgressToCallback <RunDetail <RegressionMetrics> >((metric) =>
{
WriteObject(metric);
});
var results = context.Auto()
.CreateRecommendationExperiment(10)
.Execute(trainDataset, columnInferenceResults.ColumnInformation, null, progressHandler);
WriteVerbose("Best Trainer :" + results.BestRun.TrainerName);
WriteObject(results);
}
