public override ExperimentResult <BinaryClassificationMetrics> Execute(IDataView trainData, ColumnInformation columnInformation, IEstimator <ITransformer> preFeaturizer = null, IProgress <RunDetail <BinaryClassificationMetrics> > progressHandler = null)
{
var label = columnInformation.LabelColumnName;
_experiment.SetEvaluateMetric(Settings.OptimizingMetric, label);
_experiment.SetTrainingTimeInSeconds(Settings.MaxExperimentTimeInSeconds);
// Cross val threshold for # of dataset rows --
// If dataset has < threshold # of rows, use cross val.
// Else, run experiment using train-validate split.
const int crossValRowCountThreshold = 15000;
var rowCount = DatasetDimensionsUtil.CountRows(trainData, crossValRowCountThreshold);
// TODO
// split cross validation result according to sample key as well.
if (rowCount < crossValRowCountThreshold)
{
const int numCrossValFolds = 10;
_experiment.SetDataset(trainData, numCrossValFolds);
}
else
{
var splitData = Context.Data.TrainTestSplit(trainData);
_experiment.SetDataset(splitData.TrainSet, splitData.TestSet);
}
MultiModelPipeline pipeline = new MultiModelPipeline();
if (preFeaturizer != null)
{
pipeline = pipeline.Append(preFeaturizer);
}
pipeline = pipeline.Append(Context.Auto().Featurizer(trainData, columnInformation, Features))
.Append(Context.Auto().BinaryClassification(label, Features));
_experiment.SetPipeline(pipeline);
var monitor = new BinaryClassificationTrialResultMonitor();
monitor.OnTrialCompleted += (o, e) =>
{
var detail = ToRunDetail(e);
progressHandler?.Report(detail);
};
_experiment.SetMonitor(monitor);
_experiment.Run();
var runDetails = monitor.RunDetails.Select(e => ToRunDetail(e));
var bestRun = ToRunDetail(monitor.BestRun);
var result = new ExperimentResult <BinaryClassificationMetrics>(runDetails, bestRun);
return(result);
}
private ExperimentResult <TMetrics> Execute(ColumnInformation columnInfo,
DatasetColumnInfo[] columns,
IEstimator <ITransformer> preFeaturizer,
IProgress <RunDetail <TMetrics> > progressHandler,
IRunner <RunDetail <TMetrics> > runner)
{
// Execute experiment & get all pipelines run
var experiment = new Experiment <RunDetail <TMetrics>, TMetrics>(Context, _task, OptimizingMetricInfo, progressHandler,
Settings, MetricsAgent, _trainerAllowList, columns, runner, _logger);
var runDetails = experiment.Execute();
var bestRun = GetBestRun(runDetails);
var experimentResult = new ExperimentResult <TMetrics>(runDetails, bestRun);
return(experimentResult);
}
public override ExperimentResult <BinaryClassificationMetrics> Execute(IDataView trainData, IDataView validationData, ColumnInformation columnInformation, IEstimator <ITransformer> preFeaturizer = null, IProgress <RunDetail <BinaryClassificationMetrics> > progressHandler = null)
{
var label = columnInformation.LabelColumnName;
_experiment.SetEvaluateMetric(Settings.OptimizingMetric, label);
_experiment.SetTrainingTimeInSeconds(Settings.MaxExperimentTimeInSeconds);
_experiment.SetDataset(trainData, validationData);
MultiModelPipeline pipeline = new MultiModelPipeline();
if (preFeaturizer != null)
{
pipeline = pipeline.Append(preFeaturizer);
}
pipeline = pipeline.Append(Context.Auto().Featurizer(trainData, columnInformation, "__Features__"))
.Append(Context.Auto().BinaryClassification(label, featureColumnName: Features));
_experiment.SetPipeline(pipeline);
var monitor = new BinaryClassificationTrialResultMonitor();
monitor.OnTrialCompleted += (o, e) =>
{
var detail = ToRunDetail(e);
progressHandler?.Report(detail);
};
_experiment.SetMonitor(monitor);
_experiment.Run();
var runDetails = monitor.RunDetails.Select(e => ToRunDetail(e));
var bestRun = ToRunDetail(monitor.BestRun);
var result = new ExperimentResult <BinaryClassificationMetrics>(runDetails, bestRun);
return(result);
}
