/// <summary>
/// Executes an AutoML experiment.
/// </summary>
/// <param name="trainData">The training data to be used by the AutoML experiment.</param>
/// <param name="numberOfCVFolds">The number of cross validation folds into which the training data should be divided when fitting a model.</param>
/// <param name="columnInformation">Column information for the dataset.</param>
/// <param name="preFeaturizer">Pre-featurizer that AutoML will apply to the data during an
/// experiment. (The pre-featurizer will be fit only on the training data split to produce a
/// trained transform. Then, the trained transform will be applied to both the training
/// data split and corresponding validation data split.)</param>
/// <param name="progressHandler">A user-defined object that implements
/// the <see cref="IProgress{T}"/> interface. AutoML will invoke the method
/// <see cref="IProgress{T}.Report(T)"/> after each model it produces during the
/// course of the experiment.
/// </param>
/// <returns>The cross validation experiment result.</returns>
/// <remarks>
/// Depending on the size of your data, the AutoML experiment could take a long time to execute.
/// </remarks>
public CrossValidationExperimentResult <TMetrics> Execute(IDataView trainData, uint numberOfCVFolds, ColumnInformation columnInformation = null, IEstimator <ITransformer> preFeaturizer = null, IProgress <CrossValidationRunDetail <TMetrics> > progressHandler = null)
{
UserInputValidationUtil.ValidateNumberOfCVFoldsArg(numberOfCVFolds);
var splitResult = SplitUtil.CrossValSplit(Context, trainData, numberOfCVFolds, columnInformation?.SamplingKeyColumnName);
return(ExecuteCrossVal(splitResult.trainDatasets, columnInformation, splitResult.validationDatasets, preFeaturizer, progressHandler));
}
/// <summary>
/// Executes an AutoML experiment.
/// </summary>
/// <param name="trainData">The training data to be used by the AutoML experiment.</param>
/// <param name="columnInformation">Column information for the dataset.</param>
/// <param name="preFeaturizer">Pre-featurizer that AutoML will apply to the data during an
/// experiment. (The pre-featurizer will be fit only on the training data split to produce a
/// trained transform. Then, the trained transform will be applied to both the training
/// data split and corresponding validation data split.)</param>
/// <param name="progressHandler">A user-defined object that implements
/// the <see cref="IProgress{T}"/> interface. AutoML will invoke the method
/// <see cref="IProgress{T}.Report(T)"/> after each model it produces during the
/// course of the experiment.
/// </param>
/// <returns>The experiment result.</returns>
/// <remarks>
/// Depending on the size of your data, the AutoML experiment could take a long time to execute.
/// </remarks>
public ExperimentResult <TMetrics> Execute(IDataView trainData, ColumnInformation columnInformation,
IEstimator <ITransformer> preFeaturizer = null, IProgress <RunDetail <TMetrics> > progressHandler = null)
{
// 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);
if (rowCount < crossValRowCountThreshold)
{
const int numCrossValFolds = 10;
var splitResult = SplitUtil.CrossValSplit(Context, trainData, numCrossValFolds, columnInformation?.SamplingKeyColumnName);
return(ExecuteCrossValSummary(splitResult.trainDatasets, columnInformation, splitResult.validationDatasets, preFeaturizer, progressHandler));
}
else
{
var splitResult = SplitUtil.TrainValidateSplit(Context, trainData, columnInformation?.SamplingKeyColumnName);
return(ExecuteTrainValidate(splitResult.trainData, columnInformation, splitResult.validationData, preFeaturizer, progressHandler));
}
}