private protected CrossValidationResult[] CrossValidateTrain(IDataView data, IEstimator <ITransformer> estimator,
int numFolds, string samplingKeyColumn, int?seed = null)
{
Environment.CheckValue(data, nameof(data));
Environment.CheckValue(estimator, nameof(estimator));
Environment.CheckParam(numFolds > 1, nameof(numFolds), "Must be more than 1");
Environment.CheckValueOrNull(samplingKeyColumn);
var splitColumn = DataOperationsCatalog.CreateSplitColumn(Environment, ref data, samplingKeyColumn, seed, fallbackInEnvSeed: true);
var result = new CrossValidationResult[numFolds];
int fold = 0;
// Sequential per-fold training.
// REVIEW: we could have a parallel implementation here. We would need to
// spawn off a separate host per fold in that case.
foreach (var split in DataOperationsCatalog.CrossValidationSplit(Environment, data, splitColumn, numFolds))
{
var model = estimator.Fit(split.TrainSet);
var scoredTest = model.Transform(split.TestSet);
result[fold] = new CrossValidationResult(model, scoredTest, fold);
fold++;
}
return(result);
}
