private protected override TreeEnsembleModelParameters PrepareModel(IDataView input)
{
var trainer = new FastForestRegressionTrainer(Env, _trainerOptions);
var trained = trainer.Fit(input);
return(trained.Model);
}
private FastForestRegressionModelParameters FitModel(IEnumerable <IRunResult> previousRuns)
{
Single[] targets = new Single[previousRuns.Count()];
Single[][] features = new Single[previousRuns.Count()][];
int i = 0;
foreach (RunResult r in previousRuns)
{
features[i] = SweeperProbabilityUtils.ParameterSetAsFloatArray(_host, _sweepParameters, r.ParameterSet, true);
targets[i] = (float)r.MetricValue;
i++;
}
ArrayDataViewBuilder dvBuilder = new ArrayDataViewBuilder(_host);
dvBuilder.AddColumn(DefaultColumnNames.Label, NumberDataViewType.Single, targets);
dvBuilder.AddColumn(DefaultColumnNames.Features, NumberDataViewType.Single, features);
IDataView view = dvBuilder.GetDataView();
_host.Assert(view.GetRowCount() == targets.Length, "This data view will have as many rows as there have been evaluations");
using (IChannel ch = _host.Start("Single training"))
{
// Set relevant random forest arguments.
// Train random forest.
var trainer = new FastForestRegressionTrainer(_host,
new FastForestRegressionTrainer.Options
{
FeatureFraction = _args.SplitRatio,
NumberOfTrees = _args.NumOfTrees,
MinimumExampleCountPerLeaf = _args.NMinForSplit,
LabelColumnName = DefaultColumnNames.Label,
FeatureColumnName = DefaultColumnNames.Features,
});
var predictor = trainer.Fit(view);
// Return random forest predictor.
return(predictor.Model);
}
}
