public void Dispose()
{
Booster?.Dispose();
Booster = null;
DisposeParallelTraining();
}
public Predictors <TOutput> Train(Datasets data
, Func <int, double> learningRateSchedule = null // optional: learning rate as a function of iteration (zero-based)
)
{
// For multi class, the number of labels is required.
if (!(PredictionKind != PredictionKind.MultiClassClassification || Objective.NumClass > 1))
{
throw new Exception("LightGBM requires the number of classes to be specified in the parameters for multi-class classification.");
}
if (PredictionKind == PredictionKind.Ranking)
{
if (data.Training.GetGroups() == null)
{
throw new Exception("Require Groups training data for ObjectiveType.LambdaRank");
}
if (data.Validation != null && data.Validation.GetGroups() == null)
{
throw new Exception("Require Groups validation data for ObjectiveType.LambdaRank");
}
}
TrainMetrics.Clear();
ValidMetrics.Clear();
Booster?.Dispose();
Booster = null;
Datasets = data;
var args = GetParameters(data);
Booster = Train(args, data.Training, data.Validation, TrainMetrics, ValidMetrics, learningRateSchedule);
(var model, var argsout) = Booster.GetModel();
TrainedEnsemble = model;
FeatureCount = data.Training.NumFeatures;
// check parameter strings
if (learningRateSchedule != null)
{
argsout.Learning.LearningRate = args.Learning.LearningRate;
}
var strIn = args.ToString();
var strOut = argsout.ToString();
if (strIn != strOut)
{
throw new Exception($"Parameters differ:\n{strIn}\n{strOut}");
}
var managed = CreateManagedPredictor();
var native = CreateNativePredictor();
return(new Predictors <TOutput>(managed, native));
}
public void Dispose()
{
Booster.Dispose();
}
