public override void Train(RoleMappedData trainData)
{
using (var ch = Host.Start("Training"))
{
ch.CheckValue(trainData, nameof(trainData));
trainData.CheckRegressionLabel();
trainData.CheckFeatureFloatVector();
trainData.CheckOptFloatWeight();
FeatureCount = trainData.Schema.Feature.Type.ValueCount;
ConvertData(trainData);
TrainCore(ch);
ch.Done();
}
}
private void ConvertData(RoleMappedData trainData, RoleMappedData validationData)
{
trainData.CheckFeatureFloatVector();
trainData.CheckOptFloatWeight();
CheckLabel(trainData);
var useTranspose = UseTranspose(GamTrainerOptions.DiskTranspose, trainData);
var instanceConverter = new ExamplesToFastTreeBins(Host, GamTrainerOptions.MaximumBinCountPerFeature, useTranspose, !GamTrainerOptions.FeatureFlocks, GamTrainerOptions.MinimumExampleCountPerLeaf, float.PositiveInfinity);
ParallelTraining.InitEnvironment();
TrainSet = instanceConverter.FindBinsAndReturnDataset(trainData, PredictionKind, ParallelTraining, null, false);
FeatureMap = instanceConverter.FeatureMap;
if (validationData != null)
{
ValidSet = instanceConverter.GetCompatibleDataset(validationData, PredictionKind, null, false);
}
Host.Assert(FeatureMap == null || FeatureMap.Length == TrainSet.NumFeatures);
}
/// <summary>
/// The basic training calls the optimizer
/// </summary>
public override void Train(RoleMappedData data)
{
Contracts.CheckValue(data, nameof(data));
data.CheckFeatureFloatVector(out NumFeatures);
CheckLabel(data);
data.CheckOptFloatWeight();
if (NumFeatures >= Utils.ArrayMaxSize / ClassCount)
{
throw Contracts.ExceptParam(nameof(data),
String.Format("The number of model parameters which is equal to ('# of features' + 1) * '# of classes' should be less than or equal to {0}.", Utils.ArrayMaxSize));
}
using (var ch = Host.Start("Training"))
{
TrainCore(ch, data);
ch.Done();
}
}