private void TrainCore(IChannel ch, IProgressChannel pch, RoleMappedData data, TPredictor predictor)
{
// Verifications.
_host.AssertValue(ch);
ch.CheckValue(data, nameof(data));
ValidateTrainInput(ch, data);
var featureColumns = data.Schema.GetColumns(RoleMappedSchema.ColumnRole.Feature);
ch.Check(featureColumns.Count == 1, "Only one vector of features is allowed.");
// Data dimension.
int fi = data.Schema.Feature.Index;
var colType = data.Schema.Schema.GetColumnType(fi);
ch.Assert(colType.IsVector, "Feature must be a vector.");
ch.Assert(colType.VectorSize > 0, "Feature dimension must be known.");
int nbDim = colType.VectorSize;
IDataView view = data.Data;
long nbRows = DataViewUtils.ComputeRowCount(view);
Float[] labels;
uint[] groupCount;
DMatrix dtrain;
// REVIEW xadupre: this can be avoided by using method XGDMatrixCreateFromDataIter from the XGBoost API.
// XGBoost removes NaN values from a dense matrix and stores it in sparse format anyway.
bool isDense = DetectDensity(data);
var dt = DateTime.Now;
if (isDense)
{
dtrain = FillDenseMatrix(ch, nbDim, nbRows, data, out labels, out groupCount);
ch.Info("Dense matrix created with nbFeatures={0} and nbRows={1} in {2}.", nbDim, nbRows, DateTime.Now - dt);
}
else
{
dtrain = FillSparseMatrix(ch, nbDim, nbRows, data, out labels, out groupCount);
ch.Info("Sparse matrix created with nbFeatures={0} and nbRows={1} in {2}.", nbDim, nbRows, DateTime.Now - dt);
}
// Some options are filled based on the data.
var options = _args.ToDict(_host);
UpdateXGBoostOptions(ch, options, labels, groupCount);
// For multi class, the number of labels is required.
ch.Assert(PredictionKind != PredictionKind.MultiClassClassification || options.ContainsKey("num_class"),
"XGBoost requires the number of classes to be specified in the parameters.");
ch.Info("XGBoost objective={0}", options["objective"]);
int numTrees;
Booster res = WrappedXGBoostTraining.Train(ch, pch, out numTrees, options, dtrain,
numBoostRound: _args.numBoostRound,
obj: null, verboseEval: _args.verboseEval,
xgbModel: predictor == null ? null : predictor.GetBooster(),
saveBinaryDMatrix: _args.saveXGBoostDMatrixAsBinary);
int nbTrees = res.GetNumTrees();
ch.Info("Training is complete. Number of added trees={0}, total={1}.", numTrees, nbTrees);
_model = res.SaveRaw();
_nbFeaturesXGboost = (int)dtrain.GetNumCols();
_nbFeaturesML = nbDim;
}
public int GetNumTrees()
{
return(_booster.GetNumTrees());
}
