/// <summary>
/// Initialize the Booster.
/// </summary>
/// <param name="parameters">Parameters for boosters. See <see cref="XGBoostArguments"/>.</param>
/// <param name="data">training data<see cref="DMatrix"/></param>
/// <param name="continuousTraining">Start from a trained model</param>
public Booster(Dictionary <string, string> parameters, DMatrix data, Booster continuousTraining)
{
_featureNames = null;
_featureTypes = null;
_numFeatures = (int)data.GetNumCols();
Contracts.Assert(_numFeatures > 0);
_handle = IntPtr.Zero;
WrappedXGBoostInterface.Check(WrappedXGBoostInterface.XGBoosterCreate(new[] { data.Handle }, 1, ref _handle));
if (continuousTraining != null)
{
// There should be another way than serialized then loading the model.
var saved = continuousTraining.SaveRaw();
unsafe
{
fixed(byte *buf = saved)
WrappedXGBoostInterface.Check(WrappedXGBoostInterface.XGBoosterLoadModelFromBuffer(_handle, buf, (uint)saved.Length));
}
}
if (parameters != null)
{
SetParam(parameters);
}
WrappedXGBoostInterface.Check(WrappedXGBoostInterface.XGBoosterLazyInit(_handle));
}
protected override void SaveCore(ModelSaveContext ctx)
{
Host.CheckValue(ctx, nameof(ctx));
ctx.CheckAtModel();
// *** Binary format ***
// <base>
// int (_booster.NumFeatures)
// int (_numFeaturesML) (version >= 0x00010003)
base.SaveCore(ctx);
ctx.SaveBinaryStream("xgboost.model", bw => bw.Write(_booster.SaveRaw()));
ctx.Writer.Write(_booster.NumFeatures);
ctx.Writer.Write(_numFeaturesML);
}
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;
}
