private Dataset LoadTrainingData(IChannel ch, RoleMappedData trainData, out CategoricalMetaData catMetaData)
{
// Verifications.
Host.AssertValue(ch);
ch.CheckValue(trainData, nameof(trainData));
CheckDataValid(ch, trainData);
// Load metadata first.
var factory = CreateCursorFactory(trainData);
GetMetainfo(ch, factory, out int numRow, out float[] labels, out float[] weights, out int[] groups);
catMetaData = GetCategoricalMetaData(ch, trainData, numRow);
GetDefaultParameters(ch, numRow, catMetaData.CategoricalBoudaries != null, catMetaData.TotalCats);
Dataset dtrain;
string param = LightGbmInterfaceUtils.JoinParameters(Options);
// To reduce peak memory usage, only enable one sampling task at any given time.
lock (LightGbmShared.SampleLock)
{
CreateDatasetFromSamplingData(ch, factory, numRow,
param, labels, weights, groups, catMetaData, out dtrain);
}
// Push rows into dataset.
LoadDataset(ch, factory, dtrain, numRow, Args.BatchSize, catMetaData);
// Some checks.
CheckAndUpdateParametersBeforeTraining(ch, trainData, labels, groups);
return(dtrain);
}
private protected override LightGbmRankingPredictor CreatePredictor()
{
Host.Check(TrainedEnsemble != null, "The predictor cannot be created before training is complete");
var innerArgs = LightGbmInterfaceUtils.JoinParameters(Options);
return(new LightGbmRankingPredictor(Host, TrainedEnsemble, FeatureCount, innerArgs));
}
private protected override OvaPredictor CreatePredictor()
{
Host.Check(TrainedEnsemble != null, "The predictor cannot be created before training is complete.");
Host.Assert(_numClass > 1, "Must know the number of classes before creating a predictor.");
Host.Assert(TrainedEnsemble.NumTrees % _numClass == 0, "Number of trees should be a multiple of number of classes.");
var innerArgs = LightGbmInterfaceUtils.JoinParameters(Options);
IPredictorProducing <float>[] predictors = new IPredictorProducing <float> [_tlcNumClass];
for (int i = 0; i < _tlcNumClass; ++i)
{
var pred = CreateBinaryPredictor(i, innerArgs);
var cali = new PlattCalibrator(Host, -0.5, 0);
predictors[i] = new FeatureWeightsCalibratedPredictor(Host, pred, cali);
}
string obj = (string)GetGbmParameters()["objective"];
if (obj == "multiclass")
{
return(OvaPredictor.Create(Host, OvaPredictor.OutputFormula.Softmax, predictors));
}
else
{
return(OvaPredictor.Create(Host, predictors));
}
}
private protected override IPredictorWithFeatureWeights <float> CreatePredictor()
{
Host.Check(TrainedEnsemble != null, "The predictor cannot be created before training is complete");
var innerArgs = LightGbmInterfaceUtils.JoinParameters(Options);
var pred = new LightGbmBinaryModelParameters(Host, TrainedEnsemble, FeatureCount, innerArgs);
var cali = new PlattCalibrator(Host, -0.5, 0);
return(new FeatureWeightsCalibratedPredictor(Host, pred, cali));
}
public Booster(Dictionary <string, string> parameters, Dataset trainset, Dataset validset = null)
{
var param = LightGbmInterfaceUtils.JoinParameters(parameters);
var handle = IntPtr.Zero;
LightGbmInterfaceUtils.Check(WrappedLightGbmInterface.BoosterCreate(trainset.Handle, param, ref handle));
Handle = handle;
if (validset != null)
{
LightGbmInterfaceUtils.Check(WrappedLightGbmInterface.BoosterAddValidData(Handle, validset.Handle));
_hasValid = true;
}
int numEval = 0;
BestIteration = -1;
LightGbmInterfaceUtils.Check(WrappedLightGbmInterface.BoosterGetEvalCounts(Handle, ref numEval));
// At most one metric in ML.NET.
Contracts.Assert(numEval <= 1);
if (numEval == 1)
{
_hasMetric = true;
}
}
