public void Train(ILabeledExampleCollection <LblT, ExT> dataset)
{
var binaryDataset = new LabeledDataset <LblT, ExT>(dataset.Select(le =>
new LabeledExample <LblT, ExT>(le.Label.Equals(OneLabel) ? OneLabel : OtherLabel, le.Example)));
mBinaryModel.Train(binaryDataset);
IsTrained = true;
}
public override void Train(ILabeledExampleCollection <SentimentLabel, SparseVector <double> > dataset)
{
Preconditions.CheckNotNull(dataset);
var ds = new LabeledDataset <SentimentLabel, SparseVector <double> >(dataset
.Select(le => new LabeledExample <SentimentLabel, SparseVector <double> >(le.Label, le.Example)));
mPosModel = TrainModel(ds, SentimentLabel.Positive, SentimentLabel.Negative, SentimentLabel.Neutral);
mNegModel = TrainModel(ds, SentimentLabel.Negative, SentimentLabel.Positive, SentimentLabel.Neutral);
mNeuModel = TrainModel(ds, SentimentLabel.Neutral, SentimentLabel.Positive, SentimentLabel.Negative);
IsTrained = true;
}
public override void Train(ILabeledExampleCollection <SentimentLabel, SparseVector <double> > dataset)
{
Preconditions.CheckNotNull(dataset);
var posDataset = new LabeledDataset <SentimentLabel, SparseVector <double> >(dataset.Select(le =>
new LabeledExample <SentimentLabel, SparseVector <double> >(le.Label == SentimentLabel.Positive
? SentimentLabel.Positive : SentimentLabel.Negative, le.Example)));
mPosClassifier = CreateModel();
mPosClassifier.Train(posDataset);
var negDataset = new LabeledDataset <SentimentLabel, SparseVector <double> >(dataset.Select(le =>
new LabeledExample <SentimentLabel, SparseVector <double> >(le.Label == SentimentLabel.Negative
? SentimentLabel.Negative : SentimentLabel.Positive, le.Example)));
mNegClassifier = CreateModel();
mNegClassifier.Train(negDataset);
if (PosBiasCalibration != null || NegBiasCalibration != null)
{
var labeledDataset = new LabeledDataset <SentimentLabel, SparseVector <double> >(dataset);
double?posBias = Calibrate(true, labeledDataset);
double?negBias = Calibrate(false, labeledDataset);
BiasToPosRate = posBias ?? BiasToPosRate;
BiasToNegRate = negBias ?? BiasToNegRate;
}
mPosSortedScores = mNegSortedScores = null;
mExampleScores = dataset.Select(le =>
{
Prediction <SentimentLabel> posPrediction = mPosClassifier.Predict(le.Example);
Prediction <SentimentLabel> negPrediction = mNegClassifier.Predict(le.Example);
return(new ExampleScore
{
Label = le.Label,
PosScore = posPrediction.BestClassLabel == SentimentLabel.Positive ? posPrediction.BestScore : -posPrediction.BestScore,
NegScore = negPrediction.BestClassLabel == SentimentLabel.Negative ? -negPrediction.BestScore : negPrediction.BestScore
});
}).ToArray();
UpdateDistrTable();
IsTrained = true;
}
public void Train(ILabeledExampleCollection <LblT, string> dataset)
{
Preconditions.CheckState(!IsTrained);
Preconditions.CheckNotNull(dataset);
Preconditions.CheckNotNull(BowSpace);
Preconditions.CheckNotNull(FeatureProcessor);
Preconditions.CheckNotNull(Model);
// preprocess the text
foreach (LabeledExample <LblT, string> le in dataset)
{
le.Example = FeatureProcessor.Run(le.Example);
}
// bow vectors
List <SparseVector <double> > bowData = BowSpace is DeltaBowSpace <LblT>
?(BowSpace as DeltaBowSpace <LblT>).Initialize(dataset as ILabeledDataset <LblT, string> ?? new LabeledDataset <LblT, string>(dataset))
: BowSpace.Initialize(dataset.Select(d => d.Example));
var bowDataset = new LabeledDataset <LblT, SparseVector <double> >();
for (int i = 0; i < bowData.Count; i++)
{
bowDataset.Add(dataset[i].Label, bowData[i]);
}
// train
if (OnTrainModel == null)
{
Model.Train(bowDataset);
}
else
{
OnTrainModel(this, bowDataset);
}
IsTrained = true;
}