public override void Train(ILabeledExampleCollection <SentimentLabel, SparseVector <double> > dataset)
{
Preconditions.CheckNotNull(dataset);
var ds = new LabeledDataset <SentimentLabel, SparseVector <double> >(dataset.Where(le => le.Label != SentimentLabel.Neutral)
.Select(le => new LabeledExample <SentimentLabel, SparseVector <double> >(le.Label, le.Example)));
mPosNegModel = TrainModel(ds, SentimentLabel.Positive, SentimentLabel.Negative);
ds = new LabeledDataset <SentimentLabel, SparseVector <double> >(dataset.Where(le => le.Label != SentimentLabel.Positive)
.Select(le => new LabeledExample <SentimentLabel, SparseVector <double> >(le.Label, le.Example)));
mNegNeuModel = TrainModel(ds, SentimentLabel.Negative, SentimentLabel.Neutral);
ds = new LabeledDataset <SentimentLabel, SparseVector <double> >(dataset.Where(le => le.Label != SentimentLabel.Negative)
.Select(le => new LabeledExample <SentimentLabel, SparseVector <double> >(le.Label, le.Example)));
mPosNeuModel = TrainModel(ds, SentimentLabel.Positive, SentimentLabel.Neutral);
IsTrained = true;
}
public void Train(ILabeledExampleCollection <LblT, ExT> dataset)
{
foreach (ModelLabel modelLabel in ModelLabels.Take(ModelLabels.Count() - 1))
{
modelLabel.Model.Train(dataset);
ModelLabel modelLabel_ = modelLabel;
dataset = new LabeledDataset <LblT, ExT>(dataset.Where(le => !le.Label.Equals(modelLabel_.Label)));
}
ModelLabels.Last().Model.Train(dataset);
IsTrained = true;
}
public override void Train(ILabeledExampleCollection <SentimentLabel, SparseVector <double> > dataset)
{
Preconditions.CheckNotNull(dataset);
Preconditions.CheckArgumentRange(IsCalcBounds || NegCentile >= 0 && NegCentile <= 1);
Preconditions.CheckArgumentRange(IsCalcBounds || PosCentile >= 0 && PosCentile <= 1);
var labeledDataset = (LabeledDataset <SentimentLabel, SparseVector <double> >)dataset;
if (labeledDataset.Count == 0)
{
Console.WriteLine("empty dataset");
}
TrainStats = null;
var posScores = new List <double>();
var negScores = new List <double>();
var neutralScores = new List <double>();
var trainDataset = new LabeledDataset <SentimentLabel, SparseVector <double> >(labeledDataset.Where(le => le.Label != SentimentLabel.Neutral));
var neutralDataset = IsCalcStats || IsCalcBounds
? new LabeledDataset <SentimentLabel, SparseVector <double> >(dataset.Where(le => le.Label == SentimentLabel.Neutral))
: null;
var validation = new CrossValidator <SentimentLabel, SparseVector <double> >
{
NumFolds = NumTrainFolds,
Dataset = trainDataset,
OnAfterPrediction = (sender, foldN, model, example, le, prediction) =>
{
if (le.Label == prediction.BestClassLabel)
{
if (le.Label == SentimentLabel.Positive)
{
posScores.Add(prediction.BestScore);
}
else
{
negScores.Add(-prediction.BestScore);
}
}
return(true);
},
OnAfterFold = (sender, foldN, trainSet, testSet) =>
{
if (IsCalcStats || IsCalcBounds)
{
neutralScores.AddRange(neutralDataset
.Select(le => sender.Models[0].Predict(le.Example))
.Select(p => p.BestClassLabel == SentimentLabel.Positive ? p.BestScore : -p.BestScore));
}
}
};
validation.Models.Add(CreateModel());
validation.Run();
if (IsCalcBounds)
{
double negMaxProb, negScore;
NegBound = FindMaxExclusiveProbability(neutralScores.Where(s => s < 0).Select(s => - s),
negScores.Select(s => - s), out negMaxProb, out negScore) ? -negScore : 0;
double posMaxProb, posScore;
PosBound = FindMaxExclusiveProbability(neutralScores.Where(s => s > 0),
posScores, out posMaxProb, out posScore) ? posScore : 0;
}
else
{
if (NegCentile != null)
{
NegBound = negScores.OrderByDescending(bs => bs).Skip((int)Math.Truncate(negScores.Count * NegCentile.Value)).FirstOrDefault();
}
if (PosCentile != null)
{
PosBound = posScores.OrderBy(bs => bs).Skip((int)Math.Truncate(posScores.Count * PosCentile.Value)).FirstOrDefault();
}
}
if (IsCalcStats)
{
TrainStats = CalcStats(negScores, neutralScores, posScores);
}
mBinaryClassifier = validation.Models[0];
mBinaryClassifier.Train(trainDataset);
IsTrained = true;
}
public override void Train(ILabeledExampleCollection <SentimentLabel, SparseVector <double> > dataset)
{
Preconditions.CheckNotNull(dataset);
Preconditions.CheckArgumentRange(TagDistrTable == null || TagDistrTable.NumOfDimensions == 2);
mBinModel = CreateModel();
mBinModel.Train(new LabeledDataset <SentimentLabel, SparseVector <double> >(dataset.Where(le => le.Label != SentimentLabel.Neutral)));
TagDistrTable = new EnumTagDistrTable <SentimentLabel>(1, BinWidth, -5, 5, SentimentLabel.Exclude)
{
CalcDistrFunc = (tagCounts, values, tag) => ((double)tagCounts[tag] + 1) / (tagCounts.Values.Sum() + tagCounts.Count) // use Laplace formula
};
foreach (LabeledExample <SentimentLabel, SparseVector <double> > le in dataset)
{
Prediction <SentimentLabel> prediction = mBinModel.Predict(le.Example);
TagDistrTable.AddCount(le.Label, prediction.BestClassLabel == SentimentLabel.Positive ? prediction.BestScore : -prediction.BestScore);
}
TagDistrTable.Calculate();
IsTrained = true;
}
