public override double Score <F>(IClassifier <L, F> classifier, GeneralDataset <L, F> data)
{
labelIndex = new HashIndex <L>();
labelIndex.AddAll(classifier.Labels());
labelIndex.AddAll(data.labelIndex.ObjectsList());
ClearCounts();
int[] labelsArr = data.GetLabelsArray();
for (int i = 0; i < data.Size(); i++)
{
IDatum <L, F> d = data.GetRVFDatum(i);
L guess = classifier.ClassOf(d);
AddGuess(guess, labelIndex.Get(labelsArr[i]));
}
FinalizeCounts();
return(GetFMeasure());
}
private void Train(IList <Pair <string, IFileFilter> > trainTreebankPath, Pair <string, IFileFilter> devTreebankPath, string serializedPath)
{
log.Info("Training method: " + op.TrainOptions().trainingMethod);
IList <Tree> binarizedTrees = Generics.NewArrayList();
foreach (Pair <string, IFileFilter> treebank in trainTreebankPath)
{
Sharpen.Collections.AddAll(binarizedTrees, ReadBinarizedTreebank(treebank.First(), treebank.Second()));
}
int nThreads = op.trainOptions.trainingThreads;
nThreads = nThreads <= 0 ? Runtime.GetRuntime().AvailableProcessors() : nThreads;
Edu.Stanford.Nlp.Tagger.Common.Tagger tagger = null;
if (op.testOptions.preTag)
{
Timing retagTimer = new Timing();
tagger = Edu.Stanford.Nlp.Tagger.Common.Tagger.LoadModel(op.testOptions.taggerSerializedFile);
RedoTags(binarizedTrees, tagger, nThreads);
retagTimer.Done("Retagging");
}
ICollection <string> knownStates = FindKnownStates(binarizedTrees);
ICollection <string> rootStates = FindRootStates(binarizedTrees);
ICollection <string> rootOnlyStates = FindRootOnlyStates(binarizedTrees, rootStates);
log.Info("Known states: " + knownStates);
log.Info("States which occur at the root: " + rootStates);
log.Info("States which only occur at the root: " + rootStates);
Timing transitionTimer = new Timing();
IList <IList <ITransition> > transitionLists = CreateTransitionSequence.CreateTransitionSequences(binarizedTrees, op.compoundUnaries, rootStates, rootOnlyStates);
IIndex <ITransition> transitionIndex = new HashIndex <ITransition>();
foreach (IList <ITransition> transitions in transitionLists)
{
transitionIndex.AddAll(transitions);
}
transitionTimer.Done("Converting trees into transition lists");
log.Info("Number of transitions: " + transitionIndex.Size());
Random random = new Random(op.trainOptions.randomSeed);
Treebank devTreebank = null;
if (devTreebankPath != null)
{
devTreebank = ReadTreebank(devTreebankPath.First(), devTreebankPath.Second());
}
PerceptronModel newModel = new PerceptronModel(this.op, transitionIndex, knownStates, rootStates, rootOnlyStates);
newModel.TrainModel(serializedPath, tagger, random, binarizedTrees, transitionLists, devTreebank, nThreads);
this.model = newModel;
}
