protected void TrainNe(TaggedToken[][] trainSentences, TaggedToken[][] developmentSentences)
{
NePerceptron.StartTraining();
List <int> trainOrder = new List <int>(trainSentences.Length);
for (int i = 0; i < trainSentences.Length; i++)
{
trainOrder.Add(i);
}
int bestIterations = 0;
double bestAccuracy = 0.0;
for (int iterations = 0; iterations < MaximumNeIterations; iterations++)
{
Console.WriteLine($"Starting NE iteration {iterations}");
int tokenCount = 0;
Evaluation trainEvaluation = new Evaluation();
foreach (int sentenceIndex in trainOrder)
{
TaggedToken[] trainSentence = trainSentences[sentenceIndex];
if (trainSentence.Length == 0 || trainSentence[0].NeTag < 0)
{
continue;
}
TaggedToken[] taggedSentence = new TaggedToken[trainSentence.Length];
for (int i = 0; i < trainSentence.Length; i++)
{
taggedSentence[i] = new TaggedToken(trainSentence[i]);
}
TagNe(taggedSentence, false);
trainEvaluation.Evaluate(taggedSentence, trainSentence);
if (!trainEvaluation.CheckNesEqual(taggedSentence, trainSentence))
{
NeUpdateWeights(taggedSentence, trainSentence);
}
tokenCount += trainSentence.Length;
if (tokenCount > AccumulateLimit)
{
NePerceptron.AccumulateWeights();
tokenCount = 0;
}
}
Console.WriteLine($"Training set F-score: {trainEvaluation.GetNeFScore()}");
if (developmentSentences == null)
{
if (iterations == MaximumNeIterations - 1)
{
NePerceptron.MakeBestWeight();
}
continue;
}
Evaluation developmentEvaluation = new Evaluation();
foreach (TaggedToken[] developmentSent in developmentSentences)
{
TaggedToken[] taggedSentence = new TaggedToken[developmentSent.Length];
for (int i = 0; i < developmentSent.Length; i++)
{
taggedSentence[i] = new TaggedToken(developmentSent[i]);
}
TrainingMode = false;
TagNe(taggedSentence, true);
TrainingMode = true;
developmentEvaluation.Evaluate(taggedSentence, developmentSent);
}
double developmentAccuracy = developmentEvaluation.GetNeFScore();
Console.WriteLine($"Development set F-Score: {developmentAccuracy}");
if ((developmentAccuracy - bestAccuracy) / developmentAccuracy > 0.00025)
{
bestAccuracy = developmentAccuracy;
bestIterations = iterations;
NePerceptron.MakeBestWeight();
}
else if (bestIterations <= iterations - 3)
{
Console.WriteLine("F-score not increasing, we are done.");
break;
}
}
NePerceptron.EndTraining();
}
protected void TrainPos(TaggedToken[][] trainSentences, TaggedToken[][] developmentSentences)
{
PosPerceptron.StartTraining();
List <int> trainOrder = new List <int>(trainSentences.Length);
for (int i = 0; i < trainSentences.Length; i++)
{
trainOrder.Add(i);
}
int bestIterations = 0;
double bestAccuracy = 0.0;
for (int iterations = 0; iterations < MaximumPosIterations; iterations++)
{
Console.WriteLine($"Starting POS iteration {iterations}");
int tokensCount = 0;
Evaluation trainEvaluation = new Evaluation();
foreach (int sentenceIndex in trainOrder)
{
TaggedToken[] trainSent = trainSentences[sentenceIndex];
if (trainSent.Length == 0 || trainSent[0].PosTag < 0)
{
continue;
}
TaggedToken[] taggedSent = new TaggedToken[trainSent.Length];
for (int i = 0; i < trainSent.Length; i++)
{
taggedSent[i] = new TaggedToken(trainSent[i]);
}
TagPos(taggedSent, false);
int oldPosCorrect = trainEvaluation.PosCorrect;
trainEvaluation.Evaluate(taggedSent, trainSent);
if (trainEvaluation.PosCorrect != oldPosCorrect + trainSent.Length)
{
PosUpdateWeights(taggedSent, trainSent);
}
tokensCount += trainSent.Length;
if (tokensCount > AccumulateLimit)
{
PosPerceptron.AccumulateWeights();
tokensCount = 0;
}
}
Console.WriteLine($"Training set accuracy: {trainEvaluation.GetPosAccuracy()}");
if (developmentSentences == null)
{
if (iterations == MaximumPosIterations - 1)
{
PosPerceptron.MakeBestWeight();
}
continue;
}
Evaluation developmentEvaluation = new Evaluation();
foreach (TaggedToken[] developmentSentence in developmentSentences)
{
TaggedToken[] taggedSentence = new TaggedToken[developmentSentence.Length];
for (int i = 0; i < developmentSentence.Length; i++)
{
taggedSentence[i] = new TaggedToken(developmentSentence[i]);
}
TrainingMode = false;
TagPos(taggedSentence, true);
TrainingMode = true;
developmentEvaluation.Evaluate(taggedSentence, developmentSentence);
}
double developmentAccuracy = developmentEvaluation.GetPosAccuracy();
Console.WriteLine($"Development set accuracy: {developmentAccuracy}");
if ((developmentAccuracy - bestAccuracy) / developmentAccuracy > 0.00025)
{
bestAccuracy = developmentAccuracy;
bestIterations = iterations;
PosPerceptron.MakeBestWeight();
}
else if (developmentAccuracy > bestAccuracy)
{
PosPerceptron.MakeBestWeight();
}
else if (bestIterations <= iterations - 3)
{
Console.WriteLine("Accuracy not increasing, we are done.");
break;
}
}
PosPerceptron.EndTraining();
}
