/// <exception cref="System.Exception"/>
public static void Test(PairwiseModel model, string predictionsName, bool anaphoricityModel)
{
Redwood.Log("scoref-train", "Reading compression...");
Compressor <string> compressor = IOUtils.ReadObjectFromFile(StatisticalCorefTrainer.compressorFile);
Redwood.Log("scoref-train", "Reading test data...");
IList <DocumentExamples> testDocuments = IOUtils.ReadObjectFromFile(StatisticalCorefTrainer.extractedFeaturesFile);
Redwood.Log("scoref-train", "Building test set...");
IList <Pair <Example, IDictionary <int, CompressedFeatureVector> > > allExamples = anaphoricityModel ? GetAnaphoricityExamples(testDocuments) : GetExamples(testDocuments);
Redwood.Log("scoref-train", "Testing...");
PrintWriter writer = new PrintWriter(model.GetDefaultOutputPath() + predictionsName);
IDictionary <int, ICounter <Pair <int, int> > > scores = new Dictionary <int, ICounter <Pair <int, int> > >();
WriteScores(allExamples, compressor, model, writer, scores);
if (model is MaxMarginMentionRanker)
{
writer.Close();
writer = new PrintWriter(model.GetDefaultOutputPath() + predictionsName + "_anaphoricity");
testDocuments = IOUtils.ReadObjectFromFile(StatisticalCorefTrainer.extractedFeaturesFile);
allExamples = GetAnaphoricityExamples(testDocuments);
WriteScores(allExamples, compressor, model, writer, scores);
}
IOUtils.WriteObjectToFile(scores, model.GetDefaultOutputPath() + predictionsName + ".ser");
writer.Close();
}
/// <exception cref="System.Exception"/>
public static void DoTraining(Properties props)
{
SetTrainingPath(props);
Dictionaries dictionaries = new Dictionaries(props);
SetDataPath("train");
wordCountsFile = trainingPath + "train/word_counts.ser";
CorefProperties.SetInput(props, CorefProperties.Dataset.Train);
Preprocess(props, dictionaries, true);
SetDataPath("dev");
CorefProperties.SetInput(props, CorefProperties.Dataset.Dev);
Preprocess(props, dictionaries, false);
SetDataPath("train");
dictionaries = null;
PairwiseModel classificationModel = PairwiseModel.NewBuilder(ClassificationModel, MetaFeatureExtractor.NewBuilder().Build()).Build();
PairwiseModel rankingModel = PairwiseModel.NewBuilder(RankingModel, MetaFeatureExtractor.NewBuilder().Build()).Build();
PairwiseModel anaphoricityModel = PairwiseModel.NewBuilder(AnaphoricityModel, MetaFeatureExtractor.AnaphoricityMFE()).TrainingExamples(5000000).Build();
PairwiseModelTrainer.TrainRanking(rankingModel);
PairwiseModelTrainer.TrainClassification(classificationModel, false);
PairwiseModelTrainer.TrainClassification(anaphoricityModel, true);
SetDataPath("dev");
PairwiseModelTrainer.Test(classificationModel, predictionsName, false);
PairwiseModelTrainer.Test(rankingModel, predictionsName, false);
PairwiseModelTrainer.Test(anaphoricityModel, predictionsName, true);
new Clusterer().DoTraining(ClusteringModelName);
}
public ClusteringCorefAlgorithm(Properties props, Dictionaries dictionaries, string clusteringPath, string classificationPath, string rankingPath, string anaphoricityPath, string wordCountsPath)
{
clusterer = new Clusterer(clusteringPath);
classificationModel = PairwiseModel.NewBuilder("classification", MetaFeatureExtractor.NewBuilder().Build()).ModelPath(classificationPath).Build();
rankingModel = PairwiseModel.NewBuilder("ranking", MetaFeatureExtractor.NewBuilder().Build()).ModelPath(rankingPath).Build();
anaphoricityModel = PairwiseModel.NewBuilder("anaphoricity", MetaFeatureExtractor.AnaphoricityMFE()).ModelPath(anaphoricityPath).Build();
extractor = new FeatureExtractor(props, dictionaries, null, wordCountsPath);
}
public StatisticalCorefAlgorithm(Properties props, Dictionaries dictionaries, string wordCountsFile, string modelPath, int maxMentionDistance, int maxMentionDistanceWithStringMatch, double[] thresholds)
{
extractor = new FeatureExtractor(props, dictionaries, null, wordCountsFile);
classifier = PairwiseModel.NewBuilder("classifier", MetaFeatureExtractor.NewBuilder().Build()).ModelPath(modelPath).Build();
this.maxMentionDistance = maxMentionDistance;
this.maxMentionDistanceWithStringMatch = maxMentionDistanceWithStringMatch;
this.thresholds = MakeThresholds(thresholds);
}
/// <exception cref="System.Exception"/>
public static void TrainClassification(PairwiseModel model, bool anaphoricityModel)
{
int numTrainingExamples = model.GetNumTrainingExamples();
Redwood.Log("scoref-train", "Reading compression...");
Compressor <string> compressor = IOUtils.ReadObjectFromFile(StatisticalCorefTrainer.compressorFile);
Redwood.Log("scoref-train", "Reading train data...");
IList <DocumentExamples> trainDocuments = IOUtils.ReadObjectFromFile(StatisticalCorefTrainer.extractedFeaturesFile);
Redwood.Log("scoref-train", "Building train set...");
IList <Pair <Example, IDictionary <int, CompressedFeatureVector> > > allExamples = anaphoricityModel ? GetAnaphoricityExamples(trainDocuments) : GetExamples(trainDocuments);
Redwood.Log("scoref-train", "Training...");
Random random = new Random(0);
int i = 0;
bool stopTraining = false;
while (!stopTraining)
{
Java.Util.Collections.Shuffle(allExamples, random);
foreach (Pair <Example, IDictionary <int, CompressedFeatureVector> > pair in allExamples)
{
if (i++ > numTrainingExamples)
{
stopTraining = true;
break;
}
if (i % 10000 == 0)
{
Redwood.Log("scoref-train", string.Format("On train example %d/%d = %.2f%%", i, numTrainingExamples, 100.0 * i / numTrainingExamples));
}
model.Learn(pair.first, pair.second, compressor);
}
}
Redwood.Log("scoref-train", "Writing models...");
model.WriteModel();
}
public static void WriteScores(IList <Pair <Example, IDictionary <int, CompressedFeatureVector> > > examples, Compressor <string> compressor, PairwiseModel model, PrintWriter writer, IDictionary <int, ICounter <Pair <int, int> > > scores)
{
int i = 0;
foreach (Pair <Example, IDictionary <int, CompressedFeatureVector> > pair in examples)
{
if (i++ % 10000 == 0)
{
Redwood.Log("scoref-train", string.Format("On test example %d/%d = %.2f%%", i, examples.Count, 100.0 * i / examples.Count));
}
Example example = pair.first;
IDictionary <int, CompressedFeatureVector> mentionFeatures = pair.second;
double p = model.Predict(example, mentionFeatures, compressor);
writer.Println(example.docId + " " + example.mentionId1 + "," + example.mentionId2 + " " + p + " " + example.label);
ICounter <Pair <int, int> > docScores = scores[example.docId];
if (docScores == null)
{
docScores = new ClassicCounter <Pair <int, int> >();
scores[example.docId] = docScores;
}
docScores.IncrementCount(new Pair <int, int>(example.mentionId1, example.mentionId2), p);
}
}
/// <exception cref="System.Exception"/>
public static void TrainRanking(PairwiseModel model)
{
Redwood.Log("scoref-train", "Reading compression...");
Compressor <string> compressor = IOUtils.ReadObjectFromFile(StatisticalCorefTrainer.compressorFile);
Redwood.Log("scoref-train", "Reading train data...");
IList <DocumentExamples> trainDocuments = IOUtils.ReadObjectFromFile(StatisticalCorefTrainer.extractedFeaturesFile);
Redwood.Log("scoref-train", "Training...");
for (int i = 0; i < model.GetNumEpochs(); i++)
{
Java.Util.Collections.Shuffle(trainDocuments);
int j = 0;
foreach (DocumentExamples doc in trainDocuments)
{
j++;
Redwood.Log("scoref-train", "On epoch: " + i + " / " + model.GetNumEpochs() + ", document: " + j + " / " + trainDocuments.Count);
IDictionary <int, IList <Example> > mentionToPotentialAntecedents = new Dictionary <int, IList <Example> >();
foreach (Example e in doc.examples)
{
int mention = e.mentionId2;
IList <Example> potentialAntecedents = mentionToPotentialAntecedents[mention];
if (potentialAntecedents == null)
{
potentialAntecedents = new List <Example>();
mentionToPotentialAntecedents[mention] = potentialAntecedents;
}
potentialAntecedents.Add(e);
}
IList <IList <Example> > examples = new List <IList <Example> >(mentionToPotentialAntecedents.Values);
Java.Util.Collections.Shuffle(examples);
foreach (IList <Example> es in examples)
{
if (es.Count == 0)
{
continue;
}
if (model is MaxMarginMentionRanker)
{
MaxMarginMentionRanker ranker = (MaxMarginMentionRanker)model;
bool noAntecedent = es.Stream().AllMatch(null);
es.Add(new Example(es[0], noAntecedent));
double maxPositiveScore = -double.MaxValue;
Example maxScoringPositive = null;
foreach (Example e_1 in es)
{
double score = model.Predict(e_1, doc.mentionFeatures, compressor);
if (e_1.label == 1)
{
System.Diagnostics.Debug.Assert((!noAntecedent ^ e_1.IsNewLink()));
if (score > maxPositiveScore)
{
maxPositiveScore = score;
maxScoringPositive = e_1;
}
}
}
System.Diagnostics.Debug.Assert((maxScoringPositive != null));
double maxNegativeScore = -double.MaxValue;
Example maxScoringNegative = null;
MaxMarginMentionRanker.ErrorType maxScoringEt = null;
foreach (Example e_2 in es)
{
double score = model.Predict(e_2, doc.mentionFeatures, compressor);
if (e_2.label != 1)
{
System.Diagnostics.Debug.Assert((!(noAntecedent && e_2.IsNewLink())));
MaxMarginMentionRanker.ErrorType et = MaxMarginMentionRanker.ErrorType.Wl;
if (noAntecedent && !e_2.IsNewLink())
{
et = MaxMarginMentionRanker.ErrorType.Fl;
}
else
{
if (!noAntecedent && e_2.IsNewLink())
{
if (e_2.mentionType2 == Dictionaries.MentionType.Pronominal)
{
et = MaxMarginMentionRanker.ErrorType.FnPron;
}
else
{
et = MaxMarginMentionRanker.ErrorType.Fn;
}
}
}
if (ranker.multiplicativeCost)
{
score = ranker.costs[et.id] * (1 - maxPositiveScore + score);
}
else
{
score += ranker.costs[et.id];
}
if (score > maxNegativeScore)
{
maxNegativeScore = score;
maxScoringNegative = e_2;
maxScoringEt = et;
}
}
}
System.Diagnostics.Debug.Assert((maxScoringNegative != null));
ranker.Learn(maxScoringPositive, maxScoringNegative, doc.mentionFeatures, compressor, maxScoringEt);
}
else
{
double maxPositiveScore = -double.MaxValue;
double maxNegativeScore = -double.MaxValue;
Example maxScoringPositive = null;
Example maxScoringNegative = null;
foreach (Example e_1 in es)
{
double score = model.Predict(e_1, doc.mentionFeatures, compressor);
if (e_1.label == 1)
{
if (score > maxPositiveScore)
{
maxPositiveScore = score;
maxScoringPositive = e_1;
}
}
else
{
if (score > maxNegativeScore)
{
maxNegativeScore = score;
maxScoringNegative = e_1;
}
}
}
model.Learn(maxScoringPositive, maxScoringNegative, doc.mentionFeatures, compressor, 1);
}
}
}
}
Redwood.Log("scoref-train", "Writing models...");
model.WriteModel();
}
