Examples with EvaluateDiscrete edu.illinois.cs.cogcomp.comma.utils.EvaluateDiscrete used on opensource projects

Example 1 with EvaluateDiscrete

use of edu.illinois.cs.cogcomp.comma.utils.EvaluateDiscrete in project cogcomp-nlp by CogComp.

the class ClassifierComparison method reasonForBelievingThatStructuredIsPerformingWorseDueToOverfitting.

/**
 * Structured's higher performance on the train set and lower performance on test set is
 * indicative of overfitting
 */
public static void reasonForBelievingThatStructuredIsPerformingWorseDueToOverfitting(Parser parser, boolean useGoldFeatures) throws Exception {
    List<Classifier> lbjExtractors = new ArrayList<>();
    lbjExtractors.add(new LocalCommaClassifier().getExtractor());
    Classifier lbjLabeler = new LocalCommaClassifier().getLabeler();
    StructuredCommaClassifier structured = new StructuredCommaClassifier(lbjExtractors, lbjLabeler);
    int learningRounds = 250;
    double learningRate = 0.003;
    double threshold = 0;
    double thickness = 3.5;
    EvaluateDiscrete structuredPerformanceOnTrainSet = structuredCVal(structured, parser, useGoldFeatures, true);
    EvaluateDiscrete structuredPerformanceOnTestSet = structuredCVal(structured, parser, useGoldFeatures, false);
    EvaluateDiscrete localPerformanceOnTrainSet = localCVal(useGoldFeatures, useGoldFeatures, parser, learningRounds, learningRate, threshold, thickness, true);
    EvaluateDiscrete localPerformanceOnTestSet = localCVal(useGoldFeatures, useGoldFeatures, parser, learningRounds, learningRate, threshold, thickness, false);
    System.out.println("Structured performance on train set " + structuredPerformanceOnTrainSet.getOverallStats()[2]);
    System.out.println("Structured performance on test set " + structuredPerformanceOnTestSet.getOverallStats()[2]);
    System.out.println("Local performance on train set " + localPerformanceOnTrainSet.getOverallStats()[2]);
    System.out.println("Localperformance on test set " + localPerformanceOnTestSet.getOverallStats()[2]);
}

Example 2 with EvaluateDiscrete

use of edu.illinois.cs.cogcomp.comma.utils.EvaluateDiscrete in project cogcomp-nlp by CogComp.

the class ClassifierComparison method main.

public static void main(String[] args) throws Exception {
    PrettyCorpusReader pcr = new PrettyCorpusReader(CommaProperties.getInstance().getCommaLabeledDataFile());
    CommaParser parser = new CommaParser(pcr.getSentences(), Ordering.ORDERED, true);
    System.out.println("GOLD GOLD");
    localCVal(true, true, parser, 250, 0.003, 0, 2.0, false);
    System.out.println("GOLD AUTO");
    localCVal(true, false, parser, 200, 0.003, 0, 2.0, false);
    System.out.println("AUTO AUTO");
    localCVal(false, false, parser, 250, 0.003, 0, 3.5, false);
    List<Classifier> lbjExtractors = new ArrayList<>();
    lbjExtractors.add(new LocalCommaClassifier().getExtractor());
    Classifier lbjLabeler = new LocalCommaClassifier().getLabeler();
    System.out.println("STRUCTURED GOLD");
    StructuredCommaClassifier goldStructured = new StructuredCommaClassifier(lbjExtractors, lbjLabeler);
    structuredCVal(goldStructured, parser, true, false);
    System.out.println("STRUCTURED AUTO");
    StructuredCommaClassifier autoStructured = new StructuredCommaClassifier(lbjExtractors, lbjLabeler);
    structuredCVal(autoStructured, parser, false, false);
    System.out.println("BAYRAKTAR GOLD");
    EvaluateDiscrete bayraktarGold = getBayraktarBaselinePerformance(parser, true);
    bayraktarGold.printPerformance(System.out);
    System.out.println("BAYRAKTAR AUTO");
    EvaluateDiscrete bayraktarAuto = getBayraktarBaselinePerformance(parser, false);
    bayraktarAuto.printPerformance(System.out);
    reasonForBelievingThatStructuredIsPerformingWorseDueToOverfitting(parser, false);
    printConstrainedClassifierPerformance(parser);
}

Example 3 with EvaluateDiscrete

use of edu.illinois.cs.cogcomp.comma.utils.EvaluateDiscrete in project cogcomp-nlp by CogComp.

the class StructuredCommaClassifier method test.

/**
 * @param sentences the test set
 * @param predictionFileName location to which to save the predictions of the model. If it is
 *        null, predictions are not saved
 * @return and EvaluateDiscrete object which can provide the performance statistics
 * @throws Exception
 */
public EvaluateDiscrete test(List<CommaSRLSentence> sentences, String predictionFileName) throws Exception {
    lm.setAllowNewFeatures(false);
    SLProblem sp = CommaIOManager.readProblem(sentences, lm, lbjExtractors, lbjLabeler);
    EvaluateDiscrete SLEvaluator = new EvaluateDiscrete();
    BufferedWriter writer = null;
    if (predictionFileName != null) {
        writer = new BufferedWriter(new FileWriter(predictionFileName));
    }
    for (int i = 0; i < sp.instanceList.size(); i++) {
        CommaLabelSequence gold = (CommaLabelSequence) sp.goldStructureList.get(i);
        CommaLabelSequence prediction = (CommaLabelSequence) infSolver.getBestStructure(wv, sp.instanceList.get(i));
        for (int j = 0; j < prediction.labels.size(); j++) {
            String predictedTag = prediction.labels.get(j);
            String goldTag = gold.labels.get(j);
            SLEvaluator.reportPrediction(predictedTag, goldTag);
        }
        if (predictionFileName != null) {
            CommaSequence instance = ((CommaSequence) sp.instanceList.get(i));
            instance.sortedCommas.get(i).getSentence().getAnnotatedText();
            for (int j = 0; j < prediction.labels.size(); j++) {
                int commaPosition = instance.sortedCommas.get(j).commaPosition;
                String predictedLabel = lm.getLabelString(Integer.parseInt((prediction.labels.get(j))));
                writer.write(commaPosition + "\t" + predictedLabel + "\n");
            }
            writer.write("\n");
        }
    }
    if (predictionFileName != null) {
        writer.close();
    }
    return SLEvaluator;
}

Example 4 with EvaluateDiscrete

use of edu.illinois.cs.cogcomp.comma.utils.EvaluateDiscrete in project cogcomp-nlp by CogComp.

the class ClassifierComparison method printConstrainedClassifierPerformance.

public static void printConstrainedClassifierPerformance(Parser parser) {
    List<Pair<Classifier, EvaluateDiscrete>> classifiers = new ArrayList<>();
    LocalCommaClassifier learner = new LocalCommaClassifier();
    EvaluateDiscrete unconstrainedPerformance = new EvaluateDiscrete();
    learner.setLTU(new SparseAveragedPerceptron(0.003, 0, 3.5));
    classifiers.add(new Pair<Classifier, EvaluateDiscrete>(new SubstitutePairConstrainedCommaClassifier(), new EvaluateDiscrete()));
    classifiers.add(new Pair<Classifier, EvaluateDiscrete>(new LocativePairConstrainedCommaClassifier(), new EvaluateDiscrete()));
    classifiers.add(new Pair<Classifier, EvaluateDiscrete>(new ListCommasConstrainedCommaClassifier(), new EvaluateDiscrete()));
    classifiers.add(new Pair<Classifier, EvaluateDiscrete>(new OxfordCommaConstrainedCommaClassifier(), new EvaluateDiscrete()));
    int k = 5;
    parser.reset();
    FoldParser foldParser = new FoldParser(parser, k, SplitPolicy.sequential, 0, false);
    for (int i = 0; i < k; foldParser.setPivot(++i)) {
        foldParser.setFromPivot(false);
        foldParser.reset();
        learner.forget();
        BatchTrainer bt = new BatchTrainer(learner, foldParser);
        Lexicon lexicon = bt.preExtract(null);
        learner.setLexicon(lexicon);
        bt.train(250);
        learner.save();
        foldParser.setFromPivot(true);
        foldParser.reset();
        unconstrainedPerformance.reportAll(EvaluateDiscrete.evaluateDiscrete(learner, learner.getLabeler(), foldParser));
        for (Pair<Classifier, EvaluateDiscrete> pair : classifiers) {
            foldParser.reset();
            pair.getSecond().reportAll(EvaluateDiscrete.evaluateDiscrete(pair.getFirst(), learner.getLabeler(), foldParser));
        }
    }
    for (Pair<Classifier, EvaluateDiscrete> pair : classifiers) {
        System.out.println(pair.getFirst().name + " " + pair.getSecond().getOverallStats()[2]);
    }
}

Example 5 with EvaluateDiscrete

use of edu.illinois.cs.cogcomp.comma.utils.EvaluateDiscrete in project cogcomp-nlp by CogComp.

the class ClassifierComparison method localCVal.

public static EvaluateDiscrete localCVal(boolean trainOnGold, boolean testOnGold, Parser parser, int learningRounds, double learningRate, double threshold, double thickness, boolean testOnTrain) {
    int k = 5;
    LocalCommaClassifier learner = new LocalCommaClassifier();
    learner.setLTU(new SparseAveragedPerceptron(learningRate, threshold, thickness));
    parser.reset();
    final FoldParser foldParser = new FoldParser(parser, k, SplitPolicy.sequential, 0, false);
    EvaluateDiscrete performanceRecord = new EvaluateDiscrete();
    for (int i = 0; i < k; foldParser.setPivot(++i)) {
        foldParser.setFromPivot(false);
        foldParser.reset();
        learner.forget();
        BatchTrainer bt = new BatchTrainer(learner, foldParser);
        Comma.useGoldFeatures(trainOnGold);
        Lexicon lexicon = bt.preExtract(null);
        learner.setLexicon(lexicon);
        bt.train(learningRounds);
        if (!testOnTrain)
            foldParser.setFromPivot(true);
        foldParser.reset();
        Comma.useGoldFeatures(testOnGold);
        EvaluateDiscrete currentPerformance = EvaluateDiscrete.evaluateDiscrete(learner, learner.getLabeler(), foldParser);
        performanceRecord.reportAll(currentPerformance);
    }
    // System.out.println(performanceRecord.getOverallStats()[2]);
    performanceRecord.printPerformance(System.out);
    // performanceRecord.printConfusion(System.out);
    return performanceRecord;
}