Examples with Classifier edu.illinois.cs.cogcomp.lbjava.classify.Classifier used on opensource projects

Example 1 with Classifier

use of edu.illinois.cs.cogcomp.lbjava.classify.Classifier 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 2 with Classifier

use of edu.illinois.cs.cogcomp.lbjava.classify.Classifier 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 3 with Classifier

use of edu.illinois.cs.cogcomp.lbjava.classify.Classifier in project cogcomp-nlp by CogComp.

the class BIOTester method main.

/**
     * The command line program simply instantiates an object of this class and
     * calls its {@link #test()} method.
     **/
public static void main(String[] args) {
    String classifierName = null;
    String labelerName = null;
    String parserName = null;
    String inputFile = null;
    try {
        classifierName = args[0];
        labelerName = args[1];
        parserName = args[2];
        inputFile = args[3];
        if (args.length > 4)
            throw new Exception();
    } catch (Exception e) {
        System.err.println("usage: java edu.illinois.cs.cogcomp.lbjava.edu.illinois.cs.cogcomp.lbjava.nlp.seg.BIOTester <classifier> <labeler> <parser> <test file>");
        System.exit(1);
    }
    Classifier classifier = ClassUtils.getClassifier(classifierName);
    Classifier labeler = ClassUtils.getClassifier(labelerName);
    Parser parser = ClassUtils.getParser(parserName, new Class[] { String.class }, new String[] { inputFile });
    new BIOTester(classifier, labeler, parser).test().printPerformance(System.out);
}

Example 4 with Classifier

use of edu.illinois.cs.cogcomp.lbjava.classify.Classifier 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 Classifier

use of edu.illinois.cs.cogcomp.lbjava.classify.Classifier in project cogcomp-nlp by CogComp.

the class SegmentTagPlain method main.

public static void main(String[] args) {
    String taggerName = null;
    String inputFile = null;
    String parserName = null;
    try {
        taggerName = args[0];
        inputFile = args[1];
        if (args.length > 2) {
            parserName = args[2];
            if (args.length > 3)
                throw new Exception();
        }
    } catch (Exception e) {
        System.err.println("usage: java edu.illinois.cs.cogcomp.lbjava.edu.illinois.cs.cogcomp.lbjava.nlp.seg.SegmentTagPlain <word classifier> " + "<input file> \\\n" + "                                         [<parser>]");
        System.exit(1);
    }
    Classifier tagger = ClassUtils.getClassifier(taggerName);
    Parser parser;
    if (parserName == null)
        parser = new PlainToTokenParser(new WordSplitter(new SentenceSplitter(inputFile)));
    else
        parser = ClassUtils.getParser(parserName, new Class[] { Parser.class }, new Parser[] { new WordSplitter(new SentenceSplitter(inputFile)) });
    String previous = "";
    for (Word w = (Word) parser.next(); w != null; w = (Word) parser.next()) {
        String prediction = tagger.discreteValue(w);
        if (prediction.startsWith("B-") || prediction.startsWith("I-") && !previous.endsWith(prediction.substring(2)))
            System.out.print("[" + prediction.substring(2) + " ");
        System.out.print(w.form + " ");
        if (!prediction.equals("O") && (w.next == null || tagger.discreteValue(w.next).equals("O") || tagger.discreteValue(w.next).startsWith("B-") || !tagger.discreteValue(w.next).endsWith(prediction.substring(2))))
            System.out.print("] ");
        if (w.next == null)
            System.out.println();
        previous = prediction;
    }
}