Examples with WeightVector edu.illinois.cs.cogcomp.sl.util.WeightVector used on opensource projects

Example 1 with WeightVector

use of edu.illinois.cs.cogcomp.sl.util.WeightVector in project cogcomp-nlp by CogComp.

the class SenseManager method getScores.

/**
     * Scores instance for the different labels allowed for it
     */
public double[] getScores(SenseInstance x, boolean rescoreInvalidLabels) {
    int numLabels = this.getNumLabels();
    double[] scores = new double[numLabels];
    WeightVector w;
    try {
        w = this.getModelInfo().getWeights();
        assert w != null;
    } catch (Exception e) {
        log.error("Unable to load weight vector, exception:\n{}", e);
        throw new RuntimeException(e);
    }
    for (int label = 0; label < numLabels; label++) {
        if (!this.isValidLabel(x, label) && rescoreInvalidLabels) {
            scores[label] = -50;
        } else {
            SenseStructure y = new SenseStructure(x, label, this);
            scores[label] = w.dotProduct(y.getFeatureVector());
        }
    }
    scores = MathUtilities.softmax(scores);
    return scores;
}

Example 2 with WeightVector

use of edu.illinois.cs.cogcomp.sl.util.WeightVector in project cogcomp-nlp by CogComp.

the class VerbSenseClassifierMain method train.

@CommandDescription(description = "Trains the verb-sense model.", usage = "train")
public static void train() throws Exception {
    SenseManager manager = getManager(true);
    int numThreads = Runtime.getRuntime().availableProcessors();
    ModelInfo modelInfo = manager.getModelInfo();
    String featureSet = "" + modelInfo.featureManifest.getIncludedFeatures().hashCode();
    String cacheFile = VerbSenseConfigurator.getPrunedFeatureCacheFile(featureSet, rm);
    AbstractInferenceSolver[] inference = new AbstractInferenceSolver[numThreads];
    // TODO Can I replace this with ILPInference?
    for (int i = 0; i < inference.length; i++) inference[i] = new MulticlassInference(manager);
    double c;
    FeatureVectorCacheFile cache;
    cache = new FeatureVectorCacheFile(cacheFile, manager);
    StructuredProblem cvProblem = cache.getStructuredProblem(20000);
    cache.close();
    LearnerParameters params = JLISLearner.crossvalStructSVMSense(cvProblem, inference, 4);
    c = params.getcStruct();
    log.info("c = {} after cv", c);
    cache = new FeatureVectorCacheFile(cacheFile, manager);
    StructuredProblem problem = cache.getStructuredProblem();
    cache.close();
    WeightVector w = JLISLearner.trainStructSVM(inference, problem, c);
    JLISLearner.saveWeightVector(w, manager.getModelFileName());
}

Example 3 with WeightVector

use of edu.illinois.cs.cogcomp.sl.util.WeightVector in project cogcomp-nlp by CogComp.

the class CrossValidationHelper method tryParamSerial.

private PerformanceMeasure tryParamSerial(DatasetType train, LearnerParameters param) throws Exception {
    List<PerformanceMeasure> perf = new ArrayList<>();
    for (int foldId = 0; foldId < nFolds; foldId++) {
        Pair<DatasetType, DatasetType> foldData = foldSplitter.getFoldData(train, foldId);
        log.info("Starting fold {} for params {}", foldId, param);
        WeightVector w = trainer.train(foldData.getFirst(), param, inference);
        log.info("Finished training fold {} for params {}", foldId, param);
        PerformanceMeasure p = tester.evaluate(foldData.getSecond(), w, inference[0]);
        log.info("Performance for fold {}, params {} =" + p.summarize(), foldId, param);
        perf.add(p);
    }
    return averager.average(perf);
}

Example 4 with WeightVector

use of edu.illinois.cs.cogcomp.sl.util.WeightVector in project cogcomp-nlp by CogComp.

the class WeightVectorUtils method load.

public static WeightVector load(String fileName) {
    try {
        GZIPInputStream zipin = new GZIPInputStream(new FileInputStream(fileName));
        BufferedReader reader = new BufferedReader(new InputStreamReader(zipin));
        String line;
        line = reader.readLine().trim();
        if (!line.equals("WeightVector")) {
            reader.close();
            throw new IOException("Invalid model file.");
        }
        line = reader.readLine().trim();
        int size = Integer.parseInt(line);
        WeightVector w = new WeightVector(size);
        while ((line = reader.readLine()) != null) {
            line = line.trim();
            String[] parts = line.split(":");
            int index = Integer.parseInt(parts[0]);
            float value = Float.parseFloat(parts[1]);
            w.setElement(index, value);
        }
        zipin.close();
        return w;
    } catch (Exception e) {
        log.error("Error loading model file {}", fileName);
        System.exit(-1);
    }
    return null;
}

Example 5 with WeightVector

use of edu.illinois.cs.cogcomp.sl.util.WeightVector in project cogcomp-nlp by CogComp.

the class WeightVectorUtils method loadWeightVectorFromClassPath.

public static WeightVector loadWeightVectorFromClassPath(String fileName) {
    try {
        Class<WeightVectorUtils> clazz = WeightVectorUtils.class;
        List<URL> list = IOUtils.lsResources(clazz, fileName);
        if (list.size() == 0) {
            log.error("File {} not found on the classpath", fileName);
            throw new Exception("File not found on classpath");
        }
        InputStream stream = list.get(0).openStream();
        GZIPInputStream zipin = new GZIPInputStream(stream);
        BufferedReader reader = new BufferedReader(new InputStreamReader(zipin));
        String line;
        line = reader.readLine().trim();
        if (!line.equals("WeightVector")) {
            reader.close();
            throw new IOException("Invalid model file.");
        }
        line = reader.readLine().trim();
        int size = Integer.parseInt(line);
        WeightVector w = new WeightVector(size);
        while ((line = reader.readLine()) != null) {
            line = line.trim();
            String[] parts = line.split(":");
            int index = Integer.parseInt(parts[0]);
            float value = Float.parseFloat(parts[1]);
            w.setElement(index, value);
        }
        zipin.close();
        return w;
    } catch (Exception e) {
        log.error("Error loading model file {}", fileName);
        System.exit(-1);
    }
    return null;
}