Examples with Dataset edu.cmu.minorthird.classify.Dataset used on opensource projects

Example 1 with Dataset

use of edu.cmu.minorthird.classify.Dataset in project lucida by claritylab.

the class ScoreNormalizationFilter method evaluate.

/**
	 * Performs a cross-validation on the given data set for the given features
	 * and model.
	 * 
	 * @param serializedDir directory containing serialized results
	 * @param features selected features
	 * @param model selected model
	 * @return evaluation statistics
	 */
public static Evaluation evaluate(String serializedDir, String[] features, String model) {
    // create data set with selected features from serialized results
    Dataset dataSet = createDataset(features, serializedDir);
    // create learner for selected model
    ClassifierLearner learner = createLearner(model);
    // cross-validate model on data set
    RandomElement r = new RandomElement(System.currentTimeMillis());
    Splitter splitter = new CrossValSplitter(r, NUM_FOLDS);
    CrossValidatedDataset cvDataset = new CrossValidatedDataset(learner, dataSet, splitter, true);
    Evaluation eval = cvDataset.getEvaluation();
    return eval;
}

Example 2 with Dataset

use of edu.cmu.minorthird.classify.Dataset in project lucida by claritylab.

the class ScoreNormalizationFilter method createDataset.

/**
	 * Creates a training/evaluation set from serialized judged
	 * <code>Result</code> objects.
	 * 
	 * @param features selected features
	 * @param serializedDir directory containing serialized results
	 * @return training/evaluation set
	 */
private static Dataset createDataset(String[] features, String serializedDir) {
    Dataset set = new BasicDataset();
    File[] files = FileUtils.getFilesRec(serializedDir);
    for (File file : files) {
        // one file per question
        String filename = file.getName();
        if (!filename.endsWith(".serialized"))
            continue;
        // get question ID and results
        String qid = filename.replace(".serialized", "");
        Result[] results = readSerializedResults(file);
        // create examples and add to data set
        for (Result result : results) {
            // only factoid answers with 1 extraction technique
            if (result.getScore() <= 0 || result.getScore() == Float.POSITIVE_INFINITY || result.getExtractionTechniques() == null || result.getExtractionTechniques().length != 1)
                continue;
            Example example = createExample(features, result, results, qid);
            set.add(example);
        }
    }
    return set;
}

Example 3 with Dataset

use of edu.cmu.minorthird.classify.Dataset in project lucida by claritylab.

the class ScoreNormalizationFilter method train.

/**
	 * Trains a classifier using the given training data, features and model.
	 * 
	 * @param serializedDir directory containing serialized results
	 * @param features selected features
	 * @param model selected model
	 * @return trained classifier
	 */
public static Classifier train(String serializedDir, String[] features, String model) {
    // create training set with given features from serialized results
    Dataset trainingSet = createDataset(features, serializedDir);
    // create learner for given model
    ClassifierLearner learner = createLearner(model);
    // train classifier
    Classifier classifier = new DatasetClassifierTeacher(trainingSet).train(learner);
    return classifier;
}

Example 4 with Dataset

use of edu.cmu.minorthird.classify.Dataset in project lucida by claritylab.

the class HierarchicalClassifierTrainer method makeDataset.

private Dataset makeDataset(String fileName) {
    if (trainingLabels == null) {
        loadTraining = true;
        trainingLabels = new HashSet<String>();
    }
    Dataset set = new BasicDataset();
    extractor.setUseClassLevels(useClassLevels);
    extractor.setClassLevels(learnerNames.length);
    Example[] examples = extractor.loadFile(fileName);
    for (int i = 0; i < examples.length; i++) {
        String label = examples[i].getLabel().bestClassName();
        if (classLabels.contains(label)) {
            MutableInstance instance = new MutableInstance(examples[i].getSource(), examples[i].getSubpopulationId());
            Feature.Looper bLooper = examples[i].binaryFeatureIterator();
            while (bLooper.hasNext()) {
                Feature f = bLooper.nextFeature();
                if (featureTypes.contains(f.getPart(0))) {
                    instance.addBinary(f);
                }
            }
            Feature.Looper nLooper = examples[i].numericFeatureIterator();
            while (nLooper.hasNext()) {
                Feature f = nLooper.nextFeature();
                if (featureTypes.contains(f.getPart(0))) {
                    instance.addNumeric(f, examples[i].getWeight(f));
                }
            }
            Example example = new Example(instance, examples[i].getLabel());
            MLToolkit.println(example);
            if (loadTraining) {
                trainingLabels.add(label);
                set.add(example);
            } else {
                if (!trainingLabels.contains(label))
                    MLToolkit.println("Label of test example not found in training set (discarding): " + label);
                else
                    set.add(example);
            }
        } else {
            MLToolkit.println("Discarding example for Class: " + label);
        }
    }
    if (loadTraining)
        loadTraining = false;
    MLToolkit.println("Loaded " + set.size() + " examples for experiment from " + fileName);
    return set;
}