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

Example 1 with Instance

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

the class FeatureExtractor method printFeaturesFromQuestions.

/**
     * Prints the features generated for each example in an input file.  If feature
     * types are included as command-line arguments, only those types are printed. 
     * Otherwise, all features are printed.
     * 
     * @param questionSetFileName the name of the file containing the dataset to load
     * @param features a List of the features to print
     */
public void printFeaturesFromQuestions(String questionSetFileName, List<String> features) {
    String questions = IOUtil.readFile(questionSetFileName);
    for (String question : questions.split("[\\n\\r\\f]")) {
        Instance instance = createInstance(question);
        StringBuilder sb = new StringBuilder();
        if (features.size() > 0) {
            for (Iterator it = instance.binaryFeatureIterator(); it.hasNext(); ) {
                Feature feat = (Feature) it.next();
                String name = "";
                for (String s : feat.getName()) name += "." + s;
                name = name.replaceFirst(".", "");
                if (features.contains(feat.getName()[0]))
                    sb.append(name + "  ");
            }
            System.out.println(sb.toString() + " " + question);
        } else
            System.out.println(instance + " " + question);
    }
}

Example 2 with Instance

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

the class ScoreNormalizationFilter method createExample.

/**
	 * Creates a training/evaluation example from a judged answer candidate.
	 * 
	 * @param features selected features
	 * @param result judged answer candidate
	 * @param results all answers to the question
	 * @param qid question ID
	 * @return training/evaluation example
	 */
private static Example createExample(String[] features, Result result, Result[] results, String qid) {
    // create instance with selected features
    Instance instance = createInstance(features, result, results, qid);
    // create example from the instance and its class label
    String label = result.isCorrect() ? ExampleSchema.POS_CLASS_NAME : ExampleSchema.NEG_CLASS_NAME;
    Example example = new Example(instance, new ClassLabel(label));
    return example;
}

Example 3 with Instance

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

the class ScoreNormalizationFilter method apply.

/**
	 * Normalizes the scores of the factoid answers, using the features
	 * specified in <code>SELECTED_FEATURES</code> and the classifier specified
	 * in <code>classifier</code>.
	 * 
	 * @param results array of <code>Result</code> objects
	 * @return array of <code>Result</code> objects with normalized scores
	 */
public Result[] apply(Result[] results) {
    // classifier not loaded
    if (classifier == null)
        return results;
    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;
        // create instance with selected features
        Instance instance = createInstance(SELECTED_FEATURES, result, results);
        // classify instance
        ClassLabel label = classifier.classification(instance);
        // get weight of positive class as result score
        double weight = label.posProbability();
        result.setNormScore((float) weight);
    }
    // preserve original order of results
    //		results = preserveOrderResorting(results);
    //		results = preserveOrderAveraging(results);
    results = preserveOrderTop(results);
    return results;
}

Example 4 with Instance

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

the class QuestionClassifier method getAnswerTypes.

/**
     * Classifies the question represented by the given List of Terms and parse tree 
     * as having a particular answer type and possibly subtype. 
     * 
     * @param terms the Terms that make up the question to classify
     * @param parseTreeStr the syntactic parse tree of the question, in String format
     * 
     * @return the candidate answer type / subtypes.
     * @throws Exception
     */
public List<AnswerType> getAnswerTypes(List<Term> terms, String parseTreeStr) throws Exception {
    if (!isInitialized())
        throw new Exception("getAnswerTypes called while not initialized");
    String question = "";
    for (Term term : terms) question += term.getText() + " ";
    // create the instance
    Instance instance = new MutableInstance(question);
    if (extractor != null)
        instance = extractor.createInstance(terms, parseTreeStr);
    return classify(instance);
}

Example 5 with Instance

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

the class FeatureExtractor method createExample.

/**
     * Creates an edu.cmu.minorthird.classify.Example object from one line
     * of a dataset file using {@link #createInstance(String, String)}.
     * 
     * @param datasetLine the line from the dataset file from which to create
     * the Example 
     * @return the Example created
     * @throws Exception
     */
public Example[] createExample(String datasetLine) throws Exception {
    Matcher m = datasetExamplePattern.matcher(datasetLine);
    if (!m.matches())
        throw new Exception("Malformed dataset line:\n" + datasetLine);
    String[] aTypes = null;
    aTypes = m.group(labelPosition).replaceAll(",$", "").replaceAll(",", ".").split("\\|");
    String question = m.group(questionPosition);
    String sentParse = null;
    if (parsePosition > -1)
        sentParse = m.group(parsePosition);
    Instance instance = createInstance(question, sentParse);
    Example[] result = new Example[aTypes.length];
    //create example(s) and add it to list
    for (int i = 0; i < aTypes.length; i++) {
        String newATypeName = HierarchicalClassifier.getHierarchicalClassName(aTypes[i], classLevels, useClassLevels);
        result[i] = new Example(instance, new ClassLabel(newATypeName));
    }
    return result;
}