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

Example 1 with Classifier

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

the class HierarchicalClassifier method classification.

public ClassLabel classification(Instance instance) {
    String labelName = "";
    double weight = 1;
    for (int i = 0; i < classLevels; i++) {
        Classifier currentClassifier = (Classifier) classifiers.get(labelName);
        ClassLabel currentLabel = currentClassifier.classification(instance);
        labelName = getNewLabelName(labelName, currentLabel.bestClassName(), i);
        weight *= currentLabel.bestWeight();
    }
    return new ClassLabel(labelName, weight);
}

Example 2 with Classifier

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

the class HierarchicalClassifier method explain.

public String explain(Instance instance) {
    String labelName = "";
    String explanation = "";
    for (int i = 0; i < classLevels; i++) {
        Classifier currentClassifier = (Classifier) classifiers.get(labelName);
        ClassLabel currentLabel = currentClassifier.classification(instance);
        labelName = getNewLabelName(labelName, currentLabel.bestClassName(), i);
        explanation += currentClassifier.explain(instance);
    }
    return explanation;
}

Example 3 with Classifier

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

the class ScoreNormalizationFilter method main.

/**
	 * Evaluates all combinations of features and models and trains a classifier
	 * using the best combination.
	 * 
	 * @param args {directory containing serialized results,
	 *              output directory for evaluation reports and classifier}
	 */
public static void main(String[] args) {
    // enable output of status and error messages
    MsgPrinter.enableStatusMsgs(true);
    MsgPrinter.enableErrorMsgs(true);
    // get command line parameters
    if (args.length < 2) {
        MsgPrinter.printUsage("java ScoreNormalizationFilter " + "serialized_results_dir output_dir");
        System.exit(1);
    }
    String serializedDir = args[0];
    String outputDir = args[1];
    //		// evaluate all combinations of features and models,
    //		// get best combination according to F1 measure
    //		String reportsDir = new File(outputDir, "reports").getPath();
    //		String[][] combination = evaluateAll(serializedDir, reportsDir);
    //		String[] features = combination[0];
    //		String model = combination[1][0];
    // or simply get selected features and model
    String[] features = SELECTED_FEATURES;
    String model = SELECTED_MODEL;
    // train classifier using best/selected features and model
    String msg = "Training classifier using model " + model + " with feature(s) " + StringUtils.concat(features, ", ") + " (" + MsgPrinter.getTimestamp() + ")...";
    MsgPrinter.printStatusMsg(StringUtils.repeat("-", msg.length()));
    MsgPrinter.printStatusMsg(msg);
    MsgPrinter.printStatusMsg(StringUtils.repeat("-", msg.length()));
    Classifier classifier = train(serializedDir, features, model);
    // serialize classifier to file
    String classifiersDir = new File(outputDir, "classifiers").getPath();
    String[] dataSets = FileUtils.getVisibleSubDirs(serializedDir);
    String filename = model + "_" + StringUtils.concat(features, "+") + "_" + StringUtils.concat(dataSets, "+") + ".serialized";
    try {
        FileUtils.writeSerialized(classifier, new File(classifiersDir, filename));
    } catch (IOException e) {
        MsgPrinter.printErrorMsg("Failed to serialize classifier to file " + filename + ":");
        MsgPrinter.printErrorMsg(e.toString());
        System.exit(1);
    }
    MsgPrinter.printStatusMsg("...done.");
}

Example 4 with Classifier

use of edu.cmu.minorthird.classify.Classifier 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;
}