Examples with MultiLabelClassifier edu.neu.ccs.pyramid.multilabel_classification.MultiLabelClassifier used on opensource projects

Example 1 with MultiLabelClassifier

use of edu.neu.ccs.pyramid.multilabel_classification.MultiLabelClassifier in project pyramid by cheng-li.

the class CBMLR method tune.

private static TuneResult tune(Config config, HyperParameters hyperParameters, MultiLabelClfDataSet trainSet, MultiLabelClfDataSet validSet) throws Exception {
    CBM cbm = newCBM(config, trainSet, hyperParameters);
    EarlyStopper earlyStopper = loadNewEarlyStopper(config);
    LRCBMOptimizer optimizer = getOptimizer(config, hyperParameters, cbm, trainSet);
    optimizer.initialize();
    MultiLabelClassifier classifier;
    String predictTarget = config.getString("tune.targetMetric");
    switch(predictTarget) {
        case "instance_set_accuracy":
            AccPredictor accPredictor = new AccPredictor(cbm);
            accPredictor.setComponentContributionThreshold(config.getDouble("predict.piThreshold"));
            classifier = accPredictor;
            break;
        case "instance_f1":
            PluginF1 pluginF1 = new PluginF1(cbm);
            List<MultiLabel> support = DataSetUtil.gatherMultiLabels(trainSet);
            pluginF1.setSupport(support);
            pluginF1.setPiThreshold(config.getDouble("predict.piThreshold"));
            classifier = pluginF1;
            break;
        case "instance_hamming_loss":
            MarginalPredictor marginalPredictor = new MarginalPredictor(cbm);
            marginalPredictor.setPiThreshold(config.getDouble("predict.piThreshold"));
            classifier = marginalPredictor;
            break;
        default:
            throw new IllegalArgumentException("predictTarget should be instance_set_accuracy, instance_f1 or instance_hamming_loss");
    }
    int interval = config.getInt("tune.monitorInterval");
    for (int iter = 1; true; iter++) {
        if (VERBOSE) {
            System.out.println("iteration " + iter);
        }
        optimizer.iterate();
        if (iter % interval == 0) {
            MLMeasures validMeasures = new MLMeasures(classifier, validSet);
            if (VERBOSE) {
                System.out.println("validation performance with " + predictTarget + " optimal predictor:");
                System.out.println(validMeasures);
            }
            switch(predictTarget) {
                case "instance_set_accuracy":
                    earlyStopper.add(iter, validMeasures.getInstanceAverage().getAccuracy());
                    break;
                case "instance_f1":
                    earlyStopper.add(iter, validMeasures.getInstanceAverage().getF1());
                    break;
                case "instance_hamming_loss":
                    earlyStopper.add(iter, validMeasures.getInstanceAverage().getHammingLoss());
                    break;
                default:
                    throw new IllegalArgumentException("predictTarget should be instance_set_accuracy or instance_f1");
            }
            if (earlyStopper.shouldStop()) {
                if (VERBOSE) {
                    System.out.println("Early Stopper: the training should stop now!");
                }
                break;
            }
        }
    }
    if (VERBOSE) {
        System.out.println("done!");
    }
    hyperParameters.iterations = earlyStopper.getBestIteration();
    TuneResult tuneResult = new TuneResult();
    tuneResult.hyperParameters = hyperParameters;
    tuneResult.performance = earlyStopper.getBestValue();
    return tuneResult;
}

Example 2 with MultiLabelClassifier

use of edu.neu.ccs.pyramid.multilabel_classification.MultiLabelClassifier in project pyramid by cheng-li.

the class CBMEN method tune.

private static TuneResult tune(Config config, HyperParameters hyperParameters, MultiLabelClfDataSet trainSet, MultiLabelClfDataSet validSet) throws Exception {
    CBM cbm = newCBM(config, trainSet, hyperParameters);
    EarlyStopper earlyStopper = loadNewEarlyStopper(config);
    ENCBMOptimizer optimizer = getOptimizer(config, hyperParameters, cbm, trainSet);
    if (config.getBoolean("train.randomInitialize")) {
        optimizer.randInitialize();
    } else {
        optimizer.initialize();
    }
    MultiLabelClassifier classifier;
    String predictTarget = config.getString("tune.targetMetric");
    switch(predictTarget) {
        case "instance_set_accuracy":
            AccPredictor accPredictor = new AccPredictor(cbm);
            accPredictor.setComponentContributionThreshold(config.getDouble("predict.piThreshold"));
            classifier = accPredictor;
            break;
        case "instance_f1":
            PluginF1 pluginF1 = new PluginF1(cbm);
            List<MultiLabel> support = DataSetUtil.gatherMultiLabels(trainSet);
            pluginF1.setSupport(support);
            pluginF1.setPiThreshold(config.getDouble("predict.piThreshold"));
            classifier = pluginF1;
            break;
        case "instance_hamming_loss":
            MarginalPredictor marginalPredictor = new MarginalPredictor(cbm);
            marginalPredictor.setPiThreshold(config.getDouble("predict.piThreshold"));
            classifier = marginalPredictor;
            break;
        default:
            throw new IllegalArgumentException("predictTarget should be instance_set_accuracy, instance_f1 or instance_hamming_loss");
    }
    int interval = config.getInt("tune.monitorInterval");
    for (int iter = 1; true; iter++) {
        if (VERBOSE) {
            System.out.println("iteration " + iter);
        }
        optimizer.iterate();
        if (iter % interval == 0) {
            MLMeasures validMeasures = new MLMeasures(classifier, validSet);
            if (VERBOSE) {
                System.out.println("validation performance with " + predictTarget + " optimal predictor:");
                System.out.println(validMeasures);
            }
            switch(predictTarget) {
                case "instance_set_accuracy":
                    earlyStopper.add(iter, validMeasures.getInstanceAverage().getAccuracy());
                    break;
                case "instance_f1":
                    earlyStopper.add(iter, validMeasures.getInstanceAverage().getF1());
                    break;
                case "instance_hamming_loss":
                    earlyStopper.add(iter, validMeasures.getInstanceAverage().getHammingLoss());
                    break;
                default:
                    throw new IllegalArgumentException("predictTarget should be instance_set_accuracy or instance_f1");
            }
            if (earlyStopper.shouldStop()) {
                if (VERBOSE) {
                    System.out.println("Early Stopper: the training should stop now!");
                }
                break;
            }
        }
    }
    if (VERBOSE) {
        System.out.println("done!");
    }
    hyperParameters.iterations = earlyStopper.getBestIteration();
    TuneResult tuneResult = new TuneResult();
    tuneResult.hyperParameters = hyperParameters;
    tuneResult.performance = earlyStopper.getBestValue();
    return tuneResult;
}

Example 3 with MultiLabelClassifier

use of edu.neu.ccs.pyramid.multilabel_classification.MultiLabelClassifier in project pyramid by cheng-li.

the class CBMGB method tune.

private static TuneResult tune(Config config, HyperParameters hyperParameters, MultiLabelClfDataSet trainSet, MultiLabelClfDataSet validSet) throws Exception {
    CBM cbm = newCBM(config, trainSet, hyperParameters);
    EarlyStopper earlyStopper = loadNewEarlyStopper(config);
    GBCBMOptimizer optimizer = getOptimizer(config, hyperParameters, cbm, trainSet);
    optimizer.initialize();
    MultiLabelClassifier classifier;
    String predictTarget = config.getString("tune.targetMetric");
    switch(predictTarget) {
        case "instance_set_accuracy":
            AccPredictor accPredictor = new AccPredictor(cbm);
            accPredictor.setComponentContributionThreshold(config.getDouble("predict.piThreshold"));
            classifier = accPredictor;
            break;
        case "instance_f1":
            PluginF1 pluginF1 = new PluginF1(cbm);
            List<MultiLabel> support = DataSetUtil.gatherMultiLabels(trainSet);
            pluginF1.setSupport(support);
            pluginF1.setPiThreshold(config.getDouble("predict.piThreshold"));
            classifier = pluginF1;
            break;
        case "instance_hamming_loss":
            MarginalPredictor marginalPredictor = new MarginalPredictor(cbm);
            marginalPredictor.setPiThreshold(config.getDouble("predict.piThreshold"));
            classifier = marginalPredictor;
            break;
        default:
            throw new IllegalArgumentException("predictTarget should be instance_set_accuracy, instance_f1 or instance_hamming_loss");
    }
    int interval = config.getInt("tune.monitorInterval");
    for (int iter = 1; true; iter++) {
        if (VERBOSE) {
            System.out.println("iteration " + iter);
        }
        optimizer.iterate();
        if (iter % interval == 0) {
            MLMeasures validMeasures = new MLMeasures(classifier, validSet);
            if (VERBOSE) {
                System.out.println("validation performance with " + predictTarget + " optimal predictor:");
                System.out.println(validMeasures);
            }
            switch(predictTarget) {
                case "instance_set_accuracy":
                    earlyStopper.add(iter, validMeasures.getInstanceAverage().getAccuracy());
                    break;
                case "instance_f1":
                    earlyStopper.add(iter, validMeasures.getInstanceAverage().getF1());
                    break;
                case "instance_hamming_loss":
                    earlyStopper.add(iter, validMeasures.getInstanceAverage().getHammingLoss());
                    break;
                default:
                    throw new IllegalArgumentException("predictTarget should be instance_set_accuracy or instance_f1");
            }
            if (earlyStopper.shouldStop()) {
                if (VERBOSE) {
                    System.out.println("Early Stopper: the training should stop now!");
                }
                break;
            }
        }
    }
    if (VERBOSE) {
        System.out.println("done!");
    }
    hyperParameters.iterations = earlyStopper.getBestIteration();
    TuneResult tuneResult = new TuneResult();
    tuneResult.hyperParameters = hyperParameters;
    tuneResult.performance = earlyStopper.getBestValue();
    return tuneResult;
}