Examples with MultiLabel edu.neu.ccs.pyramid.dataset.MultiLabel used on opensource projects

Example 1 with MultiLabel

use of edu.neu.ccs.pyramid.dataset.MultiLabel in project pyramid by cheng-li.

the class CBMGB method train.

private static void train(Config config, HyperParameters hyperParameters, MultiLabelClfDataSet trainSet) throws Exception {
    List<Integer> unobservedLabels = DataSetUtil.unobservedLabels(trainSet);
    if (!unobservedLabels.isEmpty()) {
        System.out.println("The following labels do not actually appear in the training set and therefore cannot be learned:");
        System.out.println(ListUtil.toSimpleString(unobservedLabels));
    }
    String output = config.getString("output.dir");
    FileUtils.writeStringToFile(new File(output, "unobserved_labels.txt"), ListUtil.toSimpleString(unobservedLabels));
    StopWatch stopWatch = new StopWatch();
    stopWatch.start();
    CBM cbm = newCBM(config, trainSet, hyperParameters);
    GBCBMOptimizer optimizer = getOptimizer(config, hyperParameters, cbm, trainSet);
    System.out.println("Initializing the model");
    optimizer.initialize();
    System.out.println("Initialization done");
    for (int iter = 1; iter <= hyperParameters.iterations; iter++) {
        System.out.println("Training progress: iteration " + iter);
        optimizer.iterate();
    }
    System.out.println("training done!");
    System.out.println("time spent on training = " + stopWatch);
    Serialization.serialize(cbm, new File(output, "model"));
    List<MultiLabel> support = DataSetUtil.gatherMultiLabels(trainSet);
    Serialization.serialize(support, new File(output, "support"));
}

Example 2 with MultiLabel

use of edu.neu.ccs.pyramid.dataset.MultiLabel in project pyramid by cheng-li.

the class CBMGB method reportHammingPrediction.

private static void reportHammingPrediction(Config config, CBM cbm, MultiLabelClfDataSet dataSet) throws Exception {
    System.out.println("============================================================");
    System.out.println("Making predictions on test set with the instance Hamming loss optimal predictor");
    String output = config.getString("output.dir");
    MarginalPredictor marginalPredictor = new MarginalPredictor(cbm);
    marginalPredictor.setPiThreshold(config.getDouble("predict.piThreshold"));
    MultiLabel[] predictions = marginalPredictor.predict(dataSet);
    MLMeasures mlMeasures = new MLMeasures(dataSet.getNumClasses(), dataSet.getMultiLabels(), predictions);
    System.out.println("test performance with the instance Hamming loss optimal predictor");
    System.out.println(mlMeasures);
    File performanceFile = Paths.get(output, "test_predictions", "instance_hamming_loss_optimal", "performance.txt").toFile();
    FileUtils.writeStringToFile(performanceFile, mlMeasures.toString());
    System.out.println("test performance is saved to " + performanceFile.toString());
    // Here we do not use approximation
    double[] setProbs = IntStream.range(0, predictions.length).parallel().mapToDouble(i -> cbm.predictAssignmentProb(dataSet.getRow(i), predictions[i])).toArray();
    File predictionFile = Paths.get(output, "test_predictions", "instance_hamming_loss_optimal", "predictions.txt").toFile();
    try (BufferedWriter br = new BufferedWriter(new FileWriter(predictionFile))) {
        for (int i = 0; i < dataSet.getNumDataPoints(); i++) {
            br.write(predictions[i].toString());
            br.write(":");
            br.write("" + setProbs[i]);
            br.newLine();
        }
    }
    System.out.println("predicted sets and their probabilities are saved to " + predictionFile.getAbsolutePath());
    System.out.println("============================================================");
}

Example 3 with MultiLabel

use of edu.neu.ccs.pyramid.dataset.MultiLabel in project pyramid by cheng-li.

the class CBMGB method reportAccPrediction.

private static void reportAccPrediction(Config config, CBM cbm, MultiLabelClfDataSet dataSet) throws Exception {
    System.out.println("============================================================");
    System.out.println("Making predictions on test set with the instance set accuracy optimal predictor");
    String output = config.getString("output.dir");
    AccPredictor accPredictor = new AccPredictor(cbm);
    accPredictor.setComponentContributionThreshold(config.getDouble("predict.piThreshold"));
    MultiLabel[] predictions = accPredictor.predict(dataSet);
    MLMeasures mlMeasures = new MLMeasures(dataSet.getNumClasses(), dataSet.getMultiLabels(), predictions);
    System.out.println("test performance with the instance set accuracy optimal predictor");
    System.out.println(mlMeasures);
    File performanceFile = Paths.get(output, "test_predictions", "instance_accuracy_optimal", "performance.txt").toFile();
    FileUtils.writeStringToFile(performanceFile, mlMeasures.toString());
    System.out.println("test performance is saved to " + performanceFile.toString());
    // Here we do not use approximation
    double[] setProbs = IntStream.range(0, predictions.length).parallel().mapToDouble(i -> cbm.predictAssignmentProb(dataSet.getRow(i), predictions[i])).toArray();
    File predictionFile = Paths.get(output, "test_predictions", "instance_accuracy_optimal", "predictions.txt").toFile();
    try (BufferedWriter br = new BufferedWriter(new FileWriter(predictionFile))) {
        for (int i = 0; i < dataSet.getNumDataPoints(); i++) {
            br.write(predictions[i].toString());
            br.write(":");
            br.write("" + setProbs[i]);
            br.newLine();
        }
    }
    System.out.println("predicted sets and their probabilities are saved to " + predictionFile.getAbsolutePath());
    System.out.println("============================================================");
}

Example 4 with MultiLabel

use of edu.neu.ccs.pyramid.dataset.MultiLabel in project pyramid by cheng-li.

the class CBMGB method reportF1Prediction.

private static void reportF1Prediction(Config config, CBM cbm, MultiLabelClfDataSet dataSet) throws Exception {
    System.out.println("============================================================");
    System.out.println("Making predictions on test set with the instance F1 optimal predictor");
    String output = config.getString("output.dir");
    PluginF1 pluginF1 = new PluginF1(cbm);
    List<MultiLabel> support = (List<MultiLabel>) Serialization.deserialize(new File(output, "support"));
    pluginF1.setSupport(support);
    pluginF1.setPiThreshold(config.getDouble("predict.piThreshold"));
    MultiLabel[] predictions = pluginF1.predict(dataSet);
    MLMeasures mlMeasures = new MLMeasures(dataSet.getNumClasses(), dataSet.getMultiLabels(), predictions);
    System.out.println("test performance with the instance F1 optimal predictor");
    System.out.println(mlMeasures);
    File performanceFile = Paths.get(output, "test_predictions", "instance_f1_optimal", "performance.txt").toFile();
    FileUtils.writeStringToFile(performanceFile, mlMeasures.toString());
    System.out.println("test performance is saved to " + performanceFile.toString());
    // Here we do not use approximation
    double[] setProbs = IntStream.range(0, predictions.length).parallel().mapToDouble(i -> cbm.predictAssignmentProb(dataSet.getRow(i), predictions[i])).toArray();
    File predictionFile = Paths.get(output, "test_predictions", "instance_f1_optimal", "predictions.txt").toFile();
    try (BufferedWriter br = new BufferedWriter(new FileWriter(predictionFile))) {
        for (int i = 0; i < dataSet.getNumDataPoints(); i++) {
            br.write(predictions[i].toString());
            br.write(":");
            br.write("" + setProbs[i]);
            br.newLine();
        }
    }
    System.out.println("predicted sets and their probabilities are saved to " + predictionFile.getAbsolutePath());
    System.out.println("============================================================");
}

Example 5 with MultiLabel

use of edu.neu.ccs.pyramid.dataset.MultiLabel in project pyramid by cheng-li.

the class Precision method precision.

@Deprecated
public static /**
     * From "Mining Multi-label Data by Grigorios Tsoumakas".
     * @param multiLabels
     * @param predictions
     * @return
     */
double precision(MultiLabel[] multiLabels, MultiLabel[] predictions) {
    double p = 0.0;
    for (int i = 0; i < multiLabels.length; i++) {
        MultiLabel label = multiLabels[i];
        MultiLabel prediction = predictions[i];
        if (prediction.getMatchedLabels().size() == 0) {
            p += 1.0;
        } else {
            p += MultiLabel.intersection(label, prediction).size() * 1.0 / prediction.getMatchedLabels().size();
        }
    }
    return p / multiLabels.length;
}