Examples with Config edu.neu.ccs.pyramid.configuration.Config used on opensource projects

Example 81 with Config

use of edu.neu.ccs.pyramid.configuration.Config in project pyramid by cheng-li.

the class ClusterLabels method main.

public static void main(String[] args) throws Exception {
    if (args.length != 1) {
        throw new IllegalArgumentException("Please specify a properties file.");
    }
    Config config = new Config(args[0]);
    System.out.println(config);
    fitModel(config);
    plot(config);
}

Example 82 with Config

use of edu.neu.ccs.pyramid.configuration.Config in project pyramid by cheng-li.

the class DataSampler method main.

public static void main(String[] args) throws Exception {
    if (args.length != 1) {
        throw new IllegalArgumentException("Please specify a properties file.");
    }
    Config config = new Config(args[0]);
    MultiLabelClfDataSet dataSet = TRECFormat.loadMultiLabelClfDataSet(config.getString("input.dataSet"), DataSetType.ML_CLF_SPARSE, true);
    List<Integer> all = IntStream.range(0, dataSet.getNumDataPoints()).boxed().collect(Collectors.toList());
    int randomSeed = config.getInt("randomSeed");
    List<Integer> keep = Sampling.sampleByPercentage(all, config.getDouble("percentage"), randomSeed);
    MultiLabelClfDataSet subset = DataSetUtil.sampleData(dataSet, keep);
    TRECFormat.save(subset, config.getString("output.dataSet"));
}

Example 83 with Config

use of edu.neu.ccs.pyramid.configuration.Config in project pyramid by cheng-li.

the class EMLevelEval method main.

public static void main(String[] args) throws Exception {
    if (args.length != 1) {
        throw new IllegalArgumentException("Please specify a properties file.");
    }
    Config config = new Config(args[0]);
    System.out.println(config);
    RegDataSet train = TRECFormat.loadRegDataSet(config.getString("input.trainData"), DataSetType.REG_SPARSE, true);
    Set<Double> unique = new HashSet<>();
    for (double d : train.getLabels()) {
        unique.add(d);
    }
    List<Double> levels = unique.stream().sorted().collect(Collectors.toList());
    RegDataSet test = TRECFormat.loadRegDataSet(config.getString("input.testData"), DataSetType.REG_SPARSE, true);
    double[] doubleTruth = test.getLabels();
    double[] doublePred = loadPrediction(config.getString("input.prediction"));
    double[] roundedPred = Arrays.stream(doublePred).map(d -> round(d, levels)).toArray();
    System.out.println("before rounding");
    System.out.println("rmse = " + RMSE.rmse(doubleTruth, doublePred));
    System.out.println("after rounding");
    System.out.println("rmse = " + RMSE.rmse(doubleTruth, roundedPred));
    System.out.println("accuracy = " + IntStream.range(0, test.getNumDataPoints()).filter(i -> doubleTruth[i] == roundedPred[i]).count() / (double) test.getNumDataPoints());
    System.out.println("the distribution of predicted label for a given true label");
    for (int l = 0; l < levels.size(); l++) {
        double level = levels.get(l);
        System.out.println("for true label " + level);
        truthToPred(test.getLabels(), roundedPred, level, levels);
    }
    System.out.println("=============================");
    System.out.println("the distribution of true label for a given predicted label");
    for (int l = 0; l < levels.size(); l++) {
        double level = levels.get(l);
        System.out.println("for predicted label " + level);
        predToTruth(test.getLabels(), roundedPred, level, levels);
    }
}

Example 84 with Config

use of edu.neu.ccs.pyramid.configuration.Config in project pyramid by cheng-li.

the class CBMEN method reportHammingPrediction.

private static void reportHammingPrediction(Config config, CBM cbm, MultiLabelClfDataSet dataSet, String name) throws Exception {
    System.out.println("============================================================");
    System.out.println("Making predictions on " + name + " 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"));
    StopWatch stopWatch = new StopWatch();
    stopWatch.start();
    MultiLabel[] predictions = marginalPredictor.predict(dataSet);
    System.out.println("time spent on prediction = " + stopWatch);
    MLMeasures mlMeasures = new MLMeasures(dataSet.getNumClasses(), dataSet.getMultiLabels(), predictions);
    System.out.println(name + " performance with the instance Hamming loss optimal predictor");
    System.out.println(mlMeasures);
    File performanceFile = Paths.get(output, name + "_predictions", "instance_hamming_loss_optimal", "performance.txt").toFile();
    FileUtils.writeStringToFile(performanceFile, mlMeasures.toString());
    System.out.println(name + " 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, name + "_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());
    boolean individualPerformance = true;
    if (individualPerformance) {
        ObjectMapper objectMapper = new ObjectMapper();
        objectMapper.writeValue(Paths.get(output, name + "_predictions", "instance_hamming_loss_optimal", "individual_performance.json").toFile(), mlMeasures.getMacroAverage());
    }
    System.out.println("============================================================");
}

Example 85 with Config

use of edu.neu.ccs.pyramid.configuration.Config in project pyramid by cheng-li.

the class CBMGB method reportAccPrediction.

private static void reportAccPrediction(Config config, CBM cbm, MultiLabelClfDataSet dataSet, String name) throws Exception {
    System.out.println("============================================================");
    System.out.println("Making predictions on " + name + " 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(name + " performance with the instance set accuracy optimal predictor");
    System.out.println(mlMeasures);
    File performanceFile = Paths.get(output, name + "_predictions", "instance_accuracy_optimal", "performance.txt").toFile();
    FileUtils.writeStringToFile(performanceFile, mlMeasures.toString());
    System.out.println(name + " 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, name + "_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());
    boolean individualPerformance = true;
    if (individualPerformance) {
        ObjectMapper objectMapper = new ObjectMapper();
        objectMapper.writeValue(Paths.get(output, name + "_predictions", "instance_accuracy_optimal", "individual_performance.json").toFile(), mlMeasures.getMacroAverage());
    }
    System.out.println("============================================================");
}