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

Example 76 with Config

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

the class CBMLR 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 77 with Config

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

the class CBMLR 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");
    StopWatch stopWatch = new StopWatch();
    stopWatch.start();
    AccPredictor accPredictor = new AccPredictor(cbm);
    accPredictor.setComponentContributionThreshold(config.getDouble("predict.piThreshold"));
    MultiLabel[] predictions = accPredictor.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 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("============================================================");
}

Example 78 with Config

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

the class CBMLR method reportGeneral.

private static void reportGeneral(Config config, CBM cbm, MultiLabelClfDataSet dataSet, String name) throws Exception {
    System.out.println("============================================================");
    System.out.println("computing other predictor-independent metrics");
    System.out.println("label averaged MAP");
    System.out.println(MAP.map(cbm, dataSet));
    System.out.println("instance averaged MAP");
    System.out.println(MAP.instanceMAP(cbm, dataSet));
    System.out.println("global AP truncated at 30");
    System.out.println(AveragePrecision.globalAveragePrecisionTruncated(cbm, dataSet, 30));
    String output = config.getString("output.dir");
    File labelProbFile = Paths.get(output, name + "_predictions", "label_probabilities.txt").toFile();
    double labelProbThreshold = config.getDouble("report.labelProbThreshold");
    try (BufferedWriter br = new BufferedWriter(new FileWriter(labelProbFile))) {
        for (int i = 0; i < dataSet.getNumDataPoints(); i++) {
            br.write(CBMInspector.topLabels(cbm, dataSet.getRow(i), labelProbThreshold));
            br.newLine();
        }
    }
    System.out.println("individual label probabilities are saved to " + labelProbFile.getAbsolutePath());
    List<Integer> unobservedLabels = Arrays.stream(FileUtils.readFileToString(new File(output, "unobserved_labels.txt")).split(",")).map(s -> s.trim()).filter(s -> !s.isEmpty()).map(s -> Integer.parseInt(s)).collect(Collectors.toList());
    // Here we do not use approximation
    double[] logLikelihoods = IntStream.range(0, dataSet.getNumDataPoints()).parallel().mapToDouble(i -> cbm.predictLogAssignmentProb(dataSet.getRow(i), dataSet.getMultiLabels()[i])).toArray();
    double average = IntStream.range(0, dataSet.getNumDataPoints()).filter(i -> !containsNovelClass(dataSet.getMultiLabels()[i], unobservedLabels)).mapToDouble(i -> logLikelihoods[i]).average().getAsDouble();
    File logLikelihoodFile = Paths.get(output, name + "_predictions", "ground_truth_log_likelihood.txt").toFile();
    FileUtils.writeStringToFile(logLikelihoodFile, PrintUtil.toMutipleLines(logLikelihoods));
    System.out.println("individual log likelihood of the " + name + " ground truth label set is written to " + logLikelihoodFile.getAbsolutePath());
    System.out.println("average log likelihood of the " + name + " ground truth label sets = " + average);
    if (!unobservedLabels.isEmpty() && name.equals("test")) {
        System.out.println("This is computed by ignoring test instances with new labels unobserved during training");
        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));
    }
}

Example 79 with Config

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

the class CBMLR method reportF1Prediction.

private static void reportF1Prediction(Config config, CBM cbm, MultiLabelClfDataSet dataSet, String name) throws Exception {
    System.out.println("============================================================");
    System.out.println("Making predictions on " + name + " 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"));
    StopWatch stopWatch = new StopWatch();
    stopWatch.start();
    MultiLabel[] predictions = pluginF1.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 F1 optimal predictor");
    System.out.println(mlMeasures);
    File performanceFile = Paths.get(output, name + "_predictions", "instance_f1_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_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());
    boolean individualPerformance = true;
    if (individualPerformance) {
        ObjectMapper objectMapper = new ObjectMapper();
        objectMapper.writeValue(Paths.get(output, name + "_predictions", "instance_f1_optimal", "individual_performance.json").toFile(), mlMeasures.getMacroAverage());
    }
    System.out.println("============================================================");
}

Example 80 with Config

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

the class Calibration method main.

public static void main(String[] args) throws Exception {
    Config config = new Config(args[0]);
    main(config);
}