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

Example 51 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 52 with Config

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

the class CBMLR 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);
    VERBOSE = config.getBoolean("output.verbose");
    new File(config.getString("output.dir")).mkdirs();
    if (config.getBoolean("tune")) {
        System.out.println("============================================================");
        System.out.println("Start hyper parameter tuning");
        StopWatch stopWatch = new StopWatch();
        stopWatch.start();
        List<TuneResult> tuneResults = new ArrayList<>();
        List<MultiLabelClfDataSet> dataSets = loadTrainValidData(config);
        List<Double> variances = config.getDoubles("tune.variance.candidates");
        List<Integer> components = config.getIntegers("tune.numComponents.candidates");
        for (double variance : variances) {
            for (int component : components) {
                StopWatch stopWatch1 = new StopWatch();
                stopWatch1.start();
                HyperParameters hyperParameters = new HyperParameters();
                hyperParameters.numComponents = component;
                hyperParameters.variance = variance;
                System.out.println("---------------------------");
                System.out.println("Trying hyper parameters:");
                System.out.println("train.numComponents = " + hyperParameters.numComponents);
                System.out.println("train.variance = " + hyperParameters.variance);
                TuneResult tuneResult = tune(config, hyperParameters, dataSets.get(0), dataSets.get(1));
                System.out.println("Found optimal train.iterations = " + tuneResult.hyperParameters.iterations);
                System.out.println("Validation performance = " + tuneResult.performance);
                tuneResults.add(tuneResult);
                System.out.println("Time spent on trying this set of hyper parameters = " + stopWatch1);
            }
        }
        Comparator<TuneResult> comparator = Comparator.comparing(res -> res.performance);
        TuneResult best;
        String predictTarget = config.getString("tune.targetMetric");
        switch(predictTarget) {
            case "instance_set_accuracy":
                best = tuneResults.stream().max(comparator).get();
                break;
            case "instance_f1":
                best = tuneResults.stream().max(comparator).get();
                break;
            case "instance_hamming_loss":
                best = tuneResults.stream().min(comparator).get();
                break;
            default:
                throw new IllegalArgumentException("tune.targetMetric should be instance_set_accuracy, instance_f1 or instance_hamming_loss");
        }
        System.out.println("---------------------------");
        System.out.println("Hyper parameter tuning done.");
        System.out.println("Time spent on entire hyper parameter tuning = " + stopWatch);
        System.out.println("Best validation performance = " + best.performance);
        System.out.println("Best hyper parameters:");
        System.out.println("train.numComponents = " + best.hyperParameters.numComponents);
        System.out.println("train.variance = " + best.hyperParameters.variance);
        System.out.println("train.iterations = " + best.hyperParameters.iterations);
        Config tunedHypers = best.hyperParameters.asConfig();
        tunedHypers.store(new File(config.getString("output.dir"), "tuned_hyper_parameters.properties"));
        System.out.println("Tuned hyper parameters saved to " + new File(config.getString("output.dir"), "tuned_hyper_parameters.properties").getAbsolutePath());
        System.out.println("============================================================");
    }
    if (config.getBoolean("train")) {
        System.out.println("============================================================");
        if (config.getBoolean("train.useTunedHyperParameters")) {
            File hyperFile = new File(config.getString("output.dir"), "tuned_hyper_parameters.properties");
            if (!hyperFile.exists()) {
                System.out.println("train.useTunedHyperParameters is set to true. But no tuned hyper parameters can be found in the output directory.");
                System.out.println("Please either run hyper parameter tuning, or provide hyper parameters manually and set train.useTunedHyperParameters=false.");
                System.exit(1);
            }
            Config tunedHypers = new Config(hyperFile);
            HyperParameters hyperParameters = new HyperParameters(tunedHypers);
            System.out.println("Start training with tuned hyper parameters:");
            System.out.println("train.numComponents = " + hyperParameters.numComponents);
            System.out.println("train.variance = " + hyperParameters.variance);
            System.out.println("train.iterations = " + hyperParameters.iterations);
            MultiLabelClfDataSet trainSet = loadTrainData(config);
            train(config, hyperParameters, trainSet);
        } else {
            HyperParameters hyperParameters = new HyperParameters(config);
            System.out.println("Start training with given hyper parameters:");
            System.out.println("train.numComponents = " + hyperParameters.numComponents);
            System.out.println("train.variance = " + hyperParameters.variance);
            System.out.println("train.iterations = " + hyperParameters.iterations);
            MultiLabelClfDataSet trainSet = loadTrainData(config);
            train(config, hyperParameters, trainSet);
        }
        System.out.println("============================================================");
    }
    if (config.getBoolean("test")) {
        System.out.println("============================================================");
        test(config);
        System.out.println("============================================================");
    }
}

Example 53 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) throws Exception {
    System.out.println("============================================================");
    System.out.println("computing other predictor-independent metrics");
    String output = config.getString("output.dir");
    File labelProbFile = Paths.get(output, "test_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, "test_predictions", "ground_truth_log_likelihood.txt").toFile();
    FileUtils.writeStringToFile(logLikelihoodFile, PrintUtil.toMutipleLines(logLikelihoods));
    System.out.println("individual log likelihood of the test ground truth label set is written to " + logLikelihoodFile.getAbsolutePath());
    System.out.println("average log likelihood of the test ground truth label sets = " + average);
    if (!unobservedLabels.isEmpty()) {
        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 54 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) 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 55 with Config

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

the class CBMGB method reportGeneral.

private static void reportGeneral(Config config, CBM cbm, MultiLabelClfDataSet dataSet) throws Exception {
    System.out.println("============================================================");
    System.out.println("computing other predictor-independent metrics");
    String output = config.getString("output.dir");
    File labelProbFile = Paths.get(output, "test_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, "test_predictions", "ground_truth_log_likelihood.txt").toFile();
    FileUtils.writeStringToFile(logLikelihoodFile, PrintUtil.toMutipleLines(logLikelihoods));
    System.out.println("individual log likelihood of the test ground truth label set is written to " + logLikelihoodFile.getAbsolutePath());
    System.out.println("average log likelihood of the test ground truth label sets = " + average);
    if (!unobservedLabels.isEmpty()) {
        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));
    }
}