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

Example 71 with Config

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

the class ArffFormat method writeConfigFile.

private static void writeConfigFile(ClfDataSet dataSet, File arffFile) {
    File configFile = new File(arffFile, ARFF_CONFIG_FILE_NAME);
    Config config = new Config();
    config.setInt(ARFF_CONFIG_NUM_DATA_POINTS, dataSet.getNumDataPoints());
    config.setInt(ARFF_CONFIG_NUM_FEATURES, dataSet.getNumFeatures());
    config.setInt(ARFF_CONFIG_NUM_CLASSES, dataSet.getNumClasses());
    try {
        config.store(configFile);
    } catch (Exception e) {
        e.printStackTrace();
    }
}

Example 72 with Config

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

the class ArffFormat method writeConfigFile.

private static void writeConfigFile(MultiLabelClfDataSet dataSet, File arffFile) {
    File configFile = new File(arffFile, ARFF_CONFIG_FILE_NAME);
    Config config = new Config();
    config.setInt(ARFF_CONFIG_NUM_DATA_POINTS, dataSet.getNumDataPoints());
    config.setInt(ARFF_CONFIG_NUM_FEATURES, dataSet.getNumFeatures());
    config.setInt(ARFF_CONFIG_NUM_CLASSES, dataSet.getNumClasses());
    try {
        config.store(configFile);
    } catch (Exception e) {
        e.printStackTrace();
    }
}

Example 73 with Config

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

the class BRRerank 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]);
    if (config.getBoolean("trainBR")) {
        System.out.println("Start training BR classifier");
        Config brConfig = produceBRConfig(config);
        CBMEN.main(brConfig);
        System.out.println("Finish training BR classifier");
    }
    Config caliConfig = produceCaliConfig(config);
    calibrate(caliConfig);
    report(config);
    calibration_eval(config);
    classification_eval(config);
}

Example 74 with Config

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

the class BRRerank method report.

private static void report(Config config) throws Exception {
    MultiLabelClfDataSet dataset = TRECFormat.loadMultiLabelClfDataSet(Paths.get(config.getString("dataPath"), "test").toFile(), DataSetType.ML_CLF_SPARSE, true);
    CBM cbm = (CBM) Serialization.deserialize(Paths.get(config.getString("outputDir"), "models", "model"));
    cbm.setAllowEmpty(true);
    MultiLabelClassifier classifier = null;
    LabelCalibrator labelCalibrator = (LabelCalibrator) Serialization.deserialize(Paths.get(config.getString("outputDir"), "models", "label_calibrator"));
    VectorCalibrator setCalibrator = (VectorCalibrator) Serialization.deserialize(Paths.get(config.getString("outputDir"), "models", "set_calibrator"));
    PredictionFeatureExtractor predictionFeatureExtractor = (PredictionFeatureExtractor) Serialization.deserialize(Paths.get(config.getString("outputDir"), "models", "calibration_feature_extractor"));
    CalibrationDataGenerator calibrationDataGenerator = new CalibrationDataGenerator(labelCalibrator, predictionFeatureExtractor);
    switch(config.getString("predictMode")) {
        case "independent":
            classifier = new IndependentPredictor(cbm, labelCalibrator);
            break;
        case "rerank":
            classifier = (Reranker) setCalibrator;
            break;
        default:
            throw new IllegalArgumentException("illegal predict.mode");
    }
    MultiLabel[] predictions = classifier.predict(dataset);
    List<CalibInfo> confidenceScores = IntStream.range(0, dataset.getNumDataPoints()).parallel().boxed().map(i -> {
        CalibrationDataGenerator.CalibrationInstance predictionInstance = calibrationDataGenerator.createInstance(cbm, dataset.getRow(i), predictions[i], dataset.getMultiLabels()[i], "accuracy");
        double calibrated = setCalibrator.calibrate(predictionInstance.vector);
        CalibInfo calibInfo = new CalibInfo();
        calibInfo.uncalibrated = predictionInstance.vector.get(0);
        calibInfo.calibrated = calibrated;
        calibInfo.accuracy = predictionInstance.correctness;
        return calibInfo;
    }).collect(Collectors.toList());
    StringBuilder stringBuilder = new StringBuilder();
    stringBuilder.append("set_prediction").append("\t").append("uncalibrated_confidence").append("\t").append("calibrated_confidence").append("\t").append("ground_truth").append("\t").append("set_accuracy").append("\n");
    for (int i = 0; i < dataset.getNumDataPoints(); i++) {
        stringBuilder.append(predictions[i]).append("\t").append(confidenceScores.get(i).uncalibrated).append("\t").append(confidenceScores.get(i).calibrated).append("\t").append(dataset.getMultiLabels()[i]).append("\t").append(predictions[i].equals(dataset.getMultiLabels()[i]) ? 1 : 0).append("\n");
    }
    FileUtils.writeStringToFile(Paths.get(config.getString("outputDir"), "reports", "set_prediction_and_confidence.txt").toFile(), stringBuilder.toString());
    System.out.println("set predictions and confidence scores are saved to " + Paths.get(config.getString("outputDir"), "reports", "set_prediction_and_confidence.txt").toString() + "\n");
}

Example 75 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;
            case "instance_log_likelihood":
                best = tuneResults.stream().max(comparator).get();
                break;
            default:
                throw new IllegalArgumentException("tune.targetMetric should be instance_set_accuracy, instance_f1, instance_hamming_loss, instance_log_likelihood");
        }
        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("============================================================");
    }
}