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

Example 91 with Config

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

the class BRLREN method train.

private static void train(Config config, HyperParameters hyperParameters, MultiLabelClfDataSet trainSet, MultiLabelClfDataSet validSet, Logger logger) throws Exception {
    List<Integer> unobservedLabels = DataSetUtil.unobservedLabels(trainSet);
    if (!unobservedLabels.isEmpty()) {
        logger.info("The following labels do not actually appear in the training set and therefore cannot be learned:");
        logger.info(ListUtil.toSimpleString(unobservedLabels));
        FileUtils.writeStringToFile(Paths.get(config.getString("output.dir"), "model_predictions", config.getString("output.modelFolder"), "analysis", "unobserved_labels.txt").toFile(), ListUtil.toSimpleString(unobservedLabels));
    }
    String output = config.getString("output.dir");
    EarlyStopper earlyStopper = loadNewEarlyStopper();
    StopWatch stopWatch = new StopWatch();
    stopWatch.start();
    CBM cbm = newCBM(config, trainSet, hyperParameters, logger);
    ENCBMOptimizer optimizer = getOptimizer(config, hyperParameters, cbm, trainSet);
    logger.info("Initializing the model");
    if (config.getBoolean("train.randomInitialize")) {
        optimizer.randInitialize();
    } else {
        optimizer.initialize();
    }
    logger.info("Initialization done");
    AccPredictor accPredictor = new AccPredictor(cbm);
    accPredictor.setComponentContributionThreshold(config.getDouble("predict.piThreshold"));
    int interval = 1;
    for (int iter = 1; true; iter++) {
        logger.info("Training progress: iteration " + iter);
        optimizer.iterate();
        if (iter % interval == 0) {
            MLMeasures validMeasures = new MLMeasures(accPredictor, validSet);
            if (VERBOSE) {
                logger.info("validation performance");
                logger.info(validMeasures.toString());
            }
            earlyStopper.add(iter, validMeasures.getInstanceAverage().getAccuracy());
            if (earlyStopper.getBestIteration() == iter) {
                Serialization.serialize(cbm, Paths.get(output, "model_predictions", config.getString("output.modelFolder"), "models", "classifier"));
            }
            if (earlyStopper.shouldStop()) {
                if (VERBOSE) {
                    logger.info("Early Stopper: the training should stop now!");
                    logger.info("Early Stopper: best iteration found = " + earlyStopper.getBestIteration());
                    logger.info("Early Stopper: best validation performance = " + earlyStopper.getBestValue());
                }
                break;
            }
        }
    }
    logger.info("training done!");
    logger.info("time spent on training = " + stopWatch);
    List<MultiLabel> support = DataSetUtil.gatherMultiLabels(trainSet);
    Serialization.serialize(support, Paths.get(output, "model_predictions", config.getString("output.modelFolder"), "models", "support"));
    CBM bestModel = (CBM) Serialization.deserialize(Paths.get(output, "model_predictions", config.getString("output.modelFolder"), "models", "classifier"));
    boolean topFeaturesToFile = true;
    if (topFeaturesToFile) {
        File analysisFolder = Paths.get(output, "model_predictions", config.getString("output.modelFolder"), "analysis").toFile();
        analysisFolder.mkdirs();
        logger.info("start writing top features");
        List<TopFeatures> topFeaturesList = IntStream.range(0, bestModel.getNumClasses()).mapToObj(k -> topFeatures(bestModel, trainSet.getFeatureList(), trainSet.getLabelTranslator(), k, 100)).collect(Collectors.toList());
        ObjectMapper mapper = new ObjectMapper();
        String file = "top_features.json";
        mapper.writeValue(new File(analysisFolder, file), topFeaturesList);
        StringBuilder sb = new StringBuilder();
        for (int l = 0; l < bestModel.getNumClasses(); l++) {
            sb.append("-------------------------").append("\n");
            sb.append(bestModel.getLabelTranslator().toExtLabel(l)).append(":").append("\n");
            for (Feature feature : topFeaturesList.get(l).getTopFeatures()) {
                sb.append(feature.simpleString()).append(", ");
            }
            sb.append("\n");
        }
        FileUtils.writeStringToFile(new File(analysisFolder, "top_features.txt"), sb.toString());
        logger.info("finish writing top features");
    }
}

Example 92 with Config

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

the class BRRerank method classification_eval.

private static void classification_eval(Config config) throws Exception {
    System.out.println("classification performance on test set");
    MultiLabelClfDataSet dataset = TRECFormat.loadMultiLabelClfDataSet(Paths.get(config.getString("dataPath"), "test").toFile(), DataSetType.ML_CLF_SPARSE, true);
    MultiLabel[] predictions = FileUtils.readLines(Paths.get(config.getString("outputDir"), "reports", "set_prediction_and_confidence.txt").toFile()).stream().skip(1).map(line -> new MultiLabel(line.split("\t")[0].replace("{", "").replace("}", ""))).toArray(MultiLabel[]::new);
    MLMeasures mlMeasures = new MLMeasures(dataset.getNumClasses(), dataset.getMultiLabels(), predictions);
    System.out.println("classification performance");
    System.out.println(mlMeasures);
    Paths.get(config.getString("outputDir")).toFile().mkdirs();
    FileUtils.writeStringToFile(Paths.get(config.getString("outputDir"), "reports", "classification_performance.txt").toFile(), mlMeasures.toString());
}

Example 93 with Config

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

the class GBRegressor 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]);
    Logger logger = Logger.getAnonymousLogger();
    String logFile = config.getString("output.log");
    FileHandler fileHandler = null;
    if (!logFile.isEmpty()) {
        new File(logFile).getParentFile().mkdirs();
        // todo should append?
        fileHandler = new FileHandler(logFile, true);
        java.util.logging.Formatter formatter = new SimpleFormatter();
        fileHandler.setFormatter(formatter);
        logger.addHandler(fileHandler);
        logger.setUseParentHandlers(false);
    }
    logger.info(config.toString());
    if (config.getBoolean("train")) {
        train(config, logger);
    }
    if (config.getBoolean("test")) {
        test(config, logger);
    }
    if (fileHandler != null) {
        fileHandler.close();
    }
}

Example 94 with Config

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

the class InstanceConcatenator 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]);
    List<String> inputs = config.getStrings("input.dataSets");
    String output = config.getString("output.dataSet");
    List<MultiLabelClfDataSet> dataSets = new ArrayList<>();
    for (String input : inputs) {
        MultiLabelClfDataSet dataSet = TRECFormat.loadMultiLabelClfDataSet(input, DataSetType.ML_CLF_SPARSE, true);
        // MultiLabelClfDataSet dataSet = TRECFormat.loadMultiLabelClfDataSet(input, DataSetType.ML_CLF_DENSE,true);
        dataSets.add(dataSet);
    }
    MultiLabelClfDataSet merged = DataSetUtil.concatenateByRow(dataSets);
    TRECFormat.save(merged, output);
}

Example 95 with Config

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

the class Meka2Trec method main.

/**
 * this is only support multi-label classification dataset.
 * @param args
 */
public static void main(String[] args) throws IOException {
    if (args.length != 1) {
        throw new IllegalArgumentException("Please specify a properties file.");
    }
    Config config = new Config(args[0]);
    System.out.println(config);
    List<String> trecs = config.getStrings("trec");
    List<String> mekas = config.getStrings("meka");
    int numLabels = config.getInt("numLabels");
    int numFeatures = config.getInt("numFeatures");
    String dataMode = config.getString("dataMode");
    for (int i = 0; i < mekas.size(); i++) {
        System.out.println("processing on: " + trecs.get(i));
        MultiLabelClfDataSet dataSet = MekaFormat.loadMLClfDataset(mekas.get(i), numFeatures, numLabels, dataMode);
        TRECFormat.save(dataSet, trecs.get(i));
    }
}