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

Example 11 with Config

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

the class CBMGB 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<Integer> leaveNums = config.getIntegers("tune.numLeaves.candidates");
        List<Integer> components = config.getIntegers("tune.numComponents.candidates");
        for (int numLeaves : leaveNums) {
            for (int component : components) {
                StopWatch stopWatch1 = new StopWatch();
                stopWatch1.start();
                HyperParameters hyperParameters = new HyperParameters();
                hyperParameters.numComponents = component;
                hyperParameters.numLeaves = numLeaves;
                System.out.println("---------------------------");
                System.out.println("Trying hyper parameters:");
                System.out.println("train.numComponents = " + hyperParameters.numComponents);
                System.out.println("train.numLeaves = " + hyperParameters.numLeaves);
                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.numLeaves = " + best.hyperParameters.numLeaves);
        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.numLeaves = " + hyperParameters.numLeaves);
            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.numLeaves = " + hyperParameters.numLeaves);
            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 12 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) throws Exception {
    System.out.println("============================================================");
    System.out.println("Making predictions on test 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("test performance with the instance set accuracy optimal predictor");
    System.out.println(mlMeasures);
    File performanceFile = Paths.get(output, "test_predictions", "instance_accuracy_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_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());
    System.out.println("============================================================");
}

Example 13 with Config

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

the class CBMGB method reportF1Prediction.

private static void reportF1Prediction(Config config, CBM cbm, MultiLabelClfDataSet dataSet) throws Exception {
    System.out.println("============================================================");
    System.out.println("Making predictions on test 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"));
    MultiLabel[] predictions = pluginF1.predict(dataSet);
    MLMeasures mlMeasures = new MLMeasures(dataSet.getNumClasses(), dataSet.getMultiLabels(), predictions);
    System.out.println("test performance with the instance F1 optimal predictor");
    System.out.println(mlMeasures);
    File performanceFile = Paths.get(output, "test_predictions", "instance_f1_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_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());
    System.out.println("============================================================");
}

Example 14 with Config

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

the class FoldPartitioner method main.

public static void main(String[] args) throws Exception {
    Config config = new Config(args[0]);
    System.out.println(config);
    String dataType = config.getString("dataSetType");
    switch(dataType) {
        case "clf":
            partitionClfData(config);
            break;
        case "reg":
            partitionRegData(config);
            break;
        case "mlclf":
            partitionMLClfData(config);
            break;
        default:
            throw new IllegalArgumentException("illegal dataSetType");
    }
}

Example 15 with Config

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

the class App1 method loadData.

//todo keep track of feature types(numerical /binary)
static MultiLabelClfDataSet loadData(Config config, MultiLabelIndex index, FeatureList featureList, IdTranslator idTranslator, int totalDim, LabelTranslator labelTranslator, String docFilter) throws Exception {
    File metaDataFolder = new File(config.getString("output.folder"), "meta_data");
    Config savedConfig = new Config(new File(metaDataFolder, "saved_config_app1"));
    int numDataPoints = idTranslator.numData();
    int numClasses = labelTranslator.getNumClasses();
    MultiLabelClfDataSet dataSet = MLClfDataSetBuilder.getBuilder().numDataPoints(numDataPoints).numFeatures(totalDim).numClasses(numClasses).density(Density.SPARSE_RANDOM).missingValue(savedConfig.getBoolean("train.feature.missingValue")).build();
    for (int i = 0; i < numDataPoints; i++) {
        String dataIndexId = idTranslator.toExtId(i);
        List<String> extMultiLabel = index.getExtMultiLabel(dataIndexId);
        if (savedConfig.getBoolean("train.label.filter")) {
            String prefix = savedConfig.getString("train.label.filter.prefix");
            extMultiLabel = extMultiLabel.stream().filter(extLabel -> extLabel.startsWith(prefix)).collect(Collectors.toList());
        }
        for (String extLabel : extMultiLabel) {
            int intLabel = labelTranslator.toIntLabel(extLabel);
            dataSet.addLabel(i, intLabel);
        }
    }
    String matchScoreTypeString = savedConfig.getString("train.feature.ngram.matchScoreType");
    FeatureLoader.MatchScoreType matchScoreType;
    switch(matchScoreTypeString) {
        case "es_original":
            matchScoreType = FeatureLoader.MatchScoreType.ES_ORIGINAL;
            break;
        case "binary":
            matchScoreType = FeatureLoader.MatchScoreType.BINARY;
            break;
        case "frequency":
            matchScoreType = FeatureLoader.MatchScoreType.FREQUENCY;
            break;
        case "tfifl":
            matchScoreType = FeatureLoader.MatchScoreType.TFIFL;
            break;
        default:
            throw new IllegalArgumentException("unknown ngramMatchScoreType");
    }
    FeatureLoader.loadFeatures(index, dataSet, featureList, idTranslator, matchScoreType, docFilter);
    dataSet.setIdTranslator(idTranslator);
    dataSet.setLabelTranslator(labelTranslator);
    return dataSet;
}