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

Example 1 with Config

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

the class CBMEN 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 2 with Config

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

the class CBMEN 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 3 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) 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 4 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) 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 5 with Config

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

the class App1 method ngramSelection.

private static void ngramSelection(Config config, MultiLabelIndex index, String docFilter, Logger logger) throws Exception {
    logger.info("start ngram selection");
    File metaDataFolder = new File(config.getString("output.folder"), "meta_data");
    FeatureLoader.MatchScoreType matchScoreType;
    String matchScoreTypeString = config.getString("train.feature.ngram.matchScoreType");
    String[] indexIds = getDocsForSplitFromQuery(index, config.getString("train.splitQuery"));
    IdTranslator idTranslator = loadIdTranslator(indexIds);
    LabelTranslator labelTranslator = (LabelTranslator) Serialization.deserialize(new File(metaDataFolder, "label_translator.ser"));
    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");
    }
    double[][] labels = loadLabels(config, index, idTranslator, labelTranslator);
    int numLabels = labels.length;
    int toKeep = config.getInt("train.feature.ngram.selectPerLabel");
    List<BoundedBlockPriorityQueue<Pair<Ngram, Double>>> queues = new ArrayList<>();
    Comparator<Pair<Ngram, Double>> comparator = Comparator.comparing(p -> p.getSecond());
    for (int l = 0; l < numLabels; l++) {
        queues.add(new BoundedBlockPriorityQueue<>(toKeep, comparator));
    }
    FeatureList featureList = (FeatureList) Serialization.deserialize(new File(metaDataFolder, "feature_list.ser"));
    featureList.getAll().stream().parallel().filter(feature -> feature instanceof Ngram).map(feature -> (Ngram) feature).filter(ngram -> ngram.getN() > 1).forEach(ngram -> {
        double[] scores = StumpSelector.scores(index, labels, ngram, idTranslator, matchScoreType, docFilter);
        for (int l = 0; l < numLabels; l++) {
            queues.get(l).add(new Pair<>(ngram, scores[l]));
        }
    });
    Set<Ngram> kept = new HashSet<>();
    StringBuilder stringBuilder = new StringBuilder();
    for (int l = 0; l < numLabels; l++) {
        stringBuilder.append("-------------------------").append("\n");
        stringBuilder.append(labelTranslator.toExtLabel(l)).append(":").append("\n");
        BoundedBlockPriorityQueue<Pair<Ngram, Double>> queue = queues.get(l);
        while (queue.size() > 0) {
            Ngram ngram = queue.poll().getFirst();
            kept.add(ngram);
            stringBuilder.append(ngram.getNgram()).append(", ");
        }
        stringBuilder.append("\n");
    }
    File selectionFile = new File(metaDataFolder, "selected_ngrams.txt");
    FileUtils.writeStringToFile(selectionFile, stringBuilder.toString());
    logger.info("finish ngram selection");
    logger.info("selected ngrams are written to " + selectionFile.getAbsolutePath());
    // after feature selection, overwrite the feature_list.ser file; rename old files
    FeatureList selectedFeatures = new FeatureList();
    for (Feature feature : featureList.getAll()) {
        if (!(feature instanceof Ngram)) {
            selectedFeatures.add(feature);
        }
        if ((feature instanceof Ngram) && ((Ngram) feature).getN() == 1) {
            selectedFeatures.add(feature);
        }
        if ((feature instanceof Ngram) && ((Ngram) feature).getN() > 1 && kept.contains(feature)) {
            selectedFeatures.add(feature);
        }
    }
    FileUtils.copyFile(new File(metaDataFolder, "feature_list.ser"), new File(metaDataFolder, "feature_list_all.ser"));
    FileUtils.copyFile(new File(metaDataFolder, "feature_list.txt"), new File(metaDataFolder, "feature_list_all.txt"));
    Serialization.serialize(selectedFeatures, new File(metaDataFolder, "feature_list.ser"));
    try (BufferedWriter bufferedWriter = new BufferedWriter(new FileWriter(new File(metaDataFolder, "feature_list.txt")))) {
        for (Feature feature : selectedFeatures.getAll()) {
            bufferedWriter.write(feature.toString());
            bufferedWriter.newLine();
        }
    }
}