Examples with Feature edu.neu.ccs.pyramid.feature.Feature used on opensource projects

Example 21 with Feature

use of edu.neu.ccs.pyramid.feature.Feature in project pyramid by cheng-li.

the class AdaBoostMHInspector method topFeatures.

public static TopFeatures topFeatures(AdaBoostMH boosting, int classIndex, int limit) {
    Map<Feature, Double> totalContributions = new HashMap<>();
    List<Regressor> regressors = boosting.getRegressors(classIndex);
    List<RegressionTree> trees = regressors.stream().filter(regressor -> regressor instanceof RegressionTree).map(regressor -> (RegressionTree) regressor).collect(Collectors.toList());
    for (RegressionTree tree : trees) {
        Map<Feature, Double> contributions = RegTreeInspector.featureImportance(tree);
        for (Map.Entry<Feature, Double> entry : contributions.entrySet()) {
            Feature feature = entry.getKey();
            Double contribution = entry.getValue();
            double oldValue = totalContributions.getOrDefault(feature, 0.0);
            double newValue = oldValue + contribution;
            totalContributions.put(feature, newValue);
        }
    }
    Comparator<Map.Entry<Feature, Double>> comparator = Comparator.comparing(Map.Entry::getValue);
    List<Feature> list = totalContributions.entrySet().stream().sorted(comparator.reversed()).limit(limit).map(Map.Entry::getKey).collect(Collectors.toList());
    TopFeatures topFeatures = new TopFeatures();
    topFeatures.setTopFeatures(list);
    topFeatures.setClassIndex(classIndex);
    LabelTranslator labelTranslator = boosting.getLabelTranslator();
    topFeatures.setClassName(labelTranslator.toExtLabel(classIndex));
    return topFeatures;
}

Example 22 with Feature

use of edu.neu.ccs.pyramid.feature.Feature in project pyramid by cheng-li.

the class MekaFormat method loadMLClfDatasetPre.

private static MultiLabelClfDataSet loadMLClfDatasetPre(File file, int numClasses, int numFeatures, int numData, Map<String, String> labelMap, Map<String, String> featureMap) throws IOException {
    MultiLabelClfDataSet dataSet = MLClfDataSetBuilder.getBuilder().numDataPoints(numData).numClasses(numClasses).numFeatures(numFeatures).build();
    // set features
    List<Feature> featureList = new LinkedList<>();
    for (int m = 0; m < numFeatures; m++) {
        String featureIndex = Integer.toString(m + numClasses);
        String featureName = featureMap.get(featureIndex);
        Feature feature = new Feature();
        feature.setIndex(m);
        feature.setName(featureName);
        featureList.add(feature);
    }
    dataSet.setFeatureList(new FeatureList(featureList));
    // set Label
    Map<Integer, String> labelIndexMap = new HashMap<>();
    for (Map.Entry<String, String> entry : labelMap.entrySet()) {
        String labelString = entry.getKey();
        String labelName = entry.getValue();
        labelIndexMap.put(Integer.parseInt(labelString), labelName);
    }
    LabelTranslator labelTranslator = new LabelTranslator(labelIndexMap);
    dataSet.setLabelTranslator(labelTranslator);
    // create feature matrix
    BufferedReader br = new BufferedReader(new FileReader(file));
    String line;
    int dataCount = 0;
    while ((line = br.readLine()) != null) {
        if ((line.startsWith("{")) && (line.endsWith("}"))) {
            line = line.substring(1, line.length() - 1);
            String[] indexValues = line.split(",");
            for (String indexValue : indexValues) {
                String[] indexValuePair = indexValue.split(" ");
                String index = indexValuePair[0];
                String value = indexValuePair[1];
                if (labelMap.containsKey(index)) {
                    double valueDouble = Double.parseDouble(value);
                    if (valueDouble == 1.0) {
                        dataSet.addLabel(dataCount, Integer.parseInt(index));
                    }
                } else if (featureMap.containsKey(index)) {
                    double valueDouble = Double.parseDouble(value);
                    int indexInt = Integer.parseInt(index);
                    dataSet.setFeatureValue(dataCount, indexInt - numClasses, valueDouble);
                } else {
                    throw new RuntimeException("Index not found in the line: " + line);
                }
            }
            dataCount++;
        }
    }
    br.close();
    return dataSet;
}

Example 23 with Feature

use of edu.neu.ccs.pyramid.feature.Feature in project pyramid by cheng-li.

the class App2 method train.

static void train(Config config, Logger logger) throws Exception {
    String output = config.getString("output.folder");
    int numIterations = config.getInt("train.numIterations");
    int numLeaves = config.getInt("train.numLeaves");
    double learningRate = config.getDouble("train.learningRate");
    int minDataPerLeaf = config.getInt("train.minDataPerLeaf");
    String modelName = "model_app3";
    //        double featureSamplingRate = config.getDouble("train.featureSamplingRate");
    //        double dataSamplingRate = config.getDouble("train.dataSamplingRate");
    StopWatch stopWatch = new StopWatch();
    stopWatch.start();
    MultiLabelClfDataSet dataSet = loadData(config, config.getString("input.trainData"));
    MultiLabelClfDataSet testSet = null;
    if (config.getBoolean("train.showTestProgress")) {
        testSet = loadData(config, config.getString("input.testData"));
    }
    int numClasses = dataSet.getNumClasses();
    logger.info("number of class = " + numClasses);
    IMLGBConfig imlgbConfig = new IMLGBConfig.Builder(dataSet).learningRate(learningRate).minDataPerLeaf(minDataPerLeaf).numLeaves(numLeaves).numSplitIntervals(config.getInt("train.numSplitIntervals")).usePrior(config.getBoolean("train.usePrior")).build();
    IMLGradientBoosting boosting;
    if (config.getBoolean("train.warmStart")) {
        boosting = IMLGradientBoosting.deserialize(new File(output, modelName));
    } else {
        boosting = new IMLGradientBoosting(numClasses);
    }
    logger.info("During training, the performance is reported using Hamming loss optimal predictor");
    logger.info("initialing trainer");
    IMLGBTrainer trainer = new IMLGBTrainer(imlgbConfig, boosting);
    boolean earlyStop = config.getBoolean("train.earlyStop");
    List<EarlyStopper> earlyStoppers = new ArrayList<>();
    List<Terminator> terminators = new ArrayList<>();
    if (earlyStop) {
        for (int l = 0; l < numClasses; l++) {
            EarlyStopper earlyStopper = new EarlyStopper(EarlyStopper.Goal.MINIMIZE, config.getInt("train.earlyStop.patience"));
            earlyStopper.setMinimumIterations(config.getInt("train.earlyStop.minIterations"));
            earlyStoppers.add(earlyStopper);
        }
        for (int l = 0; l < numClasses; l++) {
            Terminator terminator = new Terminator();
            terminator.setMaxStableIterations(config.getInt("train.earlyStop.patience")).setMinIterations(config.getInt("train.earlyStop.minIterations") / config.getInt("train.showProgress.interval")).setAbsoluteEpsilon(config.getDouble("train.earlyStop.absoluteChange")).setRelativeEpsilon(config.getDouble("train.earlyStop.relativeChange")).setOperation(Terminator.Operation.OR);
            terminators.add(terminator);
        }
    }
    logger.info("trainer initialized");
    int numLabelsLeftToTrain = numClasses;
    int progressInterval = config.getInt("train.showProgress.interval");
    for (int i = 1; i <= numIterations; i++) {
        logger.info("iteration " + i);
        trainer.iterate();
        if (config.getBoolean("train.showTrainProgress") && (i % progressInterval == 0 || i == numIterations)) {
            logger.info("training set performance");
            logger.info(new MLMeasures(boosting, dataSet).toString());
        }
        if (config.getBoolean("train.showTestProgress") && (i % progressInterval == 0 || i == numIterations)) {
            logger.info("test set performance");
            logger.info(new MLMeasures(boosting, testSet).toString());
            if (earlyStop) {
                for (int l = 0; l < numClasses; l++) {
                    EarlyStopper earlyStopper = earlyStoppers.get(l);
                    Terminator terminator = terminators.get(l);
                    if (!trainer.getShouldStop()[l]) {
                        double kl = KL(boosting, testSet, l);
                        earlyStopper.add(i, kl);
                        terminator.add(kl);
                        if (earlyStopper.shouldStop() || terminator.shouldTerminate()) {
                            logger.info("training for label " + l + " (" + dataSet.getLabelTranslator().toExtLabel(l) + ") should stop now");
                            logger.info("the best number of training iterations for the label is " + earlyStopper.getBestIteration());
                            trainer.setShouldStop(l);
                            numLabelsLeftToTrain -= 1;
                            logger.info("the number of labels left to be trained on = " + numLabelsLeftToTrain);
                        }
                    }
                }
            }
        }
        if (numLabelsLeftToTrain == 0) {
            logger.info("all label training finished");
            break;
        }
    }
    logger.info("training done");
    File serializedModel = new File(output, modelName);
    //todo pick best models
    boosting.serialize(serializedModel);
    logger.info(stopWatch.toString());
    if (earlyStop) {
        for (int l = 0; l < numClasses; l++) {
            logger.info("----------------------------------------------------");
            logger.info("test performance history for label " + l + ": " + earlyStoppers.get(l).history());
            logger.info("model size for label " + l + " = " + (boosting.getRegressors(l).size() - 1));
        }
    }
    boolean topFeaturesToFile = true;
    if (topFeaturesToFile) {
        logger.info("start writing top features");
        int limit = config.getInt("report.topFeatures.limit");
        List<TopFeatures> topFeaturesList = IntStream.range(0, boosting.getNumClasses()).mapToObj(k -> IMLGBInspector.topFeatures(boosting, k, limit)).collect(Collectors.toList());
        ObjectMapper mapper = new ObjectMapper();
        String file = "top_features.json";
        mapper.writeValue(new File(output, file), topFeaturesList);
        StringBuilder sb = new StringBuilder();
        for (int l = 0; l < boosting.getNumClasses(); l++) {
            sb.append("-------------------------").append("\n");
            sb.append(dataSet.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(output, "top_features.txt"), sb.toString());
        logger.info("finish writing top features");
    }
}