Examples with RegressionTree edu.neu.ccs.pyramid.regression.regression_tree.RegressionTree used on opensource projects

Example 11 with RegressionTree

use of edu.neu.ccs.pyramid.regression.regression_tree.RegressionTree in project pyramid by cheng-li.

the class HMLGBInspector method topFeatures.

//todo: consider newton step and learning rate
/**
     * only trees are considered
     * @param boosting
     * @param classIndex
     * @return list of feature index and feature name pairs
     */
public static TopFeatures topFeatures(HMLGradientBoosting 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 12 with RegressionTree

use of edu.neu.ccs.pyramid.regression.regression_tree.RegressionTree in project pyramid by cheng-li.

the class AdaBoostMHInspector method decisionProcess.

public static ClassScoreCalculation decisionProcess(AdaBoostMH boosting, MultiLabelClassifier.ClassProbEstimator scaling, LabelTranslator labelTranslator, Vector vector, int classIndex, int limit) {
    ClassScoreCalculation classScoreCalculation = new ClassScoreCalculation(classIndex, labelTranslator.toExtLabel(classIndex), boosting.predictClassScore(vector, classIndex));
    double prob = scaling.predictClassProb(vector, classIndex);
    classScoreCalculation.setClassProbability(prob);
    List<Regressor> regressors = boosting.getRegressors(classIndex);
    List<TreeRule> treeRules = new ArrayList<>();
    for (Regressor regressor : regressors) {
        if (regressor instanceof ConstantRegressor) {
            Rule rule = new ConstantRule(((ConstantRegressor) regressor).getScore());
            classScoreCalculation.addRule(rule);
        }
        if (regressor instanceof RegressionTree) {
            RegressionTree tree = (RegressionTree) regressor;
            TreeRule treeRule = new TreeRule(tree, vector);
            treeRules.add(treeRule);
        }
    }
    Comparator<TreeRule> comparator = Comparator.comparing(decision -> Math.abs(decision.getScore()));
    List<TreeRule> merged = TreeRule.merge(treeRules).stream().sorted(comparator.reversed()).limit(limit).collect(Collectors.toList());
    for (TreeRule treeRule : merged) {
        classScoreCalculation.addRule(treeRule);
    }
    return classScoreCalculation;
}

Example 13 with RegressionTree

use of edu.neu.ccs.pyramid.regression.regression_tree.RegressionTree 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 14 with RegressionTree

use of edu.neu.ccs.pyramid.regression.regression_tree.RegressionTree in project pyramid by cheng-li.

the class StumpSelector method score.

private static double score(DataSet dataSet, double[] labels) {
    RegTreeConfig regTreeConfig = new RegTreeConfig().setMaxNumLeaves(2);
    RegressionTree tree = RegTreeTrainer.fit(regTreeConfig, dataSet, labels);
    return tree.getRoot().getReduction();
}