Examples with SupportPredictor edu.neu.ccs.pyramid.multilabel_classification.predictor.SupportPredictor used on opensource projects

Example 1 with SupportPredictor

use of edu.neu.ccs.pyramid.multilabel_classification.predictor.SupportPredictor in project pyramid by cheng-li.

the class BRLRInspector method analyzePrediction.

public static MultiLabelPredictionAnalysis analyzePrediction(CBM cbm, LabelCalibrator labelCalibrator, VectorCalibrator setCalibrator, MultiLabelClfDataSet dataSet, MultiLabelClassifier classifier, PredictionFeatureExtractor predictionFeatureExtractor, int dataPointIndex, int ruleLimit, int labelSetLimit, double classProbThreshold) {
    MultiLabelPredictionAnalysis predictionAnalysis = new MultiLabelPredictionAnalysis();
    LabelTranslator labelTranslator = dataSet.getLabelTranslator();
    IdTranslator idTranslator = dataSet.getIdTranslator();
    predictionAnalysis.setInternalId(dataPointIndex);
    predictionAnalysis.setId(idTranslator.toExtId(dataPointIndex));
    predictionAnalysis.setInternalLabels(dataSet.getMultiLabels()[dataPointIndex].getMatchedLabelsOrdered());
    List<String> labels = dataSet.getMultiLabels()[dataPointIndex].getMatchedLabelsOrdered().stream().map(labelTranslator::toExtLabel).collect(Collectors.toList());
    predictionAnalysis.setLabels(labels);
    double[] classProbs = cbm.predictClassProbs(dataSet.getRow(dataPointIndex));
    double[] calibratedClassProbs = labelCalibrator.calibratedClassProbs(classProbs);
    PredictionCandidate trueCandidate = new PredictionCandidate();
    trueCandidate.x = dataSet.getRow(dataPointIndex);
    trueCandidate.multiLabel = dataSet.getMultiLabels()[dataPointIndex];
    trueCandidate.labelProbs = calibratedClassProbs;
    predictionAnalysis.setProbForTrueLabels(setCalibrator.calibrate(predictionFeatureExtractor.extractFeatures(trueCandidate)));
    MultiLabel predictedLabels = classifier.predict(dataSet.getRow(dataPointIndex));
    List<Integer> internalPrediction = predictedLabels.getMatchedLabelsOrdered();
    predictionAnalysis.setInternalPrediction(internalPrediction);
    List<String> prediction = internalPrediction.stream().map(labelTranslator::toExtLabel).collect(Collectors.toList());
    predictionAnalysis.setPrediction(prediction);
    PredictionCandidate predictedCandidate = new PredictionCandidate();
    predictedCandidate.x = dataSet.getRow(dataPointIndex);
    predictedCandidate.multiLabel = predictedLabels;
    predictedCandidate.labelProbs = calibratedClassProbs;
    predictionAnalysis.setProbForPredictedLabels(setCalibrator.calibrate(predictionFeatureExtractor.extractFeatures(predictedCandidate)));
    List<Integer> classes = new ArrayList<Integer>();
    for (int k = 0; k < cbm.getNumClasses(); k++) {
        if (calibratedClassProbs[k] >= classProbThreshold || dataSet.getMultiLabels()[dataPointIndex].matchClass(k) || predictedLabels.matchClass(k)) {
            classes.add(k);
        }
    }
    // todo
    List<ClassScoreCalculation> classScoreCalculations = new ArrayList<>();
    for (int k : classes) {
        ClassScoreCalculation classScoreCalculation = decisionProcess(cbm, labelTranslator, calibratedClassProbs[k], dataSet.getRow(dataPointIndex), k, ruleLimit);
        classScoreCalculations.add(classScoreCalculation);
    }
    predictionAnalysis.setClassScoreCalculations(classScoreCalculations);
    List<MultiLabelPredictionAnalysis.ClassRankInfo> labelRanking = classes.stream().map(label -> {
        MultiLabelPredictionAnalysis.ClassRankInfo rankInfo = new MultiLabelPredictionAnalysis.ClassRankInfo();
        rankInfo.setClassIndex(label);
        rankInfo.setClassName(labelTranslator.toExtLabel(label));
        rankInfo.setProb(calibratedClassProbs[label]);
        return rankInfo;
    }).collect(Collectors.toList());
    predictionAnalysis.setPredictedRanking(labelRanking);
    List<Pair<MultiLabel, Double>> topK;
    if (classifier instanceof SupportPredictor) {
        topK = TopKFinder.topKinSupport(dataSet.getRow(dataPointIndex), cbm, labelCalibrator, setCalibrator, predictionFeatureExtractor, ((SupportPredictor) classifier).getSupport(), labelSetLimit);
    } else {
        topK = TopKFinder.topK(dataSet.getRow(dataPointIndex), cbm, labelCalibrator, setCalibrator, predictionFeatureExtractor, labelSetLimit);
    }
    List<MultiLabelPredictionAnalysis.LabelSetProbInfo> labelSetRanking = topK.stream().map(pair -> {
        MultiLabel multiLabel = pair.getFirst();
        double setProb = pair.getSecond();
        MultiLabelPredictionAnalysis.LabelSetProbInfo labelSetProbInfo = new MultiLabelPredictionAnalysis.LabelSetProbInfo(multiLabel, setProb, labelTranslator);
        return labelSetProbInfo;
    }).sorted(Comparator.comparing(MultiLabelPredictionAnalysis.LabelSetProbInfo::getProbability).reversed()).limit(labelSetLimit).collect(Collectors.toList());
    predictionAnalysis.setPredictedLabelSetRanking(labelSetRanking);
    return predictionAnalysis;
}

Example 2 with SupportPredictor

use of edu.neu.ccs.pyramid.multilabel_classification.predictor.SupportPredictor in project pyramid by cheng-li.

the class BRInspector method analyzePrediction.

public static MultiLabelPredictionAnalysis analyzePrediction(MultiLabelClassifier.ClassProbEstimator classProbEstimator, LabelCalibrator labelCalibrator, VectorCalibrator setCalibrator, MultiLabelClfDataSet dataSet, MultiLabelClassifier classifier, PredictionFeatureExtractor predictionFeatureExtractor, int dataPointIndex, int ruleLimit, int labelSetLimit, double classProbThreshold) {
    MultiLabelPredictionAnalysis predictionAnalysis = new MultiLabelPredictionAnalysis();
    LabelTranslator labelTranslator = dataSet.getLabelTranslator();
    IdTranslator idTranslator = dataSet.getIdTranslator();
    predictionAnalysis.setInternalId(dataPointIndex);
    predictionAnalysis.setId(idTranslator.toExtId(dataPointIndex));
    predictionAnalysis.setInternalLabels(dataSet.getMultiLabels()[dataPointIndex].getMatchedLabelsOrdered());
    List<String> labels = dataSet.getMultiLabels()[dataPointIndex].getMatchedLabelsOrdered().stream().map(labelTranslator::toExtLabel).collect(Collectors.toList());
    predictionAnalysis.setLabels(labels);
    double[] classProbs = classProbEstimator.predictClassProbs(dataSet.getRow(dataPointIndex));
    double[] calibratedClassProbs = labelCalibrator.calibratedClassProbs(classProbs);
    PredictionCandidate trueCandidate = new PredictionCandidate();
    trueCandidate.x = dataSet.getRow(dataPointIndex);
    trueCandidate.multiLabel = dataSet.getMultiLabels()[dataPointIndex];
    trueCandidate.labelProbs = calibratedClassProbs;
    predictionAnalysis.setProbForTrueLabels(setCalibrator.calibrate(predictionFeatureExtractor.extractFeatures(trueCandidate)));
    MultiLabel predictedLabels = classifier.predict(dataSet.getRow(dataPointIndex));
    List<Integer> internalPrediction = predictedLabels.getMatchedLabelsOrdered();
    predictionAnalysis.setInternalPrediction(internalPrediction);
    List<String> prediction = internalPrediction.stream().map(labelTranslator::toExtLabel).collect(Collectors.toList());
    predictionAnalysis.setPrediction(prediction);
    PredictionCandidate predictedCandidate = new PredictionCandidate();
    predictedCandidate.x = dataSet.getRow(dataPointIndex);
    predictedCandidate.multiLabel = predictedLabels;
    predictedCandidate.labelProbs = calibratedClassProbs;
    predictionAnalysis.setProbForPredictedLabels(setCalibrator.calibrate(predictionFeatureExtractor.extractFeatures(predictedCandidate)));
    List<Integer> classes = new ArrayList<Integer>();
    for (int k = 0; k < classProbEstimator.getNumClasses(); k++) {
        if (calibratedClassProbs[k] >= classProbThreshold || dataSet.getMultiLabels()[dataPointIndex].matchClass(k) || predictedLabels.matchClass(k)) {
            classes.add(k);
        }
    }
    // todo
    List<ClassScoreCalculation> classScoreCalculations = new ArrayList<>();
    for (int k : classes) {
        ClassScoreCalculation classScoreCalculation = null;
        if (classProbEstimator instanceof IMLGradientBoosting) {
            classScoreCalculation = decisionProcess((IMLGradientBoosting) classProbEstimator, labelTranslator, calibratedClassProbs[k], dataSet.getRow(dataPointIndex), k, ruleLimit);
        }
        if (classProbEstimator instanceof CBM) {
            classScoreCalculation = decisionProcess((CBM) classProbEstimator, labelTranslator, calibratedClassProbs[k], dataSet.getRow(dataPointIndex), k, ruleLimit);
        }
        classScoreCalculations.add(classScoreCalculation);
    }
    predictionAnalysis.setClassScoreCalculations(classScoreCalculations);
    List<MultiLabelPredictionAnalysis.ClassRankInfo> labelRanking = classes.stream().map(label -> {
        MultiLabelPredictionAnalysis.ClassRankInfo rankInfo = new MultiLabelPredictionAnalysis.ClassRankInfo();
        rankInfo.setClassIndex(label);
        rankInfo.setClassName(labelTranslator.toExtLabel(label));
        rankInfo.setProb(calibratedClassProbs[label]);
        return rankInfo;
    }).collect(Collectors.toList());
    predictionAnalysis.setPredictedRanking(labelRanking);
    List<Pair<MultiLabel, Double>> topK;
    if (classifier instanceof SupportPredictor) {
        topK = TopKFinder.topKinSupport(dataSet.getRow(dataPointIndex), classProbEstimator, labelCalibrator, setCalibrator, predictionFeatureExtractor, ((SupportPredictor) classifier).getSupport(), labelSetLimit);
    } else {
        topK = TopKFinder.topK(dataSet.getRow(dataPointIndex), classProbEstimator, labelCalibrator, setCalibrator, predictionFeatureExtractor, labelSetLimit);
    }
    List<MultiLabelPredictionAnalysis.LabelSetProbInfo> labelSetRanking = topK.stream().map(pair -> {
        MultiLabel multiLabel = pair.getFirst();
        double setProb = pair.getSecond();
        MultiLabelPredictionAnalysis.LabelSetProbInfo labelSetProbInfo = new MultiLabelPredictionAnalysis.LabelSetProbInfo(multiLabel, setProb, labelTranslator);
        return labelSetProbInfo;
    }).sorted(Comparator.comparing(MultiLabelPredictionAnalysis.LabelSetProbInfo::getProbability).reversed()).limit(labelSetLimit).collect(Collectors.toList());
    predictionAnalysis.setPredictedLabelSetRanking(labelSetRanking);
    return predictionAnalysis;
}