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

Example 1 with ClassScoreCalculation

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

the class MLLogisticRegressionInspector method analyzePrediction.

public static MultiLabelPredictionAnalysis analyzePrediction(MLLogisticRegression logisticRegression, MultiLabelClfDataSet dataSet, int dataPointIndex, List<Integer> classes, int limit) {
    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);
    predictionAnalysis.setProbForTrueLabels(Double.NaN);
    MultiLabel predictedLabels = logisticRegression.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);
    predictionAnalysis.setProbForPredictedLabels(Double.NaN);
    List<ClassScoreCalculation> classScoreCalculations = new ArrayList<>();
    for (int k : classes) {
        ClassScoreCalculation classScoreCalculation = decisionProcess(logisticRegression, labelTranslator, dataSet.getRow(dataPointIndex), k, limit);
        classScoreCalculations.add(classScoreCalculation);
    }
    predictionAnalysis.setClassScoreCalculations(classScoreCalculations);
    return predictionAnalysis;
}

Example 2 with ClassScoreCalculation

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

the class MLLogisticRegressionInspector method decisionProcess.

public static ClassScoreCalculation decisionProcess(MLLogisticRegression logisticRegression, LabelTranslator labelTranslator, Vector vector, int classIndex, int limit) {
    ClassScoreCalculation classScoreCalculation = new ClassScoreCalculation(classIndex, labelTranslator.toExtLabel(classIndex), logisticRegression.predictClassScore(vector, classIndex));
    List<LinearRule> linearRules = new ArrayList<>();
    Rule bias = new ConstantRule(logisticRegression.getWeights().getBiasForClass(classIndex));
    classScoreCalculation.addRule(bias);
    for (int j = 0; j < logisticRegression.getNumFeatures(); j++) {
        Feature feature = logisticRegression.getFeatureList().get(j);
        double weight = logisticRegression.getWeights().getWeightsWithoutBiasForClass(classIndex).get(j);
        double featureValue = vector.get(j);
        double score = weight * featureValue;
        LinearRule rule = new LinearRule();
        rule.setFeature(feature);
        rule.setFeatureValue(featureValue);
        rule.setScore(score);
        rule.setWeight(weight);
        linearRules.add(rule);
    }
    Comparator<LinearRule> comparator = Comparator.comparing(decision -> Math.abs(decision.getScore()));
    List<LinearRule> sorted = linearRules.stream().sorted(comparator.reversed()).limit(limit).collect(Collectors.toList());
    for (LinearRule linearRule : sorted) {
        classScoreCalculation.addRule(linearRule);
    }
    return classScoreCalculation;
}

Example 3 with ClassScoreCalculation

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

the class LKTreeBoostTest method spam_load.

static void spam_load() throws Exception {
    System.out.println("loading ensemble");
    LKBoost lkBoost = LKBoost.deserialize(new File(TMP, "/LKTreeBoostTest/ensemble.ser"));
    ClfDataSet dataSet = TRECFormat.loadClfDataSet(new File(DATASETS, "/spam/trec_data/test.trec"), DataSetType.CLF_DENSE, true);
    System.out.println("test data:");
    System.out.println(dataSet.getMetaInfo());
    System.out.println(dataSet.getLabelTranslator());
    double accuracy = Accuracy.accuracy(lkBoost, dataSet);
    System.out.println(accuracy);
    System.out.println("auc = " + AUC.auc(lkBoost, dataSet));
    ConfusionMatrix confusionMatrix = new ConfusionMatrix(lkBoost, dataSet);
    System.out.println("confusion matrix:");
    System.out.println(confusionMatrix.printWithExtLabels());
    System.out.println("top featureList for class 0");
    System.out.println(LKBInspector.topFeatures(lkBoost, 0));
    System.out.println(new PerClassMeasures(confusionMatrix, 0));
    System.out.println(new PerClassMeasures(confusionMatrix, 1));
    System.out.println("macor-averaged:");
    System.out.println(new MacroAveragedMeasures(confusionMatrix));
    //        System.out.println(lkTreeBoost);
    int[] labels = dataSet.getLabels();
    List<double[]> classProbs = lkBoost.predictClassProbs(dataSet);
    for (int k = 0; k < dataSet.getNumClasses(); k++) {
        int numMatches = 0;
        double sumProbs = 0;
        for (int i = 0; i < dataSet.getNumDataPoints(); i++) {
            if (labels[i] == k) {
                numMatches += 1;
            }
            sumProbs += classProbs.get(i)[k];
        }
        System.out.println("for class " + k);
        System.out.println("number of matches =" + numMatches);
        System.out.println("sum of probs = " + sumProbs);
    }
    LabelTranslator labelTranslator = dataSet.getLabelTranslator();
    ClassScoreCalculation classScoreCalculation = LKBInspector.decisionProcess(lkBoost, labelTranslator, dataSet.getRow(0), 0, 10);
    ObjectMapper mapper = new ObjectMapper();
    mapper.writeValue(new File(TMP, "score_calculation.json"), classScoreCalculation);
    PredictionAnalysis predictionAnalysis = LKBInspector.analyzePrediction(lkBoost, dataSet, 0, 10);
    ObjectMapper mapper1 = new ObjectMapper();
    mapper1.writeValue(new File(TMP, "prediction_analysis.json"), predictionAnalysis);
}