Examples with PredictionAnalysis edu.neu.ccs.pyramid.classification.PredictionAnalysis used on opensource projects

Example 1 with PredictionAnalysis

use of edu.neu.ccs.pyramid.classification.PredictionAnalysis in project pyramid by cheng-li.

the class LogisticRegressionInspector method analyzePrediction.

public static PredictionAnalysis analyzePrediction(LogisticRegression logisticRegression, ClfDataSet dataSet, int dataPointIndex, int limit) {
    PredictionAnalysis predictionAnalysis = new PredictionAnalysis();
    IdTranslator idTranslator = dataSet.getIdTranslator();
    LabelTranslator labelTranslator = dataSet.getLabelTranslator();
    predictionAnalysis.setInternalId(dataPointIndex).setId(idTranslator.toExtId(dataPointIndex)).setInternalLabel(dataSet.getLabels()[dataPointIndex]).setLabel(labelTranslator.toExtLabel(dataSet.getLabels()[dataPointIndex]));
    int prediction = logisticRegression.predict(dataSet.getRow(dataPointIndex));
    predictionAnalysis.setInternalPrediction(prediction);
    predictionAnalysis.setPrediction(labelTranslator.toExtLabel(prediction));
    double[] probs = logisticRegression.predictClassProbs(dataSet.getRow(dataPointIndex));
    List<ClassProbability> classProbabilities = new ArrayList<>();
    for (int k = 0; k < probs.length; k++) {
        ClassProbability classProbability = new ClassProbability(k, labelTranslator.toExtLabel(k), probs[k]);
        classProbabilities.add(classProbability);
    }
    predictionAnalysis.setClassProbabilities(classProbabilities);
    List<ClassScoreCalculation> classScoreCalculations = new ArrayList<>();
    for (int k = 0; k < probs.length; k++) {
        ClassScoreCalculation classScoreCalculation = decisionProcess(logisticRegression, labelTranslator, dataSet.getRow(dataPointIndex), k, limit);
        classScoreCalculations.add(classScoreCalculation);
    }
    predictionAnalysis.setClassScoreCalculations(classScoreCalculations);
    return predictionAnalysis;
}

Example 2 with PredictionAnalysis

use of edu.neu.ccs.pyramid.classification.PredictionAnalysis 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);
}

Example 3 with PredictionAnalysis

use of edu.neu.ccs.pyramid.classification.PredictionAnalysis in project pyramid by cheng-li.

the class LKBInspector method analyzePrediction.

//todo  speed up
public static PredictionAnalysis analyzePrediction(LKBoost boosting, ClfDataSet dataSet, int dataPointIndex, int limit) {
    PredictionAnalysis predictionAnalysis = new PredictionAnalysis();
    IdTranslator idTranslator = dataSet.getIdTranslator();
    LabelTranslator labelTranslator = dataSet.getLabelTranslator();
    predictionAnalysis.setInternalId(dataPointIndex).setId(idTranslator.toExtId(dataPointIndex)).setInternalLabel(dataSet.getLabels()[dataPointIndex]).setLabel(labelTranslator.toExtLabel(dataSet.getLabels()[dataPointIndex]));
    int prediction = boosting.predict(dataSet.getRow(dataPointIndex));
    predictionAnalysis.setInternalPrediction(prediction);
    predictionAnalysis.setPrediction(labelTranslator.toExtLabel(prediction));
    double[] probs = boosting.predictClassProbs(dataSet.getRow(dataPointIndex));
    List<ClassProbability> classProbabilities = new ArrayList<>();
    for (int k = 0; k < probs.length; k++) {
        ClassProbability classProbability = new ClassProbability(k, labelTranslator.toExtLabel(k), probs[k]);
        classProbabilities.add(classProbability);
    }
    predictionAnalysis.setClassProbabilities(classProbabilities);
    List<ClassScoreCalculation> classScoreCalculations = new ArrayList<>();
    for (int k = 0; k < probs.length; k++) {
        ClassScoreCalculation classScoreCalculation = decisionProcess(boosting, labelTranslator, dataSet.getRow(dataPointIndex), k, limit);
        classScoreCalculations.add(classScoreCalculation);
    }
    predictionAnalysis.setClassScoreCalculations(classScoreCalculations);
    return predictionAnalysis;
}