Example usage for weka.classifiers Evaluation weightedMatthewsCorrelation

Introduction

In this page you can find the example usage for weka.classifiers Evaluation weightedMatthewsCorrelation.

Prototype

public double weightedMatthewsCorrelation() 

Source Link

Document

Calculates the weighted (by class size) matthews correlation coefficient.

Usage

From source file:adams.flow.core.EvaluationHelper.java

License:Open Source License

/**
 * Returns a statistical value from the evaluation object.
 *
 * @param eval   the evaluation object to get the value from
 * @param statistic   the type of value to return
 * @param classIndex   the class label index, for statistics like AUC
 * @return      the determined value, Double.NaN if not found
 * @throws Exception   if evaluation fails
 *///from w w w.j  a v  a  2 s .c  om
public static double getValue(Evaluation eval, EvaluationStatistic statistic, int classIndex) throws Exception {
    switch (statistic) {
    case NUMBER_CORRECT:
        return eval.correct();
    case NUMBER_INCORRECT:
        return eval.incorrect();
    case NUMBER_UNCLASSIFIED:
        return eval.unclassified();
    case PERCENT_CORRECT:
        return eval.pctCorrect();
    case PERCENT_INCORRECT:
        return eval.pctIncorrect();
    case PERCENT_UNCLASSIFIED:
        return eval.pctUnclassified();
    case KAPPA_STATISTIC:
        return eval.kappa();
    case MEAN_ABSOLUTE_ERROR:
        return eval.meanAbsoluteError();
    case ROOT_MEAN_SQUARED_ERROR:
        return eval.rootMeanSquaredError();
    case RELATIVE_ABSOLUTE_ERROR:
        return eval.relativeAbsoluteError();
    case ROOT_RELATIVE_SQUARED_ERROR:
        return eval.rootRelativeSquaredError();
    case CORRELATION_COEFFICIENT:
        return eval.correlationCoefficient();
    case SF_PRIOR_ENTROPY:
        return eval.SFPriorEntropy();
    case SF_SCHEME_ENTROPY:
        return eval.SFSchemeEntropy();
    case SF_ENTROPY_GAIN:
        return eval.SFEntropyGain();
    case SF_MEAN_PRIOR_ENTROPY:
        return eval.SFMeanPriorEntropy();
    case SF_MEAN_SCHEME_ENTROPY:
        return eval.SFMeanSchemeEntropy();
    case SF_MEAN_ENTROPY_GAIN:
        return eval.SFMeanEntropyGain();
    case KB_INFORMATION:
        return eval.KBInformation();
    case KB_MEAN_INFORMATION:
        return eval.KBMeanInformation();
    case KB_RELATIVE_INFORMATION:
        return eval.KBRelativeInformation();
    case TRUE_POSITIVE_RATE:
        return eval.truePositiveRate(classIndex);
    case NUM_TRUE_POSITIVES:
        return eval.numTruePositives(classIndex);
    case FALSE_POSITIVE_RATE:
        return eval.falsePositiveRate(classIndex);
    case NUM_FALSE_POSITIVES:
        return eval.numFalsePositives(classIndex);
    case TRUE_NEGATIVE_RATE:
        return eval.trueNegativeRate(classIndex);
    case NUM_TRUE_NEGATIVES:
        return eval.numTrueNegatives(classIndex);
    case FALSE_NEGATIVE_RATE:
        return eval.falseNegativeRate(classIndex);
    case NUM_FALSE_NEGATIVES:
        return eval.numFalseNegatives(classIndex);
    case IR_PRECISION:
        return eval.precision(classIndex);
    case IR_RECALL:
        return eval.recall(classIndex);
    case F_MEASURE:
        return eval.fMeasure(classIndex);
    case MATTHEWS_CORRELATION_COEFFICIENT:
        return eval.matthewsCorrelationCoefficient(classIndex);
    case AREA_UNDER_ROC:
        return eval.areaUnderROC(classIndex);
    case AREA_UNDER_PRC:
        return eval.areaUnderPRC(classIndex);
    case WEIGHTED_TRUE_POSITIVE_RATE:
        return eval.weightedTruePositiveRate();
    case WEIGHTED_FALSE_POSITIVE_RATE:
        return eval.weightedFalsePositiveRate();
    case WEIGHTED_TRUE_NEGATIVE_RATE:
        return eval.weightedTrueNegativeRate();
    case WEIGHTED_FALSE_NEGATIVE_RATE:
        return eval.weightedFalseNegativeRate();
    case WEIGHTED_IR_PRECISION:
        return eval.weightedPrecision();
    case WEIGHTED_IR_RECALL:
        return eval.weightedRecall();
    case WEIGHTED_F_MEASURE:
        return eval.weightedFMeasure();
    case WEIGHTED_MATTHEWS_CORRELATION_COEFFICIENT:
        return eval.weightedMatthewsCorrelation();
    case WEIGHTED_AREA_UNDER_ROC:
        return eval.weightedAreaUnderROC();
    case WEIGHTED_AREA_UNDER_PRC:
        return eval.weightedAreaUnderPRC();
    case UNWEIGHTED_MACRO_F_MEASURE:
        return eval.unweightedMacroFmeasure();
    case UNWEIGHTED_MICRO_F_MEASURE:
        return eval.unweightedMicroFmeasure();
    case BIAS:
        return eval.getPluginMetric(Bias.class.getName()).getStatistic(Bias.NAME);
    case RSQUARED:
        return eval.getPluginMetric(RSquared.class.getName()).getStatistic(RSquared.NAME);
    case SDR:
        return eval.getPluginMetric(SDR.class.getName()).getStatistic(SDR.NAME);
    case RPD:
        return eval.getPluginMetric(RPD.class.getName()).getStatistic(RPD.NAME);
    default:
        throw new IllegalArgumentException("Unhandled statistic field: " + statistic);
    }
}

---

From source file:net.sf.jclal.evaluation.measure.SingleLabelEvaluation.java

License:Open Source License

/**
 *
 * @param evaluation The evaluation//from w  ww.  j av a2  s.c  o m
 */
public void setEvaluation(Evaluation evaluation) {

    try {
        this.evaluation = evaluation;
        StringBuilder st = new StringBuilder();

        st.append("Iteration: ").append(getIteration()).append("\n");
        st.append("Labeled set size: ").append(getLabeledSetSize()).append("\n");
        st.append("Unlabelled set size: ").append(getUnlabeledSetSize()).append("\n");
        st.append("\t\n");

        st.append("Correctly Classified Instances: ").append(evaluation.pctCorrect()).append("\n");
        st.append("Incorrectly Classified Instances: ").append(evaluation.pctIncorrect()).append("\n");
        st.append("Kappa statistic: ").append(evaluation.kappa()).append("\n");
        st.append("Mean absolute error: ").append(evaluation.meanAbsoluteError()).append("\n");
        st.append("Root mean squared error: ").append(evaluation.rootMeanSquaredError()).append("\n");

        st.append("Relative absolute error: ").append(evaluation.relativeAbsoluteError()).append("\n");
        st.append("Root relative squared error: ").append(evaluation.rootRelativeSquaredError()).append("\n");
        st.append("Coverage of cases: ").append(evaluation.coverageOfTestCasesByPredictedRegions())
                .append("\n");
        st.append("Mean region size: ").append(evaluation.sizeOfPredictedRegions()).append("\n");

        st.append("Weighted Precision: ").append(evaluation.weightedPrecision()).append("\n");
        st.append("Weighted Recall: ").append(evaluation.weightedRecall()).append("\n");
        st.append("Weighted FMeasure: ").append(evaluation.weightedFMeasure()).append("\n");
        st.append("Weighted TruePositiveRate: ").append(evaluation.weightedTruePositiveRate()).append("\n");
        st.append("Weighted FalsePositiveRate: ").append(evaluation.weightedFalsePositiveRate()).append("\n");
        st.append("Weighted MatthewsCorrelation: ").append(evaluation.weightedMatthewsCorrelation())
                .append("\n");
        st.append("Weighted AreaUnderROC: ").append(evaluation.weightedAreaUnderROC()).append("\n");
        st.append("Weighted AreaUnderPRC: ").append(evaluation.weightedAreaUnderPRC()).append("\n");

        st.append("\t\t\n");

        loadMetrics(st.toString());

    } catch (Exception e) {
        Logger.getLogger(SingleLabelEvaluation.class.getName()).log(Level.SEVERE, null, e);
    }
}

---