Example usage for org.apache.commons.math.stat.correlation SpearmansCorrelation getRankCorrelation

Introduction

In this page you can find the example usage for org.apache.commons.math.stat.correlation SpearmansCorrelation getRankCorrelation.

Prototype

public PearsonsCorrelation getRankCorrelation() 

Source Link

Document

Returns a PearsonsCorrelation instance constructed from the ranked input data.

Usage

From source file:de.tudarmstadt.ukp.experiments.argumentation.convincingness.svmlib.regression.SVMLibRegressionExperimentRunner.java

@Override
public void runCrossValidation(File dataDir) throws IOException {
    Set<SinglePrediction> allPredictions = new HashSet<SinglePrediction>();

    List<File> files = new ArrayList<File>(FileUtils.listFiles(dataDir, new String[] { "libsvm.txt" }, false));

    for (File testFile : files) {
        // create training files from the rest
        Set<File> trainingFiles = new HashSet<File>(files);
        trainingFiles.remove(testFile);//from  w  w w  . j  a  v a 2s.  co  m

        //            System.out.println("Training files: " + trainingFiles);
        System.out.println("Training files size: " + trainingFiles.size());
        System.out.println("Test file: " + testFile);

        Set<SinglePrediction> predictions = runSingleFold(testFile, new ArrayList<File>(trainingFiles));

        allPredictions.addAll(predictions);
    }

    List<SinglePrediction> predictions = new ArrayList<SinglePrediction>(allPredictions);

    double[][] matrix = new double[predictions.size()][];
    for (int i = 0; i < predictions.size(); i++) {
        SinglePrediction prediction = predictions.get(i);

        matrix[i] = new double[2];
        matrix[i][0] = Double.valueOf(prediction.getGold());
        matrix[i][1] = Double.valueOf(prediction.getPrediction());
    }

    PearsonsCorrelation pearsonsCorrelation = new PearsonsCorrelation(matrix);

    try {
        double pValue = pearsonsCorrelation.getCorrelationPValues().getEntry(0, 1);
        double correlation = pearsonsCorrelation.getCorrelationMatrix().getEntry(0, 1);

        System.out.println("Correlation: " + correlation);
        System.out.println("p-Value: " + pValue);
        System.out.println("Samples: " + predictions.size());

        SpearmansCorrelation sc = new SpearmansCorrelation(new Array2DRowRealMatrix(matrix));
        double pValSC = sc.getRankCorrelation().getCorrelationPValues().getEntry(0, 1);
        double corrSC = sc.getCorrelationMatrix().getEntry(0, 1);

        System.out.println("Spearman: " + corrSC + ", p-Val " + pValSC);
    } catch (MathException e) {
        throw new IOException(e);
    }

}

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From source file:guineu.modules.dataanalysis.correlations.CorrelationTask.java

@Override
public void run() {
    setStatus(TaskStatus.PROCESSING);//from w w w  . j a v a2  s  .c om
    try {
        Dataset data1 = dataset[0];
        Dataset data2 = null;
        if (dataset.length == 1) {
            data2 = dataset[0];
        } else if (dataset.length > 1) {
            data2 = dataset[1];
        }
        Dataset newDataset = new SimpleBasicDataset("Correlation matrix");
        newDataset.addColumnName("ID");

        // Column names from the first dataset
        for (PeakListRow row : data1.getRows()) {
            String name = row.getID() + " - " + row.getName();
            newDataset.addColumnName(name);
            if (this.showPvalue) {
                newDataset.addColumnName(name + " - pValue");
            }
        }

        // Row names from the second dataset
        for (int i = 0; i < data2.getNumberRows(); i++) {
            PeakListRow row = data2.getRow(i);
            PeakListRow newRow = new SimplePeakListRowOther();
            newRow.setPeak("ID", row.getID() + " - " + row.getName());
            newDataset.addRow(newRow);
        }

        for (PeakListRow row : data1.getRows()) {
            String name = row.getID() + " - " + row.getName();
            String pValueName = name + " - pValue";
            for (int i = 0; i < data2.getNumberRows(); i++) {
                RealMatrix matrix = getCorrelation(row, data2.getRow(i), data1.getAllColumnNames());
                PearsonsCorrelation correlation = null;
                if (this.correlationType.contains("Pearsons")) {
                    correlation = new PearsonsCorrelation(matrix);
                } else if (this.correlationType.contains("Spearmans")) {
                    SpearmansCorrelation scorrelation = new SpearmansCorrelation(matrix);
                    correlation = scorrelation.getRankCorrelation();
                }
                if (correlation != null) {
                    double c = correlation.getCorrelationMatrix().getEntry(1, 0);
                    double p = correlation.getCorrelationPValues().getEntry(1, 0);
                    String cp = String.valueOf(c);

                    Color cell = Color.WHITE;
                    if (p < cutoff) {
                        if (c < 0) {
                            cell = Color.green;
                        } else {
                            cell = Color.red;
                        }

                    }
                    int pColumnIndex = 0;
                    if (this.showPvalue) {
                        newDataset.getRow(i).setPeak(pValueName, String.valueOf(p));
                        pColumnIndex = newDataset.getAllColumnNames().indexOf(pValueName) + 2;
                        newDataset.setCellColor(cell, i, pColumnIndex);
                    }

                    pColumnIndex = newDataset.getAllColumnNames().indexOf(name) + 2;
                    newDataset.getRow(i).setPeak(name, cp);

                    newDataset.setCellColor(cell, i, pColumnIndex);

                }

                if (this.correlationType.contains("Covariance")) {
                    Covariance covariance = new Covariance(matrix);
                    double c = covariance.getCovarianceMatrix().getEntry(1, 0);
                    newDataset.getRow(i).setPeak(name, String.valueOf(c));
                }
            }
        }

        GUIUtils.showNewTable(newDataset, true);
        setStatus(TaskStatus.FINISHED);
    } catch (Exception ex) {
        Logger.getLogger(CorrelationTask.class.getName()).log(Level.SEVERE, null, ex);
        setStatus(TaskStatus.ERROR);
    }
}

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From source file:guineu.modules.filter.Alignment.SerumHuNormalization.SerumHuNormalizationTask.java

private void searchBestFunction(Dataset data, PeakListRow comparedRow, int numBatches) {
    double bestValue = 0;
    int bestID = -1;
    List<String> names = data.getAllColumnNames();
    for (int i = 0; i < data.getNumberRows(); i++) {
        PeakListRow row = data.getRow(i);

        RealMatrix m = this.getMatrix(comparedRow, row, names);
        if (m != null && m.getData().length > 3) {
            SpearmansCorrelation scorrelation = new SpearmansCorrelation(m);
            PearsonsCorrelation correlation = scorrelation.getRankCorrelation();
            double value = correlation.getCorrelationMatrix().getEntry(1, 0);
            if (value > bestValue && functions.get(row.getID()) != null && row.getID() != comparedRow.getID()) {
                List<PolynomialSplineFunction> functionList = functions.get(row.getID());
                if (functionList.size() == numBatches) {
                    bestValue = value;//from   www .  j  a va2  s  .c o  m
                    bestID = row.getID();
                }
            }
        }

    }
    if (bestID > -1) {
        List<PolynomialSplineFunction> functionList = functions.get(bestID);
        functions.put(comparedRow.getID(), functionList);

    }

    this.correlated.put(comparedRow.getID(), bestID);
}

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