Examples with DiscreteVariable edu.cmu.tetrad.data.DiscreteVariable used on opensource projects

Example 46 with DiscreteVariable

use of edu.cmu.tetrad.data.DiscreteVariable in project tetrad by cmu-phil.

the class TestGeneralBootstrapTest method makeDiscreteDAG.

private static Graph makeDiscreteDAG(int numVars, int numLatentConfounders, double edgesPerNode) {
    final int numEdges = (int) (numVars * edgesPerNode);
    // System.out.println("Making list of vars");
    List<Node> vars = new ArrayList<>();
    for (int i = 0; i < numVars; i++) {
        vars.add(new DiscreteVariable(Integer.toString(i)));
    }
    // System.out.println("Making dag");
    return GraphUtils.randomGraph(vars, numLatentConfounders, numEdges, 30, 15, 15, false);
}

Example 47 with DiscreteVariable

use of edu.cmu.tetrad.data.DiscreteVariable in project tetrad by cmu-phil.

the class BayesUpdaterClassifierEditor method showRocCurve.

private void showRocCurve() {
    int tabIndex = -1;
    for (int i = 0; i < getTabbedPane().getTabCount(); i++) {
        if ("ROC Plot".equals(getTabbedPane().getTitleAt(i))) {
            getTabbedPane().remove(i);
            tabIndex = i;
            this.rocPlot = null;
            this.saveRoc.setEnabled(false);
        }
    }
    double[][] marginals = getClassifier().getMarginals();
    int ncases = getClassifier().getNumCases();
    boolean[] inCategory = new boolean[ncases];
    DataSet testData = getClassifier().getTestData();
    DiscreteVariable targetVariable = classifier.getTargetVariable();
    String targetName = targetVariable.getName();
    Node variable2 = testData.getVariable(targetName);
    int varIndex = testData.getVariables().indexOf(variable2);
    // If the target is not in the data set, don't compute a ROC plot.
    if (varIndex == -1) {
        return;
    }
    String category = (String) getCategoryDropdown().getSelectedItem();
    DiscreteVariable variable = (DiscreteVariable) variable2;
    int catIndex = variable.getIndex(category);
    for (int i = 0; i < inCategory.length; i++) {
        inCategory[i] = (testData.getInt(i, varIndex) == catIndex);
    }
    double[] scores = marginals[catIndex];
    RocCalculator rocc = new RocCalculator(scores, inCategory, RocCalculator.ASCENDING);
    double[][] points = rocc.getScaledRocPlot();
    double area = rocc.getAuc();
    NumberFormat nf = NumberFormatUtil.getInstance().getNumberFormat();
    String info = "AUC = " + nf.format(area);
    String title = "ROC Plot, " + classifier.getTargetVariable() + " = " + category;
    RocPlot plot = new RocPlot(points, title, info);
    this.rocPlot = plot;
    this.saveRoc.setEnabled(true);
    if (tabIndex == -1) {
        getTabbedPane().add("ROC Plot", plot);
    } else {
        getTabbedPane().add(plot, tabIndex);
        getTabbedPane().setTitleAt(tabIndex, "ROC Plot");
    }
}

Example 48 with DiscreteVariable

use of edu.cmu.tetrad.data.DiscreteVariable in project tetrad by cmu-phil.

the class BayesUpdaterClassifierEditor method showClassification.

private void showClassification() {
    int tabIndex = -1;
    for (int i = 0; i < getTabbedPane().getTabCount(); i++) {
        if ("Classification".equals(getTabbedPane().getTitleAt(i))) {
            getTabbedPane().remove(i);
            tabIndex = i;
        }
    }
    // Put the class information into a DataSet.
    int[] classifications = getClassifier().getClassifications();
    double[][] marginals = getClassifier().getMarginals();
    int maxCategory = 0;
    for (int classification : classifications) {
        if (classification > maxCategory) {
            maxCategory = classification;
        }
    }
    List<Node> variables = new LinkedList<>();
    DiscreteVariable targetVariable = classifier.getTargetVariable();
    DiscreteVariable classVar = new DiscreteVariable(targetVariable.getName(), maxCategory + 1);
    variables.add(classVar);
    for (int i = 0; i < marginals.length; i++) {
        String name = "P(" + targetVariable + "=" + i + ")";
        ContinuousVariable scoreVar = new ContinuousVariable(name);
        variables.add(scoreVar);
    }
    classVar.setName("Result");
    DataSet dataSet = new ColtDataSet(classifications.length, variables);
    for (int i = 0; i < classifications.length; i++) {
        dataSet.setInt(i, 0, classifications[i]);
        for (int j = 0; j < marginals.length; j++) {
            dataSet.setDouble(i, j + 1, marginals[j][i]);
        }
    }
    DataDisplay jTable = new DataDisplay(dataSet);
    JScrollPane scroll = new JScrollPane(jTable);
    if (tabIndex == -1) {
        getTabbedPane().add("Classification", scroll);
    } else {
        getTabbedPane().add(scroll, tabIndex);
        getTabbedPane().setTitleAt(tabIndex, "Classification");
    }
}

Example 49 with DiscreteVariable

use of edu.cmu.tetrad.data.DiscreteVariable in project tetrad by cmu-phil.

the class ModeInterpolator method filter.

public DataSet filter(DataSet dataSet) {
    DataSet newDataSet = dataSet.copy();
    for (int j = 0; j < dataSet.getNumColumns(); j++) {
        Node var = dataSet.getVariable(j);
        if (var instanceof DiscreteVariable) {
            DiscreteVariable variable = (DiscreteVariable) var;
            int numCategories = variable.getNumCategories();
            int[] categoryCounts = new int[numCategories];
            for (int i = 0; i < dataSet.getNumRows(); i++) {
                if (dataSet.getInt(i, j) == DiscreteVariable.MISSING_VALUE) {
                    continue;
                }
                categoryCounts[dataSet.getInt(i, j)]++;
            }
            int mode = -1;
            int max = -1;
            for (int i = 0; i < numCategories; i++) {
                if (categoryCounts[i] > max) {
                    max = categoryCounts[i];
                    mode = i;
                }
            }
            for (int i = 0; i < dataSet.getNumRows(); i++) {
                if (dataSet.getInt(i, j) == DiscreteVariable.MISSING_VALUE) {
                    newDataSet.setInt(i, j, mode);
                }
            // else {
            // newDataSet.setInt(i, j, dataSet.getInt(i, j));
            // }
            }
        } else if (dataSet.getVariable(j) instanceof ContinuousVariable) {
            double[] data = new double[dataSet.getNumRows()];
            int k = -1;
            for (int i = 0; i < dataSet.getNumRows(); i++) {
                if (!Double.isNaN(dataSet.getDouble(i, j))) {
                    data[++k] = dataSet.getDouble(i, j);
                }
            }
            Arrays.sort(data);
            double mode = Double.NaN;
            if (k >= 0) {
                mode = (data[(k + 1) / 2] + data[k / 2]) / 2.d;
            }
            for (int i = 0; i < dataSet.getNumRows(); i++) {
                if (Double.isNaN(dataSet.getDouble(i, j))) {
                    newDataSet.setDouble(i, j, mode);
                }
            // else {
            // newDataSet.setDouble(i, j, dataSet.getDouble(i, j));
            // }
            }
        }
    }
    return newDataSet;
}

Example 50 with DiscreteVariable

use of edu.cmu.tetrad.data.DiscreteVariable in project tetrad by cmu-phil.

the class BayesPm method setNumCategories.

/**
 * Sets the number of values for the given node to the given number.
 */
public void setNumCategories(Node node, int numCategories) {
    if (!nodesToVariables.containsKey(node)) {
        throw new IllegalArgumentException("Node not in BayesPm: " + node);
    }
    if (numCategories < 1) {
        throw new IllegalArgumentException("Number of categories must be >= 1: " + numCategories);
    }
    DiscreteVariable variable = nodesToVariables.get(node);
    List<String> oldCategories = variable.getCategories();
    List<String> newCategories = new LinkedList<>();
    int min = Math.min(numCategories, oldCategories.size());
    for (int i = 0; i < min; i++) {
        newCategories.add(oldCategories.get(i));
    }
    for (int i = min; i < numCategories; i++) {
        String proposedName = DataUtils.defaultCategory(i);
        if (newCategories.contains(proposedName)) {
            throw new IllegalArgumentException("Default name already in " + "list of categories: " + proposedName);
        }
        newCategories.add(proposedName);
    }
    mapNodeToVariable(node, newCategories);
}