Examples with DecisionTree org.knime.base.node.mine.decisiontree2.model.DecisionTree used on opensource projects

Example 11 with DecisionTree

use of org.knime.base.node.mine.decisiontree2.model.DecisionTree in project knime-core by knime.

the class Pruner method mdlPruning.

/**
 * Prunes a {@link DecisionTree} according to the minimum description lenght
 * (MDL) principle.
 *
 * @param decTree the decision tree to prune
 */
public static void mdlPruning(final DecisionTree decTree) {
    // traverse the tree depth first (in-fix)
    DecisionTreeNode root = decTree.getRootNode();
    mdlPruningRecurse(root);
}

Example 12 with DecisionTree

use of org.knime.base.node.mine.decisiontree2.model.DecisionTree in project knime-core by knime.

the class DecTreePredictorNodeModel method execute.

/**
 * {@inheritDoc}
 */
@Override
public PortObject[] execute(final PortObject[] inPorts, final ExecutionContext exec) throws CanceledExecutionException, Exception {
    exec.setMessage("Decision Tree Predictor: Loading predictor...");
    PMMLPortObject port = (PMMLPortObject) inPorts[INMODELPORT];
    List<Node> models = port.getPMMLValue().getModels(PMMLModelType.TreeModel);
    if (models.isEmpty()) {
        String msg = "Decision Tree evaluation failed: " + "No tree model found.";
        LOGGER.error(msg);
        throw new RuntimeException(msg);
    }
    PMMLDecisionTreeTranslator trans = new PMMLDecisionTreeTranslator();
    port.initializeModelTranslator(trans);
    DecisionTree decTree = trans.getDecisionTree();
    decTree.resetColorInformation();
    BufferedDataTable inData = (BufferedDataTable) inPorts[INDATAPORT];
    // get column with color information
    String colorColumn = null;
    for (DataColumnSpec s : inData.getDataTableSpec()) {
        if (s.getColorHandler() != null) {
            colorColumn = s.getName();
            break;
        }
    }
    decTree.setColorColumn(colorColumn);
    exec.setMessage("Decision Tree Predictor: start execution.");
    PortObjectSpec[] inSpecs = new PortObjectSpec[] { inPorts[0].getSpec(), inPorts[1].getSpec() };
    DataTableSpec outSpec = createOutTableSpec(inSpecs);
    BufferedDataContainer outData = exec.createDataContainer(outSpec);
    long coveredPattern = 0;
    long nrPattern = 0;
    long rowCount = 0;
    final long numberRows = inData.size();
    exec.setMessage("Classifying...");
    List<String> predictionValues = getPredictionStrings((PMMLPortObjectSpec) inPorts[INMODELPORT].getSpec());
    for (DataRow thisRow : inData) {
        DataCell cl = null;
        LinkedHashMap<String, Double> classDistrib = null;
        try {
            Pair<DataCell, LinkedHashMap<DataCell, Double>> pair = decTree.getWinnerAndClasscounts(thisRow, inData.getDataTableSpec());
            cl = pair.getFirst();
            LinkedHashMap<DataCell, Double> classCounts = pair.getSecond();
            classDistrib = getDistribution(classCounts);
            if (coveredPattern < m_maxNumCoveredPattern.getIntValue()) {
                // remember this one for HiLite support
                decTree.addCoveredPattern(thisRow, inData.getDataTableSpec());
                coveredPattern++;
            } else {
                // too many patterns for HiLite - at least remember color
                decTree.addCoveredColor(thisRow, inData.getDataTableSpec());
            }
            nrPattern++;
        } catch (Exception e) {
            LOGGER.error("Decision Tree evaluation failed: " + e.getMessage());
            throw e;
        }
        if (cl == null) {
            LOGGER.error("Decision Tree evaluation failed: result empty");
            throw new Exception("Decision Tree evaluation failed.");
        }
        DataCell[] newCells = new DataCell[outSpec.getNumColumns()];
        int numInCells = thisRow.getNumCells();
        for (int i = 0; i < numInCells; i++) {
            newCells[i] = thisRow.getCell(i);
        }
        if (m_showDistribution.getBooleanValue()) {
            assert predictionValues.size() >= newCells.length - 1 - numInCells : "Could not determine the prediction values: " + newCells.length + "; " + numInCells + "; " + predictionValues;
            for (int i = numInCells; i < newCells.length - 1; i++) {
                String predClass = predictionValues.get(i - numInCells);
                if (classDistrib != null && classDistrib.get(predClass) != null) {
                    newCells[i] = new DoubleCell(classDistrib.get(predClass));
                } else {
                    newCells[i] = new DoubleCell(0.0);
                }
            }
        }
        newCells[newCells.length - 1] = cl;
        outData.addRowToTable(new DefaultRow(thisRow.getKey(), newCells));
        rowCount++;
        if (rowCount % 100 == 0) {
            exec.setProgress(rowCount / (double) numberRows, "Classifying... Row " + rowCount + " of " + numberRows);
        }
        exec.checkCanceled();
    }
    if (coveredPattern < nrPattern) {
        // let the user know that we did not store all available pattern
        // for HiLiting.
        this.setWarningMessage("Tree only stored first " + m_maxNumCoveredPattern.getIntValue() + " (of " + nrPattern + ") rows for HiLiting!");
    }
    outData.close();
    m_decTree = decTree;
    exec.setMessage("Decision Tree Predictor: end execution.");
    return new BufferedDataTable[] { outData.getTable() };
}

Example 13 with DecisionTree

use of org.knime.base.node.mine.decisiontree2.model.DecisionTree in project knime-core by knime.

the class DecTreeLearnerGraphView2 method modelChanged.

/**
 * {@inheritDoc}
 */
@Override
protected void modelChanged() {
    DecisionTreeLearnerNodeModel2 model = this.getNodeModel();
    if (model != null) {
        m_hiLiteHdl.removeHiLiteListener(this);
        DecisionTree dt = model.getDecisionTree();
        if (dt != null) {
            m_graph.setColorColumn(model.getDecisionTree().getColorColumn());
            m_graph.setRootNode(dt.getRootNode());
            // retrieve HiLiteHandler from Input port
            m_hiLiteHdl = model.getInHiLiteHandler(DecisionTreeLearnerNodeModel2.DATA_INPORT);
            // and adjust menu entries for HiLite-ing
            m_hiLiteMenu.setEnabled(m_hiLiteHdl != null);
            m_hiLiteHdl.addHiLiteListener(this);
            recreateHiLite();
        } else {
            m_graph.setColorColumn(null);
            m_graph.setRootNode(null);
        }
    }
}

Example 14 with DecisionTree

use of org.knime.base.node.mine.decisiontree2.model.DecisionTree in project knime-core by knime.

the class DecTreePredictorNodeView method modelChanged.

/**
 * {@inheritDoc}
 */
@Override
protected void modelChanged() {
    DecTreePredictorNodeModel model = this.getNodeModel();
    DecisionTree dt = model.getDecisionTree();
    if (dt != null) {
        // set new model
        m_jTree.setModel(new DefaultTreeModel(dt.getRootNode()));
        // change default renderer
        m_jTree.setCellRenderer(new DecisionTreeNodeRenderer());
        // make sure no default height is assumed (the renderer's
        // preferred size should be used instead)
        m_jTree.setRowHeight(0);
        // retrieve HiLiteHandler from Input port
        m_hiLiteHdl = model.getInHiLiteHandler(DecTreePredictorNodeModel.INDATAPORT);
        // and adjust menu entries for HiLite-ing
        m_hiLiteMenu.setEnabled(m_hiLiteHdl != null);
    } else {
        m_jTree.setModel(null);
    }
}

Example 15 with DecisionTree

use of org.knime.base.node.mine.decisiontree2.model.DecisionTree in project knime-core by knime.

the class DecTreeNodeView method modelChanged.

/**
 * {@inheritDoc}
 */
@Override
protected void modelChanged() {
    NodeModel model = this.getNodeModel();
    DecisionTree dt = ((DecisionTreeLearnerNodeModel) model).getDecisionTree();
    if (dt != null) {
        // set new model
        m_jTree.setModel(new DefaultTreeModel(dt.getRootNode()));
        // change default renderer
        m_jTree.setCellRenderer(new DecisionTreeNodeRenderer());
        // make sure no default height is assumed (the renderer's
        // preferred size should be used instead)
        m_jTree.setRowHeight(0);
        // retrieve HiLiteHandler from Input port
        m_hiLiteHdl = (((DecisionTreeLearnerNodeModel) model).getInHiLiteHandler(DecisionTreeLearnerNodeModel.DATA_INPORT));
        // and adjust menu entries for HiLite-ing
        m_hiLiteMenu.setEnabled(m_hiLiteHdl != null);
    } else {
        m_jTree.setModel(null);
    }
}