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

Example 1 with DecisionTreeNode

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

the class FromDecisionTreeNodeModel method addRules.

/**
 * Adds the rules to {@code rs} (recursively on each leaf).
 *
 * @param rs The output {@link RuleSet}.
 * @param parents The parent stack.
 * @param node The actual node.
 */
private void addRules(final RuleSet rs, final List<DecisionTreeNode> parents, final DecisionTreeNode node) {
    if (node.isLeaf()) {
        SimpleRule rule = rs.addNewSimpleRule();
        if (m_rulesToTable.getScorePmmlRecordCount().getBooleanValue()) {
            // This increases the PMML quite significantly
            BigDecimal sum = BigDecimal.ZERO;
            final MathContext mc = new MathContext(7, RoundingMode.HALF_EVEN);
            final boolean computeProbability = m_rulesToTable.getScorePmmlProbability().getBooleanValue();
            if (computeProbability) {
                sum = new BigDecimal(node.getClassCounts().entrySet().stream().mapToDouble(e -> e.getValue().doubleValue()).sum(), mc);
            }
            for (final Entry<DataCell, Double> entry : node.getClassCounts().entrySet()) {
                final ScoreDistribution scoreDistrib = rule.addNewScoreDistribution();
                scoreDistrib.setValue(entry.getKey().toString());
                scoreDistrib.setRecordCount(entry.getValue());
                if (computeProbability) {
                    if (Double.compare(entry.getValue().doubleValue(), 0.0) == 0) {
                        scoreDistrib.setProbability(new BigDecimal(0.0));
                    } else {
                        scoreDistrib.setProbability(new BigDecimal(entry.getValue().doubleValue(), mc).divide(sum, mc));
                    }
                }
            }
        }
        CompoundPredicate and = rule.addNewCompoundPredicate();
        and.setBooleanOperator(BooleanOperator.AND);
        DecisionTreeNode n = node;
        do {
            PMMLPredicate pmmlPredicate = ((DecisionTreeNodeSplitPMML) n.getParent()).getSplitPred()[n.getParent().getIndex(n)];
            if (pmmlPredicate instanceof PMMLSimplePredicate) {
                PMMLSimplePredicate simple = (PMMLSimplePredicate) pmmlPredicate;
                SimplePredicate predicate = and.addNewSimplePredicate();
                copy(predicate, simple);
            } else if (pmmlPredicate instanceof PMMLCompoundPredicate) {
                PMMLCompoundPredicate compound = (PMMLCompoundPredicate) pmmlPredicate;
                CompoundPredicate predicate = and.addNewCompoundPredicate();
                copy(predicate, compound);
            } else if (pmmlPredicate instanceof PMMLSimpleSetPredicate) {
                PMMLSimpleSetPredicate simpleSet = (PMMLSimpleSetPredicate) pmmlPredicate;
                copy(and.addNewSimpleSetPredicate(), simpleSet);
            } else if (pmmlPredicate instanceof PMMLTruePredicate) {
                and.addNewTrue();
            } else if (pmmlPredicate instanceof PMMLFalsePredicate) {
                and.addNewFalse();
            }
            n = n.getParent();
        } while (n.getParent() != null);
        // Simple fix for the case when a single condition was used.
        while (and.getFalseList().size() + and.getCompoundPredicateList().size() + and.getSimplePredicateList().size() + and.getSimpleSetPredicateList().size() + and.getTrueList().size() < 2) {
            and.addNewTrue();
        }
        if (m_rulesToTable.getProvideStatistics().getBooleanValue()) {
            rule.setNbCorrect(node.getOwnClassCount());
            rule.setRecordCount(node.getEntireClassCount());
        }
        rule.setScore(node.getMajorityClass().toString());
    } else {
        parents.add(node);
        for (int i = 0; i < node.getChildCount(); ++i) {
            addRules(rs, parents, node.getChildAt(i));
        }
        parents.remove(node);
    }
}

Example 2 with DecisionTreeNode

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

the class FromDecisionTreeNodeModel method execute.

/**
 * {@inheritDoc}
 * @throws CanceledExecutionException Execution cancelled.
 * @throws InvalidSettingsException No or more than one RuleSet model is in the PMML input.
 */
@Override
protected PortObject[] execute(final PortObject[] inData, final ExecutionContext exec) throws CanceledExecutionException, InvalidSettingsException {
    PMMLPortObject decTreeModel = (PMMLPortObject) inData[0];
    PMMLDecisionTreeTranslator treeTranslator = new PMMLDecisionTreeTranslator();
    decTreeModel.initializeModelTranslator(treeTranslator);
    DecisionTree decisionTree = treeTranslator.getDecisionTree();
    decisionTree.getRootNode();
    PMMLPortObject ruleSetModel = new PMMLPortObject(decTreeModel.getSpec());
    PMMLDocument document = PMMLDocument.Factory.newInstance();
    PMML pmml = document.addNewPMML();
    PMMLPortObjectSpec.writeHeader(pmml);
    pmml.setVersion(PMMLPortObject.PMML_V4_2);
    new PMMLDataDictionaryTranslator().exportTo(document, decTreeModel.getSpec());
    RuleSetModel newRuleSetModel = pmml.addNewRuleSetModel();
    PMMLMiningSchemaTranslator.writeMiningSchema(decTreeModel.getSpec(), newRuleSetModel);
    newRuleSetModel.setFunctionName(MININGFUNCTION.CLASSIFICATION);
    newRuleSetModel.setAlgorithmName("RuleSet");
    RuleSet ruleSet = newRuleSetModel.addNewRuleSet();
    ruleSet.addNewRuleSelectionMethod().setCriterion(Criterion.FIRST_HIT);
    addRules(ruleSet, new ArrayList<DecisionTreeNode>(), decisionTree.getRootNode());
    // TODO: Return a BufferedDataTable for each output port
    PMMLPortObject pmmlPortObject = new PMMLPortObject(ruleSetModel.getSpec(), document);
    return new PortObject[] { pmmlPortObject, new RuleSetToTable(m_rulesToTable).execute(exec, pmmlPortObject) };
}

Example 3 with DecisionTreeNode

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

the class DecTreePredictorNodeView method changeSelectedHiLite.

// /////////////////////////////
// routines for HiLite Support
// /////////////////////////////
/*
     * hilite or unhilite all items that are covered by currently selected
     * branches in the tree
     *
     * @param state if true hilite, otherwise unhilite selection
     */
private void changeSelectedHiLite(final boolean state) {
    TreePath[] selectedPaths = m_jTree.getSelectionPaths();
    if (selectedPaths == null) {
        // nothing selected
        return;
    }
    for (int i = 0; i < selectedPaths.length; i++) {
        assert (selectedPaths[i] != null);
        if (selectedPaths[i] == null) {
            return;
        }
        TreePath path = selectedPaths[i];
        Object lastNode = path.getLastPathComponent();
        assert (lastNode != null);
        assert (lastNode instanceof DecisionTreeNode);
        Set<RowKey> covPat = ((DecisionTreeNode) lastNode).coveredPattern();
        if (state) {
            m_hiLiteHdl.fireHiLiteEvent(covPat);
        } else {
            m_hiLiteHdl.fireUnHiLiteEvent(covPat);
        }
    }
}

Example 4 with DecisionTreeNode

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

the class RegressionTreeLearnerNodeView method updateHiLite.

// ///////////////////////////////////////////////////
private void updateHiLite(final boolean state) {
    DecisionTreeNode selected = m_graph.getSelected();
    if (selected == null) {
        return;
    }
    Set<RowKey> covPat = new HashSet<RowKey>();
    covPat.addAll(selected.coveredPattern());
    if (state) {
        m_hiLiteHdl.fireHiLiteEvent(covPat);
    } else {
        m_hiLiteHdl.fireUnHiLiteEvent(covPat);
    }
}

Example 5 with DecisionTreeNode

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

the class RegressionTreeLearnerNodeView method recreateHiLite.

private void recreateHiLite() {
    HiLiteHandler handler = m_hiLiteHdl;
    if (handler == null) {
        return;
    }
    Set<RowKey> hilited = handler.getHiLitKeys();
    Set<DecisionTreeNode> toHilite = new HashSet<DecisionTreeNode>();
    DecisionTreeNode root = m_graph.getRootNode();
    List<DecisionTreeNode> toProcess = new LinkedList<DecisionTreeNode>();
    if (null != root) {
        toProcess.add(0, root);
    }
    // Traverse the tree breadth first
    while (!toProcess.isEmpty()) {
        DecisionTreeNode curr = toProcess.remove(0);
        // cover any pattern
        if (curr.coveredPattern().isEmpty()) {
            continue;
        }
        if (hilited.containsAll(curr.coveredPattern())) {
            // hilite subtree starting from curr
            toHilite.addAll(getSubtree(curr));
        } else {
            for (int i = 0; i < curr.getChildCount(); i++) {
                toProcess.add(0, curr.getChildAt(i));
            }
        }
    }
    m_graph.hiLite(toHilite);
}