Examples with TreeNodeRegression org.knime.base.node.mine.treeensemble2.model.TreeNodeRegression used on opensource projects

Example 1 with TreeNodeRegression

use of org.knime.base.node.mine.treeensemble2.model.TreeNodeRegression in project knime-core by knime.

the class TreeLearnerRegression method learnSingleTree.

/**
 * {@inheritDoc}
 */
@Override
public TreeModelRegression learnSingleTree(final ExecutionMonitor exec, final RandomData rd) throws CanceledExecutionException {
    final TreeTargetNumericColumnData targetColumn = getTargetData();
    final TreeData data = getData();
    final RowSample rowSampling = getRowSampling();
    final TreeEnsembleLearnerConfiguration config = getConfig();
    final IDataIndexManager indexManager = getIndexManager();
    DataMemberships rootDataMemberships = new RootDataMemberships(rowSampling, data, indexManager);
    RegressionPriors targetPriors = targetColumn.getPriors(rootDataMemberships, config);
    BitSet forbiddenColumnSet = new BitSet(data.getNrAttributes());
    boolean isGradientBoosting = config instanceof GradientBoostingLearnerConfiguration;
    if (isGradientBoosting) {
        m_leafs = new ArrayList<TreeNodeRegression>();
    }
    final TreeNodeSignature rootSignature = TreeNodeSignature.ROOT_SIGNATURE;
    final ColumnSample rootColumnSample = getColSamplingStrategy().getColumnSampleForTreeNode(rootSignature);
    TreeNodeRegression rootNode = buildTreeNode(exec, 0, rootDataMemberships, rootColumnSample, getSignatureFactory().getRootSignature(), targetPriors, forbiddenColumnSet);
    assert forbiddenColumnSet.cardinality() == 0;
    rootNode.setTreeNodeCondition(TreeNodeTrueCondition.INSTANCE);
    if (isGradientBoosting) {
        return new TreeModelRegression(rootNode, m_leafs);
    }
    return new TreeModelRegression(rootNode);
}

Example 2 with TreeNodeRegression

use of org.knime.base.node.mine.treeensemble2.model.TreeNodeRegression in project knime-core by knime.

the class LKGradientBoostedTreesLearner method calculateCoefficientMap.

private Map<TreeNodeSignature, Double> calculateCoefficientMap(final TreeModelRegression tree, final TreeData pseudoResiduals, final double numClasses) {
    final List<TreeNodeRegression> leafs = tree.getLeafs();
    final Map<TreeNodeSignature, Double> coefficientMap = new HashMap<TreeNodeSignature, Double>();
    final TreeTargetNumericColumnData pseudoTarget = (TreeTargetNumericColumnData) pseudoResiduals.getTargetColumn();
    double learningRate = getConfig().getLearningRate();
    for (TreeNodeRegression leaf : leafs) {
        final int[] indices = leaf.getRowIndicesInTreeData();
        double sumTop = 0;
        double sumBottom = 0;
        for (int index : indices) {
            double val = pseudoTarget.getValueFor(index);
            sumTop += val;
            double absVal = Math.abs(val);
            sumBottom += Math.abs(absVal) * (1 - Math.abs(absVal));
        }
        final double coefficient = (numClasses - 1) / numClasses * (sumTop / sumBottom);
        coefficientMap.put(leaf.getSignature(), learningRate * coefficient);
    }
    return coefficientMap;
}

Example 3 with TreeNodeRegression

use of org.knime.base.node.mine.treeensemble2.model.TreeNodeRegression in project knime-core by knime.

the class TreeModelPMMLTranslator method addTreeNode.

/**
 * @param pmmlNode
 * @param node
 */
private void addTreeNode(final Node pmmlNode, final AbstractTreeNode node) {
    int index = m_nodeIndex++;
    pmmlNode.setId(Integer.toString(index));
    if (node instanceof TreeNodeClassification) {
        final TreeNodeClassification clazNode = (TreeNodeClassification) node;
        pmmlNode.setScore(clazNode.getMajorityClassName());
        float[] targetDistribution = clazNode.getTargetDistribution();
        NominalValueRepresentation[] targetVals = clazNode.getTargetMetaData().getValues();
        double sum = 0.0;
        for (Float v : targetDistribution) {
            sum += v;
        }
        pmmlNode.setRecordCount(sum);
        // adding score distribution (class counts)
        for (int i = 0; i < targetDistribution.length; i++) {
            String className = targetVals[i].getNominalValue();
            double freq = targetDistribution[i];
            ScoreDistribution pmmlScoreDist = pmmlNode.addNewScoreDistribution();
            pmmlScoreDist.setValue(className);
            pmmlScoreDist.setRecordCount(freq);
        }
    } else if (node instanceof TreeNodeRegression) {
        final TreeNodeRegression regNode = (TreeNodeRegression) node;
        pmmlNode.setScore(Double.toString(regNode.getMean()));
    }
    TreeNodeCondition condition = node.getCondition();
    if (condition instanceof TreeNodeTrueCondition) {
        pmmlNode.addNewTrue();
    } else if (condition instanceof TreeNodeColumnCondition) {
        final TreeNodeColumnCondition colCondition = (TreeNodeColumnCondition) condition;
        handleColumnCondition(colCondition, pmmlNode);
    } else if (condition instanceof AbstractTreeNodeSurrogateCondition) {
        final AbstractTreeNodeSurrogateCondition surrogateCond = (AbstractTreeNodeSurrogateCondition) condition;
        setValuesFromPMMLCompoundPredicate(pmmlNode.addNewCompoundPredicate(), surrogateCond.toPMMLPredicate());
    } else {
        throw new IllegalStateException("Unsupported condition (not " + "implemented): " + condition.getClass().getSimpleName());
    }
    for (int i = 0; i < node.getNrChildren(); i++) {
        addTreeNode(pmmlNode.addNewNode(), node.getChild(i));
    }
}

Example 4 with TreeNodeRegression

use of org.knime.base.node.mine.treeensemble2.model.TreeNodeRegression in project knime-core by knime.

the class AbstractRegressionContentParser method createNode.

@Override
public final TreeNodeRegression createNode(final Node node, final TargetColumnHelper<TreeTargetNumericColumnMetaData> targetHelper, final TreeNodeSignature signature, final List<TreeNodeRegression> children) {
    double mean = Double.parseDouble(node.getScore());
    OptionalDouble totalSum = node.getExtensionList().stream().filter(e -> e.getName().equals(TranslationUtil.TOTAL_SUM_KEY)).mapToDouble(e -> Double.parseDouble(e.getValue())).findFirst();
    OptionalDouble sumSquaredDeviation = node.getExtensionList().stream().filter(e -> e.getName().equals(TranslationUtil.SUM_SQUARED_DEVIATION_KEY)).mapToDouble(e -> Double.parseDouble(e.getValue())).findFirst();
    return createNodeInternal(node, targetHelper.getMetaData(), signature, mean, totalSum.orElse(-1), sumSquaredDeviation.orElse(-1), children.toArray(new TreeNodeRegression[children.size()]));
}

Example 5 with TreeNodeRegression

use of org.knime.base.node.mine.treeensemble2.model.TreeNodeRegression in project knime-core by knime.

the class AbstractGBTModelImporter method readTreeModel.

private Pair<TreeModelRegression, Map<TreeNodeSignature, Double>> readTreeModel(final Segment segment) {
    GBTRegressionContentParser contentParser = new GBTRegressionContentParser();
    TreeModelImporter<TreeNodeRegression, TreeModelRegression, TreeTargetNumericColumnMetaData> treeImporter = new TreeModelImporter<TreeNodeRegression, TreeModelRegression, TreeTargetNumericColumnMetaData>(m_metaDataMapper, m_conditionParser, m_signatureFactory, contentParser, m_treeFactory);
    TreeModel treeModel = segment.getTreeModel();
    TreeModelRegression tree = treeImporter.importFromPMML(treeModel);
    Map<TreeNodeSignature, Double> coefficientMap = contentParser.getCoefficientMap();
    return new Pair<>(tree, coefficientMap);
}