Examples with RegressionPriors org.knime.base.node.mine.treeensemble2.data.RegressionPriors used on opensource projects

Example 11 with RegressionPriors

use of org.knime.base.node.mine.treeensemble2.data.RegressionPriors in project knime-core by knime.

the class TreeLearnerRegression method buildTreeNode.

private TreeNodeRegression buildTreeNode(final ExecutionMonitor exec, final int currentDepth, final DataMemberships dataMemberships, final ColumnSample columnSample, final TreeNodeSignature treeNodeSignature, final RegressionPriors targetPriors, final BitSet forbiddenColumnSet) throws CanceledExecutionException {
    final TreeData data = getData();
    final RandomData rd = getRandomData();
    final TreeEnsembleLearnerConfiguration config = getConfig();
    exec.checkCanceled();
    final SplitCandidate candidate = findBestSplitRegression(currentDepth, dataMemberships, columnSample, targetPriors, forbiddenColumnSet);
    if (candidate == null) {
        if (config instanceof GradientBoostingLearnerConfiguration) {
            TreeNodeRegression leaf = new TreeNodeRegression(treeNodeSignature, targetPriors, dataMemberships.getOriginalIndices());
            addToLeafList(leaf);
            return leaf;
        }
        return new TreeNodeRegression(treeNodeSignature, targetPriors);
    }
    final TreeTargetNumericColumnData targetColumn = (TreeTargetNumericColumnData) data.getTargetColumn();
    boolean useSurrogates = config.getMissingValueHandling() == MissingValueHandling.Surrogate;
    TreeNodeCondition[] childConditions;
    TreeNodeRegression[] childNodes;
    if (useSurrogates) {
        SurrogateSplit surrogateSplit = Surrogates.learnSurrogates(dataMemberships, candidate, data, columnSample, config, rd);
        childConditions = surrogateSplit.getChildConditions();
        BitSet[] childMarkers = surrogateSplit.getChildMarkers();
        assert childMarkers[0].cardinality() + childMarkers[1].cardinality() == dataMemberships.getRowCount() : "Sum of rows in children does not add up to number of rows in parent.";
        childNodes = new TreeNodeRegression[2];
        for (int i = 0; i < 2; i++) {
            DataMemberships childMemberships = dataMemberships.createChildMemberships(childMarkers[i]);
            TreeNodeSignature childSignature = getSignatureFactory().getChildSignatureFor(treeNodeSignature, (byte) i);
            ColumnSample childColumnSample = getColSamplingStrategy().getColumnSampleForTreeNode(childSignature);
            RegressionPriors childTargetPriors = targetColumn.getPriors(childMemberships, config);
            childNodes[i] = buildTreeNode(exec, currentDepth + 1, childMemberships, childColumnSample, childSignature, childTargetPriors, forbiddenColumnSet);
            childNodes[i].setTreeNodeCondition(childConditions[i]);
        }
    } else {
        SplitCandidate bestSplit = candidate;
        TreeAttributeColumnData splitColumn = bestSplit.getColumnData();
        final int attributeIndex = splitColumn.getMetaData().getAttributeIndex();
        boolean markAttributeAsForbidden = !bestSplit.canColumnBeSplitFurther();
        forbiddenColumnSet.set(attributeIndex, markAttributeAsForbidden);
        childConditions = bestSplit.getChildConditions();
        if (childConditions.length > Short.MAX_VALUE) {
            throw new RuntimeException("Too many children when splitting " + "attribute " + bestSplit.getColumnData() + " (maximum supported: " + Short.MAX_VALUE + "): " + childConditions.length);
        }
        childNodes = new TreeNodeRegression[childConditions.length];
        for (int i = 0; i < childConditions.length; i++) {
            TreeNodeCondition cond = childConditions[i];
            DataMemberships childMemberships = dataMemberships.createChildMemberships(splitColumn.updateChildMemberships(cond, dataMemberships));
            RegressionPriors childTargetPriors = targetColumn.getPriors(childMemberships, config);
            TreeNodeSignature childSignature = treeNodeSignature.createChildSignature((byte) i);
            ColumnSample childColumnSample = getColSamplingStrategy().getColumnSampleForTreeNode(childSignature);
            childNodes[i] = buildTreeNode(exec, currentDepth + 1, childMemberships, childColumnSample, childSignature, childTargetPriors, forbiddenColumnSet);
            childNodes[i].setTreeNodeCondition(cond);
        }
        if (markAttributeAsForbidden) {
            forbiddenColumnSet.set(attributeIndex, false);
        }
    }
    return new TreeNodeRegression(treeNodeSignature, targetPriors, childNodes);
}

Example 12 with RegressionPriors

use of org.knime.base.node.mine.treeensemble2.data.RegressionPriors in project knime-core by knime.

the class TreeLearnerRegression method findBestSplitRegression.

private SplitCandidate findBestSplitRegression(final int currentDepth, final DataMemberships dataMemberships, final ColumnSample columnSample, final RegressionPriors targetPriors, final BitSet forbiddenColumnSet) {
    final TreeData data = getData();
    final RandomData rd = getRandomData();
    final TreeEnsembleLearnerConfiguration config = getConfig();
    final int maxLevels = config.getMaxLevels();
    if (maxLevels != TreeEnsembleLearnerConfiguration.MAX_LEVEL_INFINITE && currentDepth >= maxLevels) {
        return null;
    }
    final int minNodeSize = config.getMinNodeSize();
    if (minNodeSize != TreeEnsembleLearnerConfiguration.MIN_NODE_SIZE_UNDEFINED) {
        if (targetPriors.getNrRecords() < minNodeSize) {
            return null;
        }
    }
    final double priorSquaredDeviation = targetPriors.getSumSquaredDeviation();
    if (priorSquaredDeviation < TreeColumnData.EPSILON) {
        return null;
    }
    final TreeTargetNumericColumnData targetColumn = getTargetData();
    SplitCandidate splitCandidate = null;
    if (currentDepth == 0 && config.getHardCodedRootColumn() != null) {
        final TreeAttributeColumnData rootColumn = data.getColumn(config.getHardCodedRootColumn());
        return rootColumn.calcBestSplitRegression(dataMemberships, targetPriors, targetColumn, rd);
    } else {
        double bestGainValue = 0.0;
        for (TreeAttributeColumnData col : columnSample) {
            if (forbiddenColumnSet.get(col.getMetaData().getAttributeIndex())) {
                continue;
            }
            SplitCandidate currentColSplit = col.calcBestSplitRegression(dataMemberships, targetPriors, targetColumn, rd);
            if (currentColSplit != null) {
                double gainValue = currentColSplit.getGainValue();
                if (gainValue > bestGainValue) {
                    bestGainValue = gainValue;
                    splitCandidate = currentColSplit;
                }
            }
        }
        return splitCandidate;
    }
}