Examples with BitSplitCandidate org.knime.base.node.mine.treeensemble2.learner.BitSplitCandidate used on opensource projects

Example 1 with BitSplitCandidate

use of org.knime.base.node.mine.treeensemble2.learner.BitSplitCandidate in project knime-core by knime.

the class TreeBitVectorColumnData method calcBestSplitRegression.

/**
 * {@inheritDoc}
 */
@Override
public SplitCandidate calcBestSplitRegression(final DataMemberships dataMemberships, final RegressionPriors targetPriors, final TreeTargetNumericColumnData targetColumn, final RandomData rd) {
    final double ySumTotal = targetPriors.getYSum();
    final double nrRecordsTotal = targetPriors.getNrRecords();
    final double criterionTotal = ySumTotal * ySumTotal / nrRecordsTotal;
    final int minChildSize = getConfiguration().getMinChildSize();
    final ColumnMemberships columnMemberships = dataMemberships.getColumnMemberships(getMetaData().getAttributeIndex());
    double onWeights = 0.0;
    double offWeights = 0.0;
    double ySumOn = 0.0;
    double ySumOff = 0.0;
    while (columnMemberships.next()) {
        final double weight = columnMemberships.getRowWeight();
        if (weight < EPSILON) {
        // ignore record: not in current branch or not in sample
        } else {
            final double y = targetColumn.getValueFor(columnMemberships.getOriginalIndex());
            if (m_columnBitSet.get(columnMemberships.getIndexInColumn())) {
                onWeights += weight;
                ySumOn += weight * y;
            } else {
                offWeights += weight;
                ySumOff += weight * y;
            }
        }
    }
    if (onWeights < minChildSize || offWeights < minChildSize) {
        return null;
    }
    final double onCriterion = ySumOn * ySumOn / onWeights;
    final double offCriterion = ySumOff * ySumOff / offWeights;
    final double gain = onCriterion + offCriterion - criterionTotal;
    if (gain > 0) {
        return new BitSplitCandidate(this, gain);
    }
    return null;
}

Example 2 with BitSplitCandidate

use of org.knime.base.node.mine.treeensemble2.learner.BitSplitCandidate in project knime-core by knime.

the class TreeBitVectorColumnData method calcBestSplitClassification.

/**
 * {@inheritDoc}
 */
@Override
public SplitCandidate calcBestSplitClassification(final DataMemberships dataMemberships, final ClassificationPriors targetPriors, final TreeTargetNominalColumnData targetColumn, final RandomData rd) {
    final NominalValueRepresentation[] targetVals = targetColumn.getMetaData().getValues();
    final IImpurity impurityCriterion = targetPriors.getImpurityCriterion();
    final int minChildSize = getConfiguration().getMinChildSize();
    // distribution of target for On ('1') and Off ('0') bits
    final double[] onTargetWeights = new double[targetVals.length];
    final double[] offTargetWeights = new double[targetVals.length];
    double onWeights = 0.0;
    double offWeights = 0.0;
    final ColumnMemberships columnMemberships = dataMemberships.getColumnMemberships(getMetaData().getAttributeIndex());
    while (columnMemberships.next()) {
        final double weight = columnMemberships.getRowWeight();
        if (weight < EPSILON) {
            // ignore record: not in current branch or not in sample
            assert false : "This code should never be reached!";
        } else {
            final int target = targetColumn.getValueFor(columnMemberships.getOriginalIndex());
            if (m_columnBitSet.get(columnMemberships.getIndexInColumn())) {
                onWeights += weight;
                onTargetWeights[target] += weight;
            } else {
                offWeights += weight;
                offTargetWeights[target] += weight;
            }
        }
    }
    if (onWeights < minChildSize || offWeights < minChildSize) {
        return null;
    }
    final double weightSum = onWeights + offWeights;
    final double onImpurity = impurityCriterion.getPartitionImpurity(onTargetWeights, onWeights);
    final double offImpurity = impurityCriterion.getPartitionImpurity(offTargetWeights, offWeights);
    final double[] partitionWeights = new double[] { onWeights, offWeights };
    final double postSplitImpurity = impurityCriterion.getPostSplitImpurity(new double[] { onImpurity, offImpurity }, partitionWeights, weightSum);
    final double gainValue = impurityCriterion.getGain(targetPriors.getPriorImpurity(), postSplitImpurity, partitionWeights, weightSum);
    return new BitSplitCandidate(this, gainValue);
}