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

Example 11 with TreeAttributeColumnData

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

the class Surrogates method learnSurrogates.

/**
 * This function searches for splits in the remaining columns of <b>colSample</b>. It is doing so by taking the
 * directions (left or right) that are induced by the <b>bestSplit</b> as new target.
 *
 * @param dataMemberships provides information which rows are in the current branch
 * @param bestSplit the best split for the current node
 * @param oldData the TreeData object that contains all attributes and the target
 * @param colSample provides information which columns are to be considered as surrogates
 * @param config the configuration
 * @param rd
 * @return a SurrogateSplit that contains the conditions for both children
 */
public static SurrogateSplit learnSurrogates(final DataMemberships dataMemberships, final SplitCandidate bestSplit, final TreeData oldData, final ColumnSample colSample, final TreeEnsembleLearnerConfiguration config, final RandomData rd) {
    TreeAttributeColumnData bestSplitCol = bestSplit.getColumnData();
    TreeNodeCondition[] bestSplitChildConditions = bestSplit.getChildConditions();
    // calculate new Target
    BitSet bestSplitLeft = bestSplitCol.updateChildMemberships(bestSplitChildConditions[0], dataMemberships);
    BitSet bestSplitRight = bestSplitCol.updateChildMemberships(bestSplitChildConditions[1], dataMemberships);
    // create DataMemberships that only contains the instances that are not missed by bestSplit
    BitSet surrogateBitSet = (BitSet) bestSplitLeft.clone();
    surrogateBitSet.or(bestSplitRight);
    DataMemberships surrogateCalcDataMemberships = dataMemberships.createChildMemberships(surrogateBitSet);
    TreeTargetNominalColumnData newTarget = createNewTargetColumn(bestSplitLeft, bestSplitRight, oldData.getNrRows(), surrogateCalcDataMemberships);
    // find best splits on new target
    ArrayList<SplitCandidate> candidates = new ArrayList<SplitCandidate>();
    ClassificationPriors newTargetPriors = newTarget.getDistribution(surrogateCalcDataMemberships, config);
    for (TreeAttributeColumnData col : colSample) {
        if (col != bestSplitCol) {
            SplitCandidate candidate = col.calcBestSplitClassification(surrogateCalcDataMemberships, newTargetPriors, newTarget, rd);
            if (candidate != null) {
                candidates.add(candidate);
            }
        }
    }
    SplitCandidate[] candidatesWithBestAtHead = new SplitCandidate[candidates.size() + 1];
    candidatesWithBestAtHead[0] = bestSplit;
    for (int i = 1; i < candidatesWithBestAtHead.length; i++) {
        candidatesWithBestAtHead[i] = candidates.get(i - 1);
    }
    return calculateSurrogates(dataMemberships, candidatesWithBestAtHead);
}

Example 12 with TreeAttributeColumnData

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

the class TreeLearnerClassification method buildTreeNode.

private TreeNodeClassification buildTreeNode(final ExecutionMonitor exec, final int currentDepth, final DataMemberships dataMemberships, final ColumnSample columnSample, final TreeNodeSignature treeNodeSignature, final ClassificationPriors targetPriors, final BitSet forbiddenColumnSet) throws CanceledExecutionException {
    final TreeData data = getData();
    final TreeEnsembleLearnerConfiguration config = getConfig();
    exec.checkCanceled();
    final boolean useSurrogates = getConfig().getMissingValueHandling() == MissingValueHandling.Surrogate;
    TreeNodeCondition[] childConditions;
    boolean markAttributeAsForbidden = false;
    final TreeTargetNominalColumnData targetColumn = (TreeTargetNominalColumnData) data.getTargetColumn();
    TreeNodeClassification[] childNodes;
    int attributeIndex = -1;
    if (useSurrogates) {
        SplitCandidate[] candidates = findBestSplitsClassification(currentDepth, dataMemberships, columnSample, treeNodeSignature, targetPriors, forbiddenColumnSet);
        if (candidates == null) {
            return new TreeNodeClassification(treeNodeSignature, targetPriors, config);
        }
        SurrogateSplit surrogateSplit = Surrogates.learnSurrogates(dataMemberships, candidates[0], data, columnSample, config, getRandomData());
        childConditions = surrogateSplit.getChildConditions();
        BitSet[] childMarkers = surrogateSplit.getChildMarkers();
        childNodes = new TreeNodeClassification[2];
        for (int i = 0; i < 2; i++) {
            DataMemberships childMemberships = dataMemberships.createChildMemberships(childMarkers[i]);
            ClassificationPriors childTargetPriors = targetColumn.getDistribution(childMemberships, config);
            TreeNodeSignature childSignature = getSignatureFactory().getChildSignatureFor(treeNodeSignature, (byte) i);
            ColumnSample childColumnSample = getColSamplingStrategy().getColumnSampleForTreeNode(childSignature);
            childNodes[i] = buildTreeNode(exec, currentDepth + 1, childMemberships, childColumnSample, childSignature, childTargetPriors, forbiddenColumnSet);
            childNodes[i].setTreeNodeCondition(childConditions[i]);
        }
    } else {
        // handle non surrogate case
        SplitCandidate bestSplit = findBestSplitClassification(currentDepth, dataMemberships, columnSample, treeNodeSignature, targetPriors, forbiddenColumnSet);
        if (bestSplit == null) {
            return new TreeNodeClassification(treeNodeSignature, targetPriors, config);
        }
        TreeAttributeColumnData splitColumn = bestSplit.getColumnData();
        attributeIndex = splitColumn.getMetaData().getAttributeIndex();
        markAttributeAsForbidden = !bestSplit.canColumnBeSplitFurther();
        forbiddenColumnSet.set(attributeIndex, markAttributeAsForbidden);
        childConditions = bestSplit.getChildConditions();
        childNodes = new TreeNodeClassification[childConditions.length];
        if (childConditions.length > Short.MAX_VALUE) {
            throw new RuntimeException("Too many children when splitting " + "attribute " + bestSplit.getColumnData() + " (maximum supported: " + Short.MAX_VALUE + "): " + childConditions.length);
        }
        // Build child nodes
        for (int i = 0; i < childConditions.length; i++) {
            DataMemberships childMemberships = null;
            TreeNodeCondition cond = childConditions[i];
            childMemberships = dataMemberships.createChildMemberships(splitColumn.updateChildMemberships(cond, dataMemberships));
            ClassificationPriors childTargetPriors = targetColumn.getDistribution(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 TreeNodeClassification(treeNodeSignature, targetPriors, childNodes, getConfig());
}

Example 13 with TreeAttributeColumnData

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

the class AbstractGradientBoostingLearner method createPredictorRecord.

/**
 * Creates a PredictorRecord from the inMemory TreeData object
 *
 * @param data
 * @param indexManager
 * @param rowIdx
 * @return a PredictorRecord for the row at <b>rowIdx</b> in <b>data</b>
 */
public static PredictorRecord createPredictorRecord(final TreeData data, final IDataIndexManager indexManager, final int rowIdx) {
    Map<String, Object> valMap = new HashMap<String, Object>();
    for (TreeAttributeColumnData column : data.getColumns()) {
        TreeAttributeColumnMetaData meta = column.getMetaData();
        valMap.put(meta.getAttributeName(), handleMissingValues(column.getValueAt(indexManager.getPositionsInColumn(meta.getAttributeIndex())[rowIdx]), column));
    }
    return new PredictorRecord(valMap);
}

Example 14 with TreeAttributeColumnData

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

the class AllColumnSampleTest method testIterator.

@Test
public void testIterator() throws Exception {
    final TreeData data = createTreeData();
    AllColumnSample sample = new AllColumnSample(data);
    int i = 0;
    for (final TreeAttributeColumnData col : sample) {
        assertEquals("Returned wrong column.", data.getColumns()[i++], col);
    }
}

Example 15 with TreeAttributeColumnData

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

the class AllColumnSampleTest method testIteratorRemove.

@Test(expected = UnsupportedOperationException.class)
public void testIteratorRemove() throws Exception {
    final TreeData data = createTreeData();
    AllColumnSample sample = new AllColumnSample(data);
    Iterator<TreeAttributeColumnData> iterator = sample.iterator();
    iterator.remove();
}