use of org.knime.base.node.mine.decisiontree2.PMMLPredicate in project knime-core by knime.
the class DecisionTreeLearnerNodeModel method buildTree.
/**
* Recursively induces the decision tree.
*
* @param table the {@link InMemoryTable} representing the data for this
* node to determine the split and after that perform
* partitioning
* @param exec the execution context for progress information
* @param depth the current recursion depth
*/
private DecisionTreeNode buildTree(final InMemoryTable table, final ExecutionContext exec, final int depth, final SplitQualityMeasure splitQualityMeasure, final ParallelProcessing parallelProcessing) throws CanceledExecutionException, IllegalAccessException {
exec.checkCanceled();
// derive this node's id from the counter
int nodeId = m_counter.getAndIncrement();
DataCell majorityClass = table.getMajorityClassAsCell();
LinkedHashMap<DataCell, Double> frequencies = table.getClassFrequencies();
// if the distribution allows for a leaf
if (table.isPureEnough()) {
// free memory
table.freeUnderlyingDataRows();
double value = m_finishedCounter.incrementAndGet(table.getSumOfWeights());
exec.setProgress(value / m_alloverRowCount, "Created node with id " + nodeId + " at level " + depth);
return new DecisionTreeNodeLeaf(nodeId, majorityClass, frequencies);
} else {
// find the best splits for all attributes
SplitFinder splittFinder = new SplitFinder(table, splitQualityMeasure, m_averageSplitpoint.getBooleanValue(), m_minNumberRecordsPerNode.getIntValue(), m_binaryNominalSplitMode.getBooleanValue(), m_maxNumNominalsForCompleteComputation.getIntValue());
// check for enough memory
checkMemory();
// get the best split among the best attribute splits
Split split = splittFinder.getSplit();
// if no best split could be evaluated, create a leaf node
if (split == null || !split.isValidSplit()) {
table.freeUnderlyingDataRows();
double value = m_finishedCounter.incrementAndGet(table.getSumOfWeights());
exec.setProgress(value / m_alloverRowCount, "Created node with id " + nodeId + " at level " + depth);
return new DecisionTreeNodeLeaf(nodeId, majorityClass, frequencies);
}
// partition the attribute lists according to this split
Partitioner partitioner = new Partitioner(table, split, m_minNumberRecordsPerNode.getIntValue());
if (!partitioner.couldBeUsefulPartitioned()) {
table.freeUnderlyingDataRows();
double value = m_finishedCounter.incrementAndGet(table.getSumOfWeights());
exec.setProgress(value / m_alloverRowCount, "Created node with id " + nodeId + " at level " + depth);
return new DecisionTreeNodeLeaf(nodeId, majorityClass, frequencies);
}
// get the just created partitions
InMemoryTable[] partitionTables = partitioner.getPartitionTables();
// recursively build the child nodes
DecisionTreeNode[] children = new DecisionTreeNode[partitionTables.length];
ArrayList<ParallelBuilding> threads = new ArrayList<ParallelBuilding>();
int i = 0;
for (InMemoryTable partitionTable : partitionTables) {
exec.checkCanceled();
if (partitionTable.getNumberDataRows() * m_numberAttributes < 10000 || !parallelProcessing.isThreadAvailable()) {
children[i] = buildTree(partitionTable, exec, depth + 1, splitQualityMeasure, parallelProcessing);
} else {
String threadName = "Build thread, node: " + nodeId + "." + i;
ParallelBuilding buildThread = new ParallelBuilding(threadName, partitionTable, exec, depth + 1, i, splitQualityMeasure, parallelProcessing);
LOGGER.debug("Start new parallel building thread: " + threadName);
threads.add(buildThread);
buildThread.start();
}
i++;
}
// already assigned to the child array
for (ParallelBuilding buildThread : threads) {
children[buildThread.getThreadIndex()] = buildThread.getResultNode();
exec.checkCanceled();
if (buildThread.getException() != null) {
for (ParallelBuilding buildThread2 : threads) {
buildThread2.stop();
}
throw new RuntimeException(buildThread.getException().getMessage());
}
}
threads.clear();
if (split instanceof SplitContinuous) {
double splitValue = ((SplitContinuous) split).getBestSplitValue();
// return new DecisionTreeNodeSplitContinuous(nodeId,
// majorityClass, frequencies, split
// .getSplitAttributeName(), children, splitValue);
String splitAttribute = split.getSplitAttributeName();
PMMLPredicate[] splitPredicates = new PMMLPredicate[] { new PMMLSimplePredicate(splitAttribute, PMMLOperator.LESS_OR_EQUAL, Double.toString(splitValue)), new PMMLSimplePredicate(splitAttribute, PMMLOperator.GREATER_THAN, Double.toString(splitValue)) };
return new DecisionTreeNodeSplitPMML(nodeId, majorityClass, frequencies, splitAttribute, splitPredicates, children);
} else if (split instanceof SplitNominalNormal) {
// else the attribute is nominal
DataCell[] splitValues = ((SplitNominalNormal) split).getSplitValues();
// return new DecisionTreeNodeSplitNominal(nodeId, majorityClass,
// frequencies, split.getSplitAttributeName(),
// splitValues, children);
int num = children.length;
PMMLPredicate[] splitPredicates = new PMMLPredicate[num];
String splitAttribute = split.getSplitAttributeName();
for (int j = 0; j < num; j++) {
splitPredicates[j] = new PMMLSimplePredicate(splitAttribute, PMMLOperator.EQUAL, splitValues[j].toString());
}
return new DecisionTreeNodeSplitPMML(nodeId, majorityClass, frequencies, splitAttribute, splitPredicates, children);
} else {
// binary nominal
SplitNominalBinary splitNominalBinary = (SplitNominalBinary) split;
DataCell[] splitValues = splitNominalBinary.getSplitValues();
// return new DecisionTreeNodeSplitNominalBinary(nodeId,
// majorityClass, frequencies, split
// .getSplitAttributeName(), splitValues,
// splitNominalBinary.getIntMappingsLeftPartition(),
// splitNominalBinary.getIntMappingsRightPartition(),
// children/* children[0]=left, ..[1] right */);
String splitAttribute = split.getSplitAttributeName();
int[][] indices = new int[][] { splitNominalBinary.getIntMappingsLeftPartition(), splitNominalBinary.getIntMappingsRightPartition() };
PMMLPredicate[] splitPredicates = new PMMLPredicate[2];
for (int j = 0; j < splitPredicates.length; j++) {
PMMLSimpleSetPredicate pred = null;
pred = new PMMLSimpleSetPredicate(splitAttribute, PMMLSetOperator.IS_IN);
pred.setArrayType(PMMLArrayType.STRING);
LinkedHashSet<String> values = new LinkedHashSet<String>();
for (int index : indices[j]) {
values.add(splitValues[index].toString());
}
pred.setValues(values);
splitPredicates[j] = pred;
}
return new DecisionTreeNodeSplitPMML(nodeId, majorityClass, frequencies, splitAttribute, splitPredicates, children);
}
}
}
use of org.knime.base.node.mine.decisiontree2.PMMLPredicate in project knime-core by knime.
the class TreeNodeRegression method createDecisionTreeNode.
/**
* @param metaData
* @return
*/
public DecisionTreeNode createDecisionTreeNode(final MutableInteger idGenerator, final TreeMetaData metaData) {
DataCell majorityCell = new StringCell(DoubleFormat.formatDouble(m_mean));
final int nrChildren = getNrChildren();
LinkedHashMap<DataCell, Double> distributionMap = new LinkedHashMap<DataCell, Double>();
distributionMap.put(majorityCell, m_totalSum);
if (nrChildren == 0) {
return new DecisionTreeNodeLeaf(idGenerator.inc(), majorityCell, distributionMap);
} else {
int id = idGenerator.inc();
DecisionTreeNode[] childNodes = new DecisionTreeNode[nrChildren];
int splitAttributeIndex = getSplitAttributeIndex();
assert splitAttributeIndex >= 0 : "non-leaf node has no split";
String splitAttribute = metaData.getAttributeMetaData(splitAttributeIndex).getAttributeName();
PMMLPredicate[] childPredicates = new PMMLPredicate[nrChildren];
for (int i = 0; i < nrChildren; i++) {
final TreeNodeRegression treeNode = getChild(i);
TreeNodeCondition cond = treeNode.getCondition();
childPredicates[i] = cond.toPMMLPredicate();
childNodes[i] = treeNode.createDecisionTreeNode(idGenerator, metaData);
}
return new DecisionTreeNodeSplitPMML(id, majorityCell, distributionMap, splitAttribute, childPredicates, childNodes);
}
}
use of org.knime.base.node.mine.decisiontree2.PMMLPredicate in project knime-core by knime.
the class PMMLDecisionTreeTranslator method addKnimeTreeNode.
private DecisionTreeNode addKnimeTreeNode(final Node pmmlNode) {
Node[] pmmlChildrenNode = pmmlNode.getNodeArray();
// TODO Handle the case that the id from PMML might not be an integer.
String nodeId = pmmlNode.getId();
int id;
try {
id = Integer.parseInt(nodeId);
} catch (NumberFormatException e) {
throw new IllegalArgumentException("Only numeric node ids are supported in KNIME. Found \"" + nodeId + "\".");
}
if (pmmlChildrenNode.length == 0) {
DecisionTreeNodeLeaf knimeLeaf = new DecisionTreeNodeLeaf(id, getMajorityClass(pmmlNode), getClassCount(pmmlNode));
return knimeLeaf;
} else {
PMMLPredicate[] pmmlPredicates = new PMMLPredicate[pmmlChildrenNode.length];
DecisionTreeNode[] children = new DecisionTreeNode[pmmlChildrenNode.length];
for (int i = 0; i < pmmlChildrenNode.length; i++) {
children[i] = addKnimeTreeNode(pmmlChildrenNode[i]);
pmmlPredicates[i] = getPredicate(pmmlChildrenNode[i]);
}
DecisionTreeNodeSplitPMML knimeNode;
if (pmmlNode.isSetDefaultChild()) {
String defaultChild = pmmlNode.getDefaultChild();
Integer knimeDefaultChildIndex;
try {
knimeDefaultChildIndex = Integer.parseInt(defaultChild);
} catch (NumberFormatException e) {
throw new IllegalArgumentException("Only numeric node ids are supported in KNIME. " + "Found \"" + defaultChild + "\" as defaultChild.");
}
knimeNode = new DecisionTreeNodeSplitPMML(id, getMajorityClass(pmmlNode), getClassCount(pmmlNode), getChildrenSplitAttribute(pmmlNode), pmmlPredicates, children, knimeDefaultChildIndex);
} else {
knimeNode = new DecisionTreeNodeSplitPMML(id, getMajorityClass(pmmlNode), getClassCount(pmmlNode), getChildrenSplitAttribute(pmmlNode), pmmlPredicates, children);
}
return knimeNode;
}
}
use of org.knime.base.node.mine.decisiontree2.PMMLPredicate in project knime-core by knime.
the class DecisionTreeLearnerNodeModel2 method buildTree.
/**
* Recursively induces the decision tree.
*
* @param table the {@link InMemoryTable} representing the data for this
* node to determine the split and after that perform
* partitioning
* @param exec the execution context for progress information
* @param depth the current recursion depth
*/
private DecisionTreeNode buildTree(final InMemoryTable table, final ExecutionContext exec, final int depth, final SplitQualityMeasure splitQualityMeasure, final ParallelProcessing parallelProcessing, final int firstSplitCol) throws CanceledExecutionException, IllegalAccessException {
exec.checkCanceled();
// derive this node's id from the counter
int nodeId = m_counter.getAndIncrement();
DataCell majorityClass = table.getMajorityClassAsCell();
LinkedHashMap<DataCell, Double> frequencies = table.getClassFrequencies();
// if the distribution allows for a leaf
if (table.isPureEnough()) {
// free memory
table.freeUnderlyingDataRows();
double value = m_finishedCounter.incrementAndGet(table.getSumOfWeights());
exec.setProgress(value / m_alloverRowCount, "Created node with id " + nodeId + " at level " + depth);
return new DecisionTreeNodeLeaf(nodeId, majorityClass, frequencies);
} else {
Split split = null;
// find best split in specified column for first split
if (depth == 0 && m_useFirstSplitCol.getBooleanValue()) {
if (table.isNominal(firstSplitCol)) {
if (m_binaryNominalSplitMode.getBooleanValue()) {
split = new SplitNominalBinary(table, firstSplitCol, splitQualityMeasure, m_minNumberRecordsPerNode.getIntValue(), m_maxNumNominalsForCompleteComputation.getIntValue());
} else {
split = new SplitNominalNormal(table, firstSplitCol, splitQualityMeasure, m_minNumberRecordsPerNode.getIntValue());
}
} else {
split = new SplitContinuous(table, firstSplitCol, splitQualityMeasure, m_averageSplitpoint.getBooleanValue(), m_minNumberRecordsPerNode.getIntValue());
}
if (Double.isNaN(split.getBestQualityMeasure()) || split.getBestQualityMeasure() == 0.0) {
m_warningMessageSb.append("The specified root split column \"").append(split.getSplitAttributeName()).append("\" does not contain a valid split.");
}
}
if (split == null) {
// no root split column found or selected
// find the best splits for all attributes
SplitFinder splittFinder = new SplitFinder(table, splitQualityMeasure, m_averageSplitpoint.getBooleanValue(), m_minNumberRecordsPerNode.getIntValue(), m_binaryNominalSplitMode.getBooleanValue(), m_maxNumNominalsForCompleteComputation.getIntValue());
// check for enough memory
checkMemory();
// get the best split among the best attribute splits
split = splittFinder.getSplit();
}
// if no best split could be evaluated, create a leaf node
if (split == null || !split.isValidSplit()) {
table.freeUnderlyingDataRows();
double value = m_finishedCounter.incrementAndGet(table.getSumOfWeights());
exec.setProgress(value / m_alloverRowCount, "Created node with id " + nodeId + " at level " + depth);
return new DecisionTreeNodeLeaf(nodeId, majorityClass, frequencies);
}
// partition the attribute lists according to this split
Partitioner partitioner = new Partitioner(table, split, m_minNumberRecordsPerNode.getIntValue());
if (!partitioner.couldBeUsefulPartitioned()) {
table.freeUnderlyingDataRows();
double value = m_finishedCounter.incrementAndGet(table.getSumOfWeights());
exec.setProgress(value / m_alloverRowCount, "Created node with id " + nodeId + " at level " + depth);
return new DecisionTreeNodeLeaf(nodeId, majorityClass, frequencies);
}
// get the just created partitions
InMemoryTable[] partitionTables = partitioner.getPartitionTables();
// recursively build the child nodes
DecisionTreeNode[] children = new DecisionTreeNode[partitionTables.length];
ArrayList<ParallelBuilding> threads = new ArrayList<ParallelBuilding>();
int i = 0;
for (InMemoryTable partitionTable : partitionTables) {
exec.checkCanceled();
if (partitionTable.getNumberDataRows() * m_numberAttributes < 10000 || !parallelProcessing.isThreadAvailable()) {
children[i] = buildTree(partitionTable, exec, depth + 1, splitQualityMeasure, parallelProcessing, firstSplitCol);
} else {
String threadName = "Build thread, node: " + nodeId + "." + i;
ParallelBuilding buildThread = new ParallelBuilding(threadName, partitionTable, exec, depth + 1, i, splitQualityMeasure, parallelProcessing);
LOGGER.debug("Start new parallel building thread: " + threadName);
threads.add(buildThread);
buildThread.start();
}
i++;
}
// already assigned to the child array
for (ParallelBuilding buildThread : threads) {
children[buildThread.getThreadIndex()] = buildThread.getResultNode();
exec.checkCanceled();
if (buildThread.getException() != null) {
for (ParallelBuilding buildThread2 : threads) {
buildThread2.stop();
}
throw new RuntimeException(buildThread.getException().getMessage());
}
}
threads.clear();
if (split instanceof SplitContinuous) {
double splitValue = ((SplitContinuous) split).getBestSplitValue();
// return new DecisionTreeNodeSplitContinuous(nodeId,
// majorityClass, frequencies, split
// .getSplitAttributeName(), children, splitValue);
String splitAttribute = split.getSplitAttributeName();
PMMLPredicate[] splitPredicates = new PMMLPredicate[] { new PMMLSimplePredicate(splitAttribute, PMMLOperator.LESS_OR_EQUAL, Double.toString(splitValue)), new PMMLSimplePredicate(splitAttribute, PMMLOperator.GREATER_THAN, Double.toString(splitValue)) };
return new DecisionTreeNodeSplitPMML(nodeId, majorityClass, frequencies, splitAttribute, splitPredicates, children);
} else if (split instanceof SplitNominalNormal) {
// else the attribute is nominal
DataCell[] splitValues = ((SplitNominalNormal) split).getSplitValues();
// return new DecisionTreeNodeSplitNominal(nodeId, majorityClass,
// frequencies, split.getSplitAttributeName(),
// splitValues, children);
int num = children.length;
PMMLPredicate[] splitPredicates = new PMMLPredicate[num];
String splitAttribute = split.getSplitAttributeName();
for (int j = 0; j < num; j++) {
splitPredicates[j] = new PMMLSimplePredicate(splitAttribute, PMMLOperator.EQUAL, splitValues[j].toString());
}
return new DecisionTreeNodeSplitPMML(nodeId, majorityClass, frequencies, splitAttribute, splitPredicates, children);
} else {
// binary nominal
SplitNominalBinary splitNominalBinary = (SplitNominalBinary) split;
DataCell[] splitValues = splitNominalBinary.getSplitValues();
// return new DecisionTreeNodeSplitNominalBinary(nodeId,
// majorityClass, frequencies, split
// .getSplitAttributeName(), splitValues,
// splitNominalBinary.getIntMappingsLeftPartition(),
// splitNominalBinary.getIntMappingsRightPartition(),
// children/* children[0]=left, ..[1] right */);
String splitAttribute = split.getSplitAttributeName();
int[][] indices = new int[][] { splitNominalBinary.getIntMappingsLeftPartition(), splitNominalBinary.getIntMappingsRightPartition() };
PMMLPredicate[] splitPredicates = new PMMLPredicate[2];
for (int j = 0; j < splitPredicates.length; j++) {
PMMLSimpleSetPredicate pred = null;
pred = new PMMLSimpleSetPredicate(splitAttribute, PMMLSetOperator.IS_IN);
pred.setArrayType(PMMLArrayType.STRING);
LinkedHashSet<String> values = new LinkedHashSet<String>();
for (int index : indices[j]) {
values.add(splitValues[index].toString());
}
pred.setValues(values);
splitPredicates[j] = pred;
}
return new DecisionTreeNodeSplitPMML(nodeId, majorityClass, frequencies, splitAttribute, splitPredicates, children);
}
}
}
use of org.knime.base.node.mine.decisiontree2.PMMLPredicate in project knime-core by knime.
the class DecisionTreeNodeSplitPMML method saveNodeSplitInternalsToPredParams.
/**
* {@inheritDoc}
*/
@Override
public void saveNodeSplitInternalsToPredParams(final ModelContentWO pConf) {
int i = 0;
for (PMMLPredicate pred : m_splitPred) {
Config predConfig = pConf.addConfig("pred" + i++);
pred.saveToPredParams(predConfig);
}
}
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