use of org.knime.base.node.mine.treeensemble.node.learner.TreeEnsembleLearnerConfiguration 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();
double[] dataMemberships = new double[data.getNrRows()];
for (int i = 0; i < dataMemberships.length; i++) {
dataMemberships[i] = rowSampling.getCountFor(i);
}
RegressionPriors targetPriors = targetColumn.getPriors(dataMemberships, config);
BitSet forbiddenColumnSet = new BitSet(data.getNrAttributes());
// TreeNodeMembershipController rootMembershipController = new TreeNodeMembershipController(data, dataMemberships);
TreeNodeMembershipController rootMembershipController = null;
TreeNodeRegression rootNode = buildTreeNode(exec, 0, dataMemberships, TreeNodeSignature.ROOT_SIGNATURE, targetPriors, forbiddenColumnSet, rootMembershipController);
assert forbiddenColumnSet.cardinality() == 0;
rootNode.setTreeNodeCondition(TreeNodeTrueCondition.INSTANCE);
return new TreeModelRegression(rootNode);
}
use of org.knime.base.node.mine.treeensemble.node.learner.TreeEnsembleLearnerConfiguration in project knime-core by knime.
the class TreeLearnerRegression method buildTreeNode.
private TreeNodeRegression buildTreeNode(final ExecutionMonitor exec, final int currentDepth, final double[] rowSampleWeights, final TreeNodeSignature treeNodeSignature, final RegressionPriors targetPriors, final BitSet forbiddenColumnSet, final TreeNodeMembershipController membershipController) throws CanceledExecutionException {
final TreeData data = getData();
final TreeEnsembleLearnerConfiguration config = getConfig();
exec.checkCanceled();
SplitCandidate bestSplit = findBestSplitRegression(currentDepth, rowSampleWeights, treeNodeSignature, targetPriors, forbiddenColumnSet, membershipController);
if (bestSplit == null) {
return new TreeNodeRegression(treeNodeSignature, targetPriors);
}
TreeAttributeColumnData splitColumn = bestSplit.getColumnData();
final int attributeIndex = splitColumn.getMetaData().getAttributeIndex();
boolean markAttributeAsForbidden = !bestSplit.canColumnBeSplitFurther();
forbiddenColumnSet.set(attributeIndex, markAttributeAsForbidden);
TreeNodeCondition[] 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);
}
TreeNodeRegression[] childNodes = new TreeNodeRegression[childConditions.length];
final double[] dataMemberships = rowSampleWeights;
final double[] childMemberships = new double[dataMemberships.length];
final TreeTargetNumericColumnData targetColumn = (TreeTargetNumericColumnData) data.getTargetColumn();
for (int i = 0; i < childConditions.length; i++) {
System.arraycopy(dataMemberships, 0, childMemberships, 0, dataMemberships.length);
TreeNodeCondition cond = childConditions[i];
splitColumn.updateChildMemberships(cond, dataMemberships, childMemberships);
RegressionPriors childTargetPriors = targetColumn.getPriors(childMemberships, config);
TreeNodeSignature childSignature = treeNodeSignature.createChildSignature((short) i);
TreeNodeMembershipController childMembershipController = splitColumn.getChildNodeMembershipController(cond, membershipController);
childNodes[i] = buildTreeNode(exec, currentDepth + 1, childMemberships, childSignature, childTargetPriors, forbiddenColumnSet, childMembershipController);
childNodes[i].setTreeNodeCondition(cond);
}
if (markAttributeAsForbidden) {
forbiddenColumnSet.set(attributeIndex, false);
}
return new TreeNodeRegression(treeNodeSignature, targetPriors, childNodes);
}
use of org.knime.base.node.mine.treeensemble.node.learner.TreeEnsembleLearnerConfiguration in project knime-core by knime.
the class TreeEnsembleRegressionLearnerNodeDialogPane method saveSettingsTo.
/**
* {@inheritDoc}
*/
@Override
protected void saveSettingsTo(final NodeSettingsWO settings) throws InvalidSettingsException {
TreeEnsembleLearnerConfiguration cfg = new TreeEnsembleLearnerConfiguration(true);
m_attributeSelectionPanel.saveSettings(cfg);
m_treeOptionsPanel.saveSettings(cfg);
m_ensembleOptionsPanel.saveSettings(cfg);
cfg.save(settings);
}
use of org.knime.base.node.mine.treeensemble.node.learner.TreeEnsembleLearnerConfiguration in project knime-core by knime.
the class TreeEnsembleRegressionLearnerNodeDialogPane method loadSettingsFrom.
/**
* {@inheritDoc}
*/
@Override
protected void loadSettingsFrom(final NodeSettingsRO settings, final DataTableSpec[] specs) throws NotConfigurableException {
final DataTableSpec inSpec = specs[0];
TreeEnsembleLearnerConfiguration cfg = new TreeEnsembleLearnerConfiguration(true);
cfg.loadInDialog(settings, inSpec);
m_attributeSelectionPanel.loadSettingsFrom(inSpec, cfg);
m_treeOptionsPanel.loadSettingsFrom(inSpec, cfg);
m_ensembleOptionsPanel.loadSettings(cfg);
}
use of org.knime.base.node.mine.treeensemble.node.learner.TreeEnsembleLearnerConfiguration in project knime-core by knime.
the class RandomForestRegressionLearnerNodeModel method loadValidatedSettingsFrom.
/**
* {@inheritDoc}
*/
@Override
protected void loadValidatedSettingsFrom(final NodeSettingsRO settings) throws InvalidSettingsException {
TreeEnsembleLearnerConfiguration config = new TreeEnsembleLearnerConfiguration(true);
config.loadInModel(settings);
m_configuration = config;
}
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