use of org.knime.base.node.mine.treeensemble.node.predictor.TreeEnsemblePredictor in project knime-core by knime.
the class RandomForestClassificationLearnerNodeModel method configure.
/**
* {@inheritDoc}
*/
@Override
protected PortObjectSpec[] configure(final PortObjectSpec[] inSpecs) throws InvalidSettingsException {
// guaranteed to not be null (according to API)
DataTableSpec inSpec = (DataTableSpec) inSpecs[0];
if (m_configuration == null) {
throw new InvalidSettingsException("No configuration available");
}
final FilterLearnColumnRearranger learnRearranger = m_configuration.filterLearnColumns(inSpec);
final String warn = learnRearranger.getWarning();
if (warn != null) {
setWarningMessage(warn);
}
DataTableSpec learnSpec = learnRearranger.createSpec();
TreeEnsembleModelPortObjectSpec ensembleSpec = m_configuration.createPortObjectSpec(learnSpec);
// the following call may return null, which is OK during configure
// but not upon execution (spec may not be populated yet, e.g.
// predecessor not executed)
// if the possible values is not null, the following call checks
// for duplicates in the toString() representation
ensembleSpec.getTargetColumnPossibleValueMap();
final TreeEnsemblePredictor outOfBagPredictor = createOutOfBagPredictor(ensembleSpec, null, inSpec);
ColumnRearranger outOfBagRearranger = outOfBagPredictor.getPredictionRearranger();
DataTableSpec outOfBagSpec = outOfBagRearranger == null ? null : outOfBagRearranger.createSpec();
DataTableSpec colStatsSpec = TreeEnsembleLearner.getColumnStatisticTableSpec();
return new PortObjectSpec[] { outOfBagSpec, colStatsSpec, ensembleSpec };
}
use of org.knime.base.node.mine.treeensemble.node.predictor.TreeEnsemblePredictor in project knime-core by knime.
the class RandomForestClassificationPredictorNodeModel method execute.
/**
* {@inheritDoc}
*/
@Override
protected PortObject[] execute(final PortObject[] inObjects, final ExecutionContext exec) throws Exception {
TreeEnsembleModelPortObject model = (TreeEnsembleModelPortObject) inObjects[0];
TreeEnsembleModelPortObjectSpec modelSpec = model.getSpec();
BufferedDataTable data = (BufferedDataTable) inObjects[1];
DataTableSpec dataSpec = data.getDataTableSpec();
final TreeEnsemblePredictor pred = new TreeEnsemblePredictor(modelSpec, model, dataSpec, m_configuration);
ColumnRearranger rearranger = pred.getPredictionRearranger();
BufferedDataTable outTable = exec.createColumnRearrangeTable(data, rearranger, exec);
return new BufferedDataTable[] { outTable };
}
use of org.knime.base.node.mine.treeensemble.node.predictor.TreeEnsemblePredictor in project knime-core by knime.
the class RandomForestClassificationPredictorNodeModel method configure.
/**
* {@inheritDoc}
*/
@Override
protected PortObjectSpec[] configure(final PortObjectSpec[] inSpecs) throws InvalidSettingsException {
TreeEnsembleModelPortObjectSpec modelSpec = (TreeEnsembleModelPortObjectSpec) inSpecs[0];
String targetColName = modelSpec.getTargetColumn().getName();
if (m_configuration == null) {
m_configuration = TreeEnsemblePredictorConfiguration.createDefault(false, targetColName);
}
modelSpec.assertTargetTypeMatches(false);
DataTableSpec dataSpec = (DataTableSpec) inSpecs[1];
final TreeEnsemblePredictor pred = new TreeEnsemblePredictor(modelSpec, null, dataSpec, m_configuration);
ColumnRearranger rearranger = pred.getPredictionRearranger();
// rearranger may be null if confidence values are appended but the
// model does not have a list of possible target values
DataTableSpec outSpec = rearranger != null ? rearranger.createSpec() : null;
return new DataTableSpec[] { outSpec };
}
use of org.knime.base.node.mine.treeensemble.node.predictor.TreeEnsemblePredictor in project knime-core by knime.
the class TreeEnsembleClassificationLearnerNodeModel method createOutOfBagPredictor.
/**
* @param ensembleSpec
* @param ensembleModel
* @param inSpec
* @return
* @throws InvalidSettingsException
*/
private TreeEnsemblePredictor createOutOfBagPredictor(final TreeEnsembleModelPortObjectSpec ensembleSpec, final TreeEnsembleModelPortObject ensembleModel, final DataTableSpec inSpec) throws InvalidSettingsException {
String targetColumn = m_configuration.getTargetColumn();
TreeEnsemblePredictorConfiguration ooBConfig = new TreeEnsemblePredictorConfiguration(false, targetColumn);
String append = targetColumn + " (Out-of-bag)";
ooBConfig.setPredictionColumnName(append);
ooBConfig.setAppendPredictionConfidence(true);
ooBConfig.setAppendClassConfidences(true);
ooBConfig.setAppendModelCount(true);
return new TreeEnsemblePredictor(ensembleSpec, ensembleModel, inSpec, ooBConfig);
}
use of org.knime.base.node.mine.treeensemble.node.predictor.TreeEnsemblePredictor in project knime-core by knime.
the class TreeEnsembleClassificationLearnerNodeModel method execute.
/**
* {@inheritDoc}
*/
@Override
protected PortObject[] execute(final PortObject[] inObjects, final ExecutionContext exec) throws Exception {
BufferedDataTable t = (BufferedDataTable) inObjects[0];
DataTableSpec spec = t.getDataTableSpec();
final FilterLearnColumnRearranger learnRearranger = m_configuration.filterLearnColumns(spec);
String warn = learnRearranger.getWarning();
BufferedDataTable learnTable = exec.createColumnRearrangeTable(t, learnRearranger, exec.createSubProgress(0.0));
DataTableSpec learnSpec = learnTable.getDataTableSpec();
TreeEnsembleModelPortObjectSpec ensembleSpec = m_configuration.createPortObjectSpec(learnSpec);
Map<String, DataCell> targetValueMap = ensembleSpec.getTargetColumnPossibleValueMap();
if (targetValueMap == null) {
throw new InvalidSettingsException("The target column does not " + "have possible values assigned. Most likely it " + "has too many different distinct values (learning an ID " + "column?) Fix it by preprocessing the table using " + "a \"Domain Calculator\".");
}
ExecutionMonitor readInExec = exec.createSubProgress(0.1);
ExecutionMonitor learnExec = exec.createSubProgress(0.8);
ExecutionMonitor outOfBagExec = exec.createSubProgress(0.1);
TreeDataCreator dataCreator = new TreeDataCreator(m_configuration, learnSpec, learnTable.getRowCount());
exec.setProgress("Reading data into memory");
TreeData data = dataCreator.readData(learnTable, m_configuration, readInExec);
m_hiliteRowSample = dataCreator.getDataRowsForHilite();
m_viewMessage = dataCreator.getViewMessage();
String dataCreationWarning = dataCreator.getAndClearWarningMessage();
if (dataCreationWarning != null) {
if (warn == null) {
warn = dataCreationWarning;
} else {
warn = warn + "\n" + dataCreationWarning;
}
}
readInExec.setProgress(1.0);
exec.setMessage("Learning trees");
TreeEnsembleLearner learner = new TreeEnsembleLearner(m_configuration, data);
TreeEnsembleModel model;
try {
model = learner.learnEnsemble(learnExec);
} catch (ExecutionException e) {
Throwable cause = e.getCause();
if (cause instanceof Exception) {
throw (Exception) cause;
}
throw e;
}
TreeEnsembleModelPortObject modelPortObject = new TreeEnsembleModelPortObject(ensembleSpec, model);
learnExec.setProgress(1.0);
exec.setMessage("Out of bag prediction");
TreeEnsemblePredictor outOfBagPredictor = createOutOfBagPredictor(ensembleSpec, modelPortObject, spec);
outOfBagPredictor.setOutofBagFilter(learner.getRowSamples(), data.getTargetColumn());
ColumnRearranger outOfBagRearranger = outOfBagPredictor.getPredictionRearranger();
BufferedDataTable outOfBagTable = exec.createColumnRearrangeTable(t, outOfBagRearranger, outOfBagExec);
BufferedDataTable colStatsTable = learner.createColumnStatisticTable(exec.createSubExecutionContext(0.0));
m_ensembleModelPortObject = modelPortObject;
if (warn != null) {
setWarningMessage(warn);
}
return new PortObject[] { outOfBagTable, colStatsTable, modelPortObject };
}
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