use of org.knime.base.node.mine.treeensemble.model.RegressionTreeModelPortObject in project knime-core by knime.
the class RegressionTreePredictorCellFactory method getCells.
/**
* {@inheritDoc}
*/
@Override
public DataCell[] getCells(final DataRow row) {
RegressionTreeModelPortObject modelObject = m_predictor.getModelObject();
final RegressionTreeModel treeModel = modelObject.getModel();
int size = 1;
DataCell[] result = new DataCell[size];
DataRow filterRow = new FilterColumnRow(row, m_learnColumnInRealDataIndices);
PredictorRecord record = treeModel.createPredictorRecord(filterRow, m_learnSpec);
if (record == null) {
// missing value
Arrays.fill(result, DataType.getMissingCell());
return result;
}
TreeModelRegression tree = treeModel.getTreeModel();
TreeNodeRegression match = tree.findMatchingNode(record);
double nodeMean = match.getMean();
result[0] = new DoubleCell(nodeMean);
return result;
}
use of org.knime.base.node.mine.treeensemble.model.RegressionTreeModelPortObject in project knime-core by knime.
the class RegressionTreeLearnerNodeModel method execute.
// /**
// * @param ensembleSpec
// * @param ensembleModel
// * @param inSpec
// * @return
// * @throws InvalidSettingsException
// */
// private TreeEnsemblePredictor createOutOfBagPredictor(final TreeEnsembleModelPortObjectSpec ensembleSpec,
// final TreeEnsembleModelPortObject ensembleModel, final DataTableSpec inSpec) throws InvalidSettingsException {
// TreeEnsemblePredictorConfiguration ooBConfig = new TreeEnsemblePredictorConfiguration(true);
// String targetColumn = m_configuration.getTargetColumn();
// 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);
// }
/**
* {@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);
ExecutionMonitor readInExec = exec.createSubProgress(0.1);
ExecutionMonitor learnExec = exec.createSubProgress(0.9);
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 tree");
// 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;
// }
RandomData rd = m_configuration.createRandomData();
TreeLearnerRegression treeLearner = new TreeLearnerRegression(m_configuration, data, rd);
TreeModelRegression regTree = treeLearner.learnSingleTree(learnExec, rd);
RegressionTreeModel model = new RegressionTreeModel(m_configuration, data.getMetaData(), regTree, data.getTreeType());
RegressionTreeModelPortObjectSpec treePortObjectSpec = new RegressionTreeModelPortObjectSpec(learnSpec);
RegressionTreeModelPortObject treePortObject = new RegressionTreeModelPortObject(model, treePortObjectSpec);
learnExec.setProgress(1.0);
m_treeModelPortObject = treePortObject;
if (warn != null) {
setWarningMessage(warn);
}
return new PortObject[] { treePortObject };
}
use of org.knime.base.node.mine.treeensemble.model.RegressionTreeModelPortObject in project knime-core by knime.
the class RegressionTreePredictorNodeModel method execute.
/**
* {@inheritDoc}
*/
@Override
protected PortObject[] execute(final PortObject[] inObjects, final ExecutionContext exec) throws Exception {
RegressionTreeModelPortObject model = (RegressionTreeModelPortObject) inObjects[0];
RegressionTreeModelPortObjectSpec modelSpec = model.getSpec();
BufferedDataTable data = (BufferedDataTable) inObjects[1];
DataTableSpec dataSpec = data.getDataTableSpec();
final RegressionTreePredictor pred = new RegressionTreePredictor(modelSpec, model, dataSpec, m_configuration);
ColumnRearranger rearranger = pred.getPredictionRearranger();
BufferedDataTable outTable = exec.createColumnRearrangeTable(data, rearranger, exec);
return new BufferedDataTable[] { outTable };
}
Aggregations