use of org.knime.core.node.BufferedDataTable in project knime-core by knime.
the class RandomForestRegressionLearnerNodeModel 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);
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 = TreeEnsembleModelPortObject.createPortObject(ensembleSpec, model, exec.createFileStore("TreeEnsemble"));
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 };
}
use of org.knime.core.node.BufferedDataTable in project knime-core by knime.
the class RandomForestRegressionPredictorNodeModel 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.core.node.BufferedDataTable in project knime-core by knime.
the class GradientBoostingClassificationPredictorNodeModel method execute.
/**
* {@inheritDoc}
*/
@Override
protected PortObject[] execute(final PortObject[] inObjects, final ExecutionContext exec) throws Exception {
GradientBoostingModelPortObject model = (GradientBoostingModelPortObject) inObjects[0];
TreeEnsembleModelPortObjectSpec modelSpec = model.getSpec();
BufferedDataTable data = (BufferedDataTable) inObjects[1];
DataTableSpec dataSpec = data.getDataTableSpec();
GradientBoostingPredictor<MultiClassGradientBoostedTreesModel> predictor = new GradientBoostingPredictor<>((MultiClassGradientBoostedTreesModel) model.getEnsembleModel(), modelSpec, dataSpec, m_configuration);
ColumnRearranger rearranger = predictor.getPredictionRearranger();
BufferedDataTable outTable = exec.createColumnRearrangeTable(data, rearranger, exec);
return new BufferedDataTable[] { outTable };
}
use of org.knime.core.node.BufferedDataTable in project knime-core by knime.
the class GradientBoostingPMMLPredictorNodeModel method execute.
/**
* {@inheritDoc}
*/
@Override
public PortObject[] execute(final PortObject[] inObjects, final ExecutionContext exec) throws Exception {
PMMLPortObject pmmlPO = (PMMLPortObject) inObjects[0];
GradientBoostingModelPortObject model = importModel(pmmlPO);
BufferedDataTable data = (BufferedDataTable) inObjects[1];
DataTableSpec dataSpec = data.getDataTableSpec();
// only happens if configure was not called previously e.g. in the generic PMML predictor
if (m_configuration == null) {
m_configuration = TreeEnsemblePredictorConfiguration.createDefault(m_isRegression, translateSpec(pmmlPO.getSpec()).getTargetColumn().getName());
}
final GradientBoostingPredictor<?> pred = new GradientBoostingPredictor<>(model.getEnsembleModel(), model.getSpec(), dataSpec, m_configuration);
ColumnRearranger rearranger = pred.getPredictionRearranger();
BufferedDataTable outTable = exec.createColumnRearrangeTable(data, rearranger, exec);
return new BufferedDataTable[] { outTable };
}
use of org.knime.core.node.BufferedDataTable in project knime-core by knime.
the class GradientBoostingPredictorNodeModel method execute.
/**
* {@inheritDoc}
*/
@Override
protected PortObject[] execute(final PortObject[] inObjects, final ExecutionContext exec) throws Exception {
GradientBoostingModelPortObject model = (GradientBoostingModelPortObject) inObjects[0];
TreeEnsembleModelPortObjectSpec modelSpec = model.getSpec();
BufferedDataTable data = (BufferedDataTable) inObjects[1];
DataTableSpec dataSpec = data.getDataTableSpec();
final GradientBoostingPredictor<GradientBoostedTreesModel> pred = new GradientBoostingPredictor<>((GradientBoostedTreesModel) model.getEnsembleModel(), modelSpec, dataSpec, m_configuration);
ColumnRearranger rearranger = pred.getPredictionRearranger();
BufferedDataTable outTable = exec.createColumnRearrangeTable(data, rearranger, exec);
return new BufferedDataTable[] { outTable };
}
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