use of org.knime.base.node.mine.treeensemble2.learner.TreeEnsembleLearner 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.base.node.mine.treeensemble2.learner.TreeEnsembleLearner 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 = TreeEnsembleModelPortObject.createPortObject(ensembleSpec, model, exec.createFileStore(UUID.randomUUID().toString() + ""));
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.base.node.mine.treeensemble2.learner.TreeEnsembleLearner in project knime-core by knime.
the class TreeEnsembleRegressionLearnerNodeModel 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(UUID.randomUUID().toString() + ""));
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.base.node.mine.treeensemble2.learner.TreeEnsembleLearner in project knime-core by knime.
the class RandomForestClassificationLearnerNodeModel 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");
// Use xgboost missing value handling
m_configuration.setMissingValueHandling(MissingValueHandling.XGBoost);
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 };
}
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