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Example 51 with BufferedDataTable

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 };
}
Also used : DataTableSpec(org.knime.core.data.DataTableSpec) TreeEnsembleModel(org.knime.base.node.mine.treeensemble2.model.TreeEnsembleModel) TreeEnsembleModelPortObjectSpec(org.knime.base.node.mine.treeensemble2.model.TreeEnsembleModelPortObjectSpec) TreeEnsembleLearner(org.knime.base.node.mine.treeensemble2.learner.TreeEnsembleLearner) InvalidSettingsException(org.knime.core.node.InvalidSettingsException) CanceledExecutionException(org.knime.core.node.CanceledExecutionException) IOException(java.io.IOException) ExecutionException(java.util.concurrent.ExecutionException) TreeEnsembleModelPortObject(org.knime.base.node.mine.treeensemble2.model.TreeEnsembleModelPortObject) ColumnRearranger(org.knime.core.data.container.ColumnRearranger) FilterLearnColumnRearranger(org.knime.base.node.mine.treeensemble2.node.learner.TreeEnsembleLearnerConfiguration.FilterLearnColumnRearranger) BufferedDataTable(org.knime.core.node.BufferedDataTable) FilterLearnColumnRearranger(org.knime.base.node.mine.treeensemble2.node.learner.TreeEnsembleLearnerConfiguration.FilterLearnColumnRearranger) TreeData(org.knime.base.node.mine.treeensemble2.data.TreeData) ExecutionMonitor(org.knime.core.node.ExecutionMonitor) CanceledExecutionException(org.knime.core.node.CanceledExecutionException) ExecutionException(java.util.concurrent.ExecutionException) TreeEnsemblePredictor(org.knime.base.node.mine.treeensemble2.node.predictor.TreeEnsemblePredictor) TreeDataCreator(org.knime.base.node.mine.treeensemble2.data.TreeDataCreator) TreeEnsembleModelPortObject(org.knime.base.node.mine.treeensemble2.model.TreeEnsembleModelPortObject) PortObject(org.knime.core.node.port.PortObject)

Example 52 with BufferedDataTable

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 };
}
Also used : TreeEnsembleModelPortObject(org.knime.base.node.mine.treeensemble2.model.TreeEnsembleModelPortObject) DataTableSpec(org.knime.core.data.DataTableSpec) ColumnRearranger(org.knime.core.data.container.ColumnRearranger) TreeEnsembleModelPortObjectSpec(org.knime.base.node.mine.treeensemble2.model.TreeEnsembleModelPortObjectSpec) BufferedDataTable(org.knime.core.node.BufferedDataTable) TreeEnsemblePredictor(org.knime.base.node.mine.treeensemble2.node.predictor.TreeEnsemblePredictor)

Example 53 with BufferedDataTable

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 };
}
Also used : DataTableSpec(org.knime.core.data.DataTableSpec) ColumnRearranger(org.knime.core.data.container.ColumnRearranger) GradientBoostingModelPortObject(org.knime.base.node.mine.treeensemble2.model.GradientBoostingModelPortObject) TreeEnsembleModelPortObjectSpec(org.knime.base.node.mine.treeensemble2.model.TreeEnsembleModelPortObjectSpec) MultiClassGradientBoostedTreesModel(org.knime.base.node.mine.treeensemble2.model.MultiClassGradientBoostedTreesModel) GradientBoostingPredictor(org.knime.base.node.mine.treeensemble2.node.gradientboosting.predictor.GradientBoostingPredictor) BufferedDataTable(org.knime.core.node.BufferedDataTable)

Example 54 with BufferedDataTable

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 };
}
Also used : DataTableSpec(org.knime.core.data.DataTableSpec) ColumnRearranger(org.knime.core.data.container.ColumnRearranger) GradientBoostingModelPortObject(org.knime.base.node.mine.treeensemble2.model.GradientBoostingModelPortObject) PMMLPortObject(org.knime.core.node.port.pmml.PMMLPortObject) GradientBoostingPredictor(org.knime.base.node.mine.treeensemble2.node.gradientboosting.predictor.GradientBoostingPredictor) BufferedDataTable(org.knime.core.node.BufferedDataTable)

Example 55 with BufferedDataTable

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 };
}
Also used : DataTableSpec(org.knime.core.data.DataTableSpec) ColumnRearranger(org.knime.core.data.container.ColumnRearranger) GradientBoostingModelPortObject(org.knime.base.node.mine.treeensemble2.model.GradientBoostingModelPortObject) TreeEnsembleModelPortObjectSpec(org.knime.base.node.mine.treeensemble2.model.TreeEnsembleModelPortObjectSpec) GradientBoostingPredictor(org.knime.base.node.mine.treeensemble2.node.gradientboosting.predictor.GradientBoostingPredictor) BufferedDataTable(org.knime.core.node.BufferedDataTable) GradientBoostedTreesModel(org.knime.base.node.mine.treeensemble2.model.GradientBoostedTreesModel)

Aggregations

BufferedDataTable (org.knime.core.node.BufferedDataTable)425 DataTableSpec (org.knime.core.data.DataTableSpec)213 ColumnRearranger (org.knime.core.data.container.ColumnRearranger)148 DataRow (org.knime.core.data.DataRow)118 BufferedDataContainer (org.knime.core.node.BufferedDataContainer)97 PortObject (org.knime.core.node.port.PortObject)96 DataCell (org.knime.core.data.DataCell)85 DataColumnSpec (org.knime.core.data.DataColumnSpec)61 InvalidSettingsException (org.knime.core.node.InvalidSettingsException)60 DefaultRow (org.knime.core.data.def.DefaultRow)56 PMMLPortObject (org.knime.core.node.port.pmml.PMMLPortObject)54 RowKey (org.knime.core.data.RowKey)52 ExecutionMonitor (org.knime.core.node.ExecutionMonitor)50 CanceledExecutionException (org.knime.core.node.CanceledExecutionException)47 SettingsModelString (org.knime.core.node.defaultnodesettings.SettingsModelString)43 IOException (java.io.IOException)41 ExecutionContext (org.knime.core.node.ExecutionContext)40 ArrayList (java.util.ArrayList)33 LinkedHashMap (java.util.LinkedHashMap)31 DoubleValue (org.knime.core.data.DoubleValue)29