Examples with TreeEnsembleModelPortObjectSpec org.knime.base.node.mine.treeensemble2.model.TreeEnsembleModelPortObjectSpec used on opensource projects

Example 26 with TreeEnsembleModelPortObjectSpec

use of org.knime.base.node.mine.treeensemble2.model.TreeEnsembleModelPortObjectSpec in project knime-core by knime.

the class GradientBoostingClassificationPredictorNodeModel 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);
    } else if (!m_configuration.isChangePredictionColumnName()) {
        m_configuration.setPredictionColumnName(TreeEnsemblePredictorConfiguration.getPredictColumnName(targetColName));
    }
    modelSpec.assertTargetTypeMatches(false);
    DataTableSpec dataSpec = (DataTableSpec) inSpecs[1];
    GradientBoostingPredictor<MultiClassGradientBoostedTreesModel> predictor = new GradientBoostingPredictor<>(null, modelSpec, dataSpec, m_configuration);
    ColumnRearranger rearranger = predictor.getPredictionRearranger();
    return new PortObjectSpec[] { rearranger.createSpec() };
}

Example 27 with TreeEnsembleModelPortObjectSpec

use of org.knime.base.node.mine.treeensemble2.model.TreeEnsembleModelPortObjectSpec in project knime-core by knime.

the class LKGradientBoostingPredictorCellFactory method createFactory.

public static LKGradientBoostingPredictorCellFactory createFactory(final GradientBoostingPredictor<MultiClassGradientBoostedTreesModel> predictor) throws InvalidSettingsException {
    TreeEnsemblePredictorConfiguration config = predictor.getConfiguration();
    DataTableSpec testSpec = predictor.getDataSpec();
    TreeEnsembleModelPortObjectSpec modelSpec = predictor.getModelSpec();
    ArrayList<DataColumnSpec> newColSpecs = new ArrayList<DataColumnSpec>();
    UniqueNameGenerator nameGen = new UniqueNameGenerator(testSpec);
    newColSpecs.add(nameGen.newColumn(config.getPredictionColumnName(), StringCell.TYPE));
    if (config.isAppendPredictionConfidence()) {
        newColSpecs.add(nameGen.newColumn("Confidence", DoubleCell.TYPE));
    }
    if (config.isAppendClassConfidences()) {
        final String targetColName = modelSpec.getTargetColumn().getName();
        final String suffix = config.getSuffixForClassProbabilities();
        for (String val : modelSpec.getTargetColumnPossibleValueMap().keySet()) {
            String colName = "P(" + targetColName + "=" + val + ")" + suffix;
            newColSpecs.add(nameGen.newColumn(colName, DoubleCell.TYPE));
        }
    }
    final Map<String, DataCell> targetValueMap = modelSpec.getTargetColumnPossibleValueMap();
    return new LKGradientBoostingPredictorCellFactory(newColSpecs.toArray(new DataColumnSpec[newColSpecs.size()]), predictor.getModel(), modelSpec.getLearnTableSpec(), modelSpec.calculateFilterIndices(testSpec), config, targetValueMap);
}

Example 28 with TreeEnsembleModelPortObjectSpec

use of org.knime.base.node.mine.treeensemble2.model.TreeEnsembleModelPortObjectSpec in project knime-core by knime.

the class GradientBoostingPredictorCellFactory method createFactory.

public static GradientBoostingPredictorCellFactory createFactory(final GradientBoostingPredictor<GradientBoostedTreesModel> predictor) throws InvalidSettingsException {
    TreeEnsembleModelPortObjectSpec modelSpec = predictor.getModelSpec();
    DataTableSpec learnSpec = modelSpec.getLearnTableSpec();
    DataTableSpec testSpec = predictor.getDataSpec();
    UniqueNameGenerator nameGen = new UniqueNameGenerator(testSpec);
    DataColumnSpec newColSpec = nameGen.newColumn(predictor.getConfiguration().getPredictionColumnName(), DoubleCell.TYPE);
    return new GradientBoostingPredictorCellFactory(newColSpec, predictor.getModel(), learnSpec, modelSpec.calculateFilterIndices(testSpec));
}

Example 29 with TreeEnsembleModelPortObjectSpec

use of org.knime.base.node.mine.treeensemble2.model.TreeEnsembleModelPortObjectSpec in project knime-core by knime.

the class GradientBoostingPredictorNodeModel 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);
    } else if (!m_configuration.isChangePredictionColumnName()) {
        m_configuration.setPredictionColumnName(TreeEnsemblePredictorConfiguration.getPredictColumnName(targetColName));
    }
    modelSpec.assertTargetTypeMatches(true);
    DataTableSpec dataSpec = (DataTableSpec) inSpecs[1];
    final GradientBoostingPredictor pred = new GradientBoostingPredictor(null, modelSpec, dataSpec, m_configuration);
    return new PortObjectSpec[] { pred.getPredictionRearranger().createSpec() };
}

Example 30 with TreeEnsembleModelPortObjectSpec

use of org.knime.base.node.mine.treeensemble2.model.TreeEnsembleModelPortObjectSpec 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);
}