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

Example 16 with TreeEnsembleModelPortObjectSpec

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

Example 17 with TreeEnsembleModelPortObjectSpec

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

the class TreeEnsembleClassificationPredictorNodeModel method createStreamableOperator.

/**
 * {@inheritDoc}
 */
@Override
public StreamableOperator createStreamableOperator(final PartitionInfo partitionInfo, final PortObjectSpec[] inSpecs) throws InvalidSettingsException {
    return new StreamableOperator() {

        @Override
        public void runFinal(final PortInput[] inputs, final PortOutput[] outputs, final ExecutionContext exec) throws Exception {
            TreeEnsembleModelPortObject model = (TreeEnsembleModelPortObject) ((PortObjectInput) inputs[0]).getPortObject();
            TreeEnsembleModelPortObjectSpec modelSpec = model.getSpec();
            DataTableSpec dataSpec = (DataTableSpec) inSpecs[1];
            final TreeEnsemblePredictor pred = new TreeEnsemblePredictor(modelSpec, model, dataSpec, m_configuration);
            ColumnRearranger rearranger = pred.getPredictionRearranger();
            StreamableFunction func = rearranger.createStreamableFunction(1, 0);
            func.runFinal(inputs, outputs, exec);
        }
    };
}

Example 18 with TreeEnsembleModelPortObjectSpec

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

the class TreeEnsembleClassificationPredictorNodeModel 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];
    final TreeEnsemblePredictor pred = new TreeEnsemblePredictor(modelSpec, null, dataSpec, m_configuration);
    ColumnRearranger rearranger = pred.getPredictionRearranger();
    // rearranger may be null if confidence values are appended but the
    // model does not have a list of possible target values
    DataTableSpec outSpec = rearranger != null ? rearranger.createSpec() : null;
    return new DataTableSpec[] { outSpec };
}

Example 19 with TreeEnsembleModelPortObjectSpec

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

the class TreeEnsembleShrinkerNodeModel method configure.

@Override
protected PortObjectSpec[] configure(final PortObjectSpec[] inSpecs) throws InvalidSettingsException {
    TreeEnsembleModelPortObjectSpec modelSpec = (TreeEnsembleModelPortObjectSpec) inSpecs[0];
    modelSpec.assertTargetTypeMatches(false);
    DataTableSpec tableSpec = (DataTableSpec) inSpecs[1];
    int targetColumnIndex = tableSpec.findColumnIndex(m_config.getTargetColumn());
    if (targetColumnIndex < 0 || !tableSpec.getColumnSpec(targetColumnIndex).getType().isCompatible(StringValue.class)) {
        throw new InvalidSettingsException("No valid target column selected");
    }
    return new PortObjectSpec[] { null };
}

Example 20 with TreeEnsembleModelPortObjectSpec

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

the class TreeEnsembleClassificationLearnerNodeModel method configure.

/**
 * {@inheritDoc}
 */
@Override
protected PortObjectSpec[] configure(final PortObjectSpec[] inSpecs) throws InvalidSettingsException {
    // guaranteed to not be null (according to API)
    DataTableSpec inSpec = (DataTableSpec) inSpecs[0];
    if (m_configuration == null) {
        throw new InvalidSettingsException("No configuration available");
    }
    final FilterLearnColumnRearranger learnRearranger = m_configuration.filterLearnColumns(inSpec);
    final String warn = learnRearranger.getWarning();
    if (warn != null) {
        setWarningMessage(warn);
    }
    DataTableSpec learnSpec = learnRearranger.createSpec();
    TreeEnsembleModelPortObjectSpec ensembleSpec = m_configuration.createPortObjectSpec(learnSpec);
    // the following call may return null, which is OK during configure
    // but not upon execution (spec may not be populated yet, e.g.
    // predecessor not executed)
    // if the possible values is not null, the following call checks
    // for duplicates in the toString() representation
    ensembleSpec.getTargetColumnPossibleValueMap();
    final TreeEnsemblePredictor outOfBagPredictor = createOutOfBagPredictor(ensembleSpec, null, inSpec);
    ColumnRearranger outOfBagRearranger = outOfBagPredictor.getPredictionRearranger();
    DataTableSpec outOfBagSpec = outOfBagRearranger == null ? null : outOfBagRearranger.createSpec();
    DataTableSpec colStatsSpec = TreeEnsembleLearner.getColumnStatisticTableSpec();
    return new PortObjectSpec[] { outOfBagSpec, colStatsSpec, ensembleSpec };
}