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

Example 21 with TreeEnsembleModelPortObjectSpec

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

the class TreeEnsembleClassificationPredictorCellFactory2 method createFactory.

/**
 * Creates a TreeEnsembleClassificationPredictorCellFactory from the provided <b>predictor</b>
 * @param predictor
 * @return an instance of TreeEnsembleClassificationPredictorCellFactory configured according to the settings of the provided
 * <b>predictor<b>
 * @throws InvalidSettingsException
 */
public static TreeEnsembleClassificationPredictorCellFactory2 createFactory(final TreeEnsemblePredictor predictor) throws InvalidSettingsException {
    DataTableSpec testDataSpec = predictor.getDataSpec();
    TreeEnsembleModelPortObjectSpec modelSpec = predictor.getModelSpec();
    TreeEnsembleModelPortObject modelObject = predictor.getModelObject();
    TreeEnsemblePredictorConfiguration configuration = predictor.getConfiguration();
    UniqueNameGenerator nameGen = new UniqueNameGenerator(testDataSpec);
    Map<String, DataCell> targetValueMap = modelSpec.getTargetColumnPossibleValueMap();
    List<DataColumnSpec> newColsList = new ArrayList<DataColumnSpec>();
    DataType targetColType = modelSpec.getTargetColumn().getType();
    String targetColName = configuration.getPredictionColumnName();
    DataColumnSpec targetCol = nameGen.newColumn(targetColName, targetColType);
    newColsList.add(targetCol);
    if (configuration.isAppendPredictionConfidence()) {
        newColsList.add(nameGen.newColumn(targetCol.getName() + " (Confidence)", DoubleCell.TYPE));
    }
    if (configuration.isAppendClassConfidences()) {
        // and this class is not called)
        assert targetValueMap != null : "Target column has no possible values";
        for (String v : targetValueMap.keySet()) {
            newColsList.add(nameGen.newColumn(v, DoubleCell.TYPE));
        }
    }
    if (configuration.isAppendModelCount()) {
        newColsList.add(nameGen.newColumn("model count", IntCell.TYPE));
    }
    // assigned
    assert modelObject == null || targetValueMap != null : "Target values must be known during execution";
    DataColumnSpec[] newCols = newColsList.toArray(new DataColumnSpec[newColsList.size()]);
    int[] learnColumnInRealDataIndices = modelSpec.calculateFilterIndices(testDataSpec);
    return new TreeEnsembleClassificationPredictorCellFactory2(predictor, targetValueMap, newCols, learnColumnInRealDataIndices);
}

Example 22 with TreeEnsembleModelPortObjectSpec

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

the class TreeEnsembleRegressionPredictorCellFactory method createFactory.

/**
 * Creates a TreeEnsembleRegressionPredictorCellFactory from the provided <b>predictor</b>
 *
 * @param predictor
 * @return an instance of TreeEnsembleRegressionPredictorCellFactory configured according to the settings of the provided
 * <b>predictor<b>
 * @throws InvalidSettingsException
 */
public static TreeEnsembleRegressionPredictorCellFactory createFactory(final TreeEnsemblePredictor predictor) throws InvalidSettingsException {
    DataTableSpec testDataSpec = predictor.getDataSpec();
    TreeEnsembleModelPortObjectSpec modelSpec = predictor.getModelSpec();
    // TreeEnsembleModelPortObject modelObject = predictor.getModelObject();
    TreeEnsemblePredictorConfiguration configuration = predictor.getConfiguration();
    UniqueNameGenerator nameGen = new UniqueNameGenerator(testDataSpec);
    List<DataColumnSpec> newColsList = new ArrayList<DataColumnSpec>();
    String targetColName = configuration.getPredictionColumnName();
    DataColumnSpec targetCol = nameGen.newColumn(targetColName, DoubleCell.TYPE);
    newColsList.add(targetCol);
    if (configuration.isAppendPredictionConfidence()) {
        newColsList.add(nameGen.newColumn(targetCol.getName() + " (Prediction Variance)", DoubleCell.TYPE));
    }
    if (configuration.isAppendModelCount()) {
        newColsList.add(nameGen.newColumn("model count", IntCell.TYPE));
    }
    DataColumnSpec[] newCols = newColsList.toArray(new DataColumnSpec[newColsList.size()]);
    int[] learnColumnInRealDataIndices = modelSpec.calculateFilterIndices(testDataSpec);
    return new TreeEnsembleRegressionPredictorCellFactory(predictor, newCols, learnColumnInRealDataIndices);
}

Example 23 with TreeEnsembleModelPortObjectSpec

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

the class GradientBoostingClassificationLearnerNodeModel method execute.

/**
 * {@inheritDoc}
 */
@Override
protected PortObject[] execute(final PortObject[] inData, final ExecutionContext exec) throws Exception {
    BufferedDataTable t = (BufferedDataTable) inData[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);
    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");
    AbstractGradientBoostingLearner learner = new LKGradientBoostedTreesLearner(m_configuration, data);
    AbstractGradientBoostingModel model;
    // m_configuration.setMissingValueHandling(MissingValueHandling.XGBoost);
    // try {
    model = learner.learn(learnExec);
    // } catch (ExecutionException e) {
    // Throwable cause = e.getCause();
    // if (cause instanceof Exception) {
    // throw (Exception)cause;
    // }
    // throw e;
    // }
    GradientBoostingModelPortObject modelPortObject = new GradientBoostingModelPortObject(ensembleSpec, model);
    learnExec.setProgress(1.0);
    if (warn != null) {
        setWarningMessage(warn);
    }
    return new PortObject[] { modelPortObject };
}

Example 24 with TreeEnsembleModelPortObjectSpec

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

the class GradientBoostingClassificationLearnerNodeModel 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);
    // }
    m_configuration.checkColumnSelection(inSpec);
    DataTableSpec learnSpec = learnRearranger.createSpec();
    TreeEnsembleModelPortObjectSpec ensembleSpec = m_configuration.createPortObjectSpec(learnSpec);
    ensembleSpec.assertTargetTypeMatches(false);
    // 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();
    return new PortObjectSpec[] { ensembleSpec };
}

Example 25 with TreeEnsembleModelPortObjectSpec

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

the class GradientBoostingRegressionLearnerNodeModel method execute.

/**
 * {@inheritDoc}
 */
@Override
protected PortObject[] execute(final PortObject[] inData, final ExecutionContext exec) throws Exception {
    BufferedDataTable t = (BufferedDataTable) inData[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");
    AbstractGradientBoostingLearner learner = new MGradientBoostedTreesLearner(m_configuration, data);
    AbstractGradientBoostingModel model;
    // try {
    model = learner.learn(learnExec);
    // } catch (ExecutionException e) {
    // Throwable cause = e.getCause();
    // if (cause instanceof Exception) {
    // throw (Exception)cause;
    // }
    // throw e;
    // }
    GradientBoostingModelPortObject modelPortObject = new GradientBoostingModelPortObject(ensembleSpec, model);
    learnExec.setProgress(1.0);
    if (warn != null) {
        setWarningMessage(warn);
    }
    return new PortObject[] { modelPortObject };
}