Examples with UniqueNameGenerator org.knime.core.util.UniqueNameGenerator used on opensource projects

Example 11 with UniqueNameGenerator

use of org.knime.core.util.UniqueNameGenerator in project knime-core by knime.

the class ModifyDateNodeModel method createColumnRearranger.

/**
 * @param inSpec table input spec
 * @return the CR describing the output
 */
@Override
protected ColumnRearranger createColumnRearranger(final DataTableSpec inSpec) {
    final ColumnRearranger rearranger = new ColumnRearranger(inSpec);
    final String[] includeList = m_colSelect.applyTo(inSpec).getIncludes();
    final int[] includeIndices = Arrays.stream(m_colSelect.applyTo(inSpec).getIncludes()).mapToInt(s -> inSpec.findColumnIndex(s)).toArray();
    // determine the data type of output
    DataType dataType;
    if (m_modifyAction.getStringValue().equals(MODIFY_OPTION_REMOVE)) {
        dataType = LocalTimeCellFactory.TYPE;
    } else {
        if (m_modifyAction.getStringValue().equals(MODIFY_OPTION_CHANGE)) {
            dataType = LocalDateTimeCellFactory.TYPE;
        } else {
            if (m_timeZone.useZone()) {
                dataType = ZonedDateTimeCellFactory.TYPE;
            } else {
                dataType = LocalDateTimeCellFactory.TYPE;
            }
        }
    }
    final ZoneId zone = m_timeZone.getZone();
    int i = 0;
    for (final String includedCol : includeList) {
        if (inSpec.getColumnSpec(includedCol).getType().equals(ZonedDateTimeCellFactory.TYPE) && m_modifyAction.getStringValue().equals(MODIFY_OPTION_CHANGE)) {
            dataType = ZonedDateTimeCellFactory.TYPE;
        }
        if (m_isReplaceOrAppend.getStringValue().equals(OPTION_REPLACE)) {
            final DataColumnSpecCreator dataColumnSpecCreator = new DataColumnSpecCreator(includedCol, dataType);
            final SingleCellFactory cellFac = createCellFactory(dataColumnSpecCreator.createSpec(), includeIndices[i++], zone);
            rearranger.replace(cellFac, includedCol);
        } else {
            final DataColumnSpec dataColSpec = new UniqueNameGenerator(inSpec).newColumn(includedCol + m_suffix.getStringValue(), dataType);
            final SingleCellFactory cellFac = createCellFactory(dataColSpec, includeIndices[i++], zone);
            rearranger.append(cellFac);
        }
    }
    return rearranger;
}

Example 12 with UniqueNameGenerator

use of org.knime.core.util.UniqueNameGenerator in project knime-core by knime.

the class DecTreePredictorNodeModel method createOutTableSpec.

private DataTableSpec createOutTableSpec(final PortObjectSpec[] inSpecs) {
    LinkedList<DataCell> predValues = null;
    if (m_showDistribution.getBooleanValue()) {
        predValues = getPredictionValues((PMMLPortObjectSpec) inSpecs[INMODELPORT]);
        if (predValues == null) {
            // no out spec can be determined
            return null;
        }
    }
    int numCols = (predValues == null ? 0 : predValues.size()) + 1;
    DataTableSpec inSpec = (DataTableSpec) inSpecs[INDATAPORT];
    UniqueNameGenerator nameGenerator = new UniqueNameGenerator(inSpec);
    DataColumnSpec[] newCols = new DataColumnSpec[numCols];
    /* Set bar renderer and domain [0,1] as default for the double cells
         * containing the distribution */
    // DataColumnProperties propsRendering = new DataColumnProperties(
    // Collections.singletonMap(
    // DataValueRenderer.PROPERTY_PREFERRED_RENDERER,
    // DoubleBarRenderer.DESCRIPTION));
    DataColumnDomain domain = new DataColumnDomainCreator(new DoubleCell(0.0), new DoubleCell(1.0)).createDomain();
    // add all distribution columns
    for (int i = 0; i < numCols - 1; i++) {
        DataColumnSpecCreator colSpecCreator = nameGenerator.newCreator(predValues.get(i).toString(), DoubleCell.TYPE);
        // colSpecCreator.setProperties(propsRendering);
        colSpecCreator.setDomain(domain);
        newCols[i] = colSpecCreator.createSpec();
    }
    // add the prediction column
    newCols[numCols - 1] = nameGenerator.newColumn("Prediction (DecTree)", StringCell.TYPE);
    DataTableSpec newColSpec = new DataTableSpec(newCols);
    return new DataTableSpec(inSpec, newColSpec);
}

Example 13 with UniqueNameGenerator

use of org.knime.core.util.UniqueNameGenerator in project knime-core by knime.

the class RegressionTreePredictorCellFactory method createFactory.

/**
 * @param predictor
 * @return factory based on RegressionTreePredictor <b>predictor</b>
 * @throws InvalidSettingsException
 */
public static RegressionTreePredictorCellFactory createFactory(final RegressionTreePredictor predictor) throws InvalidSettingsException {
    DataTableSpec testDataSpec = predictor.getDataSpec();
    RegressionTreeModelPortObjectSpec modelSpec = predictor.getModelSpec();
    RegressionTreePredictorConfiguration 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);
    DataColumnSpec[] newCols = newColsList.toArray(new DataColumnSpec[newColsList.size()]);
    int[] learnColumnInRealDataIndices = modelSpec.calculateFilterIndices(testDataSpec);
    return new RegressionTreePredictorCellFactory(predictor, newCols, learnColumnInRealDataIndices);
}

Example 14 with UniqueNameGenerator

use of org.knime.core.util.UniqueNameGenerator 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 15 with UniqueNameGenerator

use of org.knime.core.util.UniqueNameGenerator 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);
}