Examples with DataType org.knime.core.data.DataType used on opensource projects

Example 36 with DataType

use of org.knime.core.data.DataType in project knime-core by knime.

the class RegressionTreePMMLTranslatorNodeModel method containsVector.

private static boolean containsVector(final DataTableSpec learnFeatureSpec) {
    for (DataColumnSpec colSpec : learnFeatureSpec) {
        DataType type = colSpec.getType();
        boolean isVector = type.isCompatible(BitVectorValue.class) || type.isCompatible(DoubleVectorValue.class) || type.isCompatible(ByteVectorValue.class);
        if (isVector) {
            return true;
        }
    }
    return false;
}

Example 37 with DataType

use of org.knime.core.data.DataType in project knime-core by knime.

the class GradientBoostingPMMLPredictorNodeModel method configure.

/**
 * {@inheritDoc}
 */
@Override
protected PortObjectSpec[] configure(final PortObjectSpec[] inSpecs) throws InvalidSettingsException {
    PMMLPortObjectSpec pmmlSpec = (PMMLPortObjectSpec) inSpecs[0];
    DataType targetType = extractTargetType(pmmlSpec);
    if (m_isRegression && !targetType.isCompatible(DoubleValue.class)) {
        throw new InvalidSettingsException("This node expects a regression model.");
    } else if (!m_isRegression && !targetType.isCompatible(StringValue.class)) {
        throw new InvalidSettingsException("This node expectes a classification model.");
    }
    try {
        AbstractTreeModelPMMLTranslator.checkPMMLSpec(pmmlSpec);
    } catch (IllegalArgumentException e) {
        throw new InvalidSettingsException(e.getMessage());
    }
    TreeEnsembleModelPortObjectSpec modelSpec = translateSpec(pmmlSpec);
    String targetColName = modelSpec.getTargetColumn().getName();
    if (m_configuration == null) {
        m_configuration = TreeEnsemblePredictorConfiguration.createDefault(m_isRegression, targetColName);
    } else if (!m_configuration.isChangePredictionColumnName()) {
        m_configuration.setPredictionColumnName(TreeEnsemblePredictorConfiguration.getPredictColumnName(targetColName));
    }
    modelSpec.assertTargetTypeMatches(m_isRegression);
    DataTableSpec dataSpec = (DataTableSpec) inSpecs[1];
    final GradientBoostingPredictor<GradientBoostedTreesModel> pred = new GradientBoostingPredictor<>(null, modelSpec, dataSpec, m_configuration);
    return new PortObjectSpec[] { pred.getPredictionRearranger().createSpec() };
}

Example 38 with DataType

use of org.knime.core.data.DataType in project knime-core by knime.

the class GradientBoostingPMMLPredictorNodeModel method importModel.

@SuppressWarnings("unchecked")
private GradientBoostingModelPortObject importModel(final PMMLPortObject pmmlPO) {
    AbstractGBTModelPMMLTranslator<M> pmmlTranslator;
    DataType targetType = extractTargetType(pmmlPO.getSpec());
    if (targetType.isCompatible(DoubleValue.class)) {
        pmmlTranslator = (AbstractGBTModelPMMLTranslator<M>) new RegressionGBTModelPMMLTranslator();
    } else if (targetType.isCompatible(StringValue.class)) {
        pmmlTranslator = (AbstractGBTModelPMMLTranslator<M>) new ClassificationGBTModelPMMLTranslator();
    } else {
        throw new IllegalArgumentException("Currently only regression models are supported.");
    }
    pmmlPO.initializeModelTranslator(pmmlTranslator);
    if (pmmlTranslator.hasWarning()) {
        setWarningMessage(pmmlTranslator.getWarning());
    }
    return new GradientBoostingModelPortObject(new TreeEnsembleModelPortObjectSpec(pmmlTranslator.getLearnSpec()), pmmlTranslator.getGBTModel());
}

Example 39 with DataType

use of org.knime.core.data.DataType in project knime-core by knime.

the class TreeEnsembleLearnerConfiguration method checkColumnSelection.

/**
 * To be used in the configure of the learner nodes. Checks if the column selection makes sense and throws an
 * InvalidSettingsException otherwise. The sanity checks include: <br>
 * Existence and type check of fingerprint columns if specified. <br>
 * Check if any attributes are selected if no fingerprint column is used for learning.
 *
 * @param inSpec Spec of the incoming table
 * @throws InvalidSettingsException thrown if the column selection makes no sense
 */
public void checkColumnSelection(final DataTableSpec inSpec) throws InvalidSettingsException {
    FilterResult filterResult = m_columnFilterConfig.applyTo(inSpec);
    if (m_fingerprintColumn != null) {
        DataColumnSpec colSpec = inSpec.getColumnSpec(m_fingerprintColumn);
        if (colSpec == null) {
            throw new InvalidSettingsException("The fingerprint column is not contained in the incoming table.");
        }
        DataType colType = colSpec.getType();
        if (!(colType.isCompatible(BitVectorValue.class) || colType.isCompatible(ByteVectorValue.class) || colType.isCompatible(DoubleVectorValue.class))) {
            throw new InvalidSettingsException("The specified fingerprint column is not of a compatible vector type.");
        }
    } else if (filterResult.getIncludes().length > 0) {
    // ok, there are some features selected
    } else {
        throw new InvalidSettingsException("No attributes are selected.");
    }
}

Example 40 with DataType

use of org.knime.core.data.DataType in project knime-core by knime.

the class TreeEnsembleLearnerConfiguration method filterLearnColumns.

/**
 * @param spec
 * @return ColumnRearranger that filters out all columns not part of the learning columns.
 * @throws InvalidSettingsException
 */
public FilterLearnColumnRearranger filterLearnColumns(final DataTableSpec spec) throws InvalidSettingsException {
    // (ColumnRearranger is a final class in v2.5)
    if (m_targetColumn == null) {
        throw new InvalidSettingsException("Target column not set");
    }
    DataColumnSpec targetCol = spec.getColumnSpec(m_targetColumn);
    if (targetCol == null || !targetCol.getType().isCompatible(getRequiredTargetClass())) {
        throw new InvalidSettingsException("Target column \"" + m_targetColumn + "\" does not exist or is not of the " + "correct type");
    }
    FilterResult filterResult = m_columnFilterConfig.applyTo(spec);
    List<String> noDomainColumns = new ArrayList<String>();
    FilterLearnColumnRearranger rearranger = new FilterLearnColumnRearranger(spec);
    if (m_fingerprintColumn == null) {
        // use ordinary data
        Set<String> incl = new HashSet<String>(Arrays.asList(filterResult.getIncludes()));
        // the target column can possibly show up in the include list of the filter result
        // therefore we have to remove it
        incl.remove(targetCol.getName());
        for (DataColumnSpec col : spec) {
            String colName = col.getName();
            if (colName.equals(m_targetColumn)) {
                continue;
            }
            DataType type = col.getType();
            boolean ignoreColumn = false;
            boolean isAppropriateType = type.isCompatible(DoubleValue.class) || type.isCompatible(NominalValue.class);
            if (incl.remove(colName)) {
                // accept unless type mismatch
                if (!isAppropriateType) {
                    throw new InvalidSettingsException("Attribute column \"" + colName + "\" is " + "not of the expected type (must be " + "numeric or nominal).");
                } else if (shouldIgnoreLearnColumn(col)) {
                    ignoreColumn = true;
                    noDomainColumns.add(colName);
                } else {
                // accept
                }
            } else {
                ignoreColumn = true;
            }
            // }
            if (ignoreColumn) {
                rearranger.remove(colName);
            }
        }
        if (rearranger.getColumnCount() <= 1) {
            StringBuilder b = new StringBuilder("Input table has no valid " + "learning columns (need one additional numeric or " + "nominal column).");
            if (!noDomainColumns.isEmpty()) {
                b.append(" ").append(noDomainColumns.size());
                b.append(" column(s) were ignored due to missing domain ");
                b.append("information -- execute predecessor and/or ");
                b.append(" use Domain Calculator node.");
                throw new InvalidSettingsException(b.toString());
            }
        }
        if (/*!m_includeAllColumns &&*/
        !incl.isEmpty()) {
            StringBuilder missings = new StringBuilder();
            int i = 0;
            for (Iterator<String> it = incl.iterator(); it.hasNext() && i < 4; i++) {
                String s = it.next();
                missings.append(i > 0 ? ", " : "").append(s);
                it.remove();
            }
            if (!incl.isEmpty()) {
                missings.append(",...").append(incl.size()).append(" more");
            }
            throw new InvalidSettingsException("Some selected attributes " + "are not present in the input table: " + missings);
        }
    } else {
        // use fingerprint data
        DataColumnSpec fpCol = spec.getColumnSpec(m_fingerprintColumn);
        if (fpCol == null || !(fpCol.getType().isCompatible(BitVectorValue.class) || fpCol.getType().isCompatible(ByteVectorValue.class) || fpCol.getType().isCompatible(DoubleVectorValue.class))) {
            throw new InvalidSettingsException("Fingerprint columnn \"" + m_fingerprintColumn + "\" does not exist or is not " + "of correct type.");
        }
        rearranger.keepOnly(m_targetColumn, m_fingerprintColumn);
    }
    rearranger.move(m_targetColumn, rearranger.getColumnCount());
    String warn = null;
    if (!noDomainColumns.isEmpty()) {
        StringBuilder b = new StringBuilder();
        b.append(noDomainColumns.size());
        b.append(" column(s) were ignored due to missing domain");
        b.append(" information: [");
        int index = 0;
        for (String s : noDomainColumns) {
            if (index > 3) {
                b.append(", ...");
                break;
            }
            if (index > 0) {
                b.append(", ");
            }
            b.append("\"").append(s).append("\"");
            index++;
        }
        b.append("] -- change the node configuration or use a");
        b.append(" Domain Calculator node to fix it");
        warn = b.toString();
    }
    rearranger.setWarning(warn);
    return rearranger;
}