Examples with PMMLPortObjectSpecCreator org.knime.core.node.port.pmml.PMMLPortObjectSpecCreator used on opensource projects

Example 56 with PMMLPortObjectSpecCreator

use of org.knime.core.node.port.pmml.PMMLPortObjectSpecCreator in project knime-core by knime.

the class RegressionTreePMMLTranslatorNodeModel method createPMMLSpec.

private PMMLPortObjectSpec createPMMLSpec(final RegressionTreeModelPortObjectSpec treeSpec, final RegressionTreeModel model) {
    DataColumnSpec targetSpec = treeSpec.getTargetColumn();
    DataTableSpec learnFeatureSpec = treeSpec.getLearnTableSpec();
    if (containsVector(learnFeatureSpec)) {
        setWarningMessage("The model was learned on a vector column. It's possible to export the model " + "to PMML but it won't be possible to import it from the exported PMML.");
    }
    if (model == null && containsVector(learnFeatureSpec)) {
        // at this point we don't know how long the vector column is
        return null;
    } else if (model != null) {
        // possibly expand vectors with model
        learnFeatureSpec = model.getLearnAttributeSpec(learnFeatureSpec);
    }
    DataTableSpec completeLearnSpec = new DataTableSpec(learnFeatureSpec, new DataTableSpec(targetSpec));
    PMMLPortObjectSpecCreator pmmlSpecCreator = new PMMLPortObjectSpecCreator(completeLearnSpec);
    try {
        pmmlSpecCreator.setLearningCols(learnFeatureSpec);
    } catch (InvalidSettingsException e) {
        // (as of KNIME v2.5.1)
        throw new IllegalStateException(e);
    }
    pmmlSpecCreator.setTargetCol(targetSpec);
    return pmmlSpecCreator.createSpec();
}

Example 57 with PMMLPortObjectSpecCreator

use of org.knime.core.node.port.pmml.PMMLPortObjectSpecCreator in project knime-core by knime.

the class LogisticRegressionContent method createSpec.

private static PMMLPortObjectSpec createSpec(final DataTableSpec spec, final String target, final String[] learningCols) {
    PMMLPortObjectSpecCreator c = new PMMLPortObjectSpecCreator(spec);
    c.setTargetColName(target);
    c.setLearningColsNames(Arrays.asList(learningCols));
    return c.createSpec();
}

Example 58 with PMMLPortObjectSpecCreator

use of org.knime.core.node.port.pmml.PMMLPortObjectSpecCreator in project knime-core by knime.

the class LogRegLearner method init.

/**
 * Initialize instance and check if settings are consistent.
 */
private void init(final DataTableSpec inSpec, final Set<String> exclude) throws InvalidSettingsException {
    List<String> inputCols = new ArrayList<String>();
    FilterResult includedColumns = m_settings.getIncludedColumns().applyTo(inSpec);
    for (String column : includedColumns.getIncludes()) {
        inputCols.add(column);
    }
    inputCols.remove(m_settings.getTargetColumn());
    if (inputCols.isEmpty()) {
        throw new InvalidSettingsException("At least one column must " + "be included.");
    }
    DataColumnSpec targetColSpec = null;
    List<DataColumnSpec> regressorColSpecs = new ArrayList<DataColumnSpec>();
    // Auto configuration when target is not set
    if (null == m_settings.getTargetColumn() && m_settings.getIncludedColumns().applyTo(inSpec).getExcludes().length == 0) {
        for (int i = 0; i < inSpec.getNumColumns(); i++) {
            DataColumnSpec colSpec = inSpec.getColumnSpec(i);
            String colName = colSpec.getName();
            inputCols.remove(colName);
            if (colSpec.getType().isCompatible(NominalValue.class)) {
                m_settings.setTargetColumn(colName);
            }
        }
        // when there is no column with nominal data
        if (null == m_settings.getTargetColumn()) {
            throw new InvalidSettingsException("No column in " + "spec compatible to \"NominalValue\".");
        }
    }
    // remove all columns that should not be used
    inputCols.removeAll(exclude);
    m_specialColumns = new LinkedList<>();
    for (int i = 0; i < inSpec.getNumColumns(); i++) {
        DataColumnSpec colSpec = inSpec.getColumnSpec(i);
        String colName = colSpec.getName();
        final DataType type = colSpec.getType();
        if (m_settings.getTargetColumn().equals(colName)) {
            if (type.isCompatible(NominalValue.class)) {
                targetColSpec = colSpec;
            } else {
                throw new InvalidSettingsException("Type of column \"" + colName + "\" is not nominal.");
            }
        } else if (inputCols.contains(colName)) {
            if (type.isCompatible(DoubleValue.class) || type.isCompatible(NominalValue.class)) {
                regressorColSpecs.add(colSpec);
            } else if (type.isCompatible(BitVectorValue.class) || type.isCompatible(ByteVectorValue.class) || (type.isCollectionType() && type.getCollectionElementType().isCompatible(DoubleValue.class))) {
                m_specialColumns.add(colSpec);
                // We change the table spec later to encode it as a string.
                regressorColSpecs.add(new DataColumnSpecCreator(colSpec.getName(), StringCell.TYPE).createSpec());
            } else {
                throw new InvalidSettingsException("Type of column \"" + colName + "\" is not one of the allowed types, " + "which are numeric or nomial.");
            }
        }
    }
    if (null != targetColSpec) {
        // Check if target has at least two categories.
        final Set<DataCell> targetValues = targetColSpec.getDomain().getValues();
        if (targetValues != null && targetValues.size() < 2) {
            throw new InvalidSettingsException("The target column \"" + targetColSpec.getName() + "\" has one value, only. " + "At least two target categories are expected.");
        }
        String[] learnerCols = new String[regressorColSpecs.size() + 1];
        for (int i = 0; i < regressorColSpecs.size(); i++) {
            learnerCols[i] = regressorColSpecs.get(i).getName();
        }
        learnerCols[learnerCols.length - 1] = targetColSpec.getName();
        final DataColumnSpec[] updatedSpecs = new DataColumnSpec[inSpec.getNumColumns()];
        for (int i = updatedSpecs.length; i-- > 0; ) {
            final DataColumnSpec columnSpec = inSpec.getColumnSpec(i);
            final DataType type = columnSpec.getType();
            if (type.isCompatible(BitVectorValue.class) || type.isCompatible(ByteVectorValue.class)) {
                final DataColumnSpecCreator colSpecCreator = new DataColumnSpecCreator(columnSpec.getName(), StringCell.TYPE);
                colSpecCreator.setProperties(new DataColumnProperties(Collections.singletonMap("realType", type.isCompatible(BitVectorValue.class) ? "BitVector" : "ByteVector")));
                updatedSpecs[i] = colSpecCreator.createSpec();
            } else {
                updatedSpecs[i] = columnSpec;
            }
        }
        DataTableSpec updated = new DataTableSpec(updatedSpecs);
        PMMLPortObjectSpecCreator creator = new PMMLPortObjectSpecCreator(updated);
        creator.setTargetCols(Arrays.asList(targetColSpec));
        creator.setLearningCols(regressorColSpecs);
        // creator.addPreprocColNames(m_specialColumns.stream().flatMap(spec -> ));
        m_pmmlOutSpec = creator.createSpec();
        m_learner = new Learner(m_pmmlOutSpec, m_specialColumns, m_settings.getTargetReferenceCategory(), m_settings.getSortTargetCategories(), m_settings.getSortIncludesCategories());
    } else {
        throw new InvalidSettingsException("The target is " + "not in the input.");
    }
}

Example 59 with PMMLPortObjectSpecCreator

use of org.knime.core.node.port.pmml.PMMLPortObjectSpecCreator in project knime-core by knime.

the class RegressionTreeModelPortObject method createDecisionTreePMMLPortObject.

public PMMLPortObject createDecisionTreePMMLPortObject() {
    final RegressionTreeModel model = getModel();
    DataTableSpec attributeLearnSpec = model.getLearnAttributeSpec(m_spec.getLearnTableSpec());
    DataColumnSpec targetSpec = m_spec.getTargetColumn();
    PMMLPortObjectSpecCreator pmmlSpecCreator = new PMMLPortObjectSpecCreator(new DataTableSpec(attributeLearnSpec, new DataTableSpec(targetSpec)));
    try {
        pmmlSpecCreator.setLearningCols(attributeLearnSpec);
    } catch (InvalidSettingsException e) {
        // (as of KNIME v2.5.1)
        throw new IllegalStateException(e);
    }
    pmmlSpecCreator.setTargetCol(targetSpec);
    PMMLPortObjectSpec pmmlSpec = pmmlSpecCreator.createSpec();
    PMMLPortObject portObject = new PMMLPortObject(pmmlSpec);
    final TreeModelRegression tree = model.getTreeModel();
    portObject.addModelTranslater(new RegressionTreeModelPMMLTranslator(tree, model.getMetaData(), m_spec.getLearnTableSpec()));
    return portObject;
}

Example 60 with PMMLPortObjectSpecCreator

use of org.knime.core.node.port.pmml.PMMLPortObjectSpecCreator in project knime-core by knime.

the class RuleEngine2PortsNodeModel method createPMMLPortObjectSpec.

/**
 * Initializes the {@link PMMLPortObjectSpec} based on the model, input and the used column.
 *
 * @param modelSpec The preprocessing model, can be {@code null}.
 * @param spec The input table spec.
 * @param usedColumns The columns used by the rules.
 * @return The {@link PMMLPortObjectSpec} filled with proper data for configuration.
 */
private PMMLPortObjectSpec createPMMLPortObjectSpec(final DataTableSpec spec, final List<String> usedColumns) {
    // this assumes that the new column is always the last column in the spec; which is the case if
    // #createRearranger uses ColumnRearranger.append.
    String targetCol = m_settings.isReplaceColumn() ? m_settings.getReplaceColumn() : spec.getColumnSpec(spec.getNumColumns() - 1).getName();
    Set<String> set = new LinkedHashSet<String>(usedColumns);
    List<String> learnCols = new LinkedList<String>();
    for (int i = 0; i < spec.getNumColumns(); i++) {
        DataColumnSpec columnSpec = spec.getColumnSpec(i);
        String col = columnSpec.getName();
        if (!col.equals(targetCol) && set.contains(col) && (columnSpec.getType().isCompatible(DoubleValue.class) || columnSpec.getType().isCompatible(NominalValue.class) && (/*!m_skipColumns.getBooleanValue() ||*/
        columnSpec.getDomain().hasValues()))) {
            learnCols.add(spec.getColumnSpec(i).getName());
        }
    }
    PMMLPortObjectSpecCreator pmmlSpecCreator = new PMMLPortObjectSpecCreator(spec);
    pmmlSpecCreator.setLearningColsNames(learnCols);
    pmmlSpecCreator.setTargetColName(targetCol);
    return pmmlSpecCreator.createSpec();
}