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

Example 26 with PortObjectSpec

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

the class RandomForestClassificationLearnerNodeModel 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 };
}

Example 27 with PortObjectSpec

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

the class RandomForestRegressionLearnerNodeModel 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);
    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 };
}

Example 28 with PortObjectSpec

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

the class SotaNodeModel method configure.

/**
 * {@inheritDoc}
 */
@Override
protected PortObjectSpec[] configure(final PortObjectSpec[] inSpecs) throws InvalidSettingsException {
    assert inSpecs.length == 1;
    DataTableSpec inDataSpec = (DataTableSpec) inSpecs[SotaNodeModel.INPORT];
    int numberCells = 0;
    int fuzzyCells = 0;
    for (int i = 0; i < inDataSpec.getNumColumns(); i++) {
        DataType type = inDataSpec.getColumnSpec(i).getType();
        if (SotaUtil.isNumberType(type)) {
            numberCells++;
        } else if (SotaUtil.isFuzzyIntervalType(type)) {
            fuzzyCells++;
        }
    }
    StringBuffer buffer = new StringBuffer();
    if (numberCells > 0 && fuzzyCells > 0) {
        buffer.append("FuzzyIntervalCells and NumberCells are mixed ! ");
    }
    if (numberCells <= 0 && fuzzyCells <= 0) {
        buffer.append("Number of columns has to be " + "greater than zero ! ");
    }
    if (fuzzyCells <= 0 && m_sota.isUseHierarchicalFuzzyData()) {
        buffer.append("No fuzzy cells selected ! ");
    }
    if (m_sota.isUseHierarchicalFuzzyData() && m_hierarchieLevelCell == null) {
        buffer.append("No hierarchy column selected ! ");
    }
    // if buffer throw exception
    if (buffer.length() > 0) {
        throw new InvalidSettingsException(buffer.toString());
    }
    int classColIndex = inDataSpec.findColumnIndex(m_classCol.getStringValue());
    return new PortObjectSpec[] { new SotaPortObjectSpec(inDataSpec, classColIndex) };
}

Example 29 with PortObjectSpec

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

the class NaiveBayesLearnerNodeModel2 method configure.

/**
 * {@inheritDoc}
 */
@Override
protected PortObjectSpec[] configure(final PortObjectSpec[] inSpecs) throws InvalidSettingsException {
    // check the internal variables if they are valid
    final PortObjectSpec inSpec = inSpecs[TRAINING_DATA_PORT];
    if (!(inSpec instanceof DataTableSpec)) {
        throw new IllegalArgumentException("Invalid input data");
    }
    final DataTableSpec tableSpec = (DataTableSpec) inSpec;
    if (m_classifyColumnName.getStringValue() == null) {
        String predictedClassName = null;
        for (DataColumnSpec colSpec : tableSpec) {
            if (colSpec.getType().isCompatible(NominalValue.class)) {
                if (predictedClassName == null) {
                    predictedClassName = colSpec.getName();
                } else {
                    throw new InvalidSettingsException("Please define the classification column");
                }
            }
        }
        m_classifyColumnName.setStringValue(predictedClassName);
        setWarningMessage("Classification column preset to " + predictedClassName);
    }
    final String classColumn = m_classifyColumnName.getStringValue();
    final DataColumnSpec classColSpec = tableSpec.getColumnSpec(classColumn);
    if (classColSpec == null) {
        throw new InvalidSettingsException("Classification column not found in input table");
    }
    if (tableSpec.getNumColumns() < 2) {
        throw new InvalidSettingsException("Input table should contain at least 2 columns");
    }
    final int maxNoOfNominalVals = m_maxNoOfNominalVals.getIntValue();
    // and check each nominal column with a valid domain if it contains more values than allowed
    // this needs to be in sync with the NaiveBayesModel.createModelMap method!!!
    final List<String> ignoredColumns = new LinkedList<>();
    final List<String> toBigNominalColumns = new LinkedList<>();
    final List<String> learnCols = new LinkedList<>();
    for (final DataColumnSpec colSpec : tableSpec) {
        final AttributeModel model = NaiveBayesModel.getCompatibleModel(colSpec, classColumn, maxNoOfNominalVals, m_ignoreMissingVals.getBooleanValue(), m_pmmlCompatible.getBooleanValue());
        if (model == null) {
            // the column type is not supported by Naive Bayes
            ignoredColumns.add(colSpec.getName());
            continue;
        }
        final DataType colType = colSpec.getType();
        if (colType.isCompatible(NominalValue.class)) {
            final DataColumnDomain domain = colSpec.getDomain();
            if (domain != null && domain.getValues() != null) {
                if (domain.getValues().size() > maxNoOfNominalVals) {
                    // unique values
                    if (colSpec.getName().equals(classColumn)) {
                        // contains too many unique values
                        throw new InvalidSettingsException("Class column domain contains too many unique values" + " (count: " + domain.getValues().size() + ")");
                    }
                    toBigNominalColumns.add(colSpec.getName() + " (count: " + domain.getValues().size() + ")");
                }
            }
            learnCols.add(model.getAttributeName());
        }
    }
    warningMessage("The following columns will possibly be skipped due to too many values: ", toBigNominalColumns);
    warningMessage("The following columns are not supported and thus will be ignored: ", ignoredColumns);
    if (learnCols.size() < 1) {
        throw new InvalidSettingsException("Not enough valid columns");
    }
    final PMMLPortObjectSpec modelSpec = m_pmmlInEnabled ? (PMMLPortObjectSpec) inSpecs[MODEL_INPORT] : null;
    final PMMLPortObjectSpec pmmlSpec = createPMMLSpec(tableSpec, modelSpec, learnCols, classColumn);
    return new PortObjectSpec[] { pmmlSpec, NaiveBayesModel.createStatisticsTableSpec(classColSpec.getType(), m_ignoreMissingVals.getBooleanValue()) };
}

Example 30 with PortObjectSpec

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

the class NaiveBayesPredictorNodeModel2 method configure.

/**
 * {@inheritDoc}
 */
@Override
protected PortObjectSpec[] configure(final PortObjectSpec[] inSpecs) throws InvalidSettingsException {
    // check the input data
    assert (inSpecs != null && inSpecs.length == 2 && inSpecs[DATA_IN_PORT] != null && inSpecs[MODEL_IN_PORT] != null);
    final PortObjectSpec modelObject = inSpecs[MODEL_IN_PORT];
    if (!(modelObject instanceof PMMLPortObjectSpec)) {
        throw new IllegalArgumentException("Invalid input data");
    }
    final PMMLPortObjectSpec pmmlSpec = (PMMLPortObjectSpec) modelObject;
    final DataTableSpec trainingSpec = pmmlSpec.getDataTableSpec();
    if (trainingSpec == null) {
        throw new InvalidSettingsException("No model spec available");
    }
    final List<DataColumnSpec> targetCols = pmmlSpec.getTargetCols();
    if (targetCols.size() != 1) {
        throw new InvalidSettingsException("No valid class column found");
    }
    final DataColumnSpec classColumn = targetCols.get(0);
    final PortObjectSpec inSpec = inSpecs[DATA_IN_PORT];
    if (!(inSpec instanceof DataTableSpec)) {
        throw new IllegalArgumentException("TableSpec must not be null");
    }
    final DataTableSpec spec = (DataTableSpec) inSpec;
    // check the input data for columns with the wrong name or wrong type
    final List<String> unknownCols = check4UnknownCols(trainingSpec, spec);
    warningMessage("The following input columns are unknown and will be skipped: ", unknownCols);
    // check if the learned model contains columns which are not in the
    // input data
    final List<String> missingInputCols = check4MissingCols(trainingSpec, classColumn.getName(), spec);
    warningMessage("The following attributes are missing in the input data: ", missingInputCols);
    final PredictorHelper predictorHelper = PredictorHelper.getInstance();
    final DataColumnSpec resultColSpecs = NaiveBayesCellFactory.createResultColSpecs(predictorHelper.checkedComputePredictionColumnName(m_predictionColumnName.getStringValue(), m_overridePredicted.getBooleanValue(), classColumn.getName()), classColumn.getType(), spec, m_inclProbVals.getBooleanValue());
    if (resultColSpecs != null) {
        return new PortObjectSpec[] { AppendedColumnTable.getTableSpec(spec, resultColSpecs) };
    }
    return null;
}