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

Example 26 with PMMLPortObjectSpec

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

the class RegressionPredictorNodeModel method configure.

/**
 * {@inheritDoc}
 */
@Override
protected PortObjectSpec[] configure(final PortObjectSpec[] inSpecs) throws InvalidSettingsException {
    PMMLPortObjectSpec regModelSpec = (PMMLPortObjectSpec) inSpecs[0];
    DataTableSpec dataSpec = (DataTableSpec) inSpecs[1];
    if (dataSpec == null || regModelSpec == null) {
        throw new InvalidSettingsException("No input specification available");
    }
    checkModelTargetType(regModelSpec, m_expectClassificationModel);
    if (null != RegressionPredictorCellFactory.createColumnSpec(regModelSpec, dataSpec, m_settings)) {
        ColumnRearranger c = new ColumnRearranger(dataSpec);
        c.append(new RegressionPredictorCellFactory(regModelSpec, dataSpec, m_settings) {

            @Override
            public DataCell[] getCells(final DataRow row) {
                // not called during configure
                return null;
            }
        });
        DataTableSpec outSpec = c.createSpec();
        return new DataTableSpec[] { outSpec };
    } else {
        return null;
    }
}

Example 27 with PMMLPortObjectSpec

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

the class NaiveBayesLearnerNodeModel2 method execute.

/**
 * {@inheritDoc}
 */
@Override
protected PortObject[] execute(final PortObject[] inData, final ExecutionContext exec) throws CanceledExecutionException, InvalidSettingsException {
    LOGGER.debug("Entering execute of " + NaiveBayesLearnerNodeModel2.class.getName());
    assert (inData != null && ((inData.length == 2 && m_pmmlInEnabled) || (inData.length == 1 && !m_pmmlInEnabled)) && inData[TRAINING_DATA_PORT] != null);
    final PortObject inObject = inData[TRAINING_DATA_PORT];
    if (!(inObject instanceof BufferedDataTable)) {
        throw new IllegalArgumentException("Invalid input data");
    }
    final BufferedDataTable trainingTable = (BufferedDataTable) inObject;
    final boolean ignoreMissingVals = m_ignoreMissingVals.getBooleanValue();
    final boolean pmmlCompatible = m_pmmlCompatible.getBooleanValue();
    final int maxNoOfNomVals = m_maxNoOfNominalVals.getIntValue();
    m_model = new NaiveBayesModel(trainingTable, m_classifyColumnName.getStringValue(), exec, maxNoOfNomVals, ignoreMissingVals, pmmlCompatible, m_threshold.getDoubleValue());
    final List<String> missingModels = m_model.getAttributesWithMissingVals();
    if (missingModels.size() > 0) {
        final StringBuilder buf = new StringBuilder();
        buf.append("The following attributes contain missing values: ");
        for (int i = 0, length = missingModels.size(); i < length; i++) {
            if (i != 0) {
                buf.append(", ");
            }
            if (i > 3) {
                buf.append("...(see View)");
                break;
            }
            buf.append(missingModels.get(i));
        }
        setWarningMessage(buf.toString());
    }
    if (m_model.containsSkippedAttributes()) {
        setWarningMessage(m_model.getSkippedAttributesString(3));
    }
    LOGGER.debug("Exiting execute of " + NaiveBayesLearnerNodeModel2.class.getName());
    // handle the optional PMML input
    final PMMLPortObject inPMMLPort = m_pmmlInEnabled ? (PMMLPortObject) inData[MODEL_INPORT] : null;
    final DataTableSpec tableSpec = trainingTable.getSpec();
    final PMMLPortObjectSpec outPortSpec = createPMMLSpec(tableSpec, inPMMLPort == null ? null : inPMMLPort.getSpec(), m_model.getPMMLLearningCols(), m_model.getClassColumnName());
    final PMMLPortObject outPMMLPort = new PMMLPortObject(outPortSpec, inPMMLPort, tableSpec);
    outPMMLPort.addModelTranslater(new PMMLNaiveBayesModelTranslator(m_model));
    return new PortObject[] { outPMMLPort, m_model.getStatisticsTable() };
}

Example 28 with PMMLPortObjectSpec

use of org.knime.core.node.port.pmml.PMMLPortObjectSpec 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 29 with PMMLPortObjectSpec

use of org.knime.core.node.port.pmml.PMMLPortObjectSpec 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;
}

Example 30 with PMMLPortObjectSpec

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

the class LinReg2LearnerNodeModel method execute.

/**
 * {@inheritDoc}
 */
@Override
protected PortObject[] execute(final PortObject[] inObjects, final ExecutionContext exec) throws Exception {
    final BufferedDataTable data = (BufferedDataTable) inObjects[0];
    // cache the entire table as otherwise the color information
    // may be lost (filtering out the "colored" column)
    m_rowContainer = new DefaultDataArray(data, m_settings.getScatterPlotFirstRow(), m_settings.getScatterPlotRowCount());
    DataTableSpec tableSpec = data.getDataTableSpec();
    // handle the optional PMML input
    PMMLPortObject inPMMLPort = m_pmmlInEnabled ? (PMMLPortObject) inObjects[1] : null;
    PMMLPortObjectSpec inPMMLSpec = null;
    if (inPMMLPort != null) {
        inPMMLSpec = inPMMLPort.getSpec();
    } else {
        PMMLPortObjectSpecCreator creator = new PMMLPortObjectSpecCreator(tableSpec);
        inPMMLSpec = creator.createSpec();
        inPMMLPort = new PMMLPortObject(inPMMLSpec);
    }
    LinReg2Learner learner = new LinReg2Learner(new PortObjectSpec[] { tableSpec, inPMMLSpec }, m_pmmlInEnabled, m_settings);
    m_content = learner.execute(new PortObject[] { data, inPMMLPort }, exec);
    if (learner.getWarningMessage() != null && learner.getWarningMessage().length() > 0) {
        setWarningMessage(learner.getWarningMessage());
    }
    // third argument is ignored since we provide a port
    PMMLPortObject outPMMLPort = new PMMLPortObject((PMMLPortObjectSpec) learner.getOutputSpec()[0], inPMMLPort, null);
    PMMLGeneralRegressionTranslator trans = new PMMLGeneralRegressionTranslator(m_content.createGeneralRegressionContent());
    outPMMLPort.addModelTranslater(trans);
    final String warningMessage = m_content.getWarningMessage();
    if (warningMessage != null) {
        setWarningMessage(getWarningMessage() == null ? warningMessage : (getWarningMessage() + "\n" + warningMessage));
    }
    return new PortObject[] { outPMMLPort, m_content.createTablePortObject(exec) };
}