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

Example 11 with PMMLPortObjectSpec

use of org.knime.core.node.port.pmml.PMMLPortObjectSpec 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 12 with PMMLPortObjectSpec

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

the class Many2OneColPMMLNodeModel method configure.

/**
 * {@inheritDoc}
 */
@Override
protected PortObjectSpec[] configure(final PortObjectSpec[] inSpecs) throws InvalidSettingsException {
    DataTableSpec inDataSpec = (DataTableSpec) inSpecs[0];
    if (m_includedColumns.getIncludeList().size() <= 0) {
        setWarningMessage("No column selected. Node will have no effect!");
    }
    // if it is not a reg exp it must be double compatible
    if (!m_includeMethod.getStringValue().equals(IncludeMethod.RegExpPattern.name())) {
        for (String colName : m_includedColumns.getIncludeList()) {
            if (!inDataSpec.getColumnSpec(colName).getType().isCompatible(DoubleValue.class)) {
                throw new InvalidSettingsException("For selected include method '" + m_includeMethod.getStringValue() + "' only double compatible values are allowed." + " Column '" + colName + "' is not.");
            }
        }
    }
    ColumnRearranger rearranger = createRearranger(inDataSpec, getCellFactory(inDataSpec));
    if (m_pmmlEnabled) {
        PMMLPortObjectSpec pmmlSpec = (PMMLPortObjectSpec) inSpecs[1];
        PMMLPortObjectSpecCreator pmmlSpecCreator = new PMMLPortObjectSpecCreator(pmmlSpec, inDataSpec);
        return new PortObjectSpec[] { rearranger.createSpec(), pmmlSpecCreator.createSpec() };
    } else {
        return new DataTableSpec[] { rearranger.createSpec() };
    }
}

Example 13 with PMMLPortObjectSpec

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

the class LogRegLearnerNodeModel method execute.

/**
 * {@inheritDoc}
 */
@Override
protected PortObject[] execute(final PortObject[] inObjects, final ExecutionContext exec) throws Exception {
    final BufferedDataTable data = (BufferedDataTable) inObjects[0];
    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);
    }
    LogRegLearner learner = new LogRegLearner(new PortObjectSpec[] { tableSpec, inPMMLSpec }, m_pmmlInEnabled, m_settings);
    m_content = learner.execute(new PortObject[] { data, inPMMLPort }, exec);
    String warn = learner.getWarningMessage();
    if (warn != null) {
        setWarningMessage(warn);
    }
    // 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);
    return new PortObject[] { outPMMLPort, m_content.createTablePortObject(exec) };
}

Example 14 with PMMLPortObjectSpec

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

the class LogisticRegressionContent method load.

/**
 * @param parContent the content that holds the internals
 * @param spec the data table spec of the training data
 * @return a instance with he loaded values
 * @throws InvalidSettingsException when data are not well formed
 */
static LogisticRegressionContent load(final ModelContentRO parContent, final DataTableSpec spec) throws InvalidSettingsException {
    String target = parContent.getString(CFG_TARGET);
    String[] learningCols = parContent.getStringArray(CFG_LEARNING_COLS);
    PMMLPortObjectSpec pmmlSpec = createSpec(spec, target, learningCols);
    String[] factors = parContent.getStringArray(CFG_FACTORS);
    String[] covariates = parContent.getStringArray(CFG_COVARIATES);
    double[] coeff = parContent.getDoubleArray(CFG_COEFFICIENTS);
    double likelihood = parContent.getDouble(CFG_LOG_LIKELIHOOD);
    double[] covMat = parContent.getDoubleArray(CFG_COVARIANCE_MATRIX);
    int iter = parContent.getInt(CFG_ITER);
    // introduced in 2.9
    DataCell targetReferenceCategory = parContent.getDataCell(CFG_TARGET_REFERENCE_CATEGORY, null);
    boolean sortTargetCategories = parContent.getBoolean(CFG_SORT_TARGET_CATEGORIES, true);
    boolean sortFactorsCategories = parContent.getBoolean(CFG_SORT_FACTORS_CATEGORIES, true);
    return new LogisticRegressionContent(pmmlSpec, Arrays.asList(factors), Arrays.asList(covariates), targetReferenceCategory, sortTargetCategories, sortFactorsCategories, toMatrix(coeff, coeff.length), likelihood, toMatrix(covMat, coeff.length), iter);
}

Example 15 with PMMLPortObjectSpec

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

the class GeneralRegressionPredictorNodeModel 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");
    }
    RegressionPredictorSettings s = createRegressionPredictorSettings(regModelSpec, dataSpec);
    if (null != RegressionPredictorCellFactory.createColumnSpec(regModelSpec, dataSpec, s)) {
        ColumnRearranger c = new ColumnRearranger(dataSpec);
        c.append(new RegressionPredictorCellFactory(regModelSpec, dataSpec, s) {

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