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

Example 36 with PortObject

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

the class AutoBinnerApplyNodeModel method execute.

/**
 * {@inheritDoc}
 */
@Override
protected PortObject[] execute(final PortObject[] inObjects, final ExecutionContext exec) throws Exception {
    PMMLPreprocPortObject pmmlPort = (PMMLPreprocPortObject) inObjects[0];
    BufferedDataTable inTable = (BufferedDataTable) inObjects[1];
    validatePMMLPort(pmmlPort);
    PMMLPreprocDiscretize op = (PMMLPreprocDiscretize) pmmlPort.getOperations().get(0);
    AutoBinnerApply applier = new AutoBinnerApply();
    BufferedDataTable binnedData = applier.execute(op, inTable, exec);
    return new PortObject[] { binnedData };
}

Example 37 with PortObject

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

the class RegressionPredictorNodeModel method execute.

/**
 * {@inheritDoc}
 */
@Override
public PortObject[] execute(final PortObject[] inData, final ExecutionContext exec) throws Exception {
    PMMLPortObject port = (PMMLPortObject) inData[0];
    List<Node> models = port.getPMMLValue().getModels(PMMLModelType.GeneralRegressionModel);
    if (models.isEmpty()) {
        // KNIME currently (Sep '17) still uses the 'Regression' Model Type in the PolyReg learner
        @SuppressWarnings("deprecation") org.knime.base.node.mine.regression.predict.RegressionPredictorNodeModel regrPredictor = new org.knime.base.node.mine.regression.predict.RegressionPredictorNodeModel();
        @SuppressWarnings("deprecation") PortObject[] regrPredOut = regrPredictor.execute(inData, exec);
        if (regrPredOut.length > 0 && regrPredOut[0] instanceof BufferedDataTable) {
            BufferedDataTable regrPredOutTable = (BufferedDataTable) regrPredOut[0];
            // replace name of prediction column (the last column of regrPredOutTable)
            return new PortObject[] { adjustSpecOfRegressionPredictorTable(regrPredOutTable, inData, exec) };
        } else {
            return regrPredOut;
        }
    }
    PMMLGeneralRegressionTranslator trans = new PMMLGeneralRegressionTranslator();
    port.initializeModelTranslator(trans);
    BufferedDataTable data = (BufferedDataTable) inData[1];
    DataTableSpec spec = data.getDataTableSpec();
    ColumnRearranger c = createRearranger(trans.getContent(), port.getSpec(), spec);
    BufferedDataTable out = exec.createColumnRearrangeTable(data, c, exec);
    return new BufferedDataTable[] { out };
}

Example 38 with PortObject

use of org.knime.core.node.port.PortObject 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 39 with PortObject

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

the class BasisFunctionLearnerNodeModel method execute.

/**
 * Starts the learning algorithm in the learner.
 *
 * @param inData the input training data at index 0
 * @param exec the execution monitor
 * @return the output fuzzy rule model
 * @throws CanceledExecutionException if the training was canceled
 */
@Override
public PortObject[] execute(final PortObject[] inData, final ExecutionContext exec) throws CanceledExecutionException {
    BufferedDataTable data = (BufferedDataTable) inData[0];
    // find all double cell columns in the data
    DataTableSpec tSpec = data.getDataTableSpec();
    LinkedHashSet<String> columns = new LinkedHashSet<String>(tSpec.getNumColumns());
    List<String> targetHash = Arrays.asList(m_targetColumns);
    for (int c = 0; c < tSpec.getNumColumns(); c++) {
        DataColumnSpec cSpec = tSpec.getColumnSpec(c);
        String name = cSpec.getName();
        if (!targetHash.contains(name)) {
            // TODO only numeric columns allowed
            if (cSpec.getType().isCompatible(DoubleValue.class)) {
                columns.add(cSpec.getName());
            }
        }
    }
    // get all data columns without target columns
    String[] dataCols = BasisFunctionFactory.findDataColumns(tSpec, targetHash);
    columns.addAll(Arrays.asList(dataCols));
    // add target columns at the end
    columns.addAll(Arrays.asList(m_targetColumns));
    // filter selected columns from input data
    String[] cols = columns.toArray(new String[] {});
    ColumnRearranger colRe = new ColumnRearranger(tSpec);
    colRe.keepOnly(cols);
    BufferedDataTable trainData = exec.createColumnRearrangeTable(data, colRe, exec);
    // print settings info
    LOGGER.debug("distance      : " + getDistance());
    LOGGER.debug("missing       : " + getMissingFct());
    LOGGER.debug("target columns: " + Arrays.toString(m_targetColumns));
    LOGGER.debug("shrink commit : " + isShrinkAfterCommit());
    LOGGER.debug("max coverage  : " + isMaxClassCoverage());
    LOGGER.debug("max no. epochs: " + m_maxEpochs);
    // create factory
    BasisFunctionFactory factory = getFactory(trainData.getDataTableSpec());
    // start training
    BasisFunctionLearnerTable table = new BasisFunctionLearnerTable(trainData, dataCols, m_targetColumns, factory, BasisFunctionLearnerTable.MISSINGS[m_missing], m_shrinkAfterCommit, m_maxCoverage, m_maxEpochs, exec);
    DataTableSpec modelSpec = table.getDataTableSpec();
    DataColumnSpec[] modelSpecs = new DataColumnSpec[modelSpec.getNumColumns()];
    for (int i = 0; i < modelSpecs.length; i++) {
        DataColumnSpecCreator creator = new DataColumnSpecCreator(modelSpec.getColumnSpec(i));
        creator.removeAllHandlers();
        modelSpecs[i] = creator.createSpec();
    }
    // set translator mapping
    m_translator.setMapper(table.getHiLiteMapper());
    ModelContent modelInfo = new ModelContent(MODEL_INFO);
    table.saveInfos(modelInfo);
    m_modelInfo = modelInfo;
    // return rules[0] and rule_model[1]
    return new PortObject[] { exec.createBufferedDataTable(table, exec), createPortObject(new BasisFunctionModelContent(table.getDataTableSpec(), table.getBasisFunctions())) };
}

Example 40 with PortObject

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

the class NaiveBayesPredictorNodeModel2 method execute.

/**
 * {@inheritDoc}
 */
@Override
public PortObject[] execute(final PortObject[] inData, final ExecutionContext exec) throws Exception {
    LOGGER.debug("Entering execute(inData, exec) of class NaiveBayesPredictorNodeModel.");
    // check input data
    assert (inData != null && inData.length == 2 && inData[DATA_IN_PORT] != null && inData[MODEL_IN_PORT] != null);
    final PortObject dataObject = inData[DATA_IN_PORT];
    if (!(dataObject instanceof BufferedDataTable)) {
        throw new IllegalArgumentException("Invalid input data");
    }
    final BufferedDataTable data = (BufferedDataTable) dataObject;
    final PortObject modelObject = inData[MODEL_IN_PORT];
    if (!(modelObject instanceof PMMLPortObject)) {
        throw new IllegalArgumentException("Invalid input data");
    }
    final PMMLPortObject modelPort = (PMMLPortObject) modelObject;
    final Collection<Node> models = modelPort.getPMMLValue().getModels(PMMLModelType.NaiveBayesModel);
    if (models == null || models.isEmpty()) {
        throw new Exception("Node not properly configured. No Naive Bayes Model available.");
    }
    if (models.size() > 1) {
        throw new Exception("Node supports only one Naive Bayes Model at a time.");
    }
    exec.setMessage("Classifying rows...");
    ColumnRearranger rearranger = createColumnRearranger(modelPort, data.getDataTableSpec());
    final BufferedDataTable returnVal = exec.createColumnRearrangeTable(data, rearranger, exec);
    LOGGER.debug("Exiting execute(inData, exec) of class NaiveBayesPredictorNodeModel.");
    return new PortObject[] { returnVal };
}