Examples with NaiveBayesModel org.knime.base.node.mine.bayes.naivebayes.datamodel2.NaiveBayesModel used on opensource projects

Example 1 with NaiveBayesModel

use of org.knime.base.node.mine.bayes.naivebayes.datamodel2.NaiveBayesModel in project knime-core by knime.

the class NaiveBayesModel method updateModel.

/**
 * Updates the current {@link NaiveBayesModel} with the values from the
 * given {@link DataRow}.
 * @param row DataRow with values for update
 * @param tableSpec underlying DataTableSpec
 * @param classColIdx the index of the class column
 * @throws InvalidSettingsException if missing values occur in class column
 * or an attribute has too many values.
 */
public void updateModel(final DataRow row, final DataTableSpec tableSpec, final int classColIdx) throws InvalidSettingsException {
    if (row == null) {
        throw new NullPointerException("Row must not be null");
    }
    if (tableSpec == null) {
        throw new NullPointerException("TableSpec must not be null");
    }
    final DataCell classCell = row.getCell(classColIdx);
    if (classCell.isMissing()) {
        if (m_skipMissingVals) {
            return;
        }
        // check if the class value is missing
        throw new InvalidSettingsException("Missing class value found in row " + row.getKey() + " to skip missing values tick the box in the dialog");
    }
    final String classVal = classCell.toString();
    final int numColumns = tableSpec.getNumColumns();
    for (int i = 0; i < numColumns; i++) {
        final AttributeModel model = m_modelByAttrName.get(tableSpec.getColumnSpec(i).getName());
        if (model != null) {
            final DataCell cell = row.getCell(i);
            try {
                model.addValue(classVal, cell);
            } catch (final TooManyValuesException e) {
                if (model instanceof ClassAttributeModel) {
                    throw new InvalidSettingsException("Class attribute has too many unique values. " + "To avoid this exception increase the " + "maximum number of allowed nominal " + "values in the node dialog");
                }
                // delete the model if it contains too many unique values
                m_modelByAttrName.remove(model.getAttributeName());
                model.setInvalidCause("Too many values");
                m_skippedAttributes.add(model);
            }
        }
    }
    m_noOfRecs++;
}

Example 2 with NaiveBayesModel

use of org.knime.base.node.mine.bayes.naivebayes.datamodel2.NaiveBayesModel 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 3 with NaiveBayesModel

use of org.knime.base.node.mine.bayes.naivebayes.datamodel2.NaiveBayesModel in project knime-core by knime.

the class NaiveBayesLearnerNodeModel2 method loadInternals.

/**
 * {@inheritDoc}
 */
@Override
protected void loadInternals(final File nodeInternDir, final ExecutionMonitor exec) throws IOException {
    final File modelFile = new File(nodeInternDir, CFG_DATA);
    final FileInputStream modelIn = new FileInputStream(modelFile);
    // because the loadFromXML method returns the content of the root tag
    // we don't need to ask for the content of the root tag
    final ModelContentRO myModel = ModelContent.loadFromXML(modelIn);
    try {
        m_model = new NaiveBayesModel(myModel);
    } catch (final Exception e) {
        throw new IOException(e.getMessage());
    }
}

Example 4 with NaiveBayesModel

use of org.knime.base.node.mine.bayes.naivebayes.datamodel2.NaiveBayesModel in project knime-core by knime.

the class NaiveBayesPredictorNodeModel2 method createColumnRearranger.

/* Helper to create the column rearranger that does the actual work */
private ColumnRearranger createColumnRearranger(final PMMLPortObject pmmlPortObj, final DataTableSpec inSpec) {
    final PMMLNaiveBayesModelTranslator translator = new PMMLNaiveBayesModelTranslator();
    pmmlPortObj.initializeModelTranslator(translator);
    final NaiveBayesModel model = translator.getModel();
    PredictorHelper predictorHelper = PredictorHelper.getInstance();
    final String classColumnName = model.getClassColumnName();
    final String predictionColName = m_overridePredicted.getBooleanValue() ? m_predictionColumnName.getStringValue() : predictorHelper.computePredictionDefault(classColumnName);
    final NaiveBayesCellFactory appender = new NaiveBayesCellFactory(model, predictionColName, inSpec, m_inclProbVals.getBooleanValue(), m_probabilitySuffix.getStringValue());
    final ColumnRearranger rearranger = new ColumnRearranger(inSpec);
    rearranger.append(appender);
    return rearranger;
}