Examples with DataCell org.knime.core.data.DataCell used on opensource projects

Example 96 with DataCell

use of org.knime.core.data.DataCell in project knime-core by knime.

the class LogRegLearnerNodeDialogPane method createTargetOptionsPanel.

/**
 * Create options panel for the target.
 */
private JPanel createTargetOptionsPanel() {
    JPanel p = new JPanel(new GridBagLayout());
    GridBagConstraints c = new GridBagConstraints();
    c.fill = GridBagConstraints.HORIZONTAL;
    c.weightx = 0;
    c.weighty = 0;
    c.gridx = 0;
    c.gridy = 0;
    c.anchor = GridBagConstraints.BASELINE_LEADING;
    c.insets = new Insets(5, 5, 0, 0);
    p.add(new JLabel("Target Column:"), c);
    c.gridx++;
    @SuppressWarnings("unchecked") final ColumnSelectionPanel columnSelectionPanel = new ColumnSelectionPanel(new EmptyBorder(0, 0, 0, 0), NominalValue.class);
    m_selectionPanel = columnSelectionPanel;
    m_selectionPanel.addActionListener(new ActionListener() {

        @Override
        public void actionPerformed(final ActionEvent e) {
            updateTargetCategories((DataCell) m_targetReferenceCategory.getSelectedItem());
        }
    });
    p.add(m_selectionPanel, c);
    c.gridx = 0;
    c.gridy++;
    p.add(new JLabel("Reference Category:"), c);
    c.gridx++;
    m_targetReferenceCategory = new JComboBox<>();
    p.add(m_targetReferenceCategory, c);
    c.gridx = 0;
    c.gridy++;
    c.gridwidth = 3;
    c.weightx = 1;
    m_notSortTarget = new JCheckBox("Use order from target column domain (only relevant for output representation)");
    p.add(m_notSortTarget, c);
    m_selectionPanel.addItemListener(new ItemListener() {

        @Override
        public void itemStateChanged(final ItemEvent e) {
            Object selected = e.getItem();
            if (selected instanceof DataColumnSpec) {
                m_filterPanel.resetHiding();
                m_filterPanel.hideNames((DataColumnSpec) selected);
            }
        }
    });
    return p;
}

Example 97 with DataCell

use of org.knime.core.data.DataCell in project knime-core by knime.

the class LogisticRegressionContent method createTablePortObject.

/**
 * Creates a BufferedDataTable with the
 * @param exec The execution context
 * @return a port object
 */
public BufferedDataTable createTablePortObject(final ExecutionContext exec) {
    DataTableSpec tableOutSpec = new DataTableSpec("Coefficients and Statistics", new String[] { "Logit", "Variable", "Coeff.", "Std. Err.", "z-score", "P>|z|" }, new DataType[] { StringCell.TYPE, StringCell.TYPE, DoubleCell.TYPE, DoubleCell.TYPE, DoubleCell.TYPE, DoubleCell.TYPE });
    BufferedDataContainer dc = exec.createDataContainer(tableOutSpec);
    List<DataCell> logits = this.getLogits();
    List<String> parameters = this.getParameters();
    int c = 0;
    for (DataCell logit : logits) {
        Map<String, Double> coefficients = this.getCoefficients(logit);
        Map<String, Double> stdErrs = this.getStandardErrors(logit);
        Map<String, Double> zScores = this.getZScores(logit);
        Map<String, Double> pValues = this.getPValues(logit);
        for (String parameter : parameters) {
            List<DataCell> cells = new ArrayList<DataCell>();
            cells.add(new StringCell(logit.toString()));
            cells.add(new StringCell(parameter));
            cells.add(new DoubleCell(coefficients.get(parameter)));
            cells.add(new DoubleCell(stdErrs.get(parameter)));
            cells.add(new DoubleCell(zScores.get(parameter)));
            cells.add(new DoubleCell(pValues.get(parameter)));
            c++;
            dc.addRowToTable(new DefaultRow("Row" + c, cells));
        }
        List<DataCell> cells = new ArrayList<DataCell>();
        cells.add(new StringCell(logit.toString()));
        cells.add(new StringCell("Constant"));
        cells.add(new DoubleCell(this.getIntercept(logit)));
        cells.add(new DoubleCell(this.getInterceptStdErr(logit)));
        cells.add(new DoubleCell(this.getInterceptZScore(logit)));
        cells.add(new DoubleCell(this.getInterceptPValue(logit)));
        c++;
        dc.addRowToTable(new DefaultRow("Row" + c, cells));
    }
    dc.close();
    return dc.getTable();
}

Example 98 with DataCell

use of org.knime.core.data.DataCell 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 99 with DataCell

use of org.knime.core.data.DataCell in project knime-core by knime.

the class LogisticRegressionContent method getValues.

private Map<String, Double> getValues(final DataCell logit, final RealMatrix matrix) {
    assert m_targetCategories.contains(logit);
    Map<String, Double> coefficients = new HashMap<String, Double>();
    int pCount = m_beta.getColumnDimension() / (m_targetCategories.size() - 1);
    int p = 1;
    for (String colName : m_outSpec.getLearningFields()) {
        if (m_factorList.contains(colName)) {
            Iterator<DataCell> designIter = m_factorDomainValues.get(colName).iterator();
            // Omit first
            designIter.next();
            while (designIter.hasNext()) {
                DataCell dvValue = designIter.next();
                String variable = colName + "=" + dvValue;
                int k = m_targetCategories.indexOf(logit);
                double coeff = matrix.getEntry(0, p + (k * pCount));
                coefficients.put(variable, coeff);
                p++;
            }
        } else {
            String variable = colName;
            int k = m_targetCategories.indexOf(logit);
            if (m_vectorLengths.containsKey(colName)) {
                int length = m_vectorLengths.get(colName).intValue();
                for (int i = 0; i < length; ++i) {
                    double coeff = matrix.getEntry(0, p + i + (k * pCount));
                    coefficients.put(VectorHandling.valueAt(variable, i), coeff);
                }
                p += length;
            } else {
                double coeff = matrix.getEntry(0, p + (k * pCount));
                coefficients.put(variable, coeff);
                p++;
            }
        }
    }
    return coefficients;
}

Example 100 with DataCell

use of org.knime.core.data.DataCell in project knime-core by knime.

the class LogisticRegressionContent method getParameters.

/**
 * Returns the parameters. The follow the notation rule:
 *   - for covariate (numeric learning column):
 *      "column_name"
 *   - for factors (nominal learning columns) there are n-1 entries
 *     when n is the number of domain values:
 *      "column_name=domain_value"
 * @return the parameters
 */
public List<String> getParameters() {
    List<String> parameters = new ArrayList<String>();
    for (String colName : m_outSpec.getLearningFields()) {
        if (m_factorList.contains(colName)) {
            Iterator<DataCell> designIter = m_factorDomainValues.get(colName).iterator();
            // Omit first
            designIter.next();
            while (designIter.hasNext()) {
                DataCell dvValue = designIter.next();
                String variable = colName + "=" + dvValue;
                parameters.add(variable);
            }
        } else {
            String variable = colName;
            if (m_vectorLengths.containsKey(colName)) {
                int length = m_vectorLengths.get(colName).intValue();
                for (int i = 0; i < length; ++i) {
                    parameters.add(VectorHandling.valueAt(variable, i));
                }
            } else {
                parameters.add(variable);
            }
        }
    }
    return parameters;
}