Examples with PMMLPCell org.knime.base.node.mine.regression.pmmlgreg.PMMLPCell used on opensource projects

Example 6 with PMMLPCell

use of org.knime.base.node.mine.regression.pmmlgreg.PMMLPCell in project knime-core by knime.

the class LogisticRegressionContent method createGeneralRegressionContent.

/**
 * Creates a new PMML General Regression Content from this logistic
 * regression model.
 * @return the PMMLGeneralRegressionContent
 */
public PMMLGeneralRegressionContent createGeneralRegressionContent() {
    List<PMMLPredictor> factors = new ArrayList<PMMLPredictor>();
    for (String factor : m_factorList) {
        PMMLPredictor predictor = new PMMLPredictor(factor);
        factors.add(predictor);
    }
    List<PMMLPredictor> covariates = new ArrayList<PMMLPredictor>();
    for (String covariate : m_covariateList) {
        PMMLPredictor predictor = new PMMLPredictor(covariate);
        covariates.add(predictor);
    }
    // the ParameterList, the PPMatrix and the ParamMatrix
    List<PMMLParameter> parameterList = new ArrayList<PMMLParameter>();
    List<PMMLPPCell> ppMatrix = new ArrayList<PMMLPPCell>();
    List<PMMLPCell> paramMatrix = new ArrayList<PMMLPCell>();
    int pCount = m_beta.getColumnDimension() / (m_targetCategories.size() - 1);
    int p = 0;
    parameterList.add(new PMMLParameter("p" + p, "Intercept"));
    for (int k = 0; k < m_targetCategories.size() - 1; k++) {
        paramMatrix.add(new PMMLPCell("p" + p, m_beta.get(0, p + (k * pCount)), 1, m_targetCategories.get(k).toString()));
    }
    p++;
    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 pName = "p" + p;
                parameterList.add(new PMMLParameter(pName, "[" + colName + "=" + dvValue + "]"));
                ppMatrix.add(new PMMLPPCell(dvValue.toString(), colName, pName));
                for (int k = 0; k < m_targetCategories.size() - 1; k++) {
                    paramMatrix.add(new PMMLPCell(pName, m_beta.get(0, p + (k * pCount)), 1, m_targetCategories.get(k).toString()));
                }
                p++;
            }
        } else {
            String pName = "p" + p;
            parameterList.add(new PMMLParameter("p" + p, colName));
            ppMatrix.add(new PMMLPPCell("1", colName, pName));
            for (int k = 0; k < m_targetCategories.size() - 1; k++) {
                paramMatrix.add(new PMMLPCell(pName, m_beta.get(0, p + (k * pCount)), 1, m_targetCategories.get(k).toString()));
            }
            p++;
        }
    }
    // TODO PCovMatrix
    List<PMMLPCovCell> pCovMatrix = new ArrayList<PMMLPCovCell>();
    PMMLGeneralRegressionContent content = new PMMLGeneralRegressionContent(ModelType.multinomialLogistic, "KNIME Logistic Regression", FunctionName.classification, "LogisticRegression", parameterList.toArray(new PMMLParameter[0]), factors.toArray(new PMMLPredictor[0]), covariates.toArray(new PMMLPredictor[0]), ppMatrix.toArray(new PMMLPPCell[0]), pCovMatrix.toArray(new PMMLPCovCell[0]), paramMatrix.toArray(new PMMLPCell[0]));
    content.setTargetReferenceCategory(m_targetCategories.get(m_targetCategories.size() - 1).toString());
    return content;
}

Example 7 with PMMLPCell

use of org.knime.base.node.mine.regression.pmmlgreg.PMMLPCell in project knime-core by knime.

the class LogisticRegressionContent method createGeneralRegressionContent.

/**
 * Creates a new PMML General Regression Content from this logistic
 * regression model.
 * @return the PMMLGeneralRegressionContent
 */
public PMMLGeneralRegressionContent createGeneralRegressionContent() {
    List<PMMLPredictor> factors = new ArrayList<PMMLPredictor>();
    for (String factor : m_factorList) {
        PMMLPredictor predictor = new PMMLPredictor(factor);
        factors.add(predictor);
    }
    List<PMMLPredictor> covariates = new ArrayList<PMMLPredictor>();
    for (String covariate : m_covariateList) {
        PMMLPredictor predictor = new PMMLPredictor(covariate);
        covariates.add(predictor);
    }
    // the ParameterList, the PPMatrix and the ParamMatrix
    List<PMMLParameter> parameterList = new ArrayList<PMMLParameter>();
    List<PMMLPPCell> ppMatrix = new ArrayList<PMMLPPCell>();
    List<PMMLPCell> paramMatrix = new ArrayList<PMMLPCell>();
    int pCount = m_beta.getColumnDimension() / (m_targetCategories.size() - 1);
    int p = 0;
    parameterList.add(new PMMLParameter("p" + p, "Intercept"));
    for (int k = 0; k < m_targetCategories.size() - 1; k++) {
        paramMatrix.add(new PMMLPCell("p" + p, m_beta.getEntry(0, p + (k * pCount)), 1, m_targetCategories.get(k).toString()));
    }
    p++;
    final List<String> learningFields = new ArrayList<>(m_outSpec.getLearningFields());
    // learningFields.addAll(m_vectorLengths.keySet());
    for (String colName : learningFields) {
        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 pName = "p" + p;
                parameterList.add(new PMMLParameter(pName, "[" + colName + "=" + dvValue + "]"));
                ppMatrix.add(new PMMLPPCell(dvValue.toString(), colName, pName));
                for (int k = 0; k < m_targetCategories.size() - 1; k++) {
                    paramMatrix.add(new PMMLPCell(pName, m_beta.getEntry(0, p + (k * pCount)), 1, m_targetCategories.get(k).toString()));
                }
                p++;
            }
        } else {
            if (m_vectorLengths.containsKey(colName)) {
                final int length = m_vectorLengths.get(colName);
                final int pFrozen = p;
                for (int idx = 0; idx < length; ++idx) {
                    final String pName = "p" + pFrozen + "_" + idx;
                    final String predictorName = VectorHandling.valueAt(colName, idx);
                    parameterList.add(new PMMLParameter(pName, predictorName));
                    ppMatrix.add(new PMMLPPCell("1", predictorName, pName));
                    for (int k = 0; k < m_targetCategories.size() - 1; k++) {
                        paramMatrix.add(new PMMLPCell(pName, m_beta.getEntry(0, p + (k * pCount)), 1, m_targetCategories.get(k).toString()));
                    }
                    p++;
                }
            } else {
                String pName = "p" + p;
                parameterList.add(new PMMLParameter("p" + p, colName));
                ppMatrix.add(new PMMLPPCell("1", colName, pName));
                for (int k = 0; k < m_targetCategories.size() - 1; k++) {
                    paramMatrix.add(new PMMLPCell(pName, m_beta.getEntry(0, p + (k * pCount)), 1, m_targetCategories.get(k).toString()));
                }
                p++;
            }
        }
    }
    // TODO PCovMatrix
    List<PMMLPCovCell> pCovMatrix = new ArrayList<PMMLPCovCell>();
    PMMLGeneralRegressionContent content = new PMMLGeneralRegressionContent(ModelType.multinomialLogistic, "KNIME Logistic Regression", FunctionName.classification, "LogisticRegression", parameterList.toArray(new PMMLParameter[0]), factors.toArray(new PMMLPredictor[0]), covariates.toArray(new PMMLPredictor[0]), m_vectorLengths, ppMatrix.toArray(new PMMLPPCell[0]), pCovMatrix.toArray(new PMMLPCovCell[0]), paramMatrix.toArray(new PMMLPCell[0]));
    content.setTargetReferenceCategory(m_targetCategories.get(m_targetCategories.size() - 1).toString());
    return content;
}