Examples with RegressionModel org.dmg.pmml.RegressionModelDocument.RegressionModel used on opensource projects

Example 1 with RegressionModel

use of org.dmg.pmml.RegressionModelDocument.RegressionModel in project knime-core by knime.

the class PMMLRegressionTranslator method exportTo.

/**
 * {@inheritDoc}
 */
@Override
public SchemaType exportTo(final PMMLDocument pmmlDoc, final PMMLPortObjectSpec spec) {
    m_nameMapper = new DerivedFieldMapper(pmmlDoc);
    RegressionModel regressionModel = pmmlDoc.getPMML().addNewRegressionModel();
    regressionModel.setFunctionName(MININGFUNCTION.REGRESSION);
    if (m_algorithmName != null && !m_algorithmName.isEmpty()) {
        regressionModel.setAlgorithmName(m_algorithmName);
    }
    regressionModel.setModelName(m_modelName);
    regressionModel.setTargetFieldName(m_targetField);
    PMMLMiningSchemaTranslator.writeMiningSchema(spec, regressionModel);
    RegressionTableDocument.RegressionTable regressionTable = regressionModel.addNewRegressionTable();
    regressionTable.setIntercept(m_regressionTable.getIntercept());
    for (NumericPredictor p : m_regressionTable.getVariables()) {
        NumericPredictorDocument.NumericPredictor np = regressionTable.addNewNumericPredictor();
        np.setName(m_nameMapper.getDerivedFieldName(p.getName()));
        if (p.getExponent() != 1) {
            np.setExponent(BigInteger.valueOf(p.getExponent()));
        }
        np.setCoefficient(p.getCoefficient());
    }
    return RegressionModel.type;
}

Example 2 with RegressionModel

use of org.dmg.pmml.RegressionModelDocument.RegressionModel in project knime-core by knime.

the class PMMLModelWrapper method getSegmentContent.

/**
 * Returns the content of a segment as a model wrapper.
 * @param s The segment
 * @return Returns a wrapper around the model
 */
public static PMMLModelWrapper getSegmentContent(final Segment s) {
    TreeModel treemodel = s.getTreeModel();
    if (treemodel != null) {
        return new PMMLTreeModelWrapper(treemodel);
    }
    RegressionModel regrmodel = s.getRegressionModel();
    if (regrmodel != null) {
        return new PMMLRegressionModelWrapper(regrmodel);
    }
    GeneralRegressionModel genregrmodel = s.getGeneralRegressionModel();
    if (genregrmodel != null) {
        return new PMMLGeneralRegressionModelWrapper(genregrmodel);
    }
    ClusteringModel clustmodel = s.getClusteringModel();
    if (clustmodel != null) {
        return new PMMLClusteringModelWrapper(clustmodel);
    }
    NaiveBayesModel nbmodel = s.getNaiveBayesModel();
    if (nbmodel != null) {
        return new PMMLNaiveBayesModelWrapper(nbmodel);
    }
    NeuralNetwork nn = s.getNeuralNetwork();
    if (nn != null) {
        return new PMMLNeuralNetworkWrapper(nn);
    }
    RuleSetModel rsmodel = s.getRuleSetModel();
    if (rsmodel != null) {
        return new PMMLRuleSetModelWrapper(rsmodel);
    }
    SupportVectorMachineModel svmmodel = s.getSupportVectorMachineModel();
    if (svmmodel != null) {
        return new PMMLSupportVectorMachineModelWrapper(svmmodel);
    }
    return null;
}

Example 3 with RegressionModel

use of org.dmg.pmml.RegressionModelDocument.RegressionModel in project knime-core by knime.

the class PMMLPortObject method moveGlobalTransformationsToModel.

/**
 * Moves the content of the transformation dictionary to local
 * transformations of the model if a model exists.
 */
public void moveGlobalTransformationsToModel() {
    PMML pmml = m_pmmlDoc.getPMML();
    TransformationDictionary transDict = pmml.getTransformationDictionary();
    if (transDict == null || transDict.getDerivedFieldArray() == null || transDict.getDerivedFieldArray().length == 0) {
        // nothing to be moved
        return;
    }
    DerivedField[] globalDerivedFields = transDict.getDerivedFieldArray();
    LocalTransformations localTrans = null;
    if (pmml.getTreeModelArray().length > 0) {
        TreeModel model = pmml.getTreeModelArray(0);
        localTrans = model.getLocalTransformations();
        if (localTrans == null) {
            localTrans = model.addNewLocalTransformations();
        }
    } else if (pmml.getClusteringModelArray().length > 0) {
        ClusteringModel model = pmml.getClusteringModelArray(0);
        localTrans = model.getLocalTransformations();
        if (localTrans == null) {
            localTrans = model.addNewLocalTransformations();
        }
    } else if (pmml.getNeuralNetworkArray().length > 0) {
        NeuralNetwork model = pmml.getNeuralNetworkArray(0);
        localTrans = model.getLocalTransformations();
        if (localTrans == null) {
            localTrans = model.addNewLocalTransformations();
        }
    } else if (pmml.getSupportVectorMachineModelArray().length > 0) {
        SupportVectorMachineModel model = pmml.getSupportVectorMachineModelArray(0);
        localTrans = model.getLocalTransformations();
        if (localTrans == null) {
            localTrans = model.addNewLocalTransformations();
        }
    } else if (pmml.getRegressionModelArray().length > 0) {
        RegressionModel model = pmml.getRegressionModelArray(0);
        localTrans = model.getLocalTransformations();
        if (localTrans == null) {
            localTrans = model.addNewLocalTransformations();
        }
    } else if (pmml.getGeneralRegressionModelArray().length > 0) {
        GeneralRegressionModel model = pmml.getGeneralRegressionModelArray(0);
        localTrans = model.getLocalTransformations();
        if (localTrans == null) {
            localTrans = model.addNewLocalTransformations();
        }
    } else if (pmml.sizeOfRuleSetModelArray() > 0) {
        RuleSetModel model = pmml.getRuleSetModelArray(0);
        localTrans = model.getLocalTransformations();
        if (localTrans == null) {
            localTrans = model.addNewLocalTransformations();
        }
    }
    if (localTrans != null) {
        DerivedField[] derivedFields = appendDerivedFields(localTrans.getDerivedFieldArray(), globalDerivedFields);
        localTrans.setDerivedFieldArray(derivedFields);
        // remove derived fields from TransformationDictionary
        transDict.setDerivedFieldArray(new DerivedField[0]);
    }
// else do nothing as no model exists yet
}

Example 4 with RegressionModel

use of org.dmg.pmml.RegressionModelDocument.RegressionModel in project knime-core by knime.

the class ClassificationGBTModelExporter method addSoftmaxRegression.

private void addSoftmaxRegression(final Segment segment) {
    RegressionModel reg = segment.addNewRegressionModel();
    reg.setFunctionName(MININGFUNCTION.CLASSIFICATION);
    reg.setNormalizationMethod(REGRESSIONNORMALIZATIONMETHOD.SOFTMAX);
    addAggregationMiningScheme(reg);
    addRegressionOutputs(reg);
    addRegressionTables(reg);
}

Example 5 with RegressionModel

use of org.dmg.pmml.RegressionModelDocument.RegressionModel in project knime-core by knime.

the class PMMLRegressionTranslator method initializeFrom.

/**
 * {@inheritDoc}
 */
@Override
public void initializeFrom(final PMMLDocument pmmlDoc) {
    m_nameMapper = new DerivedFieldMapper(pmmlDoc);
    RegressionModel[] models = pmmlDoc.getPMML().getRegressionModelArray();
    if (models.length == 0) {
        throw new IllegalArgumentException("No regression model" + " provided.");
    } else if (models.length > 1) {
        LOGGER.warn("Multiple regression models found. " + "Only the first model is considered.");
    }
    RegressionModel regressionModel = models[0];
    if (MININGFUNCTION.REGRESSION != regressionModel.getFunctionName()) {
        LOGGER.error("Only regression is supported by KNIME.");
    }
    m_algorithmName = regressionModel.getAlgorithmName();
    m_modelName = regressionModel.getModelName();
    RegressionTableDocument.RegressionTable regressionTable = regressionModel.getRegressionTableArray(0);
    List<NumericPredictor> knimePredictors = new ArrayList<NumericPredictor>();
    for (NumericPredictorDocument.NumericPredictor pmmlPredictor : regressionTable.getNumericPredictorArray()) {
        NumericPredictor knp = new NumericPredictor(m_nameMapper.getColumnName(pmmlPredictor.getName()), pmmlPredictor.getExponent().intValue(), pmmlPredictor.getCoefficient());
        knimePredictors.add(knp);
    }
    m_regressionTable = new RegressionTable(regressionTable.getIntercept(), knimePredictors.toArray(new NumericPredictor[0]));
}