Examples with SupportVectorMachineModel org.dmg.pmml.SupportVectorMachineModelDocument.SupportVectorMachineModel used on opensource projects

Example 1 with SupportVectorMachineModel

use of org.dmg.pmml.SupportVectorMachineModelDocument.SupportVectorMachineModel 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 2 with SupportVectorMachineModel

use of org.dmg.pmml.SupportVectorMachineModelDocument.SupportVectorMachineModel 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 3 with SupportVectorMachineModel

use of org.dmg.pmml.SupportVectorMachineModelDocument.SupportVectorMachineModel in project knime-core by knime.

the class PMMLSVMTranslator method initializeFrom.

/**
 * {@inheritDoc}
 */
@Override
public void initializeFrom(final PMMLDocument pmmlDoc) {
    m_nameMapper = new DerivedFieldMapper(pmmlDoc);
    SupportVectorMachineModel[] models = pmmlDoc.getPMML().getSupportVectorMachineModelArray();
    if (models.length == 0) {
        throw new IllegalArgumentException("No support vector machine model" + " provided.");
    } else if (models.length > 1) {
        LOGGER.warn("Multiple support vector machine models found. " + "Only the first model is considered.");
    }
    SupportVectorMachineModel svmModel = models[0];
    initKernel(svmModel);
    initSVMs(svmModel);
}

Example 4 with SupportVectorMachineModel

use of org.dmg.pmml.SupportVectorMachineModelDocument.SupportVectorMachineModel in project knime-core by knime.

the class PMMLSVMTranslator method exportTo.

/**
 * {@inheritDoc}
 */
@Override
public SchemaType exportTo(final PMMLDocument pmmlDoc, final PMMLPortObjectSpec spec) {
    m_nameMapper = new DerivedFieldMapper(pmmlDoc);
    PMML pmml = pmmlDoc.getPMML();
    SupportVectorMachineModel svmModel = pmml.addNewSupportVectorMachineModel();
    PMMLMiningSchemaTranslator.writeMiningSchema(spec, svmModel);
    // add support vector machine model attributes
    svmModel.setModelName("SVM");
    svmModel.setFunctionName(MININGFUNCTION.CLASSIFICATION);
    svmModel.setAlgorithmName("Sequential Minimal Optimization (SMO)");
    svmModel.setSvmRepresentation(SVMREPRESENTATION.SUPPORT_VECTORS);
    addTargets(svmModel, spec.getTargetFields().get(0));
    addKernel(svmModel);
    addVectorDictionary(svmModel, spec.getLearningFields());
    addSVMs(svmModel);
    return SupportVectorMachineModel.type;
}

Example 5 with SupportVectorMachineModel

use of org.dmg.pmml.SupportVectorMachineModelDocument.SupportVectorMachineModel in project knime-core by knime.

the class PMMLUtils method getFirstMiningSchema.

/**
 * Retrieves the mining schema of the first model of a specific type.
 *
 * @param pmmlDoc the PMML document to extract the mining schema from
 * @param type the type of the model
 * @return the mining schema of the first model of the given type or null if
 *         there is no model of the given type contained in the pmmlDoc
 */
public static MiningSchema getFirstMiningSchema(final PMMLDocument pmmlDoc, final SchemaType type) {
    Map<PMMLModelType, Integer> models = getNumberOfModels(pmmlDoc);
    if (!models.containsKey(PMMLModelType.getType(type))) {
        return null;
    }
    PMML pmml = pmmlDoc.getPMML();
    /*
         * Unfortunately the PMML models have no common base class. Therefore a
         * cast to the specific type is necessary for being able to add the
         * mining schema.
         */
    if (AssociationModel.type.equals(type)) {
        AssociationModel model = pmml.getAssociationModelArray(0);
        return model.getMiningSchema();
    } else if (ClusteringModel.type.equals(type)) {
        ClusteringModel model = pmml.getClusteringModelArray(0);
        return model.getMiningSchema();
    } else if (GeneralRegressionModel.type.equals(type)) {
        GeneralRegressionModel model = pmml.getGeneralRegressionModelArray(0);
        return model.getMiningSchema();
    } else if (MiningModel.type.equals(type)) {
        MiningModel model = pmml.getMiningModelArray(0);
        return model.getMiningSchema();
    } else if (NaiveBayesModel.type.equals(type)) {
        NaiveBayesModel model = pmml.getNaiveBayesModelArray(0);
        return model.getMiningSchema();
    } else if (NeuralNetwork.type.equals(type)) {
        NeuralNetwork model = pmml.getNeuralNetworkArray(0);
        return model.getMiningSchema();
    } else if (RegressionModel.type.equals(type)) {
        RegressionModel model = pmml.getRegressionModelArray(0);
        return model.getMiningSchema();
    } else if (RuleSetModel.type.equals(type)) {
        RuleSetModel model = pmml.getRuleSetModelArray(0);
        return model.getMiningSchema();
    } else if (SequenceModel.type.equals(type)) {
        SequenceModel model = pmml.getSequenceModelArray(0);
        return model.getMiningSchema();
    } else if (SupportVectorMachineModel.type.equals(type)) {
        SupportVectorMachineModel model = pmml.getSupportVectorMachineModelArray(0);
        return model.getMiningSchema();
    } else if (TextModel.type.equals(type)) {
        TextModel model = pmml.getTextModelArray(0);
        return model.getMiningSchema();
    } else if (TimeSeriesModel.type.equals(type)) {
        TimeSeriesModel model = pmml.getTimeSeriesModelArray(0);
        return model.getMiningSchema();
    } else if (TreeModel.type.equals(type)) {
        TreeModel model = pmml.getTreeModelArray(0);
        return model.getMiningSchema();
    } else {
        return null;
    }
}