Examples with MiningModel org.dmg.pmml.MiningModelDocument.MiningModel used on opensource projects

Example 1 with MiningModel

use of org.dmg.pmml.MiningModelDocument.MiningModel in project knime-core by knime.

the class ClassificationGBTModelImporter method processClassSegment.

private Pair<List<TreeModelRegression>, List<Map<TreeNodeSignature, Double>>> processClassSegment(final Segment segment) {
    MiningModel m = segment.getMiningModel();
    CheckUtils.checkArgument(m.getFunctionName() == MININGFUNCTION.REGRESSION, "The mining function of a class segment mining model must be '%s' but was '%s'.", MININGFUNCTION.REGRESSION, m.getFunctionName());
    return readSumSegmentation(m.getSegmentation());
}

Example 2 with MiningModel

use of org.dmg.pmml.MiningModelDocument.MiningModel in project knime-core by knime.

the class ClassificationGBTModelExporter method addClassSegment.

private void addClassSegment(final Segmentation modelChain, final int classIdx) {
    Segment cs = modelChain.addNewSegment();
    cs.setId(Integer.toString(classIdx + 1));
    cs.addNewTrue();
    MiningModel cm = cs.addNewMiningModel();
    cm.setFunctionName(MININGFUNCTION.REGRESSION);
    // write mining schema
    PMMLMiningSchemaTranslator.writeMiningSchema(getPMMLSpec(), cm);
    addOutput(cm, classIdx);
    addTarget(cm);
    addSegmentation(cm, classIdx);
}

Example 3 with MiningModel

use of org.dmg.pmml.MiningModelDocument.MiningModel 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;
    }
}

Example 4 with MiningModel

use of org.dmg.pmml.MiningModelDocument.MiningModel in project knime-core by knime.

the class PMMLPortObject method moveDerivedFields.

/**
 * Moves the content of the transformation dictionary to local
 * transformations.
 * @param type the type of model to move the derived fields to
 * @return the {@link LocalTransformations} element containing the moved
 *      derived fields or an empty local transformation object if nothing
 *      has to be moved
 */
private LocalTransformations moveDerivedFields(final SchemaType type) {
    PMML pmml = m_pmmlDoc.getPMML();
    TransformationDictionary transDict = pmml.getTransformationDictionary();
    LocalTransformations localTrans = LocalTransformations.Factory.newInstance();
    if (transDict == null) {
        // nothing to be moved
        return localTrans;
    }
    localTrans.setDerivedFieldArray(transDict.getDerivedFieldArray());
    localTrans.setExtensionArray(transDict.getExtensionArray());
    /*
         * 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.
         */
    boolean known = true;
    if (AssociationModel.type.equals(type)) {
        AssociationModel model = pmml.getAssociationModelArray(0);
        model.setLocalTransformations(localTrans);
    } else if (ClusteringModel.type.equals(type)) {
        ClusteringModel model = pmml.getClusteringModelArray(0);
        model.setLocalTransformations(localTrans);
    } else if (GeneralRegressionModel.type.equals(type)) {
        GeneralRegressionModel model = pmml.getGeneralRegressionModelArray(0);
        model.setLocalTransformations(localTrans);
    } else if (MiningModel.type.equals(type)) {
        MiningModel model = pmml.getMiningModelArray(0);
        model.setLocalTransformations(localTrans);
    } else if (NaiveBayesModel.type.equals(type)) {
        NaiveBayesModel model = pmml.getNaiveBayesModelArray(0);
        model.setLocalTransformations(localTrans);
    } else if (NeuralNetwork.type.equals(type)) {
        NeuralNetwork model = pmml.getNeuralNetworkArray(0);
        model.setLocalTransformations(localTrans);
    } else if (RegressionModel.type.equals(type)) {
        RegressionModel model = pmml.getRegressionModelArray(0);
        model.setLocalTransformations(localTrans);
    } else if (RuleSetModel.type.equals(type)) {
        RuleSetModel model = pmml.getRuleSetModelArray(0);
        model.setLocalTransformations(localTrans);
    } else if (SequenceModel.type.equals(type)) {
        SequenceModel model = pmml.getSequenceModelArray(0);
        model.setLocalTransformations(localTrans);
    } else if (SupportVectorMachineModel.type.equals(type)) {
        SupportVectorMachineModel model = pmml.getSupportVectorMachineModelArray(0);
        model.setLocalTransformations(localTrans);
    } else if (TextModel.type.equals(type)) {
        TextModel model = pmml.getTextModelArray(0);
        model.setLocalTransformations(localTrans);
    } else if (TimeSeriesModel.type.equals(type)) {
        TimeSeriesModel model = pmml.getTimeSeriesModelArray(0);
        model.setLocalTransformations(localTrans);
    } else if (TreeModel.type.equals(type)) {
        TreeModel model = pmml.getTreeModelArray(0);
        model.setLocalTransformations(localTrans);
    } else {
        if (type != null) {
            LOGGER.error("Could not move TransformationDictionary to " + "unsupported model of type \"" + type + "\".");
        }
        known = false;
    }
    if (known) {
        // remove derived fields from TransformationDictionary
        transDict.setDerivedFieldArray(new DerivedField[0]);
        transDict.setExtensionArray(new ExtensionDocument.Extension[0]);
    }
    return localTrans;
}

Example 5 with MiningModel

use of org.dmg.pmml.MiningModelDocument.MiningModel in project knime-core by knime.

the class AbstractGBTModelPMMLTranslator method initializeFrom.

/**
 * {@inheritDoc}
 */
@Override
public void initializeFrom(final PMMLDocument pmmlDoc) {
    PMML pmml = pmmlDoc.getPMML();
    if (pmml.getHeader() == null || pmml.getHeader().getApplication() == null || !pmml.getHeader().getApplication().getName().equals("KNIME")) {
        throw new IllegalArgumentException("Currently only models created with KNIME are supported.");
    }
    List<MiningModel> mmList = pmml.getMiningModelList();
    if (mmList == null || mmList.isEmpty()) {
        throw new IllegalArgumentException("The provided PMML does not contain a Gradient Boosted Trees model.");
    }
    MiningModel model = mmList.get(0);
    MetaDataMapper<TreeTargetNumericColumnMetaData> metaDataMapper = new RegressionMetaDataMapper(pmmlDoc, getTargetFieldName(model));
    AbstractGBTModelImporter<M> importer = createImporter(metaDataMapper);
    m_gbtModel = importer.importFromPMML(pmml.getMiningModelList().get(0));
    m_learnSpec = metaDataMapper.getLearnSpec();
}