Examples with NaiveBayesModel org.dmg.pmml.NaiveBayesModelDocument.NaiveBayesModel used on opensource projects

Example 1 with NaiveBayesModel

use of org.dmg.pmml.NaiveBayesModelDocument.NaiveBayesModel 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 NaiveBayesModel

use of org.dmg.pmml.NaiveBayesModelDocument.NaiveBayesModel 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 3 with NaiveBayesModel

use of org.dmg.pmml.NaiveBayesModelDocument.NaiveBayesModel in project knime-core by knime.

the class PMMLMiningSchemaTranslator method writeMiningSchema.

/**
 * Writes the MiningSchema based upon the fields of the passed
 * {@link PMMLPortObjectSpec}.
 *
 * @param portSpec based upon this port object spec the mining schema is
 *            written
 * @param model the PMML model element to write the mining schema to
 */
public static void writeMiningSchema(final PMMLPortObjectSpec portSpec, final XmlObject model) {
    MiningSchema miningSchema = MiningSchema.Factory.newInstance();
    // avoid duplicate entries
    Set<String> learningNames = new HashSet<String>(portSpec.getLearningFields());
    Set<String> targetNames = new HashSet<String>(portSpec.getTargetFields());
    for (String colName : portSpec.getLearningFields()) {
        if (!targetNames.contains(colName)) {
            MiningField miningField = miningSchema.addNewMiningField();
            miningField.setName(colName);
            miningField.setInvalidValueTreatment(INVALIDVALUETREATMENTMETHOD.AS_IS);
        // don't write usageType = active (is default)
        }
    }
    // add all fields referenced in local transformations
    for (String colName : portSpec.getPreprocessingFields()) {
        if (!learningNames.contains(colName) && !targetNames.contains(colName)) {
            MiningField miningField = miningSchema.addNewMiningField();
            miningField.setName(colName);
            miningField.setInvalidValueTreatment(INVALIDVALUETREATMENTMETHOD.AS_IS);
        // don't write usageType = active (is default)
        }
    }
    // target columns = predicted
    for (String colName : portSpec.getTargetFields()) {
        MiningField miningField = miningSchema.addNewMiningField();
        miningField.setName(colName);
        miningField.setInvalidValueTreatment(INVALIDVALUETREATMENTMETHOD.AS_IS);
        miningField.setUsageType(FIELDUSAGETYPE.TARGET);
    }
    /* 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. */
    SchemaType type = model.schemaType();
    if (AssociationModel.type.equals(type)) {
        ((AssociationModel) model).setMiningSchema(miningSchema);
    } else if (ClusteringModel.type.equals(type)) {
        ((ClusteringModel) model).setMiningSchema(miningSchema);
    } else if (GeneralRegressionModel.type.equals(type)) {
        ((GeneralRegressionModel) model).setMiningSchema(miningSchema);
    } else if (MiningModel.type.equals(type)) {
        ((MiningModel) model).setMiningSchema(miningSchema);
    } else if (NaiveBayesModel.type.equals(type)) {
        ((NaiveBayesModel) model).setMiningSchema(miningSchema);
    } else if (NeuralNetwork.type.equals(type)) {
        ((NeuralNetwork) model).setMiningSchema(miningSchema);
    } else if (RegressionModel.type.equals(type)) {
        ((RegressionModel) model).setMiningSchema(miningSchema);
    } else if (RuleSetModel.type.equals(type)) {
        ((RuleSetModel) model).setMiningSchema(miningSchema);
    } else if (SequenceModel.type.equals(type)) {
        ((SequenceModel) model).setMiningSchema(miningSchema);
    } else if (SupportVectorMachineModel.type.equals(type)) {
        ((SupportVectorMachineModel) model).setMiningSchema(miningSchema);
    } else if (TextModel.type.equals(type)) {
        ((TextModel) model).setMiningSchema(miningSchema);
    } else if (TimeSeriesModel.type.equals(type)) {
        ((TimeSeriesModel) model).setMiningSchema(miningSchema);
    } else if (TreeModel.type.equals(type)) {
        ((TreeModel) model).setMiningSchema(miningSchema);
    } else if (NearestNeighborModel.type.equals(type)) {
        ((NearestNeighborModel) model).setMiningSchema(miningSchema);
    }
}

Example 4 with NaiveBayesModel

use of org.dmg.pmml.NaiveBayesModelDocument.NaiveBayesModel 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;
}