Examples with NeuralNetwork org.dmg.pmml.NeuralNetworkDocument.NeuralNetwork used on opensource projects

Example 6 with NeuralNetwork

use of org.dmg.pmml.NeuralNetworkDocument.NeuralNetwork in project knime-core by knime.

the class PMMLNeuralNetworkTranslator method initializeFrom.

/**
 * {@inheritDoc}
 */
@Override
public void initializeFrom(final PMMLDocument pmmlDoc) {
    m_nameMapper = new DerivedFieldMapper(pmmlDoc);
    NeuralNetwork[] models = pmmlDoc.getPMML().getNeuralNetworkArray();
    if (models.length == 0) {
        throw new IllegalArgumentException("No neural network model" + " provided.");
    } else if (models.length > 1) {
        LOGGER.warn("Multiple neural network models found. " + "Only the first model is considered.");
    }
    NeuralNetwork nnModel = models[0];
    // ------------------------------
    // initiate Neural Input
    initInputLayer(nnModel);
    // -------------------------------
    // initiate Hidden Layer
    initiateHiddenLayers(nnModel);
    // -------------------------------
    // initiate Final Layer
    initiateFinalLayer(nnModel);
    // --------------------------------
    // initiate Neural Outputs
    initiateNeuralOutputs(nnModel);
    // --------------------------------
    // initiate Neural Network properties
    ACTIVATIONFUNCTION.Enum actFunc = nnModel.getActivationFunction();
    NNNORMALIZATIONMETHOD.Enum normMethod = nnModel.getNormalizationMethod();
    if (ACTIVATIONFUNCTION.LOGISTIC != actFunc) {
        LOGGER.error("Only logistic activation function is " + "supported in KNIME MLP.");
    }
    if (NNNORMALIZATIONMETHOD.NONE != normMethod) {
        LOGGER.error("No normalization method is " + "supported in KNIME MLP.");
    }
    MININGFUNCTION.Enum functionName = nnModel.getFunctionName();
    if (MININGFUNCTION.CLASSIFICATION == functionName) {
        m_mlpMethod = MultiLayerPerceptron.CLASSIFICATION_MODE;
    } else if (MININGFUNCTION.REGRESSION == functionName) {
        m_mlpMethod = MultiLayerPerceptron.REGRESSION_MODE;
    }
    if (m_allLayers.size() < 3) {
        throw new IllegalArgumentException("Only neural networks with 3 Layers supported in KNIME MLP.");
    }
    Layer[] allLayers = new Layer[m_allLayers.size()];
    allLayers = m_allLayers.toArray(allLayers);
    m_mlp = new MultiLayerPerceptron(allLayers);
    Architecture myarch = new Architecture(allLayers[0].getPerceptrons().length, allLayers.length - 2, allLayers[1].getPerceptrons().length, allLayers[allLayers.length - 1].getPerceptrons().length);
    m_mlp.setArchitecture(myarch);
    m_mlp.setClassMapping(m_classmap);
    m_mlp.setInputMapping(m_inputmap);
    m_mlp.setMode(m_mlpMethod);
}

Example 7 with NeuralNetwork

use of org.dmg.pmml.NeuralNetworkDocument.NeuralNetwork 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 8 with NeuralNetwork

use of org.dmg.pmml.NeuralNetworkDocument.NeuralNetwork 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;
}