Examples with FieldRef org.dmg.pmml.FieldRefDocument.FieldRef used on opensource projects

Example 1 with FieldRef

use of org.dmg.pmml.FieldRefDocument.FieldRef in project knime-core by knime.

the class PMMLNeuralNetworkTranslator method addInputLayer.

/**
 * Writes the PMML input layer of the MLP.
 *
 * @param nnModel
 *            the Neural Network model.
 * @param mlp
 *            the underlying {@link MultiLayerPerceptron}.
 */
protected void addInputLayer(final NeuralNetwork nnModel, final MultiLayerPerceptron mlp) {
    Layer inputlayer = mlp.getLayer(0);
    Perceptron[] inputperceptrons = inputlayer.getPerceptrons();
    HashMap<String, Integer> inputmap = mlp.getInputMapping();
    NeuralInputs neuralInputs = nnModel.addNewNeuralInputs();
    neuralInputs.setNumberOfInputs(BigInteger.valueOf(inputperceptrons.length));
    for (int i = 0; i < inputperceptrons.length; i++) {
        NeuralInput neuralInput = neuralInputs.addNewNeuralInput();
        neuralInput.setId(0 + "," + i);
        // search corresponding input column
        String colname = "";
        for (Entry<String, Integer> e : inputmap.entrySet()) {
            if (e.getValue().equals(i)) {
                colname = e.getKey();
            }
        }
        DerivedField df = neuralInput.addNewDerivedField();
        df.setOptype(OPTYPE.CONTINUOUS);
        df.setDataType(DATATYPE.DOUBLE);
        FieldRef fieldRef = df.addNewFieldRef();
        fieldRef.setField(m_nameMapper.getDerivedFieldName(colname));
    }
}

Example 2 with FieldRef

use of org.dmg.pmml.FieldRefDocument.FieldRef in project knime-core by knime.

the class PMMLGeneralRegressionTranslator method updateVectorLengthsBasedOnDerivedFields.

/**
 * @param derivedFieldList
 */
private void updateVectorLengthsBasedOnDerivedFields(final List<DerivedField> derivedFieldList) {
    final Map<String, Integer> lengths = new LinkedHashMap<>();
    for (final DerivedField df : derivedFieldList) {
        final String name = df.getName();
        Optional<NameAndIndex> vni = VectorHandling.parse(name);
        if (vni.isPresent()) {
            final String key = vni.get().getName();
            try {
                String function = df.getApply().getFunction();
                if (!"substring".equals(function)) {
                    continue;
                }
                final List<FieldRef> fieldRefList = df.getApply().getFieldRefList();
                if (fieldRefList.isEmpty() || !key.equals(fieldRefList.get(0).getField())) {
                    LOGGER.debug("Field name is not related to the derived field name: " + fieldRefList + " <-> " + key);
                    continue;
                }
                if (2 != df.getApply().getConstantList().size()) {
                    LOGGER.debug("substring requires two parameters: " + df);
                    continue;
                }
                if (!DATATYPE.INTEGER.equals(df.getDataType())) {
                    LOGGER.debug("Array value should be integer: " + df);
                    continue;
                }
                if (!OPTYPE.CONTINUOUS.equals(df.getOptype())) {
                    LOGGER.debug("The optype should be continuous: " + df);
                    continue;
                }
                int index = vni.get().getIndex();
                int old = Math.max(0, lengths.getOrDefault(key, Integer.valueOf(0)).intValue());
                if (old <= index) {
                    lengths.put(key, index + 1);
                }
            } catch (RuntimeException e) {
                // Ignore
                LOGGER.debug(df.toString(), e);
            }
        }
    }
    LOGGER.debug(lengths);
    m_content.updateVectorLengths(lengths);
}

Example 3 with FieldRef

use of org.dmg.pmml.FieldRefDocument.FieldRef in project knime-core by knime.

the class PMMLNeuralNetworkTranslator method addInputLayer.

/**
 * Writes the PMML input layer of the MLP.
 *
 * @param nnModel
 *            the Neural Network model.
 * @param mlp
 *            the underlying {@link MultiLayerPerceptron}.
 */
protected void addInputLayer(final NeuralNetwork nnModel, final MultiLayerPerceptron mlp) {
    Layer inputlayer = mlp.getLayer(0);
    Perceptron[] inputperceptrons = inputlayer.getPerceptrons();
    HashMap<String, Integer> inputmap = mlp.getInputMapping();
    NeuralInputs neuralInputs = nnModel.addNewNeuralInputs();
    neuralInputs.setNumberOfInputs(BigInteger.valueOf(inputperceptrons.length));
    for (int i = 0; i < inputperceptrons.length; i++) {
        NeuralInput neuralInput = neuralInputs.addNewNeuralInput();
        neuralInput.setId(0 + "," + i);
        // search corresponding input column
        String colname = "";
        for (Entry<String, Integer> e : inputmap.entrySet()) {
            if (e.getValue().equals(i)) {
                colname = e.getKey();
            }
        }
        DerivedField df = neuralInput.addNewDerivedField();
        df.setOptype(OPTYPE.CONTINUOUS);
        df.setDataType(DATATYPE.DOUBLE);
        FieldRef fieldRef = df.addNewFieldRef();
        fieldRef.setField(m_nameMapper.getDerivedFieldName(colname));
    }
}

Example 4 with FieldRef

use of org.dmg.pmml.FieldRefDocument.FieldRef in project knime-core by knime.

the class PMMLNeuralNetworkTranslator method addOutputLayer.

/**
 * Writes the PMML output layer of the MLP.
 *
 * @param nnModel
 *            the neural network model.
 * @param mlp
 *            the underlying {@link MultiLayerPerceptron}.
 * @param spec
 *            the port object spec
 */
protected void addOutputLayer(final NeuralNetwork nnModel, final MultiLayerPerceptron mlp, final PMMLPortObjectSpec spec) {
    int lastlayer = mlp.getNrLayers() - 1;
    String targetCol = spec.getTargetFields().iterator().next();
    Layer outputlayer = mlp.getLayer(lastlayer);
    Perceptron[] outputperceptrons = outputlayer.getPerceptrons();
    HashMap<DataCell, Integer> outputmap = mlp.getClassMapping();
    NeuralOutputs neuralOuts = nnModel.addNewNeuralOutputs();
    neuralOuts.setNumberOfOutputs(BigInteger.valueOf(outputperceptrons.length));
    for (int i = 0; i < outputperceptrons.length; i++) {
        NeuralOutput neuralOutput = neuralOuts.addNewNeuralOutput();
        neuralOutput.setOutputNeuron(lastlayer + "," + i);
        // search corresponding output value
        String colname = "";
        for (Entry<DataCell, Integer> e : outputmap.entrySet()) {
            if (e.getValue().equals(i)) {
                colname = ((StringValue) e.getKey()).getStringValue();
            }
        }
        DerivedField df = neuralOutput.addNewDerivedField();
        df.setOptype(OPTYPE.CATEGORICAL);
        df.setDataType(DATATYPE.STRING);
        if (mlp.getMode() == MultiLayerPerceptron.CLASSIFICATION_MODE) {
            df.setOptype(OPTYPE.CATEGORICAL);
            df.setDataType(DATATYPE.STRING);
        } else if (mlp.getMode() == MultiLayerPerceptron.REGRESSION_MODE) {
            df.setOptype(OPTYPE.CONTINUOUS);
            df.setDataType(DATATYPE.DOUBLE);
        }
        if (mlp.getMode() == MultiLayerPerceptron.CLASSIFICATION_MODE) {
            NormDiscrete normDiscrete = df.addNewNormDiscrete();
            normDiscrete.setField(targetCol);
            normDiscrete.setValue(colname);
        } else if (mlp.getMode() == MultiLayerPerceptron.REGRESSION_MODE) {
            FieldRef fieldRef = df.addNewFieldRef();
            fieldRef.setField(targetCol);
        }
    }
}

Example 5 with FieldRef

use of org.dmg.pmml.FieldRefDocument.FieldRef in project knime-core by knime.

the class PMMLStringConversionTranslator method createDerivedFields.

private DerivedField[] createDerivedFields() {
    DATATYPE.Enum dataType = PMMLDataDictionaryTranslator.getPMMLDataType(m_parseType);
    OPTYPE.Enum optype = PMMLDataDictionaryTranslator.getOptype(m_parseType);
    int num = m_includeCols.size();
    DerivedField[] derivedFields = new DerivedField[num];
    for (int i = 0; i < num; i++) {
        DerivedField df = DerivedField.Factory.newInstance();
        String name = m_includeCols.get(i);
        df.setDisplayName(name);
        /* The field name must be retrieved before creating a new derived
             * name for this derived field as the map only contains the
             * current mapping. */
        String fieldName = m_mapper.getDerivedFieldName(name);
        df.setName(m_mapper.createDerivedFieldName(name));
        df.setDataType(dataType);
        df.setOptype(optype);
        FieldRef fieldRef = df.addNewFieldRef();
        fieldRef.setField(fieldName);
        derivedFields[i] = df;
    }
    return derivedFields;
}