Examples with PMML org.dmg.pmml.PMMLDocument.PMML used on opensource projects

Example 21 with PMML

use of org.dmg.pmml.PMMLDocument.PMML in project knime-core by knime.

the class PMMLPortObject method initializePMMLDocument.

/**
 * Creates a pmml document from scratch (still without a model) and stores
 * it as the PMMLValue of this class.
 * @param inData the data table spec
 */
private void initializePMMLDocument(final DataTableSpec inData) {
    m_pmmlDoc = PMMLDocument.Factory.newInstance(PMMLFormatter.getOptions());
    PMML pmml = m_pmmlDoc.addNewPMML();
    pmml.setVersion(PMML_V4_2);
    PMMLPortObjectSpec.writeHeader(pmml);
    new PMMLDataDictionaryTranslator().exportTo(m_pmmlDoc, inData);
/**
 * No model is written yet. It has to be added by the responsible
 *         PMMLTranslator.
 */
}

Example 22 with PMML

use of org.dmg.pmml.PMMLDocument.PMML 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 23 with PMML

use of org.dmg.pmml.PMMLDocument.PMML in project knime-core by knime.

the class DataColumnSpecFilterPMMLNodeModel method createPMMLOut.

private PMMLPortObject createPMMLOut(final PMMLPortObject pmmlIn, final DataTableSpec outSpec, final FilterResult res) throws XmlException {
    StringBuffer warningBuffer = new StringBuffer();
    if (pmmlIn == null) {
        return new PMMLPortObject(createPMMLSpec(null, outSpec, res));
    } else {
        PMMLDocument pmmldoc;
        try (LockedSupplier<Document> supplier = pmmlIn.getPMMLValue().getDocumentSupplier()) {
            pmmldoc = PMMLDocument.Factory.parse(supplier.get());
        }
        // Inspect models to check if they use any excluded columns
        List<PMMLModelWrapper> models = PMMLModelWrapper.getModelListFromPMMLDocument(pmmldoc);
        for (PMMLModelWrapper model : models) {
            MiningSchema ms = model.getMiningSchema();
            for (MiningField mf : ms.getMiningFieldList()) {
                if (isExcluded(mf.getName(), res)) {
                    if (warningBuffer.length() != 0) {
                        warningBuffer.append("\n");
                    }
                    warningBuffer.append(model.getModelType().name() + " uses excluded column " + mf.getName());
                }
            }
        }
        ArrayList<String> warningFields = new ArrayList<String>();
        PMML pmml = pmmldoc.getPMML();
        // Now check the transformations if they exist
        if (pmml.getTransformationDictionary() != null) {
            for (DerivedField df : pmml.getTransformationDictionary().getDerivedFieldList()) {
                FieldRef fr = df.getFieldRef();
                if (fr != null && isExcluded(fr.getField(), res)) {
                    warningFields.add(fr.getField());
                }
                Aggregate a = df.getAggregate();
                if (a != null && isExcluded(a.getField(), res)) {
                    warningFields.add(a.getField());
                }
                Apply ap = df.getApply();
                if (ap != null) {
                    for (FieldRef fieldRef : ap.getFieldRefList()) {
                        if (isExcluded(fieldRef.getField(), res)) {
                            warningFields.add(fieldRef.getField());
                            break;
                        }
                    }
                }
                Discretize d = df.getDiscretize();
                if (d != null && isExcluded(d.getField(), res)) {
                    warningFields.add(d.getField());
                }
                MapValues mv = df.getMapValues();
                if (mv != null) {
                    for (FieldColumnPair fcp : mv.getFieldColumnPairList()) {
                        if (isExcluded(fcp.getField(), res)) {
                            warningFields.add(fcp.getField());
                        }
                    }
                }
                NormContinuous nc = df.getNormContinuous();
                if (nc != null && isExcluded(nc.getField(), res)) {
                    warningFields.add(nc.getField());
                }
                NormDiscrete nd = df.getNormDiscrete();
                if (nd != null && isExcluded(nd.getField(), res)) {
                    warningFields.add(nd.getField());
                }
            }
        }
        DataDictionary dict = pmml.getDataDictionary();
        List<DataField> fields = dict.getDataFieldList();
        // Apply filter to spec
        int numFields = 0;
        for (int i = fields.size() - 1; i >= 0; i--) {
            if (isExcluded(fields.get(i).getName(), res)) {
                dict.removeDataField(i);
            } else {
                numFields++;
            }
        }
        dict.setNumberOfFields(new BigInteger(Integer.toString(numFields)));
        pmml.setDataDictionary(dict);
        pmmldoc.setPMML(pmml);
        // generate warnings and set as warning message
        for (String s : warningFields) {
            if (warningBuffer.length() != 0) {
                warningBuffer.append("\n");
            }
            warningBuffer.append("Transformation dictionary uses excluded column " + s);
        }
        if (warningBuffer.length() > 0) {
            setWarningMessage(warningBuffer.toString().trim());
        }
        PMMLPortObject outport = null;
        try {
            outport = new PMMLPortObject(createPMMLSpec(pmmlIn.getSpec(), outSpec, res), pmmldoc);
        } catch (IllegalArgumentException e) {
            if (res.getIncludes().length == 0) {
                throw new IllegalArgumentException("Excluding all columns produces invalid PMML", e);
            } else {
                throw e;
            }
        }
        return outport;
    }
}

Example 24 with PMML

use of org.dmg.pmml.PMMLDocument.PMML 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();
}

Example 25 with PMML

use of org.dmg.pmml.PMMLDocument.PMML in project knime-core by knime.

the class AbstractGBTModelPMMLTranslator method exportTo.

/**
 * {@inheritDoc}
 */
@Override
public SchemaType exportTo(final PMMLDocument pmmlDoc, final PMMLPortObjectSpec spec) {
    PMML pmml = pmmlDoc.getPMML();
    AbstractGBTModelExporter<M> exporter = createExporter(new DerivedFieldMapper(pmmlDoc));
    SchemaType st = exporter.writeModelToPMML(pmml.addNewMiningModel(), spec);
    if (exporter.hasWarning()) {
        addWarning(exporter.getWarning());
    }
    return st;
}