Examples with RuleSetModel org.dmg.pmml.RuleSetModelDocument.RuleSetModel used on opensource projects

Example 6 with RuleSetModel

use of org.dmg.pmml.RuleSetModelDocument.RuleSetModel 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 7 with RuleSetModel

use of org.dmg.pmml.RuleSetModelDocument.RuleSetModel 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 RuleSetModel

use of org.dmg.pmml.RuleSetModelDocument.RuleSetModel 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 9 with RuleSetModel

use of org.dmg.pmml.RuleSetModelDocument.RuleSetModel in project knime-core by knime.

the class RuleEngine2PortsNodeModel method computeRearrangerWithPMML.

/**
 * @param spec
 * @param rules
 * @param flowVars
 * @param ruleIdx
 * @param outcomeIdx
 * @param confidenceIdx
 * @param weightIdx
 * @param validationIdx
 * @param outputColumnName
 * @return
 * @throws InterruptedException
 * @throws InvalidSettingsException
 */
private Pair<ColumnRearranger, PortObject> computeRearrangerWithPMML(final DataTableSpec spec, final RowInput rules, final Map<String, FlowVariable> flowVars, final int ruleIdx, final int outcomeIdx, final int confidenceIdx, final int weightIdx, final int validationIdx, final String outputColumnName) throws InterruptedException, InvalidSettingsException {
    PortObject po;
    ColumnRearranger ret;
    PMMLDocument doc = PMMLDocument.Factory.newInstance();
    final PMML pmmlObj = doc.addNewPMML();
    RuleSetModel ruleSetModel = pmmlObj.addNewRuleSetModel();
    RuleSet ruleSet = ruleSetModel.addNewRuleSet();
    List<DataType> outcomeTypes = new ArrayList<>();
    PMMLRuleParser parser = new PMMLRuleParser(spec, flowVars);
    int lineNo = 0;
    DataRow ruleRow;
    while ((ruleRow = rules.poll()) != null) {
        ++lineNo;
        DataCell rule = ruleRow.getCell(ruleIdx);
        CheckUtils.checkSetting(!rule.isMissing(), "Missing rule in row: " + ruleRow.getKey());
        if (rule instanceof StringValue) {
            StringValue ruleText = (StringValue) rule;
            String r = ruleText.getStringValue().replaceAll("[\r\n]+", " ");
            if (RuleSupport.isComment(r)) {
                continue;
            }
            if (outcomeIdx >= 0) {
                r += " => " + m_settings.asStringFailForMissing(ruleRow.getCell(outcomeIdx));
            }
            ParseState state = new ParseState(r);
            try {
                PMMLPredicate condition = parser.parseBooleanExpression(state);
                SimpleRule simpleRule = ruleSet.addNewSimpleRule();
                setCondition(simpleRule, condition);
                state.skipWS();
                state.consumeText("=>");
                state.skipWS();
                Expression outcome = parser.parseOutcomeOperand(state, null);
                simpleRule.setScore(outcome.toString());
                if (confidenceIdx >= 0) {
                    DataCell confidenceCell = ruleRow.getCell(confidenceIdx);
                    if (!confidenceCell.isMissing()) {
                        if (confidenceCell instanceof DoubleValue) {
                            DoubleValue dv = (DoubleValue) confidenceCell;
                            double confidence = dv.getDoubleValue();
                            simpleRule.setConfidence(confidence);
                        }
                    }
                }
                if (weightIdx >= 0) {
                    DataCell weightCell = ruleRow.getCell(weightIdx);
                    boolean missing = true;
                    if (!weightCell.isMissing()) {
                        if (weightCell instanceof DoubleValue) {
                            DoubleValue dv = (DoubleValue) weightCell;
                            double weight = dv.getDoubleValue();
                            simpleRule.setWeight(weight);
                            missing = false;
                        }
                    }
                    if (missing && m_settings.isHasDefaultWeight()) {
                        simpleRule.setWeight(m_settings.getDefaultWeight());
                    }
                }
                CheckUtils.checkSetting(outcome.isConstant(), "Outcome is not constant in line " + lineNo + " (" + ruleRow.getKey() + ") for rule: " + rule);
                outcomeTypes.add(outcome.getOutputType());
            } catch (ParseException e) {
                ParseException error = Util.addContext(e, r, lineNo);
                throw new InvalidSettingsException("Wrong rule in line: " + ruleRow.getKey() + "\n" + error.getMessage(), error);
            }
        } else {
            CheckUtils.checkSetting(false, "Wrong type (" + rule.getType() + ") of rule: " + rule + "\nin row: " + ruleRow.getKey());
        }
    }
    ColumnRearranger dummy = new ColumnRearranger(spec);
    if (!m_settings.isReplaceColumn()) {
        dummy.append(new SingleCellFactory(new DataColumnSpecCreator(outputColumnName, RuleEngineNodeModel.computeOutputType(outcomeTypes, computeOutcomeType(rules.getDataTableSpec()), true, m_settings.isDisallowLongOutputForCompatibility())).createSpec()) {

            @Override
            public DataCell getCell(final DataRow row) {
                return null;
            }
        });
    }
    PMMLPortObject pmml = createPMMLPortObject(doc, ruleSetModel, ruleSet, parser, dummy.createSpec());
    po = pmml;
    m_copy = copy(pmml);
    String predictionConfidenceColumn = m_settings.getPredictionConfidenceColumn();
    if (predictionConfidenceColumn == null || predictionConfidenceColumn.isEmpty()) {
        predictionConfidenceColumn = RuleEngine2PortsSettings.DEFAULT_PREDICTION_CONFIDENCE_COLUMN;
    }
    ret = PMMLRuleSetPredictorNodeModel.createRearranger(pmml, spec, m_settings.isReplaceColumn(), outputColumnName, m_settings.isComputeConfidence(), DataTableSpec.getUniqueColumnName(dummy.createSpec(), predictionConfidenceColumn), validationIdx);
    return Pair.create(ret, po);
}

Example 10 with RuleSetModel

use of org.dmg.pmml.RuleSetModelDocument.RuleSetModel in project knime-core by knime.

the class PMMLRuleEditorNodeModel method createRearrangerAndPMMLModel.

private RearrangerAndPMMLModel createRearrangerAndPMMLModel(final DataTableSpec spec) throws ParseException, InvalidSettingsException {
    final PMMLDocument doc = PMMLDocument.Factory.newInstance();
    final PMML pmml = doc.addNewPMML();
    RuleSetModel ruleSetModel = pmml.addNewRuleSetModel();
    RuleSet ruleSet = ruleSetModel.addNewRuleSet();
    PMMLRuleParser parser = new PMMLRuleParser(spec, getAvailableInputFlowVariables());
    ColumnRearranger rearranger = createRearranger(spec, ruleSet, parser);
    PMMLPortObject ret = new PMMLPortObject(createPMMLPortObjectSpec(rearranger.createSpec(), parser.getUsedColumns()));
    // if (inData[1] != null) {
    // PMMLPortObject po = (PMMLPortObject)inData[1];
    // TransformationDictionary dict = TransformationDictionary.Factory.newInstance();
    // dict.setDerivedFieldArray(po.getDerivedFields());
    // ret.addGlobalTransformations(dict);
    // }
    PMMLRuleTranslator modelTranslator = new PMMLRuleTranslator();
    ruleSetModel.setFunctionName(MININGFUNCTION.CLASSIFICATION);
    ruleSet.setDefaultConfidence(defaultConfidenceValue());
    PMMLMiningSchemaTranslator.writeMiningSchema(ret.getSpec(), ruleSetModel);
    PMMLDataDictionaryTranslator ddTranslator = new PMMLDataDictionaryTranslator();
    ddTranslator.exportTo(doc, ret.getSpec());
    modelTranslator.initializeFrom(doc);
    ret.addModelTranslater(modelTranslator);
    ret.validate();
    return new RearrangerAndPMMLModel(rearranger, ret);
}