Examples with Model org.dmg.pmml.Model used on opensource projects

Example 1 with Model

use of org.dmg.pmml.Model in project pyramid by cheng-li.

the class MiningModelUtil method createModelChain.

public static MiningModel createModelChain(List<? extends Model> models, Schema schema) {
    if (models.size() < 1) {
        throw new IllegalArgumentException();
    }
    Segmentation segmentation = createSegmentation(Segmentation.MultipleModelMethod.MODEL_CHAIN, models);
    Model lastModel = Iterables.getLast(models);
    MiningModel miningModel = new MiningModel(lastModel.getMiningFunction(), ModelUtil.createMiningSchema(schema.getLabel())).setMathContext(ModelUtil.simplifyMathContext(lastModel.getMathContext())).setSegmentation(segmentation);
    return miningModel;
}

Example 2 with Model

use of org.dmg.pmml.Model in project pyramid by cheng-li.

the class MiningModelUtil method createSegmentation.

public static Segmentation createSegmentation(Segmentation.MultipleModelMethod multipleModelMethod, List<? extends Model> models, List<? extends Number> weights) {
    if ((weights != null) && (models.size() != weights.size())) {
        throw new IllegalArgumentException();
    }
    List<Segment> segments = new ArrayList<>();
    for (int i = 0; i < models.size(); i++) {
        Model model = models.get(i);
        Number weight = (weights != null ? weights.get(i) : null);
        Segment segment = new Segment().setId(String.valueOf(i + 1)).setPredicate(new True()).setModel(model);
        if (weight != null && !ValueUtil.isOne(weight)) {
            segment.setWeight(ValueUtil.asDouble(weight));
        }
        segments.add(segment);
    }
    return new Segmentation(multipleModelMethod, segments);
}

Example 3 with Model

use of org.dmg.pmml.Model in project pyramid by cheng-li.

the class MiningModelUtil method createClassification.

public static MiningModel createClassification(List<? extends Model> models, RegressionModel.NormalizationMethod normalizationMethod, boolean hasProbabilityDistribution, Schema schema) {
    CategoricalLabel categoricalLabel = (CategoricalLabel) schema.getLabel();
    // modified here
    if (categoricalLabel.size() != models.size()) {
        throw new IllegalArgumentException();
    }
    if (normalizationMethod != null) {
        switch(normalizationMethod) {
            case NONE:
            case SIMPLEMAX:
            case SOFTMAX:
                break;
            default:
                throw new IllegalArgumentException();
        }
    }
    MathContext mathContext = null;
    List<RegressionTable> regressionTables = new ArrayList<>();
    for (int i = 0; i < categoricalLabel.size(); i++) {
        Model model = models.get(i);
        MathContext modelMathContext = model.getMathContext();
        if (modelMathContext == null) {
            modelMathContext = MathContext.DOUBLE;
        }
        if (mathContext == null) {
            mathContext = modelMathContext;
        } else {
            if (!Objects.equals(mathContext, modelMathContext)) {
                throw new IllegalArgumentException();
            }
        }
        Feature feature = MODEL_PREDICTION.apply(model);
        RegressionTable regressionTable = RegressionModelUtil.createRegressionTable(Collections.singletonList(feature), Collections.singletonList(1d), null).setTargetCategory(categoricalLabel.getValue(i));
        regressionTables.add(regressionTable);
    }
    RegressionModel regressionModel = new RegressionModel(MiningFunction.CLASSIFICATION, ModelUtil.createMiningSchema(categoricalLabel), regressionTables).setNormalizationMethod(normalizationMethod).setMathContext(ModelUtil.simplifyMathContext(mathContext)).setOutput(hasProbabilityDistribution ? ModelUtil.createProbabilityOutput(mathContext, categoricalLabel) : null);
    List<Model> segmentationModels = new ArrayList<>(models);
    segmentationModels.add(regressionModel);
    return createModelChain(segmentationModels, schema);
}

Example 4 with Model

use of org.dmg.pmml.Model in project shifu by ShifuML.

the class PMMLTranslator method build.

public PMML build(List<BasicML> basicMLs) {
    if (basicMLs == null || basicMLs.size() == 0) {
        throw new IllegalArgumentException("Input ml model list is empty.");
    }
    PMML pmml = new PMML();
    // create and add header
    Header header = new Header();
    pmml.setHeader(header);
    header.setCopyright(" Copyright [2013-2018] PayPal Software Foundation\n" + "\n" + " Licensed under the Apache License, Version 2.0 (the \"License\");\n" + " you may not use this file except in compliance with the License.\n" + " You may obtain a copy of the License at\n" + "\n" + "    http://www.apache.org/licenses/LICENSE-2.0\n" + "\n" + " Unless required by applicable law or agreed to in writing, software\n" + " distributed under the License is distributed on an \"AS IS\" BASIS,\n" + " WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n" + " See the License for the specific language governing permissions and\n" + " limitations under the License.\n");
    Application application = new Application();
    header.setApplication(application);
    application.setName("shifu");
    String findContainingJar = JarManager.findContainingJar(TreeEnsemblePMMLTranslator.class);
    JarFile jar = null;
    try {
        jar = new JarFile(findContainingJar);
        final Manifest manifest = jar.getManifest();
        String version = manifest.getMainAttributes().getValue("version");
        application.setVersion(version);
    } catch (Exception e) {
        LOG.warn(e.getMessage());
    } finally {
        if (jar != null) {
            try {
                jar.close();
            } catch (IOException e) {
                LOG.warn(e.getMessage());
            }
        }
    }
    // create and set data dictionary for all bagging models
    pmml.setDataDictionary(this.dataDictionaryCreator.build(null));
    if (isOutBaggingToOne) {
        MiningModel miningModel = new MiningModel();
        miningModel.setMiningSchema(this.miningSchemaCreator.build(null));
        miningModel.setMiningFunction(MiningFunction.fromValue("regression"));
        miningModel.setTargets(((NNPmmlModelCreator) this.modelCreator).createTargets());
        AbstractSpecifCreator minningModelCreator = new MiningModelPmmlCreator(this.specifCreator.getModelConfig(), this.specifCreator.getColumnConfigList());
        minningModelCreator.build(null, miningModel);
        Segmentation seg = new Segmentation();
        miningModel.setSegmentation(seg);
        seg.setMultipleModelMethod(MultipleModelMethod.AVERAGE);
        List<Segment> list = seg.getSegments();
        int idCount = 0;
        for (BasicML basicML : basicMLs) {
            // create model element
            Model tmpmodel = this.modelCreator.build(basicML);
            // create mining schema
            tmpmodel.setMiningSchema(this.miningSchemaCreator.build(basicML));
            // create variable statistical info
            tmpmodel.setModelStats(this.modelStatsCreator.build(basicML));
            // create variable transform
            tmpmodel.setLocalTransformations(this.localTransformationsCreator.build(basicML));
            this.specifCreator.build(basicML, tmpmodel, idCount);
            Segment segment = new Segment();
            segment.setId("Segement" + String.valueOf(idCount));
            segment.setPredicate(new True());
            segment.setModel(tmpmodel);
            list.add(segment);
            idCount++;
        }
        List<Model> models = pmml.getModels();
        models.add(miningModel);
    } else {
        BasicML basicML = basicMLs.get(0);
        // create model element
        Model model = this.modelCreator.build(basicML);
        // create mining schema
        model.setMiningSchema(this.miningSchemaCreator.build(basicML));
        // create variable statistical info
        model.setModelStats(this.modelStatsCreator.build(basicML));
        // create variable transform
        model.setLocalTransformations(this.localTransformationsCreator.build(basicML));
        this.specifCreator.build(basicML, model);
        pmml.addModels(model);
    }
    return pmml;
}

Example 5 with Model

use of org.dmg.pmml.Model in project shifu by ShifuML.

the class RegressionPmmlModelCreator method build.

@Override
public Model build(BasicML basicML) {
    Model model = new RegressionModel();
    model.setTargets(createTargets());
    return model;
}