Examples with Feature org.jpmml.converter.Feature used on opensource projects

Example 56 with Feature

use of org.jpmml.converter.Feature in project jpmml-sparkml by jpmml.

the class StringIndexerModelConverter method encodeFeatures.

@Override
public List<Feature> encodeFeatures(SparkMLEncoder encoder) {
    StringIndexerModel transformer = getTransformer();
    Feature feature = encoder.getOnlyFeature(transformer.getInputCol());
    List<String> categories = new ArrayList<>();
    categories.addAll(Arrays.asList(transformer.labels()));
    String handleInvalid = transformer.getHandleInvalid();
    Field<?> field = encoder.toCategorical(feature.getName(), categories);
    if (field instanceof DataField) {
        DataField dataField = (DataField) field;
        InvalidValueTreatmentMethod invalidValueTreatmentMethod;
        switch(handleInvalid) {
            case "keep":
                invalidValueTreatmentMethod = InvalidValueTreatmentMethod.AS_IS;
                break;
            case "error":
                invalidValueTreatmentMethod = InvalidValueTreatmentMethod.RETURN_INVALID;
                break;
            default:
                throw new IllegalArgumentException(handleInvalid);
        }
        InvalidValueDecorator invalidValueDecorator = new InvalidValueDecorator().setInvalidValueTreatment(invalidValueTreatmentMethod);
        encoder.addDecorator(dataField.getName(), invalidValueDecorator);
    } else if (field instanceof DerivedField) {
    // Ignored
    } else {
        throw new IllegalArgumentException();
    }
    switch(handleInvalid) {
        case "keep":
            Apply setApply = PMMLUtil.createApply("isIn", feature.ref());
            for (String category : categories) {
                setApply.addExpressions(PMMLUtil.createConstant(category, feature.getDataType()));
            }
            categories.add(StringIndexerModelConverter.LABEL_UNKNOWN);
            Apply apply = PMMLUtil.createApply("if", setApply, feature.ref(), PMMLUtil.createConstant(StringIndexerModelConverter.LABEL_UNKNOWN, DataType.STRING));
            field = encoder.createDerivedField(FeatureUtil.createName("handleInvalid", feature), OpType.CATEGORICAL, feature.getDataType(), apply);
            break;
        default:
            break;
    }
    return Collections.<Feature>singletonList(new CategoricalFeature(encoder, field, categories));
}

Example 57 with Feature

use of org.jpmml.converter.Feature in project jpmml-sparkml by jpmml.

the class TokenizerConverter method encodeFeatures.

@Override
public List<Feature> encodeFeatures(SparkMLEncoder encoder) {
    Tokenizer transformer = getTransformer();
    Feature feature = encoder.getOnlyFeature(transformer.getInputCol());
    Apply apply = PMMLUtil.createApply("lowercase", feature.ref());
    DerivedField derivedField = encoder.createDerivedField(FeatureUtil.createName("lowercase", feature), OpType.CATEGORICAL, DataType.STRING, apply);
    return Collections.<Feature>singletonList(new DocumentFeature(encoder, derivedField, "\\s+"));
}

Example 58 with Feature

use of org.jpmml.converter.Feature in project jpmml-r by jpmml.

the class NaiveBayesConverter method encodeModel.

@Override
public Model encodeModel(Schema schema) {
    RGenericVector naiveBayes = getObject();
    RIntegerVector apriori = naiveBayes.getIntegerElement("apriori");
    RGenericVector tables = naiveBayes.getGenericElement("tables");
    CategoricalLabel categoricalLabel = (CategoricalLabel) schema.getLabel();
    List<? extends Feature> features = schema.getFeatures();
    BayesInputs bayesInputs = new BayesInputs();
    for (int i = 0; i < features.size(); i++) {
        Feature feature = features.get(i);
        String name = feature.getName();
        RDoubleVector table = tables.getDoubleElement(name);
        RStringVector tableRows = table.dimnames(0);
        RStringVector tableColumns = table.dimnames(1);
        BayesInput bayesInput = new BayesInput(name, null, null);
        if (feature instanceof CategoricalFeature) {
            CategoricalFeature categoricalFeature = (CategoricalFeature) feature;
            for (int column = 0; column < tableColumns.size(); column++) {
                TargetValueCounts targetValueCounts = new TargetValueCounts();
                List<Double> probabilities = FortranMatrixUtil.getColumn(table.getValues(), tableRows.size(), tableColumns.size(), column);
                for (int row = 0; row < tableRows.size(); row++) {
                    double count = apriori.getValue(row) * probabilities.get(row);
                    TargetValueCount targetValueCount = new TargetValueCount(tableRows.getValue(row), count);
                    targetValueCounts.addTargetValueCounts(targetValueCount);
                }
                PairCounts pairCounts = new PairCounts(tableColumns.getValue(column), targetValueCounts);
                bayesInput.addPairCounts(pairCounts);
            }
        } else if (feature instanceof ContinuousFeature) {
            ContinuousFeature continuousFeature = (ContinuousFeature) feature;
            TargetValueStats targetValueStats = new TargetValueStats();
            for (int row = 0; row < tableRows.size(); row++) {
                List<Double> stats = FortranMatrixUtil.getRow(table.getValues(), tableRows.size(), 2, row);
                double mean = stats.get(0);
                double variance = Math.pow(stats.get(1), 2);
                TargetValueStat targetValueStat = new TargetValueStat(tableRows.getValue(row), new GaussianDistribution(mean, variance));
                targetValueStats.addTargetValueStats(targetValueStat);
            }
            bayesInput.setTargetValueStats(targetValueStats);
        } else {
            throw new IllegalArgumentException();
        }
        bayesInputs.addBayesInputs(bayesInput);
    }
    BayesOutput bayesOutput = new BayesOutput().setField(categoricalLabel.getName());
    {
        TargetValueCounts targetValueCounts = new TargetValueCounts();
        RStringVector aprioriRows = apriori.dimnames(0);
        for (int row = 0; row < aprioriRows.size(); row++) {
            int count = apriori.getValue(row);
            TargetValueCount targetValueCount = new TargetValueCount(aprioriRows.getValue(row), count);
            targetValueCounts.addTargetValueCounts(targetValueCount);
        }
        bayesOutput.setTargetValueCounts(targetValueCounts);
    }
    NaiveBayesModel naiveBayesModel = new NaiveBayesModel(0d, MiningFunction.CLASSIFICATION, ModelUtil.createMiningSchema(categoricalLabel), bayesInputs, bayesOutput).setOutput(ModelUtil.createProbabilityOutput(DataType.DOUBLE, categoricalLabel));
    return naiveBayesModel;
}

Example 59 with Feature

use of org.jpmml.converter.Feature in project jpmml-r by jpmml.

the class FormulaUtil method addFeatures.

public static void addFeatures(Formula formula, List<String> names, boolean allowInteractions, RExpEncoder encoder) {
    for (int i = 0; i < names.size(); i++) {
        String name = names.get(i);
        Feature feature;
        if (allowInteractions) {
            feature = formula.resolveComplexFeature(name);
        } else {
            feature = formula.resolveFeature(name);
        }
        encoder.addFeature(feature);
    }
}

Example 60 with Feature

use of org.jpmml.converter.Feature in project jpmml-r by jpmml.

the class RandomForestConverter method encodeNode.

private <P extends Number> Node encodeNode(Predicate predicate, int i, ScoreEncoder<P> scoreEncoder, List<? extends Number> leftDaughter, List<? extends Number> rightDaughter, List<? extends Number> bestvar, List<Double> xbestsplit, List<P> nodepred, CategoryManager categoryManager, Schema schema) {
    Integer id = Integer.valueOf(i + 1);
    int var = ValueUtil.asInt(bestvar.get(i));
    if (var == 0) {
        P prediction = nodepred.get(i);
        Node result = new LeafNode(scoreEncoder.encode(prediction), predicate).setId(id);
        return result;
    }
    CategoryManager leftCategoryManager = categoryManager;
    CategoryManager rightCategoryManager = categoryManager;
    Predicate leftPredicate;
    Predicate rightPredicate;
    Feature feature = schema.getFeature(var - 1);
    Double split = xbestsplit.get(i);
    if (feature instanceof BooleanFeature) {
        BooleanFeature booleanFeature = (BooleanFeature) feature;
        if (split != 0.5d) {
            throw new IllegalArgumentException();
        }
        leftPredicate = createSimplePredicate(booleanFeature, SimplePredicate.Operator.EQUAL, booleanFeature.getValue(0));
        rightPredicate = createSimplePredicate(booleanFeature, SimplePredicate.Operator.EQUAL, booleanFeature.getValue(1));
    } else if (feature instanceof CategoricalFeature) {
        CategoricalFeature categoricalFeature = (CategoricalFeature) feature;
        String name = categoricalFeature.getName();
        List<?> values = categoricalFeature.getValues();
        java.util.function.Predicate<Object> valueFilter = categoryManager.getValueFilter(name);
        List<Object> leftValues = selectValues(values, valueFilter, split, true);
        List<Object> rightValues = selectValues(values, valueFilter, split, false);
        leftCategoryManager = categoryManager.fork(name, leftValues);
        rightCategoryManager = categoryManager.fork(name, rightValues);
        leftPredicate = createPredicate(categoricalFeature, leftValues);
        rightPredicate = createPredicate(categoricalFeature, rightValues);
    } else {
        ContinuousFeature continuousFeature = feature.toContinuousFeature();
        leftPredicate = createSimplePredicate(continuousFeature, SimplePredicate.Operator.LESS_OR_EQUAL, split);
        rightPredicate = createSimplePredicate(continuousFeature, SimplePredicate.Operator.GREATER_THAN, split);
    }
    Node result = new BranchNode(null, predicate).setId(id);
    List<Node> nodes = result.getNodes();
    int left = ValueUtil.asInt(leftDaughter.get(i));
    if (left != 0) {
        Node leftChild = encodeNode(leftPredicate, left - 1, scoreEncoder, leftDaughter, rightDaughter, bestvar, xbestsplit, nodepred, leftCategoryManager, schema);
        nodes.add(leftChild);
    }
    int right = ValueUtil.asInt(rightDaughter.get(i));
    if (right != 0) {
        Node rightChild = encodeNode(rightPredicate, right - 1, scoreEncoder, leftDaughter, rightDaughter, bestvar, xbestsplit, nodepred, rightCategoryManager, schema);
        nodes.add(rightChild);
    }
    return result;
}