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

Example 1 with BinaryFeature

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

the class TreeModelUtil method encodeNode.

public static Node encodeNode(org.apache.spark.ml.tree.Node node, PredicateManager predicateManager, Map<FieldName, Set<String>> parentFieldValues, MiningFunction miningFunction, Schema schema) {
    if (node instanceof InternalNode) {
        InternalNode internalNode = (InternalNode) node;
        Map<FieldName, Set<String>> leftFieldValues = parentFieldValues;
        Map<FieldName, Set<String>> rightFieldValues = parentFieldValues;
        Predicate leftPredicate;
        Predicate rightPredicate;
        Split split = internalNode.split();
        Feature feature = schema.getFeature(split.featureIndex());
        if (split instanceof ContinuousSplit) {
            ContinuousSplit continuousSplit = (ContinuousSplit) split;
            double threshold = continuousSplit.threshold();
            if (feature instanceof BooleanFeature) {
                BooleanFeature booleanFeature = (BooleanFeature) feature;
                if (threshold != 0.5d) {
                    throw new IllegalArgumentException();
                }
                leftPredicate = predicateManager.createSimplePredicate(booleanFeature, SimplePredicate.Operator.EQUAL, booleanFeature.getValue(0));
                rightPredicate = predicateManager.createSimplePredicate(booleanFeature, SimplePredicate.Operator.EQUAL, booleanFeature.getValue(1));
            } else {
                ContinuousFeature continuousFeature = feature.toContinuousFeature();
                String value = ValueUtil.formatValue(threshold);
                leftPredicate = predicateManager.createSimplePredicate(continuousFeature, SimplePredicate.Operator.LESS_OR_EQUAL, value);
                rightPredicate = predicateManager.createSimplePredicate(continuousFeature, SimplePredicate.Operator.GREATER_THAN, value);
            }
        } else if (split instanceof CategoricalSplit) {
            CategoricalSplit categoricalSplit = (CategoricalSplit) split;
            double[] leftCategories = categoricalSplit.leftCategories();
            double[] rightCategories = categoricalSplit.rightCategories();
            if (feature instanceof BinaryFeature) {
                BinaryFeature binaryFeature = (BinaryFeature) feature;
                SimplePredicate.Operator leftOperator;
                SimplePredicate.Operator rightOperator;
                if (Arrays.equals(TRUE, leftCategories) && Arrays.equals(FALSE, rightCategories)) {
                    leftOperator = SimplePredicate.Operator.EQUAL;
                    rightOperator = SimplePredicate.Operator.NOT_EQUAL;
                } else if (Arrays.equals(FALSE, leftCategories) && Arrays.equals(TRUE, rightCategories)) {
                    leftOperator = SimplePredicate.Operator.NOT_EQUAL;
                    rightOperator = SimplePredicate.Operator.EQUAL;
                } else {
                    throw new IllegalArgumentException();
                }
                String value = ValueUtil.formatValue(binaryFeature.getValue());
                leftPredicate = predicateManager.createSimplePredicate(binaryFeature, leftOperator, value);
                rightPredicate = predicateManager.createSimplePredicate(binaryFeature, rightOperator, value);
            } else if (feature instanceof CategoricalFeature) {
                CategoricalFeature categoricalFeature = (CategoricalFeature) feature;
                FieldName name = categoricalFeature.getName();
                List<String> values = categoricalFeature.getValues();
                if (values.size() != (leftCategories.length + rightCategories.length)) {
                    throw new IllegalArgumentException();
                }
                final Set<String> parentValues = parentFieldValues.get(name);
                com.google.common.base.Predicate<String> valueFilter = new com.google.common.base.Predicate<String>() {

                    @Override
                    public boolean apply(String value) {
                        if (parentValues != null) {
                            return parentValues.contains(value);
                        }
                        return true;
                    }
                };
                List<String> leftValues = selectValues(values, leftCategories, valueFilter);
                List<String> rightValues = selectValues(values, rightCategories, valueFilter);
                leftFieldValues = new HashMap<>(parentFieldValues);
                leftFieldValues.put(name, new HashSet<>(leftValues));
                rightFieldValues = new HashMap<>(parentFieldValues);
                rightFieldValues.put(name, new HashSet<>(rightValues));
                leftPredicate = predicateManager.createSimpleSetPredicate(categoricalFeature, leftValues);
                rightPredicate = predicateManager.createSimpleSetPredicate(categoricalFeature, rightValues);
            } else {
                throw new IllegalArgumentException();
            }
        } else {
            throw new IllegalArgumentException();
        }
        Node result = new Node();
        Node leftChild = encodeNode(internalNode.leftChild(), predicateManager, leftFieldValues, miningFunction, schema).setPredicate(leftPredicate);
        Node rightChild = encodeNode(internalNode.rightChild(), predicateManager, rightFieldValues, miningFunction, schema).setPredicate(rightPredicate);
        result.addNodes(leftChild, rightChild);
        return result;
    } else if (node instanceof LeafNode) {
        LeafNode leafNode = (LeafNode) node;
        Node result = new Node();
        switch(miningFunction) {
            case REGRESSION:
                {
                    String score = ValueUtil.formatValue(node.prediction());
                    result.setScore(score);
                }
                break;
            case CLASSIFICATION:
                {
                    CategoricalLabel categoricalLabel = (CategoricalLabel) schema.getLabel();
                    int index = ValueUtil.asInt(node.prediction());
                    result.setScore(categoricalLabel.getValue(index));
                    ImpurityCalculator impurityCalculator = node.impurityStats();
                    result.setRecordCount((double) impurityCalculator.count());
                    double[] stats = impurityCalculator.stats();
                    for (int i = 0; i < stats.length; i++) {
                        ScoreDistribution scoreDistribution = new ScoreDistribution(categoricalLabel.getValue(i), stats[i]);
                        result.addScoreDistributions(scoreDistribution);
                    }
                }
                break;
            default:
                throw new UnsupportedOperationException();
        }
        return result;
    } else {
        throw new IllegalArgumentException();
    }
}

Example 2 with BinaryFeature

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

the class OneHotEncoderModelConverter method encodeFeatures.

@Override
public List<Feature> encodeFeatures(SparkMLEncoder encoder) {
    OneHotEncoderModel transformer = getTransformer();
    String[] inputCols = transformer.getInputCols();
    boolean dropLast = transformer.getDropLast();
    List<Feature> result = new ArrayList<>();
    for (int i = 0; i < inputCols.length; i++) {
        CategoricalFeature categoricalFeature = (CategoricalFeature) encoder.getOnlyFeature(inputCols[i]);
        List<String> values = categoricalFeature.getValues();
        if (dropLast) {
            values = values.subList(0, values.size() - 1);
        }
        List<BinaryFeature> binaryFeatures = new ArrayList<>();
        for (String value : values) {
            binaryFeatures.add(new BinaryFeature(encoder, categoricalFeature.getName(), DataType.STRING, value));
        }
        result.add(new BinarizedCategoricalFeature(encoder, categoricalFeature.getName(), categoricalFeature.getDataType(), binaryFeatures));
    }
    return result;
}

Example 3 with BinaryFeature

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

the class Formula method addField.

public void addField(Field<?> field, List<String> categoryNames, List<String> categoryValues) {
    RExpEncoder encoder = getEncoder();
    if (categoryNames.size() != categoryValues.size()) {
        throw new IllegalArgumentException();
    }
    CategoricalFeature categoricalFeature;
    if ((DataType.BOOLEAN).equals(field.getDataType()) && (categoryValues.size() == 2) && ("false").equals(categoryValues.get(0)) && ("true").equals(categoryValues.get(1))) {
        categoricalFeature = new BooleanFeature(encoder, field);
    } else {
        categoricalFeature = new CategoricalFeature(encoder, field, categoryValues);
    }
    putFeature(field.getName(), categoricalFeature);
    for (int i = 0; i < categoryNames.size(); i++) {
        String categoryName = categoryNames.get(i);
        String categoryValue = categoryValues.get(i);
        BinaryFeature binaryFeature = new BinaryFeature(encoder, field, categoryValue);
        putFeature(FieldName.create((field.getName()).getValue() + categoryName), binaryFeature);
    }
    this.fields.add(field);
}

Example 4 with BinaryFeature

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

the class ScorecardConverter method encodeModel.

@Override
public Scorecard encodeModel(Schema schema) {
    RGenericVector glm = getObject();
    RDoubleVector coefficients = (RDoubleVector) glm.getValue("coefficients");
    RGenericVector family = (RGenericVector) glm.getValue("family");
    RGenericVector scConf;
    try {
        scConf = (RGenericVector) glm.getValue("sc.conf");
    } catch (IllegalArgumentException iae) {
        throw new IllegalArgumentException("No scorecard configuration information. Please initialize the \'sc.conf\' element", iae);
    }
    Double intercept = coefficients.getValue(LMConverter.INTERCEPT, true);
    List<? extends Feature> features = schema.getFeatures();
    if (coefficients.size() != (features.size() + (intercept != null ? 1 : 0))) {
        throw new IllegalArgumentException();
    }
    RNumberVector<?> odds = (RNumberVector<?>) scConf.getValue("odds");
    RNumberVector<?> basePoints = (RNumberVector<?>) scConf.getValue("base_points");
    RNumberVector<?> pdo = (RNumberVector<?>) scConf.getValue("pdo");
    double factor = (pdo.asScalar()).doubleValue() / Math.log(2);
    Map<FieldName, Characteristic> fieldCharacteristics = new LinkedHashMap<>();
    for (Feature feature : features) {
        FieldName name = feature.getName();
        if (!(feature instanceof BinaryFeature)) {
            throw new IllegalArgumentException();
        }
        Double coefficient = getFeatureCoefficient(feature, coefficients);
        Characteristic characteristic = fieldCharacteristics.get(name);
        if (characteristic == null) {
            characteristic = new Characteristic().setName(FeatureUtil.createName("score", feature));
            fieldCharacteristics.put(name, characteristic);
        }
        BinaryFeature binaryFeature = (BinaryFeature) feature;
        SimplePredicate simplePredicate = new SimplePredicate().setField(binaryFeature.getName()).setOperator(SimplePredicate.Operator.EQUAL).setValue(binaryFeature.getValue());
        Attribute attribute = new Attribute().setPartialScore(formatScore(-1d * coefficient * factor)).setPredicate(simplePredicate);
        characteristic.addAttributes(attribute);
    }
    Characteristics characteristics = new Characteristics();
    Collection<Map.Entry<FieldName, Characteristic>> entries = fieldCharacteristics.entrySet();
    for (Map.Entry<FieldName, Characteristic> entry : entries) {
        Characteristic characteristic = entry.getValue();
        Attribute attribute = new Attribute().setPartialScore(0d).setPredicate(new True());
        characteristic.addAttributes(attribute);
        characteristics.addCharacteristics(characteristic);
    }
    Scorecard scorecard = new Scorecard(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(schema.getLabel()), characteristics).setInitialScore(formatScore((basePoints.asScalar()).doubleValue() - Math.log((odds.asScalar()).doubleValue()) * factor - (intercept != null ? intercept * factor : 0))).setUseReasonCodes(false);
    return scorecard;
}

Example 5 with BinaryFeature

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

the class OneHotEncoderConverter method encodeFeatures.

@Override
public List<Feature> encodeFeatures(SparkMLEncoder encoder) {
    OneHotEncoder transformer = getTransformer();
    boolean dropLast = true;
    Option<Object> dropLastOption = transformer.get(transformer.dropLast());
    if (dropLastOption.isDefined()) {
        dropLast = (Boolean) dropLastOption.get();
    }
    CategoricalFeature categoricalFeature = (CategoricalFeature) encoder.getOnlyFeature(transformer.getInputCol());
    List<String> values = categoricalFeature.getValues();
    if (dropLast) {
        values = values.subList(0, values.size() - 1);
    }
    List<Feature> result = new ArrayList<>();
    for (String value : values) {
        result.add(new BinaryFeature(encoder, categoricalFeature.getName(), DataType.STRING, value));
    }
    return result;
}