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

Example 1 with CategoricalLabel

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

the class LogisticRegressionModelConverter method encodeModel.

@Override
public RegressionModel encodeModel(Schema schema) {
    LogisticRegressionModel model = getTransformer();
    CategoricalLabel categoricalLabel = (CategoricalLabel) schema.getLabel();
    if (categoricalLabel.size() == 2) {
        RegressionModel regressionModel = RegressionModelUtil.createBinaryLogisticClassification(schema.getFeatures(), VectorUtil.toList(model.coefficients()), model.intercept(), RegressionModel.NormalizationMethod.LOGIT, true, schema).setOutput(null);
        return regressionModel;
    } else if (categoricalLabel.size() > 2) {
        Matrix coefficientMatrix = model.coefficientMatrix();
        Vector interceptVector = model.interceptVector();
        List<? extends Feature> features = schema.getFeatures();
        List<RegressionTable> regressionTables = new ArrayList<>();
        for (int i = 0; i < categoricalLabel.size(); i++) {
            RegressionTable regressionTable = RegressionModelUtil.createRegressionTable(features, MatrixUtil.getRow(coefficientMatrix, i), interceptVector.apply(i)).setTargetCategory(categoricalLabel.getValue(i));
            regressionTables.add(regressionTable);
        }
        RegressionModel regressionModel = new RegressionModel(MiningFunction.CLASSIFICATION, ModelUtil.createMiningSchema(categoricalLabel), regressionTables).setNormalizationMethod(RegressionModel.NormalizationMethod.SOFTMAX);
        return regressionModel;
    } else {
        throw new IllegalArgumentException();
    }
}

Example 2 with CategoricalLabel

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

the class MultilayerPerceptronClassificationModelConverter method registerOutputFields.

@Override
public List<OutputField> registerOutputFields(Label label, SparkMLEncoder encoder) {
    MultilayerPerceptronClassificationModel model = getTransformer();
    List<OutputField> result = super.registerOutputFields(label, encoder);
    if (!(model instanceof HasProbabilityCol)) {
        CategoricalLabel categoricalLabel = (CategoricalLabel) label;
        result = new ArrayList<>(result);
        result.addAll(ModelUtil.createProbabilityFields(DataType.DOUBLE, categoricalLabel.getValues()));
    }
    return result;
}

Example 3 with CategoricalLabel

use of org.jpmml.converter.CategoricalLabel 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 4 with CategoricalLabel

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

the class ModelConverter method encodeSchema.

public Schema encodeSchema(SparkMLEncoder encoder) {
    T model = getTransformer();
    Label label = null;
    if (model instanceof HasLabelCol) {
        HasLabelCol hasLabelCol = (HasLabelCol) model;
        String labelCol = hasLabelCol.getLabelCol();
        Feature feature = encoder.getOnlyFeature(labelCol);
        MiningFunction miningFunction = getMiningFunction();
        switch(miningFunction) {
            case CLASSIFICATION:
                {
                    if (feature instanceof CategoricalFeature) {
                        CategoricalFeature categoricalFeature = (CategoricalFeature) feature;
                        DataField dataField = encoder.getDataField(categoricalFeature.getName());
                        label = new CategoricalLabel(dataField);
                    } else if (feature instanceof ContinuousFeature) {
                        ContinuousFeature continuousFeature = (ContinuousFeature) feature;
                        int numClasses = 2;
                        if (model instanceof ClassificationModel) {
                            ClassificationModel<?, ?> classificationModel = (ClassificationModel<?, ?>) model;
                            numClasses = classificationModel.numClasses();
                        }
                        List<String> categories = new ArrayList<>();
                        for (int i = 0; i < numClasses; i++) {
                            categories.add(String.valueOf(i));
                        }
                        Field<?> field = encoder.toCategorical(continuousFeature.getName(), categories);
                        encoder.putOnlyFeature(labelCol, new CategoricalFeature(encoder, field, categories));
                        label = new CategoricalLabel(field.getName(), field.getDataType(), categories);
                    } else {
                        throw new IllegalArgumentException("Expected a categorical or categorical-like continuous feature, got " + feature);
                    }
                }
                break;
            case REGRESSION:
                {
                    Field<?> field = encoder.toContinuous(feature.getName());
                    field.setDataType(DataType.DOUBLE);
                    label = new ContinuousLabel(field.getName(), field.getDataType());
                }
                break;
            default:
                throw new IllegalArgumentException("Mining function " + miningFunction + " is not supported");
        }
    }
    if (model instanceof ClassificationModel) {
        ClassificationModel<?, ?> classificationModel = (ClassificationModel<?, ?>) model;
        CategoricalLabel categoricalLabel = (CategoricalLabel) label;
        int numClasses = classificationModel.numClasses();
        if (numClasses != categoricalLabel.size()) {
            throw new IllegalArgumentException("Expected " + numClasses + " target categories, got " + categoricalLabel.size() + " target categories");
        }
    }
    String featuresCol = model.getFeaturesCol();
    List<Feature> features = encoder.getFeatures(featuresCol);
    if (model instanceof PredictionModel) {
        PredictionModel<?, ?> predictionModel = (PredictionModel<?, ?>) model;
        int numFeatures = predictionModel.numFeatures();
        if (numFeatures != -1 && features.size() != numFeatures) {
            throw new IllegalArgumentException("Expected " + numFeatures + " features, got " + features.size() + " features");
        }
    }
    Schema result = new Schema(label, features);
    return result;
}

Example 5 with CategoricalLabel

use of org.jpmml.converter.CategoricalLabel 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);
}