use of org.jpmml.converter.CategoryManager in project jpmml-r by jpmml.
the class RandomForestConverter method encodeTreeModel.
private <P extends Number> TreeModel encodeTreeModel(MiningFunction miningFunction, ScoreEncoder<P> scoreEncoder, List<? extends Number> leftDaughter, List<? extends Number> rightDaughter, List<P> nodepred, List<? extends Number> bestvar, List<Double> xbestsplit, Schema schema) {
RGenericVector randomForest = getObject();
Node root = encodeNode(True.INSTANCE, 0, scoreEncoder, leftDaughter, rightDaughter, bestvar, xbestsplit, nodepred, new CategoryManager(), schema);
TreeModel treeModel = new TreeModel(miningFunction, ModelUtil.createMiningSchema(schema.getLabel()), root).setMissingValueStrategy(TreeModel.MissingValueStrategy.NULL_PREDICTION).setSplitCharacteristic(TreeModel.SplitCharacteristic.BINARY_SPLIT);
if (this.compact) {
Visitor visitor = new RandomForestCompactor();
visitor.applyTo(treeModel);
}
return treeModel;
}
use of org.jpmml.converter.CategoryManager 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;
}
use of org.jpmml.converter.CategoryManager in project jpmml-sparkml by jpmml.
the class TreeModelUtil method encodeTreeModel.
private static <M extends Model<M> & DecisionTreeModel> TreeModel encodeTreeModel(MiningFunction miningFunction, ScoreEncoder scoreEncoder, M model, PredicateManager predicateManager, Schema schema) {
Node root = encodeNode(True.INSTANCE, scoreEncoder, model.rootNode(), predicateManager, new CategoryManager(), schema);
TreeModel treeModel = new TreeModel(miningFunction, ModelUtil.createMiningSchema(schema.getLabel()), root).setSplitCharacteristic(TreeModel.SplitCharacteristic.BINARY_SPLIT);
return treeModel;
}
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