org.jpmml.converter.PredicateManager Java Examples

static
public <E extends Estimator & HasTree> TreeModel encodeTreeModel(E estimator, PredicateManager predicateManager, ScoreDistributionManager scoreDistributionManager, MiningFunction miningFunction, Schema schema){
	Tree tree = estimator.getTree();

	int[] leftChildren = tree.getChildrenLeft();
	int[] rightChildren = tree.getChildrenRight();
	int[] features = tree.getFeature();
	double[] thresholds = tree.getThreshold();
	double[] values = tree.getValues();

	Node root = encodeNode(True.INSTANCE, predicateManager, scoreDistributionManager, 0, leftChildren, rightChildren, features, thresholds, values, miningFunction, schema);

	TreeModel treeModel = new TreeModel(miningFunction, ModelUtil.createMiningSchema(schema.getLabel()), root)
		.setSplitCharacteristic(TreeModel.SplitCharacteristic.BINARY_SPLIT);

	ClassDictUtil.clearContent(tree);

	return treeModel;
}
 

static
public <C extends ModelConverter<? extends M> & HasTreeOptions, M extends Model<M> & TreeEnsembleModel<T>, T extends Model<T> & DecisionTreeModel> List<TreeModel> encodeDecisionTreeEnsemble(C converter, PredicateManager predicateManager, Schema schema){
	M model = converter.getTransformer();

	Schema segmentSchema = schema.toAnonymousSchema();

	List<TreeModel> treeModels = new ArrayList<>();

	T[] trees = model.trees();
	for(T tree : trees){
		TreeModel treeModel = encodeDecisionTree(converter, tree, predicateManager, segmentSchema);

		treeModels.add(treeModel);
	}

	return treeModels;
}
 

static
public TreeModel encodeTreeModel(TreePredictor treePredictor, PredicateManager predicateManager, Schema schema){
	int[] leaf = treePredictor.isLeaf();
	int[] leftChildren = treePredictor.getLeft();
	int[] rightChildren = treePredictor.getRight();
	int[] featureIdx = treePredictor.getFeatureIdx();
	double[] thresholds = treePredictor.getThreshold();
	int[] missingGoToLeft = treePredictor.getMissingGoToLeft();
	double[] values = treePredictor.getValues();

	Node root = encodeNode(True.INSTANCE, predicateManager, 0, leaf, leftChildren, rightChildren, featureIdx, thresholds, missingGoToLeft, values, schema);

	TreeModel treeModel = new TreeModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(schema.getLabel()), root)
		.setSplitCharacteristic(TreeModel.SplitCharacteristic.BINARY_SPLIT)
		.setMissingValueStrategy(TreeModel.MissingValueStrategy.DEFAULT_CHILD);

	return treeModel;
}
 

static
private <M extends Model<M> & DecisionTreeModel> TreeModel encodeTreeModel(M model, PredicateManager predicateManager, MiningFunction miningFunction, ScoreEncoder scoreEncoder, Schema schema){
	Node root = encodeNode(True.INSTANCE, model.rootNode(), predicateManager, new CategoryManager(), scoreEncoder, schema);

	TreeModel treeModel = new TreeModel(miningFunction, ModelUtil.createMiningSchema(schema.getLabel()), root)
		.setSplitCharacteristic(TreeModel.SplitCharacteristic.BINARY_SPLIT);

	return treeModel;
}
 

public TreeModel encodeTreeModel(PredicateManager predicateManager, Schema schema){
	org.dmg.pmml.tree.Node root = encodeNode(True.INSTANCE, predicateManager, 0, schema);

	TreeModel treeModel = new TreeModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(schema.getLabel()), root)
		.setSplitCharacteristic(TreeModel.SplitCharacteristic.BINARY_SPLIT)
		.setMissingValueStrategy(TreeModel.MissingValueStrategy.DEFAULT_CHILD)
		.setMathContext(MathContext.FLOAT);

	return treeModel;
}
 

static
public <E extends Estimator & HasTree> TreeModel encodeTreeModel(E estimator, MiningFunction miningFunction, Schema schema){
	PredicateManager predicateManager = new PredicateManager();
	ScoreDistributionManager scoreDistributionManager = new ScoreDistributionManager();

	return encodeTreeModel(estimator, predicateManager, scoreDistributionManager, miningFunction, schema);
}
 

public TreeModel encodeTreeModel(PredicateManager predicateManager, Schema schema){
	Node root = encodeNode(True.INSTANCE, predicateManager, new CategoryManager(), 0, schema);

	TreeModel treeModel = new TreeModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(schema.getLabel()), root)
		.setSplitCharacteristic(TreeModel.SplitCharacteristic.BINARY_SPLIT)
		.setMissingValueStrategy(TreeModel.MissingValueStrategy.DEFAULT_CHILD);

	return treeModel;
}
 

static
public <E extends Estimator & HasEstimatorEnsemble<T>, T extends Estimator & HasTree> List<TreeModel> encodeTreeModelEnsemble(E estimator, MiningFunction miningFunction, Schema schema){
	PredicateManager predicateManager = new PredicateManager();
	ScoreDistributionManager scoreDistributionManager = new ScoreDistributionManager();

	return encodeTreeModelEnsemble(estimator, predicateManager, scoreDistributionManager, miningFunction, schema);
}
 

static
public TreeModel encodeTreeModel(TreePredictor treePredictor, Schema schema){
	PredicateManager predicateManager = new PredicateManager();

	return encodeTreeModel(treePredictor, predicateManager, schema);
}
 

public PredicateManager getPredicateManager(){
	return this.predicateManager;
}
 

public Predicate createSimplePredicate(Feature feature, SimplePredicate.Operator operator, Object value){
	PredicateManager predicateManager = getPredicateManager();

	return predicateManager.createSimplePredicate(feature, operator, value);
}
 

public Predicate createSimpleSetPredicate(Feature feature, List<?> values){
	PredicateManager predicateManager = getPredicateManager();

	return predicateManager.createSimpleSetPredicate(feature, values);
}
 

static
public MiningModel encodeHistGradientBoosting(List<TreePredictor> treePredictors, Number baselinePrediction, Schema schema){
	ContinuousLabel continuousLabel = (ContinuousLabel)schema.getLabel();

	PredicateManager predicateManager = new PredicateManager();

	Schema segmentSchema = schema.toAnonymousRegressorSchema(DataType.DOUBLE);

	List<TreeModel> treeModels = new ArrayList<>();

	for(TreePredictor treePredictor : treePredictors){
		TreeModel treeModel = TreePredictorUtil.encodeTreeModel(treePredictor, predicateManager, segmentSchema);

		treeModels.add(treeModel);
	}

	MiningModel miningModel = new MiningModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(continuousLabel))
		.setSegmentation(MiningModelUtil.createSegmentation(Segmentation.MultipleModelMethod.SUM, treeModels))
		.setTargets(ModelUtil.createRescaleTargets(null, baselinePrediction, continuousLabel));

	return miningModel;
}
 

static
protected MiningModel createMiningModel(List<RegTree> trees, List<Float> weights, float base_score, Integer ntreeLimit, Schema schema){

	if(weights != null){

		if(trees.size() != weights.size()){
			throw new IllegalArgumentException();
		}
	} // End if

	if(ntreeLimit != null){

		if(ntreeLimit > trees.size()){
			throw new IllegalArgumentException("Tree limit " + ntreeLimit + " is greater than the number of trees");
		}

		trees = trees.subList(0, ntreeLimit);

		if(weights != null){
			weights = weights.subList(0, ntreeLimit);
		}
	} // End if

	if(weights != null){
		weights = new ArrayList<>(weights);
	}

	ContinuousLabel continuousLabel = (ContinuousLabel)schema.getLabel();

	Schema segmentSchema = schema.toAnonymousSchema();

	PredicateManager predicateManager = new PredicateManager();

	List<TreeModel> treeModels = new ArrayList<>();

	boolean equalWeights = true;

	Iterator<RegTree> treeIt = trees.iterator();
	Iterator<Float> weightIt = (weights != null ? weights.iterator() : null);

	while(treeIt.hasNext()){
		RegTree tree = treeIt.next();
		Float weight = (weightIt != null ? weightIt.next() : null);

		if(tree.isEmpty()){
			weightIt.remove();

			continue;
		} // End if

		if(weight != null){
			equalWeights &= ValueUtil.isOne(weight);
		}

		TreeModel treeModel = tree.encodeTreeModel(predicateManager, segmentSchema);

		treeModels.add(treeModel);
	}

	MiningModel miningModel = new MiningModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(continuousLabel))
		.setMathContext(MathContext.FLOAT)
		.setSegmentation(MiningModelUtil.createSegmentation(equalWeights ? Segmentation.MultipleModelMethod.SUM : Segmentation.MultipleModelMethod.WEIGHTED_SUM, treeModels, weights))
		.setTargets(ModelUtil.createRescaleTargets(null, base_score, continuousLabel));

	return miningModel;
}
 

static
public <C extends ModelConverter<? extends M> & HasTreeOptions, M extends Model<M> & TreeEnsembleModel<T>, T extends Model<T> & DecisionTreeModel> List<TreeModel> encodeDecisionTreeEnsemble(C converter, Schema schema){
	PredicateManager predicateManager = new PredicateManager();

	return encodeDecisionTreeEnsemble(converter, predicateManager, schema);
}
 

static
public <C extends ModelConverter<? extends M> & HasTreeOptions, M extends Model<M> & DecisionTreeModel> TreeModel encodeDecisionTree(C converter, PredicateManager predicateManager, Schema schema){
	return encodeDecisionTree(converter, converter.getTransformer(), predicateManager, schema);
}
 

static
public <C extends ModelConverter<? extends M> & HasTreeOptions, M extends Model<M> & DecisionTreeModel> TreeModel encodeDecisionTree(C converter, Schema schema){
	PredicateManager predicateManager = new PredicateManager();

	return encodeDecisionTree(converter, predicateManager, schema);
}
 

protected MiningModel createMiningModel(List<Tree> trees, Integer numIteration, Schema schema){
	ContinuousLabel continuousLabel = (ContinuousLabel)schema.getLabel();

	Schema segmentSchema = schema.toAnonymousSchema();

	PredicateManager predicateManager = new PredicateManager();

	List<TreeModel> treeModels = new ArrayList<>();

	if(numIteration != null){

		if(numIteration > trees.size()){
			throw new IllegalArgumentException("Tree limit " + numIteration + " is greater than the number of trees");
		}

		trees = trees.subList(0, numIteration);
	}

	for(Tree tree : trees){
		TreeModel treeModel = tree.encodeTreeModel(predicateManager, segmentSchema);

		treeModels.add(treeModel);
	}

	MiningModel miningModel = new MiningModel(MiningFunction.REGRESSION, ModelUtil.createMiningSchema(continuousLabel))
		.setSegmentation(MiningModelUtil.createSegmentation(this.average_output_ ? Segmentation.MultipleModelMethod.AVERAGE : Segmentation.MultipleModelMethod.SUM, treeModels));

	return miningModel;
}