Java True类代码示例

import org.dmg.pmml.True; //导入依赖的package包/类
static
public TreeModel encodeTreeModel(org.apache.spark.ml.tree.Node node, PredicateManager predicateManager, MiningFunction miningFunction, Schema schema){
	Node root = encodeNode(node, predicateManager, Collections.<FieldName, Set<String>>emptyMap(), miningFunction, schema)
		.setPredicate(new True());

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

	String compact = TreeModelOptions.COMPACT;
	if(compact != null && Boolean.valueOf(compact)){
		Visitor visitor = new TreeModelCompactor();

		visitor.applyTo(treeModel);
	}

	return treeModel;
}
 

import org.dmg.pmml.True; //导入依赖的package包/类
static
public <E extends Estimator & HasTree> TreeModel encodeTreeModel(E estimator, PredicateManager predicateManager, 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 = new Node()
		.setId("1")
		.setPredicate(new True());

	encodeNode(root, predicateManager, 0, leftChildren, rightChildren, features, thresholds, values, miningFunction, schema);

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

	return treeModel;
}
 

import org.dmg.pmml.True; //导入依赖的package包/类
@Test
public void clean(){
	Node node = new Node()
		.setPredicate(new True())
		.setScore("1")
		.addScoreDistributions(new ScoreDistribution("0", 0), new ScoreDistribution("1", 100));

	TreeModel treeModel = new TreeModel(MiningFunction.CLASSIFICATION, new MiningSchema(), node);

	ScoreDistributionCleaner cleaner = new ScoreDistributionCleaner();
	cleaner.applyTo(treeModel);

	assertTrue(node.hasScoreDistributions());

	treeModel.setMiningFunction(MiningFunction.REGRESSION);

	cleaner.applyTo(treeModel);

	assertFalse(node.hasScoreDistributions());
}
 

import org.dmg.pmml.True; //导入依赖的package包/类
public Predicate intern(Predicate predicate){

		if(predicate instanceof SimplePredicate){
			return intern((SimplePredicate)predicate);
		} else

		if(predicate instanceof SimpleSetPredicate){
			return intern((SimpleSetPredicate)predicate);
		} else

		if(predicate instanceof True){
			return intern((True)predicate);
		} else

		if(predicate instanceof False){
			return intern((False)predicate);
		}

		return predicate;
	}
 

import org.dmg.pmml.True; //导入依赖的package包/类
@Test
public void intern(){
	ScoreDistribution left = new ScoreDistribution("event", 0.33d);
	ScoreDistribution right = new ScoreDistribution("event", 0.33d);

	Node leftChild = createNode(left);
	Node rightChild = createNode(right);

	Node root = new Node()
		.setPredicate(new True())
		.addNodes(leftChild, rightChild);

	TreeModel treeModel = new TreeModel()
		.setNode(root);

	for(int i = 0; i < 2; i++){
		assertNotSame((leftChild.getScoreDistributions()).get(i), (rightChild.getScoreDistributions()).get(i));
	}

	intern(treeModel);

	for(int i = 0; i < 2; i++){
		assertSame((leftChild.getScoreDistributions()).get(i), (rightChild.getScoreDistributions()).get(i));
	}
}
 

import org.dmg.pmml.True; //导入依赖的package包/类
public TreeModel encodeTreeModel(PredicateManager predicateManager, Schema schema){
	Node root = new Node()
		.setPredicate(new True());

	encodeNode(root, predicateManager, Collections.<FieldName, Set<String>>emptyMap(), 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;
}
 

import org.dmg.pmml.True; //导入依赖的package包/类
private void handleNodePop(Node node){
	Double recordCount = node.getRecordCount();
	String score = node.getScore();
	Predicate predicate = node.getPredicate();

	if(recordCount != null){
		node.setRecordCount(null);
	} // End if

	if(predicate instanceof True){
		Node parentNode = getParentNode();

		if(parentNode == null){
			return;
		}

		String parentScore = parentNode.getScore();
		if(parentScore != null){
			throw new IllegalArgumentException();
		}

		parentNode.setScore(score);

		List<Node> parentChildren = parentNode.getNodes();

		boolean success = parentChildren.remove(node);
		if(!success){
			throw new IllegalArgumentException();
		} // End if

		if(node.hasNodes()){
			List<Node> children = node.getNodes();

			parentChildren.addAll(children);
		}
	}
}
 

import org.dmg.pmml.True; //导入依赖的package包/类
private Predicate buildPredicate(Split split,
                                 CategoricalValueEncodings categoricalValueEncodings) {
  if (split == null) {
    // Left child always applies, but is evaluated second
    return new True();
  }

  int featureIndex = inputSchema.predictorToFeatureIndex(split.feature());
  FieldName fieldName = FieldName.create(inputSchema.getFeatureNames().get(featureIndex));

  if (split.featureType().equals(FeatureType.Categorical())) {
    // Note that categories in MLlib model select the *left* child but the
    // convention here will be that the predicate selects the *right* child
    // So the predicate will evaluate "not in" this set
    // More ugly casting
    @SuppressWarnings("unchecked")
    List<Double> javaCategories = (List<Double>) (List<?>)
        JavaConversions.seqAsJavaList(split.categories());
    Set<Integer> negativeEncodings = javaCategories.stream().map(Double::intValue).collect(Collectors.toSet());

    Map<Integer,String> encodingToValue =
        categoricalValueEncodings.getEncodingValueMap(featureIndex);
    List<String> negativeValues = negativeEncodings.stream().map(encodingToValue::get).collect(Collectors.toList());

    String joinedValues = TextUtils.joinPMMLDelimited(negativeValues);
    return new SimpleSetPredicate(fieldName,
                                  SimpleSetPredicate.BooleanOperator.IS_NOT_IN,
                                  new Array(Array.Type.STRING, joinedValues));

  } else {
    // For MLlib, left means <= threshold, so right means >
    return new SimplePredicate(fieldName, SimplePredicate.Operator.GREATER_THAN)
        .setValue(Double.toString(split.threshold()));
  }
}
 

import org.dmg.pmml.True; //导入依赖的package包/类
private static void checkNode(Node node) {
  assertNotNull(node.getId());
  List<ScoreDistribution> scoreDists = node.getScoreDistributions();
  int numDists = scoreDists.size();
  if (numDists == 0) {
    // Non-leaf
    List<Node> children = node.getNodes();
    assertEquals(2, children.size());
    Node rightChild = children.get(0);
    Node leftChild = children.get(1);
    assertInstanceOf(leftChild.getPredicate(), True.class);
    assertEquals(node.getRecordCount().doubleValue(),
                 leftChild.getRecordCount() + rightChild.getRecordCount());
    assertEquals(node.getId() + "+", rightChild.getId());
    assertEquals(node.getId() + "-", leftChild.getId());
    checkNode(rightChild);
    checkNode(leftChild);
  } else {
    // Leaf
    assertRange(numDists, 1, 2);
    ScoreDistribution first = scoreDists.get(0);
    if (numDists == 1) {
      assertEquals(1.0, first.getConfidence().doubleValue());
    } else {
      assertGreater(first.getConfidence(), 0.0);
      assertLess(first.getConfidence(), 1.0);
      ScoreDistribution second = scoreDists.get(1);
      assertGreater(second.getConfidence(), 0.0);
      assertLess(second.getConfidence(), 1.0);
    }
  }
}
 

import org.dmg.pmml.True; //导入依赖的package包/类
private void handleNodePop(Node node){
	String score = node.getScore();
	Predicate predicate = node.getPredicate();

	if(predicate instanceof True){
		Node parentNode = getParentNode();

		if(parentNode == null){
			return;
		}

		String parentScore = parentNode.getScore();
		if(parentScore != null){
			throw new IllegalArgumentException();
		}

		parentNode.setScore(score);

		List<Node> parentChildren = parentNode.getNodes();

		boolean success = parentChildren.remove(node);
		if(!success){
			throw new IllegalArgumentException();
		} // End if

		if(node.hasNodes()){
			List<Node> children = node.getNodes();

			parentChildren.addAll(children);
		}
	}
}
 

import org.dmg.pmml.True; //导入依赖的package包/类
public TreeModel encodeTreeModel(Schema schema){
	org.dmg.pmml.tree.Node root = new org.dmg.pmml.tree.Node()
		.setPredicate(new True());

	encodeNode(root, 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;
}
 

import org.dmg.pmml.True; //导入依赖的package包/类
static
public Segmentation createSegmentation(Segmentation.MultipleModelMethod multipleModelMethod, List<? extends Model> models, List<? extends Number> weights){

	if((weights != null) && (models.size() != weights.size())){
		throw new IllegalArgumentException();
	}

	List<Segment> segments = new ArrayList<>();

	for(int i = 0; i < models.size(); i++){
		Model model = models.get(i);
		Number weight = (weights != null ? weights.get(i) : null);

		Segment segment = new Segment()
			.setId(String.valueOf(i + 1))
			.setPredicate(new True())
			.setModel(model);

		if(weight != null && !ValueUtil.isOne(weight)){
			segment.setWeight(ValueUtil.asDouble(weight));
		}

		segments.add(segment);
	}

	return new Segmentation(multipleModelMethod, segments);
}
 

import org.dmg.pmml.True; //导入依赖的package包/类
private TreeModel encodeTreeModel(RGenericVector tree, Schema schema){
	Node root = new Node()
		.setPredicate(new True());

	encodeNode(root, tree, schema);

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

	return treeModel;
}
 

import org.dmg.pmml.True; //导入依赖的package包/类
private TreeModel encodeTreeModel(MiningFunction miningFunction, ScoreEncoder scoreEncoder, RGenericVector childNodeIDs, RNumberVector<?> splitVarIDs, RNumberVector<?> splitValues, RGenericVector terminalClassCounts, Schema schema){
	RNumberVector<?> leftChildIDs = (RNumberVector<?>)childNodeIDs.getValue(0);
	RNumberVector<?> rightChildIDs = (RNumberVector<?>)childNodeIDs.getValue(1);

	Node root = new Node()
		.setPredicate(new True());

	encodeNode(root, 0, scoreEncoder, leftChildIDs, rightChildIDs, splitVarIDs, splitValues, terminalClassCounts, schema);

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

	return treeModel;
}
 

import org.dmg.pmml.True; //导入依赖的package包/类
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){
	Node root = new Node()
		.setId("1")
		.setPredicate(new True());

	encodeNode(root, 0, scoreEncoder, leftDaughter, rightDaughter, bestvar, xbestsplit, nodepred, schema);

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

	return treeModel;
}