org.dmg.pmml.tree.ComplexNode Java Examples

The following examples show how to use org.dmg.pmml.tree.ComplexNode. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may check out the related API usage on the sidebar.
Example #1
Source File: ScoreDistributionInternerTest.java    From jpmml-evaluator with GNU Affero General Public License v3.0 6 votes vote down vote up 
@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 ComplexNode(True.INSTANCE)
		.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));
	}

	ScoreDistributionInterner interner = new ScoreDistributionInterner();
	interner.applyTo(treeModel);

	for(int i = 0; i < 2; i++){
		assertSame((leftChild.getScoreDistributions()).get(i), (rightChild.getScoreDistributions()).get(i));
	}
}
Example #2
Source File: ScoreDistributionInternerTest.java    From jpmml-evaluator with GNU Affero General Public License v3.0 5 votes vote down vote up 
static
private Node createNode(ScoreDistribution event){
	ScoreDistribution noEvent = new ScoreDistribution("no-event", 1d - NumberUtil.asDouble(event.getRecordCount()));

	Node node = new ComplexNode()
		.addScoreDistributions(event, noEvent);

	return node;
}
Example #3
Source File: RDFPMMLUtilsTest.java    From oryx with Apache License 2.0 4 votes vote down vote up 
private static PMML buildDummyClassificationModel(int numTrees) {
  PMML pmml = PMMLUtils.buildSkeletonPMML();

  List<DataField> dataFields = new ArrayList<>();
  DataField predictor =
      new DataField(FieldName.create("color"), OpType.CATEGORICAL, DataType.STRING);
  predictor.addValues(new Value("yellow"), new Value("red"));
  dataFields.add(predictor);
  DataField target =
      new DataField(FieldName.create("fruit"), OpType.CATEGORICAL, DataType.STRING);
  target.addValues(new Value("banana"), new Value("apple"));
  dataFields.add(target);
  DataDictionary dataDictionary =
      new DataDictionary(dataFields).setNumberOfFields(dataFields.size());
  pmml.setDataDictionary(dataDictionary);

  List<MiningField> miningFields = new ArrayList<>();
  MiningField predictorMF = new MiningField(FieldName.create("color"))
      .setOpType(OpType.CATEGORICAL)
      .setUsageType(MiningField.UsageType.ACTIVE)
      .setImportance(0.5);
  miningFields.add(predictorMF);
  MiningField targetMF = new MiningField(FieldName.create("fruit"))
      .setOpType(OpType.CATEGORICAL)
      .setUsageType(MiningField.UsageType.PREDICTED);
  miningFields.add(targetMF);
  MiningSchema miningSchema = new MiningSchema(miningFields);

  double dummyCount = 2.0;
  Node rootNode =
    new ComplexNode().setId("r").setRecordCount(dummyCount).setPredicate(new True());

  double halfCount = dummyCount / 2;

  Node left = new ComplexNode().setId("r-").setRecordCount(halfCount).setPredicate(new True());
  left.addScoreDistributions(new ScoreDistribution("apple", halfCount));
  Node right = new ComplexNode().setId("r+").setRecordCount(halfCount)
      .setPredicate(new SimpleSetPredicate(FieldName.create("color"),
                                           SimpleSetPredicate.BooleanOperator.IS_NOT_IN,
                                           new Array(Array.Type.STRING, "red")));
  right.addScoreDistributions(new ScoreDistribution("banana", halfCount));

  rootNode.addNodes(right, left);

  TreeModel treeModel = new TreeModel(MiningFunction.CLASSIFICATION, miningSchema, rootNode)
      .setSplitCharacteristic(TreeModel.SplitCharacteristic.BINARY_SPLIT)
      .setMissingValueStrategy(TreeModel.MissingValueStrategy.DEFAULT_CHILD);

  if (numTrees > 1) {
    MiningModel miningModel = new MiningModel(MiningFunction.CLASSIFICATION, miningSchema);
    List<Segment> segments = new ArrayList<>();
    for (int i = 0; i < numTrees; i++) {
      segments.add(new Segment()
          .setId(Integer.toString(i))
          .setPredicate(new True())
          .setModel(treeModel)
          .setWeight(1.0));
    }
    miningModel.setSegmentation(
        new Segmentation(Segmentation.MultipleModelMethod.WEIGHTED_MAJORITY_VOTE, segments));
    pmml.addModels(miningModel);
  } else {
    pmml.addModels(treeModel);
  }

  return pmml;
}
Example #4
Source File: RDFPMMLUtilsTest.java    From oryx with Apache License 2.0 4 votes vote down vote up 
public static PMML buildDummyRegressionModel() {
  PMML pmml = PMMLUtils.buildSkeletonPMML();

  List<DataField> dataFields = new ArrayList<>();
  dataFields.add(new DataField(FieldName.create("foo"), OpType.CONTINUOUS, DataType.DOUBLE));
  dataFields.add(new DataField(FieldName.create("bar"), OpType.CONTINUOUS, DataType.DOUBLE));
  DataDictionary dataDictionary =
      new DataDictionary(dataFields).setNumberOfFields(dataFields.size());
  pmml.setDataDictionary(dataDictionary);

  List<MiningField> miningFields = new ArrayList<>();
  MiningField predictorMF = new MiningField(FieldName.create("foo"))
      .setOpType(OpType.CONTINUOUS)
      .setUsageType(MiningField.UsageType.ACTIVE)
      .setImportance(0.5);
  miningFields.add(predictorMF);
  MiningField targetMF = new MiningField(FieldName.create("bar"))
      .setOpType(OpType.CONTINUOUS)
      .setUsageType(MiningField.UsageType.PREDICTED);
  miningFields.add(targetMF);
  MiningSchema miningSchema = new MiningSchema(miningFields);

  double dummyCount = 2.0;
  Node rootNode =
      new ComplexNode().setId("r").setRecordCount(dummyCount).setPredicate(new True());

  double halfCount = dummyCount / 2;

  Node left = new ComplexNode()
      .setId("r-")
      .setRecordCount(halfCount)
      .setPredicate(new True())
      .setScore("-2.0");
  Node right = new ComplexNode().setId("r+").setRecordCount(halfCount)
      .setPredicate(new SimplePredicate(FieldName.create("foo"),
                                        SimplePredicate.Operator.GREATER_THAN,
                                        "3.14"))
      .setScore("2.0");

  rootNode.addNodes(right, left);

  TreeModel treeModel = new TreeModel(MiningFunction.REGRESSION, miningSchema, rootNode)
      .setSplitCharacteristic(TreeModel.SplitCharacteristic.BINARY_SPLIT)
      .setMissingValueStrategy(TreeModel.MissingValueStrategy.DEFAULT_CHILD)
      .setMiningSchema(miningSchema);

  pmml.addModels(treeModel);

  return pmml;
}
Example #5
Source File: RDFUpdate.java    From oryx with Apache License 2.0 4 votes vote down vote up 
private TreeModel toTreeModel(DecisionTreeModel dtModel,
                              CategoricalValueEncodings categoricalValueEncodings,
                              IntLongMap nodeIDCounts) {

  boolean classificationTask = dtModel.algo().equals(Algo.Classification());
  Preconditions.checkState(classificationTask == inputSchema.isClassification());

  Node root = new ComplexNode();
  root.setId("r");

  Queue<Node> modelNodes = new ArrayDeque<>();
  modelNodes.add(root);

  Queue<Pair<org.apache.spark.mllib.tree.model.Node,Split>> treeNodes = new ArrayDeque<>();
  treeNodes.add(new Pair<>(dtModel.topNode(), null));

  while (!treeNodes.isEmpty()) {

    Pair<org.apache.spark.mllib.tree.model.Node,Split> treeNodePredicate = treeNodes.remove();
    Node modelNode = modelNodes.remove();

    // This is the decision that got us here from the parent, if any;
    // not the predicate at this node
    Predicate predicate = buildPredicate(treeNodePredicate.getSecond(),
                                         categoricalValueEncodings);
    modelNode.setPredicate(predicate);

    org.apache.spark.mllib.tree.model.Node treeNode = treeNodePredicate.getFirst();
    long nodeCount = nodeIDCounts.get(treeNode.id());
    modelNode.setRecordCount((double) nodeCount);

    if (treeNode.isLeaf()) {

      Predict prediction = treeNode.predict();
      int targetEncodedValue = (int) prediction.predict();
      if (classificationTask) {
        Map<Integer,String> targetEncodingToValue =
            categoricalValueEncodings.getEncodingValueMap(inputSchema.getTargetFeatureIndex());
        double predictedProbability = prediction.prob();
        Preconditions.checkState(predictedProbability >= 0.0 && predictedProbability <= 1.0);
        // Not sure how nodeCount == 0 can happen but it does in the MLlib model
        long effectiveNodeCount = Math.max(1, nodeCount);
        // Problem: MLlib only gives a predicted class and its probability, and no distribution
        // over the rest. Infer that the rest of the probability is evenly distributed.
        double restProbability = (1.0 - predictedProbability) / (targetEncodingToValue.size() - 1);

        targetEncodingToValue.forEach((encodedValue, value) -> {
          double probability = encodedValue == targetEncodedValue ? predictedProbability : restProbability;
          // Yes, recordCount may be fractional; it's a relative indicator
          double recordCount = probability * effectiveNodeCount;
          if (recordCount > 0.0) {
            ScoreDistribution distribution = new ScoreDistribution(value, recordCount);
            // Not "confident" enough in the "probability" to call it one
            distribution.setConfidence(probability);
            modelNode.addScoreDistributions(distribution);
          }
        });
      } else {
        modelNode.setScore(Double.toString(targetEncodedValue));
      }

    } else {

      Split split = treeNode.split().get();

      Node positiveModelNode = new ComplexNode().setId(modelNode.getId() + "+");
      Node negativeModelNode = new ComplexNode().setId(modelNode.getId() + "-");
      modelNode.addNodes(positiveModelNode, negativeModelNode);

      org.apache.spark.mllib.tree.model.Node rightTreeNode = treeNode.rightNode().get();
      org.apache.spark.mllib.tree.model.Node leftTreeNode = treeNode.leftNode().get();

      boolean defaultRight = nodeIDCounts.get(rightTreeNode.id()) > nodeIDCounts.get(leftTreeNode.id());
      modelNode.setDefaultChild(defaultRight ? positiveModelNode.getId() : negativeModelNode.getId());

      // Right node is "positive", so carries the predicate. It must evaluate first
      // and therefore come first in the tree
      modelNodes.add(positiveModelNode);
      modelNodes.add(negativeModelNode);
      treeNodes.add(new Pair<>(rightTreeNode, split));
      treeNodes.add(new Pair<>(leftTreeNode, null));

    }

  }

  return new TreeModel()
      .setNode(root)
      .setSplitCharacteristic(TreeModel.SplitCharacteristic.BINARY_SPLIT)
      .setMissingValueStrategy(TreeModel.MissingValueStrategy.DEFAULT_CHILD);
}
Example #6
Source File: NodeFilterer.java    From jpmml-model with BSD 3-Clause "New" or "Revised" License 4 votes vote down vote up 
@Override
public VisitorAction visit(ComplexNode complexNode){
	return super.visit(complexNode);
}
Example #7
Source File: NodeAdapter.java    From jpmml-model with BSD 3-Clause "New" or "Revised" License 4 votes vote down vote up 
@Override
public Node unmarshal(ComplexNode value){
	NodeTransformer nodeTransformer = NodeAdapter.NODE_TRANSFORMER_PROVIDER.get();

	return nodeTransformer.fromComplexNode(value);
}
Example #8
Source File: NodeAdapter.java    From jpmml-model with BSD 3-Clause "New" or "Revised" License 4 votes vote down vote up 
@Override
public ComplexNode marshal(Node node){
	NodeTransformer nodeTransformer = NodeAdapter.NODE_TRANSFORMER_PROVIDER.get();

	return nodeTransformer.toComplexNode(node);
}
Example #9
Source File: ObjectMapperTest.java    From jpmml-model with BSD 3-Clause "New" or "Revised" License 2 votes vote down vote up 
@Test
public void jsonClone() throws Exception {
	DataField dataField = new DataField(FieldName.create("x"), OpType.CATEGORICAL, DataType.BOOLEAN);

	DataDictionary dataDictionary = new DataDictionary()
		.addDataFields(dataField);

	MiningField miningField = new MiningField(FieldName.create("x"));

	MiningSchema miningSchema = new MiningSchema()
		.addMiningFields(miningField);

	assertSame(dataField.getName(), miningField.getName());

	SimplePredicate simplePredicate = new SimplePredicate(FieldName.create("x"), SimplePredicate.Operator.IS_NOT_MISSING, null);

	Node node = new ComplexNode(simplePredicate);

	TreeModel treeModel = new TreeModel()
		.setMiningSchema(miningSchema)
		.setNode(node);

	PMML pmml = new PMML()
		.setDataDictionary(dataDictionary)
		.addModels(treeModel);

	DirectByteArrayOutputStream buffer = new DirectByteArrayOutputStream(1024);

	JacksonUtil.writePMML(pmml, buffer);

	PMML jsonPmml;

	try(InputStream is = buffer.getInputStream()){
		jsonPmml = JacksonUtil.readPMML(is);
	}

	DataDictionary jsonDataDictionary = jsonPmml.getDataDictionary();

	List<DataField> jsonDataFields = jsonDataDictionary.getDataFields();

	assertEquals(1, jsonDataFields.size());

	DataField jsonDataField = jsonDataFields.get(0);

	assertEquals(dataField.getName(), jsonDataField.getName());
	assertEquals(dataField.getOpType(), jsonDataField.getOpType());
	assertEquals(dataField.getDataType(), jsonDataField.getDataType());

	List<Model> jsonModels = jsonPmml.getModels();

	assertEquals(1, jsonModels.size());

	TreeModel jsonTreeModel = (TreeModel)jsonModels.get(0);

	MiningSchema jsonMiningSchema = jsonTreeModel.getMiningSchema();

	List<MiningField> jsonMiningFields = jsonMiningSchema.getMiningFields();

	assertEquals(1, jsonMiningFields.size());

	MiningField jsonMiningField = jsonMiningFields.get(0);

	assertEquals(miningField.getName(), jsonMiningField.getName());
	assertEquals(miningField.getUsageType(), jsonMiningField.getUsageType());

	assertSame(jsonDataField.getName(), jsonMiningField.getName());

	Node jsonNode = jsonTreeModel.getNode();

	SimplePredicate jsonSimplePredicate = (SimplePredicate)jsonNode.getPredicate();

	assertEquals(simplePredicate.getField(), jsonSimplePredicate.getField());
	assertEquals(simplePredicate.getOperator(), jsonSimplePredicate.getOperator());

	assertSame(jsonDataField.getName(), jsonSimplePredicate.getField());
	assertSame(jsonMiningField.getName(), jsonSimplePredicate.getField());
}