.setDataType(DataType.INTEGER); output.addOutputFields(nodeIdField);
.addOutputFields(ModelUtil.createProbabilityField(FieldName.create("decisionFunction(" + categoricalLabel.getValue(i) + ")"), DataType.DOUBLE, categoricalLabel.getValue(i)));
@Override protected ContinuousOutputFeature toContinuousFeature(FieldName name, DataType dataType, Supplier<? extends Expression> expressionSupplier){ PMMLEncoder encoder = ensureEncoder(); Output output = getOutput(); OutputField outputField = OutputUtil.getOutputField(output, name); if(outputField == null){ Expression expression = expressionSupplier.get(); outputField = new OutputField(name, dataType) .setOpType(OpType.CONTINUOUS) .setResultFeature(ResultFeature.TRANSFORMED_VALUE) .setFinalResult(false) .setExpression(expression); output.addOutputFields(outputField); } return new ContinuousOutputFeature(encoder, output, outputField); }
.setSegmentId(id); output.addOutputFields(outputField);
@Override public TreeModel encodeModel(Schema schema){ S4Object binaryTree = getObject(); RGenericVector tree = (RGenericVector)binaryTree.getAttributeValue("tree"); Output output; switch(this.miningFunction){ case REGRESSION: output = new Output(); break; case CLASSIFICATION: CategoricalLabel categoricalLabel = (CategoricalLabel)schema.getLabel(); output = ModelUtil.createProbabilityOutput(DataType.DOUBLE, categoricalLabel); break; default: throw new IllegalArgumentException(); } output.addOutputFields(ModelUtil.createEntityIdField(FieldName.create("nodeId"))); TreeModel treeModel = encodeTreeModel(tree, schema) .setOutput(output); return treeModel; }
.setExpression(derivedField.getExpression()); output.addOutputFields(outputField);
.setExpression(derivedField.getExpression()); output.addOutputFields(outputField);
segmentOutput.addOutputFields(outputField);
.setFinalResult(false); output.addOutputFields(outputField);
.setRank(rank); output.addOutputFields(outputField);
output.addOutputFields(predictField);
.addOutputFields(outputField);
@Test public void inspectFieldAnnotations(){ PMML pmml = createPMML(); AssociationModel model = new AssociationModel(); pmml.addModels(model); assertVersionRange(pmml, Version.PMML_3_0, Version.PMML_4_3); Output output = new Output(); model.setOutput(output); assertVersionRange(pmml, Version.PMML_4_0, Version.PMML_4_3); model.setScorable(Boolean.FALSE); assertVersionRange(pmml, Version.PMML_4_1, Version.PMML_4_3); model.setScorable(null); assertVersionRange(pmml, Version.PMML_4_0, Version.PMML_4_3); OutputField outputField = new OutputField() .setRuleFeature(OutputField.RuleFeature.AFFINITY); output.addOutputFields(outputField); assertVersionRange(pmml, Version.PMML_4_1, Version.PMML_4_2); outputField.setDataType(DataType.DOUBLE); assertVersionRange(pmml, Version.PMML_4_1, Version.PMML_4_3); model.setOutput(null); assertVersionRange(pmml, Version.PMML_3_0, Version.PMML_4_3); }