/** * Create an instance of {@link Output } * */ public Output createOutput() { return new Output(); }
/** * Create an instance of {@link Output } * */ public Output createOutput() { return new Output(); }
Output output = new Output() .addOutputFields(ModelUtil.createProbabilityField(FieldName.create("decisionFunction(" + categoricalLabel.getValue(i) + ")"), DataType.DOUBLE, categoricalLabel.getValue(i)));
/** * Create the normalized output for model, since the final score should be 0 ~ 1000, instead of 0.o ~ 1.0 * * @return output for model */ protected Output createNormalizedOutput() { Output output = new Output(); output.withOutputFields(createOutputField(RAW_RESULT, OpType.CONTINUOUS, DataType.DOUBLE, ResultFeatureType.PREDICTED_VALUE)); OutputField finalResult = createOutputField(FINAL_RESULT, OpType.CONTINUOUS, DataType.DOUBLE, ResultFeatureType.TRANSFORMED_VALUE); finalResult.withExpression(createNormExpr()); output.withOutputFields(finalResult); return output; }
/** * Create the normalized output for model, since the final score should be 0 ~ 1000, instead of 0.o ~ 1.0 * * @param id * output id * @return output for model */ protected Output createNormalizedOutput(int id) { Output output = new Output(); output.withOutputFields(createOutputField(RAW_RESULT + "_" + id, OpType.CONTINUOUS, DataType.DOUBLE, ResultFeatureType.PREDICTED_VALUE)); OutputField finalResult = createOutputField(FINAL_RESULT + "_" + id, OpType.CONTINUOUS, DataType.DOUBLE, ResultFeatureType.TRANSFORMED_VALUE); finalResult.withExpression(createNormExpr(id)); output.withOutputFields(finalResult); return output; }
output = new Output();
@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; }
Output output = new Output();
Output output = new Output() .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); }