use of org.dmg.pmml.MiningField in project drools by kiegroup.
the class KiePMMLClassificationTableFactoryTest method getClassificationTable.
@Test
public void getClassificationTable() {
RegressionTable regressionTableProf = getRegressionTable(3.5, "professional");
RegressionTable regressionTableCler = getRegressionTable(27.4, "clerical");
OutputField outputFieldCat = getOutputField("CAT-1", ResultFeature.PROBABILITY, "CatPred-1");
OutputField outputFieldNum = getOutputField("NUM-1", ResultFeature.PROBABILITY, "NumPred-0");
OutputField outputFieldPrev = getOutputField("PREV", ResultFeature.PREDICTED_VALUE, null);
String targetField = "targetField";
DataField dataField = new DataField();
dataField.setName(FieldName.create(targetField));
dataField.setOpType(OpType.CATEGORICAL);
DataDictionary dataDictionary = new DataDictionary();
dataDictionary.addDataFields(dataField);
RegressionModel regressionModel = new RegressionModel();
regressionModel.setNormalizationMethod(RegressionModel.NormalizationMethod.CAUCHIT);
regressionModel.addRegressionTables(regressionTableProf, regressionTableCler);
regressionModel.setModelName(getGeneratedClassName("RegressionModel"));
Output output = new Output();
output.addOutputFields(outputFieldCat, outputFieldNum, outputFieldPrev);
regressionModel.setOutput(output);
MiningField targetMiningField = new MiningField();
targetMiningField.setUsageType(MiningField.UsageType.TARGET);
targetMiningField.setName(dataField.getName());
MiningSchema miningSchema = new MiningSchema();
miningSchema.addMiningFields(targetMiningField);
regressionModel.setMiningSchema(miningSchema);
PMML pmml = new PMML();
pmml.setDataDictionary(dataDictionary);
pmml.addModels(regressionModel);
final CommonCompilationDTO<RegressionModel> source = CommonCompilationDTO.fromGeneratedPackageNameAndFields(PACKAGE_NAME, pmml, regressionModel, new HasClassLoaderMock());
final RegressionCompilationDTO compilationDTO = RegressionCompilationDTO.fromCompilationDTORegressionTablesAndNormalizationMethod(source, regressionModel.getRegressionTables(), regressionModel.getNormalizationMethod());
KiePMMLClassificationTable retrieved = KiePMMLClassificationTableFactory.getClassificationTable(compilationDTO);
assertNotNull(retrieved);
assertEquals(regressionModel.getRegressionTables().size(), retrieved.getCategoryTableMap().size());
regressionModel.getRegressionTables().forEach(regressionTable -> assertTrue(retrieved.getCategoryTableMap().containsKey(regressionTable.getTargetCategory().toString())));
assertEquals(regressionModel.getNormalizationMethod().value(), retrieved.getRegressionNormalizationMethod().getName());
assertEquals(OP_TYPE.CATEGORICAL, retrieved.getOpType());
boolean isBinary = regressionModel.getRegressionTables().size() == 2;
assertEquals(isBinary, retrieved.isBinary());
assertEquals(isBinary, retrieved.isBinary());
assertEquals(targetMiningField.getName().getValue(), retrieved.getTargetField());
}
use of org.dmg.pmml.MiningField in project drools by kiegroup.
the class KiePMMLClassificationTableFactoryTest method setStaticGetter.
@Test
public void setStaticGetter() throws IOException {
String variableName = "variableName";
RegressionTable regressionTableProf = getRegressionTable(3.5, "professional");
RegressionTable regressionTableCler = getRegressionTable(27.4, "clerical");
OutputField outputFieldCat = getOutputField("CAT-1", ResultFeature.PROBABILITY, "CatPred-1");
OutputField outputFieldNum = getOutputField("NUM-1", ResultFeature.PROBABILITY, "NumPred-0");
OutputField outputFieldPrev = getOutputField("PREV", ResultFeature.PREDICTED_VALUE, null);
String targetField = "targetField";
DataField dataField = new DataField();
dataField.setName(FieldName.create(targetField));
dataField.setOpType(OpType.CATEGORICAL);
DataDictionary dataDictionary = new DataDictionary();
dataDictionary.addDataFields(dataField);
RegressionModel regressionModel = new RegressionModel();
regressionModel.setNormalizationMethod(RegressionModel.NormalizationMethod.CAUCHIT);
regressionModel.addRegressionTables(regressionTableProf, regressionTableCler);
regressionModel.setModelName(getGeneratedClassName("RegressionModel"));
Output output = new Output();
output.addOutputFields(outputFieldCat, outputFieldNum, outputFieldPrev);
regressionModel.setOutput(output);
MiningField miningField = new MiningField();
miningField.setUsageType(MiningField.UsageType.TARGET);
miningField.setName(dataField.getName());
MiningSchema miningSchema = new MiningSchema();
miningSchema.addMiningFields(miningField);
regressionModel.setMiningSchema(miningSchema);
PMML pmml = new PMML();
pmml.setDataDictionary(dataDictionary);
pmml.addModels(regressionModel);
final CommonCompilationDTO<RegressionModel> source = CommonCompilationDTO.fromGeneratedPackageNameAndFields(PACKAGE_NAME, pmml, regressionModel, new HasClassLoaderMock());
final RegressionCompilationDTO compilationDTO = RegressionCompilationDTO.fromCompilationDTORegressionTablesAndNormalizationMethod(source, regressionModel.getRegressionTables(), regressionModel.getNormalizationMethod());
final LinkedHashMap<String, KiePMMLTableSourceCategory> regressionTablesMap = new LinkedHashMap<>();
regressionModel.getRegressionTables().forEach(regressionTable -> {
String key = "defpack." + regressionTable.getTargetCategory().toString().toUpperCase();
KiePMMLTableSourceCategory value = new KiePMMLTableSourceCategory("", regressionTable.getTargetCategory().toString());
regressionTablesMap.put(key, value);
});
final MethodDeclaration staticGetterMethod = STATIC_GETTER_METHOD.clone();
KiePMMLClassificationTableFactory.setStaticGetter(compilationDTO, regressionTablesMap, staticGetterMethod, variableName);
String text = getFileContent(TEST_02_SOURCE);
MethodDeclaration expected = JavaParserUtils.parseMethod(text);
assertTrue(JavaParserUtils.equalsNode(expected, staticGetterMethod));
}
use of org.dmg.pmml.MiningField in project drools by kiegroup.
the class KiePMMLRegressionTableFactoryTest method getRegressionTable.
@Test
public void getRegressionTable() {
regressionTable = getRegressionTable(3.5, "professional");
RegressionModel regressionModel = new RegressionModel();
regressionModel.setNormalizationMethod(RegressionModel.NormalizationMethod.CAUCHIT);
regressionModel.addRegressionTables(regressionTable);
regressionModel.setModelName(getGeneratedClassName("RegressionModel"));
String targetField = "targetField";
DataField dataField = new DataField();
dataField.setName(FieldName.create(targetField));
dataField.setOpType(OpType.CATEGORICAL);
DataDictionary dataDictionary = new DataDictionary();
dataDictionary.addDataFields(dataField);
MiningField miningField = new MiningField();
miningField.setUsageType(MiningField.UsageType.TARGET);
miningField.setName(dataField.getName());
MiningSchema miningSchema = new MiningSchema();
miningSchema.addMiningFields(miningField);
regressionModel.setMiningSchema(miningSchema);
PMML pmml = new PMML();
pmml.setDataDictionary(dataDictionary);
pmml.addModels(regressionModel);
final CommonCompilationDTO<RegressionModel> source = CommonCompilationDTO.fromGeneratedPackageNameAndFields(PACKAGE_NAME, pmml, regressionModel, new HasClassLoaderMock());
final RegressionCompilationDTO compilationDTO = RegressionCompilationDTO.fromCompilationDTORegressionTablesAndNormalizationMethod(source, new ArrayList<>(), regressionModel.getNormalizationMethod());
KiePMMLRegressionTable retrieved = KiePMMLRegressionTableFactory.getRegressionTable(regressionTable, compilationDTO);
assertNotNull(retrieved);
commonEvaluateRegressionTable(retrieved, regressionTable);
}
use of org.dmg.pmml.MiningField in project drools by kiegroup.
the class KiePMMLRegressionTableFactoryTest method getRegressionTableBuilder.
@Test
public void getRegressionTableBuilder() {
regressionTable = getRegressionTable(3.5, "professional");
RegressionModel regressionModel = new RegressionModel();
regressionModel.setNormalizationMethod(RegressionModel.NormalizationMethod.CAUCHIT);
regressionModel.addRegressionTables(regressionTable);
regressionModel.setModelName(getGeneratedClassName("RegressionModel"));
String targetField = "targetField";
DataField dataField = new DataField();
dataField.setName(FieldName.create(targetField));
dataField.setOpType(OpType.CATEGORICAL);
DataDictionary dataDictionary = new DataDictionary();
dataDictionary.addDataFields(dataField);
MiningField miningField = new MiningField();
miningField.setUsageType(MiningField.UsageType.TARGET);
miningField.setName(dataField.getName());
MiningSchema miningSchema = new MiningSchema();
miningSchema.addMiningFields(miningField);
regressionModel.setMiningSchema(miningSchema);
PMML pmml = new PMML();
pmml.setDataDictionary(dataDictionary);
pmml.addModels(regressionModel);
final CommonCompilationDTO<RegressionModel> source = CommonCompilationDTO.fromGeneratedPackageNameAndFields(PACKAGE_NAME, pmml, regressionModel, new HasClassLoaderMock());
final RegressionCompilationDTO compilationDTO = RegressionCompilationDTO.fromCompilationDTORegressionTablesAndNormalizationMethod(source, new ArrayList<>(), regressionModel.getNormalizationMethod());
Map.Entry<String, String> retrieved = KiePMMLRegressionTableFactory.getRegressionTableBuilder(regressionTable, compilationDTO);
assertNotNull(retrieved);
Map<String, String> sources = new HashMap<>();
sources.put(retrieved.getKey(), retrieved.getValue());
commonValidateCompilation(sources);
}
use of org.dmg.pmml.MiningField in project drools by kiegroup.
the class PMMLModelTestUtils method getRandomMiningField.
public static MiningField getRandomMiningField() {
Random random = new Random();
MiningField toReturn = new MiningField(FieldName.create(RandomStringUtils.random(6, true, false)));
toReturn.setInvalidValueTreatment(getRandomInvalidValueTreatmentMethod());
toReturn.setMissingValueTreatment(getRandomMissingValueTreatmentMethod());
toReturn.setOutlierTreatment(getRandomOutlierTreatmentMethod());
DataType dataType = getRandomDataType();
toReturn.setInvalidValueReplacement(getRandomObject(dataType).toString());
toReturn.setMissingValueReplacement(getRandomObject(dataType).toString());
toReturn.setImportance(random.nextInt(10));
toReturn.setLowValue(random.nextInt(10));
toReturn.setHighValue(toReturn.getLowValue().intValue() + random.nextInt(30));
toReturn.setUsageType(getRandomUsageType());
toReturn.setOpType(getRandomOpType());
return toReturn;
}
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