Examples with DataDictionary org.dmg.pmml.DataDictionary used on opensource projects

Example 11 with DataDictionary

use of org.dmg.pmml.DataDictionary in project drools by kiegroup.

the class ModelUtilsTest method getTargetFieldName.

@Test
public void getTargetFieldName() {
    final String fieldName = "fieldName";
    MiningField.UsageType usageType = MiningField.UsageType.ACTIVE;
    MiningField miningField = getMiningField(fieldName, usageType);
    final DataField dataField = getDataField(fieldName, OpType.CATEGORICAL, DataType.STRING);
    final DataDictionary dataDictionary = new DataDictionary();
    dataDictionary.addDataFields(dataField);
    MiningSchema miningSchema = new MiningSchema();
    miningSchema.addMiningFields(miningField);
    final Model model = new RegressionModel();
    model.setMiningSchema(miningSchema);
    final List<Field<?>> fields = getFieldsFromDataDictionary(dataDictionary);
    Optional<String> retrieved = ModelUtils.getTargetFieldName(fields, model);
    assertFalse(retrieved.isPresent());
    usageType = MiningField.UsageType.PREDICTED;
    miningField = getMiningField(fieldName, usageType);
    miningSchema = new MiningSchema();
    miningSchema.addMiningFields(miningField);
    model.setMiningSchema(miningSchema);
    retrieved = ModelUtils.getTargetFieldName(fields, model);
    assertTrue(retrieved.isPresent());
    assertEquals(fieldName, retrieved.get());
}

Example 12 with DataDictionary

use of org.dmg.pmml.DataDictionary in project drools by kiegroup.

the class PMMLModelTestUtils method getPMMLWithMiningRandomTestModel.

public static PMML getPMMLWithMiningRandomTestModel() {
    PMML toReturn = new PMML();
    DataDictionary dataDictionary = getRandomDataDictionary();
    toReturn.setDataDictionary(dataDictionary);
    toReturn.addModels(getRandomMiningModel(dataDictionary));
    return toReturn;
}

Example 13 with DataDictionary

use of org.dmg.pmml.DataDictionary in project drools by kiegroup.

the class ModelUtilsTest method getOpTypeByMiningFieldsNotFound.

@Test(expected = KiePMMLInternalException.class)
public void getOpTypeByMiningFieldsNotFound() {
    final Model model = new RegressionModel();
    final DataDictionary dataDictionary = new DataDictionary();
    final MiningSchema miningSchema = new MiningSchema();
    IntStream.range(0, 3).forEach(i -> {
        String fieldName = "field" + i;
        final DataField dataField = getRandomDataField();
        dataField.setName(FieldName.create(fieldName));
        dataDictionary.addDataFields(dataField);
        final MiningField miningField = getRandomMiningField();
        miningField.setName(dataField.getName());
        miningSchema.addMiningFields(miningField);
    });
    model.setMiningSchema(miningSchema);
    ModelUtils.getOpType(getFieldsFromDataDictionary(dataDictionary), model, "NOT_EXISTING");
}

Example 14 with DataDictionary

use of org.dmg.pmml.DataDictionary in project drools by kiegroup.

the class ModelUtilsTest method getOpTypeByDataFields.

@Test
public void getOpTypeByDataFields() {
    final Model model = new RegressionModel();
    final DataDictionary dataDictionary = new DataDictionary();
    final MiningSchema miningSchema = new MiningSchema();
    IntStream.range(0, 3).forEach(i -> {
        final DataField dataField = getRandomDataField();
        dataDictionary.addDataFields(dataField);
    });
    model.setMiningSchema(miningSchema);
    dataDictionary.getDataFields().forEach(dataField -> {
        OP_TYPE retrieved = ModelUtils.getOpType(getFieldsFromDataDictionary(dataDictionary), model, dataField.getName().getValue());
        assertNotNull(retrieved);
        OP_TYPE expected = OP_TYPE.byName(dataField.getOpType().value());
        assertEquals(expected, retrieved);
    });
}

Example 15 with DataDictionary

use of org.dmg.pmml.DataDictionary in project drools by kiegroup.

the class KiePMMLClassificationTableFactoryTest method getClassificationTableBuilders.

@Test
public void getClassificationTableBuilders() {
    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());
    Map<String, KiePMMLTableSourceCategory> retrieved = KiePMMLClassificationTableFactory.getClassificationTableBuilders(compilationDTO);
    assertNotNull(retrieved);
    assertEquals(3, retrieved.size());
    retrieved.values().forEach(kiePMMLTableSourceCategory -> commonValidateKiePMMLRegressionTable(kiePMMLTableSourceCategory.getSource()));
    Map<String, String> sources = retrieved.entrySet().stream().collect(Collectors.toMap(Map.Entry::getKey, stringKiePMMLTableSourceCategoryEntry -> stringKiePMMLTableSourceCategoryEntry.getValue().getSource()));
    commonValidateCompilation(sources);
}