Examples with CategoricalPredictor org.dmg.pmml.regression.CategoricalPredictor used on opensource projects

Example 6 with CategoricalPredictor

use of org.dmg.pmml.regression.CategoricalPredictor in project drools by kiegroup.

the class KiePMMLRegressionTableFactory method getCategoricalPredictorsExpressions.

/**
 * Create the <b>CategoricalPredictor</b>s lambda <code>Expression</code>s map
 * @param categoricalPredictors
 * @param body
 * @return
 */
static Map<String, Expression> getCategoricalPredictorsExpressions(final List<CategoricalPredictor> categoricalPredictors, final BlockStmt body, final String variableName) {
    final Map<String, List<CategoricalPredictor>> groupedCollectors = categoricalPredictors.stream().collect(groupingBy(categoricalPredictor -> categoricalPredictor.getField().getValue()));
    final String categoricalPredictorMapNameBase = getSanitizedVariableName(String.format("%sMap", variableName));
    final AtomicInteger counter = new AtomicInteger();
    return groupedCollectors.entrySet().stream().map(entry -> {
        final String categoricalPredictorMapName = String.format(VARIABLE_NAME_TEMPLATE, categoricalPredictorMapNameBase, counter.getAndIncrement());
        populateWithGroupedCategoricalPredictorMap(entry.getValue(), body, categoricalPredictorMapName);
        return new AbstractMap.SimpleEntry<>(entry.getKey(), getCategoricalPredictorExpression(categoricalPredictorMapName));
    }).collect(Collectors.toMap(AbstractMap.SimpleEntry::getKey, AbstractMap.SimpleEntry::getValue));
}

Example 7 with CategoricalPredictor

use of org.dmg.pmml.regression.CategoricalPredictor in project drools by kiegroup.

the class KiePMMLRegressionModelFactoryTest method setup.

@BeforeClass
public static void setup() {
    Random random = new Random();
    Set<String> fieldNames = new HashSet<>();
    regressionTables = IntStream.range(0, 3).mapToObj(i -> {
        List<CategoricalPredictor> categoricalPredictors = new ArrayList<>();
        List<NumericPredictor> numericPredictors = new ArrayList<>();
        List<PredictorTerm> predictorTerms = new ArrayList<>();
        IntStream.range(0, 3).forEach(j -> {
            String catFieldName = "CatPred-" + j;
            String numFieldName = "NumPred-" + j;
            categoricalPredictors.add(getCategoricalPredictor(catFieldName, random.nextDouble(), random.nextDouble()));
            numericPredictors.add(getNumericPredictor(numFieldName, random.nextInt(), random.nextDouble()));
            predictorTerms.add(getPredictorTerm("PredTerm-" + j, random.nextDouble(), Arrays.asList(catFieldName, numFieldName)));
            fieldNames.add(catFieldName);
            fieldNames.add(numFieldName);
        });
        return getRegressionTable(categoricalPredictors, numericPredictors, predictorTerms, tableIntercept + random.nextDouble(), tableTargetCategory + "-" + i);
    }).collect(Collectors.toList());
    dataFields = new ArrayList<>();
    miningFields = new ArrayList<>();
    fieldNames.forEach(fieldName -> {
        dataFields.add(getDataField(fieldName, OpType.CATEGORICAL, DataType.STRING));
        miningFields.add(getMiningField(fieldName, MiningField.UsageType.ACTIVE));
    });
    targetMiningField = miningFields.get(0);
    targetMiningField.setUsageType(MiningField.UsageType.TARGET);
    dataDictionary = getDataDictionary(dataFields);
    transformationDictionary = new TransformationDictionary();
    miningSchema = getMiningSchema(miningFields);
    regressionModel = getRegressionModel(modelName, MiningFunction.REGRESSION, miningSchema, regressionTables);
    COMPILATION_UNIT = getFromFileName(KIE_PMML_REGRESSION_MODEL_TEMPLATE_JAVA);
    MODEL_TEMPLATE = COMPILATION_UNIT.getClassByName(KIE_PMML_REGRESSION_MODEL_TEMPLATE).get();
    pmml = new PMML();
    pmml.setDataDictionary(dataDictionary);
    pmml.setTransformationDictionary(transformationDictionary);
    pmml.addModels(regressionModel);
}

Example 8 with CategoricalPredictor

use of org.dmg.pmml.regression.CategoricalPredictor in project drools by kiegroup.

the class KiePMMLRegressionTableFactoryTest method populateWithGroupedCategoricalPredictorMap.

@Test
public void populateWithGroupedCategoricalPredictorMap() throws IOException {
    final List<CategoricalPredictor> categoricalPredictors = new ArrayList<>();
    for (int i = 0; i < 3; i++) {
        String predictorName = "predictorName-" + i;
        double coefficient = 1.23 * i;
        categoricalPredictors.add(PMMLModelTestUtils.getCategoricalPredictor(predictorName, i, coefficient));
    }
    final BlockStmt toPopulate = new BlockStmt();
    final String categoricalPredictorMapName = "categoricalPredictorMapName";
    KiePMMLRegressionTableFactory.populateWithGroupedCategoricalPredictorMap(categoricalPredictors, toPopulate, categoricalPredictorMapName);
    String text = getFileContent(TEST_04_SOURCE);
    BlockStmt expected = JavaParserUtils.parseBlock(String.format(text, categoricalPredictorMapName, categoricalPredictors.get(0).getValue(), categoricalPredictors.get(0).getCoefficient(), categoricalPredictors.get(1).getValue(), categoricalPredictors.get(1).getCoefficient(), categoricalPredictors.get(2).getValue(), categoricalPredictors.get(2).getCoefficient()));
    assertTrue(JavaParserUtils.equalsNode(expected, toPopulate));
}