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

Example 6 with LinearNorm

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

the class KiePMMLNormContinuousFactoryTest method getNormContinuousVariableDeclaration.

@Test
public void getNormContinuousVariableDeclaration() throws IOException {
    String variableName = "variableName";
    NormContinuous normContinuous = getRandomNormContinuous();
    List<LinearNorm> linearNorms = normContinuous.getLinearNorms();
    BlockStmt retrieved = KiePMMLNormContinuousFactory.getNormContinuousVariableDeclaration(variableName, normContinuous);
    String outlierString = OUTLIER_TREATMENT_METHOD.class.getName() + "." + OUTLIER_TREATMENT_METHOD.byName(normContinuous.getOutliers().value()).name();
    String text = getFileContent(TEST_01_SOURCE);
    Statement expected = JavaParserUtils.parseBlock(String.format(text, variableName, normContinuous.getField().getValue(), linearNorms.get(0).getOrig(), linearNorms.get(0).getNorm(), linearNorms.get(1).getOrig(), linearNorms.get(1).getNorm(), linearNorms.get(2).getOrig(), linearNorms.get(2).getNorm(), outlierString, normContinuous.getMapMissingTo()));
    assertTrue(JavaParserUtils.equalsNode(expected, retrieved));
    List<Class<?>> imports = Arrays.asList(Arrays.class, Collections.class, KiePMMLLinearNorm.class, KiePMMLNormContinuous.class, OUTLIER_TREATMENT_METHOD.class);
    commonValidateCompilationWithImports(retrieved, imports);
}

Example 7 with LinearNorm

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

the class PMMLModelTestUtils method getRandomLinearNorm.

public static LinearNorm getRandomLinearNorm() {
    Random random = new Random();
    double orig = random.nextInt(100) / 10;
    double norm = random.nextInt(100) / 10;
    return new LinearNorm(orig, norm);
}

Example 8 with LinearNorm

use of org.dmg.pmml.LinearNorm in project shifu by ShifuML.

the class WoeZscoreLocalTransformCreator method createCategoricalDerivedField.

/**
 * Create @DerivedField for categorical variable
 *
 * @param config - ColumnConfig for categorical variable
 * @param cutoff - cutoff for normalization
 * @return DerivedField for variable
 */
@Override
protected List<DerivedField> createCategoricalDerivedField(ColumnConfig config, double cutoff, ModelNormalizeConf.NormType normType) {
    List<DerivedField> derivedFields = new ArrayList<DerivedField>();
    DerivedField derivedField = super.createCategoricalDerivedField(config, cutoff, ModelNormalizeConf.NormType.WOE).get(0);
    derivedFields.add(derivedField);
    double[] meanAndStdDev = Normalizer.calculateWoeMeanAndStdDev(config, isWeightedNorm);
    // added capping logic to linearNorm
    LinearNorm from = new LinearNorm().setOrig(meanAndStdDev[0] - meanAndStdDev[1] * cutoff).setNorm(-cutoff);
    LinearNorm to = new LinearNorm().setOrig(meanAndStdDev[0] + meanAndStdDev[1] * cutoff).setNorm(cutoff);
    NormContinuous normContinuous = new NormContinuous();
    normContinuous.setField(FieldName.create(derivedField.getName().getValue()));
    normContinuous.addLinearNorms(from, to);
    normContinuous.setMapMissingTo(0.0);
    normContinuous.setOutliers(OutlierTreatmentMethod.AS_EXTREME_VALUES);
    // derived field name is consisted of FieldName and "_zscl"
    derivedFields.add(new DerivedField(OpType.CONTINUOUS, DataType.DOUBLE).setName(FieldName.create(genPmmlColumnName(NormalUtils.getSimpleColumnName(config.getColumnName()), normType))).setExpression(normContinuous));
    return derivedFields;
}