public UnivariateFeatureSelectorModel fit()

in flink-ml-lib/src/main/java/org/apache/flink/ml/feature/univariatefeatureselector/UnivariateFeatureSelector.java [103:154]
49 lines of code
7 McCabe index (conditional complexity)

    public UnivariateFeatureSelectorModel fit(Table... inputs) {
        Preconditions.checkArgument(inputs.length == 1);

        final String featuresCol = getFeaturesCol();
        final String labelCol = getLabelCol();
        final String featureType = getFeatureType();
        final String labelType = getLabelType();

        StreamTableEnvironment tEnv =
                (StreamTableEnvironment) ((TableImpl) inputs[0]).getTableEnvironment();

        Table output;
        if (CATEGORICAL.equals(featureType) && CATEGORICAL.equals(labelType)) {
            output =
                    new ChiSqTest()
                            .setFeaturesCol(featuresCol)
                            .setLabelCol(labelCol)
                            .setFlatten(true)
                            .transform(inputs[0])[0];
        } else if (CONTINUOUS.equals(featureType) && CATEGORICAL.equals(labelType)) {
            output =
                    new ANOVATest()
                            .setFeaturesCol(featuresCol)
                            .setLabelCol(labelCol)
                            .setFlatten(true)
                            .transform(inputs[0])[0];
        } else if (CONTINUOUS.equals(featureType) && CONTINUOUS.equals(labelType)) {
            output =
                    new FValueTest()
                            .setFeaturesCol(featuresCol)
                            .setLabelCol(labelCol)
                            .setFlatten(true)
                            .transform(inputs[0])[0];
        } else {
            throw new IllegalArgumentException(
                    String.format(
                            "Unsupported combination: featureType=%s, labelType=%s.",
                            featureType, labelType));
        }
        DataStream<UnivariateFeatureSelectorModelData> modelData =
                tEnv.toDataStream(output)
                        .transform(
                                "selectIndicesFromPValues",
                                TypeInformation.of(UnivariateFeatureSelectorModelData.class),
                                new SelectIndicesFromPValuesOperator(
                                        getSelectionMode(), getActualSelectionThreshold()))
                        .setParallelism(1);
        UnivariateFeatureSelectorModel model =
                new UnivariateFeatureSelectorModel().setModelData(tEnv.fromDataStream(modelData));
        ParamUtils.updateExistingParams(model, getParamMap());
        return model;
    }