fcb01e3b18c0b3304c4ff0b1f0b8b4ede518f762,ludwig/features/numerical_feature.py,NumericalFeatureMixin,get_feature_meta,#Any#Any#Any#,58

Before Change

    def get_feature_meta(column, preprocessing_parameters, backend):
        compute = backend.df_engine.compute
        if preprocessing_parameters["normalization"] is not None:
            if preprocessing_parameters["normalization"] == "zscore":
                return {
                    "mean": compute(column.astype(np.float32).mean()),
                    "std": compute(column.astype(np.float32).std())
                }
            elif preprocessing_parameters["normalization"] == "minmax":
                return {
                    "min": compute(column.astype(np.float32).min()),
                    "max": compute(column.astype(np.float32).max())
                }
            else:
                logger.info(
                    "Currently zscore and minmax are the only "
                    "normalization strategies available. No {}".format(
                        preprocessing_parameters["normalization"])
                )
                return {}
        else:
            return {}

    @staticmethod

After Change

    @staticmethod
    def get_feature_meta(column, preprocessing_parameters, backend):
        numeric_transformer = get_from_registry(
            preprocessing_parameters.get("normalization", None),
            numeric_transformation_registry
        )

        return numeric_transformer.fit_transform_params(column, backend)

    @staticmethod

Instances