Source code for getml.preprocessors.imputation

# Copyright 2022 The SQLNet Company GmbH
# This file is licensed under the Elastic License 2.0 (ELv2).
# Refer to the LICENSE.txt file in the root of the repository
# for details.

Contains routines for preprocessing data frames.

from dataclasses import dataclass

from .preprocessor import _Preprocessor
from .validate import _validate

[docs]@dataclass(repr=False) class Imputation(_Preprocessor): """ The Imputation preprocessor replaces all NULL values in numerical columns with the mean of the remaining columns. Optionally, it can additionally add a dummy column that signifies whether the original value was imputed. Args: add_dummies (bool): Whether you want to add dummy variables that signify whether the original value was imputed.. Example: .. code-block:: python imputation = getml.preprocessors.Imputation() pipe = getml.Pipeline( population=population_placeholder, peripheral=[order_placeholder, trans_placeholder], preprocessors=[imputation], feature_learners=[feature_learner_1, feature_learner_2], feature_selectors=feature_selector, predictors=predictor, share_selected_features=0.5 ) """ add_dummies: bool = False
[docs] def validate(self, params=None): """Checks both the types and the values of all instance variables and raises an exception if something is off. Args: params (dict, optional): A dictionary containing the parameters to validate. If not is passed, the own parameters will be validated. """ params = _validate(self, params) if not isinstance(params["add_dummies"], bool): raise TypeError("'add_dummies' must be a bool.")