BoxCoxTransformer

class feature_engine.transformation.BoxCoxTransformer(variables=None)[source]

The BoxCoxTransformer() applies the BoxCox transformation to numerical variables.

The Box-Cox transformation is defined as:

  • T(Y)=(Y exp(λ)−1)/λ if λ!=0

  • log(Y) otherwise

where Y is the response variable and λ is the transformation parameter. λ varies, typically from -5 to 5. In the transformation, all values of λ are considered and the optimal value for a given variable is selected.

The BoxCox transformation implemented by this transformer is that of SciPy.stats: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.boxcox.html

The BoxCoxTransformer() works only with numerical positive variables (>=0).

A list of variables can be passed as an argument. Alternatively, the transformer will automatically select and transform all numerical variables.

More details in the User Guide.

Parameters
variables: list, default=None

The list of numerical variables to transform. If None, the transformer will automatically find and select all numerical variables.

Attributes
lambda_dict_:

Dictionary with the best BoxCox exponent per variable.

variables_:

The group of variables that will be transformed.

feature_names_in_:

List with the names of features seen during fit.

n_features_in_:

The number of features in the train set used in fit.

References

1

Box and Cox. “An Analysis of Transformations”. Read at a RESEARCH MEETING, 1964. https://rss.onlinelibrary.wiley.com/doi/abs/10.1111/j.2517-6161.1964.tb00553.x

Methods

fit:

Learn the optimal lambda for the BoxCox transformation.

fit_transform:

Fit to data, then transform it.

get_feature_names_out:

Get output feature names for transformation.

get_params:

Get parameters for this estimator.

set_params:

Set the parameters of this estimator.

inverse_transform:

Convert the data back to the original representation.

transform:

Apply the BoxCox transformation.

fit(X, y=None)[source]

Learn the optimal lambda for the BoxCox transformation.

Parameters
X: pandas dataframe of shape = [n_samples, n_features]

The training input samples. Can be the entire dataframe, not just the variables to transform.

y: pandas Series, default=None

It is not needed in this transformer. You can pass y or None.

fit_transform(X, y=None, **fit_params)[source]

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters
Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

**fit_paramsdict

Additional fit parameters.

Returns
X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

get_feature_names_out(input_features=None)[source]

Get output feature names for transformation.

Parameters
input_features: str, list, default=None

If None, then the names of all the variables in the transformed dataset is returned. If list with feature names, the features in the list will be returned. This parameter exists mostly for compatibility with the Scikit-learn Pipeline.

Returns
feature_names_out: list

The feature names.

:rtype:py:class:~typing.List[Union[str, int]]
get_params(deep=True)[source]

Get parameters for this estimator.

Parameters
deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns
paramsdict

Parameter names mapped to their values.

inverse_transform(X)[source]

Convert the data back to the original representation.

Parameters
X: Pandas DataFrame of shape = [n_samples, n_features]

The data to be inverse transformed.

Returns
X_new: pandas dataframe

The dataframe with the original variables.

:rtype:py:class:~pandas.core.frame.DataFrame
set_params(**params)[source]

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters
**paramsdict

Estimator parameters.

Returns
selfestimator instance

Estimator instance.

transform(X)[source]

Apply the BoxCox transformation.

Parameters
X: Pandas DataFrame of shape = [n_samples, n_features]

The data to be transformed.

Returns
X_new: pandas dataframe

The dataframe with the transformed variables.

:rtype:py:class:~pandas.core.frame.DataFrame