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

Examples

>>> import numpy as np
>>> import pandas as pd
>>> from feature_engine.transformation import BoxCoxTransformer
>>> np.random.seed(42)
>>> X = pd.DataFrame(dict(x = np.random.lognormal(size = 100)))
>>> bct = BoxCoxTransformer()
>>> bct.fit(X)
>>> X = bct.transform(X)
>>> X.head()
          x
0  0.505485
1 -0.137595
2  0.662654
3  1.607518
4 -0.232237

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. In other words, returns the variable names of transformed dataframe.

Parameters
input_featuresarray or list, default=None

This parameter exits only for compatibility with the Scikit-learn pipeline.

  • If None, then feature_names_in_ is used as feature names in.

  • If an array or list, then input_features must match feature_names_in_.

Returns
feature_names_out: list

Transformed feature names.

rtype

List[Union[str, int]] ..

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns
routingMetadataRequest

A MetadataRequest encapsulating routing information.

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

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

DataFrame ..