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.
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
Xandywith optional parametersfit_paramsand returns a transformed version ofX.- 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
- 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.
- 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.