YeoJohnsonTransformer¶

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

The YeoJohnsonTransformer() applies the Yeo-Johnson transformation to the numerical variables.

The Yeo-Johnson transformation implemented by this transformer is that of SciPy.stats: https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.yeojohnson.html

The YeoJohnsonTransformer() works only with numerical variables.

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 containing the best lambda for the Yeo-Johnson 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: Yeo, In-Kwon and Johnson, Richard (2000). A new family of power transformations to improve normality or symmetry. Biometrika, 87, 954-959.
2: Weisberg S. “Yeo-Johnson Power Transformations”. https://www.stat.umn.edu/arc/yjpower.pdf

Methods

fit:	Learn the optimal lambda for the Yeo-Johnson 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 Yeo-Johnson transformation.

fit(X, y=None)[source]¶

Learn the optimal lambda for the Yeo-Johnson 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.

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 Yeo-Johnson transformation.

Parameters

X: Pandas DataFrame of shape = [n_samples, n_features]: The data to be transformed.

Returns

X: pandas dataframe: The dataframe with the transformed variables.
:rtype:py:class:~pandas.core.frame.DataFrame

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