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
Examples
>>> import numpy as np >>> import pandas as pd >>> from feature_engine.transformation import YeoJohnsonTransformer >>> np.random.seed(42) >>> X = pd.DataFrame(dict(x = np.random.lognormal(size = 100) - 10)) >>> yjt = YeoJohnsonTransformer() >>> yjt.fit(X) >>> X = yjt.transform(X) >>> X.head() x 0 -267042.906453 1 -444357.138990 2 -221626.115742 3 -23647.632651 4 -467264.993249
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
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. 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, thenfeature_names_in_is used as feature names in.If an array or list, then
input_featuresmust matchfeature_names_in_.
- Returns
- feature_names_out: list
Transformed feature names.
- 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.