WoEEncoder¶
- class feature_engine.encoding.WoEEncoder(variables=None, ignore_format=False, errors='ignore')[source]¶
The WoEEncoder() replaces categories by the weight of evidence (WoE). The WoE was used primarily in the financial sector to create credit risk scorecards.
The encoder will encode only categorical variables by default (type ‘object’ or ‘categorical’). You can pass a list of variables to encode. Alternatively, the encoder will find and encode all categorical variables (type ‘object’ or ‘categorical’).
With
ignore_format=Trueyou have the option to encode numerical variables as well. The procedure is identical, you can either enter the list of variables to encode, or the transformer will automatically select all variables.The encoder first maps the categories to the weight of evidence for each variable (fit). The encoder then transforms the categories into the mapped numbers (transform).
This categorical encoding is exclusive for binary classification.
Note
The log(0) is not defined and the division by 0 is not defined. Thus, if any of the terms in the WoE equation are 0 for a given category, the encoder will return an error. If this happens, try grouping less frequent categories.
More details in the User Guide.
- Parameters
- variables: list, default=None
The list of categorical variables that will be encoded. If None, the encoder will find and transform all variables of type object or categorical by default. You can also make the transformer accept numerical variables, see the next parameter.
- ignore_format: bool, default=False
Whether the format in which the categorical variables are cast should be ignored. If False, the encoder will automatically select variables of type object or categorical, or check that the variables entered by the user are of type object or categorical. If True, the encoder will select all variables or accept all variables entered by the user, including those cast as numeric.
- errors: string, default=’ignore’
Indicates what to do when categories not present in the train set are encountered during transform. If ‘raise’, then rare categories will raise an error. If ‘ignore’, then rare categories will be set as NaN and a warning will be raised instead.
- Attributes
- encoder_dict_:
Dictionary with the WoE per variable.
- variables_:
The group of variables that will be transformed.
- n_features_in_:
The number of features in the train set used in fit.
See also
feature_engine.encoding.RareLabelEncoderfeature_engine.discretisationcategory_encoders.woe.WOEEncoder
Notes
For details on the calculation of the weight of evidence visit: https://www.listendata.com/2015/03/weight-of-evidence-woe-and-information.html
NAN are introduced when encoding categories that were not present in the training dataset. If this happens, try grouping infrequent categories using the RareLabelEncoder().
There is a similar implementation in the the open-source package Category encoders
Methods
fit:
Learn the WoE per category, per variable.
transform:
Encode the categories to numbers.
fit_transform:
Fit to the data, then transform it.
inverse_transform:
Encode the numbers into the original categories.
- fit(X, y)[source]¶
Learn the WoE.
- Parameters
- X: pandas dataframe of shape = [n_samples, n_features]
The training input samples. Can be the entire dataframe, not just the categorical variables.
- y: pandas series.
Target, must be binary.
- 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_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 encoded variable back to the original values.
- Parameters
- X: pandas dataframe of shape = [n_samples, n_features].
The transformed dataframe.
- Returns
- X_tr: pandas dataframe of shape = [n_samples, n_features].
The un-transformed dataframe, with the categorical variables containing the original values.
- 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]¶
Replace categories with the learned parameters.
- Parameters
- X: pandas dataframe of shape = [n_samples, n_features].
The dataset to transform.
- Returns
- X_new: pandas dataframe of shape = [n_samples, n_features].
The dataframe containing the categories replaced by numbers.
- rtype
DataFrame..