WoEEncoder

class feature_engine.encoding.WoEEncoder(variables=None, ignore_format=False, unseen='ignore')

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=True you 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.

unseen: string, default=’ignore’

Indicates what to do, when categories not present in the train set are encountered during transform. If ‘raise’, then unseen categories will raise an error. If ‘ignore’, then unseen 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.

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.

See also

feature_engine.encoding.RareLabelEncoder

feature_engine.discretisation

category_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

Examples

>>> import pandas as pd
>>> from feature_engine.encoding import WoEEncoder
>>> X = pd.DataFrame(dict(x1 = [1,2,3,4,5], x2 = ["b", "b", "b", "a", "a"]))
>>> y = pd.Series([0,1,1,1,0])
>>> woe = WoEEncoder()
>>> woe.fit(X, y)
>>> woe.transform(X)
   x1        x2
0   1  0.287682
1   2  0.287682
2   3  0.287682
3   4 -0.405465
4   5 -0.405465

Methods

fit:	Learn the WoE per category, per variable.
transform:	Encode the categories to numbers.
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.

fit(X, y)

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)

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)

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_params(deep=True)

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)

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)

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)

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 ..