class feature_engine.encoding.PRatioEncoder(encoding_method='ratio', variables=None, ignore_format=False, errors='ignore')[source]#

The PRatioEncoder() replaces categories by the ratio of the probability of the target = 1 and the probability of the target = 0.

The target probability ratio is given by:

\[p(1) / p(0)\]

The log of the target probability ratio is:

\[log( p(1) / p(0) )\]


This categorical encoding is exclusive for binary classification.

The division by 0 is not defined and the log(0) is not defined. Thus, if p(0) = 0 for the ratio encoder, or either p(0) = 0 or p(1) = 0 for log_ratio, in any of the variables, the encoder will return an error.

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 numbers for each variable (fit). The encoder then transforms the categories into the mapped numbers (transform).

More details in the User Guide.

encoding_method: str, default=’ratio’

Desired method of encoding.

‘ratio’: probability ratio

‘log_ratio’: log probability ratio

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.


Dictionary with the probability ratio per category per variable.


The group of variables that will be transformed.


List with the names of features seen during fit.


The number of features in the train set used in fit.


NAN are introduced when encoding categories that were not present in the training dataset. If this happens, try grouping infrequent categories using the RareLabelEncoder().



Learn probability ratio per category, per variable.


Fit to data, then transform it.


Get output feature names for transformation.


Get parameters for this estimator.


Set the parameters of this estimator.


Convert the data back to the original representation.


Encode the categories to numbers.

fit(X, y)[source]#

Learn the numbers that should be used to replace the categories in each variable. That is the ratio of probability.

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 X and y with optional parameters fit_params and returns a transformed version of X.

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).


Additional fit parameters.

X_newndarray array of shape (n_samples, n_features_new)

Transformed array.


Get output feature names for transformation.

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.

feature_names_out: list

The feature names.

:rtype:py:class:~typing.List[Union[str, int]]

Get parameters for this estimator.

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.


Parameter names mapped to their values.


Convert the encoded variable back to the original values.

X: pandas dataframe of shape = [n_samples, n_features].

The transformed dataframe.

X_tr: pandas dataframe of shape = [n_samples, n_features].

The un-transformed dataframe, with the categorical variables containing the original values.


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.


Estimator parameters.

selfestimator instance

Estimator instance.


Replace categories with the learned parameters.

X: pandas dataframe of shape = [n_samples, n_features].

The dataset to transform.

X_new: pandas dataframe of shape = [n_samples, n_features].

The dataframe containing the categories replaced by numbers.