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:

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

For example in the variable colour, if the mean of the target = 1 for blue is 0.8 and the mean of the target = 0 is 0.2, blue will be replaced by: 0.8 / 0.2 = 4 if ratio is selected, or log(0.8/0.2) = 1.386 if log_ratio is selected.


This categorical encoding is exclusive for binary classification.

Let’s look at an example using the Titanic Dataset.

First, let’s load the data and separate it into train and test:

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split

from feature_engine.encoding import PRatioEncoder, RareLabelEncoder

# Load dataset
def load_titanic():
        data = pd.read_csv('')
        data = data.replace('?', np.nan)
        data['cabin'] = data['cabin'].astype(str).str[0]
        data['pclass'] = data['pclass'].astype('O')
        data['embarked'].fillna('C', inplace=True)
        return data

data = load_titanic()

# Separate into train and test sets
X_train, X_test, y_train, y_test = train_test_split(
                data.drop(['survived', 'name', 'ticket'], axis=1),
                data['survived'], test_size=0.3, random_state=0)

Before we encode the variables, I would like to group infrequent categories into one category, called ‘Rare’. For this, I will use the RareLabelEncoder() as follows:

# set up a rare label encoder
rare_encoder = RareLabelEncoder(tol=0.03, n_categories=2, variables=['cabin', 'pclass', 'embarked'])

# fit and transform data
train_t = rare_encoder.fit_transform(X_train)
test_t = rare_encoder.transform(X_train)

Now, we set up the PRatioEncoder() to replace the categories by the probability ratio, only in the 3 indicated variables:

# set up a weight of evidence encoder
pratio_encoder = PRatioEncoder(encoding_method='ratio', variables=['cabin', 'pclass', 'embarked'])

# fit the encoder, y_train)

With fit() the encoder learns the values to replace each category, which are stored in its encoder_dict_ parameter:


In the encoder_dict_ we find the probability ratio for each category in each variable to encode. This way, we can map the original value to the new value.

{'cabin': {'B': 3.1999999999999993,
 'C': 1.2903225806451615
 'D': 2.5555555555555554,
 'E': 2.5555555555555554,
 'Rare': 1.3124999999999998,
 'n': 0.4385245901639344},
 'pclass': {1: 1.6136363636363635,
  2: 0.7735849056603774,
  3: 0.34959349593495936},
  'embarked': {'C': 1.2625000000000002,
  'Q': 0.5961538461538461,
  'S': 0.5127610208816704}}

Now, we can go ahead and encode the variables:

# transform
train_t = pratio_encoder.transform(train_t)
test_t = pratio_encoder.transform(test_t)

More details#

In the following notebook, you can find more details into the PRatioEncoder() functionality and example plots with the encoded variables:

All notebooks can be found in a dedicated repository.