RareLabelCategoricalEncoder

The RareLabelCategoricalEncoder() groups infrequent categories altogether into one new category called ‘Rare’ or a different string indicated by the user. We need to specify the minimum percentage of observations a category should show to be preserved and the minimum number of unique categories a variable should have to be re-grouped.

The RareLabelCategoricalEncoder() works only with categorical variables. A list of variables can be indicated, or the encoder will automatically select all categorical variables in the train set.

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

from feature_engine import categorical_encoders as ce

def load_titanic():
    data = pd.read_csv(
        'https://www.openml.org/data/get_csv/16826755/phpMYEkMl')
    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)

# set up the encoder
encoder = ce.RareLabelCategoricalEncoder(tol=0.03, n_categories=2,
                                         variables=['cabin', 'pclass', 'embarked'],
                                         replace_with='Rare')

# fit the encoder
encoder.fit(X_train)

# transform the data
train_t = encoder.transform(X_train)
test_t = encoder.transform(X_test)

encoder.encoder_dict_
{'cabin': Index(['n', 'C', 'B', 'E', 'D'], dtype='object'),
 'pclass': array([2, 3, 1], dtype='int64'),
 'embarked': array(['S', 'C', 'Q'], dtype=object)}

You can also specify the maximum number of categories that can be considered frequent using the max_n_categories parameter.

>>> from feature_engine.categorical_encoders import RareLabelCategoricalEncoder
>>> import pandas as pd
>>> data = {'var_A': ['A'] * 10 + ['B'] * 10 + ['C'] * 2 + ['D'] * 1}
>>> data = pd.DataFrame(data)
>>> data['var_A'].value_counts()
A    10
B    10
C     2
D     1
Name: var_A, dtype: int64
>>> rare_encoder = RareLabelCategoricalEncoder(tol=0.05, n_categories=3)
>>> rare_encoder.fit_transform(data)['var_A'].value_counts()
A       10
B       10
C        2
Rare     1
Name: var_A, dtype: int64
>>> rare_encoder = RareLabelCategoricalEncoder(tol=0.05, n_categories=3, max_n_categories=2)
>>> rare_encoder.fit_transform(data)['var_A'].value_counts()
A       10
B       10
Rare     3
Name: var_A, dtype: int64

API Reference

class feature_engine.categorical_encoders.RareLabelCategoricalEncoder(tol=0.05, n_categories=10, max_n_categories=None, variables=None, replace_with='Rare')[source]

The RareLabelCategoricalEncoder() groups rare / infrequent categories in a new category called “Rare”, or any other name entered by the user.

For example in the variable colour, if the percentage of observations for the categories magenta, cyan and burgundy are < 5 %, all those categories will be replaced by the new label “Rare”.

Note, infrequent labels can also be grouped under a user defined name, for example ‘Other’. The name to replace infrequent categories is defined with the parameter replace_with.

The encoder will encode only categorical variables (type ‘object’). A list of variables can be passed as an argument. If no variables are passed as argument, the encoder will find and encode all categorical variables (object type).

The encoder first finds the frequent labels for each variable (fit).

The encoder then groups the infrequent labels under the new label ‘Rare’ or by another user defined string (transform).

Parameters
  • tol (float, default=0.05) – the minimum frequency a label should have to be considered frequent. Categories with frequencies lower than tol will be grouped.

  • n_categories (int, default=10) – the minimum number of categories a variable should have for the encoder to find frequent labels. If the variable contains less categories, all of them will be considered frequent.

  • max_n_categories (int, default=None) – the maximum number of categories that should be considered frequent. If None, all categories with frequency above the tolerance (tol) will be considered.

  • variables (list, default=None) – The list of categorical variables that will be encoded. If None, the encoder will find and select all object type variables.

  • replace_with (string, default='Rare') – The category name that will be used to replace infrequent categories.

fit(X, y=None)[source]

Learns the frequent categories for each variable.

Parameters
  • X (pandas dataframe of shape = [n_samples, n_features]) – The training input samples. Can be the entire dataframe, not just selected variables

  • y (None) – y is not required. You can pass y or None.

encoder_dict\_

The dictionary containing the frequent categories (that will be kept) for each variable. Categories not present in this list will be replaced by ‘Rare’ or by the user defined value.

Type

dictionary

transform(X)[source]

Groups rare labels under separate group ‘Rare’ or any other name provided by the user.

Parameters

X (pandas dataframe of shape = [n_samples, n_features]) – The input samples.

Returns

X_transformed – The dataframe where rare categories have been grouped.

Return type

pandas dataframe of shape = [n_samples, n_features]