The CountFrequencyCategoricalEncoder() replaces categories with the number of observations or percentage of observations per category. For example, if 10 observations show the category blue for the variable color, blue will be replaced by 10. If, using frequency, if 20% of observations show the category red, red will be replaced by 0.20.
The CountFrequencyCategoricalEncoder() 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
# Load dataset
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.CountFrequencyCategoricalEncoder(encoding_method='frequency',
variables=['cabin', 'pclass', 'embarked'])
# 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': {'n': 0.7663755458515283,
'C': 0.07751091703056769,
'B': 0.04585152838427948,
'E': 0.034934497816593885,
'D': 0.034934497816593885,
'A': 0.018558951965065504,
'F': 0.016375545851528384,
'G': 0.004366812227074236,
'T': 0.001091703056768559},
'pclass': {3: 0.5436681222707423,
1: 0.25109170305676853,
2: 0.2052401746724891},
'embarked': {'S': 0.7117903930131004,
'C': 0.19759825327510916,
'Q': 0.0906113537117904}}.. autoclass:: feature_engine.categorical_encoders.CountFrequencyCategoricalEncoder
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