Source code for category_encoders.binary

"""Binary encoding"""
from functools import partialmethod
from category_encoders import utils
from category_encoders.basen import BaseNEncoder

__author__ = 'willmcginnis'


[docs]class BinaryEncoder(BaseNEncoder): """Binary encoding for categorical variables, similar to onehot, but stores categories as binary bitstrings. Parameters ---------- verbose: int integer indicating verbosity of the output. 0 for none. cols: list a list of columns to encode, if None, all string columns will be encoded. drop_invariant: bool boolean for whether or not to drop columns with 0 variance. return_df: bool boolean for whether to return a pandas DataFrame from transform (otherwise it will be a numpy array). handle_unknown: str options are 'error', 'return_nan', 'value', and 'indicator'. The default is 'value'. Warning: if indicator is used, an extra column will be added in if the transform matrix has unknown categories. This can cause unexpected changes in dimension in some cases. handle_missing: str options are 'error', 'return_nan', 'value', and 'indicator'. The default is 'value'. Warning: if indicator is used, an extra column will be added in if the transform matrix has nan values. This can cause unexpected changes in dimension in some cases. Example ------- >>> from category_encoders import * >>> import pandas as pd >>> from sklearn.datasets import fetch_openml >>> bunch = fetch_openml(name="house_prices", as_frame=True) >>> display_cols = ["Id", "MSSubClass", "MSZoning", "LotFrontage", "YearBuilt", "Heating", "CentralAir"] >>> y = bunch.target >>> X = pd.DataFrame(bunch.data, columns=bunch.feature_names)[display_cols] >>> enc = BinaryEncoder(cols=['CentralAir', 'Heating']).fit(X, y) >>> numeric_dataset = enc.transform(X) >>> print(numeric_dataset.info()) <class 'pandas.core.frame.DataFrame'> RangeIndex: 1460 entries, 0 to 1459 Data columns (total 10 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Id 1460 non-null float64 1 MSSubClass 1460 non-null float64 2 MSZoning 1460 non-null object 3 LotFrontage 1201 non-null float64 4 YearBuilt 1460 non-null float64 5 Heating_0 1460 non-null int64 6 Heating_1 1460 non-null int64 7 Heating_2 1460 non-null int64 8 CentralAir_0 1460 non-null int64 9 CentralAir_1 1460 non-null int64 dtypes: float64(4), int64(5), object(1) memory usage: 114.2+ KB None """ encoding_relation = utils.EncodingRelation.ONE_TO_M __init__ = partialmethod(BaseNEncoder.__init__, base=2)