"""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.
This is similar to onehot, but categories are stored 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)