"""Weight of Evidence"""
import numpy as np
from category_encoders.ordinal import OrdinalEncoder
import category_encoders.utils as util
from sklearn.utils.random import check_random_state
__author__ = 'Jan Motl'
[docs]class WOEEncoder(util.BaseEncoder, util.SupervisedTransformerMixin):
"""Weight of Evidence coding for categorical features.
Supported targets: binomial. For polynomial target support, see PolynomialWrapper.
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_missing: str
options are 'return_nan', 'error' and 'value', defaults to 'value', which will assume WOE=0.
handle_unknown: str
options are 'return_nan', 'error' and 'value', defaults to 'value', which will assume WOE=0.
randomized: bool,
adds normal (Gaussian) distribution noise into training data in order to decrease overfitting (testing data are untouched).
sigma: float
standard deviation (spread or "width") of the normal distribution.
regularization: float
the purpose of regularization is mostly to prevent division by zero.
When regularization is 0, you may encounter division by zero.
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 > 200000
>>> X = pd.DataFrame(bunch.data, columns=bunch.feature_names)[display_cols]
>>> enc = WOEEncoder(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 7 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 1460 non-null float64
6 CentralAir 1460 non-null float64
dtypes: float64(6), object(1)
memory usage: 80.0+ KB
None
References
----------
.. [1] Weight of Evidence (WOE) and Information Value Explained, from
https://www.listendata.com/2015/03/weight-of-evidence-woe-and-information.html
"""
prefit_ordinal = True
encoding_relation = util.EncodingRelation.ONE_TO_ONE
def __init__(self, verbose=0, cols=None, drop_invariant=False, return_df=True,
handle_unknown='value', handle_missing='value', random_state=None, randomized=False, sigma=0.05, regularization=1.0):
super().__init__(verbose=verbose, cols=cols, drop_invariant=drop_invariant, return_df=return_df,
handle_unknown=handle_unknown, handle_missing=handle_missing)
self.ordinal_encoder = None
self._sum = None
self._count = None
self.random_state = random_state
self.randomized = randomized
self.sigma = sigma
self.regularization = regularization
def _fit(self, X, y, **kwargs):
# The label must be binary with values {0,1}
unique = y.unique()
if len(unique) != 2:
raise ValueError("The target column y must be binary. But the target contains " + str(len(unique)) + " unique value(s).")
if y.isna().any():
raise ValueError("The target column y must not contain missing values.")
if np.max(unique) < 1:
raise ValueError("The target column y must be binary with values {0, 1}. Value 1 was not found in the target.")
if np.min(unique) > 0:
raise ValueError("The target column y must be binary with values {0, 1}. Value 0 was not found in the target.")
self.ordinal_encoder = OrdinalEncoder(
verbose=self.verbose,
cols=self.cols,
handle_unknown='value',
handle_missing='value'
)
self.ordinal_encoder = self.ordinal_encoder.fit(X)
X_ordinal = self.ordinal_encoder.transform(X)
# Training
self.mapping = self._train(X_ordinal, y)
def _transform(self, X, y=None):
X = self.ordinal_encoder.transform(X)
if self.handle_unknown == 'error':
if X[self.cols].isin([-1]).any().any():
raise ValueError('Unexpected categories found in dataframe')
# Loop over columns and replace nominal values with WOE
X = self._score(X, y)
return X
def _train(self, X, y):
# Initialize the output
mapping = {}
# Calculate global statistics
self._sum = y.sum()
self._count = y.count()
for switch in self.ordinal_encoder.category_mapping:
col = switch.get('col')
values = switch.get('mapping')
# Calculate sum and count of the target for each unique value in the feature col
stats = y.groupby(X[col]).agg(['sum', 'count']) # Count of x_{i,+} and x_i
# Create a new column with regularized WOE.
# Regularization helps to avoid division by zero.
# Pre-calculate WOEs because logarithms are slow.
nominator = (stats['sum'] + self.regularization) / (self._sum + 2*self.regularization)
denominator = ((stats['count'] - stats['sum']) + self.regularization) / (self._count - self._sum + 2*self.regularization)
woe = np.log(nominator / denominator)
# Ignore unique values. This helps to prevent overfitting on id-like columns.
woe[stats['count'] == 1] = 0
if self.handle_unknown == 'return_nan':
woe.loc[-1] = np.nan
elif self.handle_unknown == 'value':
woe.loc[-1] = 0
if self.handle_missing == 'return_nan':
woe.loc[values.loc[np.nan]] = np.nan
elif self.handle_missing == 'value':
woe.loc[-2] = 0
# Store WOE for transform() function
mapping[col] = woe
return mapping
def _score(self, X, y):
for col in self.cols:
# Score the column
X[col] = X[col].map(self.mapping[col])
# Randomization is meaningful only for training data -> we do it only if y is present
if self.randomized and y is not None:
random_state_generator = check_random_state(self.random_state)
X[col] = (X[col] * random_state_generator.normal(1., self.sigma, X[col].shape[0]))
return X