"""Backward difference contrast encoding"""
import pandas as pd
from sklearn.base import BaseEstimator, TransformerMixin
from patsy.contrasts import Diff
import numpy as np
from category_encoders.ordinal import OrdinalEncoder
import category_encoders.utils as util
__author__ = 'willmcginnis'
[docs]class BackwardDifferenceEncoder(BaseEstimator, TransformerMixin):
"""Backward difference contrast coding for encoding categorical variables.
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 load_boston
>>> bunch = load_boston()
>>> y = bunch.target
>>> X = pd.DataFrame(bunch.data, columns=bunch.feature_names)
>>> enc = BackwardDifferenceEncoder(cols=['CHAS', 'RAD']).fit(X, y)
>>> numeric_dataset = enc.transform(X)
>>> print(numeric_dataset.info())
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 506 entries, 0 to 505
Data columns (total 21 columns):
intercept 506 non-null int64
CRIM 506 non-null float64
ZN 506 non-null float64
INDUS 506 non-null float64
CHAS_0 506 non-null float64
NOX 506 non-null float64
RM 506 non-null float64
AGE 506 non-null float64
DIS 506 non-null float64
RAD_0 506 non-null float64
RAD_1 506 non-null float64
RAD_2 506 non-null float64
RAD_3 506 non-null float64
RAD_4 506 non-null float64
RAD_5 506 non-null float64
RAD_6 506 non-null float64
RAD_7 506 non-null float64
TAX 506 non-null float64
PTRATIO 506 non-null float64
B 506 non-null float64
LSTAT 506 non-null float64
dtypes: float64(20), int64(1)
memory usage: 83.1 KB
None
References
----------
.. [1] Contrast Coding Systems for Categorical Variables, from
https://stats.idre.ucla.edu/r/library/r-library-contrast-coding-systems-for-categorical-variables/
.. [2] Gregory Carey (2003). Coding Categorical Variables, from
http://psych.colorado.edu/~carey/Courses/PSYC5741/handouts/Coding%20Categorical%20Variables%202006-03-03.pdf
"""
def __init__(self, verbose=0, cols=None, mapping=None, drop_invariant=False, return_df=True,
handle_unknown='value', handle_missing='value'):
self.return_df = return_df
self.drop_invariant = drop_invariant
self.drop_cols = []
self.verbose = verbose
self.mapping = mapping
self.handle_unknown = handle_unknown
self.handle_missing = handle_missing
self.cols = cols
self.ordinal_encoder = None
self._dim = None
self.feature_names = None
[docs] def fit(self, X, y=None, **kwargs):
"""Fits an ordinal encoder to produce a consistent mapping across applications and optionally finds
generally invariant columns to drop consistently.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Training vectors, where n_samples is the number of samples
and n_features is the number of features.
y : array-like, shape = [n_samples]
Target values.
Returns
-------
self : encoder
Returns self.
"""
# if the input dataset isn't already a dataframe, convert it to one (using default column names)
# first check the type
X = util.convert_input(X)
self._dim = X.shape[1]
# if columns aren't passed, just use every string column
if self.cols is None:
self.cols = util.get_obj_cols(X)
else:
self.cols = util.convert_cols_to_list(self.cols)
if self.handle_missing == 'error':
if X[self.cols].isnull().any().any():
raise ValueError('Columns to be encoded can not contain null')
# train an ordinal pre-encoder
self.ordinal_encoder = OrdinalEncoder(
verbose=self.verbose,
cols=self.cols,
handle_unknown='value',
handle_missing='value'
)
self.ordinal_encoder = self.ordinal_encoder.fit(X)
ordinal_mapping = self.ordinal_encoder.category_mapping
mappings_out = []
for switch in ordinal_mapping:
values = switch.get('mapping')
col = switch.get('col')
column_mapping = self.fit_backward_difference_coding(col, values, self.handle_missing, self.handle_unknown)
mappings_out.append({'col': col, 'mapping': column_mapping, })
self.mapping = mappings_out
X_temp = self.transform(X, override_return_df=True)
self.feature_names = X_temp.columns.tolist()
# drop all output columns with 0 variance.
if self.drop_invariant:
self.drop_cols = []
generated_cols = util.get_generated_cols(X, X_temp, self.cols)
self.drop_cols = [x for x in generated_cols if X_temp[x].var() <= 10e-5]
try:
[self.feature_names.remove(x) for x in self.drop_cols]
except KeyError as e:
if self.verbose > 0:
print("Could not remove column from feature names."
"Not found in generated cols.\n{}".format(e))
return self
[docs] @staticmethod
def fit_backward_difference_coding(col, values, handle_missing, handle_unknown):
if handle_missing == 'value':
values = values[values > 0]
values_to_encode = values.values
if len(values) < 2:
return pd.DataFrame(index=values_to_encode)
if handle_unknown == 'indicator':
values_to_encode = np.append(values_to_encode, -1)
backwards_difference_matrix = Diff().code_without_intercept(values_to_encode)
df = pd.DataFrame(data=backwards_difference_matrix.matrix, index=values_to_encode,
columns=[str(col) + '_%d' % (i, ) for i in range(len(backwards_difference_matrix.column_suffixes))])
if handle_unknown == 'return_nan':
df.loc[-1] = np.nan
elif handle_unknown == 'value':
df.loc[-1] = np.zeros(len(values_to_encode) - 1)
if handle_missing == 'return_nan':
df.loc[values.loc[np.nan]] = np.nan
elif handle_missing == 'value':
df.loc[-2] = np.zeros(len(values_to_encode) - 1)
return df
[docs] @staticmethod
def backward_difference_coding(X_in, mapping):
"""
"""
X = X_in.copy(deep=True)
cols = X.columns.values.tolist()
X['intercept'] = pd.Series([1] * X.shape[0], index=X.index)
for switch in mapping:
col = switch.get('col')
mod = switch.get('mapping')
base_df = mod.reindex(X[col])
base_df.set_index(X.index, inplace=True)
X = pd.concat([base_df, X], axis=1)
old_column_index = cols.index(col)
cols[old_column_index: old_column_index + 1] = mod.columns
cols = ['intercept'] + cols
return X.reindex(columns=cols)
[docs] def get_feature_names(self):
"""
Returns the names of all transformed / added columns.
Returns
-------
feature_names: list
A list with all feature names transformed or added.
Note: potentially dropped features are not included!
"""
if not isinstance(self.feature_names, list):
raise ValueError('Must fit data first. Affected feature names are not known before.')
else:
return self.feature_names