"""Ordinal or label encoding"""
import numpy as np
import pandas as pd
import category_encoders.utils as util
import warnings
from typing import Dict, List, Union
__author__ = 'willmcginnis'
[docs]class OrdinalEncoder(util.BaseEncoder, util.UnsupervisedTransformerMixin):
"""Encodes categorical features as ordinal, in one ordered feature.
Ordinal encoding uses a single column of integers to represent the classes. An optional mapping dict can be passed
in; in this case, we use the knowledge that there is some true order to the classes themselves. Otherwise, the classes
are assumed to have no true order and integers are selected at random.
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).
mapping: list of dicts
a mapping of class to label to use for the encoding, optional.
the dict contains the keys 'col' and 'mapping'.
the value of 'col' should be the feature name.
the value of 'mapping' should be a dictionary or pd.Series of 'original_label' to 'encoded_label'.
example mapping: [
{'col': 'col1', 'mapping': {None: 0, 'a': 1, 'b': 2}},
{'col': 'col2', 'mapping': {None: 0, 'x': 1, 'y': 2}}
]
handle_unknown: str
options are 'error', 'return_nan' and 'value', defaults to 'value', which will impute the category -1.
handle_missing: str
options are 'error', 'return_nan', and 'value, default to 'value', which treat nan as a category at fit time,
or -2 at transform time if nan is not a category during fit.
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 = OrdinalEncoder(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 int64
6 CentralAir 1460 non-null int64
dtypes: float64(4), int64(2), object(1)
memory usage: 80.0+ 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://ibgwww.colorado.edu/~carey/p5741ndir/Coding_Categorical_Variables.pdf
"""
prefit_ordinal = False
encoding_relation = util.EncodingRelation.ONE_TO_ONE
def __init__(self, verbose=0, mapping=None, cols=None, drop_invariant=False, return_df=True,
handle_unknown='value', handle_missing='value'):
super().__init__(verbose=verbose, cols=cols, drop_invariant=drop_invariant, return_df=return_df,
handle_unknown=handle_unknown, handle_missing=handle_missing)
self.mapping_supplied = mapping is not None
if self.mapping_supplied:
mapping = self._validate_supplied_mapping(mapping)
self.mapping = mapping
@property
def category_mapping(self):
return self.mapping
def _fit(self, X, y=None, **kwargs):
# reset mapping in case of refit
if not self.mapping_supplied:
self.mapping = None
_, categories = self.ordinal_encoding(
X,
mapping=self.mapping,
cols=self.cols,
handle_unknown=self.handle_unknown,
handle_missing=self.handle_missing
)
self.mapping = categories
def _transform(self, X):
X, _ = self.ordinal_encoding(
X,
mapping=self.mapping,
cols=self.cols,
handle_unknown=self.handle_unknown,
handle_missing=self.handle_missing
)
return X
[docs] @staticmethod
def ordinal_encoding(X_in, mapping=None, cols=None, handle_unknown='value', handle_missing='value'):
"""
Ordinal encoding uses a single column of integers to represent the classes. An optional mapping dict can be passed
in, in this case we use the knowledge that there is some true order to the classes themselves. Otherwise, the classes
are assumed to have no true order and integers are selected at random.
"""
return_nan_series = pd.Series(data=[np.nan], index=[-2])
X = X_in.copy(deep=True)
if cols is None:
cols = X.columns
if mapping is not None:
mapping_out = mapping
for switch in mapping:
column = switch.get('col')
col_mapping = switch['mapping']
# Convert to object to accept np.nan (dtype string doesn't)
# fillna changes None and pd.NA to np.nan
X[column] = X[column].astype("object").fillna(np.nan).map(col_mapping)
if util.is_category(X[column].dtype):
nan_identity = col_mapping.loc[col_mapping.index.isna()].array[0]
X[column] = X[column].cat.add_categories(nan_identity)
X[column] = X[column].fillna(nan_identity)
try:
X[column] = X[column].astype(int)
except ValueError as e:
X[column] = X[column].astype(float)
if handle_unknown == 'value':
X[column] = X[column].fillna(-1)
elif handle_unknown == 'error':
missing = X[column].isna()
if any(missing):
raise ValueError(f'Unexpected categories found in column {column}')
if handle_missing == 'return_nan':
X[column] = X[column].map(return_nan_series).where(X[column] == -2, X[column])
else:
mapping_out = []
for col in cols:
nan_identity = np.nan
categories = X[col].unique()
# make nan last category
if pd.isna(categories).any():
categories = [c for c in categories if not pd.isna(c)] + [nan_identity]
else:
categories = list(categories)
if util.is_category(X[col].dtype):
# Avoid using pandas category dtype meta-data if possible, see #235, #238.
if X[col].dtype.ordered:
category_set = set(categories) # convert to set for faster membership checks c.f. #407
categories = [c for c in X[col].dtype.categories if c in category_set]
if X[col].isna().any():
categories += [np.nan]
index = pd.Series(categories).fillna(nan_identity).unique()
data = pd.Series(index=index, data=range(1, len(index) + 1))
if handle_missing == 'value' and ~data.index.isna().any():
data.loc[nan_identity] = -2
elif handle_missing == 'return_nan':
data.loc[nan_identity] = -2
mapping_out.append({'col': col, 'mapping': data, 'data_type': X[col].dtype}, )
return X, mapping_out
def _validate_supplied_mapping(self, supplied_mapping: List[Dict[str, Union[str, Dict, pd.Series]]]) -> List[Dict[str, Union[str, pd.Series]]]:
"""
validate the supplied mapping and convert the actual mapping per column to a pandas series.
:param supplied_mapping: mapping as list of dicts. They actual mapping can be either a dict or pd.Series
:return: the mapping with all actual mappings being pandas series
"""
msg = "Invalid supplied mapping, must be of type List[Dict[str, Union[Dict, pd.Series]]]." \
"For an example refer to the documentation"
if not isinstance(supplied_mapping, list):
raise ValueError(msg)
for mapping_el in supplied_mapping:
if not isinstance(mapping_el, dict):
raise ValueError(msg)
if "col" not in mapping_el:
raise KeyError("Mapping must contain a key 'col' for each column to encode")
if "mapping" not in mapping_el:
raise KeyError("Mapping must contain a key 'mapping' for each column to encode")
mapping = mapping_el["mapping"]
if isinstance(mapping_el, dict):
# convert to dict in order to standardise
mapping_el["mapping"] = pd.Series(mapping)
if "data_type" not in mapping_el:
mapping_el["data_type"] = mapping_el["mapping"].index.dtype
return supplied_mapping