# Authors: Soledad Galli <solegalli@protonmail.com>
# License: BSD 3 clause
from typing import List, Optional, Union
import pandas as pd
from feature_engine._check_init_parameters.check_variables import (
_check_variables_input_value,
)
from feature_engine._docstrings.fit_attributes import (
_binner_dict_docstring,
_feature_names_in_docstring,
_n_features_in_docstring,
_variables_attribute_docstring,
)
from feature_engine._docstrings.init_parameters.all_trasnformers import (
_variables_numerical_docstring,
)
from feature_engine._docstrings.init_parameters.discretisers import (
_precision_docstring,
_return_boundaries_docstring,
_return_object_docstring,
)
from feature_engine._docstrings.methods import (
_fit_discretiser_docstring,
_fit_transform_docstring,
_transform_discretiser_docstring,
)
from feature_engine._docstrings.substitute import Substitution
from feature_engine.discretisation.base_discretiser import BaseDiscretiser
[文档]@Substitution(
return_object=_return_object_docstring,
return_boundaries=_return_boundaries_docstring,
precision=_precision_docstring,
binner_dict_=_binner_dict_docstring,
fit=_fit_discretiser_docstring,
transform=_transform_discretiser_docstring,
variables=_variables_numerical_docstring,
variables_=_variables_attribute_docstring,
feature_names_in_=_feature_names_in_docstring,
n_features_in_=_n_features_in_docstring,
fit_transform=_fit_transform_docstring,
)
class EqualWidthDiscretiser(BaseDiscretiser):
"""
The EqualWidthDiscretiser() divides continuous numerical variables into
intervals of the same width, that is, equidistant intervals. Note that the
proportion of observations per interval may vary.
The size of the interval is calculated as:
.. math::
( max(X) - min(X) ) / bins
where bins, which is the number of intervals, is determined by the user.
The EqualWidthDiscretiser() works only with numerical variables.
A list of variables can be passed as argument. Alternatively, the discretiser
will automatically select all numerical variables.
The EqualWidthDiscretiser() first finds the boundaries for the intervals for
each variable. Then, it transforms the variables, that is, sorts the values into
the intervals.
More details in the :ref:`User Guide <equal_width_discretiser>`.
Parameters
----------
{variables}
bins: int, default=10
Desired number of equal width intervals / bins.
{return_object}
{return_boundaries}
{precision}
Attributes
----------
{binner_dict_}
{variables_}
{feature_names_in_}
{n_features_in_}
Methods
-------
{fit}
{fit_transform}
{transform}
See Also
--------
pandas.cut
sklearn.preprocessing.KBinsDiscretizer
References
----------
.. [1] Kotsiantis and Pintelas, "Data preprocessing for supervised leaning,"
International Journal of Computer Science, vol. 1, pp. 111 117, 2006.
.. [2] Dong. "Beating Kaggle the easy way". Master Thesis.
https://www.ke.tu-darmstadt.de/lehre/arbeiten/studien/2015/Dong_Ying.pdf
Examples
--------
>>> import pandas as pd
>>> import numpy as np
>>> from feature_engine.discretisation import EqualWidthDiscretiser
>>> np.random.seed(42)
>>> X = pd.DataFrame(dict(x = np.random.randint(1,100, 100)))
>>> ewd = EqualWidthDiscretiser()
>>> ewd.fit(X)
>>> ewd.transform(X)["x"].value_counts()
9 15
6 15
0 13
5 11
8 9
7 8
2 8
1 7
3 7
4 7
Name: x, dtype: int64
"""
def __init__(
self,
variables: Union[None, int, str, List[Union[str, int]]] = None,
bins: int = 10,
return_object: bool = False,
return_boundaries: bool = False,
precision: int = 3,
) -> None:
if not isinstance(bins, int):
raise ValueError(f"bins must be an integer. Got {bins} instead.")
super().__init__(return_object, return_boundaries, precision)
self.bins = bins
self.variables = _check_variables_input_value(variables)
[文档] def fit(self, X: pd.DataFrame, y: Optional[pd.Series] = None):
"""
Learn the boundaries of the equal width intervals / bins for each
variable.
Parameters
----------
X: pandas dataframe of shape = [n_samples, n_features]
The training dataset. Can be the entire dataframe, not just the variables
to be transformed.
y: None
y is not needed in this encoder. You can pass y or None.
"""
# check input dataframe
X = super().fit(X)
# fit
self.binner_dict_ = {}
for var in self.variables_:
tmp, bins = pd.cut(
x=X[var],
bins=self.bins,
retbins=True,
duplicates="drop",
include_lowest=True,
)
# Prepend/Append infinities
bins = list(bins)
bins[0] = float("-inf")
bins[len(bins) - 1] = float("inf")
self.binner_dict_[var] = bins
return self
