# 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 EqualFrequencyDiscretiser(BaseDiscretiser):
"""
The EqualFrequencyDiscretiser() divides continuous numerical variables
into contiguous equal frequency intervals, that is, intervals that contain
approximately the same proportion of observations.
The EqualFrequencyDiscretiser() works only with numerical variables.
A list of variables can be passed as argument. Alternatively, the discretiser
will automatically select and transform all numerical variables.
The EqualFrequencyDiscretiser() first finds the boundaries for the intervals or
quantiles for each variable. Then it transforms the variables, that is, it sorts
the values into the intervals.
More details in the :ref:`User Guide <equal_freq_discretiser>`.
Parameters
----------
{variables}
q: int, default=10
Desired number of equal frequency 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.qcut
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 EqualFrequencyDiscretiser
>>> np.random.seed(42)
>>> X = pd.DataFrame(dict(x = np.random.randint(1,100, 100)))
>>> efd = EqualFrequencyDiscretiser()
>>> efd.fit(X)
>>> efd.transform(X)["x"].value_counts()
8 12
6 11
3 11
1 10
5 10
2 10
0 10
4 9
7 9
9 8
Name: x, dtype: int64
"""
def __init__(
self,
variables: Union[None, int, str, List[Union[str, int]]] = None,
q: int = 10,
return_object: bool = False,
return_boundaries: bool = False,
precision: int = 3,
) -> None:
if not isinstance(q, int):
raise ValueError(f"q must be an integer. Got {q} instead.")
super().__init__(return_object, return_boundaries, precision)
self.q = q
self.variables = _check_variables_input_value(variables)
[文档] def fit(self, X: pd.DataFrame, y: Optional[pd.Series] = None):
"""
Learn the limits of the equal frequency intervals.
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)
self.binner_dict_ = {}
for var in self.variables_:
tmp, bins = pd.qcut(x=X[var], q=self.q, retbins=True, duplicates="drop")
# Prepend/Append infinities to accommodate outliers
bins = list(bins)
bins[0] = float("-inf")
bins[len(bins) - 1] = float("inf")
self.binner_dict_[var] = bins
return self
