from typing import List, Optional, Union
import numpy as np
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,
power="{1/n}",
subindex="{i+1}",
)
class GeometricWidthDiscretiser(BaseDiscretiser):
"""
The `GeometricWidthDiscretiser()` divides continuous numerical variables into
intervals of increasing width. The width of each succeeding interval is larger
than the previous interval by a constant amount (cw).
The constant amount is calculated as:
.. math::
cw = (Max - Min)^{power}
were Max and Min are the variable's maximum and minimum value, and n is the number
of intervals.
The sizes of the intervals themselves are calculated with a geometric progression:
.. math::
a_{subindex} = a_i cw
Thus, the first interval's width equals cw, the second interval's width equals
2 * cw, and so on.
Note that the proportion of observations per interval may vary.
This discretisation technique is great when the distribution of the variable is
right skewed.
Note: The width of some bins might be very small. Thus, to allow this transformer
to work properly, it might help to increase the precision value, that is,
the number of decimal values allowed to define each bin. If the variable has a
narrow range or you are sorting into several bins, allow greater precision
(i.e., if precision = 3, then 0.001; if precision = 7, then 0.0001).
The :class:`GeometricWidthDiscretiser()` works only with numerical variables.
A list of variables to discretise can be indicated, or the discretiser will
automatically select all numerical variables in the train set.
More details in the :ref:`User Guide <increasing_width_discretiser>`.
Parameters
----------
{variables}
bins: int, default=10
Desired number of intervals / bins.
{return_object}
{return_boundaries}
{precision}
Attributes
----------
{binner_dict_}
{variables_}
{feature_names_in_}
{n_features_in_}
Methods
-------
{fit}
{fit_transform}
{transform}
References
----------
.. [1] J. Reiser, "Classification Systems",
https://www.slideshare.net/johnjreiser/classification-systems
.. [2] Geometric Interval Classification
http://wiki.gis.com/wiki/index.php/Geometric_Interval_Classification
.. [3] Geometric progression
https://en.wikipedia.org/wiki/Geometric_progression
"""
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 = 7,
):
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 geometric 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_:
min_, max_ = X[var].min(), X[var].max()
increment = np.power(max_ - min_, 1.0 / self.bins)
bins = np.r_[
-np.inf, min_ + np.power(increment, np.arange(1, self.bins)), np.inf
]
bins = np.sort(bins)
bins = list(bins)
self.binner_dict_[var] = bins
return self
