# Authors: Soledad Galli <solegalli@protonmail.com>
# License: BSD 3 clause
from typing import List, Optional, Union
import pandas as pd
from feature_engine.base_transformers import BaseNumericalTransformer
from feature_engine.variable_manipulation import _check_input_parameter_variables
[docs]class EqualFrequencyDiscretiser(BaseNumericalTransformer):
"""
The EqualFrequencyDiscretiser() divides continuous numerical variables
into contiguous equal frequency intervals, that is, intervals that contain
approximately the same proportion of observations.
The interval limits are determined using `pandas.qcut()`, in other words,
the interval limits are determined by the quantiles. The number of intervals,
i.e., the number of quantiles in which the variable should be divided is
determined by the user.
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.
Parameters
----------
variables: list, default=None
The list of numerical variables that will be discretised. If None, the
EqualFrequencyDiscretiser() will select all numerical variables.
q: int, default=10
Desired number of equal frequency intervals / bins. In other words the
number of quantiles in which the variables should be divided.
return_object: bool, default=False
Whether the the discrete variable should be returned casted as numeric or as
object. If you would like to proceed with the engineering of the variable as if
it was categorical, use True. Alternatively, keep the default to False.
Categorical encoders in Feature-engine work only with variables of type object,
thus, if you wish to encode the returned bins, set return_object to True.
return_boundaries: bool, default=False
Whether the output should be the interval boundaries. If True, it returns
the interval boundaries. If False, it returns integers.
Attributes
----------
binner_dict_:
Dictionary with the interval limits per variable.
variables_:
The variables to discretise.
n_features_in_:
The number of features in the train set used in fit.
Methods
-------
fit:
Find the interval limits.
transform:
Sort continuous variable values into the intervals.
fit_transform:
Fit to the data, then transform it.
See Also
--------
pandas.qcut :
https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.qcut.html
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
"""
def __init__(
self,
variables: Union[None, int, str, List[Union[str, int]]] = None,
q: int = 10,
return_object: bool = False,
return_boundaries: bool = False,
) -> None:
if not isinstance(q, int):
raise ValueError("q must be an integer")
if not isinstance(return_object, bool):
raise ValueError("return_object must be True or False")
if not isinstance(return_boundaries, bool):
raise ValueError("return_boundaries must be True or False")
self.q = q
self.variables = _check_input_parameter_variables(variables)
self.return_object = return_object
self.return_boundaries = return_boundaries
[docs] 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.
Raises
------
TypeError
- If the input is not a Pandas DataFrame
- If any of the user provided variables are not numerical
ValueError
- If there are no numerical variables in the df or the df is empty
- If the variable(s) contain null values
Returns
-------
self
"""
# check input dataframe
X = super().fit(X, y)
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
self.n_features_in_ = X.shape[1]
return self