# Authors: Soledad Galli <solegalli@protonmail.com>
# License: BSD 3 clause
from typing import List, Optional, Union
import pandas as pd
from feature_engine.dataframe_checks import _is_dataframe
from feature_engine.imputation.base_imputer import BaseImputer
from feature_engine.variable_manipulation import (
_check_input_parameter_variables,
_find_or_check_numerical_variables,
)
[docs]class EndTailImputer(BaseImputer):
"""
The EndTailImputer() replaces missing data by a value at either tail of the
distribution. It works only with numerical variables.
You can indicate the variables to be imputed in a list. Alternatively, the
EndTailImputer() will automatically find and select all variables of type numeric.
The imputer first calculates the values at the end of the distribution for each
variable (fit). The values at the end of the distribution are determined using
the Gaussian limits, the the IQR proximity rule limits, or a factor of the maximum
value:
Gaussian limits:
- right tail: mean + 3*std
- left tail: mean - 3*std
IQR limits:
- right tail: 75th quantile + 3*IQR
- left tail: 25th quantile - 3*IQR
where IQR is the inter-quartile range = 75th quantile - 25th quantile
Maximum value:
- right tail: max * 3
- left tail: not applicable
You can change the factor that multiplies the std, IQR or the maximum value
using the parameter 'fold' (we used fold=3 in the examples above).
The imputer then replaces the missing data with the estimated values (transform).
Parameters
----------
imputation_method: str, default=gaussian
Method to be used to find the replacement values. Can take 'gaussian',
'iqr' or 'max'.
**gaussian**: the imputer will use the Gaussian limits to find the values
to replace missing data.
**iqr**: the imputer will use the IQR limits to find the values to replace
missing data.
**max**: the imputer will use the maximum values to replace missing data. Note
that if 'max' is passed, the parameter 'tail' is ignored.
tail: str, default=right
Indicates if the values to replace missing data should be selected from the
right or left tail of the variable distribution. Can take values 'left' or
'right'.
fold: int, default=3
Factor to multiply the std, the IQR or the Max values. Recommended values
are 2 or 3 for Gaussian, or 1.5 or 3 for IQR.
variables: list, default=None
The list of variables to be imputed. If None, the imputer will find and
select all variables of type numeric.
Attributes
----------
imputer_dict_:
Dictionary with the values at the end of the distribution per variable.
variables_:
The group of variables that will be transformed.
n_features_in_:
The number of features in the train set used in fit.
Methods
-------
fit:
Learn values to replace missing data.
transform:
Impute missing data.
fit_transform:
Fit to the data, then transform it.
"""
def __init__(
self,
imputation_method: str = "gaussian",
tail: str = "right",
fold: int = 3,
variables: Union[None, int, str, List[Union[str, int]]] = None,
) -> None:
if imputation_method not in ["gaussian", "iqr", "max"]:
raise ValueError(
"imputation_method takes only values 'gaussian', 'iqr' or 'max'"
)
if tail not in ["right", "left"]:
raise ValueError("tail takes only values 'right' or 'left'")
if fold <= 0:
raise ValueError("fold takes only positive numbers")
self.imputation_method = imputation_method
self.tail = tail
self.fold = fold
self.variables = _check_input_parameter_variables(variables)
[docs] def fit(self, X: pd.DataFrame, y: Optional[pd.Series] = None):
"""
Learn the values at the end of the variable distribution.
Parameters
----------
X: pandas dataframe of shape = [n_samples, n_features]
The training dataset.
y: pandas Series, default=None
y is not needed in this imputation. You can pass None or y.
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
Returns
-------
self
"""
# check input dataframe
X = _is_dataframe(X)
# find or check for numerical variables
self.variables_ = _find_or_check_numerical_variables(X, self.variables)
# estimate imputation values
if self.imputation_method == "max":
self.imputer_dict_ = (X[self.variables_].max() * self.fold).to_dict()
elif self.imputation_method == "gaussian":
if self.tail == "right":
self.imputer_dict_ = (
X[self.variables_].mean() + self.fold * X[self.variables_].std()
).to_dict()
elif self.tail == "left":
self.imputer_dict_ = (
X[self.variables_].mean() - self.fold * X[self.variables_].std()
).to_dict()
elif self.imputation_method == "iqr":
IQR = X[self.variables_].quantile(0.75) - X[self.variables_].quantile(0.25)
if self.tail == "right":
self.imputer_dict_ = (
X[self.variables_].quantile(0.75) + (IQR * self.fold)
).to_dict()
elif self.tail == "left":
self.imputer_dict_ = (
X[self.variables_].quantile(0.25) - (IQR * self.fold)
).to_dict()
self.n_features_in_ = X.shape[1]
return self
# Ugly work around to import the docstring for Sphinx, otherwise not necessary
transform.__doc__ = BaseImputer.transform.__doc__