"""
This module implements the one sided selection technique.
"""
from ._noisefilter import NoiseFilter
from ._condensednn import CondensedNearestNeighbors
from ._tomeklinkremoval import TomekLinkRemoval
from .._logger import logger
_logger = logger
__all__ = ["OneSidedSelection"]
[docs]
class OneSidedSelection(NoiseFilter):
"""
References:
* BibTex::
@article{smoteNoise0,
author = {Batista, Gustavo E. A. P. A. and Prati,
Ronaldo C. and Monard, Maria Carolina},
title = {A Study of the Behavior of Several Methods
for Balancing Machine Learning Training Data},
journal = {SIGKDD Explor. Newsl.},
issue_date = {June 2004},
volume = {6},
number = {1},
month = jun,
year = {2004},
issn = {1931-0145},
pages = {20--29},
numpages = {10},
url = {http://doi.acm.org/10.1145/1007730.1007735},
doi = {10.1145/1007730.1007735},
acmid = {1007735},
publisher = {ACM},
address = {New York, NY, USA}
}
"""
[docs]
def __init__(self, n_jobs=1, **_kwargs):
"""
Constructor of the noise removal object
Args:
n_jobs (int): number of jobs
"""
super().__init__()
self.check_n_jobs(n_jobs, "n_jobs")
self.n_jobs = n_jobs
[docs]
def get_params(self, deep=False):
return {"n_jobs": self.n_jobs, **NoiseFilter.get_params(self, deep)}
[docs]
def remove_noise(self, X, y):
"""
Removes noise
Args:
X (np.array): features
y (np.array): target labels
Returns:
np.array, np.array: cleaned features and target labels
"""
_logger.info("%s: Running noise removal.", self.__class__.__name__)
self.class_label_statistics(y)
tomek = TomekLinkRemoval(n_jobs=self.n_jobs)
X_tomek, y_tomek = tomek.remove_noise(X, y) # pylint: disable=invalid-name
cnn = CondensedNearestNeighbors(n_jobs=self.n_jobs)
return cnn.remove_noise(X_tomek, y_tomek)