"""Class to perform over-sampling using SMOTE and cleaning using Tomek
links."""
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# Christos Aridas
# License: MIT
from __future__ import division
import logging
import warnings
from sklearn.utils import check_X_y
from ..base import SamplerMixin
from ..over_sampling import SMOTE
from ..under_sampling import TomekLinks
from ..utils import check_target_type, hash_X_y
[docs]class SMOTETomek(SamplerMixin):
"""Class to perform over-sampling using SMOTE and cleaning using
Tomek links.
Combine over- and under-sampling using SMOTE and Tomek links.
Parameters
----------
ratio : str, dict, or callable, optional (default='auto')
Ratio to use for resampling the data set.
- If ``str``, has to be one of: (i) ``'minority'``: resample the
minority class; (ii) ``'majority'``: resample the majority class,
(iii) ``'not minority'``: resample all classes apart of the minority
class, (iv) ``'all'``: resample all classes, and (v) ``'auto'``:
correspond to ``'all'`` with for over-sampling methods and ``'not
minority'`` for under-sampling methods. The classes targeted will be
over-sampled or under-sampled to achieve an equal number of sample
with the majority or minority class.
- If ``dict``, the keys correspond to the targeted classes. The values
correspond to the desired number of samples.
- If callable, function taking ``y`` and returns a ``dict``. The keys
correspond to the targeted classes. The values correspond to the
desired number of samples.
random_state : int, RandomState instance or None, optional (default=None)
If int, ``random_state`` is the seed used by the random number
generator; If ``RandomState`` instance, random_state is the random
number generator; If ``None``, the random number generator is the
``RandomState`` instance used by ``np.random``.
smote : object, optional (default=SMOTE())
The :class:`imblearn.over_sampling.SMOTE` object to use. If not given,
a :class:`imblearn.over_sampling.SMOTE` object with default parameters
will be given.
tomek : object, optional (default=Tomek())
The :class:`imblearn.under_sampling.Tomek` object to use. If not given,
a :class:`imblearn.under_sampling.Tomek` object with default parameters
will be given.
k : int, optional (default=None)
Number of nearest neighbours to used to construct synthetic
samples.
.. deprecated:: 0.2
``k`` is deprecated from 0.2 and will be replaced in 0.4
Give directly a :class:`imblearn.over_sampling.SMOTE` object.
m : int, optional (default=None)
Number of nearest neighbours to use to determine if a minority
sample is in danger.
.. deprecated:: 0.2
``m`` is deprecated from 0.2 and will be replaced in 0.4
Give directly a :class:`imblearn.over_sampling.SMOTE` object.
out_step : float, optional (default=None)
Step size when extrapolating.
.. deprecated:: 0.2
`out_step` is deprecated from 0.2 and will be replaced in 0.4
Give directly a :class:`imblearn.over_sampling.SMOTE` object.
kind_smote : str, optional (default=None)
The type of SMOTE algorithm to use one of the following
options: ``'regular'``, ``'borderline1'``, ``'borderline2'``,
``'svm'``.
.. deprecated:: 0.2
``kind_smote` is deprecated from 0.2 and will be replaced in 0.4
Give directly a :class:`imblearn.over_sampling.SMOTE` object.
n_jobs : int, optional (default=None)
The number of threads to open if possible.
.. deprecated:: 0.2
``n_jobs`` is deprecated from 0.2 and will be replaced in 0.4
Give directly a :class:`imblearn.over_sampling.SMOTE` object.
Notes
-----
The methos is presented in [1]_.
Supports mutli-class resampling.
Examples
--------
>>> from collections import Counter
>>> from sklearn.datasets import make_classification
>>> from imblearn.combine import \
SMOTETomek # doctest: +NORMALIZE_WHITESPACE
>>> X, y = make_classification(n_classes=2, class_sep=2,
... weights=[0.1, 0.9], n_informative=3, n_redundant=1, flip_y=0,
... n_features=20, n_clusters_per_class=1, n_samples=1000, random_state=10)
>>> print('Original dataset shape {}'.format(Counter(y)))
Original dataset shape Counter({1: 900, 0: 100})
>>> smt = SMOTETomek(random_state=42)
>>> X_res, y_res = smt.fit_sample(X, y)
>>> print('Resampled dataset shape {}'.format(Counter(y_res)))
Resampled dataset shape Counter({0: 900, 1: 900})
References
----------
.. [1] G. Batista, B. Bazzan, M. Monard, "Balancing Training Data for
Automated Annotation of Keywords: a Case Study," In WOB, 10-18, 2003.
"""
[docs] def __init__(self,
ratio='auto',
random_state=None,
smote=None,
tomek=None,
k=None,
m=None,
out_step=None,
kind_smote=None,
n_jobs=None):
super(SMOTETomek, self).__init__()
self.ratio = ratio
self.random_state = random_state
self.smote = smote
self.tomek = tomek
self.k = k
self.m = m
self.out_step = out_step
self.kind_smote = kind_smote
self.n_jobs = n_jobs
self.logger = logging.getLogger(__name__)
def _validate_estimator(self):
"Private function to validate SMOTE and ENN objects"
# Check any parameters for SMOTE was provided
# Anounce deprecation
if (self.k is not None or self.m is not None or
self.out_step is not None or self.kind_smote is not None or
self.n_jobs is not None):
warnings.warn('Parameters initialization will be replaced in'
' version 0.4. Use a SMOTE object instead.',
DeprecationWarning)
# We need to list each parameter and decide if we affect a default
# value or not
if self.k is None:
self.k = 5
if self.m is None:
self.m = 10
if self.out_step is None:
self.out_step = 0.5
if self.kind_smote is None:
self.kind_smote = 'regular'
if self.n_jobs is None:
smote_jobs = 1
else:
smote_jobs = self.n_jobs
self.smote_ = SMOTE(
ratio=self.ratio,
random_state=self.random_state,
k=self.k,
m=self.m,
out_step=self.out_step,
kind=self.kind_smote,
n_jobs=smote_jobs)
# If an object was given, affect
elif self.smote is not None:
if isinstance(self.smote, SMOTE):
self.smote_ = self.smote
else:
raise ValueError('smote needs to be a SMOTE object.'
'Got {} instead.'.format(type(self.smote)))
# Otherwise create a default SMOTE
else:
self.smote_ = SMOTE(
ratio=self.ratio, random_state=self.random_state)
# Check any parameters for ENN was provided
# Anounce deprecation
if self.n_jobs is not None:
warnings.warn('Parameters initialization will be replaced in'
' version 0.4. Use a ENN object instead.',
DeprecationWarning)
self.tomek_ = TomekLinks(ratio='all',
random_state=self.random_state,
n_jobs=self.n_jobs)
# If an object was given, affect
elif self.tomek is not None:
if isinstance(self.tomek, TomekLinks):
self.tomek_ = self.tomek
else:
raise ValueError('tomek needs to be a TomekLinks object.'
'Got {} instead.'.format(type(self.tomek)))
# Otherwise create a default TomekLinks
else:
self.tomek_ = TomekLinks(ratio='all',
random_state=self.random_state)
[docs] def fit(self, X, y):
"""Find the classes statistics before to perform sampling.
Parameters
----------
X : ndarray, shape (n_samples, n_features)
Matrix containing the data which have to be sampled.
y : ndarray, shape (n_samples, )
Corresponding label for each sample in X.
Returns
-------
self : object,
Return self.
"""
X, y = check_X_y(X, y)
y = check_target_type(y)
self.ratio_ = self.ratio
self.X_hash_, self.y_hash_ = hash_X_y(X, y)
return self
def _sample(self, X, y):
"""Resample the dataset.
Parameters
----------
X : ndarray, shape (n_samples, n_features)
Matrix containing the data which have to be sampled.
y : ndarray, shape (n_samples, )
Corresponding label for each sample in X.
Returns
-------
X_resampled : ndarray, shape (n_samples_new, n_features)
The array containing the resampled data.
y_resampled : ndarray, shape (n_samples_new)
The corresponding label of `X_resampled`
"""
self._validate_estimator()
X_res, y_res = self.smote_.fit_sample(X, y)
return self.tomek_.fit_sample(X_res, y_res)