"""Class to perform random over-sampling."""
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# Christos Aridas
# License: MIT
from __future__ import division
from collections import Counter
import numpy as np
from sklearn.utils import check_random_state
from .base import BaseOverSampler
[docs]class RandomOverSampler(BaseOverSampler):
"""Class to perform random over-sampling.
Object to over-sample the minority class(es) by picking samples at random
with replacement.
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``.
Notes
-----
Supports mutli-class resampling.
Examples
--------
>>> from collections import Counter
>>> from sklearn.datasets import make_classification
>>> from imblearn.over_sampling import \
RandomOverSampler # 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})
>>> ros = RandomOverSampler(random_state=42)
>>> X_res, y_res = ros.fit_sample(X, y)
>>> print('Resampled dataset shape {}'.format(Counter(y_res)))
Resampled dataset shape Counter({0: 900, 1: 900})
"""
[docs] def __init__(self, ratio='auto', random_state=None):
super(RandomOverSampler, self).__init__(
ratio=ratio, random_state=random_state)
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`
"""
random_state = check_random_state(self.random_state)
target_stats = Counter(y)
X_resampled = X.copy()
y_resampled = y.copy()
for class_sample, num_samples in self.ratio_.items():
index_samples = random_state.randint(
low=0, high=target_stats[class_sample], size=num_samples)
X_resampled = np.concatenate((X_resampled,
X[y == class_sample][index_samples]),
axis=0)
y_resampled = np.concatenate((y_resampled,
y[y == class_sample][index_samples]),
axis=0)
return X_resampled, y_resampled