"""Class to perform random under-sampling."""
# Authors: Guillaume Lemaitre <g.lemaitre58@gmail.com>
# Christos Aridas
# License: MIT
from __future__ import division
import numpy as np
from sklearn.utils import check_random_state
from ..base import BaseUnderSampler
[docs]class RandomUnderSampler(BaseUnderSampler):
"""Class to perform random under-sampling.
Under-sample the majority class(es) by randomly picking samples
with or without 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.
return_indices : bool, optional (default=False)
Whether or not to return the indices of the samples randomly selected
from the majority class.
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``.
replacement : boolean, optional (default=False)
Whether the sample is with (default) or without replacement.
Notes
-----
Supports mutli-class resampling.
Examples
--------
>>> from collections import Counter
>>> from sklearn.datasets import make_classification
>>> from imblearn.under_sampling import \
RandomUnderSampler # 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})
>>> rus = RandomUnderSampler(random_state=42)
>>> X_res, y_res = rus.fit_sample(X, y)
>>> print('Resampled dataset shape {}'.format(Counter(y_res)))
Resampled dataset shape Counter({0: 100, 1: 100})
"""
[docs] def __init__(self,
ratio='auto',
return_indices=False,
random_state=None,
replacement=False):
super(RandomUnderSampler, self).__init__(
ratio=ratio, random_state=random_state)
self.return_indices = return_indices
self.replacement = replacement
def _sample(self, X, y):
"""Resample the dataset.
Parameters
----------
X : ndarray, shape (n_samples, n_features)
Matrix containing the data 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`
idx_under : ndarray, shape (n_samples, )
If `return_indices` is `True`, an array will be returned
containing a boolean for each sample to represent whether
that sample was selected or not.
"""
random_state = check_random_state(self.random_state)
X_resampled = np.empty((0, X.shape[1]), dtype=X.dtype)
y_resampled = np.empty((0, ), dtype=y.dtype)
if self.return_indices:
idx_under = np.empty((0, ), dtype=int)
for target_class in np.unique(y):
if target_class in self.ratio_.keys():
n_samples = self.ratio_[target_class]
index_target_class = random_state.choice(
range(np.count_nonzero(y == target_class)),
size=n_samples,
replace=self.replacement)
else:
index_target_class = slice(None)
X_resampled = np.concatenate(
(X_resampled, X[y == target_class][index_target_class]),
axis=0)
y_resampled = np.concatenate(
(y_resampled, y[y == target_class][index_target_class]),
axis=0)
if self.return_indices:
idx_under = np.concatenate(
(idx_under, np.flatnonzero(y == target_class)[
index_target_class]), axis=0)
if self.return_indices:
return X_resampled, y_resampled, idx_under
else:
return X_resampled, y_resampled