class imblearn.under_sampling.OneSidedSelection(*, sampling_strategy='auto', random_state=None, n_neighbors=None, n_seeds_S=1, n_jobs=None)[source]

Class to perform under-sampling based on one-sided selection method.

Read more in the User Guide.

sampling_strategystr, list or callable

Sampling information to sample the data set.

  • When str, specify the class targeted by the resampling. Note the the number of samples will not be equal in each. Possible choices are:

    'majority': resample only the majority class;

    'not minority': resample all classes but the minority class;

    'not majority': resample all classes but the majority class;

    'all': resample all classes;

    'auto': equivalent to 'not minority'.

  • When list, the list contains the classes targeted by the resampling.

  • When callable, function taking y and returns a dict. The keys correspond to the targeted classes. The values correspond to the desired number of samples for each class.

random_stateint, RandomState instance, default=None

Control the randomization of the algorithm.

  • 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.

n_neighborsint or object, default=None

If int, size of the neighbourhood to consider to compute the nearest neighbors. If object, an estimator that inherits from sklearn.neighbors.base.KNeighborsMixin that will be used to find the nearest-neighbors.

n_seeds_Sint, default=1

Number of samples to extract in order to build the set S.

n_jobsint, default=None

Number of CPU cores used during the cross-validation loop. None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.

See also


Undersample by editing noisy samples.


The method is based on [1].

Supports multi-class resampling. A one-vs.-one scheme is used when sampling a class as proposed in [1]. For each class to be sampled, all samples of this class and the minority class are used during the sampling procedure.



M. Kubat, S. Matwin, “Addressing the curse of imbalanced training sets: one-sided selection,” In ICML, vol. 97, pp. 179-186, 1997.


>>> from collections import Counter
>>> from sklearn.datasets import make_classification
>>> from imblearn.under_sampling import     OneSidedSelection 
>>> 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 %s' % Counter(y))
Original dataset shape Counter({1: 900, 0: 100})
>>> oss = OneSidedSelection(random_state=42)
>>> X_res, y_res = oss.fit_resample(X, y)
>>> print('Resampled dataset shape %s' % Counter(y_res))
Resampled dataset shape Counter({1: 496, 0: 100})
sample_indices_ndarray of shape (n_new_samples)

Indices of the samples selected.

New in version 0.4.

__init__(self, *, sampling_strategy='auto', random_state=None, n_neighbors=None, n_seeds_S=1, n_jobs=None)[source]

Initialize self. See help(type(self)) for accurate signature.

Examples using imblearn.under_sampling.OneSidedSelection