imblearn.under_sampling.EditedNearestNeighbours

class imblearn.under_sampling.EditedNearestNeighbours(*, sampling_strategy='auto', n_neighbors=3, kind_sel='all', n_jobs=None)[source]

Undersample based on the edited nearest neighbour method.

This method will clean the database by removing samples close to the decision boundary.

Read more in the User Guide.

Parameters
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.

n_neighborsint or object, default=3

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.

kind_sel{‘all’, ‘mode’}, default=’all’

Strategy to use in order to exclude samples.

  • If 'all', all neighbours will have to agree with the samples of interest to not be excluded.

  • If 'mode', the majority vote of the neighbours will be used in order to exclude a sample.

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

CondensedNearestNeighbour

Undersample by condensing samples.

RepeatedEditedNearestNeighbours

Undersample by repeating ENN algorithm.

AllKNN

Undersample using ENN and various number of neighbours.

Notes

The method is based on [1].

Supports multi-class resampling. A one-vs.-rest scheme is used when sampling a class as proposed in [1].

References

1(1,2)

D. Wilson, Asymptotic” Properties of Nearest Neighbor Rules Using Edited Data,” In IEEE Transactions on Systems, Man, and Cybernetrics, vol. 2 (3), pp. 408-421, 1972.

Examples

>>> from collections import Counter
>>> from sklearn.datasets import make_classification
>>> from imblearn.under_sampling import EditedNearestNeighbours 
>>> 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})
>>> enn = EditedNearestNeighbours()
>>> X_res, y_res = enn.fit_resample(X, y)
>>> print('Resampled dataset shape %s' % Counter(y_res))
Resampled dataset shape Counter({1: 887, 0: 100})
Attributes
sample_indices_ndarray of shape (n_new_samples)

Indices of the samples selected.

New in version 0.4.

__init__(self, *, sampling_strategy='auto', n_neighbors=3, kind_sel='all', n_jobs=None)[source]

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

Examples using imblearn.under_sampling.EditedNearestNeighbours