imblearn.under_sampling.RepeatedEditedNearestNeighbours

class imblearn.under_sampling.RepeatedEditedNearestNeighbours(sampling_strategy='auto', return_indices=False, random_state=None, n_neighbors=3, max_iter=100, kind_sel='all', n_jobs=1, ratio=None)[source]

Class to perform under-sampling based on the repeated edited nearest neighbour method.

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.

return_indicesbool, optional (default=False)

Whether or not to return the indices of the samples randomly selected.

Deprecated since version 0.4: return_indices is deprecated. Use the attribute sample_indices_ instead.

random_stateint, RandomState instance or None, optional (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.

Deprecated since version 0.4: random_state is deprecated in 0.4 and will be removed in 0.6.

n_neighborsint or object, optional (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.

max_iterint, optional (default=100)

Maximum number of iterations of the edited nearest neighbours algorithm for a single run.

kind_selstr, optional (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, optional (default=1)

The number of thread to open when it is possible.

ratiostr, dict, or callable

Deprecated since version 0.4: Use the parameter sampling_strategy instead. It will be removed in 0.6.

Notes

The method is based on [Rbbc15d841963-1]. A one-vs.-rest scheme is used when sampling a class as proposed in [Rbbc15d841963-1].

Supports multi-class resampling.

References

Rbbc15d841963-1(1,2)

I. Tomek, “An Experiment with the Edited Nearest-Neighbor Rule,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 6(6), pp. 448-452, June 1976.

Examples

>>> from collections import Counter
>>> from sklearn.datasets import make_classification
>>> from imblearn.under_sampling import RepeatedEditedNearestNeighbours # 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 %s' % Counter(y))
Original dataset shape Counter({1: 900, 0: 100})
>>> renn = RepeatedEditedNearestNeighbours()
>>> X_res, y_res = renn.fit_resample(X, y)
>>> print('Resampled dataset shape %s' % Counter(y_res))
Resampled dataset shape Counter({1: 887, 0: 100})
Attributes
sample_indices_ndarray, shape (n_new_samples)

Indices of the samples selected.

New in version 0.4: sample_indices_ used instead of return_indices=True.

__init__(self, sampling_strategy='auto', return_indices=False, random_state=None, n_neighbors=3, max_iter=100, kind_sel='all', n_jobs=1, ratio=None)[source]

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

fit(self, X, y)[source]

Check inputs and statistics of the sampler.

You should use fit_resample in all cases.

Parameters
X{array-like, sparse matrix}, shape (n_samples, n_features)

Data array.

yarray-like, shape (n_samples,)

Target array.

Returns
selfobject

Return the instance itself.

fit_resample(self, X, y)[source]

Resample the dataset.

Parameters
X{array-like, sparse matrix}, shape (n_samples, n_features)

Matrix containing the data which have to be sampled.

yarray-like, shape (n_samples,)

Corresponding label for each sample in X.

Returns
X_resampled{array-like, sparse matrix}, shape (n_samples_new, n_features)

The array containing the resampled data.

y_resampledarray-like, shape (n_samples_new,)

The corresponding label of X_resampled.

fit_sample(self, X, y)[source]

Resample the dataset.

Parameters
X{array-like, sparse matrix}, shape (n_samples, n_features)

Matrix containing the data which have to be sampled.

yarray-like, shape (n_samples,)

Corresponding label for each sample in X.

Returns
X_resampled{array-like, sparse matrix}, shape (n_samples_new, n_features)

The array containing the resampled data.

y_resampledarray-like, shape (n_samples_new,)

The corresponding label of X_resampled.

get_params(self, deep=True)[source]

Get parameters for this estimator.

Parameters
deepboolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns
paramsmapping of string to any

Parameter names mapped to their values.

set_params(self, **params)[source]

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns
self

Examples using imblearn.under_sampling.RepeatedEditedNearestNeighbours