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 cleans the dataset by removing samples close to the decision boundary. It removes observations from the majority class or classes when any or most of its closest neighours are from a different class.

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 for the undersampling, i.e., if n_neighbors=3, a sample will be removed when any or most of its 3 closest neighbours are from a different class. If object, an estimator that inherits from KNeighborsMixin that will be used to find the nearest-neighbors. Note that if you want to examine the 3 closest neighbours of a sample for the undersampling, you need to pass a 4-KNN.

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

Strategy to use to exclude samples.

  • If 'all', all neighbours should be of the same class of the examined sample for it not be excluded.

  • If 'mode', most neighbours should be of the same class of the examined sample for it not be excluded.

The strategy "all" will be less conservative than 'mode'. Thus, more samples will be removed when kind_sel="all", generally.

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.

Attributes:
sampling_strategy_dict

Dictionary containing the information to sample the dataset. The keys correspond to the class labels from which to sample and the values are the number of samples to sample.

nn_estimator object

Validated K-nearest Neighbours instance created from n_neighbors parameter.

sample_indices_ndarray of shape (n_new_samples,)

Indices of the samples selected.

Added in version 0.4.

n_features_in_int

Number of features in the input dataset.

Added in version 0.9.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen during fit. Defined only when X has feature names that are all strings.

Added in version 0.10.

See also

CondensedNearestNeighbour

Undersample by condensing samples.

RepeatedEditedNearestNeighbours

Undersample by repeating the ENN algorithm.

AllKNN

Undersample using ENN with varying 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})

Methods

fit(X, y, **params)

Check inputs and statistics of the sampler.

fit_resample(X, y, **params)

Resample the dataset.

get_feature_names_out([input_features])

Get output feature names for transformation.

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

set_params(**params)

Set the parameters of this estimator.
fit(X, y, **params)[source]#

Check inputs and statistics of the sampler.

You should use fit_resample in all cases.

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

Data array.

yarray-like of shape (n_samples,)

Target array.

Returns:
selfobject

Return the instance itself.

fit_resample(X, y, **params)[source]#

Resample the dataset.

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

Matrix containing the data which have to be sampled.

yarray-like of shape (n_samples,)

Corresponding label for each sample in X.

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

The array containing the resampled data.

y_resampledarray-like of shape (n_samples_new,)

The corresponding label of X_resampled.

get_feature_names_out(input_features=None)[source]#

Get output feature names for transformation.

Parameters:
input_featuresarray-like of str or None, default=None

Input features.

  • If input_features is None, then feature_names_in_ is used as feature names in. If feature_names_in_ is not defined, then the following input feature names are generated: ["x0", "x1", ..., "x(n_features_in_ - 1)"].

  • If input_features is an array-like, then input_features must match feature_names_in_ if feature_names_in_ is defined.

Returns:
feature_names_outndarray of str objects

Same as input features.

get_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:
routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:
deepbool, default=True

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

Returns:
paramsdict

Parameter names mapped to their values.

set_params(**params)[source]#

Set the parameters of this estimator.

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

Parameters:
**paramsdict

Estimator parameters.

Returns:
selfestimator instance

Estimator instance.

Examples using imblearn.under_sampling.EditedNearestNeighbours#

Usage of pipeline embedding samplers

Usage of pipeline embedding samplers

Compare under-sampling samplers

Compare under-sampling samplers