NeighbourhoodCleaningRule#

class imblearn.under_sampling.NeighbourhoodCleaningRule(*, sampling_strategy='auto', n_neighbors=3, kind_sel='all', threshold_cleaning=0.5, n_jobs=None)[source]#

Undersample based on the neighbourhood cleaning rule.

This class uses ENN and a k-NN to remove noisy samples from the datasets.

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 estimator object, default=3

If int, size of the neighbourhood to consider to compute the nearest neighbors. If object, an estimator that inherits from KNeighborsMixin that will be used to find the nearest-neighbors. By default, it will be a 3-NN.

kind_sel{“all”, “mode”}, default=’all’

Strategy to use in order to exclude samples in the ENN sampling.

  • 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.

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

threshold_cleaningfloat, default=0.5

Threshold used to whether consider a class or not during the cleaning after applying ENN. A class will be considered during cleaning when:

Ci > C x T ,

where Ci and C is the number of samples in the class and the data set, respectively and theta is the threshold.

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 corresponds 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 object created from n_neighbors parameter.

sample_indices_ndarray of shape (n_new_samples,)

Indices of the samples selected.

New in version 0.4.

n_features_in_int

Number of features in the input dataset.

New in version 0.9.

See also

EditedNearestNeighbours

Undersample by editing noisy samples.

Notes

See the original paper: [1].

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

References

1(1,2)

J. Laurikkala, “Improving identification of difficult small classes by balancing class distribution,” Springer Berlin Heidelberg, 2001.

Examples

>>> from collections import Counter
>>> from sklearn.datasets import make_classification
>>> from imblearn.under_sampling import NeighbourhoodCleaningRule 
>>> 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})
>>> ncr = NeighbourhoodCleaningRule()
>>> X_res, y_res = ncr.fit_resample(X, y)
>>> print('Resampled dataset shape %s' % Counter(y_res))
Resampled dataset shape Counter({1: 877, 0: 100})

Methods

fit(X, y)

Check inputs and statistics of the sampler.

fit_resample(X, y)

Resample the dataset.

get_params([deep])

Get parameters for this estimator.

set_params(**params)

Set the parameters of this estimator.

fit(X, y)[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)[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_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.NeighbourhoodCleaningRule#