RepeatedEditedNearestNeighbours
- class imbalanced_ensemble.sampler.under_sampling.RepeatedEditedNearestNeighbours(*, sampling_strategy='auto', n_neighbors=3, max_iter=100, kind_sel='all', n_jobs=None)
Undersample based on the repeated edited nearest neighbour method.
This method will repeat several time the ENN algorithm.
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 adict
. 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 fromKNeighborsMixin
that will be used to find the nearest-neighbors.- max_iterint, default=100
Maximum number of iterations of the edited nearest neighbours algorithm for a single run.
- 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.
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 ajoblib.parallel_backend
context.-1
means using all processors. See Glossary for more details.
- Attributes
- sample_indices_ndarray of shape (n_new_samples,)
Indices of the samples selected.
- n_iter_int
Number of iterations run.
See also
CondensedNearestNeighbour
Undersample by condensing samples.
EditedNearestNeighbours
Undersample by editing samples.
AllKNN
Undersample using ENN and various number of neighbours.
Notes
The method is based on [1]. A one-vs.-rest scheme is used when sampling a class as proposed in [1].
Supports multi-class resampling.
References
- 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 imbalanced_ensemble.sampler.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})
Methods
fit
(X, y)Check inputs and statistics of the sampler.
fit_resample
(X, y, *[, sample_weight])Resample the dataset.
get_params
([deep])Get parameters for this estimator.
set_params
(**params)Set the parameters of this estimator.
- fit(X, y)
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, *, sample_weight=None, **kwargs)
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.
- sample_weightarray-like of shape (n_samples,), default=None
Corresponding weight for each sample in X.
If
None
, perform normal resampling and return(X_resampled, y_resampled)
.If array-like, the given
sample_weight
will be resampled along withX
andy
, and the resampled sample weights will be added to returns. The function will return(X_resampled, y_resampled, sample_weight_resampled)
.
- 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.
- sample_weight_resampledarray-like of shape (n_samples_new,), default=None
The corresponding weight of X_resampled. Only will be returned if input sample_weight is not
None
.
- get_params(deep=True)
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)
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.