OneSidedSelection¶

class
imblearn.under_sampling.
OneSidedSelection
(*, sampling_strategy='auto', random_state=None, n_neighbors=None, n_seeds_S=1, n_jobs=None)[source]¶ Class to perform undersampling based on onesided selection 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 adict
. The keys correspond to the targeted classes. The values correspond to the desired number of samples for each class.
 random_stateint, RandomState instance, 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 theRandomState
instance used bynp.random
.
 n_neighborsint or estimator object, default=None
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 nearestneighbors. IfNone
, aKNeighborsClassifier
with a 1NN rules will be used. n_seeds_Sint, default=1
Number of samples to extract in order to build the set S.
 n_jobsint, default=None
Number of CPU cores used during the crossvalidation 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.
New in version 0.4.
See also
EditedNearestNeighbours
Undersample by editing noisy samples.
Notes
The method is based on [1].
Supports multiclass resampling. A onevs.one scheme is used when sampling a class as proposed in [1]. For each class to be sampled, all samples of this class and the minority class are used during the sampling procedure.
References
 1(1,2)
M. Kubat, S. Matwin, “Addressing the curse of imbalanced training sets: onesided selection,” In ICML, vol. 97, pp. 179186, 1997.
Examples
>>> from collections import Counter >>> from sklearn.datasets import make_classification >>> from imblearn.under_sampling import OneSidedSelection >>> 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}) >>> oss = OneSidedSelection(random_state=42) >>> X_res, y_res = oss.fit_resample(X, y) >>> print('Resampled dataset shape %s' % Counter(y_res)) Resampled dataset shape Counter({1: 496, 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{arraylike, dataframe, sparse matrix} of shape (n_samples, n_features)
Data array.
 yarraylike of shape (n_samples,)
Target array.
 Returns
 selfobject
Return the instance itself.

fit_resample
(X, y)[source]¶ Resample the dataset.
 Parameters
 X{arraylike, dataframe, sparse matrix} of shape (n_samples, n_features)
Matrix containing the data which have to be sampled.
 yarraylike of shape (n_samples,)
Corresponding label for each sample in X.
 Returns
 X_resampled{arraylike, dataframe, sparse matrix} of shape (n_samples_new, n_features)
The array containing the resampled data.
 y_resampledarraylike 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.