imblearn.under_sampling.OneSidedSelection

class imblearn.under_sampling.OneSidedSelection(ratio='auto', return_indices=False, random_state=None, size_ngh=None, n_neighbors=None, n_seeds_S=1, n_jobs=1)

Class to perform under-sampling based on one-sided selection method.

Parameters:

ratio : str, dict, or callable, optional (default=’auto’)

Ratio to use for resampling the data set.

• If str, has to be one of: (i) 'minority': resample the minority class; (ii) 'majority': resample the majority class, (iii) 'not minority': resample all classes apart of the minority class, (iv) 'all': resample all classes, and (v) 'auto': correspond to 'all' with for over-sampling methods and 'not minority' for under-sampling methods. The classes targeted will be over-sampled or under-sampled to achieve an equal number of sample with the majority or minority class.
• If dict, the keys correspond to the targeted classes. The values correspond to the desired number of samples.
• If callable, function taking y and returns a dict. The keys correspond to the targeted classes. The values correspond to the desired number of samples.

return_indices : bool, optional (default=False)

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

random_state : int, RandomState instance or None, optional (default=None)

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.

size_ngh : int, optional (default=None)

Size of the neighbourhood to consider to compute the average distance to the minority point samples.

Deprecated since version 0.2: size_ngh is deprecated from 0.2 and will be replaced in 0.4 Use n_neighbors instead.

n_neighbors : int or object, optional (default=KNeighborsClassifier(n_neighbors=1))

If int, size of the neighbourhood to consider to compute the average distance to the minority point samples. If object, an object inherited from sklearn.neigbors.KNeighborsClassifier should be passed.

n_seeds_S : int, optional (default=1)

Number of samples to extract in order to build the set S.

n_jobs : int, optional (default=1)

The number of threads to open if possible.

Notes

The method is based on [R40].

Supports mutli-class resampling.

References

[R40]

(1, 2) M. Kubat, S. Matwin, “Addressing the curse of imbalanced training sets: one-sided selection,” In ICML, vol. 97, pp. 179-186, 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 {}'.format(Counter(y)))
Original dataset shape Counter({1: 900, 0: 100})
>>> oss = OneSidedSelection(random_state=42)
>>> X_res, y_res = oss.fit_sample(X, y)
>>> print('Resampled dataset shape {}'.format(Counter(y_res)))
Resampled dataset shape Counter({1: 495, 0: 100})

Methods

fit(X, y)	Find the classes statistics before to perform sampling.
fit_sample(X, y)	Fit the statistics and resample the data directly.
get_params([deep])	Get parameters for this estimator.
sample(X, y)	Resample the dataset.
set_params(**params)	Set the parameters of this estimator.

__init__(ratio='auto', return_indices=False, random_state=None, size_ngh=None, n_neighbors=None, n_seeds_S=1, n_jobs=1)

Methods

__init__([ratio, return_indices, ...])
fit(X, y)	Find the classes statistics before to perform sampling.
fit_sample(X, y)	Fit the statistics and resample the data directly.
get_params([deep])	Get parameters for this estimator.
sample(X, y)	Resample the dataset.
set_params(**params)	Set the parameters of this estimator.

fit(X, y)

Find the classes statistics before to perform sampling.

Parameters:

X : ndarray, shape (n_samples, n_features)

Matrix containing the data which have to be sampled.

y : ndarray, shape (n_samples, )

Corresponding label for each sample in X.

Returns:

self : object,

Return self.

fit_sample(X, y)

Fit the statistics and resample the data directly.

Parameters:

X : ndarray, shape (n_samples, n_features)

Matrix containing the data which have to be sampled.

y : ndarray, shape (n_samples, )

Corresponding label for each sample in X.

Returns:

X_resampled : ndarray, shape (n_samples_new, n_features)

The array containing the resampled data.

y_resampled : ndarray, shape (n_samples_new)

The corresponding label of X_resampled

get_params(deep=True)

Get parameters for this estimator.

Parameters:

deep : boolean, optional

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

Returns:

params : mapping of string to any

Parameter names mapped to their values.

sample(X, y)

Resample the dataset.

Parameters:

X : ndarray, shape (n_samples, n_features)

Matrix containing the data which have to be sampled.

y : ndarray, shape (n_samples, )

Corresponding label for each sample in X.

Returns:

X_resampled : ndarray, shape (n_samples_new, n_features)

The array containing the resampled data.

y_resampled : ndarray, shape (n_samples_new)

The corresponding label of X_resampled

set_params(**params)

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