RandomOverSampler¶

class
imblearn.over_sampling.
RandomOverSampler
(*, sampling_strategy='auto', random_state=None, shrinkage=None)[source]¶ Class to perform random oversampling.
Object to oversample the minority class(es) by picking samples at random with replacement. The bootstrap can be generated in a smoothed manner.
Read more in the User Guide.
 Parameters
 sampling_strategyfloat, str, dict or callable, default=’auto’
Sampling information to resample the data set.
When
float
, it corresponds to the desired ratio of the number of samples in the minority class over the number of samples in the majority class after resampling. Therefore, the ratio is expressed as where is the number of samples in the minority class after resampling and is the number of samples in the majority class.Warning
float
is only available for binary classification. An error is raised for multiclass classification.When
str
, specify the class targeted by the resampling. The number of samples in the different classes will be equalized. Possible choices are:'minority'
: resample only the minority 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 majority'
.When
dict
, the keys correspond to the targeted classes. The values correspond to the desired number of samples for each targeted class.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
.
 shrinkagefloat or dict, default=None
Parameter controlling the shrinkage applied to the covariance matrix. when a smoothed bootstrap is generated. The options are:
if
None
, a normal bootstrap will be generated without perturbation. It is equivalent toshrinkage=0
as well;if a
float
is given, the shrinkage factor will be used for all classes to generate the smoothed bootstrap;if a
dict
is given, the shrinkage factor will specific for each class. The key correspond to the targeted class and the value is the shrinkage factor.
The value needs of the shrinkage parameter needs to be higher or equal to 0.
New in version 0.8.
 Attributes
 sample_indices_ndarray of shape (n_new_samples,)
Indices of the samples selected.
New in version 0.4.
 shrinkage_dict or None
The perclass shrinkage factor used to generate the smoothed bootstrap sample. When
shrinkage=None
a normal bootstrap will be generated.New in version 0.8.
See also
BorderlineSMOTE
Oversample using the borderlineSMOTE variant.
SMOTE
Oversample using SMOTE.
SMOTENC
Oversample using SMOTE for continuous and categorical features.
SMOTEN
Oversample using the SMOTE variant specifically for categorical features only.
SVMSMOTE
Oversample using SVMSMOTE variant.
ADASYN
Oversample using ADASYN.
KMeansSMOTE
Oversample applying a clustering before to oversample using SMOTE.
Notes
Supports multiclass resampling by sampling each class independently. Supports heterogeneous data as object array containing string and numeric data.
When generating a smoothed bootstrap, this method is also known as Random OverSampling Examples (ROSE) [1].
Warning
Since smoothed bootstrap are generated by adding a small perturbation to the drawn samples, this method is not adequate when working with sparse matrices.
References
 1
G Menardi, N. Torelli, “Training and assessing classification rules with imbalanced data,” Data Mining and Knowledge Discovery, 28(1), pp.92122, 2014.
Examples
>>> from collections import Counter >>> from sklearn.datasets import make_classification >>> from imblearn.over_sampling import RandomOverSampler >>> 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}) >>> ros = RandomOverSampler(random_state=42) >>> X_res, y_res = ros.fit_resample(X, y) >>> print('Resampled dataset shape %s' % Counter(y_res)) Resampled dataset shape Counter({0: 900, 1: 900})
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.