imblearn.combine.SMOTETomek

class imblearn.combine.SMOTETomek(sampling_strategy='auto', random_state=None, smote=None, tomek=None, ratio=None)[source][source]

Class to perform over-sampling using SMOTE and cleaning using Tomek links.

Combine over- and under-sampling using SMOTE and Tomek links.

Read more in the User Guide.

Parameters:
sampling_strategy : float, 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 majority class over the number of samples in the minority class after resampling. Therefore, the ratio is expressed as \alpha_{os} = N_{M} / N_{rm} where N_{rm} and N_{M} are the number of samples in the minority class after resampling and the number of samples in the majority class, respectively.

    Warning

    float is only available for binary classification. An error is raised for multi-class 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 a dict. The keys correspond to the targeted classes. The values correspond to the desired number of samples for each class.

random_state : int, RandomState instance or None, optional (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 the RandomState instance used by np.random.
smote : object, optional (default=SMOTE())

The imblearn.over_sampling.SMOTE object to use. If not given, a imblearn.over_sampling.SMOTE object with default parameters will be given.

tomek : object, optional (default=Tomek())

The imblearn.under_sampling.Tomek object to use. If not given, a imblearn.under_sampling.Tomek object with default parameters will be given.

ratio : str, dict, or callable

Deprecated since version 0.4: Use the parameter sampling_strategy instead. It will be removed in 0.6.

See also

SMOTEENN
Over-sample using SMOTE followed by under-sampling using Edited Nearest Neighbours.

Notes

The methos is presented in [1].

Supports multi-class resampling. Refer to SMOTE and TomekLinks regarding the scheme which used.

References

[1](1, 2) G. Batista, B. Bazzan, M. Monard, “Balancing Training Data for Automated Annotation of Keywords: a Case Study,” In WOB, 10-18, 2003.

Examples

>>> from collections import Counter
>>> from sklearn.datasets import make_classification
>>> from imblearn.combine import SMOTETomek # 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})
>>> smt = SMOTETomek(random_state=42)
>>> X_res, y_res = smt.fit_resample(X, y)
>>> print('Resampled dataset shape %s' % Counter(y_res))
Resampled dataset shape Counter({0: 900, 1: 900})
__init__(sampling_strategy='auto', random_state=None, smote=None, tomek=None, ratio=None)[source][source]

Initialize self. See help(type(self)) for accurate signature.

fit(X, y)[source]

Check inputs and statistics of the sampler.

You should use fit_resample in all cases.

Parameters:
X : {array-like, sparse matrix}, shape (n_samples, n_features)

Data array.

y : array-like, shape (n_samples,)

Target array.

Returns:
self : object

Return the instance itself.

fit_resample(X, y)[source]

Resample the dataset.

Parameters:
X : {array-like, sparse matrix}, shape (n_samples, n_features)

Matrix containing the data which have to be sampled.

y : array-like, shape (n_samples,)

Corresponding label for each sample in X.

Returns:
X_resampled : {array-like, sparse matrix}, shape (n_samples_new, n_features)

The array containing the resampled data.

y_resampled : array-like, shape (n_samples_new,)

The corresponding label of X_resampled.

fit_sample(X, y)[source]

Resample the dataset.

Parameters:
X : {array-like, sparse matrix}, shape (n_samples, n_features)

Matrix containing the data which have to be sampled.

y : array-like, shape (n_samples,)

Corresponding label for each sample in X.

Returns:
X_resampled : {array-like, sparse matrix}, shape (n_samples_new, n_features)

The array containing the resampled data.

y_resampled : array-like, shape (n_samples_new,)

The corresponding label of X_resampled.

get_params(deep=True)[source]

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.

set_params(**params)[source]

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