imblearn.over_sampling.SMOTE

class imblearn.over_sampling.SMOTE(sampling_strategy='auto', random_state=None, k_neighbors=5, m_neighbors='deprecated', out_step='deprecated', kind='deprecated', svm_estimator='deprecated', n_jobs=1, ratio=None)[source][source]

Class to perform over-sampling using SMOTE.

This object is an implementation of SMOTE - Synthetic Minority Over-sampling Technique as presented in [1].

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.
k_neighbors : int or object, optional (default=5)

If int, number of nearest neighbours to used to construct synthetic samples. If object, an estimator that inherits from sklearn.neighbors.base.KNeighborsMixin that will be used to find the k_neighbors.

m_neighbors : int or object, optional (default=10)

If int, number of nearest neighbours to use to determine if a minority sample is in danger. Used with kind={'borderline1', 'borderline2', 'svm'}. If object, an estimator that inherits from sklearn.neighbors.base.KNeighborsMixin that will be used to find the k_neighbors.

Deprecated since version 0.4: m_neighbors is deprecated in 0.4 and will be removed in 0.6. Use BorderlineSMOTE or SVMSMOTE instead to use the intended algorithm.

out_step : float, optional (default=0.5)

Step size when extrapolating. Used with kind='svm'.

Deprecated since version 0.4: out_step is deprecated in 0.4 and will be removed in 0.6. Use SVMSMOTE instead to use the intended algorithm.

kind : str, optional (default=’regular’)

The type of SMOTE algorithm to use one of the following options: 'regular', 'borderline1', 'borderline2', 'svm'.

Deprecated since version 0.4: kind is deprecated in 0.4 and will be removed in 0.6. Use BorderlineSMOTE or SVMSMOTE instead to use the intended algorithm.

svm_estimator : object, optional (default=SVC())

If kind='svm', a parametrized sklearn.svm.SVC classifier can be passed.

Deprecated since version 0.4: out_step is deprecated in 0.4 and will be removed in 0.6. Use SVMSMOTE instead to use the intended algorithm.

n_jobs : int, optional (default=1)

The number of threads to open if possible.

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

SMOTENC
Over-sample using SMOTE for continuous and categorical features.
BorderlineSMOTE
Over-sample using the borderline-SMOTE variant.
SVMSMOTE
Over-sample using the SVM-SMOTE variant.
ADASYN
Over-sample using ADASYN.

Notes

See the original papers: [1] for more details.

Supports multi-class resampling. A one-vs.-rest scheme is used as originally proposed in [1].

References

[1](1, 2, 3, 4) N. V. Chawla, K. W. Bowyer, L. O.Hall, W. P. Kegelmeyer, “SMOTE: synthetic minority over-sampling technique,” Journal of artificial intelligence research, 321-357, 2002.

Examples

>>> from collections import Counter
>>> from sklearn.datasets import make_classification
>>> from imblearn.over_sampling import SMOTE # 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})
>>> sm = SMOTE(random_state=42)
>>> X_res, y_res = sm.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, k_neighbors=5, m_neighbors='deprecated', out_step='deprecated', kind='deprecated', svm_estimator='deprecated', n_jobs=1, 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