imblearn.over_sampling.SMOTE

class imblearn.over_sampling.SMOTE(ratio='auto', random_state=None, k=None, k_neighbors=5, m=None, m_neighbors=10, out_step=0.5, kind='regular', svm_estimator=None, n_jobs=1)[source][source]

Class to perform over-sampling using SMOTE.

This object is an implementation of SMOTE - Synthetic Minority Over-sampling Technique, and the variants Borderline SMOTE 1, 2 and SVM-SMOTE.

Read more in the User Guide.

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.

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.

k : int, optional (default=None)

Number of nearest neighbours to used to construct synthetic samples.

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

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 : int, optional (default=None)

Number of nearest neighbours to use to determine if a minority sample is in danger. Used with kind={'borderline1', 'borderline2', 'svm'}.

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

m_neighbors : int 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.

out_step : float, optional (default=0.5)

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

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

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

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

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

n_jobs : int, optional (default=1)

The number of threads to open if possible.

See also

ADASYN
Over-sample using ADASYN.

Notes

See the original papers: [R7779], [R7879], [R7979] for more details.

Supports mutli-class resampling. A one-vs.-rest scheme is used as originally proposed in [R7779].

See Benchmark over-sampling methods in a face recognition task, Evaluate classification by compiling a report, Metrics specific to imbalanced learning, Plotting Validation Curves, Comparison of the different over-sampling algorithms, and SMOTE.

References

[R7779](1, 2, 3) 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.
[R7879](1, 2) H. Han, W. Wen-Yuan, M. Bing-Huan, “Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning,” Advances in intelligent computing, 878-887, 2005.
[R7979](1, 2) H. M. Nguyen, E. W. Cooper, K. Kamei, “Borderline over-sampling for imbalanced data classification,” International Journal of Knowledge Engineering and Soft Data Paradigms, 3(1), pp.4-21, 2001.

Examples

>>> from collections import Counter
>>> from sklearn.datasets import make_classification
>>> from imblearn.over_sampling import SMOTE 
>>> 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})
>>> sm = SMOTE(random_state=42)
>>> X_res, y_res = sm.fit_sample(X, y)
>>> print('Resampled dataset shape {}'.format(Counter(y_res)))
Resampled dataset shape Counter({0: 900, 1: 900})
__init__(ratio='auto', random_state=None, k=None, k_neighbors=5, m=None, m_neighbors=10, out_step=0.5, kind='regular', svm_estimator=None, n_jobs=1)[source][source]
fit(X, y)[source]

Find the classes statistics before to perform sampling.

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:

self : object,

Return self.

fit_sample(X, y)[source]

Fit the statistics and resample the data directly.

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.

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 : {ndarray, sparse matrix}, 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)[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