imblearn.over_sampling.SVMSMOTE

class imblearn.over_sampling.SVMSMOTE(sampling_strategy='auto', random_state=None, k_neighbors=5, n_jobs=1, m_neighbors=10, svm_estimator=None, out_step=0.5)[source]

Over-sampling using SVM-SMOTE.

Variant of SMOTE algorithm which use an SVM algorithm to detect sample to use for generating new synthetic samples as proposed in [R88acb9955f91-2].

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 \alpha_{os} = N_{rm} / N_{M} where N_{rm} is the number of samples in the minority class after resampling and N_{M} is the number of samples in the majority class.

    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_stateint, 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_neighborsint 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.

n_jobsint, optional (default=1)

The number of threads to open if possible.

m_neighborsint or object, optional (default=10)

If int, number of nearest neighbours to use to determine if a minority sample is in danger. If object, an estimator that inherits from sklearn.neighbors.base.KNeighborsMixin that will be used to find the m_neighbors.

svm_estimatorobject, optional (default=SVC())

A parametrized sklearn.svm.SVC classifier can be passed.

out_stepfloat, optional (default=0.5)

Step size when extrapolating.

See also

SMOTE

Over-sample using SMOTE.

SMOTENC

Over-sample using SMOTE for continuous and categorical features.

BorderlineSMOTE

Over-sample using Borderline-SMOTE.

ADASYN

Over-sample using ADASYN.

Notes

See the original papers: [R88acb9955f91-2] for more details.

Supports multi-class resampling. A one-vs.-rest scheme is used as originally proposed in [R88acb9955f91-1].

References

R88acb9955f91-1

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.

R88acb9955f91-2(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, 2009.

Examples

>>> from collections import Counter
>>> from sklearn.datasets import make_classification
>>> from imblearn.over_sampling import SVMSMOTE 
>>> 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 = SVMSMOTE(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__(self, sampling_strategy='auto', random_state=None, k_neighbors=5, n_jobs=1, m_neighbors=10, svm_estimator=None, out_step=0.5)[source]

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

fit(self, 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.

yarray-like, shape (n_samples,)

Target array.

Returns
selfobject

Return the instance itself.

fit_resample(self, 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.

yarray-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_resampledarray-like, shape (n_samples_new,)

The corresponding label of X_resampled.

fit_sample(self, 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.

yarray-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_resampledarray-like, shape (n_samples_new,)

The corresponding label of X_resampled.

get_params(self, deep=True)[source]

Get parameters for this estimator.

Parameters
deepboolean, optional

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

Returns
paramsmapping of string to any

Parameter names mapped to their values.

set_params(self, **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

Examples using imblearn.over_sampling.SVMSMOTE