gsmote.GeometricSMOTE

class gsmote.GeometricSMOTE(sampling_strategy='auto', random_state=None, truncation_factor=1.0, deformation_factor=0.0, selection_strategy='combined', k_neighbors=5, n_jobs=1)[source]

Class to to perform over-sampling using Geometric SMOTE.

This algorithm is an implementation of Geometric SMOTE, a geometrically enhanced drop-in replacement for SMOTE 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 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_state : int, 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 the RandomState instance used by np.random.
truncation_factor : float, optional (default=0.0)

The type of truncation. The values should be in the [-1.0, 1.0] range.

deformation_factor : float, optional (default=0.0)

The type of geometry. The values should be in the [0.0, 1.0] range.

selection_strategy : str, optional (default=’combined’)

The type of Geometric SMOTE algorithm with the following options: 'combined', 'majority', 'minority'.

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

If int, number of nearest neighbours to use when synthetic samples are constructed for the minority method. If object, an estimator that inherits from sklearn.neighbors.base.KNeighborsMixin that will be used to find the k_neighbors.

n_jobs : int, optional (default=1)

The number of threads to open if possible.

Notes

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

Supports multi-class resampling. A one-vs.-rest scheme is used as originally proposed in [2].

References

[1](1, 2) G. Douzas, F. Bacao, “Geometric SMOTE: a geometrically enhanced drop-in replacement for SMOTE”, Information Sciences, vol. 501, pp. 118-135, 2019.
[2]N. V. Chawla, K. W. Bowyer, L. O. Hall, W. P. Kegelmeyer, “SMOTE: synthetic minority over-sampling technique”, Journal of Artificial Intelligence Research, vol. 16, pp. 321-357, 2002.

Examples

>>> from collections import Counter
>>> from sklearn.datasets import make_classification
>>> from gsmote import GeometricSMOTE # 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})
>>> gsmote = GeometricSMOTE(random_state=1)
>>> X_res, y_res = gsmote.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, truncation_factor=1.0, deformation_factor=0.0, selection_strategy='combined', k_neighbors=5, n_jobs=1)[source]

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

fit(X, y)

Check inputs and statistics of the sampler.

You should use fit_resample in all cases.

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

Data array.

y : array-like of shape (n_samples,)

Target array.
Returns:
self : object

Return the instance itself.
fit_resample(X, y)

Resample the dataset.

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

Matrix containing the data which have to be sampled.

y : array-like of shape (n_samples,)

Corresponding label for each sample in X.
Returns:
X_resampled : {array-like, dataframe, sparse matrix} of shape (n_samples_new, n_features)

The array containing the resampled data.

y_resampled : array-like of shape (n_samples_new,)

The corresponding label of X_resampled.
get_params(deep=True)

Get parameters for this estimator.

Parameters:
deep : bool, default=True

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

Parameter names mapped to their values.
set_params(**params)

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:
**params : dict

Estimator parameters.
Returns:
self : estimator instance

Estimator instance.

Examples using gsmote.GeometricSMOTE