aathmant/enhanced_geomtertic_smote

Enhanced_Geomtertic_SMOTE

>>> from collections import Counter
>>> from sklearn.datasets import make_classification
>>> from gsmote import EGSmote

>>> 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 = EGSmote(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}})