imblearn on cocalc

import imblearn
imblearn.__version__

from collections import Counter
from sklearn.datasets import make_classification
from imblearn.over_sampling import SMOTE  # doctest: +NORMALIZE_WHITESPACE

# implementation of SMOTE - Synthetic Minority Over-sampling Technique

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))

sm = SMOTE(random_state=42)
sm

X_res, y_res = sm.fit_resample(X, y)
print('Resampled dataset shape %s' % Counter(y_res))