34_combine_under_over_sampling.py

# The example below demonstrates how to use the SMOTEENN that combines 
# both SMOTE oversampling of the minority class and Edited Nearest 
# Neighbors undersampling of the majority class.



# example of both undersampling and oversampling
from collections import Counter
from sklearn.datasets import make_classification
from imblearn.combine import SMOTEENN
# generate dataset
X, y = make_classification(n_samples=10000, n_features=2, n_redundant=0, n_clusters_per_class=1, weights=[0.99, 0.01], flip_y=0)
# summarize class distribution
print(Counter(y))
# define sampling strategy
sample = SMOTEENN(sampling_strategy=0.5)
# fit and apply the transform
X_over, y_over = sample.fit_resample(X, y)
# summarize class distribution
print(Counter(y_over))