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yuyasugano/smote.py

Created September 25, 2020 06:54
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Sentiment analysis with NLTK and Scikit-learn TfidfVectorizer
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smote.py
from imblearn.over_sampling import SMOTE
X = train_vec
y = train_tweets['label']
print('X shape: {}, y shape: {}'.format(X.shape, y.shape))
sm = SMOTE(sampling_strategy='auto', random_state=39)
X_resampled, y_resampled = sm.fit_sample(X, y)
print('Resampled label balance:\n{}'.format(y_resampled.value_counts()))
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