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wmlba/oversampling.py

Last active April 21, 2019 16:02
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oversampling.py
from imblearn.over_sampling import SMOTE
# Resample the minority class. You can change the strategy to 'auto' if you are not sure.
sm = SMOTE(sampling_strategy='minority', random_state=7)
# Fit the model to generate the data.
oversampled_trainX, oversampled_trainY = sm.fit_sample(credit_df.drop('Class', axis=1), credit_df['Class'])
oversampled_train = pd.concat([pd.DataFrame(oversampled_trainY), pd.DataFrame(oversampled_trainX)], axis=1)
oversampled_train.columns = normalized_df.columns
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