I am working on an imbalanced data for classification and I tried to use SMOTE previously to oversampling the training data. However, this time I think I need to use a leave-on group out (LOGO) cross-validation because I want to leave one subject out on each CV.

I am not sure if I can explain it nicely, but, as my understanding, to do k-fold CV using SMOTE we can loop the SMOTE on every fold, as I saw in this code on another post. Below is an example of SMOTE implementation on the k-fold CV.

from sklearn.model_selection import KFold
from imblearn.over_sampling import SMOTE
from sklearn.metrics import f1_score

kf = KFold(n_splits=5)

for fold, (train_index, test_index) in enumerate(kf.split(X), 1):
    X_train = X[train_index]
    y_train = y[train_index]  
    X_test = X[test_index]
    y_test = y[test_index]  
    sm = SMOTE()
    X_train_oversampled, y_train_oversampled = sm.fit_sample(X_train, y_train)
    model = ...  # classification model example
    model.fit(X_train, y_train)  
    y_pred = model.predict(X_test)
    print(f'For fold {fold}:')
    print(f'Accuracy: {model.score(X_test, y_test)}')
    print(f'f-score: {f1_score(y_test, y_pred)}')

Without SMOTE, I tried to do this to do LOGO CV. But by doing this, I will be using a super imbalanced dataset.

X = X
y = np.array(df.loc[:, df.columns == 'label'])
groups = df["cow_id"].values #because I want to leave cow data with same ID on each run
logo = LeaveOneGroupOut()

logo.get_n_splits(X_std, y, groups)

cv=logo.split(X_std, y, groups)

for train_index, test_index in cv:
    print("Train Index: ", train_index, "\n")
    print("Test Index: ", test_index)
    X_train, X_test, y_train, y_test = X[train_index], X[test_index], y[train_index], y[test_index]
    model.fit(X_train, y_train.ravel())
    scores.append(model.score(X_test, y_test.ravel()))

My question will be: How should I implement SMOTE inside a loop of leave one group out CV, I am confused about how to define the group list for the synthetic training data.

I'd be happy to provide more information. Thanks!


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.