why roc-auc score in naive svm is greater than svm+oversample?

Question

I working on sentiment analysis in imbalanced dataset.I have problem with naive svm classifer which gives better roc-auc score than svm+sampling.This is naive svm results(first sad brackets is roc-auc score,second is G-mean score and third is f1_measure)

and this is oversample+svm results:

also this is my code for svm :

    clf=SVC(kernel='linear',C=1,probability=True)
    clf.fit(tf_idf_train3, polarity_train)
    probs = clf.predict_proba(tf_idf_test3)
    preds = probs[:,1]
    fpr, tpr, threshold = metrics.roc_curve(polarity_test, preds)
    pred = clf.predict(tf_idf_test3)
    roc_auc=roc_auc_score(polarity_test,preds,average='macro')
    print(classification_report(polarity_test,pred))
    print(confusion_matrix(polarity_test,pred))
    gmean=geometric_mean_score(polarity_test,pred,average='macro')
    f1=f1_score(polarity_test, pred, average='macro')

and this is my code for svm+oversample:

    clf=SVC(kernel='linear',C=1,probability=True)
    X_resample, y_resampled = ros.fit_resample(tf_idf_train3, polarity_train)
    clf.fit(X_resample, y_resampled)
    probs = clf.predict_proba(tf_idf_test3)
    preds = probs[:,1]
    fpr, tpr, threshold = metrics.roc_curve(polarity_test, preds)
    pred = clf.predict(tf_idf_test3)
    roc_auc=roc_auc_score(polarity_test,preds ,average='macro')
    print(classification_report(polarity_test,pred))
    print(confusion_matrix(polarity_test,pred))
    gmean=geometric_mean_score(polarity_test,pred,average='macro')
    f1=f1_score(polarity_test, pred, average='macro')