def cv_loop(X, y, model, N):
    mean_auc = 0.
    for i in range(N):
        X_train, X_cv, y_train, y_cv = cross_validation.train_test_split(
                                       X, y, test_size=.20,
                                       random_state = i*SEED)
        model.fit(X_train, y_train)
        preds = model.predict_proba(X_cv)[:,1]
        auc = metrics.auc_score(y_cv, preds)
        print "AUC (fold %d/%d): %f" % (i + 1, N, auc)
        mean_auc += auc
    return N/mean_auc
dataset/ETHPy150Open zigahertz/2013-Sep-HR-ML-sprint/py/nightmaremode.py/cv_loop/swapped operands of `/`