select_columns_outside_argmaxf1.py

updated = False
for k in range(N):
    start = time.time()

    y_pred_proba = np.array(Y_pred_proba[:,k].todense()).ravel()
    y_test = np.array(Y_test[:,k].todense()).ravel()
    fp = partial(f, y_pred_proba, y_test, mlcm, k)

    optimal_thresholds_star = argmaxf1(y_pred_proba, y_test, optimal_thresholds, mlcm, k, nb_thresholds)

    if fp(optimal_thresholds_star) > fp(optimal_thresholds):
        optimal_thresholds = optimal_thresholds_star
        Y_pred = Y_pred_proba > optimal_thresholds
        mlcm = multilabel_confusion_matrix(Y_test, Y_pred)
        updated = True

    print(f"label {k} - updated {updated} - time elapsed {time.time()-start:.2f}s")