def METHOD_NAME(weight_filepath, vector_list, index_list=None, verbose=VERBOSE_SVM):
    if index_list is None:
        index_list = list(range(len(vector_list)))
    # Init score and class holders
    score_dict = {index: [] for index in index_list}
    class_dict = {index: [] for index in index_list}
    # Load models
    model_tup = ut.load_cPkl(weight_filepath, verbose=verbose)
    model, scaler = model_tup
    # Normalize
    vector_list = scaler.transform(vector_list)
    # calculate decisions and predictions
    # score_list = model.decision_function(vector_list)
    score_list = model.predict_proba(vector_list)
    # Take only the positive probability
    score_list = score_list[:, 1]
    class_list = model.predict(vector_list)
    # Zip together results
    zipped = zip(index_list, score_list, class_list)
    for index, score_, class_ in zipped:
        score_dict[index].append(score_)
        class_dict[index].append(class_)
    # Return scores and classes
    return score_dict, class_dict