[{'role': 'system', 'content': "You are an expert in machine learning and your task is to classify the code snippets into categories from the provided list of categories. List of categories: ['helper_functions', 'load_data', 'data_exploration', 'data_preprocessing', 'evaluation', 'modelling', 'prediction', 'result_visualization', 'save_results', 'comment_only']"}, {'role': 'user', 'content': 'Identify to which category the provided code belongs and assign the code snippet to at least 1 but up to 5 categories based on the probabilities, ignore if the probability of the category is negligible. Strictly provide your response as a list. Avoid providing any additional information, commentary, or personal opinions.\\nPython Code:def get_prior(df):\n    # count how many times neither antecedent is correct for the pronoun\n    Neither_count = len(df) - sum(df["A-coref"]  |  df["B-coref"])\n    # count the  A coreferences\n    A_count = sum(df["A-coref"])\n    # count the B coreferences\n    B_count = sum(df["B-coref"])\n    # total number of samples\n    test_total = len(df)\n    # compute the prior probabilities of the three classes\n    Neither_prior = Neither_count/test_total\n    A_prior = A_count/test_total\n    B_prior = B_count/test_total\n    print("Prior probabilities:")\n    print("Neither: "+str(Neither_prior),"A: "+str(A_prior),"B: "+str(B_prior))\n    # sanity check whether everything adds up\n    assert Neither_count + A_count + B_count == test_total\n    return A_prior, B_prior, Neither_prior\n\nA_prior,B_prior,Neither_prior = get_prior(gap_test)\n\n'}, {'role': 'assistant', 'content': "['data_preprocessing']"}]