[{'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:#TODO: takes 44m ish, consider multi-processing, multi-cores, run in GPU\n#TODO: create data_generate func\nfrom joblib import parallel_backend\nstart_time = time.time()\n\ncountry = train['Country_Region'].drop_duplicates()\ntrain_df = train.copy()\ntrain_df.rename(columns={'Date': 'date', 'ConfirmedCases': 'cc_cases', 'Fatalities': 'ft_cases', 'Country_Region': 'country', 'Province_State': 'province'}, inplace=True)\nlags = np.arange(1,8,1)  # lag of 1 to 7\n\nwith parallel_backend('threading', n_jobs = -1):\n    with tqdm(total = len(list(train_df['date'].unique()))) as pbar:\n        for d in train_df['date'].drop_duplicates():\n            for i in country:\n                province = train_df[train_df['country'] == i]['province'].drop_duplicates()\n                for j in province:\n                    mask = (train_df['date'] == d) & (train_df['country'] == i) & (train_df['province'] == j)            \n                    for lag in lags:\n                        mask_org = (train_df['date'] == (d - pd.Timedelta(days=lag))) & (train_df['country'] == i) & (train_df['province'] == j)\n                        try:\n                            train_df.loc[mask, 'cc_cases_' + str(lag)] = train_df.loc[mask_org, 'cc_cases'].values\n                        except:\n                            train_df.loc[mask, 'cc_cases_' + str(lag)] = 0\n\n                        try:\n                            train_df.loc[mask, 'ft_cases_' + str(lag)] = train_df.loc[mask_org, 'ft_cases'].values\n                        except:\n                            train_df.loc[mask, 'ft_cases_' + str(lag)] = 0\n            pbar.update(1)\nprint('Time spent for building features is {} minutes'.format(round((time.time()-start_time)/60,1)))"}, {'role': 'assistant', 'content': "['data_preprocessing']"}]