[{'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 14m ish, consider multi-processing, multi-cores, run in GPU\n#TODO: create data_generate func\nstart_time = time.time()\ntest['Country_Region'] = test['Country_Region'].astype(str)\ntest['Province_State'] = test['Province_State'].astype(str)\ncountry = test['Country_Region'].drop_duplicates()\nadj_input_cols = [e for e in input_cols if e not in ('province_encoder', 'country_encoder', 'date_int')]\n# fill data for overlapped days\ntest_df = test.copy().join(pd.DataFrame(columns = adj_input_cols + output_cols))\ntest_df.rename(columns={'Date': 'date', 'Country_Region': 'country', 'Province_State': 'province'}, inplace=True)\nlags = np.arange(1,8,1)  # lag of 1 to 7\ntest_overlap_mask = (test_df['date'] <= train_df['date'].max())\ntrain_overlap_mask = (train_df['date'] >= test_df['date'].min())\ntest_df.loc[test_overlap_mask, input_cols + output_cols] = train_df.loc[train_overlap_mask, input_cols + output_cols].values\n\n# predict data for forward days\npred_dt_range = pd.date_range(start = train_df['date'].max() + pd.Timedelta(days=1), end = test_df['date'].max(), freq = '1D') # test_df['date'].max()\nwith tqdm(total = len(pred_dt_range)) as pbar:\n    for d in pred_dt_range:\n        \n        for i in country:\n            \n            province = test_df[test_df['country'] == i]['province'].drop_duplicates()\n            \n            for j in province:\n                \n                mask = (test_df['date'] == d) & (test_df['country'] == i) & (test_df['province'] == j)\n                \n                \n                # update input features for the predicted day\n                for lag in lags:\n                    mask_org = (test_df['date'] == (d - pd.Timedelta(days=lag))) & (test_df['country'] == i) & (test_df['province'] == j)\n                    try:\n                        test_df.loc[mask, 'cc_cases_' + str(lag)] = test_df.loc[mask_org, 'cc_cases'].values\n                    except:\n                        test_df.loc[mask, 'cc_cases_' + str(lag)] = 0\n\n                    try:\n                        test_df.loc[mask, 'ft_cases_' + str(lag)] = test_df.loc[mask_org, 'ft_cases'].values\n                    except:\n                        test_df.loc[mask, 'ft_cases_' + str(lag)] = 0\n                \n                test_X  = test_df.loc[mask, input_cols]\n            \n                # predict for comfirmed cases\n                test_X_cc = test_X[cc_input]\n                X_test_cc= test_X_cc\n                # X_test_cc =  X_scaler_cc.transform(test_X_cc) # intput/output 2D array-like\n                # X_test_cc = X_test_cc.reshape(X_test_cc.shape[0], 1, X_test_cc.shape[1])\n                X_test_cc = X_test_cc.to_numpy().reshape(X_test_cc.shape[0], 1, X_test_cc.shape[1])\n                next_cc = model_cc.predict(X_test_cc)\n                # next_cc_scaled = y_scaler_cc.inverse_transform(next_cc)\n                next_cc_scaled = next_cc\n                \n                # predict for fatal cases\n                test_X_ft = test_X[ft_input]\n                X_test_ft = test_X_ft\n                # X_test_ft =  X_scaler_ft.transform(test_X_ft) # intput/output 2D array-like\n                # X_test_ft = X_test_ft.reshape(X_test_ft.shape[0], 1, X_test_ft.shape[1])\n                X_test_ft = X_test_ft.to_numpy().reshape(X_test_ft.shape[0], 1, X_test_ft.shape[1])\n                next_ft = model_cc.predict(X_test_ft)\n                # next_ft_scaled = y_scaler_cc.inverse_transform(next_ft)\n                next_ft_scaled = next_ft\n                # print(d, ' - ', i, ' - ', j,  ' - Predicted Confirmed Cases are ', next_cc_scaled, ' - Predicted Fatal Cases are ', next_ft_scaled)\n                \n                # update yhat for next day\n                test_df.loc[mask, 'cc_cases'] = next_cc_scaled\n                test_df.loc[mask, 'ft_cases'] = next_ft_scaled\n                        \n        pbar.update(1)\n        \nprint('Time spent for building features is {} minutes'.format(round((time.time()-start_time)/60,1)))"}, {'role': 'assistant', 'content': "['data_preprocessing']"}]