[{'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:nCountries= train_data[\'Country_Region\'].unique() \nisState = bool\nx_train = range(n_train_days)\nx_test  = range(n_train_days+n_test_days-overlap_days)\n\nfor country in tqdm(nCountries): \n    fig,(ax0,ax1) = plt.subplots(1,2,figsize=(20,8))\n    fig,(ax2,ax3) = plt.subplots(1,2,figsize=(20,8))\n    #print(\'\\\n\\\n\\\n\\\n country ==>\', country) \n    \n    df_country_train = train_data[train_data[\'Country_Region\']==country] \n    df_country_test = test_data[test_data[\'Country_Region\']==country]  \n    \n    if country != \'China\':\n        df_country_train = df_country_train.reset_index().loc[df_country_train.reset_index().Date>\'2020-02-22\'] #17\n        n_days_sans_China =train_data.Date.nunique() - df_country_train.Date.nunique()        \n    else:\n        df_country_train = df_country_train.reset_index()\n        n_days_sans_China = 0\n        \n    n_train_days =df_country_train.Date.nunique()    \n    x_train = range(n_train_days)\n    x_test  = range(n_train_days+n_test_days-overlap_days)   \n    nvalues = df_country_train[\'Province_State\'].isna().nunique() #fix for problem with Denmark data\n    \n    if (df_country_train[\'Province_State\'].isna().unique()==True).any() and nvalues<2: \n        isState = False        \n        y_train_f = df_country_train[\'Fatalities\']\n        y_train_c = df_country_train[\'ConfirmedCases\']  \n        y_train_cn = (df_country_train[\'ConfirmedCases\'] - df_country_train[\'ConfirmedCases\'].shift(1)).fillna(0.0)\n        y_train_fn = (df_country_train[\'Fatalities\'] - df_country_train[\'Fatalities\'].shift(1)).fillna(0.0)\n        \n        if y_train_f.empty == False:\n            lower, upper = get_bounds_fatal (country, isState, y_train_f)\n            #print(lower, upper)\n            popt_f, pcov_f = curve_fit(Gompertz, x_train, y_train_f, method=\'trf\', bounds=(lower,upper))\n            a_max, estimated_c, estimated_t0 = popt_f\n            y_predict_f = Gompertz(a_max, estimated_c, x_test, estimated_t0)            \n            #print(\'\\\nfatalities ==>, max: \',a_max, \', slope: %.2f\'% estimated_c, \', inflection point: \', \n             #     estimated_t0, \', r2 score: %.2f\'% r2_score(y_train_f[:], y_predict_f[0:n_train_days]))\n            y_fn = np.array([])\n            fn = [y_predict_f[i]-y_predict_f[i-1] if i!=0 else y_predict_f[i] for i in range(len(y_predict_f))]    \n            y_predict_fn = np.append(y_fn, fn)\n   \n            \n        if y_train_c.empty == False:  \n            lower_c, upper_c = get_bounds_confirm (country, isState, y_train_c)\n            #print(lower_c, upper_c)\n            popt_c, pcov_c = curve_fit(Gompertz, x_train, y_train_c, method=\'trf\', bounds=(lower_c,upper_c))\n            a_max_c, estimated_c_c, estimated_t0_c = popt_c\n            y_predict_c = Gompertz(a_max_c, estimated_c_c, x_test, estimated_t0_c)\n            #print(\'\\\nconfirmed ==> max: \',a_max_c, \', slope: %.2f\'% estimated_c_c, \', inflection point: \', \n             #     estimated_t0_c, \', r2 score: %.2f\'% r2_score(y_train_c[:], y_predict_c[0:n_train_days]))\n            y_cn = np.array([])\n            cn = [y_predict_c[i]-y_predict_c[i-1] if i!=0 else y_predict_c[i] for i in range(len(y_predict_c))]    \n            y_predict_cn = np.append(y_cn, cn)\n            \n        ## ===== Move the x-axis of trained and test datasets to allign with dates in China ======\n        extend_days_test = [i+len(x_test) for i in range(n_days_sans_China)]\n        x_test       = np.append(x_test, extend_days_test)                         \n        y_predict_c  = np.pad(y_predict_c, (n_days_sans_China, 0), \'constant\')\n        y_predict_cn = np.pad(y_predict_cn,(n_days_sans_China, 0), \'constant\')\n        y_predict_f  = np.pad(y_predict_f, (n_days_sans_China, 0), \'constant\')\n        inflection_f = estimated_t0+n_days_sans_China\n        y_predict_fn = np.pad(y_predict_fn, (n_days_sans_China, 0), \'constant\')\n            \n        extend_days_train = [i+len(x_train) for i in range(n_days_sans_China)]\n        x_train      = np.append(x_train, extend_days_train)           \n        y_train_c    = np.pad(y_train_c, (n_days_sans_China, 0), \'constant\')\n        y_train_cn   = np.pad(y_train_cn, (n_days_sans_China, 0), \'constant\')\n        y_train_f    = np.pad(y_train_f, (n_days_sans_China, 0), \'constant\')\n        y_train_fn   = np.pad(y_train_fn, (n_days_sans_China, 0), \'constant\')\n        inflection_c = estimated_t0_c+n_days_sans_China           \n        \n        ax0.plot(x_test, y_predict_c, linewidth=2, label=\'predict_\'+country) \n        ax0.plot(x_train, y_train_c, linewidth=2, color=\'r\', linestyle=\'dotted\', label=\'train_\'+country)\n        ax0.set_title("Prediction vs Training for Confirmed Cases")\n        ax0.set_xlabel("Number of days")\n        ax0.set_ylabel("Confirmed Cases")\n        ax0.legend()\n        test_data.loc[test_data[\'Country_Region\']==country,\'ConfirmedCases\'] = y_predict_c[-n_test_days:]\n        \n        ax1.plot(x_test, y_predict_f, linewidth=2, label=\'predict_\'+country) \n        ax1.plot(x_train, y_train_f, linewidth=2, color=\'r\', linestyle=\'dotted\', label=\'train_\'+country)    \n        ax1.set_title("Prediction vs Training for Fatalities")\n        ax1.set_xlabel("Number of days")\n        ax1.set_ylabel("Fatalities")\n        ax1.legend()\n        test_data.loc[test_data[\'Country_Region\']==country,\'Fatalities\'] = y_predict_f[-n_test_days:] \n                \n        ax2.plot(x_test, y_predict_cn, linewidth=2, label=\'predict_\'+country) \n        ax2.scatter(x_train, y_train_cn, linewidth=2, color=\'r\', linestyle=\'dotted\', label=\'train_\'+country)   \n        ax2.set_title("New Confirmed Cases")\n        ax2.set_xlabel("Number of days")\n        ax2.set_ylabel("New Confirmed Cases")\n        ax2.legend()#loc=\'center left\',bbox_to_anchor=(1.0, 0.5))\n    \n        ax3.plot(x_test, y_predict_fn, linewidth=2, label=\'predict_\'+country) \n        ax3.scatter(x_train, y_train_fn, linewidth=2, color=\'r\', linestyle=\'dotted\', label=\'train_\'+country)   \n        ax3.set_title("New Fatalities")\n        ax3.set_xlabel("Number of days")\n        ax3.set_ylabel("New Fatalities")\n        ax3.legend()#loc=\'center left\',bbox_to_anchor=(1.0, 0.5))\n    \n    else: # use Province/State data when available\n        isState = True\n        state_list = []\n        y_predict_c_dict = {}; y_train_c_dict = {}\n        y_predict_cn_dict = {}; y_train_cn_dict = {}\n        y_predict_f_dict = {}; y_train_f_dict = {}\n        y_predict_fn_dict = {}; y_train_fn_dict = {}\n        for state in df_country_train[\'Province_State\'].unique():\n            df_state_train = df_country_train[df_country_train[\'Province_State\']==state] #state\n            df_state_test = df_country_test[df_country_test[\'Province_State\']==state]   \n            state_list.append(state)\n            y_train_f = df_state_train[\'Fatalities\']\n            y_train_c = df_state_train[\'ConfirmedCases\']  \n            y_train_cn = (df_state_train[\'ConfirmedCases\'] - df_state_train[\'ConfirmedCases\'].shift(1)).fillna(0.0)\n            y_train_fn = (df_state_train[\'Fatalities\'] - df_state_train[\'Fatalities\'].shift(1)).fillna(0.0)\n            \n            if y_train_f.empty== False:                 \n                lower, upper = get_bounds_fatal (country, isState, y_train_f)\n                popt_f, pcov_f = curve_fit(Gompertz, x_train, y_train_f, method=\'trf\', bounds=(lower,upper))\n                a_max, estimated_c, estimated_t0 = popt_f\n                y_predict_f = Gompertz(a_max, estimated_c, x_test, estimated_t0) \n                y_predict_f_dict[state] =  y_predict_f\n                y_train_f_dict[state]   =  y_train_f                \n                #print(\'\\\nfatalities state ==>, max: \',a_max, \', slope: %.2f\'% estimated_c, \', inflection point: \', \n                #    estimated_t0, \', r2 score: %.2f\'% r2_score(y_train_f[:], y_predict_f[0:70]))\n                y_fn = np.array([])\n                fn = [y_predict_f[i]-y_predict_f[i-1] if i!=0 else y_predict_f[i] for i in range(len(y_predict_f))]    \n                y_predict_fn = np.append(y_fn, fn)\n                y_predict_fn_dict[state] = y_predict_fn\n                y_train_fn_dict[state]   = y_train_fn\n                                \n            if y_train_c.empty == False:  \n                lower_c, upper_c = get_bounds_confirm (country, isState, y_train_c)\n                popt_c, pcov_c = curve_fit(Gompertz, x_train, y_train_c, method=\'trf\', bounds=(lower_c,upper_c))\n                a_max_c, estimated_c_c, estimated_t0_c = popt_c\n                y_predict_c = Gompertz(a_max_c, estimated_c_c, x_test, estimated_t0_c)\n                y_predict_c_dict[state] =  y_predict_c\n                y_train_c_dict[state]   =  y_train_c\n                #print(\'\\\nconfirmed state ==> max: \',a_max_c, \', slope: %.2f\'% estimated_c_c, \', inflection point: \', \n                #  estimated_t0_c, \', r2 score: %.2f\'% r2_score(y_train_c[:], y_predict_c[0:70]))                \n                y_cn = np.array([])\n                cn = [y_predict_c[i]-y_predict_c[i-1] if i!=0 else y_predict_c[i] for i in range(len(y_predict_c))]    \n                y_predict_cn = np.append(y_cn, cn)\n                y_predict_cn_dict[state] = y_predict_cn\n                y_train_cn_dict[state]   = y_train_cn\n                            \n        ## ====== Plot and Store the Results: ======\n        ## ====== Move the x-axis of trained and test datasets to allign with dates in China ======       \n        extend_days_test = [i+len(x_test) for i in range(n_days_sans_China)]\n        x_test      = np.append(x_test, extend_days_test) \n        extend_days_train = [i+len(x_train) for i in range(n_days_sans_China)]\n        x_train     = np.append(x_train, extend_days_train)           \n            \n        for state, y_predict in y_predict_f_dict.items():\n            y_predict = np.pad(y_predict, (n_days_sans_China, 0), \'constant\') \n            ax1.plot(x_test, y_predict, linewidth=2, label=country+\'_\'+state) \n            ax1.legend(loc=\'center left\',bbox_to_anchor=(1.0, 0.5)) \n            test_data.loc[(test_data[\'Country_Region\']==country)&(test_data[\'Province_State\']==state),\'Fatalities\'] = y_predict[-n_test_days:]\n        for state, y_train in y_train_f_dict.items():\n            y_train   = np.pad(y_train, (n_days_sans_China, 0), \'constant\')\n            ax1.plot(x_train, y_train, linewidth=2, color=\'r\', linestyle=\'dotted\', label=\'train_\'+state)             \n        ax1.set_title("Prediction vs Training for Fatalities")\n        ax1.set_xlabel("Number of days")\n        ax1.set_ylabel("Fatalities")   \n        \n        \n        for state, y_predict in y_predict_c_dict.items():\n            y_predict = np.pad(y_predict, (n_days_sans_China, 0), \'constant\') \n            ax0.plot(x_test, y_predict, linewidth=2, label=country+\'_\'+state) \n            #ax0.legend(loc=\'center left\',bbox_to_anchor=(1.0, 0.5)) \n            test_data.loc[(test_data[\'Country_Region\']==country)&(test_data[\'Province_State\']==state),\'ConfirmedCases\'] = y_predict[-n_test_days:]\n        for state, y_train in y_train_c_dict.items():\n            y_train   = np.pad(y_train, (n_days_sans_China, 0), \'constant\')\n            ax0.plot(x_train, y_train, linewidth=2, color=\'r\', linestyle=\'dotted\', label=\'train_\'+country+\'_\'+state)             \n        ax0.set_title("Prediction vs Training for ConfirmedCases")\n        ax0.set_xlabel("Number of days")\n        ax0.set_ylabel("Confirmed Cases") \n        \n        for state, y_predict in y_predict_fn_dict.items():\n            y_predict = np.pad(y_predict, (n_days_sans_China, 0), \'constant\') \n            ax3.plot(x_test, y_predict, linewidth=2, label=country+\'_\'+state) \n            ax3.legend(loc=\'center left\',bbox_to_anchor=(1.0, 0.5)) \n        for state, y_train in y_train_fn_dict.items():\n            y_train   = np.pad(y_train, (n_days_sans_China, 0), \'constant\')\n            ax3.scatter(x_train, y_train, linewidth=2, color=\'r\', linestyle=\'dotted\', label=\'train_\'+state)    \n        ax3.set_title("New Fatalities")\n        ax3.set_xlabel("Number of days")\n        ax3.set_ylabel("New Fatalities")\n        \n        \n        for state, y_predict in y_predict_cn_dict.items():\n            y_predict = np.pad(y_predict, (n_days_sans_China, 0), \'constant\') \n            ax2.plot(x_test, y_predict, linewidth=2, label=country+\'_\'+state) \n            #ax2.legend(loc=\'center left\',bbox_to_anchor=(1.0, 0.5)) \n            test_data.loc[(test_data[\'Country_Region\']==country)&(test_data[\'Province_State\']==state),\'ConfirmedCases\'] = y_predict[-n_test_days:]\n        for state, y_train in y_train_cn_dict.items():\n            y_train   = np.pad(y_train, (n_days_sans_China, 0), \'constant\')\n            ax2.scatter(x_train, y_train, linewidth=2, color=\'r\', linestyle=\'dotted\', label=\'train_\'+country+\'_\'+state)\n        ax2.set_title("New Confirmed Cases")\n        ax2.set_xlabel("Number of days")\n        ax2.set_ylabel("New Confirmed Cases")\''}, {'role': 'assistant', 'content': "['result_visualization']"}]