def run(): fn = Name_functions.full_GRAEC_table() # Clear Test Scores file with open(fn, 'w+') as wf: wf.write('Beta;Tau;P;S;Score_type;Score_Value\n') for S in Parameters.S_values: Filefunctions.make_directory(Name_functions.S_score_folder(S)) print('S = {}'.format(S)) parse_graec(S) parse_previous(S) parse_naive(S)
def parse_graec(s): # do GRAEC SCORE enumeration_encoder = Human_Functions.load_dict_from_csv(Name_functions.S_GRAEC_enumeration_dictionary(s)) best_acc_train = -1 best_acc_test = -1 best_acc_enum = -1 best_f1_train = -1 best_f1_test = -1 best_f1_enum = -1 with open(Name_functions.full_GRAEC_table(), 'a+') as wf: for e in enumeration_encoder: df_train = DataFrameOperations.import_df(fn=Name_functions.S_GRAEC_train_predictions(s, e)) acc_train = Metrics.accuracy(df_train['True_label'], df_train['Predicted_label']) f1_train = Metrics.f1(df_train['True_label'], df_train['Predicted_label']) df_test = DataFrameOperations.import_df(fn=Name_functions.S_GRAEC_test_predictions(s, e)) acc_test = Metrics.accuracy(df_test['True_label'], df_test['Predicted_label']) f1_test = Metrics.f1(df_test['True_label'], df_test['Predicted_label']) wf.write(enumeration_encoder[e] + ';{};ACC;{}\n'.format(s, acc_test)) wf.write(enumeration_encoder[e] + ';{};F1;{}\n'.format(s, f1_test)) if acc_train > best_acc_train: best_acc_train = acc_train best_acc_test = acc_test best_acc_enum = e if f1_train > best_f1_train: best_f1_train = f1_train best_f1_test = f1_test best_f1_enum = e with open(Name_functions.S_GRAEC_score(s, 'accuracy'), 'w+') as wf: wf.write(enumeration_encoder[best_acc_enum] + '\n') wf.write('{}\n'.format(best_acc_test)) with open(Name_functions.S_GRAEC_score(s, 'f1'), 'w+') as wf: wf.write(enumeration_encoder[best_f1_enum] + '\n') wf.write('{}\n'.format(best_f1_test))