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))
