if __name__ == '__main__':
ut.list_to_csv(head=head)
result = []
df_list = []
name_list = ut_light.gen_list_mix(main_path=dataset_path,data_type='bgs',mix_size="*sq_L3_I5_*")
for index, name in enumerate(name_list):
for threshold in thresholds:
algo_name = "SEA_{}_{}".format(bin_sizes,block_type)
ut.checkFolderandCreate("{}\\ROC\\{}".format(main_path, algo_name))
pickle_ROC_file = '{}\\ROC\\{}\\_{}_threshold{}.pkl'.format(main_path, algo_name, name, threshold)
if not os.path.exists(pickle_ROC_file):
result = []
data = ut_light.load_light_select_dataset(input_path=dataset_path, file_name=name, is_mix=is_mix)
print("##### cal name : {} , threshold = {} #####".format(name, threshold))
ROC_row = genROCFile(name=name, data=data, threshold=threshold)
result.append(ROC_row)
df = pd.DataFrame(result, columns=ROC_head)
with open(pickle_ROC_file, 'wb') as output:
pickle.dump(df, output)
else:
print("##### Load name : {} , threshold = {} #####".format(name, threshold))
with open(pickle_ROC_file, 'rb') as pickle_input:
df = pickle.load(pickle_input)
df_list.append(df)
df = ut.union_DF(df_list=df_list, head_list=ROC_head)
print("a")
plt = ut.plot_roc_curve_dy(df=df, name_list=name_list)
plt.show()
directorys = os.listdir(input_path)
directorys_matching = fnmatch.filter(directorys, pattern)
return directorys_matching
if __name__ == '__main__':
name_list = ut_light.gen_list_title(data_type='bgs')
# name_list = ut_light.gen_list_mix(dataset_path, data_type='bgs',mix_size=mix_size)
path = "{}\\ROC".format(main_path)
directorys = get_direct_from_pattern(input_path=path,
input_pattern=pattern)
for directory in directorys:
path_directory = "{}\\ROC\\{}".format(main_path, directory)
pk_list = os.listdir(path_directory)
df_list = []
for index, pk_file in enumerate(pk_list):
pickle_ROC_file = "{}\\{}".format(path_directory, pk_file)
with open(pickle_ROC_file, 'rb') as pickle_input:
df = pickle.load(pickle_input)
df_list.append(df)
# df_list.append(ut_light.last_df_list(head_list=ROC_head))
df = ut.union_DF(df_list=df_list, head_list=ROC_head)
plt = ut.plot_roc_curve_dy(df=df,
name_list=name_list,
algo_name=directory)
plt.savefig("{}\\pic\\{}_{}.png".format(main_path, directory,
mix_size.replace("*", "")))
plt.show()
plt.clf()
# ut.dftocsv(dataframe_input = df,file_name="csv\\{}.csv".format(directory))