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single_read_result_longtail.py
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single_read_result_longtail.py
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import re
import os
import copy
import numpy as np
import matplotlib.pyplot as plt
from itertools import product
if __name__ == '__main__':
if not os.path.exists("single_result_files"):
os.makedirs("single_result_files")
np.set_printoptions(precision=2)
# algos = ['iql', 'iqln_10', 'iqln_100', 'iqln2_10', 'iqln2_100', 'iqln3_10', 'iqln3_100', 'sacn_10', 'sacn_100', 'edac_10', 'edac_100', 'td3_plus_bc', 'cql']
algos = ['iqln_100']
# colors = ['#226E9C', '#045275', '#7CCBA2', '#FF1F5B', '#E9002D', '#00CD6C', '#00B000', '#228B3B', '#9CCEA7', '#C40F5B', '#EB85B0', '#F08F6E', '#B10026']
weight_temps = ["3.0", "10.0"]
expectiles = ["0.99"]
# replay_types = ['bc', 'ewc', 'si', 'gem', 'agem']
experiences = ['q', 'v', 'vq_diff']
datasets = ["halfcheetah-random-v0"]
task_nums = ["300_0_0_0_0_0_0"]
# max_save_nums = ['1000', '10000', '100000']
clone_actors = ['', 'cloneactor']
max_save_nums = ['25', '50', '75', '100']
colors = ['#226E9C', '#045275', '#7CCBA2', '#FF1F5B', '#E9002D', '#00CD6C', '#00B000', '#9CCEA7']
colors = dict(zip(max_save_nums, colors))
entropy_times = ['0', '0.2']
seeds = ['0']
continual_types = ["orl_1_orl_1"]
if not os.path.isdir('single_output_files'):
raise NotImplementedError
else:
file_list = os.listdir('single_output_files')
# assert False
results = dict()
for algo, weight_temp, expectile, experience, continual_type, (dataset, task_num), max_save_num, entropy_time, clone_actor in product(algos, weight_temps, expectiles, experiences, continual_types, zip(datasets, task_nums), max_save_nums, entropy_times, clone_actors):
if 'orl' not in continual_type and max_save_num != '0':
continue
elif 'orl' in continual_type and max_save_num == '0':
continue
if 'iqln' not in algo and 'sacn' not in algo and 'edac' not in algo:
algo_file = algo + '_10'
else:
algo_file = algo
item_name = algo_file + '_' + weight_temp + '_' + expectile + '_0.7_0.7_' + dataset + '_' + task_num + '_50000_' + continual_type + '_' + entropy_time + '_' + clone_actor + '_' + experience + '_' + max_save_num# + '_clearnetwork'
item_name_split = algo + ';' + weight_temp + ';' + expectile + ';' + dataset + ';' + task_num + ';' + continual_type + ';' + clone_actor + ';' + experience + ';' + max_save_num
save_name = 'result_' + item_name
# print(save_name)
mean_lasts, mean_accs, mean_bwts, real_accs = [], [], [], []
for seed in seeds:
# print(f'seed: {seed}')
file_model = 'output_' + item_name + '_' + str(seed)
file_time = -1
file_name_match = None
for file_name in file_list:
if re.match(file_model, file_name) is not None:
file_time_new = int(file_name[-18:-4])
if file_time < file_time_new:
file_time = file_time_new
file_name_match = file_name
if file_name_match is None:
continue
file_name = 'single_output_files/' + file_name_match
task_num_split = task_num.split('_')
task_length = len(task_num_split)
real_envs = []
if not os.path.isfile(file_name):
print(f'file {file_name} not exist')
continue
with open(file_name, 'r') as fr:
conti = False
while True:
line = fr.readline()
if line == '':
break
try:
epoch_num = 50
if re.search('epoch=' + str(epoch_num), line) is not None:
pattern = re.compile(r"'0_environment': " + r"[-+]?[0-9]*\.?[0-9]+")
match = re.search(pattern, line)
if match is not None:
match_str = line[match.start() + 17: match.end()]
real_envs.append(float(match_str))
except:
conti = True
break
if conti:
continue
if len(real_envs) < task_length:
continue
bwts = []
accs = []
mean_acc = sum(real_envs) / len(real_envs)
mean_accs.append(mean_acc)
mean_bwt = max(real_envs) - sum(real_envs) / len(real_envs)
mean_bwts.append(mean_bwt)
mean_lasts.append(real_envs[-1])
real_accs.append(real_envs)
with open(f'single_result_files/result{file_name_match[6:]}.txt', 'w') as fw:
print(f'real_envs: {real_envs}', file=fw)
if len(mean_lasts) > 0:
real_accs = np.array(real_accs)
real_accs = [np.mean(np.array([x[i] for x in real_accs])) for i in range(len(real_accs[0]))]
mean_lasts = np.array(mean_lasts)
mean_accs = np.array(mean_accs)
mean_bwts = np.array(mean_bwts)
mean_last = mean_lasts.mean()
mean_acc = mean_accs.mean()
mean_bwt = mean_bwts.mean()
var_last = mean_last.var()
var_acc = mean_accs.var()
var_bwt = mean_bwts.var()
with open(f'single_result_files/{save_name}.txt', 'w') as fw:
print(f'mean_last: {mean_last}', file=fw)
print(f'mean_acc: {mean_acc}', file=fw)
print(f'mean_bwt: {mean_bwt}', file=fw)
print(f'var_last: {var_last}', file=fw)
print(f'var_acc: {var_acc}', file=fw)
print(f'var_bwt: {var_bwt}', file=fw)
with open(f'single_result_files/results.txt', 'a') as fw:
print(f'save_name: {save_name}', file=fw)
print(f'mean_last: {mean_last}', file=fw)
print(f'mean_acc: {mean_acc}', file=fw)
print(f'mean_bwt: {mean_bwt}', file=fw)
print(f'var_last: {var_last}', file=fw)
print(f'var_acc: {var_acc}', file=fw)
print(f'var_bwt: {var_bwt}', file=fw)
print('', file=fw)
results[max_save_num] = (mean_last, mean_acc, mean_bwt, var_last, var_acc, var_bwt, real_accs)
# algos = ['iql', 'iqln_100', 'sacn_100', 'edac_100', 'td3_plus_bc', 'cql']
plt.figure()
plt.cla()
plt.rc("legend")
for max_save_num in max_save_nums:
if max_save_num not in results.keys():
print(f"{max_save_num=}")
if max_save_num in results.keys():
real_acc = results[max_save_num][-1]
x = np.arange(len(real_acc))
plt.plot(x, real_acc, label=algo.split('_')[0], c=colors[max_save_num])
plt.legend(loc="lower left")
plt.xlabel("Train Process")
plt.ylabel("Reward")
# save_path = f"pictures/result_longtail_clearnetwork.png"
save_path = f"pictures/result_longtail.png"
print(f"save to {save_path}")
plt.savefig(save_path, bbox_inches="tight")
plt.close()