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model_pool.py
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import json
import os
import pandas as pd
import random
import numpy as np
import pickle
from math import isnan
from config import DIC_AGENTS, DIC_ENVS
validation_set = [
"synthetic-over-WE254-EW221-NS671-SN747-1893.xml",
# "synthetic-over-WE484-EW484-NS700-SN649-2317.xml",
# "synthetic-over-WE495-EW511-NS634-SN736-2376.xml",
"synthetic-over-WE499-EW450-NS502-SN447-1898.xml",
"synthetic-over-WE510-EW445-NS489-SN524-1968.xml",
"synthetic-under-WE221-EW300-NS509-SN524-1554.xml",
"synthetic-under-WE239-EW262-NS690-SN637-1828.xml",
# "synthetic-under-WE240-EW277-NS509-SN544-1570.xml",
# "synthetic-under-WE247-EW279-NS232-SN242-1000.xml",
# "synthetic-under-WE259-EW228-NS265-SN271-1023.xml"
]
DIC_MIN_DURATION = {
200: 26,
300: 26,
350: 27,
400: 28,
450: 29,
500: 30,
550: 34,
600: 38,
650: 40
}
def get_traffic_volume(file_name, run_cnt):
scale = run_cnt / 3600 # run_cnt > traffic_time, no exact scale
if "synthetic" in file_name:
sta = file_name.rfind("-") + 1
print(file_name, int(int(file_name[sta:-4]) * scale))
return int(int(file_name[sta:-4]) * scale)
elif "cross" in file_name:
sta = file_name.find("equal_") + len("equal_")
end = file_name.find(".xml")
return int(int(file_name[sta:end]) * scale * 4) # lane_num = 4
class ModelPool():
def __init__(self, dic_path, dic_exp_conf):
self.dic_path = dic_path
self.exp_conf = dic_exp_conf
self.num_best_model = self.exp_conf["NUM_BEST_MODEL"]
if os.path.exists(os.path.join(self.dic_path["PATH_TO_WORK_DIRECTORY"], "best_model.pkl")):
self.best_model_pool = pickle.load(
open(os.path.join(self.dic_path["PATH_TO_WORK_DIRECTORY"], "best_model.pkl"), "rb"))
else:
self.best_model_pool = []
def single_test(self, cnt_round):
print("Start testing model pool")
records_dir = self.dic_path["PATH_TO_WORK_DIRECTORY"]
# run_cnt = 360
if_gui = False
nan_thres = 80
dic_agent_conf = json.load(open(os.path.join(records_dir, "agent.conf"), "r"))
dic_exp_conf = json.load(open(os.path.join(records_dir, "exp.conf"), "r"))
dic_traffic_env_conf = json.load(open(os.path.join(records_dir, "traffic_env.conf"), "r"))
# dic_exp_conf["RUN_COUNTS"] = run_cnt
run_cnt = dic_exp_conf["RUN_COUNTS"]
dic_traffic_env_conf["IF_GUI"] = if_gui
# dump dic_exp_conf
if os.path.exists(os.path.join(records_dir, "test_exp.conf")):
json.dump(dic_exp_conf, open(os.path.join(records_dir, "test_exp.conf"), "w"))
if dic_exp_conf["MODEL_NAME"] in dic_exp_conf["LIST_MODEL_NEED_TO_UPDATE"]:
dic_agent_conf["EPSILON"] = 0 # dic_agent_conf["EPSILON"] # + 0.1*cnt_gen
dic_agent_conf["MIN_EPSILON"] = 0
agent_name = dic_exp_conf["MODEL_NAME"]
agent = DIC_AGENTS[agent_name](
dic_agent_conf=dic_agent_conf,
dic_traffic_env_conf=dic_traffic_env_conf,
dic_path=self.dic_path,
cnt_round=0, # useless
)
# try:
if 1:
# test
agent.load_network("round_{0}".format(cnt_round))
path_to_log = os.path.join(self.dic_path["PATH_TO_WORK_DIRECTORY"], "test_round",
"round_{0}".format(cnt_round))
if not os.path.exists(path_to_log):
os.makedirs(path_to_log)
env = DIC_ENVS[dic_traffic_env_conf["SIMULATOR_TYPE"]](
path_to_log=path_to_log,
path_to_work_directory=self.dic_path["PATH_TO_WORK_DIRECTORY"],
dic_traffic_env_conf=dic_traffic_env_conf)
done = False
state = env.reset()
step_num = 0
while not done and step_num < int(dic_exp_conf["RUN_COUNTS"] / dic_traffic_env_conf["MIN_ACTION_TIME"]):
action_list = []
for one_state in state:
action = agent.choose_action(step_num, one_state)
action_list.append(action)
next_state, reward, done, _ = env.step(action_list)
state = next_state
step_num += 1
env.bulk_log()
env.end_sumo()
# summary items (duration) from csv
df_vehicle_inter_0 = pd.read_csv(os.path.join(path_to_log, "vehicle_inter_0.csv"),
sep=',', header=0, dtype={0: str, 1: float, 2: float},
names=["vehicle_id", "enter_time", "leave_time"])
duration = df_vehicle_inter_0["leave_time"].values - df_vehicle_inter_0["enter_time"].values
dur = np.mean([time for time in duration if not isnan(time)])
real_traffic_vol = 0
nan_num = 0
for time in duration:
if not isnan(time):
real_traffic_vol += 1
else:
nan_num += 1
traffic_vol = get_traffic_volume(dic_exp_conf["TRAFFIC_FILE"][0], run_cnt)
print(nan_num, nan_thres, self.best_model_pool)
if nan_num < nan_thres:
cnt = 0
for i in range(len(self.best_model_pool)):
if self.best_model_pool[i][1] > dur:
break
cnt += 1
self.best_model_pool.insert(cnt, [cnt_round, dur])
num_max = min(len(self.best_model_pool), self.exp_conf["NUM_BEST_MODEL"])
self.best_model_pool = self.best_model_pool[:num_max]
# log best models through rounds
print(self.best_model_pool)
f = open(os.path.join(self.dic_path["PATH_TO_WORK_DIRECTORY"], "best_model_pool.log"), "a")
f.write("round: %d " % cnt_round)
for i in range(len(self.best_model_pool)):
f.write("id: %d, duration: %f, " % (self.best_model_pool[i][0], self.best_model_pool[i][1]))
f.write("\n")
f.close()
print("model pool ends")
# except:
# print("fail to test model:%s"%model_round)
# pass
def model_compare(self, cnt_round):
print("Start testing model pool")
records_dir = self.dic_path["PATH_TO_WORK_DIRECTORY"]
# run_cnt = 360
if_gui = False
nan_thres = 80
dic_agent_conf = json.load(open(os.path.join(records_dir, "agent.conf"), "r"))
dic_exp_conf = json.load(open(os.path.join(records_dir, "exp.conf"), "r"))
dic_sumo_env_conf = json.load(open(os.path.join(records_dir, "sumo_env.conf"), "r"))
# dic_exp_conf["RUN_COUNTS"] = run_cnt
run_cnt = dic_exp_conf["RUN_COUNTS"]
dic_sumo_env_conf["IF_GUI"] = if_gui
# dump dic_exp_conf
if os.path.exists(os.path.join(records_dir, "test_exp.conf")):
json.dump(dic_exp_conf, open(os.path.join(records_dir, "test_exp.conf"), "w"))
# try:
path_to_log = os.path.join(records_dir, "test_round", "round_%d"%cnt_round)
if 1:
# summary items (duration) from csv
df_vehicle_inter_0 = pd.read_csv(os.path.join(path_to_log, "vehicle_inter_0.csv"),
sep=',', header=0, dtype={0: str, 1: float, 2: float},
names=["vehicle_id", "enter_time", "leave_time"])
duration = df_vehicle_inter_0["leave_time"].values - df_vehicle_inter_0["enter_time"].values
dur = np.mean([time for time in duration if not isnan(time)])
real_traffic_vol = 0
nan_num = 0
for time in duration:
if not isnan(time):
real_traffic_vol += 1
else:
nan_num += 1
traffic_vol = get_traffic_volume(dic_exp_conf["TRAFFIC_FILE"][0], run_cnt)
print(nan_num, nan_thres, self.best_model_pool)
if nan_num < nan_thres:
cnt = 0
for i in range(len(self.best_model_pool)):
if self.best_model_pool[i][1] > dur:
break
cnt += 1
self.best_model_pool.insert(cnt, [cnt_round, dur])
num_max = min(len(self.best_model_pool), self.exp_conf["NUM_BEST_MODEL"])
self.best_model_pool = self.best_model_pool[:num_max]
# log best models through rounds
print(self.best_model_pool)
f = open(os.path.join(self.dic_path["PATH_TO_WORK_DIRECTORY"], "best_model_pool.log"), "a")
f.write("round: %d " % cnt_round)
for i in range(len(self.best_model_pool)):
f.write("id: %d, duration: %f, " % (self.best_model_pool[i][0], self.best_model_pool[i][1]))
f.write("\n")
f.close()
print("model pool ends")
def get(self):
if not self.best_model_pool:
return
else:
ind = random.randint(0, len(self.best_model_pool) - 1)
return self.best_model_pool[ind][0]
def dump_model_pool(self):
if self.best_model_pool:
pickle.dump(self.best_model_pool,
open(os.path.join(self.dic_path["PATH_TO_WORK_DIRECTORY"], "best_model.pkl"), "wb"))