def run_stage(self, Ti, up_info):
for i in range(len(self.agents)):
last_arg = deepcopy(self.agents[i].stage_arg)
self.agents[i].stage_arg = arg.init_stage_arg(
self.global_arg, self.main_env.arg, self.agents[i].agent_arg,
last_arg, Ti)
for i in range(self.global_arg['Ts']):
logging.info("frame %3d , Ti:%3d" % (i, Ti))
# 运行Frame
self.run_frame(Ti, i, up_info)
for k in range(self.global_arg["Nagent"]):
csv_info = [
Ti + i,
self.main_env.getValue(self.agents[k].state_now, Ti),
self.agents[k].frame_arg['PSM']['f-req'],
int(self.agents[k].frame_arg['PROC']['action']),
self.agents[k].frame_arg['PSM']['a-need'],
up_info['nkinfo']['max'], up_info['nkinfo']['min'],
up_info['nkinfo']['mid'], up_info['nkinfo']['avg'],
up_info['nkinfo']['p0.75'], up_info['nkinfo']['p0.25']
]
moniter.AppendToCsv(csv_info, all_config['result_csv_path'][k])
agent_value = [
self.main_env.getValue(self.agents[k].state_now, Ti)
for k in range(self.global_arg["Nagent"])
]
csv_info = [Ti + i] \
+ agent_value \
+ [sum(agent_value) / len(agent_value)] \
+ [up_info['nkinfo'][key] for key in ['max', 'min', 'mid', 'avg', 'p0.75', 'p0.25']]
moniter.AppendToCsv(csv_info, all_config['result_csv_path'][-1])
def __init__(self):
# 环境初始化
self.global_arg = arg.init_global_arg()
env_arg = arg.init_env_arg(self.global_arg)
# 增加nk的一个读入操作
self.main_env = Env(env_arg)
for model_type in ['st', 'ed']:
if all_config['checkpoint']['env'][model_type]['enable']:
self.main_env.nkmodel_load(all_config['checkpoint']['env']['path'], model_type)
self.main_env.nkmodel_save(all_config["nkmodel_path"][model_type], model_type)
# 个体初始化
self.agents = []
csv_head_agent = ['agent_no'] + ['st_state'] + ['st_value'] + ['insight'] + ['xplr'] + ['xplt'] + ['enable']
moniter.AppendToCsv(csv_head_agent, all_config['agent_csv_path'])
for i in range(self.global_arg["Nagent"]):
# 个体随机初始位置
start_st_label = [randint(0, self.main_env.P - 1) for j in range(self.main_env.N)]
state_start = State(start_st_label)
self.agents.append(Agent(arg.init_agent_arg(self.global_arg,
self.main_env.arg),
self.main_env))
self.agents[i].state_now = deepcopy(state_start)
self.agents[i].agent_id = i
# 去除了一开始给一个全局area，改为添加一个包含起点的点area
start_area = Area(self.agents[i].state_now, [False] * self.main_env.N, 0)
start_area.info = get_area_sample_distr(env=self.main_env, area=start_area, state=self.agents[i].state_now,
T_stmp=0, sample_num=1, dfs_r=1)
start_area.sign = Sign(i, 0, 'start')
self.agents[i].renew_m_info(start_area, 0)
self.a_plan = None
logging.info("state:%s, st_value:%s,insight:%.5s ,xplr:%.5s, xplt:%.5s, enable:%.5s" % (
str(self.agents[i].state_now),
self.main_env.getValue(self.agents[i].state_now, 0),
self.agents[i].agent_arg['a']['insight'],
self.agents[i].agent_arg['a']['xplr'],
self.agents[i].agent_arg['a']['xplt'],
self.agents[i].agent_arg['a']['enable']))
# 记录agent信息
csv_info_agent = ['agent%d' % i] \
+ [self.agents[i].state_now] \
+ [self.main_env.getValue(self.agents[i].state_now, 0)] \
+ [self.agents[i].agent_arg['a']['insight']] \
+ [self.agents[i].agent_arg['a']['xplr']] \
+ [self.agents[i].agent_arg['a']['xplt']] \
+ [self.agents[i].agent_arg['a']['enable']]
moniter.AppendToCsv(csv_info_agent, all_config['agent_csv_path'])
# 社会网络初始化
soclnet_arg = arg.init_soclnet_arg(self.global_arg, env_arg)
self.socl_net = SoclNet(soclnet_arg)
self.socl_net.new_flat_init() # 修改初始化方法
# self.socl_net.flat_init()
if all_config['checkpoint']['socl_network']['enable']:
self.socl_net.power_load(all_config['checkpoint']['socl_network']['power'])
self.socl_net.relat_load(all_config['checkpoint']['socl_network']['relat'])
self.record = Record()
self.metric = metrics.register_all_metrics(metrics.Metrics())
def run_exp(self):
up_info = {}
# 单个agent的结果表
for k in range(self.global_arg["Nagent"]):
csv_head = ['frame', 'state', 'value', 'area_v', 'area_center', 'act', 'goal', 'goal_value']
moniter.AppendToCsv(csv_head, all_config['result_csv_path'][k])
# 结果汇总表
# 添加agent max和agent min
csv_head_value = ['frame'] \
+ ["agent%d" % (k) for k in range(self.global_arg['Nagent'])] \
+ ["agent_avg", "agent_max", "agent_min"] \
+ ['peakmax', 'peakmin', 'peakavg'] \
+ ['adj_avg']
moniter.AppendToCsv(csv_head_value, all_config['value_csv_path'][-1])
csv_head_area = ['frame'] \
+ ["agent%d" % (k) for k in range(self.global_arg['Nagent'])] \
+ ["agent_avg"] \
+ ['nkmax']
moniter.AppendToCsv(csv_head_area, all_config['area_csv_path'])
csv_head_act = ['frame'] \
+ ["agent%d" % (k) for k in range(self.global_arg['Nagent'])]
moniter.AppendToCsv(csv_head_act, all_config['act_csv_path'])
stage_num = self.global_arg['T'] // self.global_arg['Ts']
# self.main_env.getModelDistri() # 为了作图，仅测试时调用！！
up_info['nkinfo'] = self.main_env.getModelPeakDistri() # 将nkinfo变为peakvalue
# all_peak_value = self.main_env.getAllPeakValue()
# moniter.DrawHist(all_peak_value, all_config['peak_hist'])
meet_req = {}
for i in range(stage_num):
Ti = i * self.global_arg['Ts'] + 1
logging.info("stage %3d, Ti:%3d" % (i, Ti))
self.main_env.T_clock = Ti
# 每个stage遍历一遍当前模型，获取分布信息
# 减少运算量，只算第一帧
# up_info['nkinfo'] = self.main_env.getModelDistri()
# logging.debug("max_value:{max}".format(**up_info['nkinfo']))
# 运行一个Stage，Ti表示每个Stage的第一帧
meet_req = self.run_stage(Ti, meet_req, up_info)
moniter.DumpToJson(self.metric.get_data(), all_config['metrics_json_path'])
moniter.DumpToJson(leadership_bill.leader_bill.to_json(), all_config['leadership_bill_json_path'])
def run_exp(self):
up_info = {}
for i in range(self.global_arg["Nagent"]):
self.agents.append(
Agent(arg.init_agent_arg(self.global_arg, self.main_env.arg)))
self.agents[i].state_now = State(
[0 for _ in range(self.main_env.N)])
self.agents[i].inter_area.info = get_area_sample_distr(
env=self.main_env,
T=0,
area=self.agents[i].inter_area,
state=self.agents[i].state_now,
sample_num=self.main_env.arg['ACT']['hqxx']['sample_n'],
dfs_r=self.main_env.arg['ACT']['hqxx']['dfs_p'])
self.agents[i].inter_area.info['start_t'] = 0
stage_num = self.global_arg['T'] // self.global_arg['Ts']
for k in range(self.global_arg["Nagent"]):
csv_head = [
'frame', 'SSMfi', 'SSM_f-req', 'proc_action', 'SSM_f_need',
'nkmax', 'nkmin', 'nkmid', 'nkavg', 'nk0.75', "nk0.25"
]
# 'peakmax', 'peakmin', 'peakmid', 'peakavg', 'peak0.75', "peak0.25"]
moniter.AppendToCsv(csv_head, all_config['result_csv_path'][k])
csv_head = ['frame'] \
+ ["%s%d" % (using_brain[k].func_name, k) for k in range(self.global_arg['Nagent'])] \
+ ["agent_avg"] \
+ ['nkmax', 'nkmin', 'nkmid', 'nkavg', 'nk0.75', "nk0.25"]
moniter.AppendToCsv(csv_head, all_config['result_csv_path'][-1])
for i in range(stage_num):
Ti = i * self.global_arg['Ts'] + 1
logging.info("stage %3d , Ti:%3d" % (i, Ti))
up_info['nkinfo'] = self.main_env.getModelDistri(Ti)
# up_info['nk_peak'] = self.main_env.getModelPeakDistri(Ti)
# 运行一个Stage，Ti表示每个Stage的第一帧
self.run_stage(Ti, up_info)
def run_stage(self, Ti, meet_req, up_info):
# 将Agent上一个stage的最终状态拷贝过来
for i in range(len(self.agents)):
last_arg = deepcopy(self.agents[i].stage_arg)
self.agents[i].stage_arg = arg.init_stage_arg(self.global_arg,
self.main_env.arg,
self.agents[i].agent_arg,
last_arg,
Ti)
meet_req = {}
# NOTE cid传了个up_info进去，避免重复遍历
self.record.add_env_record(self.main_env, Ti, up_info)
self.record.add_socl_net_record(self.socl_net, Ti)
for i in range(self.global_arg['Ts']):
logging.info("frame %3d , Ti:%3d" % (i, Ti))
self.record.add_agents_record(self.main_env, self.agents, Ti + i)
# 运行Frame， 并将运行后生成的会议请求记录下来
meet_req = self.run_all_frame(Ti, i, meet_req, up_info)
# 输出每个个体的具体信息
for k in range(self.global_arg["Nagent"]):
tmp_goal = ''
tmp_goal_value = ''
if not self.agents[k].a_plan is None:
tmp_goal = self.agents[k].a_plan.goal
tmp_goal_value = self.agents[k].a_plan.goal_value
csv_info_result = [
Ti + i,
str(self.agents[k].state_now),
self.main_env.getValue(self.agents[k].state_now, Ti),
self.agents[k].get_max_area().info['max'],
str(self.agents[k].get_max_area().center),
str(self.agents[k].policy_now) + '&' + str(self.agents[k].meeting_now),
str(tmp_goal),
tmp_goal_value
]
moniter.AppendToCsv(csv_info_result, all_config['result_csv_path'][k])
# 输出当前value
agent_value = [self.main_env.getValue(self.agents[k].state_now, Ti) for k in
range(self.global_arg["Nagent"])]
agent_avg = sum(agent_value) / len(agent_value)
csv_info_value = [Ti + i] \
+ agent_value \
+ [agent_avg, max(agent_value), min(agent_value)] \
+ [up_info['nkinfo'][key] for key in ['max', 'min', 'avg']] \
+ [(agent_avg - up_info['nkinfo']['min']) / (
up_info['nkinfo']['max'] - up_info['nkinfo']['min'])]
moniter.AppendToCsv(csv_info_value, all_config['value_csv_path'][-1])
# 输出max_area
agent_max_area = [self.agents[k].get_max_area().info['max'] for k in
range(self.global_arg["Nagent"])]
csv_info_area = [Ti + i] \
+ agent_max_area \
+ [sum(agent_max_area) / len(agent_max_area)] \
+ [up_info['nkinfo']['max']]
moniter.AppendToCsv(csv_info_area, all_config['area_csv_path'])
# NOTE cid 添加act信息(相应增加agent类里的变量）
act_list = [self.agents[k].policy_now + '&' + self.agents[k].meeting_now for k in
range(self.global_arg["Nagent"])]
csv_info_act = [Ti + i] \
+ act_list
moniter.AppendToCsv(csv_info_act, all_config['act_csv_path'])
# 按stage输出
if self.global_arg['mul_agent']:
# net_title, net_data = self.record.output_socl_net_per_frame(Ti + i)
power_save_path = os.path.join(all_config['network_csv_path'], "power_%04d.csv" % (Ti))
relat_save_path = os.path.join(all_config['network_csv_path'], "relat_%04d.csv" % (Ti))
self.socl_net.power_save(power_save_path)
self.socl_net.relat_save(relat_save_path)
# P1-05 增加Socil Network的结果输出
self.metric.calc_metric(['stage'], Ti,
socl_net=self.socl_net,
agents=self.agents,
env=self.main_env)
return meet_req
# 其他汇总信息，位于每个exp的总目录下
all_config['value_csv_path'] = []
all_config['value_csv_path'].append(
os.path.join("result", all_config['batch_id'], exp_id, "%s_value.csv" % (exp_id))
)
# max_area输出
all_config['area_csv_path'] = os.path.join("result", all_config['batch_id'], exp_id, "%s_area.csv" % (exp_id))
# 添加一个act的记录文件
all_config['act_csv_path'] = os.path.join("result", all_config['batch_id'], exp_id, "%s_act.csv" % (exp_id))
all_config['nkmodel_path'] = {}
for model_type in ['st', 'ed']:
all_config['nkmodel_path'][model_type] = os.path.join("result", all_config['batch_id'], exp_id,
"%s_nkmodel_%s.pickle" % (exp_id, model_type))
all_config['agent_csv_path'] = os.path.join("result", all_config['batch_id'], exp_id,
"%s_agent_attribute.csv" % (exp_id))
all_config['peak_hist'] = os.path.join("result", all_config['batch_id'], exp_id, "%s_peak_hist.png" % (exp_id))
all_config['total_hist'] = os.path.join("result", all_config['batch_id'], exp_id,
"%s_total_hist.png" % (exp_id))
all_config['metrics_json_path'] = os.path.join("result", all_config['batch_id'], exp_id,
"%s_metrics.json" % exp_id)
all_config['leadership_bill_json_path'] = os.path.join("result", all_config['batch_id'], exp_id,
"%s_leadership_bill.json" % exp_id)
logging.info("run exp %3d" % exp_num)
main_control = MulControl()
main_control.run_exp() # 开始运行实验
moniter.AppendToCsv(exp_list, all_config['exp_list_path'])