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EVO_STGCN.py
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EVO_STGCN.py
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import argparse
from ExperimentDataLogger import *
from Env import *
import numpy as np
from copy import *
import wandb
import geatpy as ea
import shutil
def is_debug():
gettrace = getattr(sys, 'gettrace', None)
if gettrace is None:
return False
elif gettrace():
return True
else:
return False
class MyProblem(ea.Problem):
def __init__(self, env, logger):
name = 'Auto-STGCN'
maxormins = [-1]
Dim = 24
# [LF, BS, ILR, OF, SIPM, TIPM, FES,PBIndex, SIPM, TIPM, FES,PBIndex, SIPM, TIPM,FES,PBIndex,SIPM, TIPM, FES,PBIndex,IS,OS,FI,MBOF]
varTypes = np.array([1] * Dim)
lb = [1, 1, 1, 1] + [1, 1, 1, 0] + [1, 1, 1, -1] * 3 + [1, 1, 1, 1]
assert len(lb) == Dim
lbin = [1] * Dim
ub = [2, 3, 3, 3] + [4, 3, 4, 0] + [4, 3, 4, 1] + [4, 3, 4, 2] + [4, 3, 4, 3] + [2, 2, 3, 2]
assert len(ub) == Dim
ubin = [1] * Dim
self.env = env
self.logger = logger
ea.Problem.__init__(self, name, 1, maxormins, Dim, varTypes, lb, ub, lbin, ubin)
def aimFunc(self, pop):
vars = pop.Phen
self.logger.plus_episode()
rewards = []
vars = vars.astype(np.int)
for i in range(vars.shape[0]):
self.env.reset()
var = vars[i, :]
action_1 = var[0:4]
action_2 = var[-4:]
blocks = []
blocks.append(var[4:8])
blocks.append(var[8:12])
blocks.append(var[12:16])
blocks.append(var[16:20])
self.env.step(action_1)
self.env.step(action_2)
reward = 0
for block in blocks:
flag = False
if block[-1] == -1:
block = np.array([-1, -1, -1, -1])
flag = True
_, reward, _, _ = self.env.step(block)
if flag:
break
blocks.insert(0, action_1)
blocks.insert(1, action_2)
self.logger(action=list(map(lambda x: x.tolist(), blocks)), reward=reward)
rewards.append(reward)
self.logger.flush_log()
pop.ObjV = np.array(rewards).reshape(vars.shape[0], 1)
max_reward = np.max(np.array(rewards))
self.logger.append_log_file(f'ObjV:{pop.ObjV}')
wandb.log({'reward': max_reward})
# if not isinstance(pop.ObjV, np.ndarray):
# raise RuntimeError('error: 目标函数值矩阵ObjV不是numpy array')
# elif pop.ObjV.ndim != 2 or pop.ObjV.shape[0] != pop.sizes or pop.ObjV.shape[1] != self.problem.M:
# raise RuntimeError(f'error: 目标函数值矩阵ObjV的shape错误，为{pop.ObjV.shape}, ndim应为2, shape[0]应为{pop.sizes}, shape[1]应为{self.problem.M}')
def ea_select_model(config, log_name):
#####################
# set up parameters #
######################
if isinstance(config['ctx'], list):
ctx = [mx.gpu(i) for i in config['ctx']]
elif isinstance(config['ctx'], int):
ctx = mx.gpu(config['ctx'])
else:
raise Exception("config_ctx error:" + str(config['ctx']))
logger = Logger(log_name, config, False)
#######################
# init Env #
#######################
env = GNNEnv(config, ctx, logger)
##############
# training #
##############
problem = MyProblem(env, logger)
Encoding = 'RI'
NIND = 50
Field = ea.crtfld(Encoding, problem.varTypes, problem.ranges, problem.borders)
population = ea.Population(Encoding, Field, NIND)
myAlgorithm = ea.soea_DE_best_1_L_templet(problem, population)
myAlgorithm.MAXGEN = 39
myAlgorithm.logTras = 1
myAlgorithm.verbose = True
myAlgorithm.drawing = 1
[NDSet, population] = myAlgorithm.run()
NDSet.save()
"""==================================输出结果=============================="""
print('用时：%f 秒' % myAlgorithm.passTime)
print('评价次数：%d 次' % myAlgorithm.evalsNum)
print('非支配个体数：%d 个' % NDSet.sizes) if NDSet.sizes != 0 else print('没有找到可行解！')
if myAlgorithm.log is not None and NDSet.sizes != 0:
print('eval', myAlgorithm.log['eval'][-1])
print('f_opt', myAlgorithm.log['f_opt'][-1])
print('f_max', myAlgorithm.log['f_max'][-1])
print('f_avg', myAlgorithm.log['f_avg'][-1])
print('f_min', myAlgorithm.log['f_min'][-1])
print('f_std', myAlgorithm.log['f_std'][-1])
"""=========================进化过程指标追踪分析========================="""
metricName = [['eval']]
Metrics = np.array([myAlgorithm.log[metricName[i][0]] for i in range(len(metricName))]).T
# 绘制指标追踪分析图
ea.trcplot(Metrics, labels=metricName, titles=metricName)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--data', type=str, default=None)
parser.add_argument('--train_length', type=int, default=None)
parser.add_argument('--pred_length', type=int, default=None)
parser.add_argument('--split_ratio', type=list, default=None)
parser.add_argument('--time_max', type=float, default=None)
parser.add_argument('--epochs', type=int, default=None)
parser.add_argument('--epsilon_initial', type=float, default=None)
parser.add_argument('--epsilon_decay_step', type=int, default=None)
parser.add_argument('--epsilon_decay_ratio', type=float, default=None)
parser.add_argument('--gamma', type=float, default=None)
parser.add_argument('--lr', type=float, default=None)
parser.add_argument('--episodes', type=int, default=None)
parser.add_argument('--n', type=int, default=None)
parser.add_argument('--ctx', type=int, default=None)
args = parser.parse_args()
config_filename = './Config/default.json'
with open(config_filename, 'r') as f:
config = json.loads(f.read())
# override default config
dataset = args.data.upper()
if dataset == 'PEMS03':
config["id_filename"] = "data/PEMS03/PEMS03.txt"
config["num_of_vertices"] = 358
elif dataset == 'PEMS04':
config["id_filename"] = None
config["num_of_vertices"] = 307
elif dataset == 'PEMS07':
config["id_filename"] = None
config["num_of_vertices"] = 883
elif dataset == 'PEMS08':
config["id_filename"] = None
config["num_of_vertices"] = 170
else:
raise Exception(f'Input data is {args.data}, only support PEMS03/04/07/08')
config["adj_filename"] = f"data/{dataset}/{dataset}.csv"
config["graph_signal_matrix_filename"] = f"data/{dataset}/{dataset}.npz"
config["pearsonr_adj_filename"] = f"data/{dataset}/{dataset}_pearsonr.npz"
arg_dict = copy(vars(args))
for key, value in vars(args).items():
if value is None:
arg_dict.pop(key)
config.update(arg_dict)
print(json.dumps(config, sort_keys=True, indent=4))
if is_debug():
log_name = 'debug'
if os.path.exists('Log/debug_retrain/'):
shutil.rmtree('Log/debug_retrain/')
else:
log_name = input('log_name:\n')
wandb.init(project="GNN2", config=config, notes=log_name)
ea_select_model(config, log_name)