各位老师，py出现如下错误该如何处理呢？谢谢了
Traceback (most recent call last):
File "C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexes\base.py", line 3361, in get_loc
return self._engine.get_loc(casted_key)
File "pandas\_libs\index.pyx", line 76, in pandas._libs.index.IndexEngine.get_loc
File "pandas\_libs\index.pyx", line 108, in pandas._libs.index.IndexEngine.get_loc
File "pandas\_libs\hashtable_class_helper.pxi", line 5198, in pandas._libs.hashtable.PyObjectHashTable.get_item
File "pandas\_libs\hashtable_class_helper.pxi", line 5206, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 'open'
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "E:/main_test.py", line 148, in <module>
is_intraday=True, is_lack_margin=is_lack_margin, args=args)
File "E:\environment.py", line 137, in __init__
self.Data=preProcessData(data_fn)
File "E:\environment.py", line 51, in preProcessData
df[newLabel] = df[cn]
File "C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\frame.py", line 3458, in __getitem__
indexer = self.columns.get_loc(key)
File "C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\indexes\base.py", line 3363, in get_loc
raise KeyError(key) from err
KeyError: 'open'
[Python] 纯文本查看复制代码
import numpy as npimport argparse
from copy import deepcopy
import random
import torch
from timeit import default_timer as timer
from evaluator import Evaluator
from rdpg import RDPG
from util import *
from environment import environment
torch.cuda.empty_cache()
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='PyTorch on Financial trading--iRDPG algorithm')
##### Model Setting #####
# parser.add_argument('--rnn_mode', default='lstm', type=str, help='RNN mode: LSTM/GRU')
parser.add_argument('--rnn_mode', default='gru', type=str, help='RNN mode: LSTM/GRU')
parser.add_argument('--input_size', default=14, type=int, help='num of features for input state')
parser.add_argument('--seq_len', default=15, type=int, help='sequence length of input state')
parser.add_argument('--num_rnn_layer', default=2, type=int, help='num of rnn layer')
parser.add_argument('--hidden_rnn', default=128, type=int, help='hidden num of lstm layer')
parser.add_argument('--hidden_fc1', default=256, type=int, help='hidden num of 1st-fc layer')
parser.add_argument('--hidden_fc2', default=64, type=int, help='hidden num of 2nd-fc layer')
parser.add_argument('--hidden_fc3', default=32, type=int, help='hidden num of 3rd-fc layer')
parser.add_argument('--init_w', default=0.005, type=float, help='initialize model weights')
##### Learning Setting #####
parser.add_argument('--r_rate', default=0.0001, type=float, help='gru layer learning rate')
parser.add_argument('--c_rate', default=0.0001, type=float, help='critic net learning rate')
parser.add_argument('--a_rate', default=0.0001, type=float, help='policy net learning rate (only for DDPG)')
parser.add_argument('--beta1', default=0.3, type=float, help='mometum beta1 for Adam optimizer')
parser.add_argument('--beta2', default=0.9, type=float, help='mometum beta2 for Adam optimizer')
parser.add_argument('--sch_step_size', default=16*150, type=float, help='LR_scheduler: step_size')
parser.add_argument('--sch_gamma', default=0.5, type=float, help='LR_scheduler: gamma')
parser.add_argument('--bsize', default=100, type=int, help='minibatch size')
##### RL Setting #####
parser.add_argument('--warmup', default=100, type=int, help='only filling the replay memory without training')
parser.add_argument('--discount', default=0.95, type=float, help='future rewards discount rate')
parser.add_argument('--a_update_freq', default=3, type=int, help='actor update frequecy (per N steps)')
parser.add_argument('--Reward_max_clip', default=15., type=float, help='max DSR reward for clipping')
parser.add_argument('--tau', default=0.002, type=float, help='moving average for target network')
##### original Replay Buffer Setting #####
parser.add_argument('--rmsize', default=12000, type=int, help='memory size')
parser.add_argument('--window_length', default=1, type=int, help='')
##### Exploration Setting #####
parser.add_argument('--ou_theta', default=0.18, type=float, help='noise theta of Ornstein Uhlenbeck Process')
parser.add_argument('--ou_sigma', default=0.3, type=float, help='noise sigma of Ornstein Uhlenbeck Process')
parser.add_argument('--ou_mu', default=0.0, type=float, help='noise mu of Ornstein Uhlenbeck Process')
parser.add_argument('--epsilon_decay', default=100000, type=int, help='linear decay of exploration policy')
##### Training Trajectory Setting #####
parser.add_argument('--exp_traj_len', default=16, type=int, help='segmented experiece trajectory length')
parser.add_argument('--train_num_episodes', default=2000, type=int, help='train iters each episode')
### Also use in Test (Evaluator) Setting ###
parser.add_argument('--max_episode_length', default=240, type=int, help='the max episode length is 240 minites in one day')
parser.add_argument('--test_episodes', default=243, type=int, help='how many episode to perform during testing periods')
##### PER Demostration Buffer #####
parser.add_argument('--is_PER_replay', default=True, help='conduct PER momery or not')
parser.add_argument('--is_pretrain', default=True, action='store_true', help='conduct pretrain or not')
parser.add_argument('--Pretrain_itrs', default=10, type=int, help='number of pretrain iterations')
parser.add_argument('--is_demo_warmup', default=True, action='store_true', help='Execute demonstration buffer')
parser.add_argument('--PER_size', default=40000, type=int, help='memory size for PER')
parser.add_argument('--p_alpha', default=0.3, type=int, help='the power of priority for each experience')
parser.add_argument('--lambda_balance', default=50, type=int, help='priority coeffient for weighting the gradient term')
parser.add_argument('--priority_const', default=0.1, type=int, help='priority constant for demonstration experiences')
parser.add_argument('--small_const', default=0.001, type=int, help='priority constant for agent experiences')
##### Behavior Cloning #####
parser.add_argument('--is_BClone', default=True, action='store_true', help='conduct behavior cloning or not')
parser.add_argument('--is_Qfilt', default=False, action='store_true', help='conduct Q-filter or not')
parser.add_argument('--use_Qfilt', default=100, type=int, help='set the episode after warmup to use Q-filter')
parser.add_argument('--lambda_Policy', default=0.7, type=int, help='The weight for actor loss')
# parser.add_argument('--lambda_BC', default=0.5, type=int, help='The weight for BC loss after Q-filter, default is equal to (1-lambda_Policy)')
##### Other Setting #####
parser.add_argument('--seed', default=627, type=int, help='seed number')
parser.add_argument('--date', default=629, type=int, help='date for output file name')
parser.add_argument('--save_threshold', default=20, type=int, help='lack margin stop ratio')
parser.add_argument('--lackM_ratio', default=0.7, type=int, help='lack margin stop ratio')
parser.add_argument('--debug', default=True, dest='debug', action='store_true')
parser.add_argument('--checkpoint', default="checkpoints", type=str, help='Checkpoint path')
parser.add_argument('--logdir', default='log')
parser.add_argument('--mode', default='test', type=str, help='support option: train/test')
# parser.add_argument('--mode', default='train', type=str, help='support option: train/test')
args = parser.parse_args()
#######################################################################################################
####################################################################################################
'''##### Run Task #####'''
if args.seed > 0:
np.random.seed(args.seed)
random.seed(args.seed)
is_lack_margin = True
# is_lack_margin = False
##### Demonstration Setting #####
if args.is_demo_warmup:
data_fn = "data_preprocess/IF_tech_oriDT.csv"
demo_env = environment(data_fn=data_fn, data_mode='random', duration='train', is_demo=True,
is_intraday=True, is_lack_margin=is_lack_margin, args=args)
else:
demo_env = None
##### Run Training #####
start_time = timer()
if args.mode == 'train':
print('##### Run Training #####')
### train_env setting ###
data_mode = 'random' # random select a day for a trading episode (240 minutes)
duration = 'train' # training period from 2016/1/1 to 2018/5/8
data_fn = "data_preprocess/IF_prophetic.csv"
train_env = environment(data_fn=data_fn, data_mode=data_mode, duration=duration, is_demo=False,
is_intraday=True, is_lack_margin=is_lack_margin, args=args)
### Run training ###
rdpg = RDPG(demo_env, train_env, args)
rdpg.train(args.train_num_episodes, args.checkpoint, args.debug)
end_time = timer()
minutes, seconds = (end_time - start_time)//60, (end_time - start_time)%60
print(f"\nTraining time taken: {minutes} minutes {seconds:.1f} seconds")
##### Run Testing #####
elif args.mode == 'test':
torch.cuda.empty_cache()
print('##### Run Testing #####')
### test_env setting ###
# is_demo = True
is_demo = False
data_mode = 'time_order'
duration = 'test' # testing period from 2018/5/9 to 2019/5/8
is_lack_margin = True
# data_fn = "data_preprocess/IF_prophetic.csv"
data_fn = "data_preprocess/IC_prophetic.csv"
test_env = environment(data_fn=data_fn, data_mode=data_mode, duration=duration, is_demo=is_demo,
is_intraday=True, is_lack_margin=is_lack_margin, args=args)
rdpg = RDPG(demo_env, test_env, args)
description = 'iRDPG_agent'
model_fn = description +'.pkl'
rdpg.test(args.checkpoint, model_fn, description, lackM=is_lack_margin, debug=args.debug)
end_time = timer()
minutes, seconds = (end_time - start_time)//60, (end_time - start_time)%60
print(f"\nTesting time taken: {minutes} minutes {seconds:.1f} seconds")
else:
raise RuntimeError('undefined mode {}'.format(args.mode))
发表于 2023-9-7 17:09
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发表于 2023-9-7 17:51
