def create_notebook_pool():
notebook_pool = []
in_result = []
cursor, db = create_connection()
sql = 'select distinct notebook_id from result'
cursor.execute(sql)
sql_res = cursor.fetchall()
for row in sql_res:
in_result.append(int(row[0]))
in_notebook = []
sql = 'select distinct id from notebook where isRandom=1'
cursor.execute(sql)
sql_res = cursor.fetchall()
for row in sql_res:
in_notebook.append(int(row[0]))
sql = 'select pair.nid from pair,dataset where pair.did=dataset.id and dataset.server_ip = \'' + ip + '\''
cursor.execute(sql)
sql_res = cursor.fetchall()
for row in sql_res:
if int(row[0]) not in in_result:
continue
if int(row[0]) not in in_notebook:
continue
if int(row[0]) not in notebook_pool:
notebook_pool.append(int(row[0]))
return notebook_pool
def check_model(notebook_id):
model_dic = eval(CONFIG.get('models', 'model_dic'))
cursor, db = create_connection()
sql = 'select model_type from result where notebook_id = ' + str(notebook_id)
cursor.execute(sql)
sql_res = cursor.fetchall()
model_list = np.zeros([len(model_dic.keys())])
check = False
for row in sql_res:
if row[0] in model_dic.keys():
model_id = model_dic[row[0]]-1
model_list[model_id] = 1
check = True
return check,model_list
def train(notebook_root, dataset_root, ip):
def check_model(notebook_id):
model_dic = eval(CONFIG.get('models', 'model_dic'))
cursor, db = create_connection()
sql = 'select model_type from result where notebook_id = ' + str(
notebook_id)
cursor.execute(sql)
sql_res = cursor.fetchall()
model_list = np.zeros([len(model_dic.keys())])
check = False
for row in sql_res:
if row[0] in model_dic.keys():
model_id = model_dic[row[0]] - 1
model_list[model_id] = 1
check = True
return check, model_list
def create_notebook_pool():
notebook_pool = []
in_result = []
cursor, db = create_connection()
sql = 'select distinct notebook_id from result'
cursor.execute(sql)
sql_res = cursor.fetchall()
for row in sql_res:
in_result.append(int(row[0]))
in_notebook = []
sql = 'select distinct id from notebook where isRandom=1'
cursor.execute(sql)
sql_res = cursor.fetchall()
for row in sql_res:
in_notebook.append(int(row[0]))
sql = 'select pair.nid from pair,dataset where pair.did=dataset.id and dataset.server_ip = \'' + ip + '\''
cursor.execute(sql)
sql_res = cursor.fetchall()
for row in sql_res:
if int(row[0]) not in in_result:
continue
if int(row[0]) not in in_notebook:
continue
if int(row[0]) not in notebook_pool:
notebook_pool.append(int(row[0]))
return notebook_pool
notebook_pool = create_notebook_pool()
train_config = eval(CONFIG.get('train', 'train'))
nepisode = train_config['nepisode']
obs_dim = train_config['obs_dim']
ope_dic = eval(CONFIG.get('operators', 'operations'))
learning_rate = train_config['learning_rate']
gamma = train_config['gamma']
dense_dim = train_config['dense_dim']
column_num = train_config['column_num']
act_1_dim = 0
for item in ope_dic:
if ope_dic[item]['index'] > act_1_dim:
act_1_dim = ope_dic[item]['index'] # 27
agent = PolicyGradient(act_1_dim=act_1_dim + 1,
act_2_dim=column_num,
obs_dim=obs_dim,
dense_dim=dense_dim,
lr=learning_rate,
gamma=gamma)
if os.path.exists('reward_list.npy'):
print('exists')
reward_list = list(np.load('./reward_list.npy', allow_pickle=True))
else:
reward_list = []
if os.path.exists('loss_pg.npy'):
print('loss exists')
loss_list = list(np.load('./loss_pg.npy', allow_pickle=True))
else:
loss_list = []
iteration = 0
if os.path.exists('act_reward_pg.npy'):
print('exists')
act_reward = np.load('./act_reward_pg.npy', allow_pickle=True).item()
else:
act_reward = {}
for i_episode in range(nepisode):
ep_rwd = 0
notebook_id = random.choice(notebook_pool)
print("\033[0;35;40m" + "notebook_id:" + str(notebook_id) + "\033[0m")
notebook_path = notebook_root + str(notebook_id) + '.ipynb'
notebook_code = get_code_txt(notebook_path)
res_line_number = -1
s_t, len_data = rpc_client_get_origin_state(notebook_id, notebook_code,
column_num, ip)
# print(s_t)
check_result, model_list = check_model(notebook_id)
while s_t == 'run failed' or check_result == False:
notebook_pool.remove(notebook_id)
notebook_id = random.choice(notebook_pool)
print("\033[0;34;40m" + "notebook_id:" + str(notebook_id) +
"\033[0m")
notebook_path = notebook_root + str(notebook_id) + '.ipynb'
notebook_code = get_code_txt(notebook_path)
s_t, len_data = rpc_client_get_origin_state(
notebook_id, notebook_code, column_num, ip)
check_result, model_list = check_model(notebook_id)
s_t_p = s_t
s_t = np.ravel(s_t)
type_ = np.array([int(np.load('type.npy', allow_pickle=True))])
if len(s_t) == 1900:
s_t = np.concatenate((type_, s_t), axis=0)
if len(s_t) == 1901:
s_t = np.concatenate((s_t, model_list), axis=0)
# pprint.pprint(s_t)
if len(s_t) == 0:
continue
while True:
terminal1 = False
if int(np.load('type.npy', allow_pickle=True)) != 1:
terminal1 = True
action1, action2 = agent.step(s_t) # 已知当前状态，通过网络预测预测下一步的动作(这里要改)
# print(len_data)
# print(len(s_t_p))
check_res = check_action_by_rule(action1 + 1,
action2,
s_t_p,
len_data,
column_num=column_num)
count = 0
s_t_plus_1 = np.zeros([1942])
if check_res == False:
reward = -1.0
terminal = True
# while cehck_res == False and count < 10:
# print('changed_act1:',action1)
# count += 1
# action1,action2 = agent.step_1() # 已知当前状态，通过网络预测预测下一步的动作(这里要改)
# cehck_res = check_action_by_rule(action1 + 1, action2 + 1, s_t_p,len_data,column_num=column_num)
else:
if action2 == column_num - 1:
target_content = {
'operation': action1 + 1,
'data_object': -1,
}
else:
target_content = {
'operation': action1 + 1,
'data_object': action2,
}
# print('?>>?')
# print('act:',act)
# 执行动作，得到新状态，立即回报，是否终止
# s_t = []
eventlet.monkey_patch()
try:
with eventlet.Timeout(60, False): # 设置超时时间为2秒
s_t, action, reward, terminal, s_t_plus_1, notebook_code, res_line_number, len_data_plus_1 = rpc_client_do_an_action(
notebook_id, notebook_code, target_content,
column_num, res_line_number, ip)
except:
break
if s_t == []:
break
# print('?>>?')
if reward == -2:
continue
# print("\033[0;36;40m" + "s_t:" + str(s_t) + "\033[0m")
s_t = np.ravel(s_t)
type_ = np.array([int(np.load('type.npy', allow_pickle=True))])
if int(np.load('type.npy', allow_pickle=True)) != 1:
terminal = True
if len(s_t) == 1900:
s_t = np.concatenate((type_, s_t), axis=0)
if len(s_t) == 1901:
s_t = np.concatenate((s_t, model_list), axis=0)
s_t_p = s_t_plus_1
s_t_plus_1 = np.ravel(s_t_plus_1)
if len(s_t_plus_1) == 1900:
s_t_plus_1 = np.concatenate(([0], s_t_plus_1), axis=0)
if len(s_t_plus_1) == 1901:
s_t_plus_1 = np.concatenate((s_t_plus_1, model_list),
axis=0)
s_t = s_t_plus_1
len_data = len_data_plus_1
act = (action1, action2)
agent.memory.store_transition(s_t, act, reward) # 放入采样池(这里要改)
ep_rwd += reward
print("\033[0;36;40m" + "reward:" + str(reward) + "\033[0m")
print("\033[0;36;40m" + "terminal:" + str(terminal) + "\033[0m")
print("\033[0;36;40m" + "act:" + str(act) + "\033[0m")
if terminal or terminal1:
loss = agent.learn() # 一个完整过程终止，开始优化网络
loss_list.append(loss)
print('Ep: %i' % i_episode, "|Ep_r: %f" % ep_rwd)
sql = 'update notebook set trained_time = trained_time + 1 where id=' + str(
notebook_id)
cursor, db = create_connection()
cursor.execute(sql)
db.commit()
if act[0] not in act_reward:
act_reward[act[0]] = []
act_reward[act[0]].append(ep_rwd)
reward_list.append(ep_rwd)
# if i_episode % 50 == 0:
np.save('./reward_list.npy', reward_list)
np.save('loss_pg', loss_list)
np.save('act_reward_pg', act_reward)
break