# 約定した時は、trade_sleep_timeを返す
sleep_time = trade_wrapper.tradeDecisionWrapper(base_time)
base_time = sleepTransaction(sleep_time, test_mode, base_time)
# order_flagがある時は、stl_sleep_timeを返す
# 決済した時は、stl_sleep_ltime or stl_sleep_vtimeを返す
# その他はsleep_time = 0を返す
sleep_time = trade_wrapper.stlDecisionWrapper(base_time)
base_time = sleepTransaction(sleep_time, test_mode, base_time)
# StopLossの時はstl_sleep_ltimeを返す
# LimitOrderの時はstl_sleep_vtimeを返す
# その他はsleep_time = 0を返す
sleep_time = trade_wrapper.checkPosition(base_time)
base_time = sleepTransaction(sleep_time, test_mode, base_time)
if test_mode:
now = datetime.now()
if base_time > now or base_time > end_time:
trade_wrapper.removeOnfile()
raise ValueError("Complete Back Test")
except:
sendmail = SendMail("*****@*****.**", "*****@*****.**",
property_path)
message = traceback.format_exc()
debug_logger.info(message)
sendmail.set_msg(message)
sendmail.send_mail()
from netcup_crawler import NetcupCrawler
from send_mail import SendMail
crawler = NetcupCrawler()
mail = SendMail()
entries = crawler.fetch()
if crawler.check(entries):
crawler.save(entries)
subject = "Netcup - Neues Sonderangebot"
body = ""
for entry in entries:
body += "<br>Angebot: " + entry.title + "<br> Preis: " + entry.price + "<br>"
recepients = {"*****@*****.**", "*****@*****.**"}
mail.send_mail(subject, body, recepients)
print("Email gesendet.")
else:
print("Keine Email gesendet.")
def start():
# Command line arguments
ap = argparse.ArgumentParser()
ap.add_argument('-t',
'--tune',
type=bool,
default=False,
help="pass -t True for hyperparameter tuning")
args = vars(ap.parse_args())
batch_size = 20
n_epochs = 1
# loads data and returns train, valid and test data
train_load, valid_load, test_load, classes = load_data(batch_size)
if not args['tune']:
learning_rate = 0.001
optimizer = 'Adam'
model = define_model()
cr, op = loss_and_optim(model, learning_rate, optimizer)
print("Training the network without hyperparameter tuning")
train(n_epochs, model, train_load, valid_load, cr, op)
accuracy = test(batch_size, model, test_load, classes, cr, op)
accuracy = int(accuracy)
with open('accuracy.txt', 'w') as f:
f.write(str(accuracy))
else:
n_epochs = 2
learning_rates = [0.01, 0.001]
optimizers = ['Adam', 'SGD']
acc = []
max_acc = 0
best_lr = 0
best_opt = ''
for learning_rate in learning_rates:
for optimizer in optimizers:
print(
"\n ** Training with {} optimizer and {} learning rate **\n"
.format(optimizer, learning_rate))
model = define_model()
cr, op = loss_and_optim(model, learning_rate, optimizer)
model = train(n_epochs, model, train_load, valid_load, cr, op)
accuracy = test(batch_size, model, test_load, classes, cr, op)
if accuracy > max_acc:
max_acc = accuracy
best_opt = optimizer
best_lr = learning_rate
# Save the best performing model
torch.save(model.state_dict(), 'model_cifar.pt')
print("Saving best model...")
print(
"\nBest Learning Rate : {}\nBest Optimizer : {}\nBest Accuracy: {}"
.format(best_lr, best_opt, str(max_acc)))
# Send email user about the model performance and best hyperparameter so far
print("Sending email to the user...")
s = SendMail(str(max_acc) + '%', best_lr, best_opt)
s.send_mail()
max_acc = int(max_acc)
with open('accuracy.txt', 'w') as f:
f.write(str(max_acc))
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from receive_mail import FetchEmail
from extract_content import extract_content
from send_mail import SendMail
# receivemail
# 下载对应邮箱的附件图片，下载到对应的文件夹 received
mail_server = ''
username = ''
password = '' # 此处填写授权码
fetch_email = FetchEmail(mail_server, username, password)
emails = fetch_email.fetch_unread_messages()
# 调用相应的代码进行处理，识别内容，生成xlsx
save_dir_path = "to_send"
extract_content(save_dir_path)
# sendmail
from_mail = ''
tolist = ['']
username = ''
password = '' # 此处填写授权码
send = SendMail(from_mail, tolist, username, password)
send.send_mail('./to_send')
def main_process():
# 1. 调用receive_mail.py
# 下载对应邮箱的附件图片，下载到对应的文件夹 received
receiver_mail_server = config['receiver']['mail_server']
receiver_username = config['receiver']['username']
receiver_password = config['receiver']['password']
logger.info("Receive from mail_server is :{}, username is :{}!",
receiver_mail_server,
receiver_username,
feature="f-strings")
flag = True
while flag:
try:
fetch_email = FetchEmail(receiver_mail_server, receiver_username,
receiver_password)
emails = fetch_email.fetch_unread_messages()
logger.info("Receiving from mail_server...please wait...")
if emails:
flag = False
logger.info("Receive mail attachment success!")
time.sleep(1)
except Exception as e:
logger.debug("Receive mail attachment error, error is {}!",
e,
feature="f-strings")
# 2. 调用extract_content.py
# 对下载的图片进行识别，识别后内容生成xls
try:
save_dir_path = "to_send"
extract_content(save_dir_path)
logger.info("Extract mail attachment success!")
except Exception as e:
logger.debug("Extract mail attachment error, error is {}!",
e,
feature="f-strings")
# 3. 调用sendmail.py
# 将生成的xls发送到对应的邮箱列表
sender_mail_server = config['sender']['sender_mail_server']
to_username = config['sender']['to_username']
sender_username = config['sender']['sender_username']
sender_password = config['sender']['sender_password'] # 此处填写授权码
logger.info(
"Send mail from sender_mail_server is :{}, sender_username is :{}, to_username is :{}!",
sender_mail_server,
sender_username,
to_username,
feature="f-strings")
try:
send = SendMail(sender_mail_server, to_username, sender_username,
sender_password)
send.send_mail('./to_send')
logger.info("Send extract xls success!")
except Exception as e:
logger.debug("Send extract xls error, error is {}!",
e,
feature="f-strings")
