def __init__(self, username: str, telegram_chat_ids: str):
self.sleeper = Sleeper(30)
self.username = username
self.existing_like_id_set = get_like_id_set(self.get_likes())
logging.info('Init monitor succeed.\nUsername: {}\nLike ids: {}'.format(
self.username, self.existing_like_id_set))
self.telegram_notifier = TelegramNotifier(chat_ids=telegram_chat_ids,
username=username,
module='Like')
self.last_log_time = datetime.now()
def __init__(self, username: str, telegram_chat_ids: str):
self.sleeper = Sleeper(120)
self.username = username
self.user_id = get_user_id(username)
self.following_users = self.get_all_following_users(self.user_id)
logging.info(
'Init monitor succeed.\nUsername: {}\nUser id: {}\nFollowing users: {}'
.format(username, self.user_id, self.following_users))
self.telegram_notifier = TelegramNotifier(chat_ids=telegram_chat_ids,
username=username,
module='Following')
self.last_log_time = datetime.now()
def __init__(self, username: str, telegram_chat_ids: str):
self.sleeper = Sleeper(10)
self.user_id = get_user_id(username)
tweets = self.get_tweets()
self.last_tweet_id = tweets[0]['id']
logging.info(
'Init monitor succeed.\nUsername: {}\nUser id: {}\nLast tweet: {}'.
format(username, self.user_id, tweets[0]))
self.telegram_notifier = TelegramNotifier(chat_ids=telegram_chat_ids,
username=username,
module='Tweet')
self.last_log_time = datetime.now()
def __init__(self, chat_ids: str, username: str, module: str):
if not chat_ids:
logging.warning('Telegram id not set, skip initialization of telegram notifier.')
self.bot = None
return
token = get_token('TELEGRAM_TOKEN')
if not token:
raise ValueError('TELEGRAM_TOKEN is null, please fill in it.')
self.bot = telegram.Bot(token=token)
self.chat_ids = chat_ids.split(',')
self.username = username
self.module = module
self.sleeper = Sleeper(1)
self.send_message('Init telegram bot succeed.')
# Imports
import torch
import math
import copy
from sleeper import Sleeper
sleeper = Sleeper()
class Academy:
def __init__(self,
net,
data,
gpu = False,
autoencoder_trainer = False):
"""
Trains and test models on datasets
Args:
net (Pytorch model class): Network to train and test
data (Tensor Object): Data to train and test with
gpu (bool, optional): If True then GPU's will be used. Defaults to False.
autoencoder_trainer (bool, optional): If True labels will equal inputs. Defaults to False.
"""
super(Academy,self).__init__()
# Declare GPU usage
self.gpu = gpu
# Push net to CPU or GPU
self.net = net if self.gpu == False else net.cuda()