def PostContext(self):
tUser = self.GetUser()
tDonor = DonorRecord()
tDonatorRsName = self.request.get('name')
#logging.debug("Quantity Entered: " + str(self.request.get('quantity')))
tDonatorAmount = int(self.request.get('quantity'))
tDonatorNote = self.request.get('memo')
tDonatorForumName = self.request.get('fname')
tFormEmail = self.request.get('email')
tAgentGold = self.request.get('agentgold')
tDonor.donorAgent = tUser.email()
tDonor.donorForumName = tDonatorForumName
tDonor.donorMemo = tDonatorNote
tDonor.donorRsName = tDonatorRsName
tDonor.donorGoldAmount = int(tDonatorAmount)
tDonor.put()
tAgent = Agent()
try:
if (len(tFormEmail) > 0):
tAgent = Agent().GetAgentByEmail(tFormEmail)
else:
tAgent = Agent().GetAgentByEmail(str(tUser.email()))
except:
tAgent = Agent().GetAgentByEmail(str(tUser.email()))
if (tAgent.agentGoldSupply == None):
tAgent.agentGoldSupply = 0
if (tDonatorAmount < 0):
#logging.debug(str(tAgentGold))
tDonatorAmount = tDonatorAmount / 1000000.0
if (tAgentGold == 'on'):
tCommission = tDonatorAmount * 0.65 * -1.0
else:
tAgent.agentGoldSupply += int(tDonatorAmount * 1000000.0)
tCommission = tDonatorAmount * 0.05 * -1.0
#logging.debug("Agent Commission: " + str(tDonatorAmount * -1.0))
tAgent.agentCurrentCommission += tCommission
tAgent.agentTotalCommission += tCommission
else:
tAgent.agentGoldSupply += tDonatorAmount
tAgent.put()
return {}
def post(self):
tAgent = Agent()
tUser = users.get_current_user()
if (tUser):
tAgent = Agent().GetAgentByEmail(tUser.email())
tAgent.put()
#logging.debug("Action by Agent: " + tUser.email())
if (tAgent.agentOnline == False):
self.response.out.write("offline")
else:
self.response.out.write("online")
def test_train(self):
env = Env(balance=250000,
FX_DATA_FILE='../data/raw/FX_Demo/sample_USD_JPY_S5.pickle')
agent = Agent(input_data_shape=(10, ))
mount_agent = Agent(actions=10, input_data_shape=(10, ))
print(len(env.fx_time_data_buy))
trainer = Trainer_priority(env,
agent,
mount_agent,
data_end_index=len(env.fx_time_data_buy) -
2)
trainer.train()
def PostContext(self):
tContext = {}
if (self.IsUserAdmin()):
try:
tAgentKey = self.request.get('custid')
tAgent = Agent()
tAgent = Agent().get(tAgentKey)
tAgent.agentCurrentCommission = 0.0
tAgent.put()
self.response.out.write("Success")
except:
self.response.out.write("Error")
tContext['nowrite'] = True
return tContext
def post(self):
tUrl = "https://api-3t.paypal.com/nvp"
tPaypalPayload = {}
tPaypal = PaypalRefund()
tAgent = Agent()
tOrder = Order()
tUser = users.get_current_user()
tTransId = str(self.request.get('orderid'))
tAgentEmail = str(tUser.email())
tAgent = Agent().GetAgentByEmail(tAgentEmail)
tRefundAgent = tAgentEmail
tOrderQuery = Order.all()
tOrderQuery.filter("orderTransactionId", tTransId)
#logging.debug("Transaction id: " + tTransId)
tOrder = tOrderQuery.get()
if (tOrder.orderDeliver != 'True'):
tPaypalPayload['METHOD'] = "RefundTransaction"
tPaypalPayload['TRANSACTIONID'] = tTransId
tPayloadEncoded = tPaypal.GeneratePayload(tPaypalPayload)
request_cookies = mechanize.CookieJar()
request_opener = mechanize.build_opener(
mechanize.HTTPCookieProcessor(request_cookies))
request_opener.addheaders = [('Content-Type',
'application/x-www-form-urlencoded')]
mechanize.install_opener(request_opener)
tResponse = mechanize.urlopen(url=tUrl,
timeout=25.0,
data=tPayloadEncoded)
#logging.debug("Mechanize Package")
#logging.debug("Url: " + tUrl)
#logging.debug("Data: " + str(tPaypalPayload))
tResult = tResponse.read()
#logging.debug(tResult)
tOrder.orderIsRefunded = "True"
tOrder.orderRefundAgent = tRefundAgent
tOrder.orderLocked = "True"
tOrder.orderRefundId = ""
tOrder.put()
self.response.out.write("Order Locked and Refunded")
def main():
env = gym.make(FLAGS.env_name)
agent = Agent(num_actions=env.action_space.n, config=FLAGS)
if FLAGS.train: # Train mode
for _ in range(FLAGS.num_episodes):
terminal = False
observation = env.reset()
for _ in range(random.randint(1, FLAGS.no_op_steps)):
last_observation = observation
observation, _, _, _ = env.step(0) # Do nothing
state = agent.get_initial_state(observation, last_observation)
while not terminal:
last_observation = observation
action = agent.get_action(state)
observation, reward, terminal, _ = env.step(action)
# env.render()
processed_observation = preprocess(observation, last_observation)
state = agent.run(state, action, reward, terminal, processed_observation)
else: # Test mode
# env.monitor.start(ENV_NAME + '-test')
for _ in range(FLAGS.num_episodes_at_test):
terminal = False
observation = env.reset()
for _ in range(random.randint(1, FLAGS.no_op_steps)):
last_observation = observation
observation, _, _, _ = env.step(0) # Do nothing
state = agent.get_initial_state(observation, last_observation)
while not terminal:
last_observation = observation
action = agent.get_action_at_test(state)
observation, _, terminal, _ = env.step(action)
env.render()
processed_observation = preprocess(observation, last_observation)
state = np.append(state[1:, :, :], processed_observation, axis=0)
def PostContext(self):
tContext = {}
tAgent = Agent()
tUser = self.GetUser()
tAgent = Agent.GetAgentByEmail(tUser.email())
tSaveAgent = False
tNewNick = self.request.get('nick')
tSoundOpt = self.request.get('sound')
if (tNewNick != None and len(tNewNick) > 0):
tAgent.agentNickName = tNewNick
tSaveAgent = True
if (tSoundOpt != None and len(tSoundOpt) > 0):
tSoundOpt = str(tSoundOpt)
if (tSoundOpt == "true"):
tAgent.agentSoundPreference = 'True'
tSaveAgent = True
elif (tSoundOpt == "false"):
tAgent.agentSoundPreference = 'False'
tSaveAgent = True
if (tSaveAgent):
tAgent.put()
self.LOCATION = "/"
self.REDIRECT = True
return tContext
def GetContext(self):
tAgent = Agent()
tAgentList = []
tAgentOrders = []
tAgentDonations = []
tAgentRequest = self.request.get('agent')
context = {}
tAgentList = Agent.all()
context['agents'] = tAgentList
if (tAgentRequest != ""):
tAgent = Agent.get(tAgentRequest)
tAgentOrdersQuery = Order.all()
tAgentOrdersQuery.filter('orderAgent', str(tAgentRequest))
tAgentOrdersQuery.order('-orderCompleted')
tAgentOrders = tAgentOrdersQuery.fetch(100)
tAgentDonorsQuery = DonorRecord.all()
tAgentDonorsQuery.filter('donorAgent', tAgent.agentId)
tAgentDonorsQuery.order('-donorDate')
tAgentDonations = tAgentDonorsQuery.fetch(100)
#logging.debug("Agent Order Count: " + str(len(tAgentOrders)))
#logging.debug("Agent Donation Count: " + str(len(tAgentDonations)))
context['agent'] = tAgent
context['orders'] = tAgentOrders
context['donations'] = tAgentDonations
context['extended'] = 'True'
return context
def run(params, task_id, job_dir, name=None):
keys = params.keys()
agent_keys = [key for key in keys if 'agent' in key]
meta = params.pop('__meta__')
verbose = meta['verbose']
trial_num = meta['trial_num']
protocol = meta['protocol']
experiment_name = meta['experiment_name']
prepare_environment(meta)
# Load Agents Based on Model
agents = []
for agent_key in agent_keys:
agent_params = params.pop(agent_key)
agents += [Agent(agent_dict=agent_params, name=agent_key)]
params['agents'] = agents
# Load Protocol and Train (Results callback will collect results)
module_name = 'protocols.%s.train' % (protocol)
train = getattr(import_module(module_name), 'train')
info, results = train(**params,
verbose=False)
# AFTER DONE TRAINING SAVE RESULTS FILE
results.insert(0, {'experiment_name': experiment_name,
'protocol': protocol,
'trial_num': trial_num,
**info})
if name is not None:
result_file = '%s.npy' % name
else:
result_file = '%i.npy' % task_id
np.save(result_file, results)
save_file(job_dir, result_file)
def agent_worker(config, policy, learner_w_queue, global_episode, n_agent,
log_dir, training_on, replay_queue, update_step):
agent = Agent(config,
policy,
global_episode=global_episode,
n_agent=n_agent,
log_dir=log_dir)
agent.run(training_on, replay_queue, learner_w_queue, update_step)
def main():
# Training settings
parser = argparse.ArgumentParser(description='PyTorch Poly Echo')
parser = add_experiment_args(parser)
args = parser.parse_args()
np.set_printoptions(formatter={'float': '{: 0.3f}'.format})
params_file = os.path.join(WORK_DIR, '%s.json' % args.task_id)
with open(params_file) as file:
params = json.load(file)
keys = params.keys()
agent_keys = [key for key in keys if 'agent' in key]
num_agents = len(agent_keys)
meta = params.pop('__meta__')
verbose = meta['verbose']
trial_num = meta['trial_num']
protocol = meta['protocol']
experiment_name = meta['experiment_name']
prepare_environment(meta)
agents = []
for agent_key in agent_keys:
agent_params = params.pop(agent_key)
agents += [Agent(agent_dict=agent_params, name=agent_key)]
if num_agents == 2:
A = agents[0]
B = agents[1]
else:
A = agents[0]
B = agents[0]
# Load Protocol
module_name = 'protocols.%s.trainer' % (protocol)
trainer = getattr(import_module(module_name), 'trainer')
total_batches_sent = 0
results, symbols_sent = [], []
test_SNR_dbs = get_test_SNR_dbs()[
params['bits_per_symbol']]['ber_roundtrip']
num_iterations = params['num_iterations']
total_iterations = num_iterations + (1 if num_agents == 2 else 0)
for i in range(total_iterations):
B, A, batches_sent = trainer(agents=[A, B],
bits_per_symbol=params['bits_per_symbol'],
batch_size=params['batch_size'],
train_SNR_db=params['train_SNR_db'],
signal_power=params['signal_power'],
backwards_only=(i == num_iterations))
total_batches_sent += batches_sent
if i % params['results_every'] == 0:
result = test(agent1=agents[0],
agent2=agents[1] if num_agents == 2 else None,
bits_per_symbol=params['bits_per_symbol'],
test_SNR_dbs=test_SNR_dbs,
signal_power=params['signal_power'],
test_batch_size=params['test_batch_size'])
test_bers = result['test_bers']
print('Test: Roundtrip BER: ', test_bers)
symbols_sent += [total_batches_sent * params['batch_size']]
results += [result]
def get(self):
tAgent = Agent()
tAgentsOnline = []
tCurrentTime = datetime.datetime.now()
tIncrement = datetime.timedelta(minutes = -30)
tTime = tIncrement + tCurrentTime
tAgentQuery = Agent().all()
tAgentQuery.filter("agentOnline", True)
tAgentQuery.filter("agentLastActive <", tTime)
tAgentsOnline = tAgentQuery.fetch(10)
if (len(tAgentsOnline) > 0):
for tAgent in tAgentsOnline:
tAgent.agentOnline = False
tAgent.put()
def get(self):
tAgent = Agent()
tUser = users.get_current_user()
if (tUser):
tAgent = Agent().GetAgentByEmail(tUser.email())
if (tAgent.agentOnline == True):
if (tAgent.agentNotify == True):
tAgent.agentNotify = False
tAgent.put()
self.response.out.write("1")
elif (tAgent.agentNotify == False):
self.response.out.write("0")
else:
self.response.out.write("0")
tAgent.agentNotify = False
tAgent.put()
else:
self.response.out.write("2")
def agent_worker(config, policy, learner_w_queue, global_episode, i, agent_type,
experiment_dir, training_on, replay_queue, update_step):
agent = Agent(config,
policy=policy,
global_episode=global_episode,
n_agent=i,
agent_type=agent_type,
log_dir=experiment_dir)
agent.run(training_on, replay_queue, learner_w_queue, update_step)
def test_evaluate(self):
self.env = Env(
balance=250000,
FX_DATA_FILE='../data/raw/FX_Demo/sample_USD_JPY_S5.pickle')
self.agent = Agent()
self.agent.model.compile(optimizer=Adam(), loss="mse")
state = (self.env.balance, self.env.stock_balance)
y = self.agent.evaluate(state=state)
assert y.shape == (3, )
assert any(y) is True
def post(self):
tOrderKey = self.request.get('orderid')
#logging.debug("tOrderKey: " + tOrderKey)
tPaOrder = PaOrder()
tPaOrder = PaOrder.get(tOrderKey)
tUser = users.get_current_user()
tAgent = Agent().GetAgentByEmail(str(tUser.email()))
if (tPaOrder.paOrderDeliver == False and tPaOrder.paOrderLock == False
and tAgent.agentIsEnabled == True):
tGoldAmount = tPaOrder.paAmountInt
tGoldAmountLong = tGoldAmount
tGoldAmount = tGoldAmount / 1000000
if (tAgent.agentGoldSupply == None):
tAgent.agentGoldSupply = 0
tCommission = tGoldAmount * 0.05 + 0.50
tAgent.agentGoldSupply = int(
tAgent.agentGoldSupply) - int(tGoldAmountLong)
tAgent.agentCurrentCommission = tAgent.agentCurrentCommission + tCommission
tAgent.agentTotalCommission = tAgent.agentTotalCommission + tCommission
tAgentOrders = tAgent.agentOrders #Add order to agent pa orders
tAgentOrders.append(tOrderKey)
tAgent.agentOrders = tAgentOrders
tAgent.agentCurrentOrderTotal = tAgent.agentCurrentOrderTotal + 1
tAgentKey = tAgent.put()
tPaOrder.paDeliveryAgent = str(tAgent.agentId)
tPaOrder.paDeliveryAgentNick = tAgent.agentNickName
tPaOrder.paOrderDeliver = True
tPaOrder.paOrderLock = True
tKey = tPaOrder.put()
#logging.debug("Delivery by Agent: " + str(tAgentKey))
#logging.debug("Delivery of Order: " + str(tKey))
self.response.headers[
'Cache-Control'] = 'Cache-Control: no-cache, must-revalidate'
self.response.headers['Content-Type'] = 'Content-Type: plain/text'
self.response.out.write("Order Delivered")
else:
#logging.debug('Attempted to Deliver ' + tOrderKey + " by Agent " + tAgent.agentId)
self.response.headers[
'Cache-Control'] = 'Cache-Control: no-cache, must-revalidate'
self.response.headers['Content-Type'] = 'Content-Type: plain/text'
self.response.out.write("Order Not Deliverable")
def get(self):
tAgentQuery = Agent().all()
tAgentQuery.filter('agentOnline', True)
tAgents = tAgentQuery.fetch(10)
if (len(tAgents) > 0):
self.response.out.write(str(len(tAgents)))
else:
self.response.out.write(str(0))
exit
def test_act(self):
self.env = Env(
balance=250000,
FX_DATA_FILE='../data/raw/FX_Demo/sample_USD_JPY_S5.pickle')
self.agent = Agent()
self.agent.model.compile(optimizer=Adam(), loss="mse")
state = (self.env.balance, self.env.stock_balance)
action = self.agent.act(state, epsilon=0.1)
action_state = False
if action == 0 or action == 1 or action == 2:
action_state = True
assert action_state is True
def GetContext(self):
tContext = {}
if (self.IsUserAdmin()):
tAgentsQuery = Agent().all()
tAgentsQuery.order("agentNickName")
tAgentsQuery.filter("agentIsEnabled", True)
tAgents = tAgentsQuery.fetch(100)
tAgent = Agent()
for tAgent in tAgents:
tAgent.__setattr__(
'agentGoldSupplyEocString',
NumberToGp.ConvertIntToBet(tAgent.agentGoldSupplyEoc))
tAgent.__setattr__(
'agentGoldSupply07String',
NumberToGp.ConvertIntToBet(tAgent.agentGoldSupply07))
tContext['agents'] = tAgents
return tContext
else:
self.redirect("/")
def main():
parser = argparse.ArgumentParser(
description='Execute train reinforcement learning.')
parser.add_argument(
'--dataset_name',
type=str,
default="../data/raw/FX_Demo/sample10000_USD_JPY_S5.pickle",
help='an integer for the accumulator')
args = parser.parse_args()
print(args.dataset_name)
env = Env(balance=250000, FX_DATA_FILE=args.dataset_name)
agent = Agent(input_data_shape=(10, ))
mount_agent = Agent(actions=10, input_data_shape=(10, ))
trainer = Trainer(env,
agent,
mount_agent,
Adam(lr=1e-6),
data_end_index=len(env.fx_time_data_buy) - 2)
trainer.train()
def GetAssignedAgent(self, pOrder=None):
tAgent = Agent()
tPaypal = PaypalOrder()
tAgents = []
Switch = {}
tOrder = Order()
tOrder = pOrder
#Need to implement these methods
#Switch[(1,2)] = tPaypal.UseFullAndBackupAgents
#Switch[(0,2)] = tPaypal.UseBackupAgent
#Switch[(2,2)] = tPaypal.UseFullAgent
Switch[(0, 0)] = tPaypal.AssignNoAgent
Switch[(0, 1)] = tPaypal.UseBackupAgent
Switch[(1, 0)] = tPaypal.UseFullAgent
Switch[(1, 1)] = tPaypal.UseFullAndBackupAgents
Switch[(2, 0)] = tPaypal.UseFullAgent
Switch[(2, 1)] = tPaypal.UseFullAndBackupAgents
Switch[(3, 0)] = tPaypal.UseFullAgent
Switch[(3, 1)] = tPaypal.UseFullAgent
#Based on the raw online numbers of each group
tCurrentState = (tPaypal.GetNumberofOnlineFullAgents(),
tPaypal.GetNumberofOnlineBackupAgents())
#logging.debug("Current State" + str(tCurrentState))
#The end agent will be handled in each function
tAgent = Switch[tCurrentState]()
if (tOrder != None):
try:
#logging.debug("Agent Current Total: " + str(tAgent.agentCurrentOrderTotal))
#logging.debug("Order Quantity: " + str(tOrder.orderQuantity))
tAgent.agentCurrentOrderTotal = tAgent.agentCurrentOrderTotal + int(
tOrder.orderQuantity)
#logging.debug("New Agent Current Total: " + str(tAgent.agentCurrentOrderTotal))
tAgent.agentNotify = True
tAgent.put()
#logging.debug("GetAssignedAgent returning agent: " + str(tAgent.agentId))
return tAgent.agentId
except:
#logging.debug("Hit an error")
return "No Agent Online"
else:
try:
return str(tAgent.agentId)
except:
return "No Agent Online"
def UseFullAgent(self):
tAgent = Agent()
tAgents = []
tPaypal = PaypalOrder()
tOnlineAgents = tPaypal.GetNumberofOnlineFullAgents()
tAvailableAgents = tPaypal.GetNumberofAvailableFullAgents()
if (tOnlineAgents > 0 and tAvailableAgents == 0):
tPaypal.ResetOnlineAgents()
tAgents = tPaypal.GetAvailableFullAgents()
try:
tAgent = tAgents[0]
return tAgent
except:
return "No Agent Online"
def post(self):
tUser = users.get_current_user()
tAgent = Agent().GetAgentByEmail(tUser.email())
tStatus = tAgent.agentOnline
if (tStatus == False):
tAgent.agentOnline = True
tAgent.agentCurrentOrderTotal = 100
tReturn = "You're Online!"
else:
tAgent.agentOnline = False
tReturn = "You're Offline!"
tAgent.put()
self.response.out.write(tReturn)
exit
def UseFullAndBackupAgents(self):
tAgent = Agent()
tAgents = []
tPaypal = PaypalOrder()
tOnlineBackups = tPaypal.GetNumberofOnlineBackupAgents()
tAvailableBackups = tPaypal.GetNumberofAvailableBackupAgents()
tOnlineAgents = tPaypal.GetNumberofOnlineFullAgents()
tAvailableAgents = tPaypal.GetNumberofAvailableFullAgents()
tTotalOnline = tOnlineAgents + tOnlineBackups
tTotalAvailable = tAvailableAgents + tAvailableBackups
if (tTotalOnline > 0 and tTotalAvailable == 0):
tPaypal.ResetOnlineAgents()
tAgents = tPaypal.GetAvailableAgents()
try:
tAgent = tAgents[0]
return tAgent
except:
return "No Agent Online"
def UseBackupAgent(self):
#logging.debug("UseBackupAgent Called")
tAgent = Agent()
tAgents = []
tPaypal = PaypalOrder()
tOnlineBackups = tPaypal.GetNumberofOnlineBackupAgents()
tAvailableBackups = tPaypal.GetNumberofAvailableBackupAgents()
#logging.debug("Online Backups: " + str(tOnlineBackups))
#logging.debug("Available Backups: " + str(tAvailableBackups))
if (tOnlineBackups > 0 and tAvailableBackups == 0):
#logging.debug("Resetting Online Agents")
tPaypal.ResetOnlineAgents()
tAgents = tPaypal.GetAvailableBackupAgents()
try:
tAgent = tAgents[0]
#logging.debug("UseBackupAgent Returning " + str(tAgent.agentId))
return tAgent
except:
#logging.debug("Error in UseBackupAgent")
return "No Agent Online"
def add_agent(self):
self.db.new_agent(Agent(rnd_fullname(), rnd_salary()))
def post(self):
tUser = self.GetUser()
locale.setlocale(locale.LC_ALL, "")
tContext = {}
tContext['login'] = users.create_login_url(self.request.uri)
tContext['logout'] = users.create_logout_url(self.request.uri)
tContext['error'] = ''
tContext['TIME'] = str(datetime.datetime.now())
if (tUser == None and self.REQUIRE_AUTH_POST == True):
if (self.GetLocation() != "../views/index.html"):
self.redirect("/")
else:
tTemplate = os.path.join(os.path.dirname(__file__),
"../views/index.html")
self.response.out.write(render(tTemplate, tContext))
return
else:
self.USER = tUser
tContext['user'] = tUser
tPostContext = self.PostContext()
tContext.update(tPostContext)
tLocation = self.GetLocation()
tRedirect = self.GetRedirect()
#logging.debug("User:"******"Context: " + str(tContext))
#logging.debug("Location: " + str(tLocation))
#logging.debug("Redirect: " + str(tRedirect))
#if(tContext.has_key('agent')):
#tAgent = tContext['agent']
#else:
try:
tAgent = Agent().GetAgentByEmail(str(tUser.email()))
except:
tAgent = Agent()
if (tAgent.agentSoundDelay == None
or tAgent.agentSoundDelay == ""):
tAgent.agentSoundDelay = 10000
if (tAgent.agentSoundSelection == None
or tAgent.agentSoundSelection == ""):
tAgent.agentSoundSelection = "beep"
if (tAgent.agentSoundRepeat == None
or tAgent.agentSoundRepeat == ""):
tAgent.agentSoundRepeat = 1
#logging.debug("Sound delay: " + str(tAgent.agentSoundDelay))
#logging.debug("Sound selection: " + str(tAgent.agentSoundSelection))
#logging.debug("Sound repeat: " + str(tAgent.agentSoundRepeat))
tContext['agent'] = tAgent
if (tContext.has_key('nowrite')):
if (tContext['nowrite'] == True):
return
else:
if (tRedirect == False):
tTemplate = os.path.join(os.path.dirname(__file__),
tLocation)
self.response.out.write(render(tTemplate, tContext))
else:
self.redirect(tLocation)
def get(self):
tUser = self.GetUser()
locale.setlocale(locale.LC_ALL, "")
tContext = {}
tContext['login'] = users.create_login_url(self.request.uri)
tContext['logout'] = users.create_logout_url(self.request.uri)
tContext['error'] = ''
tContext['TIME'] = str(datetime.datetime.now())
if (tUser == None):
if (self.GetLocation() != "../views/index.html"):
self.redirect("/")
else:
tContext['error'] = 'Login is required to access the portal'
tTemplate = os.path.join(os.path.dirname(__file__),
"../views/index.html")
self.response.out.write(render(tTemplate, tContext))
return
else:
self.USER = tUser
tContext['user'] = tUser
tContext.update(self.GetContext())
tLocation = self.GetLocation()
tRedirect = self.GetRedirect()
#logging.debug("User: "******"Context: " + str(tContext))
#logging.debug("Location: " + str(tLocation))
#logging.debug("Redirect: " + str(tRedirect))
#if(tContext.has_key('agent')):
# tAgent = tContext['agent']
#else:
try:
tAgent = Agent().GetAgentByEmail(str(tUser.email()))
except:
tAgent = Agent()
if (tAgent.agentSoundDelay == None
or tAgent.agentSoundDelay == ""):
tAgent.agentSoundDelay = 10000
if (tAgent.agentSoundSelection == None
or tAgent.agentSoundSelection == ""):
tAgent.agentSoundSelection = "beep"
if (tAgent.agentSoundRepeat == None
or tAgent.agentSoundRepeat == ""):
tAgent.agentSoundRepeat = 1
#logging.debug("Sound delay: " + str(tAgent.agentSoundDelay))
#logging.debug("Sound selection: " + str(tAgent.agentSoundSelection))
#logging.debug("Sound repeat: " + str(tAgent.agentSoundRepeat))
tContext['agent'] = tAgent
logging.debug('Context: ' + str(tContext))
if tAgent.agentIsAdmin:
tContext['isAdmin'] = 'True'
if (tAgent.agentIsEnabled == False):
tContext['error'] = 'Your agent access is not active'
tTemplate = os.path.join(os.path.dirname(__file__),
"../views/index.html")
self.response.out.write(render(tTemplate, tContext))
return
if (tRedirect == False):
tTemplate = os.path.join(os.path.dirname(__file__), tLocation)
self.response.out.write(render(tTemplate, tContext))
else:
self.redirect(tLocation)
def Agent(self):
return Agent(self)
def run(jobs_file, job_id=None, plot=False, echo_symlink_to=None, job_date=None):
# pr.enable()
with open(jobs_file) as jfile:
jobs = json.load(jfile)
if isinstance(jobs, dict):
# ToDo: make this more explicit, but basically, you can give me a json of SINGLE param dict, aka rerun a
# single job that was spit out (ex: echo/experiments/gradient_passing/QPSK_neural_and_neural/results/0.json)
jobs = [jobs]
elif job_id is not None: # 0 = False you dummy
plot = plot
jobs = [jobs[job_id]]
else:
# NO PLOTTING IF YOU ARE RUNNING A BUNCH OF JOBS...NO!
plot = False
for params in jobs:
params_copy = deepcopy(params)
keys = params.keys()
agent_keys = [key for key in keys if 'agent' in key]
meta = params.pop('__meta__')
verbose = meta['verbose']
job_id = meta['job_id']
trial_num = meta['trial_num']
protocol = meta['protocol']
experiment_name = meta['experiment_name']
experiment_dir = os.path.abspath(os.path.join(ECHO_DIR, 'experiments', protocol, experiment_name))
results_dir = os.path.abspath(os.path.join(experiment_dir, 'results'))
# DEAL WITH SYMLINKING FOR RUNNING ON BRC
if echo_symlink_to is not None:
assert os.path.isdir(echo_symlink_to), "Invalid symlink path"
if os.path.isdir(results_dir) and not os.path.islink(results_dir):
old_results_dir = os.path.abspath(os.path.join(experiment_dir, 'old_results'))
os.makedirs(old_results_dir, exist_ok=True)
n = len(os.listdir(old_results_dir))
os.rename(results_dir, os.path.abspath(os.path.join(old_results_dir, '%i' % n)))
_experiment_dir = os.path.abspath(os.path.join(echo_symlink_to, 'experiments', protocol, experiment_name))
job_date = "results" + (job_date if job_date is not None else "")
_results_dir = os.path.abspath(os.path.join(_experiment_dir, job_date))
os.makedirs(_results_dir, exist_ok=True)
if os.path.islink(results_dir) and os.readlink(results_dir) != _results_dir:
try:
os.remove(results_dir)
except OSError:
pass
if not os.path.islink(results_dir):
try:
os.symlink(_results_dir, results_dir)
except OSError as e:
if e.errno == errno.EEXIST:
assert os.readlink(results_dir) == _results_dir
else:
raise e
else:
os.makedirs(results_dir, exist_ok=True)
results_file = '%s/%i.npy' % (results_dir, job_id)
if os.path.isfile(results_file) and plot:
print("result already found")
else:
params_file = '%s/%i.json' % (results_dir, job_id)
with open(params_file, 'w') as pf:
pf.write(json.dumps(params_copy, indent=4))
if verbose:
print("...running run_experiment.py with:", protocol, experiment_name)
prepare_environment(meta)
# Load Agents Based on Model
agents = []
for agent_key in agent_keys:
agent_params = params.pop(agent_key)
agents += [Agent(agent_dict=agent_params, name=agent_key, verbose=verbose)]
params['agents'] = agents
# Load Protocol and Train (Results callback will collect results)
module_name = 'protocols.%s.train' % (protocol)
train = getattr(import_module(module_name), 'train')
info, results = train(**params,
verbose=verbose,
plot_callback=lambda **kwargs: None)
# AFTER DONE TRAINING SAVE RESULTS FILE
results.insert(0, {'protocol': protocol,
'trial_num': trial_num,
'experiment_name': experiment_name,
**info})
np.save(results_file, results)
if verbose:
print("...params for this job have been saved into:", params_file)
print("...results for this job have been saved into:", results_file)
# pr.disable()
# pr.dump_stats('%s%i.pstat'% (experiment_name,job_id) )
if plot:
from importlib import util
if util.find_spec('matplotlib') is not None:
from plot_experiment import animated_plot
animated_plot(results=results)
else:
print("Cannot plot; matplotlib not found")
return ()
