def open_wallet(self):
"""
Open wallet authentication for making transactions
:return:
"""
return transactions(self.client)
def transactions_handler():
request_body = request.get_json()
username = request_body['username']
password = request_body['password']
from_date = request_body['from_date']
transaction_entries = transactions.transactions(username, password,
from_date)
return jsonify(transaction_entries)
def processTransaction():
try:
t_file = open('file/jennyposb.txt', 'r')
lines = []
for trans in t_file:
list = trans.split(',')
e = jennytrans.transactions(list[0], list[1], int(list[2]))
lines.append(e)
return lines
except IOError:
print('File cannot be found')
except ValueError:
print('Invalid integer')
except ZeroDivisionError:
print('Second number cannot be 0')
except:
print('An unknown error occured')
def generate_dataset(basepath, usern, trans_multi=1.0, minimum_trans=1):
"""
Generate a dataset containing customers, products, and transactions
Keyword arguments:
basepath --directory to put the files in, do not end with /
usern -- number of users (affects number of transactions)
trans_multi -- multiplier of default transaction volumes
affects numbers of transactions, we simulate a customer
purchase habit such as 90% of sales from 10% of customers
minimum_trans -- minimum number of transactions per customer
"""
# Discard customer/product 0
cust = users.make_users(usern + 1)[1:]
prod = products.fetch_products()[1:]
tran = transactions.transactions(cust,
prod,
multiplier=trans_multi,
base=minimum_trans)[1:]
cust.to_csv(join(basepath, 'cust.csv'), float_format='%.2f')
prod.to_csv(join(basepath, 'prod.csv'), float_format='%.2f')
tran.to_csv(join(basepath, 'tran.csv'), float_format='%.2f')
config.padIdx = dataset.numItemsTrain
else:
config.padIdx = dataset.numItemsTest
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
logger.info('start training')
if config.retrival:
model = torch.load(config.saveRoot + '_' + str(config.testEpoch))
if config.model == 'FPMC':
recall, ndcg = evalByBas(model, trans, config, device)
userEmbeddings = model.EUI.cpu().weight.data.numpy().copy()
np.save('embeddings/userEmb_' + config.dataset + '_FPMC',
userEmbeddings)
trans = transactions(dataset, config)
else:
recall, ndcg = evalByUser(model, dataset, config, device,
config.isTrain)
W = model.itemEmb.cpu().weight.data.numpy().copy()
bias = model.out.cpu().bias.data.numpy().copy()
np.savetxt('weights/' + config.model + '_' + config.dataset + '_' +
str(config.testOrder) + '_bias.txt',
bias,
fmt='%.4f',
delimiter=',')
np.savetxt('weights/' + config.model + '_' + config.dataset + '_' +
str(config.testOrder) + '_W.txt',
W,
fmt='%.4f',
delimiter=',')
def do_transactions(self, line):
transactions.transactions()
def do_transactions(self, line):
transactions.transactions()
noofagents = int(input("Enter noofagents: "))
agents = []
for x in range(1, noofagents + 1):
agent_name = input("Enter agent_name: ")
location_Id = input("Enter location_Id: ")
agents = agent(agent_name, location_Id)
agentsagent_name = insertagentsToDb(agent)
agents.agent_name = agentsagent_name
nooftransactions = int(input("Enter nooftransactions: "))
transactions = []
for x in range(1, nooftransactions + 1):
amount = int(input("Enter amount: "))
balance = int(input("Enter balance: "))
member_Id = input("Enter mermber_Id: ")
clubcards_Id = input("Enter clubcards_Id: ")
agents_Id = input("Enter agents_Id: ")
transactions = transactions(amount, balance, member_Id, clubcards_Id,
agents_Id)
transactionsamount = inserttransactionsToDb(transactions)
transactions.amount = transactionsamount
for tran in transactions:
print(tran.member_Id, tran.amount, tran.balance)
for mem in members:
print(mem.member_name, mem.merchant_Id, mem.phone_no)