def total_ranges():
return (
("prev_year", utils.prev_year_range()),
("this_year", utils.year_range()),
("prev_month", utils.prev_month_range()),
("this_month", utils.month_range()),
("prev_week", utils.prev_week_range()),
("this_week", utils.week_range()),
("this_day", utils.day_range()),
("prev_day", utils.prev_day_range()))
def test_week_range(self):
d = datetime(2010, 4, 16, 23, 3)
start, end = utils.week_range(d)
self.assertEqual(datetime(2010, 4, 11), start)
self.assertEqual(datetime(2010, 4, 17, 23, 59, 59, 999999), end)
hours = db.list_hours()
servers = db.list_servers()
result = Result()
default_bandwidth = db.get_bandwidth_avg()
default_max_user = db.get_max_user_avg()
result.set_default(default_bandwidth, default_max_user)
total_iteration = len(weekdays) * len(hours) * len(servers)
print('Train...')
pbar = tqdm(total=total_iteration)
for weekday, hour, server in itertools.product(weekdays, hours, servers):
pbar.update(1)
dates_train = list(week_range(start_date_train, end_date_train, weekday))
data = db.filter(UPDATE_TIME=dates_train, HOUR_ID=hour, SERVER_NAME=server)
if len(data) == 0:
continue
x_train = [row['UPDATE_TIME'] for row in data]
y1_train = [row['BANDWIDTH_TOTAL'] for row in data]
y2_train = [row['MAX_USER'] for row in data]
dates_predict = list(week_range(start_date_predict, end_date_predict, weekday))
model = Model()
model.fit(x_train, y1_train, y2_train)
y1_predict, y2_predict = model.predict(dates_predict)
for date, y1, y2 in zip(dates_predict, y1_predict, y2_predict):
