def run_opt(city, budget, start_date, important_dates, infectratio_range=None,
dummy_range=None, unob_flow_num=None, repeat_time=1, init_samples=None,
training_end_date=None, json_name='data_run.json', seed=3, loss_ord=0.0,
touch_range=None,iso_range=None):
assert infectratio_range is not None and dummy_range is not None
days_predict = 0
# load data
real_data = pd.read_csv(get_data_path(True))
history_real = prepareData(real_data)
flow_out_data = None
# initialize models
infectratio = InfectRatio(1, [infectratio_range], [True])
if touch_range is None:
touchratio = TouchRatio(1, [[0.999, 1.0000]], [True])
else:
touchratio = TouchRatio(1, [touch_range], [True])
touchratiointra = TouchRatio(1, [[0, 1]], [True])
obs = ObservationRatio(1, [[0.0, 0.3]], [True])
dead = DeadRatio(1, [[0., 0.01]], [True])
if iso_range is None:
isoratio = IsolationRatio(1, [[0.03, 0.12]], [True])
else:
isoratio = IsolationRatio(1, [iso_range], [True])
dummy = DummyModel(1, [dummy_range], [True, True])
cure_ratio = InfectRatio(1, [[0., 0.15]], [True])
simulator = Simulator(city, infectratio, touchratio, obs, dead, dummy, isoratio, touchratiointra, cure_ratio, important_dates,
unob_flow_num=unob_flow_num, flow_out_data=flow_out_data, training_date_end=training_end_date)
test_date = datetime.strptime(history_real['date'].max(), '%Y-%m-%d').date() - timedelta(days_predict)
history_real = history_real[history_real['adcode'] == city]
history_real = history_real[history_real['date'] >= str(start_date)]
history_train = history_real[history_real['date'] <= str(test_date)]
x, y = simulator.fit(history_train, budget=budget, server_num=get_core_num(),
repeat=repeat_time, seed=seed, intermediate_freq=10000, init_samples=init_samples,
loss_ord=loss_ord)
print('best_solution: x = ', x, 'y = ', y)
simulator.set_param(x)
run_simulation(x, city, 60, 60, start_date, get_important_date(city), unob_flow_num=unob_flow_num, json_name=json_name)
return x
def run_opt(city,
budget,
start_date,
important_dates,
infectratio_range=None,
dummy_range=None,
unob_flow_num=None,
repeat_time=1,
init_samples=None,
training_date_end=None,
json_name='data_run.json',
seed=1,
loss_ord=0.0,
unob_period=None,
obs_period=None,
iso_period=None,
cure_period=None,
isoratio_it=None):
if city == 420000:
infectratio_range = [0., 0.05]
dummy_range = [0.0000, 400.00001]
else:
assert infectratio_range is not None and dummy_range is not None
days_predict = 0
# load data
real_data = pd.read_csv(get_data_path())
history_real = prepareData(real_data)
flow_out_data = flowOutData()
# initialize models
infectratio = InfectRatio(1, [infectratio_range], [True])
touchratio = TouchRatio(1, [[0.0, 0.6]], [True])
touchratiointra = TouchRatio(1, [[0, 1]], [True])
obs = ObservationRatio(1, [[0.0, 0.3]], [True])
dead = DeadRatio(1, [[0., 0.01]], [True])
if isoratio_it is None:
isoratio = IsolationRatio(1, [[0.2, 0.5]], [True])
else:
isoratio = IsolationRatio(1, [isoratio_it], [True])
dummy = DummyModel(1, [dummy_range], [True, True])
cure_ratio = InfectRatio(1, [[0., 0.1]], [True])
# set the time of applying touchratio
simulator = Simulator(city,
infectratio,
touchratio,
obs,
dead,
dummy,
isoratio,
touchratiointra,
cure_ratio,
important_dates,
unob_flow_num=unob_flow_num,
flow_out_data=flow_out_data,
training_date_end=training_date_end)
# set period here
simulator.set_period()
test_date = datetime.strptime(history_real['date'].max(),
'%Y-%m-%d').date() - timedelta(days_predict)
history_real = history_real[history_real['adcode'] == city]
history_real = history_real[history_real['date'] >= str(start_date)]
history_train = history_real[history_real['date'] <= str(test_date)]
x, y = simulator.fit(history_train,
budget=budget,
server_num=get_core_num(),
repeat=repeat_time,
seed=seed,
intermediate_freq=10000,
init_samples=init_samples,
loss_ord=loss_ord)
print('best_solution: x = ', x, 'y = ', y)
simulator.set_param(x)
run_simulation(x,
city,
60,
60,
start_date,
get_important_date(city),
unob_flow_num=unob_flow_num,
json_name=json_name)
duration = len(real_data["date"].unique()) - 1
sim_res, _ = simulator.simulate(str(start_date), duration)
print('RMSE: ', get_newly_loss(sim_res, history_real))
return x, sim_res