def __init__(self, patient_name=None, speed=0, oracle=-1, reward_fun=neg_risk):
'''
patient_name must be 'adolescent#001' to 'adolescent#010',
or 'adult#001' to 'adult#010', or 'child#001' to 'child#010'
'''
self._gym_disable_underscore_compat = True
seeds = [0, 0, 0, 0, 0] #self._seed()
# have to hard code the patient_name, gym has some interesting
# error when choosing the patient
if patient_name is None:
patient_name = 'adolescent#002'
patient = T1DPatient.withName(patient_name)
# sensor = CGMSensor.withName('Navigator', seed=seeds[1]) # Sample frequency = 1 min
# sensor = CGMSensor.withName('Dexcom', seed=seeds[1]) # Sample frequency = 3 min
sensor = CGMSensor.withName('GuardianRT', seed=seeds[1]) # Sample frequency = 5 min
hour = 0 #self.np_random.randint(low=0.0, high=24.0)
start_time = datetime(2018, 1, 1, hour, 0, 0)
scenario = RandomScenario(start_time=start_time, seed=seeds[2])
# scenario = WeightScenario(weight=patient._params.BW, start_time=start_time, seed=seeds[2])
pump = InsulinPump.withName('Insulet')
self.env = _NS_T1DSimEnv_old(patient, sensor, pump, scenario, speed, oracle)
self.reward_fun = reward_fun
self.target = 140
self.max_horizon = 1 # Sampling in policy parameter space, not in action space of env.
self.oracle = oracle
def local_build_env(pname, controller_name=''):
patient = T1DPatient.withName(pname)
cgm_sensor = CGMSensor.withName(cgm_sensor_name, seed=cgm_seed)
insulin_pump = InsulinPump.withName(insulin_pump_name)
scen = copy.deepcopy(scenario)
env = T1DSimEnv(patient, cgm_sensor, insulin_pump, scen, controller_name=controller_name, results_path=results_path)
return env
def run_sim_PID(no_runs, patients, runtime, meals, controller_params):
'''
Run the simulation a single time on a list of patients with the PID controller.
Parameters
----------
no_runs: int
the number of separate simulation runs.
patients: list of str
a list of patient name strings. Patient name strings can be found in the params/Quest.csv file inside simGlucose.
runtime: int
simulation time, in hours.
meals: (timedelta, int)
a tuple containing the time of meal (as referenced from simulation start) and the meal size, in grams.
targetBG: int
the target blood glucose for the controller, in mg/dl
lowBG: int
the pump suspension glucose for the controller, in mg/dl
Returns
-------
A pandas dataframe containing the simulation results.
axis=0: time, type datetime.datetime
axis=1: MultiIndex
level 0: data category, type str
level 1: patient id, type str
level 2: run number, type int (starts at 1)
'''
sensor = CGMSensor.withName('Dexcom')
pump = InsulinPump.withName('Insulet')
scenario = CustomScenario(start_time = datetime(2020, 1, 1, 0,0,0), scenario=meals)
sim_objs = []
keys = []
for run in range(0, no_runs):
for pname in patients:
sim_objs.append(SimObj(T1DSimEnv(T1DPatient.withName(pname),
sensor,
pump,
copy.deepcopy(scenario)), # because random numbers.
controller.PIDController(controller_params, pname),
timedelta(hours=runtime),
animate=False,
path=None))
keys.append((run + 1, pname))
p_start = time.time()
print('Running batch simulation of {} items...'.format(len(patients * no_runs)))
p = pathos.pools.ProcessPool()
results = p.map(sim, sim_objs)
print('Simulation took {} seconds.'.format(time.time() - p_start))
return pd.concat(results, axis=1, keys=keys)
def pidsim():
for idx,patient in enumerate(patients):
patient = T1DPatient.withName(patient)
sensor = CGMSensor.withName('Dexcom', seed=1)
pump = InsulinPump.withName('Insulet')
p,i,d = pidparams[idx]
for seed in range (10,20):
scenario = RandomScenario(start_time=start_time, seed=randint(10, 99999))
env = T1DSimEnv(patient, sensor, pump, scenario)
# Create a controller
controller = FoxPIDController(112.517,kp=p, ki=i, kd=d)
# Put them together to create a simulation object
s1 = SimObj(env, controller, timedelta(days=10), animate=False, path=path+str(seed))
results1 = sim(s1)
print('Complete:',patient.name,'-',seed)
print('All done!')
def _create_env_from_random_state(self):
# Derive a random seed. This gets passed as a uint, but gets
# checked as an int elsewhere, so we need to keep it below
# 2**31.
seed2 = seeding.hash_seed(self.np_random.randint(0, 1000)) % 2**31
seed3 = seeding.hash_seed(seed2 + 1) % 2**31
seed4 = seeding.hash_seed(seed3 + 1) % 2**31
hour = self.np_random.randint(low=0.0, high=24.0)
start_time = datetime(2018, 1, 1, hour, 0, 0)
patient = T1DPatient.withName(self.patient_name, random_init_bg=True, seed=seed4)
sensor = CGMSensor.withName(self.SENSOR_HARDWARE, seed=seed2)
scenario = RandomScenario(start_time=start_time, seed=seed3)
pump = InsulinPump.withName(self.INSULIN_PUMP_HARDWARE)
env = _T1DSimEnv(patient, sensor, pump, scenario)
return env, seed2, seed3, seed4
def seed(self, seed=None):
print('_seed called')
self.np_random, seed1 = seeding.np_random(seed=seed)
# Derive a random seed. This gets passed as a uint, but gets
# checked as an int elsewhere, so we need to keep it below
# 2**31.
seed2 = seeding.hash_seed(seed1 + 1) % 2**31
seed3 = seeding.hash_seed(seed2 + 1) % 2**31
hour = self.np_random.randint(low=0.0, high=24.0)
start_time = datetime(2021, 1, 1, hour, 0, 0)
patient = T1DPatient.withName(self.patient_name)
sensor = CGMSensor.withName(self.SENSOR_HARDWARE, seed=seed2)
scenario = RandomScenario(start_time=start_time, seed=seed3)
pump = InsulinPump.withName(self.INSULIN_PUMP_HARDWARE)
self.env = _T1DSimEnv(patient, sensor, pump, scenario)
return [seed1, seed2, seed3]
def __init__(self, patient_name=None, reward_fun=None):
'''
patient_name must be 'adolescent#001' to 'adolescent#010',
or 'adult#001' to 'adult#010', or 'child#001' to 'child#010'
'''
seeds = self._seed()
# have to hard code the patient_name, gym has some interesting
# error when choosing the patient
if patient_name is None:
patient_name = 'adolescent#001'
patient = T1DPatient.withName(patient_name)
sensor = CGMSensor.withName('Dexcom', seed=seeds[1])
hour = self.np_random.randint(low=0.0, high=24.0)
start_time = datetime(2018, 1, 1, hour, 0, 0)
scenario = RandomScenario(start_time=start_time, seed=seeds[2])
pump = InsulinPump.withName('Insulet')
self.env = _T1DSimEnv(patient, sensor, pump, scenario)
self.reward_fun = reward_fun
def bbsim():
for patient in patients:
patient = T1DPatient.withName(patient)
sensor = CGMSensor.withName('Dexcom', seed=1)
pump = InsulinPump.withName('Insulet')
for seed in range(10, 20):
scenario = RandomScenario(start_time=start_time,
seed=randint(10, 99999))
env = T1DSimEnv(patient, sensor, pump, scenario)
# Create a controller
controller = BBController()
# Put them together to create a simulation object
s1 = SimObj(env,
controller,
timedelta(days=10),
animate=False,
path=path + str(seed))
results1 = sim(s1)
print('Complete:', patient.name, '-', seed)
print('All done!')
def __init__(self, patient_name=None, reward_fun=None, Initial_Bg=0):
'''
patient_name must be 'adolescent#001' to 'adolescent#010',
or 'adult#001' to 'adult#010', or 'child#001' to 'child#010'
'''
seeds = self._seed()
# have to hard code the patient_name, gym has some interesting
# error when choosing the patient
if patient_name is None:
patient_name = 'adolescent#001'
patient = T1DPatient.withName(patient_name, Initial_Bg)
sensor = CGMSensor.withName('GuardianRT', seed=seeds[1]) #Dexcom
hour = 20 #self.np_random.randint(low=0.0, high=24.0)
start_time = datetime(2018, 1, 1, hour, 0, 0)
# scenario = RandomScenario(start_time=start_time, seed=seeds[2])
# custom scenario is a list of tuples (time, meal_size)
# scen = [(0,float(Initial_Bg)),(13, 45), (16, 10), (18, 35), (22, 10)]#, (23, 10)]
scen = [(13, 45), (16, 10), (18, 35), (22, 10)] #, (23, 10)]
scenario = CustomScenario(start_time=start_time, scenario=scen)
pump = InsulinPump.withName('Insulet')
self.env = _T1DSimEnv(patient, sensor, pump, scenario)
self.reward_fun = reward_fun
def _create_env_from_random_state(self):
#print('_create_env_from_random_state simglucose extended observation')
# Derive a random seed. This gets passed as a uint, but gets
# checked as an int elsewhere, so we need to keep it below
# 2**31.
seed2 = seeding.hash_seed(self.np_random.randint(0, 1000)) % 2**31
seed3 = seeding.hash_seed(seed2 + 1) % 2**31
seed4 = seeding.hash_seed(seed3 + 1) % 2**31
hour = self.np_random.randint(low=0.0, high=24.0)
if self.saved_state is not None:
seed2=self.saved_state[0]
seed3=self.saved_state[1]
seed4=self.saved_state[2]
hour=self.saved_state[3]
print('Using state', seed2, seed3, seed4, hour)
start_time = datetime(2021, 1, 1, hour, 0, 0)
patient = T1DPatient.withName(self.patient_name, random_init_bg=True, seed=seed4)
sensor = CGMSensor.withName(self.SENSOR_HARDWARE, seed=seed2)
scenario = RandomScenario(start_time=start_time, seed=seed3)
pump = InsulinPump.withName(self.INSULIN_PUMP_HARDWARE)
env = _T1DSimEnvExtendedObs(patient, sensor, pump, scenario,n_samples=self._n_samples, append_action=self.append_action )
self.time_per_step=int(sensor.sample_time)
self.time_in_env=0
return env, seed2, seed3, seed4, hour
def test_results_consistency(self):
# Test data
results_exp = pd.read_csv(TESTDATA_FILENAME, index_col=0)
results_exp.index = pd.to_datetime(results_exp.index)
# specify start_time as the beginning of today
start_time = datetime(2018, 1, 1, 0, 0, 0)
# --------- Create Random Scenario --------------
# Create a simulation environment
patient = T1DPatient.withName('adolescent#001')
sensor = CGMSensor.withName('Dexcom', seed=1)
pump = InsulinPump.withName('Insulet')
scenario = RandomScenario(start_time=start_time, seed=1)
env = T1DSimEnv(patient, sensor, pump, scenario)
# Create a controller
controller = BBController()
# Put them together to create a simulation object
s = SimObj(env, controller, timedelta(
days=2), animate=False, path=save_folder)
results = sim(s)
assert_frame_equal(results, results_exp)
dfs = run_sim_PID(n, adults, t, meals, PIDparams)
filename = 'dfs/' + 'p_' + str(PIDparams[0]) + ' i_' + str(PIDparams[1]) + ' d_' + str(PIDparams[2]) + ' target_' + str(PIDparams[3]) + '.bz2'
dfs.to_pickle(filename)
=======
pname = "adult#001"
t = 9
meals = [(timedelta(hours=2), 50)]
sensor = CGMSensor.withName('Dexcom')
pump = InsulinPump.withName('Insulet')
scenario = CustomScenario(start_time = datetime(2020, 1, 1, 0,0,0), scenario=meals)
keys = []
# forward horizon
horizon = 50
controller_params = (140, 80, horizon)
obj= SimObj(T1DSimEnv(T1DPatient.withName(pname),
sensor,
pump,
copy.deepcopy(scenario)), # because random numbers.
controller.MPCNaive(controller_params, pname),
timedelta(hours=t),
animate=False,
path=None)
keys.append((1, pname))
p_start = time.time()
results = sim(obj)
print('Simulation took {} seconds.'.format(time.time() - p_start))
dfs = results
filename = 'mpc_test.bz2'
dfs.to_pickle(filename)
>>>>>>> MPC-exploration
fBG = 3.5506*(np.log(bg)**.8353-3.7932)
risk = 10 * (fBG)**2
return -1*risk
def cameron_reward(bg_hist, **kwargs):
bg = bg_hist[-1]
a = .2370 # 1/(mg/dL)
b = -36.21
c = 6.0e-5 # (1/(mg/dL)**3)
d = 177 # mg/dL
if bg < d:
risk = a*bg+b+(c*(d-bg)**3)
else:
risk = a*bg+b
return -1*risk
person_options = (['child#0{}'.format(str(i).zfill(2)) for i in range(1, 11)]+['adolescent#0{}'.format(str(i).zfill(2)) for i in range(1, 11)]+['adult#0{}'.format(str(i).zfill(2)) for i in range(1, 11)])
for i,p in enumerate(person_options):
patient_id = p.split('#')[0] + str(i + 1)
# Create a simulation environment
print(p)
patient = T1DPatient.withName(p)
register(id='simglucose-'+p+'-v0',entry_point='simglucose.envs:T1DSimEnv',kwargs={'patient_name': p},'reward_fun': reward_target)
env = gym.make('simglucose-'+p+'-v0')
model = ACKTR(MlpLstmPolicy, env, verbose=1)
model.learn(total_timesteps=250000)
model.save('mlplstm_trained-'+p+'-reward_target')
print('Model Trained and Saved for : '+ p)
from simglucose.simulation.scenario_gen import RandomScenario
from simglucose.simulation.scenario import CustomScenario
from simglucose.simulation.sim_engine import SimObj, sim, batch_sim
from datetime import timedelta
from datetime import datetime
# specify start_time as the beginning of today
now = datetime.now()
start_time = datetime.combine(now.date(), datetime.min.time())
# --------- Create Random Scenario --------------
# Specify results saving path
path = './results'
# Create a simulation environment
patient = T1DPatient.withName('adolescent#001')
sensor = CGMSensor.withName('Dexcom', seed=1)
pump = InsulinPump.withName('Insulet')
scenario = RandomScenario(start_time=start_time, seed=1)
env = T1DSimEnv(patient, sensor, pump, scenario)
# Create a controller
controller = BBController()
# Put them together to create a simulation object
s1 = SimObj(env, controller, timedelta(days=1), animate=False, path=path)
results1 = sim(s1)
print(results1)
# --------- Create Custom Scenario --------------
# Create a simulation environment
def test_batch_sim(self):
# specify start_time as the beginning of today
now = datetime.now()
start_time = datetime.combine(now.date(), datetime.min.time())
# --------- Create Random Scenario --------------
# Create a simulation environment
patient = T1DPatient.withName('adolescent#001')
sensor = CGMSensor.withName('Dexcom', seed=1)
pump = InsulinPump.withName('Insulet')
scenario = RandomScenario(start_time=start_time, seed=1)
env = T1DSimEnv(patient, sensor, pump, scenario)
# Create a controller
controller = BBController()
# Put them together to create a simulation object
s1 = SimObj(env,
controller,
timedelta(days=2),
animate=True,
path=save_folder)
results1 = sim(s1)
# --------- Create Custom Scenario --------------
# Create a simulation environment
patient = T1DPatient.withName('adolescent#001')
sensor = CGMSensor.withName('Dexcom', seed=1)
pump = InsulinPump.withName('Insulet')
# custom scenario is a list of tuples (time, meal_size)
scen = [(7, 45), (12, 70), (16, 15), (18, 80), (23, 10)]
scenario = CustomScenario(start_time=start_time, scenario=scen)
env = T1DSimEnv(patient, sensor, pump, scenario)
# Create a controller
controller = BBController()
# Put them together to create a simulation object
s2 = SimObj(env,
controller,
timedelta(days=2),
animate=False,
path=save_folder)
results2 = sim(s2)
# --------- batch simulation --------------
s1.reset()
s2.reset()
s1.animate = False
s = [s1, s2]
results_para = batch_sim(s, parallel=True)
s1.reset()
s2.reset()
s = [s1, s2]
results_serial = batch_sim(s, parallel=False)
assert_frame_equal(results_para[0], results1)
assert_frame_equal(results_para[1], results2)
for r1, r2 in zip(results_para, results_serial):
assert_frame_equal(r1, r2)