A Type-1 Diabetes simulator implemented in Python for Reinforcement Learning purpose
This simulator is a python implementation of the FDA-approved UVa/Padova Simulator (2008 version) for research purpose only. The simulator includes 30 virtual patients, 10 adolescents, 10 adults, 10 children.
HOW TO CITE: Jinyu Xie. Simglucose v0.2.1 (2018) [Online]. Available: https://github.com/jxx123/simglucose. Accessed on: Month-Date-Year.
Notice: simglucose no longer supports python 3.7 and 3.8, please update to >=3.9 verison. Thanks!
Announcement (08/20/2023): simglucose now supports gymnasium! Check examples/run_gymnasium.py for usage.
Animation | CVGA Plot | BG Trace Plot | Risk Index Stats |
---|---|---|---|
![]() |
![]() |
![]() |
![]() |
risk[t-1] - risk[t]
. risk[t]
is the risk index at time t
defined in this paper.parallel=False
).from simglucose.simulation.scenario_gen import RandomScenario
) and a customized scenario generator (from simglucose.simulation.scenario import CustomScenario
). Commandline user-interface will guide you through the scenario settings.animate
and parallel
cannot be set to True
at the same time in macOS. Most backends of matplotlib in macOS is not thread-safe. Windows has not been tested. Let me know the results if anybody has tested it out.It is highly recommended using pip
to install simglucose
, follow this link to install pip.
Auto installation:
pip install simglucose
Manual installation:
git clone https://github.com/jxx123/simglucose.git
cd simglucose
If you have pip
installed, then
pip install -e .
If you do not have pip
, then
python setup.py install
If rllab (optional) is installed, the package will utilize some functionalities in rllab.
Note: there might be some minor differences between auto install version and manual install version. Use git clone
and manual installation to get the latest version.
Run the simulator user interface
from simglucose.simulation.user_interface import simulate
simulate()
You are free to implement your own controller, and test it in the simulator. For example,
from simglucose.simulation.user_interface import simulate
from simglucose.controller.base import Controller, Action
class MyController(Controller):
def __init__(self, init_state):
self.init_state = init_state
self.state = init_state
def policy(self, observation, reward, done, **info):
'''
Every controller must have this implementation!
----
Inputs:
observation - a namedtuple defined in simglucose.simulation.env. For
now, it only has one entry: blood glucose level measured
by CGM sensor.
reward - current reward returned by environment
done - True, game over. False, game continues
info - additional information as key word arguments,
simglucose.simulation.env.T1DSimEnv returns patient_name
and sample_time
----
Output:
action - a namedtuple defined at the beginning of this file. The
controller action contains two entries: basal, bolus
'''
self.state = observation
action = Action(basal=0, bolus=0)
return action
def reset(self):
'''
Reset the controller state to inital state, must be implemented
'''
self.state = self.init_state
ctrller = MyController(0)
simulate(controller=ctrller)
These two examples can also be found in examples\ folder.
In fact, you can specify a lot more simulation parameters through simulation
:
simulate(sim_time=my_sim_time,
scenario=my_scenario,
controller=my_controller,
start_time=my_start_time,
save_path=my_save_path,
animate=False,
parallel=True)
import gym
# Register gym environment. By specifying kwargs,
# you are able to choose which patient or patients to simulate.
# patient_name must be 'adolescent#001' to 'adolescent#010',
# or 'adult#001' to 'adult#010', or 'child#001' to 'child#010'
# It can also be a list of patient names
# You can also specify a custom scenario or a list of custom scenarios
# If you chose a list of patient names or a list of custom scenarios,
# every time the environment is reset, a random patient and scenario will be
# chosen from the list
from gym.envs.registration import register
from simglucose.simulation.scenario import CustomScenario
from datetime import datetime
start_time = datetime(2018, 1, 1, 0, 0, 0)
meal_scenario = CustomScenario(start_time=start_time, scenario=[(1,20)])
register(
id='simglucose-adolescent2-v0',
entry_point='simglucose.envs:T1DSimEnv',
kwargs={'patient_name': 'adolescent#002',
'custom_scenario': meal_scenario}
)
env = gym.make('simglucose-adolescent2-v0')
observation = env.reset()
for t in range(100):
env.render(mode='human')
print(observation)
# Action in the gym environment is a scalar
# representing the basal insulin, which differs from
# the regular controller action outside the gym
# environment (a tuple (basal, bolus)).
# In the perfect situation, the agent should be able
# to control the glucose only through basal instead
# of asking patient to take bolus
action = env.action_space.sample()
observation, reward, done, info = env.step(action)
if done:
print("Episode finished after {} timesteps".format(t + 1))
break
import gym
from gym.envs.registration import register
def custom_reward(BG_last_hour):
if BG_last_hour[-1] > 180:
return -1
elif BG_last_hour[-1] < 70:
return -2
else:
return 1
register(
id='simglucose-adolescent2-v0',
entry_point='simglucose.envs:T1DSimEnv',
kwargs={'patient_name': 'adolescent#002',
'reward_fun': custom_reward}
)
env = gym.make('simglucose-adolescent2-v0')
reward = 1
done = False
observation = env.reset()
for t in range(200):
env.render(mode='human')
action = env.action_space.sample()
observation, reward, done, info = env.step(action)
print(observation)
print("Reward = {}".format(reward))
if done:
print("Episode finished after {} timesteps".format(t + 1))
break
from rllab.algos.ddpg import DDPG
from rllab.envs.normalized_env import normalize
from rllab.exploration_strategies.ou_strategy import OUStrategy
from rllab.policies.deterministic_mlp_policy import DeterministicMLPPolicy
from rllab.q_functions.continuous_mlp_q_function import ContinuousMLPQFunction
from rllab.envs.gym_env import GymEnv
from gym.envs.registration import register
register(
id='simglucose-adolescent2-v0',
entry_point='simglucose.envs:T1DSimEnv',
kwargs={'patient_name': 'adolescent#002'}
)
env = GymEnv('simglucose-adolescent2-v0')
env = normalize(env)
policy = DeterministicMLPPolicy(
env_spec=env.spec,
# The neural network policy should have two hidden layers, each with 32 hidden units.
hidden_sizes=(32, 32)
)
es = OUStrategy(env_spec=env.spec)
qf = ContinuousMLPQFunction(env_spec=env.spec)
algo = DDPG(
env=env,
policy=policy,
es=es,
qf=qf,
batch_size=32,
max_path_length=100,
epoch_length=1000,
min_pool_size=10000,
n_epochs=1000,
discount=0.99,
scale_reward=0.01,
qf_learning_rate=1e-3,
policy_learning_rate=1e-4
)
algo.train()
You can create the simulation objects, and run batch simulation. For example,
from simglucose.simulation.env import T1DSimEnv
from simglucose.controller.basal_bolus_ctrller import BBController
from simglucose.sensor.cgm import CGMSensor
from simglucose.actuator.pump import InsulinPump
from simglucose.patient.t1dpatient import T1DPatient
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
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=1), animate=False, path=path)
results2 = sim(s2)
print(results2)
# --------- batch simulation --------------
# Re-initialize simulation objects
s1.reset()
s2.reset()
# create a list of SimObj, and call batch_sim
s = [s1, s2]
results = batch_sim(s, parallel=True)
print(results)
Run analysis offline (example/offline_analysis.py):
from simglucose.analysis.report import report
import pandas as pd
from pathlib import Path
# get the path to the example folder
exmaple_pth = Path(__file__).parent
# find all csv with pattern *#*.csv, e.g. adolescent#001.csv
result_filenames = list(exmaple_pth.glob(
'results/2017-12-31_17-46-32/*#*.csv'))
patient_names = [f.stem for f in result_filenames]
df = pd.concat(
[pd.read_csv(str(f), index_col=0) for f in result_filenames],
keys=patient_names)
report(df)
policy
method gets access to all the current patient state through info['patient_state']
.gym.make('simglucose-v0')
to make the environment.simglucose.envs.T1DSimEnv
.Shoot me any bugs, enhancements or even discussion by creating issues.
The following instruction is originally from the contribution instructions of sklearn.
The preferred workflow for contributing to simglucose is to fork the main repository on GitHub, clone, and develop on a branch. Steps:
Fork the project repository by clicking on the 'Fork' button near the top right of the page. This creates a copy of the code under your GitHub user account. For more details on how to fork a repository see this guide.
Clone your fork of the simglucose repo from your GitHub account to your local disk:
$ git clone git@github.com:YourLogin/simglucose.git
$ cd simglucose
Create a feature
branch to hold your development changes:
$ git checkout -b my-feature
Always use a feature
branch. It's good practice to never work on the master
branch!
Develop the feature on your feature branch. Add changed files using git add
and then git commit
files:
$ git add modified_files
$ git commit
to record your changes in Git, then push the changes to your GitHub account with:
$ git push -u origin my-feature
Follow these instructions to create a pull request from your fork. This will email the committers.
(If any of the above seems like magic to you, please look up the Git documentation on the web, or ask a friend or another contributor for help.)