def train(config, expe_name, trial_id, beta_default):
"""
Main training script. Defines the cost function and epidemiological model to form the environment model.
Parameters
----------
config: str
Identifier of the configuration parameters.
expe_name: str
String to describe your experiment, will be used for the logging directory.
trial_id: int
Trial identifier for logging path.
beta_default: float
Additional parameter for our experiments.
"""
# Get the configuration
params = get_params(config_id=config, expe_name=expe_name)
params.update(trial_id=trial_id)
params['cost_params']['beta_default'] = beta_default
# Set seeds
set_seeds(params['seed'])
# Get the epidemiological model
model = get_model(model_id=params['model_id'],
params=params['model_params'])
# Update cost function params
params['cost_params']['N_region'] = int(
model.pop_sizes[params['model_params']['region']])
params['cost_params']['N_country'] = int(
sum(list(model.pop_sizes.values())))
# Get cost function
cost_function = get_cost_function(cost_function_id=params['cost_id'],
params=params['cost_params'])
# Create the optimization problem as a Gym-like environment
env = get_env(env_id=params['env_id'],
cost_function=cost_function,
model=model,
simulation_horizon=params['simulation_horizon'],
seed=params['seed'])
# Setup logdir
params = get_logdir(params=params)
# Get DQN algorithm parameterized by beta
algorithm = get_algorithm(algo_id=params['algo_id'],
env=env,
params=params)
# Run the training loop
algorithm.learn(num_train_steps=params['num_train_steps'])
def setup_for_replay(folder,
seed=np.random.randint(1e6),
deterministic_model=False):
from epidemioptim.environments.models import get_model
from epidemioptim.environments.cost_functions import get_cost_function
from epidemioptim.environments.gym_envs import get_env
from epidemioptim.optimization import get_algorithm
# print('Replaying: ', folder)
with open(folder + 'params.json', 'r') as f:
params = json.load(f)
if deterministic_model:
params['model_params']['stochastic'] = False
params[
'logdir'] = None #get_repo_path() + 'data/results/experiments' + params['logdir'].split('EpidemicDiscrete-v0')[1]
model = get_model(model_id=params['model_id'],
params=params['model_params'])
# update reward params
params['cost_params']['N_region'] = int(
model.pop_sizes[params['model_params']['region']])
params['cost_params']['N_country'] = int(
np.sum(list(model.pop_sizes.values())))
set_seeds(seed)
cost_function = get_cost_function(cost_function_id=params['cost_id'],
params=params['cost_params'])
# Form the Gym-like environment
env = get_env(env_id=params['env_id'],
cost_function=cost_function,
model=model,
simulation_horizon=params['simulation_horizon'],
seed=seed)
# Get DQN algorithm parameterized by beta
algorithm = get_algorithm(algo_id=params['algo_id'],
env=env,
params=params)
if params['algo_id'] == 'NSGAII':
algorithm.load_model(folder + 'res_eval.pk')
else:
algorithm.load_model(folder + 'models/best_model.cp')
return algorithm, cost_function, env, params
def setup_for_replay(folder,
seed=np.random.randint(1e6),
deterministic_model=False):
from epidemioptim.environments.models import get_model
from epidemioptim.environments.cost_functions import get_cost_function
from epidemioptim.environments.gym_envs import get_env
from epidemioptim.optimization import get_algorithm
print('Replaying: ', folder)
with open(folder + 'params.json', 'r') as f:
params = json.load(f)
if deterministic_model:
params['model_params']['stochastic'] = False
params[
'logdir'] = None #get_repo_path() + 'data/results/experiments' + params['logdir'].split('EpidemicDiscrete-v0')[1]
model = get_model(model_id=params['model_id'],
params=params['model_params'])
set_seeds(seed)
cost_function = get_cost_function(cost_function_id=params['cost_id'],
params=params['cost_params'])
# Form the Gym-like environment
env = get_env(env_id=params['env_id'],
cost_function=cost_function,
model=model,
simulation_horizon=params['simulation_horizon'],
seed=seed)
# Get DQN algorithm parameterized by beta
algorithm = get_algorithm(algo_id=params['algo_id'],
env=env,
params=params)
# if params['algo_id'] == 'NGSA':
# algorithm.load_model(folder + 'res_eval.pk')
# else:
algorithm.load_model(folder + 'models/best_model.cp')
return algorithm, cost_function, env, params
# Compute the real SIDEP incidence from a CSV file
# def get_incidence():
# PATH_TO_DATA = get_repo_path() + '/data/jane_model_data/inci.csv'
# data = pd.read_csv(PATH_TO_DATA, delimiter=";")
# true_data = pd.DataFrame(data['jour'])
# true_data['T']=data['P']
# week_grouped = true_data.groupby(['jour'])['T'].sum()/2
# data = week_grouped.values.tolist()
# #pre = np.zeros(132).tolist()
# #post = np.zeros(214).tolist()
# #pre.extend(data)
# #pre.extend(post)
# yhat = scipy.signal.savgol_filter(data, 53, 3)
# # for i in range(455, 731):
# # yhat[i] = 0
# return yhat