def __init__(self,
params=sib.Params(),
window_length=14,
maxit0=10,
maxit1=10,
damp0=0,
damp1=0.5,
tol=1e-3,
memory_decay=1.0,
tau=None,
print_callback=lambda t, err, f: print(t, err)):
self.description = "class for BP inference of openABM loop"
self.authors = "Indaco Biazzo, Alessandro Ingrosso, Alfredo Braunstein"
self.pseed = params.pseed
self.prob_sus = params.psus / (1 - self.pseed)
self.params = params
self.window_length = window_length
self.maxit0 = maxit0
self.maxit1 = maxit1
self.damp0 = damp0
self.damp1 = damp1
self.tol = tol
self.window_length = window_length
self.memory_decay = memory_decay
self.print_callback = print_callback
self.tau = tau
def run_sib_instance(self, inst, callback_fun=callback):
mu = self.params["mu"]
sib_pars = sib.Params(prob_r=sib.Gamma(mu=mu))
sib_fg = sib.FactorGraph(sib_pars, self.contacts_sib,
self.obs_all_sib[inst])
sib.iterate(sib_fg, maxit=1000, tol=6e-6, callback=callback_fun)
return sib_fg
intervention_options=intervention_options)
##### TRACING SCENARIO #######
scenario_trac = Scenario(model,
seed=seed + 1,
ranking_options=dict(ranking=RANKINGS["tracing"],
tau=trac_tau),
observation_options=observation_options,
intervention_options=intervention_options)
pseed = 1e-4
psus = 0.52
mu_r = np.log(1 + mu)
pautoinf = args.pautoinf
bp_ranker = bp_ranker_class(params=sib.Params(prob_r=sib.Exponential(mu=mu_r),
pseed=pseed,
psus=psus,
pautoinf=pautoinf),
maxit0=20,
maxit1=20,
tol=1e-3,
memory_decay=1e-5,
window_length=window_bp,
tau=tau_bp)
bp_ranker.init(N, T)
bp_ranker.__name__ = "bp"
scenario_bp = Scenario(
model,
seed=seed,
ranking_options=dict(ranking=bp_ranker.step_scenario),
observation_options=observation_options,
intervention_options=intervention_options,