def startup(self,
abc_scenario: ABCScenario,
n: int,
initial_state: cdict,
initial_extra: cdict,
**kwargs) -> Tuple[cdict, cdict]:
initial_state, initial_extra = SMCSampler.startup(self, abc_scenario, n,
initial_state, initial_extra, **kwargs)
n = len(initial_state.value)
if not hasattr(initial_state, 'prior_potential') and is_implemented(abc_scenario.prior_potential):
random_keys = random.split(initial_extra.random_key, n + 1)
initial_extra.random_key = random_keys[-1]
initial_state.prior_potential = vmap(abc_scenario.prior_potential)(initial_state.value,
random_keys[:n])
if not hasattr(initial_state, 'simulated_data'):
random_keys = random.split(initial_extra.random_key, n + 1)
initial_extra.random_key = random_keys[-1]
initial_state.simulated_data = vmap(abc_scenario.likelihood_sample)(initial_state.value,
random_keys[:n])
if not hasattr(initial_state, 'distance'):
initial_state.distance = vmap(abc_scenario.distance_function)(initial_state.simulated_data)
if not hasattr(initial_state, 'threshold'):
if self.threshold_schedule is None:
initial_state.threshold = jnp.zeros(n) + jnp.inf
else:
initial_state.threshold = jnp.zeros(n) + self.threshold_schedule[0]
if not hasattr(initial_state, 'ess'):
initial_state.ess = jnp.zeros(n) + n
return initial_state, initial_extra
def startup(self, abc_scenario: ABCScenario, sampler: MCMCSampler, n: int,
initial_state: cdict, initial_extra: cdict,
**kwargs) -> Tuple[cdict, cdict]:
initial_state, initial_extra = super().startup(abc_scenario, sampler,
n, initial_state,
initial_extra, **kwargs)
if sampler.parameters.stepsize is None:
initial_extra.parameters.stepsize = 1.0
if sampler.parameters.threshold is None:
initial_extra.parameters.threshold = 50.
initial_state.threshold = initial_extra.parameters.threshold
initial_extra.parameters.stepsize = jnp.ones(
abc_scenario.dim) * initial_extra.parameters.stepsize
initial_state.stepsize = initial_extra.parameters.stepsize
initial_extra.post_mean = initial_state.value
initial_extra.diag_post_cov = initial_extra.parameters.stepsize * abc_scenario.dim / 2.38**2
return initial_state, initial_extra
def adapt(self,
previous_ensemble_state: cdict,
previous_extra: cdict,
new_ensemble_state: cdict,
new_extra: cdict) -> Tuple[cdict, cdict]:
n = new_ensemble_state.value.shape[0]
next_threshold = self.next_threshold(new_ensemble_state, new_extra)
new_ensemble_state.threshold = jnp.ones(n) * next_threshold
new_extra.parameters.threshold = next_threshold
new_ensemble_state.log_weight = self.log_weight(previous_ensemble_state, previous_extra,
new_ensemble_state, new_extra)
new_ensemble_state.ess = jnp.ones(n) * ess_log_weight(new_ensemble_state.log_weight)
alive_inds = previous_ensemble_state.log_weight > -jnp.inf
new_extra.alpha_mean = (new_ensemble_state.alpha * alive_inds).sum() / alive_inds.sum()
new_ensemble_state, new_extra = self.adapt_mcmc_params(previous_ensemble_state, previous_extra,
new_ensemble_state, new_extra)
return new_ensemble_state, new_extra
def clean_chain(self,
abc_scenario: ABCScenario,
chain_ensemble_state: cdict) -> cdict:
chain_ensemble_state.threshold = chain_ensemble_state.threshold[:, 0]
chain_ensemble_state.ess = chain_ensemble_state.ess[:, 0]
return chain_ensemble_state
