def startup(self, scenario: Scenario, n: int, initial_state: cdict,
initial_extra: cdict, **kwargs) -> Tuple[cdict, cdict]:
if not hasattr(scenario, 'prior_sample'):
raise TypeError(
f'Likelihood tempering requires scenario {scenario.name} to have prior_sample implemented'
)
initial_state, initial_extra = super().startup(scenario, n,
initial_state,
initial_extra, **kwargs)
random_keys = random.split(initial_extra.random_key, 2 * n + 1)
initial_extra.random_key = random_keys[-1]
initial_state.prior_potential = vmap(scenario.prior_potential)(
initial_state.value, random_keys[:n])
initial_state.likelihood_potential = vmap(
scenario.likelihood_potential)(initial_state.value,
random_keys[n:(2 * n)])
initial_state.potential = initial_state.prior_potential
initial_state.temperature = jnp.zeros(n)
initial_state.log_weight = jnp.zeros(n)
initial_state.ess = jnp.zeros(n) + n
scenario.temperature = 0.
return initial_state, initial_extra
def clean_chain_ar(self, abc_scenario: ABCScenario, chain_state: cdict):
threshold = jnp.quantile(chain_state.distance,
self.parameters.acceptance_rate)
self.parameters.threshold = float(threshold)
chain_state.log_weight = jnp.where(chain_state.distance < threshold,
0., -jnp.inf)
return chain_state
def startup(self, scenario: Scenario, n: int, initial_state: cdict,
initial_extra: cdict, **kwargs) -> Tuple[cdict, cdict]:
initial_state, initial_extra = super().startup(scenario, n,
initial_state,
initial_extra, **kwargs)
if not hasattr(initial_state, 'log_weight'):
initial_state.log_weight = jnp.zeros(n)
if not hasattr(initial_state, 'ess'):
initial_state.ess = jnp.zeros(n) + n
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_temperature = self.next_temperature(new_ensemble_state, new_extra)
new_ensemble_state.temperature = jnp.ones(n) * next_temperature
new_ensemble_state.log_weight = previous_ensemble_state.log_weight \
+ self.log_weight(previous_ensemble_state, previous_extra,
new_ensemble_state, new_extra)
new_ensemble_state.ess = jnp.ones(n) * jnp.exp(
log_ess_log_weight(new_ensemble_state.log_weight))
return new_ensemble_state, new_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 startup(self, scenario: Scenario, n: int, initial_state: cdict,
initial_extra: cdict, **kwargs) -> Tuple[cdict, cdict]:
self.mcmc_sampler.correction = check_correction(
self.mcmc_sampler.correction)
initial_state, initial_extra = super().startup(scenario, n,
initial_state,
initial_extra, **kwargs)
first_temp = self.next_temperature(initial_state, initial_extra)
scenario.temperature = first_temp
initial_state.temperature += first_temp
initial_state.potential = initial_state.prior_potential + first_temp * initial_state.likelihood_potential
initial_state.log_weight = -first_temp * initial_state.likelihood_potential
initial_state.ess = jnp.repeat(
jnp.exp(log_ess_log_weight(initial_state.log_weight)), n)
initial_state, initial_extra = vmap(
lambda state: self.mcmc_sampler.startup(
scenario, n, state, initial_extra))(initial_state)
initial_extra = initial_extra[0]
return initial_state, initial_extra
def startup(self,
abc_scenario: ABCScenario,
n: int,
initial_state: cdict = None,
initial_extra: cdict = None,
**kwargs) -> Tuple[cdict, cdict]:
if initial_state is None:
if is_implemented(abc_scenario.prior_sample):
initial_extra.random_key, sub_key = random.split(
initial_extra.random_key)
init_vals = abc_scenario.prior_sample(sub_key)
else:
init_vals = jnp.zeros(abc_scenario.dim)
initial_state = cdict(value=init_vals)
self.max_iter = n - 1
initial_state, initial_extra = super().startup(abc_scenario, n,
initial_state,
initial_extra, **kwargs)
initial_state.log_weight = self.log_weight(abc_scenario, initial_state,
initial_extra)
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]:
new_ensemble_state.log_weight = previous_ensemble_state.log_weight + new_ensemble_state.log_weight
return new_ensemble_state, new_extra
