def append_simulations(
self,
theta: Tensor,
x: Tensor,
from_round: int = 0,
) -> "RatioEstimator":
r"""
Store parameters and simulation outputs to use them for later training.
Data are stored as entries in lists for each type of variable (parameter/data).
Stores $\theta$, $x$, prior_masks (indicating if simulations are coming from the
prior or not) and an index indicating which round the batch of simulations came
from.
Args:
theta: Parameter sets.
x: Simulation outputs.
from_round: Which round the data stemmed from. Round 0 means from the prior.
With default settings, this is not used at all for `SNRE`. Only when
the user later on requests `.train(discard_prior_samples=True)`, we
use these indices to find which training data stemmed from the prior.
Returns:
NeuralInference object (returned so that this function is chainable).
"""
validate_theta_and_x(theta, x)
self._theta_roundwise.append(theta)
self._x_roundwise.append(x)
self._prior_masks.append(
mask_sims_from_prior(int(from_round), theta.size(0)))
self._data_round_index.append(int(from_round))
return self
def append_simulations(
self, theta: Tensor, x: Tensor, proposal: Optional[Any] = None,
) -> "PosteriorEstimator":
r"""
Store parameters and simulation outputs to use them for later training.
Data are stored as entries in lists for each type of variable (parameter/data).
Stores $\theta$, $x$, prior_masks (indicating if simulations are coming from the
prior or not) and an index indicating which round the batch of simulations came
from.
Args:
theta: Parameter sets.
x: Simulation outputs.
proposal: The distribution that the parameters $\theta$ were sampled from.
Pass `None` if the parameters were sampled from the prior. If not
`None`, it will trigger a different loss-function.
Returns:
NeuralInference object (returned so that this function is chainable).
"""
validate_theta_and_x(theta, x)
self._check_proposal(proposal)
if (
proposal is None
or proposal is self._prior
or (
isinstance(proposal, RestrictedPrior) and proposal._prior is self._prior
)
):
# The `_data_round_index` will later be used to infer if one should train
# with MLE loss or with atomic loss (see, in `train()`:
# self._round = max(self._data_round_index))
self._data_round_index.append(0)
self._prior_masks.append(mask_sims_from_prior(0, theta.size(0)))
else:
if not self._data_round_index:
# This catches a pretty specific case: if, in the first round, one
# passes data that does not come from the prior.
self._data_round_index.append(1)
else:
self._data_round_index.append(max(self._data_round_index) + 1)
self._prior_masks.append(mask_sims_from_prior(1, theta.size(0)))
self._theta_roundwise.append(theta)
self._x_roundwise.append(x)
self._proposal_roundwise.append(proposal)
return self
def append_simulations(
self,
theta: Tensor,
x: Tensor,
proposal: Optional[Any] = None,
) -> "PosteriorEstimator":
r"""
Store parameters and simulation outputs to use them for later training.
Data are stored as entries in lists for each type of variable (parameter/data).
Stores $\theta$, $x$, prior_masks (indicating if simulations are coming from the
prior or not) and an index indicating which round the batch of simulations came
from.
Args:
theta: Parameter sets.
x: Simulation outputs.
proposal: The distribution that the parameters $\theta$ were sampled from.
Pass `None` if the parameters were sampled from the prior. If not
`None`, it will trigger a different loss-function.
Returns:
NeuralInference object (returned so that this function is chainable).
"""
validate_theta_and_x(theta, x)
self._check_proposal(proposal)
self._prior_masks = [None for _ in self._prior_masks]
if (proposal is None or proposal is self._prior
or (isinstance(proposal, RestrictedPrior)
and proposal._prior is self._prior)):
# The `_data_round_index` will later be used to infer if one should train
# with MLE loss or with atomic loss (see, in `train()`:
# self._round = max(self._data_round_index))
self._data_round_index.append(0)
self._prior_masks.append(mask_sims_from_prior(0, theta.size(0)))
else:
if not self._data_round_index:
# This catches a pretty specific case: if, in the first round, one
# passes data that does not come from the prior.
self._data_round_index.append(1)
else:
self._data_round_index.append(max(self._data_round_index) + 1)
self._prior_masks.append(mask_sims_from_prior(1, theta.size(0)))
# Delete old samples as we do not want to use them
self._theta_roundwise = [None for _ in self._theta_roundwise]
self._x_roundwise = [None for _ in self._x_roundwise]
self._proposal_roundwise = [None for _ in self._proposal_roundwise]
self._theta_roundwise.append(theta)
self._x_roundwise.append(x)
self._proposal_roundwise.append(proposal)
if self._prior is None or isinstance(self._prior, ImproperEmpirical):
if proposal is not None:
raise ValueError(
"You had not passed a prior at initialization, but now you "
"passed a proposal. If you want to run multi-round SNPE, you have "
"to specify a prior (set the `.prior` argument or re-initialize "
"the object with a prior distribution). If the samples you passed "
"to `append_simulations()` were sampled from the prior, you can "
"run single-round inference with "
"`append_simulations(..., proposal=None)`.")
theta_prior = self.get_simulations()[0]
self._prior = ImproperEmpirical(theta_prior,
ones(theta_prior.shape[0]))
return self