def get_simulations(self,
starting_round: int = 0
) -> Tuple[Tensor, Tensor, Tensor]:
r"""
Return all $(\theta, x, label)$ pairs that have been passed to this object.
The label had been inferred from the `valid_or_invalid_criterion`.
"""
theta = get_simulations_since_round(self._theta_roundwise,
self._data_round_index,
starting_round)
x = get_simulations_since_round(self._x_roundwise,
self._data_round_index, starting_round)
label = get_simulations_since_round(self._label_roundwise,
self._data_round_index,
starting_round)
return theta, x, label
def get_simulations(
self,
starting_round: int = 0,
exclude_invalid_x: bool = True,
warn_on_invalid: bool = True,
) -> Tuple[Tensor, Tensor, Tensor]:
r"""
Returns all $\theta$, $x$, and prior_masks from rounds >= `starting_round`.
If requested, do not return invalid data.
Args:
starting_round: The earliest round to return samples from (we start counting
from zero).
exclude_invalid_x: Whether to exclude simulation outputs `x=NaN` or `x=±∞`
during training.
warn_on_invalid: Whether to give out a warning if invalid simulations were
found.
Returns: Parameters, simulation outputs, prior masks.
"""
theta = get_simulations_since_round(
self._theta_roundwise, self._data_round_index, starting_round
)
x = get_simulations_since_round(
self._x_roundwise, self._data_round_index, starting_round
)
prior_masks = get_simulations_since_round(
self._prior_masks, self._data_round_index, starting_round
)
# Check for NaNs in simulations.
is_valid_x, num_nans, num_infs = handle_invalid_x(x, exclude_invalid_x)
# Check for problematic z-scoring
warn_if_zscoring_changes_data(x)
if warn_on_invalid:
warn_on_invalid_x(num_nans, num_infs, exclude_invalid_x)
warn_on_invalid_x_for_snpec_leakage(
num_nans, num_infs, exclude_invalid_x, type(self).__name__, self._round
)
return theta[is_valid_x], x[is_valid_x], prior_masks[is_valid_x]