def destandardizing_net(batch_t: Tensor, min_std: float = 1e-7) -> nn.Module:
"""Net that de-standardizes the output so the NN can learn the standardized target.
Args:
batch_t: Batched tensor from which mean and std deviation (across
first dimension) are computed.
min_std: Minimum value of the standard deviation to use when z-scoring to
avoid division by zero.
Returns:
Neural network module for z-scoring
"""
is_valid_t, *_ = handle_invalid_x(batch_t, True)
t_mean = torch.mean(batch_t[is_valid_t], dim=0)
if len(batch_t > 1):
t_std = torch.std(batch_t[is_valid_t], dim=0)
t_std[t_std < min_std] = min_std
else:
t_std = 1
logging.warning(
"""Using a one-dimensional batch will instantiate a Standardize transform
with (mean, std) parameters which are not representative of the data. We
allow this behavior because you might be loading a pre-trained. If this is
not the case, please be sure to use a larger batch.""")
return Destandardize(t_mean, t_std)
def append_simulations(self, theta: Tensor,
x: Tensor) -> "RestrictionEstimator":
r"""
Store parameters and simulation outputs to use them for training later.
Data ar stored as entries in lists for each type of variable (parameter/data).
Args:
theta: Parameter sets.
x: Simulation outputs.
Returns:
`RestrictionEstimator` object (returned so that this function is chainable).
"""
validate_theta_and_x(theta, x)
if self._valid_or_invalid_criterion == "nan":
label, _, _ = handle_invalid_x(x)
else:
label = self._valid_or_invalid_criterion(x)
label = label.long()
if self._data_round_index:
self._data_round_index.append(self._data_round_index[-1] + 1)
else:
self._data_round_index.append(0)
self._theta_roundwise.append(theta)
self._x_roundwise.append(x)
self._label_roundwise.append(label)
return self
def test_handle_invalid_x(x_shape, set_seed):
x = torch.rand(x_shape)
x[x < 0.1] = float("nan")
x[x > 0.9] = float("inf")
x_is_valid, *_ = handle_invalid_x(x, exclude_invalid_x=True)
assert torch.isfinite(x[x_is_valid]).all()
