Laplace approximation#

class fortuna.prob_model.posterior.laplace.laplace_approximator.LaplacePosteriorApproximator(tune_prior_log_variance=True)[source]#

Laplace posterior approximator.

property posterior_method_kwargs: Dict[str, Any]#
class fortuna.prob_model.posterior.laplace.laplace_posterior.LaplacePosterior(joint, posterior_approximator)[source]#

Bases: Posterior

Laplace approximation posterior class.

Parameters:
fit(train_data_loader, val_data_loader=None, fit_config=FitConfig(), map_fit_config=None, **kwargs)[source]#

Fit the posterior distribution. A posterior state will be internally stored.

Parameters:

  • train_data_loader (DataLoader) – Training data loader.

  • val_data_loader (Optional[DataLoader]) – Validation data loader.

  • fit_config (FitConfig) – A configuration object.

Returns:

A status including metrics describing the fitting process.

Return type:

Status

prior_log_variance_tuning(val_data_loader, n_posterior_samples=10, mode='cv', min_prior_log_var=-3, max_prior_log_var=3, grid_size=20, distribute=False)[source]#
Return type:

Array

sample(rng=None, **kwargs)[source]#

Sample from the posterior distribution.

Parameters:

rng (Optional[PRNGKeyArray]) – A random number generator. If not passed, this will be taken from the attributes of this class.

Returns:

A sample from the posterior distribution.

Return type:

JointState

class fortuna.prob_model.posterior.laplace.laplace_state.LaplaceState(step, apply_fn, params, tx, opt_state, encoded_name=(76, 97, 112, 108, 97, 99, 101, 83, 116, 97, 116, 101), frozen_params=None, dynamic_scale=None, mutable=None, calib_params=None, calib_mutable=None, grad_accumulated=None, prior_log_var=0.0, _encoded_which_params=None)[source]#

Bases: PosteriorState

prior_log_var#

Prior log-variance value.

Type:

float

encoded_name#

Laplace state name encoded as an array.

Type:

jnp.ndarray

classmethod convert_from_map_state(map_state, hess_lik_diag, prior_log_var, which_params)[source]#

Convert a MAP state into a Laplace state.

Parameters:

  • map_state (MAPState) – A MAP state.

  • hess_lik_diag (Union[Params, Tuple[Params, ...]]) – Diagonal of the approximated Hessian of the likelihood.

  • prior_log_var (float) – Prior log-variance value. If None, initialize it to 100.

  • which_params (Tuple[List[AnyKey], ...]) – Sequences of keys pointing to the parameters over which std is defined. If which_params is None, std must be defined for all parameters.

Returns:

A Laplace state instance.

Return type:

LaplaceState

classmethod init(params, mutable=None, optimizer=None, calib_params=None, calib_mutable=None, grad_accumulated=None, dynamic_scale=None, **kwargs)#

Initialize a posterior distribution state.

Parameters:

  • params (Params) – The parameters characterizing an approximation of the posterior distribution.

  • optimizer (Optional[OptaxOptimizer]) – An Optax optimizer associated with the posterior state.

  • mutable (Optional[Mutable]) – The mutable objects characterizing an approximation of the posterior distribution.

  • calib_params (Optional[CalibParams]) – The parameters objects characterizing an approximation of the posterior distribution.

  • calib_mutable (Optional[CalibMutable]) – The calibration mutable objects characterizing an approximation of the posterior distribution.

  • grad_accumulated (Optional[jnp.ndarray]) – The gradients accumulated in consecutive training steps (used only when gradient_accumulation_steps > 1).

  • dynamic_scale (Optional[dynamic_scale.DynamicScale]) – Dynamic loss scaling for mixed precision gradients.

Returns:

A posterior distribution state.

Return type:

Any

classmethod init_from_dict(d, optimizer=None, **kwargs)#

Initialize a posterior distribution state from a dictionary.

Parameters:

  • d (Dict) – A dictionary including attributes of the posterior state.

  • optimizer (Optional[OptaxOptimizer]) – An optax optimizer to assign to the posterior state.

Returns:

A posterior state.

Return type:

PosteriorState