Maximum-A-Posteriori (MAP)#

class fortuna.prob_model.posterior.map.map_approximator.MAPPosteriorApproximator[source]#

Maximum-A-Posteriori posterior approximator. It is responsible to define how the posterior distribution is approximated.

property posterior_method_kwargs: Dict[str, Any]#
class fortuna.prob_model.posterior.map.map_posterior.MAPPosterior(joint, posterior_approximator)[source]#

Bases: Posterior

Maximum-a-Posteriori (MAP) approximate 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

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.map.map_state.MAPState(step, apply_fn, params, tx, opt_state, encoded_name=(77, 65, 80, 83, 116, 97, 116, 101), frozen_params=None, dynamic_scale=None, mutable=None, calib_params=None, calib_mutable=None, grad_accumulated=None)[source]#

Bases: PosteriorState

encoded_name#

MAP state name encoded as an array.

Type:

jnp.ndarray

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