aspire.samplers.smc.minipcn

aspire.samplers.smc.minipcn#

Classes#

MiniPCNSMC

MiniPCN SMC sampler.

Module Contents#

class aspire.samplers.smc.minipcn.MiniPCNSMC(log_likelihood, log_prior, dims, prior_flow, xp, dtype=None, parameters=None, rng=None, preconditioning_transform=None)[source]#

Bases: aspire.samplers.smc.base.SMCSampler

MiniPCN SMC sampler.

Parameters:

  • log_likelihood (Callable)

  • log_prior (Callable)

  • dims (int)

  • prior_flow (aspire.flows.base.Flow)

  • xp (Callable)

  • dtype (Any | str | None)

  • parameters (list[str] | None)

  • rng (array_api_compat.numpy.random.Generator | orng.ArrayRNG | None)

  • preconditioning_transform (Callable | None)

rng = None[source]#
log_prob(x, beta=None)[source]#

Compute the log probability of the samples.

Input samples are in the transformed space.

Parameters:
Return type:

Any

sample(n_samples, n_steps=None, min_beta_step=None, max_beta_step=None, max_n_steps=None, adaptive=True, target_efficiency=0.5, target_efficiency_rate=1.0, n_final_samples=None, sampler_kwargs=None, rng=None, checkpoint_callback=None, checkpoint_every=None, checkpoint_file_path=None, resume_from=None, beta_tolerance=1e-06, store_sample_history=True)[source]#

Sample using the SMC sampler.

Parameters:

  • n_samples (int) – The number of samples (particles) to use in the SMC sampler.

  • n_steps (int, optional) – The number of SMC iterations to perform. Must be specified if adaptive=False. Default is None.

  • adaptive (bool, optional) – Whether to adaptively determine the beta schedule. Default is True.

  • min_beta_step (float, optional) – The minimum beta step size when using adaptive beta. Default is None, which means no minimum step size.

  • max_beta_step (float, optional) – The maximum beta step size when using adaptive beta. Default is None, which means no maximum step size.

  • max_n_steps (int, optional) – The maximum number of SMC iterations to perform when using adaptive beta. Default is None, which means no maximum.

  • target_efficiency (float or tuple, optional) – The target sample efficiency (ESS / n_samples) to aim for at each SMC iteration. Can be a single float in (0, 1) or a tuple of two floats specifying a range to adapt between. Default is 0.5.

  • target_efficiency_rate (float, optional) – When using a tuple for target_efficiency, this controls the rate at which the target efficiency adapts from the first value to the second value as beta increases. Default is 1.0 (linear adaptation).

  • n_final_samples (int, optional) – If specified, the number of final samples to produce after the SMC iterations. If not specified, the number of final samples will be the same as n_samples. Default is None.

  • checkpoint_callback (callable, optional) – A callback function to call with a checkpoint dictionary at regular intervals during sampling. Default is None (no checkpointing).

  • checkpoint_every (int, optional) – The number of iterations between checkpoints when using checkpoint callback. Default is None, which means no regular checkpointing.

  • checkpoint_file_path (str, optional) – If using checkpoint_callback, this can be used to specify a file path to save checkpoints to. Default is None.

  • resume_from (str, bytes, or dict, optional) – If specified, this can be used to resume sampling from a previous checkpoint. Can be a file path, bytes object, or checkpoint dictionary. Default is None (start from scratch).

  • store_sample_history (bool, optional) – Whether to store the history of samples at each iteration in self.history.sample_history. Default is True.

  • beta_tolerance (float, optional) – Tolerance for determining convergence of beta when using adaptive beta. Default is given by DEFAULT_BETA_TOLERANCE.

  • sampler_kwargs (dict | None)

  • rng (numpy.random.Generator | None)

Returns:

final_samples – The final samples after running the SMC sampler, with log evidence and log evidence error estimates.

Return type:

SMCSamples

Raises:

ValueError – If both n_steps is None and adaptive is False, or if target_efficiency is not in (0, 1) when a float, or if target_efficiency tuple is not valid, or if log probabilities contain NaN values.

mutate(particles, beta, n_steps=None)[source]#

Mutate particles using the MiniPCN sampler.

Parameters:

  • particles (SMCSamples) – The current particles to be mutated.

  • beta (float) – The current inverse temperature.

  • n_steps (int, optional) – The number of MCMC steps to take. If None, uses the default from sampler_kwargs.

Returns:

The mutated particles.

Return type:

SMCSamples