Sampler#

class pocomc.Sampler(prior: callable, likelihood: callable, n_dim: int = None, n_ess: int = 1000, n_active: int = 250, likelihood_args: list = None, likelihood_kwargs: dict = None, vectorize: bool = False, pool=None, flow=None, train_config: dict = None, precondition: bool = True, n_prior: int = None, sample: str = None, max_steps: int = None, patience: int = None, ess_threshold: int = None, output_dir: str = None, output_label: str = None, random_state: int = None)#

Preconditioned Monte Carlo class.

Parameters:

  • prior (callable) – Class implementing the prior distribution.

  • likelihood (callable) – Function returning the log likelihood of a set of parameters.

  • n_dim (int) – The total number of parameters/dimensions (Optional as it can be infered from the prior class).

  • n_ess (int) – The effective sample size maintained during the run (default is n_ess=1000).

  • n_active (int) – The number of active particles (default is n_active=250). It must be smaller than n_ess.

  • likelihood_args (list) – Extra arguments to be passed to likelihood (default is likelihood_args=None).

  • likelihood_kwargs (dict) – Extra arguments to be passed to likelihood (default is likelihood_kwargs=None).

  • vectorize (bool) – If True, vectorize likelihood calculation (default is vectorize=False).

  • pool (pool) – Provided MPI or multiprocessing pool for parallelisation (default is pool=None).

  • flow (torch.nn.Module or None) – Normalizing flow (default is None). The default is a Masked Autoregressive Flow (MAF) with 6 blocks of 3x64 layers and residual connections.

  • train_config (dict or None) – Configuration for training the normalizing flow (default is train_config=None). Options include a dictionary with the following keys: "validation_split", "epochs", "batch_size", "patience", "learning_rate", "annealing", "gaussian_scale", "laplace_scale", "noise", "shuffle", "clip_grad_norm", "verbose".

  • precondition (bool) – If True, use preconditioned MCMC (default is precondition=True). If False, use standard MCMC without normalizing flow.

  • n_prior (int) – Number of prior samples to draw (default is n_prior=2*(n_ess//n_active)*n_active).

  • sample (str) – Type of MCMC sampler to use (default is sample="pcn"). Options are "pcn" (Preconditioned Crank-Nicolson) or "rwm" (Random-Walk Metropolis).

  • max_steps (int) – Maximum number of MCMC steps (default is max_steps=5*n_dim).

  • patience (int) – Number of steps for early stopping of MCMC (default is patience=None). If patience=None, MCMC terminates automatically.

  • ess_threshold (int) – Effective sample size threshold for resampling (default is ess_threshold=4*n_dim).

  • output_dir (str or None) – Output directory for storing the state files of the sampler. Default is None which creates a states directory. Output files can be used to resume a run.

  • output_label (str or None) – Label used in state files. Defaullt is None which corresponds to "pmc". The saved states are named as "{output_dir}/{output_label}_{i}.state" where i is the iteration index. Output files can be used to resume a run.

  • random_state (int or None) – Initial random seed.

evidence()#

Return the log evidence estimate and error.

load_state(path: str | Path)#

Load state of sampler from file.

Parameters:

path (Union[str, Path]) – Path from which to load state.

posterior(resample=False, trim_importance_weights=True, return_logw=False, ess_trim=0.99, bins_trim=1000)#

Return posterior samples.

Parameters:

  • resample (bool) – If True, resample particles (default is resample=False).

  • trim_importance_weights (bool) – If True, trim importance weights (default is trim_importance_weights=True).

  • return_logw (bool) – If True, return log importance weights (default is return_logw=False).

  • ess_trim (float) – Effective sample size threshold for trimming (default is ess_trim=0.99).

  • bins_trim (int) – Number of bins for trimming (default is bins_trim=1_000).

Returns:

  • samples (np.ndarray) – Samples from the posterior.

  • weights (np.ndarray) – Importance weights.

  • logl (np.ndarray) – Log likelihoods.

  • logp (np.ndarray) – Log priors.

property results#

Return results.

Returns:

results – Dictionary containing the results.

Return type:

dict

run(n_total: int = 5000, n_evidence: int = 5000, progress: bool = True, resume_state_path: str | Path = None, save_every: int = None)#

Run Preconditioned Monte Carlo.

Parameters:

  • n_total (int) – The total number of effectively independent samples to be collected (default is n_total=5000).

  • n_evidence (int) – The number of importance samples used to estimate the evidence (default is n_evidence=5000). If n_evidence=0, the evidence is not estimated using importance sampling and the SMC estimate is used instead. If preconditioned=False, the evidence is estimated using SMC and n_evidence is ignored.

  • progress (bool) – If True, print progress bar (default is progress=True).

  • resume_state_path (Union[str, Path]) – Path of state file used to resume a run. Default is None in which case the sampler does not load any previously saved states. An example of using this option to resume or continue a run is e.g. resume_state_path = "states/pmc_1.state".

  • save_every (int or None) – Argument which determines how often (i.e. every how many iterations) pocoMC saves state files to the output_dir directory. Default is None in which case no state files are stored during the run.

save_state(path: str | Path)#

Save current state of sampler to file.

Parameters:

path (Union[str, Path]) – Path to save state.