pybop.samplers.base_pints_sampler

Classes

BasePintsSampler	Base class for PINTS samplers.
PintsSamplerOptions	Pints sampler options.

Module Contents

class pybop.samplers.base_pints_sampler.BasePintsSampler(log_pdf: pybop.problems.log_pdf.LogPDF, sampler: type[pints.SingleChainMCMC | pints.MultiChainMCMC], options: PintsSamplerOptions | None = None)

Bases: pybop.BaseSampler

Base class for PINTS samplers.

This class extends the BaseSampler class to provide a common interface for PINTS samplers. The class provides a sample() method that can be used to sample from the posterior distribution using a PINTS sampler.

Parameters:

• log_pdf (pybop.LogPDF) – The negative unnormalised posterior distribution.

• sampler (pints.MCMCSampler) – The PINTS sampler to be used for sampling.

• options (Optional[PintsSamplerOptions]) – Options for the sampler, by default None.

_ask_for_samples()

_check_initial_phase()

Set initial phase if needed

_check_stopping_criteria()

Verify that at least one stopping criterion is defined.

_end_initial_phase()

_finalise_logging()

_initialise_chain_processor()

Initialise the appropriate chain processor based on configuration.

_initialise_logging()

_initialise_storage()

_process_chains()

Process chains using the appropriate processor.

static default_options() → PintsSamplerOptions

Get the default options for the sampler.

run() → numpy.ndarray

Executes the Monte Carlo sampling process and generates samples from the posterior distribution.

This method orchestrates the entire sampling process, managing iterations, evaluations, logging, and stopping criteria. It initialises the necessary structures, handles both single and multi-chain scenarios, and manages parallel evaluation based on the configuration.

Returns:

The results including a numpy array containing the samples from the posterior distribution if chains are stored in memory, otherwise None.

Return type:

SamplingResult

Raises:

• ValueError – If no stopping criterion is set (i.e., _max_iterations is None).

• Details: –

  • Checks and ensures at least one stopping criterion is set.

  • Initialises iterations, evaluations, and other required

  structures. - Sets up the parallel evaluator based on the configuration. - Handles the initial phase, if applicable, and manages intermediate steps in the sampling process. - Logs progress and relevant information based on the logging configuration. - Iterates through the sampling process, evaluating the log PDF, updating chains, and managing the stopping criteria. - Finalises and returns the collected samples, or None if chains are not stored in memory.

_chain_files = None

_chains_in_memory = True

_evaluation_files = None

_initial_phase

_initial_phase_iterations = 250

_log_filename = None

_log_to_screen = True

_loop_iters = 0

_max_iterations = 500

_needs_sensitivities

_sampler

_single_chain

_verbose = False

_warm_up = 0

property active

property chains_in_memory

iter_time = 0.0

property iteration

property max_iterations

property n_samples

property needs_sensitivities

property sampled_logpdf

property sampled_prior

property samplers

property single_chain

class pybop.samplers.base_pints_sampler.PintsSamplerOptions

Bases: pybop.samplers.base_sampler.SamplerOptions

Pints sampler options.

n_chains

The number of chains to concurrently sample from (default: 1).

Type:

int

max_iterations

Maximum number of iterations to run (default: 500).

Type:

int

verbose

If True, additional information will be printed (default: False).

Type:

bool

warm_up_iterations

Number of iterations to warm up the sampler (default: 250).

Type:

int

chains_in_memory

Whether to store the chains in memory (default: True).

Type:

bool

log_to_screen

If True (default), the sampler will print information during the sampling.

Type:

bool

log_filename

The name of the file to save the sampler log to (default: None).

Type:

str

chain_files

The name of the file to save the chains in (default: None).

Type:

list

evaluation_files

The name of the file to save the evaluations in (default: None).

Type:

list

validate()

Validate the options.

Raises:

ValueError – If the options are invalid.

chain_files: list[str] | None = None

chains_in_memory: bool = True

evaluation_files: list[str] | None = None

initial_phase_iterations: int = 250

log_filename: str | None = None

log_to_screen: bool = True

max_iterations: int = 500

verbose: bool = False

warm_up_iterations: int = 0