_AbstractSampler#

class hmclab.Samplers._AbstractSampler(seed: Optional[int] = None)[source]#

Bases: abc.ABC

Abstract base class for Markov chain Monte Carlo samplers.

__init__(seed: Optional[int] = None) → None[source]#

Constructor that sets the random number generator for the sampler. Pass a seed to make a Markov chain reproducible (given that all settings are re-used.)

Parameters: seed (int) – Description of parameter seed.

Methods

`__init__`	Constructor that sets the random number generator for the sampler.
`get_diagnostics`
`load_results`
`print_results`	Print Jupyter widget from _repr_html_() to stdout.

Attributes

`accepted_proposals`	An integer representing the amount of accepted proposals.
`amount_of_writes`	A positive integer representing the amount of times the sampler has written to disk.
`current_model`	A NumPy array containing the model at the current state of the Markov chain.
`current_proposal`	An integer representing the current proposal index (before thinning) of the Markov chain.
`current_proposal_after_thinning`	An integer representing the current proposal index after thinning of the Markov chain.
`current_x`	A float containing \(\chi = -\log\left( p\\right)\) (i.e.
`diagnostic_mode`	Boolean describing whether functions need to be profiled for performance.
`dimensions`	An integer representing the dimensionality in which the MCMC sampler works.
`disable_progressbar`	A bool describing whether or not to disable the TQDM progressbar
`distribution`	The _AbstractDistribution-derived object on which the sampler works.
`end_time`	Time (and date) at which sampling was terminated / finished.
`exchange_interval`	Integer describing how many samples lie between the attempted swap of states between samplers.
`exchange_schedule`	A pre-communicated exchange schedule determining when and which samplers try to exchange states.
`functions_to_diagnose`	Array of functions to be profiled, typically contains functions only from the abstract base class.
`main_thread_keyboard_interrupt`
`max_time`	A float representing the maximum time in seconds that sampling is allowed to take before it is automatically terminated.
`name`	The name of the sampler
`online_thinning`	An integer representing the interval between stored samples.
`parallel`	A boolean describing if this sampler works in an array of multiple samplers.
`pipe_matrix`	A matrix of two-way communicators between samplers.
`progressbar_refresh_rate`	A float representing how many seconds lies between an update of the progress bar statistics (acceptance rate etc.).
`proposals`	An integer representing the amount of requested proposals for a run.
`proposed_model`	A NumPy array containing the model at the proposed state of the Markov chain.
`proposed_x`	A float containing \(\chi = -\log\left( p\\right)\) (i.e.
`ram_buffer`	A NumPy ndarray containing the samples that are as of yet not written to disk.
`ram_buffer_size`	A positive integer indicating the size of the RAM buffer in amount of samples.
`rng`
`sampler_index`	An integer describing the index of the parallel sampler in the array of samplers.
`sampler_specific_functions_to_diagnose`	Array of sampler specific functions to be profiled.
`samples_hdf5_dataset`	A string containing the HDF5 group of the hdf5 file to which samples will be stored.
`samples_hdf5_filehandle`	A HDF5 file handle of the hdf5 file to which samples will be stored.
`samples_hdf5_filename`	A string containing the path+filename of the hdf5 file to which samples will be stored.
`start_time`	Time (and date) at which sampling was started.
`times_started`	An integer representing how often this sampler object has started sampling.

name: str = 'Monte Carlo sampler abstract base class'#: The name of the sampler

dimensions: int = None#: An integer representing the dimensionality in which the MCMC sampler works.

distribution: hmclab.Distributions.base._AbstractDistribution = None#: The _AbstractDistribution-derived object on which the sampler works.

samples_hdf5_filename: str = None#: A string containing the path+filename of the hdf5 file to which samples will be stored.

samples_hdf5_filehandle = None#: A HDF5 file handle of the hdf5 file to which samples will be stored.

samples_hdf5_dataset = None#: A string containing the HDF5 group of the hdf5 file to which samples will be stored.

ram_buffer_size: int = None#: A positive integer indicating the size of the RAM buffer in amount of samples.

ram_buffer: numpy.ndarray = None#: A NumPy ndarray containing the samples that are as of yet not written to disk.

current_model: numpy.ndarray = None#: A NumPy array containing the model at the current state of the Markov chain.

proposed_model: numpy.ndarray = None#: A NumPy array containing the model at the proposed state of the Markov chain.

current_x: float = None#: A float containing \(\chi = -\log\left( p\\right)\) (i.e. the misfit, negative log probability) of the distribution at the current state of the Markov chain.

proposed_x: float = None#: A float containing \(\chi = -\log\left( p\\right)\) (i.e. the misfit, negative log probability) of the distribution at the proposed state of the Markov chain.

amount_of_writes: int = None#: A positive integer representing the amount of times the sampler has written to disk.

progressbar_refresh_rate: float = 0.25#: A float representing how many seconds lies between an update of the progress bar statistics (acceptance rate etc.).

max_time: float = None#: A float representing the maximum time in seconds that sampling is allowed to take before it is automatically terminated. The value None is used for unlimited time.

times_started: int = 0#: An integer representing how often this sampler object has started sampling.

proposals: int = None#: An integer representing the amount of requested proposals for a run.

online_thinning: int = None#: An integer representing the interval between stored samples. Defaults to 1, i.e. all samples are stored.

current_proposal: int = 0#: An integer representing the current proposal index (before thinning) of the Markov chain.

current_proposal_after_thinning: int = 0#: An integer representing the current proposal index after thinning of the Markov chain.

accepted_proposals: int = 0#: An integer representing the amount of accepted proposals.

disable_progressbar: bool = False#: A bool describing whether or not to disable the TQDM progressbar

end_time: datetime.datetime = None#: Time (and date) at which sampling was terminated / finished.

start_time: datetime.datetime = None#: Time (and date) at which sampling was started.

diagnostic_mode: bool = True#: Boolean describing whether functions need to be profiled for performance.

functions_to_diagnose: List = []#: Array of functions to be profiled, typically contains functions only from the abstract base class.

sampler_specific_functions_to_diagnose: List = []#: Array of sampler specific functions to be profiled.

parallel: bool = False#: A boolean describing if this sampler works in an array of multiple samplers.

sampler_index: int = 0#: An integer describing the index of the parallel sampler in the array of samplers. Essential for two-way communication.

exchange_schedule = None#: A pre-communicated exchange schedule determining when and which samplers try to exchange states.

pipe_matrix = None#: A matrix of two-way communicators between samplers. Indexed using sampler_index.

exchange_interval: int = None#: Integer describing how many samples lie between the attempted swap of states between samplers.

print_results() → None[source]#: Print Jupyter widget from _repr_html_() to stdout.