RWMH#

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

Bases: hmclab.Samplers._AbstractSampler

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

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.
`autotune`
`get_diagnostics`
`load_results`
`plot_acceptance_rate`
`plot_stepsizes`
`print_results`	Print Jupyter widget from _repr_html_() to stdout.
`sample`	Sampling using the Metropolis-Hastings algorithm.

Attributes

`acceptance_rates`	A NumPy ndarray containing all past acceptance rates.
`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.
`autotuning`	A boolean indicating if the stepsize is tuned towards a specific acceptance rate using diminishing adaptive parameters.
`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.
`learning_rate`	A float tuning the rate of decrease at which a new step size is adapted, according to rate = (iteration_number) ** (-learning_rate).
`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.
`minimal_stepsize`	Minimal step length which is chosen if timestep becomes zero or negative during autotuning.
`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.
`stepsize`	A parameter describing the standard deviation of a multivariate normal (MVN) used as the proposal distribution for Random Walk Metropolis-Hastings.
`stepsizes`	A NumPy ndarray containing all past step lengths for the RWMH algorithm.
`target_acceptance_rate`	A float representing the optimal acceptance rate used for autotuning, if autotuning is True.
`times_started`	An integer representing how often this sampler object has started sampling.

stepsize: Union[float, numpy.ndarray] = 1.0#: A parameter describing the standard deviation of a multivariate normal (MVN) used as the proposal distribution for Random Walk Metropolis-Hastings. Using a _numpy.ndarray column vector (shape dimensions × 1) will give every dimensions a unique step length. Correlations in the MVN are not yet implemented. Has a strong influence on acceptance rate. An essential tuning parameter.

autotuning: bool = False#: A boolean indicating if the stepsize is tuned towards a specific acceptance rate using diminishing adaptive parameters.

learning_rate: float = 0.75#: A float tuning the rate of decrease at which a new step size is adapted, according to rate = (iteration_number) ** (-learning_rate). Needs to be larger than 0.5 and equal to or smaller than 1.0.

target_acceptance_rate: float = 0.65#: A float representing the optimal acceptance rate used for autotuning, if autotuning is True.

acceptance_rates: numpy.ndarray = None#: A NumPy ndarray containing all past acceptance rates. Collected if autotuning is True.

stepsizes: numpy.ndarray = None#: A NumPy ndarray containing all past step lengths for the RWMH algorithm. Collected if autotuning is True.

minimal_stepsize: float = 1e-18#: Minimal step length which is chosen if timestep becomes zero or negative during autotuning.

sample(samples_hdf5_filename: str, distribution: hmclab.Distributions.base._AbstractDistribution, stepsize: Union[float, numpy.ndarray] = 1.0, initial_model: Optional[numpy.ndarray] = None, proposals: int = 100, online_thinning: int = 1, diagnostic_mode: bool = False, ram_buffer_size: Optional[int] = None, overwrite_existing_file: bool = False, max_time: Optional[float] = None, autotuning: bool = False, target_acceptance_rate: float = 0.65, learning_rate: float = 0.75, queue=None, disable_progressbar=False)[source]#

Sampling using the Metropolis-Hastings algorithm.

Parameters

samples_hdf5_filename (str) – String containing the (path+)filename of the HDF5 file to contain the samples. A required parameter.
distribution (_AbstractDistribution) – The distribution to sample. Should be an instance of a subclass of _AbstractDistribution. A required parameter.
stepsize (_Union[float, _numpy.ndarray]) – A parameter describing the standard deviation of a multivariate normal (MVN) used as the proposal distribution for Random Walk Metropolis-Hastings. Using a _numpy.ndarray column vector (shape dimensions × 1) will give every dimensions a unique step length. Correlations in the MVN are not yet implemented. Has a strong influence on acceptance rate. An essential tuning parameter.
initial_model (_numpy) – A NumPy column vector (shape dimensions × 1) containing the starting model of the Markov chain. This model will not be written out as a sample.
proposals (int) – An integer representing the amount of proposals the algorithm should make.
online_thinning (int) – An integer representing the degree of online thinning, i.e. the interval between storing samples.
diagnostic_mode (bool) – A boolean describing if subroutines of sampling should be timed. Useful for finding slow parts of the algorithm. Will add overhead to each function.
ram_buffer_size (int) – An integer representing how many samples should be kept in RAM before writing to storage.
overwrite_existing_file (bool) – A boolean describing whether or not to silently overwrite existing files. Use with caution.
max_time (float) – 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.
autotuning (bool) – A boolean describing whether or not stepsize is automatically adjusted. Uses a diminishing adapting scheme to satisfy detailed balance.
target_acceptance_rate (float) – A float between 0.0 and 1.0 that is the target acceptance rate of autotuning. The algorithm will try to achieve this acceptance rate.
learning_rate (float) – A float larger than 0.5 but smaller than or equal to 1.0, describing how aggressively the stepsize is updated. Lower is more aggresive. Arbitrary keyword arguments.

Raises

AssertionError – For any unspecified invalid entry.
ValueError – For any invalid value of algorithm settings.
TypeError – For any invalid value of algorithm settings.

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