# Source code for pycbc.inference.sampler.base_multitemper

# Copyright (C) 2018  Collin Capano
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# =============================================================================
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#                                   Preamble
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# =============================================================================
#
"""Provides constructor classes provide support for parallel tempered MCMC
samplers."""

from __future__ import absolute_import

import logging
from six import string_types
import numpy
import h5py
from pycbc.filter import autocorrelation
[docs] @staticmethod def betas_from_config(cp, section): """Loads number of temperatures or betas from a config file. This looks in the given section for: * ntemps : The number of temperatures to use. Either this, or inverse-temperatures-file must be provided (but not both). * inverse-temperatures-file : Path to an hdf file containing the inverse temperatures ("betas") to use. The betas will be retrieved from the file's .attrs['betas']. Either this or ntemps must be provided (but not both). Parameters ---------- cp : WorkflowConfigParser instance Config file object to parse. section : str The name of the section to look in. Returns ------- ntemps : int or None The number of temperatures to use, if it was provided. betas : array The array of betas to use, if a inverse-temperatures-file was provided. """ if cp.has_option(section, "ntemps") and \ cp.has_option(section, "inverse-temperatures-file"): raise ValueError("Must specify either ntemps or " "inverse-temperatures-file, not both.") if cp.has_option(section, "inverse-temperatures-file"): # get the path of the file containing inverse temperatures values. inverse_temperatures_file = cp.get(section, "inverse-temperatures-file") with h5py.File(inverse_temperatures_file, "r") as fp: try: betas = numpy.array(fp.attrs['betas']) # betas must be in decending order betas = numpy.sort(betas)[::-1] ntemps = betas.shape[0] except KeyError: raise AttributeError("No attribute called betas") else: # get the number of temperatures betas = None ntemps = int(cp.get(section, "ntemps")) return ntemps, betas
[docs]def compute_acf(filename, start_index=None, end_index=None, chains=None, parameters=None, temps=None): """Computes the autocorrleation function for independent MCMC chains with parallel tempering. Parameters ----------- filename : str Name of a samples file to compute ACFs for. start_index : int, optional The start index to compute the acl from. If None (the default), will try to use the burn in iteration for each chain; otherwise, will start at the first sample. end_index : {None, int} The end index to compute the acl to. If None, will go to the end of the current iteration. chains : optional, int or array Calculate the ACF for only the given chains. If None (the default) ACFs for all chains will be estimated. parameters : optional, str or array Calculate the ACF for only the given parameters. If None (the default) will calculate the ACF for all of the model params. temps : optional, (list of) int or 'all' The temperature index (or list of indices) to retrieve. If None (the default), the ACF will only be computed for the coldest (= 0) temperature chain. To compute an ACF for all temperates pass 'all', or a list of all of the temperatures. Returns ------- dict : Dictionary parameter name -> ACF arrays. The arrays have shape ntemps x nchains x niterations. """ acfs = {} with loadfile(filename, 'r') as fp: if parameters is None: parameters = fp.variable_params if isinstance(parameters, string_types): parameters = [parameters] temps = _get_temps_idx(fp, temps) if chains is None: chains = numpy.arange(fp.nchains) for param in parameters: subacfs = [] for tk in temps: subsubacfs = [] for ci in chains: samples = fp.read_raw_samples( param, thin_start=start_index, thin_interval=1, thin_end=end_index, chains=ci, temps=tk)[param] thisacf = autocorrelation.calculate_acf(samples).numpy() subsubacfs.append(thisacf) # stack the chains subacfs.append(subsubacfs) # stack the temperatures acfs[param] = numpy.stack(subacfs) return acfs
[docs]def compute_acl(filename, start_index=None, end_index=None, min_nsamples=10): """Computes the autocorrleation length for independent MCMC chains with parallel tempering. ACLs are calculated separately for each chain. Parameters ----------- filename : str Name of a samples file to compute ACLs for. start_index : {None, int} The start index to compute the acl from. If None, will try to use the number of burn-in iterations in the file; otherwise, will start at the first sample. end_index : {None, int} The end index to compute the acl to. If None, will go to the end of the current iteration. min_nsamples : int, optional Require a minimum number of samples to compute an ACL. If the number of samples per walker is less than this, will just set to inf. Default is 10. Returns ------- dict A dictionary of ntemps x nchains arrays of the ACLs of each parameter. """ # following is a convenience function to calculate the acl for each chain # defined here so that we can use map for this below def _getacl(si): # si: the samples loaded for a specific chain; may have nans in it si = si[~numpy.isnan(si)] if len(si) < min_nsamples: acl = numpy.inf else: acl = autocorrelation.calculate_acl(si) if acl <= 0: acl = numpy.inf return acl acls = {} with loadfile(filename, 'r') as fp: tidx = numpy.arange(fp.ntemps) for param in fp.variable_params: these_acls = numpy.zeros((fp.ntemps, fp.nchains)) for tk in tidx: samples = fp.read_raw_samples( param, thin_start=start_index, thin_interval=1, thin_end=end_index, temps=tk, flatten=False)[param] # flatten out the temperature samples = samples[0, ...] # samples now has shape nchains x maxiters if samples.shape[-1] < min_nsamples: these_acls[tk, :] = numpy.inf else: these_acls[tk, :] = list(map(_getacl, samples)) acls[param] = these_acls # report the mean ACL: take the max over the temps and parameters act = acl_from_raw_acls(acls)*fp.thinned_by finite = act[numpy.isfinite(act)] logging.info("ACTs: min %s, mean (of finite) %s, max %s", str(act.min()), str(finite.mean() if finite.size > 0 else numpy.inf), str(act.max())) return acls
[docs]def acl_from_raw_acls(acls): """Calculates the ACL for one or more chains from a dictionary of ACLs. This is for parallel tempered MCMCs in which the chains are independent of each other. The ACL for each chain is maximized over the temperatures and parameters. Parameters ---------- acls : dict Dictionary of parameter names -> ntemps x nchains arrays of ACLs (the thing returned by :py:func:compute_acl). Returns ------- array The ACL of each chain. """ return numpy.array(list(acls.values())).max(axis=0).max(axis=0)
[docs]def ensemble_compute_acf(filename, start_index=None, end_index=None, per_walker=False, walkers=None, parameters=None, temps=None): """Computes the autocorrleation function for a parallel tempered, ensemble MCMC. By default, parameter values are averaged over all walkers at each iteration. The ACF is then calculated over the averaged chain for each temperature. An ACF per-walker will be returned instead if per_walker=True. Parameters ---------- filename : str Name of a samples file to compute ACFs for. start_index : int, optional The start index to compute the acl from. If None (the default), will try to use the number of burn-in iterations in the file; otherwise, will start at the first sample. end_index : int, optional The end index to compute the acl to. If None (the default), will go to the end of the current iteration. per_walker : bool, optional Return the ACF for each walker separately. Default is False. walkers : int or array, optional Calculate the ACF using only the given walkers. If None (the default) all walkers will be used. parameters : str or array, optional Calculate the ACF for only the given parameters. If None (the default) will calculate the ACF for all of the model params. temps : (list of) int or 'all', optional The temperature index (or list of indices) to retrieve. If None (the default), the ACF will only be computed for the coldest (= 0) temperature chain. To compute an ACF for all temperates pass 'all', or a list of all of the temperatures. Returns ------- dict : Dictionary of arrays giving the ACFs for each parameter. If per-walker is True, the arrays will have shape ntemps x nwalkers x niterations. Otherwise, the returned array will have shape ntemps x niterations. """ acfs = {} with loadfile(filename, 'r') as fp: if parameters is None: parameters = fp.variable_params if isinstance(parameters, string_types): parameters = [parameters] temps = _get_temps_idx(fp, temps) for param in parameters: subacfs = [] for tk in temps: if per_walker: # just call myself with a single walker if walkers is None: walkers = numpy.arange(fp.nwalkers) arrays = [ensemble_compute_acf(filename, start_index=start_index, end_index=end_index, per_walker=False, walkers=ii, parameters=param, temps=tk)[param][0, :] for ii in walkers] # we'll stack all of the walker arrays to make a single # nwalkers x niterations array; when these are stacked # below, we'll get a ntemps x nwalkers x niterations # array subacfs.append(numpy.vstack(arrays)) else: samples = fp.read_raw_samples( param, thin_start=start_index, thin_interval=1, thin_end=end_index, walkers=walkers, temps=tk, flatten=False)[param] # contract the walker dimension using the mean, and # flatten the (length 1) temp dimension samples = samples.mean(axis=1)[0, :] thisacf = autocorrelation.calculate_acf( samples).numpy() subacfs.append(thisacf) # stack the temperatures acfs[param] = numpy.stack(subacfs) return acfs
[docs]def ensemble_compute_acl(filename, start_index=None, end_index=None, min_nsamples=10): """Computes the autocorrleation length for a parallel tempered, ensemble MCMC. Parameter values are averaged over all walkers at each iteration and temperature. The ACL is then calculated over the averaged chain. Parameters ----------- filename : str Name of a samples file to compute ACLs for. start_index : int, optional The start index to compute the acl from. If None (the default), will try to use the number of burn-in iterations in the file; otherwise, will start at the first sample. end_index : int, optional The end index to compute the acl to. If None, will go to the end of the current iteration. min_nsamples : int, optional Require a minimum number of samples to compute an ACL. If the number of samples per walker is less than this, will just set to inf. Default is 10. Returns ------- dict A dictionary of ntemps-long arrays of the ACLs of each parameter. """ acls = {} with loadfile(filename, 'r') as fp: if end_index is None: end_index = fp.niterations tidx = numpy.arange(fp.ntemps) for param in fp.variable_params: these_acls = numpy.zeros(fp.ntemps) for tk in tidx: samples = fp.read_raw_samples( param, thin_start=start_index, thin_interval=1, thin_end=end_index, temps=tk, flatten=False)[param] # contract the walker dimension using the mean, and flatten # the (length 1) temp dimension samples = samples.mean(axis=1)[0, :] if samples.size < min_nsamples: acl = numpy.inf else: acl = autocorrelation.calculate_acl(samples) if acl <= 0: acl = numpy.inf these_acls[tk] = acl acls[param] = these_acls maxacl = numpy.array(list(acls.values())).max() logging.info("ACT: %s", str(maxacl*fp.thinned_by)) return acls