# Copyright (C) 2016 Collin Capano
# This program is free software; you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by the
# Free Software Foundation; either version 3 of the License, or (at your
# option) any later version.
#
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General
# Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
"""
This modules provides classes and functions for using the emcee_pt sampler
packages for parameter estimation.
"""
import logging
import numpy
import emcee
from pycbc.pool import choose_pool
from .base import (BaseSampler, setup_output)
from .base_mcmc import (BaseMCMC, EnsembleSupport, raw_samples_to_dict,
get_optional_arg_from_config)
from .base_multitemper import (MultiTemperedSupport,
ensemble_compute_acf, ensemble_compute_acl)
from ..burn_in import EnsembleMultiTemperedMCMCBurnInTests
from pycbc.inference.io import EmceePTFile
from .. import models
# This is a hack that will allow us to continue using emcee's abandoned
# PTSampler, which relied on `numpy.float`, until the end of time.
numpy.float = float
if emcee.__version__ >= '3.0.0':
raise ImportError
[docs]class EmceePTSampler(MultiTemperedSupport, EnsembleSupport, BaseMCMC,
BaseSampler):
"""This class is used to construct a parallel-tempered MCMC sampler from
the emcee package's PTSampler.
Parameters
----------
model : model
A model from ``pycbc.inference.models``.
ntemps : int
Number of temeratures to use in the sampler.
nwalkers : int
Number of walkers to use in sampler.
betas : array
An array of inverse temperature values to be used in emcee_pt's
temperature ladder. If not provided, ``emcee_pt`` will use the number
of temperatures and the number of dimensions of the parameter space to
construct the ladder with geometrically spaced temperatures.
loglikelihood_function : str, optional
Set the function to call from the model for the ``loglikelihood``.
Default is ``loglikelihood``.
nprocesses : int, optional
The number of parallel processes to use. Default is 1
(no paralleliztion).
use_mpi : bool, optional
Use MPI for parallelization. Default (False) will use python's
multiprocessing.
"""
name = "emcee_pt"
_io = EmceePTFile
burn_in_class = EnsembleMultiTemperedMCMCBurnInTests
def __init__(self, model, ntemps, nwalkers, betas=None,
checkpoint_interval=None, checkpoint_signal=None,
loglikelihood_function=None,
nprocesses=1, use_mpi=False):
self.model = model
# create a wrapper for calling the model
if loglikelihood_function is None:
loglikelihood_function = 'loglikelihood'
# frustratingly, emcee_pt does not support blob data, so we have to
# turn it off
model_call = models.CallModel(model, loglikelihood_function,
return_all_stats=False)
# these are used to help paralleize over multiple cores / MPI
models._global_instance = model_call
model_call = models._call_global_model
prior_call = models._call_global_model_logprior
self.pool = choose_pool(mpi=use_mpi, processes=nprocesses)
# construct the sampler: PTSampler needs the likelihood and prior
# functions separately
ndim = len(model.variable_params)
self._sampler = emcee.PTSampler(ntemps, nwalkers, ndim,
model_call, prior_call, pool=self.pool,
betas=betas)
self.nwalkers = nwalkers
self._ntemps = ntemps
self._checkpoint_interval = checkpoint_interval
self._checkpoint_signal = checkpoint_signal
@property
def io(self):
return self._io
@property
def base_shape(self):
return (self.ntemps, self.nwalkers,)
@property
def betas(self):
return self._sampler.betas
[docs] @staticmethod
def compute_acf(filename, **kwargs):
r"""Computes the autocorrelation function.
Calls :py:func:`base_multitemper.ensemble_compute_acf`; see that
function for details.
Parameters
----------
filename : str
Name of a samples file to compute ACFs for.
\**kwargs :
All other keyword arguments are passed to
:py:func:`base_multitemper.ensemble_compute_acf`.
Returns
-------
dict :
Dictionary of arrays giving the ACFs for each parameter. If
``per-walker=True`` is passed as a keyword argument, the arrays
will have shape ``ntemps x nwalkers x niterations``. Otherwise, the
returned array will have shape ``ntemps x niterations``.
"""
return ensemble_compute_acf(filename, **kwargs)
[docs] @staticmethod
def compute_acl(filename, **kwargs):
r"""Computes the autocorrelation length.
Calls :py:func:`base_multitemper.ensemble_compute_acl`; see that
function for details.
Parameters
-----------
filename : str
Name of a samples file to compute ACLs for.
\**kwargs :
All other keyword arguments are passed to
:py:func:`base_multitemper.ensemble_compute_acl`.
Returns
-------
dict
A dictionary of ntemps-long arrays of the ACLs of each parameter.
"""
return ensemble_compute_acl(filename, **kwargs)
[docs] @classmethod
def from_config(cls, cp, model, output_file=None, nprocesses=1,
use_mpi=False):
"""Loads the sampler from the given config file.
The following options are retrieved in the ``[sampler]`` section:
* ``name`` :
Required. This must match the samlper's name.
* ``nwalkers`` :
Required. The number of walkers to use.
* ``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).
* ``niterations`` :
The number of iterations to run the sampler for. Either this or
``effective-nsamples`` must be provided (but not both).
* ``effective-nsamples`` :
Run the sampler until the given number of effective samples are
obtained. A ``checkpoint-interval`` must also be provided in this
case. Either this or ``niterations`` must be provided (but not
both).
* ``thin-interval`` :
Thin the samples by the given value before saving to disk. May
provide this, or ``max-samples-per-chain``, but not both. If
neither options are provided, will save all samples.
* ``max-samples-per-chain`` :
Thin the samples such that the number of samples per chain per
temperature that are saved to disk never exceeds the given value.
May provide this, or ``thin-interval``, but not both. If neither
options are provided, will save all samples.
* ``checkpoint-interval`` :
Sets the checkpoint interval to use. Must be provided if using
``effective-nsamples``.
* ``checkpoint-signal`` :
Set the checkpoint signal, e.g., "USR2". Optional.
* ``logl-function`` :
The attribute of the model to use for the loglikelihood. If
not provided, will default to ``loglikelihood``.
Settings for burn-in tests are read from ``[sampler-burn_in]``. In
particular, the ``burn-in-test`` option is used to set the burn in
tests to perform. See
:py:func:`MultiTemperedMCMCBurnInTests.from_config` for details. If no
``burn-in-test`` is provided, no burn in tests will be carried out.
Parameters
----------
cp : WorkflowConfigParser instance
Config file object to parse.
model : pycbc.inference.model.BaseModel instance
The model to use.
output_file : str, optional
The name of the output file to checkpoint and write results to.
nprocesses : int, optional
The number of parallel processes to use. Default is 1.
use_mpi : bool, optional
Use MPI for parallelization. Default is False.
Returns
-------
EmceePTSampler :
The sampler instance.
"""
section = "sampler"
# check name
assert cp.get(section, "name") == cls.name, (
"name in section [sampler] must match mine")
# get the number of walkers to use
nwalkers = int(cp.get(section, "nwalkers"))
# get the temps/betas
ntemps, betas = cls.betas_from_config(cp, section)
# get the checkpoint interval, if it's specified
checkpoint_interval = cls.checkpoint_from_config(cp, section)
checkpoint_signal = cls.ckpt_signal_from_config(cp, section)
# get the loglikelihood function
logl = get_optional_arg_from_config(cp, section, 'logl-function')
obj = cls(model, ntemps, nwalkers, betas=betas,
checkpoint_interval=checkpoint_interval,
checkpoint_signal=checkpoint_signal,
loglikelihood_function=logl, nprocesses=nprocesses,
use_mpi=use_mpi)
# set target
obj.set_target_from_config(cp, section)
# add burn-in if it's specified
obj.set_burn_in_from_config(cp)
# set prethin options
obj.set_thin_interval_from_config(cp, section)
# Set up the output file
setup_output(obj, output_file)
if not obj.new_checkpoint:
obj.resume_from_checkpoint()
else:
obj.set_start_from_config(cp)
return obj
@property
def samples(self):
"""A dict mapping ``variable_params`` to arrays of samples currently
in memory.
The arrays have shape ``ntemps x nwalkers x niterations``.
"""
# emcee stores samples to it's chain attribute as a
# nwalker x niterations x ndim array
raw_samples = self._sampler.chain
return raw_samples_to_dict(self, raw_samples)
@property
def model_stats(self):
"""Returns the log likelihood ratio and log prior as a dict of arrays.
The returned array has shape ntemps x nwalkers x niterations.
Unfortunately, because ``emcee_pt`` does not have blob support, this
will only return the loglikelihood and logprior (with the logjacobian
set to zero) regardless of what stats the model can return.
.. warning::
Since the `logjacobian` is not saved by `emcee_pt`, the `logprior`
returned here is the log of the prior pdf in the sampling
coordinate frame rather than the variable params frame. This
differs from the variable params frame by the log of the Jacobian
of the transform from one frame to the other. If no sampling
transforms were used, then the `logprior` is the same.
"""
# likelihood has shape ntemps x nwalkers x niterations
logl = self._sampler.lnlikelihood
# get prior from posterior
logp = self._sampler.lnprobability - logl
logjacobian = numpy.zeros(logp.shape)
return {'loglikelihood': logl, 'logprior': logp,
'logjacobian': logjacobian}
[docs] def clear_samples(self):
"""Clears the chain and blobs from memory.
"""
# store the iteration that the clear is occuring on
self._lastclear = self.niterations
self._itercounter = 0
# now clear the chain
self._sampler.reset()
[docs] def set_state_from_file(self, filename):
"""Sets the state of the sampler back to the instance saved in a file.
"""
with self.io(filename, 'r') as fp:
rstate = fp.read_random_state()
# set the numpy random state
numpy.random.set_state(rstate)
[docs] def run_mcmc(self, niterations):
"""Advance the ensemble for a number of samples.
Parameters
----------
niterations : int
Number of samples to get from sampler.
"""
pos = self._pos
if pos is None:
pos = self._p0
res = self._sampler.run_mcmc(pos, niterations)
p, _, _ = res[0], res[1], res[2]
# update the positions
self._pos = p
[docs] def write_results(self, filename):
"""Writes samples, model stats, acceptance fraction, and random state
to the given file.
Parameters
-----------
filename : str
The file to write to. The file is opened using the ``io`` class
in an an append state.
"""
with self.io(filename, 'a') as fp:
# write samples
fp.write_samples(self.samples,
parameters=self.model.variable_params,
last_iteration=self.niterations)
# write stats
fp.write_samples(self.model_stats, last_iteration=self.niterations)
# write accpetance
fp.write_acceptance_fraction(self._sampler.acceptance_fraction)
# write random state
fp.write_random_state()
[docs] @classmethod
def calculate_logevidence(cls, filename, thin_start=None, thin_end=None,
thin_interval=None):
"""Calculates the log evidence from the given file using ``emcee_pt``'s
thermodynamic integration.
Parameters
----------
filename : str
Name of the file to read the samples from. Should be an
``EmceePTFile``.
thin_start : int
Index of the sample to begin returning stats. Default is to read
stats after burn in. To start from the beginning set thin_start
to 0.
thin_interval : int
Interval to accept every i-th sample. Default is to use the
`fp.acl`. If `fp.acl` is not set, then use all stats
(set thin_interval to 1).
thin_end : int
Index of the last sample to read. If not given then
`fp.niterations` is used.
Returns
-------
lnZ : float
The estimate of log of the evidence.
dlnZ : float
The error on the estimate.
"""
with cls._io(filename, 'r') as fp:
logls = fp.read_raw_samples(['loglikelihood'],
thin_start=thin_start,
thin_interval=thin_interval,
thin_end=thin_end,
temps='all', flatten=False)
logls = logls['loglikelihood']
# we need the betas that were used
betas = fp.betas
# annoyingly, theromdynaimc integration in PTSampler is an instance
# method, so we'll implement a dummy one
ntemps = fp.ntemps
nwalkers = fp.nwalkers
ndim = len(fp.variable_params)
dummy_sampler = emcee.PTSampler(ntemps, nwalkers, ndim, None,
None, betas=betas)
return dummy_sampler.thermodynamic_integration_log_evidence(
logls=logls, fburnin=0.)
def _correctjacobian(self, samples):
"""Corrects the log jacobian values stored on disk.
Parameters
----------
samples : dict
Dictionary of the samples.
"""
# flatten samples for evaluating
orig_shape = list(samples.values())[0].shape
flattened_samples = {p: arr.ravel()
for p, arr in list(samples.items())}
# convert to a list of tuples so we can use map function
params = list(flattened_samples.keys())
size = flattened_samples[params[0]].size
logj = numpy.zeros(size)
for ii in range(size):
these_samples = {p: flattened_samples[p][ii] for p in params}
these_samples = self.model.sampling_transforms.apply(these_samples)
self.model.update(**these_samples)
logj[ii] = self.model.logjacobian
return logj.reshape(orig_shape)
[docs] def finalize(self):
"""Calculates the log evidence and writes to the checkpoint file.
If sampling transforms were used, this also corrects the jacobian
stored on disk.
The thin start/interval/end for calculating the log evidence are
retrieved from the checkpoint file's thinning attributes.
"""
if self.model.sampling_transforms is not None:
# fix the lobjacobian values stored on disk
logging.info("Correcting logjacobian values on disk")
with self.io(self.checkpoint_file, 'r') as fp:
samples = fp.read_raw_samples(self.variable_params,
thin_start=0,
thin_interval=1, thin_end=None,
temps='all', flatten=False)
logjacobian = self._correctjacobian(samples)
# write them back out
for fn in [self.checkpoint_file, self.backup_file]:
with self.io(fn, "a") as fp:
fp[fp.samples_group]['logjacobian'][()] = logjacobian
logging.info("Calculating log evidence")
# get the thinning settings
with self.io(self.checkpoint_file, 'r') as fp:
thin_start = fp.thin_start
thin_interval = fp.thin_interval
thin_end = fp.thin_end
# calculate
logz, dlogz = self.calculate_logevidence(
self.checkpoint_file, thin_start=thin_start, thin_end=thin_end,
thin_interval=thin_interval)
logging.info("log Z, dlog Z: {}, {}".format(logz, dlogz))
# write to both the checkpoint and backup
for fn in [self.checkpoint_file, self.backup_file]:
with self.io(fn, "a") as fp:
fp.write_logevidence(logz, dlogz)