def save_sample(self, fname, name):
sample = np.atleast_2d(np.loadtxt(fname))
if not sample.size:
return None
collection = SampleCollection(self.model, self.output, name=str(name))
for row in sample:
collection.add(
row[2:2 + self.n_sampled],
derived=row[2 + self.n_sampled:2 + self.n_sampled + self.n_derived],
weight=row[0],
logpriors=row[-(self.n_priors + self.n_likes):-self.n_likes],
loglikes=row[-self.n_likes:])
# make sure that the points are written
collection.out_update()
return collection
class polychord(Sampler):
r"""
PolyChord sampler \cite{Handley:2015fda,2015MNRAS.453.4384H}, a nested sampler
tailored for high-dimensional parameter spaces with a speed hierarchy.
"""
# Name of the PolyChord repo and version to download
_pc_repo_name = "PolyChord/PolyChordLite"
_pc_repo_version = "1.20.1"
_base_dir_suffix = "polychord_raw"
_clusters_dir = "clusters"
_at_resume_prefer_old = Sampler._at_resume_prefer_old + ["blocking"]
_at_resume_prefer_new = Sampler._at_resume_prefer_new + ["callback_function"]
pypolychord: Any
# variables from yaml
do_clustering: bool
num_repeats: int
confidence_for_unbounded: float
callback_function: Callable
blocking: Any
measure_speeds: bool
oversample_power: float
nlive: NumberWithUnits
path: str
logzero: float
max_ndead: int
def initialize(self):
"""Imports the PolyChord sampler and prepares its arguments."""
try:
install_path = (lambda _p: self.get_path(_p) if _p else None)(
self.packages_path)
self.pc = load_external_module(
"pypolychord", path=self.path, install_path=install_path,
min_version=self._pc_repo_version,
get_import_path=get_compiled_import_path, logger=self.log,
not_installed_level="debug")
except ComponentNotInstalledError as excpt:
raise ComponentNotInstalledError(
self.log, (f"Could not find PolyChord: {excpt}. "
"To install it, run `cobaya-install polychord`"))
with NoLogging(logging.CRITICAL):
settings = load_external_module(
"pypolychord.settings", path=self.path, install_path=install_path,
get_import_path=get_compiled_import_path, logger=self.log)
# Prepare arguments and settings
self.n_sampled = len(self.model.parameterization.sampled_params())
self.n_derived = len(self.model.parameterization.derived_params())
self.n_priors = len(self.model.prior)
self.n_likes = len(self.model.likelihood)
self.nDims = self.model.prior.d()
self.nDerived = (self.n_derived + self.n_priors + self.n_likes)
if self.logzero is None:
self.logzero = np.nan_to_num(-np.inf)
if self.max_ndead == np.inf:
self.max_ndead = -1
self._quants_d_units = ["nlive", "max_ndead"]
for p in self._quants_d_units:
if getattr(self, p) is not None:
setattr(self, p, NumberWithUnits(
getattr(self, p), "d", scale=self.nDims, dtype=int).value)
self._quants_nlive_units = ["nprior", "nfail"]
for p in self._quants_nlive_units:
if getattr(self, p) is not None:
setattr(self, p, NumberWithUnits(
getattr(self, p), "nlive", scale=self.nlive, dtype=int).value)
# Fill the automatic ones
if getattr(self, "feedback", None) is None:
values = {logging.CRITICAL: 0, logging.ERROR: 0, logging.WARNING: 0,
logging.INFO: 1, logging.DEBUG: 2}
self.feedback = values[self.log.getEffectiveLevel()]
# Prepare output folders and prefixes
if self.output:
self.file_root = self.output.prefix
self.read_resume = self.output.is_resuming()
else:
output_prefix = share_mpi(hex(int(self._rng.random() * 16 ** 6))[2:]
if is_main_process() else None)
self.file_root = output_prefix
# dummy output -- no resume!
self.read_resume = False
self.base_dir = self.get_base_dir(self.output)
self.raw_clusters_dir = os.path.join(self.base_dir, self._clusters_dir)
self.output.create_folder(self.base_dir)
if self.do_clustering:
self.clusters_folder = self.get_clusters_dir(self.output)
self.output.create_folder(self.clusters_folder)
self.mpi_info("Storing raw PolyChord output in '%s'.", self.base_dir)
# Exploiting the speed hierarchy
if self.blocking:
blocks, oversampling_factors = self.model.check_blocking(self.blocking)
else:
if self.measure_speeds:
self.model.measure_and_set_speeds(n=self.measure_speeds,
random_state=self._rng)
blocks, oversampling_factors = self.model.get_param_blocking_for_sampler(
oversample_power=self.oversample_power)
self.mpi_info("Parameter blocks and their oversampling factors:")
max_width = len(str(max(oversampling_factors)))
for f, b in zip(oversampling_factors, blocks):
self.mpi_info("* %" + "%d" % max_width + "d : %r", f, b)
# Save blocking in updated info, in case we want to resume
self._updated_info["blocking"] = list(zip(oversampling_factors, blocks))
blocks_flat = list(chain(*blocks))
self.ordering = [
blocks_flat.index(p) for p in self.model.parameterization.sampled_params()]
self.grade_dims = [len(block) for block in blocks]
# Steps per block
# NB: num_repeats is ignored by PolyChord when int "grade_frac" given,
# so needs to be applied by hand.
# In num_repeats, `d` is interpreted as dimension of each block
self.grade_frac = [
int(o * read_dnumber(self.num_repeats, dim_block))
for o, dim_block in zip(oversampling_factors, self.grade_dims)]
# Assign settings
pc_args = ["nlive", "num_repeats", "nprior", "nfail", "do_clustering",
"feedback", "precision_criterion", "logzero",
"max_ndead", "boost_posterior", "posteriors", "equals",
"cluster_posteriors", "write_resume", "read_resume",
"write_stats", "write_live", "write_dead", "write_prior",
"maximise", "compression_factor", "synchronous", "base_dir",
"file_root", "grade_dims", "grade_frac", "nlives"]
# As stated above, num_repeats is ignored, so let's not pass it
pc_args.pop(pc_args.index("num_repeats"))
self.pc_settings = settings.PolyChordSettings(
self.nDims, self.nDerived, seed=(self.seed if self.seed is not None else -1),
**{p: getattr(self, p) for p in pc_args if getattr(self, p) is not None})
# prior conversion from the hypercube
bounds = self.model.prior.bounds(
confidence_for_unbounded=self.confidence_for_unbounded)
# Check if priors are bounded (nan's to inf)
inf = np.where(np.isinf(bounds))
if len(inf[0]):
params_names = list(self.model.parameterization.sampled_params())
params = [params_names[i] for i in sorted(set(inf[0]))]
raise LoggedError(
self.log, "PolyChord needs bounded priors, but the parameter(s) '"
"', '".join(params) + "' is(are) unbounded.")
locs = bounds[:, 0]
scales = bounds[:, 1] - bounds[:, 0]
# This function re-scales the parameters AND puts them in the right order
self.pc_prior = lambda x: (locs + np.array(x)[self.ordering] * scales).tolist()
# We will need the volume of the prior domain, since PolyChord divides by it
self.logvolume = np.log(np.prod(scales))
# Prepare callback function
if self.callback_function is not None:
self.callback_function_callable = (
get_external_function(self.callback_function))
self.last_point_callback = 0
# Prepare runtime live and dead points collections
self.live = SampleCollection(self.model, None, name="live")
self.dead = SampleCollection(self.model, self.output, name="dead")
# Done!
if is_main_process():
self.log.debug("Calling PolyChord with arguments:")
for p, v in inspect.getmembers(self.pc_settings, lambda a: not (callable(a))):
if not p.startswith("_"):
self.log.debug(" %s: %s", p, v)
def dumper(self, live_points, dead_points, logweights, logZ, logZstd):
if self.callback_function is None:
return
# Store live and dead points and evidence computed so far
self.live.reset()
for point in live_points:
self.live.add(
point[:self.n_sampled],
derived=point[self.n_sampled:self.n_sampled + self.n_derived],
weight=np.nan,
logpriors=point[self.n_sampled + self.n_derived:
self.n_sampled + self.n_derived + self.n_priors],
loglikes=point[self.n_sampled + self.n_derived + self.n_priors:
self.n_sampled + self.n_derived + self.n_priors +
self.n_likes])
for logweight, point in zip(logweights[self.last_point_callback:],
dead_points[self.last_point_callback:]):
self.dead.add(
point[:self.n_sampled],
derived=point[self.n_sampled:self.n_sampled + self.n_derived],
weight=np.exp(logweight),
logpriors=point[self.n_sampled + self.n_derived:
self.n_sampled + self.n_derived + self.n_priors],
loglikes=point[self.n_sampled + self.n_derived + self.n_priors:
self.n_sampled + self.n_derived + self.n_priors +
self.n_likes])
self.logZ, self.logZstd = logZ, logZstd
self._correct_unphysical_fraction()
# Callback function
if self.callback_function is not None:
try:
self.callback_function_callable(self)
except Exception as e:
self.log.error("The callback function produced an error: %r", str(e))
self.last_point_callback = len(self.dead)
def run(self):
"""
Prepares the posterior function and calls ``PolyChord``'s ``run`` function.
"""
# Prepare the posterior
# Don't forget to multiply by the volume of the physical hypercube,
# since PolyChord divides by it
def logpost(params_values):
result = self.model.logposterior(params_values)
loglikes = result.loglikes
if len(loglikes) != self.n_likes:
loglikes = np.full(self.n_likes, np.nan)
derived = result.derived
if len(derived) != self.n_derived:
derived = np.full(self.n_derived, np.nan)
derived = list(derived) + list(result.logpriors) + list(loglikes)
return (max(result.logpost + self.logvolume, self.pc_settings.logzero),
derived)
sync_processes()
self.mpi_info("Calling PolyChord...")
self.pc.run_polychord(logpost, self.nDims, self.nDerived, self.pc_settings,
self.pc_prior, self.dumper)
self.process_raw_output()
@property
def raw_prefix(self):
return os.path.join(
self.pc_settings.base_dir, self.pc_settings.file_root)
def dump_paramnames(self, prefix):
labels = self.model.parameterization.labels()
with open(prefix + ".paramnames", "w") as f_paramnames:
for p in self.model.parameterization.sampled_params():
f_paramnames.write("%s\t%s\n" % (p, labels.get(p, "")))
for p in self.model.parameterization.derived_params():
f_paramnames.write("%s*\t%s\n" % (p, labels.get(p, "")))
for p in self.model.prior:
f_paramnames.write("%s*\t%s\n" % (
"logprior" + derived_par_name_separator + p,
r"\pi_\mathrm{" + p.replace("_", r"\ ") + r"}"))
for p in self.model.likelihood:
f_paramnames.write("%s*\t%s\n" % (
"loglike" + derived_par_name_separator + p,
r"\log\mathcal{L}_\mathrm{" + p.replace("_", r"\ ") + r"}"))
def save_sample(self, fname, name):
sample = np.atleast_2d(np.loadtxt(fname))
if not sample.size:
return None
collection = SampleCollection(self.model, self.output, name=str(name))
for row in sample:
collection.add(
row[2:2 + self.n_sampled],
derived=row[2 + self.n_sampled:2 + self.n_sampled + self.n_derived],
weight=row[0],
logpriors=row[-(self.n_priors + self.n_likes):-self.n_likes],
loglikes=row[-self.n_likes:])
# make sure that the points are written
collection.out_update()
return collection
def _correct_unphysical_fraction(self):
"""
Correction for the fraction of the prior that is unphysical -- see issue #77
"""
if not hasattr(self, "_frac_unphysical"):
with open(self.raw_prefix + ".prior_info", "r", encoding="utf-8-sig") as pf:
lines = list(pf.readlines())
get_value_str = lambda line: line[line.find("=") + 1:]
get_value_str_var = lambda var: get_value_str(
next(line for line in lines if line.lstrip().startswith(var)))
nprior = int(get_value_str_var("nprior"))
ndiscarded = int(get_value_str_var("ndiscarded"))
self._frac_unphysical = nprior / ndiscarded
if self._frac_unphysical != 1:
self.log.debug(
"Correcting for unphysical region fraction: %g", self._frac_unphysical)
self.logZ += np.log(self._frac_unphysical)
if hasattr(self, "clusters"):
for cluster in self.clusters.values():
cluster["logZ"] += np.log(self._frac_unphysical)
def process_raw_output(self):
"""
Loads the sample of live points from ``PolyChord``'s raw output and writes it
(if ``txt`` output requested).
"""
if is_main_process():
self.log.info("Loading PolyChord's results: samples and evidences.")
self.dump_paramnames(self.raw_prefix)
self.collection = self.save_sample(self.raw_prefix + ".txt", "1")
# Load clusters, and save if output
if self.pc_settings.do_clustering:
self.clusters = {}
clusters_raw_regexp = re.compile(
re.escape(self.pc_settings.file_root + "_") + r"\d+\.txt")
cluster_raw_files = sorted(find_with_regexp(
clusters_raw_regexp, os.path.join(
self.pc_settings.base_dir, self._clusters_dir), walk_tree=True))
for f in cluster_raw_files:
i = int(f[f.rfind("_") + 1:-len(".txt")])
if self.output:
old_folder = self.output.folder
self.output.folder = self.clusters_folder
sample = self.save_sample(f, str(i))
if self.output:
# noinspection PyUnboundLocalVariable
self.output.folder = old_folder
self.clusters[i] = {"sample": sample}
# Prepare the evidence(s) and write to file
pre = "log(Z"
active = "(Still active)"
with open(self.raw_prefix + ".stats", "r", encoding="utf-8-sig") as statsfile:
lines = [line for line in statsfile.readlines() if line.startswith(pre)]
for line in lines:
logZ, logZstd = [float(n.replace(active, "")) for n in
line.split("=")[-1].split("+/-")]
component = line.split("=")[0].lstrip(pre + "_").rstrip(") ")
if not component:
self.logZ, self.logZstd = logZ, logZstd
elif self.pc_settings.do_clustering:
i = int(component)
self.clusters[i]["logZ"], self.clusters[i]["logZstd"] = logZ, logZstd
self.log.debug(
"RAW log(Z) = %g +/- %g ; RAW Z in [%.8g, %.8g] (68%% C.L. log-gaussian)",
self.logZ, self.logZstd,
*[np.exp(self.logZ + n * self.logZstd) for n in [-1, 1]])
self._correct_unphysical_fraction()
if self.output:
out_evidences = dict(logZ=self.logZ, logZstd=self.logZstd)
if getattr(self, "clusters", None):
out_evidences["clusters"] = {}
for i in sorted(list(self.clusters)):
out_evidences["clusters"][i] = dict(
logZ=self.clusters[i]["logZ"],
logZstd=self.clusters[i]["logZstd"])
fname = os.path.join(self.output.folder,
self.output.prefix + Extension.evidence)
yaml_dump_file(fname, out_evidences, comment="log-evidence",
error_if_exists=False)
# TODO: try to broadcast the collections
# if get_mpi():
# bcast_from_0 = lambda attrname: setattr(self,
# attrname, get_mpi_comm().bcast(getattr(self, attrname, None), root=0))
# map(bcast_from_0, ["collection", "logZ", "logZstd", "clusters"])
if is_main_process():
self.log.info("Finished! Raw PolyChord output stored in '%s', "
"with prefix '%s'",
self.pc_settings.base_dir, self.pc_settings.file_root)
self.log.info(
"log(Z) = %g +/- %g ; Z in [%.8g, %.8g] (68%% C.L. log-gaussian)",
self.logZ, self.logZstd,
*[np.exp(self.logZ + n * self.logZstd) for n in [-1, 1]])
def products(self):
"""
Auxiliary function to define what should be returned in a scripted call.
Returns:
The sample ``SampleCollection`` containing the sequentially
discarded live points.
"""
if is_main_process():
products = {
"sample": self.collection, "logZ": self.logZ, "logZstd": self.logZstd}
if self.pc_settings.do_clustering:
products.update({"clusters": self.clusters})
return products
else:
return {}
@classmethod
def get_base_dir(cls, output):
if output:
return output.add_suffix(cls._base_dir_suffix, separator="_")
return os.path.join(gettempdir(), cls._base_dir_suffix)
@classmethod
def get_clusters_dir(cls, output):
if output:
return output.add_suffix(cls._clusters_dir, separator="_")
@classmethod
def output_files_regexps(cls, output, info=None, minimal=False):
# Resume file
regexps_tuples = [
(re.compile(re.escape(output.prefix + ".resume")), cls.get_base_dir(output))]
if minimal:
return regexps_tuples
return regexps_tuples + [
# Raw products base dir
(None, cls.get_base_dir(output)),
# Main sample
(output.collection_regexp(name=None), None),
# Evidence
(re.compile(re.escape(output.prefix + Extension.evidence)), None),
# Clusters
(None, cls.get_clusters_dir(output))
]
@classmethod
def get_version(cls):
return None
@classmethod
def get_path(cls, path):
return os.path.realpath(
os.path.join(path, "code",
cls._pc_repo_name[cls._pc_repo_name.find("/") + 1:]))
@classmethod
def is_compatible(cls):
import platform
if platform.system() == "Windows":
return False
return True
@classmethod
def is_installed(cls, reload=False, **kwargs):
if not kwargs.get("code", True):
return True
try:
return bool(load_external_module(
"pypolychord", path=kwargs["path"],
get_import_path=get_compiled_import_path,
min_version=cls._pc_repo_version, reload=reload,
logger=get_logger(cls.__name__), not_installed_level="debug"))
except ComponentNotInstalledError:
return False
@classmethod
def install(cls, path=None, force=False, code=False, data=False,
no_progress_bars=False):
if not code:
return True
log = get_logger(__name__)
log.info("Downloading PolyChord...")
success = download_github_release(os.path.join(path, "code"), cls._pc_repo_name,
cls._pc_repo_version,
no_progress_bars=no_progress_bars,
logger=log)
if not success:
log.error("Could not download PolyChord.")
return False
log.info("Compiling (Py)PolyChord...")
from subprocess import Popen, PIPE
# Needs to re-define os' PWD,
# because MakeFile calls it and is not affected by the cwd of Popen
cwd = os.path.join(path, "code",
cls._pc_repo_name[cls._pc_repo_name.find("/") + 1:])
my_env = os.environ.copy()
my_env.update({"PWD": cwd})
if "CC" not in my_env:
my_env["CC"] = "mpicc"
if "CXX" not in my_env:
my_env["CXX"] = "mpicxx"
process_make = Popen([sys.executable, "setup.py", "build"],
cwd=cwd, env=my_env, stdout=PIPE, stderr=PIPE)
out, err = process_make.communicate()
if process_make.returncode:
log.info(out.decode("utf-8"))
log.info(err.decode("utf-8"))
log.error("Python build failed!")
return False
return True
class Evaluate(Sampler):
file_base_name = 'evaluate'
override: Mapping[str, float]
N: int
def initialize(self):
"""
Creates a 1-point collection to store the point
at which the posterior is evaluated.
"""
try:
self.N = int(self.N)
except ValueError:
raise LoggedError(
self.log,
"Could not convert the number of samples to an integer: %r", self.N)
self.one_point = SampleCollection(self.model, self.output, name="1")
self.log.info("Initialized!")
def run(self):
"""
First gets a reference point. If a single reference point is not given,
the point is sampled from the reference pdf. If that one is not defined either,
the point is sampled from the prior.
Then it evaluates the prior and likelihood(s) and stores them in the one-member
sample collection.
"""
for i in range(self.N):
if self.N > 1:
self.log.info("Evaluating sample #%d ------------------------------",
i + 1)
self.log.info("Looking for a reference point with non-zero prior.")
reference_values = self.model.prior.reference(random_state=self._rng)
reference_point = dict(
zip(self.model.parameterization.sampled_params(), reference_values))
for p, v in (self.override or {}).items():
if p not in reference_point:
raise LoggedError(
self.log, "Parameter '%s' used in override not known. "
"Known parameters names are %r.",
p, self.model.parameterization.sampled_params())
reference_point[p] = v
self.log.info("Reference point:\n " + "\n ".join(
["%s = %g" % pv for pv in reference_point.items()]))
self.log.info("Evaluating prior and likelihoods...")
self.logposterior = self.model.logposterior(reference_point)
self.one_point.add(
list(reference_point.values()), derived=self.logposterior.derived,
logpost=self.logposterior.logpost, logpriors=self.logposterior.logpriors,
loglikes=self.logposterior.loglikes)
self.log.info("log-posterior = %g", self.logposterior.logpost)
self.log.info("log-prior = %g", self.logposterior.logprior)
for j, name in enumerate(self.model.prior):
self.log.info(
" logprior_" + name + " = %g", self.logposterior.logpriors[j])
if self.logposterior.logprior > -np.inf:
self.log.info("log-likelihood = %g", self.logposterior.loglike)
for j, name in enumerate(self.model.likelihood):
self.log.info(
" chi2_" + name + " = %g", (-2 * self.logposterior.loglikes[j]))
self.log.info("Derived params:")
for name, value in zip(self.model.parameterization.derived_params(),
self.logposterior.derived):
self.log.info(" " + name + " = %g", value)
else:
self.log.info("Likelihoods and derived parameters not computed, "
"since the prior is null.")
# Write the output: the point and its prior, posterior and likelihood.
self.one_point.out_update()
def products(self):
"""
Auxiliary function to define what should be returned in a scripted call.
Returns:
The sample ``SampleCollection`` containing the
sequentially discarded live points.
"""
return {"sample": self.one_point}