def test_post_prior(tmpdir):
# Generate original chain
info: InputDict = {
"output": os.path.join(tmpdir, "gaussian"), "force": True,
"params": info_params, "sampler": info_sampler,
"likelihood": {"one": None}, "prior": {"gaussian": sampled_pdf}}
info_post: InputDict = {
"output": info["output"], "force": True,
"post": {"suffix": "foo", 'skip': 0.1,
"remove": {"prior": {"gaussian": None}},
"add": {"prior": {"target": target_pdf_prior}}}}
_, sampler = run(info)
if mpi.is_main_process():
mcsamples_in = loadMCSamples(info["output"], settings={'ignore_rows': 0.1})
target_mean, target_cov = mpi.share(_get_targets(mcsamples_in))
else:
target_mean, target_cov = mpi.share()
for mem in [False, True]:
post(info_post, sample=sampler.products()["sample"] if mem else None)
# Load with GetDist and compare
if mpi.is_main_process():
mcsamples = loadMCSamples(
info_post["output"] + _post_ + info_post["post"]["suffix"])
new_mean = mcsamples.mean(["a", "b"])
new_cov = mcsamples.getCovMat().matrix
mpi.share((new_mean, new_cov))
else:
new_mean, new_cov = mpi.share()
assert np.allclose(new_mean, target_mean)
assert np.allclose(new_cov, target_cov)
def test_post_params():
# Tests:
# - added simple dynamical derived parameter "a+b"
# - added dynamical derived parameter that depends on *new* chi2__target
# - added new fixed input "c" + new derived-from-external-function "cprime"
# Generate original chain
info = {
"params": info_params, "sampler": info_sampler_dummy,
"likelihood": {"gaussian": sampled_pdf}}
updated_info_gaussian, sampler_gaussian = run(info)
products_gaussian = sampler_gaussian.products()
info_post = {
"post": {"suffix": "foo",
"remove": {"params": {"a_plus_b": None}},
"add": {
"likelihood": {
"target": {"external": target_pdf, "output_params": ["cprime"]}},
"params": {
"c": 1.234,
"a_minus_b": {"derived": "lambda a,b: a-b"},
"my_chi2__target": {
"derived": "lambda chi2__target: chi2__target"},
"cprime": None}}}}
info_post.update(updated_info_gaussian)
products = post(info_post, products_gaussian["sample"]).products
# Compare parameters
assert np.allclose(
products["sample"]["a"] - products["sample"]["b"],
products["sample"]["a_minus_b"])
assert np.allclose(
products["sample"]["cprime"], info_post["post"]["add"]["params"]["c"])
assert np.allclose(
products["sample"]["my_chi2__target"], products["sample"]["chi2__target"])
def test_post_prior(tmpdir):
# Generate original chain
info = {
_output_prefix: os.path.join(str(tmpdir), "gaussian"),
_force: True,
_params: info_params,
kinds.sampler: info_sampler,
kinds.likelihood: {
"one": None
},
_prior: {
"gaussian": sampled_pdf
}
}
run(info)
info_post = {
_output_prefix: info[_output_prefix],
_force: True,
_post: {
_post_suffix: "foo",
_post_remove: {
_prior: {
"gaussian": None
}
},
_post_add: {
_prior: {
"target": target_pdf_prior
}
}
}
}
post(info_post)
# Load with GetDist and compare
mcsamples = loadMCSamples(info_post[_output_prefix] + _post_ +
info_post[_post][_post_suffix])
new_mean = mcsamples.mean(["a", "b"])
new_cov = mcsamples.getCovMat().matrix
assert abs(KL_norm(target["mean"], target["cov"], new_mean,
new_cov)) < 0.02
def test_post_prior(tmpdir):
# Generate original chain
info = {
"output": os.path.join(str(tmpdir), "gaussian"),
"force": True,
"params": info_params,
"sampler": info_sampler,
"likelihood": {
"one": None
},
"prior": {
"gaussian": sampled_pdf
}
}
run(info)
info_post = {
"output": info["output"],
"force": True,
"post": {
"suffix": "foo",
"remove": {
"prior": {
"gaussian": None
}
},
"add": {
"prior": {
"target": target_pdf
}
}
}
}
post(info_post)
# Load with GetDist and compare
mcsamples = loadMCSamples(info_post["output"] + "_post_" +
info_post["post"]["suffix"])
new_mean = mcsamples.mean(["a", "b"])
new_cov = mcsamples.getCovMat().matrix
assert abs(KL_norm(target["mean"], target["cov"], new_mean,
new_cov)) < 0.02
def test_post_params():
# Tests:
# - added simple dynamical derived parameter "a+b"
# - added dynamical derived parameter that depends on *new* chi2__target
# - added new fixed input "c" + new derived-from-external-function "cprime"
# Generate original chain
info = {
_params: info_params,
_sampler: info_sampler_dummy,
_likelihood: {
"gaussian": sampled_pdf
}
}
updated_info_gaussian, products_gaussian = run(info)
info_post = {
_post: {
_post_suffix: "foo",
_post_remove: {
_params: {
"a_plus_b": None
}
},
_post_add: {
_likelihood: {
"target": target_pdf
},
_params: {
"c": 1.234,
"a_minus_b": {
_p_derived: "lambda a,b: a-b"
},
"my_chi2__target": {
_p_derived: "lambda chi2__target: chi2__target"
},
"cprime": None
}
}
}
}
info_post.update(updated_info_gaussian)
updated_info, products = post(info_post, products_gaussian["sample"])
# Compare parameters
assert np.allclose(products["sample"]["a"] - products["sample"]["b"],
products["sample"]["a_minus_b"])
assert np.allclose(products["sample"]["cprime"],
info_post[_post][_post_add][_params]["c"])
assert np.allclose(products["sample"]["my_chi2__target"],
products["sample"]["chi2__target"])
def run(
info_or_yaml_or_file: Union[InputDict, str, os.PathLike],
packages_path: Optional[str] = None,
output: Union[str, LiteralFalse, None] = None,
debug: Union[bool, int, None] = None,
stop_at_error: Optional[bool] = None,
resume: bool = False,
force: bool = False,
no_mpi: bool = False,
test: bool = False,
override: Optional[InputDict] = None,
) -> Union[InfoSamplerTuple, PostTuple]:
"""
Run from an input dictionary, file name or yaml string, with optional arguments
to override settings in the input as needed.
:param info_or_yaml_or_file: input options dictionary, yaml file, or yaml text
:param packages_path: path where external packages were installed
:param output: path name prefix for output files, or False for no file output
:param debug: true for verbose debug output, or a specific logging level
:param stop_at_error: stop if an error is raised
:param resume: continue an existing run
:param force: overwrite existing output if it exists
:param no_mpi: run without MPI
:param test: only test initialization rather than actually running
:param override: option dictionary to merge into the input one, overriding settings
(but with lower precedence than the explicit keyword arguments)
:return: (updated_info, sampler) tuple of options dictionary and Sampler instance,
or (updated_info, results) if using "post" post-processing
"""
# This function reproduces the model-->output-->sampler pipeline one would follow
# when instantiating by hand, but alters the order to performs checks and dump info
# as early as possible, e.g. to check if resuming possible or `force` needed.
if no_mpi or test:
mpi.set_mpi_disabled()
with mpi.ProcessState("run"):
info: InputDict = load_info_overrides(info_or_yaml_or_file, debug,
stop_at_error, packages_path,
override)
if test:
info["test"] = True
# If any of resume|force given as cmd args, ignore those in the input file
if resume or force:
if resume and force:
raise ValueError("'rename' and 'force' are exclusive options")
info["resume"] = bool(resume)
info["force"] = bool(force)
if info.get("post"):
if isinstance(output, str) or output is False:
info["post"]["output"] = output or None
return post(info)
if isinstance(output, str) or output is False:
info["output"] = output or None
logger_setup(info.get("debug"), info.get("debug_file"))
logger_run = get_logger(run.__name__)
# MARKED FOR DEPRECATION IN v3.0
# BEHAVIOUR TO BE REPLACED BY ERROR:
check_deprecated_modules_path(info)
# END OF DEPRECATION BLOCK
# 1. Prepare output driver, if requested by defining an output_prefix
# GetDist needs to know the original sampler, so don't overwrite if minimizer
try:
which_sampler = list(info["sampler"])[0]
except (KeyError, TypeError):
raise LoggedError(
logger_run,
"You need to specify a sampler using the 'sampler' key "
"as e.g. `sampler: {mcmc: None}.`")
infix = "minimize" if which_sampler == "minimize" else None
with get_output(prefix=info.get("output"),
resume=info.get("resume"),
force=info.get("force"),
infix=infix) as out:
# 2. Update the input info with the defaults for each component
updated_info = update_info(info)
if is_debug(logger_run):
# Dump only if not doing output
# (otherwise, the user can check the .updated file)
if not out and mpi.is_main_process():
logger_run.info(
"Input info updated with defaults (dumped to YAML):\n%s",
yaml_dump(sort_cosmetic(updated_info)))
# 3. If output requested, check compatibility if existing one, and dump.
# 3.1 First: model only
out.check_and_dump_info(info,
updated_info,
cache_old=True,
ignore_blocks=["sampler"])
# 3.2 Then sampler -- 1st get the last sampler mentioned in the updated.yaml
# TODO: ideally, using Minimizer would *append* to the sampler block.
# Some code already in place, but not possible at the moment.
try:
last_sampler = list(updated_info["sampler"])[-1]
last_sampler_info = {
last_sampler: updated_info["sampler"][last_sampler]
}
except (KeyError, TypeError):
raise LoggedError(logger_run, "No sampler requested.")
sampler_name, sampler_class = get_sampler_name_and_class(
last_sampler_info)
check_sampler_info((out.reload_updated_info(use_cache=True)
or {}).get("sampler"),
updated_info["sampler"],
is_resuming=out.is_resuming())
# Dump again, now including sampler info
out.check_and_dump_info(info, updated_info, check_compatible=False)
# Check if resumable run
sampler_class.check_force_resume(
out, info=updated_info["sampler"][sampler_name])
# 4. Initialize the posterior and the sampler
with Model(updated_info["params"],
updated_info["likelihood"],
updated_info.get("prior"),
updated_info.get("theory"),
packages_path=info.get("packages_path"),
timing=updated_info.get("timing"),
allow_renames=False,
stop_at_error=info.get("stop_at_error",
False)) as model:
# Re-dump the updated info, now containing parameter routes and version
updated_info = recursive_update(updated_info, model.info())
out.check_and_dump_info(None,
updated_info,
check_compatible=False)
sampler = sampler_class(
updated_info["sampler"][sampler_name],
model,
out,
name=sampler_name,
packages_path=info.get("packages_path"))
# Re-dump updated info, now also containing updates from the sampler
updated_info["sampler"][sampler_name] = \
recursive_update(updated_info["sampler"][sampler_name],
sampler.info())
out.check_and_dump_info(None,
updated_info,
check_compatible=False)
mpi.sync_processes()
if info.get("test", False):
logger_run.info(
"Test initialization successful! "
"You can probably run now without `--%s`.", "test")
return InfoSamplerTuple(updated_info, sampler)
# Run the sampler
sampler.run()
return InfoSamplerTuple(updated_info, sampler)
def test_post_likelihood():
"""
Swaps likelihood "gaussian" for "target".
It also tests aggregated chi2's by removing and adding a likelihood to an existing
type.
"""
# Generate original chain
orig_interval = OutputOptions.output_inteveral_s
try:
OutputOptions.output_inteveral_s = 0
info_params_local = deepcopy(info_params)
info_params_local["dummy"] = 0
dummy_loglike_add = 0.1
dummy_loglike_remove = 0.01
info = {
"output": None,
"force": True,
"params": info_params_local,
"sampler": info_sampler,
"likelihood": {
"gaussian": {
"external": sampled_pdf,
"type": "A"
},
"dummy": {
"external": lambda dummy: 1,
"type": "BB"
},
"dummy_remove": {
"external": lambda dummy: dummy_loglike_add,
"type": "BB"
}
}
}
info_out, sampler = run(info)
samples_in = mpi.gather(sampler.products()["sample"])
if mpi.is_main_process():
mcsamples_in = MCSamplesFromCobaya(info_out, samples_in)
else:
mcsamples_in = None
info_out.update({
"post": {
"suffix": "foo",
"remove": {
"likelihood": {
"gaussian": None,
"dummy_remove": None
}
},
"add": {
"likelihood": {
"target": {
"external": target_pdf,
"type": "A",
"output_params": ["cprime"]
},
"dummy_add": {
"external": lambda dummy: dummy_loglike_remove,
"type": "BB"
}
}
}
}
})
info_post_out, products_post = post(info_out,
sampler.products()["sample"])
samples = mpi.gather(products_post["sample"])
# Load with GetDist and compare
if mcsamples_in:
target_mean, target_cov = mpi.share(_get_targets(mcsamples_in))
mcsamples = MCSamplesFromCobaya(info_post_out,
samples,
name_tag="sample")
new_mean = mcsamples.mean(["a", "b"])
new_cov = mcsamples.getCovMat().matrix
mpi.share((new_mean, new_cov))
else:
target_mean, target_cov = mpi.share()
new_mean, new_cov = mpi.share()
assert np.allclose(new_mean, target_mean)
assert np.allclose(new_cov, target_cov)
assert allclose(products_post["sample"]["chi2__A"],
products_post["sample"]["chi2__target"])
assert allclose(
products_post["sample"]["chi2__BB"],
products_post["sample"]["chi2__dummy"] +
products_post["sample"]["chi2__dummy_add"])
finally:
OutputOptions.output_inteveral_s = orig_interval
def test_post_likelihood(tmpdir):
"""
Swaps likelihood "gaussian" for "target".
It also tests aggregated chi2's by removing and adding a likelihood to an existing
type.
"""
# Generate original chain
info_params_local = deepcopy(info_params)
info_params_local["dummy"] = 0
dummy_loglike_add = 0.1
dummy_loglike_remove = 0.01
info = {
_output_prefix: os.path.join(str(tmpdir), "gaussian"),
_force: True,
_params: info_params_local,
kinds.sampler: info_sampler,
kinds.likelihood: {
"gaussian": {
"external": sampled_pdf,
"type": "A"
},
"dummy": {
"external": lambda dummy: 1,
"type": "B"
},
"dummy_remove": {
"external": lambda dummy: dummy_loglike_add,
"type": "B"
}
}
}
info_run_out, sampler_run = run(info)
info_post = {
_output_prefix: info[_output_prefix],
_force: True,
_post: {
_post_suffix: "foo",
_post_remove: {
kinds.likelihood: {
"gaussian": None,
"dummy_remove": None
}
},
_post_add: {
kinds.likelihood: {
"target": {
"external": target_pdf,
"type": "A",
"output_params": ["cprime"]
},
"dummy_add": {
"external": lambda dummy: dummy_loglike_remove,
"type": "B"
}
}
}
}
}
info_post_out, products_post = post(info_post)
# Load with GetDist and compare
mcsamples = loadMCSamples(info_post[_output_prefix] + _post_ +
info_post[_post][_post_suffix])
new_mean = mcsamples.mean(["a", "b"])
new_cov = mcsamples.getCovMat().matrix
assert abs(KL_norm(target["mean"], target["cov"], new_mean,
new_cov)) < 0.02
assert np.allclose(products_post["sample"]["chi2__A"],
products_post["sample"]["chi2__target"])
assert np.allclose(
products_post["sample"]["chi2__B"],
products_post["sample"]["chi2__dummy"] +
products_post["sample"]["chi2__dummy_add"])
def run_script():
warn_deprecation()
import os
import argparse
parser = argparse.ArgumentParser(description="Cobaya's run script.")
parser.add_argument("input_file",
nargs=1,
action="store",
metavar="input_file.yaml",
help="An input file to run.")
parser.add_argument("-" + _packages_path_arg[0],
"--" + _packages_path_arg_posix,
action="store",
nargs=1,
metavar="/packages/path",
default=[None],
help="Path where external packages were installed.")
# MARKED FOR DEPRECATION IN v3.0
modules = "modules"
parser.add_argument("-" + modules[0],
"--" + modules,
action="store",
nargs=1,
required=False,
metavar="/packages/path",
default=[None],
help="To be deprecated! "
"Alias for %s, which should be used instead." %
_packages_path_arg_posix)
# END OF DEPRECATION BLOCK -- CONTINUES BELOW!
parser.add_argument("-" + _output_prefix[0],
"--" + _output_prefix,
action="store",
nargs=1,
metavar="/some/path",
default=[None],
help="Path and prefix for the text output.")
parser.add_argument("-" + _debug[0],
"--" + _debug,
action="store_true",
help="Produce verbose debug output.")
continuation = parser.add_mutually_exclusive_group(required=False)
continuation.add_argument(
"-" + _resume[0],
"--" + _resume,
action="store_true",
help="Resume an existing chain if it has similar info "
"(fails otherwise).")
continuation.add_argument("-" + _force[0],
"--" + _force,
action="store_true",
help="Overwrites previous output, if it exists "
"(use with care!)")
parser.add_argument("--%s" % _test_run,
action="store_true",
help="Initialize model and sampler, and exit.")
parser.add_argument("--version", action="version", version=__version__)
parser.add_argument("--no-mpi",
action='store_true',
help="disable MPI when mpi4py installed but MPI does "
"not actually work")
arguments = parser.parse_args()
if arguments.no_mpi or getattr(arguments, _test_run, False):
set_mpi_disabled()
if any((os.path.splitext(f)[0] in ("input", "updated"))
for f in arguments.input_file):
raise ValueError("'input' and 'updated' are reserved file names. "
"Please, use a different one.")
load_input = import_MPI(".input", "load_input")
given_input = arguments.input_file[0]
if any(given_input.lower().endswith(ext) for ext in _yaml_extensions):
info = load_input(given_input)
output_prefix_cmd = getattr(arguments, _output_prefix)[0]
output_prefix_input = info.get(_output_prefix)
info[_output_prefix] = output_prefix_cmd or output_prefix_input
else:
# Passed an existing output_prefix? Try to find the corresponding *.updated.yaml
updated_file = get_info_path(*split_prefix(given_input),
kind="updated")
try:
info = load_input(updated_file)
except IOError:
raise ValueError(
"Not a valid input file, or non-existent run to resume")
# We need to update the output_prefix to resume the run *where it is*
info[_output_prefix] = given_input
# If input given this way, we obviously want to resume!
info[_resume] = True
# solve packages installation path cmd > env > input
# MARKED FOR DEPRECATION IN v3.0
if getattr(arguments, modules) != [None]:
logger_setup()
logger = logging.getLogger(__name__.split(".")[-1])
logger.warning(
"*DEPRECATION*: -m/--modules will be deprecated in favor of "
"-%s/--%s in the next version. Please, use that one instead.",
_packages_path_arg[0], _packages_path_arg_posix)
# BEHAVIOUR TO BE REPLACED BY ERROR:
if getattr(arguments, _packages_path_arg) == [None]:
setattr(arguments, _packages_path_arg, getattr(arguments, modules))
# BEHAVIOUR TO BE REPLACED BY ERROR:
check_deprecated_modules_path(info)
# END OF DEPRECATION BLOCK
info[_packages_path] = \
getattr(arguments, _packages_path_arg)[0] or info.get(_packages_path)
info[_debug] = getattr(arguments, _debug) or info.get(
_debug, _debug_default)
info[_test_run] = getattr(arguments, _test_run, False)
# If any of resume|force given as cmd args, ignore those in the input file
resume_arg, force_arg = [
getattr(arguments, arg) for arg in [_resume, _force]
]
if any([resume_arg, force_arg]):
info[_resume], info[_force] = resume_arg, force_arg
if _post in info:
post(info)
else:
run(info)
def run_script():
warn_deprecation()
import os
import argparse
parser = argparse.ArgumentParser(description="Cobaya's run script.")
parser.add_argument("input_file",
nargs=1,
action="store",
metavar="input_file.yaml",
help="An input file to run.")
parser.add_argument(
"-" + _modules_path_arg[0],
"--" + _modules_path_arg,
action="store",
nargs=1,
metavar="/some/path",
default=[None],
help="Path where modules were automatically installed.")
parser.add_argument("-" + _output_prefix[0],
"--" + _output_prefix,
action="store",
nargs=1,
metavar="/some/path",
default=[None],
help="Path and prefix for the text output.")
parser.add_argument("-" + _debug[0],
"--" + _debug,
action="store_true",
help="Produce verbose debug output.")
continuation = parser.add_mutually_exclusive_group(required=False)
continuation.add_argument(
"-" + _resume[0],
"--" + _resume,
action="store_true",
help="Resume an existing chain if it has similar info "
"(fails otherwise).")
continuation.add_argument("-" + _force[0],
"--" + _force,
action="store_true",
help="Overwrites previous output, if it exists "
"(use with care!)")
parser.add_argument("--version", action="version", version=__version__)
args = parser.parse_args()
if any([(os.path.splitext(f)[0] in ("input", "updated"))
for f in args.input_file]):
raise ValueError("'input' and 'updated' are reserved file names. "
"Please, use a different one.")
load_input = import_MPI(".input", "load_input")
given_input = args.input_file[0]
if any(given_input.lower().endswith(ext) for ext in _yaml_extensions):
info = load_input(given_input)
output_prefix_cmd = getattr(args, _output_prefix)[0]
output_prefix_input = info.get(_output_prefix)
info[_output_prefix] = output_prefix_cmd or output_prefix_input
else:
# Passed an existing output_prefix? Try to find the corresponding *.updated.yaml
updated_file = (
given_input +
(_separator_files if not given_input.endswith(os.sep) else "") +
_updated_suffix + _yaml_extensions[0])
try:
info = load_input(updated_file)
except IOError:
raise ValueError(
"Not a valid input file, or non-existent sample to resume")
# We need to update the output_prefix to resume the sample *where it is*
info[_output_prefix] = given_input
# If input given this way, we obviously want to resume!
info[_resume] = True
# solve modules installation path cmd > env > input
path_cmd = getattr(args, _modules_path_arg)[0]
path_env = os.environ.get(_modules_path_env, None)
path_input = info.get(_path_install)
info[_path_install] = path_cmd or (path_env or path_input)
info[_debug] = getattr(args, _debug) or info.get(_debug, _debug_default)
info[_resume] = getattr(args, _resume, _resume_default)
info[_force] = getattr(args, _force, False)
if _post in info:
post(info)
else:
run(info)
