def test_mcmc_drag_results():
info: InputDict = yaml_load(yaml_drag)
info['likelihood'] = {
'g1': {
'external': GaussLike
},
'g2': {
'external': GaussLike2
}
}
updated_info, sampler = run(info)
products = sampler.products()
from getdist.mcsamples import MCSamplesFromCobaya
products["sample"] = mpi.allgather(products["sample"])
gdample = MCSamplesFromCobaya(updated_info,
products["sample"],
ignore_rows=0.2)
assert abs(gdample.mean('a') - 0.2) < 0.03
assert abs(gdample.mean('b')) < 0.03
assert abs(gdample.std('a') - 0.293) < 0.03
assert abs(gdample.std('b') - 0.4) < 0.03
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_cosmo_run_resume_post(tmpdir, packages_path=None):
# only vary As, so fast chain. Chain does not need to converge (tested elsewhere).
info['output'] = os.path.join(tmpdir, 'testchain')
if packages_path:
info["packages_path"] = process_packages_path(packages_path)
run(info, force=True)
# note that continuing from files leads to text-file precision at read in, so a mix of
# precision in the output SampleCollection returned from run
run(info, resume=True, override={'sampler': {'mcmc': {'Rminus1_stop': 0.2}}})
updated_info, sampler = run(info['output'] + '.updated' + Extension.dill,
resume=True,
override={'sampler': {'mcmc': {'Rminus1_stop': 0.05}}})
results = mpi.allgather(sampler.products()["sample"])
samp = MCSamplesFromCobaya(updated_info, results, ignore_rows=0.2)
assert np.isclose(samp.mean('As100'), 100 * samp.mean('As'))
# post-processing
info_post: PostDict = {'add': {'params': {'h': None},
"likelihood": {"test_likelihood2": likelihood2}},
'remove': {'likelihood': ["test_likelihood"]},
'suffix': 'testpost',
'skip': 0.2, 'thin': 4
}
output_info, products = run(updated_info, override={'post': info_post}, force=True)
results2 = mpi.allgather(products["sample"])
samp2 = MCSamplesFromCobaya(output_info, results2)
samp_test = samp.copy()
samp_test.weighted_thin(4)
sigma8 = samp_test.getParams().sigma8
samp_test.reweightAddingLogLikes(-(sigma8 - 0.7) ** 2 / 0.1 ** 2
+ (sigma8 - 0.75) ** 2 / 0.07 ** 2)
assert np.isclose(samp_test.mean('sigma8'), samp2.mean('sigma8'))
# from getdist-format chain files
root = os.path.join(tmpdir, 'getdist_format')
if mpi.is_main_process():
samp.saveChainsAsText(root)
mpi.sync_processes()
from_txt = dict(updated_info, output=root)
post_from_text = dict(info_post, skip=0) # getdist already skipped
output_info, products = run(from_txt, override={'post': post_from_text}, force=True)
samp_getdist = MCSamplesFromCobaya(output_info, mpi.allgather(products["sample"]))
assert not products["stats"]["points_removed"]
assert samp2.numrows == samp_getdist.numrows
assert np.isclose(samp2.mean('sigma8'), samp_getdist.mean('sigma8'))
# again with inferred-inputs for params
info_conv = cosmomc_root_to_cobaya_info_dict(root)
# have to manually add consistent likelihoods if re-computing
info_conv['likelihood'] = info['likelihood']
info_conv['theory'] = info['theory']
post_from_text = dict(info_post, skip=0, suffix='getdist2') # getdist already skipped
output_info, products = run(info_conv, override={'post': post_from_text},
output=False)
samp_getdist2 = MCSamplesFromCobaya(output_info, mpi.allgather(products["sample"]))
assert np.isclose(samp2.mean('sigma8'), samp_getdist2.mean('sigma8'))
# from save info, no output
info_post['output'] = None
output_info, products = run({'output': info['output'], 'post': info_post}, force=True)
results3 = mpi.allgather(products["sample"])
samp3 = MCSamplesFromCobaya(output_info, results3)
assert np.isclose(samp3.mean("sigma8"), samp2.mean("sigma8"))
assert np.isclose(samp3.mean("joint"), samp2.mean("joint"))
samps4 = loadMCSamples(info['output'] + '.post.testpost')
assert np.isclose(samp3.mean("joint"), samps4.mean("joint"))
# test recover original answer swapping likelihoods back
info_revert = {'add': {'likelihood': info['likelihood']},
'remove': {'likelihood': ["test_likelihood2"]},
'suffix': 'revert',
'skip': 0, 'thin': 1,
'output': None
}
output_info, products = run({'output': info['output'] + '.post.testpost',
'post': info_revert}, force=True)
results_revert = mpi.allgather(products["sample"])
samp_revert = MCSamplesFromCobaya(output_info, results_revert)
samp_thin = MCSamplesFromCobaya(updated_info, results, ignore_rows=0.2)
samp_thin.weighted_thin(4)
assert samp_thin.numrows == samp_revert.numrows + products["stats"]["points_removed"]
if not products["stats"]["points_removed"]:
assert np.isclose(samp_revert.mean("sigma8"), samp_thin.mean("sigma8"))
else:
assert abs(samp_revert.mean("sigma8") - samp_thin.mean("sigma8")) < 0.01
assert not products["stats"]["points_removed"]
# no remove
info_post = {
'add': {'params': {'h': None}, "likelihood": {"test_likelihood2": likelihood2}},
'suffix': 'test2', 'skip': 0.2, 'thin': 4}
output_info, products = run(updated_info, override={'post': info_post}, force=True)
results2 = mpi.allgather(products["sample"])
samp2 = MCSamplesFromCobaya(output_info, results2)
assert "chi2__type1" in samp2.paramNames.list()
# check what has been saved to disk is consistent
samps4 = loadMCSamples(updated_info['output'] + '.post.test2')
assert samp2.paramNames.list() == samps4.paramNames.list()
assert np.isclose(samp2.mean("sigma8"), samps4.mean("sigma8"))
# adding new theory derived
info_post['add']['theory'] = {'new_param_theory': BTheory}
output_info, products = run(updated_info, override={'post': info_post}, output=False)
results3 = mpi.allgather(products["sample"])
samp3 = MCSamplesFromCobaya(output_info, results3)
assert np.isclose(samp3.mean("sigma8"), samp2.mean("sigma8"))
assert np.isclose(samp3.mean("As1000"), samp2.mean("As") * 1000)
info_post['add']['theory'] = {'new_param_theory': CTheory}
with pytest.raises(LoggedError) as e, NoLogging(logging.ERROR):
run(updated_info, override={'post': info_post}, output=False)
assert 'Parameter AsX no known value' in str(e)
