def test__loads_from_config_file_if_not_input(self):
multi_nest = af.MultiNest(
prior_passer=af.PriorPasser(sigma=2.0,
use_errors=False,
use_widths=False),
n_live_points=40,
sampling_efficiency=0.5,
const_efficiency_mode=True,
evidence_tolerance=0.4,
importance_nested_sampling=False,
multimodal=False,
n_iter_before_update=90,
null_log_evidence=-1.0e80,
max_modes=50,
mode_tolerance=-1e88,
seed=0,
verbose=True,
resume=False,
context=1,
write_output=False,
log_zero=-1e90,
max_iter=1,
init_MPI=True,
)
assert multi_nest.prior_passer.sigma == 2.0
assert multi_nest.prior_passer.use_errors is False
assert multi_nest.prior_passer.use_widths is False
assert multi_nest.config_dict_search["n_live_points"] == 40
assert multi_nest.config_dict_search["sampling_efficiency"] == 0.5
multi_nest = af.MultiNest()
assert multi_nest.prior_passer.sigma == 3.0
assert multi_nest.prior_passer.use_errors is True
assert multi_nest.prior_passer.use_widths is True
assert multi_nest.config_dict_search["n_live_points"] == 50
assert multi_nest.config_dict_search["sampling_efficiency"] == 0.6
model = af.ModelMapper(mock_class_1=mock.MockClassx4)
fitness = af.MultiNest.Fitness(
analysis=None,
model=model,
samples_from_model=multi_nest.samples_from,
stagger_resampling_likelihood=False,
paths=None)
assert fitness.model == model
def test__read_total_samples_from_file_resume(self,
multi_nest_resume_path):
multi_nest = af.MultiNest()
create_resume(file_path=multi_nest.paths.path)
total_samples = mn.total_samples_from_file_resume(
file_resume=path.join(multi_nest.paths.path,
"multinestresume.dat"))
assert total_samples == 12345
def test__log_evidence_from_file_summary(self, multi_nest_summary_path):
multi_nest = af.MultiNest()
multi_nest.paths = af.DirectoryPaths(
path_prefix=path.join("non_linear", "multinest"))
create_summary_4_parameters(file_path=multi_nest.paths.samples_path)
log_evidence = mn.log_evidence_from_file_summary(
file_summary=path.join(multi_nest.paths.samples_path,
"multinestsummary.txt"),
prior_count=4,
)
assert log_evidence == 0.02
def test__samples_from_model(self, multi_nest_samples_path,
multi_nest_resume_path,
multi_nest_summary_path):
multi_nest = af.MultiNest()
multi_nest.paths = af.DirectoryPaths(
path_prefix=path.join("non_linear", "multinest"))
create_weighted_samples_4_parameters(
file_path=multi_nest.paths.samples_path)
create_resume(file_path=multi_nest.paths.samples_path)
create_summary_4_parameters(file_path=multi_nest.paths.samples_path)
model = af.ModelMapper(mock_class=mock.MockClassx4)
model.mock_class.two = af.LogUniformPrior(lower_limit=1e-8,
upper_limit=10.0)
samples = multi_nest.samples_from(model=model)
assert samples.parameter_lists == [
[1.1, 2.1, 3.1, 4.1],
[0.9, 1.9, 2.9, 3.9],
[1.0, 2.0, 3.0, 4.0],
[1.0, 2.0, 3.0, 4.0],
[1.0, 2.0, 3.0, 4.0],
[1.0, 2.0, 3.0, 4.0],
[1.0, 2.0, 3.0, 4.0],
[1.0, 2.0, 3.0, 4.0],
[1.0, 2.0, 3.0, 4.0],
[1.0, 2.0, 3.0, 4.0],
]
value = -0.5 * 9999999.9
assert samples.log_likelihood_list == 10 * [value]
assert samples.log_prior_list == pytest.approx([
0.243902, 0.256410, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25, 0.25
], 1.0e-4)
assert samples.weight_list == [
0.02, 0.02, 0.01, 0.05, 0.1, 0.1, 0.1, 0.1, 0.2, 0.3
]
assert samples.total_samples == 12345
assert samples.log_evidence == 0.02
assert samples.number_live_points == 50
def test__read_quantities_from_weighted_samples_file(
self, multi_nest_samples_path):
multi_nest = af.MultiNest()
multi_nest.paths = af.DirectoryPaths(
path_prefix=path.join("non_linear", "multinest"))
create_weighted_samples_4_parameters(file_path=multi_nest.paths.path)
parameters = mn.parameters_from_file_weighted_samples(
file_weighted_samples=path.join(multi_nest.paths.path,
"multinest.txt"),
prior_count=4,
)
assert parameters == [
[1.1, 2.1, 3.1, 4.1],
[0.9, 1.9, 2.9, 3.9],
[1.0, 2.0, 3.0, 4.0],
[1.0, 2.0, 3.0, 4.0],
[1.0, 2.0, 3.0, 4.0],
[1.0, 2.0, 3.0, 4.0],
[1.0, 2.0, 3.0, 4.0],
[1.0, 2.0, 3.0, 4.0],
[1.0, 2.0, 3.0, 4.0],
[1.0, 2.0, 3.0, 4.0],
]
log_likelihood_list = mn.log_likelihood_list_from_file_weighted_samples(
file_weighted_samples=path.join(multi_nest.paths.path,
"multinest.txt"))
value = -0.5 * 9999999.9
assert log_likelihood_list == 10 * [value]
weight_list = mn.weight_list_from_file_weighted_samples(
file_weighted_samples=path.join(multi_nest.paths.path,
"multinest.txt"))
assert weight_list == [
0.02, 0.02, 0.01, 0.05, 0.1, 0.1, 0.1, 0.1, 0.2, 0.3
]
- https://github.com/JohannesBuchner/PyMultiNest
- http://johannesbuchner.github.io/PyMultiNest/index.html#
"""
multi_nest = af.MultiNest(
path_prefix="searches",
name="MultiNest",
nlive=50,
n_live_points=50,
sampling_efficiency=0.2,
const_efficiency_mode=False,
evidence_tolerance=0.5,
multimodal=False,
importance_nested_sampling=False,
max_modes=100,
mode_tolerance=-1e90,
max_iter=0,
n_iter_before_update=5,
null_log_evidence=-1e90,
seed=0,
verbose=False,
resume=True,
context=0,
write_output=True,
log_zero=-1e100,
init_MPI=False,
iterations_per_update=500,
number_of_cores=2,
)
result = multi_nest.fit(model=model, analysis=analysis)
"""