def run(self) -> SensitivityResult:
"""
Run fits and comparisons for all perturbations, returning
a list of results.
"""
results = list()
for result in Process.run_jobs(self._make_jobs(),
number_of_cores=self.number_of_cores):
results.append(result)
return SensitivityResult(results)
def fit_parallel(self, model, analysis, grid_priors):
"""
Perform the grid search in parallel, with all the optimisation for each grid square being performed on a
different process.
Parameters
----------
analysis
An analysis
grid_priors
Priors describing the position in the grid
Returns
-------
result: GridSearchResult
The result of the grid search
"""
grid_priors = list(set(grid_priors))
results = []
lists = self.make_lists(grid_priors)
physical_lists = self.make_physical_lists(grid_priors)
results_list = [["index"] +
list(map(model.name_for_prior, grid_priors)) +
["likelihood_merit"]]
jobs = list()
for index, values in enumerate(lists):
jobs.append(
self.job_for_analysis_grid_priors_and_values(
analysis=copy.deepcopy(analysis),
model=model,
grid_priors=grid_priors,
values=values,
index=index,
))
for result in Process.run_jobs(jobs, self.number_of_cores):
results.append(result)
results = sorted(results)
results_list.append(result.result_list_row)
self.write_results(results_list)
return GridSearchResult([result.result for result in results], lists,
physical_lists)
def run(self) -> SensitivityResult:
"""
Run fits and comparisons for all perturbations, returning
a list of results.
"""
self.logger.info("Running")
headers = [
"index", *self._headers, "log_likelihood_base",
"log_likelihood_perturbed", "log_likelihood_difference"
]
physical_values = list(self._physical_values)
results = list()
for result in Process.run_jobs(self._make_jobs(),
number_of_cores=self.number_of_cores):
if isinstance(result, Exception):
raise result
results.append(result)
results = sorted(results)
os.makedirs(self.search.paths.output_path, exist_ok=True)
with open(self.results_path, "w+") as f:
writer = csv.writer(f)
writer.writerow(headers)
for result_ in results:
values = physical_values[result_.number]
writer.writerow(
padding(item) for item in [
result_.number,
*values,
result_.log_likelihood_base,
result_.log_likelihood_perturbed,
result_.log_likelihood_difference,
])
return SensitivityResult(results)