Skip to content

fixes to figure of merit #1166

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
Jammy2211 merged 1 commit into from
Dec 10, 2025
Merged

fixes to figure of merit #1166

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
14 changes: 6 additions & 8 deletions autofit/non_linear/fitness.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -173,6 +173,7 @@ def call(self, parameters):

# Penalize NaNs in the log-likelihood
log_likelihood = self._xp.where(self._xp.isnan(log_likelihood), self.resample_figure_of_merit, log_likelihood)
log_likelihood = self._xp.where(self._xp.isinf(log_likelihood), self.resample_figure_of_merit, log_likelihood)

# Determine final figure of merit
if self.fom_is_log_likelihood:
Expand Down Expand Up @@ -222,19 +223,16 @@ def call_wrap(self, parameters):
figure_of_merit = self._call(parameters)

if self.convert_to_chi_squared:
figure_of_merit *= -0.5

if self.fom_is_log_likelihood:
log_likelihood = figure_of_merit
log_likelihood = -0.5 * figure_of_merit
else:
log_likelihood = figure_of_merit

if not self.fom_is_log_likelihood:
log_prior_list = self._xp.array(self.model.log_prior_list_from_vector(vector=parameters, xp=self._xp))
log_likelihood = figure_of_merit - self._xp.sum(log_prior_list)
log_likelihood -= self._xp.sum(log_prior_list)

self.manage_quick_update(parameters=parameters, log_likelihood=log_likelihood)

if self.convert_to_chi_squared:
log_likelihood *= -2.0

if self.store_history:

self.parameters_history_list.append(np.array(parameters))
Expand Down
27 changes: 19 additions & 8 deletions autofit/non_linear/search/mle/bfgs/search.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -136,21 +136,32 @@ def _fit(
maxiter = self.config_dict_options.get("maxiter", 1e8)

while total_iterations < maxiter:
iterations_remaining = maxiter - total_iterations

iterations_remaining = maxiter - total_iterations
iterations = min(self.iterations_per_full_update, iterations_remaining)

if iterations > 0:
config_dict_options = self.config_dict_options
config_dict_options["maxiter"] = iterations

search_internal = optimize.minimize(
fun=fitness._jit,
x0=x0,
method=self.method,
options=config_dict_options,
**self.config_dict_search
)
if analysis._use_jax:

search_internal = optimize.minimize(
fun=fitness._jit,
x0=x0,
method=self.method,
options=config_dict_options,
**self.config_dict_search
)
else:

search_internal = optimize.minimize(
fun=fitness.__call__,
x0=x0,
method=self.method,
options=config_dict_options,
**self.config_dict_search
)

total_iterations += search_internal.nit

Expand Down
Loading