Skip to content

feat: create a warning when fitted parameter is at the bounds #2526

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

Draft
wants to merge 2 commits into
base: main
Choose a base branch
from
Draft

feat: create a warning when fitted parameter is at the bounds #2526

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
4412a76
initial attempt to handle warnings at bounds
kratsg Jun 26, 2024
071c32a
add additional todo comments
kratsg Jun 26, 2024
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
24 changes: 22 additions & 2 deletions src/pyhf/optimize/mixins.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -67,15 +67,32 @@ def _internal_minimize(
raise exceptions.FailedMinimization(result)
return result

def _internal_postprocess(self, fitresult, stitch_pars, return_uncertainties=False):
def _internal_postprocess(
self, fitresult, stitch_pars, /, par_bounds, *, return_uncertainties=False
):
"""
Post-process the fit result.

Args:
fitresult (scipy.optimize.OptimizeResult): Fit result from :func:`_internal_minimize`
stitch_pars (:obj:`func`): callable that stitches fixed parameters into the unfixed parameters
par_bounds (:obj:`list` of :obj:`list`/:obj:`tuple`): The extrema of values the model parameters
are allowed to reach in the fit.
The shape should be ``(n, 2)`` for ``n`` model parameters.
return_uncertainties (:obj:`bool`): Return uncertainties on the fitted parameters. Default is off (``False``).

Returns:
fitresult (scipy.optimize.OptimizeResult): A modified version of the fit result.
"""
tensorlib, _ = get_backend()

# TODO: check how to handle this for batching
# TODO: handle skipping fixed parameters
# TODO: handle various backends
for par_index, (fitted_par, bound) in enumerate(zip(fitresult.x, par_bounds)):
if fitted_par in bound:
log.warning(f'parameter at index {par_index} is at the bounds')

# stitch in missing parameters (e.g. fixed parameters)
fitted_pars = stitch_pars(tensorlib.astensor(fitresult.x))

Expand Down Expand Up @@ -195,7 +212,10 @@ def minimize(
**minimizer_kwargs, options=kwargs, par_names=par_names
)
result = self._internal_postprocess(
result, stitch_pars, return_uncertainties=return_uncertainties
result,
stitch_pars,
par_bounds=minimizer_kwargs['bounds'],
return_uncertainties=return_uncertainties,
)

_returns = [result.x]
Expand Down