feat: Log warning when fitted parameter is at the bounds - #2526
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Clean up the rough draft: unpack (lower, upper) from each bound for clarity, fix to use % style logging rather than f-string, and improve the warning message to include the fitted value and both bound values. Add regression tests for lower bound, upper bound, and interior cases using _internal_postprocess directly with a synthetic OptimizeResult so the check is independent of optimizer floating-point behaviour. Closes #2525 Co-Authored-By: Giordon Stark <kratsg@gmail.com>
…ound check The three TODOs around the at-bounds warning loop are all safe to resolve: batching is not yet implemented (fitresult.x is always 1D), fixed parameters are already excluded because shim() strips them from both fitresult.x and par_bounds before optimization, and the in-tuple comparison is safe across all backends since fitresult.x is always a numpy array from scipy/iminuit. Replace the TODOs with an explanatory comment documenting why the alignment is guaranteed. Co-Authored-By: Giordon Stark <kratsg@gmail.com>
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Note Reviews pausedIt looks like this branch is under active development. To avoid overwhelming you with review comments due to an influx of new commits, CodeRabbit has automatically paused this review. You can configure this behavior by changing the Use the following commands to manage reviews:
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📝 WalkthroughWalkthroughThe optimizer now warns when free fitted parameters reach finite bounds or produce non-finite values. Postprocessing preserves full parameter names and bound metadata. Tests cover direct checks and SciPy and Minuit fits. ChangesOptimizer bound warnings
Estimated code review effort: 4 (Complex) | ~45 minutes Mergeability Score: 🔵 Low · up to The PR adds warnings when fitted parameters reach bounds without changing fit results; it is mergeable with owner follow-up for a minor lint issue in test helpers. Suggested reviewers: 🚥 Pre-merge checks | ✅ 5✅ Passed checks (5 passed)
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Inline comments:
In `@src/pyhf/optimize/mixins.py`:
- Around line 71-73: Update the _internal_postprocess method signature so *
appears before par_bounds, making par_bounds keyword-only while preserving the
existing return_uncertainties keyword-only behavior and positional-only
arguments.
- Around line 224-227: Update the call to _internal_postprocess() to pass the
free-parameter bounds from minimizer_kwargs["bounds"] rather than the full-model
par_bounds, preserving alignment when fixed parameters occur before free
parameters. Add a regression case covering a non-terminal fixed parameter and
verifying bound warnings use the correct free-parameter bounds.
In `@tests/test_optim.py`:
- Around line 635-638: Strengthen the warning assertions in the test around
warning_records so both bound-warning cases verify the parameter index, fitted
value, and lower and upper bound values in addition to the existing “bounds”
text check. Use the expected values for each case, while preserving the current
single-warning assertion when expect_warning is true.
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| result, | ||
| stitch_pars, | ||
| par_bounds=par_bounds, | ||
| return_uncertainties=return_uncertainties, |
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🎯 Functional Correctness | 🟡 Minor | ⚡ Quick win
🧩 Analysis chain
🏁 Script executed:
#!/bin/bash
set -euo pipefail
ast-grep outline src/pyhf/optimize/common.py --match shim --view expanded
rg -n -C 8 'variable_bounds|minimizer_kwargs|do_stitch' src/pyhf/optimize/common.py
rg -n -C 5 '_internal_postprocess\s*\(|par_bounds=' src testsRepository: scikit-hep/pyhf
Length of output: 9612
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#!/bin/bash
set -euo pipefail
printf '%s\n' '--- mixins.py post-processing and minimize flow ---'
sed -n '60,125p' src/pyhf/optimize/mixins.py
sed -n '185,245p' src/pyhf/optimize/mixins.py
printf '%s\n' '--- stitching and bound-related tests ---'
rg -n -C 12 'do_stitch|fixed_params|fixed_vals|is at a bound|par_bounds' tests/test_optim.py src/pyhf/optimize
printf '%s\n' '--- relevant optimizer call paths ---'
rg -n -C 8 'shim\(|_internal_minimize\(|_internal_postprocess\(' src/pyhfRepository: scikit-hep/pyhf
Length of output: 50371
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#!/bin/bash
set -euo pipefail
python3 - <<'PY'
# Model the exact transformations in common.py and the zip-based warning
# check in mixins.py without importing or executing repository code.
def free_bounds(all_bounds, fixed_indices):
variable_indices = [
index for index in range(len(all_bounds)) if index not in fixed_indices
]
return variable_indices, [all_bounds[index] for index in variable_indices]
all_bounds = [(0, 10), (100, 200), (1000, 2000)]
fixed_indices = [1]
free_indices, free_bounds_value = free_bounds(all_bounds, fixed_indices)
free_values = [0, 2000]
wrong_pairs = list(zip(free_values, all_bounds))
correct_pairs = list(zip(free_values, free_bounds_value))
print(f"free_indices={free_indices}")
print(f"minimizer_kwargs_bounds={free_bounds_value}")
print(f"pairs_with_original_bounds={wrong_pairs}")
print(f"pairs_with_free_bounds={correct_pairs}")
assert free_bounds_value == [all_bounds[0], all_bounds[2]]
assert wrong_pairs != correct_pairs
assert wrong_pairs[1][1] != correct_pairs[1][1]
PY
printf '%s\n' '--- focused source assertions ---'
sed -n '100,148p' src/pyhf/optimize/common.py
sed -n '88,106p' src/pyhf/optimize/mixins.py
sed -n '604,642p' tests/test_optim.pyRepository: scikit-hep/pyhf
Length of output: 4107
Pass the free-parameter bounds to post-processing.
_internal_postprocess() compares free fitted values with full-model bounds. A non-terminal fixed parameter misaligns later bounds and produces incorrect bound warnings. Use minimizer_kwargs["bounds"] and add a regression case.
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In `@src/pyhf/optimize/mixins.py` around lines 224 - 227, Update the call to
_internal_postprocess() to pass the free-parameter bounds from
minimizer_kwargs["bounds"] rather than the full-model par_bounds, preserving
alignment when fixed parameters occur before free parameters. Add a regression
case covering a non-terminal fixed parameter and verifying bound warnings use
the correct free-parameter bounds.
…er handling * Move the at-bound check onto the stitched full parameter vector in _internal_postprocess so it aligns with the full-model par_bounds in both the do_stitch=True case (fitresult.x holds only the free parameters) and the default case, and report the model parameter name and index via pdf.config.par_names. * Skip parameters held constant in the fit: a parameter deliberately fixed at a bound (e.g. the POI fixed at zero for discovery test statistics) is not a fit pathology. * Compare against bounds with a tolerance relative to the bound range instead of exact equality: iminuit's sin-transformed limits stop near but not exactly at bounds and scipy's SLSQP rails within O(1e-14), so exact comparison misses genuinely railed fits. * Make the new _internal_postprocess arguments keyword-only with safe defaults so existing callers and subclasses keep working; skip the check when par_bounds is None (direct optimizer API) and support one-sided (None) bounds. * Aggregate to a single warning per fit to avoid flooding logs in toy studies. * Extend the regression tests with stitched fixed-parameter alignment, tolerance, fixed-POI, unbounded, one-sided-bound, and end-to-end (scipy and minuit) cases. Addresses code review feedback on PR 2526. Assisted-by: ClaudeCode:claude-fable-5
…ilder * Replace the module-level _AT_BOUND_RTOL constant with an optional rtol parameter of _at_bound_warning_messages, as it was only used there; the rationale for the default moves into the docstring. * Add type annotations to _at_bound_warning_messages, restructuring the one-sided bound handling into an explicit if/elif chain so mypy can narrow the optional bounds. Assisted-by: ClaudeCode:claude-fable-5
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Inline comments:
In `@src/pyhf/optimize/mixins.py`:
- Around line 51-63: Update the bound-handling logic around the tolerance
calculation and proximity check to normalize non-finite endpoints, including
±inf, to None before any arithmetic or comparisons. Ensure fully unbounded
ranges are skipped and one-sided infinite ranges use only the finite endpoint,
then add regressions covering both cases without changing behavior for finite
bounds.
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* Replace the bound-range-scaled tolerance with rtol * max(1, |bound|) per side: the range-scaled form flagged interior fits as at-bound for wide bounds (tolerance 1.0 for bounds (0, 1e6)) while still missing minuit fits railed at a bound. * For the minuit optimizer, additionally use iminuit's own per-parameter at-limit determination (within half an uncertainty of a bound, cf. iminuit.util.FMin.has_parameters_at_limit), mapped back to model parameter indices under do_stitch=True. In a 300-toy study this now agrees 208/208 with iminuit; the tolerance-only form missed 35% of railed fits. * Use one-sided comparisons so values beyond a bound (constraint violations) also warn, and surface non-finite fitted values instead of silently swallowing them. * Treat None and non-finite (inf) bounds as unbounded: (-inf, inf) bounds previously warned for every parameter. Skip degenerate bounds that pin a parameter to a single value, as they are deliberate. * Accept scipy.optimize.Bounds instances for par_bounds (previously a TypeError after a successful fit on the direct optimizer API) and skip the check with a warning on a par_bounds length mismatch instead of silently zip-truncating. * Guard par_names shorter than the parameter vector (non-pyhf model configs) with an index-label fallback instead of IndexError. * Gate the check on log.isEnabledFor(WARNING) and use a set for the fixed-index skip to keep the hot path cheap. * Strip fixed parameter names for the minimizer without mutating the model config's par_names list in place. * Assert on individual warning lines in tests (the aggregated single-record warning previously capped observable warnings at one), assert a fitted value distinct from the bound, and add regression tests for inf/wide/degenerate/inverted bounds, NaN values, short par_names, scipy Bounds instances, and the minuit at-limit path. Addresses xhigh code review findings on PR 2526. Assisted-by: ClaudeCode:claude-fable-5
State explicitly in the _minuit_at_limit_flags docstring that iminuit (as of v2.32.0) only exposes the OR-aggregated FMin.has_parameters_at_limit, which is why the criterion is re-evaluated per parameter here, and that the function can be replaced if iminuit gains a per-parameter API. Assisted-by: ClaudeCode:claude-fable-5
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Inline comments:
In `@src/pyhf/optimize/mixins.py`:
- Around line 236-243: Normalize SciPy Bounds before the do_stitch=True flow
reaches shim(), converting lb/ub into indexable parameter-bound pairs before any
slicing; update the bounds handling around _internal_postprocess() without
changing existing pair behavior. Add a regression in tests/test_optim.py lines
833-857 covering Bounds with fixed_vals and do_stitch=True; retain the existing
do_stitch=False coverage.
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…ck edge cases
* Word the two detection criteria distinctly: numeric rails keep
"is at a bound" while parameters flagged only by iminuit's statistical
criterion say "is within half its uncertainty of a bound (iminuit
at-limit criterion)", as such values need not be numerically at a
bound (e.g. mu_hat = 0.2 +/- 1.0 with bounds (0, 10)).
* Raise the default detection tolerance to rtol=1e-4, covering minuit's
measured rail distances (up to O(1e-5)) so railed minuit fits warn as
at-bound even when the at-limit heuristic is unavailable.
* Normalize scipy.optimize.Bounds instances to pairs in minimize()
before shim(), so all optimizer and do_stitch code paths support
them, and drop the hasattr("lb") duck-typing in postprocessing.
* Only exonerate degenerate (pinning) bounds when the fitted value
actually sits at the pin; a value violating pinned bounds now warns.
* Gate the minuit parameter walk on the O(1)
fmin.has_parameters_at_limit aggregate, hoist the fixed-index set out
of the mapping comprehension (was O(npars * nfixed) per fit), and
replace the unreachable length-mismatch fallback with zip-based
mapping.
* Report fitted values at full precision (repr) so near-bound values
are distinguishable from the bound, display inf bounds as given, and
guard at_limit indexing against short flag lists.
* Tests: enforce the one-record-per-fit aggregation invariant, filter
warning lines per line rather than per record, cover the par_bounds
length-mismatch guard and the fixed/unbounded skip in the at-limit
flags (resolving the codecov-flagged lines), and run the Bounds
instance end-to-end on both optimizers.
Addresses xhigh code review findings on PR 2526.
Assisted-by: ClaudeCode:claude-fable-5
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Inline comments:
In `@src/pyhf/optimize/mixins.py`:
- Around line 108-111: Update the non-finite fitted-parameter warning in the
relevant optimization flow to include the configured bounds as
bounds=(raw_lower, raw_upper) before returning. Ensure raw_lower and raw_upper
are formatted or made available before the math.isfinite(fitted_par) check,
while preserving the existing parameter label and fitted value details.
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…olations * Scale the detection tolerance with the bound range (or max(1, |bound|) when only one side is bounded) instead of max(1, |bound|) per side, which degenerated to an absolute 1e-4 window for any bound with |bound| <= 1. pyhf's own shapesys and staterror gammas default to bounds of (1e-10, 10), so a gamma fitted at 5e-5 was reported as at its lower bound, and any bound range narrower than 2 * rtol was flagged unconditionally. rtol returns to 1e-8. * Report a value strictly outside its bounds as "is outside its bounds" rather than "is at a bound": a minimization returning a point the bounds should have excluded is a stronger condition than railing. * Include the bounds in the non-finite parameter message, which previously returned before the bounds were formatted. * Document both detection criteria in the minimize() docstring, which described only the numeric one, and drop the docstring's claim that scipy.optimize.Bounds is a supported par_bounds type: the instance is still normalized so it cannot break the fit, but pyhf.infer.mle.fit does not accept one, so advertising it would overpromise. * Tests: cover the gamma-style small-bound-value and narrow-range interior cases, the outside-bounds wording, bounds in the non-finite message, and drive a real iminuit.Minuit through the at-limit flags so a change to the iminuit Param API is caught in tests rather than in every minuit fit. Addresses xhigh code review findings on PR 2526. Assisted-by: ClaudeCode:claude-opus-5
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Inline comments:
In `@tests/test_optim.py`:
- Around line 606-618: Add the return annotation list[str] to both private
helpers, _warning_lines and _at_bound_warning_lines, without changing their
existing behavior.
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_internal_postprocess called list(par_bounds) after shim() had already
handed the same object to the minimizer, so a one-shot iterable was
exhausted by the time the at-bound check indexed it. The check then took
the length-mismatch branch and reported
length of par_bounds (0) does not match the number of model
parameters (2), skipping check for parameters at bounds
replacing a genuine at-bound diagnostic with a spurious one. The fit
itself was unaffected: the same railed mu = 7.16e-16 came back for every
input form, only the reporting differed.
Reachable through the direct optimizer API, where zip(lows, highs) is a
natural way to build bounds. pyhf.infer.mle.fit is unaffected, as
_validate_fit_inputs already exhausts the iterator there.
Materialize in minimize() alongside the scipy.optimize.Bounds
normalization, before shim() sees it, and drop the now-redundant copy in
_internal_postprocess.
Assisted-by: ClaudeCode:claude-opus-5[1m]
fitresult.minuit was fetched defensively with getattr, but the very next
expression dereferenced minuit.fmin.has_parameters_at_limit unguarded,
so a diagnostic-only code path could abort an otherwise successful fit:
Minuit constructed, migrad never run
AttributeError: 'NoneType' object has no attribute
'has_parameters_at_limit'
non-iminuit object on .minuit
AttributeError: 'object' object has no attribute 'fmin'
iminuit.Minuit.fmin is None until migrad has run. This is new exposure:
before the at-bound check, fitresult.minuit was only touched inside the
`uncertainties is not None` branch. pyhf's own minuit_optimizer cannot
reach it, since _internal_minimize raises FailedMinimization first, but
OptimizerMixin is documented for building custom optimizers and
_internal_postprocess is autodoc-published, so mocked and third-party
results can.
Assisted-by: ClaudeCode:claude-opus-5[1m]
The rtol docstring claimed "scipy's SLSQP rails within O(1e-14)" without
qualification. That holds only when the parameter is stripped from the
minimization. With do_stitch=False (the default) and fixed parameters,
opt_scipy._minimize minimizes under an equality constraint instead, and
SLSQP then stops O(1e-7) from the bound -- outside the default 1e-7
tolerance, so the at-bound warning is not emitted at all.
Measured on the same model, data, bounds and optimizer, varying only the
flag:
do_stitch=False gamma[1]=9.999999881921008 dist=1.18e-07 no warning
do_stitch=True gamma[1]=10.0 dist=0.00e+00 warning
Document the limitation rather than widen the tolerance: raising rtol to
catch this would also make a staterror/shapesys gamma at 5e-5 against
(1e-10, 10) start warning, which is a judgement about what pyhf should
report rather than a bug fix, and belongs in its own discussion.
Assisted-by: ClaudeCode:claude-opus-5[1m]
…terion Mutation testing against the at-bound check found several behaviours the suite exercised but did not assert, so the corresponding production logic could be changed without any test failing. Message kind. _warning_lines matches "fit result for parameter", the prefix shared by all three message kinds, so nothing distinguished "is at a bound" from "is outside its bounds" -- the latter was asserted nowhere. Parametrize on the expected kind instead of a bool, so both rewording the violation message and disabling the violation branch outright are now caught. Tolerance scaling. The interior cases sat far from every candidate tolerance, so replacing the range-scaled tolerance with a flat rtol, with rtol * abs(upper), or with a floor of 1 all went unnoticed. Add cases that bracket the scaling: 1e-7 is inside a (0, 10) window but outside a (0, 1) one, the range still governs when the near endpoint is small (-10, 1), and a 1e-4-wide window must not be floored at 1. Infinite bounds and values. (-inf, inf) is self-neutralising, since -inf + inf is nan, so it did not discriminate; a (0, inf) interior case does. Bounds are reported as supplied rather than as normalized, and an inf fit result is reported as non-finite rather than compared to the bounds. iminuit criterion. The stub sat orders of magnitude from the threshold, leaving the 0.5 * error factor, the upper side of the min(), and both one-sided-limit fallbacks unpinned. Add a _minuit_param_stub helper and cases that bracket the threshold, sit near the upper limit, and exercise each one-sided limit. Also pin the flag mapping: the early return that keeps do_stitch=False results unremapped, and a free parameter that is not at a limit staying False through the remap. The O(1) aggregate gate is now covered by a has_parameters_at_limit False case, and the scipy.optimize.Bounds test is parametrized over do_stitch with fixed_vals, which is the regression CodeRabbit asked for on PR #2526: shim() slices par_bounds per parameter under do_stitch, which a Bounds instance does not support. Assisted-by: ClaudeCode:claude-opus-5[1m]
Detection of an at-bound parameter used the scaled tolerance, but the
escalation to the more severe "is outside its bounds" message compared
exactly. A value already classified as at-bound was therefore upgraded to
a constraint violation over floating-point noise:
value=-0.0 -> is at a bound
value=-1e-17 -> is outside its bounds
value= 1e-17 -> is at a bound
Numerically identical results drew the two most different messages, and
1e-17 of slop against a bound of 0 earned the claim that "the
minimization returned a point the bounds should have excluded" -- which
the same docstring contradicts by noting optimizers "stop near but not
exactly at bounds".
Judge the escalation against the same tolerance as the detection, so
only a value genuinely beyond the bound is reported as violating it.
Nothing that previously reported "is outside its bounds" beyond the
tolerance changes: 2.5 against (3, 10), 5.0 against (1, 1) and the
inverted (10, 3) case all still escalate.
This does not address the pinned-bounds case, where lower == upper makes
the tolerance exactly 0 and a 1-ulp drift still escalates; that is
tracked separately with the rest of the tolerance-heuristic work.
Assisted-by: ClaudeCode:claude-opus-5[1m]
The fixed-parameter skip sat above the finiteness check, so a NaN or inf
at a fixed index produced no diagnostic at all while the identical value
at a free index was reported:
NaN at a free index -> "... 'a' (index 0) is not finite: value=nan"
NaN at a fixed index -> (nothing)
Skipping fixed parameters is right for the *bound* comparison, since a
parameter pinned at a bound is deliberate, but it should not extend to
finiteness. A fixed parameter holding NaN is a real symptom -- a
corrupted fixed_vals, or a non-finite init_pars entry stitched back in --
and silence is the one outcome that helps nobody.
Move the skip below the finiteness check. A fixed parameter with a finite
value at a bound stays silent as before.
Note that unpacking the bounds now also runs for fixed indices, so
malformed bounds at a fixed index raise where they were previously
skipped. Accepted: nearly every malformed bound shape already fails
inside scipy before the fit completes, and the alternative duplicates the
bound-string formatting the message needs.
Also tighten test_parameter_at_bounds_warning_fixed_parameter, which
asserted only the absence of at-bound lines and so would not have noticed
a spurious non-finite message for a finite fixed parameter.
Assisted-by: ClaudeCode:claude-opus-5[1m]
test_parameter_at_bounds_warning_without_minuit_fmin constructed an iminuit.Minuit inside its parametrize list, so the object was built when the module was imported -- even when the test was deselected, and regardless of --disable-backend minuit. It was also shared mutable state whose "fmin is None" premise, the entire point of the minuit_before_migrad case, held only because nothing else happened to call migrad on it. Parametrize over factories and construct in the body, matching the make_par_bounds pattern used by the neighbouring test. Verified that importing the module now builds zero Minuit instances; the parametrize ids are unchanged. Assisted-by: ClaudeCode:claude-opus-5[1m]
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@kratsg I think that we should probably look at the full state of what I've added from "fix: Correct at-bound warning alignment, tolerance, and fixed-parameter handling" onward and evaluate how much of this is needed and how much of this is my sloppy debugging of minimization issues that are outside the scope of this PR. Your input would be welcome here, but you should also feel free to just move any and all commits that I've made to another branch and revert things to a state that we can discuss where the PR scope should develop towards. |
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Pull Request Description
Resolves #2525.
When nuisance parameters hit their bounds during fitting (e.g. in a large-model toy run), there was previously no indication that this had happened, making it difficult to diagnose suspect fit results. This PR adds a
WARNINGlog message from_internal_postprocesswhenever a fitted parameter lands exactly on a bound, reporting the parameter index, the fitted value, and the bound range.The implementation iterates over the free parameters in
fitresult.xalongside their bounds (both already stripped of fixed parameters byshim()before optimization). Three architectural concerns (batching, fixed-parameter skipping, and backend compatibility) were investigated (using CLAUDE) and confirmed to be non-issues given the current design; a clarifying comment documents why the alignment is guaranteed.Checklist Before Requesting Reviewer
Before Merging
For the PR Assignees:
Summary by CodeRabbit
Bug Fixes
Tests