fix: Manually set uncertainties for fixed parameters to 0 (#1919)

* Manually set the uncertainties for fixed parameters to zero after minimization,
and remove setting the uncertainty/step_sizes during minimization, to avoid warnings
from iminuit. For iminuit v2.12.2+ "assigning zero to errors is invalid, but in the
past this was not caught." This approach harmonizes with cabinetry for fixed
parameters for iminuit v2.12.2+.
   - c.f. scikit-hep/iminuit#762
   - c.f. scikit-hep/cabinetry#346
   - c.f. https://iminuit.readthedocs.io/en/stable/changelog.html#july-15-2022
     > fix a bug in error heuristic when parameters have negative values and prevent
     > assigning negative values to errors
* Remove tests/test_optim.py test_step_sizes_fixed_parameters_minuit as the
uncertainties/step sizes values are no longer set during minimization and so are no
longer in pyhf's control.

src/pyhf/optimize/mixins.py

@@ -1,9 +1,11 @@
 """Helper Classes for use of automatic differentiation."""
-from pyhf.tensor.manager import get_backend
+import logging
+
+import numpy as np
+
 from pyhf import exceptions
 from pyhf.optimize.common import shim
-
-import logging
+from pyhf.tensor.manager import get_backend
 
 log = logging.getLogger(__name__)
 
@@ -82,6 +84,13 @@ def _internal_postprocess(self, fitresult, stitch_pars, return_uncertainties=Fal
         if uncertainties is not None:
             # extract number of fixed parameters
             num_fixed_pars = len(fitted_pars) - len(fitresult.x)
+
+            # FIXME: Set uncertainties for fixed parameters to 0 manually
+            # https://github.com/scikit-hep/iminuit/issues/762
+            # https://github.com/scikit-hep/pyhf/issues/1918
+            # https://github.com/scikit-hep/cabinetry/pull/346
+            uncertainties = np.where(fitresult.minuit.fixed, 0.0, uncertainties)
+
             # stitch in zero-uncertainty for fixed values
             uncertainties = stitch_pars(
                 tensorlib.astensor(uncertainties),

src/pyhf/optimize/opt_minuit.py

@@ -50,14 +50,12 @@ def _get_minimizer(
         par_names=None,
     ):
 
-        step_sizes = [(b[1] - b[0]) / float(self.steps) for b in init_bounds]
         fixed_vals = fixed_vals or []
         # Minuit wants True/False for each parameter
         fixed_bools = [False] * len(init_pars)
         for index, val in fixed_vals:
             fixed_bools[index] = True
             init_pars[index] = val
-            step_sizes[index] = 0.0
 
         # Minuit requires jac=callable
         if do_grad:
@@ -68,7 +66,6 @@ def _get_minimizer(
             jac = None
 
         minuit = iminuit.Minuit(wrapped_objective, init_pars, grad=jac, name=par_names)
-        minuit.errors = step_sizes
         minuit.limits = init_bounds
         minuit.fixed = fixed_bools
         minuit.print_level = self.verbose

tests/test_optim.py

@@ -520,20 +520,6 @@ def test_init_pars_sync_fixed_values_minuit(mocker):
     assert minimizer.fixed == [True, False, False]
 
 
-def test_step_sizes_fixed_parameters_minuit(mocker):
-    opt = pyhf.optimize.minuit_optimizer()
-
-    # patch all we need
-    from pyhf.optimize import opt_minuit
-
-    minuit = mocker.patch.object(getattr(opt_minuit, 'iminuit'), 'Minuit')
-    minimizer = opt._get_minimizer(None, [9, 9, 9], [(0, 10)] * 3, fixed_vals=[(0, 1)])
-
-    assert minuit.called
-    assert minimizer.fixed == [True, False, False]
-    assert minimizer.errors == [0.0, 0.01, 0.01]
-
-
 def test_solver_options_behavior_scipy(mocker):
     opt = pyhf.optimize.scipy_optimizer(solver_options={'arbitrary_option': 'foobar'})