feat: Propagate model parameter names to optimizers

src/pyhf/optimize/mixins.py

@@ -29,11 +29,23 @@ def __init__(self, **kwargs):
             )
 
     def _internal_minimize(
-        self, func, x0, do_grad=False, bounds=None, fixed_vals=None, options={}
+        self,
+        func,
+        x0,
+        do_grad=False,
+        bounds=None,
+        fixed_vals=None,
+        options={},
+        par_names=None,
     ):
 
         minimizer = self._get_minimizer(
-            func, x0, bounds, fixed_vals=fixed_vals, do_grad=do_grad
+            func,
+            x0,
+            bounds,
+            fixed_vals=fixed_vals,
+            do_grad=do_grad,
+            par_names=par_names,
         )
         result = self._minimize(
             minimizer,
@@ -157,7 +169,21 @@ def minimize(
             do_stitch=do_stitch,
         )
 
-        result = self._internal_minimize(**minimizer_kwargs, options=kwargs)
+        # handle non-pyhf ModelConfigs
+        try:
+            par_names = pdf.config.par_names()
+        except AttributeError:
+            par_names = None
+
+        # need to remove parameters that are fixed in the fit
+        if par_names and do_stitch and fixed_vals:
+            for index, _ in fixed_vals:
+                par_names[index] = None
+            par_names = [name for name in par_names if name]
+
+        result = self._internal_minimize(
+            **minimizer_kwargs, options=kwargs, par_names=par_names
+        )
         result = self._internal_postprocess(
             result, stitch_pars, return_uncertainties=return_uncertainties
         )

src/pyhf/optimize/opt_minuit.py

@@ -40,7 +40,13 @@ def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
 
     def _get_minimizer(
-        self, objective_and_grad, init_pars, init_bounds, fixed_vals=None, do_grad=False
+        self,
+        objective_and_grad,
+        init_pars,
+        init_bounds,
+        fixed_vals=None,
+        do_grad=False,
+        par_names=None,
     ):
 
         step_sizes = [(b[1] - b[0]) / float(self.steps) for b in init_bounds]
@@ -60,7 +66,7 @@ def _get_minimizer(
             wrapped_objective = objective_and_grad
             jac = None
 
-        minuit = iminuit.Minuit(wrapped_objective, init_pars, grad=jac)
+        minuit = iminuit.Minuit(wrapped_objective, init_pars, grad=jac, name=par_names)
         minuit.errors = step_sizes
         minuit.limits = init_bounds
         minuit.fixed = fixed_bools

src/pyhf/optimize/opt_scipy.py

@@ -31,7 +31,13 @@ def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
 
     def _get_minimizer(
-        self, objective_and_grad, init_pars, init_bounds, fixed_vals=None, do_grad=False
+        self,
+        objective_and_grad,
+        init_pars,
+        init_bounds,
+        fixed_vals=None,
+        do_grad=False,
+        par_names=None,
     ):
         return scipy.optimize.minimize
 

src/pyhf/pdf.py

@@ -319,6 +319,36 @@ def par_slice(self, name):
         """
         return self.par_map[name]['slice']
 
+    def par_names(self, fstring='{name}[{index}]'):
+        """
+        The names of the parameters in the model including binned-parameter indexing.
+
+        Args:
+            fstring (:obj:`str`): Format string for the parameter names using `name` and `index` variables. Default: '{name}[{index}]'.
+
+        Returns:
+            :obj:`list`: Names of the model parameters.
+
+        Example:
+            >>> import pyhf
+            >>> model = pyhf.simplemodels.uncorrelated_background(
+            ...     signal=[12.0, 11.0], bkg=[50.0, 52.0], bkg_uncertainty=[3.0, 7.0]
+            ... )
+            >>> model.config.par_names()
+            ['mu', 'uncorr_bkguncrt[0]', 'uncorr_bkguncrt[1]']
+            >>> model.config.par_names(fstring='{name}_{index}')
+            ['mu', 'uncorr_bkguncrt_0', 'uncorr_bkguncrt_1']
+        """
+        _names = []
+        for name in self.par_order:
+            _npars = self.param_set(name).n_parameters
+            if _npars == 1:
+                _names.append(name)
+                continue
+
+            _names.extend([fstring.format(name=name, index=i) for i in range(_npars)])
+        return _names
+
     def param_set(self, name):
         """
         The :class:`~pyhf.parameters.paramset` for the model parameter ``name``.

tests/test_optim.py

@@ -573,3 +573,17 @@ def test_bad_solver_options_scipy(mocker):
     model = pyhf.simplemodels.uncorrelated_background([50.0], [100.0], [10.0])
     data = pyhf.tensorlib.astensor([125.0] + model.config.auxdata)
     assert pyhf.infer.mle.fit(data, model).tolist()
+
+
+def test_minuit_param_names(mocker):
+    pyhf.set_backend('numpy', 'minuit')
+    pdf = pyhf.simplemodels.uncorrelated_background([5], [10], [3.5])
+    data = [10] + pdf.config.auxdata
+    _, result = pyhf.infer.mle.fit(data, pdf, return_result_obj=True)
+    assert 'minuit' in result
+    assert result.minuit.parameters == ('mu', 'uncorr_bkguncrt')
+
+    pdf.config.par_names = mocker.Mock(return_value=None)
+    _, result = pyhf.infer.mle.fit(data, pdf, return_result_obj=True)
+    assert 'minuit' in result
+    assert result.minuit.parameters == ('x0', 'x1')

tests/test_simplemodels.py

@@ -13,6 +13,7 @@ def test_correlated_background():
     assert model.config.channels == ["single_channel"]
     assert model.config.samples == ["background", "signal"]
     assert model.config.par_order == ["mu", "correlated_bkg_uncertainty"]
+    assert model.config.par_names() == ['mu', 'correlated_bkg_uncertainty']
     assert model.config.suggested_init() == [1.0, 0.0]
 
 
@@ -23,6 +24,11 @@ def test_uncorrelated_background():
     assert model.config.channels == ["singlechannel"]
     assert model.config.samples == ["background", "signal"]
     assert model.config.par_order == ["mu", "uncorr_bkguncrt"]
+    assert model.config.par_names() == [
+        'mu',
+        'uncorr_bkguncrt[0]',
+        'uncorr_bkguncrt[1]',
+    ]
     assert model.config.suggested_init() == [1.0, 1.0, 1.0]