use pyhf API for parameter labels, fix MINOS via pyhf (parameter name…

…s now passed through)

src/cabinetry/fit/__init__.py

@@ -85,15 +85,7 @@ def _fit_model_pyhf(
     fit_results = FitResults(bestfit, uncertainty, labels, corr_mat, best_twice_nll)
 
     if minos is not None:
-        parameters_translated = []
-        for minos_par in minos:
-            par_index = model_utils._parameter_index(minos_par, labels)
-            if par_index != -1:
-                # pyhf does not hand over parameter names, all parameters are known as
-                # x0, x1, etc.
-                parameters_translated.append(f"x{par_index}")
-
-        _run_minos(result_obj.minuit, parameters_translated, labels)
+        _run_minos(result_obj.minuit, minos, labels)
 
     return fit_results
 
@@ -243,13 +235,11 @@ def _run_minos(
             knows parameters)
     """
     for par_name in minos:
-        # get index of current parameter in labels (to translate its name if iminuit
-        # did not receive the parameter labels)
-        par_index = model_utils._parameter_index(par_name, minuit_obj.parameters)
-        if par_index == -1:
-            # parameter not found, skip calculation (can only happen with custom fit)
+        if par_name not in minuit_obj.parameters:
+            # parameter not found, skip calculation
+            log.warning(f"parameter {par_name} not found in model")
             continue
-        log.info(f"running MINOS for {labels[par_index]}")
+        log.info(f"running MINOS for {par_name}")
         minuit_obj.minos(par_name)
 
     log.info("MINOS results:")
@@ -503,7 +493,7 @@ def scan(
     # get index of parameter with name par_name
     par_index = model_utils._parameter_index(par_name, labels)
     if par_index == -1:
-        raise ValueError(f"could not find parameter {par_name} in model")
+        raise ValueError(f"parameter {par_name} not found in model")
 
     # run a fit with the parameter not held constant, to find the best-fit point
     fit_results = _fit_model(model, data, custom_fit=custom_fit)

src/cabinetry/model_utils.py

@@ -77,15 +77,7 @@ def parameter_names(model: pyhf.pdf.Model) -> List[str]:
     Returns:
         List[str]: names of fit parameters
     """
-    labels = []
-    for parname in model.config.par_order:
-        for i_par in range(model.config.param_set(parname).n_parameters):
-            labels.append(
-                f"{parname}[bin_{i_par}]"
-                if model.config.param_set(parname).n_parameters > 1
-                else parname
-            )
-    return labels
+    return model.config.par_names()
 
 
 def asimov_data(model: pyhf.Model, with_aux: bool = True) -> List[float]:

tests/fit/test_fit.py

@@ -68,7 +68,7 @@ def test__fit_model_pyhf(mock_minos, example_spec, example_spec_multibin):
     fit_results = fit._fit_model_pyhf(model, data, minos=["Signal strength", "abc"])
     assert mock_minos.call_count == 1
     # first argument to minos call is the Minuit instance
-    assert mock_minos.call_args[0][1] == ["x1"]
+    assert mock_minos.call_args[0][1] == ["Signal strength", "abc"]
     assert mock_minos.call_args[0][2] == ["staterror_Signal-Region", "Signal strength"]
     assert mock_minos.call_args[1] == {}
 
@@ -222,18 +222,6 @@ def func_to_minimize(pars):
     assert "b =  1.5909 -0.7262 +0.4738" in [rec.message for rec in caplog.records]
     caplog.clear()
 
-    # proper labels not known to iminuit
-    m = iminuit.Minuit(
-        func_to_minimize,
-        [1.0, 1.0],
-    )
-    m.errordef = 1
-    m.migrad()
-    fit._run_minos(m, ["x0"], ["a", "b"])
-    assert "running MINOS for a" in [rec.message for rec in caplog.records]
-    assert "a =  1.3827 -0.8713 +0.5715" in [rec.message for rec in caplog.records]
-    caplog.clear()
-
     # unknown parameter, MINOS does not run
     m = iminuit.Minuit(
         func_to_minimize,
@@ -480,7 +468,7 @@ def test_scan(mock_fit, example_spec):
     assert mock_fit.call_args[1]["custom_fit"] is True
 
     # unknown parameter
-    with pytest.raises(ValueError, match="could not find parameter abc in model"):
+    with pytest.raises(ValueError, match="parameter abc not found in model"):
         fit.scan(model, data, "abc")
 
 

tests/test_integration.py

@@ -148,7 +148,7 @@ def test_integration(tmp_path, ntuple_creator, caplog):
     assert np.allclose(fit_results.goodness_of_fit, 0.24679341)
 
     # minos result
-    assert "Signal_norm                    =  1.6895 -0.9580 +0.9052" in [
+    assert "Signal_norm                =  1.6895 -0.9580 +0.9052" in [
         rec.message for rec in caplog.records
     ]
     caplog.clear()