From 0ad8a7d43273208269f201f8a333f3f22007f390 Mon Sep 17 00:00:00 2001
From: Giordon Stark <kratsg@gmail.com>
Date: Fri, 4 Sep 2020 11:07:17 -0400
Subject: [PATCH] update fixed_poi_fit() to call fit() essentially

---
 src/pyhf/infer/mle.py | 32 ++++++++------------------------
 1 file changed, 8 insertions(+), 24 deletions(-)

diff --git a/src/pyhf/infer/mle.py b/src/pyhf/infer/mle.py
index 8a8cff69aa..a5c544ae26 100644
--- a/src/pyhf/infer/mle.py
+++ b/src/pyhf/infer/mle.py
@@ -49,7 +49,7 @@ def twice_nll(pars, data, pdf):
 
 def fit(data, pdf, init_pars=None, par_bounds=None, fixed_params=None, **kwargs):
     r"""
-    Run a unconstrained maximum likelihood fit.
+    Run a maximum likelihood fit.
     This is done by minimizing the objective function :func:`~pyhf.infer.mle.twice_nll`
     of the model parameters given the observed data.
     This is used to produce the maximal likelihood :math:`L\left(\hat{\mu}, \hat{\boldsymbol{\theta}}\right)`
@@ -87,6 +87,7 @@ def fit(data, pdf, init_pars=None, par_bounds=None, fixed_params=None, **kwargs)
         pdf (~pyhf.pdf.Model): The statistical model adhering to the schema model.json
         init_pars (`list`): Values to initialize the model parameters at for the fit
         par_bounds (`list` of `list`\s or `tuple`\s): The extrema of values the model parameters are allowed to reach in the fit
+        fixed_params (`list`): Parameters to be held constant in the fit.
         kwargs: Keyword arguments passed through to the optimizer API
 
     Returns:
@@ -155,6 +156,7 @@ def fixed_poi_fit(
         pdf (~pyhf.pdf.Model): The statistical model adhering to the schema model.json
         init_pars (`list`): Values to initialize the model parameters at for the fit
         par_bounds (`list` of `list`\s or `tuple`\s): The extrema of values the model parameters are allowed to reach in the fit
+        fixed_params (`list`): Parameters to be held constant in the fit.
         kwargs: Keyword arguments passed through to the optimizer API
 
     Returns:
@@ -166,28 +168,10 @@ def fixed_poi_fit(
             'No POI is defined. A POI is required to fit with a fixed POI.'
         )
 
-    _, opt = get_backend()
-    init_pars = init_pars or pdf.config.suggested_init()
-    par_bounds = par_bounds or pdf.config.suggested_bounds()
-    fixed_params = fixed_params or pdf.config.suggested_fixed()
+    init_pars = [*(init_pars or pdf.config.suggested_init())]
+    fixed_params = [*(fixed_params or pdf.config.suggested_fixed())]
 
-    # get fixed vals from the model
-    fixed_vals = [
-        (index, init)
-        for index, (init, is_fixed) in enumerate(zip(init_pars, fixed_params))
-        if is_fixed
-    ]
-    # add the fixed POI
-    fixed_vals = fixed_vals + [(pdf.config.poi_index, poi_val)]
-    # de-dupe and use last-appended result for each index
-    fixed_vals = list(dict(fixed_vals).items())
+    init_pars[pdf.config.poi_index] = poi_val
+    fixed_params[pdf.config.poi_index] = True
 
-    return opt.minimize(
-        twice_nll,
-        data,
-        pdf,
-        init_pars,
-        par_bounds,
-        fixed_vals,
-        **kwargs,
-    )
+    return fit(data, pdf, init_pars, par_bounds, fixed_params, **kwargs)