feat: Add hypotest kwargs to pyhf.infer.intervals.upperlimit (#1613)

* Add hypotest_kwargs to pyhf.infer.intervals.upperlimit to allow passing in configuration options to the underlying hypotest call
* Add test for hypotest_kwargs use
* Add Aryan Roy to contributor list

docs/contributors.rst

@@ -25,3 +25,4 @@ Contributors include:
 - Saransh Chopra
 - Sviatoslav Sydorenko
 - Mason Proffitt
+- Aryan Roy

src/pyhf/infer/intervals.py

@@ -15,7 +15,7 @@ def _interp(x, xp, fp):
     return tb.astensor(np.interp(x, xp.tolist(), fp.tolist()))
 
 
-def upperlimit(data, model, scan, level=0.05, return_results=False):
+def upperlimit(data, model, scan, level=0.05, return_results=False, **hypotest_kwargs):
     """
     Calculate an upper limit interval ``(0, poi_up)`` for a single
     Parameter of Interest (POI) using a fixed scan through POI-space.
@@ -44,6 +44,8 @@ def upperlimit(data, model, scan, level=0.05, return_results=False):
         scan (:obj:`iterable`): Iterable of POI values.
         level (:obj:`float`): The threshold value to evaluate the interpolated results at.
         return_results (:obj:`bool`): Whether to return the per-point results.
+        hypotest_kwargs (:obj:`string`): Kwargs for the calls to
+         :class:`~pyhf.infer.hypotest` to configure the fits.
 
     Returns:
         Tuple of Tensors:
@@ -56,7 +58,7 @@ def upperlimit(data, model, scan, level=0.05, return_results=False):
     """
     tb, _ = get_backend()
     results = [
-        hypotest(mu, data, model, test_stat="qtilde", return_expected_set=True)
+        hypotest(mu, data, model, return_expected_set=True, **hypotest_kwargs)
         for mu in scan
     ]
     obs = tb.astensor([[r[0]] for r in results])

tests/test_infer.py

@@ -34,6 +34,22 @@ def test_upperlimit(tmpdir, hypotest_args):
     )
 
 
+def test_upperlimit_with_kwargs(tmpdir, hypotest_args):
+    """
+    Check that the default return structure of pyhf.infer.hypotest is as expected
+    """
+    _, data, model = hypotest_args
+    results = pyhf.infer.intervals.upperlimit(
+        data, model, scan=np.linspace(0, 5, 11), test_stat="qtilde"
+    )
+    assert len(results) == 2
+    observed_limit, expected_limits = results
+    assert observed_limit == pytest.approx(1.0262704738584554)
+    assert expected_limits == pytest.approx(
+        [0.65765653, 0.87999725, 1.12453992, 1.50243428, 2.09232927]
+    )
+
+
 def test_mle_fit_default(tmpdir, hypotest_args):
     """
     Check that the default return structure of pyhf.infer.mle.fit is as expected