Rename chi_sq_conf_lvl -> gaussian_conf_lvl

pyomo/contrib/pyros/tests/test_uncertainty_sets.py

@@ -1868,8 +1868,8 @@ def test_normal_construction_and_update(self):
             # evaluate chisquare CDF for 2 degrees of freedom
             # using simplified formula
             1 - np.exp(-scale / 2),
-            eset.chi_sq_conf_lvl,
-            err_msg="EllipsoidalSet chi-squared confidence level not as expected",
+            eset.gaussian_conf_lvl,
+            err_msg="EllipsoidalSet Gaussian confidence level not as expected",
         )
 
         # check attributes update
@@ -1898,11 +1898,11 @@ def test_normal_construction_and_update(self):
             # evaluate chisquare CDF for 2 degrees of freedom
             # using simplified formula
             1 - np.exp(-new_scale / 2),
-            eset.chi_sq_conf_lvl,
-            err_msg="EllipsoidalSet chi-square confidence level update not as expected",
+            eset.gaussian_conf_lvl,
+            err_msg="EllipsoidalSet Gaussian confidence level update not as expected",
         )
 
-    def test_normal_construction_and_update_chi_sq_conf_lvl(self):
+    def test_normal_construction_and_update_gaussian_conf_lvl(self):
         """
         Test EllipsoidalSet constructor and setter
         work normally when arguments are appropriate.
@@ -1912,19 +1912,19 @@ def test_normal_construction_and_update_chi_sq_conf_lvl(self):
             center=[0, 0, 0],
             shape_matrix=np.eye(3),
             scale=None,
-            chi_sq_conf_lvl=init_conf_lvl,
+            gaussian_conf_lvl=init_conf_lvl,
         )
 
-        self.assertEqual(eset.chi_sq_conf_lvl, init_conf_lvl)
+        self.assertEqual(eset.gaussian_conf_lvl, init_conf_lvl)
         np.testing.assert_allclose(
             sp.stats.chi2.isf(q=1 - init_conf_lvl, df=eset.dim),
             eset.scale,
             err_msg="EllipsoidalSet scale not as expected",
         )
 
         new_conf_lvl = 0.99
-        eset.chi_sq_conf_lvl = new_conf_lvl
-        self.assertEqual(eset.chi_sq_conf_lvl, new_conf_lvl)
+        eset.gaussian_conf_lvl = new_conf_lvl
+        self.assertEqual(eset.gaussian_conf_lvl, new_conf_lvl)
         np.testing.assert_allclose(
             sp.stats.chi2.isf(q=1 - new_conf_lvl, df=eset.dim),
             eset.scale,
@@ -1970,9 +1970,9 @@ def test_error_on_neg_scale(self):
         with self.assertRaisesRegex(ValueError, exc_str):
             eset.scale = neg_scale
 
-    def test_error_invalid_chi_sq_conf_lvl(self):
+    def test_error_invalid_gaussian_conf_lvl(self):
         """
-        Test error when attempting to initialize with chi-squared
+        Test error when attempting to initialize with Gaussian
         confidence level outside range.
         """
         center = [0, 0]
@@ -1987,30 +1987,30 @@ def test_error_invalid_chi_sq_conf_lvl(self):
                 center=center,
                 shape_matrix=shape_matrix,
                 scale=None,
-                chi_sq_conf_lvl=invalid_conf_lvl,
+                gaussian_conf_lvl=invalid_conf_lvl,
             )
 
         # error on updating valid ellipsoid
-        eset = EllipsoidalSet(center, shape_matrix, scale=None, chi_sq_conf_lvl=0.95)
+        eset = EllipsoidalSet(center, shape_matrix, scale=None, gaussian_conf_lvl=0.95)
         with self.assertRaisesRegex(ValueError, exc_str):
-            eset.chi_sq_conf_lvl = invalid_conf_lvl
+            eset.gaussian_conf_lvl = invalid_conf_lvl
 
         # negative confidence level
-        eset = EllipsoidalSet(center, shape_matrix, scale=None, chi_sq_conf_lvl=0.95)
+        eset = EllipsoidalSet(center, shape_matrix, scale=None, gaussian_conf_lvl=0.95)
         with self.assertRaisesRegex(ValueError, exc_str):
-            eset.chi_sq_conf_lvl = -0.1
+            eset.gaussian_conf_lvl = -0.1
 
-    def test_error_scale_chi_sq_conf_lvl_construction(self):
+    def test_error_scale_gaussian_conf_lvl_construction(self):
         """
         Test exception raised if neither or both of
-        `scale` and `chi_sq_conf_lvl` are None.
+        `scale` and `gaussian_conf_lvl` are None.
         """
-        exc_str = r"Exactly one of `scale` and `chi_sq_conf_lvl` should be None"
+        exc_str = r"Exactly one of `scale` and `gaussian_conf_lvl` should be None"
         with self.assertRaisesRegex(ValueError, exc_str):
-            EllipsoidalSet([0], [[1]], scale=None, chi_sq_conf_lvl=None)
+            EllipsoidalSet([0], [[1]], scale=None, gaussian_conf_lvl=None)
 
         with self.assertRaisesRegex(ValueError, exc_str):
-            EllipsoidalSet([0], [[1]], scale=1, chi_sq_conf_lvl=0.95)
+            EllipsoidalSet([0], [[1]], scale=1, gaussian_conf_lvl=0.95)
 
     def test_error_on_shape_matrix_with_wrong_size(self):
         """

pyomo/contrib/pyros/uncertainty_sets.py

@@ -2380,11 +2380,11 @@ class EllipsoidalSet(UncertaintySet):
         Square of the factor by which to scale the semi-axes
         of the ellipsoid (i.e. the eigenvectors of the shape
         matrix). The default is `1`.
-    chi_sq_conf_lvl : numeric type or None, optional
-        (Fractional) chi-squared confidence level of the multivariate
+    gaussian_conf_lvl : numeric type or None, optional
+        (Fractional) confidence level of the multivariate
         normal distribution with mean `center` and covariance
         matrix `shape_matrix`.
-        Exactly one of `scale` and `chi_sq_conf_lvl` should be
+        Exactly one of `scale` and `gaussian_conf_lvl` should be
         None; otherwise, an exception is raised.
 
     Examples
@@ -2428,7 +2428,7 @@ class EllipsoidalSet(UncertaintySet):
     ...     center=np.zeros(4),
     ...     shape_matrix=np.diag(range(1, 5)),
     ...     scale=None,
-    ...     chi_sq_conf_lvl=0.95,
+    ...     gaussian_conf_lvl=0.95,
     ... )
     >>> conf_ellipsoid.center
     array([0, 0, 0, 0])
@@ -2439,24 +2439,24 @@ class EllipsoidalSet(UncertaintySet):
            [0, 0, 0. 4]])
     >>> conf_ellipsoid.scale
     ...9.4877...
-    >>> conf_ellipsoid.chi_sq_conf_lvl
+    >>> conf_ellipsoid.gaussian_conf_lvl
     0.95
 
     """
 
-    def __init__(self, center, shape_matrix, scale=1, chi_sq_conf_lvl=None):
+    def __init__(self, center, shape_matrix, scale=1, gaussian_conf_lvl=None):
         """Initialize self (see class docstring)."""
         self.center = center
         self.shape_matrix = shape_matrix
 
-        if scale is not None and chi_sq_conf_lvl is None:
+        if scale is not None and gaussian_conf_lvl is None:
             self.scale = scale
-        elif scale is None and chi_sq_conf_lvl is not None:
-            self.chi_sq_conf_lvl = chi_sq_conf_lvl
+        elif scale is None and gaussian_conf_lvl is not None:
+            self.gaussian_conf_lvl = gaussian_conf_lvl
         else:
             raise ValueError(
-                "Exactly one of `scale` and `chi_sq_conf_lvl` should be "
-                f"None (got {scale=}, {chi_sq_conf_lvl=})"
+                "Exactly one of `scale` and `gaussian_conf_lvl` should be "
+                f"None (got {scale=}, {gaussian_conf_lvl=})"
             )
 
     @property
@@ -2599,24 +2599,24 @@ def scale(self, val):
             )
 
         self._scale = val
-        self._chi_sq_conf_lvl = sp.stats.chi2.cdf(x=val, df=self.dim)
+        self._gaussian_conf_lvl = sp.stats.chi2.cdf(x=val, df=self.dim)
 
     @property
-    def chi_sq_conf_lvl(self):
+    def gaussian_conf_lvl(self):
         """
-        numeric type : (Fractional) chi-squared confidence level of the
-        multivariate normal distribution with mean ``self.origin``
+        numeric type : (Fractional) confidence level of the
+        multivariate Gaussian distribution with mean ``self.origin``
         and covariance ``self.shape_matrix`` for ellipsoidal region
         with square magnification factor ``self.scale``.
         """
-        return self._chi_sq_conf_lvl
+        return self._gaussian_conf_lvl
 
-    @chi_sq_conf_lvl.setter
-    def chi_sq_conf_lvl(self, val):
+    @gaussian_conf_lvl.setter
+    def gaussian_conf_lvl(self, val):
         validate_arg_type(
-            "chi_sq_conf_lvl", val, valid_num_types, "a valid numeric type", False
+            "gaussian_conf_lvl", val, valid_num_types, "a valid numeric type", False
         )
-        self._chi_sq_conf_lvl = val
+        self._gaussian_conf_lvl = val
         self._scale = sp.stats.chi2.isf(q=1 - val, df=self.dim)
         if np.isnan(self._scale) or np.isinf(self._scale):
             raise ValueError(