fix tests

Author: bknueven

pyomo/contrib/pynumero/algorithms/solvers/tests/test_cyipopt_interfaces.py

@@ -156,9 +156,9 @@ def test_model1_CyIpoptNLP_scaling(self):
 
         cynlp = CyIpoptNLP(PyomoNLP(m))
         obj_scaling, x_scaling, g_scaling = cynlp.scaling_factors()
-        self.assertTrue(obj_scaling is None)
-        self.assertTrue(x_scaling is None)
-        self.assertTrue(g_scaling is None)
+        self.assertTrue(obj_scaling == 1.0)
+        self.assertTrue((x_scaling == 1.0).all())
+        self.assertTrue((g_scaling == 1.0).all())
 
     def _check_model1(self, nlp, cynlp):
         # test x_init

pyomo/contrib/pynumero/interfaces/tests/test_external_grey_box_model.py

@@ -1827,18 +1827,47 @@ def create_model_two_equalities_two_outputs(self, external_model):
         m.egb.outputs['Pout'].setub(70)
         return m
 
-    def test_scaling_all_missing(self):
+    def test_scaling_all_one(self):
         m = self.create_model_two_equalities_two_outputs(
             ex_models.PressureDropTwoEqualitiesTwoOutputs()
         )
         m.obj = pyo.Objective(expr=(m.egb.outputs['Pout'] - 20) ** 2)
         pyomo_nlp = PyomoGreyBoxNLP(m)
         fs = pyomo_nlp.get_obj_scaling()
+        self.assertEqual(fs, 1.0)
+
+        comparison_x_order = [
+            'egb.inputs[Pin]',
+            'egb.inputs[c]',
+            'egb.inputs[F]',
+            'egb.inputs[P1]',
+            'egb.inputs[P3]',
+            'egb.outputs[P2]',
+            'egb.outputs[Pout]',
+            'hin',
+            'hout',
+        ]
+        x_order = pyomo_nlp.variable_names()
         xs = pyomo_nlp.get_primals_scaling()
+        comparison_xs = np.asarray([1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=np.float64)
+        check_vectors_specific_order(
+            self, xs, x_order, comparison_xs, comparison_x_order
+        )
+
+        comparison_c_order = [
+            'egb.pdrop1',
+            'egb.pdrop3',
+            'egb.P2_con',
+            'egb.Pout_con',
+            'incon',
+            'outcon',
+        ]
+        c_order = pyomo_nlp.constraint_names()
         cs = pyomo_nlp.get_constraints_scaling()
-        self.assertIsNone(fs)
-        self.assertIsNone(xs)
-        self.assertIsNone(cs)
+        comparison_cs = np.asarray([1, 1, 1, 1, 1, 1], dtype=np.float64)
+        check_vectors_specific_order(
+            self, cs, c_order, comparison_cs, comparison_c_order
+        )
 
     def test_scaling_pyomo_model_only(self):
         m = self.create_model_two_equalities_two_outputs(

pyomo/contrib/pynumero/interfaces/tests/test_pyomo_grey_box_nlp.py

@@ -2248,18 +2248,49 @@ def create_model_two_equalities_two_outputs(self, external_model):
         m.egb.outputs['Pout'].setub(70)
         return m
 
-    def test_scaling_all_missing(self):
+    def test_scaling_all_one(self):
         m = self.create_model_two_equalities_two_outputs(
             ex_models.PressureDropTwoEqualitiesTwoOutputs()
         )
         m.obj = pyo.Objective(expr=(m.egb.outputs['Pout'] - 20) ** 2)
         pyomo_nlp = PyomoNLPWithGreyBoxBlocks(m)
+
+        comparison_x_order = [
+            'egb.inputs[Pin]',
+            'egb.inputs[c]',
+            'egb.inputs[F]',
+            'egb.inputs[P1]',
+            'egb.inputs[P3]',
+            'egb.outputs[P2]',
+            'egb.outputs[Pout]',
+            'hin',
+            'hout',
+        ]
+        x_order = pyomo_nlp.primals_names()
+        comparison_c_order = [
+            'egb.pdrop1',
+            'egb.pdrop3',
+            'egb.output_constraints[P2]',
+            'egb.output_constraints[Pout]',
+            'incon',
+            'outcon',
+        ]
+        c_order = pyomo_nlp.constraint_names()
+
         fs = pyomo_nlp.get_obj_scaling()
+        self.assertEqual(fs, 1.0)
+
         xs = pyomo_nlp.get_primals_scaling()
+        comparison_xs = np.asarray([1, 1, 1, 1, 1, 1, 1, 1, 1], dtype=np.float64)
+        check_vectors_specific_order(
+            self, xs, x_order, comparison_xs, comparison_x_order
+        )
+
         cs = pyomo_nlp.get_constraints_scaling()
-        self.assertIsNone(fs)
-        self.assertIsNone(xs)
-        self.assertIsNone(cs)
+        comparison_cs = np.asarray([1, 1, 1, 1, 1, 1], dtype=np.float64)
+        check_vectors_specific_order(
+            self, cs, c_order, comparison_cs, comparison_c_order
+        )
 
     def test_scaling_pyomo_model_only(self):
         m = self.create_model_two_equalities_two_outputs(