Finishing test clean-up for sensitivity_toolbox

pyomo/contrib/sensitivity_toolbox/examples/parameter.py

@@ -15,7 +15,7 @@
 
 from __future__ import print_function
 from pyomo.environ import ConcreteModel, Param, Var, Objective, Constraint, NonNegativeReals, value
-from pyomo.contrib.sensitivity_toolbox.sens import sipopt
+from pyomo.contrib.sensitivity_toolbox.sens import sensitivity_calculation
 
 def create_model():
     ''' Create a concrete Pyomo model for this example
@@ -52,9 +52,9 @@ def run_example(print_flag=True):
     m.perturbed_eta2 = Param(initialize = 1.0)
 
 
-    m_sipopt = sipopt(m,[m.eta1,m.eta2],
-                        [m.perturbed_eta1,m.perturbed_eta2],
-                        streamSoln=True)
+    m_sipopt = sensitivity_calculation('sipopt',m,[m.eta1,m.eta2],
+                                       [m.perturbed_eta1,m.perturbed_eta2],
+                                       streamSoln=True)
 
 
 

pyomo/contrib/sensitivity_toolbox/examples/parameter_kaug.py

@@ -15,7 +15,7 @@
 
 
 from pyomo.environ import * 
-from pyomo.contrib.sensitivity_toolbox.sens import kaug
+from pyomo.contrib.sensitivity_toolbox.sens import sensitivity_calculation
 
 def create_model():
     ''' Create a concrete Pyomo model for this example
@@ -35,7 +35,7 @@ def create_model():
 
     return m 
 
-def example(print_flag=True):
+def run_example(print_flag=True):
     '''
     Execute the example
     
@@ -52,11 +52,9 @@ def example(print_flag=True):
     m.perturbed_eta2 = Param(initialize = 1.0)
 
 
-    m_kaug_dsdp = kaug(m,[m.eta1,m.eta2],
-                        [m.perturbed_eta1,m.perturbed_eta2],
-                        streamSoln=True)
-
-
+    m_kaug_dsdp = sensitivity_calculation('kaug',m,[m.eta1,m.eta2],
+                                          [m.perturbed_eta1,m.perturbed_eta2],
+                                          streamSoln=True)    
 
     if print_flag:
         print("\nOriginal parameter values:")
@@ -99,4 +97,4 @@ def example(print_flag=True):
     return d
 
 if __name__=='__main__':
-    d = example()
+    d = run_example()

pyomo/contrib/sensitivity_toolbox/sens.py

@@ -19,8 +19,9 @@
 
 _log = logging.getLogger('pyomo.contrib.sensitivity_toolbox')
 
-@deprecated('The sipopt function has been deprecated. Use the sensitivity_calculation() '
-            'function with method="sipopt" to access this functionality.', 
+@deprecated("The sipopt function has been deprecated. Use the sensitivity_calculation() "
+            "function with method='sipopt' to access this functionality.",
+            logger='pyomo.contrib.sensitivity_toolbox',
             version='TBD')
 def sipopt(instance, paramSubList, perturbList,
            cloneModel=True, streamSoln=False, keepfiles=False):    
@@ -29,8 +30,8 @@ def sipopt(instance, paramSubList, perturbList,
 
     return m
 
-@deprecated('The kaug function has been deprecated. Use the sensitivity_calculation() '
-            'function with method="kaug" to access this functionality.', 
+@deprecated("The kaug function has been deprecated. Use the sensitivity_calculation() "
+            "function with method='kaug' to access this functionality.", 
             version='TBD')
 def kaug(instance, paramSubList, perturbList,
          cloneModel=True, streamSoln=False, keepfiles=False, optarg=None):

pyomo/contrib/sensitivity_toolbox/tests/test_sens.py

@@ -24,6 +24,7 @@
 from pyomo.core.expr.current import identify_variables
 from pyomo.contrib.sensitivity_toolbox.sens import sipopt, kaug, sensitivity_calculation
 import pyomo.contrib.sensitivity_toolbox.examples.parameter as param_ex
+import pyomo.contrib.sensitivity_toolbox.examples.parameter_kaug as param_kaug_ex
 import pyomo.contrib.sensitivity_toolbox.examples.feedbackController as fc
 import pyomo.contrib.sensitivity_toolbox.examples.rangeInequality as ri
 import pyomo.contrib.sensitivity_toolbox.examples.HIV_Transmission as hiv
@@ -45,11 +46,12 @@ def test_sipopt_deprecated(self):
         with LoggingIntercept(output, 'pyomo.contrib.sensitivity_toolbox', logging.WARNING):
             sipopt(m,[m.eta1,m.eta1],
                    [m.perturbed_eta1,m.perturbed_eta2],
-                   cloneModel=False)        
-        self.assertIn('DEPRECATED: The sipopt function has been deprecated. Use the '
-                      'sensitivity_calculation() function with method="sipopt"',
+                   cloneModel=False)    
+        self.assertIn("DEPRECATED: The sipopt function has been deprecated. Use the "
+                      "sensitivity_calculation() function with method='sipopt' to access",
                       output.getvalue().replace('\n', ' '))
 
+
     @unittest.skipIf(not opt.available(False), "ipopt_sens is not available")
     def test_sipopt_equivalent(self):
         m1 = param_ex.create_model()
@@ -60,16 +62,16 @@ def test_sipopt_equivalent(self):
         m2.perturbed_eta1 = Param(initialize = 4.0)
         m2.perturbed_eta2 = Param(initialize = 1.0)
 
-        out1 = StringIO()
-        out2 = StringIO()
-        sipopt(m1,[m1.eta1,m1.eta2],
+        m11 = sipopt(m1,[m1.eta1,m1.eta2],
                [m1.perturbed_eta1,m1.perturbed_eta2],
-               cloneModel=False)        
-        sensitivity_calculation('sipopt',m2,[m2.eta1,m2.eta2],
+                    cloneModel=True)        
+        m22 = sensitivity_calculation('sipopt',m2,[m2.eta1,m2.eta2],
                                 [m2.perturbed_eta1,m2.perturbed_eta2],
-                                cloneModel=False)        
-        m1.pprint(ostream=out1)
-        m2.pprint(ostream=out2)
+                                cloneModel=True)        
+        out1 = StringIO()
+        out2 = StringIO()
+        m11._sipopt_data.constList.pprint(ostream=out1)
+        m22._sipopt_data.constList.pprint(ostream=out2)
         self.assertMultiLineEqual(out1.getvalue(), out2.getvalue())
 
     @unittest.skipIf(not opt_kaug.available(False), "k_aug is not available")
@@ -84,8 +86,8 @@ def test_kaug_deprecated(self):
             kaug(m,[m.eta1,m.eta1],
                  [m.perturbed_eta1,m.perturbed_eta2],
                  cloneModel=False)        
-        self.assertIn('DEPRECATED: The kaug function has been deprecated. Use the '
-                      'sensitivity_calculation() function with method="kaug"',
+        self.assertIn("DEPRECATED: The kaug function has been deprecated. Use the "
+                      "sensitivity_calculation() function with method='kaug'",
                       output.getvalue().replace('\n', ' '))
 
     @unittest.skipIf(not opt_kaug.available(False), "k_aug is not available")
@@ -99,16 +101,16 @@ def test_kaug_equivalent(self):
         m2.perturbed_eta1 = Param(initialize = 4.0)
         m2.perturbed_eta2 = Param(initialize = 1.0)
 
+        m11 = kaug(m1,[m1.eta1,m1.eta2],
+                   [m1.perturbed_eta1,m1.perturbed_eta2],
+                   cloneModel=True)        
+        m22 = sensitivity_calculation('kaug',m2,[m2.eta1,m2.eta2],
+                                      [m2.perturbed_eta1,m2.perturbed_eta2],
+                                      cloneModel=True)        
         out1 = StringIO()
         out2 = StringIO()
-        kaug(m1,[m1.eta1,m1.eta2],
-             [m1.perturbed_eta1,m1.perturbed_eta2],
-             cloneModel=False)        
-        sensitivity_calculation('kaug',m2,[m2.eta1,m2.eta2],
-                                [m2.perturbed_eta1,m2.perturbed_eta2],
-                                cloneModel=False)        
-        m1.pprint(ostream=out1)
-        m2.pprint(ostream=out2)
+        m11.pprint(ostream=out1)
+        m22.pprint(ostream=out2)
         self.assertMultiLineEqual(out1.getvalue(), out2.getvalue())
 
 
@@ -133,35 +135,30 @@ def test_bad_arg(self):
 
         # verify ValueError thrown when param and perturb list are different
         # lengths
-        try:
-            Result = sipopt(m,list_one,list_two)
-            self.fail("Expected ValueError: for different length lists")
-        except ValueError:
-            pass
+        with self.assertRaises(ValueError) as context:
+            Result = sensitivity_calculation('sipopt',m,list_one,list_two)
+        self.assertTrue("Length of paramSubList argument does not equal "
+                        "length of perturbList" in str(context.exception))
 
         # verify ValueError thrown when param list has a Var in it
-        try:
-            Result = sipopt(m,list_three,list_two)
-            self.fail("Expected ValueError: variable sent through paramSubList")
-        except ValueError:
-            pass
+        with self.assertRaises(ValueError) as context:
+            Result = sensitivity_calculation('sipopt',m,list_three,list_one)
+        self.assertTrue("paramSubList argument is expecting a list of Params" in str(context.exception))
 
         # verify ValueError thrown when perturb list has Var in it
-        try:
-            Result = sipopt(m,list_one,list_three)
-            self.fail("Expected ValueError: variable sent through perturbList")
-        except ValueError:
-            pass
+        with self.assertRaises(ValueError) as context:
+            Result = sensitivity_calculation('sipopt',m,list_one,list_three)
+        self.assertTrue("perturbList argument is expecting a list of Params" in str(context.exception))
 
         # verify ValueError thrown when param list has an unmutable param
-        try:
-            Result = sipopt(m,list_four,list_one)
-            self.fail("Expected ValueError:" 
-                       "unmutable param sent through paramSubList")
-        except ValueError:
-            pass
-
+        with self.assertRaises(ValueError) as context:
+            Result = sensitivity_calculation('sipopt',m,list_four,list_one)
+        self.assertTrue("parameters within paramSubList must be mutable" in str(context.exception))
 
+        # verify ValueError thrown when an invalid method is specified
+        with self.assertRaises(ValueError) as context:
+            Result = sensitivity_calculation('foo',m,list_four,list_one)
+        self.assertTrue("method should be 'sipopt' or 'kaug'" in str(context.exception))
 
     # test feedbackController Solution when the model gets cloned
     @unittest.skipIf(not scipy_available, "scipy is required for this test")
@@ -174,7 +171,7 @@ def test_clonedModel_soln(self):
         m_orig.perturbed_a = Param(initialize=-0.25)
         m_orig.perturbed_H = Param(initialize=0.55)
 
-        m_sipopt = sipopt(m_orig,[m_orig.a,m_orig.H],
+        m_sipopt = sensitivity_calculation('sipopt',m_orig,[m_orig.a,m_orig.H],
                             [m_orig.perturbed_a,m_orig.perturbed_H],
                             cloneModel=True)        
 
@@ -269,7 +266,7 @@ def test_noClone_soln(self):
         m_orig.perturbed_a = Param(initialize=-0.25)
         m_orig.perturbed_H = Param(initialize=0.55)
 
-        m_sipopt = sipopt(m_orig,[m_orig.a,m_orig.H],
+        m_sipopt = sensitivity_calculation('sipopt',m_orig,[m_orig.a,m_orig.H],
                             [m_orig.perturbed_a,m_orig.perturbed_H],
                             cloneModel=False)
 
@@ -341,8 +338,6 @@ def test_noClone_soln(self):
         self.assertAlmostEqual(value(m_sipopt.J),0.0048956783,8)
 
 
-
-
     # test indexed param mapping to var and perturbed values
     @unittest.skipIf(not scipy_available, "scipy is required for this test")
     @unittest.skipIf(not opt.available(False), "ipopt_sens is not available")
@@ -366,7 +361,7 @@ def test_indexedParamsMapping(self):
 
         m.aaDelta = Param(initialize =0.0001001)
 
-        m_sipopt = sipopt(m, [m.eps,m.qq,m.aa],
+        m_sipopt = sensitivity_calculation('sipopt',m, [m.eps,m.qq,m.aa],
                              [m.epsDelta,m.qqDelta,m.aaDelta])
 
         # param to var data
@@ -400,7 +395,7 @@ def test_constraintSub(self):
         m.pert_a = Param(initialize=0.01)
         m.pert_b = Param(initialize=1.01)
 
-        m_sipopt = sipopt(m,[m.a,m.b], [m.pert_a,m.pert_b])
+        m_sipopt = sensitivity_calculation('sipopt',m,[m.a,m.b], [m.pert_a,m.pert_b])
 
         # verify substitutions in equality constraint
         self.assertTrue(m_sipopt.C_equal.lower.ctype is Param and
@@ -441,8 +436,7 @@ def test_constraintSub(self):
     @unittest.skipIf(not opt.available(False), "ipopt_sens is not available")
     def test_parameter_example(self):
 
-        from pyomo.contrib.sensitivity_toolbox.examples.parameter import run_example
-        d = run_example()
+        d = param_ex.run_example()
 
         d_correct = {'eta1':4.5, 'eta2':1.0, 'x1_init':0.15, 'x2_init':0.15, 'x3_init':0.0,
             'cost_sln':0.5, 'x1_sln':0.5, 'x2_sln':0.5, 'x3_sln':0.0, 'eta1_pert':4.0,
@@ -456,53 +450,7 @@ def test_parameter_example(self):
 
 
     # Test kaug
-    # Perform the same tests as ipopt_sens 
-    # test arguments
-    @unittest.skipIf(not opt_kaug.available(False), "k_aug is not available")
-    @unittest.skipIf(not opt_dotsens.available(False), "dot_sens is not available")
-    def test_kaug_bad_arg_kaug(self):
-        m = ConcreteModel()
-        m.t = ContinuousSet(bounds=(0,1))
-
-        m.a = Param(initialize=1, mutable=True)
-        m.b = Param(initialize=2, mutable=True)
-        m.c = Param(initialize=3, mutable=False)
-
-        m.x = Var(m.t)
-
-        list_one = [m.a,m.b]
-        list_two = [m.a,m.b,m.c]
-        list_three = [m.a, m.x]
-        list_four = [m.a,m.c]
-
-        # verify ValueError thrown when param and perturb list are different
-        # lengths
-        with self.assertRaises(ValueError):
-            Result = kaug(m, list_one, list_two)
-
-        # verify ValueError thrown when param list has a Var in it
-        try:
-            Result = kaug(m,list_three,list_two)
-            self.fail("Expected ValueError: variable sent through paramSubList")
-        except ValueError:
-            pass
-
-        # verify ValueError thrown when perturb list has Var in it
-        try:
-            Result = kaug(m,list_one,list_three)
-            self.fail("Expected ValueError: variable sent through perturbList")
-        except ValueError:
-            pass
-
-        # verify ValueError thrown when param list has an unmutable param
-        try:
-            Result = kaug(m,list_four,list_one)
-            self.fail("Expected ValueError:"
-                       "unmutable param sent through paramSubList")
-        except ValueError:
-            pass
-
-
+    # Perform the same tests as for sipopt
     # test feedbackController Solution when the model gets cloned
     @unittest.skipIf(not scipy_available, "scipy is required for this test")
     @unittest.skipIf(not opt_kaug.available(False), "k_aug is not available")
@@ -514,7 +462,7 @@ def test_kaug_clonedModel_soln_kaug(self):
         m_orig.perturbed_a = Param(initialize=-0.25)
         m_orig.perturbed_H = Param(initialize=0.55)
 
-        m_kaug = kaug(m_orig,[m_orig.a,m_orig.H],
+        m_kaug = sensitivity_calculation('kaug',m_orig,[m_orig.a,m_orig.H],
                                [m_orig.perturbed_a,m_orig.perturbed_H],
                                 cloneModel=True)
 
@@ -609,7 +557,7 @@ def test_noClone_soln_kaug(self):
         m_orig.perturbed_a = Param(initialize=-0.25)
         m_orig.perturbed_H = Param(initialize=0.55)
 
-        m_kaug = kaug(m_orig,[m_orig.a,m_orig.H],
+        m_kaug = sensitivity_calculation('kaug',m_orig,[m_orig.a,m_orig.H],
                              [m_orig.perturbed_a,m_orig.perturbed_H],
                              cloneModel=False)
 
@@ -696,7 +644,7 @@ def test_indexedParamsMapping_kaug(self):
 
         m.aaDelta = Param(initialize =0.0001001)
 
-        m_kaug = kaug(m, [m.eps,m.qq,m.aa],
+        m_kaug = sensitivity_calculation('kaug',m, [m.eps,m.qq,m.aa],
                          [m.epsDelta,m.qqDelta,m.aaDelta])
 
         # param to var data
@@ -732,16 +680,15 @@ def test_constraintSub_kaug(self):
         # m_kaug = kaug(m,[m.a,m.b], [m.pert_a,m.pert_b])
         # verify ValueError thrown when param list has an unmutable param
         with self.assertRaises(Exception) as context:
-            m_kaug = kaug(m,[m.a,m.b], [m.pert_a,m.pert_b])
+            m_kaug = sensitivity_calculation('kaug',m,[m.a,m.b], [m.pert_a,m.pert_b])
         self.assertTrue('kaug does not support inequality constraints.' in str(context.exception))
 
     # Test example `parameter_kaug.py`
     @unittest.skipIf(not opt_kaug.available(False), "k_aug is not available")
     @unittest.skipIf(not opt_dotsens.available(False), "dot_sens is not available")    
     def test_parameter_example_kaug(self):
 
-        from parameter_kaug import example
-        d = example()
+        d = param_kaug_ex.run_example()
 
         d_correct = {'eta1':4.5, 'eta2':1.0, 'x1_init':0.15, 'x2_init':0.15, 'x3_init':0.0,
                      'eta1_pert':4.0, 'eta2_pert':1.0, 'x1_pert':0.3333333,'x2_pert':0.6666667,