Implemented Bethany's feedback in test_parmest.py

Author: slilonfe5

pyomo/contrib/parmest/tests/test_parmest.py

@@ -34,258 +34,6 @@
 testdir = this_file_dir()
 
 
-@unittest.skipIf(
-    not parmest.parmest_available,
-    "Cannot test parmest: required dependencies are missing",
-)
-@unittest.skipIf(not ipopt_available, "The 'ipopt' command is not available")
-class TestExceptionRooneyBiegler(unittest.TestCase):
-
-    def setUp(self):
-        self.data = pd.DataFrame(
-            data=[[1, 8.3], [2, 10.3], [3, 19.0], [4, 16.0], [5, 15.6], [7, 19.8]],
-            columns=["hour", "y"],
-        )
-
-        # create the Rooney-Biegler model
-        def rooney_biegler_model():
-            """
-            Formulates the Pyomo model of the Rooney-Biegler example
-
-            Returns:
-                m: Pyomo model
-            """
-            m = pyo.ConcreteModel()
-
-            m.asymptote = pyo.Var(within=pyo.NonNegativeReals, initialize=10)
-            m.rate_constant = pyo.Var(within=pyo.NonNegativeReals, initialize=0.2)
-
-            m.hour = pyo.Var(within=pyo.PositiveReals, initialize=0.1)
-            m.y = pyo.Var(within=pyo.NonNegativeReals)
-
-            @m.Constraint()
-            def response_rule(m):
-                return m.y == m.asymptote * (1 - pyo.exp(-m.rate_constant * m.hour))
-
-            return m
-
-        # create the Experiment class
-        class RooneyBieglerExperiment(Experiment):
-            def __init__(self, experiment_number, hour, y):
-                self.y = y
-                self.hour = hour
-                self.experiment_number = experiment_number
-                self.model = None
-
-            def get_labeled_model(self):
-                self.create_model()
-                self.finalize_model()
-                self.label_model()
-
-                return self.model
-
-            def create_model(self):
-                m = self.model = rooney_biegler_model()
-
-                return m
-
-            def finalize_model(self):
-                m = self.model
-
-                # fix the input variable
-                m.hour.fix(self.hour)
-
-                return m
-
-            def label_model(self):
-                m = self.model
-
-                # add unknown parameters
-                m.unknown_parameters = pyo.Suffix(direction=pyo.Suffix.LOCAL)
-                m.unknown_parameters.update(
-                    (k, pyo.value(k)) for k in [m.asymptote, m.rate_constant]
-                )
-
-                return m
-
-        # extract the input and output variables
-        hour_data = self.data["hour"]
-        y_data = self.data["y"]
-
-        # create the experiments list
-        rooney_biegler_exp_list = []
-        for i in range(self.data.shape[0]):
-            rooney_biegler_exp_list.append(
-                RooneyBieglerExperiment(i, hour_data[i], y_data[i])
-            )
-
-        self.exp_list = rooney_biegler_exp_list
-
-    def test_parmest_exception(self):
-        """
-        Test the exception raised by parmest as a result of not defining
-        the "experiment_outputs" attribute
-        """
-        with self.assertRaises(RuntimeError) as context:
-            parmest.Estimator(self.exp_list, obj_function="SSE", tee=True)
-
-        self.assertIn("experiment_outputs", str(context.exception))
-
-
-@unittest.skipIf(
-    not parmest.parmest_available,
-    "Cannot test parmest: required dependencies are missing",
-)
-@unittest.skipIf(not ipopt_available, "The 'ipopt' solver is not available")
-class TestExceptionReactorDesign_DAE(unittest.TestCase):
-    # Based on a reactor example in `Chemical Reactor Analysis and Design Fundamentals`,
-    # https://sites.engineering.ucsb.edu/~jbraw/chemreacfun/
-    # https://sites.engineering.ucsb.edu/~jbraw/chemreacfun/fig-html/appendix/fig-A-10.html
-
-    def setUp(self):
-        def ABC_model(data):
-
-            if isinstance(data, pd.DataFrame):
-                meas_t = data.index  # time index
-            else:  # dictionary
-                meas_t = list(data["ca"].keys())  # nested dictionary
-
-            ca0 = 1.0
-            cb0 = 0.0
-            cc0 = 0.0
-
-            m = pyo.ConcreteModel()
-
-            m.k1 = pyo.Var(initialize=0.5, bounds=(1e-4, 10))
-            m.k2 = pyo.Var(initialize=3.0, bounds=(1e-4, 10))
-
-            m.time = dae.ContinuousSet(bounds=(0.0, 5.0), initialize=meas_t)
-
-            # initialization and bounds
-            m.ca = pyo.Var(m.time, initialize=ca0, bounds=(-1e-3, ca0 + 1e-3))
-            m.cb = pyo.Var(m.time, initialize=cb0, bounds=(-1e-3, ca0 + 1e-3))
-            m.cc = pyo.Var(m.time, initialize=cc0, bounds=(-1e-3, ca0 + 1e-3))
-
-            m.dca = dae.DerivativeVar(m.ca, wrt=m.time)
-            m.dcb = dae.DerivativeVar(m.cb, wrt=m.time)
-            m.dcc = dae.DerivativeVar(m.cc, wrt=m.time)
-
-            def _dcarate(m, t):
-                if t == 0:
-                    return pyo.Constraint.Skip
-                else:
-                    return m.dca[t] == -m.k1 * m.ca[t]
-
-            m.dcarate = pyo.Constraint(m.time, rule=_dcarate)
-
-            def _dcbrate(m, t):
-                if t == 0:
-                    return pyo.Constraint.Skip
-                else:
-                    return m.dcb[t] == m.k1 * m.ca[t] - m.k2 * m.cb[t]
-
-            m.dcbrate = pyo.Constraint(m.time, rule=_dcbrate)
-
-            def _dccrate(m, t):
-                if t == 0:
-                    return pyo.Constraint.Skip
-                else:
-                    return m.dcc[t] == m.k2 * m.cb[t]
-
-            m.dccrate = pyo.Constraint(m.time, rule=_dccrate)
-
-            return m
-
-        class ReactorDesignExperimentDAE(Experiment):
-
-            def __init__(self, data):
-
-                self.data = data
-                self.model = None
-
-            def create_model(self):
-                self.model = ABC_model(self.data)
-
-            def label_model(self):
-
-                m = self.model
-
-                if isinstance(self.data, pd.DataFrame):
-                    meas_time_points = self.data.index
-                else:
-                    meas_time_points = list(self.data["ca"].keys())
-
-                m.experiment_outputs = pyo.Suffix(direction=pyo.Suffix.LOCAL)
-                m.experiment_outputs.update(
-                    (m.ca[t], self.data["ca"][t]) for t in meas_time_points
-                )
-                m.experiment_outputs.update(
-                    (m.cb[t], self.data["cb"][t]) for t in meas_time_points
-                )
-                m.experiment_outputs.update(
-                    (m.cc[t], self.data["cc"][t]) for t in meas_time_points
-                )
-
-            def get_labeled_model(self):
-                self.create_model()
-                self.label_model()
-
-                return self.model
-
-        # This example tests data formatted in 3 ways
-        # Each format holds 1 scenario
-        # 1. dataframe with time index
-        # 2. nested dictionary {ca: {t, val pairs}, ... }
-        data = [
-            [0.000, 0.957, -0.031, -0.015],
-            [0.263, 0.557, 0.330, 0.044],
-            [0.526, 0.342, 0.512, 0.156],
-            [0.789, 0.224, 0.499, 0.310],
-            [1.053, 0.123, 0.428, 0.454],
-            [1.316, 0.079, 0.396, 0.556],
-            [1.579, 0.035, 0.303, 0.651],
-            [1.842, 0.029, 0.287, 0.658],
-            [2.105, 0.025, 0.221, 0.750],
-            [2.368, 0.017, 0.148, 0.854],
-            [2.632, -0.002, 0.182, 0.845],
-            [2.895, 0.009, 0.116, 0.893],
-            [3.158, -0.023, 0.079, 0.942],
-            [3.421, 0.006, 0.078, 0.899],
-            [3.684, 0.016, 0.059, 0.942],
-            [3.947, 0.014, 0.036, 0.991],
-            [4.211, -0.009, 0.014, 0.988],
-            [4.474, -0.030, 0.036, 0.941],
-            [4.737, 0.004, 0.036, 0.971],
-            [5.000, -0.024, 0.028, 0.985],
-        ]
-        data = pd.DataFrame(data, columns=["t", "ca", "cb", "cc"])
-        data_df = data.set_index("t")
-        data_dict = {
-            "ca": {k: v for (k, v) in zip(data.t, data.ca)},
-            "cb": {k: v for (k, v) in zip(data.t, data.cb)},
-            "cc": {k: v for (k, v) in zip(data.t, data.cc)},
-        }
-
-        # Create an experiment list
-        self.exp_list_df = [ReactorDesignExperimentDAE(data_df)]
-        self.exp_list_dict = [ReactorDesignExperimentDAE(data_dict)]
-
-    def test_parmest_exception(self):
-        """
-        Test the exception raised by parmest as a result of not defining
-        the "unknown_parameters" attribute
-        """
-        with self.assertRaises(RuntimeError) as context:
-            parmest.Estimator(self.exp_list_df, obj_function="SSE")
-
-        self.assertIn("unknown_parameters", str(context.exception))
-
-        with self.assertRaises(RuntimeError) as context:
-            parmest.Estimator(self.exp_list_dict, obj_function="SSE")
-
-        self.assertIn("unknown_parameters", str(context.exception))
-
-
 @unittest.skipIf(
     not parmest.parmest_available,
     "Cannot test parmest: required dependencies are missing",
@@ -327,6 +75,39 @@ def SSE(model):
             exp_list, obj_function=SSE, solver_options=solver_options, tee=True
         )
 
+    def test_parmest_exception(self):
+        """
+        Test the exception raised by parmest when the "experiment_outputs"
+        attribute is not defined in the model
+        """
+        from pyomo.contrib.parmest.examples.rooney_biegler.rooney_biegler import (
+            RooneyBieglerExperiment,
+        )
+
+        # create an instance of the RooneyBieglerExperiment class
+        # without the "experiment_outputs" attribute
+        class RooneyBieglerExperimentException(RooneyBieglerExperiment):
+            def label_model(self):
+                m = self.model
+
+                # add the unknown parameters
+                m.unknown_parameters = pyo.Suffix(direction=pyo.Suffix.LOCAL)
+                m.unknown_parameters.update(
+                    (k, pyo.ComponentUID(k)) for k in [m.asymptote, m.rate_constant]
+                )
+
+        # create an experiment list
+        exp_list = []
+        for i in range(self.data.shape[0]):
+            exp_list.append(RooneyBieglerExperimentException(self.data.loc[i, :]))
+
+        # check the exception raised by parmest due to not defining
+        # the "experiment_outputs"
+        with self.assertRaises(RuntimeError) as context:
+            parmest.Estimator(exp_list, obj_function="SSE", tee=True)
+
+        self.assertIn("experiment_outputs", str(context.exception))
+
     def test_theta_est(self):
         objval, thetavals = self.pest.theta_est()
 
@@ -1137,6 +918,51 @@ def get_labeled_model(self):
         self.m_df = ABC_model(data_df)
         self.m_dict = ABC_model(data_dict)
 
+        # create an instance of the ReactorDesignExperimentDAE class
+        # without the "unknown_parameters" attribute
+        class ReactorDesignExperimentException(ReactorDesignExperimentDAE):
+            def label_model(self):
+
+                m = self.model
+
+                if isinstance(self.data, pd.DataFrame):
+                    meas_time_points = self.data.index
+                else:
+                    meas_time_points = list(self.data["ca"].keys())
+
+                m.experiment_outputs = pyo.Suffix(direction=pyo.Suffix.LOCAL)
+                m.experiment_outputs.update(
+                    (m.ca[t], self.data["ca"][t]) for t in meas_time_points
+                )
+                m.experiment_outputs.update(
+                    (m.cb[t], self.data["cb"][t]) for t in meas_time_points
+                )
+                m.experiment_outputs.update(
+                    (m.cc[t], self.data["cc"][t]) for t in meas_time_points
+                )
+
+        # create an experiment list without the "unknown_parameters" attribute
+        exp_list_df_no_params = [ReactorDesignExperimentException(data_df)]
+        exp_list_dict_no_params = [ReactorDesignExperimentException(data_dict)]
+
+        self.exp_list_df_no_params = exp_list_df_no_params
+        self.exp_list_dict_no_params = exp_list_dict_no_params
+
+    def test_parmest_exception(self):
+        """
+        Test the exception raised by parmest when the "unknown_parameters"
+        attribute is not defined in the model
+        """
+        with self.assertRaises(RuntimeError) as context:
+            parmest.Estimator(self.exp_list_df_no_params, obj_function="SSE")
+
+        self.assertIn("unknown_parameters", str(context.exception))
+
+        with self.assertRaises(RuntimeError) as context:
+            parmest.Estimator(self.exp_list_dict_no_params, obj_function="SSE")
+
+        self.assertIn("unknown_parameters", str(context.exception))
+
     def test_dataformats(self):
         obj1, theta1 = self.pest_df.theta_est()
         obj2, theta2 = self.pest_dict.theta_est()