Updated documentation files driver.rst and datarec.rst

Author: slilonfe5

doc/OnlineDocs/explanation/analysis/parmest/datarec.rst

@@ -50,5 +50,9 @@ The following example returns model values from a Pyomo Expression.
    ...     return expr
 
    >>> pest = parmest.Estimator(exp_list, obj_function=SSE, solver_options=None)
+INFO: Model has expected labels.
+WARNING: DEPRECATED: You're using a deprecated input to the `obj_function` argument by passing a custom function.
+This usage will be removed in a future release. Please update to the new parmest interface using the built-in 'SSE'
+and 'SSE_weighted' objectives. (deprecated in 6.9.3.dev0)
    >>> obj, theta, var_values = pest.theta_est(return_values=['response_function'])
    >>> #print(var_values)

doc/OnlineDocs/explanation/analysis/parmest/driver.rst

@@ -84,6 +84,10 @@ Section.
 
     >>> import pyomo.contrib.parmest.parmest as parmest
     >>> pest = parmest.Estimator(exp_list, obj_function=SSE)
+INFO: Model has expected labels.
+WARNING: DEPRECATED: You're using a deprecated input to the `obj_function` argument by passing a custom function.
+This usage will be removed in a future release. Please update to the new parmest interface using the built-in 'SSE'
+and 'SSE_weighted' objectives. (deprecated in 6.9.3.dev0)
 
 Optionally, solver options can be supplied, e.g.,
 
@@ -92,6 +96,10 @@ Optionally, solver options can be supplied, e.g.,
 
     >>> solver_options = {"max_iter": 6000}
     >>> pest = parmest.Estimator(exp_list, obj_function=SSE, solver_options=solver_options)
+INFO: Model has expected labels.
+WARNING: DEPRECATED: You're using a deprecated input to the `obj_function` argument by passing a custom function.
+This usage will be removed in a future release. Please update to the new parmest interface using the built-in 'SSE'
+and 'SSE_weighted' objectives. (deprecated in 6.9.3.dev0)
 
 
 List of experiment objects

pyomo/contrib/parmest/parmest.py

@@ -1662,18 +1662,22 @@ def cov_est(
         """
         Covariance matrix calculation using all scenarios in the data
 
-        Argument:
-            method: string ``method`` object specified by the user
-                    options - 'finite_difference', 'reduced_hessian',
-                    and 'automatic_differentiation_kaug'
-            solver: string ``solver`` object specified by the user, e.g., 'ipopt'
-            cov_n: integer, number of datapoints specified by the user which is used
-                   in the objective function
-            step: float used for relative perturbation of the parameters, e.g.,
-                  step=0.02 is a 2% perturbation
+        Parameters
+        ----------
+        method: string, optional
+            Options - 'finite_difference', 'reduced_hessian',
+            and 'automatic_differentiation_kaug'
+        solver: string, optional
+            E.g., 'ipopt'
+        cov_n: integer, required
+            Number of datapoints used in the covariance calculation
+        step: float, optional
+            The value used for relative perturbation of the parameters, e.g.,
+            step=0.02 is a 2% perturbation
 
-        Returns:
-            cov: pd.DataFrame, covariance matrix of the estimated parameters
+        Returns
+        -------
+        cov: pd.DataFrame, covariance matrix of the estimated parameters
         """
         # check if the solver input is a string
         if not isinstance(solver, str):
@@ -1687,7 +1691,7 @@ def cov_est(
 
         # check if the user-supplied number of datapoints is an integer
         if not isinstance(cov_n, int):
-            raise TypeError("Expected an integer for 'cov_n' argument.")
+            raise TypeError("Expected an integer for the 'cov_n' argument.")
 
         # number of unknown parameters
         num_unknowns = max(