Adding regularization objective option to parmest

pyomo/contrib/parmest/examples/rooney_biegler/regularization_example.py

@@ -0,0 +1,77 @@
+# ____________________________________________________________________________________
+#
+# Pyomo: Python Optimization Modeling Objects
+# Copyright (c) 2008-2026 National Technology and Engineering Solutions of Sandia, LLC
+# Under the terms of Contract DE-NA0003525 with National Technology and Engineering
+# Solutions of Sandia, LLC, the U.S. Government retains certain rights in this
+# software.  This software is distributed under the 3-clause BSD License.
+# ____________________________________________________________________________________
+
+from pyomo.common.dependencies import pandas as pd
+import pyomo.contrib.parmest.parmest as parmest
+from pyomo.contrib.parmest.examples.rooney_biegler.rooney_biegler import (
+    RooneyBieglerExperiment,
+)
+
+
+def main():
+
+    # Rooney & Biegler Reference Values
+    # a = 19.14, b = 0.53
+    theta_ref = pd.Series({'asymptote': 20.0, 'rate_constant': 0.8})
+
+    # Create a 'Stiff' Prior for 'asymptote' but leave 'rate_constant' flexible
+    prior_FIM = pd.DataFrame(
+        [[1000.0, 0.0], [0.0, 0.0]],
+        index=['asymptote', 'rate_constant'],
+        columns=['asymptote', 'rate_constant'],
+    )
+
+    # Data
+    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 an experiment list
+    exp_list = []
+    for i in range(data.shape[0]):
+        exp_list.append(RooneyBieglerExperiment(data.loc[i, :]))
+
+    # Create an instance of the parmest estimator
+    pest = parmest.Estimator(
+        exp_list,
+        obj_function="SSE",
+        regularization='L2',
+        prior_FIM=prior_FIM,
+        theta_ref=theta_ref,
+        regularization_weight=None,
+    )
+
+    # Parameter estimation and covariance
+    obj, theta = pest.theta_est()
+    cov = pest.cov_est()
+
+    if parmest.graphics.seaborn_available:
+        parmest.graphics.pairwise_plot(
+            (theta, cov, 100),
+            theta_star=theta,
+            alpha=0.8,
+            distributions=['MVN'],
+            title='Theta estimates within 80% confidence region',
+        )
+
+    # Assert statements compare parameter estimation (theta) to an expected value
+    # relative_error = abs(theta['asymptote'] - 19.1426) / 19.1426
+    # assert relative_error < 0.01
+    # relative_error = abs(theta['rate_constant'] - 0.5311) / 0.5311
+    # assert relative_error < 0.01
+
+    return obj, theta, cov
+
+
+if __name__ == "__main__":
+    obj, theta, cov = main()
+    print("Estimated parameters (theta):", theta)
+    print("Objective function value at theta:", obj)
+    print("Covariance of parameter estimates:", cov)