datarec.rst

Data Reconciliation

The optional argument return_values in :class:`~pyomo.contrib.parmest.parmest.Estimator.theta_est` can be used for data reconciliation or to return model values based on the specified objective.

For data reconciliation, the m.unknown_parameters is empty and the objective function is defined to minimize measurement to model error. Note that the model used for data reconciliation may differ from the model used for parameter estimation.

The functions :class:`~pyomo.contrib.parmest.graphics.grouped_boxplot` or :class:`~pyomo.contrib.parmest.graphics.grouped_violinplot` can be used to visually compare the original and reconciled data.

The following example from the reactor design subdirectory returns reconciled values for experiment outputs (ca, cb, cc, and cd) and then uses those values in parameter estimation (k1, k2, and k3).

.. literalinclude:: ../../../../pyomo/contrib/parmest/examples/reactor_design/datarec_example.py
   :language: python

The following example returns model values from a Pyomo Expression.

>>> import pandas as pd
>>> import pyomo.contrib.parmest.parmest as parmest
>>> from pyomo.contrib.parmest.examples.rooney_biegler.rooney_biegler import RooneyBieglerExperiment

>>> # Generate 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, :]))

>>> # Define objective
>>> def SSE(model):
...     expr = (model.experiment_outputs[model.y]
...             - model.response_function[model.experiment_outputs[model.hour]]
...            ) ** 2
...     return expr

>>> pest = parmest.Estimator(exp_list, obj_function=SSE, solver_options=None)
>>> obj, theta, var_values = pest.theta_est(return_values=['response_function'])
>>> #print(var_values)