Include PyBaMM solution in OptimizationResult

[pipliggins]

Description

Saves the latest pybamm solution object into a Problem class, to add into an OptimisationResult.

Issue reference

Fixes #548

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[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 99.15%. Comparing base (c8b00e6) to head (6adcabd).
Report is 25 commits behind head on develop.

Additional details and impacted files

@@             Coverage Diff             @@
##           develop     #573      +/-   ##
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+ Coverage    99.08%   99.15%   +0.07%     
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  Files           52       56       +4     
  Lines         3605     3913     +308     
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+ Hits          3572     3880     +308     
  Misses          33       33              

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[BradyPlanden]

This is looking great - thanks @pipliggins! To ensure the pybamm solution matches the optimal parameter values, I would consider updating the code to run the forward model with the "x_best" values. The final optimisation position "should" be close to the found optimal, but there are cases where this doesn't occur (e.g. poorly tuned gradient-based methods, or population based optimisers that are exploring the search space in batches).

[pipliggins]

This is looking great - thanks @pipliggins! To ensure the pybamm solution matches the optimal parameter values, I would consider updating the code to run the forward model with the "x_best" values. The final optimisation position "should" be close to the found optimal, but there are cases where this doesn't occur (e.g. poorly tuned gradient-based methods, or population based optimisers that are exploring the search space in batches).

Hey @BradyPlanden I'm just coming back to this - can you point me at which part you're talking about?

[BradyPlanden]

Hi @pipliggins - hopefully this helps!

[BradyPlanden]

This returns the solution of the last parameter values ran by the optimiser, whereas the user would want the solution from the best parameter combination. This will be the value stored in x parameter above. One method would be add to call the forward model with the x_best values a final time before accessing self.cost.problem.solution. This only needs to be done for the Pints' based methods since the SciPy optimisers return the optimal value from their self._run_optimiser() i.e. we don't have the same granular access.

[pipliggins]

Ahhh okay makes sense, thanks!