Added a lazy result object.

[PaulJonasJost]

Added a result object, that reads the results only if needed. Connected to #1405

Things to consider still

• Do we want a casing limit, if yes how would that look like?
• Should the lazy result actually be a subclass of the optimizer result?
• Connected with the above, when printing the lazy result, all attributes are shown as None

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Codecov Report

Attention: Patch coverage is 73.20261% with 41 lines in your changes missing coverage. Please review.

Project coverage is 83.24%. Comparing base (e746456) to head (6f76f46).

Files	Patch %	Lines
pypesto/result/optimize.py	71.72%	41 Missing ⚠️

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Additional details and impacted files

@@             Coverage Diff             @@
##           develop    #1421      +/-   ##
===========================================
- Coverage    83.36%   83.24%   -0.13%     
===========================================
  Files          161      161              
  Lines        13247    13396     +149     
===========================================
+ Hits         11043    11151     +108     
- Misses        2204     2245      +41     

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[dweindl]

* [ ]  Do we want a casing limit, if yes how would that look like?

Not sure what you mean.

* [ ]  Should the lazy result actually be a subclass of the optimizer result?

I'd say so.

Something to be discussed: Should changes to the result just affect the OptimizerResult instance or should they be written to file?

* [ ]  Connected with the above, when printing the lazy result, all attributes are shown as `None`

Why aren't they loaded on demand then?

[PaulJonasJost]

Not sure what you mean.

Sorry meant caching. If we load very big hessians, we might want to throw them out later again? 🤔

Something to be discussed: Should changes to the result just affect the OptimizerResult instance or should they be written to file?

That's an interesting thought. I would say per default no, as it would not be a result object created by optimization but changed thereafter. But a function would probably be good to propagate changes to the file?

Why aren't they loaded on demand then?

Not entirely sure, it might be because in the OptimizerResult they are attributes while here they are properties? And so since LazyResult is a subclass, it will get initiated with those attributes as well 🤔 When calling optimizer_result.id it gives back the appropriate value, just not in the __repr__/print

[dilpath]

I have been using hdfdict [1] with pydantic objects, no issues so far in my basic use cases (including numpy arrays), which has lazy loading on by default. Hopefully the lazy loading persists when a pydantic data object is reconstructed from HDF5 via hdfdict, but I haven't tested this yet.

So one alternative to this PR would be to refactor our results to be pydantic classes. I guess this might anyway occur when PEtab Result is supported in pyPESTO.

[1] https://github.com/SiggiGue/hdfdict/blob/master/hdfdict/hdfdict.py

[PaulJonasJost]

@dilpath as we talked about, I think it makes sense to merge it in and when petabResult is out, we might anyways make it obsolete? In that case i think this as an intermediate step should be fine.

[dilpath]

Yes, fine for me 👍