muon_shield_optimisation's People
muon_shield_optimisation's Issues
Use persistent optimisers
We could use https://github.com/scikit-optimize/scikit-optimize/blob/master/examples/ask-and-tell.ipynb and https://github.com/scikit-optimize/scikit-optimize/blob/master/examples/store-and-load-results.ipynb to use persistent optimisers and to store and load them.
Move creation of geofiles to workers
Recover good simulation results which were not processed.
Plan:
- Update
point
saving to mark jobs as processed (viaModifyJob
) and point to thejob_id
of thepoint
they belong to. - Run over old simulation results and gather sub-jobs by metadata
- Try to match groups of simulation jobs to existing points, if none found, process and save, if no
point
for the parameters exists yet.
Once this is done de-duplication as per #8 could be implemented, I think.
@FilatovArtm: Any comments?
Change disney_optimize.py in order to commit docker jobs
Save optimisation results in `point` type jobs on `Disneyland`
Create docker image
Depends on olantwin/ship-shield#1
Don't simulate points multiple times (or change seed?)
Currently we can get into situations where the algorithm simulates some points multiple times (especially in the toy-studies such as Mitesh's test optimisations). Just reading the known answer (or changing the seed, or maybe just stopping the optimisation) in that case might be better.
Use space transformations to control quantisation of grid, increase step sizes
This could be used to keep implement different step sizes in a flexible way. Shrinking the overall search space is very important to get convergence.
`Hadd` histograms automatically
Write script to test point retrieval in isolation.
Useful for #5
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