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
`Hadd` histograms automatically
Create docker image
Depends on olantwin/ship-shield#1
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.
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`
Write script to test point retrieval in isolation.
Useful for #5
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.
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