bsmart.scans.DEAP

Optimisation using genetic algorithm DEAP

BSMArt scan written by M. Goodsell

Requires:

pip3 install deap

https://github.com/DEAP/deap

Information

BSMArt Name: DEAP

Requires:

• deap

• numpy

Settings:

• Points: Int

DEAP

• Population Size: Int

• Generations: Int

• Global Only: Bool

• n_cores: Int

class bsmart.scans.DEAP.NewScan(inputs, log)

Bases: Scan

Scanner class for NF Scans

get_losses(observables)

Returns a list of losses.

The C-function loss should be zero if the observable is within allowed bounds, and greater than zero outside it.

If we include a validity condition, then other observables, evaluated afterwards, will be returned as NaN.

-> We should assign these the maximum loss ~ 710.

This is then compatible with the explicit use of ‘hierarchical observables’, where all other observables are set to the maximum.

initialise()

method to allow the user scan to overload run settings etc during the initialisation process

postprocess(Point, observables, data_point, temp_dir, log, lock=None)

return the likelihood; we won’t get this far if the point failed to be generated

run()

DEAP scan

Version 1: just use it as an optimisation over a single global likelihood @todo: allow multiple likelihoods for different observables, with hierarchical evaluation