https://neurord.github.io/ajustador/
https://github.com/neurord/ajustador/
- create directory, such as my_NSGopt_dir, with the following files
main optimization scripts, example is gpNpas_opt.py, in https://github.com/neurord/optimization_scripts/blob/master/gp_opt/, which
specifies model type (moose_nerp package)
specifies neuron type
specifies data name (i.e., which experimental trace)
specifies optimization parameters, such as generations and population size
creates and moves to output directory according to some naming convention, e.g.
rootdir=os.getcwd()+'/output' dirname='cmaes_'+dataname+'_'+str(seed)+'_'+str(popsiz) if not in dirname in os.listdir(rootdir): os.mkdir(rootdir+dirname) os.chdir(rootdir+dirname)
fit_commands.py
param_fitness_chan.py, which specifies
- which parameters to change, and their ranges
- the fitness function, and weights on fitness features
- in that same my_NSGopt_dir:
- create empty /output subdirectory
- copy (or link)
- moose_nerp/moose_nerp
- dill module
- adjustador/ajustador
- make sure __init__ in ajustador does not import xml.py, loadconc.py, nrd_fitness.py, drawing.py, nrd_output.py
- make sure ajustador/helpers/save_params.py does not import xml
- copy (or link) the data directory, and python file that specifies the data as class Params, e.g. from waves:
create a zip file from the directory _above_ the directory you just created
zip -r NSGopt.zip my_NSGopt_dir/