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commenting and hyperparameters, also training dataset option

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finish commenting, included some other hyperparameters that could be handful
Included the possibility to refer to some past optimization data to skip training
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castaneda 2025-03-21 16:05:18 +01:00
parent a9fd8cebed
commit c50b21677b
1 changed files with 17 additions and 4 deletions
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scripts/NNDy.py
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@ -40,6 +40,11 @@ if __name__ == '__main__':
#if no value is to be set from here globalPar must be an empty dictionary
globalPar = {}
# $$$ TRAINING DATASET $$$
# if previous run of the same optimization -meaning with the same type of optimizer, same type and number of input parameters- have been performed, the past dataset can be fed to skip the initial training
# indicate the path for the learner archive file, to be found in the /M-LOOP_archives/ folder
training_filename =
# $$$ INPUT PARAMETERS $$$
#indicate variables to be optimized, in the following we will call them "input parameters"
@ -85,17 +90,25 @@ if __name__ == '__main__':
num_params = num_params,
min_boundary = min_boundary, max_boundary = max_boundary,
first_params = inputPar,
param_names = inputPar_names,
param_names = inputPar_names,
#if retrieving dataset from previous runs
training_filename = training_filename,
#other settings
# %of allowed variation (from 0 to 1) - wrt each parameter range - from current best parameters found, limits the exploration around the current global minimum of the cost function
trust_region = ,
trust_region = 1,
#output parameters over which cost is computed are noisy quantities
cost_has_noise = True,
#if False, waits for the experiment to be performed every time so that every new optimization iteration trains on an enlarged training set
no_delay = False)
#for other possible settings for the optimizer see documentation https://m-loop.readthedocs.io/en/latest/tutorials.html
no_delay = False,
default_bad_cost = 0, #default cost for bad run
default_bad_uncertainty = 0, #default uncertainty for bad run
update_hyperparameters = True #whether hyperparameters should be tuned to avoid overfitting. Default False.
#for other possible settings for the optimizer see documentation https://m-loop.readthedocs.io/en/latest/tutorials.html
)
#To run M-LOOP and find the optimal parameters just use the controller method optimize
controller.optimize()