pdmlabs.mango.metatuner

Meta Tuner Class: Used to optimize across a set of models: - selecting intelligently the order of functions to optimize Current implementation: Bare Metal functionality for testing. ToDo: Improve code with better config management and remove hardcoded parameters

Classes

MetaTuner(param_dict_list, objective_list, ...)

class pdmlabs.mango.metatuner.MetaTuner(param_dict_list, objective_list, **kwargs)

Bases: object

class Config(n_iter: int = 20, n_init: int = 2)

Bases: object

n_init: int = 2

n_iter: int = 20

static calculateDomainSize(param_dict)

Calculating the domain size to be explored for finding optimum of bayesian optimizer

get_max_y_value(Y_dict_array)

run()

runExponentialTuner()

Steps: 1-Create DS obj for each obj of param_dict_list 2-Sample randomly from the DS objects and evaluate the objective functions. 3- Now use the GPR for each objective to select next batch 4- Select the best values based on fxn(surrogate) from GPR. 5- Modify the surrogate selections so as to avoid getting struck.