for testing and deploying your application
for finding and fixing issues
for empowering human code reviews
# Author: Simon Blanke
# Email: [email protected]
# License: MIT License
import numpy as np
from .dictionary import DictClass
def gfo2hyper(search_space, para):
values_dict = {}
for _, key in enumerate(search_space.keys()):
pos_ = int(para[key])
values_dict[key] = search_space[key][pos_]
return values_dict
class ObjectiveFunction(DictClass):
def __init__(self, objective_function, optimizer, callbacks, catch, nth_process):
super().__init__()
self.objective_function = objective_function
self.optimizer = optimizer
self.callbacks = callbacks
self.catch = catch
self.nth_process = nth_process
self.nth_iter = 0
def run_callbacks(self, type_):
if self.callbacks and type_ in self.callbacks:
[callback(self) for callback in self.callbacks[type_]]
def __call__(self, search_space):
# wrapper for GFOs
def _model(para):
self.nth_iter = len(self.optimizer.pos_l)
para = gfo2hyper(search_space, para)
self.para_dict = para
try:
self.run_callbacks("before")
results = self.objective_function(self)
self.run_callbacks("after")
except tuple(self.catch.keys()) as e:
results = self.catch[e.__class__]
return results
_model.__name__ = self.objective_function.__name__
return _model
SimonBlanke /
Hyperactive
