import hyperengine
import numpy as np
def rosenbrock(hyperparams):
return (hyperparams["x"]-1)**2 + 10*(hyperparams["x"]**2-hyperparams["y"])**2
class BlackBoxSolver:
def __init__(self, func):
self.func=func
self._val_loss_curve = []
def train(self):
loss=self.func()
self._val_loss_curve.append(loss)
return self._reducer(self._val_loss_curve)
def _reducer(self, *args, **kwargs):
return np.min(*args, **kwargs)
def terminate(self):
pass
def solver_generator(hyperparams):
return BlackBoxSolver(partial(rosenbrock, hyperparams))
class IterLimitedHyperTuner(hyperengine.HyperTuner):
def __init__(self, hyper_params_spec, solver_generator, iterLimit, *args, **kwargs):
j=0
def solver_generator_limited(hyperparams):
nonlocal j
if j<iterLimit:
j+=1
return solver_generator(hyperparams)
else:
raise StopIteration()
super().__init__(hyper_params_spec, solver_generator_limited, *args, **kwargs)
def tune(self):
try:
super().tune()
except StopIteration as ex:
minLossPointNum=np.argmin(self.strategy.values)
return dict(zip(self.parsed._spec.keys(), self.strategy.points[minLossPointNum]))
spec=hp.new({
"x": hp.uniform(-10, 10),
"y": hp.uniform(-10, 10),
})
tuner=IterLimitedHyperTuner(spec, solver_generator, iterLimit=10, strategy='bayesian') #'portfolio'
tuner.tune()
