class BOOptimizer(object):
def __init__(self, hptuning_config):
self.hptuning_config = hptuning_config
self.n_initial_trials = self.hptuning_config.bo.n_initial_trials
self.space = SearchSpace(hptuning_config=hptuning_config)
self.utility_function = UtilityFunction(
config=hptuning_config.bo.utility_function,
seed=hptuning_config.seed)
self.n_warmup = hptuning_config.bo.utility_function.n_warmup or 5000
self.n_iter = hptuning_config.bo.utility_function.n_iter or 150
def _maximize(self):
""" Find argmax of the acquisition function."""
if not self.space.is_observations_valid():
return None
y_max = self.space.y.max()
self.utility_function.gaussian_process.fit(self.space.x, self.space.y)
return self.utility_function.max_compute(y_max=y_max,
bounds=self.space.bounds,
n_warmup=self.n_warmup,
n_iter=self.n_iter)
def add_observations(self, configs, metrics):
# Turn configs and metrics into data points
self.space.add_observations(configs=configs, metrics=metrics)
def get_suggestion(self):
x = self._maximize()
return self.space.get_suggestion(x)
class BOOptimizer(object):
def __init__(self, hptuning_config):
self.hptuning_config = hptuning_config
self.n_initial_trials = self.hptuning_config.bo.n_initial_trials
self.space = SearchSpace(hptuning_config=hptuning_config)
self.utility_function = UtilityFunction(
config=hptuning_config.bo.utility_function, seed=hptuning_config.seed)
self.n_warmup = hptuning_config.bo.utility_function.n_warmup or 5000
self.n_iter = hptuning_config.bo.utility_function.n_iter or 150
def _maximize(self):
""" Find argmax of the acquisition function."""
if not self.space.is_observations_valid():
return None
y_max = self.space.y.max()
self.utility_function.gaussian_process.fit(self.space.x, self.space.y)
return self.utility_function.max_compute(y_max=y_max,
bounds=self.space.bounds,
n_warmup=self.n_warmup,
n_iter=self.n_iter)
def add_observations(self, configs, metrics):
# Turn configs and metrics into data points
self.space.add_observations(configs=configs, metrics=metrics)
def get_suggestion(self):
x = self._maximize()
return self.space.get_suggestion(x)
def test_space_get_suggestion(self):
space1 = SearchSpace(hptuning_config=self.manager1.hptuning_config)
suggestion = space1.get_suggestion(suggestion=[1, 1, 1])
assert suggestion == {'feature1': 1, 'feature2': 1, 'feature3': 1}
suggestion = space1.get_suggestion(suggestion=[1, 1.2, 2])
assert suggestion == {'feature1': 1, 'feature2': 1.25, 'feature3': 2}
suggestion = space1.get_suggestion(suggestion=[1, 1.5, 3])
assert suggestion == {'feature1': 1, 'feature2': 1.5, 'feature3': 3}
space2 = SearchSpace(hptuning_config=self.manager2.hptuning_config)
suggestion = space2.get_suggestion(suggestion=[1, 1, 1, 1, 1, 0, 0])
assert suggestion == {'feature1': 1,
'feature2': 1,
'feature3': 1,
'feature4': 1,
'feature5': 'a'}
suggestion = space2.get_suggestion(suggestion=[1, 1.2, 2, 3, 0, 0, 1])
assert suggestion == {'feature1': 1,
'feature2': 1,
'feature3': 2,
'feature4': 3,
'feature5': 'c'}
suggestion = space2.get_suggestion(suggestion=[1, 1.8, 3, 3, 0, 1, 0])
assert suggestion == {'feature1': 1,
'feature2': 2,
'feature3': 3,
'feature4': 3,
'feature5': 'b'}
def test_space_get_suggestion(self):
space1 = SearchSpace(hptuning_config=self.manager1.hptuning_config)
suggestion = space1.get_suggestion(suggestion=[1, 1, 1])
assert suggestion == {'feature1': 1, 'feature2': 1, 'feature3': 1}
suggestion = space1.get_suggestion(suggestion=[1, 1.2, 2])
assert suggestion == {'feature1': 1, 'feature2': 1.25, 'feature3': 2}
suggestion = space1.get_suggestion(suggestion=[1, 1.5, 3])
assert suggestion == {'feature1': 1, 'feature2': 1.5, 'feature3': 3}
space2 = SearchSpace(hptuning_config=self.manager2.hptuning_config)
suggestion = space2.get_suggestion(suggestion=[1, 1, 1, 1, 1, 0, 0])
assert suggestion == {'feature1': 1,
'feature2': 1,
'feature3': 1,
'feature4': 1,
'feature5': 'a'}
suggestion = space2.get_suggestion(suggestion=[1, 1.2, 2, 3, 0, 0, 1])
assert suggestion == {'feature1': 1,
'feature2': 1,
'feature3': 2,
'feature4': 3,
'feature5': 'c'}
suggestion = space2.get_suggestion(suggestion=[1, 1.8, 3, 3, 0, 1, 0])
assert suggestion == {'feature1': 1,
'feature2': 2,
'feature3': 3,
'feature4': 3,
'feature5': 'b'}