class BOOptimizer:
def __init__(self, config):
self.space = SearchSpace(config=config)
self.utility_function = UtilityFunction(config=config.utility_function,
seed=config.seed)
self.num_warmup = config.utility_function.num_warmup or 5
self.num_iterations = config.utility_function.num_iterations or 10
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,
num_warmup=self.num_warmup,
num_iterations=self.num_iterations,
)
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(config=self.manager1.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(config=self.manager2.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 __init__(self, config):
self.space = SearchSpace(config=config)
self.utility_function = UtilityFunction(config=config.utility_function,
seed=config.seed)
self.num_warmup = config.utility_function.num_warmup or 5
self.num_iterations = config.utility_function.num_iterations or 10
def test_add_observation_to_space_search(self):
space1 = SearchSpace(config=self.manager1.config)
assert space1.x == []
assert space1.y == []
configs = [
{
"feature1": 1,
"feature2": 1,
"feature3": 1
},
{
"feature1": 2,
"feature2": 1.2,
"feature3": 2
},
{
"feature1": 3,
"feature2": 1.3,
"feature3": 3
},
]
metrics = [1, 2, 3]
space1.add_observations(configs=configs, metrics=metrics)
assert len(space1.x) == 3
assert len(space1.y) == 3
for i, feature in enumerate(space1.features):
if feature == "feature1":
assert np.all(space1.x[:, i] == [1, 2, 3])
elif feature == "feature2":
assert np.all(space1.x[:, i] == [1, 1.2, 1.3])
elif feature == "feature3":
assert np.all(space1.x[:, i] == [1, 2, 3])
assert np.all(space1.y == np.array([-1, -2, -3]))
space2 = SearchSpace(config=self.manager2.config)
configs = [
{
"feature1": 1,
"feature2": 1,
"feature3": 1,
"feature4": 1,
"feature5": "a",
},
{
"feature1": 2,
"feature2": 1.2,
"feature3": 2,
"feature4": 4,
"feature5": "b",
},
{
"feature1": 3,
"feature2": 1.3,
"feature3": 3,
"feature4": 3,
"feature5": "a",
},
]
metrics = [1, 2, 3]
space2.add_observations(configs=configs, metrics=metrics)
assert len(space2.x) == 3
assert len(space2.y) == 3
for i, feature in enumerate(space2.features):
if feature == "feature1":
assert np.all(space2.x[:, i] == [1, 2, 3])
elif feature == "feature2":
assert np.all(space2.x[:, i] == [1, 1.2, 1.3])
elif feature == "feature3":
assert np.all(space2.x[:, i] == [1, 2, 3])
elif feature == "feature4":
assert np.all(space2.x[:, i] == [1, 4, 3])
elif feature == "feature5":
assert np.all(space2.x[:, i:i +
3] == [[1, 0, 0], [0, 1, 0], [1, 0, 0]])
assert np.all(space2.y == np.array(metrics))
def test_space_search(self):
# Space 1
space1 = SearchSpace(config=self.manager1.config)
assert space1.dim == 3
assert len(space1.bounds) == 3
assert len(space1.discrete_features) == 3
assert len(space1.categorical_features) == 0 # pylint:disable=len-as-condition
for i, feature in enumerate(space1.features):
# Bounds
if feature == "feature1":
assert np.all(space1.bounds[i] == [1, 3])
elif feature == "feature2":
assert np.all(space1.bounds[i] == [1, 2])
elif feature == "feature3":
assert np.all(space1.bounds[i] == [1, 5])
for feature in space1.features:
# Features
if feature == "feature1":
assert np.all(space1.discrete_features["feature1"]["values"] ==
[1, 2, 3])
elif feature == "feature2":
assert np.all(space1.discrete_features["feature2"]["values"] ==
np.asarray([1.0, 1.25, 1.5, 1.75, 2.0]))
elif feature == "feature3":
assert np.all(space1.discrete_features["feature3"]["values"] ==
np.asarray([1, 2, 3, 4]))
# Space 2
space2 = SearchSpace(config=self.manager2.config)
assert space2.dim == 7
assert len(space2.bounds) == 7
assert len(space2.discrete_features) == 3
assert len(space2.categorical_features) == 1
assert len(space2.features) == 5
for i, feature in enumerate(space2.features):
# Bounds
if feature == "feature1":
assert np.all(space2.bounds[i] == [1, 5])
elif feature == "feature2":
assert np.all(space2.bounds[i] == [1, 5])
elif feature == "feature3":
assert np.all(space2.bounds[i] == [1, 6])
elif feature == "feature4":
assert np.all(space2.bounds[i] == [1, 5])
elif feature == "feature5":
assert np.all(space2.bounds[i] == [0, 1])
# One feature left is continuous
# One categorical Features
assert space2.categorical_features == {
"feature5": {
"values": ["a", "b", "c"],
"number": 3
}
}
# 3 discrete Features
assert space2.discrete_features["feature1"]["values"] == [
1, 2, 3, 4, 5
]
assert np.all(space2.discrete_features["feature2"]["values"] ==
np.asarray([1, 2, 3, 4, 5]))
assert np.all(space2.discrete_features["feature3"]["values"] ==
np.asarray([1, 2, 3, 4, 5]))