def test_optimization_problem():
parameter1 = Parameter(
name="param1", category="categorical", search_space={"values": ["a", "b"]}
)
parameter2 = Parameter(name="param2", category="uniform", search_space={"low": 1, "high": 2})
parameters = [parameter1, parameter2]
optimization_problem = OptimizationProblem(parameters)
observation1 = Observation(sample={"param1": "a", "param2": 1.5}, loss=1.5)
optimization_problem.add_observation(observation1)
observation2 = Observation(sample={"param1": "b", "param2": 1.8}, loss=1.8)
optimization_problem.add_observation(observation2)
observation3 = Observation(sample={"param1": "b", "param2": 1.05}, loss=0.1)
optimization_problem.add_observation(observation3)
assert type(optimization_problem.parameters) == list
assert len(optimization_problem.observations) == 3
assert optimization_problem.parameters_name == set(["param1", "param2"])
assert observation1.sample in optimization_problem.samples
assert len(optimization_problem.samples) == 3
assert optimization_problem.best_sample == {"param1": "b", "param2": 1.05}
assert optimization_problem.sorted_observations[0].sample == {"param1": "b", "param2": 1.05}
assert optimization_problem.finite is False
assert len(optimization_problem.find_observations({"param1": "b", "param2": 1.05})) == 1
a, b = optimization_problem.observations_quantile(0.5)
assert len(a) == 1
assert len(b) == 2
assert optimization_problem.get_best_k_samples(1)[0].sample == {"param1": "b", "param2": 1.05}
def test_parameter_from_dict_bad_sample():
data = {
"loss": 0.8,
"sample": [2],
}
with pytest.raises(ValidationError):
Observation.from_dict(data)
def test_parameter_from_dict_missing_loss():
data = {
"sample": {
"alpha": 2
},
}
with pytest.raises(ValidationError):
Observation.from_dict(data)
def minimize(
f,
optimization_problem_parameters,
optimizer_type="parzen_estimator",
number_of_evaluation=100,
seed=None,
debug=False,
):
logger = logging.getLogger("benderopt")
np.random.seed(seed=seed)
samples = []
optimization_problem = OptimizationProblem.from_list(
optimization_problem_parameters)
optimizer = optimizers[optimizer_type](optimization_problem)
for i in range(number_of_evaluation):
logger.info("Evaluating {0}/{1}...".format(i + 1,
number_of_evaluation))
sample = optimizer.suggest()
samples.append(sample)
loss = f(**sample)
logger.debug("f={0} for optimizer suggestion: {1}.".format(
loss, sample))
observation = Observation.from_dict({"loss": loss, "sample": sample})
optimization_problem.add_observation(observation)
if debug is True:
return samples
return optimization_problem.best_sample
def test_parameter_from_dict():
data = {
"sample": {"alpha": 2},
"loss": 0.8
}
observation = Observation.from_dict(data)
assert observation.parameters_name == set(["alpha"])
def test_optimization_problem_add_bad_observation():
optimization_problem = OptimizationProblem.from_list(
[
{"name": "param1", "category": "categorical", "search_space": {"values": ["a", "b"]}},
{"name": "param2", "category": "uniform", "search_space": {"low": 1, "high": 2}},
]
)
observation2 = Observation(sample={"lol": "b", "param2": 1.8}, loss=1.8)
with pytest.raises(ValidationError):
optimization_problem.add_observation(observation2)
def minimize(
f,
optimization_problem,
optimizer_type="parzen_estimator",
number_of_evaluation=100,
seed=None,
debug=False,
):
np.random.seed(seed=seed)
samples = []
optimizer = optimizers[optimizer_type](optimization_problem)
for _ in range(number_of_evaluation):
sample = optimizer.suggest()
samples.append(sample)
loss = f(**sample)
observation = Observation.from_dict({"loss": loss, "sample": sample})
optimization_problem.add_observation(observation)
if debug is True:
return samples
return optimization_problem.best_sample
def minimize(
f,
optimization_problem_parameters,
optimizer_type="parzen_estimator",
number_of_evaluation=100,
seed=None,
debug=False,
):
logger = logging.getLogger("benderopt")
RNG.seed(seed)
samples = []
optimization_problem = OptimizationProblem.from_list(optimization_problem_parameters)
if isinstance(optimizer_type, str):
optimizer_type = optimizers[optimizer_type]
if not issubclass(optimizer_type, BaseOptimizer):
raise ValueError(
"optimizer_type should either be a string or a subclass of BaseOptimizer, got {}".format(
optimizer_type
)
)
optimizer = optimizer_type(optimization_problem)
for i in range(number_of_evaluation):
logger.info("Evaluating {0}/{1}...".format(i + 1, number_of_evaluation))
sample = optimizer.suggest()
samples.append(sample)
loss = f(**sample)
logger.debug("f={0} for optimizer suggestion: {1}.".format(loss, sample))
observation = Observation.from_dict({"loss": loss, "sample": sample})
optimization_problem.add_observation(observation)
if debug is True:
return samples
return optimization_problem.best_sample
def test_parameter_init():
observation = Observation(sample={"alpha": 2}, loss=0.8)
assert observation.parameters_name == set(["alpha"])
def test_parameter_from_dict_missing_sample():
data = {"loss": 0.8}
with pytest.raises(ValidationError):
Observation.from_dict(data)
def test_parameter_bad_sample_format():
with pytest.raises(ValidationError):
Observation(sample=[2], loss=0.8)