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 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_from_dict_missing_sample():
data = {"loss": 0.8}
with pytest.raises(ValidationError):
Observation.from_dict(data)