def test_convergence_gaussian_process(self):
np.random.seed(0)
ray.init(local_mode=True, num_cpus=1, num_gpus=1)
# This is the space of parameters to explore
space = {"x": tune.uniform(0, 20)}
resources_per_trial = {"cpu": 1, "gpu": 0}
# Following bayesian optimization
gp = BayesOptSearch(random_search_steps=10)
gp.repeat_float_precision = 5
gp = ConcurrencyLimiter(gp, 1)
# Execution of the BO.
analysis = tune.run(
loss,
metric="loss",
mode="min",
# stop=EarlyStopping("loss", mode="min", patience=5),
search_alg=gp,
config=space,
num_samples=100, # Number of iterations
resources_per_trial=resources_per_trial,
raise_on_failed_trial=False,
fail_fast=True,
verbose=1)
assert len(analysis.trials) in {13, 40, 43} # it is 43 on the cluster?
assert math.isclose(analysis.best_config["x"], 0, abs_tol=1e-5)
def test_convergence_gaussian_process(self):
np.random.seed(0)
ray.init(local_mode=True, num_cpus=1, num_gpus=1)
space = {
"x": (0, 20) # This is the space of parameters to explore
}
resources_per_trial = {"cpu": 1, "gpu": 0}
# Following bayesian optimization
gp = BayesOptSearch(space,
metric="loss",
mode="min",
random_search_steps=10)
gp.repeat_float_precision = 5
gp = ConcurrencyLimiter(gp, 1)
# Execution of the BO.
analysis = tune.run(
loss,
# stop=EarlyStopping("loss", mode="min", patience=5),
search_alg=gp,
config={},
num_samples=100, # Number of iterations
resources_per_trial=resources_per_trial,
raise_on_failed_trial=False,
fail_fast=True,
verbose=1)
assert len(analysis.trials) == 41
ray.shutdown()
def testConvergenceBayesOpt(self):
from ray.tune.suggest.bayesopt import BayesOptSearch
np.random.seed(0)
# Following bayesian optimization
searcher = BayesOptSearch(random_search_steps=10)
searcher.repeat_float_precision = 5
searcher = ConcurrencyLimiter(searcher, 1)
analysis = self._testConvergence(searcher, patience=100)
assert len(analysis.trials) < 50
assert math.isclose(analysis.best_config["x"], 0, abs_tol=1e-5)
