def testCFO(self):
from ray.tune.suggest.flaml import CFO
searcher = CFO(space=self.config, metric=self.metric_name, mode="max")
self._save(searcher)
searcher = CFO(space=self.config, metric=self.metric_name, mode="max")
self._restore(searcher)
def testCFO(self):
self.skipTest(
"Broken in FLAML, reenable once "
"https://github.com/microsoft/FLAML/pull/263 is merged"
)
from ray.tune.suggest.flaml import CFO
out = tune.run(
_invalid_objective,
search_alg=CFO(
points_to_evaluate=[
{"report": 1.0},
{"report": 2.1},
{"report": 3.1},
{"report": 4.1},
]
),
config=self.config,
metric="_metric",
mode="max",
num_samples=16,
reuse_actors=False,
)
best_trial = out.best_trial
self.assertLessEqual(best_trial.config["report"], 2.0)
def testConvergenceCFO(self):
from ray.tune.suggest.flaml import CFO
np.random.seed(0)
searcher = CFO()
analysis = self._testConvergence(searcher, patience=200)
assert math.isclose(analysis.best_config["x"], 0, abs_tol=1e-2)
def testCFO(self):
from ray.tune.suggest.flaml import CFO
out = tune.run(_invalid_objective,
search_alg=CFO(),
config=self.config,
metric="_metric",
mode="max",
num_samples=16,
reuse_actors=False)
best_trial = out.best_trial
self.assertLessEqual(best_trial.config["report"], 2.0)
def set_basic_conf(self):
space = {
"height": tune.uniform(-100, 100),
"width": tune.randint(0, 100),
}
def cost(param, reporter):
reporter(loss=(param["height"] - 14)**2 - abs(param["width"] - 3))
search_alg = CFO(
space=space,
metric="loss",
mode="min",
seed=20,
)
return search_alg, cost
def run_cfo_tune(smoke_test=False):
algo = CFO()
algo = ConcurrencyLimiter(algo, max_concurrent=4)
scheduler = AsyncHyperBandScheduler()
analysis = tune.run(
easy_objective,
metric="mean_loss",
mode="min",
search_alg=algo,
scheduler=scheduler,
num_samples=10 if smoke_test else 100,
config={
"steps": 100,
"width": tune.uniform(0, 20),
"height": tune.uniform(-100, 100),
# This is an ignored parameter.
"activation": tune.choice(["relu", "tanh"])
})
print("Best hyperparameters found were: ", analysis.best_config)
