def test_basic(self):
space = {
"x": ["hp_choice", "x", np.linspace(-10, 10, num=21)],
"y": ["hp_choice", "y", np.linspace(-10, 10, num=21)],
}
hpo = Hyperopt(space=space, method="tpe")
tape = []
# does it do anything?
tape = run_n_steps(hpo, 2, tape)
start = best_loss(tape)
tape = run_n_steps(hpo, 200, tape)
end = best_loss(tape)
self.assertGreater(start, end)
# test repeatability
tid, next_suggestion = hpo.suggest()
hpo2 = Hyperopt(space=space, method="tpe")
for entry in tape:
if entry[0] == "suggest":
tid, s = hpo2.suggest()
self.assertEqual(tid, entry[1])
self.assertEqual(s, entry[2])
else:
hpo2.submit(entry[1], loss=entry[2])
tid, next_suggestion2 = hpo2.suggest()
self.assertEqual(next_suggestion, next_suggestion2)
def test_quadratic(self):
# this is a somewhat redundant test, but it'll put the
# pool through a slightly more realistic situation and hopefully
# show us when and where it fails.
space = {
"x": ["hp_loggrid", "x", -2.0, 2.0, 161],
"y": ["hp_loggrid", "y", -2.0, 2.0, 161],
"z": ["hp_loggrid", "z", -2.0, 2.0, 161],
"a": ["hp_loggrid", "a", -2.0, 2.0, 161],
"b": ["hp_loggrid", "b", -2.0, 2.0, 161],
"c": ["hp_loggrid", "c", -2.0, 2.0, 161],
"wait": ["hp_choice", "wait", [0.0, 0.1]],
}
hpo = Hyperopt(space=space)
pool = EvaluationPool(
max_workers=40,
evaluator_config={"mock_eval2": {}},
trial_timeout=0.07,
)
search = Hyperopt(space=space)
tasks = {}
for i in range(10):
t, s = hpo.suggest()
tasks[t] = s
futures = {pool.schedule(s): t for t, s in tasks.items()}
for d in futures:
pool.finish(d)
pool.shutdown()
self.assertEqual(len(pool.evals), 10)