def _test_repeater(self, num_samples, repeat):
class TestSuggestion(Searcher):
index = 0
def suggest(self, trial_id):
self.index += 1
return {"test_variable": 5 + self.index}
def on_trial_complete(self, *args, **kwargs):
return
searcher = TestSuggestion(metric="episode_reward_mean")
repeat_searcher = Repeater(searcher, repeat=repeat, set_index=False)
alg = SearchGenerator(repeat_searcher)
experiment_spec = {
"run": "__fake",
"num_samples": num_samples,
"stop": {
"training_iteration": 1
}
}
alg.add_configurations({"test": experiment_spec})
runner = TrialRunner(search_alg=alg)
while not runner.is_finished():
runner.step()
return runner.get_trials()
def create_searcher():
class TestSuggestion(Searcher):
def __init__(self, index):
self.index = index
self.returned_result = []
super().__init__(metric="episode_reward_mean", mode="max")
def suggest(self, trial_id):
self.index += 1
return {"test_variable": self.index}
def on_trial_complete(self, trial_id, result=None, **kwargs):
self.returned_result.append(result)
def save(self, checkpoint_path):
with open(checkpoint_path, "wb") as f:
pickle.dump(self.__dict__, f)
def restore(self, checkpoint_path):
with open(checkpoint_path, "rb") as f:
self.__dict__.update(pickle.load(f))
searcher = TestSuggestion(0)
searcher = ConcurrencyLimiter(searcher, max_concurrent=2)
searcher = Repeater(searcher, repeat=3, set_index=False)
search_alg = SearchGenerator(searcher)
experiment_spec = {
"run": "__fake",
"num_samples": 20,
"stop": {"training_iteration": 2},
}
experiments = [Experiment.from_json("test", experiment_spec)]
search_alg.add_configurations(experiments)
return search_alg
def testNestedSuggestion(self):
class TestSuggestion(Searcher):
def suggest(self, trial_id):
return {"a": {"b": {"c": {"d": 4, "e": 5}}}}
searcher = TestSuggestion()
alg = SearchGenerator(searcher)
alg.add_configurations({"test": {"run": "__fake"}})
trial = alg.next_trial()
self.assertTrue("e=5" in trial.experiment_tag)
self.assertTrue("d=4" in trial.experiment_tag)
