def _generate_trials(self, experiment_spec, output_path=""):
"""Generates trials with configurations from `_suggest`.
Creates a trial_id that is passed into `_suggest`.
Yields:
Trial objects constructed according to `spec`
"""
if "run" not in experiment_spec:
raise TuneError("Must specify `run` in {}".format(experiment_spec))
for _ in range(experiment_spec.get("num_samples", 1)):
trial_id = Trial.generate_id()
while True:
suggested_config = self._suggest(trial_id)
if suggested_config is None:
yield None
else:
break
spec = copy.deepcopy(experiment_spec)
spec["config"] = merge_dicts(spec["config"], suggested_config)
flattened_config = resolve_nested_dict(spec["config"])
self._counter += 1
tag = "{0}_{1}".format(str(self._counter),
format_vars(flattened_config))
yield create_trial_from_spec(spec,
output_path,
self._parser,
experiment_tag=tag,
trial_id=trial_id)
def create_trial_if_possible(self, experiment_spec: Dict,
output_path: str) -> Optional[Trial]:
logger.debug("creating trial")
trial_id = Trial.generate_id()
suggested_config = self.searcher.suggest(trial_id)
if suggested_config == Searcher.FINISHED:
self._finished = True
logger.debug("Searcher has finished.")
return
if suggested_config is None:
return
spec = copy.deepcopy(experiment_spec)
spec["config"] = merge_dicts(spec["config"],
copy.deepcopy(suggested_config))
# Create a new trial_id if duplicate trial is created
flattened_config = resolve_nested_dict(spec["config"])
self._counter += 1
tag = "{0}_{1}".format(str(self._counter),
format_vars(flattened_config))
trial = create_trial_from_spec(
spec,
output_path,
self._parser,
evaluated_params=flatten_dict(suggested_config),
experiment_tag=tag,
trial_id=trial_id)
return trial
def _generate_trials(self, experiment_spec, output_path=""):
"""Generates trials with configurations from `_suggest`.
Creates a trial_id that is passed into `_suggest`.
Yields:
Trial objects constructed according to `spec`
"""
if "run" not in experiment_spec:
raise TuneError("Must specify `run` in {}".format(experiment_spec))
for _ in range(experiment_spec.get("num_samples", 1)):
trial_id = Trial.generate_id()
while True:
suggested_config = self._suggest(trial_id)
if suggested_config is None:
yield None
else:
break
spec = copy.deepcopy(experiment_spec)
spec["config"] = merge_dicts(spec["config"], suggested_config)
flattened_config = resolve_nested_dict(spec["config"])
self._counter += 1
tag = "{0}_{1}".format(
str(self._counter), format_vars(flattened_config))
yield create_trial_from_spec(
spec,
output_path,
self._parser,
experiment_tag=tag,
trial_id=trial_id)
def test_resolve_dict(self):
config = {
"a": {
"b": 1,
"c": 2,
},
"b": {"a": 3},
}
resolved = resolve_nested_dict(config)
for k, v in [(("a", "b"), 1), (("a", "c"), 2), (("b", "a"), 3)]:
self.assertEqual(resolved.get(k), v)
