def test_unflatten_list_dict_multi_level_nested(self):
result = unflatten_list_dict({"a/0/c/d": 1, "a/1/c": 2, "a/2": 3, "e": 4})
assert result == {"a": [{"c": {"d": 1}}, {"c": 2}, 3], "e": 4}
result = unflatten_list_dict(
{"0/a/0/b": 1, "0/a/1": 2, "1/0": 3, "1/1": 4, "1/2/c": 5, "2": 6}
)
assert result == [{"a": [{"b": 1}, 2]}, [3, 4, {"c": 5}], 6]
def test_unflatten_list_dict_output_type(self):
in_ = OrderedDict({"a/0": 0, "a/1": 1, "c/d": 2, "e": 3})
out = unflatten_list_dict(in_)
assert type(out) is OrderedDict
in_ = OrderedDict({"0/a": 0, "1/b": 1, "2/c": 2, "3/d": 3})
out = unflatten_list_dict(in_)
assert type(out) is list
def suggest(self, trial_id: str) -> Optional[Dict]:
if not self._space:
raise RuntimeError(
UNDEFINED_SEARCH_SPACE.format(cls=self.__class__.__name__,
space="space"))
if not self._metric or not self._mode:
raise RuntimeError(
UNDEFINED_METRIC_MODE.format(cls=self.__class__.__name__,
metric=self._metric,
mode=self._mode))
max_concurrent = (self._max_concurrent
if self._max_concurrent > 0 else float("inf"))
if len(self.running) >= max_concurrent:
return None
if self._points_to_evaluate:
config = self._points_to_evaluate.pop(0)
else:
# This parameter is not used in hpbandster implementation.
config, _ = self.bohber.get_config(None)
self.trial_to_params[trial_id] = copy.deepcopy(config)
self.running.add(trial_id)
return unflatten_list_dict(config)
def test_unflatten_list_dict_one_level_nested(self):
result = unflatten_list_dict({"a/0": 0, "a/1": 1, "c/d": 2, "e": 3})
assert result == {"a": [0, 1], "c": {"d": 2}, "e": 3}
result = unflatten_list_dict({"0/a": 0, "1/b": 1, "2/c": 2, "3": 3})
assert result == [{"a": 0}, {"b": 1}, {"c": 2}, 3]
