def __call__(
self,
search_space: Optional[SearchSpace] = None,
experiment: Optional[Experiment] = None,
data: Optional[Data] = None,
silently_filter_kwargs: bool = True, # TODO[Lena]: default to False
**kwargs: Any,
) -> ModelBridge:
assert self.value in MODEL_KEY_TO_MODEL_SETUP, f"Unknown model {self.value}"
# All model bridges require either a search space or an experiment.
assert search_space or experiment, "Search space or experiment required."
model_setup_info = MODEL_KEY_TO_MODEL_SETUP[self.value]
model_class = model_setup_info.model_class
bridge_class = model_setup_info.bridge_class
if not silently_filter_kwargs:
validate_kwarg_typing( # TODO[Lena]: T46467254, pragma: no cover
typed_callables=[model_class, bridge_class],
search_space=search_space,
experiment=experiment,
data=data,
**kwargs,
)
# Create model with consolidated arguments: defaults + passed in kwargs.
model_kwargs = consolidate_kwargs(
kwargs_iterable=[get_function_default_arguments(model_class), kwargs],
keywords=get_function_argument_names(model_class),
)
model = model_class(**model_kwargs)
# Create `ModelBridge`: defaults + standard kwargs + passed in kwargs.
bridge_kwargs = consolidate_kwargs(
kwargs_iterable=[
get_function_default_arguments(bridge_class),
model_setup_info.standard_bridge_kwargs,
{"transforms": model_setup_info.transforms},
kwargs,
],
keywords=get_function_argument_names(
function=bridge_class, omit=["experiment", "search_space", "data"]
),
)
# Create model bridge with the consolidated kwargs.
model_bridge = bridge_class(
search_space=search_space or not_none(experiment).search_space,
experiment=experiment,
data=data,
model=model,
**bridge_kwargs,
)
# Store all kwargs on model bridge, to be saved on generator run.
model_bridge._set_kwargs_to_save(
model_key=self.value,
model_kwargs=_encode_callables_as_references(model_kwargs),
bridge_kwargs=_encode_callables_as_references(bridge_kwargs),
)
return model_bridge
def __call__(
self,
search_space: Optional[SearchSpace] = None,
experiment: Optional[Experiment] = None,
data: Optional[Data] = None,
silently_filter_kwargs: bool = True, # TODO[Lena]: default to False
**kwargs: Any,
) -> ModelBridge:
assert self.value in MODEL_KEY_TO_MODEL_SETUP
# All model bridges require either a search space or an experiment.
assert search_space or experiment, "Search space or experiment required."
model_setup_info = MODEL_KEY_TO_MODEL_SETUP[self.value]
model_class = model_setup_info.model_class
bridge_class = model_setup_info.bridge_class
if not silently_filter_kwargs:
validate_kwarg_typing( # TODO[Lena]: T46467254, pragma: no cover
typed_callables=[model_class, bridge_class],
search_space=search_space,
experiment=experiment,
data=data,
**kwargs,
)
# Create model with consolidated arguments: defaults + passed in kwargs.
model_kwargs = consolidate_kwargs(
kwargs_iterable=[get_function_default_arguments(model_class), kwargs],
keywords=get_function_argument_names(model_class),
)
model = model_class(**model_kwargs)
# Create `ModelBridge`: defaults + standard kwargs + passed in kwargs.
bridge_kwargs = consolidate_kwargs(
kwargs_iterable=[
get_function_default_arguments(bridge_class),
model_setup_info.standard_bridge_kwargs,
{"transforms": model_setup_info.transforms},
kwargs,
],
keywords=get_function_argument_names(
function=bridge_class, omit=["experiment", "search_space", "data"]
),
)
# Create model bridge with the consolidated kwargs.
model_bridge = bridge_class(
search_space=search_space or not_none(experiment).search_space,
experiment=experiment,
data=data,
model=model,
**bridge_kwargs,
)
# Temporarily ignore Botorch callable & torch-typed arguments, as those
# are not serializable to JSON out-of-the-box. TODO[Lena]: T46527142
if isinstance(model, TorchModel):
model_kwargs = {kw: p for kw, p in model_kwargs.items() if not callable(p)}
bridge_kwargs = {
kw: p for kw, p in bridge_kwargs.items() if kw[:5] != "torch"
}
# Store all kwargs on model bridge, to be saved on generator run.
model_bridge._set_kwargs_to_save(
model_key=self.value, model_kwargs=model_kwargs, bridge_kwargs=bridge_kwargs
)
return model_bridge
def test_validate_kwarg_typing(self):
def typed_callable(arg1: int, arg2: str = None) -> None:
pass
def typed_callable_with_dict(arg3: int, arg4: Dict[str, int]) -> None:
pass
def typed_callable_valid(arg3: int, arg4: str = None) -> None:
pass
def typed_callable_dup_keyword(arg2: int, arg4: str = None) -> None:
pass
def typed_callable_with_callable(
arg1: int, arg2: Callable[[int], Dict[str, int]]) -> None:
pass
def typed_callable_extra_arg(arg1: int, arg2: str, arg3: bool) -> None:
pass
# pass
try:
kwargs = {"arg1": 1, "arg2": "test", "arg3": 2}
validate_kwarg_typing([typed_callable, typed_callable_valid],
**kwargs)
except Exception:
self.assertTrue(False, "Exception raised on valid kwargs")
# pass with complex data structure
try:
kwargs = {"arg1": 1, "arg2": "test", "arg3": 2, "arg4": {"k1": 1}}
validate_kwarg_typing([typed_callable, typed_callable_with_dict],
**kwargs)
except Exception:
self.assertTrue(False, "Exception raised on valid kwargs")
# callable as arg (same arg count but diff type)
try:
kwargs = {"arg1": 1, "arg2": typed_callable}
validate_kwarg_typing([typed_callable_with_callable], **kwargs)
except Exception:
self.assertTrue(False, "Exception raised on valid kwargs")
# callable as arg (diff arg count)
try:
kwargs = {"arg1": 1, "arg2": typed_callable_extra_arg}
validate_kwarg_typing([typed_callable_with_callable], **kwargs)
except Exception:
self.assertTrue(False, "Exception raised on valid kwargs")
# kwargs contains extra keywords
with self.assertRaises(ValueError):
kwargs = {"arg1": 1, "arg2": "test", "arg3": 3, "arg5": 4}
typed_callables = [typed_callable, typed_callable_valid]
validate_kwarg_typing(typed_callables, **kwargs)
# callables have duplicate keywords
with patch.object(logger, "debug") as mock_debug:
kwargs = {"arg1": 1, "arg2": "test", "arg4": "test_again"}
typed_callables = [typed_callable, typed_callable_dup_keyword]
validate_kwarg_typing(typed_callables, **kwargs)
mock_debug.assert_called_once_with(
f"`{typed_callables}` have duplicate keyword argument: arg2.")
# mismatch types
with patch.object(logger, "warning") as mock_warning:
kwargs = {"arg1": 1, "arg2": "test", "arg3": "test_again"}
typed_callables = [typed_callable, typed_callable_valid]
validate_kwarg_typing(typed_callables, **kwargs)
expected_message = (
f"`{typed_callable_valid}` expected argument `arg3` to be of type"
f" {type(1)}. Got test_again (type: {type('test_again')}).")
mock_warning.assert_called_once_with(expected_message)
# mismatch types with Dict
with patch.object(logger, "warning") as mock_warning:
str_dic = {"k1": "test"}
kwargs = {"arg1": 1, "arg2": "test", "arg3": 2, "arg4": str_dic}
typed_callables = [typed_callable, typed_callable_with_dict]
validate_kwarg_typing(typed_callables, **kwargs)
expected_message = (
f"`{typed_callable_with_dict}` expected argument `arg4` to be of type"
f" typing.Dict[str, int]. Got {str_dic} (type: {type(str_dic)})."
)
mock_warning.assert_called_once_with(expected_message)
# mismatch types with callable as arg
with patch.object(logger, "warning") as mock_warning:
kwargs = {"arg1": 1, "arg2": "test_again"}
typed_callables = [typed_callable_with_callable]
validate_kwarg_typing(typed_callables, **kwargs)
expected_message = (
f"`{typed_callable_with_callable}` expected argument `arg2` to be of"
f" type typing.Callable[[int], typing.Dict[str, int]]. "
f"Got test_again (type: {type('test_again')}).")
mock_warning.assert_called_once_with(expected_message)
