def object_to_json(object: Any) -> Any:
"""Convert an Ax object to a JSON-serializable dictionary.
The root node passed to this function should always be an instance of a
core Ax class or a JSON-compatible python builtin. The sub-fields of the
input will then be recursively passed to this function.
e.g. if we pass an instance of Experiment, we will first fall through
to the line `object_dict = ENCODER_REGISTRY[_type](object)`, which
will convert the Experiment to a (shallow) dictionary, where search
subfield remains "unconverted", i.e.:
{"name": <name: string>, "search_space": <search space: SearchSpace>}.
We then pass each item of the dictionary back into this function to
recursively convert the entire object.
"""
object = numpy_type_to_python_type(object)
_type = type(object)
if _type in (str, int, float, bool, type(None)):
return object
elif _type is list:
return [object_to_json(x) for x in object]
elif _type is tuple:
return tuple(object_to_json(x) for x in object)
elif _type is dict:
return {k: object_to_json(v) for k, v in object.items()}
elif _type is OrderedDict:
return {
"__type": _type.__name__,
"value": [(k, object_to_json(v)) for k, v in object.items()],
}
elif _type is datetime.datetime:
return {
"__type": _type.__name__,
"value": datetime.datetime.strftime(object,
"%Y-%m-%d %H:%M:%S.%f"),
}
elif _type is pd.DataFrame:
return {"__type": _type.__name__, "value": object.to_json()}
elif _is_named_tuple(object):
return {
"__type": _type.__name__,
**{k: object_to_json(v)
for k, v in object._asdict().items()},
}
elif issubclass(_type, enum.Enum):
return {"__type": _type.__name__, "name": object.name}
elif _type is np.ndarray or issubclass(_type, np.ndarray):
return {"__type": _type.__name__, "value": object.tolist()}
elif isclass(object) and issubclass(object, Transform):
# There is no other way to check is object is of type Type[Transform].
_type = Type[Transform]
if _type not in ENCODER_REGISTRY:
err = (
f"Object {object} passed to `object_to_json` (of type {_type}) is "
f"not registered with a corresponding encoder in ENCODER_REGISTRY."
)
raise JSONEncodeError(err)
object_dict = ENCODER_REGISTRY[_type](object)
return {k: object_to_json(v) for k, v in object_dict.items()}
def object_to_json(obj: Any) -> Any:
"""Convert an Ax object to a JSON-serializable dictionary.
The root node passed to this function should always be an instance of a
core Ax class or a JSON-compatible python builtin. The sub-fields of the
input will then be recursively passed to this function.
e.g. if we pass an instance of Experiment, we will first fall through
to the line `object_dict = ENCODER_REGISTRY[_type](object)`, which
will convert the Experiment to a (shallow) dictionary, where search
subfield remains "unconverted", i.e.:
{"name": <name: string>, "search_space": <search space: SearchSpace>}.
We then pass each item of the dictionary back into this function to
recursively convert the entire object.
"""
obj = numpy_type_to_python_type(obj)
_type = type(obj)
# Type[MyClass] encoding (encoding of classes, not instances)
if isclass(obj):
for class_type in CLASS_ENCODER_REGISTRY:
if issubclass(obj, class_type):
obj_dict = CLASS_ENCODER_REGISTRY[class_type](obj)
return {k: object_to_json(v) for k, v in obj_dict.items()}
raise ValueError(
f"{obj} is a class. Add it to the CLASS_ENCODER_REGISTRY "
"(and remove it from the ENCODER_REGISTRY if needed).")
if _type in ENCODER_REGISTRY:
obj_dict = ENCODER_REGISTRY[_type](obj)
return {k: object_to_json(v) for k, v in obj_dict.items()}
# Python built-in types + `typing` module types
if _type in (str, int, float, bool, type(None)):
return obj
elif _type is list:
return [object_to_json(x) for x in obj]
elif _type is tuple:
return tuple(object_to_json(x) for x in obj)
elif _type is dict:
return {k: object_to_json(v) for k, v in obj.items()}
elif _is_named_tuple(obj):
return { # pragma: no cover
"__type": _type.__name__,
**{k: object_to_json(v) for k, v in obj._asdict().items()},
}
elif dataclasses.is_dataclass(obj):
return {
"__type": _type.__name__,
**{
k: object_to_json(v)
for k, v in dataclasses.asdict(obj).items()
},
}
# Types from libraries, commonly used in Ax (e.g., numpy, pandas, torch)
elif _type is OrderedDict:
return {
"__type": _type.__name__,
"value": [(k, object_to_json(v)) for k, v in obj.items()],
}
elif _type is datetime.datetime:
return {
"__type": _type.__name__,
"value": datetime.datetime.strftime(obj, "%Y-%m-%d %H:%M:%S.%f"),
}
elif _type is pd.DataFrame:
return {"__type": _type.__name__, "value": obj.to_json()}
elif issubclass(_type, enum.Enum):
return {"__type": _type.__name__, "name": obj.name}
elif _type is np.ndarray or issubclass(_type, np.ndarray):
return {"__type": _type.__name__, "value": obj.tolist()}
elif _type is torch.Tensor:
return {
"__type": _type.__name__,
# TODO: check size and add warning for large tensors: T69137799
"value": obj.tolist(),
"dtype": object_to_json(obj.dtype),
"device": object_to_json(obj.device),
}
elif _type.__module__ == "torch":
# Torch does not support saving to string, so save to buffer first
return {
"__type": f"torch_{_type.__name__}",
"value": torch_type_to_str(obj)
}
err = (f"Object {obj} passed to `object_to_json` (of type {_type}) is "
f"not registered with a corresponding encoder in ENCODER_REGISTRY.")
raise JSONEncodeError(err)