What is the recommended way to include properties in dataclasses in asdict or serialization?

user_2750819 0
Viewed 2952

Note this is similar to How to get @property methods in asdict?.

I have a (frozen) nested data structure like the following. A few properties that are (purely) dependent on the fields are defined.

import copy
import dataclasses
import json
from dataclasses import dataclass

@dataclass(frozen=True)
class Bar:
    x: int
    y: int

    @property
    def z(self):
        return self.x + self.y

@dataclass(frozen=True)
class Foo:
    a: int
    b: Bar

    @property
    def c(self):
        return self.a + self.b.x - self.b.y

I can serialize the data structure as follows:

class CustomEncoder(json.JSONEncoder):
    def default(self, o):
        if dataclasses and dataclasses.is_dataclass(o):
            return dataclasses.asdict(o)
        return json.JSONEncoder.default(self, o)

foo = Foo(1, Bar(2,3))
print(json.dumps(foo, cls=CustomEncoder))

# Outputs {"a": 1, "b": {"x": 2, "y": 3}}

However, I would like to also serialize the properties (@property). Note I do not want to turn the properties into fields using __post_init__ as I would like to keep the dataclass' frozen. I do not want to use obj.__setattr__ to work around the frozen fields. I also do not want to pre-compute the values of the properties outside the class and pass them in as fields.

The current solution I am using is to explicitly write out how each object is serialized as follows:

class CustomEncoder2(json.JSONEncoder):
    def default(self, o):
        if isinstance(o, Foo):
            return {
                "a": o.a,
                "b": o.b,
                "c": o.c
            }
        elif isinstance(o, Bar):
            return {
                "x": o.x,
                "y": o.y,
                "z": o.z
            }
        return json.JSONEncoder.default(self, o)

foo = Foo(1, Bar(2,3))
print(json.dumps(foo, cls=CustomEncoder2))

# Outputs {"a": 1, "b": {"x": 2, "y": 3, "z": 5}, "c": 0} as desired

For a few levels of nesting, this is manageable but I am hoping for a more general solution. For example, here is a (hacky) solution that monkey-patches the _asdict_inner implementation from the dataclasses library.

def custom_asdict_inner(obj, dict_factory):
    if dataclasses._is_dataclass_instance(obj):
        result = []
        for f in dataclasses.fields(obj):
            value = custom_asdict_inner(getattr(obj, f.name), dict_factory)
            result.append((f.name, value))
        # Inject this one-line change
        result += [(prop, custom_asdict_inner(getattr(obj, prop), dict_factory)) for prop in dir(obj) if not prop.startswith('__')]
        return dict_factory(result)
    elif isinstance(obj, tuple) and hasattr(obj, '_fields'):
        return type(obj)(*[custom_asdict_inner(v, dict_factory) for v in obj])
    elif isinstance(obj, (list, tuple)):
        return type(obj)(custom_asdict_inner(v, dict_factory) for v in obj)
    elif isinstance(obj, dict):
        return type(obj)((custom_asdict_inner(k, dict_factory),
                          custom_asdict_inner(v, dict_factory))
                         for k, v in obj.items())
    else:
        return copy.deepcopy(obj)

dataclasses._asdict_inner = custom_asdict_inner

class CustomEncoder3(json.JSONEncoder):
    def default(self, o):
        if dataclasses and dataclasses.is_dataclass(o):
            return dataclasses.asdict(o)
        return json.JSONEncoder.default(self, o)

foo = Foo(1, Bar(2,3))
print(json.dumps(foo, cls=CustomEncoder3))

# Outputs {"a": 1, "b": {"x": 2, "y": 3, "z": 5}, "c": 0} as desired

Is there a recommended way to achieve what I am trying to do?

python-dataclasses immutability python-3.x serialization python
2 Answers

It seems to contradict a convenient dataclass feature:

Class(**asdict(obj)) == obj  # only for classes w/o nested dataclass attrs

If you don't find any relevant pypi package you can always add a 2-liner like this:

from dataclasses import asdict as std_asdict

def asdict(obj):
    return {**std_asdict(obj),
            **{a: getattr(obj, a) for a in getattr(obj, '__add_to_dict__', [])}}

Then you can specify in a custom but short manner which ones you want in dicts:

@dataclass
class A:
    f: str
    __add_to_dict__ = ['f2']

    @property
    def f2(self):
        return self.f + '2'



@dataclass
class B:
    f: str

print(asdict(A('f')))
print(asdict(B('f')))

:

{'f2': 'f2', 'f': 'f'}
{'f': 'f'}
user_145060620

There's no "recommended" way to include them that I know of.

Here's something that seems to work and I think meets your numerous requirements. It defines a custom encoder that calls its own _asdict() method when the object is a dataclass instead of monkey-patching the (private) dataclasses._asdict_inner() function and encapsulates (bundles) the code within the customer encoder that makes use of it.

Like you, I used the current implementation of dataclasses.asdict() as a guide/template since what you're asking for is basically just a customized version of that. The current value of each field that's a property is obtained by calling its __get__ method.

import copy
import dataclasses
from dataclasses import dataclass, field
import json
import re
from typing import List

class MyCustomEncoder(json.JSONEncoder):
    is_special = re.compile(r'^__[^\d\W]\w*__\Z', re.UNICODE)  # Dunder name.

    def default(self, obj):
        return self._asdict(obj)

    def _asdict(self, obj, *, dict_factory=dict):
        if not dataclasses.is_dataclass(obj):
            raise TypeError("_asdict() should only be called on dataclass instances")
        return self._asdict_inner(obj, dict_factory)

    def _asdict_inner(self, obj, dict_factory):
        if dataclasses.is_dataclass(obj):
            result = []
            # Get values of its fields (recursively).
            for f in dataclasses.fields(obj):
                value = self._asdict_inner(getattr(obj, f.name), dict_factory)
                result.append((f.name, value))
            # Add values of non-special attributes which are properties.
            is_special = self.is_special.match  # Local var to speed access.
            for name, attr in vars(type(obj)).items():
                if not is_special(name) and isinstance(attr, property):
                    result.append((name, attr.__get__(obj)))  # Get property's value.
            return dict_factory(result)
        elif isinstance(obj, tuple) and hasattr(obj, '_fields'):
            return type(obj)(*[self._asdict_inner(v, dict_factory) for v in obj])
        elif isinstance(obj, (list, tuple)):
            return type(obj)(self._asdict_inner(v, dict_factory) for v in obj)
        elif isinstance(obj, dict):
            return type(obj)((self._asdict_inner(k, dict_factory),
                              self._asdict_inner(v, dict_factory)) for k, v in obj.items())
        else:
            return copy.deepcopy(obj)


if __name__ == '__main__':

    @dataclass(frozen=True)
    class Bar():
        x: int
        y: int

        @property
        def z(self):
            return self.x + self.y


    @dataclass(frozen=True)
    class Foo():
        a: int
        b: Bar

        @property
        def c(self):
            return self.a + self.b.x - self.b.y

        # Added for testing.
        d: List = field(default_factory=lambda: [42])  # Field with default value.


    foo = Foo(1, Bar(2,3))
    print(json.dumps(foo, cls=MyCustomEncoder))

Output:

{"a": 1, "b": {"x": 2, "y": 3, "z": 5}, "d": [42], "c": 0}
user_3552300
Related