How dangerous is setting self.__class__ to something else?

Question

Say I have a class, which has a number of subclasses.

I can instantiate the class. I can then set its __class__ attribute to one of the subclasses. I hav

Answer 1

On arbitrary classes, this is extremely unlikely to work, and is very fragile even if it does. It's basically the same thing as pulling the underlying function objects out of the methods of one class, and calling them on objects which are not instances of the original class. Whether or not that will work depends on internal implementation details, and is a form of very tight coupling.

That said, changing the __class__ of objects amongst a set of classes that were particularly designed to be used this way could be perfectly fine. I've been aware that you can do this for a long time, but I've never yet found a use for this technique where a better solution didn't spring to mind at the same time. So if you think you have a use case, go for it. Just be clear in your comments/documentation what is going on. In particular it means that the implementation of all the classes involved have to respect all of their invariants/assumptions/etc, rather than being able to consider each class in isolation, so you'd want to make sure that anyone who works on any of the code involved is aware of this!

Answer 2

Here's an example of one way you could do the same thing without changing __class__. Quoting @unutbu in the comments to the question:

Suppose you were modeling cellular automata. Suppose each cell could be in one of say 5 Stages. You could define 5 classes Stage1, Stage2, etc. Suppose each Stage class has multiple methods.

class Stage1(object):
  …

class Stage2(object):
  …

…

class Cell(object):
  def __init__(self):
    self.current_stage = Stage1()
  def goToStage2(self):
    self.current_stage = Stage2()
  def __getattr__(self, attr):
    return getattr(self.current_stage, attr)

If you allow changing __class__ you could instantly give a cell all the methods of a new stage (same names, but different behavior).

Same for changing current_stage, but this is a perfectly normal and pythonic thing to do, that won't confuse anyone.

Plus, it allows you to not change certain special methods you don't want changed, just by overriding them in Cell.

Plus, it works for data members, class methods, static methods, etc., in ways every intermediate Python programmer already understands.

If you refuse to change __class__, then you might have to include a stage attribute, and use a lot of if statements, or reassign a lot of attributes pointing to different stage's functions

Yes, I've used a stage attribute, but that's not a downside—it's the obvious visible way to keep track of what the current stage is, better for debugging and for readability.

And there's not a single if statement or any attribute reassignment except for the stage attribute.

And this is just one of multiple different ways of doing this without changing __class__.

Answer 3

Well, not discounting the problems cautioned about at the start. But it can be useful in certain cases.

First of all, the reason I am looking this post up is because I did just this and __slots__ doesn't like it. (yes, my code is a valid use case for slots, this is pure memory optimization) and I was trying to get around a slots issue.

I first saw this in Alex Martelli's Python Cookbook (1st ed). In the 3rd ed, it's recipe 8.19 "Implementing Stateful Objects or State Machine Problems". A fairly knowledgeable source, Python-wise.

Suppose you have an ActiveEnemy object that has different behavior from an InactiveEnemy and you need to switch back and forth quickly between them. Maybe even a DeadEnemy.

If InactiveEnemy was a subclass or a sibling, you could switch class attributes. More exactly, the exact ancestry matters less than the methods and attributes being consistent to code calling it. Think Java interface or, as several people have mentioned, your classes need to be designed with this use in mind.

Now, you still have to manage state transition rules and all sorts of other things. And, yes, if your client code is not expecting this behavior and your instances switch behavior, things will hit the fan.

But I've used this quite successfully on Python 2.x and never had any unusual problems with it. Best done with a common parent and small behavioral differences on subclasses with the same method signatures.

No problems, until my __slots__ issue that's blocking it just now. But slots are a pain in the neck in general.

I would not do this to patch live code. I would also privilege using a factory method to create instances.

But to manage very specific conditions known in advance? Like a state machine that the clients are expected to understand thoroughly? Then it is pretty darn close to magic, with all the risk that comes with it. It's quite elegant.

Python 3 concerns? Test it to see if it works but the Cookbook uses Python 3 print(x) syntax in its example, FWIW.

Answer 4

In the comments I proposed modeling cellular automata as a possible use case for dynamic __class__s. Let's try to flesh out the idea a bit:

---

Using dynamic __class__:

class Stage(object):
    def __init__(self, x, y):
        self.x = x
        self.y = y

class Stage1(Stage):
    def step(self):
        if ...:
            self.__class__ = Stage2

class Stage2(Stage):
    def step(self):
        if ...:
            self.__class__ = Stage3

cells = [Stage1(x,y) for x in range(rows) for y in range(cols)]

def step(cells):
    for cell in cells:
        cell.step()
    yield cells

---

For lack of a better term, I'm going to call this

The traditional way: (mainly abarnert's code)

class Stage1(object):
    def step(self, cell):
        ...
        if ...:
            cell.goToStage2()

class Stage2(object):
    def step(self, cell):
        ...
        if ...:        
            cell.goToStage3()

class Cell(object):
    def __init__(self, x, y):
        self.x = x
        self.y = y
        self.current_stage = Stage1()
    def goToStage2(self):
        self.current_stage = Stage2()
    def __getattr__(self, attr):
        return getattr(self.current_stage, attr)

cells = [Cell(x,y) for x in range(rows) for y in range(cols)]

def step(cells):
    for cell in cells:
        cell.step(cell)
    yield cells

---

Comparison:

• The traditional way creates a list of Cell instances each with a current stage attribute.

  The dynamic __class__ way creates a list of instances which are subclasses of Stage. There is no need for a current stage attribute since __class__ already serves this purpose.

• The traditional way uses goToStage2, goToStage3, ... methods to switch stages.

  The dynamic __class__ way requires no such methods. You just reassign __class__.

• The traditional way uses the special method __getattr__ to delegate some method calls to the appropriate stage instance held in the self.current_stage attribute.

  The dynamic __class__ way does not require any such delegation. The instances in cells are already the objects you want.

• The traditional way needs to pass the cell as an argument to Stage.step. This is so cell.goToStageN can be called.

  The dynamic __class__ way does not need to pass anything. The object we are dealing with has everything we need.

---

Conclusion:

Both ways can be made to work. To the extent that I can envision how these two implementations would pan-out, it seems to me the dynamic __class__ implementation will be

• simpler (no Cell class),

• more elegant (no ugly goToStage2 methods, no brain-teasers like why you need to write cell.step(cell) instead of cell.step()),

• and easier to understand (no __getattr__, no additional level of indirection)

Answer 5

The other answers have done a good job of discussing the question of why just changing __class__ is likely not an optimal decision.

Below is one example of a way to avoid changing __class__ after instance creation, using __new__. I'm not recommending it, just showing how it could be done, for the sake of completeness. However it is probably best to do this using a boring old factory rather than shoe-horning inheritance into a job for which it was not intended.

class ChildDispatcher:
    _subclasses = dict()
    def __new__(cls, *args, dispatch_arg, **kwargs):
        # dispatch to a registered child class
        subcls = cls.getsubcls(dispatch_arg)
        return super(ChildDispatcher, subcls).__new__(subcls)
    def __init_subclass__(subcls, **kwargs):
        super(ChildDispatcher, subcls).__init_subclass__(**kwargs)
        # add __new__ contructor to child class based on default first dispatch argument
        def __new__(cls, *args, dispatch_arg = subcls.__qualname__, **kwargs):
            return super(ChildDispatcher,cls).__new__(cls, *args, **kwargs)
        subcls.__new__ = __new__
        ChildDispatcher.register_subclass(subcls)

    @classmethod
    def getsubcls(cls, key):
        name = cls.__qualname__
        if cls is not ChildDispatcher:
            raise AttributeError(f"type object {name!r} has no attribute 'getsubcls'")
        try:
            return ChildDispatcher._subclasses[key]
        except KeyError:
            raise KeyError(f"No child class key {key!r} in the "
                           f"{cls.__qualname__} subclasses registry")

    @classmethod
    def register_subclass(cls, subcls):
        name = subcls.__qualname__
        if cls is not ChildDispatcher:
            raise AttributeError(f"type object {name!r} has no attribute "
                                 f"'register_subclass'")
        if name not in ChildDispatcher._subclasses:
            ChildDispatcher._subclasses[name] = subcls
        else:
            raise KeyError(f"{name} subclass already exists")

class Child(ChildDispatcher): pass

c1 = ChildDispatcher(dispatch_arg = "Child")
assert isinstance(c1, Child)
c2 = Child()
assert isinstance(c2, Child)

Answer 6

Here's a list of things I can think of that make this dangerous, in rough order from worst to least bad:

• It's likely to be confusing to someone reading or debugging your code.
• You won't have gotten the right __init__ method, so you probably won't have all of the instance variables initialized properly (or even at all).
• The differences between 2.x and 3.x are significant enough that it may be painful to port.
• There are some edge cases with classmethods, hand-coded descriptors, hooks to the method resolution order, etc., and they're different between classic and new-style classes (and, again, between 2.x and 3.x).
• If you use __slots__, all of the classes must have identical slots. (And if you have the compatible but different slots, it may appear to work at first but do horrible things…)
• Special method definitions in new-style classes may not change. (In fact, this will work in practice with all current Python implementations, but it's not documented to work, so…)
• If you use __new__, things will not work the way you naively expected.
• If the classes have different metaclasses, things will get even more confusing.

Meanwhile, in many cases where you'd think this is necessary, there are better options:

• Use a factory to create an instance of the appropriate class dynamically, instead of creating a base instance and then munging it into a derived one.
• Use __new__ or other mechanisms to hook the construction.
• Redesign things so you have a single class with some data-driven behavior, instead of abusing inheritance.

As a very most common specific case of the last one, just put all of the "variable methods" into classes whose instances are kept as a data member of the "parent", rather than into subclasses. Instead of changing self.__class__ = OtherSubclass, just do self.member = OtherSubclass(self). If you really need methods to magically change, automatic forwarding (e.g., via __getattr__) is a much more common and pythonic idiom than changing classes on the fly.