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Make base class variant over base classes

I have two (non-trivial) classes like this:
class MyClass(Base):
def __init__(self, etc, *args, **kwargs):
super().__init__(*args, **kwargs)
self.etc = etc
def do_something(self, arg1, arg2):
# not actually a function call, just to make this as minimal as possible
output = computation_with_a_bunch_of_code(arg1, arg2)
return super().do_something(output)
Don't get too attached to the exact format, the super call may be in the really long computation (possibly multiple times), but the only difference is the super delegated to.
I've tried looking at related questions and none quite did what I wanted. For instance, This recommended one with multiple inheritance doesn't rely on super calls the way mine does.
While I'd love to just instantiate base like an object like so:
class MyFakeBaseClass:
def __init__(self, etc, base):
self.base = base
self.etc = etc
def do_something(self, arg1, arg2):
output = computation_with_a_bunch_of_code(arg1, arg2)
return self.base.do_something(output)
The library I'm using requires the ultimate base class of whatever I pass in to be one of its base classes (I think it uses metaclass subtyping magic to do things to your overridden methods, because duck typing doesn't generally work). While I could use the API's lowest level abstract class as my base, I'd have to do a lot of legwork to manually override a ton of methods with self.base.method() which would end up being about as much boilerplate code as normal code I'm replacing anyway, and a nightmare to read.
My ideal solution would look something like:
class _BaseClass(???):
def __init__(self, etc, *args, **kwargs):
super().__init__(*args, **kwargs)
self.etc = etc
def do_something(self, arguments):
arguments = computation_with_a_bunch_of_code()
return super().do_something(arguments)
class RealOne(_BaseClass(SuperOne)): pass
class RealTwo(_BaseClass(SuperTwo)): pass
I've tried typing.Generic since on the surface it seemed similar from examples, but given a Generic[T], it doesn't seem to use T as a real concrete super class when calling super. And on further research, it appears you can't really use it this way anyway, due to when the relevant values are set if nothing else.
Currently I've settled on:
def _actually_do_something(base, obj, arg1, arg2):
output = computation_with_a_bunch_of_code(arg1, arg2)
return base.do_something(output)
class MyClass(Base):
# ... etc ...
def do_something(self, arg1, arg2):
base = super()
return _actually_do_something(base, self, arg1, arg2)
Which is fine, but with multiple methods doing this it's a lot of boilerplate, and a frustrating amount of indirection, somewhat to me, but definitely to other people working on this who I've shown it to. It's not completely unmaintainable, but if one exists I'd like a cleaner solution.
It seems to me that the only other solution within my (unfortunate) constraints is metaclasses, but I couldn't find anything that quite did what I wanted. Most examples were either trivial (setting an attribute), or didn't specify how to use a dynamic base class. I'd welcome a simpler solution even more, but as far as I can tell decorators and the like can't quite do this. PEP 638 (Syntactic Macros) seems like it'd be an easy avenue to what I want, but as far as I can tell it's not implemented yet, and I'm unfortunately tied to environments that don't have 3.9 available yet anyway if it is (I'm stuck on 3.6, specifically). It's possible the answer is "just do what you're doing now and update to that whenever it comes out," but again, if there's something relatively simple, compact, and nice looking I'm missing I'd like to know.

Avoiding long constructors while inheriting without hiding constructor, optional arguments or functionality

I have a particular problem, but I will make the example more general.
I have a Parent class with a mandatory constructor parameter and a few optional ones, each with a default value. Then, I inherit Child from it and add a mandatory parameter, and inherit GrandChild from Child and add another mandatory parameter to the constructor. The result is similar to this:
class Parent():
def __init__(self, arg1, opt_arg1='opt_arg1_default_val', opt_arg2='opt_arg2_default_val',
opt_arg3='opt_arg3_default_val', opt_arg4='opt_arg4_default_val'):
self.arg1 = arg1
self.opt_arg1 = opt_arg1
self.opt_arg2 = opt_arg2
self.opt_arg3 = opt_arg3
self.opt_arg4 = opt_arg4
class Child(Parent):
def __init__(self, arg1, arg2, opt_arg1, opt_arg2, opt_arg3, opt_arg4):
super().__init__(arg1, opt_arg1, opt_arg2, opt_arg3, opt_arg4)
self.arg2 = arg2
class GrandChild(Child):
def __init__(self, arg1, arg2, arg3, opt_arg1, opt_arg2, opt_arg3, opt_arg4):
super().__init__(arg1, arg2, opt_arg1, opt_arg2, opt_arg3, opt_arg4)
self.arg3 = arg3
The problem is that this looks rather ugly, especially if I want to inherit more classes from Child, I'd have to copy/paste all the arguments in that new class's constructor.
In search for a solution, I found here that I can solve this problem using **kwargs like so:
class Parent():
def __init__(self, arg1, opt_arg1='opt_arg1_default_val', opt_arg2='opt_arg2_default_val',
opt_arg3='opt_arg3_default_val', opt_arg4='opt_arg4_default_val'):
self.arg1 = arg1
self.opt_arg1 = opt_arg1
self.opt_arg2 = opt_arg2
self.opt_arg3 = opt_arg3
self.opt_arg4 = opt_arg4
class Child(Parent):
def __init__(self, arg1, arg2, **kwargs):
super().__init__(arg1, **kwargs)
self.arg2 = arg2
class GrandChild(Child):
def __init__(self, arg1, arg2, arg3,**kwargs):
super().__init__(arg1, arg2,**kwargs)
self.arg3 = arg3
However, I am not sure if this is the right way.
There is also a slight inconvenience while creating objects of these classes. I am using PyCharm to develop, and in this case the IDE has a useful method of displaying a function/class constructor arguments. For instance, in the first example,
This makes it much easier to develop and can help future developers as well since they can see what other arguments the function has. However, in the second example, the optional arguments are not shown anymore:
And I do not think it is a good practice to use **kwargs in this case, since one would have to dig deeper into the code up to the Parent class to check what optional arguments it has.
I've also looked into using the Builder pattern, but then all I do is move the arguments list from my classes to builder classes, and I have the same problem, builders with lots of arguments that when inherited will create even more arguments on top of the already existing ones. Also in Python, as much as I see, Builder doesn't really make much sense considering all class members are public and can be accessed without needing setters and getters.
Any ideas on how to solve this constructor problem?

The basic idea is to write code that generates the __init__ method for you, with all the parameters specified explicitly rather than via *args and/or **kwargs, and without even needing to repeat yourself with all those self.arg1 = arg1 lines.
And, ideally, it can make it easy to add type annotations that PyCharm can use for popup hints and/or static type checking.1
And, while you're at it, why not build a __repr__ that displays the same values? And maybe even an __eq__, and a __hash__, and maybe lexicographical comparison operators, and conversion to and from a dict whose keys match the attributes for each JSON persistence, and…
Or, even better, use a library that takes care of that for you.
Python 3.7 comes with such a library, dataclasses. Or you can use a third-party library like attrs, that works with Python 3.4 and (with some limitations) 2.7. Or, for simple cases (where your objects are immutable, and you want them to work like a tuple of their attributes in specified order), you can use namedtuple, which works back to 3.0 and 2.6.
Unfortunately, dataclasses doesn't quite work for your use case. If you just write this:
from dataclasses import dataclass
#dataclass
class Parent:
arg1: str
opt_arg1: str = 'opt_arg1_default_val'
opt_arg2: str = 'opt_arg2_default_val'
opt_arg3: str = 'opt_arg3_default_val'
opt_arg4: str = 'opt_arg4_default_val'
#dataclass
class Child(Parent):
arg2: str
… you'll get an error, because it tries to place the mandatory parameter arg2 after the default-values parameters opt_arg1 through opt_arg4.
dataclasses doesn't have any way to reorder parameters (Child(arg1, arg2, opt_arg1=…), or to force them to be keyword-only parameters (Child(*, arg1, opt_arg1=…, arg2)). attrs doesn't have that functionality out of the box, but you can add it.
So, it's not quite as trivial as you'd hope, but it's doable.
But if you wanted to write this yourself, how would you create the __init__ function dynamically?
The simplest option is exec.
You've probably heard that exec is dangerous. But it's only dangerous if you're passing in values that came from your user. Here, you're only passing in values that came from your own source code.
It's still ugly—but sometimes it's the best answer anyway. The standard library's namedtuple used to be one giant exec template., and even the current version uses exec for most of the methods, and so does dataclasses.
Also, notice that all of these modules store the set of fields somewhere in a private class attribute, so subclasses can easily read the parent class's fields. If you didn't do that, you could use the inspect module to get the Signature for your base class's (or base classes', for multiple inheritance) initializer and work it out from there. But just using base._fields is obviously a lot simpler (and allows storing extra metadata that doesn't normally go in signatures).
Here's a dead simple implementation that doesn't handle most of the features of attrs or dataclasses, but does order all mandatory parameters before all optionals.
def makeinit(cls):
fields = ()
optfields = {}
for base in cls.mro():
fields = getattr(base, '_fields', ()) + fields
optfields = {**getattr(base, '_optfields', {}), **optfields}
optparams = [f"{name} = {val!r}" for name, val in optfields.items()]
paramstr = ', '.join(['self', *fields, *optparams])
assignstr = "\n ".join(f"self.{name} = {name}" for name in [*fields, *optfields])
exec(f'def __init__({paramstr}):\n {assignstr}\ncls.__init__ = __init__')
return cls
#makeinit
class Parent:
_fields = ('arg1',)
_optfields = {'opt_arg1': 'opt_arg1_default_val',
'opt_arg2': 'opt_arg2_default_val',
'opt_arg3': 'opt_arg3_default_val',
'opt_arg4': 'opt_arg4_default_val'}
#makeinit
class Child(Parent):
_fields = ('arg2',)
Now, you've got exactly the __init__ methods you wanted on Parent and Child, fully inspectable2 (including help), and without having to repeat yourself.
1. I don't use PyCharm, but I know that well before 3.7 came out, their devs were involved in the discussion of #dataclass and were already working on adding explicit support for it to their IDE, so it doesn't even have to evaluate the class definition to get all that information. I don't know if it's available in the current version, but if not, I assume it will be. Meanwhile, #dataclass already just works for me with IPython auto-completion, emacs flycheck, and so on, which is good enough for me. :)
2. … at least at runtime. PyCharm may not be able to figure things out statically well enough to do popup completion.

Inheritance best practice : *args, **kwargs or explicitly specifying parameters [closed]

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I often find myself overwriting methods of a parent class, and can never decide if I should explicitly list given parameters or just use a blanket *args, **kwargs construct. Is one version better than the other? Is there a best practice? What (dis-)advantages am I missing?
class Parent(object):
def save(self, commit=True):
# ...
class Explicit(Parent):
def save(self, commit=True):
super(Explicit, self).save(commit=commit)
# more logic
class Blanket(Parent):
def save(self, *args, **kwargs):
super(Blanket, self).save(*args, **kwargs)
# more logic
Perceived benefits of explicit variant
More explicit (Zen of Python)
easier to grasp
function parameters easily accessed
Perceived benefits of blanket variant
more DRY
parent class is easily interchangeable
change of default values in parent method is propagated without touching other code

Liskov Substitution Principle
Generally you don't want you method signature to vary in derived types. This can cause problems if you want to swap the use of derived types. This is often referred to as the Liskov Substitution Principle.
Benefits of Explicit Signatures
At the same time I don't think it's correct for all your methods to have a signature of *args, **kwargs. Explicit signatures:
help to document the method through good argument names
help to document the method by specifying which args are required and which have default values
provide implicit validation (missing required args throw obvious exceptions)
Variable Length Arguments and Coupling
Do not mistake variable length arguments for good coupling practice. There should be a certain amount of cohesion between a parent class and derived classes otherwise they wouldn't be related to each other. It is normal for related code to result in coupling that reflects the level of cohesion.
Places To Use Variable Length Arguments
Use of variable length arguments shouldn't be your first option. It should be used when you have a good reason like:
Defining a function wrapper (i.e. a decorator).
Defining a parametric polymorphic function.
When the arguments you can take really are completely variable (e.g. a generalized DB connection function). DB connection functions usually take a connection string in many different forms, both in single arg form, and in multi-arg form. There are also different sets of options for different databases.
...
Are You Doing Something Wrong?
If you find you are often creating methods which take many arguments or derived methods with different signatures you may have a bigger issue in how you're organizing your code.

My choice would be:
class Child(Parent):
def save(self, commit=True, **kwargs):
super(Child, self).save(commit, **kwargs)
# more logic
It avoids accessing commit argument from *args and **kwargs and it keeps things safe if the signature of Parent:save changes (for example adding a new default argument).
Update : In this case, having the *args can cause troubles if a new positional argument is added to the parent. I would keep only **kwargs and manage only new arguments with default values. It would avoid errors to propagate.

If you are certain that Child will keep the signature, surely the explicit approach is preferable, but when Child will change the signature I personally prefer to use both approaches:
class Parent(object):
def do_stuff(self, a, b):
# some logic
class Child(Parent):
def do_stuff(self, c, *args, **kwargs):
super(Child, self).do_stuff(*args, **kwargs)
# some logic with c
This way, changes in the signature are quite readable in Child, while the original signature is quite readable in Parent.
In my opinion this is also the better way when you have multiple inheritance, because calling super a few times is quite disgusting when you don't have args and kwargs.
For what it's worth, this is also the preferred way in quite a few Python libs and frameworks (Django, Tornado, Requests, Markdown, to name a few). Although one should not base his choices on such things, I'm merely implying that this approach is quite widespread.

Not really an answer but more a side note: If you really, really want to make sure the default values for the parent class are propagated to the child classes you can do something like:
class Parent(object):
default_save_commit=True
def save(self, commit=default_save_commit):
# ...
class Derived(Parent):
def save(self, commit=Parent.default_save_commit):
super(Derived, self).save(commit=commit)
However I have to admit this looks quite ugly and I would only use it if I feel I really need it.

I prefer explicit arguments because auto complete allows you to see the method signature of the function while making the function call.

In addition to the other answers:
Having variable arguments may "decouple" the parent from the child, but creates a coupling between the object created and the parent, which I think is worse, because now you created a "long distance" couple (more difficult to spot, more difficult to maintain, because you may create several objects in your application)
If you're looking for decoupling, take a look at composition over inheritance

python classes/subclasses with mutually exclusive optional arguments using a instance of the superclass as a possible constructor

I am trying to define in python a class B, subclass of A, that could accept multiple ways of being instantiated to be very flexible. A is a simple class with few members. I know how to write A to allow a mix of positional/keywords arguments and having default values if some arguments are omitted.
My problem comes with class B. B is simply A with an extra member. But when I create an instance of B, I would like to have the choice between:
an instance created from a mix of positional/keyword arguments (pretty much like A)
an instance created specifying the extra member and an existing instance (or a copy) of A
created using only an existing instance of A and the extra member gets a default value.
making mutually exclusive the use of an existing instance of A and positional/keywords arguments
edit: something like this
class A:
def __init__(self, x1=1, x2='a', x3=4.0)
self.x1=x1
self.x2=x2
self.x3=x3
class B(A):
def __init__(self, x1=1, x2='a', x3=4.0, x4='t')
self.x4=x4
A.__init__(x1,x2,x3)
but then I would like to use it like this:
a1=A()
b1=B() # takes all defaults
b2=B(x4='r') # x1,x2,x3 gets defaults from A
b3=B(a1,x4='z') # use a1, an instance of A
b4=B(x1,x2,x3,x4) # defines manually all arguments
b5=B(a1,x1=2) # this one should not work because x1 can come from the instance a1 or x1

The simplest thing is probably to define B with a nice straightforward initialiser with the same signature as A uses and then additional factory functions defined as class methods to do the more complicated scenarios. That way you won't get messed up when two different methods of construction actually take exactly indistinguishable arguments.
So something like:
class B(A):
def __init__(self, some, args):
A.__init__(self, some, args)
#classmethod
def fromA(cls, theA, extra=some_default):
newB = cls(theA.some, theA.args)
newB.extra = extra
return newB

In my experience the best way of dealing with complex cases like that is to use *args, **kwargs and process them manually through a series of conditionals, e.g, in B.__init__:
if isinstance(arg[0], A):
if has_other_args:
raise TypeError
else:
do_something()
else:
do_something_else()
However, this can still get really complicated so factory functions (as suggested by Duncan) are probably the best approach.
Side note: Whenever I find myself in a position of wanting this sort of functionality, its usually an indication that something's not right with my design. Even if I implement it, I usually end up replacing the entire structure with something neater.

Is super() broken in Python-2.x? [closed]

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Closed 10 years ago.
It's often stated that super should be avoided in Python 2. I've found in my use of super in Python 2 that it never acts the way I expect unless I provide all arguments such as the example:
super(ThisClass, self).some_func(*args, **kwargs)
It seems to me this defeats the purpose of using super(), it's neither more concise, or much better than TheBaseClass.some_func(self, *args, **kwargs). For most purposes method resolution order is a distant fairy tale.
Other than the fact that 2.7 is the last major release to Python 2, why does super remain broken in Python 2?
How and why has Python 3's super changed? Are there any caveats?
When and why should I use super going forward?

super() is not broken -- it just should not be considered the standard way of calling a method of the base class. This did not change with Python 3.x. The only thing that changed is that you don't need to pass the arguments self, cls in the standard case that self is the first parameter of the current function and cls is the class currently being defined.
Regarding your question when to actually use super(), my answer would be: hardly ever. I personally try to avoid the kind of multiple inheritance that would make super() useful.
Edit: An example from real life that I once ran into: I had some classes defining a run() method, some of which had base classes. I used super() to call the inherited constructors -- I did not think it mattered because I was using single inheritance only:
class A(object):
def __init__(self, i):
self.i = i
def run(self, value):
return self.i * value
class B(A):
def __init__(self, i, j):
super(B, self).__init__(i)
self.j = j
def run(self, value):
return super(B, self).run(value) + self.j
Just imagine there were several of these classes, all with individual constructor prototypes, and all with the same interface to run().
Now I wanted to add some additional functionality to all of these classes, say logging. The additional functionality required an additional method to be defined on all these classes, say info(). I did not want to invade the original classes, but rather define a second set of classes inheriting from the original ones, adding the info() method and inheriting from a mix-in providing the actual logging. Now, I could not use super() in the constructor any more, so I used direct calls:
class Logger(object):
def __init__(self, name):
self.name = name
def run_logged(self, value):
print "Running", self.name, "with info", self.info()
return self.run(value)
class BLogged(B, Logger):
def __init__(self, i, j):
B.__init__(self, i, j)
Logger.__init__("B")
def info(self):
return 42
Here things stop working. The super() call in the base class constructor suddenly calls Logger.__init__(), and BLogged can't do anything about it. There is actually no way to make this work, except for removing the super() call in B itself.
[Another Edit: I don't seem to have made my point, judging from all the comments here and below the other answers. Here is how to make this code work using super():
class A(object):
def __init__(self, i, **kwargs):
super(A, self).__init__(**kwargs)
self.i = i
def run(self, value):
return self.i * value
class B(A):
def __init__(self, j, **kwargs):
super(B, self).__init__(**kwargs)
self.j = j
def run(self, value):
return super(B, self).run(value) + self.j
class Logger(object):
def __init__(self, name, **kwargs):
super(Logger,self).__init__(**kwargs)
self.name = name
def run_logged(self, value):
print "Running", self.name, "with info", self.info()
return self.run(value)
class BLogged(B, Logger):
def __init__(self, **kwargs):
super(BLogged, self).__init__(name="B", **kwargs)
def info(self):
return 42
b = BLogged(i=3, j=4)
Compare this with the use of explicit superclass calls. You decide which version you prefer.]
This and similar stories are why I think that super() should not be considered the standard way of calling methods of the base class. It does not mean super() is broken.

super() is not broken, in Python 2 or Python 3.
Let's consider the arguments from the blog post:
It doesn't do what it sounds like it does.
OK, you may agree or disagree on that, it's pretty subjective. What should it have been called then? super() is a replacement for calling the superclass directly, so the name seems fine to me. It does NOT call the superclass directly, because if that was all it did, it would be pointless, as you could do that anyway. OK, admittedly, that may not be obvious, but the cases where you need super() are generally not obvious. If you need it, you are doing some pretty hairy multiple inheritance. It's not going to be obvious. (Or you are doing a simple mixin, in which case it will be pretty obvious and behave as you expect even if you didn't read the docs).
If you can call the superclass directly, that's probably what you'll end up doing. That's the easy and intuitive way of doing it. super() only comes into play when that doesn't work.
It doesn't mesh well with calling the superclass directly.
Yes, because it's designed to solve a problem with doing that. You can call the superclass directly if, and only if, you know exactly what class that is. Which you don't for mixins, for example, or when your class hierarchy is so messed up that you actually are merging two branches (which is the typical example in all examples of using super()).
So as long as every class in your class hierarchy has a well defined place, calling the superclass directly works. If you don't, then it does not work, and in that case you must use super() instead. That's the point of super() that it figures out what the "next superclass" is according to the MRO, without you explicitly having to specify it, because you can't always do that because you don't always know what it is, for example when using mixins.
The completely different programming language Dylan, a sort of lisp-thingy, solves this in another way that can't be used in Python because it's very different.
Eh. OK?
super() doesn't call your superclass.
Yeah, you said that.
Don't mix super() and direct calling.
Yeah, you said that too.
So, there is two arguments against it: 1. The name is bad. 2. You have to use it consistently.
That does not translate to it being "broken" or that it should be "avoided".

You seem to imply in your post that
def some_func(self, *args, **kwargs):
self.__class__.some_func(self, *args, **kwargs)
is not an infinite recursion. It is, and super would be more correct.
Also, yes, you are required to pass all arguments to super(). This is a bit like complaining that max() doesn't work like expected unless you pass it all the numbers you want to check.
In 3.x, however, fewer arguments are needed: you can do super().foo(*args, **kwargs) instead of super(ThisClass, self).foo(*args, **kwargs).
Anyway, I'm unsure as to any situations when super should be avoided. Its behavior is only "weird" when MI is involved, and when MI is involved, super() is basically your only hope for a correct solution. In Single-Inheritance it's just slightly wordier than SuperClass.foo(self, *args, **kwargs), and does nothing different.
I think I agree with Sven that this sort of MI is worth avoiding, but I don't agree that super is worth avoiding. If your class is supposed to be inherited, super offers users of your class hope of getting MI to work, if they're weird in that way, so it makes your class more usable.

Did you read the article that you link it? It doesn't conclude that super should be avoided but that you should be wary of its caveats when using it. These caveats are summarized by the article, though I would disagree with their suggestions.
The main point of the article is that multiple inheritance can get messy, and super doesn't help as much as the author would want. However doing multiple inheritance without super is often even more complicated.
If you're not doing multiple inheritance, super gives you the advantage that anyone inheriting from your class can add simple mixins and their __init__ would be properly called. Just remember to always call the __init__ of the superclass, even when you're inheriting from object, and to pass all the remaining arguments (*a and **kw) to it. When you're calling other methods from the parent class also use super, but this time use their proper signature that you already know (i.e. ensure that they have the same signature in all classes).
If you're doing multiple inheritance you'd have to dig deeper than that, and probably re-read the same article more carefully to be aware of the caveats. And it's also only during multiple inheritance when you might a situation where an explicit call to the parent might be better than super, but without a specific scenario nobody can tell you whether super should be used or not.
The only change in super in Python 3.x is that you don't need to explicitly pass the current class and self to it. This makes super more attractive, because using it would mean no hardcoding of either the parent class or the current class.

#Sven Marnach:
The problem with your example is that you mix explicit superclass calls B.__init__ and Logger.__init__ in Blogged with super() in B. That won't work. Either you use all explicit superclass calls or use super() on all classes. When you use super() you need to use it on all classes involved, including A I think. Also in your example I think you could use explicit superclass calls in all classes, i.e use A.__init__ in class B.
When there is no diamond inheritance I think super() doesn't have much advantage. The problem is, however, that you don't know in advance if you will get into any diamond inheritance in the future so in that case it would be wise to use super() anyway (but then use it consistently). Otherwise you would end up having to change all classes at a later time or run into problems.