I have a class MyClass with a complex __init__ function.
This class had a method my_method(self) which I would like to test.
my_method only needs attribute my_attribute from the class instance.
Is there a way I can mock class instances without calling __init__ and by setting the attributes of each class instance instead?
What I have:
# my_class.py
from utils import do_something
class MyClass(object):
def __init__(self, *args, **kwargs):
# complicated function which I would like to bypass when initiating a mocked instance class
pass
def my_method(self):
return do_something(self.my_attribute)
What I tried
#mock.patch("my_class.MyClass")
def test_my_method(class_mock, attribute):
instance = class_mock.return_value
instance.my_attribute = attribute
example_instance = my_class.MyClass()
out_my_method = example_instance.my_method()
# then perform some assertions on `out_my_method`
however this still makes usage of __init__ which I hope we can by-pass or mock.
As I mentioned in the comments, one way to test a single method without having to create an instance is:
MyClass.my_method(any_object_with_my_attribute)
The problem with this, as with both options in quamrana's answer, is that we have now expanded the scope of any future change just because of the tests. If a change to my_method requires access to an additional attribute, we now have to change both the implementation and something else (the SuperClass, the MockMyClass, or in this case any_object_with_my_attribute_and_another_one).
Let's have a more concrete example:
import json
class MyClass:
def __init__(self, filename):
with open(filename) as f:
data = json.load(f)
self.foo = data.foo
self.bar = data.bar
self.baz = data.baz
def my_method(self):
return self.foo ** 2
Here any test that requires an instance of MyClass. is painful because of the file access in __init__. A more testable implementation would split apart the detail of how the data is accessed and the initialisation of a valid instance:
class MyClass:
def __init__(self, foo, bar, baz):
self.foo = foo
self.bar = bar
self.baz = baz
def my_method(self):
return self.foo ** 2
#classmethod
def from_json(cls, filename):
with open(filename) as f:
data = json.load(f)
return cls(data.foo, data.bar, data.baz)
You have to refactor MyClass("path/to/file") to MyClass.from_json("path/to/file"), but wherever you already have the data (e.g. in your tests) you can use e.g. MyClass(1, 2, 3) to create the instance without requiring a file (you only need to consider the file in the tests of from_json itself). This makes it clearer what the instance actually needs, and allows the introduction of other ways to construct an instance without changing the interface.
There are at least two options I can see:
Extract a super class:
class SuperClass:
def __init__(self, attribute):
self.my_attribute = attribute
def my_method(self):
return do_something(self.my_attribute)
class MyClass(SuperClass):
def __init__(self, *args, **kwargs):
super().__init__(attribute) # I don't know where attribute comes from
# complicated function which I would like to bypass when initiating a mocked instance class
Your tests can instantiate SuperClass and call my_method().
Inherit from MyClass as is and make your own simple __init__():
class MockMyClass(MyClass):
def __init__(self, attribute):
self.my_attribute = attribute
Now your test code can instantiate MockMyClass with the required attribute and call my_method()
For instance, you could write the test as follows
def test_my_method(attribute):
class MockMyClass(MyClass):
def __init__(self, attribute):
self.my_attribute = attribute
out_my_method = MockMyClass(attribute).my_method()
# perform assertions on out_my_method
Related
Let's say I have a class and would like to implement a method which creates an instance of that class. What I have is 2 options:
static method,
class method.
An example:
class DummyClass:
def __init__(self, json):
self.dict = json
#staticmethod
def from_json_static(json):
return DummyClass(json)
#classmethod
def from_json_class(cls, json):
return cls(json)
Both of the methods work:
dummy_dict = {"dummy_var": 124}
dummy_instance = DummyClass({"test": "abc"})
dummy_instance_from_static = dummy_instance.from_json_static(dummy_dict)
print(dummy_instance_from_static.dict)
> {'dummy_var': 124}
dummy_instance_from_class = DummyClass.from_json_class(dummy_dict)
print(dummy_instance_from_class.dict)
> {'dummy_var': 124}
What I often see in codes of other people is the classmethod design instead of staticmethod. Why is this the case?
Or, rephrasing the question to possibly get a more comprehensive answer: what are the pros and cons of creating a class instance via classmethod vs staticmethod in Python?
Two big advantages of the #classmethod approach:
First, you don't hard-code the name. Given modern refactoring tools in IDEs, this isn't as big of a deal, but it is nice to not have your code break if you change the name of your Foo, class to Bar::
class Bar:
#statmicmethod
def make_me():
return Foo()
Another advantage (at least, you should understand the difference!) is how this behaves with inheritance:
class Foo:
#classmethod
def make_me_cm(cls):
return cls()
#staticmethod
def make_me_sm():
return Foo()
class Bar(Foo):
pass
print(Bar.make_me_cm()) # it's a Bar instance
print(Bar.make_me_sm()) # it's a Foo instance
Lets define simple class decorator function, which creates subclass and adds 'Dec' to original class name only:
def decorate_class(klass):
new_class = type(klass.__name__ + 'Dec', (klass,), {})
return new_class
Now apply it on a simple subclass definition:
class Base(object):
def __init__(self):
print 'Base init'
#decorate_class
class MyClass(Base):
def __init__(self):
print 'MyClass init'
super(MyClass, self).__init__()
Now, if you try instantiate decorated MyClass, it will end up in an infinite loop:
c = MyClass()
# ...
# File "test.py", line 40, in __init__
# super(MyClass, self).__init__()
# RuntimeError: maximum recursion depth exceeded while calling a Python object
It seems, super can't handle this case and does not skip current class from inheritance chain.
The question, how correctly use class decorator on classes using super ?
Bonus question, how get final class from proxy-object created by super ? Ie. get object class from super(Base, self).__init__ expression, as determined parent class defining called __init__.
If you just want to change the class's .__name__ attribute, make a decorator that does that.
from __future__ import print_function
def decorate_class(klass):
klass.__name__ += 'Dec'
return klass
class Base(object):
def __init__(self):
print('Base init')
#decorate_class
class MyClass(Base):
def __init__(self):
print('MyClass init')
super(MyClass, self).__init__()
c = MyClass()
cls = c.__class__
print(cls, cls.__name__)
Python 2 output
MyClass init
Base init
<class '__main__.MyClassDec'> MyClassDec
Python 3 output
MyClass init
Base init
<class '__main__.MyClass'> MyClassDec
Note the difference in the repr of cls. (I'm not sure why you'd want to change a class's name though, it sounds like a recipe for confusion, but I guess it's ok for this simple example).
As others have said, an #decorator isn't intended to create a subclass. You can do it in Python 3 by using the arg-less form of super (i.e., super().__init__()). And you can make it work in both Python 3 and Python 2 by explicitly supplying the parent class rather than using super.
from __future__ import print_function
def decorate_class(klass):
name = klass.__name__
return type(name + 'Dec', (klass,), {})
class Base(object):
def __init__(self):
print('Base init')
#decorate_class
class MyClass(Base):
def __init__(self):
print('MyClass init')
Base.__init__(self)
c = MyClass()
cls = c.__class__
print(cls, cls.__name__)
Python 2 & 3 output
MyClass init
Base init
<class '__main__.MyClassDec'> MyClassDec
Finally, if we just call decorate_class using normal function syntax rather than as an #decorator we can use super.
from __future__ import print_function
def decorate_class(klass):
name = klass.__name__
return type(name + 'Dec', (klass,), {})
class Base(object):
def __init__(self):
print('Base init')
class MyClass(Base):
def __init__(self):
print('MyClass init')
super(MyClass, self).__init__()
MyClassDec = decorate_class(MyClass)
c = MyClassDec()
cls = c.__class__
print(cls, cls.__name__)
The output is the same as in the last version.
Since your decorator returns an entirely new class with different name, for that class MyClass object doesn't even exist. This is not the case class decorators are intended for. They are intended to add additional functionality to an existing class, not outright replacing it with some other class.
Still if you are using Python3, solution is simple -
#decorate_class
class MyClass(Base):
def __init__(self):
print 'MyClass init'
super().__init__()
Otherwise, I doubt there is any straight-forward solution, you just need to change your implementation. When you are renaming the class, you need to rewrite overwrite __init__ as well with newer name.
The problem is that your decorator creates a subclass of the original one. That means that super(Myclass) now point to... the original class itself!
I cannot even explain how the 0 arg form of super manages to do the job in Python 3, I could not find anything explicit in the reference manual. I assume it must use the class in which it is used at the time of declaration. But I cannot imagine a way to get that result in Python2.
If you want to be able to use super in the decorated class in Python 2, you should not create a derived class, but directly modify the original class in place.
For example, here is a decorator that prints a line before and after calling any method:
def decorate_class(klass):
for name, method in klass.__dict__.iteritems(): # iterate the class attributes
if isinstance(method, types.FunctionType): # identify the methods
def meth(*args, **kwargs): # define a wrapper
print "Before", name
method(*args, **kwargs)
print "After", name
setattr(klass, name, meth) # tell the class to use the wrapper
return klass
With your example it gives as expected:
>>> c = MyClass()
Before __init__
MyClass init
Base init
After __init__
If you have multiple layers of inheritance and know that a particular variable exists, is there a way to trace back to where the variable originated? Without having to navigate backwards by looking through each file and classes. Possibly calling some sort of function that will do it?
Example:
parent.py
class parent(object):
def __init__(self):
findMe = "Here I am!"
child.py
from parent import parent
class child(parent):
pass
grandson.py
from child import child
class grandson(child):
def printVar(self):
print self.findMe
Try to locate where the findMe variable came from with a function call.
If the "variable" is an instance variable - , so , if at any point in chain of __init__ methods you do:
def __init__(self):
self.findMe = "Here I am!"
It is an instance variable from that point on, and cannot, for all effects, be made distinct of any other instance variable. (Unless you put in place a mechanism, like a class with a special __setattr__ method, that will keep track of attributes changing, and introspect back which part of the code set the attribute - see last example on this answer)
Please also note that on your example,
class parent(object):
def __init__(self):
findMe = "Here I am!"
findMe is defined as a local variable to that method and does not even exist after __init__ is finished.
Now, if your variable is set as a class attribute somewhere on the inheritance chain:
class parent(object):
findMe = False
class childone(parent):
...
It is possible to find the class where findMe is defined by introspecting each class' __dict__ in the MRO (method resolution order) chain . Of course, there is no way, and no sense, in doing that without introspecting all classes in the MRO chain - except if one keeps track of attributes as defined, like in the example bellow this - but introspecting the MRO itself is a oneliner in Python:
def __init__(self):
super().__init__()
...
findme_definer = [cls for cls in self.__class__.__mro__ if "findMe" in cls.__dict__][0]
Again - it would be possible to have a metaclass to your inheritance chain which would keep track of all defined attributes in the inheritance tree, and use a dictionary to retrieve where each attribute is defined. The same metaclass could also auto-decorate all __init__ (or all methods), and set a special __setitem__ so that it could track instance attributes as they are created, as listed above.
That can be done, is a bit complicated, would be hard to maintain, and probably is a signal you are taking the wrong approach to your problem.
So, the metaclass to record just class attributes could simply be (python3 syntax - define a __metaclass__ attribute on the class body if you are still using Python 2.7):
class MetaBase(type):
definitions = {}
def __init__(cls, name, bases, dct):
for attr in dct.keys():
cls.__class__.definitions[attr] = cls
class parent(metaclass=MetaBase):
findMe = 5
def __init__(self):
print(self.__class__.definitions["findMe"])
Now, if one wants to find which of the superclasses defined an attribute of the currentclass, just a "live" tracking mechanism, wrapping each method in each class can work - it is a lot trickier.
I've made it - even if you won't need this much, this combines both methods - keeping track of class attributes in the class'class definitions and on an instance _definitions dictionary - since in each created instance an arbitrary method might have been the last to set a particular instance attribute: (This is pure Python3, and maybe not that straighforward porting to Python2 due to the "unbound method" that Python2 uses, and is a simple function in Python3)
from threading import current_thread
from functools import wraps
from types import MethodType
from collections import defaultdict
def method_decorator(func, cls):
#wraps(func)
def wrapper(self, *args, **kw):
self.__class__.__class__.current_running_class[current_thread()].append(cls)
result = MethodType(func, self)(*args, **kw)
self.__class__.__class__.current_running_class[current_thread()].pop()
return result
return wrapper
class MetaBase(type):
definitions = {}
current_running_class = defaultdict(list)
def __init__(cls, name, bases, dct):
for attrname, attr in dct.items():
cls.__class__.definitions[attr] = cls
if callable(attr) and attrname != "__setattr__":
setattr(cls, attrname, method_decorator(attr, cls))
class Base(object, metaclass=MetaBase):
def __setattr__(self, attr, value):
if not hasattr(self, "_definitions"):
super().__setattr__("_definitions", {})
self._definitions[attr] = self.__class__.current_running_class[current_thread()][-1]
return super().__setattr__(attr,value)
Example Classes for the code above:
class Parent(Base):
def __init__(self):
super().__init__()
self.findMe = 10
class Child1(Parent):
def __init__(self):
super().__init__()
self.findMe1 = 20
class Child2(Parent):
def __init__(self):
super().__init__()
self.findMe2 = 30
class GrandChild(Child1, Child2):
def __init__(self):
super().__init__()
def findall(self):
for attr in "findMe findMe1 findMe2".split():
print("Attr '{}' defined in class '{}' ".format(attr, self._definitions[attr].__name__))
And on the console one will get this result:
In [87]: g = GrandChild()
In [88]: g.findall()
Attr 'findMe' defined in class 'Parent'
Attr 'findMe1' defined in class 'Child1'
Attr 'findMe2' defined in class 'Child2'
Python 3 doesn't allow you to reference a class inside its body (except in methods):
class A:
static_attribute = A()
def __init__(self):
...
This raises a NameError in the second line because 'A' is not defined.
Alternatives
I have quickly found one workaround:
class A:
#property
#classmethod
def static_property(cls):
return A()
def __init__(self):
...
Although this isn't exactly the same since it returns a different instance every time (you could prevent this by saving the instance to a static variable the first time).
Are there simpler and/or more elegant alternatives?
EDIT:
I have moved the question about the reasons for this restriction to a separate question
The expression A() can't be run until the class A has been defined. In your first block of code, the definition of A is not complete at the point you are trying to execute A().
Here is a simpler alternative:
class A:
def __init__(self):
...
A.static_attribute = A()
When you define a class, Python immediately executes the code within the definition. Note that's different than defining a function where Python compiles the code, but doesn't execute it.
That's why this will create an error:
class MyClass(object):
a = 1 / 0
But this won't:
def my_func():
a = 1 / 0
In the body of A's class definition, A is not yet defined, so you can't reference it until after it's been defined.
There are several ways you can accomplish what you're asking, but it's not clear to me why this would be useful in the first place, so if you can provide more details about your use case, it'll be easier to recommend which path to go down.
The simplest would be what khelwood posted:
class A(object):
pass
A.static_attribute = A()
Because this is modifying class creation, using a metaclass could be appropriate:
class MetaA(type):
def __new__(mcs, name, bases, attrs):
cls = super(MetaA, mcs).__new__(mcs, name, bases, attrs)
cls.static_attribute = cls()
return cls
class A(object):
__metaclass__ = MetaA
Or you could use descriptors to have the instance lazily created or if you wanted to customize access to it further:
class MyDescriptor(object):
def __get__(self, instance, owner):
owner.static_attribute = owner()
return owner.static_attribute
class A(object):
static_attribute = MyDescriptor()
Using the property decorator is a viable approach, but it would need to be done something like this:
class A:
_static_attribute = None
#property
def static_attribute(self):
if A._static_attribute is None:
A._static_attribute = A()
return A._static_attribute
def __init__(self):
pass
a = A()
print(a.static_attribute) # -> <__main__.A object at 0x004859D0>
b = A()
print(b.static_attribute) # -> <__main__.A object at 0x004859D0>
You can use a class decorator:
def set_static_attribute(cls):
cls.static_attribute = cls()
return cls
#set_static_attribute
class A:
pass
Now:
>>>> A.static_attribute
<__main__.A at 0x10713a0f0>
Applying the decorator on top of the class makes it more explicit than setting static_attribute after a potentially long class definition. The applied decorator "belongs" to the class definition. So if you move the class around in your source code you will more likely move it along than an extra setting of the attribute outside the class.
Here's a very simple example of what I'm trying to get around:
class Test(object):
some_dict = {Test: True}
The problem is that I cannot refer to Test while it's still being defined
Normally, I'd just do this:
class Test(object):
some_dict = {}
def __init__(self):
if self.__class__.some_dict == {}:
self.__class__.some_dict = {Test: True}
But I never create an instance of this class. It's really just a container to hold a group of related functions and data (I have several of these classes, and I pass around references to them, so it is necessary for Test to be it's own class)
So my question is, how could I refer to Test while it's being defined, or is there something similar to __init__ that get's called as soon as the class is defined? If possible, I want self.some_dict = {Test: True} to remain inside the class definition. This is the only way I know how to do this so far:
class Test(object):
#classmethod
def class_init(cls):
cls.some_dict = {Test: True}
Test.class_init()
The class does in fact not exist while it is being defined. The way the class statement works is that the body of the statement is executed, as a block of code, in a separate namespace. At the end of the execution, that namespace is passed to the metaclass (such as type) and the metaclass creates the class using the namespace as the attributespace.
From your description, it does not sound necessary for Test to be a class. It sounds like it should be a module instead. some_dict is a global -- even if it's a class attribute, there's only one such attribute in your program, so it's not any better than having a global -- and any classmethods you have in the class can just be functions.
If you really want it to be a class, you have three options: set the dict after defining the class:
class Test:
some_dict = {}
Test.some_dict[Test] = True
Use a class decorator (in Python 2.6 or later):
def set_some_dict(cls):
cls.some_dict[cls] = True
#set_some_dict
class Test:
some_dict = {}
Or use a metaclass:
class SomeDictSetterType(type):
def __init__(self, name, bases, attrs):
self.some_dict[self] = True
super(SomeDictSetterType, self).__init__(name, bases, attrs)
class Test(object):
__metaclass__ = SomeDictSetterType
some_dict = {}
You could add the some_dict attribute after the main class definition.
class Test(object):
pass
Test.some_dict = {Test: True}
I've tried to use classes in this way in the past, and it gets ugly pretty quickly (for example, all the methods will need to be class methods or static methods, and you will probably realise eventually that you want to define certain special methods, for which you will have to start using metaclasses). It could make things a lot easier if you just use class instances instead - there aren't really any downsides.
A (weird-looking) alternative to what others have suggested: you could use __new__:
class Test(object):
def __new__(cls):
cls.some_dict = {cls: True}
Test()
You could even have __new__ return a reference to the class and use a decorator to call it:
def instantiate(cls):
return cls()
#instantiate
class Test(object):
def __new__(cls):
cls.some_dict = {cls: True}
return cls
You can also use a metaclass (a function here but there are other ways):
def Meta(name, bases, ns):
klass = type(name, bases, ns)
setattr(klass, 'some_dict', { klass: True })
return klass
class Test(object):
__metaclass__ = Meta
print Test.some_dict
Thomas's first example is very good, but here's a more Pythonic way of doing the same thing.
class Test:
x = {}
#classmethod
def init(cls):
# do whatever setup you need here
cls.x[cls] = True
Test.init()