I am trying sort a list of strings in a way that uses a special comparison. I am trying to use functools.total_ordering, but I'm not sure whether it's filling out the undefined comparisons correctly.
The two I define ( > and ==) work as expected, but < does not. In particular, I print all three and I get that a > b and a < b. How is this possible? I would think that total_ordering would simply define < as not > and not ==. The result of my < test is what you would get with regular str comparison, leading me to believe that total_ordering isn't doing anything.
Perhaps the problem is that I am inheriting str, which already has __lt__ implemented? If so, is there a fix to this issue?
from functools import total_ordering
#total_ordering
class SortableStr(str):
def __gt__(self, other):
return self+other > other+self
#Is this necessary? Or will default to inherited class?
def __eq__(self, other):
return str(self) == str(other)
def main():
a = SortableStr("99")
b = SortableStr("994")
print(a > b)
print(a == b)
print(a < b)
if __name__ == "__main__":
main()
OUTPUT:
True
False
True
You're right that the built-in str comparison operators are interfering with your code. From the docs
Given a class defining one or more rich comparison ordering methods, this class decorator supplies the rest.
So it only supplies the ones not already defined. In your case, the fact that some of them are defined in a parent class is undetectable to total_ordering.
Now, we can dig deeper into the source code and find the exact check
roots = {op for op in _convert if getattr(cls, op, None) is not getattr(object, op, None)}
So it checks if the values are equal to the ones defined in the root object object. We can make that happen
#total_ordering
class SortableStr(str):
__lt__ = object.__lt__
__le__ = object.__le__
__ge__ = object.__ge__
def __gt__(self, other):
return self+other > other+self
#Is this necessary? Or will default to inherited class?
def __eq__(self, other):
return str(self) == str(other)
Now total_ordering will see that __lt__, __le__, and __ge__ are equal to the "original" object values and overwrite them, as desired.
This all being said, I would argue that this is a poor use of inheritance. You're violating Liskov substitution at the very least, in that mixed comparisons between str and SortableStr are going to, to put it lightly, produce counterintuitive results.
My more general recommendation is to favor composition over inheritance and, rather than defining a thing that "is a" specialized string, consider defining a type that "contains" a string and has specialized behavior.
#total_ordering
class SortableStr:
def __init__(self, value):
self.value = value
def __gt__(self, other):
return self.value + other.value > other.value + self.value
def __eq__(self, other):
return self.value == other.value
There, no magic required. Now SortableStr("99") is a valid object that is not a string but exhibits the behavior you want.
Not sure if this is correct, but glancing at the documentation of functools.total_ordering, this stands out to me:
Given a class defining one or more rich comparison ordering methods,
this class decorator supplies the rest.
Emphasis mine. Your class inherits __lt__ from str, so it does not get re-implemented by total_ordering since it isn't missing. That's my best guess.
I have a class MyClass, which contains two member variables foo and bar:
class MyClass:
def __init__(self, foo, bar):
self.foo = foo
self.bar = bar
I have two instances of this class, each of which has identical values for foo and bar:
x = MyClass('foo', 'bar')
y = MyClass('foo', 'bar')
However, when I compare them for equality, Python returns False:
>>> x == y
False
How can I make python consider these two objects equal?
You should implement the method __eq__:
class MyClass:
def __init__(self, foo, bar):
self.foo = foo
self.bar = bar
def __eq__(self, other):
if not isinstance(other, MyClass):
# don't attempt to compare against unrelated types
return NotImplemented
return self.foo == other.foo and self.bar == other.bar
Now it outputs:
>>> x == y
True
Note that implementing __eq__ will automatically make instances of your class unhashable, which means they can't be stored in sets and dicts. If you're not modelling an immutable type (i.e. if the attributes foo and bar may change the value within the lifetime of your object), then it's recommended to just leave your instances as unhashable.
If you are modelling an immutable type, you should also implement the data model hook __hash__:
class MyClass:
...
def __hash__(self):
# necessary for instances to behave sanely in dicts and sets.
return hash((self.foo, self.bar))
A general solution, like the idea of looping through __dict__ and comparing values, is not advisable - it can never be truly general because the __dict__ may have uncomparable or unhashable types contained within.
N.B.: be aware that before Python 3, you may need to use __cmp__ instead of __eq__. Python 2 users may also want to implement __ne__, since a sensible default behaviour for inequality (i.e. inverting the equality result) will not be automatically created in Python 2.
You override the rich comparison operators in your object.
class MyClass:
def __lt__(self, other):
# return comparison
def __le__(self, other):
# return comparison
def __eq__(self, other):
# return comparison
def __ne__(self, other):
# return comparison
def __gt__(self, other):
# return comparison
def __ge__(self, other):
# return comparison
Like this:
def __eq__(self, other):
return self._id == other._id
If you're dealing with one or more classes that you can't change from the inside, there are generic and simple ways to do this that also don't depend on a diff-specific library:
Easiest, unsafe-for-very-complex-objects method
pickle.dumps(a) == pickle.dumps(b)
pickle is a very common serialization lib for Python objects, and will thus be able to serialize pretty much anything, really. In the above snippet, I'm comparing the str from serialized a with the one from b. Unlike the next method, this one has the advantage of also type checking custom classes.
The biggest hassle: due to specific ordering and [de/en]coding methods, pickle may not yield the same result for equal objects, especially when dealing with more complex ones (e.g. lists of nested custom-class instances) like you'll frequently find in some third-party libs. For those cases, I'd recommend a different approach:
Thorough, safe-for-any-object method
You could write a recursive reflection that'll give you serializable objects, and then compare results
from collections.abc import Iterable
BASE_TYPES = [str, int, float, bool, type(None)]
def base_typed(obj):
"""Recursive reflection method to convert any object property into a comparable form.
"""
T = type(obj)
from_numpy = T.__module__ == 'numpy'
if T in BASE_TYPES or callable(obj) or (from_numpy and not isinstance(T, Iterable)):
return obj
if isinstance(obj, Iterable):
base_items = [base_typed(item) for item in obj]
return base_items if from_numpy else T(base_items)
d = obj if T is dict else obj.__dict__
return {k: base_typed(v) for k, v in d.items()}
def deep_equals(*args):
return all(base_typed(args[0]) == base_typed(other) for other in args[1:])
Now it doesn't matter what your objects are, deep equality is assured to work
>>> from sklearn.ensemble import RandomForestClassifier
>>>
>>> a = RandomForestClassifier(max_depth=2, random_state=42)
>>> b = RandomForestClassifier(max_depth=2, random_state=42)
>>>
>>> deep_equals(a, b)
True
The number of comparables doesn't matter as well
>>> c = RandomForestClassifier(max_depth=2, random_state=1000)
>>> deep_equals(a, b, c)
False
My use case for this was checking deep equality among a diverse set of already trained Machine Learning models inside BDD tests. The models belonged to a diverse set of third-party libs. Certainly implementing __eq__ like other answers here suggest wasn't an option for me.
Covering all the bases
You may be in a scenario where one or more of the custom classes being compared do not have a __dict__ implementation. That's not common by any means, but it is the case of a subtype within sklearn's Random Forest classifier: <type 'sklearn.tree._tree.Tree'>. Treat these situations on a case by case basis - e.g. specifically, I decided to replace the content of the afflicted type with the content of a method that gives me representative information on the instance (in this case, the __getstate__ method). For such, the second-to-last row in base_typed became
d = obj if T is dict else obj.__dict__ if '__dict__' in dir(obj) else obj.__getstate__()
Edit: for the sake of organization, I replaced the hideous oneliner above with return dict_from(obj). Here, dict_from is a really generic reflection made to accommodate more obscure libs (I'm looking at you, Doc2Vec)
def isproperty(prop, obj):
return not callable(getattr(obj, prop)) and not prop.startswith('_')
def dict_from(obj):
"""Converts dict-like objects into dicts
"""
if isinstance(obj, dict):
# Dict and subtypes are directly converted
d = dict(obj)
elif '__dict__' in dir(obj):
# Use standard dict representation when available
d = obj.__dict__
elif str(type(obj)) == 'sklearn.tree._tree.Tree':
# Replaces sklearn trees with their state metadata
d = obj.__getstate__()
else:
# Extract non-callable, non-private attributes with reflection
kv = [(p, getattr(obj, p)) for p in dir(obj) if isproperty(p, obj)]
d = {k: v for k, v in kv}
return {k: base_typed(v) for k, v in d.items()}
Do mind none of the above methods yield True for objects with the same key-value pairs in a differing order, as in
>>> a = {'foo':[], 'bar':{}}
>>> b = {'bar':{}, 'foo':[]}
>>> pickle.dumps(a) == pickle.dumps(b)
False
But if you want that you could use Python's built-in sorted method beforehand anyway.
With Dataclasses in Python 3.7 (and above), a comparison of object instances for equality is an inbuilt feature.
A backport for Dataclasses is available for Python 3.6.
(Py37) nsc#nsc-vbox:~$ python
Python 3.7.5 (default, Nov 7 2019, 10:50:52)
[GCC 8.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from dataclasses import dataclass
>>> #dataclass
... class MyClass():
... foo: str
... bar: str
...
>>> x = MyClass(foo="foo", bar="bar")
>>> y = MyClass(foo="foo", bar="bar")
>>> x == y
True
Implement the __eq__ method in your class; something like this:
def __eq__(self, other):
return self.path == other.path and self.title == other.title
Edit: if you want your objects to compare equal if and only if they have equal instance dictionaries:
def __eq__(self, other):
return self.__dict__ == other.__dict__
As a summary :
It's advised to implement __eq__ rather than __cmp__, except if you run python <= 2.0 (__eq__ has been added in 2.1)
Don't forget to also implement __ne__ (should be something like return not self.__eq__(other) or return not self == other except very special case)
Don`t forget that the operator must be implemented in each custom class you want to compare (see example below).
If you want to compare with object that can be None, you must implement it. The interpreter cannot guess it ... (see example below)
class B(object):
def __init__(self):
self.name = "toto"
def __eq__(self, other):
if other is None:
return False
return self.name == other.name
class A(object):
def __init__(self):
self.toto = "titi"
self.b_inst = B()
def __eq__(self, other):
if other is None:
return False
return (self.toto, self.b_inst) == (other.toto, other.b_inst)
Depending on your specific case, you could do:
>>> vars(x) == vars(y)
True
See Python dictionary from an object's fields
You should implement the method __eq__:
class MyClass:
def __init__(self, foo, bar, name):
self.foo = foo
self.bar = bar
self.name = name
def __eq__(self,other):
if not isinstance(other,MyClass):
return NotImplemented
else:
#string lists of all method names and properties of each of these objects
prop_names1 = list(self.__dict__)
prop_names2 = list(other.__dict__)
n = len(prop_names1) #number of properties
for i in range(n):
if getattr(self,prop_names1[i]) != getattr(other,prop_names2[i]):
return False
return True
When comparing instances of objects, the __cmp__ function is called.
If the == operator is not working for you by default, you can always redefine the __cmp__ function for the object.
Edit:
As has been pointed out, the __cmp__ function is deprecated since 3.0.
Instead you should use the “rich comparison” methods.
I wrote this and placed it in a test/utils module in my project. For cases when its not a class, just plan ol' dict, this will traverse both objects and ensure
every attribute is equal to its counterpart
No dangling attributes exist (attrs that only exist on one object)
Its big... its not sexy... but oh boi does it work!
def assertObjectsEqual(obj_a, obj_b):
def _assert(a, b):
if a == b:
return
raise AssertionError(f'{a} !== {b} inside assertObjectsEqual')
def _check(a, b):
if a is None or b is None:
_assert(a, b)
for k,v in a.items():
if isinstance(v, dict):
assertObjectsEqual(v, b[k])
else:
_assert(v, b[k])
# Asserting both directions is more work
# but it ensures no dangling values on
# on either object
_check(obj_a, obj_b)
_check(obj_b, obj_a)
You can clean it up a little by removing the _assert and just using plain ol' assert but then the message you get when it fails is very unhelpful.
Below works (in my limited testing) by doing deep compare between two object hierarchies. In handles various cases including the cases when objects themselves or their attributes are dictionaries.
def deep_comp(o1:Any, o2:Any)->bool:
# NOTE: dict don't have __dict__
o1d = getattr(o1, '__dict__', None)
o2d = getattr(o2, '__dict__', None)
# if both are objects
if o1d is not None and o2d is not None:
# we will compare their dictionaries
o1, o2 = o1.__dict__, o2.__dict__
if o1 is not None and o2 is not None:
# if both are dictionaries, we will compare each key
if isinstance(o1, dict) and isinstance(o2, dict):
for k in set().union(o1.keys() ,o2.keys()):
if k in o1 and k in o2:
if not deep_comp(o1[k], o2[k]):
return False
else:
return False # some key missing
return True
# mismatched object types or both are scalers, or one or both None
return o1 == o2
This is a very tricky code so please add any cases that might not work for you in comments.
class Node:
def __init__(self, value):
self.value = value
self.next = None
def __repr__(self):
return str(self.value)
def __eq__(self,other):
return self.value == other.value
node1 = Node(1)
node2 = Node(1)
print(f'node1 id:{id(node1)}')
print(f'node2 id:{id(node2)}')
print(node1 == node2)
>>> node1 id:4396696848
>>> node2 id:4396698000
>>> True
Use the setattr function. You might want to use this when you can't add something inside the class itself, say, when you are importing the class.
setattr(MyClass, "__eq__", lambda x, y: x.foo == y.foo and x.bar == y.bar)
If you want to get an attribute-by-attribute comparison, and see if and where it fails, you can use the following list comprehension:
[i for i,j in
zip([getattr(obj_1, attr) for attr in dir(obj_1)],
[getattr(obj_2, attr) for attr in dir(obj_2)])
if not i==j]
The extra advantage here is that you can squeeze it one line and enter in the "Evaluate Expression" window when debugging in PyCharm.
I tried the initial example (see 7 above) and it did not work in ipython. Note that cmp(obj1,obj2) returns a "1" when implemented using two identical object instances. Oddly enough when I modify one of the attribute values and recompare, using cmp(obj1,obj2) the object continues to return a "1". (sigh...)
Ok, so what you need to do is iterate two objects and compare each attribute using the == sign.
Instance of a class when compared with == comes to non-equal. The best way is to ass the cmp function to your class which will do the stuff.
If you want to do comparison by the content you can simply use cmp(obj1,obj2)
In your case cmp(doc1,doc2) It will return -1 if the content wise they are same.
I'm confused as to how the == operator works in Python 3. From the docs, eq(a, b) is equivalent to a == b. Also eq and __eq__ are equivalent.
Take the following example:
class Potato:
def __eq__(self, other):
print("In Potato's __eq__")
return True
>> p = Potato()
>> p == "hello"
In Potato's __eq__ # As expected, p.__eq__("hello") is called
True
>> "hello" == p
In Potato's __eq__ # Hmm, I expected this to be false because
True # this should call "hello".__eq__(p)
>> "hello".__eq__(p)
NotImplemented # Not implemented? How does == work for strings then?
AFAIK, the docs only talk about the == -> __eq__ mapping, but don't say anything about what happens either one of the arguments is not an object (e.g. 1 == p), or when the first object's __eq__ is NotImplemented, like we saw with "hello".__eq(p).
I'm looking for the general algorithm that is employed for equality... Most, if not all other SO answers, refer to Python 2's coercion rules, which don't apply anymore in Python 3.
You're mixing up the functions in the operator module and the methods used to implement those operators. operator.eq(a, b) is equivalent to a == b or operator.__eq__(a, b), but not to a.__eq__(b).
In terms of the __eq__ method, == and operator.eq work as follows:
def eq(a, b):
if type(a) is not type(b) and issubclass(type(b), type(a)):
# Give type(b) priority
a, b = b, a
result = a.__eq__(b)
if result is NotImplemented:
result = b.__eq__(a)
if result is NotImplemented:
result = a is b
return result
with the caveat that the real code performs method lookup for __eq__ in a way that bypasses instance dicts and custom __getattribute__/__getattr__ methods.
When you do this:
"hello" == potato
Python first calls "hello".__eq__(potato). That return NotImplemented, so Python tries it the other way: potato.__eq__("hello").
Returning NotImplemented doesn't mean there's no implementation of .__eq__ on that object. It means that the implementation didn't know how to compare to the value that was passed in. From https://docs.python.org/3/library/constants.html#NotImplemented:
Note When a binary (or in-place) method returns NotImplemented the
interpreter will try the reflected operation on the other type (or
some other fallback, depending on the operator). If all attempts
return NotImplemented, the interpreter will raise an appropriate
exception. Incorrectly returning NotImplemented will result in a
misleading error message or the NotImplemented value being returned to
Python code. See Implementing the arithmetic operations for examples.
I'm confused as to how the == operator works in Python 3. From the docs, eq(a, b) is equivalent to a == b. Also eq and __eq__ are equivalent.
No that is only the case in the operator module. The operator module is used to pass an == as a function for instance. But operator has not much to do with vanilla Python itself.
AFAIK, the docs only talk about the == -> eq mapping, but don't say anything about what happens either one of the arguments is not an object (e.g. 1 == p), or when the first object's.
In Python everything is an object: an int is an object, a "class" is an object", a None is an object, etc. We can for instance get the __eq__ of 0:
>>> (0).__eq__
<method-wrapper '__eq__' of int object at 0x55a81fd3a480>
So the equality is implemented in the "int class". As specified in the documentation on the datamodel __eq__ can return several values: True, False but any other object (for which the truthiness will be calculated). If on the other hand NotImplemented is returned, Python will fallback and call the __eq__ object on the object on the other side of the equation.
class A(object):
def __cmp__(self):
print '__cmp__'
return object.__cmp__(self)
def __eq__(self, rhs):
print '__eq__'
return True
a1 = A()
a2 = A()
print a1 in set([a1])
print a1 in set([a2])
Why does first line prints True, but second prints False? And neither enters operator eq?
I am using Python 2.6
Set __contains__ makes checks in the following order:
'Match' if hash(a) == hash(b) and (a is b or a==b) else 'No Match'
The relevant C source code is in Objects/setobject.c::set_lookkey() and in Objects/object.c::PyObject_RichCompareBool().
You need to define __hash__ too. For example
class A(object):
def __hash__(self):
print '__hash__'
return 42
def __cmp__(self, other):
print '__cmp__'
return object.__cmp__(self, other)
def __eq__(self, rhs):
print '__eq__'
return True
a1 = A()
a2 = A()
print a1 in set([a1])
print a1 in set([a2])
Will work as expected.
As a general rule, any time you implement __cmp__ you should implement a __hash__ such that for all x and y such that x == y, x.__hash__() == y.__hash__().
Sets and dictionaries gain their speed by using hashing as a fast approximation of full equality checking. If you want to redefine equality, you usually need to redefine the hash algorithm so that it is consistent.
The default hash function uses the identity of the object, which is pretty useless as a fast approximation of full equality, but at least allows you to use an arbitrary class instance as a dictionary key and retrieve the value stored with it if you pass exactly the same object as a key. But it means if you redefine equality and don't redefine the hash function, your objects will go into a dictionary/set without complaining about not being hashable, but still won't actually work the way you expect them to.
See the official python docs on __hash__ for more details.
A tangential answer, but your question and my testing made me curious. If you ignore the set operator which is the source of your __hash__ problem, it turns out your question is still interesting.
Thanks to the help I got on this SO question, I was able to chase the in operator through the source code to it's root. Near the bottom I found the PyObject_RichCompareBool function which indeed tests for identity (see the comment about "Quick result") before testing for equality.
So unless I misunderstand the way things work, the technical answer to your question is first identity and then equality, through the equality test itself. Just to reiterate, that is not the source of the behavior you were seeing but just the technical answer to your question.
If I misunderstood the source, somebody please set me straight.
int
PyObject_RichCompareBool(PyObject *v, PyObject *w, int op)
{
PyObject *res;
int ok;
/* Quick result when objects are the same.
Guarantees that identity implies equality. */
if (v == w) {
if (op == Py_EQ)
return 1;
else if (op == Py_NE)
return 0;
}
res = PyObject_RichCompare(v, w, op);
if (res == NULL)
return -1;
if (PyBool_Check(res))
ok = (res == Py_True);
else
ok = PyObject_IsTrue(res);
Py_DECREF(res);
return ok;
}
Sets seem to use hash codes, then identity, before comparing for equality. The following code:
class A(object):
def __eq__(self, rhs):
print '__eq__'
return True
def __hash__(self):
print '__hash__'
return 1
a1 = A()
a2 = A()
print 'set1'
set1 = set([a1])
print 'set2'
set2 = set([a2])
print 'a1 in set1'
print a1 in set1
print 'a1 in set2'
print a1 in set2
outputs:
set1
__hash__
set2
__hash__
a1 in set1
__hash__
True
a1 in set2
__hash__
__eq__
True
What happens seems to be:
The hash code is computed when an element is inserted into a hash. (To compare with the existing elements.)
The hash code for the object you're checking with the in operator is computed.
Elements of the set with the same hash code are inspected by first checking whether they're the same object as the one you're looking for, or if they're logically equal to it.
Take the following example:
>>> class C(object):
... def __init__(self, p):
... self.p = p
... def __eq__(self, o):
... return True
...
>>> C(1) is C(2)
False
>>> C(1) == C(2)
True
>>> C(1) != C(2)
True # <- Why?!?
So now the two objects are equal and not-equal at the same time. I though the two operations are opposing?!
Python’s “Data model” explains it all:
There are no implied relationships among the comparison operators. The truth of x==y does not imply that x!=y is false. Accordingly, when defining __eq__(), one should also define __ne__() so that the operators will behave as expected.
In C(1) != C(2), it’s using the default implementation, where objects are equal only to themselves and unequal to everything else.
Defining __cmp__ can be simpler, as it is used as a fallback for all comparison operations, not just some of them:
... def __cmp__(self, o):
... return 0
>>> C(1) != C(2)
False
There is a separate function for != which is __ne__ which is implicitly defined to compare the instance members.
What you want to do is:
def __ne__(self, other):
return not self.__eq__(other)
or some variant of this.
You have to define both __ne__ and __eq__. And you probably ought to consider implementing __cmp__ too!