Question

Short version: What’s the best hashing algorithm for a multiset implemented as a dictionary of unordered items?

I’m trying to hash an immutable multiset (which is a bag or multiset in other languages: like a mathematical set except that it can hold more than one of each element) implemented as a dictionary. I’ve created a subclass of the standard library class collections.Counter, similar to the advice here: Python hashable dicts, which recommends a hash function like so:

class FrozenCounter(collections.Counter):
    # ...
    def __hash__(self):
        return hash(tuple(sorted(self.items())))

Creating the full tuple of items takes up a lot of memory (relative to, say, using a generator) and hashing will occur in an extremely memory intensive part of my application. More importantly, my dictionary keys (multiset elements) probably won’t be order-able.

I’m thinking of using this algorithm:

def __hash__(self):
    return functools.reduce(lambda a, b: a ^ b, self.items(), 0)

I figure using bitwise XOR means order doesn’t matter for the hash value unlike in the hashing of a tuple? I suppose I could semi-implement the Python tuple-hashing alogrithm on the unordered stream of tuples of my data. See https://github.com/jonashaag/cpython/blob/master/Include/tupleobject.h (search in the page for the word ‘hash’) — but I barely know enough C to read it.

Thoughts? Suggestions? Thanks.

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(If you’re wondering why I’m messing around with trying to hash a multiset: The input data for my problem are sets of multisets, and within each set of multisets, each multiset must be unique. I’m working on a deadline and I’m not an experienced coder, so I wanted to avoid inventing new algorithms where possible. It seems like the most Pythonic way to make sure I have unique of a bunch of things is to put them in a set(), but the things must be hashable.)

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What I’ve gathered from the comments

Both @marcin and @senderle gave pretty much the same answer: use hash(frozenset(self.items())). This makes sense because items() “views” are set-like. @marcin was first but I gave the check mark to @senderle because of the good research on the big-O running times for different solutions. @marcin also reminds me to include an __eq__ method — but the one inherited from dict will work just fine. This is how I’m implementing everything — further comments and suggestions based on this code are welcome:

class FrozenCounter(collections.Counter):
    # Edit: A previous version of this code included a __slots__ definition.
    # But, from the Python documentation: "When inheriting from a class without
    # __slots__, the __dict__ attribute of that class will always be accessible,
    # so a __slots__ definition in the subclass is meaningless."
    # http://docs.python.org/py3k/reference/datamodel.html#notes-on-using-slots
    # ...
    def __hash__(self):
        "Implements hash(self) -> int"
        if not hasattr(self, '_hash'):
            self._hash = hash(frozenset(self.items()))
        return self._hash

Answer 1

Since the dictionary is immutable, you can create the hash when the dictionary is created and return it directly. My suggestion would be to create a frozenset from items (in 3+; iteritems in 2.7), hash it, and store the hash.

To provide an explicit example:

>>>> frozenset(Counter([1, 1, 1, 2, 3, 3, 4]).iteritems())
frozenset([(3, 2), (1, 3), (4, 1), (2, 1)])
>>>> hash(frozenset(Counter([1, 1, 1, 2, 3, 3, 4]).iteritems()))
-3071743570178645657
>>>> hash(frozenset(Counter([1, 1, 1, 2, 3, 4]).iteritems()))
-6559486438209652990

To clarify why I prefer a frozenset to a tuple of sorted items: a frozenset doesn’t have to sort the items, and so the initial hash completes in O(n) time rather than O(n log n) time. This can be seen from the frozenset_hash and set_next implementations.

See also this great answer from Raymond Hettinger describing his implementation of the frozenset hash function. There he explicitly explains how the hash function avoids having to sort values to get a stable, order insensitive value.