Python: Fast extraction of intersections among all possible 2-combinations in a large number of lists

I have a dataset of ca. 9K lists of variable length (1 to 100K elements). I need to calculate the length of the开发者_StackOverflow中文版 intersection of all possible 2-list combinations in this dataset. Note that elements in each list are unique so they can be stored as sets in python.

What is the most efficient way to perform this in python?

Edit I forgot to specify that I need to have the ability to match the intersection values to the corresponding pair of lists. Thanks everybody for the prompt response and apologies for the confusion!

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If your sets are stored in s, for example:

s = [set([1, 2]), set([1, 3]), set([1, 2, 3]), set([2, 4])]

Then you can use itertools.combinations to take them two by two, and calculate the intersection (note that, as Alex pointed out, combinations is only available since version 2.6). Here with a list comrehension (just for the sake of the example):

from itertools import combinations
[ i[0] & i[1] for i in combinations(s,2) ]

Or, in a loop, which is probably what you need:

for i in combinations(s, 2):
    inter = i[0] & i[1]
    # processes the intersection set result "inter"

So, to have the length of each one of them, that "processing" would be:

    l = len(inter)

This would be quite efficient, since it's using iterators to compute every combinations, and does not prepare all of them in advance.

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Edit: Note that with this method, each set in the list "s" can actually be something else that returns a set, like a generator. The list itself could simply be a generator if you are short on memory. It could be much slower though, depending on how you generate these elements, but you wouldn't need to have the whole list of sets in memory at the same time (not that it should be a problem in your case).

For example, if each set is made from a function gen:

def gen(parameter):
    while more_sets():
        # ... some code to generate the next set 'x'
        yield x

with open("results", "wt") as f_results:
    for i in combinations(gen("data"), 2):
        inter = i[0] & i[1]
        f_results.write("%d\n" % len(inter))

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Edit 2: How to collect indices (following redrat's comment).

Besides the quick solution I answered in comment, a more efficient way to collect the set indices would be to have a list of (index, set) instead of a list of set.

Example with new format:

s = [(0, set([1, 2])), (1, set([1, 3])), (2, set([1, 2, 3]))]

If you are building this list to calculate the combinations anyway, it should be simple to adapt to your new requirements. The main loop becomes:

with open("results", "wt") as f_results:
    for i in combinations(s, 2):
        inter = i[0][1] & i[1][1]
        f_results.write("length of %d & %d: %d\n" % (i[0][0],i[1][0],len(inter))

In the loop, i[0] and i[1] would be a tuple (index, set), so i[0][1] is the first set, i[0][0] its index.

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As you need to produce a (N by N/2) matrix of results, i.e., O(N squared) outputs, no approach can be less than O(N squared) -- in any language, of course. (N is "about 9K" in your question). So, I see nothing intrinsically faster than (a) making the N sets you need, and (b) iterating over them to produce the output -- i.e., the simplest approach. IOW:

def lotsofintersections(manylists):
  manysets = [set(x) for x in manylists]
  moresets = list(manysets)
  for  s in reversed(manysets):
    moresets.pop()
    for z in moresets:
      yield s & z

This code's already trying to add some minor optimization (e.g. by avoiding slicing or popping off the front of lists, which might add other O(N squared) factors).

If you have many cores and/or nodes available and are looking for parallel algorithms, it's a different case of course -- if that's your case, can you mention the kind of cluster you have, its size, how nodes and cores can best communicate, and so forth?

Edit: as the OP has casually mentioned in a comment (!) that they actually need the numbers of the sets being intersected (really, why omit such crucial parts of the specs?! at least edit the question to clarify them...), this would only require changing this to:

  L = len(manysets)
  for i, s in enumerate(reversed(manysets)):
    moresets.pop()
    for j, z in enumerate(moresets):
      yield L - i, j + 1, s & z

(if you need to "count from 1" for the progressive identifiers -- otherwise obvious change).

But if that's part of the specs you might as well use simpler code -- forget moresets, and:

  L = len(manysets)
  for i xrange(L):
    s = manysets[i]
    for j in range(i+1, L):
      yield i, j, s & manysets[z]

this time assuming you want to "count from 0" instead, just for variety;-)

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Try this:

_lists = [[1, 2, 3, 7], [1, 3], [1, 2, 3], [1, 3, 4, 7]]
_sets = map( set, _lists )
_intersection = reduce( set.intersection, _sets )

And to obtain the indexes:

_idxs = [ map(_i.index, _intersection ) for _i in _lists ]

Cheers,

José María García

PS: Sorry I misunderstood the question