Converting python collaborative filtering code to use Map Reduce

Using Python, I'm computing cosine similarity across items.

given event data that represents a purchase (user,item), I have a list of all items 'bought' by my users.

Given this input data

(user,item)
X,1
X,2
Y,1
Y,2
Z,2
Z,3

I build a python dictionary

{1: ['X','Y'], 2 : ['X','Y','Z'], 3 : ['Z']}

From that dictionary, I generate a bought/not bought matrix, also another dictionary(bnb).

{1 : [1,1,0], 2 : [1,1,1], 3 : [0,0,1]} 

From there, I'm computing similarity between (1,2) by calculating cosine between (1,1,0) and (1,1,1), yielding 0.816496

I'm doing this by:

items=[1,2,3]
for item in items:
  for sub in items:
    if sub >= item:    #as to not calculate similarity on the inverse
      sim = coSim( bnb[item], bnb[sub] )

I think the brute force approach is killing me and it only runs slower as the data gets larger. Using my trusty laptop, this calculation runs for hours when dealing with 8500 users and 3500 items.

I'm trying to compute similarity for all items in my dict and it's taking longer than I'd like it to. I think this is a good candidate for MapReduce but I'm having trouble 'thinking' in terms of key/value pairs.

Alternatively, is the issue with my approach and not necessa开发者_StackOverflow中文版rily a candidate for Map Reduce?

This is not actually a "MapReduce" function but it should give you some significant speedup without all of the hassle.

I would actually use numpy to "vectorize" the operation and make your life easier. From this you'll just need to loop through this dictionary and apply the vectorized function comparing this item against all others.

import numpy as np
bnb_items = bnb.values()
for num in xrange(len(bnb_items)-1):
    sims = cosSim(bnb_items[num], bnb_items[num+1:]

def cosSim(User, OUsers):
""" Determinnes the cosine-similarity between 1 user and all others.
Returns an array the size of OUsers with the similarity measures

User is a single array of the items purchased by a user.
OUsers is a LIST of arrays purchased by other users.

"""

    multidot = np.vectorize(np.vdot)
    multidenom = np.vectorize(lambda x: np.sum(x)*np.sum(User))

    #apply the dot-product between this user and all others
    num = multidot(OUsers, User)

    #apply the magnitude multiplication across this user and all others
    denom = multidenom(OUsers)

    return num/denom

I haven't tested this code so there may be some silly errors but the idea should get you 90% of the way.

This should have a SIGNIFICANT speedup. If you still need a speed up there is a wonderful blog post which implements a "Slope One" recommendation system here.

Hope that helps, Will