from math import sqrt critics = {'Lisa Rose': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.5, 'Just My Luck': 3.0, 'Superman Returns': 3.5, 'You, Me and Dupree': 2.5, 'The Night Listener': 3.0}, 'Gene Seymour': {'Lady in the Water': 3.0, 'Snakes on a Plane': 3.5, 'Just My Luck': 1.5, 'Superman Returns': 5.0, 'The Night Listener': 3.0, 'You, Me and Dupree': 3.5}, 'Michael Phillips': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.0, 'Superman Returns': 3.5, 'The Night Listener': 4.0}, 'Claudia Puig': {'Snakes on a Plane': 3.5, 'Just My Luck': 3.0, 'The Night Listener': 4.5, 'Superman Returns': 4.0, 'You, Me and Dupree': 2.5}, 'Mick LaSalle': {'Lady in the Water': 3.0, 'Snakes on a Plane': 4.0, 'Just My Luck': 2.0, 'Superman Returns': 3.0, 'The Night Listener': 3.0, 'You, Me and Dupree': 2.0}, 'Jack Matthews': {'Lady in the Water': 3.0, 'Snakes on a Plane': 4.0, 'The Night Listener': 3.0, 'Superman Returns': 5.0, 'You, Me and Dupree': 3.5}, 'Toby': {'Snakes on a Plane':4.5,'You, Me and Dupree':1.0,'Superman Returns':4.0}} def sim_distance(prefs, person1, person2): # Get the list of shared items si = {} for item in prefs[person1]: if item in prefs[person2]: si[item] = 1 # if they have no rating in common, return 0 if len(si) == 0: return 0 # Add up the squaresof all the differences sum_of_squares = sum(pow(prefs[person1][item] - prefs[person2][item], 2) for item in prefs[person1] if item in prefs[person2]) return 1/(1+sum_of_squares) def sim_pearson(prefs,p1,p2): # Get the list of mutually rated items si={} for item in prefs[p1]: if item in prefs[p2]: si[item]=1 n=len(si) if n==0: return 0 sum1=sum([prefs[p1][it] for it in si]) sum2=sum([prefs[p2][it] for it in si]) sum1Sq=sum([pow(prefs[p1][it],2) for it in si]) sum2Sq=sum([pow(prefs[p2][it],2) for it in si]) pSum=sum([prefs[p1][it]*prefs[p2][it] for it in si]) num=pSum-(sum1*sum2/n) den=sqrt((sum1Sq-pow(sum1,2)/n)*(sum2Sq-pow(sum2,2)/n)) if den==0: return 0 r=num/den return r def topMatches(prefs, person, n=5, similarity=sim_pearson): scores = [(similarity(prefs, person, other), other) for other in prefs if other != person] scores.sort() scores.reverse() return scores[0:n] def getRecommendations(prefs, person, similarity=sim_pearson): totals = {} simSums = {} for other in prefs: if other == person: continue sim = similarity(prefs, person, other) if sim <= 0: continue for item in prefs[other]: if item not in prefs[person] or prefs[person][item] == 0: totals.setdefault(item, 0) totals[item] += prefs[other][item] * sim simSums.setdefault(item, 0) simSums[item] += sim rankings = [(totals/simSums[item], item) for (item, totals) in totals.items()] rankings.sort() rankings.reverse() return rankings def transformPrefs(prefs): result = {} for person in prefs: for item in prefs[person]: result.setdefault(item, {}) result[item][person] = prefs[person][item] return result def calculateSimiliarItems(prefs, n=10): result = {} itemPrefs = transformPrefs(prefs) c = 0 for item in itemPrefs: c += 1 if c % 100 == 0: print "%d / %d" % (c, len(itemPrefs)) scores = topMatches(itemPrefs, item, n=n, similarity=sim_distance) result[item] = scores return result def getRecommendedItems(prefs, itemMatch, user): userRatings = prefs[user] scores = {} totalSim = {} for (item, rating) in userRatings.items(): for (similarity, item2) in itemMatch[item]: if item2 in userRatings: continue scores.setdefault(item2, 0) scores[item2] += similarity * rating totalSim.setdefault(item2, 0) totalSim[item2] += similarity rankings = [(score/totalSim[item], item) for item,score in scores.items()] rankings.sort() rankings.reverse() return rankings def loadMovieLens(path='/home/xa4a/merc/c_i/ml-data'): movies = {} for line in open(path + '/u.item'): (id, title) = line.split('|')[0:2] movies[id] = title prefs = {} for line in open(path + '/u.data'): (user, movieid, rating, ts) = line.split('\t') prefs.setdefault(user,{}) prefs[user][movies[movieid]] = float(rating) return prefs