Created
March 27, 2009 21:44
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'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}} | |
from math import sqrt | |
# Returns a distance-based similarity score for person1 and person2 | |
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 ratings in common, return 0 | |
if len(si)==0: return 0 | |
# Add up the squares of 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) | |
# Returns the Pearson correlation coefficient for p1 and p2 | |
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 | |
# if they are no ratings in common, return 0 | |
if len(si)==0: return 0 | |
# Sum calculations | |
n=len(si) | |
# Sums of all the preferences | |
sum1=sum([prefs[p1][it] for it in si]) | |
sum2=sum([prefs[p2][it] for it in si]) | |
# Sums of the squares | |
sum1Sq=sum([pow(prefs[p1][it],2) for it in si]) | |
sum2Sq=sum([pow(prefs[p2][it],2) for it in si]) | |
# Sum of the products | |
pSum=sum([prefs[p1][it]*prefs[p2][it] for it in si]) | |
# Calculate r (Pearson score) | |
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 | |
# Returns the best matches for person from the prefs dictionary. | |
# Number of results and similarity function are optional params. | |
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] | |
# Gets recommendations for a person by using a weighted average | |
# of every other user's rankings | |
def getRecommendations(prefs,person,similarity=sim_pearson): | |
totals={} | |
simSums={} | |
for other in prefs: | |
# don't compare me to myself | |
if other==person: continue | |
sim=similarity(prefs,person,other) | |
# ignore scores of zero or lower | |
if sim<=0: continue | |
for item in prefs[other]: | |
# only score movies I haven't seen yet | |
if item not in prefs[person] or prefs[person][item]==0: | |
# Similarity * Score | |
totals.setdefault(item,0) | |
totals[item]+=prefs[other][item]*sim | |
# Sum of similarities | |
simSums.setdefault(item,0) | |
simSums[item]+=sim | |
# Create the normalized list | |
rankings=[(total/simSums[item],item) for item,total in totals.items()] | |
# Return the sorted list | |
rankings.sort() | |
rankings.reverse() | |
return rankings | |
def transformPrefs(prefs): | |
result={} | |
for person in prefs: | |
for item in prefs[person]: | |
result.setdefault(item,{}) | |
# Flip item and person | |
result[item][person]=prefs[person][item] | |
return result | |
def calculateSimilarItems(prefs,n=10): | |
# Create a dictionary of items showing which other items they | |
# are most similar to. | |
result={} | |
# Invert the preference matrix to be item-centric | |
itemPrefs=transformPrefs(prefs) | |
c=0 | |
for item in itemPrefs: | |
# Status updates for large datasets | |
c+=1 | |
if c%100==0: print "%d / %d" % (c,len(itemPrefs)) | |
# Find the most similar items to this one | |
scores=topMatches(itemPrefs,item,n=n,similarity=sim_distance) | |
result[item]=scores | |
return result | |
def getRecommendedItems(prefs,itemMatch,user): | |
userRatings=prefs[user] | |
scores={} | |
totalSim={} | |
# Loop over items rated by this user | |
for (item,rating) in userRatings.items( ): | |
# Loop over items similar to this one | |
for (similarity,item2) in itemMatch[item]: | |
# Ignore if this user has already rated this item | |
if item2 in userRatings: continue | |
# Weighted sum of rating times similarity | |
scores.setdefault(item2,0) | |
scores[item2]+=similarity*rating | |
# Sum of all the similarities | |
totalSim.setdefault(item2,0) | |
totalSim[item2]+=similarity | |
# Divide each total score by total weighting to get an average | |
rankings=[(score/totalSim[item],item) for item,score in scores.items( )] | |
# Return the rankings from highest to lowest | |
rankings.sort( ) | |
rankings.reverse( ) | |
return rankings | |
def loadMovieLens(path='/data/movielens'): | |
# Get movie titles | |
movies={} | |
for line in open(path+'/u.item'): | |
(id,title)=line.split('|')[0:2] | |
movies[id]=title | |
# Load data | |
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 |
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