Dinara koten

import pydelicious
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, Michael 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):
	si={}
	for item in prefs[person1]:
		if item in prefs[person2]:
			si[item]=1
	if len(si)==0: return 0
	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):
	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]

# 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:
		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=[(total/simSums[item],item) for item,total 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 calculateSimilarItems(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='ml-100k'):
	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





'''example for testing code'''

# itemsim=calculateSimilarItems(critics)
# print(getRecommendedItems(critics,itemsim,'Toby'))
# print(itemsim)

# print(sim_distance(critics,'Lisa Rose','Gene Seymour'))
# print(sim_pearson(critics,'Lisa Rose','Gene Seymour'))

prefs=loadMovieLens()
print(prefs['87'])
print(getRecommendations(prefs,'87')[0:30])
itemsim=calculateSimilarItems(prefs,n=50)
print(getRecommendedItems(prefs,itemsim,'87')[0:30])