#SampleSet with movies
movies={'Marcel Caraciolo': {'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},
'Luciana Nunes': {'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}, 
'Leopoldo Pires': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.0,
 'Superman Returns': 3.5, 'The Night Listener': 4.0},
'Lorena Abreu': {'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},
'Steve Gates': {'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}, 
'Sheldom': {'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},
'Penny Frewman': {'Snakes on a Plane':4.5,'You, Me and Dupree':1.0,'Superman Returns':4.0}}



def loadDataset(path=""):
	""" To load the dataSet"
		Parameter: The folder where the data files are stored
		Return: the dictionary with the data
	"""
	#Recover the titles of the books
	books = {}
	for line in open(path+"BX-Books.csv"):
		line = line.replace('"', "")
		(id,title) = line.split(";") [0:2]
		books[id] = title
	
	#Load the data
	prefs = {}
	count = 0
	for line in open(path+"BX-Book-Ratings.csv"):
		line = line.replace('"', "")
		line = line.replace("\\","")
		(user,bookid,rating) = line.split(";")
		try:
			if float(rating) > 0.0:
				prefs.setdefault(user,{})
				prefs[user][books[bookid]] = float(rating)
		except ValueError:
			count+=1
			print "value error found! " + user + bookid + rating
		except KeyError:
			count +=1
			print "key error found! " + user + " " + bookid
	return prefs


from math import sqrt

#Returns a distance-base 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 rating in common, return 0
	if len(si) == 0: 
		return 0

	#Add up the squares of all 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 rating in common, return 0
	if len(si) == 0:
		return 0

	#sum calculations
	n = len(si)

	#sum of all preferences
	sum1 = sum([prefs[p1][it] for it in si])
	sum2 = sum([prefs[p2][it] for it in si])

	#Sum 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 the results and similiraty 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 books 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


#Function to transform Person, item - > Item, person
def transformPrefs(prefs):
	results = {}
	for person in prefs:
		for item in prefs[person]:
			results.setdefault(item,{})

			#Flip item and person
			results[item][person] = prefs[person][item]
	return results





#Create a dictionary of items showing which other items they are most similar to.

def calculateSimilarItems(prefs,n=10):
	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