def makerecs(name, items, ratings, size, top):
notseen = []
seen = []
newdic = {}
recommend = RecommenderEngine.recommendations(name, items, ratings, size)
for title in items:
for k, v in recommend:
if title == k:
newdic[title] = v
for num in range(len(ratings[name])):
if ratings[name][num] == 0:
title = items[num]
value = newdic[title]
notseen.append((title, value))
else:
title = items[num]
value = newdic[title]
seen.append((title, value))
seen = [
item for item in sorted(seen, key=operator.itemgetter(1), reverse=True)
]
notseen = [
item
for item in sorted(notseen, key=operator.itemgetter(1), reverse=True)
]
topsim = seen[:(top)]
#if student has seen it
toprec = notseen[:(top)]
#if student hasn't seen it
return ((topsim, toprec))
def makerecs(name, items, ratings, numUsers, top):
'''
This function calculates the top recommendations and returns a two-tuple consisting of two lists.
The second list is the top items not seen/rated by name (string)
'''
newAverages = RecommenderEngine.recommendations(name, items, ratings,
numUsers)
eaten = []
notEaten = []
i = 0
for item in newAverages:
if ratings[name][items.index(item[0])] != 0:
eaten.append(newAverages[i])
i += 1
if ratings[name][items.index(item[0])] == 0:
notEaten.append(newAverages[i])
i += 1
eaten = sorted(eaten, key=lambda x: x[0])
eaten = sorted(eaten, key=lambda x: x[1], reverse=True)
eaten = eaten[0:top]
notEaten = sorted(notEaten, key=lambda x: x[0])
notEaten = sorted(notEaten, key=lambda x: x[1], reverse=True)
notEaten = notEaten[0:top]
return (eaten, notEaten)
def makerecs(name, items, ratings, numUsers, top):
'''
This function calculates the top recommendations and returns a two-tuple consisting of two lists.
The second list is the top items not seen/rated by name (string)
'''
newList = RecommenderEngine.recommendations(name, items, ratings, numUsers)
topThree = []
person = []
for i in ratings:
if i == name:
person = ratings[i]
break
counter = 0
for i in range(0, len(items)):
if counter == top:
break
if person[i] == 0:
tuple = (items[i], newList[i][1])
topThree.append(tuple)
counter += 1
if len(topThree):
for i in range(0, top):
topThree.append(newList[i])
return topThree
def driver():
(items,ratings) = SmallDukeEatsReader.getdata()
print("items = ",items)
print("ratings = ", ratings)
avg = RecommenderEngine.averages(items,ratings)
if avg == [('DivinityCafe', 4.0), ('TheCommons', 3.0), ('Tandoor', 2.4285714285714284), ('IlForno', 1.8),
('FarmStead', 1.4), ('LoopPizzaGrill', 1.0), ('TheSkillet', 0.0), ('PandaExpress', -0.2),
('McDonalds', -0.3333333333333333)]:
print("averages work")
else:
print("averages fails")
similarities = RecommenderEngine.similarities("Sung-Hoon", ratings)
if similarities == [('Wei', 1), ('Sly one', -1), ('Melanie', -2), ('Sarah Lee', -6), ('J J', -14), ('Harry', -24),
('Nana Grace', -29)]:
print("similarities work")
else:
print("similarities fails")
recommendations = RecommenderEngine.recommendations("Sarah Lee", ["DivinityCafe", "FarmStead", "IlForno","LoopPizzaGrill", "McDonalds", "PandaExpress",
"Tandoor", "TheCommons", "TheSkillet"], {"Sarah Lee":[3, 3, 3, 3, 0, -3, 5, 0, -3], "Melanie":
[5, 0, 3, 0, 1, 3, 3, 3, 1],"J J":[0, 1, 0, -1, 1, 1, 3, 0, 1], "Sly one":[5, 0, 1, 3, 0, 0, 3, 3, 3],
"Sung-Hoon": [0, -1, -1, 5, 1, 3, -3, 1, -3], "Nana Grace":[5, 0, 3, -5, -1, 0, 1, 3, 0], "Harry":
[5, 3, 0, -1, -3, -5, 0, 5, 1], "Wei":[1, 1, 0, 3, -1, 0, 5, 3, 0]}, 3)
if recommendations == [('Tandoor', 149.5), ('TheCommons', 128.0), ('DivinityCafe', 123.33333333333333), ('FarmStead', 69.5),
('TheSkillet', 66.0), ('LoopPizzaGrill', 62.0), ('IlForno', 33.0), ('McDonalds', -69.5), ('PandaExpress', -165.0)]:
print("recommendations work")
else:
print("recommendations fails")
def driver():
'''
Testing recommender assignment functions with Duke eateries
'''
(items, ratings) = SmallDukeEatsReader.getdata()
# print("items = ",items)
# print("ratings = ", ratings)
avg = RecommenderEngine.averages(items, ratings)
# print("average",avg)
if avg == correctAvg:
print('averages works')
else:
print('average fails')
for key in ratings:
slist = RecommenderEngine.similarities(key, ratings)
# print(key,slist)
r3 = RecommenderEngine.recommendations(key, items, ratings, 3)
# print("top",r3)
if key == 'Sung-Hoon' and slist == correctSim:
print('similarities works')
elif key == 'Sung-Hoon' and slist != correctSim:
print('similarities fails')
if key == 'Sarah Lee' and r3 == correctRec:
print('recommendations works')
elif key == 'Sarah Lee' and r3 != correctRec:
print('recommendations fails')
def makerecs(name, items, ratings, size, top):
movies = []
top5seen = []
top5notseen = []
bestups = RecommenderEngine.recommendations(name, items, ratings, size)
f = open("data/movies.txt")
for line in f:
data = line.strip().split(",")
if data[0] == name:
movies.append(data[1])
for tup in bestups:
if tup[0] in movies:
top5seen.append(tup)
top5seen = [top5seen[x][0] for x in range(top)]
for tup in bestups:
if tup[0] not in movies:
top5notseen.append(tup)
top5notseen = [top5notseen[x][0] for x in range(top)]
return top5seen, top5notseen
def driver():
(items, ratings) = SmallDukeEatsReader.getdata()
print("items = ", items)
print("ratings = ", ratings)
avg = RecommenderEngine.averages(items, ratings)
print("average", avg)
for key in ratings:
slist = RecommenderEngine.similarities(key, ratings)
print(key, slist)
r3 = RecommenderEngine.recommendations(key, items, ratings, 3)
print("top", r3)
def driver():
'''(items,ratings) = SmallDukeEatsReader.getdata()
print("items = ",items)
print("ratings = ", ratings)'''
avg = RecommenderEngine.averages(items, ratings)
print("average", avg)
slist = RecommenderEngine.similarities("student1", ratings)
print(slist)
for key in ratings:
slist = RecommenderEngine.similarities(key, ratings)
print(key, slist)
r3 = RecommenderEngine.recommendations("student1", items, ratings, 2)
print(r3)
print(MovieReader.getdata()[:5])
print(BookReader.getdata()[:5])
def makerecs(name, items, ratings, size, top):
'''
This function calculates the top recommendations and returns a two-tuple consisting of two lists.
The first list is the top items rated by the rater called name (string).
The second list is the top items not seen/rated by name (string)
'''
recs=RecommenderEngine.recommendations(name,items,ratings,size)
seen=[]
notseen=[]
for r in recs:
item=r[0]
loc=items.index(item)
if ratings[name][loc]==0 and len(notseen)!=top:
notseen.append(r)
if ratings[name][loc]!=0 and len(seen)!=top:
seen.append(r)
if len(seen)==top and len(notseen)==top:
return (seen,notseen)
return(seen,notseen)
def driver():
'''
This function uses the file SmallDukeEatsReads to test functions
in RecommenderEngine by outputting validation messages.
'''
(items, ratings) = SmallDukeEatsReader.getdata()
print("items = ", items) #len 9
print("ratings = ", ratings)
avg = RecommenderEngine.averages(items, ratings)
print("average", avg)
if avg == [('DivinityCafe', 4.0), ('TheCommons', 3.0),
('Tandoor', 2.4285714285714284), ('IlForno', 1.8),
('FarmStead', 1.4), ('LoopPizzaGrill', 1.0),
('TheSkillet', 0.0), ('PandaExpress', -0.2),
('McDonalds', -0.3333333333333333)]:
print("averages works")
else:
print("averages fails")
for key in ratings:
slist = RecommenderEngine.similarities(key, ratings)
print(key, slist)
if key == "Sung-Hoon":
if slist == [('Wei', 1), ('Sly one', -1), ('Melanie', -2),
('Sarah Lee', -6), ('J J', -14), ('Harry', -24),
('Nana Grace', -29)]:
print("similarities works")
else:
print("similarities fails")
r3 = RecommenderEngine.recommendations(key, items, ratings, 3)
if key == "Sarah Lee":
if r3 == [('Tandoor', 149.5), ('TheCommons', 128.0),
('DivinityCafe', 123.33333333333333),
('FarmStead', 69.5), ('TheSkillet', 66.0),
('LoopPizzaGrill', 62.0), ('IlForno', 33.0),
('McDonalds', -69.5), ('PandaExpress', -165.0)]:
print("recommendations works")
else:
print("recommendations fails")
print("top", r3)
def driver():
(items, ratings) = SmallDukeEatsReader.getdata()
print("items = ", items)
print("ratings = ", ratings)
avg = RecommenderEngine.averages(items, ratings)
print("average", avg)
answer = [('DivinityCafe', 4.0), ('TheCommons', 3.0),
('Tandoor', 2.4285714285714284), ('IlForno', 1.8),
('FarmStead', 1.4), ('LoopPizzaGrill', 1.0), ('TheSkillet', 0.0),
('PandaExpress', -0.2), ('McDonalds', -0.3333333333333333)]
if avg == answer:
print("average works")
else:
print("average fails")
for key in ratings:
slist = RecommenderEngine.similarities(key, ratings)
print(key, slist)
answer = [('Wei', 1), ('Sly one', -1), ('Melanie', -2),
('Sarah Lee', -6), ('J J', -14), ('Harry', -24),
('Nana Grace', -29)]
if answer == slist and key == 'Sung-Hoon':
print("similarities works")
if answer != slist and key == 'Sung-Hoon':
print("similarities fails")
r3 = RecommenderEngine.recommendations(key, items, ratings, 3)
print("top", r3)
answer = [('Tandoor', 149.5), ('TheCommons', 128.0),
('DivinityCafe', 123.33333333333333), ('FarmStead', 69.5),
('TheSkillet', 66.0), ('LoopPizzaGrill', 62.0),
('IlForno', 33.0), ('McDonalds', -69.5),
('PandaExpress', -165.0)]
if answer == r3 and key == 'Sarah Lee':
print("recommendations works")
if answer != r3 and key == 'Sarah Lee':
print("recommendations fails")
def driver():
(items, ratings) = SmallDukeEatsReader.getdata()
print("items = ", items)
print("ratings = ", ratings)
avg = RecommenderEngine.averages(items, ratings)
if avg == [('DivinityCafe', 4.0), ('TheCommons', 3.0),
('Tandoor', 2.4285714285714284), ('IlForno', 1.8),
('FarmStead', 1.4), ('LoopPizzaGrill', 1.0),
('TheSkillet', 0.0), ('PandaExpress', -0.2),
('McDonalds', -0.3333333333333333)]:
print("AVERAGES WORKS")
else:
print("AVERAGES FAILS")
print("average", avg)
for key in ratings:
slist = RecommenderEngine.similarities(key, ratings)
print(key, slist)
r3 = RecommenderEngine.recommendations(key, items, ratings, 3)
print("top", r3)
def makerecs(name, items, ratings, numUsers, top):
'''
This function calculates the top recommendations and returns a two-tuple consisting of two lists.
The first list is the top items rated by the rater called name (string).
The second list is the top items not seen/rated by name (string)
'''
#[('A Beautiful Mind', 335.0), ('Black Swan', 335.0), ('Anchorman: The Legend of Ron Burgundy', 247.5)]
avgTups = RecommenderEngine.recommendations(name, items, ratings, numUsers)
#Correct. the movies the person has rated.
hasRated = []
#get the movies the person has rated.
for i in range(len(items)):
if ratings[name][i] != 0: #check to see the movie has been rated
hasRated.append(items[i]) #append that movie into hasRated.
#pick out the top hasRated values to put in favorites.
favorites = [
] #Correct. [('Anchorman: The Legend of Ron Burgundy', 247.5), ('Avatar', 215.5), ('The Godfather', 201.0)]
#the (movie, avgrating) for movies the person rated.
for tuple in avgTups:
if tuple[0] in hasRated: #the movie has been rated
favorites.append(tuple)
#print(favorites)
topfavs = favorites[:top]
#print(topfavs)
#pick out the top notRated values to put in recLst.
recLst = [
] #notRate[:top] [('A Beautiful Mind', 335.0), ('Black Swan', 335.0), ('A Nightmare on Elm Street', 96.0)]
for tuple in avgTups:
if tuple[
0] not in hasRated: #pick out movies that have not been rated.
recLst.append(tuple)
#print(recLst)
toprec = recLst[:top]
return (topfavs, toprec)
for r in recs:
item=r[0]
loc=items.index(item)
if ratings[name][loc]==0 and len(notseen)!=top:
notseen.append(r)
if ratings[name][loc]!=0 and len(seen)!=top:
seen.append(r)
if len(seen)==top and len(notseen)==top:
return (seen,notseen)
return(seen,notseen)
if __name__ == '__main__':
name = "student1367"
items = ['127 Hours', 'The Godfather', '50 First Dates', 'A Beautiful Mind', 'A Nightmare on Elm Street', 'Alice in Wonderland', 'Anchorman: The Legend of Ron Burgundy', 'Austin Powers in Goldmember', 'Avatar', 'Black Swan']
ratings = {'student1367': [2, 3, -5, 2, 3, 1, 5, 1, 3, 4],
'student1046': [2, 3, -5, 2, 3, 1, 5, 1, 3, 4],
'student1206': [2, 3, -5, 2, 3, 1, 5, 1, 3, 4],
'student1103': [2, 3, -5, 2, 3, 1, 5, 1, 3, 4]}
size = 2
top = 3
print(RecommenderEngine.recommendations(name,items,ratings,size))
print(makerecs(name,items,ratings,size,top))
def test_recommendations(self):
self.assertEqual(
RecommenderEngine.recommendations("student1", items, ratings, 2),
[('Cat', 8.5), ('Dog', 6.5), ('Bear', 0.0)])