def get_correlation_coefficient_from_titles(self, title_a, title_b):
movie_a_ids = loadmovielens.give_me_movie_id(title_a, self.items_dictionary)
movie_b_ids = loadmovielens.give_me_movie_id(title_b, self.items_dictionary)
if len(movie_a_ids) == 0 or len(movie_b_ids) == 0:
exit("Movie titles not found")
if len(movie_a_ids) > 1 or len(movie_b_ids) > 1:
exit("Ambigous search titles")
return self.get_correlation_coefficient_from_ids(movie_a_ids[0][0], movie_b_ids[0][0])
"""
test_cases = ast.literal_eval(sys.argv[1])
results = str(my_info()) + '\t\t'
for test_case in test_cases:
mode = test_case[0]
id_1 = int(test_case[1])
id_2 = int(test_case[2])
if mode == 'jc':
results += str(Jaccard_Coefficient(id_1, id_2)) + '\t\t'
elif mode == 'cc':
results += str(Correlation_Coefficient(id_1, id_2)) + '\t\t'
else:
exit('bad command')
print results + '\n'
if __name__ == '__main__':
main()
"""print my_info()"""
"""print reader.read_movie_lens_data()"""
print reader.give_me_movie_id('story', movie_dictionary)
print reader.give_me_movie_id('GoldenEye', movie_dictionary)
ratings, movie_dictionary, user_ids, item_ids, movie_names = reader.read_movie_lens_data()
print len(ratings)
i = 0
for x in range(0, len(ratings)):"search full db, when find, sum ratings
i += 1
"""print ratings[i], movie_names[i]"""
print "i %d" % (i)
print ratings[1], movie_names[1]
for movie_id in map_from_movie_to_user_id_list:
ret.append((movie_id,
fast_jaccard_coefficient(map_from_movie_to_user_id_list[movie_id],
map_from_movie_to_user_id_list[target_movie_id])))
ret = sorted(ret, key=lambda x: -x[1])
while len(ret) > k + 1:
ret.pop()
return ret[1:k + 1]
print "--- Jaccard coefficient between 'Toy Story' and 'GoldenEye' ---"
toy_story_id = reader.give_me_movie_id('Toy Story', movie_dictionary)[0][0]
golden_eye_id = reader.give_me_movie_id('GoldenEye', movie_dictionary)[0][0]
print jaccard_coefficient(toy_story_id, golden_eye_id)
print "--- Jaccard coefficient between 'Three Colors: Red' and 'Three Colors: Blue' ---"
red_id = reader.give_me_movie_id('Three Colors: Red', movie_dictionary)[0][0]
blue_id = reader.give_me_movie_id('Three Colors: Blue', movie_dictionary)[0][0]
print jaccard_coefficient(red_id, blue_id)
print "--- Closest 5 movies to 'Taxi Driver'---"
taxi_driver_id = reader.give_me_movie_id('Taxi Driver', movie_dictionary)[0][0]
taxi_passengers = find_k_closest(taxi_driver_id, 5)
for t in taxi_passengers:
users1 = set(seen[id].keys())
for movie in item_ids:
users2 = set(seen[movie].keys())
seenBoth = users1 & users2
if len(seenBoth) < 3:
continue
scores = []
users = []
for user in seenBoth:
scores = np.append(scores, seen[id][user])
scores = np.append(scores, seen[movie][user])
users = np.append(users, user)
users = np.append(users, user)
coef = np.corrcoef(scores, users)[0][1]
corcoefs[coef] = movie
order = sorted(corcoefs, reverse=True)
print movie_dictionary[id], id
print "-----"
for i in range(1, 6):
print movie_dictionary[corcoefs[
order[i]]], "Correlation coefficient:", round(order[i], 3)
id1 = reader.give_me_movie_id('toy', movie_dictionary)[0][0]
id2 = reader.give_me_movie_id('little big', movie_dictionary)[0][0]
#corCoef(id1, id2)
corCoefOne(id1)
for movie_id in map_from_movie_to_user_id_list:
ret.append((movie_id,
fast_jaccard_coefficient(
map_from_movie_to_user_id_list[movie_id],
map_from_movie_to_user_id_list[target_movie_id])))
ret = sorted(ret, key=lambda x: -x[1])
while len(ret) > k + 1:
ret.pop()
return ret[1:k + 1]
print "--- Jaccard coefficient between 'Toy Story' and 'GoldenEye' ---"
toy_story_id = reader.give_me_movie_id('Toy Story', movie_dictionary)[0][0]
golden_eye_id = reader.give_me_movie_id('GoldenEye', movie_dictionary)[0][0]
print jaccard_coefficient(toy_story_id, golden_eye_id)
print "--- Jaccard coefficient between 'Three Colors: Red' and 'Three Colors: Blue' ---"
red_id = reader.give_me_movie_id('Three Colors: Red', movie_dictionary)[0][0]
blue_id = reader.give_me_movie_id('Three Colors: Blue', movie_dictionary)[0][0]
print jaccard_coefficient(red_id, blue_id)
print "--- Closest 5 movies to 'Taxi Driver'---"
taxi_driver_id = reader.give_me_movie_id('Taxi Driver', movie_dictionary)[0][0]
taxi_passengers = find_k_closest(taxi_driver_id, 5)
for t in taxi_passengers:
seenBoth = users1 & users2
if len(seenBoth) < len(users1) // 5:
continue
scores1 = []
scores2 = []
for user in seenBoth:
scores1 = np.append(scores1, seen[id][user])
scores2 = np.append(scores2, seen[movie][user])
if np.std(scores1) == 0 or np.std(scores2) == 0:
continue
coef = np.corrcoef(scores1, scores2)[0][1]
corcoefs[coef] = movie
n[coef] = len(seenBoth)
order = sorted(corcoefs, reverse=True)
print movie_dictionary[id], id
print "-----"
for i in range(1, 6):
print movie_dictionary[corcoefs[order[i]]], "Correlation:", round(
order[i], 3), len(seen[corcoefs[order[i]]])
id1 = reader.give_me_movie_id('Three colors', movie_dictionary)[0][0]
jaccardForOne(id1)
corCoefOne(id1)
