def get_movies_dict_from_file():
movies_dict = {}
pth = get_dataset_dir() + 'ml-100k/ml-100k/u.item'
with open(pth) as f:
for line in f:
ln_lst = line.split('|')
movies_dict[ln_lst[0]] = (ln_lst[1], ln_lst[2])
return movies_dict
def read():
file_name = get_dataset_dir() + '/ml-100k/ml-100k/u.item'
rid_name = {}
with io.open(file_name, 'r', encoding='ISO-8859-1') as file:
for line in file:
line = line.split('|')
rid_name[line[0]] = (line[1], line[2])
return rid_name
def read_item_names():
file_name = get_dataset_dir() + '/ml-100k/ml-100k/u.item'
rid_to_name = {}
with io.open(file_name, 'r', encoding='ISO-8859-1') as f:
for line in f:
line = line.split('|')
rid_to_name[line[0]] = line[1]
return rid_to_name
def id_to_user():
file_name = get_dataset_dir() + '/ml-100k/ml-100k/u.user'
rid_to_user = {}
with io.open(file_name, 'r', encoding='ISO-8859-1') as f:
for line in f:
line = line.split('|')
rid_to_user[line[0]] = {}
rid_to_user[line[0]]['Identificador'] = line[0]
rid_to_user[line[0]]['Idade'] = line[1]
rid_to_user[line[0]]['Genero'] = line[2]
return rid_to_user
def get_rid_to_item_mapping():
"""Read the u.item file from MovieLens 100-k dataset and return two
mappings to convert raw ids into movie names and movie names into raw ids.
"""
file_name = get_dataset_dir() + '/ml-100k/ml-100k/u.item'
rid_to_name = {}
with io.open(file_name, 'r', encoding='ISO-8859-1') as f:
for line in f:
line = line.split('|')
rid_to_name[line[0]] = (line[1], line[2])
return rid_to_name
def read_user_names():
"""Read the u.item file from MovieLens 100-k dataset and return two
mappings to convert raw ids into movie names and movie names into raw ids.
"""
file_name = get_dataset_dir() + '/ml-100k/ml-100k/u.data'
user_id = {}
name_to_rid = {}
with io.open(file_name, 'r', encoding='ISO-8859-1') as f:
for line in f:
line = line.split('\t')
user_id[line[0]] = line[0]
return user_id
def read_item_names():
"""Read the u.item file from MovieLens 100-k dataset and return two
mappings to convert raw ids into movie names and movie names into raw ids.
"""
file_name = get_dataset_dir() + "/ml-100k/ml-100k/u.item"
rid_to_name = {}
name_to_rid = {}
with io.open(file_name, "r", encoding="ISO-8859-1") as f:
for line in f:
line = line.split("|")
rid_to_name[line[0]] = line[1]
name_to_rid[line[1]] = line[0]
return rid_to_name, name_to_rid
def read_item_names():
"""Read the u.item file from MovieLens 100-k dataset and return two
mappings to convert raw ids into movie names and movie names into raw ids.
"""
file_name = get_dataset_dir() + '/ml-100k/ml-100k/u.item'
rid_to_name = {}
name_to_rid = {}
with io.open(file_name, 'r', encoding='ISO-8859-1') as f:
for line in f:
line = line.split('|')
rid_to_name[line[0]] = line[1]
name_to_rid[line[1]] = line[0]
return rid_to_name, name_to_rid
# Loads move dataset
data = Dataset.load_builtin("ml-100k")
# Gets top 5 rated movies for each user
ratings = pd.DataFrame({"userID": [rating[0] for rating in data.raw_ratings],
"movieID": [rating[1] for rating in data.raw_ratings],
"Rating": [rating[2] for rating in data.raw_ratings]}) \
.groupby(["userID"])
# Saves those that are 4 or 5 stars
topMovies = [[
rating[1] for rating in np.array(
ratings.get_group(str(uid)).sort_values(
["Rating"], ascending=False).head(5)) if rating[2] > 3.0
] for uid in list(ratings.groups)]
# Runs apriori algorithm on movies
L, supportData = apriori(topMovies)
rules = generateRules(L, supportData)
# Builds dict to convert IDs to names
file_name = get_dataset_dir() + '/ml-100k/ml-100k/u.item'
id_to_name = {}
with io.open(file_name, 'r', encoding='ISO-8859-1') as f:
for line in f:
line = line.split('|')
id_to_name[int(line[0])] = line[1]
pntRules(rules, id_to_name)
'user_based': True,
'min_support': films_count
}
algo = surprise.KNNBaseline(k=k, sim_options=sim_options)
algo.fit(trainset)
testset = trainset.build_anti_testset()
testset = filter(lambda x: x[0] == id, testset)
predictions = algo.test(testset)
top_n = defaultdict(list)
for uid, iid, _, est, _ in predictions:
top_n[uid].append((iid, round(est, 3)))
for uid, user_ratings in top_n.items():
user_ratings.sort(key=lambda x: x[1], reverse=True)
top_n[uid] = user_ratings[:5]
file_name = surprise.get_dataset_dir() + '/ml-100k/ml-100k/u.item'
item = {}
with open(file_name, 'r') as f:
for line in f:
line = line.split('|')
item[line[0]] = (line[1], line[2])
print(f'User {id}:')
for movie_id, rating in top_n[id]:
print(str(movie_id) + "\t" + str(rating) + "\t" + str(item[movie_id]))
# Create list of tuples to append to test set
for i in range(0, 5):
temp_input = input("Rating for " + movies[i] + ": ")
while int(temp_input) not in [1, 2, 3, 4, 5]:
temp_input = input("Rating for " + movies[i] + ": ")
my_tuple = (uid_for_new_user, m_id[i], float(temp_input), time)
input_list.append(my_tuple)
return input_list
if __name__ == "__main__":
# Get Data
# file_path = os.path.expanduser('C:/cygwin64/home/jaipe/Machine Learning/ml-100k/u.data')
file_path = os.path.expanduser(get_dataset_dir() +
'/ml-100k/ml-100k/u.data')
reader = Reader(line_format='user item rating timestamp', sep='\t')
data = Dataset.load_from_file(file_path, reader=reader)
# Get the mappings: raw id <-> movie name
rid_to_name, name_to_rid = read_item_names()
# Ask for user ratings
new_uid = '1500'
my_input_list = get_user_recs(uid_for_new_user=new_uid, dev=False)
# Add to new user data to train on
for tup in my_input_list:
data.raw_ratings.append(tup)
