def task_10():
md = MovieData('data/movies.dat')
uim = UserItemData('data/user_ratedmovies.dat', min_ratings=1000)
rp = ItemBasedPredictor()
rec = Recommender(rp)
rec.fit(uim)
movies = uim.data['movieID'].unique()
movies.sort()
comparisons = []
for m1 in movies:
for m2 in movies:
if not m1 < m2:
continue
sim = rp.similarity(m1, m2)
comparisons.append((m1, m2, sim))
print(m1, m2, sim)
comparisons.sort(key=itemgetter(2), reverse=True)
for result in comparisons[:21]:
print("Movie1: {}, Movie2: {}, similarity: {}".format(
md.get_title(result[0]), md.get_title(result[1]), result[2]))
def task_7():
md = MovieData('data/movies.dat')
uim = UserItemData('data/user_ratedmovies.dat')
ap = STDPredictor(100)
rec = Recommender(ap)
rec.fit(uim)
rec_items = rec.recommend(78, n=5, rec_seen=False)
for idmovie, val in rec_items:
print("Movie: {}, score: {}".format(md.get_title(idmovie), val))
def task_12():
md = MovieData('data/movies.dat')
uim = UserItemData('data/user_ratedmovies.dat', min_ratings=1000)
rp = ItemBasedPredictor()
rec = Recommender(rp)
rec.fit(uim)
rec_items = rec.recommend(133337, n=15, rec_seen=False)
for idmovie, val in rec_items:
print("Movie: {}, score: {}".format(md.get_title(idmovie), val))
def task_3():
md = MovieData('data/movies.dat')
uim = UserItemData('data/user_ratedmovies.dat')
rp = RandomPredictor(1, 5)
rp.fit(uim)
pred = rp.predict(78)
print(type(pred))
items = [1, 3, 20, 50, 100]
for item in items:
print("Movie: {}, score: {}".format(md.get_title(item), pred[item]))
def task_13():
md = MovieData('data/movies.dat')
uim = UserItemData('data/user_ratedmovies.dat', min_ratings=1000)
rp = SlopeOnePredictor()
rec = Recommender(rp)
rec.fit(uim)
print("Predictions for 78: ")
rec_items = rec.recommend(78, n=15, rec_seen=False)
for idmovie, val in rec_items:
print("Movie: {}, score: {}".format(md.get_title(idmovie), val))
def task_11():
md = MovieData('data/movies.dat')
uim = UserItemData('data/user_ratedmovies.dat', min_ratings=1000)
rp = ItemBasedPredictor()
rec = Recommender(rp)
rec.fit(uim)
rec_items = rp.similar_items(7361, 30)
print(
'Movies similar to "The Lord of the Rings: The Fellowship of the Ring": '
)
for idmovie, val in rec_items:
print("Movie: {}, score: {}, id: {}".format(md.get_title(idmovie), val,
idmovie))
def task_14():
md = MovieData('data/movies.dat')
uim = UserItemData('data/user_ratedmovies.dat',
min_ratings=1000,
to_date=datetime.datetime(2008, 1, 1))
rp = SlopeOnePredictor()
rec = Recommender(rp)
rec.fit(uim)
uim_test = UserItemData('data/user_ratedmovies.dat',
min_ratings=200,
from_date=datetime.datetime(2008, 1, 2))
mse, mae, precision, recall, f = rec.evaluate(uim_test, 20)
print(mse, mae, precision, recall, f)
def task_8():
md = MovieData('data/movies.dat')
uim = UserItemData('data/user_ratedmovies.dat', min_ratings=1000)
# uim = UserItemData('data/user_ratedmovies.dat')
rp = ItemBasedPredictor()
rec = Recommender(rp)
rec.fit(uim)
# print(uim.data)
print(
"Similarity between the movies 'Men in black'(1580) and 'Ghostbusters'(2716): ",
rp.similarity(1580, 2716))
print(
"Similarity between the movies 'Men in black'(1580) and 'Schindler's List'(527): ",
rp.similarity(1580, 527))
print(
"Similarity between the movies 'Men in black'(1580) and 'Independence day'(780): ",
rp.similarity(1580, 780))
def task_2():
md = MovieData('data/movies.dat')
print(md.get_title(1))