def test_simple_factorization(self):
save_path = './test/'
k=3
MF = MatrixFactorization(save_path=save_path)
MF.train(self.ratings, k=k)
self.assertEqual(MF.user_factors.shape[0], len(set(self.ratings['user_id'])))
self.assertEqual(MF.user_factors.shape[1], k)
def test_simple_factorization(self):
save_path = './test/'
k = 3
MF = MatrixFactorization(save_path=save_path)
MF.train(self.ratings, k=k)
self.assertEqual(MF.user_factors.shape[0],
len(set(self.ratings['user_id'])))
self.assertEqual(MF.user_factors.shape[1], k)
def evaluate_funksvd_recommender():
save_path = './models/funkSVD/'
K = 20
timestr = time.strftime("%Y%m%d-%H%M%S")
file_name = '{}-funksvd-k.csv'.format(timestr)
with open(file_name, 'a', 1) as logfile:
logfile.write("rak,pak,mae,k\n")
builder = MatrixFactorization(save_path)
for k in np.arange(0, 20, 2):
recommender = FunkSVDRecs(save_path + 'model/')
er = EvaluationRunner(0,
builder,
recommender,
k,
params={
'k': 20,
'save_path': save_path + 'model/'
})
result = er.calculate(20, 10)
builder = None
map = result['map']
mae = result['mae']
ar = result['ar']
logfile.write("{}, {}, {}, {}, {}, {}\n".format(ar, map, mae, k))
logfile.flush()
def test_factorization(self):
ratings = pd.DataFrame(
[['1', "SW", Decimal(5), '2013-10-12 23:21:27+00:00'],
['1', "SW2", Decimal(5), '2014-10-12 23:22:27+00:00'],
['1', "SW3", Decimal(5), '2015-11-12 23:20:27+00:00'],
['1', "MM", Decimal(2), '2015-08-12 23:20:27+00:00'],
['1', "MM2", Decimal(1), '2015-10-12 22:20:27+00:00'],
['2', "SW", Decimal(4), '2013-10-12 23:21:27+00:00'],
['2', "SW2", Decimal(5), '2014-10-12 23:22:27+00:00'],
['2', "SW3", Decimal(5), '2015-11-12 23:20:27+00:00'],
['2', "MM", Decimal(1), '2015-08-12 23:20:27+00:00'],
['2', "MM2", Decimal(2), '2015-10-12 22:20:27+00:00'],
['3', "SW", Decimal(5), '2013-10-12 23:21:27+00:00'],
['3', "SW2", Decimal(5), '2014-10-12 23:22:27+00:00'],
['4', "SW", Decimal(1), '2013-10-12 23:21:27+00:00'],
['4', "SW2", Decimal(2), '2014-10-12 23:22:27+00:00'],
['4', "SW3", Decimal(1), '2015-11-12 23:20:27+00:00'],
['4', "MM", Decimal(5), '2015-08-12 23:20:27+00:00'],
['4', "MM2", Decimal(5), '2015-10-12 22:20:27+00:00'],
['5', "SW", Decimal(2), '2013-10-12 23:21:27+00:00'],
['5', "SW2", Decimal(1), '2014-10-12 23:22:27+00:00'],
['5', "SW3", Decimal(2), '2015-11-12 23:20:27+00:00'],
['5', "MM", Decimal(4), '2015-08-12 23:20:27+00:00'],
['5', "MM2", Decimal(5), '2015-10-12 22:20:27+00:00'],
], columns=['user_id', 'movie_id', 'rating', 'rating_timestamp'])
ratings['rating'] = ratings['rating'].astype(Decimal)
save_path = './test/small_model/'
k = 2
MF = MatrixFactorization(save_path=save_path)
MF.train(ratings, k=k)
self.assertEqual(MF.user_factors.shape[0], len(set(ratings['user_id'])))
self.assertEqual(MF.user_factors.shape[1], k)
u_inx = MF.u_inx['3']
i_inx = MF.i_inx['SW3']
r_sw3 = MF.predict(u_inx, i_inx)
print(MF.item_factors)
print("user factors {}".format(MF.user_factors))
print("u_inx {}".format(MF.u_inx))
print("i_inx {}".format(MF.i_inx))
print("user bias {}".format(dict(MF.user_bias)))
print("item bias {}".format(dict(MF.item_bias)))
i_inx = MF.i_inx['MM']
recommendation = {i: MF.predict(u_inx, MF.i_inx[i]) for i in set(ratings['movie_id'])}
print(recommendation)
print(sorted(recommendation, key=recommendation.__getitem__))
r_mm2 = MF.predict(u_inx, i_inx)
print("SW3 {} vs. MM2 {}".format(r_sw3, r_mm2))
self.assertGreater(r_sw3, r_mm2)
def test_factorization(self):
ratings = pd.DataFrame([
['1', "SW", Decimal(5), '2013-10-12 23:21:27+00:00'],
['1', "SW2", Decimal(5), '2014-10-12 23:22:27+00:00'],
['1', "SW3", Decimal(5), '2015-11-12 23:20:27+00:00'],
['1', "MM", Decimal(2), '2015-08-12 23:20:27+00:00'],
['1', "MM2", Decimal(1), '2015-10-12 22:20:27+00:00'],
['2', "SW", Decimal(4), '2013-10-12 23:21:27+00:00'],
['2', "SW2", Decimal(5), '2014-10-12 23:22:27+00:00'],
['2', "SW3", Decimal(5), '2015-11-12 23:20:27+00:00'],
['2', "MM", Decimal(1), '2015-08-12 23:20:27+00:00'],
['2', "MM2", Decimal(2), '2015-10-12 22:20:27+00:00'],
['3', "SW", Decimal(5), '2013-10-12 23:21:27+00:00'],
['3', "SW2", Decimal(5), '2014-10-12 23:22:27+00:00'],
['4', "SW", Decimal(1), '2013-10-12 23:21:27+00:00'],
['4', "SW2", Decimal(2), '2014-10-12 23:22:27+00:00'],
['4', "SW3", Decimal(1), '2015-11-12 23:20:27+00:00'],
['4', "MM", Decimal(5), '2015-08-12 23:20:27+00:00'],
['4', "MM2", Decimal(5), '2015-10-12 22:20:27+00:00'],
['5', "SW", Decimal(2), '2013-10-12 23:21:27+00:00'],
['5', "SW2", Decimal(1), '2014-10-12 23:22:27+00:00'],
['5', "SW3", Decimal(2), '2015-11-12 23:20:27+00:00'],
['5', "MM", Decimal(4), '2015-08-12 23:20:27+00:00'],
['5', "MM2", Decimal(5), '2015-10-12 22:20:27+00:00'],
],
columns=[
'user_id', 'movie_id', 'rating',
'rating_timestamp'
])
ratings['rating'] = ratings['rating'].astype(Decimal)
save_path = './test/small_model/'
k = 2
MF = MatrixFactorization(save_path=save_path)
MF.train(ratings, k=k)
self.assertEqual(MF.user_factors.shape[0],
len(set(ratings['user_id'])))
self.assertEqual(MF.user_factors.shape[1], k)
u_inx = MF.u_inx['3']
i_inx = MF.i_inx['SW3']
r_sw3 = MF.predict(u_inx, i_inx)
print(MF.item_factors)
print("user factors {}".format(MF.user_factors))
print("u_inx {}".format(MF.u_inx))
print("i_inx {}".format(MF.i_inx))
print("user bias {}".format(dict(MF.user_bias)))
print("item bias {}".format(dict(MF.item_bias)))
i_inx = MF.i_inx['MM']
recommendation = {
i: MF.predict(u_inx, MF.i_inx[i])
for i in set(ratings['movie_id'])
}
print(recommendation)
print(sorted(recommendation, key=recommendation.__getitem__))
r_mm2 = MF.predict(u_inx, i_inx)
print("SW3 {} vs. MM2 {}".format(r_sw3, r_mm2))
self.assertGreater(r_sw3, r_mm2)
def setUp(self):
self.ratings = pd.DataFrame(
[
['1', STAR_WARS, 9, '2013-10-12 23:21:27+00:00'],
['1', WONDER_WOMAN, 10, '2014-10-12 23:22:27+00:00'],
['1', AVENGERS, 10, '2015-11-12 23:20:27+00:00'],
['1', WOLVERINE, 8, '2015-08-12 23:20:27+00:00'],
['1', PIRATES_OF, 10, '2015-10-12 22:20:27+00:00'],
['1', HARRY, 10, '2015-10-12 23:21:27+00:00'],
['1', CAPTAIN_AMERICA, 10, '2014-10-12 23:20:27+00:00'],
['1', ALIEN, 6, '2015-10-12 23:22:27+00:00'],
['1', JACQUES, 6, '2015-10-12 11:20:27+00:00'],
['2', STAR_WARS, 10, '2013-10-12 23:20:27+00:00'],
['2', WONDER_WOMAN, 10, '2014-10-12 23:20:27+00:00'],
['2', AVENGERS, 9, '2016-10-12 23:20:27+00:00'],
['2', PIRATES_OF, 6, '2010-10-12 23:20:27+00:00'],
['2', CAPTAIN_AMERICA, 10, '2005-10-12 23:20:27+00:00'],
['2', DR_STRANGELOVE, 10, '2015-01-12 23:20:27+00:00'],
['3', STAR_WARS, 9, '2013-10-12 20:20:27+00:00'],
['3', AVENGERS, 10, '2015-10-12 10:20:27+00:00'],
['3', PIRATES_OF, 9, '2013-03-12 23:20:27+00:00'],
['3', HARRY, 8, '2016-10-13 23:20:27+00:00'],
['3', DR_STRANGELOVE, 10, '2016-09-12 23:20:27+00:00'],
['4', STAR_WARS, 8, '2013-10-12 23:20:27+00:00'],
['4', WONDER_WOMAN, 8, '2014-10-12 23:20:27+00:00'],
['4', AVENGERS, 9, '2015-10-12 23:20:27+00:00'],
['4', PIRATES_OF, 5, '2013-10-12 23:20:27+00:00'],
['4', HARRY, 6, '2014-10-12 23:20:27+00:00'],
['4', ALIEN, 8, '2015-10-12 23:20:27+00:00'],
['4', DR_STRANGELOVE, 9, '2015-10-12 23:20:27+00:00'],
['5', STAR_WARS, 6, '2013-10-12 23:20:27+00:00'],
['5', AVENGERS, 1, '2014-10-12 23:20:27+00:00'],
['5', WOLVERINE, 2, '2015-10-12 23:20:27+00:00'],
['5', PIRATES_OF, 2, '2016-10-12 23:20:27+00:00'],
['5', HARRY, 10, '2016-10-12 23:20:27+00:00'],
['5', CAPTAIN_AMERICA, 1, '2016-10-12 23:20:27+00:00'],
['5', ALIEN, 4, '2016-10-12 23:20:27+00:00'],
['5', DR_STRANGELOVE, 3, '2016-10-12 23:20:27+00:00'],
['5', JACQUES, 10, '2016-10-12 23:20:27+00:00'],
['6', STAR_WARS, 8, '2013-10-12 23:20:27+00:00'],
['6', WONDER_WOMAN, 8, '2014-10-12 23:20:27+00:00'],
['6', AVENGERS, 8, '2014-10-12 23:20:27+00:00'],
['6', WOLVERINE, 8, '2015-10-12 23:20:27+00:00'],
['6', PIRATES_OF, 6, '2016-10-12 23:20:27+00:00'],
['6', HARRY, 10, '2016-10-12 23:20:27+00:00'],
['6', JACQUES, 8, '2016-10-12 23:20:27+00:00'],
['7', AVENGERS, 10, '2014-10-12 23:20:27+00:00'],
['7', PIRATES_OF, 3, '2016-10-12 23:20:27+00:00'],
['7', HARRY, 1, '2016-10-12 23:20:27+00:00'],
['7', ALIEN, 8, '2016-10-12 23:20:27+00:00'],
['7', DR_STRANGELOVE, 10, '2016-10-12 23:20:27+00:00'],
['8', STAR_WARS, 9, '2013-10-12 23:20:27+00:00'],
['8', WONDER_WOMAN, 7, '2014-10-12 23:20:27+00:00'],
['8', AVENGERS, 7, '2014-10-12 23:20:27+00:00'],
['8', WOLVERINE, 7, '2015-10-12 23:20:27+00:00'],
['8', PIRATES_OF, 8, '2016-10-12 23:20:27+00:00'],
['8', HARRY, 8, '2016-10-12 23:20:27+00:00'],
['8', ALIEN, 8, '2016-10-12 23:20:27+00:00'],
['8', DR_STRANGELOVE, 8, '2016-10-12 23:20:27+00:00'],
['8', JACQUES, 10, '2016-10-12 23:20:27+00:00'],
['9', WONDER_WOMAN, 7, '2014-10-12 23:20:27+00:00'],
['9', AVENGERS, 8, '2014-10-12 23:20:27+00:00'],
['9', WOLVERINE, 8, '2015-10-12 23:20:27+00:00'],
['9', PIRATES_OF, 7, '2016-10-12 23:20:27+00:00'],
['9', HARRY, 8, '2016-10-12 23:20:27+00:00'],
['9', CAPTAIN_AMERICA, 10, '2016-10-12 23:20:27+00:00'],
['9', DR_STRANGELOVE, 10, '2016-10-12 23:20:27+00:00'],
['9', JACQUES, 7, '2016-10-12 23:20:27+00:00'],
['10', AVENGERS, 7, '2014-10-12 23:20:27+00:00'],
['10', ALIEN, 10, '2016-10-12 23:20:27+00:00'],
['10', CAPTAIN_AMERICA, 6, '2016-10-12 23:20:27+00:00'],
['10', DR_STRANGELOVE, 8, '2016-10-12 23:20:27+00:00'],
],
columns=['user_id', 'movie_id', 'rating', 'rating_timestamp'])
self.save_path = './test/'
self.k = 3
MF = MatrixFactorization(save_path=self.save_path)
MF.train(self.ratings, k=self.k)
def setUp(self):
self.ratings = pd.DataFrame(
[['1', STAR_WARS, 9, '2013-10-12 23:21:27+00:00'],
['1', WONDER_WOMAN, 10, '2014-10-12 23:22:27+00:00'],
['1', AVENGERS, 10, '2015-11-12 23:20:27+00:00'],
['1', WOLVERINE, 8, '2015-08-12 23:20:27+00:00'],
['1', PIRATES_OF, 10, '2015-10-12 22:20:27+00:00'],
['1', HARRY, 10, '2015-10-12 23:21:27+00:00'],
['1', CAPTAIN_AMERICA, 10, '2014-10-12 23:20:27+00:00'],
['1', ALIEN, 6, '2015-10-12 23:22:27+00:00'],
['1', JACQUES, 6, '2015-10-12 11:20:27+00:00'],
['2', STAR_WARS, 10, '2013-10-12 23:20:27+00:00'],
['2', WONDER_WOMAN, 10, '2014-10-12 23:20:27+00:00'],
['2', AVENGERS, 9, '2016-10-12 23:20:27+00:00'],
['2', PIRATES_OF, 6, '2010-10-12 23:20:27+00:00'],
['2', CAPTAIN_AMERICA, 10, '2005-10-12 23:20:27+00:00'],
['2', DR_STRANGELOVE, 10, '2015-01-12 23:20:27+00:00'],
['3', STAR_WARS, 9, '2013-10-12 20:20:27+00:00'],
['3', AVENGERS, 10, '2015-10-12 10:20:27+00:00'],
['3', PIRATES_OF, 9, '2013-03-12 23:20:27+00:00'],
['3', HARRY, 8, '2016-10-13 23:20:27+00:00'],
['3', DR_STRANGELOVE, 10, '2016-09-12 23:20:27+00:00'],
['4', STAR_WARS, 8, '2013-10-12 23:20:27+00:00'],
['4', WONDER_WOMAN, 8, '2014-10-12 23:20:27+00:00'],
['4', AVENGERS, 9, '2015-10-12 23:20:27+00:00'],
['4', PIRATES_OF, 5, '2013-10-12 23:20:27+00:00'],
['4', HARRY, 6, '2014-10-12 23:20:27+00:00'],
['4', ALIEN, 8, '2015-10-12 23:20:27+00:00'],
['4', DR_STRANGELOVE, 9, '2015-10-12 23:20:27+00:00'],
['5', STAR_WARS, 6, '2013-10-12 23:20:27+00:00'],
['5', AVENGERS, 1, '2014-10-12 23:20:27+00:00'],
['5', WOLVERINE, 2, '2015-10-12 23:20:27+00:00'],
['5', PIRATES_OF, 2, '2016-10-12 23:20:27+00:00'],
['5', HARRY, 10, '2016-10-12 23:20:27+00:00'],
['5', CAPTAIN_AMERICA, 1, '2016-10-12 23:20:27+00:00'],
['5', ALIEN, 4, '2016-10-12 23:20:27+00:00'],
['5', DR_STRANGELOVE, 3, '2016-10-12 23:20:27+00:00'],
['5', JACQUES, 10, '2016-10-12 23:20:27+00:00'],
['6', STAR_WARS, 8, '2013-10-12 23:20:27+00:00'],
['6', WONDER_WOMAN, 8, '2014-10-12 23:20:27+00:00'],
['6', AVENGERS, 8, '2014-10-12 23:20:27+00:00'],
['6', WOLVERINE, 8, '2015-10-12 23:20:27+00:00'],
['6', PIRATES_OF, 6, '2016-10-12 23:20:27+00:00'],
['6', HARRY, 10, '2016-10-12 23:20:27+00:00'],
['6', JACQUES, 8, '2016-10-12 23:20:27+00:00'],
['7', AVENGERS, 10, '2014-10-12 23:20:27+00:00'],
['7', PIRATES_OF, 3, '2016-10-12 23:20:27+00:00'],
['7', HARRY, 1, '2016-10-12 23:20:27+00:00'],
['7', ALIEN, 8, '2016-10-12 23:20:27+00:00'],
['7', DR_STRANGELOVE, 10, '2016-10-12 23:20:27+00:00'],
['8', STAR_WARS, 9, '2013-10-12 23:20:27+00:00'],
['8', WONDER_WOMAN, 7, '2014-10-12 23:20:27+00:00'],
['8', AVENGERS, 7, '2014-10-12 23:20:27+00:00'],
['8', WOLVERINE, 7, '2015-10-12 23:20:27+00:00'],
['8', PIRATES_OF, 8, '2016-10-12 23:20:27+00:00'],
['8', HARRY, 8, '2016-10-12 23:20:27+00:00'],
['8', ALIEN, 8, '2016-10-12 23:20:27+00:00'],
['8', DR_STRANGELOVE, 8, '2016-10-12 23:20:27+00:00'],
['8', JACQUES, 10, '2016-10-12 23:20:27+00:00'],
['9', WONDER_WOMAN, 7, '2014-10-12 23:20:27+00:00'],
['9', AVENGERS, 8, '2014-10-12 23:20:27+00:00'],
['9', WOLVERINE, 8, '2015-10-12 23:20:27+00:00'],
['9', PIRATES_OF, 7, '2016-10-12 23:20:27+00:00'],
['9', HARRY, 8, '2016-10-12 23:20:27+00:00'],
['9', CAPTAIN_AMERICA, 10, '2016-10-12 23:20:27+00:00'],
['9', DR_STRANGELOVE, 10, '2016-10-12 23:20:27+00:00'],
['9', JACQUES, 7, '2016-10-12 23:20:27+00:00'],
['10', AVENGERS, 7, '2014-10-12 23:20:27+00:00'],
['10', ALIEN, 10, '2016-10-12 23:20:27+00:00'],
['10', CAPTAIN_AMERICA, 6, '2016-10-12 23:20:27+00:00'],
['10', DR_STRANGELOVE, 8, '2016-10-12 23:20:27+00:00'],
], columns=['user_id', 'movie_id', 'rating', 'rating_timestamp'])
self.save_path = './test/'
self.k=3
MF = MatrixFactorization(save_path=self.save_path)
MF.train(self.ratings, k=self.k)