def test05_topk_MT(self):
set_log_level(2)
data_opt = self.get_ml100k_mm_opt()
opt = ALSOption().get_default_option()
opt.d = 20
opt.num_workers = 1
als = ALS(opt, data_opt=data_opt)
als.initialize()
als.train()
als.build_userid_map()
all_keys = als._idmanager.userids
start_t = time.time()
naive = als.topk_recommendation(all_keys, topk=5)
naive_elapsed = time.time() - start_t
pals = ParALS(als)
pals.num_workers = 4
start_t = time.time()
qkeys1, topks1, scores1 = pals.topk_recommendation(all_keys,
topk=5,
repr=True)
par_elapsed = time.time() - start_t
self.assertEqual(len(qkeys1), len(naive))
for q, t in zip(qkeys1, topks1):
self.assertEqual(naive[q], t)
self.assertTrue(naive_elapsed > par_elapsed * 1.5)
def test06_topk_pool(self):
set_log_level(2)
data_opt = self.get_ml100k_mm_opt()
opt = ALSOption().get_default_option()
opt.d = 20
opt.num_workers = 1
als = ALS(opt, data_opt=data_opt)
als.initialize()
als.train()
pals = ParALS(als)
pool = np.array([i for i in range(5)], dtype=np.int32)
als.build_userid_map()
all_keys = als._idmanager.userids[::][:10]
naive = als.topk_recommendation(all_keys, topk=10, pool=pool)
qkeys1, topks1, scores1 = pals.topk_recommendation(all_keys, topk=10, pool=pool, repr=True)
for q, t in zip(qkeys1, topks1):
self.assertEqual(naive[q], t)