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 test01_most_similar(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)
random_keys = [
k for k, _ in als.most_similar('49.Star_Wars_(1977)', topk=128)
]
random_indexes = als.get_index_pool(random_keys)
naive = [als.most_similar(k, topk=10) for k in random_keys]
topks0 = [[k for k, _ in result] for result in naive]
scores0 = np.array([[v for _, v in result] for result in naive])
self.assertEqual(scores0.shape, (
128,
10,
), msg='check even size')
scores0 = scores0.reshape(len(naive), 10)
pals.num_workers = 1
topks1, scores1 = pals.most_similar(random_keys, topk=10, repr=True)
topks2, scores2 = pals.most_similar(random_indexes, topk=10, repr=True)
for a, b in combinations([topks0, topks1, topks2], 2):
self.assertEqual(a, b)
for a, b in combinations([scores0, scores1, scores2], 2):
self.assertTrue(np.allclose(a, b))
def test5_validation(self):
opt = ALSOption().get_default_option()
opt.d = 5
opt.num_iters = 20
opt.validation = aux.Option({'topk': 10})
opt.tensorboard = aux.Option({'root': './tb', 'name': 'als'})
self._test5_validation(ALS, opt)
def test11_train_ml_20m_on_gpu(self):
opt = ALSOption().get_default_option()
opt.num_workers = 8
opt.d = 100
opt.validation = aux.Option({'topk': 10})
opt.compute_loss_on_training = True
opt.accelerator = True
opt.num_cg_max_iters = 3
self._test7_train_ml_20m(ALS, opt)
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)
def test00_tensorboard(self):
set_log_level(2)
opt = ALSOption().get_default_option()
opt.d = 5
opt.validation = aux.Option({'topk': 10})
opt.tensorboard = aux.Option({'root': './tb', 'name': 'als'})
data_opt = MatrixMarketOptions().get_default_option()
data_opt.input.main = self.ml_100k + 'main'
data_opt.input.uid = self.ml_100k + 'uid'
data_opt.input.iid = self.ml_100k + 'iid'
data_opt.data.value_prepro = aux.Option({'name': 'OneBased'})
als = ALS(opt, data_opt=data_opt)
als.initialize()
als.train()
results = als.get_validation_results()
self.assertTrue(results['ndcg'] > 0.025)
self.assertTrue(results['map'] > 0.015)
def test4_optimize(self):
set_log_level(2)
opt = ALSOption().get_default_option()
opt.d = 5
opt.num_workers = 2
opt.model_path = 'als.bin'
opt.validation = aux.Option({'topk': 10})
optimize_option = aux.Option({
'loss': 'val_rmse',
'max_trials': 10,
'deployment': True,
'start_with_default_parameters': True,
'space': {
'd': ['randint', ['d', 10, 20]],
'reg_u': ['uniform', ['reg_u', 0.1, 0.3]],
'reg_i': ['uniform', ['reg_i', 0.1, 0.3]],
'alpha': ['randint', ['alpha', 8, 10]]
}
})
opt.optimize = optimize_option
opt.evaluation_period = 1
opt.tensorboard = aux.Option({'root': './tb',
'name': 'als'})
data_opt = MatrixMarketOptions().get_default_option()
data_opt.input.main = self.ml_100k + 'main'
data_opt.input.uid = self.ml_100k + 'uid'
data_opt.input.iid = self.ml_100k + 'iid'
data_opt.data.value_prepro = aux.Option({'name': 'OneBased'})
als = ALS(opt, data_opt=data_opt)
als.init_factors()
als.train()
default_result = als.get_validation_results()
als.optimize()
base_loss = default_result['rmse'] # val_rmse
optimize_loss = als.get_optimization_data()['best']['val_rmse']
self.assertTrue(base_loss > optimize_loss)
als.load('als.bin')
loss = als.get_validation_results()
self.assertAlmostEqual(loss['rmse'], optimize_loss)
os.remove('als.bin')
def test02_most_similar(self):
set_log_level(1)
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_itemid_map()
pals = ParALS(als)
all_keys = als._idmanager.itemids[::]
start_t = time.time()
[als.most_similar(k, topk=10) for k in all_keys]
naive_elapsed = time.time() - start_t
pals.num_workers = 4
start_t = time.time()
pals.most_similar(all_keys, topk=10, repr=True)
parals_elapsed = time.time() - start_t
self.assertTrue(naive_elapsed > parals_elapsed * 3.0)
def test10_fast_most_similar(self):
opt = ALSOption().get_default_option()
opt.d = 5
opt.validation = aux.Option({'topk': 10})
self._test10_fast_most_similar(ALS, opt)
def test9_compact_serialization(self):
opt = ALSOption().get_default_option()
opt.d = 5
opt.validation = aux.Option({'topk': 10})
self._test9_compact_serialization(ALS, opt)
def test6_topk(self):
opt = ALSOption().get_default_option()
opt.d = 5
opt.validation = aux.Option({'topk': 10})
self._test6_topk(ALS, opt)
def test4_train(self):
opt = ALSOption().get_default_option()
opt.d = 20
self._test4_train(ALS, opt)
