def test_ib_recommender(self):
ctx = blob_ctx.get()
rating_table = expr.sparse_rand((N_USERS, N_ITEMS),
dtype=np.float64,
density=0.1,
format = "csr",
tile_hint=(N_USERS, N_ITEMS/ctx.num_workers))
model = ItemBasedRecommender(rating_table)
model.precompute()
def test_ib_recommender(self):
ctx = blob_ctx.get()
FLAGS.opt_auto_tiling = 0
rating_table = expr.sparse_rand(
(N_USERS, N_ITEMS),
dtype=np.float64,
density=0.1,
format="csr",
tile_hint=(N_USERS, N_ITEMS / ctx.num_workers))
model = ItemBasedRecommender(rating_table)
model.precompute()
def benchmark_ib_recommander(ctx, timer):
print "#worker:", ctx.num_workers
N_ITEMS = 800
N_USERS = 8000
rating_table = expr.sparse_rand((N_USERS, N_ITEMS),
dtype=np.float64,
density=0.1,
format = "csr")
t1 = datetime.now()
model = ItemBasedRecommender(rating_table)
model.precompute()
t2 = datetime.now()
cost_time = millis(t1, t2)
print "total cost time:%s ms" % cost_time
def benchmark_ib_recommander(ctx, timer):
print "#worker:", ctx.num_workers
N_ITEMS = 800
N_USERS = 8000
rating_table = expr.sparse_rand((N_USERS, N_ITEMS),
dtype=np.float64,
density=0.1,
format="csr")
t1 = datetime.now()
model = ItemBasedRecommender(rating_table)
model.precompute()
t2 = datetime.now()
cost_time = millis(t1, t2)
print "total cost time:%s ms" % cost_time
