def evaluate(model, train_mat, test_mat, config, logger, device):
logger.info("Start evaluation")
model.eval()
device = torch.device(config.device)
with torch.no_grad():
user_num, item_num = train_mat.shape
# users = torch.from_numpy(np.random.choice(user_num, min(user_num, 5000), False)).to(device)
users = np.random.choice(user_num, min(5000, user_num), False)
evals = Eval()
m = evals.evaluate_item(train_mat[users, :],
test_mat[users, :],
users,
model,
device,
topk=50)
return m
def evaluate(self, train, test):
m, n = train.shape
u, v = self.get_uv()
users = np.random.choice(m, min(m, 50000), False)
m = Eval.evaluate_item(train[users, :], test[users, :], u[users, :], v, topk=-1)
return m