eval.py 文件源码

def eval_pred(dr_model, ub):
    '''
        evaluate dream model for predicting next basket on all training users
        in batches
    '''
    item_embedding = dr_model.encode.weight
    dr_model.eval()
    dr_hidden = dr_model.init_hidden(dr_model.config.batch_size)
    start_time = time()
    id_u, score_u = [], [] # user's id, user's score
    num_batchs = ceil(len(ub) / dr_model.config.batch_size)
    for i,x in enumerate(batchify(ub, dr_model.config.batch_size)):
        print(i)
        baskets, lens, uids = x
        _, dynamic_user, _ = dr_model(baskets, lens, dr_hidden)# shape: batch_size, max_len, embedding_size
        dr_hidden = repackage_hidden(dr_hidden)
        for i,l,du in zip(uids, lens, dynamic_user):
            du_latest = du[l - 1].unsqueeze(0) # shape: 1, embedding_size
            score_up = torch.mm(du_latest, item_embedding.t()) # shape: 1, num_item
            score_u.append(score_up.cpu().data.numpy())
            id_u.append(i)
    elapsed = time() - start_time 
    print('[Predicting] Elapsed: {02.2f}'.format(elapsed))
    return score_ub, id_u