def evaluation(): """evaluation""" num_user = train_graph.graph_info()["node_num"][0] num_item = train_graph.graph_info()["node_num"][1] eval_class = BGCFEvaluate(parser, train_graph, test_graph, parser.Ks) for _epoch in range(parser.eval_interval, parser.num_epoch+1, parser.eval_interval) \ if parser.device_target == "Ascend" else range(parser.num_epoch, parser.num_epoch+1): bgcfnet_test = BGCF([parser.input_dim, num_user, num_item], parser.embedded_dimension, parser.activation, [0.0, 0.0, 0.0], num_user, num_item, parser.input_dim) load_checkpoint(parser.ckptpath + "/bgcf_epoch{}.ckpt".format(_epoch), net=bgcfnet_test) forward_net = ForwardBGCF(bgcfnet_test) user_reps, item_reps = TestBGCF(forward_net, num_user, num_item, parser.input_dim, test_graph_dataset) test_recall_bgcf, test_ndcg_bgcf, \ test_sedp, test_nov = eval_class.eval_with_rep(user_reps, item_reps, parser) if parser.log_name: log.write( 'epoch:%03d, recall_@10:%.5f, recall_@20:%.5f, ndcg_@10:%.5f, ndcg_@20:%.5f, ' 'sedp_@10:%.5f, sedp_@20:%.5f, nov_@10:%.5f, nov_@20:%.5f\n' % (_epoch, test_recall_bgcf[1], test_recall_bgcf[2], test_ndcg_bgcf[1], test_ndcg_bgcf[2], test_sedp[0], test_sedp[1], test_nov[1], test_nov[2])) else: print( 'epoch:%03d, recall_@10:%.5f, recall_@20:%.5f, ndcg_@10:%.5f, ndcg_@20:%.5f, ' 'sedp_@10:%.5f, sedp_@20:%.5f, nov_@10:%.5f, nov_@20:%.5f\n' % (_epoch, test_recall_bgcf[1], test_recall_bgcf[2], test_ndcg_bgcf[1], test_ndcg_bgcf[2], test_sedp[0], test_sedp[1], test_nov[1], test_nov[2]))
parser.add_argument("--activation", type=str, default="tanh", choices=["relu", "tanh"], help="activation function") args = parser.parse_args() context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target) if args.device_target == "Ascend": context.set_context(device_id=args.device_id) if __name__ == "__main__": num_user, num_item = 7068, 3570 network = BGCF([args.input_dim, num_user, num_item], args.embedded_dimension, args.activation, [0.0, 0.0, 0.0], num_user, num_item, args.input_dim) load_checkpoint(args.ckpt_file, net=network) forward_net = ForwardBGCF(network) users = Tensor(np.zeros([ num_user, ]).astype(np.int32)) items = Tensor(np.zeros([ num_item, ]).astype(np.int32)) neg_items = Tensor(np.zeros([num_item, 1]).astype(np.int32)) u_test_neighs = Tensor( np.zeros([num_user, args.row_neighs]).astype(np.int32))
def train(): """Train""" num_user = train_graph.graph_info()["node_num"][0] num_item = train_graph.graph_info()["node_num"][1] num_pairs = train_graph.graph_info()['edge_num'][0] bgcfnet = BGCF([parser.input_dim, num_user, num_item], parser.embedded_dimension, parser.activation, parser.neighbor_dropout, num_user, num_item, parser.input_dim) train_net = TrainBGCF(bgcfnet, parser.num_neg, parser.l2, parser.learning_rate, parser.epsilon, parser.dist_reg) train_net.set_train(True) itr = train_ds.create_dict_iterator(parser.num_epoch, output_numpy=True) num_iter = int(num_pairs / parser.batch_pairs) for _epoch in range(1, parser.num_epoch + 1): epoch_start = time.time() iter_num = 1 for data in itr: u_id = Tensor(data["users"], mstype.int32) pos_item_id = Tensor(convert_item_id(data["items"], num_user), mstype.int32) neg_item_id = Tensor( convert_item_id(data["neg_item_id"], num_user), mstype.int32) pos_users = Tensor(data["pos_users"], mstype.int32) pos_items = Tensor(convert_item_id(data["pos_items"], num_user), mstype.int32) u_group_nodes = Tensor(data["u_group_nodes"], mstype.int32) u_neighs = Tensor(convert_item_id(data["u_neighs"], num_user), mstype.int32) u_gnew_neighs = Tensor( convert_item_id(data["u_gnew_neighs"], num_user), mstype.int32) i_group_nodes = Tensor( convert_item_id(data["i_group_nodes"], num_user), mstype.int32) i_neighs = Tensor(data["i_neighs"], mstype.int32) i_gnew_neighs = Tensor(data["i_gnew_neighs"], mstype.int32) neg_group_nodes = Tensor( convert_item_id(data["neg_group_nodes"], num_user), mstype.int32) neg_neighs = Tensor(data["neg_neighs"], mstype.int32) neg_gnew_neighs = Tensor(data["neg_gnew_neighs"], mstype.int32) train_loss = train_net(u_id, pos_item_id, neg_item_id, pos_users, pos_items, u_group_nodes, u_neighs, u_gnew_neighs, i_group_nodes, i_neighs, i_gnew_neighs, neg_group_nodes, neg_neighs, neg_gnew_neighs) if iter_num == num_iter: print( 'Epoch', '%03d' % _epoch, 'iter', '%02d' % iter_num, 'loss', '{}, cost:{:.4f}'.format(train_loss, time.time() - epoch_start)) iter_num += 1 if _epoch % parser.eval_interval == 0: save_checkpoint( bgcfnet, parser.ckptpath + "/bgcf_epoch{}.ckpt".format(_epoch))
def train_and_eval(): """Train and eval""" num_user = train_graph.graph_info()["node_num"][0] num_item = train_graph.graph_info()["node_num"][1] num_pairs = train_graph.graph_info()['edge_num'][0] bgcfnet = BGCF([parser.input_dim, num_user, num_item], parser.embedded_dimension, parser.activation, parser.neighbor_dropout, num_user, num_item, parser.input_dim) train_net = TrainBGCF(bgcfnet, parser.num_neg, parser.l2, parser.learning_rate, parser.epsilon, parser.dist_reg) train_net.set_train(True) eval_class = BGCFEvaluate(parser, train_graph, test_graph, parser.Ks) itr = train_ds.create_dict_iterator(parser.num_epoch, output_numpy=True) num_iter = int(num_pairs / parser.batch_pairs) for _epoch in range(1, parser.num_epoch + 1): epoch_start = time.time() iter_num = 1 for data in itr: u_id = Tensor(data["users"], mstype.int32) pos_item_id = Tensor(convert_item_id(data["items"], num_user), mstype.int32) neg_item_id = Tensor( convert_item_id(data["neg_item_id"], num_user), mstype.int32) pos_users = Tensor(data["pos_users"], mstype.int32) pos_items = Tensor(convert_item_id(data["pos_items"], num_user), mstype.int32) u_group_nodes = Tensor(data["u_group_nodes"], mstype.int32) u_neighs = Tensor(convert_item_id(data["u_neighs"], num_user), mstype.int32) u_gnew_neighs = Tensor( convert_item_id(data["u_gnew_neighs"], num_user), mstype.int32) i_group_nodes = Tensor( convert_item_id(data["i_group_nodes"], num_user), mstype.int32) i_neighs = Tensor(data["i_neighs"], mstype.int32) i_gnew_neighs = Tensor(data["i_gnew_neighs"], mstype.int32) neg_group_nodes = Tensor( convert_item_id(data["neg_group_nodes"], num_user), mstype.int32) neg_neighs = Tensor(data["neg_neighs"], mstype.int32) neg_gnew_neighs = Tensor(data["neg_gnew_neighs"], mstype.int32) train_loss = train_net(u_id, pos_item_id, neg_item_id, pos_users, pos_items, u_group_nodes, u_neighs, u_gnew_neighs, i_group_nodes, i_neighs, i_gnew_neighs, neg_group_nodes, neg_neighs, neg_gnew_neighs) if iter_num == num_iter: print( 'Epoch', '%03d' % _epoch, 'iter', '%02d' % iter_num, 'loss', '{}, cost:{:.4f}'.format(train_loss, time.time() - epoch_start)) iter_num += 1 if _epoch % parser.eval_interval == 0: if os.path.exists("ckpts/bgcf.ckpt"): os.remove("ckpts/bgcf.ckpt") save_checkpoint(bgcfnet, "ckpts/bgcf.ckpt") bgcfnet_test = BGCF([parser.input_dim, num_user, num_item], parser.embedded_dimension, parser.activation, [0.0, 0.0, 0.0], num_user, num_item, parser.input_dim) load_checkpoint("ckpts/bgcf.ckpt", net=bgcfnet_test) forward_net = ForwardBGCF(bgcfnet_test) user_reps, item_reps = TestBGCF(forward_net, num_user, num_item, parser.input_dim, test_graph_dataset) test_recall_bgcf, test_ndcg_bgcf, \ test_sedp, test_nov = eval_class.eval_with_rep(user_reps, item_reps, parser) if parser.log_name: log.write( 'epoch:%03d, recall_@10:%.5f, recall_@20:%.5f, ndcg_@10:%.5f, ndcg_@20:%.5f, ' 'sedp_@10:%.5f, sedp_@20:%.5f, nov_@10:%.5f, nov_@20:%.5f\n' % (_epoch, test_recall_bgcf[1], test_recall_bgcf[2], test_ndcg_bgcf[1], test_ndcg_bgcf[2], test_sedp[0], test_sedp[1], test_nov[1], test_nov[2])) else: print( 'epoch:%03d, recall_@10:%.5f, recall_@20:%.5f, ndcg_@10:%.5f, ndcg_@20:%.5f, ' 'sedp_@10:%.5f, sedp_@20:%.5f, nov_@10:%.5f, nov_@20:%.5f\n' % (_epoch, test_recall_bgcf[1], test_recall_bgcf[2], test_ndcg_bgcf[1], test_ndcg_bgcf[2], test_sedp[0], test_sedp[1], test_nov[1], test_nov[2]))
def bgcf(*args, **kwargs): return BGCF(*args, **kwargs)