# Copyright 2020 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================ """ BGCF training script. """ import os import time import datetime from mindspore import Tensor import mindspore.context as context from mindspore.common import dtype as mstype from mindspore.train.serialization import save_checkpoint, load_checkpoint from src.bgcf import BGCF from src.metrics import BGCFEvaluate from src.config import parser_args from src.utils import BGCFLogger, convert_item_id from src.callback import ForwardBGCF, TrainBGCF, TestBGCF from src.dataset import load_graph, create_dataset, TestGraphDataset 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) 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])) if __name__ == "__main__": context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False) parser = parser_args() train_graph, test_graph, sampled_graph_list = load_graph(parser.datapath) train_ds = create_dataset(train_graph, sampled_graph_list, batch_size=parser.batch_pairs) test_graph_dataset = TestGraphDataset(train_graph, sampled_graph_list, num_samples=parser.raw_neighs, num_bgcn_neigh=parser.gnew_neighs, num_neg=parser.num_neg) if parser.log_name: now = datetime.datetime.now().strftime("%b_%d_%H_%M_%S") name = "bgcf" + '-' + parser.log_name + '-' + parser.dataset log_save_path = './log-files/' + name + '/' + now log = BGCFLogger(logname=name, now=now, foldername='log-files', copy=False) log.open(log_save_path + '/log.train.txt', mode='a') for arg in vars(parser): log.write(arg + '=' + str(getattr(parser, arg)) + '\n') else: for arg in vars(parser): print(arg + '=' + str(getattr(parser, arg))) train_and_eval()