【传知代码】知识图谱推理（论文复现）-4. 核心代码

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# start
 check all output paths
    checkPath('./results/')
    checkPath(f'./results/{dataset}/')
    checkPath(f'{loader.task_dir}/saveModel/')

    model = BaseModel(opts, loader)
    opts.perf_file = f'results/{dataset}/{model.modelName}_perf.txt'
    print(f'==> perf_file: {opts.perf_file}')

    config_str = '%.4f, %.4f, %.6f,  %d, %d, %d, %d, %.4f,%s\n' % (
    opts.lr, opts.decay_rate, opts.lamb, opts.hidden_dim, opts.attn_dim, opts.n_layer, opts.n_batch, opts.dropout,
    opts.act)
    print(config_str)
    with open(opts.perf_file, 'a+') as f:
        f.write(config_str)

    if args.weight != None:
        model.loadModel(args.weight)
        model._update()
        model.model.updateTopkNums(opts.n_node_topk)

    if opts.train:
        writer = SummaryWriter(log_dir=f'./tensorboard_logs/{dataset}')
        # training mode
        best_v_mrr = 0
        for epoch in range(opts.epoch):
            epoch_loss = model.train_batch()
            if epoch_loss is not None:
                writer.add_scalar('Training Loss', epoch_loss, epoch)
            else:
                print("Warning: Skipping logging of Training Loss due to NoneType.")
            model.train_batch()
            # eval on val/test set
            if (epoch + 1) % args.eval_interval == 0:
                result_dict, out_str = model.evaluate(eval_val=True, eval_test=True)
                v_mrr, t_mrr = result_dict['v_mrr'], result_dict['t_mrr']
                writer.add_scalar('Validation MRR', result_dict['v_mrr'], epoch)
                writer.add_scalar('Validation Hits@1', result_dict['v_h1'], epoch)
                writer.add_scalar('Validation Hits@10', result_dict['v_h10'], epoch)
                writer.add_scalar('Test MRR', result_dict['t_mrr'], epoch)
                writer.add_scalar('Test Hits@1', result_dict['t_h1'], epoch)
                writer.add_scalar('Test Hits@10', result_dict['t_h10'], epoch)
                print(out_str)
                with open(opts.perf_file, 'a+') as f:
                    f.write(out_str)
                if v_mrr > best_v_mrr:
                    best_v_mrr = v_mrr
                    best_str = out_str
                    print(str(epoch) + '\t' + best_str)
                    BestMetricStr = f'ValMRR_{str(v_mrr)[:5]}_TestMRR_{str(t_mrr)[:5]}'
                    model.saveModelToFiles(BestMetricStr, deleteLastFile=False)

        # show the final result
        print(best_str)
        writer.close()
        model.writer.close()

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