不做PSI，纵向直接从对齐好的数据训练可以吗?

[FancyXun]

请教个问题，如果我训练每次都做psi感觉没太大必要，有时候我希望psi做完一次后，就专注做模型的训练。但是我看所有的例子好像都需要做一次psi，然后进行纵向训练，https://github.com/FederatedAI/FATE/blob/d9253c4dedd4799b3d68de5c63cd261fbb3af033/examples/pipeline/coordinated_lr/test_lr.py#L62 有没有可能，我实现（psi）对齐后数据，以纵向Lr为例，在dag图里面直接从 reader - > lr. 我尝试修改了下，如下

lr_0 = CoordinatedLR("lr_0",
                         epochs=10,
                         batch_size=300,
                         optimizer={"method": "SGD", "optimizer_params": {"lr": 0.1}, "penalty": "l2", "alpha": 0.001},
                         init_param={"fit_intercept": True, "method": "zeros"},
                         train_data=reader_0.outputs["output_data"],
                         learning_rate_scheduler={"method": "linear", "scheduler_params": {"start_factor": 0.7,
                                                                                           "total_iters": 100}})
pipeline.add_tasks([reader_0,  lr_0, evaluation_0])

但是host端报错: NoneType

[mgqa34]

可以单独跑一个reader->psi的任务，然后这一步的输出会有对齐后的数据表，后续可以直接作为reader的输入。

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