FATE —— 二.1.1 Homo-NN二进制分类任务 _组件

说明
FATE版本为1.10.0单机部署版， win10+
本教程允许您快速开始使用Homo NN 。默认情况下，您可以在与其他FATE算法组件相同的过程中使用Homo NN组件：使用FATE附带的读取器和转换器接口输入表数据并转换数据格式，然后将数据输入算法组件。然后NN组件将使用您定义的模型、优化器和损失函数进行训练和模型聚合。
在FATE-1.10中，正在开发中的Homo NN增加了对的支持。您可以遵循的用法，使用的内置层来定义模型并提交模型。同时，您可以使用附带的损失函数和优化器。
下面是一个基本的二进制分类任务Homo NN任务。有两个客户端的参与方ID分别为10000和9999 ， 10000被指定为服务器端聚合模型。
开始上载表格数据
一开始，我们将数据上传到FATE 。我们可以使用管道直接上传数据。在这里，我们上载两个文件：来宾的.csv和主机的.csv 。请注意，在本教程中，我们使用的是独立版本，如果您使用的是集群版本，则需要在每台计算机上上载相应的数据。
from pipeline.backend.pipeline import PipeLine# pipeline Class# [9999(guest), 10000(host)] as client# [10000(arbiter)] as serverguest = 9999host = 10000arbiter = 10000pipeline_upload = PipeLine().set_initiator(role='guest', party_id=guest).set_roles(guest=guest, host=host, arbiter=arbiter)partition = 4# upload a datasetpath_to_fate_project = '../../../../'guest_data = http://www.kingceram.com/post/{"name": "breast_homo_guest", "namespace": "experiment"}host_data = http://www.kingceram.com/post/{"name": "breast_homo_host", "namespace": "experiment"}pipeline_upload.add_upload_data(file="examples/data/breast_homo_guest.csv", # file in the example/datatable_name=guest_data["name"],# table namenamespace=guest_data["namespace"],# namespacehead=1, partition=partition)# data infopipeline_upload.add_upload_data(file="examples/data/breast_homo_host.csv", # file in the example/datatable_name=host_data["name"],# table namenamespace=host_data["namespace"],# namespacehead=1, partition=partition)# data infopipeline_upload.upload(drop=1)
乳房数据集是一个具有30个特征的二进制数据集：
import pandas as pddf = pd.read_csv('../examples/data/breast_homo_guest.csv')# 这里根据文件夹位置自行调整df
编写并执行脚本

文章插图
上传完成后，我们可以开始编写管道脚本以提交FATE任务。
导入管道组件

# torchimport torch as tfrom torch import nn# pipelinefrom pipeline.component.homo_nn import HomoNN, TrainerParam# HomoNN Component, TrainerParam for setting trainer parameterfrom pipeline.backend.pipeline import PipeLine# pipeline classfrom pipeline.component import Reader, DataTransform, Evaluation # Data I/O and Evaluationfrom pipeline.interface import Data# Data Interaces for defining data flow

我们可以检查Homo NN组件的参数：
print(HomoNN.__doc__)

Parameters----------name, name of this componenttrainer, trainer paramdataset, dataset paramtorch_seed, global random seedloss, loss function from fate_torchoptimizer, optimizer from fate_torchmodel, a fate torch sequential defining the model structure

请确保执行以下函数，该函数可以修改某些torch类，以便可以通过管道解析和提交您在脚本中定义的torch层、顺序、优化器和损失函数。
from pipeline import fate_torch_hookt = fate_torch_hook(t)

# create a pipeline to submitting the jobguest = 9999host = 10000arbiter = 10000pipeline = PipeLine().set_initiator(role='guest', party_id=guest).set_roles(guest=guest, host=host, arbiter=arbiter)# read uploaded datasettrain_data_0 = {"name": "breast_homo_guest", "namespace": "experiment"}train_data_1 = {"name": "breast_homo_host", "namespace": "experiment"}reader_0 = Reader(name="reader_0")reader_0.get_party_instance(role='guest', party_id=guest).component_param(table=train_data_0)reader_0.get_party_instance(role='host', party_id=host).component_param(table=train_data_1)# The transform component converts the uploaded data to the DATE standard formatdata_transform_0 = DataTransform(name='data_transform_0')data_transform_0.get_party_instance(role='guest', party_id=guest).component_param(with_label=True, output_format="dense")data_transform_0.get_party_instance(role='host', party_id=host).component_param(with_label=True, output_format="dense")"""Define Pytorch model/ optimizer and loss"""model = nn.Sequential(nn.Linear(30, 1),nn.Sigmoid())loss = nn.BCELoss()optimizer = t.optim.Adam(model.parameters(), lr=0.01)"""Create Homo-NN Component"""nn_component = HomoNN(name='nn_0',model=model, # set modelloss=loss, # set lossoptimizer=optimizer, # set optimizer# Here we use fedavg trainer# TrainerParam passes parameters to fedavg_trainer, see below for details about Trainertrainer=TrainerParam(trainer_name='fedavg_trainer', epochs=3, batch_size=128, validation_freqs=1),torch_seed=100 # random seed)# define work flowpipeline.add_component(reader_0)pipeline.add_component(data_transform_0, data=http://www.kingceram.com/post/Data(data=reader_0.output.data))pipeline.add_component(nn_component, data=Data(train_data=data_transform_0.output.data))pipeline.add_component(Evaluation(name='eval_0'), data=Data(data=nn_component.output.data))pipeline.compile()pipeline.fit()

打开可视化工具可以看到进程。
获取组件输出
# get predict scorespipeline.get_component('nn_0').get_output_data()
# get summarypipeline.get_component('nn_0').get_summary()

{'best_epoch': 2,'loss_history': [0.8317702709315632, 0.683187778825802, 0.5690162255375396],'metrics_summary': {'train': {'auc': [0.732987012987013,0.9094372294372294,0.9561904761904763],'ks': [0.4153246753246753, 0.6851948051948051, 0.7908225108225109]}},'need_stop': False}

and
在这个版本中， Homo NN的训练逻辑和联邦聚合逻辑都在类中实现。是FATE Homo NN的默认训练器，它实现了标准的算法。的功能是：
我们可以在FATE中检查的参数，这些可用参数可以填写在中。
from federatedml.nn.homo.trainer.fedavg_trainer import FedAVGTrainer
查看的文档以了解可用参数。提交任务时，可以使用传递这些参数
print(FedAVGTrainer.__doc__)
【FATE —— 二.1.1 Homo-NN二进制分类任务】

Parameters----------epochs: int >0, epochs to trainbatch_size: int, -1 means full batchsecure_aggregate: bool, default is True, whether to use secure aggregation. if enabled, will add random number mask to local models. These random number masks will eventually cancel out to get 0.weighted_aggregation: bool, whether add weight to each local model when doing aggregation. if True, According to origin paper, weight of a client is: n_local / n_global, where n_local is the sample number locally and n_global is the sample number of all clients.if False, simply averaging these models.early_stop: None, 'diff' or 'abs'. if None, disable early stop; if 'diff', use the loss difference between two epochs as early stop condition, if differences < tol, stop training ; if 'abs', if loss < tol,stop training tol: float, tol value for early stopaggregate_every_n_epoch: None or int. if None, aggregate model on the end of every epoch, if int, aggregate every n epochs.cuda: bool, use cuda or notpin_memory: bool, for pytorch DataLoadershuffle: bool, for pytorch DataLoaderdata_loader_worker: int, for pytorch DataLoader, number of workers when loading datavalidation_freqs: None or int. if int, validate your model and send validate results to fate-board every n epoch. if is binary classification task, will use metrics 'auc', 'ks', 'gain', 'lift', 'precision'. if is multi classification task, will use metrics 'precision', 'recall', 'accuracy'. if is regression task, will use metrics 'mse', 'mae', 'rmse', 'explained_variance', 'r2_score'checkpoint_save_freqs: save model every n epoch, if None, will not save checkpoint.task_type: str, 'auto', 'binary', 'multi', 'regression'. this option decides the return format of this trainer, and the evaluation type when running validation. if auto, will automatically infer your task type from labels and predict results.

到目前为止，我们已经对Homo NN有了基本的了解，并利用它完成了基本的建模任务。此外， Homo NN提供了为更高级的用例定制模型、数据集和训练器的能力。有关详细信息，请参阅提供的其他教程