FATE —— 二.2.6 Homo

FATE —— 二.2.6 Homo

前言

在本教程中，我们将演示如何使用培训师用户界面返回格式化的预测结果，评估模型的性能，保存模型，并在仪表板上显示损失曲线和性能分数。这些接口允许您的培训师与FATE框架集成，使其更易于使用。

由于官方网站的示例代码有一定的错误，所以在此进行声明，改正后的_proximal_term如下所示：

def _proximal_term(self, model_a, model_b):diff_ = 0for p1, p2 in zip(model_a.parameters(), model_b.parameters()):diff_ += ((p1-p2.detach())**2).sum()return diff_

在本教程中，我们将继续开发我们的玩具FedProx训练器。

FedProx的玩具实现

在上一个教程中，我们通过演示FedProx算法的玩具实现提供了一个具体的示例。在FedProx中，训练过程与标准FedAVG算法略有不同，因为在计算损失时，需要从当前模型和全局模型计算近端项。代码在这里：

 import save_to_fate

%%save_to_fate trainer fedprox.py
import copy
import torch as t
ainer_base import TrainerBase
from torch.utils.data import DataLoader
# We need to use aggregator client&server class for federation
from federatedml.framework.homo.aggregator.secure_aggregator import SecureAggregatorClient, SecureAggregatorServer
# We use LOGGER to output logs
from federatedml.util import LOGGERclass ToyFedProxTrainer(TrainerBase):def __init__(self, epochs, batch_size, u):super(ToyFedProxTrainer, self).__init__()# trainer parametersself.epochs = epochsself.batch_size = batch_sizeself.u = u# Given two model, we compute the proximal termdef _proximal_term(self, model_a, model_b):diff_ = 0for p1, p2 in zip(model_a.parameters(), model_b.parameters()):diff_ += ((p1-p2.detach())**2).sum()return diff_# implement the train function, this function will be called by client side# contains the local training process and the federation partdef train(self, train_set, validate_set=None, optimizer=None, loss=None, extra_data={}):sample_num = len(train_set)aggregator = Noneif self.fed_mode:aggregator = SecureAggregatorClient(True, aggregate_weight=sample_num, communicate_match_suffix='fedprox')  # initialize aggregator# set dataloaderdl = DataLoader(train_set, batch_size=self.batch_size, num_workers=4)for epoch in range(self.epochs):# the local training processLOGGER.debug('running epoch {}'.format(epoch))global_model = copy.del)loss_sum = 0# batch training processfor batch_data, label in _grad()pred = del(batch_data)loss_term_a = loss(pred, label)loss_term_b = self._proximal_del, global_model)loss_ = loss_term_a + (self.u/2) * loss_term_bloss_.backward()loss_sum += float(loss_.detach().numpy())optimizer.step()# pring lossLOGGER.debug('epoch loss is {}'.format(loss_sum))# the aggregation processif aggregator is not del = del_del)converge_status = aggregator.loss_aggregation(loss_sum)# implement the aggregation function, this function will be called by the sever sidedef server_aggregate_procedure(self, extra_data={}):# initialize aggregatorif self.fed_mode:aggregator = SecureAggregatorServer(communicate_match_suffix='fedprox')# the aggregation process is simple: every epoch the server aggregate model and loss oncefor i in range(self.epochs):del_aggregation()merge_loss, _ = aggregator.loss_aggregation()

用户界面

现在我们向您介绍TrainerBase类提供的用户界面，我们将使用这些功能来改进我们的培训师。

格式预测结果

此函数将组织预测结果并返回StdReturnFormat对象，该对象将包装结果。您可以在预测函数的末尾使用此函数返回FATE框架可以解析并显示在命运板上的标准格式。这种标准化格式还允许下游组件（例如评估组件）使用预测结果。

此函数接受四个参数：

• sample_ids：示例ID列表

• predict_result：预测得分的张量

• true_label：真标签张量

• task_type：正在执行的任务类型。默认值为“auto”，它将自动推断任务类型。其他选项包括“二进制”、“多”和“回归”。目前，FATE仪表板仅支持显示二进制/多分类和回归任务。如果选择“自动”，则将自动推断任务类型。

稍后我们将在FedProx培训器中实现预测。

import torch as t 
from typing import Listdef format_predict_result(self, sample_ids: List, predict_result: t.Tensor,true_label: t.Tensor, task_type: str = None):...

callback_metric和callback_loss

顾名思义，这两个功能使您能够保存数据点，并在命盘上显示自定义评估指标和损失曲线。

使用回调度量函数时，需要提供度量名称、浮点值，并指定度量类型（'train'或'validate'）和历元索引。使用回调损失函数时，需要提供浮点损失值和历元索引。您的数据将显示在命盘上。

def callback_metric(self, metric_name: str, value: float, metric_type='train', epoch_idx=0):...def callback_loss(self, loss: float, epoch_idx: int):...

总结

此函数允许您在字典中保存训练过程的摘要，例如丢失历史和最佳时期。任务完成后，您可以从管道中检索此摘要。

def summary(self, summary_dict: dict):...

保存和检查点

您可以使用“save”保存模型，并使用“checkpoint”功能设置模型检查点。需要注意的是：

• “save”仅将模型存储在内存中，因此您保存的模型将是上次使用“save”功能保存的模型。

• “checkpoint”直接将模型保存到磁盘。

• “save”只能在客户端（在“train”函数中）调用，而“checkpoint”应该在客户端和服务器端（在“rain”和“server_aggregate_proccure”函数中调用）调用，以确保检查点机制正常工作。

函数中的“extra_data”参数允许您在字典中保存其他数据。这在热启动模型时非常有用，因为您可以使用“train”和“server_aggregate_proccure”函数中的“extra_data”参数检索保存的数据。

def save(self,model=None,epoch_idx=-1,optimizer=None,converge_status=False,loss_history=None,best_epoch=-1,extra_data={}): ...def checkpoint(self,epoch_idx,model=None,optimizer=None,converge_status=False,loss_history=None,best_epoch=-1,extra_data={}): ...

评价

此界面允许您通过自动计算各种性能指标来评估模型。计算的指标取决于数据集和任务的类型

• 二进制分类：“AUC”和“ks”

• 多级分类：“准确度”、“精度”和“召回”

• 回归：“rmse”和“mae”

您可以在参数中指定数据集的类型（“训练”或“验证”）和任务类型（“二元”、“多元”或“回归”）。如果未指定任务类型，将自动从您的分数和标签中推断出任务类型。

def evaluation(self, sample_ids: list, pred_scores: t.Tensor, label: t.Tensor, dataset_type='train',epoch_idx=0, task_type=None):...

改进的FedProx训练器

在本节中，我们将使用前面介绍的接口来改进我们的FedProx Trainer，使其成为一个更全面的培训工具。我们：

• 我们实现了predict函数，并返回格式化的结果

• 添加求值函数

• 培训结束时保存模型

• 回调损失以保存损失曲线

• 我们计算准确度分数，然后使用回调度量显示。

 import save_to_fate

%%save_to_fate trainer fedprox_v2.py
import copy
import torch as t
ainer_base import TrainerBase
dataset.base import Dataset
from torch.utils.data import DataLoader
# We need to use aggregator client&server class for federation
from federatedml.framework.homo.aggregator.secure_aggregator import SecureAggregatorClient, SecureAggregatorServer
# We use LOGGER to output logs
from federatedml.util import LOGGERclass ToyFedProxTrainer(TrainerBase):def __init__(self, epochs, batch_size, u):super(ToyFedProxTrainer, self).__init__()# trainer parametersself.epochs = epochsself.batch_size = batch_sizeself.u = u# Given two model, we compute the proximal termdef _proximal_term(self, model_a, model_b):diff_ = 0for p1, p2 in zip(model_a.parameters(), model_b.parameters()):diff_ += ((p1-p2.detach())**2).sum()return diff_# implement the train function, this function will be called by client side# contains the local training process and the federation partdef train(self, train_set, validate_set=None, optimizer=None, loss=None, extra_data={}):sample_num = len(train_set)aggregator = Noneif self.fed_mode:aggregator = SecureAggregatorClient(True, aggregate_weight=sample_num, communicate_match_suffix='fedprox')  # initialize aggregator# set dataloaderdl = DataLoader(train_set, batch_size=self.batch_size, num_workers=4)loss_history = []for epoch in range(self.epochs):# the local training processLOGGER.debug('running epoch {}'.format(epoch))global_model = copy.del)loss_sum = 0# batch training processfor batch_data, label in _grad()pred = del(batch_data)loss_term_a = loss(pred, label)loss_term_b = self._proximal_del, global_model)loss_ = loss_term_a + (self.u/2) * loss_term_bLOGGER.debug('loss is {} loss a is {} loss b is {}'.format(loss_, loss_term_a, loss_term_b))loss_.backward()loss_sum += float(loss_.detach().numpy())optimizer.step()# print lossLOGGER.debug('epoch loss is {}'.format(loss_sum))loss_history.append(loss_sum)# we callback loss hereself.callback_loss(loss_sum, epoch)# we evaluate out model heresample_ids, preds, labels = self._predict(train_set)self.evaluation(sample_ids, preds, labels, 'train', task_type='binary', epoch_idx=epoch)# we manually compute accuracy:acc = ((preds > 0.5 + 0) == labels).sum() / len(labels)acc = float(acc.detach().numpy())self.callback_metric('my_accuracy', acc, epoch_idx=epoch)# the aggregation processif aggregator is not del = del_del)converge_status = aggregator.loss_aggregation(loss_sum)# We will save model at the end of the trainingself.del, epoch, optimizer)# We will save model summaryself.summary({'loss_history': loss_history})# implement the aggregation function, this function will be called by the sever sidedef server_aggregate_procedure(self, extra_data={}):# initialize aggregatorif self.fed_mode:aggregator = SecureAggregatorServer(communicate_match_suffix='fedprox')# the aggregation process is simple: every epoch the server aggregate model and loss oncefor i in range(self.epochs):del_aggregation()merge_loss, _ = aggregator.loss_aggregation()def _predict(self, dataset: Dataset):len_ = len(dataset)dl = DataLoader(dataset, batch_size=len_)preds, labels = None, Nonefor data, l in dl:preds = del(data)labels = lsample_ids = _sample_ids()return sample_ids, preds, labels# We implement the predict function heredef predict(self, dataset):sample_ids, preds, labels = self._predict(dataset)return self.format_predict_result(sample_ids, preds, labels, 'binary')

提交Pipeline

在这里，我们提交了一个新的Pipeline来测试我们的新trainer

# torch
import torch as t
from torch import nn
from pipeline import fate_torch_hook
fate_torch_hook(t)
# pipeline
from pipelineponent.homo_nn import HomoNN, TrainerParam  # HomoNN Component, TrainerParam for setting trainer parameter
from pipeline.backend.pipeline import PipeLine  # pipeline class
from pipelineponent import Reader, DataTransform, Evaluation # Data I/O and Evaluation
from pipeline.interface import Data  # Data Interaces for defining data flow# create a pipeline to submitting the job
guest = 9999
host = 10000
arbiter = 10000
pipeline = PipeLine().set_initiator(role='guest', party_id=guest).set_roles(guest=guest, host=host, arbiter=arbiter)# read uploaded dataset
train_data_0 = {"name": "breast_homo_guest", "namespace": "experiment"}
train_data_1 = {"name": "breast_homo_host", "namespace": "experiment"}
reader_0 = Reader(name="reader_0")
_party_instance(role='guest', party_id=guest)ponent_param(table=train_data_0)
_party_instance(role='host', party_id=host)ponent_param(table=train_data_1)# The transform component converts the uploaded data to the DATE standard format
data_transform_0 = DataTransform(name='data_transform_0')
data__party_instance(role='guest', party_id=guest)ponent_param(with_label=True, output_format="dense")
data__party_instance(role='host', party_id=host)ponent_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='fedprox_v2', epochs=3, batch_size=128, u=0.5),torch_seed=100 # random seed)# define work flow
pipeline.add_component(reader_0)
pipeline.add_component(data_transform_0, data=Data(data=reader_0.output.data))
pipeline.add_component(nn_component, data=Data(train_data=data_transform_0.output.data))
pipelinepile()
pipeline.fit()