在聯邦學習中將數據拆分為訓練和測試

Question

我是聯邦學習的新手，我目前正在按照官方 TFF 文檔試驗 model。 但我遇到了一個問題，希望能在這里找到一些解釋。

我正在使用自己的數據集，數據分布在多個文件中，每個文件都是一個客戶端（因為我正計划構建模型）。 並且已經定義了因變量和自變量。

現在，我的問題是如何在聯邦學習中將數據拆分為每個客戶端（文件）中的訓練和測試集？ 就像我們通常在集中式 ML 模型中所做的一樣下面的代碼是我到目前為止實現的：請注意我的代碼是受官方文檔和這篇文章的啟發，它與我的應用程序幾乎相似，但它旨在拆分客戶端作為培訓和測試客戶本身，而我的目標是拆分這些客戶內部的數據。

dataset_paths = {
  'client_0': '/content/drive/MyDrive/Colab Notebooks/1.csv',
  'client_1': '/content/drive/MyDrive/Colab Notebooks/2.csv',
  'client_2': '/content/drive/MyDrive/Colab Notebooks/3.csv'
}
record_defaults = [int(), int(), int(), int(), float(),float(),float(),
                   float(),float(),float(), int(), int(),float(),float(),int()]

@tf.function
def create_tf_dataset_for_client_fn(dataset_path):
   return tf.data.experimental.CsvDataset(dataset_path,
                                          record_defaults=record_defaults,
                                          header=True)

@tf.function
def add_parsing(dataset):
  def parse_dataset(*x):
    ## x defines the dependant varable & y defines the independant 
    return OrderedDict([('x', x[-1]), ('y', x[1:-1])])
  return dataset.map(parse_dataset, num_parallel_calls=tf.data.AUTOTUNE)

source = tff.simulation.datasets.FilePerUserClientData(
  dataset_paths, create_tf_dataset_for_client_fn) 

source = source.preprocess(add_parsing)
## Creat the the datasets from client data 
dataset_creation=source.create_tf_dataset_for_client(source.client_ids[0-2])
print(dataset_creation)
>>> _VariantDataset element_spec=OrderedDict([('x', TensorSpec(shape=(), dtype=tf.int32, name=None)), ('y', (TensorSpec(shape=(), dtype=tf.int32, name=None), TensorSpec(shape=(), dtype=tf.int32, name=None), TensorSpec(shape=(), dtype=tf.int32, name=None), TensorSpec(shape=(), dtype=tf.float32, name=None), TensorSpec(shape=(), dtype=tf.float32, name=None), TensorSpec(shape=(), dtype=tf.float32, name=None), TensorSpec(shape=(), dtype=tf.float32, name=None), TensorSpec(shape=(), dtype=tf.float32, name=None), TensorSpec(shape=(), dtype=tf.float32, name=None), TensorSpec(shape=(), dtype=tf.int32, name=None)))])>
## Convert the x into array(I think it is necessary for spliting to training and testing sets ) 
test= tf.nest.map_structure(lambda x: x.numpy(),next(iter(dataset_creation)))
print(test)
>>> OrderedDict([('x', 1), ('y', (0, 1, 9, 85.0, 7.75, 85.0, 95.0, 75.0, 50.0, 6))])

我對監督 ML 的理解是將數據分成訓練集和測試集，如下面的代碼所示，我不確定如何在聯邦學習中做到這一點，以及它是否會以這種方式工作？

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.4, random_state = 42) 

所以，我正在尋找這個問題的解釋，以便我可以進入培訓階段。

Answer 1

請參閱本教程。 您應該能夠根據客戶及其數據創建兩個數據集（訓練和測試）：

import tensorflow as tf
import tensorflow_federated as tff
from collections import OrderedDict

record_defaults = [int(), int(), int(), int(), float(),float(),float(),float(),float(),float(), int(), int()]

@tf.function
def create_tf_dataset_for_client_fn(dataset_path):
   return tf.data.experimental.CsvDataset(dataset_path, record_defaults=record_defaults, header=True)
   
@tf.function
def add_parsing(dataset):
  def parse_dataset(*x):
    return OrderedDict([('label', x[:-1]), ('features', x[1:-1])])
  return dataset.map(parse_dataset, num_parallel_calls=tf.data.AUTOTUNE)

def split_train_test(client_ids):
  train, test = [], []
  for x in client_ids:
    d = source.create_tf_dataset_for_client(x)
    d_length = d.reduce(0, lambda x,_: x+1).numpy()
    d = d.shuffle(d_length)
    train.append(list(d.take(int(d_length*.8)))) 
    test.append(list(d.skip(int(d_length*.2))))
  return train[0], test[0]

dataset_paths = {'client1': '/content/client1.csv', 'client2': '/content/client2.csv', 
                 'client3': '/content/client2.csv', 'client4': '/content/client2.csv'}
source = tff.simulation.datasets.FilePerUserClientData(
  dataset_paths, create_tf_dataset_for_client_fn) 

client_ids = sorted(source.client_ids)

federated_train_data, federated_test_data = split_train_test(client_ids)
print(*federated_train_data, sep='\n')

(<tf.Tensor: shape=(), dtype=int32, numpy=24>, <tf.Tensor: shape=(), dtype=int32, numpy=17>, <tf.Tensor: shape=(), dtype=int32, numpy=27>, <tf.Tensor: shape=(), dtype=int32, numpy=4>, <tf.Tensor: shape=(), dtype=float32, numpy=0.17308392>, <tf.Tensor: shape=(), dtype=float32, numpy=1.889401>, <tf.Tensor: shape=(), dtype=float32, numpy=1.6235029>, <tf.Tensor: shape=(), dtype=float32, numpy=-0.56010467>, <tf.Tensor: shape=(), dtype=float32, numpy=-1.0171211>, <tf.Tensor: shape=(), dtype=float32, numpy=0.43558818>, <tf.Tensor: shape=(), dtype=int32, numpy=40>, <tf.Tensor: shape=(), dtype=int32, numpy=14>)
(<tf.Tensor: shape=(), dtype=int32, numpy=8>, <tf.Tensor: shape=(), dtype=int32, numpy=32>, <tf.Tensor: shape=(), dtype=int32, numpy=14>, <tf.Tensor: shape=(), dtype=int32, numpy=11>, <tf.Tensor: shape=(), dtype=float32, numpy=-0.91828436>, <tf.Tensor: shape=(), dtype=float32, numpy=0.29887632>, <tf.Tensor: shape=(), dtype=float32, numpy=-0.4598584>, <tf.Tensor: shape=(), dtype=float32, numpy=-1.1088414>, <tf.Tensor: shape=(), dtype=float32, numpy=-0.4057387>, <tf.Tensor: shape=(), dtype=float32, numpy=-2.1537204>, <tf.Tensor: shape=(), dtype=int32, numpy=15>, <tf.Tensor: shape=(), dtype=int32, numpy=45>)
(<tf.Tensor: shape=(), dtype=int32, numpy=11>, <tf.Tensor: shape=(), dtype=int32, numpy=17>, <tf.Tensor: shape=(), dtype=int32, numpy=17>, <tf.Tensor: shape=(), dtype=int32, numpy=2>, <tf.Tensor: shape=(), dtype=float32, numpy=0.93560874>, <tf.Tensor: shape=(), dtype=float32, numpy=-2.4382026>, <tf.Tensor: shape=(), dtype=float32, numpy=-1.7638668>, <tf.Tensor: shape=(), dtype=float32, numpy=0.65431964>, <tf.Tensor: shape=(), dtype=float32, numpy=-0.7130539>, <tf.Tensor: shape=(), dtype=float32, numpy=-0.96356>, <tf.Tensor: shape=(), dtype=int32, numpy=15>, <tf.Tensor: shape=(), dtype=int32, numpy=18>)
(<tf.Tensor: shape=(), dtype=int32, numpy=42>, <tf.Tensor: shape=(), dtype=int32, numpy=27>, <tf.Tensor: shape=(), dtype=int32, numpy=34>, <tf.Tensor: shape=(), dtype=int32, numpy=8>, <tf.Tensor: shape=(), dtype=float32, numpy=0.3965425>, <tf.Tensor: shape=(), dtype=float32, numpy=-0.2588629>, <tf.Tensor: shape=(), dtype=float32, numpy=-0.84179455>, <tf.Tensor: shape=(), dtype=float32, numpy=0.114052325>, <tf.Tensor: shape=(), dtype=float32, numpy=-0.9591451>, <tf.Tensor: shape=(), dtype=float32, numpy=0.94621265>, <tf.Tensor: shape=(), dtype=int32, numpy=28>, <tf.Tensor: shape=(), dtype=int32, numpy=7>)

如果您遵循我鏈接的教程，您應該能夠將拆分數據直接提供給tff.learning.from_keras_model 。