File size: 3,888 Bytes
9f61031 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 | # PFLlib: Personalized Federated Learning Algorithm Library
# Copyright (C) 2021 Jianqing Zhang
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
import numpy as np
import os
import torch
def read_data(dataset, idx, is_train=True):
if is_train:
train_data_dir = os.path.join('../dataset', dataset, 'train/')
train_file = train_data_dir + str(idx) + '.npz'
with open(train_file, 'rb') as f:
train_data = np.load(f, allow_pickle=True)['data'].tolist()
return train_data
else:
test_data_dir = os.path.join('../dataset', dataset, 'test/')
test_file = test_data_dir + str(idx) + '.npz'
with open(test_file, 'rb') as f:
test_data = np.load(f, allow_pickle=True)['data'].tolist()
return test_data
def read_client_data(dataset, idx, is_train=True):
if "News" in dataset:
return read_client_data_text(dataset, idx, is_train)
elif "Shakespeare" in dataset:
return read_client_data_Shakespeare(dataset, idx)
if is_train:
train_data = read_data(dataset, idx, is_train)
X_train = torch.Tensor(train_data['x']).type(torch.float32)
y_train = torch.Tensor(train_data['y']).type(torch.int64)
train_data = [(x, y) for x, y in zip(X_train, y_train)]
return train_data
else:
test_data = read_data(dataset, idx, is_train)
X_test = torch.Tensor(test_data['x']).type(torch.float32)
y_test = torch.Tensor(test_data['y']).type(torch.int64)
test_data = [(x, y) for x, y in zip(X_test, y_test)]
return test_data
def read_client_data_text(dataset, idx, is_train=True):
if is_train:
train_data = read_data(dataset, idx, is_train)
X_train, X_train_lens = list(zip(*train_data['x']))
y_train = train_data['y']
X_train = torch.Tensor(X_train).type(torch.int64)
X_train_lens = torch.Tensor(X_train_lens).type(torch.int64)
y_train = torch.Tensor(train_data['y']).type(torch.int64)
train_data = [((x, lens), y) for x, lens, y in zip(X_train, X_train_lens, y_train)]
return train_data
else:
test_data = read_data(dataset, idx, is_train)
X_test, X_test_lens = list(zip(*test_data['x']))
y_test = test_data['y']
X_test = torch.Tensor(X_test).type(torch.int64)
X_test_lens = torch.Tensor(X_test_lens).type(torch.int64)
y_test = torch.Tensor(test_data['y']).type(torch.int64)
test_data = [((x, lens), y) for x, lens, y in zip(X_test, X_test_lens, y_test)]
return test_data
def read_client_data_Shakespeare(dataset, idx, is_train=True):
if is_train:
train_data = read_data(dataset, idx, is_train)
X_train = torch.Tensor(train_data['x']).type(torch.int64)
y_train = torch.Tensor(train_data['y']).type(torch.int64)
train_data = [(x, y) for x, y in zip(X_train, y_train)]
return train_data
else:
test_data = read_data(dataset, idx, is_train)
X_test = torch.Tensor(test_data['x']).type(torch.int64)
y_test = torch.Tensor(test_data['y']).type(torch.int64)
test_data = [(x, y) for x, y in zip(X_test, y_test)]
return test_data
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