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# 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 sys
import random
import torchtext
from utils.dataset_utils import check, separate_data, split_data, save_file
from torchtext.data.utils import get_tokenizer
from torchtext.vocab import build_vocab_from_iterator
random.seed(1)
np.random.seed(1)
num_clients = 20
max_len = 200
dir_path = "SogouNews/"
# Allocate data to users
def generate_dataset(dir_path, num_clients, niid, balance, partition):
if not os.path.exists(dir_path):
os.makedirs(dir_path)
# Setup directory for train/test data
config_path = dir_path + "config.json"
train_path = dir_path + "train/"
test_path = dir_path + "test/"
if check(config_path, train_path, test_path, num_clients, niid, balance, partition):
return
# Get Sogou_News data
trainset, testset = torchtext.datasets.SogouNews(root=dir_path+"rawdata")
trainlabel, traintext = list(zip(*trainset))
testlabel, testtext = list(zip(*testset))
dataset_text = []
dataset_label = []
dataset_text.extend(traintext)
dataset_text.extend(testtext)
dataset_label.extend(trainlabel)
dataset_label.extend(testlabel)
num_classes = len(set(dataset_label))
print(f'Number of classes: {num_classes}')
tokenizer = get_tokenizer('basic_english')
vocab = build_vocab_from_iterator(map(tokenizer, iter(dataset_text)), specials=["<unk>"])
vocab.set_default_index(vocab["<unk>"])
text_pipeline = lambda x: vocab(tokenizer(x))
label_pipeline = lambda x: int(x) - 1
def text_transform(text, label, max_len=0):
label_list, text_list = [], []
for _text, _label in zip(text, label):
label_list.append(label_pipeline(_label))
text_ = text_pipeline(_text)
padding = [0 for i in range(max_len-len(text_))]
text_.extend(padding)
text_list.append(text_[:max_len])
return label_list, text_list
label_list, text_list = text_transform(dataset_text, dataset_label, max_len)
text_lens = [len(text) for text in text_list]
# max_len = max(text_lens)
# label_list, text_list = text_transform(dataset_text, dataset_label, max_len)
text_list = [(text, l) for text, l in zip(text_list, text_lens)]
text_list = np.array(text_list)
label_list = np.array(label_list)
# dataset = []
# for i in range(num_classes):
# idx = label_list == i
# dataset.append(text_list[idx])
X, y, statistic = separate_data((text_list, label_list), num_clients, num_classes,
niid, balance, partition)
train_data, test_data = split_data(X, y)
save_file(config_path, train_path, test_path, train_data, test_data, num_clients, num_classes,
statistic, niid, balance, partition)
print("The size of vocabulary:", len(vocab))
if __name__ == "__main__":
niid = True if sys.argv[1] == "noniid" else False
balance = True if sys.argv[2] == "balance" else False
partition = sys.argv[3] if sys.argv[3] != "-" else None
generate_dataset(dir_path, num_clients, niid, balance, partition) |