ACL2020 pytorch复现 (TextING) Every Document Owns Its Structure- Inductive Text Classification

一、模型图

二、构图方式

滑动窗口，捕捉共现边

 

三、消息传递

聚合，通过GRU训练。

 

四，读出

 

五，资源下载

TextING

The implementation of TextING

Require

Python 3.7.6
torch 1.5.1
torch-geometric 1.6.1

Source

Download corpus.zip
https://download.youkuaiyun.com/download/qq_28969139/13061605

zip ./corpus
corpus/20ng.labels.txt
corpus/20ng.texts.txt
...

--------------------------
Download golve.6B.zip
https://download.youkuaiyun.com/download/qq_28969139/14033550

zip ./source
source/glove.6B.50d.txt
source/glove.6B.100d.txt
...

Run 'python handle_glove.py'

Preprocess

Run 'python preprocess.py'
Run 'python prebuild.py'

Run

Run 'python train.py'

 

1目录结构

2代码

config.py

# static params
args = {
    '20ng':
        {'train_size': 11314,
         'test_size': 7532,
         'valid_size': 1131,
         "num_classes": 20
         },
    'aclImdb':
        {'train_size': 25000,
         'test_size': 25000,
         'valid_size': 2500
         },
    'ag_news':
        {'train_size': 120000,
         'test_size': 7600,
         'valid_size': 12000
         },
    'dblp':
        {'train_size': 61479,
         'test_size': 20000,
         'valid_size': 6148
         },
    'mr':
        {'train_size': 7108,
         'test_size': 3554,
         'valid_size': 711,
         "num_classes": 2
         },
    'ohsumed':
        {'train_size': 3357,
         'test_size': 4043,
         'valid_size': 336,
         "num_classes": 23
         },
    'R8':
        {'train_size': 5485,
         'test_size': 2189,
         'valid_size': 548,
         "num_classes": 8
         },
    'R52':
        {'train_size': 6532,
         'test_size': 2568,
         'valid_size': 653,
         "num_classes": 52
         },
    'TREC':
        {'train_size': 5452,
         'test_size': 500,
         'valid_size': 545
         },
    'WebKB':
        {'train_size': 2803,
         'test_size': 1396,
         'valid_size': 280
         },
    'wiki':
        {'train_size': 3000,
         'test_size': 127000,
         'valid_size': 300
         }
}

 

dataset.py

from config import args
import joblib
import numpy as np
from torch_geometric.data import Data, DataLoader
import torch
import random
from tqdm import tqdm


class MyDataLoader(object):

    def __init__(self, dataset, batch_size, mini_batch_size=0):
        self.total = len(dataset)
        self.dataset = dataset
        self.batch_size = batch_size
        self.mini_batch_size = mini_batch_size
        if mini_batch_size == 0:
            self.mini_batch_size = self.batch_size

    def __getitem__(self, item):
        ceil = (item + 1) * self.batch_size
        sub_dataset = self.dataset[ceil - self.batch_size:ceil]
        if ceil >= self.total:
            random.shuffle(self.dataset)
        return DataLoader(sub_dataset, batch_size=self.mini_batch_size)

    def __len__(self):
        if self.total == 0: return 0
        return (self.total - 1) // self.batch_size + 1


def split_train_valid_test(data, train_size, valid_part=0.1):
    train_data