kaize0409 / hypergat_textclassification Goto Github PK

@kaize0409

Thanks your fantastic work !

Hello !
I can use the generate_LDA.py now!
But I still have something confused!
parser.add_argument('--topn', type=int, default=10, help='top n keywords') parser.add_argument('--topics', type=int, help='number of topics')

I know --topn is like your paper mention that top K words right?
But How to determine the number of --topics ?
The number of labels? Can if I use --topics 20 on R8 dataset, it will be error like scipy.sparse.coo_matrix .
So like R8 has --topics :8 R52 has --topics :52
I'm nor really sure:(

Also I am trying to modify the framework of HyperGAT to fit on my Chinese multi label text classification data.
Which have approximately 2500 text with 9 labels.
Have you ever try your HyperGAT on multi label classification task?
such like Kaggle's toxic comment classification datasets ?

Thank you for this code.

Could you please provide the remaining datasets(20ng, ohsumed, and MR). or the link to datasets with preprocessing script.

Thanks

Hi, thanks your work!
I have some confusion about the code.
I want to know what self.word_context means, and why concat with x1(pair_h = torch.cat((q1, x1), dim=-1)，
q1 = self.word_context.weight[0:].view(1, -1).repeat(x1.shape[0],1).view(x1.shape[0], self.out_features))?
It doesn't seem to be reflected in the formula.

When AGGR(edge) aggregates features of hyperedges to nodes, pair_h = torch.cat((q1, y1), dim=-1) , q1 are hyperedge features, y1 are node features. So, I guess whether q1 is the hyperedge feature when nodes features aggregate to hyperedges features?
If the guess is correct? But why self.word_context = nn.Embedding(1, self.out_features), instead of self.word_context = nn.Embedding(n_hyperedge, self.out_features). Don't we need to distinguish features of hyperedges?

When I use your model to fit MoviewReview datasets which is from TextGCN. I met problem as follows. Thanks a lot in advance~

min_len_of_sentence : 0
max_len_of_sentence : 27
min_num_of_sentence : 1
max_num_of_sentence : 5
average_len_of_sentence: 9.062759575449931
average_num_of_sentence: 1.0162258488088538
Total_num_of_sentence : 10835
Counter({'1': 5331, '0': 5331})
Total_number_of_words: 4284
Total_number_of_categories: 2

epoch: 0
start training: 2020-12-16 16:58:18.184139
Traceback (most recent call last):
File "run.py", line 109, in
main()
File "run.py", line 82, in main
train_model(model, train_data, args)
File "/data/yuhai/tc/HyperGAT/model.py", line 114, in train_model
targets, scores = forward(model, alias_inputs, HT, items, targets, node_masks)
File "/data/yuhai/tc/HyperGAT/model.py", line 98, in forward
node = model(items, HT)
File "/home/yuhai/workspace/anaconda3/envs/torch1.4/lib/python3.6/site-packages/torch/nn/modules/module.py", line 532, in call
result = self.forward(*input, **kwargs)
File "/data/yuhai/tc/HyperGAT/model.py", line 89, in forward
nodes = self.hgnn(hidden, HT)
File "/home/yuhai/workspace/anaconda3/envs/torch1.4/lib/python3.6/site-packages/torch/nn/modules/module.py", line 532, in call
result = self.forward(*input, **kwargs)
File "/data/yuhai/tc/HyperGAT/model.py", line 40, in forward
x = self.gat2(x, H)
File "/home/yuhai/workspace/anaconda3/envs/torch1.4/lib/python3.6/site-packages/torch/nn/modules/module.py", line 532, in call
result = self.forward(*input, **kwargs)
File "/data/yuhai/tc/HyperGAT/layers.py", line 102, in forward
assert not torch.isnan(pair_e).any()
AssertionError

Thanks your work!
I want to implement on my own Chinese data.
May I asked how to generate LDA.p data?
I have generate tcorpus and label.
Thanks!

Your paper is very good. I want to reproduce the experiment in your paper. When do you expose the code?