Neural LDA Approximation

Approximation of the latent dirichlet allocation via deep neural networks.

Preliminaries

Download NLTK corpora which are used for defining stopwords and lemmatizing stuff:

python -m nltk.downloader stopwords
python -m nltk.downloader reuters
python -m nltk.downloader wordnet

Download the english wikipedia dump with:

mkdir ./data/wikipedia_dump/
cd ./data/wikipedia_dump/
wget -nc https://dumps.wikimedia.org/enwiki/latest/enwiki-latest-pages-articles.xml.bz2

Preprocess the files by calling the gensim script in the same directory as the dump:

python -m gensim.scripts.make_wiki <PATH_TO_WIKI_DUMP> ./wiki

(This will take about 7-8 hours depending on your CPU) Then decompress the id->word mapping by using bzip2 e.g.:

bunzip2 wiki_wordids.txt.bz2

Training the LDA will also take several hours. So make sure to use the --num_workers flag to speed up the computation accordingly to your computers specs.

How to fix ImportError: cannot import name 'zero_gradients' from 'torch.autograd.gradcheck':

• Clone https://github.com/BorealisAI/advertorch
• cd into the directory and install the latest Git version with:
• python -m pip install -e .

Usage:

1. Attack

python main.py [-h] [--gpu | -g GPU] [--num_workers | -w WORKERS] [--num_topics | -t TOPICS] [--from_scratch | -s]

e.g.

python main.py --attack_id 33 --l2_attack --advs_eps 100

Arguments

Argument	Type	Description
-h, --help	None	shows argument help message
-g, --gpu	INT	specifies which GPU should be used [0, 1] (default=0)
-b, --batch_size	INT	specifies the batch size (default=128)
-e, --epochs	INT	specifies the training epochs (default=100)
-l, --learning_rate	FLOAT	specifies the learning rate (default=0.01)
-w, --num_workers	INT	number of workers to compute the LDA model (default=4)
-t, --num_topics	INT	number of topics which the LDA and the DNN tries to assign the text into
-a, --attack_id	INT	specifies the word id for the target of the adversarial attack
-ae, --advs_eps	FLOAT	specifies the epsilon for the adversarial attack
-ai, --prob_attack	BOOL	flag to activate whole probability distribution target attack
-r, --random_test	BOOL	enables random test documents for evaluation
-s, --from_scratch	BOOL	flag to ignore every pretrained model and datasets and create everything from scratch
-ts, --topic_stacking	BOOL	flat to use the topic stacking attack to evaluate the performance for more than one target topic at the same time
-l2, --l2_attack	BOOL	flag to activate the L2 norm attack (rounded floats) instead of the LINF (whole integers)
-f, --full_attack	BOOL	flag to attack every topic
-v, --verbose	BOOL	flag to set Gensim to verbose mode to print the LDA information during it's training

2. LDA Matching

python lda_matching.py [--benchmark | -b]

3. LDA Stability Test

python test_lda_stability.py [--corpus_sizes | -cs]

e.g.

python test_lda_stability.py --corpus_sizes 0.01 0.1 0.5 1.0