arxiv sanity preserver

This project is a web interface that attempts to tame the overwhelming flood of papers on bioRxiv. It allows researchers to keep track of recent papers, search for papers, sort papers by similarity to any paper, see recent popular papers, to add papers to a personal library, and to get personalized recommendations of (new or old) bioRxiv papers. This code will be available/is available at www.biorxiv-sanity.com., where it's serving 25,000+ Arxiv papers from Machine Learning (cs.[CV|AI|CL|LG|NE]/stat.ML) over the last ~3 years. With this code base you could replicate the website to any of your favorite subsets of Arxiv by simply changing the categories in fetch_papers.py.

Code layout

There are two large parts of the code:

Indexing code. Uses Arxiv API to download the most recent papers in any categories you like, and then downloads all papers, extracts all text, creates tfidf vectors based on the content of each paper. This code is therefore concerned with the backend scraping and computation: building up a database of arxiv papers, calculating content vectors, creating thumbnails, computing SVMs for people, etc.

User interface. Then there is a web server (based on Flask/Tornado/sqlite) that allows searching through the database and filtering papers by similarity, etc.

Dependencies

You will need:

numpy
feedparser (to process xml files)
scikit learn (for tfidf vectorizer, training of SVM)
flask (for serving the results)
flask_limiter
tornado (if you want to run the flask server in production)
dateutil
scipy
sqlite3 - for database (accounts, library support, etc.)

Most of these are easy to get through pip, e.g.:

$ virtualenv env                # optional: use virtualenv
$ source env/bin/activate       # optional: use virtualenv
$ pip install -r requirements.txt

You will also need ImageMagick and pdftotext, which you can install on Ubuntu as sudo apt-get install imagemagick poppler-utils. Bleh, that's a lot of dependencies isn't it.

Processing pipeline

The processing pipeline requires you to run a series of scripts, and at this stage I really encourage you to manually inspect each script, as they may contain various inline settings you might want to change. In order, the processing pipeline is:

Run fetch_papers.py to query arxiv API and create a file db.p that contains all information for each paper. This script is where you would modify the query, indicating which parts of arxiv you'd like to use. Note that if you're trying to pull too many papers arxiv will start to rate limit you. You may have to run the script multiple times, and I recommend using the arg --start-index to restart where you left off when you were last interrupted by arxiv.
Run download_pdfs.py, which iterates over all papers in parsed pickle and downloads the papers into folder pdf
Run parse_pdf_to_text.py to export all text from pdfs to files in txt
Run thumb_pdf.py to export thumbnails of all pdfs to thumb
Run analyze.py to compute tfidf vectors for all documents based on bigrams. Saves a tfidf.p, tfidf_meta.p and sim_dict.p pickle files.
Run buildsvm.py to train SVMs for all users (if any), exports a pickle user_sim.p
Run make_cache.py for various preprocessing so that server starts faster (and make sure to run sqlite3 as.db < schema.sql if this is the very first time ever you're starting arxiv-sanity, which initializes an empty database).
Start the mongodb daemon in the background. Mongodb can be installed by following the instructions here - https://docs.mongodb.com/tutorials/install-mongodb-on-ubuntu/.

Start the mongodb server with - sudo service mongod start.
Verify if the server is running in the background : The last line of /var/log/mongodb/mongod.log file must be - [initandlisten] waiting for connections on port <port>

Run the flask server with serve.py. Visit localhost:5000 and enjoy sane viewing of papers!

Optionally you can also run the twitter_daemon.py in a screen session, which uses your Twitter API credentials (stored in twitter.txt) to query Twitter periodically looking for mentions of papers in the database, and writes the results to the pickle file twitter.p.

I have a simple shell script that runs these commands one by one, and every day I run this script to fetch new papers, incorporate them into the database, and recompute all tfidf vectors/classifiers. More details on this process below.

protip: numpy/BLAS: The script analyze.py does quite a lot of heavy lifting with numpy. I recommend that you carefully set up your numpy to use BLAS (e.g. OpenBLAS), otherwise the computations will take a long time. With ~25,000 papers and ~5000 users the script runs in several hours on my current machine with a BLAS-linked numpy.

Running online

If you'd like to run the flask server online (e.g. AWS) run it as python serve.py --prod.

You also want to create a secret_key.txt file and fill it with random text (see top of serve.py).

Current workflow

Running the site live is not currently set up for a fully automatic plug and play operation. Instead it's a bit of a manual process and I thought I should document how I'm keeping this code alive right now. I have a script that performs the following update early morning after arxiv papers come out (~midnight PST):

python fetch_papers.py
python download_pdfs.py
python parse_pdf_to_text.py
python thumb_pdf.py
python analyze.py
python buildsvm.py
python make_cache.py

I run the server in a screen session, so screen -S serve to create it (or -r to reattach to it) and run:

python serve.py --prod --port 80