shakespeare

Identify relevant scientific papers with simple machine learning techniques

Installation

run:

python setup.py install --user

to install shakespeare in ~/.local.

This will install the shakespeare python library, and also a script shakespeare that handles training, content fetching and content filtering.

To install an example knowledge set, copy examples' contents to $HOME/.shakespeare

Depends on bibtexparser, feedparser scikit-learn packages, which can be installed via pip

pip install --user bibtexparser scikit-learn feedparser

Features

• fetch functions for the following journals

  • Phys Rev A-X
  • PRL
  • PNAS
  • Nature + Nature:Stuff
  • Science
  • Small
  • ACS Nano, Nano Letters
  • Soft Matter
  • Langmuir
  • Angewandte Chemie
  • JCP, JCP B

• Fetch functions for arXiv

• support for BibTex Files

• Naive bayes training and classification

Usage

Train naive_bayes algorithm

shakespeare -g thegoodstuff.bib -b thebadstuff.bib --train

Find papers from nature nano and PNAS

shakespeare -j natnano pnas -o cool_papers.md

Find papers from the arxiv cond-mat.soft and math, then review the algorithms selection

shakespeare -a cond-mat.soft math --feedback

Help printout

usage: shakespeare    [-h] [-o OUTPUT] [-b [BIBFILES [BIBFILES ...]]]
                      [-j [JOURNALS [JOURNALS ...]]] [-a [ARXIV [ARXIV ...]]]
                      [--all_sources] [--all_good_sources] [--train]
                      [-g GOOD_SOURCE] [-m METHOD] [-k KNOWLEDGE]
                       [--overwrite-knowledge] [--feedback] [--review_all]
optional arguments:
  -h, --help            show this help message and exit
  -o OUTPUT, --output OUTPUT
                        output file name. only supports markdown right now.
  -b [BIBFILES [BIBFILES ...]], --bibtex [BIBFILES [BIBFILES ...]]
                        bibtex files to fetch
  -j [JOURNALS [JOURNALS ...]], --journals [JOURNALS [JOURNALS ...]]
                        journals to fetch. Currently supports physreve
                        physrevd jchemphysb physreva physrevc pnas nature
                        jchemphys science natmat physrevb acsnano jphyschem
                        nanoletters natphys prl small angewantechemie langmuir
                        physrevx natnano.
  -a [ARXIV [ARXIV ...]], --arXiv [ARXIV [ARXIV ...]]
                        arXiv categories to fetch
  --all_sources         flag to search from all sources.
  --all_good_sources    flag to search from good sources. Specfied in your
                        config file.
  --train               flag to train. All sources beside "--train-input-good"
                        are treated as bad/irrelevant papers
  -g GOOD_SOURCE, --train_input_good GOOD_SOURCE
                        bibtex file containing relevant articles.
  -m METHOD, --method METHOD
                        Methods to try to find relevent papers. Right now,
                        only all, title, author, and abstract are valid fields
  -k KNOWLEDGE, --knowledge KNOWLEDGE
                        path to database containing information about good and
                        bad keywords. If you are training, you must specifiy
                        this, as it will be where your output is written
  --overwrite-knowledge
                        flag to overwrite knowledge,if training
  --feedback            flag to give feedback after sorting content
  --review_all          review all the new selections. Otherwise, you will
                         only review the good selections

TODO

• Add support for a config file for setting defaults (e.g., which journals for search for when using the --all_good_sources command