LearnProgrammingBot is a bot for reddit that uses scikit-learn and supervised learning techniques to categorise submissions and reply with useful and appropriate links.
It is intended to answer common questions on /r/learnprogramming that can be found on the wiki, but in theory it should be suitable for any subreddit provided that it is trained properly. The default training set should be fine for most programming subreddits, but it can be extended at any time.
LearnProgrammingBot requires scikit-learn, praw and sqlalchemy. Due to this, the installation instructions are slightly different depending on which platform you are using. It should work with both Python 2 and Python 3 (unit tests are coming soon)
Before continuing, download the code (either through the source zip or releases
tab) and extract it if necessary, or clone using git. Then, open a command
prompt or terminal and cd to the directory where you have extracted the code.
As an administrator, in the command prompt, run:
pip install -r requirements.txt
sudo pip install -r requirements.txt
sudo apt-get install python-scipy
sudo pip install sqlalchemy scikit-learn praw
You'll need to enter a few variables into settings.py. Just follow the
instructions on lines preceded by # (comments) and fill in the correct data.
To run, use ./main.py run in the terminal. This will run continuously until
killed using Ctrl+C or an exception. You might find useful logging information
in bot.log if the bot does crash. Feel free to report an issue if you do find a
bug!
Currently, the classifier only recognises 3 types of post classes:
- 'good' - the post is a good question for /r/learnprogramming
- 'faq' - the post contains a common question that is probably on the FAQ
- 'bad' - the post is formatted badly, off topic or does not contain enough detail.
As of commit 9742b376ef4e845ac45cbd96e86dfe7156dc913e, the classifier's accuracy is as follows:
- Correct classification = 81%
- False negative = 13%
- False positive = 5%
- Wrong category = 1%
False negatives are counted as any time that the actual class was not 'good' and the classifier returned 'good'. False positives occur when the actual class was 'good' but the classifier did not return 'good'. Wrong category classifications occur when the classifier returned a different negative classification (i.e. 'faq' instead of 'bad')
- Unit tests
- More modular approach so that extra modules can be installed to make the bot more customisable.
We're happy to accept contributors - you don't need to be an expert, just file an issue about getting started and we can start there!
MIT License. Please see the LICENSE file!