I feel ya. But remember your users probably don't care. You are trying to convey information, and your diagram does so effectively. Everything else is sugar. But if you are dissatisfied then you could also take a look at code2flow.com which is very quick and easy to use. I'd be quite curious if it solves your issue. I suspect it will.
Eh, the point is avoid unnecessary work. If you have just one diagram, creating it by hand is not a problem. If you have 10 or 20, you want automation. Especially since you'll want to keep the diagram continually updated, maybe tweakable with different styles; editing all diagrams by hand every time for every colour change is arduous compared to just running a single command to re-render.
ReStructuredText with Sphinx is wonderful. The syntax isn't the most intuitive, however once you have overcome this small hurdle, it can boost your productivity enormously.
In terms of deployment, using Sphinx enables you to easily deploy to readthedocs.io, or github pages.
Since it is a static site generate, it also enables you to use bootstrap, any javascript front-end library you wish to use, so nothing stops you from having web-application type behaviour and interactions in your posts. It also integrates nicely with extensions, such as mermaid, to make graphs inline with your posts. It can include code snippets from code, offers syntax highlighting and a lot more.
Hi Spooky23. Thank you. I'll be writing more posts on the topic over the christmas period, so keep checking. I also find it more interesting with data I find interesting.
You are indeed right, and I encourage anyone interested in computing the precision metrics to do so.
The focus of this post is the GoLang proxy used for the caching. It's actually used in a CI / CD environment, but I'm finding it incredibly useful for a whole variety of tasks.
In the scikit-learn article the classifier scores over 90% precision, so I'd expect it would be possible to do the same.
I'll be posting more about this over the xmas period, so I'll write a part II, where I compute the precision metrics.
Another thing worth noting is that it's not caching the HN comments, while it is using the reddit comments. Despite using tfidf, this still completely skews the results towards reddit as opposed to HN. So that's something else any interested reader can look into.
But results and how well the classifier performs really just depends on the quality and amount of training data you have. So would be interesting to see how this does if you can get a bunch more data from each of the subreddits and have some more test examples!
Thank you Jack. I think your point is important so I'll add a note at the end of the article. I'll also add link to your article as I think it's complimentary.
Thank you. Post author here. I'm glad you enjoyed it. I'll write many more blog posts over the christmas period on the same topic, so if you enjoyed then do keep checking.
Indeed. I've been very busy with work, but I'll dedicate a lot more time to articles like this, and shall list them all on the frontpage of http://ioloop.io. So if you just bookmark that for now, you should find more content like this before the New Year.
What I really like about this technique is that I can play with my scikit-learn while being offline, which seems to go hand in hand with the holiday travels ahead.
