Fishy Fun with Doc2Vec

Using a fishkeeping forum corpus with everyone’s favorite vector representation

I wanted to play around with word2vec but did not want to use the typical data sets (IMDB, etc.). So, I said, what if I were to do some web scraping of one of my favorite fishkeeping forums and attempt to apply word2vec to find “experts” within the forum. Well, turns out this is a much longer journey than I originally thought it would be, but an interesting one nonetheless.

This is a first blog post of hopefully several of my adventures with word2vec/doc2vec. I have a few ideas on how to leverage this corpus using deep learning to auto-generate text, so stay tuned, and if interested, drop me a line or leave a comment!


So word2vec was originally developed by Google researchers and many people have discussed the algorithm. Word2vec provides a vector representation of a sequence of words using a not-deep neural network. Doc2vec adds additional information (namely context, or paragraph context) to the word embeddings. The original paper on Paragraph Vector can be found at A quick literature search revealed I wanted to use doc2vec instead of word2vec for my particular use case since I wanted to compare user posts (essentially multiple paragaphs) instead of just words.

Later, I found this very informative online video from PyData Berlin 2017 where another data scientist used doc2vec to analyze comments on news websites. I thought that was cool, and further fueled my interest to tinker with this algorithm in my spare time… fast forward a few hours, and its almost daylight and I’m still here typing away…

I highly recommend watching this video for additional context:   

What I’m trying to do

I’d like to do the following:

  • analyze user posts on to identify who are the “experts” on fishkeeping and plants/aquascaping
  • have fun with doc2vec while doing this

Continue reading


Computer Vision meets Fish Tank

One day I got curious… what if I programmed my computer to track the fish swimming in my fish tank? That led me to tinkering with an open source software library called OpenCV. I fiddled around with the settings, tried a few things, and saved the output as a video, seen below. There’s a lot of research in computer science around object recognition and identification … this mini-project was just an attempt to have some fun poking around with some “older” computer vision technologies. Let me know what you think!


Python API to


Approximately 10% of American households have fish as pets.
It is estimated that 95% of fish deaths can be attributed to improper housing or nutrition. Many times fish are sold or given away without any guidance to the new pet owner, such as goldfish giveaways at carnivals or at birthdays. Some fish have myths associated with them, such as the betta fish (siamese fighting fish) that supposedly can live in dirty water in small bowls. is a website that helps aquarists plan how to stock their fish tank. Users specify their tank size, their filtration, and what fish they intend to keep in the tank. The site will calculate the stocking level and filtration capacity given the inputs. This is a useful tool to get a rough estimate on a fish tank’s stocking level, it even lets you know whether the fish are compatible with one another, if you have more than one species in the tank. AqAdvisor is sometimes criticized for “not being accurate”, so the output generated should be not be treated as gospel; nonetheless, it gives a reasonable starting point, and is generally very useful for beginner fishkeepers.

Why I created this tool

I started using AqAdvisor and got annoyed at the archaic design. It’s not a RESTful API, it’s a clunky web site that takes a while to load. I was doing lots of research and found myself wanting a better useful experience. I also had some free time on my hands one long holiday weekend so I decided to give myself a little programming exercise of creating a python API to the site.

How to use the tool

The easiest way to use the tool is to use the ipython notebook as a starting point. First, create a stocking, then a tank, and then make a call to the AqAdvisor service. Because of the clunky web interface, multiple calls to must be made if you want to have more than one fish species in a tank (as is would be the case for a community tank). The auto-generated AqAdvisor URL will be printed for each call out to the website. This is useful in case you want to jump over to the web UI, you can just copy and paste the URL into your web browser and continue from there.

Use the common (English) name for the fish you are looking for. PyAqAdvisor will do a “fuzzy match” to AqAdvisor’s species list and match the closet one. This way you can specify your stocking list as “cardinal tetra” and not worry about the scientic name.

Please look at examples/ and examples/example.ipynb for more information.

Here’s an example of how easy it use the new API:

from pyaqadvisor import Tank, Stocking

if __name__ == '__main__':

  stocking = Stocking().add('cardinal tetra', 5)\
   .add('panda cory', 6)\
   .add('lemon_tetra', 12)\
   .add('pearl gourami', 4)

  print "My user-specified stocking is: ", stocking
  print "I translate this into: ", stocking.aqadvisor_stock_list

  t = Tank('55g').add_filter("AquaClear 30").add_stocking(stocking)
  print "Aqadvisor tells me: ",
  print t.get_stocking_level()

Github Repo: PyAqAdvisor


  • PyAqAdvisor currently only works for freshwater fish species. If you are interested in saltwater fish, please contact me.

Generate heart rate charts from MapMyRide TCX files

So I had some free time over Columbus Day weekend and figured why not spend it on a fun programming project. My politically-incorrectly named GhettoTCX project emerged after some quick fussing around with TCX (XML) file.

Ghetto TCX

GhettoTCX will parse a TCX file from Garmin, MapMyRide, etc. and generate some basic plots. The most interesting plot type is the heart rate zone chart. It can create a panel of plots, by parsing all the filed in a given directory.

It’s called GhettoTCX because it’s a no-frills, nothing fancy, not even a true TCX file parser. It simply searches for some keywords and pulls out heartbeat info and lat/long data. And not even at the same time, you need to the read the file twice if you want to plot both.

Heart Rate plots
Heart Rate plots

The example code and python code repository can be found on the project’s github page.

There are “better” TCX/XML file parsers out there. This one was meant to do one thing (actually two things), quickly and easily: plot heart rate (and heart rate zones). It can also plot lat/long data points onto a scatterplot, but it is seriously no-frills when you can get nice google maps charts on MapMyRide and practically any other fitness app out there.

It started out (and ended) as a fun weekend programming project… if you are curious about your heart rate zone, and are too cheap cost-conscious to pay the monthly subscription fee to MapMyRide for the heart rate zone chart, you can use this free tool instead. Enjoy!

Map Reduce is dead, long live Spark!

Map Reduce is dead, long live Spark!

That’s the impression I, and I think most people attending the conference, walked away with after Strata NY 2014.  Most of the interesting presentations were centered on Spark.  Only corporate IT presentations about “in progress hadoop implementations” were about Map Reduce.

So who’s working on Spark?  Cool startups and vendors (preparing for enterprise IT departments to move on to Spark in a year or two).

Who’s working on Map Reduce? Corporate IT departments migrating off legacy BI systems onto the promised land of Hadoop (dream come true, or nightmare around the corner, not sure which one it will be for people).

It makes sense. Map Reduce has been tested and is ‘safe’ now for enterprise IT teams to start deploying it into production systems.  Spark is still very new and untested.  Too risky for a Fortune 500 to dive into replacing legacy systems with a still-in-diapers open source software “solution.”  Nonetheless, I am sure every technical worker will be drooling to “prototype” or create proof of concepts with Spark after this conference.

Reflections on Strata NYC 2014

I had a chance to attend Strata in New York back in October.  I had been wanting to attend Strata for a few years, but had not had a chance until now.  A few impressions: (in the form of brief bullets)

  • It’s huge! (Over 3,000 attendees)
  • Very corporate!  (A bit too corporate, too stuffy, seemed like legal departments censored some presentations)
  • All the cool kids are using/learning Spark (and Scala)
  • Map Reduce is old news.
  • Enterprises move slow like dinosaurs, are just figuring out what Map Reduce is
  • Way too many vendors
  • Not enough interesting/inspiring presentations

Those were just my impressions, others may have other opinions.