Andrle and I each used to have our own Netflix accounts. For a time, after we got married, we kept both; mine was used for disc rentals, and hers for streaming. Eventually we realized we were mostly just streaming, so we canceled my account and went to a single shared account. Sadly, this meant I lost over 1400 movie ratings, since Netflix provides no official way to export. After a while, I gave up on ever getting my ratings back.
Finally, earlier this month, Netflix embraced their role as a household service and added profiles. I decided to pay for a month of streaming and reactivate my old account to see if I could get my ratings out. I found this browser script which, after applying a patch described there, gave me a JSON file containing all of my ratings. This is also a useful backup to have in case Netflix ever goes away (unlikely).
Unfortunately there is still no way to easily import ratings into Netflix, so I wrote a very basic Chrome extension that would read the exported JSON file and click through each movie, rating it. It’s available on GitHub. Make sure to read the instructions included with the code; it’s straightforward but requires some poweruser comfort to follow the steps, since I didn’t bother with an interface. (Incidentally, this is one thing I love about having programming skills – that sense of having more power and control over my own data.)
Obviously it would be nice if various online services practiced across-the-board data liberation (though with Facebook and Twitter adding export, it’s getting better), but hopefully this is one tiny step in helping other nerdy family units transfer their precious Netflix ratings.
Although it’s been almost a month since the end of the semester, I wanted to share some of the final project that consumed most of my time and mental capacity in April. I was taking CS266: Bio-inspired Multi-agent Systems from Prof. Rhadika Nagpal. It was a very fun course, emphasizing seminar-style discussion of research papers, with labs working with actual robot hardware and culminating in a final project that had both a physical robot competition component and a more open-ended simulation component.
My contribution to the final project focused on the simulation, while my partners Andrew Reiter and Pierre-Emile Duhamel focused on the robots. We decided to use the recommended MASON simulation library to implement a virtual version of the competitive foraging task, and then coupled that with a genetic programming implementation to attempt to evolve robot strategies. You can read a lot more detail about our methods and results in our final paper (pdf), including our approaches using the actual e-puck hardware. The simulation is discussed in Section 3. Long story short, the GP approach worked, in that it did successfully learn strategies, but I think that our fitness function could use some work in order to develop strategies that do something more interesting than the manually written strategies.
The complete source code is available on GitHub under a BSD license.
Various videos (some of which I’ve already posted elsewhere) are below the cut.
Tweetworks Python API
Version 1.0.0b1 of the tweetworks package for Python 2.6 is now available. This package implements the web service API for Tweetworks, a Web 2.0 service that facilitates threaded conversations on top of Twitter.
This is definitely a beta, because while I’ve tested everything I can think of, I haven’t tried writing anything seriously complicated with it, although I certainly plan to. Comments and questions are welcome here, or find me in the Tweetworks Developers group or as @UltraNurd. I admit that the documentation is a little light at the moment.
If you’re interested in using Tweetworks programmatically from Python, or want to know more about the service, read on.