app (not working properly on Chrome): [http://bayeisan-optimization.25800bfd.svc.dockerapp.io:8000/bo]

[https://github.com/majidaldo/GTK3webserver]

I recently made a notebook about Gaussian processes (GP). Now, Gaussian processes are used by Bayesian optimization. In Bayesian optimization, the goal is to optimize a function with as few (probably expensive) function evaluations as possible. Here's a good tutorial.

To see how it works, I implemented a basic optimizer following the mathematics introduced in the referenced tutorial (which wasn't as difficult as the math behind the GP!). I began from the code for the GP, to see how a Bayesian optimizer (BO) would optimize a toy 1-D problem.

But then, I wondered how the performance of a human would compare to the BO. So I made a game! In this game, the goal is to, of course, find the maximum of the function is as few tries as possible. Both players in each trial will attempt to find the maximum. A running average of the number of tries is kept.

After some experimentation playing the game, it seems there is a lower bound for the average number of tries needed. For the way I have my game set up (50 possible choices), the BO needed 10 point something +/- point something tries. Furthermore, trying different so-called acquisition functions for the BO did not have much of an effect. This figure is based on thousands of trials (central limit theorem at work). And playing as the creator of my game, it was difficult for me to keep below 10 tries as an average. Of course, I can't play thousands of times.

These results imply that a human would not be able to optimize a function in three dimensions or more in less tries than a BO. There are simply too many variables to keep track of and it is very difficult if not impossible to visualize which is part of the point of using BOs.

Some features of the program:

- For aesthetic reasons, the y-axis has to be fixed based on the range of values of the function. But then the visual clue of having a point close to the top edge of the plot gives the human player an unfair advantage. The solution is to add some random margin between the maximum and the edge. So, if you happen to get a point that is close to the top edge, choose its neighbors!
- To generate a 'random' smooth function, I get some normally distributed points and sprinkle them on the domain. Then I use splines to connect them. It works surprisingly well!

Implementation notes:

Now, the work involved in putting the game on the web, so that you, the reader, can easily engage in it, deserves its own post or two! Once I had the 'desktop' application running, I swam through oceans of technology until I got it to the form presented here. I should mention here that I came across a program similar to mine on Wolfram|Alpha but I can't find it anymore.

So here is the game served on the web. It's a Docker container managed for free, for now, by tutum.co but hosted almost for free on AWS for a few more months as of the date of this post. If you can spare 1GB of HD space and 100MB of memory to host my program, let me know!

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