Practical ML in Python: HR/K Prediction

Editor's Notes

1. Data is everywhere clickstream data users are bad at managing fb permissions; you can get a lot out of the graph APIThere’s value in learning about data - how people use your site- feature or advertisement personalizationOne thing that enables this is that resources are cheap these days
2. Python is a fantastic programming environment for data processing and analyticson one end of the spectrum, quick and dirty scripts... or full-featured applications ready for a deployment at scaleWide variety of toolkits for off-the-shelf analysis or building out your own data processing applications
3. For this talk... discussing one flavor of analytics and machine learning, the classification problemintuition: training set: what you know about the world train a classifier to predict things that you don’t
4. As a concrete example, I started playing around with some baseball stats to illustrate how one might go about building ML applications in pythoneven if you’re not into baseball, you know that the iconic visions of success and failure are the home run and the strikeout in all the movies, hitting a home run is equivalent to getting the girl and striking out is seen as a major setback
5. As with any machine learning problem, you want to get your data into a classifier-consumable format. That is, labeled feature sets. For each play in the game, keep track of the game state and output a labeled feature bundle representing the situation and its outcome: HR, K, (other)
6. speed: offline: deadline ~ hours, daysrealtime: user waiting on the other side (user actions: => milliseconds)transparency:seeing what’s going on with an algorithm in case the docs aren’t clearmodifying or patching an algorithm to meet your needssupport:maturity, active development how strong is the community around the project? are there tutorials available?
7. interface with external packages if you’ve done some analysis already and want to transition to python without throwing away codepython toolkits provide sets of algorithms, mostly python implementationsoften use external packages with C bindings, some even use other toolkitsDIY: use the external packages yourself
8. to give a sampling of what’s available, i chose some toolkits that were last updated within a yearAs a disclaimer... -Not exhaustive, just a sampling -some of these tools I’ve used, some I haven’t! -I’m sure I’ve missed your favorite, and for that I apologizedifferent packages focus on different things, so one isn’t necessarily going to suit all of your needs
9. buzz around scikit-learn last year - checked it out recently and it’s been built out a lot
10. NumPy: fast and efficient arraysSciPy: scientific tools and algorithms built on NumPyCan also use popular C/C++ implementations using python bindingspython is a modular language, so you can always sub out your implementation without disrupting your workflow too muchnow, as an example of applying these toolkits...
11. speed isn’t criticalspeed is critical (imagine that you’re a coach) baseball is slow, but it’s not THAT slow
12. identifies predictive features certain values are strongly correlated with certain labelssklearn- wasn’t clear on the documented usage, looked at the code
13. for a coach
14. don’t we need to train our classifier to run our web application?save them on disk!pickle or pull out a textual representation(another argument for using a package that allows you to do this)why compute things twice?use generatorslots and lots of dataavoid keeping it all in memorysingle pass algorithm (bayes)first-pass conversion to compact data (numpy vectors, not python objects)not always possible, but keep it in mindtake advantage of multiple cores - if your processing step has a minimal memory footprint (just one line at a time), do it on multiple cores - multiple processes on different input files or multiprocessing module is great at this
15. you don't need to know everything about the algorithms you use …but you can't just blindly apply these things and hope that they magically workml-class.org: free class, provides an excellent foundation and starting point for understanding MLin no time, you, too, can be a number muncher
16. source code for SluggerML on github; kind of a mess, and I’m sorry about thatand I’m @mattspitz on the twitters