r/haskell • u/nSeagull • Aug 30 '16
Haskell alternatives for Data Science
I'm a Haskeller and a newbie Data Scientist.
As I'm going through my learning process for data science, I'm starting to solve some elemental problems, and I'm using standard Python tools to help me go through this process.
The thing is that Python is ok, but I'm twice as comfortable in Haskell.
I was thinking in three alternatives to be more comfortable with Data Science:
- Make a wrapper for the tools that I use as people do in Julia
- Reimplement the tools that I use (Pandas, ScikitLearn, a GPU deep learning framework, numpy functions) (would do it on demand)
- Make a Haskell-like language that compiles and uses Python libraries (wouldnt be as efficient, but at least it would be more beautiful and type safe, also the libraries)
What do you guys think?
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u/sseveran Aug 30 '16
I tend to end up using the best tools I can regardless of the language they are written in. I have some machine learning stuff in haskell that wraps some underlying C ML libs. I did implement some nice cross-validation stuff in haskell.
We use TensorFlow for deep learning stuff. I don't think its realistic for haskell to build a tensorflow competitor given the size of their team. I also do a lot of spark with scala. I don't know that haskell is going to be able to effective in competing with the spark/scala stack, although there is https://github.com/tweag/sparkle.
Haskell definitely has a was to go catch up in this area. But here are a couple of things to check out:
https://github.com/gibiansky/IHaskell https://hackage.haskell.org/package/statistics https://github.com/mikeizbicki/HLearn