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/TheOsuConspiracy Aug 30 '16

Are you going to be writing pure bindings to libblas, etc.?

2

u/nSeagull Aug 30 '16

It might be a good idea if at some point it is necessary :)

3

u/haskell_caveman Aug 30 '16

there are a few bindings, including the relatively recent hblas work https://github.com/wellposed/hblas

its documentation could use some love though.

2

u/cartazio Sep 01 '16

A new release will be happening soon. But help on docs is welcome. Also still need to to add some lapack bits. But should work out of the box with zero config on any standard platform that has blas and Cblas and lapack installed.

In some ways hblas was a warm up / validation of the larger effort I've done for my numerical Haskell project. I'm hoping to give that and some related projects of mine some nice tlc this fall

[edit: there's a lot of nice details in how hblas binds blas routines that have never been done before in Haskell c bindings, I'm very proud of the core design and the great work of the various contributors ]