Monthly Downloads: 29
Programming language: Haskell
License: BSD 3-clause "New" or "Revised" License
Tags: Machine Learning     Data Mining     HLearn    
Latest version: v2.0.0.0

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HLearn is a high performance machine learning library written in Haskell. For example, it currently has the fastest nearest neighbor implementation for arbitrary metric spaces (see this blog post).

HLearn is also a research project. The research goal is to discover the "best possible" interface for machine learning. This involves two competing demands: The library should be as fast as low-level libraries written in C/C++/Fortran/Assembly; but it should be as flexible as libraries written in high level languages like Python/R/Matlab. Julia is making amazing progress in this direction, but HLearn is more ambitious. In particular, HLearn's goal is to be faster than the low level languages and more flexible than the high level languages.

To achieve this goal, HLearn uses a very different interface than standard learning libraries. The H in HLearn stands for three separate concepts that are fundamental to HLearn's design:

  1. The H stands for Haskell. Machine learning is about estimating functions from data, so it makes sense that a functional programming language would be well suited for machine learning. But Functional programming languages are not widely used in machine learning because they traditionally lack strong support for the fast numerical computations required for learning algorithms. HLearn uses the SubHask library to get this fast numeric support in Haskell. The two libraries are being developed in tandem with each other. <!--Languages like Agda/Coq/Idris provide more advanced type systems,--> <!--but their compilers lack the support for real world optimizations needed for numerical applications.--> <!--Haskell strikes a nice balance.-->

  2. The H stands for Homomorphisms. Homomorphisms are a fundamental concept in abstract algebra, and HLearn exploits the algebraic structures inherrent in learning systems. The following table gives a brief overview of what these structures give us:

    Structure What we get
    Monoid parallel batch training
    Monoid online training
    Monoid fast cross-validation
    Abelian group "untraining" of data points
    Abelian group more fast cross-validation
    R-Module weighted data points
    Vector space fractionally weighted data points
    Functor fast simple preprocessing of data
    Monad fast complex preprocessing of data
  3. The H stands for the History monad. One of the most difficult tasks of developing a new learning algorithm is debugging the optimization procedure. There has previously been essentially no work on making this debugging process easier, and the History monad tries to solve this problem. It lets you thread debugging information throughout the optimization code without modifying the original code. Furthermore, there is no runtime overhead associated with this technique.

The downside of HLearn's ambition is that it currently does not implement many of the popular machine learning techniques.

More Documentation

Due to the rapid pace of development, HLearn's documentation is sparse. That said, the examples folder is a good place to start. The haddock documentation embedded within the code is decent; but unfortunately, hackage is unable to compile the haddocks because it uses an older version of GHC.

HLearn has several academic papers:

There are also a number of blog posts on my personal website. Unfortunately, they are mostly out of date with the latest version of HLearn. They might help you understand some of the main concepts in HLearn, but the code they use won't work at all.


<!--If you want to contribute, I'd be happy to help you get started.--> I'd love to have you contribute, and I'd be happy to help you get started! Just create an issue to let me know you're interested and we can work something out.