Roadmap

[LukeMathWalker]

In terms of functionality, the mid-term end goal is to achieve an offering of ML algorithms and pre-processing routines comparable to what is currently available in Python's scikit-learn.

These algorithms can either be:

• re-implemented in Rust;
• re-exported from an existing Rust crate, if available on crates.io with a compatible interface.

In no particular order, focusing on the main gaps:

• Clustering:

  • DBSCAN
  • Spectral clustering;
  • Hierarchical clustering;
  • OPTICS.

• Preprocessing:

  • PCA
  • ICA
  • Normalisation
  • CountVectoriser
  • TFIDF
  • t-SNE

• Supervised Learning:

  • Linear regression;
  • Ridge regression;
  • LASSO;
  • ElasticNet;
  • Support vector machines;
  • Nearest Neighbours;
  • Gaussian processes; (integrating friedrich - tracking issue Integrating friedrich into linfa nestordemeure/friedrich#1)
  • Decision trees;
  • Random Forest
  • Naive Bayes
  • Logistic Regression
  • Ensemble Learning
  • Least Angle Regression
  • PLS

The collection is on purpose loose and non-exhaustive, it will evolve over time - if there is an ML algorithm that you find yourself using often on a day to day, please feel free to contribute it 💯

[Nimpruda]

Hi, I'm eager to help I'll take Linear regression, Lasso and ridge.

[LukeMathWalker]

Cool! I worked a bit on linear regression a while ago - you can find a very vanilla implementation of it here: https://github.com/rust-ndarray/ndarray-examples/tree/master/linear_regression @Nimpruda

[InCogNiTo124]

What does Normalization mean, is it like sklearn's StandardScaler or something else?

[LukeMathWalker]

Exactly @InCogNiTo124.

[ADMoreau]

This is an interesting project and I will work on the PCA implementation

[nestordemeure]

I am the author of the friedrich crate which implements Gaussian Processes.

While it is still a work in progress, it is fully featured and I would be happy to help integrate it into the project if you have directions to do so.

[LukeMathWalker]

That would be awesome @nestordemeure - I'll have a look at the project and I'll get back to you! Should I open an issue on friedrich's repository when I am ready? Or would you prefer it to be tracked here on the linfa repository?

[nestordemeure]

Both are ok with me.

An issue in friedrich's repository might help avoid overcrowning linfa with issues but do as you prefer.

[mstallmo]

I'd love to take the Nearest Neighbors implementation

[milesgranger]

I think this is really great, I just started on a sklearn like implementation of their pipelines, here but more or less for experimentation without anything serious. I'll be sure to keep my eye on issues/goals here and help out where I can. Thanks for the initiative! 👏

[ChristopherRabotin]

Hi there! First off, I don't have any experience in ML, but I read a lot about it (and listen to way too many podcasts on the topic). I'm interested in jumping in. I have quite some experience developing in Rust, and specifically high fidelity simulation tools (cf nyx and hifitime).

I wrote an Ant Colony Optimizer in Rust. ACOs are great for traversing graphs which represent a solution space, a problem which is considered NP hard if I'm not mistaken. Is that something used at all in ML? If so, would it be of interest to this library, or is there a greater interest (for now) to focus on the problems listed in the first post?

Cheers