Peroxide VS linfa

Rust’s standard library is relatively small by design and doesn’t contain any tools for numeric integration. However, you can probably find a crate on crates.io that does what you need. A quick search suggests Peroxide.

peroxide seems really cool, but I haven't had the opportunity to use it much yet.

peroxide covers some of this area. It could work as a starting point for something in the ML or probabilistic programming domains.

I've been playing with data analytics and ml in rust for the last couple of weeks. A typical ML job requires transforming some data to feed the ml model to the then train the model. For ML I've been using linfa (https://github.com/rust-ml/linfa) which is surprisingly nice. I've been experimenting with ndarray and polars for data transformation (linfa uses ndarray) - from a UX standpoint. I'm pretty surprised by polars' performance (https://h2oai.github.io/db-benchmark/), which sits on top of arrow2, and it's definitely a great candidate for OLAP tasks. But I couldn't find any comparison between ndarray and polars, has anyone had any meaningful experience with the two or/and can point me to a benchmark comparison?

The most comprehensive current view of the Rust machine learning ecosystem at the moment is probably at https://www.arewelearningyet.com/ (I sometimes help maintain this site)

Rust has a weird mix at the moment, and not one that's likely to significantly change within the next 12 months, at least. Certain tools are genuinely best-in-class, especially around simple operations on insane amounts of data. Rust kills it in that space due to its native speed and focus on concurrency.

There's also growing projects like Linfa [1]. that while not at the level of scikit-learn, have significantly increased their coverage on common data science/classical ML problems in the past couple years, along with improved tooling. The space does have a few pure-Rust projects coming down the pipeline around autodifferentiation, GPU compute, etc. that are likely to yield some really valuable results in deep learning, but that aren't quite available and will take some time to pick up some traction even once they're released. At the same time, areas like data visualization are unlikely to reach parity with something like matplotlib/pyplot in the near future.

Python is the de-facto standard, and will be for some time, but Rust's ability to build accessible high-level APIs on top of performant, language-native libraries is attracting some attention and I wouldn't be surprised to start seeing ingress in the certain areas over the next few years, where instead of the Python/C++ combination, it's just Rust all the way down.

[1] https://github.com/rust-ml/linfa

take a look at https://github.com/rust-ml/linfa for machine learning related crates

Check out smartcore and linfa. At work I was badly in need of an NMF function similar to MATLAB's one these days but not enough time to write one myself. If you're good at math and machine learning, this sounds like a task you could try tackling.

For other algorithms, there is not yet a single library to rule them all (linfa might become that at some point) but searching for the algorithm you need on crate.io is likely to give you some results (obligatory plug to Friedrich, my gaussian process implementation).

To all folks in love with Rust programming language, **linfa** is a promising library to check out: a complete porting of the well known scikit-learn library, which enables common preprocessing tasks and classical ML algorithms such as clustering, linear learners, logistic regression, and decision trees as well as support vector machines and Bayesian algorithms such as Naive Bayes. We all know that Python has the 98% of the machine learning languages market share, but if I looked to something else, a super-fast Rust implementation would be my first stop.

for a start I will implement the TryFrom for Dataset under a feature flag. But to be really useful some of the algorithms have to start using something like DatasetBase here Records are currently bounded by an associated type for the element type, we would have to relax that too. Just read your blogpost on polars 👍