fast_vector_similarity VS rust-ndarray

Yes, check out my library for vector similarity that has various other measures which are more discriminative:

https://github.com/Dicklesworthstone/fast_vector_similarity

pip install fast_vector_similarity

Got it. I'd encourage you to expose more of that functionality at the level of your application if possible. I think there is a lot of potential in using more than just cosine similarity, especially when there are lots of candidates and you really want to sharpen up the top few recommendations to the best ones. You might find this open-source library I made recently useful for that:

https://github.com/Dicklesworthstone/fast_vector_similarity

I've had good results from starting with cosine similarity (using FAISS) and then "enriching" the top results from that with more sophisticated measures of similarity from my library to get the final ranking.

You can see how I did something similar in my library here:

https://github.com/Dicklesworthstone/fast_vector_similarity/...

Basically you use ndarray instead of numpy, try to vectorize anything you can, and for the for loops that can’t be vectorized, you can use rayon to do them in parallel.

Yeah, like the other commenter said, everything is in this file here:

https://github.com/Dicklesworthstone/fast_vector_similarity/...

If you also make your project using Rust and Maturin, you can literally just copy and paste that into your project because it's totally generic, and if the repo is public, GitHub will just run it all for you for free.

The only thing is you need to create an account on PyPi (pip) and add 2-Factor Auth so you can generate an API key. Then you go into the repo settings and go to secrets, and create a Github Actions secret with the name PYPI_API_TOKEN and make the value your PyPi token. That's it! It will not only compile all the wheels for you but even upload the project to PyPi for you using the settings found in your pyproject.toml file, like this:

https://github.com/Dicklesworthstone/fast_vector_similarity/...

I would love some examples of how to do non-trivial data interop between Rust and Python. My experience is that PyO3/Maturin is excellent when converting between simple datatypes but conversions get difficult when there are non-standard types, e.g. Python Numpy arrays or Rust ndarrays or whatever other custom thing.

Polars seems to have a good model where it uses the Arrow in memory format, which has implementations in Python and Rust, and makes a lot of the ndarray stuff easier. However, if the Rust libraries are not written with Arrow first, they become quite hard to work with. For example, there are many libraries written with https://github.com/rust-ndarray/ndarray, which is challenging to interop with Numpy.

(I am not an expert at all, please correct me if my characterizations are wrong!)

Thanks for sharing. I read this issue on why ndarray does not have a dynamically typed array: https://github.com/rust-ndarray/ndarray/issues/651

You can get involved with the ndarray project

Sadly Ndarray does look a little abandoned to me: https://github.com/rust-ndarray/ndarray

The date of the last commit of [ndarray](https://github.com/rust-ndarray/ndarray) lies 6 month in the past while many recent issues are open and untouched.

While looking through ndarrays src, I came across a set of functions that explicitly unroll 8 variables on each iteration of a loop, with the comment eightfold unrolled so that floating point can be vectorized (even with strict floating point accuracy semantics). I don't understand why floats would be affected by unrolling, and in general I'm confused as to how explicit unrolling differs from iterating through each element one by one. I assumed this would be a scenario where the compiler would optimize best anyway, which seems to be confirmed (at least in the context of using iter() rather than for) here. Could anyone give a little context into what this, or any explicit unrolling achieves?

Burn is different: it is built around the Backend trait which encapsulates tensor primitives. Even the reverse mode automatic differentiation is just a backend that wraps another one using the decorator pattern. The goal is to make it very easy to create optimized backends and support different devices and use cases. For now, there are only 3 backends: NdArray (https://github.com/rust-ndarray/ndarray) for a pure rust solution, Tch (https://github.com/LaurentMazare/tch-rs) for an easy access to CUDA and cuDNN optimized operations and the ADBackendDecorator making any backend differentiable. I am now refactoring the internal backend API to make it as easy as possible to plug in new ones.

Looks like it's an open request

https://github.com/rust-ndarray/ndarray/issues/281

Answer: you can’t with this crate. I implemented a dynamic n-dim solution myself but it uses views of integer indices that get copied to a new array, which have indexes to another flattened array in order to avoid duplication of possibly massive amounts of n-dimensional data; using the crate alone, copying all the array data would be unavoidable.

Ultimately I’ve had to make my own axis shifting and windowing mechanisms. But the crate is still a useful lib and continuing effort.

While I don’t mind getting into the weeds, these kinds of side efforts can really impact context focus so it’s just something to be aware of.

In principle you're trying to convert between columnar and row-based data layouts, something that happens fairly often in data science. I bet there's some hyper-efficient SIMD magic that could be invoked for these slicing operations (and maybe the iterator solution does exactly that). Might be worth taking a look at how the relevant Rust libraries like ndarray do it.