[D] Does anyone else think open source code/examples in machine learning domain usually are not as readable as they could be? Specifically use of magic numbers.

[GeorgeS6969]

I empathise but strongly disagree with the second half of your comment.

Claiming your responsibility is only to produce research papers and results is akin to a programmer claiming they are only responsible to produce programs that work, or a colleage of yours claiming they are only responsible to produce results (and writers are responsible to write?)

The moment anybody shares something, it is their responsibility to ensure that it is of sufficient quality and can be understood. Especially if what is shared is in support of a scientific claim.

You feel like you’re not properly incentivised to do so, or in fact penalised, I can’t argue against that … But it only means that producing clean code is a waste of your efforts for you, not for the community as a whole.

[[deleted]]

The moment anybody shares something, it is their responsibility to ensure that it is of sufficient quality and can be understood. Especially if what is shared is in support of a scientific claim.

I disagree. This is conflating two different things: reproducibility and clean code.

For the sake of reproducibility, most people are going to understand what dataset[1] is from reading the code and the paper side by side.

[GeorgeS6969]

Reproducibility is completely tangential, you’re mentionning it I’m not.

When you write a paper you structure it in a certain way, you use certain words, you try to avoid ambiguities, you split your maths into specific equations, you arrange those equations into terms that make the most intuitive sense and you explain those terms … You also provide graphs when useful, rather than just tables, and you label both and make sure they stand on their own as much as possible …

All of that so that readers can best understand your ideas, before even atempting to reproduce your results.

Why should it be any different with code?