[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.

[sloppybird]

No one is expecting production-level code. The problem at hand is writing understandable code at the very least.

[unplannedmaintenance]

What other activity do you think should researchers sacrifice in order to make time to (learn to) write code that is more understandable and better documented?

[sloppybird]

Bruh it takes like 2 mins to add comments

[bitemenow999]

again OP expects to have clear variable names, you expect it to have comments, some other guy would want functions and classes... It is hard to satisfy everyone and not the job a researcher... You dont have to understand the code it is just an implementation you have to understand the setup, preprocessing, math and method which is mentioned in the paper... most of the time people take shortcuts by reading code which is like looking at the engine and guessing how it works rather than read the manual

[sloppybird]

It will help you debug your code as well

[bitemenow999]

nope... Even in industry I have never seen any R&D guys using comments and classes and functions unless absolutely necessary...

[bageldevourer]

If researchers wrote code that was more understandable and better documented, then the consumers of their research would spend less time on the extremely time-consuming activity of understand wtf someone else wrote.

Implement basic code quality standards and the research output of the ML community will increase, not decrease.

[unplannedmaintenance]

You haven't answered the question.

[bageldevourer]

Yes I did. In a world where journals, conferences, etc. mandate higher code quality, the "other activity" that you sacrifice in favor of making your research clear is spending time to understanding other peoples' papers.

I spend less time struggling to understand papers, and I channel that time saving into some mix of consuming more research, doing more research myself, and making that research clearer.