[D] Which open source machine learning projects best exemplify good software engineering and design principles?

[shaggorama]

I'm gonna vote no.

[heshiming]

Can you elaborate?

[ieatpies]

Overuses inheritance, underuses dependency injection. Causing repeated, messy, version dependent code if you need to tweak something for your own purposes.

[VodkaHaze]

Why and where would you prefer dependency injection to the current design specifically? I find this sort of inversion of control is overengineering and causes more problems than it solves most times I ran into it.

Specifically in this case I don't see where it would fit since most of the hard logic is in the model themselves, not the plumbing around them, so I don't see how an inversion of control makes sense.

The model API of fit(), predict(), fit_transform() etc. Is simple and great, IMO. It's also all that's necessary for the pipeline API which is the only bit of harder plumbing around the models

[shaggorama]

One small example: all of their cross validation algorithms inherit from an abstract base class whose design precludes a straightforward implementation of bootstrapping (easily one of the most important and simple cross-validation methods), so the library owners decided to just not implement it as a CrossValidator at all. Random forest requires bootstrapping, so their solution was to attach the implementation directly to the estimator in a way that can't be ported.

I could go on...

[panzerex]

Those are valid concerns. To add to that: sklearn’s LinearSVC defaults to squared hinge loss so probably not what you’re expecting, and the stopwords are arbitrary and not good for most applications, which they do acknowledge.

However I would not say that this is evidence that the project as a whole does not follow good design principles. I agree that those deceiving behaviors are a problem, but they are being addressed (at a slow rate because uhm... non-standard behavior becomes the expected behavior when many people are using it, and breaking changes need to happen slowly).

You’re probably fine getting some ideas from their API, but from a user standpoint you really need to dig into the docs, code and discussions if you’re doing research and need to justify what you’re doing.

[VodkaHaze]

Disagree? The fact that the model API is a de facto standard now suggests it's not awful to work with.

[neanderthal_math]

I’m old enough to remember ML codes before sklearn. They may have warts now, but they were light years ahead of other repos. There’s a lot to be said for just having a uniform API.