Proposal for New Supervised Learning Data Simulation Classes in C++ for MLPACK Library about mlpack

[rcurtin]

Nice, I think this could make for more compelling examples than "generate random uniform data"! 👍

It's worth pointing out that mlpack already has a number of distribution-like classes: GaussianDistribution, GammaDistribution, LaplaceDistribution, DiscreteDistribution, and so forth. (See src/mlpack/core/dists/.) Now, it would be cool to generate data directly from one of these distribution classes, but there are some issues: those distribution classes are typically aimed at (1) generating random samples via Random(), and (2) evaluating probabilities via Probability(), but that second function is totally irrelevant here---we just want to generate datasets. Even the signature of (1) is not quite right, as for existing distributions it just generates a single point.

So, certainly some additional infrastructure is necessary to generate labeled synthetic datasets, but I do think that whatever we write should be "aware" of the distribution code and make use of it when possible in the implementation (and add new distributions as needed).

A minor pedantic thought is that after #3269, pretty much everything in mlpack is directly in the mlpack:: namespace for convenience (with the exception of a couple things in util:: and a couple things in data::). So, I'd personally prefer to avoid a simulate:: namespace.

At least personally I wouldn't worry about Open Question (2) too much; I think if we provide something relatively barebones at first, it will get immediately used in the documentation, and that's probably good enough for now.

[mlpack-bot]

This issue has been automatically marked as stale because it has not had any recent activity. It will be closed in 7 days if no further activity occurs. Thank you for your contributions! 👍

[coatless]

Sounds like an expansion for distributions is in order to handle multi-point generation. With respect to random() is this using a poorly spec'd PRNG?

On the note of namespaces, maybe this should go under util:: or where the train/test split is found?

[rcurtin]

Sounds like an expansion for distributions is in order to handle multi-point generation.

Possibly, it would be great to keep things unified, but if it doesn't make sense (or if the amount of work for adapting older distributions is not feasible), in my view it's okay to keep them different.

With respect to random() is this using a poorly spec'd PRNG?

It uses std::mt19937, not sure if that qualifies as "poor" (I am not an RNG expert).

On the note of namespaces, maybe this should go under util:: or where the train/test split is found?

I really think a flat namespace is fine, since there aren't really going to be any naming conflicts, but Split() is in the data:: namespace (as is Load() and Save()), and I suppose we could use that too. util:: is primarily for internal mlpack tooling, but this would be user-facing.

[arthiondaena]

@rcurtin I am trying to find a good beginner's issue, do you think this feature request can be implemented by a beginner to learn about mlpack.