Add distribution parameters as named positional arguments

[thomasaarholt]

Description of your problem

PyMC sideliner here, who is finally taking a proper crack at it with 4.0. I find the distribution docstrings difficult to parse when viewing them in my editor. As an example, the Bernoulli distribution in jupyter notebook (I imagine a lot of people are using notebooks):

import pymc as pm
pm.Bernoulli()

And with VSCode (I am using this):

Two points:

1. My main gripe is with the arguments being shown as pm.Bernoulli(name, *args, **kwargs)/(name: Unknown, *args: Unknown, **kwargs: Unknown) -> TensorVariable. It would be much nicer to actually show the parameters taken by the distribution (in this case, p).
2. The second point is a bit more complicated: VSCode tries to show the docstring of the __init__ method rather than the class docstring*. Since Bernoulli doesn't have an explicit __init__ method, it inherits from the first one it finds, which seems to be the __new__ method of the Distribution class. Note that if you position the cursor (|) as Bernoulli|() when invoking the documentation, you get the Bernoulli class docs and not the parent __new__ doc. It would be nice to supply a simple docstring to the __init__ method in order to override the confusing parent Distribution docstring.

I am quite happy to take a crack at this, though I do recognize that there are several other issues concerned with similar things #5308, #5353, #5358)

• class docstring vs init docstring:

class A:
    "class docstring"
    def __init__(self, a: int, b: str = "hi"):
        "init docstring"
        print(a, b)

Versions and main components

• PyMC/PyMC3 Version: 4.0
• Python Version: 3.9

[michaelosthege]

Yeah, this is indeed very annoying.

By deleting the Distribution.__new__ docstring, VS Code shows the right docstring, but still with the uninformative signature.

Maybe one can monkeypatch autogenerated __init__ methods onto all distributions that don't have them?
The .dist() classmethods are the ones that count and __init__ is just being forwarded internally.
Creating __init__s for all distributions with basically the same signature as the .dist() classmethods would be a lot of redundancy =/

[ricardoV94]

We should be using functions instead of classes

[thomasaarholt]

@michaelosthege I see you're the author of the distribution.__new__ aspect. I just went digging a bit into the code, and saw issues like #5308. Did you need to use __new__ due to how pymc checks if it is in a with model: context? Or were there other reasons? (I've read up on what __new__ does, but it's unclear to me why it was needed here)

I still think init methods for the parameters is a good idea, despite the redundancy, at least until someone has a better idea. As it currently stands, it does raise the barrier for new users.

@ricardoV94 I see your bullet list in #5308 - how are you proposing using functions instead of classes?

[ricardoV94]

@ricardoV94 I see your bullet list in #5308 - how are you proposing using functions instead of classes?

If you follow the call stack you'll notice that we never do anything with the classes other than accessing the static property cls.rv_op and the (easily static) classmethod cls.dist. That could easily be done in a function.

[michaelosthege]

I think @ricardoV94 is right that we should refactor distributions to be functions.

This is, however, a major refactoring and needs to be done right. The logic from pm.Distribution doesn't go away and will have to be refactored into some other structure (maybe a decorator?) to make sure it still applies to all distribution implementations.

Also the more complicated distributions like GPs, timeseries, ODEs will have to be considered.
It's probably a good idea to refactor many auxiliary things into functions.

Just to thinking out loud how we could get there:

• Refactoring logic from pm.Distribution into functions
• Implementing a def Normal() function-based alternative to figure out how it should look like / what needs to be done
• Transition period where both function- and class-based distributions exist while they're being migrated one by one?

One of the bigger questions I have is how to deal with pm.Normal(name, mu, sigma) vs pm.Normal.dist(mu, sigma).

• Requiring all parameters to be explicit kwargs?
• Requiring nullable name args every time?
• pm.Normal vs. pm.normal to mimick the underlying Aesara RVs while still providing the alternative parametrizations?

---

We should continue this discussion elsewehere (a Discussion?), by the way. It's beyond the scope of this issue.

[ricardoV94]

Agree with this being out of context, but regarding:

One of the bigger questions I have is how to deal with pm.Normal(name, mu, sigma) vs pm.Normal.dist(mu, sigma).

I also thought about pm.normal, but I think it's not distinctive enough. Perhaps pm.normal_dist or pm.Normal_dist could do it.

[canyon289]

Sidenote: I independently ran into this same issue, and agree that a whole refactoring is a major challenge.

Perhaps if there is a way to alleviate this issue without refactoring in the meanwhile we can consider that here