I never said macros were required. I said implementing this type of code without OOP required more boilerplate, and MLJ uses macros to reduce that boilerplate.
As I understand module imports in Julia: Each module developer exports a list of publicly facing objects. Obviously "fit" and "model" would be among them. If you import two modules that both export a new "nn" subtype of shared parent type "model", and both extend "fit" and "predict" and etc to accept their own subtype "nn", then you have to manually specify which module you are referring to every time you call nn, or fit, or predict, or whatever. Is that wrong? If I just import PyTorch, import TensorFlow, and then call "mymodel = TensorFlow.nn; fit(mymodel, mydata)" then Julia doesn't know that the "fit" I am calling is the TensorFlow implementation and not the PyTorch implementation; what if I had WANTED to use module A's "fit" on module B's model, and they intentionally adopted the same abstract type system to enable this interoperability? So instead I have to write "mymodel = TensorFlow.nn; TensorFlow.fit(mymodel, mydata); TensorFlow.predict(mymodel, mynewdata)". Obviously the extra typing is mildly annoying but the bigger problem is potentially introducing bugs by mismatching modules, and the developer's cognitive overload of having to keep track of modules. Python style OOP is a more elegant solution to the namespace problem and results in more readable, maintainble code, at least in my opinion. Anyways, maybe Julia has a more elegant solution I'm not aware of, if so I'd love to hear it.
In other words, one package would define `fit(mymodel::TensorFlowModel)` and the other would define `fit(mymodel:PyTorchModel)`, and then when you call `fit` it'll just dispatch to the appropriate one depending on the type of `mymodel`.
This dispatch-oriented style also allows a shocking degree of composability, e.g. [1], where a lot of packages will just work together, such that you could for example just use the equivalent of PyTorch or TensorFlow on the equivalent of (e.g.) NumPy arrays without having to convert anything.
If you mean "what about the case where both packages just call their model type `Model`", while I've never run into that, the worst case scenario is just that you have to fall back to the Python style explicit Module.function usage (which was always allowed anyways...). And if you if you don't like names being exported, you can always just `import` a package instead of `using` it:
help?> import
search: import
import
import Foo will load the module or package Foo. Names from the imported Foo module can
be accessed with dot syntax (e.g. Foo.foo to access the name foo). See the manual
section about modules for details.
I am aware of the ability to do eg "import TensorFlow; model = TensorFlow.model; TensorFlow.fit(model,data)"
As I mentioned previously, I find Python's OOP "model.fit" syntax to be better, for a variety of reasons.
Thank you for your engagement. Have a nice day.
m1 = TensorFlow.model()
fit(m1, data)
m2 = Pytorch.model()
fit(m2, data)
YensorFlow.fit and Pytorch.fit are just different methods of the same function.
You've formed some strong opinions based on a pretty big misunderstanding.
<edit>I'm also nervous of relying on the recourse of asking package maintainers to edit their variable names to improve compatibility with the random third package I want to use; maybe the culture is different in Julia but in Python that's a good way to get laughed out of the issue tracker :) </edit>
If I were you I would maybe ask people like @systemvoltage who took the plunge and wrote a big project in Julia only to find they had trouble maintaining the project. Maybe one reason he can't upgrade without breaking everything is because of namespace collisions amongst his many dependencies? If not that, it's something like that.
I know in Julia I can just precede every function call and object instantiation with "modulename." and solve the namespace problem that way. What I want to do instead, what I do in python, is bind one namespace of function methods to each object, so that as I code, I don't have to remember which module each each object came from. That is the appeal to me of "model.fit" over "module.fit(model)".
EDIT: This is not some "shave a few seconds off coding time" quality of life issue. This is a mission critical requirements in many enterprise contexts.
Scenario A: Model Development Team trains a model, serializes it, and sends the serialized model object to Production. You want Production to have to lookup which software Package the Model Development Team used for each model, just so they know which "predict" function to call? No, "predict" should just be packaged with the model object as a method, along with "retrain", "diagnose", etc.
Scenario B: I want to fit a dozen different types of models, from multiple packages, on the same data, to evaluate how they each do, and build build some sort of ensemble meta model. So I need to loop over each model object in my complicated ensembling logic. You want me to also programmatically keep track of which module each model comes from?
These are important, happens-every-day use cases in the enterprise.
I think the best solution for this in Julia is to package the model state with all the "method" functions in one struct. Again, this is what MLJ does. This is the closest Julia gets to OOP. But then you either need a lot of boilerplate code, or a Macro to unpack it all for you behind the scenes.
The bigger issue for production in my experience is about packaging the right model with the right version. I don't think anyone has to do `module1.fit` everywhere, since `fit` would've likely come from the same source.
That solves both of your scenarios
No.
As described in the great-great-great-great-great-great-great-great-great-parent comment, the problem I am describing is that of trying to combine models from multiple software authors. You may not have that problem. It may not be a common problem among Julia's academic user base. I do have that problem.
Thanks for reading.
Can I trouble you to make a post either on discourse or on the slack? I'd really like this to get in front of the broader julia community and core devs, and you're the best person to do that. Maybe there's a solution of which I'm unaware....or there could be some design discussions to come out of this.
https://julialang.org/community/ (slack and discourse link)
To be 100% honest with you, there's pretty much 0% chance of me adopting Julia in the next 12 months. I evaluated it before embarking on my current project, but ended up going with Python, and now I have several thousand lines of Python code that work fine, and I'm not going to rewrite it all in the near future. At some point in the medium term, I'll re-evaluate Julia. But until then, I don't want to lead anyone on any wild goose chase. Even if you solved this problem, and all my problems, I'm just not in a position to switch right now. So for that reason I think I'll hold off on issuing a community wide call for help. But I'm cautiously optimistic that at some point in the future I'll be writing Julia professionally.
A lot of this is also probably cultural rather than language features. The first thing they teach any Python data science boot camp initiate is: never "from numpy import *", always "import numpy as np" and yet in Julia "using" appears more common than "import"...
I also wouldn't read too much into my one example. It was initially meant just as an illustrative point, but somehow I was so bad at explaining it that it took tons of comments for me to get my minor point across. I do think the MLJ guys are properly on the right track, and that should work fine for most people who don't mind Macros. Maybe I'm in the minority in hating Macros.
The more commonly cited issues around compile time caching, tooling, etc. are boring to list off yet again, but probably the right areas of focus for the community, in terms of both impact and ease of implementation.
More generally, I really do think you're better off talking to people like @systemvoltage, who have actually given Julia more of a chance than I have. If I worked for Julia Computing I'd be reaching out to him and begging to get his brain dump on all the challenges he faced. In any business, it's always easier to reduce churn and learn from your (unhappy) customers, then it is to convert new prospects, whether that business is programming languages or investment banking.
Best of luck. Sincerely.
I'm a bit confused still though why you say it's a "missing" feature, given that as we discussed above, there is absolutely nothing to stop anyone who wants to use the "Python OOP" style of namespacing in Julia from doing so? Most of us don't seem to find it necessary or to prefer it personally, but that doesn't restrict anyone else from choosing it.
If (and only if, I have not looked at our hypothetical model and fit GF, but for the sake of argument, I will assume that it does) "fit" specialises on "model" will a "mode = AnOtherModel" cause "fit(model, x, y)" to be exquivalet to Python's "model.fit(x, y)". If you need to provide a custom "fit" method, you do so by providing a method specialised on AnOtherModel to the "fit" GF.
At no point is there a macro involved.
As for the "module a, model nn" and "module b, model nn", I would naively assume that they actually are different models, and therefore something specialising on "a.nn" will not get dispatched to when you pass a "b.nn".
Disclaimer: I don't actually know Julia, at all. But I have written substantial amounts of CLOS code (and Python, but I like CL and CLOS better).