moonscript VS scikit-learn

Now I like lua, and think single pass is the way to go for interpreted, since you don't have the disadvantage of a slow compile time no matter how big your codebase gets, BUT its not great to write in. things like +=, ++, are not possible, which means the only solution is to transpile into it, which has led to some good languages like moonscript[0], teal[1] which offers static type checking, an absolute must as your codebase grows.

[0]: https://moonscript.org/

That can be very productive and clever, but be - and stay - aware that such polyglot solutions tend to be maintenance headaches in the longer run.

There is a really nice open source project out there that allows you to train your hearing and your sightreading, but it's written in the authors own language which in turn compiles to JavaScript and the headache to set up their toolchain is such that I haven't bothered fixing any of the bugs that I'm aware of (and there are plenty).

https://sightreading.training/

https://github.com/leafo/sightreading.training

It's written in a language called 'Moonscript':

https://github.com/leafo/moonscript

Which compiles to Lua. Which compiles to JS.

Madness. Nice madness, but still, it stopped me from being a contributor.

RE: the cost of switching at this point, what about languages that compile to Lua? Like https://moonscript.org/. That would let you keep the legacy code, no?

In the case of Lapis, it is actually written in Moonscript, which needs a few more things.

There's also some languages made to compile straight to Lua: - MoonScript is the most popular Lua wrapper - it's built to be more Python-like, featuring indentation-based scopes, function calls without parentheses, lambda syntax, list comprehension, and much more. - Yuescript is a modern update to MoonScript that adds more features (I haven't used it myself, so I'm not entirely sure exactly how it differs from MS). - Teal is a version of Lua that adds static typing for better code standards.

A programmer-friendly language that compiles to Lua.

This is a sample code for learning, written in Moonscript for TIC-80:

Thank you for your interest. There are some interesting examples in the SWE-bench-lite benchmark which are resolved by AutoCodeRover:

- From sympy: https://github.com/sympy/sympy/issues/13643. AutoCodeRover's patch for it: https://github.com/nus-apr/auto-code-rover/blob/main/results...

- Another one from scikit-learn: https://github.com/scikit-learn/scikit-learn/issues/13070. AutoCodeRover's patch (https://github.com/nus-apr/auto-code-rover/blob/main/results...) modified a few lines below (compared to the developer patch) and wrote a different comment.

There are more examples in the results directory (https://github.com/nus-apr/auto-code-rover/tree/main/results).

sklearn is adding support through the dataframe interchange protocol (https://github.com/scikit-learn/scikit-learn/issues/25896). scipy, as far as I know, doesn't explicitly support dataframes (it just happens to work when you wrap a Series in `np.array` or `np.asarray`). I don't know about PyTorch but in general you can convert to numpy.

Wow, from the upvotes on this comment, it really seems like a lot of people think that this is the correct behavior! I have to say I disagree, but if that's what you think, don't just sit there upvoting comments on Reddit; instead go to this PR and tell the Scikit-Learn maintainers not to "fix" this "bug", which they are currently planning to do!

I was in a similar boat to yours - Worked in data science and since then have made a move to data engineering and software engineering for ML services.

I would recommend you look into the Design Patterns book by the Gang of Four. I found it particularly helpful to make extensible code that doesn't break specially with abstract classes, builders and factories. I would also recommend looking into the book The Object Oriented Thought Process to understand why traditional OOP is build the way it is.

You can also look into the source code of popular data science libraries such as sklearn (https://github.com/scikit-learn/scikit-learn/tree/main/sklea...) and see how a lot of them have Base classes to define shared functionality between object of the same nature.

As others mentioned, I would also encourage you to try and implement design patterns in your everyday work - maybe you can make a Factory to load models or preprocessors that follow the same Abstract class?

It looks like you've been the victim of some misinformation. As Dr_Birdbrain said, an SVM is a convex problem with unique global optimum. sklearn.SVC relies on libsvm which initializes the weights to 0 [0]. The random state is only used to shuffle the data to make probability estimates with Platt scaling [1]. Of the random_state parameter, the sklearn documentation for SVC [2] says

Controls the pseudo random number generation for shuffling the data for probability estimates. Ignored when probability is False. Pass an int for reproducible output across multiple function calls. See Glossary.

[0] https://github.com/scikit-learn/scikit-learn/blob/2a2772a87b...

[1] https://en.wikipedia.org/wiki/Platt_scaling

[2] https://scikit-learn.org/stable/modules/generated/sklearn.sv...

Scikit-learn Documentation

Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy.

The ML component is based on scikit-learn which differentiates it from purely list-based filters. It couples this with a full-featured wireless router (RaspAP) in a single device, so it fulfills the needs of a use case not entirely addressed by Pi-hole.