scikit-learn VS RegExr

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.

When thinking about how I might compare an arrangement to the contiguous group of damaged springs, I used regexr.com to experiment with very specific regexs that used the numbers.

There are plenty of online regex tools to test and experiment with regex patterns. Some popular ones include RegExr, RegEx101, and RegexPlanet.

Using regexr.com it at least appears to work as expected.

If you are going to use RE's, use something like https://regexr.com/ to double check that they're doing what you want. I was suspicious of your 'cols = re.findall(r'\d+ .....', i)' line, and indeed it does miss some columns. You should rethink your column detection, and either not use REs or learn how to use capture groups and \w. There would then be no reason to use yet another RE in your column iterator to extract the numbers which you've already detected.

First time posting here, let me know if I need to edit post to conform to any rules. My issue is that I'm trying to match regex pattern to separate out the number of cubes drawn and its color but my Matcher object seems to not be returning any matches so it's throwing a no match found exception when I try to call digitMatcher.group(). I have tested my regex pattern on sites like regexr and it seems to pass there but it's not working for some reason here. I use the same type of regex on day one and it work there so I'm not sure where my regex pattern is failing here. I'm talking about specifically in my isGameValid() method where I create a matcher base on a pattern I made above. Through debugging I know that I separated the string color pairing correctly and that my Matcher object has the correct regex pattern, it's just not matching for some reason. Any help would be appreciated. Code below:

Regexr has been an invaluable tool as a beginner.

RegExr simplifies working with regular expressions. This online tool provides a visual interface for building and testing regex patterns in real-time, making regex less intimidating.

Ho did you arrive at the regex? I usually use a website to , such as https://regex101.com/, https://regexr.com/, https://regex-generator.olafneumann.org/ in combination of each other, as some explain better than the other.

I pulled this into regexr.com and it yielded the same results except it removed :41: