mage VS scikit-learn

You can find the MAGE repository on this link.

We will import these 118521 edges, and act as if they are undirected. The Node2Vec algorithm in MAGE accepts parameters whether to treat graph from Memgraph as directed or undirected.

Memgraph introduces the concept of query modules, collections of user-written Cypher procedures intended to extend Cypher by implementing complex graph algorithms. Various query modules are already implemented and ready to use in an open-source project MAGE.

However, there is one more thing that we should really think about when syncing Memgraph and Elasticsearch and that is expandability. The challenge is to build a solution that will need the minimum amount of change when a new method is required. That is why we decided to use Memgraph’s Pythonic capabilities and create a new query module that uses Elasticsearch’s API inside Memgraph’s graph library MAGE.

Memgraph Advanced Graph Extensions, or for short, MAGE, is an open-source graph library that contains implementations of standard and incremental graph algorithms. You can use any of the algorithms as well as implement your own.

MAGE is an open-source repository that contains many Memgraph modules in various programming languages. The overall idea is to extend Memgraph with capabilities that are not part of the core engine. One of the critical design decisions early on was the compatibility with various programming languages to support different application stacks and developers with varying skill sets. Up until now, only C/C++ and Python were first-class citizens of the Memgraph ecosystem, but Rust is quickly running through the front doors!

Memgraph is a graph database in C++ but heavy on Rust as well. The client -> https://github.com/memgraph/rsmgclient (still early, please report any issues). It's possible to write query modules for Memgraph in Rust -> https://github.com/memgraph/mage/tree/main/rust (the "rusty" wrapper).

If you want to do more with your graph data, visit the Memgraph MAGE graph library (and throw us a star ⭐) and take a look at all of the graph algorithms that have been implemented. You can also implement and submit your own algorithms and utility procedures.

Finally, if you are working on your query module and would like to share it with other developers, take a look at the contributing guidelines. We would be more than happy to provide feedback and add the module to the MAGE repository.

We at Memgraph recognize your challenges. In this article, you will learn about the merits of online community detection methods and get acquainted with the LabelRankT algorithm by Xie et al., now available in MAGE 1.1.

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.