CodeTriage
scikit-learn
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CodeTriage | scikit-learn | |
---|---|---|
80 | 81 | |
1,377 | 58,046 | |
0.7% | 1.0% | |
7.4 | 9.9 | |
4 months ago | 6 days ago | |
Ruby | Python | |
MIT License | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
CodeTriage
- Ask HN: Anyone looking for contributors for their open source projects
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💼 50 Tips to Land a Remote Tech Job Based on My 45-Day Journey to 2 Offers
3. Open Source Contribution
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Docs Deserve More Respect
I wrote a book with a chapter on how to write docs for other people’s code https://howtoopensource.dev
I also wrote an open source tool for writing and testing tutorials https://github.com/zombocom/rundoc and another that will email you undocumented methods of open source code so you can practice writing documentation https://www.codetriage.com/.
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Where to Find Open Source Projects for Contribution?
CodeTriage helps you contribute to open source by “picking a handful of open issues and delivering them directly to your inbox”. (Source: CodeTriage)
- Ask HN: What’s the best way to start contributing to Open Source?
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Idea for project for intermediate c developper
Here are open source projects listed https://www.codetriage.com/ You can filter for "C".
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Cookpad to discontinue Ruby interpreter development - let's help Koichi and Mame land a new job or support them via GH sponsors
The biggest untaped potential (IMHO) is not one company funding 1 full time maintainer, but EVERY company allowing and encouraging EVERY developer to help and work with open source. This was the basis of my web app https://www.codetriage.com/. I have a chapter on it in my book How to Open Source (https://howtoopensource.dev/), and I talked to Yehuda about it for about an hour after my last talk at Philly ETE.
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What do i do to become hireable?
You can also use websites like up-for-grabs, goodfirstissue, or CodeTriage to find projects with open issues. Find one that looks easy or interesting to you and comment on it, asking if you can take a shot at it.
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Student looking to contribute to open source
I recommend these resources to help you contribute https://www.codetriage.com/ (free) and https://howtoopensource.dev/ (paid). DM if you can’t afford a copy.
- Are there any open source projects on Github that a person can get involved in if they want to start helping with coding projects? I was thinking if a person wanted to get some credit for coding something that actually got implemented in a project?
scikit-learn
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AutoCodeRover resolves 22% of real-world GitHub in SWE-bench lite
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).
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Polars
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.
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[D] Major bug in Scikit-Learn's implementation of F-1 score
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!
- Contraction Clustering (RASTER): A fast clustering algorithm
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Ask HN: Learning new coding patterns – how to start?
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?
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Transformers as Support Vector Machines
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...
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How to Build and Deploy a Machine Learning model using Docker
Scikit-learn Documentation
- Planning to get a laptop for ML/DL, is this good enough at the price point or are there better options at/below this price point?
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Link Prediction With node2vec in Physics Collaboration Network
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.
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WiFilter is a RaspAP install extended with a squidGuard proxy to filter adult content. Great solution for a family, schools and/or public access point
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.
What are some alternatives?
first-contributions - 🚀✨ Help beginners to contribute to open source projects
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
Cataclysm-DDA - Cataclysm - Dark Days Ahead. A turn-based survival game set in a post-apocalyptic world.
Surprise - A Python scikit for building and analyzing recommender systems
awesome-for-beginners - A list of awesome beginners-friendly projects.
Keras - Deep Learning for humans
htop - htop - an interactive process viewer
tensorflow - An Open Source Machine Learning Framework for Everyone
good-first-issue - Make your first open-source contribution.
gensim - Topic Modelling for Humans
Open-Source-Ruby-and-Rails-Apps - Awesome Ruby and Rails Open Source applications 🌈
H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.