onelinerhub
scikit-learn
onelinerhub | scikit-learn | |
---|---|---|
15 | 81 | |
788 | 58,130 | |
0.5% | 0.5% | |
10.0 | 9.9 | |
about 2 months ago | 7 days ago | |
PHP | 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.
onelinerhub
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Best Websites For Coders
Library or micro code solutions : Community library of micro code pieces for popular issues.
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Aligning text horizontally and vertically with PHP GD
We use (400 - $p[2])/2 to calculate x coordinate so our text is centered horizontally. Open original or edit on Github.
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10 Useful vim commands for coders
More useful Vim commands, add your commands on Github.
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Getting and setting headers in Node.js HTTP server
Open original or edit on Github.
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3 code pieces to work with file path in Node.js
Original version, improve this code.
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Help us collect modern Python NumPy code solutions
The suggested solution https://github.com/Onelinerhub/onelinerhub/blob/34467e427cc6... still runs out of memory with many files.
I believe the original intention behind using mode="a" was to append to the output file while reading the input files at the same time. This way, there is no need for an ever-growing string array.
But there are still many other issues like using default platform string encoding instead of detecting it properly or at least using utf-8, checking for ".txt" anywhere in the path instead of at the end, and not closing the input files with a context manager like the output file, which suggests that this code is just pierced together from various sources.
A robust solution would require many more lines.
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Hacker News top posts: Mar 28, 2022
Help us collect modern Python NumPy code solutions\ (23 comments)
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Many thanks to all of you – new UI onelinerhub.com
Thank you all for great support in helping our ad-free non-profit code library project.
We've launched new UI today (thanks to your support and sponsoring!), please help us test it and make sure everything works fine. Please leave feedback using Github issues: https://github.com/nonunicorn/onelinerhub/issues
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?
scripts - Collection of useful scripts for Linux (git, docker, LUKS, Archlinux...)
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
high-assurance-rust - A free book about developing secure and robust systems software.
Surprise - A Python scikit for building and analyzing recommender systems
fornjot - Early-stage b-rep CAD kernel, written in the Rust programming language.
Keras - Deep Learning for humans
flower - Flower: A Friendly Federated Learning Framework
tensorflow - An Open Source Machine Learning Framework for Everyone
slickstack - Lightning-fast WordPress on Nginx
gensim - Topic Modelling for Humans
pi-encrypted-boot-ssh - 🔑 Raspberry Pi Encrypted Boot with Remote SSH
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.