A sci-kit image experiment #skimage #scikit Code at: https://github.com/villares/sketch-a-day/tree/main/2024/sketch_2024_12_06
More sketch-a-day: https://abav.lugaralgum.com/sketch-a-day
I really need your support to keep going, if you can, donate any amount at: https://www.paypal.com/donate/?hosted_button_id=5B4MZ78C9J724 #Processing #Python #py5 #CreativeCoding

NumPy, SciPy, Scikit and Cython for Numeric Computing

Maghreb, Cairo
https://www.meetup.com/softwarehubcairo/events/302346506/

Turk, Ankara
https://www.meetup.com/softwarehub/events/302346460/

Mashreq, Riyadh
https://www.meetup.com/softwarehubriyadh/events/302346561/

RSVP is required.

All members of the Ummah are invited !

#Python
#Programming
#Algorithms
#Numeric
#numpy
#scipy
#scikit
#cython
#matplotlib
#Ummah
#Qatar
#Doha
#Linux
#Palestine
#Cairo
#Ankara
#Riyadh
#Mashreq
#Maghreb
#SoftwareFreedom
#GNU

Notes on linear regression using scikit-learn and chapter 15 of Numerical Recipes https://felixquinihildebet.wordpress.com/2024/07/16/notes-on-linear-regression-using-scikit-learn-and-chapter-15-of-numerical-recipes/ #Fortran, #LinearRegression, #Scikit-learn

Didn't do it. The 3.11 runtime helped. But Python's latency with #scikit-learn is still at-least twice as high on #FreeBSD as it is on #Debian. The reasons for this are not immediately clear to me. This doesn't bode well for my argument that BSD is a good alternative to Linux for data science workflows. 😅. I guess because I've always used Bhyve + Docker + Debian for DS, I never noticed the speed issues before. I'll send a note to the FreeBSD Python mailing list. Maybe they'll know.

📝🐍 Шпаргалка з #Python з акцентом на #DataScience

Зокрема, наводяться приклади використання #JupyterNotebook, #NumPy, #SciPy, #Pandas, #Scikit, #Matplotlib, #Seaborn, #Bokeh.

The Python community is huge and diverse, in some aspects. I always like to point how professional software development is just a fraction of the huge number of ways that software is created. I remember seeing threads here o Mastodon of #Python developers who didn't understand why people used #conda (from Anaconda inc.) package management... Well, it is 2024 and there are parts of #scikit, scikit-geometry, you can't install with #pip :(( ... I wish I could have it on pip, to be honest, but large parts of the scientific community will use conda, and that's it.

It's a miracle! I've managed to make #SciKit Image tests work offline in #Gentoo!

That said:

1. Upstream includes most of the test data in sdist. Except they don't use it, and instead fetch it all from the GitHub repository. Sigh. (If you're upstream, please pretend you didn't see this and do not remove the data from sdist. Thanks.)
2. On top of that, they fetch more test data from an additional GitLab repository, and move and rename it around.
3. Yes, you've read right. They keep code on GitHub, but test data on GitLab.com (not self-hosted).

https://gitweb.gentoo.org/repo/gentoo.git/commit/?id=0110ec020dbdb46c859e584609ab056fcafa10c6

#Python

Wydarzył się cud! Udało mi się doprowadzić testy #SciKit Image do działania bez Internetu w #Gentoo!

Na marginesie:

1. Autorzy włączają większość niezbędnych danych w archiwum sdist. Tyle że ich nie używają, i zamiast tego ściągają je drugi raz, prosto z repozytorium na GitHubie. Wzdych. (Jeżeli czyta to któryś z autorów, proszę o udawanie, żeście tego nie widzieili i nie usuwanie tych plików z archiwum sdist. Dzięki.)
2. Na dodatek, pobierają dodatkowe dane dla testów z dodatkowego repozytorium na GitLabie, i przenoszą i zmieniają nazwy plików.
3. Tak, dobrze widzicie. Kod jest na GitHubie, dane dla testów na GitLab.com (nie własnej instancji).

https://gitweb.gentoo.org/repo/gentoo.git/commit/?id=0110ec020dbdb46c859e584609ab056fcafa10c6

#Python

and, if needed, modify it.

there are many python libraries out there:

#OpenCV
#SciPy
#imageio
#scikit-image (skimage)
#matplotlib
#Wand
#Pillow (PIL Fork)

#SciKit-Build copied the broken build directory naming logic from #setuptools. As a result, it uses a "3.10" directory both for CPython 3.10 and for #PyPy 3.10. Whereas with setuptools this has minor consequences and can be trivially worked around (via overriding build directories), this isn't the case for SciKit-Build. Here it means that the subsequent build for CPython 3.10 reuses the cache from PyPy 3.10 and installs PyPy 3.10 modules. Yes, seriously, completely wrong, useless files.

The best part? They know about it, and they just don't care: "I don't think we should do better than setuptools". Sure, great. Except that we can trivially globally override build directory in setuptools, and SciKit-Build just forces "_skbuild" on us.

https://github.com/scikit-build/scikit-build/issues/633#issuecomment-1021846731

#Gentoo #Python #PEP517

#SciKit-Build skopiowało zepsutą logikę nazywania katalogu budowy z #setuptools. Tak więc używa katalogu "3.10" zarówno dla CPythona 3.10, jak i dla #PyPy 3.10. Podczas gdy w setuptools ma to pomniejsze konsekwencje i można to łatwo obejść (zmieniając katalog budowy), ze SciKit-Build nie ma tak łatwo. Budowa dla CPythona 3.10 używa cache'a z PyPy 3.10 i instaluje moduły z PyPy 3.10. Tak, serio, kompletnie niewłaściwe, bezużyteczne pliki.

A najlepsze w tym wszystkim? Autorzy o tym doskonale wiedzą, i mają to w dupie: "Nie sądzę, że powinniśmy zrobić to lepiej niż setuptools" (tłum. własne). No super, po prostu zajebiście. Tyle że w setuptools można trywalnie zmienić katalog budowania, a SciKit-Build wymusza "_skbuild".

https://github.com/scikit-build/scikit-build/issues/633#issuecomment-1021846731

#Gentoo #Python #PEP517

https://kandi.openweaver.com/collections/python/how-to-use-skimage.exposure-function

In scikit-image, you use other Python libraries to load and manipulate image data. You can use the functions provided by skimage for image analysis.

#scikit #scikit-image #python #kandi #openweaver

Can you name that algorithm based on this dataflow representation?

It's Linear Discriminant Analysis as implemented by Scikit Learn!

I finished up a notebook showing how you can build an Array API compatible library with the egglog e-graph library in Python and use that to optimize a #scikit-learn algorithm with #numba

https://egg-smol-python.readthedocs.io/en/stable/tutorials/sklearn.html

For more context, I gave a talk on the broader goals this summer:

https://egg-smol-python.readthedocs.io/en/stable/explanation/2023_07_presentation.html

https://www.youtube.com/watch?v=Pbi2uV9vWPg

#pydata #egraph #python

A Minimal Example of Machine Learning (with scikit-learn)

https://levelup.gitconnected.com/a-minimal-example-of-machine-learning-with-scikit-learn-4e98d5dcc6e7

#machinelearning
#ArtificialIntelligence
#deeplearning
#sklearn
#scikitlearn
#scikit
#python
#coding
#learning
#intelligence
#machine
#learn
#prediction
#predictions
#weatherprediction
#weather

Learn more on our github: https://github.com/MouseLand/rastermap. #Rastermap is fast thanks to #python #numpy, #scipy, #numba, and #scikit-learn. The GUI is powered by #pyqt and #pyqtgraph, and supports npz, npy, mat and #nwb ophys files.

⏱️ Say goodbye to hours spent troubleshooting.

🎯 It integrates with popular Python ML frameworks such as #PyTorch, #TensorFlow, #HuggingFace, #Scikit-Learn, #XGBoost, and #LangChain.

[3/4]

Inne wiadomości ze świata #Python: nowy backend #pep517 #scikit-build-core okazuje się psuć #setuptools na jeszcze jeden sposób. Tym razem błąd zauważono przy wykorzystaniu #pdm-backend.

A, fail2ban też psuje. Może to ten sam problem.

scikit-build-core: https://github.com/scikit-build/scikit-build-core/issues/426
fail2ban: https://bugs.gentoo.org/909535
poprzedni bug (psuł rozszerzenia #RustLang): https://github.com/scikit-build/scikit-build-core/issues/413

In other #Python news, the new #scikit-build-core #pep517 backend turns out to break building extensions via #setuptools in yet another way, this time noticed via #pdm-backend.

Oh, and there's also this bug where it breaks fail2ban too. Perhaps it's the same issue.

scikit-build-core: https://github.com/scikit-build/scikit-build-core/issues/426
fail2ban: https://bugs.gentoo.org/909535
previous bug (breaking #RustLang extensions): https://github.com/scikit-build/scikit-build-core/issues/413

🌘 GitHub - Dobiasd/treebomination: 將 scikit-learn 決策樹轉換為 Keras 模型
➤ 將 scikit-learn 決策樹轉換為 Keras 模型
✤ https://github.com/Dobiasd/treebomination
這篇文章介紹了一個名為 Treebomination 的 Python 庫，可以將 scikit-learn 決策樹轉換為 Keras 模型。這個庫可以用於將決策樹轉換為神經網絡，並且可以進一步微調以提高準確性。作者還提到，這個庫不適用於對預測性能、精度和應用程序大小有要求的情況。
+ 這個庫看起來很有趣，但我不確定它是否適用於我的項目。
+ 我很喜歡這個庫，因為它可以將決策樹轉換為神經網絡，這樣我就可以使用 Keras 進行進一步的微調。
#機器學習 #Python #Keras #scikit-learn #決策樹

🌘 PostgresML籌集470萬美元，推出基於Postgres的無伺服器AI應用數據庫
➤ PostgresML的擴展將AI任務帶入數據庫引擎，減少應用程序開發人員的複雜性，並產生一系列性能、成本和質量優勢。
https://postgresml.org/blog/postgresml-raises-4.7M-to-launch-serverless-ai-application-databases-based-on-
#PostgresML #AI #數據庫 #無伺服器 #應用程序 #擴展 #開源 #MIT許可證 #Rust #機器學習 #GPU #PgCat #pgvector #HuggingFace #Torch #Tensorflow #XGBoost #LightGBM #Scikit Learn #Instacart #OneSignal #Amplify Partners #Max Mullen #Brandon Leonardo #Jack Altman #Rafael Corrales #Greg Rosen #Jeremy Stanley #James Yu