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GaelVaroquaux, to random

Software systems, more than any other engineering activity, create a technological world that results from social dynamics and constructs.
This is because the space of possibilities is much wider, and there are many more objects interacting than in other industrial endeavors.

Big thinkers of urban planning, designing spaces and cities accounting for interactions connected their thinking with sociology and related.

People thinking software at the ecosystem level probably should do the same.

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olberger,
@olberger@mastodon.social avatar

@GaelVaroquaux
True.

What's the context behind your toot, if I may?

GaelVaroquaux, to random

Avec la #LoiImmigration, le gouvernement manie la xénophobie, et veut inscrire la discrimination dans la loi.

C'est le programme de l'extrême droite, un programme de division et non de construction, un programme qui met notre démocratie sur une pente dangereuse.

GaelVaroquaux, to random

Une interview sur scikit-learn : la vision du projet, comment penser à l'impact, au lien avec la société, à la dynamique open-source... 45mn où je parle de ce qui nous motive, de ce que nous avons appris sur les données et l'humain...

https://www.youtube.com/watch?v=I5RoWUyJgT8

Ce fut un grand plaisir, merci beaucoup à l'équipe, hymaïa dont Yoann Benoit.
Je me rends compte que j'ai une meilleure énonciation en français 🙂

GaelVaroquaux,

Mais ce soir, si vous êtes francophone, il y a plus important: le naufrage inquiétant de notre démocratie qui se doit de réunir et de construire:
https://mastodon.social/@GaelVaroquaux/111609337085524577

GaelVaroquaux, to random

Join us: this is open source, and the power of such a project is the ability to build in common.

Let's create together a much-needed tool for data science
https://github.com/skrub-data/skrub/

GaelVaroquaux, to random

Skrub is very young, and there is a lot more that needs done.

For instance, we want to support multiple dataframe backends and lazy modes.

Our dream is to streamline developing and put in production machine-learning by coupling the scikit_learn API to database operations.
7/8

GaelVaroquaux, to random

🎉First release of skrub 0.1.0 http://skrub-data.org

Couple dataframes and databases to machine learning to facilitate data prep

✨Less data wrangling, more machine learning✨

This is a young project that I am very excited about:
🧵👇
1/8

GaelVaroquaux,

Likewise, skrub.to_datetime (https://skrub-data.org/stable/generated/skrub.to_datetime.html) takes a complete dataframe, tries to detect which columns are dates or time and converts them to the Datetime type:
5/8

GaelVaroquaux,

Each functionality comes as a scikit_learn
transformer:
Joiner (https://skrub-data.org/stable/generated/skrub.Joiner.html), DateTimeEncoder (https://skrub-data.org/stable/generated/skrub.DatetimeEncoder.html)

Separate "fit" and "transform" avoid prediction-time problems.
They enable hyper-parameter tuning (eg adding a "day of the week column")
6/8

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GaelVaroquaux, to random

🎉 Tool for better documentation!! Release of sphinx-gallery, to automatically integrate narrative 🐍 examples in documentations
https://sphinx-gallery.github.io/stable/index.html

Highlight: a light recommender system to show related examples

An illustration of sphinx-gallery:
https://scikit-learn.org/dev/auto_examples/inspection/plot_linear_model_coefficient_interpretation.html
(from @sklearn 's gallery). Note the links to function docs.

Sphinx-gallery comes with awesome features such as
◼online execution with binder or jupyterlite
◼mini-galleries eg to link an object's docstring to its examples

A screenshot of a long example in scikit-learn documentation discussion interpretation of features in linear models
Part of scikit-learn's gallery of examples
Examples on a given object (here scikit-learn's TransformTargetRegressor) linked at the end of the object's docstring

GaelVaroquaux, to statistics

Sampling bias in practice: conducting a survey on the Paris metro platform...

if you ask people where they get off, you'll get a different distribution depending on where on the platform you stand: people choose their position close to the exits at arrival.

#statistics #epidemiology #socialscience

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GaelVaroquaux, to random

I’ll be giving the online lecture on "Representation learning on relational data to automate data preparation" on November 15th, 7pm EEST at AIHouse Ukraine.

Join the lecture, learn and support Ukraine
https://aihouse.org.ua/en/ai-for-ukraine/

GaelVaroquaux, to random

📑 "healthwashing": verb [ I or T ]
to make people believe that your computer-science grant or paper is about trying to improve health, while it really is an excuse to do maths and maybe you have a few biomedical signals on a thumb drive
⚕️💻

GaelVaroquaux,

@cazencott it's a step in the right direction.

Even better if you have an idea of the public-health stakes: what can be done to actually make a difference on patients' life.

I find research just applying canonical CS questions to biomedical data without heakth thinking a bit boring, I must say.

cazencott,

@GaelVaroquaux applying your favorite hammer to a biomedical dataset without consideration for the actual problem rarely ends up well.

But as someone whose applications are mostly several steps removed from patient care, I often feel a bit self conscious about the "health" label...

GaelVaroquaux, to random

🤖 I am honored to have been appointed to the government-level panel of experts on AI 🇫🇷.

We are tasked with suggesting a national vision and strategy in France.
The panel is made of experts on different topics: economics, law, computer science, from academia, industry, non-profits
https://gael-varoquaux.info/science/comite-de-lintelligence-artificielle-vision-et-strategie-nationale.html

GaelVaroquaux,

@morenonatural that's not the point. Nobody (me in particular) should see this as a reward, but as a duty 😀

GaelVaroquaux,

@morenonatural and thanks, by the way 😊

GaelVaroquaux, to random

✨Slides on causal inference: Individualizing treatment effects — transportability and model selection

https://speakerdeck.com/gaelvaroquaux/individualizing-treatment-effects-transportability-and-model-selection

Selecting models for causal inference, choice of variables for best bias-variance tradeoff, and choice of a relevant summary statistics (not risk difference for binary outcomes)

Presented at #ECMLPKDD2023

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GaelVaroquaux, to random

I'm in #ECMLPKDD2023:
Presenting https://link.springer.com/article/10.1007/s10994-022-06277-7 in room A9i today 2pm
Exciting work on embeddings in databases

Tomorrow in the Causal Machine Learning for Operational Decision Making workshop, I'll be giving a keynote on various results on individualizing treatment effet: how to select models, to choose covariates, and summary statistics
https://upliftworkshop.ipipan.waw.pl

GaelVaroquaux, to photography

And now, for something not about science & tech: #photography

Brittany harbor

I took this photo in "Le Guilvinec", a small harbor in Brittany ( #bretagne ).
https://www.flickr.com/photos/gaelvaroquaux/53187708200/

GaelVaroquaux, to random

But others capture the profits?
Yes, this does not bother me. I feel I can structure more the ecosystem by investing in core open technology.

More money? I make enough for a happy life. It would help advance technology I'm interested in, but less than a thriving community 4/5

GaelVaroquaux,

Of course, money is what supports scikit-learn: scikit-learn @ inria foundation and its quansight, QuantStack, nvidia... employ core contributors.

For profit ventures are good: they impact users, they fund technology. To me, what's important is coming back to open code & communities

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