We just released skrub 0.2.0. This release markedly simplifies learning on complex dataframes.
The highlight of the release is the tabular_learner function, which facilitates creating pipelines that readily perform machine learning on dataframes, adding preprocessing to a scikit-learn compatible learner …
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Open-source efforts around scikit-learn at Inria are spinning off to a new enterprise, Probabl, in charge of sustainable development of a data-science commons.
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François Chollet rightfully said that people often underestimate the impact of scikit-learn. I give here a few illustrations to back his claim.
A few days ago, François Chollet (the creator of Keras, the library that that democratized deep learning) posted:
Indeed, scikit-learn continues to be the most popular machine …
The Mayavi Python software, and my personal history: A thread on Python and scipy ecosystems, building open source codebase, and meeting really cool and friendly people
I am writing today as a goodbye to the project: I used to be one of the core contributors and maintainers but have been …
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Join us to work on reinventing data-science practices and tools to produce robust analysis with less data curation.
It is well known that data cleaning and preparation are a heavy burden to the data scientist.
In the dirty data project, we have been conducting machine-learning research …
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With the growth of scikit-learn and the wider PyData ecosystem, we want to recruit in the Inria scikit-learn team for a new role. Departing from our usual focus on excellence in algorithms, statistics, or code, we want to add to the team someone with some technical understanding, but an …
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This post discuss the difficulties of communicating while developing open-source projects and tries to gives some simple advice.
A large software project is above all a social exercise in which technical experts try to reach good decisions together, for instance on github pull requests. But communication is difficult, in …
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An important acknowledgement for a different view of doing science: open, collaborative, and more than a proof of concept.
A few days ago, Loïc Estève, Alexandre Gramfort, Olivier Grisel, Bertrand Thirion, and myself received the “Académie des Sciences Inria prize for transfer”, for our contributions to the scikit-learn project …
We have just announced that a foundation will be supporting scikit-learn at Inria [1]: scikit-learn.fondation-inria.fr
This is an exciting turn for us, because it enables us to receive private funding. As a result, we will be able to have secure employment for some existing core …
Two weeks ago, we held a scikit-learn sprint in Austin and Paris. Here is a brief report, on progresses and challenges.
Several sprints
We actually held two sprint in Austin: one open sprint, at the scipy conference sprints, which was open to new contributors, and one core sprint, for more …
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Scientific progress calls for reproducing results. Due to limited resources, this is difficult even in computational sciences. Yet, reproducibility is only a means to an end. It is not enough by itself to enable new scientific results. Rather, new discoveries must build on reuse and modification of the state …
Two week ago, we held in Paris a large international sprint on scikit-learn. It was incredibly productive and fun, as always. We are still busy merging in the work, but I think that know is a good time to try to summarize the sprint.
We had a …
To my friends developing data science for the social media, marketing, and advertising industries,
It is time to accept that we have our share of responsibility in the outcome of the US elections and the vote on Brexit. We are not creating the society that we would like. Facebook, Twitter …
New persistence in joblib enables low-overhead storage of big data contained in arbitrary objects
At MLOSS 15 we brainstormed on reproducible science, discussing why we care about software in computer science. Here is a summary blending notes from the discussions with my opinion.
“Without engineering, science is not more than philosophy” — the community
How do we enable better Science? Why do we do software …
After 6 months of efforts, We just released version 0.2 of nilearn, dedicated to making machine learning in neuroimaging easier and more powerful.
This release integrates the features of the july sprint, and more.
Better documentation …
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The 2015 edition of the machine learning open source software (MLOSS) workshop was full of very mature discussions that I strive to report here.
I give links to the videos. Some machine-learning researchers have great thoughts about growing communities of coders, about code as a process and a deliverable …
A couple of weeks ago, we had in Paris the second international nilearn sprint, dedicated to making machine learning in neuroimaging easier and more powerful.
It was such a fantastic experience, as nilearn is really shaping up as a simple yet powerful tool, and there is a lot of enthusiasm …
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tl;dr: Reproducibilty is a noble cause and scientific software a promising vessel. But excess of reproducibility can be at odds with the housekeeping required for good software engineering. Code that “just works” should not be taken for granted.
This post advocates for a progressive consolidation effort of scientific …
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This year again we will have an exciting workshop on the leading-edge machine-learning open-source software. This subject is central to many, because software is how we propagate, reuse, and apply progress in machine learning.
Want to present a project? The deadline for the call for papers is Apr 28th …
