Mailman 3 September 2023 - scikit-learn

[ANN] scikit-learn 0.24.0rc1 is online!
by Olivier Grisel Jan. 19, 2024

Jan. 19, 2024

Please help us test the first release candidate for scikit-learn 0.24.0: pip install scikit-learn==0.24.0rc1 Changelog: https://scikit-learn.org/0.24/whats_new/v0.24.html In particular, if you maintain a project with a dependency on scikit-learn, please let us know about any regression. Feel free to also retweet the announcement to get more people to test it before the final release (probably in 1 week or 2): https://twitter.com/scikit_learn/status/1334562221498753026 Thanks to anybody who helped make this happen! -- Olivier

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Introducing PyMilo: A New Way to Transport Pre-trained ML Models
by Sepand Haghighi Sept. 28, 2023

Sept. 28, 2023

Dear all, We are thrilled to introduce PyMilo, an open-source Python package that can revolutionize the way you transport pre-trained machine-learning models. PyMilo offers an efficient, secure, and transparent method that aims to eliminate the risks associated with binary or pickle formats. Why PyMilo? The motivation behind developing this package is simple but significant: to provide a safer and more reliable way to share machine learning models. As we embark on this journey, we acknowledge that PyMilo is still in its early stages of development. Currently, it supports only a limited number of machine learning models provided by Scikit-learn. Your Feedback Matters We firmly believe in the power of community collaboration. This is why we're reaching out to you, the Scikit-learn users, to ask for your support in utilizing PyMilo and providing us with your invaluable feedback. Your insights can help us enhance the package's interface and prioritize future developments. How You Can Contribute - Try PyMilo with your Scikit-learn models and let us know about your experience.- Share your thoughts on improving PyMilo's functionality and usability.- Report any issues or bugs you encounter. Your cooperation would be precious to us as we work towards making PyMilo a robust and indispensable tool for the machine learning community. Get Started To start using PyMilo, you can find detailed documentation and installation instructions on our GitHub repository: GitHub - openscilab/pymilo: PyMilo: Python for ML I/O | | | | | | | | | | | GitHub - openscilab/pymilo: PyMilo: Python for ML I/O PyMilo: Python for ML I/O. Contribute to openscilab/pymilo development by creating an account on GitHub. | | | Join us in shaping the future of model transportation with Pymilo! Thank you for your time and support. We look forward to your active participation in this exciting endeavor.

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Request / Proposal: integrating IEEE paper in scikit-learn as "feature_selection.EFS / EFSCV" and cancer_benchmark datasets
by Dalibor Hrg Sept. 25, 2023

Sept. 25, 2023

Dear scikit-learn mailing list similarly to standing feature_selection. <https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection…>*RFE and RFECV*, this is a request to openly discuss the *PROPOSAL* and requirements of *feature_selection.EFS and/or EFSCV* which would stand for "Evolutionary Feature Selection" with starting 8 algorithms or methods to be used with scikit-learn estimators, just as published in IEEE https://arxiv.org/abs/2303.10182 by the authors of paper. They agreed to help integrate it (in cc). *PROPOSAL* Implement/integrate https://arxiv.org/abs/2303.10182 paper into scikit-learn: *1) CODE* - implementing *feature_selection.EFS and/or EFSC*V (a space for evolutionary computing community interested in feature selection) RFE is: feature_selection. <https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection…> *RFE*(estimator, *[, ...]) Feature ranking with recursive feature elimination. feature_selection.RFECV <https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection…> (estimator, *[, ...]) Recursive feature elimination with cross-validation to select features. The "EFS" could be: feature_selection. <https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection…> *EFS*(estimator, *[, ...]) Feature ranking and feature elimination with *8 different algorithms, SFE, SFE-PSO* etc. *<- new algorithms could be added and benchmarked with evolutionary computing, swarm, genetic etc. * feature_selection. <https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection…> *EFSCV*(estimator, *[, ...]) Feature elimination with cross-validation to select features *2) DATASETS & CANCER BENCHMARK* - curating and integrating fetch of *cancer_benchmark* 40 datasets, directly in scikit-learn or externally pullable somehow and maintained (space for contributing expanding high-dimensional datasets on cancer topics). fetch_c <https://scikit-learn.org/stable/modules/generated/sklearn.datasets.fetch_ca…> ancer-benchmark(*[,, ...]) Loads 40 individual cancer related high-dimensional datasets for benchmarking feature selection methods (classification). *3) TUTORIAL / WEBSITE* - writing tutorial to replicate IEEE paper results with *feature_selection.EFS and/or EFSCV* on *cancer_benchmark (40 datasets)* I have identified IEEE work https://arxiv.org/abs/2303.10182 to be of very interesting novelty in working with high-dimensional datasets as it reports small subsets of predictive features selected with SVM, KNN across 40 datasets. Replicability under BSD-3 and high quality under scikit-learn could assure benchmarking novel feature selection algorithms easier - in my very first opinion. Since this is the very first touch of myself with IEEE paper authors and the scikit-learn list altogether, we would welcome some help/guide how integration could work out, and if there is any interest on that line at all. Kind regards Dalibor Hrg https://www.linkedin.com/in/daliborhrg/ On Sat, Sep 23, 2023 at 9:08 AM Alexandre Gramfort < alexandre.gramfort(a)inria.fr> wrote: > Dear Dalibor > > you should discuss this on the main scikit-learn mailing list. > > https://mail.python.org/mailman/listinfo/scikit-learn > > Alex > > On Fri, Sep 22, 2023 at 12:19 PM Dalibor Hrg <dalibor.hrg(a)gmail.com> > wrote: > >> Dear sklearn feature_selection.RFE Team and IEEE Authors (in-cc), >> >> This is a request to openly discuss the idea of potential for >> feature_selection. >> <https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection…> >> *EFS* which would stand for "Evolutionary Feature Selection" or shortly >> EFS with starting 8 algorithms as published in IEEE >> https://arxiv.org/abs/2303.10182 by the authors on high-dimensional >> datasets. I have identified this work to be of very interesting novelty in >> working with high-dimensional datasets, especially for health fields, and >> it could mean a lot to the ML community and scikit-learn project - in my >> very first opinion. >> >> A Jupyter Notebook and scikit-learn tutorial replicating this IEEE >> paper/work as feature_selection. >> <https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection…> >> *EFS *and 8 algorithms in it could be a near term goal. And eventually, >> scikit-learn EFSCV and diverse classification algorithms could be >> benchmarked for "joint paper" in JOSS, or a health journal. >> >> My initial idea (doesn't need to be that way or is open to discussion) >> has some first thought like this: >> >> RFE has: >> >> feature_selection. >> <https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection…> >> *RFE*(estimator, *[, ...]) >> >> Feature ranking with recursive feature elimination. >> >> feature_selection.RFECV >> <https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection…> >> (estimator, *[, ...]) >> >> Recursive feature elimination with cross-validation to select features. >> The "EFS" could have: >> >> feature_selection. >> <https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection…> >> *EFS*(estimator, *[, ...]) >> >> Feature ranking and feature elimination with *8 different algorithms, >> SFE, SFE-PSO* etc. *<- new algorithms could be added and benchmarked >> with evolutionary computing, swarm, genetic etc. * >> >> feature_selection. >> <https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection…> >> *EFSCV*(estimator, *[, ...]) >> >> Feature elimination with cross-validation to select features >> Looking forward to an open discussion and if Evolutionary Feature >> Selection EFS is something for sklearn project, or maybe a separate pip >> install package. >> >> Kind regards >> Dalibor Hrg >> https://www.linkedin.com/in/daliborhrg/ >> >> On Fri, Sep 22, 2023 at 10:50 AM Behrooz Ahadzade <b.ahadzade(a)yahoo.com> >> wrote: >> >>> >>> >>> Dear Dalibor Hrg, >>> >>> Thank you very much for your attention to the SFE algorithm. Thank you >>> very much for the time you took to guide me and my colleagues. According to >>> your guidance, we will add this algorithm to the scikit-learn library as >>> soon as possible. >>> >>> Kind regards, >>> Ahadzadeh. >>> On Wednesday, September 13, 2023 at 12:22:04 AM GMT+3:30, Dalibor Hrg < >>> dalibor.hrg(a)gmail.com> wrote: >>> >>> >>> Dear Authors, >>> >>> you have done some amazing work on feature selection here published in >>> IEEE: https://arxiv.org/abs/2303.10182 . I have noticed Python code >>> here without a LICENSE file or any info on this: >>> https://github.com/Ahadzadeh2022/SFE and in the paper some links are >>> mentioned to download data. >>> >>> I would be interested with you that we: >>> >>> Step 1) make and release a pip package, publish this code in JOSS >>> https://joss.readthedocs.io i.e. >>> https://joss.theoj.org/papers/10.21105/joss.04611 under BSD-3 license >>> and replicate IEEE paper table results. All 8 algorithms could be in >>> potentially one class "EFS" meaning "Evolutionary Feature Selection", >>> selectable as 8 options among them SFE. Or something like that. >>> >>> Step 2) try integrate and work with scikit-learn people, I would >>> recommend it to integrate this under >>> https://scikit-learn.org/stable/modules/classes.html#module-sklearn.feature… similarly >>> to sklearn.feature_selection.RFE. I believe this would be a great >>> contribution to the best open library for ML, scikit-learn. >>> >>> I am unsure what is the status of datasets and licenses therein?. But, >>> the datasets could be fetched externally from OpenML.org repository, for >>> example >>> https://scikit-learn.org/stable/datasets/loading_other_datasets.html or >>> CERN Zenodo where "benchmark datasets" could be expanded. It depends a bit >>> on the dataset licenses? >>> >>> Overall, I hope this can hugely maximize your published work visibility >>> but also for others to credit you in papers in a more citable and >>> replicable way. I believe your IEEE paper and work definitely deserve a >>> spot in scikit-learn. There is need for some replicable code on >>> "Evolutionary Methods for Feature Selection" and such Benchmark in >>> life-science datasets, and you have done some great work so far. >>> >>> Let me know what you think. >>> >>> Best regards, >>> Dalibor Hrg >>> >>> https://www.linkedin.com/in/daliborhrg/ >>> >>

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[ANN] scikit-learn 1.3.1 is online!
by Guillaume Lemaître Sept. 21, 2023

Sept. 21, 2023

scikit-learn 1.3.1 is out on pypi.org and conda-forge! This is a maintenance release that fixes several regressions introduced in version 1.3 <https://scikit-learn.org/stable/whats_new/v1.2.html#version-1-2-1> https://scikit-learn.org/ <https://scikit-learn.org/stable/whats_new/v1.3.html#version-1-3-1> stable/whats_new/v1.3.html# <https://scikit-learn.org/stable/whats_new/v1.3.html#version-1-3-1> version-1-3-1 <https://scikit-learn.org/stable/whats_new/v1.3.html#version-1-3-1> You can upgrade with pip as usual: pip install -U scikit-learn The conda-forge builds will be available shortly, which you can then install using: conda install -c conda-forge scikit-learn Thanks to all contributors who helped on this release. Guillaume, On the behalf of the Scikit-learn maintainers team. -- Guillaume Lemaitre Scikit-learn @ Inria Foundation https://glemaitre.github.io/

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CLUSTER ANALYSIS AND THE SEARCH OF A SAMPLE MODE
by Ulderico Santarelli Sept. 18, 2023

Sept. 18, 2023

*I am an old guy who started programming around the seventies of the last century* with ASSEMBLER 360, then FORTRAN, PL1, APL, IBM APPLICATION SYSTEM and, last, the marvelous SAS. Having heard around about the powerful, flexible, functionally complete PYTHON UNIVERSE”, encompassing an advanced Object-Oriented Language and a very wide family of packages, I decided to run an exercise about a problem I've been tackling since my youth (have a look at the Bibliography). I succeeded in completing it in a few days and I'm attaching my solution to the problem of finding the points in a sample that are "central" in a surrounding topological neighborhood. They are eligible as centroids for a Cluster Analysis after the aggregation of "too near points'. The solution is based on the search of potential wells in a suitable potential field, similar to the one all of us studied in high school. Therefore, too near points may be in the same potential well. No more words, have a look at the attachment. My coding is that of a beginner. I'm sure everybody would find more efficient coding. As a comment: I started studying Python around May 15th 2023. My best regards. Ulderico Santarelli.

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(no subject)
by Ulderico Santarelli Sept. 14, 2023

Sept. 14, 2023

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