Mailman 3 May 2021 - 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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Open Source: sustainability and etiquette
by Reshama Shaikh Oct. 11, 2021

Oct. 11, 2021

Hello, I've seen some excellent resources that have explained open source, its sustainability, challenges and *indirectly, the etiquette*. I am starting to compile the list here [a]. This keynote by Stuart Geiger is a must-watch: The Invisible Work of Maintaining & Sustaining Open Source Software [b] There is one more video by Emily someone who was at Microsoft, but is now a professor somewhere, and I am trying to track that video down. I think it's from 2017. I'll add it to the list once I find it. If anyone knows the full name of the speaker, please share. [a] https://www.dataumbrella.org/open-source/open-source-sustainability [b] https://www.youtube.com/watch?v=PM3iltcaIL8 Best, Reshama --- Reshama Shaikh she/her Blog <https://reshamas.github.io> | Twitter <https://twitter.com/reshamas> | LinkedIn <https://www.linkedin.com/in/reshamas/> | GitHub <https://github.com/reshamas> Data Umbrella <https://www.dataumbrella.org> NYC PyLadies <https://meet.meetup.com/wf/click?upn=pEEcc35imY7Cq0tG1vyTt6zEs68RbcMfjPcajN…>

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Spanish translation proposal for Scikit-Learn documentation
by Mariangela Petrizzo Aug. 17, 2021

Aug. 17, 2021

Dear Scikit-Learn team! I am Mariángela Petrizzo, I am writing to you as a member of Qu4nt, a team dedicated to the use of open source tools for the development of software solutions with emphasis on data science. We have a strong interest in translating the Scikit-Learn documentation into Spanish. Our team is made up of members from various scientific fields, including some university faculty in linguistics and computer sciences, with a wide experience in Python as well as several libraries used for data analysis and machine learning, and also contribute locally as evangelists of its use in Spanish-speaking communities, in particular, the leader initiated the translation of some Software Carpentry lessons into Spanish. That is why we have been discussing the opportunity to offer our contribution to the Python project, promoting the translation into Spanish of the documentation of some of the libraries with the greatest impact in our areas of interest. Talking with David Mertz, to whom we are sending a copy of this email, we have explored options, and the idea of working with Scikit-learn has really seemed to be an exceptional opportunity for all of us and the community. He's very enthusiastic about the idea of generating a spanish translation of Scientific Python libraries like Scikit-learn. For us, this translation project has to be done through a completely open work on Github, taking as reference the restructured text sources for Sphinx from a git fork, using the tools provided by Sphinx itself for internationalization: https://www.sphinx-doc.org/en/1.8/intl.html, and applying tags to perform planned updates. In addition, as with any open source project, the main mechanism for quality assurance comes from the users themselves who will have the channels available for submitting issues. Our intention is to secure all the infrastructure and mechanisms to make this possible: making the process transparent through Github, using as much as possible tools like Transifex to facilitate participation, and providing guidelines for contributors as part of the project. Of course, this project cannot be realized without your support. We therefore come to you to inquire about your willingness to accompany and support this project. We would love to hear your feedback on our proposal. Best regards, Mariángela -- María Ángela Petrizzo Páez [image: https://] [image: https://]about.me/petrizzo <https://about.me/petrizzo?promo=email_sig&utm_source=product&utm_medium=ema…> Descárgate Redes para la Comprensión de la Política <http://www.elperroylarana.gob.ve/redes-para-la-comprension-de-la-politica/> *A quienes conservan la esperanza que no es lo último que se pierde, sino lo primero que se siembra y, por tanto, lo más radical.* El único modo de vencer el secuestro del conocimiento es comprender sus razones. La manera de revertirlo, es hacernos hackers de los secuestros cotidianos a cambio de no morir sin saber lo que somos ¡Piensa para vivir, actúa para hackear! Cada día, una acción procomún a la vez. *“Tengo horror de aquellos cuyas palabras van más allá que sus actos”* *Albert Camus* *“El poder, lejos de estorbar al saber, lo produce.” - **Michael Foucault* Usuario Linux # 498889 Miembro Red de Politólogas - #NoSinMujeres <http://www.nosinmujeres.com/> https://hotelescuela.academia.edu/MariangelaPetrizzoPaez http://orcid.org/0000-0001-9483-4185 PEII - Nivel B

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[Data Umbrella] scikit-learn Latin America sprint (June 2021)
by Reshama Shaikh July 28, 2021

July 28, 2021

Hello, Data Umbrella is organizing a scikit-learn open source sprint, with a focus on * LATIN AMERICA * on June 26, 2021. This scikit-learn sprint is a 4-hour online hands-on "hackathon" where we work on issues in the scikit-learn GitHub repo to get started in contributing to open source in a structured setting. There is 2-3 hours of pre-work and participants will work with a pair programming partner during the sprint. Sprint website: https://latam2021.dataumbrella.org/home Sprint application: https://forms.gle/cxav9eE9ZkiULfbm7 Tweet: https://twitter.com/DataUmbrella/status/1394661734275821573 LinkedIn post: https://www.linkedin.com/feed/update/urn:li:activity:6800434144624070656/ Meetup event: https://www.meetup.com/data-umbrella/events/278298428/ FAQ: Q: If I am not in Latin America, can I still apply and participate? A: Priority will be given to folks in the Latin America region. If there are open spots, we will open it up to other regions. In the meantime, we have resources available for all to get started in contributing to scikit-learn. Check them out here: https://latam2021.dataumbrella.org/about/prep-work Any questions, please email us: data.umbrella.dei(a)gmail.com Cheers, Reshama --- Reshama Shaikh she/her Blog <https://reshamas.github.io> | Twitter <https://twitter.com/reshamas> | LinkedIn <https://www.linkedin.com/in/reshamas/> | GitHub <https://github.com/reshamas> Data Umbrella <https://www.dataumbrella.org> NYC PyLadies <https://meet.meetup.com/wf/click?upn=pEEcc35imY7Cq0tG1vyTt6zEs68RbcMfjPcajN…>

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[Data Umbrella] 3 Components for Reviewing a Pull Request (PR)
by Reshama Shaikh June 4, 2021

June 4, 2021

Hello, Thomas Fan, a core contributor to scikit-learn, will be presenting on "Reviewing a Pull Request." This live webinar is scheduled for Wednesday, June 2 at 6pm EDT. Sign-up info is here: https://www.meetup.com/data-umbrella/events/278045166/ This presentation will be recorded and shared on YouTube about a day after the event. You can look for it here: https://www.youtube.com/c/DataUmbrella/featured Best, Reshama --- Reshama Shaikh she/her Blog <https://reshamas.github.io> | Twitter <https://twitter.com/reshamas> | LinkedIn <https://www.linkedin.com/in/reshamas/> | GitHub <https://github.com/reshamas> Data Umbrella <https://www.dataumbrella.org> NYC PyLadies <https://meet.meetup.com/wf/click?upn=pEEcc35imY7Cq0tG1vyTt6zEs68RbcMfjPcajN…>

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custom scorer needs group information: how?
by Emanuele Olivetti June 3, 2021

June 3, 2021

Dear scikit-learn Community, I'd like to create a custom scorer to be used with GroupKFold and GridSearchCV. The issue is that I need to use the grouping information also "inside" the custom scorer, to compute the desired metric. How to do that? Here follows a simplified example to explain in detail the issue. Given this basic and common scenario: --- X = <feature values> y = <labels> groups = np.array([0,0,1,1,1,1,2,2,3,3,3,...]) parameters = {'n_estimators': [10,100,1000], 'max_depth': [5,10,15]} gkf = GroupKFold(n_splits=3) clf = GridSearchCV(RandomForestClassifier(), parameters, scoring=my_scorer) --- how to create my_scorer so that it computes, let's say, the "average accuracy across groups"? Meaning that my_scorer should know not only y_true and y_pred but also their grouping structure. In principle, it should be something like in the following snippet, which needs the group information "for the specific slice of data evaluated" (which I call y_groups below)... a piece of information that I don't know how to propagate there: --- def my_score(y_true, y_pred, y_groups): for group in np.unique(y_groups): idx = y_group==group result.append((y_true[idx] == y_pred[idx]).mean()) return np.mean(result) my_scorer = make_scorer(my_score) --- How can I make a custom scorer that uses inside the group information for the specific predictions to be scored? Thanks in advance for your help, Emanuele

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Call for papers for the 4th international workshop on machine learning in clinical neuroimaging
by MLCN Workshop May 30, 2021

May 30, 2021

Dear Colleagues, *Please find below the call for papers for the International Workshop of Machine Learning in Clinical Neuroimaging (MLCN) which is held virtually on the 27th of September 2021 at MICCAI 2021 in Strasbourg, France. We welcome contributions to novel machine learning methods and their applications to clinical neuroimaging data.* The submission deadline is *25 June 2021*, and all MLCN accepted papers will be eligible for the best paper award of 500 USD. Top accepted papers will be invited to submit an extended version to the MELBA journal. For more information, please visit https://mlcnws.com/call-for-papers/*.* Best wishes, The MLCN 2021 steering and organizing committees Christos Davatzikos, Andre Marquand, Jonas Richiardi, Emma Robinson Ahmed Abdulkadir, Mohamad Habes, Seyed Mostafa Kia, Vinod Kumar, Jane Maryam Rondina, Chantal Tax, Thomas Wolfers ================================================================================================ Call for Papers The International Workshop of Machine Learning in Clinical Neuroimaging ( https://mlcnws.com/), a satellite event of MICCAI (https://miccai202 <https://miccai2021.org/>1 <https://miccai2020.org/>.org <https://miccai2021.org/>), calls for original papers in the field of clinical neuroimaging data analysis with machine learning. The two tracks of the workshop include methodological innovations as well as clinical applications. This highly interdisciplinary topic provides an excellent platform to connect researchers of varying disciplines and to collectively advance the field in multiple directions. In the machine learning track, we seek novel contributions that address current methodological gaps in analyzing high-dimensional, longitudinal, and heterogeneous clinical neuroscientific data using stable, scalable, and interpretable machine learning models. Topics of interest include but are not limited to: - Big data - Spatio-temporal brain data analysis - Structural data analysis - Graph theory and complex network analysis - Longitudinal data analysis - Model stability and interpretability - Model scalability in large neuroimaging datasets - Multi-source data integration and multi-view learning - Multi-site data analysis, from preprocessing to modeling - Domain adaptation, data harmonization, and transfer learning in neuroimaging - Unsupervised methods for stratifying brain disorders - Deep learning in clinical neuroimaging - Model uncertainty in clinical predictions - … In the *clinical neuroimaging *track*,* the applications of existing machine learning algorithms are evaluated to move towards precision medicine for complex brain disorders. The discovery of biological markers in medicine is an important challenge across different fields and various experimental procedures and designs are used to detect biological signatures that can be utilized for improvement in diagnostic, treatment, or for other beneficial ends. However, for most complex brain disorders, we do not have reliable biomarkers today. The application of advanced machine learning methods may help to reach this goal. Therefore, we invite the community to submit conference contributions on machine learning approaches with the goal to improve our understanding of complex brain disorders, moving the field closer towards precision medicine. Topics of interest include but are not limited to: - Biomarker discovery - Refinement of nosology and diagnostics - Biological validation of clinical syndromes - Treatment outcome prediction - Course prediction - Analysis of wearable sensors - Neurogenetics and brain imaging genetics - Mechanistic modeling - Brain aging - The presentation of clinical neuroimaging databases to stimulate developments in machine learning - … ------------------------------ Submission Process: The workshop seeks high-quality, original, and unpublished work that addresses one or more challenges described above. Papers should be submitted electronically in Springer Lecture Notes in Computer Science (LCNS) style (see https://miccai2021.org/en/PAPER-SUBMISSION-GUIDELINES.html for detailed author guidelines) using the CMT system at https://cmt3.research.microsoft.com/MLCN2021. The page limit is 8-pages (text, figures, and tables) plus up to 2-pages of references. We review the submissions in a double-blind process. Please make sure that your submission is anonymous. Accepted papers will be published in a joint proceeding with the MICCAI 2021 conference. ------------------------------ MLCN Special Issue at the MELBA journal: This year, we will invite the top accepted papers to submit an extended version of their contribution to the MLCN special issue at the Journal of Machine Learning for Biomedical Imaging (MELBA) <https://www.melba-journal.org/about>. The invited papers will go through an independent review process by the journal. ------------------------------ Best Paper Award: All MLCN accepted papers will be eligible for the best paper award. The recipient of the award will be chosen by the MLCN scientific committee based on the scientific quality, novelty, and clarity of contributions. The winner will be announced at the end of the workshop and will receive a 500 USD honorarium. ------------------------------ Important Dates: - Paper submission deadline: June 25, 2021, 11:59 PM Pacific Time - Notification of Acceptance: July 16, 2021 - Camera-ready Submission: July 30, 2021 - Workshop Date: September 27, 2021

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problem importing sklearn in python
by olivier combescure May 26, 2021

May 26, 2021

hi, i've always that problem importing from sklearn.cluster import KMeans result : Traceback (most recent call last): File ".\test_imports.py", line 3, in <module> from sklearn.cluster import KMeans File "C:\Users\ocomb\anaconda3\lib\site-packages\sklearn\__init__.py", line 82, in <module> from .base import clone File "C:\Users\ocomb\anaconda3\lib\site-packages\sklearn\base.py", line 17, in <module> from .utils import _IS_32BIT File "C:\Users\ocomb\anaconda3\lib\site-packages\sklearn\utils\__init__.py", line 23, in <module> from .class_weight import compute_class_weight, compute_sample_weight File "C:\Users\ocomb\anaconda3\lib\site-packages\sklearn\utils\class_weight.py", line 7, in <module> from .validation import _deprecate_positional_args File "C:\Users\ocomb\anaconda3\lib\site-packages\sklearn\utils\validation.py", line 26, in <module> from .fixes import _object_dtype_isnan, parse_version File "C:\Users\ocomb\anaconda3\lib\site-packages\sklearn\utils\fixes.py", line 20, in <module> import scipy.stats File "C:\Users\ocomb\anaconda3\lib\site-packages\scipy\stats\__init__.py", line 391, in <module> from .stats import * File "C:\Users\ocomb\anaconda3\lib\site-packages\scipy\stats\stats.py", line 180, in <module> from . import distributions File "C:\Users\ocomb\anaconda3\lib\site-packages\scipy\stats\distributions.py", line 8, in <module> from ._distn_infrastructure import (entropy, rv_discrete, rv_continuous, File "C:\Users\ocomb\anaconda3\lib\site-packages\scipy\stats\_distn_infrastructure.py", line 23, in <module> from scipy import optimize File "C:\Users\ocomb\anaconda3\lib\site-packages\scipy\optimize\__init__.py", line 400, in <module> from .optimize import * File "C:\Users\ocomb\anaconda3\lib\site-packages\scipy\optimize\optimize.py", line 36, in <module> from ._numdiff import approx_derivative File "C:\Users\ocomb\anaconda3\lib\site-packages\scipy\optimize\_numdiff.py", line 6, in <module> from scipy.sparse.linalg import LinearOperator File "C:\Users\ocomb\anaconda3\lib\site-packages\scipy\sparse\linalg\__init__.py", line 114, in <module> from .eigen import * File "C:\Users\ocomb\anaconda3\lib\site-packages\scipy\sparse\linalg\eigen\__init__.py", line 9, in <module> from .arpack import * File "C:\Users\ocomb\anaconda3\lib\site-packages\scipy\sparse\linalg\eigen\arpack\__init__.py", line 20, in <module> from .arpack import * File "C:\Users\ocomb\anaconda3\lib\site-packages\scipy\sparse\linalg\eigen\arpack\arpack.py", line 42, in <module> from . import _arpack ImportError: DLL load failed while importing _arpack: La procédure spécifiée est introuvable. anaconda 64 bit -- L'absence de virus dans ce courrier électronique a été vérifiée par le logiciel antivirus Avast. https://www.avast.com/antivirus

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Monthly meeting May 25th 2021
by Chiara Marmo May 20, 2021

May 20, 2021

Dear list, The scikit-learn monthly meeting will take place on **Tuesday** May 25th at 1PM UTC: <https://www.timeanddate.com/worldclock/meetingdetails.html?year=2021&month=…> <https://www.timeanddate.com/worldclock/meetingdetails.html?year=2021&month=…> https://www.timeanddate.com/worldclock/meetingdetails.html?year=2021&month=… <https://www.google.com/url?q=https://www.timeanddate.com/worldclock/meeting…> While these meetings are mainly for core-devs to discuss the current topics, non core-devs and other project maintainers are welcome. Feel free to join, using the following link: https://meet.google.com/xhq-yoga-rtf If you plan to attend and you would like to discuss something specific about your contribution please add your name (or github pseudo) in the " Contributors <https://hackmd.io/0yokz72CTZSny8y3Re648Q#Contributors>" section, of the public pad: <https://hackmd.io/qtKt7pTNSXanU-MJOIMxbw> https://hackmd.io/0yokz72CTZSny8y3Re648Q Best Chiara

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check_estimator _NotAnArray
by Sole Galli May 12, 2021

May 12, 2021

Hello everyone, I am trying to get Feature-engine transformers pass the check_estimator tests and there is one test, that I am not too sure what it is intended for. The transformers fail the check_transformer_data_not_an_array because the input is a _NotAnArray class, and Feature-engine transformers don't like that. What is this check intended for? Is it to ensure compatibility with some other sklearn class? if yes, which ones? I would appreciate any info or links to docs/ issues. Thanks a lot! Sole

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