Mailman 3 July 2022 - 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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[ANNOUNCEMENT] scikit-learn 1.0 release
by Adrin June 30, 2023

June 30, 2023

Hi everyone, We're happy to announce the 1.0 release which you can install via pip or conda: pip install -U scikit-learn or conda install -c conda-forge scikit-learn You can read the release highlights under https://scikit-learn.org/stable/auto_examples/release_highlights/plot_relea… and the long list of the changes under https://scikit-learn.org/stable/whats_new/v1.0.html New major features include: mandatory keyword arguments in many places, Spline Transformers, Quantile Regressor, Feature Names Support, a more flexible plotting API, Online One-Class SVM, and much more! This version supports Python versions 3.7 to 3.9. A big thanks to all contributors for making this release possible. Regards, Adrin, On the behalf of the scikit-learn maintainer team.

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question regarding 'RANSACRegressor' object has no attribute 'inlier_mask_'
by Shang-Rou Hsieh July 29, 2022

July 29, 2022

To whom it may concern, Belows are the codes: - - - - - from sklearn.linear_model import RANSACRegressor ransac = RANSACRegressor(LinearRegression(), max_trials=100, # default min_samples=0.95, loss='absolute_error', # default residual_threshold=None, # default random_state=123) inlier_mask = ransac.inlier_mask_ - - - - Here is the error message: AttributeError: 'RANSACRegressor' object has no attribute 'inlier_mask_' SO I checked the attributes of RANSACRegressor using dir (RANSACRegressor) and I do not find 'inlier_mask_' Any advise?Henry

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Call for Participation ClinSpEn at Biomedical WMT Shared Task (WMT/EMNLP 2022
by Martin Krallinger July 20, 2022

July 20, 2022

Call for Participation ClinSpEn @ Biomedical WMT Shared Task (WMT/EMNLP 2022) Automatic Translation of Clinical cases, ontologies & medical entities: Spanish - English https://temu.bsc.es/clinspen/ ClinSpEn is part of the Biomedical WMT 2022 shared task, having the aim to promote the development and evaluation of machine translation systems adapted to the medical domain with three highly relevant sub-tracks: clinical cases, medical controlled vocabularies/ontologies, and clinical terms and entities extracted from medical content. Key information: - ClinSpEn sub-track: https://temu.bsc.es/clinspen/ - Biomedical WMT: https://statmt.org/wmt22/biomedical-translation-task.html - Main WMT: https://statmt.org/wmt22/ - EMNLP conference: https://2022.emnlp.org/ - Sample/Training Data: - Clinical Cases: https://doi.org/10.5281/zenodo.6497350 - Clinical Terms: https://doi.org/10.5281/zenodo.6497372 - Ontology Concepts: https://doi.org/10.5281/zenodo.6497388 - BioWMT Registration Form: https://tinyurl.com/mtvdytmt - ClinSpEn Registration Form (for support and updates): https://temu.bsc.es/clinspen/registration/ Motivation Machine translation applied to the clinical domain is a specially challenging task due to the complexity of medical language and the heavy use of health-related technical terms and medical expressions. Therefore, there is a large community of specialized medical translators, able to deal with medical narratives, terminologies or the use of ambiguous abbreviations and acronyms. Taking into account the relevance, impact and diversity of health-related content, as well as the rapidly growing number of publications, EHRs, clinical trials, informed consent documents and medical terminologies there is a pressing need to be able to generate more robust medical machine translation resources together with independent quality evaluation scenarios. Recent advances in machine translation technologies, together with the use of other NLP components, are showing promising results, thus domain adaptation of MT approaches can have a significant impact in unlocking key information from medical content. The ClinSpEn sub-task of Biomedical WMT proposes three different highly relevant sub-tracks, each associated with highly relevant medical machine translation application scenarios:: - ClinSpEn-CC (Clinical Cases) subtask: translation of clinical case documents from English to Spanish, a type of document relevant both for processing medical literature as well as clinical records. - ClinSpEn-CT (Clinical Terms): translation of clinical terms and entity mentions from Spanish to English. The used terms were directly extracted from medical literature and clinical records, with particular focus on diseases, symptoms, findings, procedures and professions. - ClinSpEn-OC (Ontology Concepts): translation of clinical controlled vocabularies and ontology concepts from English to Spanish. Ontologies and structured vocabularies represent a key resource for semantic interoperability, entity linking, biomedical knowledge bases and precision medicine, and thus there is a pressing need to generate multilingual biomedical ontologies for a range of clinical applications. A decently-sized sample set for each data type has been released. Participants may use it to test their existing systems or try out new ones. In addition to the manually translated test set by professional medical translators, participants will also have access to a larger background collection for each of the three substracks, which might serve as additional resources and to promote scalability and robustness assessment of machine translation technology. Schedule - Test and Background Set Release: July 21st, 2022 - Participant Predictions Due: July 28th, 2022 - Paper Submission Deadline: September 7th, 2022 - Notification of Acceptance (peer-reviews): October 9th, 2022 - Camera-ready Version Due: October 16th, 2022 - WMT @ EMNLP: December 7th and 8th, 2022 [All deadlines are in AoE (Anywhere on Earth)] Registration Participants must register using the official BioWMT Registration Form, which is available at https://tinyurl.com/mtvdytmt. Additionally, we’ve created a registration form specific for the ClinSpEn sub-tracks which will be used to keep participants updated. Register at: https://temu.bsc.es/clinspen/registration/. Publications and WMT workshop Teams participating in the ClinSpEn subtrack of Biomedical WMT will be invited to contribute a systems description paper for the WMT 2022 Working Notes proceedings. More information on the paper’s specifications, formatting guidelines and review process at: https://statmt.org/wmt22/index.html. If you are interested in Machine Translation, the biomedical domain or other language combinations, remember to check out the Biomedical WMT site and the rest of this year’s sub-tracks and language pairs: https://statmt.org/wmt22/biomedical-translation-task.html ClinSpEn Organizers - Salvador Lima-López (Barcelona Supercomputing Center, Spain) - Darryl Johan Estrada (Barcelona Supercomputing Center, Spain) - Eulàlia Farré-Maduell (Barcelona Supercomputing Center, Spain) - Martin Krallinger (Barcelona Supercomputing Center, Spain) Biomedical WMT Organizers - Rachel Bawden (University of Edinburgh, UK) - Giorgio Maria Di Nunzio (University of Padua, Italy) - Darryl Johan Estrada (Barcelona Supercomputing Center, Spain) - Eulàlia Farré-Maduell (Barcelona Supercomputing Center, Spain) - Cristian Grozea (Fraunhofer Institute, Germany) - Antonio Jimeno Yepes (University of Melbourne, Australia) - Salvador Lima-López (Barcelona Supercomputing Center, Spain) - Martin Krallinger (Barcelona Supercomputing Center, Spain) - Aurélie Névéol (Université Paris Saclay, CNRS, LISN, France) - Mariana Neves (German Federal Institute for Risk Assessment, Germany) - Roland Roller (DFKI, Germany) - Amy Siu (Beuth University of Applied Sciences, Germany) - Philippe Thomas (DFKI, Germany) - Federica Vezzani (University of Padua, Italy) - Maika Vicente Navarro, Maika Spanish Translator, Melbourne, Australia - Dina Wiemann (Novartis, Switzerland) - Lana Yeganova (NCBI/NLM/NIH, USA

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scikit-learn monthly developer meeting: Monday July 25, 2022
by Thomas J. Fan July 19, 2022

July 19, 2022

Dear all, The scikit-learn developer monthly meeting will take place on Monday July 25, 2022 at 15:00 UTC. - Video call link: https://meet.google.com/ews-uszu-djs - Meeting notes / agenda: https://hackmd.io/0yokz72CTZSny8y3Re648Q - Local times: https://www.timeanddate.com/worldclock/meetingdetails.html?year=2022&month=… The goal of this meeting is to discuss ongoing development topics for the project. Everybody is welcome. As usual, please follow the code of conduct of the project: https://github.com/scikit-learn/scikit-learn/blob/main/CODE_OF_CONDUCT.md Regards, Thomas

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VOTE SLEP018 - Pandas Output for Transformers
by Thomas J. Fan July 17, 2022

July 17, 2022

Hi everyone, SLEP018 <https://scikit-learn-enhancement-proposals.readthedocs.io/en/latest/slep018…> introduces a set_output API for outputting pandas DataFrames in transformers. An implementation for this SLEP is available in PR #23734 <https://github.com/scikit-learn/scikit-learn/pull/23734>. Feedback is welcome! If you are a scikit-learn core developer please cast your vote in this PR <https://github.com/scikit-learn/enhancement_proposals/pull/72>. According to our governance model <https://scikit-learn.org/stable/governance.html#decision-making-process>, the vote will be open for a month (till 17th August), and the motion is accepted if 2/3 of the cast votes are in favor. Best, Thomas

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Double hinge loss
by Maroan K July 11, 2022

July 11, 2022

Hi, Could anyone please give the derivative of the double hinge loss (DH)? DH = max(-wx,max(0, 1/2 - 1/2 * wx)) I guess the derivative of DH is usually -wx , is this right? Sorry if this is not the right place to ask such a question.

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