From kmaroank at gmail.com Mon Jul 11 01:19:09 2022 From: kmaroank at gmail.com (Maroan K) Date: Mon, 11 Jul 2022 08:19:09 +0300 Subject: [scikit-learn] Double hinge loss Message-ID: 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. -------------- next part -------------- An HTML attachment was scrubbed... URL: From alan.isaac at gmail.com Mon Jul 11 08:42:09 2022 From: alan.isaac at gmail.com (Alan G. Isaac) Date: Mon, 11 Jul 2022 08:42:09 -0400 Subject: [scikit-learn] Double hinge loss In-Reply-To: References: Message-ID: On 7/11/2022 1:19 AM, Maroan K wrote: > > 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. 1. The right place for such a question is e.g. https://math.stackexchange.com/ 2. Since it is hinged, it is not everywhere differentiable. Alan Isaac -------------- next part -------------- A non-text attachment was scrubbed... Name: temp.png Type: image/png Size: 10550 bytes Desc: not available URL: From thomasjpfan at gmail.com Sun Jul 17 13:12:05 2022 From: thomasjpfan at gmail.com (Thomas J. Fan) Date: Sun, 17 Jul 2022 12:12:05 -0500 Subject: [scikit-learn] VOTE SLEP018 - Pandas Output for Transformers Message-ID: Hi everyone, SLEP018 introduces a set_output API for outputting pandas DataFrames in transformers. An implementation for this SLEP is available in PR #23734 . Feedback is welcome! If you are a scikit-learn core developer please cast your vote in this PR . According to our governance model , 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 -------------- next part -------------- An HTML attachment was scrubbed... URL: From thomasjpfan at gmail.com Mon Jul 18 22:52:05 2022 From: thomasjpfan at gmail.com (Thomas J. Fan) Date: Mon, 18 Jul 2022 22:52:05 -0400 Subject: [scikit-learn] scikit-learn monthly developer meeting: Monday July 25, 2022 Message-ID: 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=7&day=25&hour=15&min=0&sec=0&p1=1440&p2=240&p3=248&p4=195&p5=179&p6=224 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 -------------- next part -------------- An HTML attachment was scrubbed... URL: From krallinger.martin at gmail.com Wed Jul 20 07:23:18 2022 From: krallinger.martin at gmail.com (Martin Krallinger) Date: Wed, 20 Jul 2022 13:23:18 +0200 Subject: [scikit-learn] Call for Participation ClinSpEn at Biomedical WMT Shared Task (WMT/EMNLP 2022 Message-ID: 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-L?pez (Barcelona Supercomputing Center, Spain) - Darryl Johan Estrada (Barcelona Supercomputing Center, Spain) - Eul?lia Farr?-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) - Eul?lia Farr?-Maduell (Barcelona Supercomputing Center, Spain) - Cristian Grozea (Fraunhofer Institute, Germany) - Antonio Jimeno Yepes (University of Melbourne, Australia) - Salvador Lima-L?pez (Barcelona Supercomputing Center, Spain) - Martin Krallinger (Barcelona Supercomputing Center, Spain) - Aur?lie N?v?ol (Universit? 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 -------------- next part -------------- An HTML attachment was scrubbed... URL: From srhsieh at yahoo.com Fri Jul 29 17:27:29 2022 From: srhsieh at yahoo.com (Shang-Rou Hsieh) Date: Fri, 29 Jul 2022 21:27:29 +0000 (UTC) Subject: [scikit-learn] question regarding 'RANSACRegressor' object has no attribute 'inlier_mask_' References: <1719874980.2277622.1659130049290.ref@mail.yahoo.com> Message-ID: <1719874980.2277622.1659130049290@mail.yahoo.com> 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 -------------- next part -------------- An HTML attachment was scrubbed... URL: From g.lemaitre58 at gmail.com Fri Jul 29 18:06:17 2022 From: g.lemaitre58 at gmail.com (=?utf-8?Q?Guillaume_Lema=C3=AEtre?=) Date: Sat, 30 Jul 2022 00:06:17 +0200 Subject: [scikit-learn] question regarding 'RANSACRegressor' object has no attribute 'inlier_mask_' In-Reply-To: <1719874980.2277622.1659130049290@mail.yahoo.com> References: <1719874980.2277622.1659130049290.ref@mail.yahoo.com> <1719874980.2277622.1659130049290@mail.yahoo.com> Message-ID: <12CB3095-84A7-4623-A29B-F2B87F532D3A@gmail.com> You need to fit the estimator to access the fitted attribute: In [1]: from sklearn.linear_model import RANSACRegressor ...: from sklearn.datasets import make_regression ...: X, y = make_regression( ...: n_samples=200, n_features=2, noise=4.0, random_state=0) ...: reg = RANSACRegressor(random_state=0).fit(X, y) In [2]: In [2]: reg.inlier_mask_ Out[2]: array([ True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True, True]) Cheers, -- Guillaume Lemaitre Scikit-learn @ Inria Foundation https://glemaitre.github.io/ > On 29 Jul 2022, at 23:27, Shang-Rou Hsieh via scikit-learn wrote: > > 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 > > > _______________________________________________ > scikit-learn mailing list > scikit-learn at python.org > https://mail.python.org/mailman/listinfo/scikit-learn -------------- next part -------------- An HTML attachment was scrubbed... URL: From srhsieh at yahoo.com Fri Jul 29 18:41:37 2022 From: srhsieh at yahoo.com (Shang-Rou Hsieh) Date: Fri, 29 Jul 2022 22:41:37 +0000 (UTC) Subject: [scikit-learn] question regarding 'RANSACRegressor' object has no attribute 'inlier_mask_' In-Reply-To: <12CB3095-84A7-4623-A29B-F2B87F532D3A@gmail.com> References: <1719874980.2277622.1659130049290.ref@mail.yahoo.com> <1719874980.2277622.1659130049290@mail.yahoo.com> <12CB3095-84A7-4623-A29B-F2B87F532D3A@gmail.com> Message-ID: <251038468.2288582.1659134497759@mail.yahoo.com> Thanks. I will give it a try. On Friday, July 29, 2022 at 03:06:19 PM PDT, Guillaume Lema?tre wrote: You need to fit the estimator to access the fitted attribute: In [1]: from sklearn.linear_model import RANSACRegressor?? ...: from sklearn.datasets import make_regression?? ...: X, y = make_regression(?? ...: ? ? n_samples=200, n_features=2, noise=4.0, random_state=0)?? ...: reg = RANSACRegressor(random_state=0).fit(X, y) In [2]:? In [2]: reg.inlier_mask_Out[2]:?array([ True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True,? True,? True,? True,? True,? True,? True,? True,? ? ? ? True,? True]) Cheers,--Guillaume Lemaitre Scikit-learn @ Inria Foundation https://glemaitre.github.io/ On 29 Jul 2022, at 23:27, Shang-Rou Hsieh via scikit-learn wrote: 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 _______________________________________________ scikit-learn mailing list scikit-learn at python.org https://mail.python.org/mailman/listinfo/scikit-learn -------------- next part -------------- An HTML attachment was scrubbed... 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