🌐 Mailman 3 May 2019 - scikit-learn - python.org

Fwd: Proposing Encoder class to encode Ordinal attributes
by prudhviraj nitjsr May 14, 2019

May 14, 2019

Hi, Can someone please respond. Any response would be appreciated Thanks ---------- Forwarded message --------- From: prudhviraj nitjsr <prudhvirajnitjsr(a)gmail.com> Date: Sun, May 12, 2019 at 1:38 AM Subject: Proposing Encoder class to encode Ordinal attributes To: <scikit-learn(a)python.org> Hi All, Recently, when i was solving some ML problem, I came accross an attribute which has Ordinal Values . Eg: Student ID | Subjects ======================================== 1 | ['Math'] 2 | ['Math','Python'] 3 | ['C'] 4 | ['Python','Statistics'] ======================================== Here, attribute Subjects is a list which contains list of subjects the student is interested in. We have sklearn.preprocessing.OneHotEncoder which encodes a single Categorical variable by creating multiple columns. Similarily, I want to propose different encoder that encodes this type of list and creates new columns , one column for each subject. Allowed values are 1/0 which specifies whether student is interested in this subject or not. I'm new to Open Source contribution. Can someone tell me If there is an existing feature that handles this type of data or If I can start working on this feature. Any response would be appreciated. Thanks Prudvi RajKumar

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Proposing Encoder class to encode Ordinal values of an attribute
by prudhviraj nitjsr May 11, 2019

May 11, 2019

Hi All, Recently, when i was solving some ML problem, I came accross an attribute which has Ordinal Values . Eg: Student ID | Subjects ======================================== 1 | ['Math'] 2 | ['Math','Python'] 3 | ['C'] 4 | ['Python','Statistics'] ======================================== Here, attribute Subjects is a list which contains list of subjects the student is interested in. We have sklearn.preprocessing.OneHotEncoder which encodes a single Categorical variable by creating multiple columns. Similarily, I want to propose different encoder that encodes this type of list and creates new columns , one column for each subject. Allowed values are 1/0 which specifies whether student is interested in this subject or not. I'm new to Open Source contribution. Can someone tell me If there is an existing feature that handles this type of data or If I can start working on this feature. Any response would be appreciated. Thanks Prudvi RajKumar

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MEDDOCAN Shared task for Named Entity Recognition and Classification with Scikit-Learn
by Martin Krallinger May 2, 2019

May 2, 2019

*IberLEF/SEPLN: CFP MEDDOCAN track & task prize: named entity recognition and sensitive personal information identification* ***** *CFP MEDDOCAN track **** *First Medical Document Anonymization * *http://temu.bsc.es/meddocan <http://temu.bsc.es/meddocan>* *SEAD – Plan TL Sponsoring Track Awards* Sub-tracks: 1,000€, 500€ and 200€ (first, second, third team) *Task description* Scikit-Learn has been successfully used for Named Entity Recognition and Classification tasks in the past, showing that it is specially competitive for fining mentions of entities in running text. Clinical records with protected health information (PHI) cannot be directly shared as is, due to privacy constraints, making it particularly cumbersome to carry out NLP research in the medical domain. A necessary precondition for accessing clinical records outside of hospitals is their de-identification, i.e., the exhaustive removal (or replacement) of all mentioned PHI phrases. The practical relevance of anonymization or de-identification of clinical texts motivated the proposal of two shared tasks, the 2006 and 2014 de-identification tracks, organized under the umbrella of the i2b2 (*i2b2.org <http://i2b2.org>*) community evaluation effort. The i2b2 effort has deeply influenced the clinical NLP community worldwide, but was focused on documents in English and covering characteristics of US-healthcare data providers. As part of the IberLEF 2019 (*https://sites.google.com/view/iberlef-2019 <https://sites.google.com/view/iberlef-2019>*) initiative, we announce *the first community challenge task specifically devoted to the anonymization of medical documents in Spanish*, called the MEDDOCAN (Medical Document Anonymization) track. In order to carry out these tasks we have prepared a synthetic corpus of 1000 clinical case studies. This corpus was selected manually by a practicing physician and augmented with PHI information from discharge summaries and medical genetics clinical records. The MEDDOCAN task will be structured into *two sub-tracks*: - NER offset and entity type classification - Sensitive span detection. *Publications* Teams will be invited to send a workshop proceedings systems description paper, similarly to previous *IberEval* events. We plan to* invite selected works *for full publication in a *Q1 Journal – Special Issue devoted to MEDDOCAN*. Invitation to the special issue will consider multiple aspects such as performance, novelty of the system, availability of the underlying system (software/web-service) as well as the workshop presentation. *Important Dates* - March 18, 2019: Sample set and Evaluation script released. - March 20, 2019: Training set released. - April 4, 2019: Development set released. - April 29, 2019: Test set released (includes background set). - May 17, 2019: End of evaluation period (system submissions). - May 20, 2019: Results posted and Test set with GS annotations released. - May 31, 2019: Working notes paper submission. - June 14, 2019: Notification of acceptance (peer-reviews). - June 28, 2019: Camera ready paper submission. - September 24, 2019: IberLEF 2019 Workshop, Bilbao Spain *Task organizers* - Aitor Gonzalez-Agirre, Barcelona Supercomputing Center. - Ander Intxaurrondo, Barcelona Supercomputing Center. - Jose Antonio Lopez-Martin, Hospital 12 de Octubre. - Montserrat Marimon, Barcelona Supercomputing Center. - Felipe Soares, Barcelona Supercomputing Center. - Marta Villegas, Barcelona Supercomputing Center. - Martin Krallinger, Barcelona Supercomputing Center. *Scientific committee * • Hercules Dalianis, DSV/Stockholm University, Sweden • Christoph Dieterich, Klaus-Tschira-Institute for Computational Cardiology, University Hospital Heidelberg, Germany • Jelena Jacimovic, University of Belgrade, Serbia • Bradley Malin, Vanderbilt University Medical Center, USA • Øystein Nytrø, Norwegian University of Science and Technology, Norway • Patrick Ruch, SIB Text Mining, HES-SO & Swiss Institute of Bioinformatics, Switzerland • Angus Roberts, King’s College London, UK • Arturo Romero Gutiérrez, Ministerio de Sanidad, Servicios Sociales e Igualdad, Spain • Ozlem Uzuner, George Mason University, USA • Alfonso Valencia, Barcelona Supercomputing Center, Spain ============================ Martin Krallinger, Dr. -------------------------------------------------------------------- Head of Biological Text Mining Unit Structural Biology and BioComputing Programme Spanish National Cancer Research Centre (CNIO) -------------------------------------------------------------------- Oficina Técnica General (OTG) del Plan TL en el área de Biomedicina de la Secretaria de Estado de Telecomunicaciones y para la Sociedad de la Información ============================

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Release Candidate for Scikit-learn 0.21
by Joel Nothman May 2, 2019

May 2, 2019

PyPI now has source and binary releases for Scikit-learn 0.21rc2. * Documentation at https://scikit-learn.org/0.21 * Release Notes at https://scikit-learn.org/0.21/whats_new * Download source or wheels at https://pypi.org/project/scikit-learn/0.21rc2/ Please try out the software and help us edit the release notes before a final release. Highlights include: * neighbors.NeighborhoodComponentsAnalysis for supervised metric learning, which learns a weighted euclidean distance for k-nearest neighbors. https://scikit-learn.org/0.21/modules/neighbors.html#nca * ensemble.HistGradientBoostingClassifier and ensemble.HistGradientBoostingRegressor: experimental implementations of efficient binned gradient boosting machines. https://scikit-learn.org/0.21/modules/ensemble.html#gradient-tree-boosting * impute.IterativeImputer: a non-trivial approach to missing value imputation. https://scikit-learn.org/0.21/modules/impute.html#multivariate-feature-impu… * cluster.OPTICS: a new density-based clustering algorithm. https://scikit-learn.org/0.21/modules/clustering.html#optics * better printing of estimators as strings, with an option to hide default parameters for compactness: https://scikit-learn.org/0.21/auto_examples/plot_changed_only_pprint_parame… * for estimator and library developers: a way to tag your estimator so that it can be treated appropriately with check_estimator. https://scikit-learn.org/0.21/developers/contributing.html#estimator-tags There are many other enhancements and fixes listed in the release notes ( https://scikit-learn.org/0.21/whats_new). Please note that Scikit-learn has new dependencies: * joblib >= 0.11, which used to be vendored within Scikit-learn * OpenMP, unless the environment variable SKLEARN_NO_OPENMP=1 when the code is compiled (and cythonized) Happy Learning! >From the Scikit-learn core dev team.

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