🌐 Mailman 3 March 2021 - scikit-learn - python.org

[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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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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CFP MESINESP2 (BioASQ / CLEF2021 shared task) on semantic indexing of heterogenous health content: literature, clinical trials and patents
by Martin Krallinger March 26, 2021

March 26, 2021

**** CFP MESINESP2 track:* *Medical Semantic Indexing (BioASQ – CLEF 2021) **** https://temu.bsc.es/mesinesp2/ Plan TL Award for MESINESP2 winners There is a pressing need to improve information access, retrieval, classification, semantic annotation as well as integration across multiple document types, in particular for health-related content such as literature, clinical trials and medicinal patents. This is especially true for multilingual content from heterogeneous sources (cross-genre), where for instance many of the initially reported COVID-19 case reports were published in a variety of languages, a considerable fraction being non-English publications. Due to the significant practical impact of advanced semantic indexing technologies in health, and the direct collaboration and interest in the generated results by collaborating international and national healthcare organizations (BIREME/WHO, ISCIII/Spain) we are organizing the MESINESP2 shared task in collaboration with the well-established BioASQ (CLEF2021) initiative. A variety of complementary strategies were explored so far for semantic indexing of health-content including (extreme) multi-label classification, multilingual X-BERT, transformers, graph matching, text similarity, string matching/term indexing, named entity recognition or machine translation components. Inspired by the settings of past BioASQ tracks and our BioCreative corpora (CHEMPROT, BC4CHEMD/CHEMDNER) included in popular benchmark datasets like BioBERT, we propose the following three MESINESP2 subtracks: *1. MESINESP-L – Scientific Literature *(sub-track 1): This track will require automatic indexing with DeCS terms (similar to MeSH) of abstracts using two highly used databases in Spanish (IBECS and LILACS). *2. MESINESP-T – Clinical trials *(sub-track 2): This track will require automatic indexing with DeCS terms of clinical trials from REEC (Registro Español de Estudios Clínicos). *3. MESINESP-P – Patents *(sub-track 3)*: *This track will require automatic indexing with DeCS terms the content of Spanish patents extracted from Google Patents. *Key information* 1. MESINESP2 web, info & detailed description: https://temu.bsc.es/mesinesp2 2. Registration for MESINESP2: http://clef2021-labs-registration.dei.unipd.it/ and register to *Task 3 – Task MESINESP: Medical Semantic Indexing In Spanish *(Which is part of the workshop “BioASQ - Large-scale biomedical semantic indexing and question answering”) 3. Datasets: https://zenodo.org/record/4634129#.YFu0MZ1KiUl <https://zenodo.org/record/4634129#.YFu0MZ1KiUl> *Task impact* We foresee that the systems resulting from MESINESP2 will provide directly useful for a variety of use case scenarios beyond literature indexing, including competitive intelligence, prior art searches, complex search queries for systematic reviews, evidence-based medicine, decision making, as well database curation, elaboration of clinical practice guidelines. Moreover the document selection criteria of MESINESP2 considered additional scenarios of future tasks on semantic indexing of medical records. *Important dates* March, 17: Train set and guidelines release March, 17: First development set release April, 15: Test and Background set release April, 30: BioASQ9 Lab @CLEF 2021 Registration Deadline April, 30: End of the evaluation period May, 28: Submission of Participant Papers at CLEF2021 July, 2: Camera-ready paper submission Sep 21-24: CLEF 2021 Conference *Publications and workshop* The MESINESP2 track results will be presented at the BioASQ workshop *allocated at CLEF 2021* (http://clef2021.clef-initiative.eu). Participating teams will be invited to present their systems and obtained results. Moreover, participating teams will be invited to submit their system description papers for publication at the *CLEF 2021 Working Notes proceedings*. *MESINESP2 awards* There will be awards for the top-scoring teams promoted by the Spanish Plan for the Advancement of Language Technology (Plan TL) and the Barcelona Supercomputing Center (BSC). *Main Track organizers* ● *Martin Krallinger*, Barcelona Supercomputing Center (BSC), Spain. ● *Luis Gascó*, Barcelona Supercomputing Center (BSC), Spain. ● *Anastasios Nentidis*, National Center for Scientific Research Demokritos, Greece. ● *Elena Primo-Peña,* Biblioteca Nacional de Ciencias de Salud. Instituto de Salud Carlos III, Spain. ● *Cristina Bojo Canales, *Biblioteca Nacional de Ciencias de la Salud. Instituto de Salud Carlos III, Spain. ● *George Paliouras*, National Center for Scientific Research Demokritos, Greece. ● *Anastasia Krithara*, National Center for Scientific Research Demokritos, Greece. ● *Renato Murasaki, *BIREME – Organización Panamericana de la Salud (WHO), Brasil. *Scientific Committee* ● *David Camacho*, Applied Intelligence and Data Analysis Research Group, Universidad Politécnica de Madrid (Spain) ● *Oscar Corcho*, Ontology Engineering Group, Universidad Politécnica de Madrid (Spain) ● *Parminder Batia*, Amazon Health AI (USA) ● *Irena Spasic*, School of Computer Science & Informatics, co-Director of the Data Innovation Research Institute, Cardiff University (UK) ● *Jose Luis Redondo García, *Amazon Alexa, Amazon (UK) ● *Carlos Badenes-Olmedo*, Ontology Engineering Group, Universidad Politécnica de Madrid (Spain)

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running examples
by James Bunn March 24, 2021

March 24, 2021

Hi, I am a new user trying to run the Visualization of MLP weights on MNIST example for neural networks. I am not able to get the example to run. I loaded the scikitlearn and matplotlib packages called in the program, but still it will not work. Is there any more I need to do? My error text is below. Thank you, James "C:\Users\James\PycharmProjects\MATH541 Project\venv\Scripts\python.exe" C:/Users/James/Documents/MATH541/plot_mnist_filters.py C:\Users\James\PycharmProjects\MATH541 Project\venv\lib\site-packages\sklearn\datasets\_openml.py:65: RuntimeWarning: Invalid cache, redownloading file warn("Invalid cache, redownloading file", RuntimeWarning) ===================================== Visualization of MLP weights on MNIST ===================================== Sometimes looking at the learned coefficients of a neural network can provide insight into the learning behavior. For example if weights look unstructured, maybe some were not used at all, or if very large coefficients exist, maybe regularization was too low or the learning rate too high. This example shows how to plot some of the first layer weights in a MLPClassifier trained on the MNIST dataset. The input data consists of 28x28 pixel handwritten digits, leading to 784 features in the dataset. Therefore the first layer weight matrix have the shape (784, hidden_layer_sizes[0]). We can therefore visualize a single column of the weight matrix as a 28x28 pixel image. To make the example run faster, we use very few hidden units, and train only for a very short time. Training longer would result in weights with a much smoother spatial appearance. The example will throw a warning because it doesn't converge, in this case this is what we want because of CI's time constraints. Traceback (most recent call last): File "C:\Users\James\PycharmProjects\MATH541 Project\venv\lib\site-packages\sklearn\utils\__init__.py", line 1081, in check_pandas_support import pandas # noqa ModuleNotFoundError: No module named 'pandas' The above exception was the direct cause of the following exception: Traceback (most recent call last): File "C:\Users\James\PycharmProjects\MATH541 Project\venv\lib\site-packages\sklearn\datasets\_openml.py", line 61, in wrapper return f(*args, **kw) File "C:\Users\James\PycharmProjects\MATH541 Project\venv\lib\site-packages\sklearn\datasets\_openml.py", line 518, in _load_arff_response parsed_arff = parse_arff(arff) File "C:\Users\James\PycharmProjects\MATH541 Project\venv\lib\site-packages\sklearn\datasets\_openml.py", line 332, in _convert_arff_data_dataframe pd = check_pandas_support('fetch_openml with as_frame=True') File "C:\Users\James\PycharmProjects\MATH541 Project\venv\lib\site-packages\sklearn\utils\__init__.py", line 1084, in check_pandas_support raise ImportError( ImportError: fetch_openml with as_frame=True requires pandas. During handling of the above exception, another exception occurred: Traceback (most recent call last): File "C:\Users\James\PycharmProjects\MATH541 Project\venv\lib\site-packages\sklearn\utils\__init__.py", line 1081, in check_pandas_support import pandas # noqa ModuleNotFoundError: No module named 'pandas' The above exception was the direct cause of the following exception: Traceback (most recent call last): File "C:\Users\James\Documents\MATH541\plot_mnist_filters.py", line 36, in <module> X, y = fetch_openml('mnist_784', version=1, return_X_y=True) File "C:\Users\James\PycharmProjects\MATH541 Project\venv\lib\site-packages\sklearn\utils\validation.py", line 63, in inner_f return f(*args, **kwargs) File "C:\Users\James\PycharmProjects\MATH541 Project\venv\lib\site-packages\sklearn\datasets\_openml.py", line 915, in fetch_openml bunch = _download_data_to_bunch(url, return_sparse, data_home, File "C:\Users\James\PycharmProjects\MATH541 Project\venv\lib\site-packages\sklearn\datasets\_openml.py", line 633, in _download_data_to_bunch out = _retry_with_clean_cache(url, data_home)( File "C:\Users\James\PycharmProjects\MATH541 Project\venv\lib\site-packages\sklearn\datasets\_openml.py", line 69, in wrapper return f(*args, **kw) File "C:\Users\James\PycharmProjects\MATH541 Project\venv\lib\site-packages\sklearn\datasets\_openml.py", line 518, in _load_arff_response parsed_arff = parse_arff(arff) File "C:\Users\James\PycharmProjects\MATH541 Project\venv\lib\site-packages\sklearn\datasets\_openml.py", line 332, in _convert_arff_data_dataframe pd = check_pandas_support('fetch_openml with as_frame=True') File "C:\Users\James\PycharmProjects\MATH541 Project\venv\lib\site-packages\sklearn\utils\__init__.py", line 1084, in check_pandas_support raise ImportError( ImportError: fetch_openml with as_frame=True requires pandas. Process finished with exit code 1

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Monthly meeting March 29th 2021
by Chiara Marmo March 22, 2021

March 22, 2021

Dear list, The scikit-learn monthly meeting will take place on Monday March 29th at 8PM 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…> <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/3KKinuJ-TSeVY_Rs9IeyqQ#Contributors>" section, of the public pad: <https://hackmd.io/qtKt7pTNSXanU-MJOIMxbw> https://hackmd.io/3KKinuJ-TSeVY_Rs9IeyqQ Best Chiara

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Outlier Detection Problem
by Pham Gia Minh March 21, 2021

March 21, 2021

Hello scikit-learn group. I am a beginner to libsvm. I'm having an outlier detection problem. I train the data in the format <label> <index1>: <value1> <index2>: <value2> ... in one-class SVM and get a model file, then I predict test file and model file to get be accuracy. I want to draw one-class borders using the same interface as this article: https://scikit-learn.org/stable/auto_examples/miscellaneous/plot_anomaly_co… but different input got me error (not in type <index:value>). Can you help me? I hope for help in solving this problem. Thank you very much.

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[Vote] SLEP006: Routing sample-aligned metadata
by Joel Nothman March 9, 2021

March 9, 2021

With thanks to Alex, Adrin and Christian, we have a proposal to implement what we used to call "sample props" that should be expressive enough for us to resolve tens of issues and PRs, but will be largely unobtrusive for most current users. Core developers, please cast your vote in this PR <https://github.com/scikit-learn/enhancement_proposals/pull/52> after considering the proposal here <https://scikit-learn-enhancement-proposals.readthedocs.io/en/latest/slep006…>, which has a partial implementation in #16079 <https://github.com/scikit-learn/scikit-learn/pull/16079>. In brief, the problem we are trying to solve: Scikit-learn has limited support for information pertaining to each sample (henceforth “sample properties”) to be passed through an estimation pipeline. The user can, for instance, pass fit parameters to all members of a FeatureUnion, or to a specified member of a Pipeline using dunder (__) prefixing: >>> from sklearn.pipeline import Pipeline>>> from sklearn.linear_model import LogisticRegression>>> pipe = Pipeline([('clf', LogisticRegression())])>>> pipe.fit([[1, 2], [3, 4]], [5, 6],... clf__sample_weight=[.5, .7]) Several other meta-estimators, such as GridSearchCV, support forwarding these fit parameters to their base estimator when fitting. Yet a number of important use cases are currently not supported. Features we currently do not support and wish to include: - passing sample properties (e.g. sample_weight <https://scikit-learn.org/stable/glossary.html#term-sample_weight>) to a scorer used in cross-validation - passing sample properties (e.g. groups <https://scikit-learn.org/stable/glossary.html#term-groups>) to a CV splitter in nested cross validation - passing sample properties (e.g. sample_weight <https://scikit-learn.org/stable/glossary.html#term-sample_weight>) to some scorers and not others in a multi-metric cross-validation setup Solution: Each consumer requests A meta-estimator provides along to its children only what they request. A meta-estimator needs to request, on behalf of its children, any metadata that descendant consumers request. Each object that could receive metadata should have a method called get_metadata_request() which returns a dict that specifies which metadata is consumed by each of its methods (keys of this dictionary are therefore method names, e.g. fit <https://scikit-learn.org/stable/glossary.html#term-fit>, transform <https://scikit-learn.org/stable/glossary.html#term-transform> etc.). Estimators supporting weighted fitting may return {} by default, but have a method called request_sample_weight which allows the user to specify the requested sample_weight <https://scikit-learn.org/stable/glossary.html#term-sample_weight> in each of its methods. make_scorer accepts request_metadata as keyword parameter through which the user can specify what metadata is requested. Regards, Joel

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scikit-learn github label wiki
by Chiara Marmo March 9, 2021

March 9, 2021

Dear list, dear triagers, dear core-devs I have started a page on the scikit-learn wiki, about the use of labels in the scikit-learn github repository: https://github.com/scikit-learn/scikit-learn/wiki/label The page is meant to provide help in labeling issues and pull requests, improving the standardization of the workflow and perhaps accelerate the automation process (thanks to the identification of 'obvious' procedures). The idea in the end is to make the review more understandable for the contributors but also making reviewers trust the system a bit more ... :) Triagers and core devs , feel free to comment and edit there. List, feel free to comment and ask questions about the triaging process in this mailing list, any help is welcome! Thanks for your attention. Best, Chiara

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Data Umbrella: AFME Sprint Report
by Reshama Shaikh March 8, 2021

March 8, 2021

Hello, The Data Umbrella AFME (Africa & Middle East) scikit-learn sprint was on February 6, 2021, and the report is now available. [a] For folks who are interested in contributing, or an educator who would like to share with students, there are resources for getting started in contributing to scikit-learn. [b] [a] https://reshamas.github.io/data-umbrella-afme-2021-scikit-learn-sprint-repo… [b] https://www.dataumbrella.org/open-source/contributing-to-scikit-learn 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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Question RE: skLearn Logistic Regression
by The Helmbolds March 6, 2021

March 6, 2021

I have a case with binary results and 1-D features, like: X = np.array(-3,-2,-1,0,1,2,3,) and y = np.array(0, 0, 0, 1, 1, 1, 1) only longer arrays (about 180 entries in each array) of this general type. So this should be the "simplest" case. Altho I've tried several variations of the Logistic input formats, in LogisticRegression.fit(X, y) they keep being rejected with the most common error message being Missing argument y I assure you I do indeed have an array "y" that is passed to "fit" So----What do I have to do to get Logistic Regression to accept 1-D features?

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