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

Version 0.21! and plot_tree!
by Andrew Howe Dec. 5, 2019

Dec. 5, 2019

I want to say thank you to all the sklearn developers. The breadth and quality of this software is truly breathtaking. Specifically, I want to say thank you very very much for the plot_tree function! I have wasted a lot of effort in the past, on multiple OSes, getting everything to work so I could view the tree.export_graphviz results. Having this new function to plot the trees natively in matplotlib is extremely useful. Thanks again! Andrew <~~~~~~~~~~~~~~~~~~~~~~~~~~~> J. Andrew Howe, PhD LinkedIn Profile <http://www.linkedin.com/in/ahowe42> ResearchGate Profile <http://www.researchgate.net/profile/John_Howe12/> Open Researcher and Contributor ID (ORCID) <http://orcid.org/0000-0002-3553-1990> Github Profile <http://github.com/ahowe42> Personal Website <http://www.andrewhowe.com> I live to learn, so I can learn to live. - me <~~~~~~~~~~~~~~~~~~~~~~~~~~~>

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Using sklearn-crfsuite on Production Systems
by Astha Agarwal Aug. 5, 2019

Aug. 5, 2019

Hi, I'm wondering if anyone is using sklearn-crfsuite on production systems? Is this library suitable for usage in industry on production systems (and not academia) for non-big data problems? Thanks, Astha

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Difference in normalization between Lasso and LogisticRegression + L1
by Jesse Livezey June 10, 2019

June 10, 2019

Hi everyone, I noticed recently that in the Lasso implementation (and docs), the MSE term is normalized by the number of samples https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lass… but for LogisticRegression + L1, the logloss does not seem to be normalized by the number of samples. One consequence is that the strength of the regularization depends on the number of samples explicitly. For instance, in Lasso, if you tile a dataset N times, you will learn the same coef, but in LogisticRegression, you will learn a different coef. Is this the intended behavior of LogisticRegression? I was surprised by this. Either way, it would be helpful to document this more clearly in the Logistic Regression docs (I can make a PR.) https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Logi… Jesse

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Google code reviews
by Andreas Mueller June 7, 2019

June 7, 2019

Hi All. What do you think of https://www.pullrequest.com/googleserve/? It's sponsored code reviews. Could be interesting, right? Best, Andy

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Mahalanobis distance metric in OPTICS
by Omkar Kumbhar May 31, 2019

May 31, 2019

Hello, I was having issues while fitting OPTICS using Mahalanobis metric. I tried many things and had a hard time fitting it to my data distribution. I have replicated the issue in the ipython notebook below. You could also take a look at the html version of the notebook to look at the issues. Can you guide me on how to resolve this bug? PFA, ipython notebook to replicate the issue html of ipynb to look at stack traces. Thanks & Regards, Omkar Kumbhar Associate Data Scientist Innoplexus Consulting Services Pvt. Ltd. www.innoplexus.com Mob : +91- 9579464473 Landline: +91-20-66527300 The Intelligence MachineTM that is disrupting the traditional data and analytics services model © 2011-19 Innoplexus Consulting Services Pvt. Ltd. Unless otherwise explicitly stated, all rights including those in copyright in the content of this e-mail are owned by Innoplexus Consulting Services Pvt Ltd. and all related legal entities. The contents of this e-mail shall not be copied, reproduced, or transmitted in any form without the written permission of Innoplexus Consulting Services Pvt Ltd or that of the copyright owner. The receipt of this mail is the acknowledgement of the receipt of contents; if the recipient is not the intended addressee then the recipient shall notify the sender immediately. The contents are provided for information only and no opinions expressed should be relied on without further consultation with Innoplexus Consulting Services Pvt Ltd. and all related legal entities. While all endeavors have been made to ensure accuracy, Innoplexus Consulting Services Pvt. Ltd. makes no warranty or representation to its accuracy, completeness or fairness and persons who rely on it do so entirely at their own risk. The information herein may be changed or withdrawn at any time without notice. Innoplexus Consulting Services Pvt. Ltd. will not be liable to any client or third party for the accuracy of the information supplied through this service. Innoplexus Consulting Services Pvt. Ltd. accepts no responsibility or liability for the contents of any other site, whether linked to this site or not, or any consequences from your acting upon the contents of another site. -- <https://www.innoplexus.com/news/bio-international-convention>

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PyCM 2.2 released : A general benchmark based comparison of classification models
by Sepand Haghighi May 30, 2019

May 30, 2019

Hi folks Recently we have released new version of PyCM, library for confusion matrix statistical analysis. I thought you might find it interesting. http://www.pycm.ir https://github.com/sepandhaghighi/pycm Changelog : - Negative likelihood ratio interpretation (NLRI) added - Cramer's benchmark (SOA5) added - Matthews correlation coefficient interpretation (MCCI) added #204 - Matthews's benchmark (SOA6) added #204 - F1 macro added - F1 micro added - Accuracy macro added #205 - Compare class score calculation modified - Parameters recommendation for multi-class dataset modified - Parameters recommendation for imbalance dataset modified - README.md modified - Document modified - Logo updated Best RegardsSepand Haghighi

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Re: [scikit-learn] scikit-learn Digest, Vol 38, Issue 18
by Régis Cardoso May 30, 2019

May 30, 2019

Dear, What the dependencies are you talking? Because I have install the Numpy, Scipy and Joblib, this is the necessary programs, ok? I used also this paper below, it is a very nice paper about the programs to data science from Raspberry Pi, but isn't working, when I try $ pytest sklearn, the Raspberry don't working, because don't find the sklearn. I don't know that to do now, I need the sklearn to development my work and don't have more ideia to resolve this problem. https://geoffboeing.com/2016/03/scientific-python-raspberry-pi/ [https://geoffboeing.com/wp-content/uploads/2016/03/raspberry-pi-3-300x225.j…]<https://geoffboeing.com/2016/03/scientific-python-raspberry-pi/> Scientific Python for Raspberry Pi - Geoff Boeing<https://geoffboeing.com/2016/03/scientific-python-raspberry-pi/> A guide to setting up the Python scientific stack, well-suited for geospatial analysis, on a Raspberry Pi 3. The whole process takes just a few minutes. The Raspberry Pi 3 was announced two weeks ago and presents a substantial step up in computational power over its predecessors. It can serve as a functional Wi-Fi connected … Continue reading Scientific Python for Raspberry Pi geoffboeing.com Cardoso, Regis ________________________________ De: scikit-learn <scikit-learn-bounces+regis_cardos=hotmail.com(a)python.org> em nome de scikit-learn-request(a)python.org <scikit-learn-request(a)python.org> Enviado: quarta-feira, 29 de maio de 2019 09:59 Para: scikit-learn(a)python.org Assunto: scikit-learn Digest, Vol 38, Issue 18 Send scikit-learn mailing list submissions to scikit-learn(a)python.org To subscribe or unsubscribe via the World Wide Web, visit https://mail.python.org/mailman/listinfo/scikit-learn or, via email, send a message with subject or body 'help' to scikit-learn-request(a)python.org You can reach the person managing the list at scikit-learn-owner(a)python.org When replying, please edit your Subject line so it is more specific than "Re: Contents of scikit-learn digest..." Today's Topics: 1. Problems with installation Scikit Learn (R?gis Cardoso) 2. Re: Problems with installation Scikit Learn (Guillaume Lema?tre) ---------------------------------------------------------------------- Message: 1 Date: Wed, 29 May 2019 12:16:09 +0000 From: R?gis Cardoso <regis_cardos(a)hotmail.com> To: "scikit-learn(a)python.org" <scikit-learn(a)python.org> Subject: [scikit-learn] Problems with installation Scikit Learn Message-ID: <CP2P15201MB1988BAF383841EE146D140EAF21F0(a)CP2P15201MB1988.LAMP152.PROD.OUTLOOK.COM> Content-Type: text/plain; charset="iso-8859-1" Dear, I subscribled on scikit-learn subscription results now. My name is R?gis i am estudying word2vec and artificial neural network using the scikit-learn and i am trying to install the scikit-learn in a Rasp Berry Pi 3, but i don't achieving. I am using the all comands below and i don't have sucess with the installation. 1? Try - pip install -U scikit-learn 2? Try - sudo install scikit-learn 3? Try - sudo apt-get install gfortran libopenblas-dev liblapack-dev sudo pip install scikit-learn 4? Try - sudo pip3 install scikit-learn 5? Try - pip install scikit-learn I would like to know if there is other form to install the scikit-learn in a Rasp Berry Pi 3? I have the Python 3.6 intalled in my board on this case and also there are installed the Numpy, Scipy and Joblib. Regards, Cardoso, Regis

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MemoryError when evaluate clustering with gridsearchcv
by lampahome May 30, 2019

May 30, 2019

I read a large data into memory and it cost about 2GB ram(I have 4GB ram) Size get from sys.getsizeof(train_X) *63963248* And I evalute clustering with gridsearchcv below: def grid_search_clu(X): def cv_scorer(estimator, X): estimator.fit(X) cluster_labels = estimator.labels_ if hasattr(estimator, 'labels_') else estimator.predict(X)#estimator.predict(X)#.labels_ num_labels = len(set(cluster_labels)) num_samples = len(X) if num_labels == 1 or num_labels == num_samples: return -1 else: return -metrics.davies_bouldin_score(X, cluster_labels) m = cluster.Birch(n_clusters=None, compute_labels=True) m_param = {'branching_factor' : range(10,60,10), 'threshold' : np.arange(0.1, 0.6, 0.1).round(decimals=3) } clf = GridSearchCV(m, m_param, cv=[(slice(None), slice(None))], scoring=cv_scorer, verbose=1, n_jobs=1, return_train_score=False).fit(X) And I got memoryerror, how should I do to solve this? Adjust the parameters' range? thx

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scikit-learn for automatic text indexing/classification shared task : MESINESP /BioASQ
by Martin Krallinger May 29, 2019

May 29, 2019

*** Call for Participation Medical Semantic indexing in Spanish *** Medical Semantic indexing in Spanish BioASQ MESINESP Task http://temu.bsc.es/mesinesp/ Task description Scikit-learn has been successfully used for a variety of text classification tasks on documents in a range of different languages. As part of the BioASQ challenges on biomedical semantic indexing and question answering (http://bioasq.org/), we organize the first task on semantic indexing of Spanish medical texts. The task will address the automatic classification/indexing with structured medical vocabularies (DeCS terms) of abstracts from the IBECS and LILACS databases written in Spanish. The main aim is to promote the development of semantic indexing tools of practical relevance of non-English content, determining the current-state-of-the art, identifying challenges and comparing the strategies and results to those published for English data. In order to measure classification performance, an on-line evaluation system will be maintained. As the true annotations of the articles are not available beforehand, the evaluation procedure will run continuously by providing online results. The participating systems will be assessed for their performance based on two measures, one hierarchical and one flat: the Lowest Common Ancestor F-measure (LCA-F) and the label-based micro F-measure, respectively. Deadlines for submission: The task will run in Autumn 2019 (detailed schedule TBA). Participants, after downloading the released test sets, will have to submit results within a limited time window. The task will run for two consecutive periods (batches) of 5 weeks each. The first batch will start on October, 2019. For further details, please refer to: Additional information is available at http://temu.bsc.es/mesinesp/ and http://participants-area.bioasq.org/general_information/Taskaspanish/ Best regards, Martin Krallinger

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Problems with installation Scikit Learn
by Régis Cardoso May 29, 2019

May 29, 2019

Dear, I subscribled on scikit-learn subscription results now. My name is Régis i am estudying word2vec and artificial neural network using the scikit-learn and i am trying to install the scikit-learn in a Rasp Berry Pi 3, but i don't achieving. I am using the all comands below and i don't have sucess with the installation. 1º Try - pip install -U scikit-learn 2º Try - sudo install scikit-learn 3º Try - sudo apt-get install gfortran libopenblas-dev liblapack-dev sudo pip install scikit-learn 4º Try - sudo pip3 install scikit-learn 5º Try - pip install scikit-learn I would like to know if there is other form to install the scikit-learn in a Rasp Berry Pi 3? I have the Python 3.6 intalled in my board on this case and also there are installed the Numpy, Scipy and Joblib. Regards, Cardoso, Regis

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