Mailman 3 June 2019 - scikit-learn

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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Scikit Learn in a Cray computer
by Mauricio Reis June 30, 2019

June 30, 2019

I'd like to understand how parallelism works in the DBScan routine in SciKit Learn running on the Cray computer and what should I do to improve the results I'm looking at. I have adapted the existing example in [https://scikit-learn.org/stable/auto_examples/cluster/plot_dbscan.html#sphx…] to run with 100,000 points and thus enable one processing time allowing reasonable evaluation of times obtained. I changed the parameter "n_jobs = x", "x" ranging from 1 to 6. I repeated several times the same experiments and calculated the average values of the processing time. n_jobs time 1 21,3 2 15,1 3 14,8 4 15,2 5 15,5 6 15,0 I then get the times that appear in the table above and in the attached image. As can be seen, there was only effective gain when "n_jobs = 2" and no difference for larger quantities. And yet, the gain was only less than 30%!! Why were the gains so small? Why was there no greater gain for a greater value of the "n_jobs" parameter? Is it possible to improve the results I have obtained? -- Ats., Mauricio Reis

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BIRCH fro streaming data
by Shukla Sharma June 30, 2019

June 30, 2019

Dear Scikit Team, I am working to build a recommendation model on streaming data. Algorithm used: BIRCH # trained with 1000 records birch_model.partial_fit(x) label count is 1000 and cluster count is 4 joblib_file = filename joblib.dump(birch_model, joblib_file) joblib_model = joblib.load(joblib_file) # trained with 100 new records records joblib_model .partial_fit(x) label count is 100 and cluster count is 3 Is it a correct way to apply on streaming data? I could not find any documentation which explains that "when we retrain the model with a new set of records it also carries previous information". Thanks & Regards, Shukla Sharma

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[DEADLINE EXTENSION]: The 2nd International Workshop on Machine Learning in Clinical Neuroimaging (MLCN 2019): ENTERING THE ERA OF BIG DATA VIA TRANSFER LEARNING AND DATA HARMONIZATION
by MLCN Workshop June 30, 2019

June 30, 2019

Dear Colleagues,The submission deadline for MLCN 2019 workshop as a satellite event of the MICCAI 2019 conference has been extended to 14/07/2019. For more information, please visit https://mlcnws.com/call-for-papers/ . Call for Papers Recent advances in neuroimaging and machine learning provide an exceptional opportunity for investigators and physicians to discover complex relationships between brain, behaviors, and mental and neurological disorders. The MLCN 2019 workshop (https://mlcnws.com), as a satellite event of MICCAI 2019 (https://www.miccai2019.org), aims to bring together researchers in both theory and application from various fields in domains such as *e.g.* machine learning, neuroimaging, predictive clinical neuroscience, *etc.* Topics of interests include, but are not limited to: - Transfer learning in clinical neuroimaging - Model stability in transfer learning - Data prerequisites for successful transfer learning - Domain adaptation in neuroimaging - Data harmonization across sites - Data pooling – practical issues - Cross-domain learning in neuroimaging - Interpretability for transfer learning - Unsupervised methods for domain adaptation - Multi-site data analysis, from preprocessing to modeling - Big data in clinical neuroimaging - Scalable machine learning methods - Benefits, problems, and solutions of working with very large datasets SUBMISSION PROCESS: The workshop seeks high quality, original, and unpublished work on algorithms, theory, and applications of machine learning in clinical neuroimaging related to big data, transfer learning, and data harmonization. Papers should be submitted electronically in Springer Lecture Notes in Computer Science (LCNS) style ( https://www.springer.com/gp/computer-science/lncs/conference-proceedings-gu…) with up to 8-pages and using the CMT system at https://cmt3.research.microsoft.com/MLCN2019. The MLCN workshop uses a double-blind review process in the evaluation phase, thus authors must ensure anonymous submissions. Accepted papers will be published in a joint proceeding with the MICCAI 2019 conference. IMPORTANT DATES: - Paper submission deadline: July 14, 2019 (23:59 PST) - Notification of Acceptance: August 5, 2019 - Camera-ready Submission: August 12, 2019 - Workshop Date: October 13, 2019 Best regards, MLCN 2019 Organizing Committee <https://mlcnws.com/organization/>, Email: mlcnworkshop(a)gmail.com Website: https://mlcnws.com/ twitter: @MLCNworkshop <https://twitter.com/MLCNworkshop>

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ANN: imbalanced-learn 0.5.0
by Christos Aridas June 29, 2019

June 29, 2019

Hi all, The new release of imbalanced-learn is already available via pip and conda! The changelog can be found here: http://imbalanced-learn.org/en/stable/whats_new.html#version-0-5 The documentation is available at: http://imbalanced-learn.org/en/stable/ Cheers, Chris

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baggingClassifier with pipeline
by Roxana Danger June 28, 2019

June 28, 2019

Hello, I would like to use the BaggingClassifier whose base estimator is a pipeline with multiple transformations including a DataFrameMapper from sklearn_pandas. I am getting an error during the fitting the DataFrameMapper as the first step of the BaggingClassifier is to convert the DataFrame to an array (see in BaseBagging._fit method). Similar problem happen using directly sklearn.Pipeline instead of the DataFrameMapper. in both cases, a DataFrame is expected as input, but, instead, an array is provided to the Pipeline. Is there anyway I can overcome this problem? Many thanks, Roxana

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titanic dataset, use for book
by Sole Galli June 28, 2019

June 28, 2019

Hello Scikit-learn team, I am currently writing a book for Packt on feature engineering, where I plan to show how to use the newest sklearn transformers. Could I confirm with you whether I can use the titanic dataset located here: titanic_url = ('https://raw.githubusercontent.com/amueller/' 'scipy-2017-sklearn/091d371/notebooks/datasets/titanic3.csv') in the book? The code where I found it, seems to have a BSD license, but I am not sure whether the license extends to the use of the dataset as well. https://scikit-learn.org/stable/auto_examples/compose/plot_column_transform… Many thanks and I look forward to hearing from you Kind regards Sole

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Any drawbacks when using partial_fit?
by lampahome June 27, 2019

June 27, 2019

I try to use Birch to cluster time-series data incrementally. Because insufficient memory, so I train it batch by batch. Every batch is 1000 samples and for 50 batch. I found when I only train the first batch, it cluster well. After first trained, I train following batch with the same model and use partial_fit to train them. I found the clustering result become worse after I trained many rounds until finish. Some samples will mix into another cluster which that seems very different with another samples in the same cluster. Is there any way to make it better? Or I use the wrong method?

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Any way to pre-calculate number of cluster roughly?
by lampahome June 27, 2019

June 27, 2019

I see many ways like elbow method, silhouette score, they all define the cluster number after clustering. Especially the elbow method, I need to monitor the relation with cluster number and find the elbow. But if the dataset is too huge to let me find the elbow and I don't even how many cluster number actually. Any way to pre-calculate number of cluster roughly? thx

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