🌐 Mailman 3 February 2022 - 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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[ANNOUNCEMENT] scikit-learn 1.0 release
by Adrin June 30, 2023

June 30, 2023

Hi everyone, We're happy to announce the 1.0 release which you can install via pip or conda: pip install -U scikit-learn or conda install -c conda-forge scikit-learn You can read the release highlights under https://scikit-learn.org/stable/auto_examples/release_highlights/plot_relea… and the long list of the changes under https://scikit-learn.org/stable/whats_new/v1.0.html New major features include: mandatory keyword arguments in many places, Spline Transformers, Quantile Regressor, Feature Names Support, a more flexible plotting API, Online One-Class SVM, and much more! This version supports Python versions 3.7 to 3.9. A big thanks to all contributors for making this release possible. Regards, Adrin, On the behalf of the scikit-learn maintainer team.

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Blog posting
by 박상진 Feb. 28, 2022

Feb. 28, 2022

Hello. I am a college student studying machine learning in Korea. I would like to study and organize articles like scikit-learn's wonderful tutorial on my personal blog and post them in Korean. Is there something that might be a problem? I wish scikit-learn continued success and prosperity in the future. Thank you.

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Batch Incremental Learning from Scikit-Multiflow
by Farzana Anowar Feb. 25, 2022

Feb. 25, 2022

Hello Scikit-learn community, I hope you all are doing well! I am currently working with BatchIncrementalClassifier from Scikit-multiflow package. For this BatchIncrementalClassifier, the following example is given: # Setup a data stream stream = SEAGenerator(random_state=1) # Pre-training the classifier with 200 samples X, y = stream.next_sample(200) batch_incremental_cfier = BatchIncrementalClassifier() batch_incremental_cfier.partial_fit(X, y) # Preparing the processing of 5000 samples and correct prediction count n_samples = 0 correct_cnt = 0 while n_samples < 5000 and stream.has_more_samples(): X, y = stream.next_sample() y_pred = batch_incremental_cfier.predict(X) if y[0] == y_pred[0]: correct_cnt += 1 batch_incremental_cfier.partial_fit(X, y) n_samples += 1 # Display results print('Batch Incremental ensemble classifier example') print('{} samples analyzed'.format(n_samples)) print('Performance: {}'.format(correct_cnt / n_samples)) Now my questions are: 1. For pre-training the model, the classifier used 200 samples from the stream, and then it does the prequential evaluation (test-train) on 5000 samples. So, the 200 samples, are they considered as the 1st batch of data from the stream that is just used for pre-training and when the 2nd batch of data (5000) becomes available it does the evaluation based on the pre-train model??? (This makes sense to me, as in this way, we will have influence from the previous pre-trained model) or 2. Is this one batch (200+5000) from the stream where 1st 200 samples have been used to pre-train and the rest of the samples are used for evaluation?? And when the next batch will arrive from the stream, will it does the same thing (200 for pre-training and the rest of them for evaluation)?? (If this is the case, are not we training from the scratch each time which does not keep the BatchIncrementalClassifier as an incremental classifier anymore?) Thanks! -- Best Regards, Farzana Anowar, PhD Candidate Department of Computer Science University of Regina

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scikit-learn monthly developer meeting: Monday February 28 2022
by Thomas J. Fan Feb. 18, 2022

Feb. 18, 2022

Dear all, The scikit-learn developer monthly meeting will take place on Monday February 28 2022 at 20:00 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=… 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

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willing to attend to the sprint on March 12
by Şeyma Bayrak Feb. 15, 2022

Feb. 15, 2022

Hi all, seeing the recent twitter post: How is it possible to attend to the scikit-learn sprint in Paris on March 12? One idea is to implement the "diffusion map embedding" method there - a nonlinear dimensionality technique ;) Best, Şeyma.

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How to implement a callable distance metruc function in scikit-learn TSNE
by Sihawi Khalid Feb. 10, 2022

Feb. 10, 2022

Hi all! I have been using the TSNE module in python’s sklearn to visualize n-dimensional binary valued dataset using ‘hamming’ as the distance metric. I plan on packing the n-dimensional dataset into bits in a uint8 array using numpy’s ‘packbits’ function. To use the TSNE module on the packed dataset, I would have to unpack the new dataset and use the ‘hamming’ distance metric as I have been doing in my original dataset. I however want to write a simple callable function that will unpack just two instances in the packed dataset and measure the hamming distance. In my initial attempt, I used a lambda function as a metric which takes two input arrays, unack them and outputs the hamming distance between the two. Here's an example given 'myPackedDataset' is the packed dataset: tsneCluster = TSNE(learning_rate=rate_val, perplexity=per_val, n_iter=iter_val, metric=lambda x, y: distance.hamming(np.unpackbits(x), np.unpackbits(y))) fit_result = tsne.fit_transform(myPackedDataset) I get this error at the TSNE declaration: TypeError: Expected an input array of unsigned byte data type I am still trying to understand how the callable function option of the TSNE's metric parameter works but I would really appreciate any help.

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Converting Scikit-learn Models to C code
by David Tian Feb. 10, 2022

Feb. 10, 2022

Hi, I use scikit-learn to train machine learning models such as neural networks and SVM in my research. I would like to convert the model files (.joblib files) to C code. I wonder whether there are any software tools to do this? Thank you. David

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Call for Proposals for SciPy 2022!
by Arliss Collins Feb. 6, 2022

Feb. 6, 2022

[image: image.png] Hello Everyone, We are reaching out to you about the 21st annual Scientific Computing with Python Conference — also known as SciPy 2022 <https://www.scipy2022.scipy.org/> — that will be held July 11-17, 2022 in Austin, Texas. The call for submissions <https://www.scipy2022.scipy.org/participate> is now open, and we would love to receive proposals from folks in your organization. We seek submissions for talks and posters <https://www.scipy2022.scipy.org/talk-poster-presentations> by *February 11*. This year, in addition to the general conference track and domain mini-symposia, we are accepting submissions for two specialized tracks: (1) Machine Learning and Data Science, and (2) Data Life Cycle. We are also seeking tutorial <https://www.scipy2022.scipy.org/tutorials> submissions by *February 15.* This year's conference will be held in-person in Austin, Texas. Registration <https://www.scipy2022.scipy.org/attend> opens February 3 and early bird rates end May 1. Scholarships are available this year – if you’d like to apply for a scholarship to attend, please fill out this form <https://docs.google.com/forms/d/e/1FAIpQLSdZ_xCt0HbNOhsn3UR4qFf8dhqs0td47Ij…>. The deadline for scholarships <https://www.scipy2022.scipy.org/financial-aid> is *March 13*. The conference will conclude with two days of Sprints <https://www.scipy2022.scipy.org/sprints> focused on developing and maintaining open source Python scientific software. In addition we are proud to offer again our Mentorship Program and networking events with focused topics like “Careers in Data Science.” You can find out more about our programming at https://www.scipy2022.scipy.org/about. The full schedule will be published later in May. In the meantime, please check out last year’s schedule <https://www.scipy2021.scipy.org/schedule> and videos of last year’s talks and events <https://www.youtube.com/playlist?list=PLYx7XA2nY5GesARqNMImG3NnX3_bWq-lT> to get a better idea of what SciPy conferences are like. SciPy 2022 is shaping up to be a fantastic conference. We hope you'll join us in July! SciPy Organizers

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Scikit-learn got a prize in France
by Gael Varoquaux Feb. 6, 2022

Feb. 6, 2022

Hi everyone, It has just been announced that scikit-learn has received a prize for open-source scientific software from the French government: https://twitter.com/Osec2022/status/1489973348637585416 I knew that we were short-listed, but I only got confirmation that we would receive it a few minutes ago. I'm listed on the tweet, but the prize is for the software, and not an individual, and to the whole community, which I of course mentioned in the acceptance speech. As far as I know, this is not going to make any one of us rich, unfortunately: I think that there is no money involved. Thanks a lot for being part of this adventure, Gaël -- Gael Varoquaux Research Director, INRIA http://gael-varoquaux.info http://twitter.com/GaelVaroquaux

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