Mailman 3 December 2018 - scikit-learn

MLPClassifier on WIndows 10 is 4 times slower than that on macOS?
by Kouichi Matsuda Dec. 18, 2018

Dec. 18, 2018

Hi Hi everyone, I am writing a scikit-learn program to use MLPClassifier to learn Fashion-MNIST. The following is the program. It's very simple. When I ran it on Windows 10 (Core-i7-8565U, 1.8GHz, 16GB) note book, it took about 4 minutes. However, when I ran it on MacBook(macOS), it took about 1 minutes. Does anyone help me to understand the reason why Windows 10 is so slow? Am I missing something? Thanks, import os import gzip import numpy as np #from https://github.com/zalandoresearch/fashion-mnist/blob/master/utils/mnist_re… def load_mnist(path, kind='train'): labels_path = os.path.join(path,'%s-labels-idx1-ubyte.gz' % kind) images_path = os.path.join(path,'%s-images-idx3-ubyte.gz' % kind) with gzip.open(labels_path, 'rb') as lbpath: labels = np.frombuffer(lbpath.read(), dtype=np.uint8, offset=8) with gzip.open(images_path, 'rb') as imgpath: images = np.frombuffer(imgpath.read(), dtype=np.uint8, offset=16) images = images.reshape(len(labels), 784) return images, labels x_train, y_train = load_mnist('data', kind='train') x_test, y_test = load_mnist('data', kind='t10k') from sklearn.neural_network import MLPClassifier import time import datetime print(datetime.datetime.today()) start = time.time() mlp = MLPClassifier() mlp.fit(x_train, y_train) print((time.time() - start)/ 60) --- MATSUDA, Kouichi, Ph.D.

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plan to add the association rule classification algorithm in scikit learn
by rui min Dec. 18, 2018

Dec. 18, 2018

Dear scikit-learn developers, I am Rui from Spain, Granada University. Currently I am planning to write an association rule algorithm in scikit-learn. I don’t know if anyone is working on that. So avoid duplication of the work, I would like to ask here. Hope to hear from you soon. Best Regards Rui

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check_estimator and score_samples method
by Jason Rudy Dec. 13, 2018

Dec. 13, 2018

Hi all, I'm working on updating py-earth for some recent changes in scikit-learn and cython. It seems like check_estimator has been significantly improved, and I'm working through making py-earth compliant with it. I've hit the following issue, though. It seems check_estimator tests score_samples using only X as an argument, and py-earth's score_samples requires y as well. So, my question is: must score_samples work with just X (and therefore maybe I should just remove it from py-earth) or is it okay to have a score_samples that requires y, and I should try to find a workaround for check_estimator? Best, Jason

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Difference between linear model and tree-based regressor?
by lampahome Dec. 13, 2018

Dec. 13, 2018

Linear model like linear reg, Lasso reg, Elastic net reg...etc. Tree-based like ExtTree reg, Random forest reg...etc What's the difference between them? I observe one point is below: 1. linear model can be extrapolated? tree-based can't does it

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Why some regression algo can predict multiple out?
by lampahome Dec. 11, 2018

Dec. 11, 2018

As title, apart from sklearn.multioutput.MultiOutputRegressor, almost regression algo in sklearn only can predict 1-d output. Ex: predict 1-d output sklearn.linear_model.SGDRegressor fit(X, y, coef_init=None, intercept_init=None, sample_weight=None) y : numpy array, shape (n_samples,) Ex: predict multiple output sklearn.linear_model.ElasticNet fit(X, y, check_input=True) y : ndarray, shape (n_samples,) or (n_samples, n_targets) There're two kind of output for regression methods. What's the difference?

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Re: [scikit-learn] Question about contributing to scikit-learn
by eamanu15 Dec. 10, 2018

Dec. 10, 2018

Hello Parker, I can tell you my experience. I start to contribute to sklearn two month ago, and I start with code review, this way I can learn how sklearn is written and how is the workflow, read issue and try to solve them. Then, I make some PR. I can tell that the core devs are very friendly and help you always. Specially, I had more contact with Joel Nothman and Andreas Mueller (thanks guys). So, I hope this help you in some way =) Regards! Emmanuel

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Agglomerative clustering
by Jitesh Khandelwal Dec. 10, 2018

Dec. 10, 2018

Hi everyone, I am using agglomerative clustering with an L1 distance matrix as input and the "complete" linkage option. I want to impose an additional constraint. When 2 clusters are combined and the cost of combination is equal for multiple cluster pairs, I want to choose the pair for which the combined cluster has the least size. What is the cleanest and easiest way of achieving this? Thanks, Jitesh

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Re: [scikit-learn] New core dev: Adrin Jalali
by Hanmin Qin Dec. 9, 2018

Dec. 9, 2018

Welcome and thanks for contributing! Hanmin Qin ----- Original Message ----- From: Gael Varoquaux <gael.varoquaux(a)normalesup.org> To: Scikit-learn mailing list <scikit-learn(a)python.org> Subject: Re: [scikit-learn] New core dev: Adrin Jalali Date: 2018-12-09 01:18 Indeed, welcome Adrin, and thanks a lot for your investment on the package! Gaël On Sat, Dec 08, 2018 at 09:26:15AM -0500, Andreas Mueller wrote: > Congratulations and welcome Adrin! > On 12/5/18 5:32 PM, Joel Nothman wrote: > The Scikit-learn core development team has welcomed a new member, Adrin > Jalali, who has been doing some really amazing work in contributing code > and reviews since July (aside from occasional contributions since 2014). > Congratulations and welcome, Adrin! > _______________________________________________ > scikit-learn mailing list > scikit-learn(a)python.org > https://mail.python.org/mailman/listinfo/scikit-learn > _______________________________________________ > scikit-learn mailing list > scikit-learn(a)python.org > https://mail.python.org/mailman/listinfo/scikit-learn -- Gael Varoquaux Senior Researcher, INRIA Parietal NeuroSpin/CEA Saclay , Bat 145, 91191 Gif-sur-Yvette France Phone: ++ 33-1-69-08-79-68 http://gael-varoquaux.info http://twitter.com/GaelVaroquaux _______________________________________________ scikit-learn mailing list scikit-learn(a)python.org https://mail.python.org/mailman/listinfo/scikit-learn

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New core dev: Adrin Jalali
by Joel Nothman Dec. 8, 2018

Dec. 8, 2018

The Scikit-learn core development team has welcomed a new member, Adrin Jalali, who has been doing some really amazing work in contributing code and reviews since July (aside from occasional contributions since 2014). Congratulations and welcome, Adrin!

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Question about contributing to scikit-learn
by parker x Dec. 8, 2018

Dec. 8, 2018

Dear scikit-learn developers, My name is Parker, and I'm a data scientist. Scikit-learn is a great ML library that I work frequently for work and personal projects. I have always wanted to contribute something to the scikit-learn community, and I am wondering if you could give some opinions on following two ideas for contribution. My first idea is to integrate another python library 'imbalanced-learn' into scikit-learn so that people could also use scikit-learn to deal with imbalance issues. Another idea is to combine those scikit-learn built-in feature selection functions into one automated feature selection function that might benefit those users who are not familiar with feature selection process. Looking forward to your suggestions! And thank you very much for your time! Best, Parker

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