🌐 Mailman 3 June 2018 - scikit-learn - python.org

Jeff Levesque: association rules
by Jeffrey Levesque June 12, 2018

June 12, 2018

Hi guys, What are some good approaches for association rules. Is there something built in, or do people sometimes use alternate packages, maybe apache spark? Thank you, Jeff Levesque https://github.com/jeff1evesque

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Jeff Levesque: profit functionality
by Jeffrey Levesque June 12, 2018

June 12, 2018

Hi guys, Does sklearn have both probit, and logic functionality? Thank you, Jeff Levesque https://github.com/jeff1evesque

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scikit-learn-contrib: building Cython, cpp files
by Dylan Fernando June 11, 2018

June 11, 2018

Hi, I would like to publish this: https://github.com/dil12321/scikit-learn/tree/aode https://github.com/scikit-learn/scikit-learn/pull/11093 as a scikit-learn-contrib project. However, I'm not sure how to write the setup.py file so that aode_helper.cpp and _aode.pyx get included in the package, and run correctly. How should I write setup.py? Regards, Dylan

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Deadline Extension: the first International Workshop on Machine Learning in Clinical Neuroimaging (MLCN 2018)
by MLCN Workshop June 11, 2018

June 11, 2018

Dear Colleagues, The paper submission deadline for MLCN 2018 <https://mlcn2018.com/> workshop has been extended to June 25, 2018. ------------------------------------------------------------ ------------------------------------------------------ CALL FOR PAPERS: Recent advances in neuroimaging and statistical machine learning provide an exceptional opportunity for investigators and physicians to discover complex relationships between brain, behaviors, and mental and neurological disorders. MLCN 2018 workshop, as a satellite event of MICCAI 2018, aims to bring together researchers in both theory and application from various fields in domains of spatial statistics, pattern recognition in neuroimaging, and predictive clinical neuroscience. Topics of interests include but are not limited to: - Applications of spatio-temporal modeling in predictive clinical neuroscience - Spatial regularization in decoding clinical neuroimaging data - Spatial statistics in neuroimaging - Learning with structured inputs and outputs in clinical neuroscience - Multi-task learning in analyzing structured neuroimaging data - Deep learning in analyzing structured neuroimaging data - Model stability and interpretability in clinical neuroscience ------------------------------------------------------------ --------------------------------------------------------- CONFIRMED SPEAKERS: Christos Davatzikos (University of Pennsylvania) Gaël Varoquaux (Parietal team, INRIA) Jian Kang (University of Michigan) ------------------------------------------------------------ ---------------------------------------------------------- SUBMISSION PROCESS: The workshop seeks high quality, original, and unpublished work on algorithms, theory, and applications of machine learning in clinical neuroimaging and spatially structured data analysis. Papers should be submitted electronically in Springer Lecture Notes in Computer Science (LCNS) style of up to 8-pages papers using the CMT system at https://cmt3.research.microsoft.com/MLCN2018. This 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 conference. ------------------------------------------------------------ ----------------------------------------------------------- IMPORTANT DATES: Paper submission deadline: June 25, 2018 Notification of Acceptance: July 16, 2018 Camera-ready Submission: July 23, 2018 Workshop Date: September 20, 2018 ------------------------------------------------------------ ------------------------------------------------------ Best regards, MLCN 2018 Organizing Committee, Email: mlcnworkshop(a)gmail.com Website: https://mlcn2018.com/ twitter: @MLCN2018

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Trained model repository?
by Francois Dion June 8, 2018

June 8, 2018

Does anybody know of a repo or site that has scikit-learn pre-trained models / pipelines? There are specific projects that might include a model in their github repo (I've done that for a PyData talk in the past), and I've also seen specific frameworks including some pre-trained neural networks (keras and caffe2 for example), but I don't think there's anything for scikit models. I've asked around and on twitter, but nothing. I figured, if anybody would know, it would have to be on the sklearn list. Francois

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PyCM: Multiclass confusion matrix library in Python
by Sepand Haghighi June 6, 2018

June 6, 2018

PyCM is a multi-class confusion matrix library written in Python that supports both input data vectors and direct matrix, and a proper tool for post-classification model evaluation that supports most classes and overall statistics parameters. PyCM is the swiss-army knife of confusion matrices, targeted mainly at data scientists that need a broad array of metrics for predictive models and an accurate evaluation of large variety of classifiers. Github Repo : https://github.com/sepandhaghighi/pycm Webpage : http://pycm.shaghighi.ir/ JOSS Paper : https://doi.org/10.21105/joss.00729

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2018 John Hunter Excellence in Plotting Contest
by Nelle Varoquaux June 6, 2018

June 6, 2018

Hello everyone, Sorry about the cross-posting. There's a couple more days to submit to the John Hunter Excellence in Plotting Competition! If you have any scientific plot worth sharing, submit an entry before June 8th. For more information, see below. Thanks, Nelle In memory of John Hunter, we are pleased to be reviving the SciPy John Hunter Excellence in Plotting Competition for 2018. This open competition aims to highlight the importance of data visualization to scientific progress and showcase the capabilities of open source software. Participants are invited to submit scientific plots to be judged by a panel. The winning entries will be announced and displayed at the conference. John Hunter’s family and NumFocus are graciously sponsoring cash prizes for the winners in the following amounts: - 1st prize: $1000 - 2nd prize: $750 - 3rd prize: $500 - Entries must be submitted by June, 8th to the form at https://goo.gl/forms/7q86zgu5OYUOjODH3 <https://goo.gl/forms/7q86zgu5OYUOjODH3>. - Winners will be announced at Scipy 2018 in Austin, TX. - Participants do not need to attend the Scipy conference. - Entries may take the definition of “visualization” rather broadly. Entries may be, for example, a traditional printed plot, an interactive visualization for the web, or an animation. - Source code for the plot must be provided, in the form of Python code and/or a Jupyter notebook, along with a rendering of the plot in a widely used format. This may be, for example, PDF for print, standalone HTML and Javascript for an interactive plot, or MPEG-4 for a video. If the original data can not be shared for reasons of size or licensing, "fake" data may be substituted, along with an image of the plot using real data. - Each entry must include a 300-500 word abstract describing the plot and its importance for a general scientific audience. - Entries will be judged on their clarity, innovation and aesthetics, but most importantly for their effectiveness in communicating a real-world problem. Entrants are encouraged to submit plots that were used during the course of research or work, rather than merely being hypothetical. - SciPy reserves the right to display any and all entries, whether prize-winning or not, at the conference, use in any materials or on its website, with attribution to the original author(s). SciPy John Hunter Excellence in Plotting Competition Co-Chairs Thomas Caswell Michael Droettboom Nelle Varoquaux

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Re: [scikit-learn] PyCM: Multiclass confusion matrix library in Python
by Sepand Haghighi June 4, 2018

June 4, 2018

Hi Stuart Thanks ;-) Activation threshold is in our plan and will be added in next release (in the next few weeks) Best RegardsSepand Haghighi On Thursday, May 31, 2018, 9:56:43 PM GMT+4:30, Stuart Reynolds <stuart(a)stuartreynolds.net> wrote: Hi Sepand, Thanks for this -- looks useful. I had to write something similar (for the binary case) and wish scikit had something like this. I wonder if there's something similar for the binary class case where, the prediction is a real value (activation) and from this we can also derive - CMs for all prediction cutoff (or set of cutoffs?) - scores over all cutoffs (AUC, AP, ...) For me, in analyzing (binary class) performance, reporting scores for a single cutoff is less useful than seeing how the many scores (tpr, ppv, mcc, relative risk, chi^2, ...) vary at various false positive rates, or prediction quantiles. Does your library provide any tools for the binary case where we add an activation threshold? Thanks again for releasing this and providing pip packaging. - Stuart On Thu, May 31, 2018 at 6:05 AM, Sepand Haghighi via scikit-learn <scikit-learn(a)python.org> wrote: > PyCM is a multi-class confusion matrix library written in Python that > supports both input data vectors and direct matrix, and a proper tool for > post-classification model evaluation that supports most classes and overall > statistics parameters. PyCM is the swiss-army knife of confusion matrices, > targeted mainly at data scientists that need a broad array of metrics for > predictive models and an accurate evaluation of large variety of > classifiers. > > Github Repo : https://github.com/sepandhaghighi/pycm > > Webpage : http://pycm.shaghighi.ir/ > > JOSS Paper : https://doi.org/10.21105/joss.00729 > > > > > > > > > > _______________________________________________ > scikit-learn mailing list > scikit-learn(a)python.org > https://mail.python.org/mailman/listinfo/scikit-learn >

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Novel efficient one-shot optimizer for regression
by pyformulas June 2, 2018

June 2, 2018

Hi, I created an algorithm that may solve linear regression problems with less time complexity than Singular Value Decomposition. It only requires the gradient and the diagonal of the hessian to calculate the optimal weights. I attached the Tensorflow code below. I haven't been able to get it to work in pure NumPy yet, but I'm sure someone will be able port it if it really does what it purports to do. import numpy as np Y = np.arange(10).reshape(10,1)**0.5 bias_X = np.ones(10).reshape(10,1) X_feature1 = Y**3 X_feature2 = Y**4 X_feature3 = Y**5 X = np.concatenate((bias_X, X_feature1, X_feature2, X_feature3), axis=1) num_features = 4 import tensorflow as tf X_in = tf.placeholder(tf.float64, [None,num_features]) Y_in = tf.placeholder(tf.float64, [None,1]) W = tf.placeholder(tf.float64, [num_features,1]) W_squeezed = tf.squeeze(W) Y_hat = tf.expand_dims(tf.tensordot(X_in, W_squeezed, ([1],[0])), axis=1) loss = tf.reduce_mean(Y - Y_hat)**2 gradient = tf.gradients(loss, [W_squeezed])[0] gradient_2nd = tf.diag_part(tf.hessians(loss, [W_squeezed])[0]) vertex_offset = -gradient/gradient_2nd/num_features W_star = W_squeezed + vertex_offset W_star = tf.expand_dims(W_star, axis=1) with tf.Session() as sess: random_W = np.random.normal(size=(num_features,1)).astype(np.float64) result1 = sess.run([loss, W_star, gradient, gradient_2nd], feed_dict={X_in:X, Y_in:Y, W:random_W}) random_loss = result1[0] optimal_W = result1[1] print('Random loss:',result1[0]) print('Gradient:', result1[-2]) print("2nd-order Gradient:", result1[-1]) print("W:") print(random_W) print() print("W*:") print(result1[1]) print() optimal_loss = sess.run(loss, feed_dict={X_in:X, Y_in:Y, W:optimal_W}) print('Optimal loss:', optimal_loss)

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