Mailman 3 June 2019 - scikit-learn

Is there any general way to make clustering huge time-series dataset better?
by lampahome June 20, 2019

June 20, 2019

I have a huge time-series dataset and should load batch by batch. My procedures like below: Scale to (0~1) Shuffle (because I use Birch not MiniBatchKMeans) Train Birch model with partial_fit Evaluate with silhouette_score (large is better) Why I use Birch is because it have partial_fit and no need to specify the cluster number But...I found evaluting by silhouette_score and db score, it will cluster with fewer cluster numbers. When I look into the data, it should cluster more than the clustering results. Should I change the evaluating way? or else? thx

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Full-time job opportunity -- software engineer for open source project
by Matteo Caorsi June 19, 2019

June 19, 2019

Who we are: L2F is a start-up based on the EPFL Innovation Park (Lausanne, CH). We are currently working at the frontier of machine learning and topological data analysis, in collaboration with several academic partners. Our Mission: We are developing an open source library implementing new topological data analysis algorithms that are being designed by our research team. The library shall be user-friendly, well documented, high-performance and well integrated with state-of-the-art machine learning libraries (such as scikit-learn and Keras). We are offering a full-time job in our company to help us develop this library. The candidate will work in the L2F research team. Profile description: We are looking for a computer scientist matching these characteristics: * 2+ years of experience in software engineering * Skilled with Python and C++ (in particular, at ease wrapping C++ code for Python) * Aware of how open source communities work. Better if he/she contributed in open-source collaborations, such as scikit-learn. * At ease writing specifications, developer documentation and good user documentation * Fluent with continuous integration, Git and common developer tools * Skilled in testing architectures (unit tests, integration tests,…) How to apply: Applicants can write an e-mail to Dr. Matteo Caorsi (m.caorsi(a)l2f.ch<mailto:m.caorsi@l2f.ch> ) attaching their CV and a short letter detailing their relevant experience and motivation. Starting date: This position is available for immediate start for the right candidate.

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Re: [scikit-learn] Semi-supervised methods
by gael.varoquaux＠normalesup.org June 18, 2019

June 18, 2019

Hi Jonathan, It's very important that you keep discussions on the list, to keep everybody informed, and also to make sure that I am not the bottleneck (I deal terribly with email). Once again, I want to stress that getting code in scikit-learn is a long process (maybe unfortunately). I think that working on a package getting these algorithm out first, before trying to move some upstream to scikit-learn, is the best option. I am saying this despite the fact that I really want scikit-learn to grow and consolidate useful algorithms in one package. It's just a question of being efficient. Cheers, Gaël On Tue, Jun 18, 2019 at 06:31:13PM +0200, Jonatan Gøttcke wrote: > I’ve been reading the sites on scikit-learn now, and my methods actually follow > the methodology of .fit and .predict and all of the graph-methods implemented, > are the very fundamental and established graph approaches for semi-supervised > learning as described by Zhu & Gholdberg in their ”Introduction to > semi-supervised learning”. > Even though the methods fit the bill very well, do you think I should push it > to scikit-learn contrib? And is there a graph algorithm Expert in the Group, > or a semi-supervised maintainer or something, that I can discuss my > implemenations with 😊 > Thanks for getting back so quickly btw. > Cheers > Jonatan M. Gøttcke > CEO @ OpGo > +45 23 65 01 96 -- Gael Varoquaux Senior Researcher, INRIA http://gael-varoquaux.info http://twitter.com/GaelVaroquaux

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Semi-supervised learning contributions
by Jonatan Gøttcke June 18, 2019

June 18, 2019

Hi Scikit-Learn developers. I’m a masters student in Data Mining and Machine Learning from Denmark. I’m finishing my masters in two months time. As a part of my thesis I’m comparing a theoretically sound and interesting semi-supervised algorithm I came up with, to a bunch of other algorithms. The problem was, that all of these basic graph based algorithms didn’t exist in scikit-learn, so I’ve implemented them following a scikit-learn like ”methodology”, but it’s not 100% compatible. Compatibility will require a bit more work, but dependant on how much it is (I don’t know cause I’ve never contributed to scikit-learn before), I might have time to put that in before the deadline (or I could do it as a part of my PhD, and get that as an easy first publication). I’m sure you are more interested in getting the basic graph algorithms in there, than my own interesting (and still unpublished) methods 😉. I would however love to hear if you guys are interested in getting this contribution into scikit-learn.? Cheers Jonatan M. Gøttcke CEO @ OpGo +45 23 65 01 96

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How use get_depth
by Wendley Silva June 17, 2019

June 17, 2019

Hi all, I tried several ways to use the get_depth() method from DecisionTreeRegression, but I always get the same error: self.clf.*get_depth()* AttributeError: *'DecisionTreeRegressor' object has no attribute 'get_depth'* I researched the internet and found no solution. Any idea how to use it correctly? *Description of get_depth():* https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTree… Thanks in advance. Best, *Wendley S. Silva* Universidade Federal do Ceará - Brasil +55 (88) 3695.4608 wendley(a)ufc.br www.ec.ufc.br/wendley Rua Cel. Estanislau Frota, 563, Centro, Sobral-CE, Brasil - CEP 62.010-560

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Concatenate posterior probabilities of different datasets obtained from different models
by Naiping Dong June 13, 2019

June 13, 2019

Hi all, I have several small datasets, each is composed by two classes. The posterior probabilities of different datasets are predicted by different models, which are constructed either by different models having the attribute "predict_proba" or the same algorithm trained by different training data. I wonder whether there exists a method to concatenate these probabilities as a single array so that I can do some inferences from much larger number of probabilities. Thanks in advance. Best regards, Elkan

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How is linear regression in scikit-learn done? Do you need train and test split?
by C W June 13, 2019

June 13, 2019

Hello everyone, I'm new to scikit learn. I see that many tutorial in scikit-learn follows the work-flow along the lines of 1) tranform the data 2) split the data: train, test 3) instantiate the sklearn object and fit 4) predict and tune parameter But, linear regression is done in least squares, so I don't think train test split is necessary. So, I guess I can just use the entire dataset? Thanks in advance!

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LogisticRegression
by Eric J. Van der Velden June 12, 2019

June 12, 2019

Hello, I am learning sklearn from my book of Geron. On page 137 he learns the model of petal widths. When I implements logistic regression myself as I learned from my Coursera course or from my book of Bishop I find that the following parameters are found where the cost function is minimal: In [6219]: w Out[6219]: array([[-21.12563996], [ 12.94750716]]) I used Gradient Descent and Newton-Raphson, both give the same answer. My question is: how can I see after fit() which parameters LogisticRegression() has found? One other question also: when I read the documentation page, https://scikit-learn.org/stable/modules/linear_model.html#logistic-regressi…, I see a different cost function as I read in the books. Thanks.

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How to tune parameters when using partial_fit
by lampahome June 11, 2019

June 11, 2019

as title, I try to cluster a huge data, but I don't know how to tune parameters when clustering. If it's a small dataset, I can use gridsearchcv, but how to if it's a huge data? thx

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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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