Mailman 3 June 2017 - scikit-learn

ANN scikit-learn 0.18.2 numpy 1.12 compatibility release
by Andreas Mueller June 22, 2017

June 22, 2017

Hey everybody. I just pushed the minor release 0.18.2 to pypi. This release contains minor fixed to ensure compatibility with numpy 1.12, mostly for the examples. There is a small fix in the gaussian process module, too. Check the changelog here: http://scikit-learn.org/stable/whats_new.html#version-0-18-2 Stay tuned for the 0.19 release candidate, that we're putting together right now! Cheers, Andy

3 3

R user trying to learn Python
by C W June 20, 2017

June 20, 2017

Dear Scikit-learn, What are some good ways and resources to learn Python for data analysis? I am extremely frustrated using this thing. Everything comes after a dot! Why would you type the sam thing at the beginning of every line. It's not efficient. code 1: y_sin = np.sin(x) y_cos = np.cos(x) I know you can import the entire package without the "as np", but I see np.something as the standard. Why? Code 2: model = LogisticRegression() model.fit(X_train, y_train) model.score(X_test, y_test) In R, everything is saved to a variable. In the code above, what if I accidentally ran model.fit(), I would not know. Code 3: from sklearn import linear_model reg = linear_model.Ridge (alpha = .5) reg.fit ([[0, 0], [0, 0], [1, 1]], [0, .1, 1]) In the code above, sklearn > linear_model > Ridge, one lives inside the other, it feels that there are multiple layer, how deep do I have to dig in? Can someone explain the mentality behind this setup? Thank you very much! M

9 13

Need Help Random Forest Imputation Model as in R
by Akash Devgun June 19, 2017

June 19, 2017

Please let me know .... Do you have random Forest Imputation model in python-scikit learn similar to rfImpute in R has ? Thanks

5 9

Documentation proposal
by Gaël Pegliasco June 16, 2017

June 16, 2017

Hi, First of all, thanks to all contributors for developping a such rich, simple, well documented and easy to use machine learning library for Python ; which, clearly, plays a big role in Python world domination in AI ! As I'm using it more and more these past month, I've written a french tutorial on machine learning introduction: * The Theory (no code here, only describing AI with Python and machine learning concepts with real examples): https://makina-corpus.com/blog/metier/2017/initiation-au-machine-learning-a… * The Practice (using Scikit-Learn) https://makina-corpus.com/blog/metier/2017/initiation-au-machine-learning-a… Another iris tutorial, but with much more details than most I've read using this database and using both supervised and unsupervised learning I've received a few positive returns regarding these 2 articles and others requests to translate it into english. I think that as to translate it into english, you may find it useful to include it into Scikit-Learn official documentation/examples ? So, if you think it can be useful I could work on it as soon as next week. Anyway, any feedback is welcome, especially because I'm not an expert and that it may not be error safe! Thanks again for your great work and keep going on ! Gaël, -- Makina Corpus <http://makina-corpus.com> Newsletters <http://makina-corpus.com/formulaires/newsletters> | Formations <http://makina-corpus.com/formation> | Twitter <https://twitter.com/makina_corpus>

4 3

Sampling capabilities and uncertainty quantification
by roy June 15, 2017

June 15, 2017

Hi, From the doc, I have not found a sampling class. To train a gaussian process for example, It is preferable to use sampling techniques (low discrepancy sequences, uniform designs, etc.). Do you plan to do such a thing? Also, I was wondering if you were planning to add variance analysis (Sobol indices). You already have some PCA so it would be complementary. I have some python codes for both topics if needed. Sincerely, Pamphile

2 1

Help with data parsing (link to stack exchange question)
by Daniel Harris June 14, 2017

June 14, 2017

Hello, I hope this is the correct email address for questions regarding support. I posted my question here on stack exchange: https://bioinformatics.stackexchange.com/q/702/842 Thank you, Daniel

2 1

Re: [scikit-learn] Sk-Learn Presentation
by Gael Varoquaux June 14, 2017

June 14, 2017

You're welcome to download and reuse the PDF from any of the presentations that I have given: https://www.slideshare.net/GaelVaroquaux/presentations None of them is an unbiased comprehensive overview of scikit-learn (not sure how much this possible). Gaël On Tue, Jun 13, 2017 at 08:55:15PM -0300, Nykollas Alves wrote: > I subscribed me. > Em 13/06/2017 20:43, <scikit-learn-owner(a)python.org> escreveu: > This list allows posts by subscribers only. Please subscribe at > https://mail.python.org/mailman/listinfo/scikit-learn to post to the > list. > ---------- Mensagem encaminhada ---------- > From: Nykollas Alves <nicolauarti09(a)gmail.com> > To: scikit-learn(a)python.org > Cc: > Bcc: > Date: Tue, 13 Jun 2017 20:43:18 -0300 > Subject: Sk-Learn Presentation > Good Night, > I'm Nykollas, a student of IS, I will make a presentation in my university > about scikit-learn. > I would like to know if yous have a Power Point template based in > scikit-learn to provide me. > If not, i understand.! > Thanks in advance, > Sorry my english, i not is native american. -- Gael Varoquaux 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

1 0

Need contributors to finish open PRs
by Joel Nothman June 14, 2017

June 14, 2017

We appear to have a number of pull requests where the original contributors are no longer available to address comments and complete them. Some of them even have complete positive reviews. Many are listed here: https://github.com/scikit-learn/scikit-learn/pulls?utf8=%E2%9C%93&q=is%3Aop… Due to their number, it is difficult for core devs to address the comments, as well as deal with reviewing and managing an upcoming release. It would be great to have volunteers take them over (check out the PR branch as per https://help.github.com/articles/checking-out-pull-requests-locally/, add commits, push the branch to your fork, create a pull request noting that you are continuing a previous one and whether you have addressed all comments). Thanks

1 0

Random Forest max_features and boostrap construction parameters interpretation
by Brown J.B. June 6, 2017

June 6, 2017

Dear community, This is a question regarding how to interpret the documentation and semantics of the random forest constructors. In forest.py (of version 0.17 which I am still using), the documentation regarding the number of features to consider states on lines 742-745 of the source code that the search may effectively inspect more than `max_features` when determining the features to pick from in order to split a node. It also states that it is tree specific. Am I correct in: Interpretation #1 - For bootstrap=True, sampling with replacement occurs for the number of training instances available, meaning that the subsample presented to a particular tree will have some probability of containing overlaps and therefore not the full input training set, but for bootstrap=False, the entire dataset will be presented to each tree? Interpretation #2 - Particularly, with the way I interpret the documentation stating that "The sub-sample size is always the same as the original input sample size...", it seems to me that bootstrap=False then provides the entire training dataset to each decision tree, and it is a matter of which feature was randomly selected first from the features given that determines what the tree will become. That would suggest that, if bootstrap=False, and if the number of trees is high but the feature dimensionality is very low, then there is a high possibility that multiple copies of the same tree will emerge from the forest. Interpretation #3 - the feature subset is not subsampled per tree, but rather all features are presented for the subsampled training data provided to a tree ? For example, if the dimensionality is 400 on a 6000-input training dataset that has randomly been subsampled (with bootstrap=True) to yield 4700 unique training samples, then the tree builder will consider all 400 dimensions/features with respect to the 4700 samples, picking at most `max_features` number of features (out of 400) for building splits in the tree? So by default (sqrt/auto), there would be at most 20 splits in the tree? Confirmations, denials, and corrections to my interpretations are _highly_ welcome. As always, my great thanks to the community. With kind regards, J.B. Brown Kyoto University Graduate School of Medicine

2 3

How to best understand scikit-learn and know its modules and methods?
by C W June 5, 2017

June 5, 2017

Dear scikit learn list, I am new to scikit-learn. I am getting confused about LinearRegression. For example, from sklearn.datasets import load_boston from sklearn.linear_model import LinearRegression boston = load_boston() X = boston.data y = boston.target model1 = LinearRegression() model1.fit(X, y) print(model.coef) I got a few questions: 1) When I do model1.fit(X, y), don't I have to save it? Does object model1 automatically gets trained/updated? Since I don't see any output, how do I know what has been done to the model1? 2) Is there a command to see what's masked under sklearn, like sklearn.datasets, sklearn.linear_model, and all of it? 3) Why do we need load_boston() to load boston data? I thought we just imported it, so it should be ready to use. Thank you very much! Mike

3 3