Re: [scikit-learn] [ANN] scikit-learn 1.1 release

awesome !  Alex On Thu, May 12, 2022 at 6:53 PM Gael Varoquaux < gael.varoqu...@normalesup.org> wrote: > Wohoo!! Thank you so much. This is so exciting: all those nice > improvements reaching so much users. > > Gaël > > On Thu, May 12, 2022 at 05:20:24PM +0200, Jeremie du Boisberranger wrote:

Re: [scikit-learn] [TC Vote] Technical Committee vote: line length

> Keep current 88 characters: > Revert to 79 characters: Alex Gramfort ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn

Re: [scikit-learn] custom scorer needs group information: how?

hi Emanuelle, I would suggest you have a look at https://github.com/scikit-learn/enhancement_proposals/pull/55 it's work in progress though Alex ___ scikit-learn mailing list scikit-learn@python.org

Re: [scikit-learn] Efficient forward stepwise regression

hi Matt, I'd like to implement a forward stepwise regression algorithm using the > efficient procedure described in the first problem here > . It does not > seem that such a model exists anywhere in Python. Would it be useful for me > to

Re: [scikit-learn] running examples

hi James, you need to install pandas pip install pandas or conda install pandas should fix your pb Alex ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn

Re: [scikit-learn] Some typos in documentation and errors

Hi, the best way to get this fixed is to send us a PR updating this file: https://github.com/scikit-learn/scikit-learn/blob/master/doc/modules/model_evaluation.rst thanks for your help Alex On Sun, Aug 30, 2020 at 7:38 AM 최우정 wrote: > > To whom it may concern > > I am a good user of

Re: [scikit-learn] Vote: Add Adrin Jalali to the scikit-learn technical committee

+1 ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn

Re: [scikit-learn] A basic question about kmeans algorithms elkan and llyod

hi, I suspect Elkan is really winning when you have many centroids so the conclusion is not systematic my 2c Alex On Thu, Mar 26, 2020 at 3:18 AM mc_george...@hotmail.com < mc_george...@hotmail.com> wrote: > Hi admins, > > > > My team is working on optimization on scikit-learn staff now. When

Re: [scikit-learn] Using a new random number generator in libsvm and liblinear

I don't foresee any issue with that. Alex ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn

Re: [scikit-learn] Vote on SLEP010: n_features_in_ attribute

+1 on SLEP + adding an estimator tag if it does not apply eg Text vectorizers etc. Alex ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn

Re: [scikit-learn] scikit-learn twitter account

hi, it's me. I own the scikit-learn twitter account. Shall I transfer it to you ogrisel? Alex On Fri, Nov 15, 2019 at 6:58 PM Olivier Grisel wrote: > I am not sure who has the rights to manage the twitter account. I just > sent a password reset request to

Re: [scikit-learn] Decision tree results sometimes different with scaled data

another reason is that we take as threshold the mid point between sample values which is not invariant to arbitrary scaling of the features Alex On Tue, Oct 22, 2019 at 11:56 AM Guillaume Lemaître wrote: > Even with the same random state, it can happen that several features will > lead to a

Re: [scikit-learn] Vote on SLEP009: keyword only arguments

Yes I am +1 for positional arguments for the __init__ of the estimators. Alex On Mon, Sep 16, 2019 at 10:25 PM Albert Thomas wrote: > Hi all, > > Just a few comments about this SLEP from a contributor and user of the > library :). > > I think it is important for users to be able to quickly

Re: [scikit-learn] Vote on SLEP009: keyword only arguments

> But overall I'm + 1 on forcing kwargs for all __init__ methods. yes I think it will help for __init__ methods Alex PS : I don't shoot people (usually...) ___ scikit-learn mailing list scikit-learn@python.org

Re: [scikit-learn] Vote on SLEP009: keyword only arguments

hi, Adrin do you suggest this for everything or maybe just for __init__ params of estimators and stuff that can come after X, y in fit eg sample_weights? would: clf.fit(X, y) still be allowed? Alex ___ scikit-learn mailing list

Re: [scikit-learn] Continues monitoring of benchmark performances

it's the same projects. Alex ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn

Re: [scikit-learn] Monthly meetings between core developers

hi, I kind of like the project boards we used for sprints: https://github.com/scikit-learn/scikit-learn/projects/11 the outcome of the core devs meeting could be to agree what should be listed on such a priority board. my 2c Alex On Thu, Jul 18, 2019 at 10:59 AM Olivier Grisel wrote: > > I

Re: [scikit-learn] Difference in normalization between Lasso and LogisticRegression + L1

see https://github.com/scikit-learn/scikit-learn/pulls?utf8=%E2%9C%93=is%3Apr+is%3Aclosed+scale_C+ for historical perspective on this issue. Alex On Wed, May 29, 2019 at 11:32 PM Stuart Reynolds wrote: > > I looked into like a while ago. There were differences in which algorithms >

Re: [scikit-learn] API Discussion: Where shall we put the plotting functions?

I also think that YellowBrick folks did a great job and that we should not reinvent the wheel or at least have clear idea of how we differ in scope with respect to YellowBrick my 2c Alex On Thu, Apr 4, 2019 at 1:02 AM Eric Ma wrote: > This is not a strongly-held suggestion - but what about

Re: [scikit-learn] LASSO: Predicted values show negative correlation with observed values on random data

in your example with random data Lasso leads to coef_ of zeros so you get as prediction : np.mean(Y[train]) you'll see the same phenomenon if you do: pred = np.r_[pred, np.mean(Y[train])] Alex ___ scikit-learn mailing list scikit-learn@python.org

Re: [scikit-learn] Sprint discussion points?

we should also see if we can have a lot of CI machines for the 5 days as it's always the blocker to move fast during 1 week. my 2c Alex On Wed, Feb 20, 2019 at 7:35 AM Roman Yurchak via scikit-learn wrote: > > Thanks for putting the draft schedule together! > > Personally I will be there 3 days

Re: [scikit-learn] VOTE: scikit-learn governance document

+1 for me too Alex On Sat, Feb 9, 2019 at 10:06 PM Gilles Louppe wrote: > Hi Andy, > > I read through to document. Even though I have not been really active > these past months/years, I think it summarizes well our governance > model. > > +1. > > Gilles > > On Sat, 9 Feb 2019 at 12:01, Adrin

Re: [scikit-learn] Any clustering algo to cluster multiple timing series data?

you can have a look at : https://tslearn.readthedocs.io/en/latest/ Alex On Thu, Jan 17, 2019 at 9:01 AM Mikkel Haggren Brynildsen wrote: > > You can use it to get a single similarity / closeness number between two > timeseries and then feed that into a clustering algorithm. > > > > For

Re: [scikit-learn] Next Sprint

ok for me Alex On Thu, Dec 20, 2018 at 8:35 PM Adrin wrote: > > It'll be the least favourable week of February for me, but I can make do. > > On Thu, 20 Dec 2018 at 18:45 Andreas Mueller wrote: >> >> Works for me! >> >> On 12/19/18 5:33 PM, Gael Varoquaux wrote: >> > I would propose the week

Re: [scikit-learn] Dimension Reduction - MDS

hi Guillaume, I cannot use our MDS solver at this scale. Even if you fit it in RAM it will be slow. I would play with https://github.com/lmcinnes/umap unless you really what a classic MDS. Alex On Thu, Oct 11, 2018 at 10:31 AM Guillaume Favelier wrote: > > Hello J.B, > > Thank you for your

Re: [scikit-learn] RidgeCV and cv=LeaveOneGroupOut

you cannot do this indeed. The groups cannot be passed to the cv.split method as they are not exposed as samples_props in the fit. This is still a hole in our API... ___ scikit-learn mailing list scikit-learn@python.org

Re: [scikit-learn] RidgeCV with multiple targets returns a single alpha. Is it possible to get one alpha per target?

you should call RidgeCV on all targets separately. HTH Alex On Tue, Aug 7, 2018 at 12:46 PM Christophe Pallier wrote: > > Hello, > > I'd like to use RidgeCV to find the optimal alpha for each colunm (ntargets) > of the DV variable. > > It lloks like itthe fit() computes a single alpha. Is

Re: [scikit-learn] Jeff Levesque: profit functionality

no only logit with LogisticRegression estimator. Alex ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn

Re: [scikit-learn] Question about LDA's coef_ attribute

no it stores the direction of the decision function to match the API of linear models. HTH Alex On Mon, Oct 16, 2017 at 3:27 PM, Serafeim Loukas wrote: > Dear Scikit-learn community, > > Since the documentation of the LDA >

Re: [scikit-learn] Truncated svd not working for complex matrices

I agree with Gaël on this. If you want to support complex values just copy the estimators / functions you want and maintain them in a separate package. +1 to error when complex are passed. ___ scikit-learn mailing list scikit-learn@python.org

Re: [scikit-learn] Contribution - Markov Clustering

hi, did you have a look at : http://scikit-learn.org/stable/faq.html#what-are-the-inclusion-criteria-for-new-algorithms Alex ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn

Re: [scikit-learn] Scipy 2017

Hi Andy, I'll be happy to share the stage with you for a tutorial. Alex On Tue, Feb 21, 2017 at 3:52 PM, Andreas Mueller wrote: > Hey folks. > Who's coming to scipy this year? > Any volunteers for tutorials? I'm happy to be part of it but doing 7h by > myself is a bit much

Re: [scikit-learn] Disable Travis Cache

hi, I just flushed all the caches. HTH Alex On Sun, Aug 7, 2016 at 2:39 PM, Raghav R V wrote: > Could someone disable the Travis cache once and for all please? > > I have seen several frustrating incidents where the Travis fails the PR > because of this caching of old files.

Re: [scikit-learn] Is there any official position on PEP484/mypy?

> I am still worried that this is going to add even more complexity to > contributing: people will contribute without knowing type hint, CI will > break, they won't understand why it breaks, won't be able to reproduce > it, and it will stall PRs. +1 same feeling here. A

Re: [scikit-learn] Using fit_intercept with sparse matrices

> Jaidev is suggesting that fit_intercept=False makes no sense if the data is > sparse. But I think that depends on your target variable. It can make sense **not** to fit intercept e.g. if it has no impact on perf it is faster to optimize without one

Re: [scikit-learn] Using fit_intercept with sparse matrices

On Mon, Jul 4, 2016 at 12:13 PM, Jaidev Deshpande wrote: > My point was, would it not be useful to raise a warning when the input is > sparse and the user does _not_ want to fit the intercept? I don't get it. Just fit_intercept=False should do it. why a warning??? A

Re: [scikit-learn] Using fit_intercept with sparse matrices

note: the Lasso and ElasticNet code do fit the intercept without breaking sparsity. Alex ___ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn