Dear Roman, My concern is actually not about not mentioning the scaling but about not mentioning the centering. That is, the sklearn PCA removes the mean but it does not mention it in the help file. This was quite messy for me to debug as I expected it to either: 1/ center and scale simultaneously or / not scale and not center either. It would be beneficial to explicit the behavior in the help file in my opinion. Ismael
On Mon, Oct 16, 2017 at 8:02 AM, <scikit-learn-requ...@python.org> wrote: > Send scikit-learn mailing list submissions to > scikit-learn@python.org > > To subscribe or unsubscribe via the World Wide Web, visit > https://mail.python.org/mailman/listinfo/scikit-learn > or, via email, send a message with subject or body 'help' to > scikit-learn-requ...@python.org > > You can reach the person managing the list at > scikit-learn-ow...@python.org > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of scikit-learn digest..." > > > Today's Topics: > > 1. unclear help file for sklearn.decomposition.pca (Ismael Lemhadri) > 2. Re: unclear help file for sklearn.decomposition.pca > (Roman Yurchak) > 3. Question about LDA's coef_ attribute (Serafeim Loukas) > 4. Re: Question about LDA's coef_ attribute (Alexandre Gramfort) > 5. Re: Question about LDA's coef_ attribute (Serafeim Loukas) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Sun, 15 Oct 2017 18:42:56 -0700 > From: Ismael Lemhadri <lemha...@stanford.edu> > To: scikit-learn@python.org > Subject: [scikit-learn] unclear help file for > sklearn.decomposition.pca > Message-ID: > <CANpSPFTgv+Oz7f97dandmrBBayqf_o9w=18oKHCF > n0u5dnz...@mail.gmail.com> > Content-Type: text/plain; charset="utf-8" > > Dear all, > The help file for the PCA class is unclear about the preprocessing > performed to the data. > You can check on line 410 here: > https://github.com/scikit-learn/scikit-learn/blob/ef5cb84a/sklearn/ > decomposition/pca.py#L410 > that the matrix is centered but NOT scaled, before performing the singular > value decomposition. > However, the help files do not make any mention of it. > This is unclear for someone who, like me, just wanted to compare that the > PCA and np.linalg.svd give the same results. In academic settings, students > are often asked to compare different methods and to check that they yield > the same results. I expect that many students have confronted this problem > before... > Best, > Ismael Lemhadri > -------------- next part -------------- > An HTML attachment was scrubbed... > URL: <http://mail.python.org/pipermail/scikit-learn/ > attachments/20171015/c465bde7/attachment-0001.html> > > ------------------------------ > > Message: 2 > Date: Mon, 16 Oct 2017 15:16:45 +0200 > From: Roman Yurchak <rth.yurc...@gmail.com> > To: Scikit-learn mailing list <scikit-learn@python.org> > Subject: Re: [scikit-learn] unclear help file for > sklearn.decomposition.pca > Message-ID: <b2abdcfd-4736-929e-6304-b93832932...@gmail.com> > Content-Type: text/plain; charset=utf-8; format=flowed > > Ismael, > > as far as I saw the sklearn.decomposition.PCA doesn't mention scaling at > all (except for the whiten parameter which is post-transformation scaling). > > So since it doesn't mention it, it makes sense that it doesn't do any > scaling of the input. Same as np.linalg.svd. > > You can verify that PCA and np.linalg.svd yield the same results, with > > ``` > >>> import numpy as np > >>> from sklearn.decomposition import PCA > >>> import numpy.linalg > >>> X = np.random.RandomState(42).rand(10, 4) > >>> n_components = 2 > >>> PCA(n_components, svd_solver='full').fit_transform(X) > ``` > > and > > ``` > >>> U, s, V = np.linalg.svd(X - X.mean(axis=0), full_matrices=False) > >>> (X - X.mean(axis=0)).dot(V[:n_components].T) > ``` > > -- > Roman > > On 16/10/17 03:42, Ismael Lemhadri wrote: > > Dear all, > > The help file for the PCA class is unclear about the preprocessing > > performed to the data. > > You can check on line 410 here: > > https://github.com/scikit-learn/scikit-learn/blob/ef5cb84a/sklearn/ > decomposition/pca.py#L410 > > <https://github.com/scikit-learn/scikit-learn/blob/ef5cb84a/sklearn/ > decomposition/pca.py#L410> > > that the matrix is centered but NOT scaled, before performing the > > singular value decomposition. > > However, the help files do not make any mention of it. > > This is unclear for someone who, like me, just wanted to compare that > > the PCA and np.linalg.svd give the same results. In academic settings, > > students are often asked to compare different methods and to check that > > they yield the same results. I expect that many students have confronted > > this problem before... > > Best, > > Ismael Lemhadri > > > > > > _______________________________________________ > > scikit-learn mailing list > > scikit-learn@python.org > > https://mail.python.org/mailman/listinfo/scikit-learn > > > > > > ------------------------------ > > Message: 3 > Date: Mon, 16 Oct 2017 15:27:48 +0200 > From: Serafeim Loukas <seral...@gmail.com> > To: scikit-learn@python.org > Subject: [scikit-learn] Question about LDA's coef_ attribute > Message-ID: <58c6d0da-9de5-4ef5-97c1-48159831f...@gmail.com> > Content-Type: text/plain; charset="us-ascii" > > Dear Scikit-learn community, > > Since the documentation of the LDA (http://scikit-learn.org/ > stable/modules/generated/sklearn.discriminant_analysis. > LinearDiscriminantAnalysis.html <http://scikit-learn.org/ > stable/modules/generated/sklearn.discriminant_analysis. > LinearDiscriminantAnalysis.html>) is not so clear, I would like to ask if > the lda.coef_ attribute stores the eigenvectors from the SVD decomposition. > > Thank you in advance, > Serafeim > -------------- next part -------------- > An HTML attachment was scrubbed... > URL: <http://mail.python.org/pipermail/scikit-learn/ > attachments/20171016/4263df5c/attachment-0001.html> > > ------------------------------ > > Message: 4 > Date: Mon, 16 Oct 2017 16:57:52 +0200 > From: Alexandre Gramfort <alexandre.gramf...@inria.fr> > To: Scikit-learn mailing list <scikit-learn@python.org> > Subject: Re: [scikit-learn] Question about LDA's coef_ attribute > Message-ID: > <CADeotZricOQhuHJMmW2Z14cqffEQyndYoxn-OgKAvTMQ7V0Y2g@mail. > gmail.com> > Content-Type: text/plain; charset="UTF-8" > > 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 <seral...@gmail.com> > wrote: > > Dear Scikit-learn community, > > > > Since the documentation of the LDA > > (http://scikit-learn.org/stable/modules/generated/ > sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html) > > is not so clear, I would like to ask if the lda.coef_ attribute stores > the > > eigenvectors from the SVD decomposition. > > > > Thank you in advance, > > Serafeim > > > > _______________________________________________ > > scikit-learn mailing list > > scikit-learn@python.org > > https://mail.python.org/mailman/listinfo/scikit-learn > > > > > ------------------------------ > > Message: 5 > Date: Mon, 16 Oct 2017 17:02:46 +0200 > From: Serafeim Loukas <seral...@gmail.com> > To: Scikit-learn mailing list <scikit-learn@python.org> > Subject: Re: [scikit-learn] Question about LDA's coef_ attribute > Message-ID: <413210d2-56ae-41a4-873f-d171bb365...@gmail.com> > Content-Type: text/plain; charset="us-ascii" > > Dear Alex, > > Thank you for the prompt response. > > Are the eigenvectors stored in some variable ? > Does the lda.scalings_ attribute contain the eigenvectors ? > > Best, > Serafeim > > > On 16 Oct 2017, at 16:57, Alexandre Gramfort < > alexandre.gramf...@inria.fr> wrote: > > > > 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 <seral...@gmail.com> > wrote: > >> Dear Scikit-learn community, > >> > >> Since the documentation of the LDA > >> (http://scikit-learn.org/stable/modules/generated/ > sklearn.discriminant_analysis.LinearDiscriminantAnalysis.html) > >> is not so clear, I would like to ask if the lda.coef_ attribute stores > the > >> eigenvectors from the SVD decomposition. > >> > >> Thank you in advance, > >> Serafeim > >> > >> _______________________________________________ > >> scikit-learn mailing list > >> scikit-learn@python.org > >> https://mail.python.org/mailman/listinfo/scikit-learn > >> > > _______________________________________________ > > scikit-learn mailing list > > scikit-learn@python.org > > https://mail.python.org/mailman/listinfo/scikit-learn > > -------------- next part -------------- > An HTML attachment was scrubbed... > URL: <http://mail.python.org/pipermail/scikit-learn/ > attachments/20171016/505c7da3/attachment.html> > > ------------------------------ > > Subject: Digest Footer > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > > > ------------------------------ > > End of scikit-learn Digest, Vol 19, Issue 25 > ******************************************** >
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