[scikit-learn] Unclear help file about sklearn.decomposition.pca
Raphael C
drraph at gmail.com
Tue Oct 17 11:44:55 EDT 2017
How about including the scaling that people might want to use in the
User Guide examples?
Raphael
On 17 October 2017 at 16:40, Andreas Mueller <t3kcit at gmail.com> wrote:
> In general scikit-learn avoids automatic preprocessing.
> That's a convention to give the user more control and decrease surprising
> behavior (ostensibly).
> So scikit-learn will usually do what the algorithm is supposed to do, and
> nothing more.
>
> I'm not sure what the best way do document this is, as this has come up with
> different models.
> For example the R wrapper of libsvm does automatic scaling, while we apply
> the SVM.
>
> We could add "this model does not do any automatic preprocessing" to all
> docstrings, but that seems
> a bit redundant. We could add it to
> https://github.com/scikit-learn/scikit-learn/pull/9517, but
> that is probably not where you would have looked.
>
> Other suggestions welcome.
>
>
> On 10/16/2017 03:29 PM, Ismael Lemhadri wrote:
>
> Thank you all for your feedback.
> The initial problem I came with wasnt the definition of PCA but what the
> sklearn method does. In practice I would always make sure the data is both
> centered and scaled before performing PCA. This is the recommended method
> because without scaling, the biggest direction could wrongly seem to explain
> a huge fraction of the variance.
> So my point was simply to clarify in the help file and the user guide what
> the PCA class does precisely to leave no unclarity to the reader. Moving
> forward I have now submitted a pull request on github as initially suggested
> by Roman on this thread.
> Best,
> Ismael
>
> On Mon, 16 Oct 2017 at 11:49 AM, <scikit-learn-request at python.org> wrote:
>>
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>> Today's Topics:
>>
>> 1. Re: 1. Re: unclear help file for sklearn.decomposition.pca
>> (Andreas Mueller)
>> 2. Re: 1. Re: unclear help file for sklearn.decomposition.pca
>> (Oliver Tomic)
>>
>>
>> ----------------------------------------------------------------------
>>
>> Message: 1
>> Date: Mon, 16 Oct 2017 14:44:51 -0400
>> From: Andreas Mueller <t3kcit at gmail.com>
>> To: scikit-learn at python.org
>> Subject: Re: [scikit-learn] 1. Re: unclear help file for
>> sklearn.decomposition.pca
>> Message-ID: <35142868-fce9-6cb3-eba3-015a0b106163 at gmail.com>
>> Content-Type: text/plain; charset="utf-8"; Format="flowed"
>>
>>
>>
>> On 10/16/2017 02:27 PM, Ismael Lemhadri wrote:
>> > @Andreas Muller:
>> > My references do not assume centering, e.g.
>> > http://ufldl.stanford.edu/wiki/index.php/PCA
>> > any reference?
>> >
>> It kinda does but is not very clear about it:
>>
>> This data has already been pre-processed so that each of the
>> features\textstyle x_1and\textstyle x_2have about the same mean (zero)
>> and variance.
>>
>>
>>
>> Wikipedia is much clearer:
>> Consider a datamatrix
>> <https://en.wikipedia.org/wiki/Matrix_%28mathematics%29>,*X*, with
>> column-wise zeroempirical mean
>> <https://en.wikipedia.org/wiki/Empirical_mean>(the sample mean of each
>> column has been shifted to zero), where each of the/n/rows represents a
>> different repetition of the experiment, and each of the/p/columns gives
>> a particular kind of feature (say, the results from a particular sensor).
>> https://en.wikipedia.org/wiki/Principal_component_analysis#Details
>>
>> I'm a bit surprised to find that ESL says "The SVD of the centered
>> matrix X is another way of expressing the principal components of the
>> variables in X",
>> so they assume scaling? They don't really have a great treatment of PCA,
>> though.
>>
>> Bishop <http://www.springer.com/us/book/9780387310732> and Murphy
>> <https://mitpress.mit.edu/books/machine-learning-0> are pretty clear
>> that they subtract the mean (or assume zero mean) but don't standardize.
>> -------------- next part --------------
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>>
>> ------------------------------
>>
>> Message: 2
>> Date: Mon, 16 Oct 2017 20:48:29 +0200
>> From: Oliver Tomic <olivertomic at zoho.com>
>> To: "Scikit-learn mailing list" <scikit-learn at python.org>
>> Cc: <scikit-learn at python.org>
>> Subject: Re: [scikit-learn] 1. Re: unclear help file for
>> sklearn.decomposition.pca
>> Message-ID: <15f26840d65.e97b33c25239.3934951873824890747 at zoho.com>
>> Content-Type: text/plain; charset="utf-8"
>>
>> Dear Ismael,
>>
>>
>>
>> PCA should always involve at the least centering, or, if the variables are
>> to contribute equally, scaling. Here is a reference from the scientific area
>> named "chemometrics". In Chemometrics PCA used not only for dimensionality
>> reduction, but also for interpretation of variance by use of scores,
>> loadings, correlation loadings, etc.
>>
>>
>>
>> If you scroll down to subsection "Preprocessing" you will find more info
>> on centering and scaling.
>>
>>
>> http://pubs.rsc.org/en/content/articlehtml/2014/ay/c3ay41907j
>>
>>
>>
>> best
>>
>> Oliver
>>
>>
>>
>>
>> ---- On Mon, 16 Oct 2017 20:27:11 +0200 Ismael Lemhadri
>> <lemhadri at stanford.edu> wrote ----
>>
>>
>>
>>
>> @Andreas Muller:
>>
>> My references do not assume centering, e.g.
>> http://ufldl.stanford.edu/wiki/index.php/PCA
>>
>> any reference?
>>
>>
>>
>>
>>
>>
>>
>> On Mon, Oct 16, 2017 at 10:20 AM, <scikit-learn-request at python.org>
>> wrote:
>>
>> Send scikit-learn mailing list submissions to
>>
>> scikit-learn at python.org
>>
>>
>>
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>>
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>>
>> or, via email, send a message with subject or body 'help' to
>>
>> scikit-learn-request at python.org
>>
>>
>>
>> You can reach the person managing the list at
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>>
>>
>>
>> When replying, please edit your Subject line so it is more specific
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>>
>>
>>
>>
>>
>> Today's Topics:
>>
>>
>>
>> 1. Re: unclear help file for sklearn.decomposition.pca
>>
>> (Andreas Mueller)
>>
>>
>>
>>
>>
>> ----------------------------------------------------------------------
>>
>>
>>
>> Message: 1
>>
>> Date: Mon, 16 Oct 2017 13:19:57 -0400
>>
>> From: Andreas Mueller <t3kcit at gmail.com>
>>
>> To: scikit-learn at python.org
>>
>> Subject: Re: [scikit-learn] unclear help file for
>>
>> sklearn.decomposition.pca
>>
>> Message-ID: <04fc445c-d8f3-a3a9-4ab2-0535826a2d03 at gmail.com>
>>
>> Content-Type: text/plain; charset="utf-8"; Format="flowed"
>>
>>
>>
>> The definition of PCA has a centering step, but no scaling step.
>>
>>
>>
>> On 10/16/2017 11:16 AM, Ismael Lemhadri wrote:
>>
>> > 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-request at python.org
>>
>> > <mailto:scikit-learn-request at python.org>> wrote:
>>
>> >
>>
>> > Send scikit-learn mailing list submissions to
>>
>> > scikit-learn at python.org <mailto:scikit-learn at python.org>
>>
>> >
>>
>> > To subscribe or unsubscribe via the World Wide Web, visit
>>
>> > https://mail.python.org/mailman/listinfo/scikit-learn
>>
>> > <https://mail.python.org/mailman/listinfo/scikit-learn>
>>
>> > or, via email, send a message with subject or body 'help' to
>>
>> > scikit-learn-request at python.org
>>
>> > <mailto:scikit-learn-request at python.org>
>>
>> >
>>
>> > You can reach the person managing the list at
>>
>> > scikit-learn-owner at python.org
>> <mailto:scikit-learn-owner at 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 <lemhadri at stanford.edu
>>
>> > <mailto:lemhadri at stanford.edu>>
>>
>> > To: scikit-learn at python.org
>> <mailto:scikit-learn at python.org>
>>
>> > Subject: [scikit-learn] unclear help file for
>>
>> > ? ? ? ? sklearn.decomposition.pca
>>
>> > Message-ID:
>>
>> > ? ? ? ?
>>
>> >
>> <CANpSPFTgv+Oz7f97dandmrBBayqf_o9w=18oKHCFN0u5DNzj+g at mail.gmail.com
>>
>> > <mailto:18oKHCFN0u5DNzj%2Bg at 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
>>
>> >
>> <https://github.com/scikit-learn/scikit-learn/blob/ef5cb84a/sklearn/%0Adecomposition/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
>>
>> >
>> <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.yurchak at gmail.com
>>
>> > <mailto:rth.yurchak at gmail.com>>
>>
>> > To: Scikit-learn mailing list <scikit-learn at python.org
>>
>> > <mailto:scikit-learn at python.org>>
>>
>> > Subject: Re: [scikit-learn] unclear help file for
>>
>> > ? ? ? ? sklearn.decomposition.pca
>>
>> > Message-ID: <b2abdcfd-4736-929e-6304-b93832932043 at gmail.com
>>
>> >
>> <mailto:b2abdcfd-4736-929e-6304-b93832932043 at 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>
>>
>> > >
>>
>> >
>> <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 at python.org
>> <mailto:scikit-learn at python.org>
>>
>> > > https://mail.python.org/mailman/listinfo/scikit-learn
>>
>> > <https://mail.python.org/mailman/listinfo/scikit-learn>
>>
>> > >
>>
>> >
>>
>> >
>>
>> >
>>
>> > ------------------------------
>>
>> >
>>
>> > Message: 3
>>
>> > Date: Mon, 16 Oct 2017 15:27:48 +0200
>>
>> > From: Serafeim Loukas <seralouk at gmail.com
>> <mailto:seralouk at gmail.com>>
>>
>> > To: scikit-learn at python.org
>> <mailto:scikit-learn at python.org>
>>
>> > Subject: [scikit-learn] Question about LDA's coef_ attribute
>>
>> > Message-ID: <58C6D0DA-9DE5-4EF5-97C1-48159831F5A9 at gmail.com
>>
>> >
>> <mailto:58C6D0DA-9DE5-4EF5-97C1-48159831F5A9 at 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>
>>
>> >
>> <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
>>
>> >
>> <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.gramfort at inria.fr
>>
>> > <mailto:alexandre.gramfort at inria.fr>>
>>
>> > To: Scikit-learn mailing list <scikit-learn at python.org
>>
>> > <mailto:scikit-learn at python.org>>
>>
>> > Subject: Re: [scikit-learn] Question about LDA's coef_ attribute
>>
>> > Message-ID:
>>
>> > ? ? ? ?
>>
>> >
>> <CADeotZricOQhuHJMmW2Z14cqffEQyndYoxn-OgKAvTMQ7V0Y2g at mail.gmail.com
>>
>> >
>> <mailto:CADeotZricOQhuHJMmW2Z14cqffEQyndYoxn-OgKAvTMQ7V0Y2g at 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
>>
>> > <seralouk at gmail.com <mailto:seralouk at 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
>>
>> >
>> <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 at python.org
>> <mailto:scikit-learn at python.org>
>>
>> > > https://mail.python.org/mailman/listinfo/scikit-learn
>>
>> > <https://mail.python.org/mailman/listinfo/scikit-learn>
>>
>> > >
>>
>> >
>>
>> >
>>
>> > ------------------------------
>>
>> >
>>
>> > Message: 5
>>
>> > Date: Mon, 16 Oct 2017 17:02:46 +0200
>>
>> > From: Serafeim Loukas <seralouk at gmail.com
>> <mailto:seralouk at gmail.com>>
>>
>> > To: Scikit-learn mailing list <scikit-learn at python.org
>>
>> > <mailto:scikit-learn at python.org>>
>>
>> > Subject: Re: [scikit-learn] Question about LDA's coef_ attribute
>>
>> > Message-ID: <413210D2-56AE-41A4-873F-D171BB36539D at gmail.com
>>
>> >
>> <mailto:413210D2-56AE-41A4-873F-D171BB36539D at 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.gramfort at inria.fr
>> <mailto:alexandre.gramfort at 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
>>
>> > <seralouk at gmail.com <mailto:seralouk at 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
>>
>> >
>> <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 at python.org
>> <mailto:scikit-learn at python.org>
>>
>> > >> https://mail.python.org/mailman/listinfo/scikit-learn
>>
>> > <https://mail.python.org/mailman/listinfo/scikit-learn>
>>
>> > >>
>>
>> > > _______________________________________________
>>
>> > > scikit-learn mailing list
>>
>> > > scikit-learn at python.org
>> <mailto:scikit-learn at python.org>
>>
>> > > https://mail.python.org/mailman/listinfo/scikit-learn
>>
>> > <https://mail.python.org/mailman/listinfo/scikit-learn>
>>
>> >
>>
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