[scikit-learn] scikit-learn Digest, Vol 19, Issue 37
Brown J.B.
jbbrown at kuhp.kyoto-u.ac.jp
Tue Oct 17 23:30:28 EDT 2017
2017-10-18 12:18 GMT+09:00 Ismael Lemhadri <lemhadri at stanford.edu>:
> How about editing the various chunks of code concerned to add the option
> to scale the parameters, and set it by default to NOT scale? This would
> make what happens clear without the redundancy Andreas mentioned, and would
> add more convenience to the user shall they want to scale their data.
>
>From my perspectives:
That's a very nice, rational idea.
For end users, it preserves compatibility of existing codebases, but allows
both near-effortless updating of code for those who want to use
Scikit-learn's scaling as well as ease of application for new users and
tools.
One issue of caution would be where the scaling occurs, such as globally
before any cross-validation, or per-split with the transformation stored
and applied to prediction data per fold of CV.
One more keyword argument would need to be added to allow user
specification of this, and a state variable would have to be stored and
accessible from the methods of the parent estimator.
J.B.
>
>
>> Today's Topics:
>>
>> 1. Re: Unclear help file about sklearn.decomposition.pca (Raphael C)
>>
>>
>> ----------------------------------------------------------------------
>>
>> Message: 1
>> Date: Tue, 17 Oct 2017 16:44:55 +0100
>> From: Raphael C <drraph at gmail.com>
>> To: Scikit-learn mailing list <scikit-learn at python.org>
>> Subject: Re: [scikit-learn] Unclear help file about
>> sklearn.decomposition.pca
>> Message-ID:
>> <CAFHc1QZigBoA0erY2hwJht2kiAenp=QTQDSb3uc0Uzs5SoEC7Q at mail.gm
>> ail.com>
>> Content-Type: text/plain; charset="UTF-8"
>>
>> 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:
>> >>
>> >> Send scikit-learn mailing list submissions to
>> >> scikit-learn at 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-request at python.org
>> >>
>> >> You can reach the person managing the list at
>> >> 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. 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 --------------
>> >> An HTML attachment was scrubbed...
>> >> URL:
>> >> <http://mail.python.org/pipermail/scikit-learn/attachments/
>> 20171016/81b3014b/attachment-0001.html>
>> >>
>> >> ------------------------------
>> >>
>> >> 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
>> >>
>> >>
>> >>
>> >> 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-request at python.org
>> >>
>> >>
>> >>
>> >> You can reach the person managing the list at
>> >>
>> >> 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. 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 pytho
>> n.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/ef5cb8
>> 4a/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/attachment
>> s/20171015/c465bde7/attachment-0001.html
>> >>
>> >> >
>> >> <http://mail.python.org/pipermail/scikit-learn/attachment
>> s/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-b9
>> 3832932043 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/ef5cb8
>> 4a/sklearn/decomposition/pca.py#L410>
>> >>
>> >> > >
>> >>
>> >> >
>> >> <https://github.com/scikit-learn/scikit-learn/blob/ef5cb8
>> 4a/sklearn/decomposition/pca.py#L410
>> >>
>> >> >
>> >> <https://github.com/scikit-learn/scikit-learn/blob/ef5cb8
>> 4a/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-9DE5-4EF5-97C1-48
>> 159831F5A9 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/attachment
>> s/20171016/4263df5c/attachment-0001.html
>> >>
>> >> >
>> >> <http://mail.python.org/pipermail/scikit-learn/attachment
>> s/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-OgKAvTMQ7V0Y
>> 2g 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-D1
>> 71BB36539D 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/mailma
>> n/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>
>> ;
>> >>
>> >> >
>> >>
>> >> > -------------- next part --------------
>> >>
>> >> > An HTML attachment was scrubbed...
>> >>
>> >> > URL:
>> >>
>> >> >
>> >> <http://mail.python.org/pipermail/scikit-learn/attachment
>> s/20171016/505c7da3/attachment.html
>> >>
>> >> >
>> >> <http://mail.python.org/pipermail/scikit-learn/attachment
>> s/20171016/505c7da3/attachment.html>>
>> >>
>> >> >
>> >>
>> >> > ------------------------------
>> >>
>> >> >
>> >>
>> >> > Subject: Digest Footer
>> >>
>> >> >
>> >>
>> >> > _______________________________________________
>> >>
>> >> > 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>
>> ;
>> >>
>> >> >
>> >>
>> >> >
>> >>
>> >> > ------------------------------
>> >>
>> >> >
>> >>
>> >> > End of scikit-learn Digest, Vol 19, Issue 25
>> >>
>> >> > ********************************************
>> >>
>> >> >
>> >>
>> >> >
>> >>
>> >> >
>> >>
>> >> >
>> >>
>> >> > _______________________________________________
>> >>
>> >> > scikit-learn mailing list
>> >>
>> >> > scikit-learn at 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/attachment
>> s/20171016/f47e63a9/attachment.html>
>> >>
>> >>
>> >>
>> >> ------------------------------
>> >>
>> >>
>> >>
>> >> Subject: Digest Footer
>> >>
>> >>
>> >>
>> >> _______________________________________________
>> >>
>> >> scikit-learn mailing list
>> >>
>> >> scikit-learn at python.org
>> >>
>> >> https://mail.python.org/mailman/listinfo/scikit-learn
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> ------------------------------
>> >>
>> >>
>> >>
>> >> End of scikit-learn Digest, Vol 19, Issue 28
>> >>
>> >> ********************************************
>> >>
>> >>
>> >>
>> >>
>> >>
>> >>
>> >> _______________________________________________
>> >>
>> >> scikit-learn mailing list
>> >>
>> >> scikit-learn at 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/
>> 20171016/620a9401/attachment.html>
>> >>
>> >> ------------------------------
>> >>
>> >> Subject: Digest Footer
>> >>
>> >> _______________________________________________
>> >> scikit-learn mailing list
>> >> scikit-learn at python.org
>> >> https://mail.python.org/mailman/listinfo/scikit-learn
>> >>
>> >>
>> >> ------------------------------
>> >>
>> >> End of scikit-learn Digest, Vol 19, Issue 31
>> >> ********************************************
>> >
>> > --
>> >
>> > Sent from a mobile phone and may contain errors
>> >
>> >
>> > _______________________________________________
>> > scikit-learn mailing list
>> > scikit-learn at python.org
>> > https://mail.python.org/mailman/listinfo/scikit-learn
>> >
>> >
>> >
>> > _______________________________________________
>> > scikit-learn mailing list
>> > scikit-learn at python.org
>> > https://mail.python.org/mailman/listinfo/scikit-learn
>> >
>>
>>
>> ------------------------------
>>
>> Subject: Digest Footer
>>
>> _______________________________________________
>> scikit-learn mailing list
>> scikit-learn at python.org
>> https://mail.python.org/mailman/listinfo/scikit-learn
>>
>>
>> ------------------------------
>>
>> End of scikit-learn Digest, Vol 19, Issue 37
>> ********************************************
>>
>
>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at 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/20171018/9902e06b/attachment-0001.html>
More information about the scikit-learn
mailing list