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, <[email protected]
<mailto:[email protected]>> wrote:
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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 <[email protected]
<mailto:[email protected]>>
To: [email protected] <mailto:[email protected]>
Subject: [scikit-learn] unclear help file for
sklearn.decomposition.pca
Message-ID:
<CANpSPFTgv+Oz7f97dandmrBBayqf_o9w=18okhcfn0u5dnz...@mail.gmail.com
<mailto:18okhcfn0u5dnzj%[email protected]>>
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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
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Message: 2
Date: Mon, 16 Oct 2017 15:16:45 +0200
From: Roman Yurchak <[email protected]
<mailto:[email protected]>>
To: Scikit-learn mailing list <[email protected]
<mailto:[email protected]>>
Subject: Re: [scikit-learn] unclear help file for
sklearn.decomposition.pca
Message-ID: <[email protected]
<mailto:[email protected]>>
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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
> [email protected] <mailto:[email protected]>
> 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 <[email protected] <mailto:[email protected]>>
To: [email protected] <mailto:[email protected]>
Subject: [scikit-learn] Question about LDA's coef_ attribute
Message-ID: <[email protected]
<mailto:[email protected]>>
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
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------------------------------
Message: 4
Date: Mon, 16 Oct 2017 16:57:52 +0200
From: Alexandre Gramfort <[email protected]
<mailto:[email protected]>>
To: Scikit-learn mailing list <[email protected]
<mailto:[email protected]>>
Subject: Re: [scikit-learn] Question about LDA's coef_ attribute
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<mailto:cadeotzricoqhuhjmmw2z14cqffeqyndyoxn-ogkavtmq7v0...@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
<[email protected] <mailto:[email protected]>> 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
> [email protected] <mailto:[email protected]>
> 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 <[email protected] <mailto:[email protected]>>
To: Scikit-learn mailing list <[email protected]
<mailto:[email protected]>>
Subject: Re: [scikit-learn] Question about LDA's coef_ attribute
Message-ID: <[email protected]
<mailto:[email protected]>>
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
<[email protected] <mailto:[email protected]>>
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
<[email protected] <mailto:[email protected]>> 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
>> [email protected] <mailto:[email protected]>
>> https://mail.python.org/mailman/listinfo/scikit-learn
<https://mail.python.org/mailman/listinfo/scikit-learn>
>>
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> scikit-learn mailing list
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