[scikit-learn] Nested Leave One Subject Out (LOSO) cross validation with scikit

Ludovico Coletta ludo25_90 at hotmail.com
Wed Dec 7 07:41:27 EST 2016


Dear scikit experts,


I did as you suggested, but it is not exactly what I would like to do ( I also read this: http://stackoverflow.com/questions/40400351/nested-cross-validation-with-stratifiedshufflesplit-in-sklearn)

Perhaps I should ask my question in another way: it is possible to split the nested cv folds just once? It seems to me that this is not possible, do you have any hints?


Thanks you for time

Ludovico



________________________________
Da: Ludovico Coletta <ludo25_90 at hotmail.com>
Inviato: lunedì 5 dicembre 2016 15.42
A: scikit-learn at python.org
Oggetto: Re: Nested Leave One Subject Out (LOSO) cross validation with scikit


thank you for the quick answer!


The problem is that I have a different number of features for each cv folds, therefore I thought that I had to handle each cv fold separately. I did like you suggested, but training set of the outer cv is then further splitted 3 times (stratified kfold), which I think is suboptimal (for feature selection I indeed implemented a nested loso).


One question: would it be so bad if I had nested loso for feature selection but the default stratified kfold for hyperparameter optimization? It would be some kind of double dipping in the nested cv, but the final set left out for test is not concerned.


The other point is that maybe I got something wrong in the whole process.


I have 26 subjects.


CV 1: subject 26 is left out for the final test, subjects 1:24 are used for hyperparameter optimization, subject 25 is used to select the best hyperpameters


CV 2: subject 1 is left out for the final test, subjects 2:25 are used for hyperparameter optimization, subject 26 is used to select the best hyperpameters.


So until the end. Is that correct?


Sorry for the trivial questions, but I am quite a beginner with both Python and ML

Best
Ludovico
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Today's Topics:

   1. Re: Markov Clustering? (Gael Varoquaux)
   2. Re: Markov Clustering? (Raphael C)
   3. Re: Nested Leave One Subject Out (LOSO) cross validation with
      scikit (Andy)
   4. Re: Nested Leave One Subject Out (LOSO) cross validation with
      scikit (Andy)


----------------------------------------------------------------------

Message: 1
Date: Mon, 5 Dec 2016 14:45:52 +0100
From: Gael Varoquaux <gael.varoquaux at normalesup.org>
To: Scikit-learn user and developer mailing list
        <scikit-learn at python.org>
Subject: Re: [scikit-learn] Markov Clustering?
Message-ID: <20161205134552.GK2327874 at phare.normalesup.org>
Content-Type: text/plain; charset=iso-8859-1

Interestingly, a couple of days before this thread was started a
researcher in a top lab of a huge private-sector company had mentionned
to me that they found this algorithm very useful in practice (sorry for
taking time to point this out, I just needed to check with him that
indeed it was this specific algorithm).

G

On Sun, Dec 04, 2016 at 08:18:54AM +0000, Raphael C wrote:
> I think you get a better view of the importance of Markov Clustering in
> academia from https://scholar.google.co.uk/scholar?hl=en&as_sdt=0,5&q=
> Markov+clustering .

> Raphael

> On Sat, 3 Dec 2016 at 22:43 Allan Visochek <avisochek3 at gmail.com> wrote:

>     Thanks for pointing that out, I sort of picked it up by word of mouth so
>     I'd assumed it had a bit more precedence in the academic world. ?

>     I'll look into it a little more, but I'd definitely be interested in
>     contributing something else if that doesn't work out.

>     -Allan

>     On Sat, Dec 3, 2016 at 4:45 PM, Andy <t3kcit at gmail.com> wrote:

>         Hey Allan.

>         None of the references apart from the last one seems to be published in
>         a peer-reviewed place, is that right?
>         And "A stochastic uncoupling process for graphs" has 13 citations since
>         2000. Unless there is a more prominent
>         publication or evidence of heavy use, I think it's disqualified.
>         Academia is certainly not the only metric for evaluation, so if you
>         have others, that's good, too ;)

>         Best,
>         Andy

>         On 12/03/2016 04:33 PM, Allan Visochek wrote:

>             Hey Andy,

>             This algorithm does operate on sparse graphs so it may be beyond
>             the scope of sci-kit learn, let me know what you think.?
>             The website is here, it includes a brief description of how the
>             algorithm operates under Documentation -> Overview1 and Overview2.?
>             The references listed on the website are included below.

>             Best,
>             -Allan


>             [1]?Stijn van Dongen.?Graph Clustering by Flow Simulation. PhD
>             thesis, University of Utrecht, May 2000.
>             http://www.library.uu.nl/digiarchief/dip/diss/1895620/inhoud.htm

>             [2]?Stijn van Dongen.?A cluster algorithm for graphs. Technical
>             Report INS-R0010, National Research Institute for Mathematics and
>             Computer Science in the Netherlands, Amsterdam, May 2000.
>             http://www.cwi.nl/ftp/CWIreports/INS/INS-R0010.ps.Z

>             [3]?Stijn van Dongen.?A stochastic uncoupling process for graphs.
>             Technical Report INS-R0011, National Research Institute for
>             Mathematics and Computer Science in the Netherlands, Amsterdam, May
>             2000.
>             http://www.cwi.nl/ftp/CWIreports/INS/INS-R0011.ps.Z

>             [4]?Stijn van Dongen.?Performance criteria for graph clustering and
>             Markov cluster experiments. Technical Report INS-R0012, National
>             Research Institute for Mathematics and Computer Science in the
>             Netherlands, Amsterdam, May 2000.
>             http://www.cwi.nl/ftp/CWIreports/INS/INS-R0012.ps.Z

>             [5]?Enright A.J., Van Dongen S., Ouzounis C.A.?An efficient
>             algorithm for large-scale detection of protein families, Nucleic
>             Acids Research 30(7):1575-1584 (2002).


>             On Sat, Dec 3, 2016 at 3:34 PM, Andy <t3kcit at gmail.com> wrote:

>                 Hi Allan.
>                 Can you provide the original paper?
>                 It this something usually used on sparse graphs? We do have
>                 algorithms that operate on data-induced
>                 graphs, like SpectralClustering, but we don't really implement
>                 general graph algorithms (there's no PageRank or community
>                 detection).

>                 Andy


>                 On 12/03/2016 12:19 PM, Allan Visochek wrote:

>                     Hi there,

>                     My name is Allan Visochek, I'm a data scientist and web
>                     developer and I love scikit-learn so first of all, thanks
>                     so much for the work that you do.?

>                     I'm reaching out because I've found the markov clustering
>                     algorithm to be quite useful for me in some of my work and
>                     noticed that there is no implementation in scikit-learn, is
>                     anybody working on this? If not, id be happy to take this
>                     on. I'm new to open source, but I've been working with
>                     python for a few years now.?

>                     Best,
>                     -Allan



>                     _______________________________________________
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--
    Gael Varoquaux
    Researcher, INRIA Parietal
    NeuroSpin/CEA Saclay , Bat 145, 91191 Gif-sur-Yvette France
    Phone:  ++ 33-1-69-08-79-68
    http://gael-varoquaux.info            http://twitter.com/GaelVaroquaux
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------------------------------

Message: 2
Date: Mon, 5 Dec 2016 13:51:26 +0000
From: Raphael C <drraph at gmail.com>
To: Scikit-learn user and developer mailing list
        <scikit-learn at python.org>
Subject: Re: [scikit-learn] Markov Clustering?
Message-ID:
        <CAFHc1QZEEwBWWP7BNRCGKEVvNAgUDEQ=3TKBWjkjjtTgc+SmAA at mail.gmail.com>
Content-Type: text/plain; charset=UTF-8

And...

 [1] Stijn van Dongen. Graph Clustering by Flow Simulation. PhD
thesis, University of Utrecht, May 2000.
http://www.library.uu.nl/digiarchief/dip/diss/1895620/inhoud.htm

has

1201 citations.

I think it's fair to say the method is very widely known and used.

Raphael

On 5 December 2016 at 13:45, Gael Varoquaux
<gael.varoquaux at normalesup.org> wrote:
> Interestingly, a couple of days before this thread was started a
> researcher in a top lab of a huge private-sector company had mentionned
> to me that they found this algorithm very useful in practice (sorry for
> taking time to point this out, I just needed to check with him that
> indeed it was this specific algorithm).
>
> G
>
> On Sun, Dec 04, 2016 at 08:18:54AM +0000, Raphael C wrote:
>> I think you get a better view of the importance of Markov Clustering in
>> academia from https://scholar.google.co.uk/scholar?hl=en&as_sdt=0,5&q=
>> Markov+clustering .
>
>> Raphael
>
>> On Sat, 3 Dec 2016 at 22:43 Allan Visochek <avisochek3 at gmail.com> wrote:
>
>>     Thanks for pointing that out, I sort of picked it up by word of mouth so
>>     I'd assumed it had a bit more precedence in the academic world.
>
>>     I'll look into it a little more, but I'd definitely be interested in
>>     contributing something else if that doesn't work out.
>
>>     -Allan
>
>>     On Sat, Dec 3, 2016 at 4:45 PM, Andy <t3kcit at gmail.com> wrote:
>
>>         Hey Allan.
>
>>         None of the references apart from the last one seems to be published in
>>         a peer-reviewed place, is that right?
>>         And "A stochastic uncoupling process for graphs" has 13 citations since
>>         2000. Unless there is a more prominent
>>         publication or evidence of heavy use, I think it's disqualified.
>>         Academia is certainly not the only metric for evaluation, so if you
>>         have others, that's good, too ;)
>
>>         Best,
>>         Andy
>
>>         On 12/03/2016 04:33 PM, Allan Visochek wrote:
>
>>             Hey Andy,
>
>>             This algorithm does operate on sparse graphs so it may be beyond
>>             the scope of sci-kit learn, let me know what you think.
>>             The website is here, it includes a brief description of how the
>>             algorithm operates under Documentation -> Overview1 and Overview2.
>>             The references listed on the website are included below.
>
>>             Best,
>>             -Allan
>
>
>>             [1] Stijn van Dongen. Graph Clustering by Flow Simulation. PhD
>>             thesis, University of Utrecht, May 2000.
>>             http://www.library.uu.nl/digiarchief/dip/diss/1895620/inhoud.htm
>
>>             [2] Stijn van Dongen. A cluster algorithm for graphs. Technical
>>             Report INS-R0010, National Research Institute for Mathematics and
>>             Computer Science in the Netherlands, Amsterdam, May 2000.
>>             http://www.cwi.nl/ftp/CWIreports/INS/INS-R0010.ps.Z
>
>>             [3] Stijn van Dongen. A stochastic uncoupling process for graphs.
>>             Technical Report INS-R0011, National Research Institute for
>>             Mathematics and Computer Science in the Netherlands, Amsterdam, May
>>             2000.
>>             http://www.cwi.nl/ftp/CWIreports/INS/INS-R0011.ps.Z
>
>>             [4] Stijn van Dongen. Performance criteria for graph clustering and
>>             Markov cluster experiments. Technical Report INS-R0012, National
>>             Research Institute for Mathematics and Computer Science in the
>>             Netherlands, Amsterdam, May 2000.
>>             http://www.cwi.nl/ftp/CWIreports/INS/INS-R0012.ps.Z
>
>>             [5] Enright A.J., Van Dongen S., Ouzounis C.A. An efficient
>>             algorithm for large-scale detection of protein families, Nucleic
>>             Acids Research 30(7):1575-1584 (2002).
>
>
>>             On Sat, Dec 3, 2016 at 3:34 PM, Andy <t3kcit at gmail.com> wrote:
>
>>                 Hi Allan.
>>                 Can you provide the original paper?
>>                 It this something usually used on sparse graphs? We do have
>>                 algorithms that operate on data-induced
>>                 graphs, like SpectralClustering, but we don't really implement
>>                 general graph algorithms (there's no PageRank or community
>>                 detection).
>
>>                 Andy
>
>
>>                 On 12/03/2016 12:19 PM, Allan Visochek wrote:
>
>>                     Hi there,
>
>>                     My name is Allan Visochek, I'm a data scientist and web
>>                     developer and I love scikit-learn so first of all, thanks
>>                     so much for the work that you do.
>
>>                     I'm reaching out because I've found the markov clustering
>>                     algorithm to be quite useful for me in some of my work and
>>                     noticed that there is no implementation in scikit-learn, is
>>                     anybody working on this? If not, id be happy to take this
>>                     on. I'm new to open source, but I've been working with
>>                     python for a few years now.
>
>>                     Best,
>>                     -Allan
>
>
>
>>                     _______________________________________________
>>                     scikit-learn mailing list
>>                     scikit-learn at python.org
>>                     https://mail.python.org/mailman/listinfo/scikit-learn
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>
>>                 _______________________________________________ scikit-learn
>>                 mailing list scikit-learn at python.org https://mail.python.org/
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>
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>
>
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>
>
>
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>
>
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>
>
> --
>     Gael Varoquaux
>     Researcher, INRIA Parietal
>     NeuroSpin/CEA Saclay , Bat 145, 91191 Gif-sur-Yvette France
>     Phone:  ++ 33-1-69-08-79-68
>     http://gael-varoquaux.info            http://twitter.com/GaelVaroquaux
Gael Varoquaux (@GaelVaroquaux) | Twitter<http://twitter.com/GaelVaroquaux>
twitter.com
The latest Tweets from Gael Varoquaux (@GaelVaroquaux). Researcher and geek: ►Brain, Data, & Computational science ►#python #pydata #sklearn ►Machine learning for fMRI ►Photography on @artgael. Paris, France


Gaël Varoquaux: computer / data / brain science<http://gael-varoquaux.info/>
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------------------------------

Message: 3
Date: Mon, 5 Dec 2016 08:51:47 -0500
From: Andy <t3kcit at gmail.com>
To: Scikit-learn user and developer mailing list
        <scikit-learn at python.org>
Subject: Re: [scikit-learn] Nested Leave One Subject Out (LOSO) cross
        validation with scikit
Message-ID: <2864ea23-e6ca-cf83-599f-f8ec149e8d67 at gmail.com>
Content-Type: text/plain; charset=windows-1252; format=flowed



On 12/04/2016 04:27 PM, Raghav R V wrote:
> Hi!
>
> It looks like you are using the old `sklearn.cross_validation`'s
> LeaveOneLabelOut cross-validator. It has been deprecated since v0.18.
>
> Use the `LeaveOneLabelOut` from `sklearn.model_selection`, that should
> fix your issue I think (thought I have not looked into your code in
> detail).
>
You mean LeaveOneGroupOut, right?


------------------------------

Message: 4
Date: Mon, 5 Dec 2016 08:54:01 -0500
From: Andy <t3kcit at gmail.com>
To: Scikit-learn user and developer mailing list
        <scikit-learn at python.org>
Subject: Re: [scikit-learn] Nested Leave One Subject Out (LOSO) cross
        validation with scikit
Message-ID: <6e970af0-faeb-bf81-3e9f-28dcc5df9168 at gmail.com>
Content-Type: text/plain; charset="windows-1252"; Format="flowed"

I'm not sure what the issue with your custom CV is but this seems like a
complicated way to implement this.
Try model_selection.LeaveOneGroupOut, which directly implements LOSO

On 12/04/2016 03:12 PM, Ludovico Coletta wrote:
> Dear scikit experts,
>
> I'm struggling with the implementation of a nested cross validation.
>
> My data: I have 26 subjects (13 per class) x 6670 features. I used a
> feature reduction algorithm (you may have heard about Boruta) to
> reduce the dimensionality of my data. Problems start now: I defined
> LOSO as outer partitioning schema. Therefore, for each of the 26 cv
> folds I used 24 subjects for feature reduction. This lead to a
> different number of features in each cv fold. Now, for each cv fold I
> would like to use the same 24 subjects for hyperparameter optimization
> (SVM with rbf kernel).
>
> This is what I did:
>
> /cv = list(LeaveOneout(len(y))) # in y I stored the labels/
> /
> /
> /inner_train = [None] * len(y)/
> /
> /
> /inner_test =  [None] * len(y)/
> /
> /
> /ii = 0/
> /
> /
> /while ii < len(y):/
> /    cv = list(LeaveOneOut(len(y))) /
> /    a = cv[ii][0]/
> /    a = a[:-1]/
> /    inner_train[ii] = a/
> /
> /
> /    b = cv[ii][0]/
> /    b = np.array(b[((len(cv[0][0]))-1)])/
> /    inner_test[ii]=b/
> /
> /
> /    ii = ii + 1/
> /
> /
> /custom_cv = zip(inner_train,inner_test) # inner cv/
> /
> /
> /
> /
> /pipe_logistic = Pipeline([('scl', StandardScaler()),('clf',
> SVC(kernel="rbf"))])/
> /
> /
> /parameters = [{'clf__C':  np.logspace(-2, 10, 13),
> 'clf__gamma':np.logspace(-9, 3, 13)}]/
> /
> /
> /
> /
> /
> /
> /scores = [None] * (len(y)) /
> /
> /
> /ii = 0/
> /
> /
> /while ii < len(scores):/
> /
> /
> /    a = data[ii][0] # data for train/
> /    b = data[ii][1] # data for test/
> /    c = np.concatenate((a,b)) # shape: number of subjects * number of
> features/
> /    d = cv[ii][0] # labels for train/
> /    e = cv[ii][1] # label for test/
> /    f = np.concatenate((d,e))/
> /
> /
> /    grid_search = GridSearchCV(estimator=pipe_logistic,
> param_grid=parameters, verbose=1, scoring='accuracy', cv=
> zip(([custom_cv[ii][0]]), ([custom_cv[ii][1]])))/
> /
> /
> /    scores[ii] = cross_validation.cross_val_score(grid_search, c,
> y[f], scoring='accuracy', cv = zip(([cv[ii][0]]), ([cv[ii][1]])))/
> /
> /
> /    ii = ii + 1/
> However, I got the following error message: index 25 is out of bounds
> for size 25
>
> Would it be so bad if I do not perform a nested LOSO but I use the
> default setting for hyperparameter optimization?
>
> Any help would be really appreciated
>
>
>
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