[scikit-learn] scikit-learn Digest, Vol 6, Issue 40
Joel Nothman
joel.nothman at gmail.com
Mon Sep 26 18:06:10 EDT 2016
Hi Arafin,
You appear to be talking about a situation in which your dataset is divided
into subsets in which the data are highly correlated (but perhaps
conditionally independent given the subject / group identifier). In
Scikit-learn 0.18 these might be called "grouped cross validation"
strategies. See
http://scikit-learn.org/dev/modules/cross_validation.html#cross-validation-iterators-for-grouped-data
.
(In earlier versions of Scikit-learn, you will find the corresponding CV
objects as LabelKFold, LeaveOneLabelOut, etc., but we decided to rename
them for clarity when redesigning CV objects and moving them to the new
sklearn.model_selection subpackage.)
I hope that helps.
Joel
On 27 September 2016 at 07:06, Afarin Famili <
Afarin.Famili at utsouthwestern.edu> wrote:
> Hi David,
>
> When applying Train_test_split to the sample space, we have a single row
> per subject. I am looking for some other function like Train_test_split
> that can deal with pairs of rows (for each subject), which does not lead to
> a biased accuracy. We are studying memory and have a row of features for
> successful memory encoding, and a second row for unsuccessful memory
> encoding in each of the subjects. Our target space being 1 for successful
> and 0 for unsuccessful encoding respectively.
> How do you recommend me to split this set of data in order to get a
> reasonable/unbiased accuracy?
>
> Thanks,
> Afarin
>
>
>
> ________________________________________
> From: scikit-learn <scikit-learn-bounces+afarin.famili=utsouthwestern.edu@
> python.org> on behalf of scikit-learn-request at python.org <
> scikit-learn-request at python.org>
> Sent: Monday, September 26, 2016 2:43 PM
> To: scikit-learn at python.org
> Subject: scikit-learn Digest, Vol 6, Issue 40
>
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> Today's Topics:
>
> 1. header intact (Afarin Famili)
> 2. Is there a built-in function for pairs of data? (Afarin Famili)
> 3. Re: Is there a built-in function for pairs of data?
> (Pedro Pazzini)
> 4. Re: Is there a built-in function for pairs of data?
> (David Nicholson)
> 5. Large computation time for homogeneous data with
> agglomerative clustering (Md. Khairullah)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Mon, 26 Sep 2016 18:03:27 +0000
> From: Afarin Famili <Afarin.Famili at UTSouthwestern.edu>
> To: "scikit-learn at python.org" <scikit-learn at python.org>
> Subject: [scikit-learn] header intact
> Message-ID: <1474913007611.80841 at UTSouthwestern.edu>
> Content-Type: text/plain; charset="iso-8859-1"
>
> ?
>
>
>
> ________________________________
>
> UT Southwestern
>
>
> Medical Center
>
>
>
> The future of medicine, today.
>
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> ------------------------------
>
> Message: 2
> Date: Mon, 26 Sep 2016 18:06:49 +0000
> From: Afarin Famili <Afarin.Famili at UTSouthwestern.edu>
> To: "scikit-learn at python.org" <scikit-learn at python.org>
> Subject: [scikit-learn] Is there a built-in function for pairs of
> data?
> Message-ID: <1474913209751.36283 at UTSouthwestern.edu>
> Content-Type: text/plain; charset="iso-8859-1"
>
>
> Dear Scikit-learn team,
>
>
> We need to deal with pairs of data in our classification task. I was
> wondering if there is already a built-in function in Scikit-learn that can
> partition the pairs of data into train and test sets?
>
>
> Regards,
>
> Afarin
>
>
>
> ________________________________
>
> UT Southwestern
>
>
> Medical Center
>
>
>
> The future of medicine, today.
>
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>
> ------------------------------
>
> Message: 3
> Date: Mon, 26 Sep 2016 15:47:26 -0300
> From: Pedro Pazzini <pedropazzini at gmail.com>
> To: Scikit-learn user and developer mailing list
> <scikit-learn at python.org>
> Subject: Re: [scikit-learn] Is there a built-in function for pairs of
> data?
> Message-ID:
> <CAAY8FkB2LjnegwFbn=gSOawLBcBQ3dnYa6BxDxN6-cvLT1RsfA at mail.
> gmail.com>
> Content-Type: text/plain; charset="utf-8"
>
> Like this?:
> http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.
> train_test_split.html
>
> 2016-09-26 15:06 GMT-03:00 Afarin Famili <Afarin.Famili at utsouthwestern.edu
> >:
>
> >
> > Dear Scikit-learn team,
> >
> >
> > We need to deal with pairs of data in our classification task. I was
> > wondering if there is already a built-in function in Scikit-learn that
> can
> > partition the pairs of data into train and test sets?
> >
> >
> > Regards,
> >
> > Afarin
> >
> >
> >
> > ------------------------------
> >
> > UT Southwestern
> >
> > Medical Center
> >
> > The future of medicine, today.
> >
> > _______________________________________________
> > scikit-learn mailing list
> > scikit-learn at python.org
> > https://mail.python.org/mailman/listinfo/scikit-learn
> >
> >
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> ------------------------------
>
> Message: 4
> Date: Mon, 26 Sep 2016 14:53:05 -0400
> From: David Nicholson <nicholdav at gmail.com>
> To: Scikit-learn user and developer mailing list
> <scikit-learn at python.org>
> Subject: Re: [scikit-learn] Is there a built-in function for pairs of
> data?
> Message-ID:
> <CAMabFbXamB5KzQY9_WU+8BFxpSECbs2fSiQqad18zi9zmOjvVQ
> @mail.gmail.com>
> Content-Type: text/plain; charset="utf-8"
>
> Do you mean like train_test_split?
> http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.
> train_test_split.html
>
> On Sep 26, 2016 14:43, "Afarin Famili" <Afarin.Famili at utsouthwestern.edu>
> wrote:
>
> >
> > Dear Scikit-learn team,
> >
> >
> > We need to deal with pairs of data in our classification task. I was
> > wondering if there is already a built-in function in Scikit-learn that
> can
> > partition the pairs of data into train and test sets?
> >
> >
> > Regards,
> >
> > Afarin
> >
> >
> >
> > ------------------------------
> >
> > UT Southwestern
> >
> > Medical Center
> >
> > The future of medicine, today.
> >
> > _______________________________________________
> > scikit-learn mailing list
> > scikit-learn at python.org
> > https://mail.python.org/mailman/listinfo/scikit-learn
> >
> >
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> ------------------------------
>
> Message: 5
> Date: Mon, 26 Sep 2016 21:43:05 +0200
> From: "Md. Khairullah" <md.khairullah at gmail.com>
> To: scikit-learn at python.org
> Subject: [scikit-learn] Large computation time for homogeneous data
> with agglomerative clustering
> Message-ID:
> <CA+xrTcKMkwSN2Y7jFg12nEx-Ch_V5bw7eLhG5UO39wN+ebBozg at mail.
> gmail.com>
> Content-Type: text/plain; charset="utf-8"
>
> Dear Scikit-learners,
> This is my first post here and I hope you experts can help me a lot.
>
> We are using the agglomerative clustering with ward's linkage and
> connectivity constraint. The data size is around 205,000 (each is a single
> scalar feature). The data set is dynamic (in time) and we need to apply
> clustering at different time thorough the process. Initially all data is 0
> and they increase gradually. Alternatively, in the early stage the data is
> more homogeneous and the heterogeneity among the data increases gradually.
> If the clustering is applied at the final stage (most heterogeneous data,
> but off course having patterns/clusters) requesting 20 clusters it takes
> only 61s of CPU time. But, if clustering is run in an early stage (more
> homogeneous data but all are not 0 and off course there are
> patterns/clusters in the data) with the same settings the time rises up to
> 1h 5m. The CPU time is in-between of these two if the data come from an
> in-between time stamp. I also tried the the other linkage options too, but
> the situation does not improve. My understanding is that the homogeneity is
> playing the role.
>
> Have you experienced this too? What solution do you suggest?
>
> Thanks in advance for your attention and help.
>
> --
> Best regards
>
> Md. Khairullah
> PhD Student, KU Leuven
> Numerical Analysis and Applied Mathematics Section
> Celestijnenlaan 200a - box 2402
> 3001 Leuven
> room: 03.18
> tel. +32 16 37 39 66
> fax +32 16 3 27996
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