Hi Robert,
Gotcha. Sorry I misunderstood. And ditto by the way.
John
On Sun, Apr 21, 2013 at 6:43 PM, <
scikit-learn-general-requ...@lists.sourceforge.net> wrote:
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> 1. Rotations Code? (Skipper Seabold)
> 2. Re: Metric Learning Algorithms (John Collins) (Robert McGibbon)
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> ----------------------------------------------------------------------
>
> Message: 1
> Date: Sun, 21 Apr 2013 21:36:57 -0400
> From: Skipper Seabold <jsseab...@gmail.com>
> Subject: [Scikit-learn-general] Rotations Code?
> To: scikit-learn-general@lists.sourceforge.net
> Message-ID:
> <CAKF=Djv=O=
> fhjvvfocm9cr7+vzqm8l3prjtzb1aj3o5isja...@mail.gmail.com>
> Content-Type: text/plain; charset=ISO-8859-1
>
> Hi,
>
> Does anyone have any code for computing rotations of components after
> PCA or FactorAnalysis, etc. E.g., varimax?
>
> Thanks,
>
> Skipper
>
>
>
> ------------------------------
>
> Message: 2
> Date: Sun, 21 Apr 2013 18:43:44 -0700
> From: Robert McGibbon <rmcgi...@gmail.com>
> Subject: Re: [Scikit-learn-general] Metric Learning Algorithms (John
> Collins)
> To: scikit-learn-general@lists.sourceforge.net
> Message-ID: <2c3332ed-0e63-4d0f-8f74-6abd48d5e...@gmail.com>
> Content-Type: text/plain; charset="iso-8859-1"
>
> John,
>
> I just meant that if the scikit's maintainers didn't think it was within
> the scope of the project, I'd still be interested in assembling and
> contributing to
> a collection of metric learning algorithms in the scikit's style.
>
> -Robert
>
>
> On Apr 21, 2013, at 6:25 PM, John Collins wrote:
>
> > Hi Robert, Ken,
> >
> > Robert,
> >
> > I'm not convinced we would need a separate interface. Perhaps I'm wrong
> because I've not really been exposed to all of the metric learning
> techniques, but in all the ones I've seen the goal is to learn a matrix A.
> Calling say <metric_learning_technique_X>.fit(X, y) could create A. Perhaps
> the response variable y could be not-necessarily fully specified as is
> usually the case with a metric learning approach, i.e. some elements are
> None. Then <metric_learning_technique_X>.predict(newX) would do the usual
> thing.
> >
> > In any case I would also be interested in helping to implement, though I
> think it would fit nicely within the scope of sklearn.
> >
> > John
> >
> >
> > On Sun, Apr 21, 2013 at 4:30 PM, <
> scikit-learn-general-requ...@lists.sourceforge.net> wrote:
> > Send Scikit-learn-general mailing list submissions to
> > scikit-learn-general@lists.sourceforge.net
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> >
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> > or, via email, send a message with subject or body 'help' to
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> > than "Re: Contents of Scikit-learn-general digest..."
> >
> >
> > Today's Topics:
> >
> > 1. Re: Random patches and coordinates (Nicolas Tr?segnie)
> > 2. Re: Metric Learning Algorithms (Robert McGibbon)
> > 3. Re: Random patches and coordinates (Gael Varoquaux)
> > 4. Re: Metric Learning Algorithms (Robert McGibbon)
> > 5. Re: Random patches and coordinates (Nicolas Tr?segnie)
> >
> >
> > ----------------------------------------------------------------------
> >
> > Message: 1
> > Date: Mon, 22 Apr 2013 00:34:19 +0200
> > From: Nicolas Tr?segnie <nicolas.treseg...@gmail.com>
> > Subject: Re: [Scikit-learn-general] Random patches and coordinates
> > To: scikit-learn-general@lists.sourceforge.net
> > Message-ID: <5174696b.7070...@gmail.com>
> > Content-Type: text/plain; charset="iso-8859-1"
> >
> > Hi Alex,
> >
> > Indeed, this solution wouldn't break anything but I generally avoid
> > letting the parameters change the return type of a function. Is this
> > approach used somewhere else in scikit-learn?
> >
> > After the modification of extract_patches_2d, I will probably modify
> > reconstruct_from_patches_2d. I would add the possibility to:
> >
> > * disable the averaging
> > * use the coordinates to reconstruct the image with only a subset of
> > the patches
> >
> > Nicolas
> >
> > On 04/21/2013 10:01 PM, Alexandre Gramfort wrote:
> > > hi Nicolas,
> > >
> > > I would add a bool parameter to extract_patches_2d such as
> > > return_coordinates. If True it returns (patches, coords)
> > >
> > > Alex
> > >
> > >
> ------------------------------------------------------------------------------
> > > Precog is a next-generation analytics platform capable of advanced
> > > analytics on semi-structured data. The platform includes APIs for
> building
> > > apps and a phenomenal toolset for data science. Developers can use
> > > our toolset for easy data analysis & visualization. Get a free account!
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> > ------------------------------
> >
> > Message: 2
> > Date: Sun, 21 Apr 2013 15:35:52 -0700
> > From: Robert McGibbon <rmcgi...@gmail.com>
> > Subject: Re: [Scikit-learn-general] Metric Learning Algorithms
> > To: scikit-learn-general@lists.sourceforge.net
> > Message-ID: <91613893-bd92-49d5-a76b-9e30a0949...@gmail.com>
> > Content-Type: text/plain; charset="windows-1252"
> >
> > This would be AWESOME.
> >
> > I have code implementing Shen, C.; Kim, J.; Wang, L. Scalable
> large-margin Mahalanobis distance metric learning. IEEE Trans. Neural
> Networks 2010, 21, 1524?1530, but it yeah, it's not up to sklearn standards
> either.
> >
> > -Robert
> >
> > On Apr 21, 2013, at 12:49 PM, Kenneth C. Arnold wrote:
> >
> > > I have implemented a few metric learning algorithms myself. The
> quality of that code is nowhere near sklearn standards, but I may have some
> incentive to improve it soon.
> > >
> > > -Ken
> > >
> > >
> > > On Sun, Apr 21, 2013 at 3:42 PM, John Collins <johnsso...@gmail.com>
> wrote:
> > > Has anybody or does anybody have plans to implement metric learning
> algorithms like ITML in sklearn?
> > >
> > > If not, I would like to consider working on this.
> > >
> > > Thanks,
> > >
> > > John
> > >
> > >
> ------------------------------------------------------------------------------
> > > Precog is a next-generation analytics platform capable of advanced
> > > analytics on semi-structured data. The platform includes APIs for
> building
> > > apps and a phenomenal toolset for data science. Developers can use
> > > our toolset for easy data analysis & visualization. Get a free account!
> > > http://www2.precog.com/precogplatform/slashdotnewsletter
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> > > Scikit-learn-general mailing list
> > > Scikit-learn-general@lists.sourceforge.net
> > > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
> > >
> > >
> > >
> ------------------------------------------------------------------------------
> > > Precog is a next-generation analytics platform capable of advanced
> > > analytics on semi-structured data. The platform includes APIs for
> building
> > > apps and a phenomenal toolset for data science. Developers can use
> > > our toolset for easy data analysis & visualization. Get a free account!
> > >
> http://www2.precog.com/precogplatform/slashdotnewsletter_______________________________________________
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> > Message: 3
> > Date: Sun, 21 Apr 2013 16:38:13 -0600
> > From: Gael Varoquaux <gael.varoqu...@normalesup.org>
> > Subject: Re: [Scikit-learn-general] Random patches and coordinates
> > To: scikit-learn-general@lists.sourceforge.net
> > Message-ID: <20130421223813.gc3...@phare.normalesup.org>
> > Content-Type: text/plain; charset=us-ascii
> >
> > Would a transformer, with an associated inverse_transform, be useful
> > here? It seems to me that it would be the right pattern, however I don't
> > have the code in mind, so I may be wrong.
> >
> >
> >
> > ------------------------------
> >
> > Message: 4
> > Date: Sun, 21 Apr 2013 15:40:21 -0700
> > From: Robert McGibbon <rmcgi...@gmail.com>
> > Subject: Re: [Scikit-learn-general] Metric Learning Algorithms
> > To: scikit-learn-general@lists.sourceforge.net
> > Message-ID: <6f63ec10-ec76-408e-b007-f4a671769...@gmail.com>
> > Content-Type: text/plain; charset="windows-1252"
> >
> > If people were interested in putting together a separate package in the
> style of the scikit collecting metric learning algorithms with a common
> API, I would love to contribute to that too.
> >
> > -Robert
> >
> > On Apr 21, 2013, at 3:35 PM, Robert McGibbon wrote:
> >
> > > This would be AWESOME.
> > >
> > > I have code implementing Shen, C.; Kim, J.; Wang, L. Scalable
> large-margin Mahalanobis distance metric learning. IEEE Trans. Neural
> Networks 2010, 21, 1524?1530, but it yeah, it's not up to sklearn standards
> either.
> > >
> > > -Robert
> > >
> > > On Apr 21, 2013, at 12:49 PM, Kenneth C. Arnold wrote:
> > >
> > >> I have implemented a few metric learning algorithms myself. The
> quality of that code is nowhere near sklearn standards, but I may have some
> incentive to improve it soon.
> > >>
> > >> -Ken
> > >>
> > >>
> > >> On Sun, Apr 21, 2013 at 3:42 PM, John Collins <johnsso...@gmail.com>
> wrote:
> > >> Has anybody or does anybody have plans to implement metric learning
> algorithms like ITML in sklearn?
> > >>
> > >> If not, I would like to consider working on this.
> > >>
> > >> Thanks,
> > >>
> > >> John
> > >>
> > >>
> ------------------------------------------------------------------------------
> > >> Precog is a next-generation analytics platform capable of advanced
> > >> analytics on semi-structured data. The platform includes APIs for
> building
> > >> apps and a phenomenal toolset for data science. Developers can use
> > >> our toolset for easy data analysis & visualization. Get a free
> account!
> > >> http://www2.precog.com/precogplatform/slashdotnewsletter
> > >> _______________________________________________
> > >> Scikit-learn-general mailing list
> > >> Scikit-learn-general@lists.sourceforge.net
> > >> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
> > >>
> > >>
> > >>
> ------------------------------------------------------------------------------
> > >> Precog is a next-generation analytics platform capable of advanced
> > >> analytics on semi-structured data. The platform includes APIs for
> building
> > >> apps and a phenomenal toolset for data science. Developers can use
> > >> our toolset for easy data analysis & visualization. Get a free
> account!
> > >>
> http://www2.precog.com/precogplatform/slashdotnewsletter_______________________________________________
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> > ------------------------------
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> > Message: 5
> > Date: Mon, 22 Apr 2013 01:29:59 +0200
> > From: Nicolas Tr?segnie <nicolas.treseg...@gmail.com>
> > Subject: Re: [Scikit-learn-general] Random patches and coordinates
> > To: scikit-learn-general@lists.sourceforge.net
> > Message-ID: <51747677.4010...@gmail.com>
> > Content-Type: text/plain; charset="iso-8859-1"
> >
> > I think you are right. Moreover, a transformer already exists here
> > <
> https://github.com/scikit-learn/scikit-learn/blob/85ec0fd1ae904f275f608b11044a2476ed4723e6/sklearn/feature_extraction/image.py#L380
> >.
> >
> > I see two solutions:
> >
> > * The transform() method could return the coordinates and the
> > inverse_transform() method could take them as argument. The
> > Transformer API
> > <
> http://scikit-learn.org/0.13/developers/index.html#different-objects>
> > doesn't specify if the transform() method can take more than one
> > argument (two, to be exact) and I didn't find inverse_transform() in
> > the API page. So I don't know if this approach would be consistent
> > with all the others estimators/transformers.
> > * The transformer could also retain the coordinates but then:
> > o It would be specific to images of a certain size.
> > o What kind of behaviour would be expected if the transform()
> > method is called more than once? Use the same coordinates to
> > extract patches in the new set of images?
> > o If the transform() method was used to extract patches from
> > various images, these images would need to be reconstructed
> > together.
> >
> > Another question for later:
> >
> > * The two functions extract_patches_2d and reconstruct_from_patches_2d
> > are part of the public API (they are not prefixed with _). I think
> > they sould be deprecated if a create a more complex transformer.
> > What do you think?
> >
> >
> > On 04/22/2013 12:38 AM, Gael Varoquaux wrote:
> > > Would a transformer, with an associated inverse_transform, be useful
> > > here? It seems to me that it would be the right pattern, however I
> don't
> > > have the code in mind, so I may be wrong.
> > >
> > >
> ------------------------------------------------------------------------------
> > > Precog is a next-generation analytics platform capable of advanced
> > > analytics on semi-structured data. The platform includes APIs for
> building
> > > apps and a phenomenal toolset for data science. Developers can use
> > > our toolset for easy data analysis & visualization. Get a free account!
> > > http://www2.precog.com/precogplatform/slashdotnewsletter
> > > _______________________________________________
> > > Scikit-learn-general mailing list
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> ------------------------------------------------------------------------------
> > Precog is a next-generation analytics platform capable of advanced
> > analytics on semi-structured data. The platform includes APIs for
> building
> > apps and a phenomenal toolset for data science. Developers can use
> > our toolset for easy data analysis & visualization. Get a free account!
> > http://www2.precog.com/precogplatform/slashdotnewsletter
> >
> > ------------------------------
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> > End of Scikit-learn-general Digest, Vol 39, Issue 41
> > ****************************************************
> >
> >
> ------------------------------------------------------------------------------
> > Precog is a next-generation analytics platform capable of advanced
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> building
> > apps and a phenomenal toolset for data science. Developers can use
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>
> ------------------------------------------------------------------------------
> Precog is a next-generation analytics platform capable of advanced
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> apps and a phenomenal toolset for data science. Developers can use
> our toolset for easy data analysis & visualization. Get a free account!
> http://www2.precog.com/precogplatform/slashdotnewsletter
>
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analytics on semi-structured data. The platform includes APIs for building
apps and a phenomenal toolset for data science. Developers can use
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