[scikit-learn] Nested Leave One Subject Out (LOSO) cross validation with scikit
Ludovico Coletta
ludo25_90 at hotmail.com
Wed Dec 7 17:25:14 EST 2016
Thank you for the good news! [😊]
Best
Ludovico
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Inviato: mercoledì 7 dicembre 2016 22.06
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Today's Topics:
1. Re: Nested Leave One Subject Out (LOSO) cross validation with
scikit (Andreas Mueller)
----------------------------------------------------------------------
Message: 1
Date: Wed, 7 Dec 2016 16:06:29 -0500
From: Andreas Mueller <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: <e3971a8a-a4eb-2536-4e78-95edd2fece2e at gmail.com>
Content-Type: text/plain; charset="windows-1252"; Format="flowed"
PredefinedSplit allows to define your split but I think it's better if
you look at this pr:
https://github.com/scikit-learn/scikit-learn/pull/7990
[https://avatars2.githubusercontent.com/u/7454015?v=3&s=400]<https://github.com/scikit-learn/scikit-learn/pull/7990>
[WIP] Cache pipeline by glemaitre · Pull Request #7990 · scikit-learn/scikit-learn<https://github.com/scikit-learn/scikit-learn/pull/7990>
github.com
Reference Issue Address the discussions in #3951 Other related issues and PR: #2086 #5082 #5080 What does this implement/fix? Explain your changes. It implements a version of Pipeline which allows ...
That allows you to cache steps in a pipeline so you don't have to
recompute the feature selection for each parameter.
On 12/07/2016 03:13 PM, Ludovico Coletta wrote:
>
> Thank you for the answer.
>
>
> I also thought about ShuffleSplit (n_splits=1), but I need to control
> which indices are used for training and which for testing in the
> nested folds. The problem is that I did feature selection before
> hyperparameters optimization (with a nested Leave One Out schema) and
> now I need the same partitioning for hyperparameters optimization. The
> reason why I did this is that the feature selection step is incredibly
> slow, I hope I can get rid of that step in the permutation test. Is
> not clear to me if I have to include feature selection in the
> permutation test as well.
>
>
> Maybe LeavePOut is what I need.
>
>
> Best
>
> Ludovico
>
>
>
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>
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>
> or, via email, send a message with subject or body 'help' to
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> When replying, please edit your Subject line so it is more specific
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>
> Today's Topics:
>
> 1. Re: New to scikit (Andreas Mueller)
> 2. Re: Nested Leave One Subject Out (LOSO) cross validation with
> scikit (Andreas Mueller)
> 3. return type of StandardScaler (Nilay Shrivastava)
> 4. Re: return type of StandardScaler (Bharat Didwania .)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Wed, 7 Dec 2016 11:33:38 -0500
> From: Andreas Mueller <t3kcit at gmail.com>
> To: Scikit-learn user and developer mailing list
> <scikit-learn at python.org>
> Subject: Re: [scikit-learn] New to scikit
> Message-ID: <f1d5d579-0646-d05d-4bde-9d44c34ec2a3 at gmail.com>
> Content-Type: text/plain; charset="windows-1252"; Format="flowed"
>
> http://scikit-learn.org/dev/developers/contributing.html#deprecation
>
> On 12/07/2016 09:42 AM, Chinmay Talegaonkar wrote:
> > Yeah, I found an easy bug. Looking for some help in writing
> > deprecation cycles for a bug.
> >
> > On Wed, Dec 7, 2016 at 8:05 PM, Siddharth Gupta
> > <siddharthgupta234 at gmail.com <mailto:siddharthgupta234 at gmail.com>>
> wrote:
> >
> > Great! Welcome to the community. I would suggest you to check out
> > the issues page on the github repo, raise hand to the issues you
> > feel like you can give a go to, check out the issues that are
> > tagged as require contributor. Issues are a good way to start,
> > they will direct you about the areas of the code base to explore.
> >
> > On Dec 7, 2016 6:02 PM, "Chinmay Talegaonkar"
> > <chinmay0301 at gmail.com <mailto:chinmay0301 at gmail.com>> wrote:
> >
> > Hi everyone,
> > I have a prior experience in python, and
> > have started learning machine learning recently. I wanted to
> > contribute to scikit, can anyone suggest a relatively easy
> > codebase to explore.
> > Thanks in advance!
> >
> >
> > _______________________________________________
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> >
> >
> > --
> > --
> > *Chinmay Talegaonkar*
> > Cultural and Events Coordinator, Mood Indigo
> > ..............................................
> >
> >
> > +91-8879178724
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> > www.moodi.org<http://www.moodi.org> <http://www.moodi.org> <http://www.moodi.org/>
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> Message: 2
> Date: Wed, 7 Dec 2016 11:33:00 -0500
> From: Andreas Mueller <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: <e9e342f5-d09b-92b7-e304-a0bfa37aecb6 at gmail.com>
> Content-Type: text/plain; charset="windows-1252"; Format="flowed"
>
>
>
> On 12/07/2016 07:41 AM, Ludovico Coletta wrote:
> >
> > 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?
> >
> >
> Not sure I understand your question.
> You can do a single split by using ShuffleSplit(n_splits=1) for example.
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> Message: 3
> Date: Wed, 7 Dec 2016 22:14:18 +0530
> From: Nilay Shrivastava <nilay.euler16 at gmail.com>
> To: scikit-learn at python.org
> Subject: [scikit-learn] return type of StandardScaler
> Message-ID:
> <CAKLfaQRA_qGdYPrDdtG58nhyBQurWBCT2_2NZKzRiOmww5rgJw at mail.gmail.com>
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>
> StandardScaler returns numpy array even if the object passed is a pandas
> dataframe, shouldn't it return a dataframe?
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> Message: 4
> Date: Wed, 7 Dec 2016 08:48:47 -0800
> From: "Bharat Didwania ." <bharat.didwania.eee14 at itbhu.ac.in>
> To: Scikit-learn user and developer mailing list
> <scikit-learn at python.org>
> Subject: Re: [scikit-learn] return type of StandardScaler
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>
> you can use pandas.get_dummies()
> <http://pandas.pydata.org/pandas-docs/stable/generated/pandas.get_dummies.html>.
> It will perform one hot encoding on categorical columns, and produce a
> dataframe as the result. From there you can use
> pandas.concat([existing_df,
> new_df],axis=0) to add the new columns to your existing dataframe. This
> will avoid the use of a numpy array.
>
>
> On Wed, Dec 7, 2016 at 8:44 AM, Nilay Shrivastava
> <nilay.euler16 at gmail.com>
> wrote:
>
> > StandardScaler returns numpy array even if the object passed is a pandas
> > dataframe, shouldn't it return a dataframe?
> >
> >
> > _______________________________________________
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