They have been modified for other purposes; and the LibSVM version we have
has been modified to support sample_weight.
On 29 July 2014 17:05, Manoj Kumar <[email protected]> wrote:
> Hi Sean,
>
> Thanks for the link. I was not aware of that, but I'm really not sure to
> what extent we modify the shipped liblinear source files.
>
> For example there is an issue related to warm restart that I pinged Fabian
> about .
> https://github.com/scikit-learn/scikit-learn/issues/11#issuecomment-50328761
>
> Also there are other issues like the max_iter param is harcoded to 1000 in
> liblinear, which makes it difficult, to compare the effects of really low
> tolerances with different solvers.
>
> @Joel and other core devs
> Sorry for the dumb question but what is the status on modifying the the
> liblinear source files?
>
>
>
>
>
>
> On Tue, Jul 29, 2014 at 2:44 AM, Joel Nothman <[email protected]>
> wrote:
>
>> There is actually an open PR to import the sample_weight changes into the
>> scikit-learn copy of liblinear:
>> https://github.com/scikit-learn/scikit-learn/pull/2784. It would
>> appreciate some love, or someone to executively decide that it's not worth
>> including.
>>
>>
>> On 29 July 2014 10:36, Sean Violante <[email protected]> wrote:
>>
>>> it wasn't clear from the blog post but
>>> are you aware that liblinear has a modification that handles sample
>>> weights
>>> http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/#weights_for_data_instances
>>>
>>> [fyi what I would be interested in (and I am not sure this is
>>> implemented in that mod) is where one can aggregate the target]
>>> ie in dealing with categorical data one would group over the input data,
>>> weighting =group size and have target variable=average over group.
>>>
>>>
>>> On Tue, Jul 29, 2014 at 2:03 AM, <
>>> [email protected]> wrote:
>>>
>>>> Send Scikit-learn-general mailing list submissions to
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>>>> than "Re: Contents of Scikit-learn-general digest..."
>>>>
>>>>
>>>> Today's Topics:
>>>>
>>>> 1. Re: Evaluation measure for imbalanced data (Yogesh Karpate)
>>>> 2. [GSoC] - Logistic Regression CV (Manoj Kumar)
>>>> 3. Re: RBK Kernel - Query (umang patel)
>>>>
>>>>
>>>> ----------------------------------------------------------------------
>>>>
>>>> Message: 1
>>>> Date: Tue, 29 Jul 2014 00:12:02 +0200
>>>> From: Yogesh Karpate <[email protected]>
>>>> Subject: Re: [Scikit-learn-general] Evaluation measure for imbalanced
>>>> data
>>>> To: [email protected]
>>>> Message-ID:
>>>> <
>>>> cag7mfdsbnfqmqemddfxxzouhfhpnusbivjxcf1ozyegmvzu...@mail.gmail.com>
>>>> Content-Type: text/plain; charset="utf-8"
>>>>
>>>> Dear Hamed,
>>>> Can you share the code of "balanced accuracy" as you mentioned in last
>>>> mail.
>>>>
>>>>
>>>> On Tue, Jul 29, 2014 at 12:07 AM, Hamed Zamani <[email protected]>
>>>> wrote:
>>>>
>>>> > Dear Mario,
>>>> >
>>>> > Yes of course. Sorry I forgot to mention GMeans. It is also one of the
>>>> > measures which have been used frequently.
>>>> >
>>>> > -- Hamed
>>>> >
>>>> >
>>>> >
>>>> > On Tue, Jul 29, 2014 at 2:24 AM, Mario Michael Krell <
>>>> [email protected]>
>>>> > wrote:
>>>> >
>>>> >> Dear Hamed,
>>>> >>
>>>> >> I think it would be a good idea to also consider gmean when extending
>>>> >> scikit. It is the geometric mean of TNR and TPR instead of the
>>>> arithmetic
>>>> >> mean used for the balanced accuracy.
>>>> >>
>>>> >> Greets
>>>> >>
>>>> >> Mario
>>>> >>
>>>> >> On 28.07.2014, at 19:00,
>>>> >> [email protected] wrote:
>>>> >>
>>>> >> Dear Joel,
>>>> >>
>>>> >> Sorry for the delay. I was in a trip and I couldn't check my email.
>>>> >>
>>>> >> To the best of my knowledge and according to the kind responses in
>>>> this
>>>> >> email thread, we cannot claim that an specific measure is better
>>>> than the
>>>> >> others for imbalanced data. In other words, there are some evaluation
>>>> >> measure suitable for imbalanced data and each of them has its own
>>>> >> advantages. Hence, choosing the best evaluation measure totally
>>>> depends on
>>>> >> the application which you are working on.
>>>> >>
>>>> >> Anyway, "Matthew's Correlation Coefficient", "AUC of ROC",
>>>> "F-measure",
>>>> >> "Balanced Accuracy", and generally "Weighted Accuracy" have been used
>>>> >> frequently in the literature. Among these measures, only "balanced
>>>> >> accuracy" is not developed in scikit-learn and I think it is
>>>> worthwhile to
>>>> >> add it to this library. I have developed it before and if you want I
>>>> can
>>>> >> add it to the project or send it to you.
>>>> >>
>>>> >> Kind Regards,
>>>> >> Hamed
>>>> >>
>>>> >>
>>>> >>
>>>> >>
>>>> >>
>>>> ------------------------------------------------------------------------------
>>>> >> Infragistics Professional
>>>> >> Build stunning WinForms apps today!
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>>>> >>
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>>>> >>
>>>> >> _______________________________________________
>>>> >> Scikit-learn-general mailing list
>>>> >> [email protected]
>>>> >> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
>>>> >>
>>>> >>
>>>> >
>>>> >
>>>> >
>>>> ------------------------------------------------------------------------------
>>>> > Infragistics Professional
>>>> > Build stunning WinForms apps today!
>>>> > Reboot your WinForms applications with our WinForms controls.
>>>> > Build a bridge from your legacy apps to the future.
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>>>> > _______________________________________________
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>>>> > [email protected]
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>>>> >
>>>> >
>>>>
>>>>
>>>> --
>>>> Warm Regards
>>>> Yogesh Karpate
>>>> -------------- next part --------------
>>>> An HTML attachment was scrubbed...
>>>>
>>>> ------------------------------
>>>>
>>>> Message: 2
>>>> Date: Tue, 29 Jul 2014 01:38:44 +0200
>>>> From: Manoj Kumar <[email protected]>
>>>> Subject: [Scikit-learn-general] [GSoC] - Logistic Regression CV
>>>> To: [email protected]
>>>> Message-ID:
>>>> <
>>>> cafqad-nkckkapj8r8gez37pjd35ghwkwah_bd7s-avuhrfp...@mail.gmail.com>
>>>> Content-Type: text/plain; charset="utf-8"
>>>>
>>>> Hi, A update on the new Logistic Regression CV model in scikit-learn
>>>>
>>>>
>>>> http://manojbits.wordpress.com/2014/07/28/scikit-learn-logistic-regression-cv-2/
>>>>
>>>>
>>>> --
>>>> Regards,
>>>> Manoj Kumar,
>>>> GSoC 2014, Scikit-learn
>>>> Mech Undergrad
>>>> http://manojbits.wordpress.com
>>>> -------------- next part --------------
>>>> An HTML attachment was scrubbed...
>>>>
>>>> ------------------------------
>>>>
>>>> Message: 3
>>>> Date: Mon, 28 Jul 2014 20:02:56 -0400
>>>> From: umang patel <[email protected]>
>>>> Subject: Re: [Scikit-learn-general] RBK Kernel - Query
>>>> To: [email protected]
>>>> Message-ID:
>>>> <
>>>> camgx1excdd36fbnhxm_vmjry6adccnaesc3zpnxs5ojwn36...@mail.gmail.com>
>>>> Content-Type: text/plain; charset="utf-8"
>>>>
>>>> Hi Andy ,
>>>>
>>>> I nvr got answer . Could you please re- answer if possible . I will
>>>> really
>>>> appraciate it .
>>>>
>>>> Thank you.
>>>>
>>>>
>>>>
>>>> On Mon, Jul 28, 2014 at 2:53 PM, Andy <[email protected]> wrote:
>>>>
>>>> > Please do not repost.
>>>> > You got an answer on the issue if I recall correctly.
>>>> > If you want to know more, pick up a textbook on machine learning,
>>>> such as
>>>> > ESL (free pdf:
>>>> >
>>>> http://web.stanford.edu/~hastie/local.ftp/Springer/OLD/ESLII_print4.pdf
>>>> ),
>>>> > Kevin Murpy's book or the Bishop.
>>>> >
>>>> >
>>>> > On 07/27/2014 08:22 PM, umang patel wrote:
>>>> >
>>>> > Hello all ,
>>>> >
>>>> > I asked the following question on "Issues" and I was advised to
>>>> mail on
>>>> > the following email id I i have furthur queries .
>>>> >
>>>> > "
>>>> > Is it possible to get weight of features in rbf kernel .
>>>> >
>>>> > It is written under coeff_ that it is possible only with linear
>>>> kernel .
>>>> > Is it mathematically possible to get for rfb kernel , if yes then how
>>>> ?
>>>> > "
>>>> >
>>>> >
>>>> > Could and one please direct me to relevant paper or please explain
>>>> why it
>>>> > is not possible to get coeff_ for rbf kernel .
>>>> >
>>>> > Thank you.
>>>> >
>>>> >
>>>> > On Wed, Jul 23, 2014 at 8:33 AM, umang patel <[email protected]>
>>>> > wrote:
>>>> >
>>>> >> Hello all ,
>>>> >>
>>>> >> I asked the following question on "Issues" and I was advised to
>>>> mail on
>>>> >> the following email id I i have furthur queries .
>>>> >>
>>>> >> "
>>>> >> Is it possible to get weight of features in rbf kernel .
>>>> >>
>>>> >> It is written under coeff_ that it is possible only with linear
>>>> kernel .
>>>> >> Is it mathematically possible to get for rfb kernel , if yes then
>>>> how ?
>>>> >> "
>>>> >>
>>>> >>
>>>> >> Could and one please direct me to relevant paper or please explain
>>>> why
>>>> >> it is not possible to get coeff_ for rbf kernel .
>>>> >>
>>>> >> Thank you.
>>>> >>
>>>> >
>>>> >
>>>> >
>>>> >
>>>> ------------------------------------------------------------------------------
>>>> > Want fast and easy access to all the code in your enterprise? Index
>>>> and
>>>> > search up to 200,000 lines of code with a free copy of Black Duck
>>>> > Code Sight - the same software that powers the world's largest code
>>>> > search on Ohloh, the Black Duck Open Hub! Try it now.
>>>> http://p.sf.net/sfu/bds
>>>> >
>>>> >
>>>> >
>>>> > _______________________________________________
>>>> > Scikit-learn-general mailing listScikit-learn-general
>>>> @lists.sourceforge.nethttps://
>>>> lists.sourceforge.net/lists/listinfo/scikit-learn-general
>>>> >
>>>> >
>>>> >
>>>> >
>>>> >
>>>> ------------------------------------------------------------------------------
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>>>> >
>>>> >
>>>> -------------- next part --------------
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>>>> ------------------------------
>>>>
>>>>
>>>> ------------------------------------------------------------------------------
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>>>> ------------------------------
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>>>>
>>>> End of Scikit-learn-general Digest, Vol 54, Issue 67
>>>> ****************************************************
>>>>
>>>
>>>
>>>
>>> ------------------------------------------------------------------------------
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>>>
>>
>>
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>>
>>
>
>
> --
> Regards,
> Manoj Kumar,
> GSoC 2014, Scikit-learn
> Mech Undergrad
> http://manojbits.wordpress.com
>
>
> ------------------------------------------------------------------------------
> Infragistics Professional
> Build stunning WinForms apps today!
> Reboot your WinForms applications with our WinForms controls.
> Build a bridge from your legacy apps to the future.
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