Hi Dayvid,
For now, a number of projects that follow the scikit-learn interface but
for one reason or another (often just out of scope) are listed at
https://github.com/scikit-learn/scikit-learn/wiki/Third-party-projects-and-code-snippets
.
I would recommend against keeping everything in a scikit-learn fork. It
would be much cleaner to maintain. You want your users to download your
package separately from PyPI, and use it with the current version of
scikit-learn, not whatever you happen to have in your repository.
The easiest way to do that is just create a new github project and copy
your files across.
Whether or not these algorithms become included in the main scikit-learn
repository depends to a large extent on how popular they are, e.g. if their
seminal works have a lot of citation growth (and perhaps I've
underestimated its importance already). At a minimum, to show that the
techniques are worth including, you will need to show evidence that they
are popular in the community, and provide examples
<http://scikit-learn.org/stable/auto_examples> to show the implementation
is effective in practice, and explain why they do not depart from
scikit-learn's functional scope (which can't afford to creep due to
maintenance costs).
Given that the technique seems to be KNN-focused, it could be incorporated
as an extension to the KNN classifier/regressor classes, by providing an
option to reduce the population when fitting, rather than a set of separate
estimators.
On 20 June 2014 09:30, Dayvid Victor <[email protected]> wrote:
> Hi Joel,
>
> Thanks for your feedback. Let me see if I got this straight,
> you think I should open a new repository and then add an entry
> in the Wiki?
>
> Do you have an example of some other project that did the same?
> How do I organize it, do I start a new project or I build a new project
> inside my sklearn fork?
>
> Also, do you think in the future it might be included in the
> scikits-learn repository?
>
> Thanks,
>
>
>
> On Thu, Jun 19, 2014 at 4:07 PM, Joel Nothman <[email protected]>
> wrote:
>
>> PS: Kyle, from a brief look, I would summarise it as sampling a small set
>> of KNN centroids.
>>
>>
>> On 19 June 2014 12:05, Joel Nothman <[email protected]> wrote:
>>
>>> Hi Dayvid,
>>>
>>> Although it could potentially be included in scikit-learn, it looks like
>>> your components do not require modifying the existing codebase, and could
>>> be construed as an entirely independent project. This could be referenced
>>> from the Scikit-learn Wiki or similar, without having to decide whether
>>> these are within the scope (besides quality of code, testing, documentation
>>> and exemplification) for inclusion in the main project. Given the current
>>> burden on scikit-learn devs (e.g. 171 open pull requests), including a
>>> somewhat orthogonal set of algorithms (which may not be seminal enough for
>>> inclusion anyway) seems unlikely at present. I suggest you make the
>>> instance reduction directory a separate repository, and invite comment
>>> there.
>>>
>>> Cheers,
>>>
>>> - Joel
>>>
>>>
>>> On 19 June 2014 11:46, Dayvid Victor <[email protected]> wrote:
>>>
>>>> Hi Kyle,
>>>>
>>>> (sorry for the long answer).
>>>>
>>>> Instance Reduction techniques aims to reduce the amount of data
>>>> manipulated in order
>>>> to perform a classification/prediction ...
>>>>
>>>> Depending on the approach, they can remove noisy-data and outliers,
>>>> remove redundant data, generate new generalized data by combining existing
>>>> data. Undersampling, Oversampling, Hybrid-Sampling for Imbalanced Datasets.
>>>>
>>>> This figure is from a paper I was co-author 2 years ago, and I think it
>>>> shows what Instance Reduction does:
>>>>
>>>> https://dl.dropboxusercontent.com/u/23695780/asgp.png
>>>>
>>>>
>>>> These are two surveys that presents a taxonomy of prototype selection
>>>> and prototype generation, they do not have many citations (arround 100),
>>>> but they do reference the most important techniques and papers until 2012.
>>>>
>>>> - S. García, J. Derrac, J. R. Cano, and F. Herrera, “Prototype
>>>> selection for nearest neighbor classification: Taxonomy and empirical
>>>> study,” Pattern Analysis and Machine Intelligence, IEEE Transactions on,
>>>> vol. 34, no. 3, pp. 417–435, 2012.
>>>> - I. Triguero, J. Derrac, S. García, and F. Herrera, “A taxonomy
>>>> and experimental study on prototype generation for nearest neighbor
>>>> classification,” Systems, Man, and Cybernetics, Part C: Applications and
>>>> Reviews, IEEE Transactions on, vol. 42, no. 1, pp. 86–100, 2012
>>>>
>>>>
>>>> Actually, the idea is to contribute with the main ideas that handles:
>>>> High Noisy-Data, Outliers, (Edition, Condensation and Hybrid approaches);
>>>> Only the important techniques of each.
>>>>
>>>>
>>>> Thanks,
>>>>
>>>>
>>>> On Wed, Jun 18, 2014 at 2:45 PM, Kyle Kastner <[email protected]>
>>>> wrote:
>>>>
>>>>> Do you have any references for this technique? What is it typically
>>>>> used for?
>>>>>
>>>>>
>>>>> On Wed, Jun 18, 2014 at 12:26 PM, Dayvid Victor <[email protected]
>>>>> > wrote:
>>>>>
>>>>>> Hi there,
>>>>>>
>>>>>> Is anybody working on an Instance Reduction module for sklearn?
>>>>>>
>>>>>> I started working on those and I already have more than 10 IR (PS and
>>>>>> PG)
>>>>>> algorithms implemented (only 7 are in the repository right now);
>>>>>>
>>>>>> If anybody is working on this, I'd love to help you out; If not, but
>>>>>> anybody want to contribute to this module, please, reach me in my email!
>>>>>>
>>>>>>
>>>>>> https://github.com/dvro/scikit-learn/tree/instance_reduction/sklearn/instance_reduction
>>>>>>
>>>>>> Thanks,
>>>>>> --
>>>>>> Dayvid Victor R. de Oliveira
>>>>>> PhD Candidate in Computer Science at Federal University of
>>>>>> Pernambuco (UFPE)
>>>>>> MSc in Computer Science at Federal University of Pernambuco (UFPE)
>>>>>> BSc in Computer Engineering - Federal University of Pernambuco (UFPE)
>>>>>>
>>>>>>
>>>>>> ------------------------------------------------------------------------------
>>>>>> HPCC Systems Open Source Big Data Platform from LexisNexis Risk
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>>>>>>
>>>>>>
>>>>>
>>>>>
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>>>>> HPCC Systems Open Source Big Data Platform from LexisNexis Risk
>>>>> Solutions
>>>>> Find What Matters Most in Your Big Data with HPCC Systems
>>>>> Open Source. Fast. Scalable. Simple. Ideal for Dirty Data.
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>>>>>
>>>>>
>>>>
>>>>
>>>> --
>>>> *Dayvid Victor R. de Oliveira*
>>>> PhD Candidate in Computer Science at Federal University of Pernambuco
>>>> (UFPE)
>>>> MSc in Computer Science at Federal University of Pernambuco (UFPE)
>>>> BSc in Computer Engineering - Federal University of Pernambuco (UFPE)
>>>>
>>>>
>>>> ------------------------------------------------------------------------------
>>>> HPCC Systems Open Source Big Data Platform from LexisNexis Risk
>>>> Solutions
>>>> Find What Matters Most in Your Big Data with HPCC Systems
>>>> Open Source. Fast. Scalable. Simple. Ideal for Dirty Data.
>>>> Leverages Graph Analysis for Fast Processing & Easy Data Exploration
>>>> http://p.sf.net/sfu/hpccsystems
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>>>>
>>>>
>>>
>>
>>
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>> Find What Matters Most in Your Big Data with HPCC Systems
>> Open Source. Fast. Scalable. Simple. Ideal for Dirty Data.
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>>
>
>
> --
> *Dayvid Victor R. de Oliveira*
> PhD Candidate in Computer Science at Federal University of Pernambuco
> (UFPE)
> MSc in Computer Science at Federal University of Pernambuco (UFPE)
> BSc in Computer Engineering - Federal University of Pernambuco (UFPE)
>
>
> ------------------------------------------------------------------------------
> HPCC Systems Open Source Big Data Platform from LexisNexis Risk Solutions
> Find What Matters Most in Your Big Data with HPCC Systems
> Open Source. Fast. Scalable. Simple. Ideal for Dirty Data.
> Leverages Graph Analysis for Fast Processing & Easy Data Exploration
> http://p.sf.net/sfu/hpccsystems
> _______________________________________________
> Scikit-learn-general mailing list
> [email protected]
> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
>
>
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