@Jacob,
I understand your concern about the new algorithms. It will be lost effort
to make coding, updating, documentation for a unsuccessful algorithm.
Thanks for the tips.
@federico,
lightning library is, close to what in my mind but not the same. I think,
there should be an easy way to see how
I've added this PR, and I addressed in the comments some of your concerns
(publications, comparison to affinity propagation, etc).
https://github.com/scikit-learn/scikit-learn/pull/9329
I'd love for you to review, since this is my first PR in the scikit learn
repository
On Wed, Jul 12, 2017 at 1
If this is the first time you contribute, please make sure to
carefully read the contributors guide till the end:
http://scikit-learn.org/stable/developers/contributing.html
In particular, make sure to follow the estimators API conventions for
your PR to get a chance to be reviewed. In particular
You don't need our permission to submit a PR, go ahead! We welcome PRs.
On Mon, Jul 10, 2017 at 9:36 PM, Uri Goren wrote:
> I have,
> The only criterion that I am unsure about is the number citations.
>
> In the literature Markov clustering is usually compared to affinity
> prolongation, which a
I have,
The only criterion that I am unsure about is the number citations.
In the literature Markov clustering is usually compared to affinity
prolongation, which also has a similar number of citations.
I have attached my implementation in my github account for you to review.
Do I have your appr
hi,
did you have a look at :
http://scikit-learn.org/stable/faq.html#what-are-the-inclusion-criteria-for-new-algorithms
Alex
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On Mon, Jul 10, 2017 at 04:10:09PM +, federico vaggi wrote:
> There is a fantastic library called lightning where the optimization
> routines are first class citizens:
> http://contrib.scikit-learn.org/lightning/ - you can take a look there.
> However, lightning focuses on convex optimization,
Hi,
I'd like to implement the Markov clustering algorithm,
Any objections?
On Jul 10, 2017 7:10 PM, "federico vaggi" wrote:
Hey Gurhan,
sklearn doesn't really neatly separate optimizers from the models they
optimize at the level of API (except in a few cases). In order to make the
package mor
Hey Gurhan,
sklearn doesn't really neatly separate optimizers from the models they
optimize at the level of API (except in a few cases). In order to make the
package more friendly to newer user, each model has excellent optimizer
defaults that you can use, and only in a few cases does it make sen
Howdy
This question and the one right after in the FAQ are probably relevant re:
inclusion of new algorithms:
http://scikit-learn.org/stable/faq.html#what-are-the-inclusion-criteria-for-new-algorithms.
The gist is that we only include well established algorithms, and there are
no end to those. I t
p. m.
Para: Scikit-learn user and developer mailing list
Asunto: Re: [scikit-learn] Contribution to sklearn: Cross validation of time
series
Hey Andres.
I think there might be a PR for that.
Can you explain the minimum size of the training set? How is that used?
I thought the other main option would b
Hi Andres, hi Andy,
Indeed in real life I also needed to cross-validate time series in a different
manner than TimeSeriesSplit implemented in sklearn does.
I fully support the idea of such a contribution Andres.
As Andy mentioned, the main option would be a « rolling window » or as I use to
say
Hey Andres.
I think there might be a PR for that.
Can you explain the minimum size of the training set? How is that used?
I thought the other main option would be "rolling window" cross validation
to use a fixed length cv training set.
So the two options to me were rolling window and what we're d
Hi Konstantinos.
There is an IRC channel but it's not that busy any more.
You could try the gitter channel at
http://gitter.im/scikit-learn/scikit-learn
The issue that you cited is ok, but this one might be easier to start with:
https://github.com/scikit-learn/scikit-learn/issues/8194
You need
Hi,
Thank you all for the info. It is not my first contribution to a project (I
made little contributions to xgboost and tensorflow), even though I think it is
really interesting what Oliver said, specially because of the very curated
structure and guidelines of the project. BTW, I’ve found su
If this is your first contribution to the project, I would strongly
suggest to start by contributing a small bug fix or improvement to get
accustomed to the kind of things the core devs expect when reviewing a
PR.
Also please read the contributors guide :
http://scikit-learn.org/dev/developers/co
I remember that there was a discussion regarding stacking in general after we
implemented the majority voting classifier, and I just found a PR with some
stacking implementation that seems to be in progress
https://github.com/scikit-learn/scikit-learn/pull/6674
> On Sep 20, 2016, at 8:02 PM, J
Have you searched the issue tracker for Stacking and the relationship
between your proposal and others in the works?
https://github.com/scikit-learn/scikit-learn/search?q=stacking&type=Issues&utf8=%E2%9C%93
On 21 September 2016 at 02:04, Iván Vallés Pérez
wrote:
> Hello,
>
> My name is Iván Val
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