[SciPy-User] [ANN] scikit.statsmodels 0.2.0 release
Fri Feb 19 10:01:58 CST 2010
On Fri, Feb 19, 2010 at 10:42 AM, Bruce Southey <email@example.com> wrote:
> On 02/18/2010 06:04 PM, Gael Varoquaux wrote:
>> On Thu, Feb 18, 2010 at 06:59:14PM -0500, firstname.lastname@example.org wrote:
>>> but I haven't seen anything yet that would be BSD compatible.
>> We actually have some code in the lab. It is based on proximal iterations,
>> or dual optimisation. We are going to release some of it as BSD pretty
>> soon. I believe we will also implement a path algorithm, but I am not
>> sure whether it will be a lars or the new Friedman toy.
>>> I think lars is not too difficult, that's one of the reason I have it
>>> in mind, and I have seen some applications in econometrics where the
>>> forecasting performance of lars in combination with PCA was the best
>>> but not lars alone.
>> I have implemented a lars (not releasing it, as it is buggy). There are a
>> few difficulties that are not outlined in the article :). I am willing to
>> work with you on the lars (mostly by dumping the code to you, with the
>> comments pointing to the problems). But I would advice you to wait a
>> month, as we have a working sessions planned exactly for pushing code
>> that solves these problems in scikit.learn.
> That would be great!
> I really do think that the scikits learn and statsmodels must talk
> together now that learn has had a release as well ( I don't recall
> seeing it mentioned hint hint!). What would be nice is the acceptance of
> input data types between learn and statsmodels especially for things
> like logistic regression. While I understand the need for duplicate
> functions, it may be desirable share at least code since both code bases
> are still relatively 'new'.
We are trying to interface or relate in some way or other to closely
related packages, http://statsmodels.sourceforge.net/related.html and
sharing code is very useful among compatible licensed packages.
However, the focus is different, and statsmodels currently targets
pure python and we don't want to depend on packages that require that
users are able to install and link to boost libraries. We are a bit
careful with dependencies and other requirements if it should remain
possible to integrate statsmodels (or parts of it) into scipy.
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