[Scikit-learn-general] Announcing lightning v0.1

[Fabian Pedregosa]

Dear all,

I'm pleased to announce the first public release of lightning (
http://contrib.scikit-learn.org/lightning/).

lightning is a library for large-scale linear classification, regression
and ranking in Python.

Highlights:

* follows the scikit-learn API conventions.
* supports natively both dense and sparse data representations.
* computationally demanding parts implemented in Cython.

The release can be downloaded from pypi:

https://pypi.python.org/pypi/sklearn-contrib-lightning

and prebuild conda packages can be installed with the command

conda install -c https://conda.anaconda.org/scikit-learn-contrib
lightning

Check out the website for more information:

http://contrib.scikit-learn.org/lightning/


Happy hacking!

the lightning team

[Fred Mailhot]

I imagine a lot of people might be interested in this, but be in a position
where they need to justify bringing in a new package that mimics sklearn,
rather than just using the linear models that are already available there.
Could you day a but more about how/why this is better?

Thanks!
Fred.

Post by Fabian Pedregosa
Dear all,
I'm pleased to announce the first public release of lightning (
http://contrib.scikit-learn.org/lightning/).
lightning is a library for large-scale linear classification, regression
and ranking in Python.
* follows the scikit-learn API conventions.
* supports natively both dense and sparse data representations.
* computationally demanding parts implemented in Cython.
https://pypi.python.org/pypi/sklearn-contrib-lightning
and prebuild conda packages can be installed with the command
conda install -c https://conda.anaconda.org/scikit-learn-contrib
lightning
http://contrib.scikit-learn.org/lightning/
Happy hacking!
the lightning team
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[Mathieu Blondel]

With lightning, you can train linear models on large-scale data using
recent state-of-the-art optimization algorithms which are too cutting-edge
for including in scikit-learn (e.g., SDCA or SAGA). If you just want to
train a logistic regression on 1000 samples, you don't need lightning :)

Mathieu

Post by Fred Mailhot
I imagine a lot of people might be interested in this, but be in a
position where they need to justify bringing in a new package that mimics
sklearn, rather than just using the linear models that are already
available there. Could you day a but more about how/why this is better?
Thanks!
Fred.

Post by Fabian Pedregosa
Dear all,
I'm pleased to announce the first public release of lightning (
http://contrib.scikit-learn.org/lightning/).
lightning is a library for large-scale linear classification, regression
and ranking in Python.
* follows the scikit-learn API conventions.
* supports natively both dense and sparse data representations.
* computationally demanding parts implemented in Cython.
https://pypi.python.org/pypi/sklearn-contrib-lightning
and prebuild conda packages can be installed with the command
conda install -c https://conda.anaconda.org/scikit-learn-contrib
lightning
http://contrib.scikit-learn.org/lightning/
Happy hacking!
the lightning team
------------------------------------------------------------------------------
Transform Data into Opportunity.
Accelerate data analysis in your applications with
Intel Data Analytics Acceleration Library.
Click to learn more.
http://pubads.g.doubleclick.net/gampad/clk?id=278785351&iu=/4140
_______________________________________________
Scikit-learn-general mailing list
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general

------------------------------------------------------------------------------
Transform Data into Opportunity.
Accelerate data analysis in your applications with
Intel Data Analytics Acceleration Library.
Click to learn more.
http://pubads.g.doubleclick.net/gampad/clk?id=278785351&iu=/4140
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Scikit-learn-general mailing list
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