There are several ways of achieving this. One is to build scikit-learn in
place by going into the sklearn clone and typing
make in
or alternatively
python setup.py build_ext --inplace # (i think)
Then you can use the environment variable PYTHONPATH, set to the github
clone, and python will gi
On Monday, August 1, 2016, Andreas Mueller wrote:
> Hi.
> The best is probably to use a virtual environment or conda environment
> specific for this changed version of scikit-learn.
> In that environment you could just run an "install" and it would not mess
> with your other environments.
+1!
Hi,
I would highly recommend you to work with virtual environments here. E.g.,
look into Anaconda/Miniconda (http://conda.pydata.org/miniconda.html,
http://conda.pydata.org/docs/using/using.html), which makes this process most
convenient in my opinion. Alternatively, I would use Python’s virt
Hi.
The best is probably to use a virtual environment or conda environment
specific for this changed version of scikit-learn.
In that environment you could just run an "install" and it would not
mess with your other environments.
If you don't want to go that way, you can also do ``python setup.p
I'm looking for the best way to install sklearn into a specific folder so
I can make changes for my work, without worrying about bugging my main
sklearn installation (as I use the default version for some experiments
too).
I tried to clone the git repository and use "python setup.py install", but
On Fri, Jul 29, 2016 at 8:57 PM, Gael Varoquaux <
gael.varoqu...@normalesup.org> wrote:
>
> Can you summarize once again in very simple terms what would be the big
> benefits?
>
Benefits for regular scikit-learn users
1. Reliable information on method signatures in a standarized way
("reliable"
