[ml] Wednesday, 10/27/2010 @ 7:30pm: Linear Classifier Workshop w/ scikits.learn

David Faden dfaden at gmail.com
Thu Oct 28 01:40:42 PDT 2010


Here's a hacky way that worked for me to get started with scikits.learning
under Mac OS X:
1. Install Sage <http://www.sagemath.org/>. (I dropped it in /Applications
as suggested in the docs.) This brings with it its own custom Python system
with all of the dependencies present already -- numpy, scipy, matplotlib and
associated libraries.

2. Download the source for scikits.learn <
http://sourceforge.net/projects/scikit-learn/files/> and unpack them:
$ tar zxvf scikits.learn-0.5.tar.gz

3. Set PYTHONPATH to point to Sage's local directory: (I think this may not
be necessary.)
$ export PYTHONPATH=/Applications/sage/local/lib/python/site-packages/

4. Change into scikits.learn source directory and build, using the sage
frontend (which I guess is just a souped up Python interpreter):
$ cd scikits.learn-0.5
$ /Applications/sage/sage setup.py install

5. Try it out
$ /Applications/sage/sage
Despite having the "sage:" prompt, you still have a Python interpreter there
to play with. The logistic regression example here <
http://scikit-learn.sourceforge.net/auto_examples/logistic_l1_l2_coef.html>
worked for me with no modification. (I haven't gotten a chance to go through
the actual examples for our class, but I'm hopeful that if this works so
will probably most other stuff.)

On Wed, Oct 27, 2010 at 6:12 PM, Mike Schachter <mike at mindmech.com> wrote:

> I posted the code to ml-noisebridge's sourceforge git repository. It
> probably needs some more work, but you can find it in the scikits.linear
> subdirectory of this repo:
>
> git clone git://
> ml-noisebridge.git.sourceforge.net/gitroot/ml-noisebridge/ml-noisebridge
>
>
>
> On Wed, Oct 27, 2010 at 5:06 PM, Mike Schachter <mike at mindmech.com> wrote:
>
>> Two more things:
>>
>> Don't forget to install scipy:
>>
>> http://www.scipy.org/
>>
>> And by "linear classification" i actually meant "comparing
>> support vector machines and k-nearest neighbors"
>>
>>
>>
>>
>>
>>
>> On Wed, Oct 27, 2010 at 12:14 PM, Mike Schachter <mike at mindmech.com>wrote:
>>
>>> There are some prerequisites:
>>>
>>> Python 2.5+
>>>
>>> Numpy: http://numpy.scipy.org/
>>>
>>> Matplotlib: http://matplotlib.sourceforge.net/
>>>
>>> scikits.learn: http://scikit-learn.sourceforge.net/
>>>
>>> Try to have these installed before we get started.
>>>
>>>    mike
>>>
>>>
>>>
>>>
>>>
>>> On Tue, Oct 26, 2010 at 2:08 PM, Mike Schachter <mike at mindmech.com>wrote:
>>>
>>>> Hey everyone,
>>>>
>>>> Tomorrow I'll be guiding an impromptu workshop with
>>>> scikits.learn. We'll use a sample dataset and try our
>>>> hands at classifying it with linear classifiers and perhaps
>>>> even support vector machines. See you there!
>>>>
>>>> http://scikit-learn.sourceforge.net/
>>>>
>>>>   mike
>>>>
>>>>
>>>
>>
>
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>
>
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