[ml] Wednesday, 10/27/2010 @ 7:30pm: Linear Classifier Workshop w/ scikits.learn
Adam Skory
askory at gmail.com
Thu Oct 28 09:19:55 PDT 2010
Sage looks pretty promising, but another way to get numpy and
matplotlib up and running is to use iPython; starting ipython with the
-pylab argument magically imports the good bits of numpy, scipy, and
matplotlib.
(really, iPython is so awesome I use it as my default shell...)
-Skory
On Thu, Oct 28, 2010 at 4:40 AM, David Faden <dfaden at gmail.com> wrote:
> 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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