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

[Adam Skory]

On Thu, Oct 28, 2010 at 1:46 PM, Ethan Herdrick <info at reatlas.com> wrote:
> But if numpy, scipy and matplotlib aren't already installed then that
> magical import still won't work.  That's the step that most people
> were and are having trouble with, I think.
>
> Or does iPython come with it's own build of those things?

iPython from Debian-ish distros certainly comes with, or, at least I
never had to install any of those other packages after installing
iPython. YMMV.

> On Thu, Oct 28, 2010 at 9:19 AM, Adam Skory <askory at gmail.com> wrote:
>> 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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