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

[Ethan Herdrick]

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?


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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