On Sat, Jan 14, 2012 at 08:18:07PM +0100, Lars Buitinck wrote:
> I already mentioned this in an email earlier today, but in case not
> everyone read that: the Python Natural Language Toolkit (NLTK) now has
> a wrapper for scikit-learn classifiers in its bleeding-edge version.
That's really, really
On 14 January 2012 20:23, Olivier Grisel wrote:
> 2012/1/14 Lars Buitinck :
> > Dear all,
> >
> > I already mentioned this in an email earlier today, but in case not
> > everyone read that: the Python Natural Language Toolkit (NLTK) now has
> > a wrapper for scikit-learn classifiers in its bleedi
2012/1/14 Lars Buitinck :
> Dear all,
>
> I already mentioned this in an email earlier today, but in case not
> everyone read that: the Python Natural Language Toolkit (NLTK) now has
> a wrapper for scikit-learn classifiers in its bleeding-edge version. I
> hope this will make scikit-learn easier t
Dear all,
I already mentioned this in an email earlier today, but in case not
everyone read that: the Python Natural Language Toolkit (NLTK) now has
a wrapper for scikit-learn classifiers in its bleeding-edge version. I
hope this will make scikit-learn easier to use for NLP people; it
mainly tran
2012/1/14 Gael Varoquaux :
> On Sat, Jan 14, 2012 at 01:40:53PM +0100, Olivier Grisel wrote:
>> Do you think that the default tolerance set in scikit-learn is inadequate?
>
> No. Datasets for which it doesn't work well are, in my experience,
> aberrant datasets for which the optimization problem is
On Sat, Jan 14, 2012 at 01:40:53PM +0100, Olivier Grisel wrote:
> Do you think that the default tolerance set in scikit-learn is inadequate?
No. Datasets for which it doesn't work well are, in my experience,
aberrant datasets for which the optimization problem is ill-posed.
Gael
2012/1/14 Gael Varoquaux :
> On Wed, Dec 21, 2011 at 06:36:03PM -, luca.fias...@iwr.uni-heidelberg.de
> wrote:
>> first thing I would like to say that I'm not so experienced with python
>> therefore I might do something really stupid which I cannot see.
>> Nevertheless I don't manege to unders
Very interesting.
I added an issue to track this:
https://github.com/scikit-learn/scikit-learn/issues/555
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2012/1/6 Dhruvkaran Mehta :
> Is there a convenient way in scikits to go from "string" features like:
...
> to a numpy matrix like:
...
At present, no. But I donated a scikit-learn wrapper to NLTK
yesterday, which translates NLTK's featuresets (dicts mapping feature
names to numeric or boolean
On Sat, Jan 14, 2012 at 2:38 AM, Alexandre Gramfort
wrote:
> I've put up a demo of adaptive lasso a.k.a. reweighed l1:
>
> https://gist.github.com/1610922
Cute! Do you by any chance have a reference that gives an intuitive
motivation for the calculation of the weights?
Stéfan
-
On Wed, Dec 21, 2011 at 06:36:03PM -, luca.fias...@iwr.uni-heidelberg.de
wrote:
> first thing I would like to say that I'm not so experienced with python
> therefore I might do something really stupid which I cannot see.
> Nevertheless I don't manege to understand why the following script thro
hi sklearners,
I've put up a demo of adaptive lasso a.k.a. reweighed l1:
https://gist.github.com/1610922
If anyone is willing to make it a proper estimator in the main code
base please do so.
Cheers,
Alex
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hi,
it works if you change the tolerance of the optimality check.
Set tol=1e-9:
>>> clf = svm.OneClassSVM(nu=0.1, kernel="rbf", gamma=0.1, tol=1e-9)
Alex
PS : next time use a gist on github to avoid pasting code in an email.
On Wed, Dec 21, 2011 at 7:36 PM, wrote:
> Hi All,
> first thing I
Hi scikit-learn users,
*Is there a convenient way in scikits to go from "string" features like:*
"uc_berkeley", "google", 1
"stanford", "intel", 1
.
.
.
"uiuc", "texas_instruments", 0
*to a numpy matrix like:*
"uc_berkeley", "stanford", ..., "uiuc", "google", "intel",
"texas_instruments", "boo
Hi All,
first thing I would like to say that I'm not so experienced with python
therefore I might do something really stupid which I cannot see.
Nevertheless I don't manege to understand why the following script throw
an assertion error.
Indeed, training the one class svm with a dataset or with a s
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