On Monday, March 23, 2015, Gael Varoquaux gael.varoqu...@normalesup.org
wrote:
On Mon, Mar 23, 2015 at 10:27:00AM +0530, Vinayak Mehta wrote:
I believe that it is the same thing as cosine similarity. If that's
indeed the case, you could add a note in the cosine similarity
docstring
to
The cosine similarity and Pearson correlation are the same if the data is
centered but are different in general.
The routine in SciPy is between two vectors; metrics in scikit-learn are
between matrices.
So +1 to add Pearson correlation to scikit-learn.
On Mon, Mar 23, 2015 at 3:24 PM, Gael
On 03/21/2015 08:54 PM, Artem wrote:
Are there any objections on Joel's variant of y? It serves my needs,
but is quite different from what one can usually find in scikit-learn.
--
Another point I want to bring up is metric-aware KMeans. Currently it
works with Euclidean distance only,
Have you had a look at the issues tagged easy?
On 03/22/2015 05:47 PM, Boyuan Deng wrote:
Hi all:
This is the link to my proposal for the Cross-validation and
Meta-estimators for Semi-supervised Learning topic:
On 03/22/2015 07:57 PM, Raghav R V wrote:
2. Given that there is a huge interest among students in learning
about ML, do you think it would be within the scope of/beneficial to
skl to have all the exercises and/or concepts, from a good quality
book (ESL / PRML / Murphy) or an academic
For practical purposes, I currently know of 2 (3?) sklearn books
published with PACKT. There is also an OReilly book coming up:
http://shop.oreilly.com/product/0636920030515.do
2 general books, 1 cookbook and I think there is another one
half-written as well. Didn't know about O'Reilly, good
@Gael
I believe that it is the same thing as cosine similarity. If that's
indeed the case, you could add a note in the cosine similarity
docstring
to stress it.
I think it is somewhat different from cosine similarity.
Then you'll have to tell me how, because I am being dense and I
On Mon, Mar 23, 2015 at 07:56:33AM +0100, Michael Eickenberg wrote:
I think it is somewhat different from cosine similarity.
Then you'll have to tell me how, because I am being dense and I don't see
the difference.
Both are scalar products of two normalized data vectors.
Hi Vinayak:
scipy.stats implemented pearsonr() like that because it's a statistics
routine. It treats 0 in the input data as indeed value 0.
But in the context of recommender systems, unrated is different from
score 0 (though we usually use 0 to represent unrated when score must
be
can you please also upload it to melange?
On 03/22/2015 08:52 PM, Raghav R V wrote:
2 things :
* The subject should have been Multiple Metric Support in grid_search
and cross_validation modules and other general improvements and not
multiple metric learning! Sorry for that!
* The link was
Hi Vinayak.
Have you decided on your application topic?
I am trying to get a bit of an overview, and I think you haven't
submitted anything yet.
There are two other applications for the hyperparameter topic and one
for the cross-validation and gridsearch improvements.
Since Ragv is already
Hi Christof.
Can you please also post it on melange?
Reviews will be coming soon ;)
Andy
On 03/19/2015 05:12 PM, Christof Angermueller wrote:
Hi All,
you can find my proposal for the hyperparameter optimization topic here:
* http://goo.gl/XHuav8
*
Theoretical justifications of using kernel PCA is that the data needs to be
projected onto span of eigenvectors of a covariance matrix (section 3.1.4
of Kulis' survey
http://web.cse.ohio-state.edu/~kulis/pubs/ftml_metric_learning.pdf). Does
kernel approximation whiten the data?
Either way,
Thanks for all the good comments!! I'll replace that section of my proposal
with some other more important work! :)
On Mon, Mar 23, 2015 at 7:53 PM, Matthieu Brucher
matthieu.bruc...@gmail.com wrote:
For practical purposes, I currently know of 2 (3?) sklearn books
published with PACKT.
Hi Sklearn,
I'm using Kernel PCA with the rbf kernel for projecting data into 3
dimensions for viewing alongside normal PCA and a stereographic projection
class that I wrote myself. Both the PCA and SGP classes seem to be
functioning correctly on this data set, but when I get to the .fit()
I am not aware of anyone tracking liblinear.
There is certainly no automatic update.
On 03/23/2015 08:05 PM, Charles Martin wrote:
On liblinear--can you clarify for me how you incorporate updates from
the main site?
Do you make an effort to stay up to date with latest changes directly
by
Hi Vinayak,
The wiki page just lists a subset of possible topics for which candidates
already showed concrete interest. I think an application for low-rank matrix
completion would be more than welcome. It’s very important to work on a topic
that you are interested in directly, versus just
Hi Aurélien
Thanks for your comments! Can you say anything on kernelization as part of
a model, not KPCA? I'm especially interested in a kernelized version of
ITML. I think, kernel metric learning methods don't scale well, since one
has to work a huge matrix of size n_samples x n_samples, which
It's worth noting that there was a similar project
https://github.com/scikit-learn/scikit-learn/pull/2387 2 years ago, but
unfortunately it wasn't completed. I made some work upon that, but I didn't
get any feedback.
On Tue, Mar 24, 2015 at 3:23 AM, Vlad Niculae zephy...@gmail.com wrote:
Hi
Very good points, Artem! The PR you link to contains important discussion on
API issues. I’m sorry I missed your PR.
On 23 Mar 2015, at 20:33, Artem barmaley@gmail.com wrote:
It's worth noting that there was a similar project 2 years ago, but
unfortunately it wasn't completed. I made
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