Hi Nipun.
We discussed this and basically think structured learning is off-topic
for sklearn at the moment.
I am building a structured learning library, but it is still changing
quite a bit.
It is not so clear to me what happens with the HMMs.
And I guess we should decide that soon.
I think throwing them out would probably be the best idea.
But then there should be a new place for them to go to.
So I think we should keep them until we find a new place.
Pandas might be a good idea?
Or starting a new lib. But that is a lot of work.
Maybe ask on the Pandas mailing list?
Cheers,
Andy
On 03/22/2013 02:11 PM, nipun batra wrote:
Might be a bit off topic. Is Structured Learning still not a priority
for sklearn?
I would have ideally liked to have put my development code in sklearn
for HMM's (since what i need would goes beyond what is currently
implemented in sklearn). I have started porting Murphy's HMM toolbox
<http://www.cs.ubc.ca/%7Emurphyk/Software/HMM/hmm.html>.
Apart from the standard Discrete and Gaussian HMM, i would be
developing atleast the following:
1. Factorial HMM
2. Multiple input/output HMM
3. Conditional HMM
4. Semi HMM
Started committing on Github only since a couple of days here
<https://github.com/nipunreddevil/PyHMM>. Would like to see it one day
merge in the main branch! Being a grad student it's best not to
overcommit, but i plan to do major chunk of this implementation for my
thesis work during the summer break.
Just wanted to know if someone is willing to lend a helping hand.
On Fri, Mar 22, 2013 at 5:53 PM, Andreas Mueller
<[email protected] <mailto:[email protected]>> wrote:
Hi Anne.
Thanks for the offer.
I'm not sure we want a newtons method implementation. There is on
in liblinear. but that is one-vs-rest.
If we start reimplementing parts of liblinear, we might open
pandoras box ;)
In principal I could imagine a "MultinomialLogisticRegression"
estimator. The speed should be comparable with LinearSVC, though,
which might not be that easy.
Currently, an SGD implementation would be great.
Cheers,
Andy
On 03/22/2013 01:17 PM, Anne Dwyer wrote:
Andy,
I wrote Python code for Newton's method logistic regression and a
plot of the hyperplane. Is this something the GSoC project would
be interested in or is it too low level?
Anne Dwyer
On Fri, Mar 22, 2013 at 6:58 AM, Andreas Mueller
<[email protected] <mailto:[email protected]>> wrote:
Hi Ricardo.
I think you forgot to mention what [1] and [2] are.
What is the difference between a relative neighborhood graph
and a neighborhood graph?
To me that sounds a bit to special purpose for the moment.
We need Logistic Regression first (which might also be a good
GSoC project)!
Just my opinion though ;)
Cheers,
Andy
On 03/22/2013 06:49 AM, Ricardo Corral C. wrote:
Ok, this is a brief description of what I'm interested in.
Recently, I faced a problem of evaluating the quality of a method to
obtain features from protein structures.
I adopted the approach given in [1] to measure separability of my
classes independently of my capacity of make good predictions.
This is basically a hypothesis testing of whether or not the
distribution of classes over feature vectors is somewhat random.
This test is made over the construction of a Relative Neighbourhood
Graph, which is O(n^3), thus, so prohibitive for practical use.
There is an efficient method for constructing RNG on the plane
described in [2] O(n*log(n)), but O(n^2) for a higher d dimension (in
fact O(n^2*f(d)) with f(d) <= (2*sqrt(d) +2)^d...).
Actually, I have the test implemented, and I'm refining a speedup of
RNG construction based on the Half-Space Proximal (HSP) graph. This is
O(n^2log(n)), and there is no dependence of dimension other than time
consumed in calculating distances.
This is made by doing RNG test over edges in HSP (attached images for
clarify this).
Could this be of interest for sklearn users? And if so, be considered
for GSoC?
On Thu, Mar 21, 2013 at 12:02 PM, Andreas Mueller
<[email protected]> <mailto:[email protected]> wrote:
On 03/21/2013 06:56 PM, Ricardo Corral C. wrote:
I would like to contribute with an idea different from those listed.
Is this the place to describe my proposal?
I think posting it on the mailing list (at least a short description)
would be a good start.
Also starting to contribute ;)
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