Here is some updates about my code which is still very slow,
I have only 3 labels, 130 features (68 feature types which increases to 130 due to different combination of y's). As training I'm using 5 training sample each having around 1500 residues. I'm using semi-markov model which uses nestedTrainer for training. The maxMermory is 3500. Under these conditions each single iteration of training takes around 2 hours. Also my features are all simple (just additions and subtractions) since I precalculate the possible outputs and store them in a large array.

Any ideas?



From: Sunita Sarawagi <sunita@cse.iitb.ac.in>
To: Hamid Reza Hassanzadeh <hassanzadeh@gatech.edu>
Cc: crf-users@lists.sourceforge.net
Sent: Saturday, March 24, 2012 1:53 AM
Subject: Re: [Crf-users] Running Time

Are you using straight CRF with only level-1 Markov dependency, or a higher-order Markov dependency, or a semi-crf?

Is there no dependence on y in the feature function, or do you have a wrapper around this feature that takes a cross-product of this feature with all possible y-s?

Hamid Reza Hassanzadeh wrote:
> Dear CRF users,
> I'm testing the crf package with a sequence of some 30000 residues length with a simple base feature over each residue. The feature is simply like this:
> public boolean startScanFeaturesAt(...)
> {
> ...
> if (x(pos-5:pos)=="xxxxxx" && x(pos:pos+2)=="yyy")
> return true;
> ...
> }
> And the execution time is as long as 1hour while this normally takes some 5 minutes with Mallet. The situation becomes even much worse when I use a longer training sequence.
> Am I making a mistake? When I feed the training file as test file I get 100% accuracy for both cases (mallet and crf). Thus there should not be any mistake. In that case what can be the cause of this huge difference?
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