Linear Chain CRF with SGD method

[chyikwei]

Hi,

I tried to implement linear chain CRF with SGD method based on these two paper:

C. Sutton, "An Introduction to Conditional Random Fields for Relational Learning"

N. Schraudolph, "Accelerated Training of Conditional Random Fields with Stochastic Gradient Methods"

For performance, I tested it on CoNLL 2000 shared task and got:

Model	iteration	F1 training	F1 testing	training time
CRF	5	0.894	0.844	68 mins
Structure Perceptron	10	0.892	0.834	1 min

I think the biggest problem in my implementation is the training time. I will try to work on parallel training or improve the cython code.

Maybe you can review it and give me some feedback. Any suggestions are welcomed. Thanks!

[larsmans]

Sweet! As for the time, have you tried profiling with kernprof.py?

I suspect calling logsumexp from inside Cython might have to with this, too.

[chyikwei]

I have not tried it yet. Will do this next week.

[chyikwei]

Just profiling LinearChainCRF.fit() and here are some results:

1. computer posterior takes about 20% of total time:

Line: 146, Time Per Hit: 12021.4, % Time: 19.2 post_state, post_trans, ll = _posterior(score, None, b_trans, b_init, b_final)

2. compute w_update takes 41% of the total time:

Line: 162, Time Per Hit: 26131.2, % Time: 41.7 w_update = lr * (safe_sparse_dot(y_t_i.T, X_i) - safe_sparse_dot(post_state.T, X_i) - (reg * w))

3. compute objective function takes about 20% of total time:

Line: 149, Time Per Hit: 7178.1, % Time: 11.5 feature_val = np.sum(w_true * w)

Line: 153, Time Per Hit: 6169.9, % Time: 9.9 sum_obj_val += feature_val + trans_val + init_val + final_val - ll - (0.5 * reg * np.sum(w * w))

[chyikwei]

Saved some time on computing w_update, new profiling result on w_update:

Line: 162, Time Per Hit: 10535.2, % Time: 25.1 w_update = lr * (safe_sparse_dot(y_t_i.T, X_i) - safe_sparse_dot(post_state.T, X_i) - (reg * w))