else:
min_occ_user = 35
min_occ_prog = 300
leave_k_out = 10
# perform leave k out.
top_n = 10
# performance computed on top N;
# number of repetitions.
total_iteration = 3
# recommendation algorithms
method_list = [ LMaFit(latent_factor=lafactor), RandUV(latent_factor=lafactor), \
HierLat(latent_factor=lafactor), NMF(latent_factor=lafactor),
PMF(latent_factor=lafactor), TriUHV(latent_factor=lafactor) ]
#method_list = [ NMF(latent_factor=lafactor),\
# PMF(latent_factor=lafactor), TriUHV(latent_factor=lafactor) ];
# main method.
result = experiment_leave_k_out(exp_name, daily_data_file, min_occ_user, min_occ_prog, \
method_list, leave_k_out, total_iteration, top_n, True)
# display results (average RMSE).
for method_name, method_iter_perf in result.items():
print 'Method: ' + method_name
print '>>Average precision : %.5f' % (sum(x['prec']
for x in method_iter_perf) /
len(method_iter_perf))
print '>>Average recall : %.5f' % (sum(x['recall']
feedback_data.normalize_row()
# build model with 3 latent factors.
r = 5
# the L_2 norm regularizer
lamb = 0.2
# the stopping delta value
delta = 0.01
# the maximium iteration number
maxiter = 500
# TriUHV_model = TriUHV(r,lamb,delta,maxiter, verbose = True);
# TriUHV_model.train(feedback_data);
'''
# test.
loc_row = [200, 4, 105];
loc_col = [ 10, 22, 4];
print 'Prediction:'
print TriUHV_model.predict(loc_row, loc_col);
'''
# test cold start.
row = [0, 0, 1, 1, 2, 3, 3]
col = [0, 3, 1, 4, 0, 1, 3]
data = [1, 1, 1, 1, 1, 1, 1]
[f1, f2] = feedback_data.blind_k_out([1])
LMaFit_model = TriUHV(r, lamb, delta, 5, verbose=True)
LMaFit_model.train(f1)
# the L_2 norm regularizer
lamb = 0.2;
# the stopping delta value
delta = 0.01;
# the maximium iteration number
maxiter = 500;
# TriUHV_model = TriUHV(r,lamb,delta,maxiter, verbose = True);
# TriUHV_model.train(feedback_data);
'''
# test.
loc_row = [200, 4, 105];
loc_col = [ 10, 22, 4];
print 'Prediction:'
print TriUHV_model.predict(loc_row, loc_col);
'''
# test cold start.
row = [0, 0, 1, 1, 2, 3, 3];
col = [0, 3, 1, 4, 0, 1, 3];
data = [1, 1, 1, 1, 1, 1, 1];
[f1, f2] = feedback_data.blind_k_out([1]);
LMaFit_model = TriUHV(r,lamb,delta,5, verbose = True);
LMaFit_model.train(f1);