n_dim, n_particle, nt))
else:
model_file = os.path.join(dest, 'train_test_var_%.2f_%.2f_dim_%d_par_%d_test_%d.pkl' % (
var_x, var_comp, n_dim, n_particle, nt))
if not os.path.exists(model_file):
# change file extension from pkl to txt for writing log
log = os.path.splitext(model_file)[0] + ".txt"
eval_log = os.path.splitext(model_file)[0] + "_eval.txt"
if os.path.exists(log):
os.remove(log)
if os.path.exists(eval_log):
os.remove(eval_log)
if model == 'brescal':
_model = PFBayesianRescal(n_dim, var_x=var_x, n_particles=n_particle, compute_score=False, parallel=False,
log=log, eval_log=eval_log, dest=model_file, sample_all=True)
elif model == 'bcomp_add':
_model = PFBayesianCompRescal(n_dim, compositionality='additive', var_x=var_x, var_comp=var_comp,
n_particles=n_particle, compute_score=False, log=log, dest=model_file,
eval_log=eval_log)
elif model == 'bcomp_mul':
_model = PFBayesianCompRescal(n_dim, compositionality='multiplicative', var_x=var_x, var_comp=var_comp,
n_particles=n_particle, compute_score=False, log=log, dest=model_file,
eval_log=eval_log)
elif model == 'logit':
_model = PFBayesianLogitRescal(n_dim, log=log, dest=model_file, compute_score=False)
else:
raise Exception('No such model exists %s' % model)
seq = _model.fit(T, obs_mask=obs_mask, max_iter=max_iter, test_mask=test_mask)
else:
output_file = os.path.join(dest, '%s_%.2f_%d_training_error.pkl' % (model, p, n_dim))
if not os.path.exists(output_file):
if model == 'brescal':
model_file = os.path.join(dest, 'sRESCAL_varx_%.2f_dim_%d_par_%d_test_%d.pkl' % (
var_x, n_dim, n_particle, nt))
elif model == 'logit':
model_file = os.path.join(dest, 'sRESCAL_dim_%d_par_%d_test_%d.pkl' % (
n_dim, n_particle, nt))
else:
model_file = os.path.join(dest, 'sRESCAL_var_%.2f_%.2f_dim_%d_par_%d_test_%d.pkl' % (
var_x, var_comp, n_dim, n_particle, nt))
if model == 'brescal':
_model = PFBayesianRescal(n_dim, var_x=var_x, n_particles=n_particle, compute_score=False,
parallel=False, sample_all=True)
elif model == 'bcomp_add':
_model = PFBayesianCompRescal(n_dim, compositionality='additive', var_x=var_x, var_comp=var_comp,
n_particles=n_particle, compute_score=False)
elif model == 'bcomp_mul':
_model = PFBayesianCompRescal(n_dim, compositionality='multiplicative', var_x=var_x,
var_comp=var_comp,
n_particles=n_particle, compute_score=False)
elif model == 'logit':
_model = PFBayesianLogitRescal(n_dim, compute_score=False, n_particles=n_particle)
elif model == 'logit_mul':
_model = PFBayesianCompRescal(n_dim, compositionality='logit_mul',
n_particles=n_particle, compute_score=False)
else:
raise Exception('No such model exists %s' % model)
