if good_partition is True:
poi_list = sorted(poi_set)
break
# train
ssvm = SSVM(inference_train=inference_method,
inference_pred=inference_method,
dat_obj=dat_obj,
share_params=SSVM_SHARE_PARAMS,
multi_label=SSVM_MULTI_LABEL,
C=ssvm_C,
poi_info=poi_info_i.loc[poi_list].copy())
if ssvm.train(sorted(trajid_set_train), n_jobs=N_JOBS) is True:
for j in test_ix: # test
ps_cv, L_cv = keys_cv[j]
y_hat_list = ssvm.predict(ps_cv, L_cv)
if y_hat_list is not None:
F1, pF1, tau = evaluate(dat_obj, keys_cv[j], y_hat_list)
F1_ssvm.append(F1)
pF1_ssvm.append(pF1)
Tau_ssvm.append(tau)
else:
for j in test_ix:
F1_ssvm.append(0)
pF1_ssvm.append(0)
Tau_ssvm.append(0)
mean_F1 = np.mean(F1_ssvm)
mean_pF1 = np.mean(pF1_ssvm)
mean_Tau = np.mean(Tau_ssvm)
'start POI of query %s does not exist in training set.\n' %
str(keys[i]))
sys.exit(0)
best_C = bestC[(ps, L)]
print('\n--------------- Query: (%d, %d), Best_C: %f ---------------\n' %
(ps, L, best_C))
# train model using all examples in training set and measure performance on test set
ssvm = SSVM(inference_train=inference_method,
inference_pred=inference_method,
dat_obj=dat_obj,
share_params=SSVM_SHARE_PARAMS,
multi_label=SSVM_MULTI_LABEL,
C=best_C,
poi_info=poi_info_i)
if ssvm.train(sorted(trajid_set_i), n_jobs=N_JOBS) is True:
y_hat_list = ssvm.predict(ps, L)
print(y_hat_list)
if y_hat_list is not None:
recdict_ssvm[(ps, L)] = {
'PRED': y_hat_list,
'W': ssvm.osssvm.w,
'C': ssvm.C
}
fssvm = os.path.join(
data_dir, 'ssvm-' + SSVM_VARIANT + '-' + dat_obj.dat_suffix[dat_ix] +
'-%d.pkl' % (qix))
pickle.dump(recdict_ssvm, open(fssvm, 'bw'))
