roc_max.append(roc_auc_score(y_test, comb_by_max))
prn_max.append(precision_n_scores(y_test, comb_by_max))
print('ite', t + 1, 'comb by max,', 'ROC:',
roc_auc_score(y_test, comb_by_max), 'precision@n:',
precision_n_scores(y_test, comb_by_max))
# combination by aom
comb_by_aom = aom(test_scores_norm, 5, 20)
roc_aom.append(roc_auc_score(y_test, comb_by_aom))
prn_aom.append(precision_n_scores(y_test, comb_by_aom))
print('ite', t + 1, 'comb by aom,', 'ROC:',
roc_auc_score(y_test, comb_by_aom), 'precision@n:',
precision_n_scores(y_test, comb_by_aom))
# combination by moa
comb_by_moa = moa(test_scores_norm, 5, 20)
roc_moa.append(roc_auc_score(y_test, comb_by_moa))
prn_moa.append(precision_n_scores(y_test, comb_by_moa))
print('ite', t + 1, 'comb by moa,', 'ROC:',
roc_auc_score(y_test, comb_by_moa), 'precision@n:',
precision_n_scores(y_test, comb_by_moa))
print()
##########################################################################
print('summary of {ite} iterations'.format(ite=ite))
print('comb by mean, ROC: {roc}, precision@n: {prn}'.format(
roc=np.mean(roc_mean), prn=np.mean(prn_mean)))
print('comb by max, ROC: {roc}, precision@n: {prn}'.format(
roc=np.mean(roc_max), prn=np.mean(prn_max)))
print('comb by aom, ROC: {roc}, precision@n: {prn}'.format(
axis=1)
test_target_list.extend(
[target_test_weighted_pear, target_test_weighted_euc])
method_list.extend([
'w_mean_pear',
'w_mean_euc',
])
# generate threshold sum
target_test_threshold = np.sum(test_scores_norm.clip(0), axis=1)
test_target_list.append(target_test_threshold)
method_list.append('threshold')
# generate average of maximum (AOM) and maximum of average (MOA)
target_test_aom = aom(test_scores_norm, n_buckets, n_clf)
target_test_moa = moa(test_scores_norm, n_buckets, n_clf)
test_target_list.extend([target_test_aom, target_test_moa])
method_list.extend(['aom', 'moa'])
###################################################################
# use mean as the pseudo target
for k in final_k_list:
tree = KDTree(X_train_norm)
dist_arr, ind_arr = tree.query(X_test_norm, k=k)
m_list = [
'a_dist_d', 'a_dist_r', 'a_dist_n', 'a_pear_d', 'a_pear_r',
'a_pear_n'
]
# initialize different buckets
pred_scores_best = np.zeros([X_test.shape[0], len(m_list)])