def evaluation(sess, model):
ano_scores = []
for _, batch_data in DataInput(x, test_batch_size):
_ano_score = model.eval(sess, batch_data)
# Extend
ano_scores += list(_ano_score)
ano_scores = np.array(ano_scores).reshape((-1, 1))
# Calculate auc
auroc = calc_auroc(y, ano_scores)
print('Eval_auroc:{:.4f}'.format(auroc))
prec, rec, f1 = calc_metric(y, ano_scores)
print('Prec:{:.4f}\tRec:{:.4f}\tF1:{:.4f}\n'.format(prec, rec, f1))
draw_prc(y, ano_scores, key='ResDEAAE_' + 'cross-e')
def evaluation(sess, model, ratio):
(sub_ano, sub_ano_label), _ = _split_dataset(ano, ano_label,
mapping_ratio[ratio])
x = np.concatenate((norm, sub_ano), axis=0)
y = np.concatenate((norm_label, sub_ano_label), axis=0)
ano_scores = []
for _, batch_data in DataInput(x, test_batch_size):
_ano_score = model.eval(sess, batch_data)
# Extend
ano_scores += list(_ano_score)
ano_scores = np.array(ano_scores).reshape((-1, 1))
# Calculate auc
auroc = calc_auroc(y, ano_scores)
print('Anomaly ratio:{:.4f}\tEval_auroc:{:.4f}'.format(ratio, auroc))
prec, rec, f1 = calc_metric(y, ano_scores)
print('Prec:{:.4f}\tRec:{:.4f}\tF1:{:.4f}\n'.format(prec, rec, f1))
def _eval(sess, model, test_data, label):
ano_scores = []
for _, batch_data in DataInput(test_data, test_batch_size):
_ano_score = model.eval(sess, batch_data)
# Extend
ano_scores += list(_ano_score)
ano_scores = np.array(ano_scores).reshape((-1, 1))
# Calculate auroc
auroc = calc_auroc(label, ano_scores)
# Calculate metric
prec, rec, f1 = 0., 0., 0. # for sake of computation speed
# prec, rec, f1 = calc_metric(label, ano_scores)
global best_auroc
if best_auroc < auroc:
best_auroc = auroc
model.save(sess, '{}/ckpt'.format(save_path))
return auroc, prec, rec, f1
def evaluation(sess, model):
ano_scores = []
for _, batch_data in DataInput(x, test_batch_size):
_ano_score = model.eval(sess, batch_data)
# Extend
ano_scores += list(_ano_score)
ano_scores = np.array(ano_scores).reshape((-1, 1))
with open('scores.pkl', 'wb') as f:
pickle.dump((y, ano_scores), f, pickle.HIGHEST_PROTOCOL)
# Calculate auc
auroc = calc_auroc(y, ano_scores)
print('Eval_auroc:{:.4f}'.format(auroc))
prec, rec, f1 = calc_metric(y, ano_scores)
print('Prec:{:.4f}\tRec:{:.4f}\tF1:{:.4f}\n'.format(prec, rec, f1))
draw_prc(y, ano_scores, key='DEAAE_' + method)