with open('{}_dataset.pkl'.format(mode), 'rb') as f:
train_set = pickle.load(f)
val_set = pickle.load(f)
test_set = pickle.load(f)
x_test, y_test = test_set
print('test set', x_test.shape)
with tf.Session() as sess:
model = BiWGAN(input_dim, method, weight, degree)
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
model.restore(sess, '{}/ckpt'.format(save_path))
ano_scores = []
for _, batch_test_data in DataInput(x_test, test_batch_size):
_ano_score, _, _ = model.eval(sess, batch_test_data)
# extend
ano_scores += list(_ano_score)
ano_scores = np.array(ano_scores).reshape((-1, 1))
# Highest 80% are anomalous
prec, rec, f1 = calc_metric(y_test, ano_scores, percentile=80)
# Calculate auc
auprc = calc_auc(y_test, ano_scores)
print('Prec:{:.4f} | Rec:{:.4f} | F1:{:.4f} | AUPRC:{:.4f}'.format(
prec, rec, f1, auprc))
x_test, y_test = test_set
(x, y), _ = _split_dataset(x_test, y_test, percentage=0.2)
print('test set:', x.shape)
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')
with tf.Session() as sess:
model = BiWGAN(count_list, method, weight=weight, degree=degree)
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
model.restore(sess, 'train_logs_fm_0.8932/talkingdata/fm/0.9/1/ckpt')
# model.restore(sess, 'train_logs_ce_0.9252/talkingdata/cross-e/0.9/1/ckpt')
evaluation(sess, model)
x_test, y_test = test_set
(x, y), _ = _split_dataset(x_test, y_test, percentage=0.2)
print('test set:', x.shape)
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='DEGBM_'+'cross-e')
with tf.Session() as sess:
model = BiWGAN(count_list, method, weight=weight, degree=degree)
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
model.restore(sess, 'train_logs_0.8884/talkingdata/fm/0.9/1/ckpt')
# model.restore(sess, 'train_logs_ce_0.9252/talkingdata/cross-e/0.9/1/ckpt')
evaluation(sess, model)