def predict(self, data_points, load_path):
res = []
self.graph = tf.Graph()
with self.graph.as_default():
with tf.Session() as sess:
saver = tf.train.import_meta_graph(load_path + '.meta')
saver.restore(sess, load_path)
# get weights and ops
graph = tf.get_default_graph()
# get weights and ops
self.fm_col_vals = self.graph.get_operation_by_name(
"ModelInput/features").outputs[0]
self.training = self.graph.get_tensor_by_name(
"ModelInput/training_flag:0")
self.output_prob = self.graph.get_operation_by_name(
"ModelOutput/truediv").outputs[0]
###
print('Start Predict.')
for batch_id, (ft, labels) in DataInput(
data_points, self.batch_size, self.fm_cols,
self.label_name):
feed_vals = {}
feed_vals[self.fm_col_vals] = ft
feed_vals[self.training] = False
predictions = sess.run(self.output_prob,
feed_dict=feed_vals)
res.append(predictions)
return np.concatenate(res)
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)
def _eval(sess, model, test_data, label):
ano_scores = []
for _, batch_test_data in DataInput(test_data, 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(label, ano_scores)
# Calculate auprc
_auprc = calc_auc(label, ano_scores)
global best_f1
if best_f1 < f1:
best_f1 = f1
global best_auprc
if best_auprc < _auprc:
best_auprc = _auprc
model.save(sess, '{}/ckpt'.format(save_path))
return prec, rec, f1, _auprc
prec, rec, f1, auprc = _eval(sess, model, x_val, y_val)
print('Prec:{:.4f} | Rec:{:.4f} | F1:{:.4f} | Eval_auprc:{:.4f}'.
format(prec, rec, f1, auprc))
sys.stdout.flush()
# Start training
start_time = time.time()
for i in range(nb_epochs):
print('==== Training epoch {} ===='.format(i))
sys.stdout.flush()
# shuffle for each epoch
x_train, y_train = _shuffle(x_train, y_train)
loss_dis_sum, loss_gen_sum, loss_enc_sum = 0., 0., 0.
for j, batch_data in DataInput(x_train, train_batch_size):
# Update discriminator
if (j % d_g_iter != 0) or (j == 0):
loss_dis, loss_gen, loss_enc = model.train(sess,
batch_data,
learning_rate,
train_d=True)
# Update generator and encoder
else:
loss_dis, loss_gen, loss_enc = model.train(sess,
batch_data,
learning_rate,
train_d=False)
loss_dis_sum += loss_dis
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))
# draw prc curve
# draw_prc(y_test, ano_scores)
def train(self,
train_points,
eval_points,
epoch_num,
ob_step=5,
save_path=None,
load_path=None): #[B,T,H]
if load_path is None:
self.graph = self.build_graph()
else:
self.graph = tf.Graph()
with self.graph.as_default():
with tf.Session() as sess:
if load_path is None:
saver = tf.train.Saver()
tf.initialize_all_variables().run()
else:
saver = tf.train.import_meta_graph(load_path + '.meta')
saver.restore(sess, load_path)
# get weights and ops
self.fm_col_vals = self.graph.get_operation_by_name(
"ModelInput/features").outputs[0]
self.labels = self.graph.get_tensor_by_name(
"ModelInput/labels:0")
self.training = self.graph.get_tensor_by_name(
"ModelInput/training_flag:0")
self.update = self.graph.get_operation_by_name(
"Optimizer/GradientDescent")
self.loss = self.graph.get_operation_by_name(
"Loss/Mean").outputs[0]
self.output_prob = self.graph.get_operation_by_name(
"ModelOutput/truediv").outputs[0]
### 训练
print('Start Training')
step = 0
cnt = 0
metric_train_loss = 0
train_preds = []
train_labels = []
for ep in range(epoch_num):
print("############## epoch %d###############" % ep)
random.shuffle(train_points)
for batch_id, (ft, labels) in DataInput(
train_points, self.batch_size, self.fm_cols,
self.label_name):
feed_vals = {}
feed_vals[self.fm_col_vals] = ft
feed_vals[self.labels] = np.expand_dims(
np.array(labels), axis=1)
feed_vals[self.training] = True
_, l, predictions = sess.run(
[self.update, self.loss, self.output_prob],
feed_dict=feed_vals)
metric_train_loss += l * len(labels)
cnt += len(labels)
train_preds.append(predictions)
train_labels.append(labels)
#if ob_step steps have passed,we print current training result
if step > 0 and step % ob_step == 0:
accuracy = self.accuracy(
np.concatenate(train_preds, axis=0),
np.expand_dims(np.array(
list(chain(*train_labels))),
axis=1))
print('Minibatch loss at step %d: %f' %
(step, metric_train_loss / cnt))
print('Minibatch accuracy: %.1f%%\n' %
(100 * accuracy))
train_preds = []
train_labels = []
metric_train_loss = 0
cnt = 0
###
#if one epoch finishes, we start evaluation on test set
if step == self.epoch_size - 1:
step = 0
eval_cnt = 0
eval_loss = 0
eval_preds = []
eval_labels = []
for batch_id, (ft, labels) in DataInput(
eval_points, self.batch_size, self.fm_cols,
self.label_name):
feed_vals = {}
feed_vals[self.fm_col_vals] = ft
feed_vals[self.training] = False
feed_vals[self.labels] = np.expand_dims(
np.array(labels), axis=1)
l, predictions = sess.run(
[self.loss, self.output_prob],
feed_dict=feed_vals)
eval_loss += l * len(labels)
cnt += len(labels)
eval_preds.append(predictions)
eval_labels.append(labels)
accuracy = self.accuracy(
np.concatenate(eval_preds, axis=0),
np.expand_dims(np.array(
list(chain(*eval_labels))),
axis=1))
print('DEV_SET loss at step %d: %f' %
(step, eval_loss / cnt))
print('DEV_SET accuracy: %.1f%%\n' %
(100 * accuracy))
else:
step += 1
#保存
if save_path is not None:
saver.save(sess, save_path)
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
print('Objective1_Value: %.4f\t Objective2_Value: %.4f\t' %
evaluate(sess, model, train_set))
sys.stdout.flush()
lr = 1
start_time = time.time()
last_auc = 0.0
for epoch in range(100):
random.shuffle(train_set)
random.shuffle(test_set)
epoch_size = round(len(train_set) / batch_size)
loss_sum = 0.0
for _, uij in DataInput(train_set, batch_size):
loss = model.train(sess, uij, lr)
loss_sum += loss
print('Epoch %d Train_Loss: %.4f' %
(model.global_epoch_step.eval(), loss_sum))
print('Epoch %d DONE\tCost time: %.2f' %
(model.global_epoch_step.eval(), time.time() - start_time))
print('Objective1_Value: %.4f\t Objective2_Value: %.4f\t' %
evaluate(sess, model, train_set))
#print('Objective1_Value: %.4f\t Objective2_Value: %.4f\t' % evaluate(sess, model, test_set))
sys.stdout.flush()
model.global_epoch_step_op.eval()
if epoch % 5 == 0:
update_target_graph('primary_dqn', 'target_dqn')
end_time = time.time()
testset2 = testset2[:len(testset2)//batch_size*batch_size]
gpu_options = tf.GPUOptions(allow_growth=True)
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
model = Model(user_count, item_count, batch_size)
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
print('Domain_A_Initialized_AUC: %.4f\tDomain_B_Initialized_AUC: %.4f' % compute_auc(sess, model, testset1, testset2))
sys.stdout.flush()
start_time = time.time()
last_auc = 0.0
for _ in range(1000):
loss_sum = 0.0
for uij in DataInput(trainset1, batch_size):
loss = model.train_1(sess, uij, lr)
loss_sum += loss
for uij in DataInput(trainset2, batch_size):
loss = model.train_2(sess, uij, lr)
loss_sum += loss
model.train_orth(sess, uij[0], lr)
test_auc_1, test_auc_2 = compute_auc(sess, model, testset1, testset2)
train_auc_1, train_auc_2 = compute_auc(sess, model, trainset1, trainset2)
print('Epoch %d \tDomain A Train_AUC: %.4f\tTest_AUC: %.4F' % (model.global_epoch_step.eval(), train_auc_1, test_auc_1))
print('Epoch %d \tDomain B Train_AUC: %.4f\tTest_AUC: %.4F' % (model.global_epoch_step.eval(), train_auc_2, test_auc_2))
print('Epoch %d \tTrain_loss: %.4f' % (model.global_epoch_step.eval(), loss_sum))
print('Epoch %d DONE\tCost time: %.2f' % (model.global_epoch_step.eval(), time.time()-start_time))
sys.stdout.flush()
model.global_epoch_step_op.eval()
hit_1, hit_2 = compute_hr(sess, model, testset1, testset2)
