# t-lambda processing (iteration in [1,400])
if random.random() >= (0.025*iteration):
action = [random.randint(0,1)]
#get reward of the action
reward_action=10*abs(2*confidence[0][0]-1)
#get the variance of operate number
self.frame.Normalize_label()
operatenum_pre=Evaluator.evaluate(self.dataset.imgID,self.frame.label,[1,2])
#check the action is True or False
action_result=self.frame.setPerception(action)
if action_result==False:
reward_action=-reward_action
#save history
self.puthistory(package,action,reward_action,operatenum_pre)
#calculate Metric
self.frame.Normalize_label()
self.Recall=Evaluate.Recall(self.dataset.imgID,self.frame.label)
self.Precision=Evaluate.Precision(self.dataset.imgID,self.frame.label)
self.operatenum=Evaluator.evaluate(self.dataset.imgID,self.frame.label,[1,2])
self.Recall_edge=Evaluate.Recall_edge(self.dataset.imgID,self.frame.label,1)
self.Precision_edge=Evaluate.Precision_edge(self.dataset.imgID,self.frame.label)
print self.dataset.size,self.Recall_edge,self.Precision_edge,self.operatenum
if __name__=='__main__':
t=test()
def begintest(self, iteration=0):
# model_R_p2p = svm_load_model(
# os.path.join(
# self.config.get('REID', 'REWARD_MODEL_SAVED_PATH'),
# 'model_r_p2p.model'))
# model_R_p2G = svm_load_model(
# os.path.join(
# self.config.get('REID', 'REWARD_MODEL_SAVED_PATH'),
# 'model_r_p2g.model'))
# model_R_G2G = svm_load_model(
# os.path.join(
# self.config.get('REID', 'REWARD_MODEL_SAVED_PATH'),
# 'model_r_g2g.model'))
with open(
os.path.join(
self.config.get('REID', 'REWARD_MODEL_SAVED_PATH'),
'model_r_p2p.model')) as f:
model_R_p2p = pickle.load(f)
with open(
os.path.join(
self.config.get('REID', 'REWARD_MODEL_SAVED_PATH'),
'model_r_p2g.model')) as f:
model_R_p2G = pickle.load(f)
with open(
os.path.join(
self.config.get('REID', 'REWARD_MODEL_SAVED_PATH'),
'model_r_g2g.model')) as f:
model_R_G2G = pickle.load(f)
is_first_iteration = False
model_dir = self.config.get('REID', 'REWARD_MODEL_SAVED_PATH')
if os.path.exists(os.path.join(model_dir, 'model_q_p2p.model')):
model_Q_p2p = xgb.Booster(
model_file=os.path.join(model_dir, 'model_q_p2p.model'))
model_Q_p2G = xgb.Booster(
model_file=os.path.join(model_dir, 'model_q_p2g.model'))
model_Q_G2G = xgb.Booster(
model_file=os.path.join(model_dir, 'model_q_g2g.model'))
else:
is_first_iteration = True
data = list(list())
data.append([0, 0, 0])
data_Q = list(list())
data_Q.append([0, 0, 0])
index = 0
reward = 0
decision = Dicision.Dicision()
t01 = time.time()
while self.frame.checkState(check_batch=True):
package = self.frame.getObservation()
index += 1
if type(package) == int:
print 'Done!'
break
data[0] = package
question_type = len(package)
model = None
if question_type == 3: #point-----point
if not is_first_iteration:
model = model_Q_p2p.copy()
tp = 'P2P'
#Reward Function
# action_R, _, confidence = svm_predict([0], data, model_R_p2p,
# '-b 1 -q')
# confidence = model_R_p2p.predict_proba(data)
w = model_R_p2p.coef_[0]
b = model_R_p2p.intercept_[0]
#Reward Value Function: action = 0
temp = package[:]
temp.insert(0, 0)
data_Q[0] = temp
DM_data = xgb.DMatrix(np.array(data_Q))
if not is_first_iteration:
value_0 = model_Q_p2p.predict(DM_data)
else:
value_0 = [random.random()]
del temp[0]
#Reward Value Function: action = 1
temp.insert(0, 1)
data_Q[0] = temp
DM_data = xgb.DMatrix(np.array(data_Q))
if not is_first_iteration:
value_1 = model_Q_p2p.predict(DM_data)
else:
value_1 = [random.random()]
#choose the most awarded action
if value_1[0] >= value_0[0]:
action = [1]
else:
action = [0]
elif question_type == 3 + self.frame.k_size: #point-----Group or group---point
if not is_first_iteration:
model = model_Q_p2G.copy()
tp = 'P2G'
#Reward Function
# action_R, _, confidence = svm_predict([0], data, model_R_p2G,
# '-b 1 -q')
# confidence = model_R_p2G.predict_proba(data)
w = model_R_p2G.coef_[0]
b = model_R_p2G.intercept_[0]
#Reward Value Function: action = 0
temp = package[:]
temp.insert(0, 0)
data_Q[0] = temp
DM_data = xgb.DMatrix(np.array(data_Q))
if not is_first_iteration:
value_0 = model_Q_p2G.predict(DM_data)
else:
value_0 = [random.random()]
del temp[0]
#Reward Value Function: action = 1
temp.insert(0, 1)
data_Q[0] = temp
DM_data = xgb.DMatrix(np.array(data_Q))
if not is_first_iteration:
value_1 = model_Q_p2G.predict(DM_data)
else:
value_1 = [random.random()]
#choose the most awarded action
if value_1[0] >= value_0[0]:
action = [1]
else:
action = [0]
else:
if not is_first_iteration:
model = model_Q_G2G.copy()
tp = 'G2G'
#Reward Function
# action_R, _, confidence = svm_predict([0], data, model_R_G2G,
# '-b 1 -q')
# confidence = model_R_G2G.predict_proba(data)
w = model_R_G2G.coef_[0]
b = model_R_G2G.intercept_[0]
#Reward Value Function: action = 0
temp = package[:]
temp.insert(0, 0)
data_Q[0] = temp
DM_data = xgb.DMatrix(np.array(data_Q))
if not is_first_iteration:
value_0 = model_Q_G2G.predict(DM_data)
else:
value_0 = [random.random()]
del temp[0]
#Reward Value Function: action = 1
temp.insert(0, 1)
data_Q[0] = temp
DM_data = xgb.DMatrix(np.array(data_Q))
if not is_first_iteration:
value_1 = model_Q_G2G.predict(DM_data)
else:
value_1 = [random.random()]
#choose the most awarded action
if value_1[0] > value_0[0]:
action = [1]
else:
action = [0]
#获取操作量原数量
# t-lambda processing (iteration in [1,400])
if random.random() >= (0.025 * iteration):
action = [random.randint(0, 1)]
# get reward of the action
# reward_action = 10 * abs(2 * confidence[0] - 1)
reward_action = abs(np.sum(np.multiply(w, package)) + b)
#get the variance of operate number
self.frame.Normalize_label()
operatenum_pre = Evaluator.evaluate(self.dataset.imgID,
self.frame.label, [0])
#check the action is True or False
action_result = self.frame.setPerception(action, save=False)
if action_result == False:
reward_action = -reward_action
#save history
self.puthistory(package, action, reward_action, operatenum_pre,
model)
if not self.inference:
#calculate Metric
self.frame.Normalize_label()
self.Recall = Evaluate.Recall(self.dataset.imgID, self.frame.label)
self.Precision = Evaluate.Precision(self.dataset.imgID,
self.frame.label)
self.operatenum = Evaluator.evaluate(self.dataset.imgID,
self.frame.label, [0])
self.Recall_edge = Evaluate.Recall_edge(self.dataset.imgID,
self.frame.label, 0)
self.Precision_edge = Evaluate.Precision_edge(
self.dataset.imgID, self.frame.label)
print self.dataset.size, self.Recall_edge, self.Precision_edge, self.operatenum
with open(
os.path.join(
self.config.get('REID', 'REWARD_MODEL_SAVED_PATH'),
'xgboost_output_nstepsarsa_origin.log'), 'a') as f:
f.write('{}, {}, {}, {}\n'.format(self.dataset.size,
self.Recall_edge,
self.Precision_edge,
self.operatenum))
