class Corrp():
def __init__(self):
#self.obj=Reason()
self.obj = None
self.decision = None
self.time = None
self.location = None
self.suplearn = None
def init_pred(self): #remember later to change it to override method
self.obj = Reason()
time = ['(currenttime=rush)', '(currenttime=break)']
location = ['(atlocation=at_entrance)', '(atlocation=at_exit)']
lstm = ['(classifier=zero)', '(classifier=one)']
decision = ['interested', 'not_interested']
time_rand = random.choice(time)
loc_rand = random.choice(location)
lstm_rand = random.choice(lstm)
int_prob = float(
self.obj.query('reason.plog', self.decision, time_rand, lstm_rand,
loc_rand))
self.obj.delete('reason.plog')
print int_prob
print int_prob
return int_prob
def init_state(self):
self.decision = random.choice(['interested', 'not_interested'])
print 'the random decision is:', self.decision
self.time = random.choice(['break', 'rush'])
print 'The random time is ', self.time
self.location = random.choice(['at_entrance', 'at_exit'])
print 'The random location is : ', self.location
self.lstm()
print 'The classifier output is: ', self.classifier
return self.decision
def get_state_index(self, state):
return self.model.states.index(state)
def get_obs_index(self, obs):
return self.model.observations.index(obs)
def lstm(self):
pred = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
#pred_int = [0.9,1.0]
#pred_not_int = [0.0,0.1,0.2]
if random.choice(pred) >= 0.5:
self.suplearn = 1
self.classifier = 'one'
else:
self.suplearn = 0
self.classifier = 'zero'
def run(self):
init = self.init_state()
prob = self.init_pred()
cost = 0
#print ( 'b shape is,', b.shape )
#print b
success = 0
tp = 0
tn = 0
fp = 0
fn = 0
if prob > 0.5:
if 'interested' == init:
success = 1
tp = 1
print 'Trial was successfull'
elif 'not_interested' == init:
success = 0
fp = 1
print 'Trial was unsuccessful'
else:
if 'not_interested' == init:
tn = 1
success = 1
print 'Trial was successful'
elif 'interested' == init:
fn = 1
#print ('finished ')
return cost, success, tp, tn, fp, fn
def trial_num(self, num):
total_success = 0
total_cost = 0
total_tp = 0
total_tn = 0
total_fp = 0
total_fn = 0
for i in range(num):
random.seed(i)
c, s, tp, tn, fp, fn = self.run()
total_cost += c
total_success += s
total_tp += tp
total_tn += tn
total_fp += fp
total_fn += fn
print 'Average total reward is:', total_cost / num
print 'Average total success is: ', float(total_success) / num
Precision = float(total_tp) / (total_tp + total_fp)
print 'Precision is ', Precision
Recall = float(total_tp) / (total_tp + total_fn)
print 'Recall is ', Recall
F1score = 2.0 * Precision * Recall / (Precision + Recall)
print 'F1 score', F1score
class Corrp():
def __init__(self, pomdpfile='program.pomdp'):
#self.obj=Reason()
self.obj = None
self.model = Model(filename='program.pomdp', parsing_print_flag=False)
self.policy = Policy(5, 4, output='program.policy')
print self.model.states
self.decision = None
self.time = None
self.location = None
self.suplearn = None
def init_belief(self): #remember later to change it to override method
self.obj = Reason()
time = [
'(currenttime=morning)', '(currenttime=afternoon)',
'(currenttime=evening)'
]
location = ['(atlocation=classroom)', '(atlocation=library)']
#lstm = ['(classifier=zero)','(classifier=one)']
decision = ['interested', 'not_interested']
time_rand = random.choice(time)
loc_rand = random.choice(location)
#lstm_rand = random.choice(lstm)
lstm_rand = self.lstm()
int_prob = float(
self.obj.query('reason.plog', self.decision, time_rand, lstm_rand,
loc_rand))
self.obj.delete('reason.plog')
print int_prob
init_belief = [1.0 - int_prob, int_prob, 1.0 - int_prob, int_prob, 0]
b = np.zeros(len(init_belief))
for i in range(len(init_belief)):
b[i] = init_belief[i] / sum(init_belief)
print b
return b
def init_state(self):
#state=random.choice(['not_forward_not_interested','not_forward_interested'])
self.decision = random.choice(['interested', 'not_interested'])
print 'the random decision is:', self.decision
self.time = random.choice(['morning', 'afternoon', 'evening'])
print 'The random time is ', self.time
self.location = random.choice(['library', 'classroom'])
print 'The random location is : ', self.location
self.lstm()
print 'The classifier output is: ', self.classifier
if self.decision == 'interested':
state = random.choice(
['not_forward_interested', 'forward_interested'])
else:
state = random.choice(
['not_forward_not_interested', 'forward_not_interested'])
print state
s_idx = self.get_state_index(state)
print s_idx
return s_idx, state
def get_state_index(self, state):
return self.model.states.index(state)
def get_obs_index(self, obs):
return self.model.observations.index(obs)
def random_observe(self, a_idx):
if self.model.actions[a_idx] == 'move_forward':
obs = random.choice(['physical', 'no_physical'])
elif self.model.actions[a_idx] == 'greet':
obs = random.choice(['verbal', 'no_verbal'])
else:
obs = 'na'
#l=len(self.model.observations)-1
#o_idx=randint(0,l)
o_idx = self.get_obs_index(obs)
print('random observation is: ', self.model.observations[o_idx])
return o_idx
def observe_logical(self, a_idx):
if self.model.actions[
a_idx] == 'move_forward' and self.decision == 'interested':
obs = 'physical'
elif self.model.actions[
a_idx] == 'move_forward' and self.decision == 'not_interested':
obs = 'no_physical'
elif self.model.actions[
a_idx] == 'greet' and self.decision == 'interested':
obs = 'verbal'
elif self.model.actions[
a_idx] == 'greet' and self.decision == 'not_interested':
obs = 'no_verbal'
else:
obs = 'na'
#l=len(self.model.observations)-1
#o_idx=randint(0,l)
o_idx = self.get_obs_index(obs)
print('random observation is: ', self.model.observations[o_idx])
return o_idx
#def update(self, a_idx,o_idx,b ):
# temp = np.matmul(self.model.trans_mat[0,:,:],self.model.obs_mat[0,:,:])
# temp = np.matmul(b,temp)
# b = temp/np.sum(temp)
# return b
def update(self, a_idx, o_idx, b):
b = np.dot(b, self.model.trans_mat[a_idx, :])
b = [
b[i] * self.model.obs_mat[a_idx, i, o_idx]
for i in range(len(self.model.states))
]
b = b / sum(b)
return b
def lstm(self):
predone = [1, 1, 1, 1, 1, 1, 1, 0, 0, 0]
prezero = [0, 0, 0, 0, 0, 0, 0, 1, 1, 1]
if self.decision == 'interested':
if random.choice(predone) == 1:
self.classifier = 'one'
return '(classifier=one)'
else:
self.classifier = 'zero'
return '(classifier=zero)'
else:
if random.choice(predone) == 1:
self.classifier = 'one'
return '(classifier=one)'
else:
self.classifier = 'zero'
return '(classifier=zero)'
#self.suplearn = 0
#self.classifier = 'zero'
def run(self):
s_idx, temp = self.init_state()
b = self.init_belief()
cost = 0
#print ( 'b shape is,', b.shape )
#print b
while True:
a_idx = self.policy.select_action(b)
a = self.model.actions[a_idx]
print('action selected', a)
o_idx = self.random_observe(a_idx)
#o_idx= self.observe_logical(a_idx)
#print ('transition matrix shape is', self.model.trans_mat.shape)
#print self.model.trans_mat[a_idx,:,:]
#print ('observation matrix shape is', self.model.obs_mat.shape)
#print self.model.trans_mat[a_idx,:,:]
print s_idx
cost = cost + self.model.reward_mat[a_idx, s_idx]
print('Total reward is,', cost)
b = self.update(a_idx, o_idx, b)
print b
success = 0
tp = 0
tn = 0
fp = 0
fn = 0
if 'report' in a:
if 'not_interested' in a and 'not_interested' in temp:
success = 1
tn = 1
print 'Trial was successfull'
elif 'report_interested' in a and 'forward_interested' in temp:
success = 1
tp = 1
print 'Trial was successful'
elif 'report_interested' in a and 'forward_not_interested' in temp:
fp = 1
print 'Trial was unsuccessful'
elif 'not_interested' in a and 'forward_interested' in temp:
fn = 1
print('finished ')
break
return cost, success, tp, tn, fp, fn
def trial_num(self, num):
total_success = 0
total_cost = 0
total_tp = 0
total_tn = 0
total_fp = 0
total_fn = 0
for i in range(num):
random.seed(i)
c, s, tp, tn, fp, fn = self.run()
total_cost += c
total_success += s
total_tp += tp
total_tn += tn
total_fp += fp
total_fn += fn
print 'Average total reward is:', total_cost / num
print 'Average total success is: ', float(total_success) / num
Precision = float(total_tp) / (total_tp + total_fp)
print 'Precision is ', Precision
Recall = float(total_tp) / (total_tp + total_fn)
print 'Recall is ', Recall
F1score = 2.0 * Precision * Recall / (Precision + Recall)
print 'F1 score', F1score