def unit_test_reset_belief( pomdp ):
final_belief = tbh_pomdp_util.reset_belief( pomdp )
pomdp_dict = { 'belief_for_policy': final_belief,
'final_belief': final_belief,
'new_machine_action': None,
'reset_belief': True }
return pomdp_dict
def pomdp_choose_action( pomdp, Q, belief_for_policy, action_code_list ):
new_machine_action_number = tbh_pomdp_util.solve_qmdp( belief_for_policy, pomdp, Q )
new_machine_action = action_code_list[new_machine_action_number]
#print "ACTION NO., ACTION", new_machine_action_number, new_machine_action
# Determine if we need to reset the policy
reset_belief = determine_reset_belief( new_machine_action )
if reset_belief:
final_belief = tbh_pomdp_util.reset_belief( pomdp )
else:
final_belief = belief_for_policy
pomdp_dict = { 'belief_for_policy': belief_for_policy,
'final_belief': final_belief,
'new_machine_action': new_machine_action,
'reset_belief': reset_belief }
return pomdp_dict
def process_nbest_handler( self , param_dict ):
# ----- COMMON TO ALL DMs ----- #
# Fill in action parts away from defaults:
# Using confidence scores
feature_set_list = []
# if we want all 10 hypotheses:
# use this for computing confidence scores
### custom for this experiment ###
### for computing the confidence score
action_code_list = []
for index, nbest_item in enumerate( param_dict['nbest_list'] ):
feature_set = \
tbh_compute_text_feature_set.compute_feature_set( \
[ nbest_item ], self.feature_type_list )
action_code = self.determine_action_code( feature_set )
action_code_list.append( action_code )
if len( action_code_list ) > 0:
top_action_code = action_code_list[0]
confidence_score = 0
for code in action_code_list:
if code == top_action_code:
confidence_score += 0.1
confidence_score = confidence_score / float( len( action_code_list ) ) * 10 - 0.03
else:
confidence_score = 0
print "CONFIDENCE SCORE:", confidence_score
### end of computing confidence score
#### LOAD ENTRIES INTO PARAM_DICT
param_dict['confidence_score'] = confidence_score
feature_set = \
tbh_compute_text_feature_set.compute_feature_set( \
param_dict['nbest_list'] , self.feature_type_list )
# add feature set to param_dict for storage and other purposes
param_dict[ 'feature_set' ] = feature_set
# for the pomdp
# Search over the features to determine the right action
# --- CUSTOM TO YOUR DM --- #
# Adjust things based on this action dictionary and send to
# everyone who might want to use it
# ----- COMMON TO ALL DMs ----- #
# Send the action_dict to everyone that might need it: common
# to all dialog managers; you probably don't want to change
# this part of the code.
#if action_dict['skype_manager_phone_action'] is not None:
# self._process_phone_event( action_dict['skype_manager_phone_action'] )
if self.current_experiment_log.experiment_name[0] == "POMDP":
action_dict = self.determine_action_confidence( param_dict )
self.machine_action = action_dict['pomdp_dict']['new_machine_action']
self.belief_for_policy = action_dict['pomdp_dict']['belief_for_policy']
print "\n================="
print "CURRENT POLICY"
print self.belief_for_policy
print "================="
#print "==========BELIEF=========="
#print self.belief_for_policy
#print "=========================="
# Update the belief as appropriate
self.pomdp.belief = action_dict['pomdp_dict']['final_belief']
current_turn = tbh_experiment_controller.experiment_turn( \
time = time.time(), \
utterance_file = None, \
param_dict = param_dict, \
utterance_hypothesis = param_dict['nbest_list'][0]['text'],
hypothesis_code = \
self.determine_action_code( param_dict['feature_set'] ), \
confidence_score = confidence_score, \
system_response = \
action_dict['pomdp_dict']['new_machine_action'] )
elif self.current_experiment_log.experiment_name[0] == "THRESHOLD":
threshold = 0.75
action_dict = self.determine_action_threshold( param_dict, threshold )
self.belief_for_policy = tbh_pomdp_util.reset_belief( self.pomdp )
self.pomdp.belief = tbh_pomdp_util.reset_belief( self.pomdp )
if confidence_score < threshold:
self.machine_action = action_dict['pomdp_dict']['new_machine_action']
current_turn = tbh_experiment_controller.experiment_turn( \
time = time.time(), \
utterance_file = None, \
param_dict = param_dict, \
utterance_hypothesis = param_dict['nbest_list'][0]['text'],
hypothesis_code = \
self.determine_action_code( param_dict['feature_set'] ), \
confidence_score = confidence_score, \
system_response = \
action_dict['pomdp_dict']['new_machine_action'] )
else:
self.machine_action = action_dict['pomdp_dict']['new_machine_action']
current_turn = tbh_experiment_controller.experiment_turn( \
time = time.time(), \
utterance_file = None, \
param_dict = param_dict, \
utterance_hypothesis = param_dict['nbest_list'][0]['text'],
hypothesis_code = \
self.determine_action_code( param_dict['feature_set'] ), \
confidence_score = confidence_score, \
system_response = \
self.determine_action_code( param_dict['feature_set'] ) )
else:
raise NameError( 'neither option as name of experiment' )
self.current_experiment_log.turn_list.append( current_turn )
# print self.current_experiment_log.turn_list
# add this to the param_dict
param_dict[ 'action_dict' ] = action_dict
if len( self.current_experiment_log.turn_list ) > 0:
for turn in self.current_experiment_log.turn_list:
print turn.time, turn.utterance_file, turn.utterance_hypothesis, turn.hypothesis_code, turn.confidence_score, turn.system_response
self._processed_nbest_event( param_dict )