def calc_True_Reward(action_sequence, qa_info, adaptive_flag = False):
entity_mask = qa_info['entity_mask'] if 'entity_mask' in qa_info.keys() else {}
relation_mask = qa_info["relation_mask"] if 'relation_mask' in qa_info.keys() else {}
type_mask = qa_info['type_mask'] if 'type_mask' in qa_info.keys() else {}
int_mask = qa_info['int_mask'] if 'int_mask' in qa_info.keys() else {}
# Update(add) elements in dict.
masking_elements = {**entity_mask, **relation_mask, **type_mask, **int_mask}
new_action = list()
# Default separator of split() method is any whitespace.
mask_name_list = ["ENTITY", "RELATION", "TYPE", "INT"]
for act in action_sequence:
# Unmasking.
if any(name in str(act) for name in mask_name_list):
correct_flag = False
for k, v in masking_elements.items():
if act == v:
act = k
correct_flag = True
break
# If the action could not find the corresponding mask in the dict,
# then the predicted action is not semantically correct, the reward should be returned as -1.0.
if not correct_flag:
# print('%s: %s is wrong!' % (str(qa_info['qid']), str(action_sequence)))
return -1.0
new_action.append(act)
symbolic_seq = list2dict(new_action)
# print (symbolic_seq)
symbolic_exe = Symbolics(symbolic_seq)
answer = symbolic_exe.executor()
if adaptive_flag:
return calc_adaptative_reward(answer, qa_info)
else:
return calc_01_reward(answer, qa_info)
def calc_True_Reward(action_sequence, qa_info):
entity_mask = qa_info['entity_mask'] if 'entity_mask' in qa_info.keys() else {}
relation_mask = qa_info["relation_mask"] if 'relation_mask' in qa_info.keys() else {}
type_mask = qa_info['type_mask'] if 'type_mask' in qa_info.keys() else {}
# Update(add) elements in dict.
masking_elements = {**entity_mask, **relation_mask, **type_mask}
new_action = list()
# Default separator of split() method is any whitespace.
for act in action_sequence:
for k, v in masking_elements.items():
if act == v:
act = k
break
new_action.append(act)
symbolic_seq = list2dict(new_action)
# print (symbolic_seq)
symbolic_exe = Symbolics(symbolic_seq)
answer = symbolic_exe.executor()
return calc_adaptative_reward(answer, qa_info)
# -*- coding: utf-8 -*-
# @Time : 2019/09/02 20:25
# @Author : Devin Hua
'''
input:
<class 'list'>: ['A2', '(', 'Q6619679', 'P674', 'Q838948', ')', 'A2', '(', 'Q6619679', '-', 'P674', 'Q36649', ')', 'A6', '(', 'Q944203', ')', 'A11', '(', ')']
output:
<class 'list'>: [{'A2': ['Q3895768', 'P1441', 'Q3895768']}, {'A2': ['Q15961987', '-P1441', 'Q95074']}, {'A15': ['Q347947', '', '']}]
'''
from SymbolicExecutor.transform_util import list2dict
if __name__ == "__main__":
list_temp = [
'A2', '(', ')', ')', '(', 'Q6619679', '-', 'P674', 'Q36649', 'A6',
'Q944203'
]
print(list2dict(list_temp))
print(len(""))