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gen_triple.py
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gen_triple.py
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# coding=utf-8
import os
import json
from config import config
def get_latest_model_predict_data_dir(new_epochs_ckpt_dir=None):
def new_report(test_report):
lists = os.listdir(test_report)
lists.sort(key=lambda fn: os.path.getmtime(test_report + "/" + fn))
file_new = os.path.join(test_report, lists[-1])
return file_new
if new_epochs_ckpt_dir is None:
input_new_epochs = os.path.join(
os.path.abspath(os.path.join(os.path.dirname(__file__), "output")), "sequnce_infer_out")
new_ckpt_dir = new_report(input_new_epochs)
input_new_epochs_ckpt = os.path.join(input_new_epochs, new_ckpt_dir)
new_epochs_ckpt_dir = new_report(input_new_epochs_ckpt)
if not os.path.exists(new_ckpt_dir):
raise ValueError("path do not exist!{}".format(new_epochs_ckpt_dir))
return new_epochs_ckpt_dir
schemas_dict_relation_2_object_subject_type = config.schema
class File_Management(object):
def __init__(self, TEST_DATA_DIR=None, MODEL_OUTPUT_DIR=None, Competition_Mode=True):
self.TEST_DATA_DIR = TEST_DATA_DIR
self.MODEL_OUTPUT_DIR = get_latest_model_predict_data_dir(MODEL_OUTPUT_DIR)
self.Competition_Mode = Competition_Mode
def file_path_and_name(self):
text_sentence_file_path = os.path.join(self.TEST_DATA_DIR, "text_and_one_predicate.txt")
token_in_file_path = os.path.join(self.TEST_DATA_DIR, "token_in_not_UNK_and_one_predicate.txt")
predicate_token_label_file_path = os.path.join(self.MODEL_OUTPUT_DIR, "token_label_predictions.txt")
file_path_list = [text_sentence_file_path, token_in_file_path, predicate_token_label_file_path]
file_name_list = ["text_sentence_list", "token_in_not_NUK_list ", "token_label_list",]
if not self.Competition_Mode:
spo_out_file_path = os.path.join(self.TEST_DATA_DIR, "spo_out.txt")
if os.path.exists(spo_out_file_path):
file_path_list.append(spo_out_file_path)
file_name_list.append("reference_spo_list")
return file_path_list, file_name_list
def read_file_return_content_list(self):
file_path_list, file_name_list = self.file_path_and_name()
content_list_summary = []
for file_path in file_path_list:
with open(file_path, "r", encoding='utf-8') as f:
content_list = f.readlines()
content_list = [content.replace("\n", "") for content in content_list]
content_list_summary.append(content_list)
if self.Competition_Mode:
content_list_length_summary = [(file_name, len(content_list)) for content_list, file_name in
zip(content_list_summary, file_name_list)]
file_line_number = self._check_file_line_numbers(content_list_length_summary)
else:
file_line_number = len(content_list_summary[0])
print("first file line number: ", file_line_number)
print("do not check file line! if you need check file line, set Competition_Mode=True")
print("\n")
return content_list_summary, file_line_number
def _check_file_line_numbers(self, content_list_length_summary):
content_list_length_file_one = content_list_length_summary[0][1]
for file_name, file_line_number in content_list_length_summary:
assert file_line_number == content_list_length_file_one
return content_list_length_file_one
class Sorted_relation_and_entity_list_Management(File_Management):
def __init__(self, TEST_DATA_DIR, MODEL_OUTPUT_DIR, Competition_Mode=False):
File_Management.__init__(self, TEST_DATA_DIR=TEST_DATA_DIR, MODEL_OUTPUT_DIR=MODEL_OUTPUT_DIR, Competition_Mode=Competition_Mode)
self.relationship_label_list = config.class_label
self.Competition_Mode = Competition_Mode
def get_input_list(self,):
content_list_summary, self.file_line_number = self.read_file_return_content_list()
if len(content_list_summary) == 4:
[text_sentence_list, token_in_not_NUK_list, token_label_list, reference_spo_list] = content_list_summary
elif len(content_list_summary) == 3:
[text_sentence_list, token_in_not_NUK_list, token_label_list] = content_list_summary
reference_spo_list = [None] * len(text_sentence_list)
else:
raise ValueError("check code!")
return text_sentence_list, token_in_not_NUK_list, token_label_list, reference_spo_list
def _merge_WordPiece_and_single_word(self, entity_sort_list):
entity_sort_tuple_list = []
for a_entity_list in entity_sort_list:
entity_content = ""
entity_type = None
for idx, entity_part in enumerate(a_entity_list):
if idx == 0:
entity_type = entity_part
if entity_type[:2] not in ["B-", "I-"]:
break
else:
if entity_part.startswith("##"):
entity_content += entity_part.replace("##", "")
else:
entity_content += entity_part
if entity_content != "":
entity_sort_tuple_list.append((entity_type[2:], entity_content))
return entity_sort_tuple_list
def preprocessing_reference_spo_list(self, refer_spo_str):
refer_spo_list = refer_spo_str.split("[SPO_SEP]")
refer_spo_list = [spo.split(" ") for spo in refer_spo_list]
refer_spo_list = [dict([('predicate', spo[0]),
('object_type', spo[2]), ('subject_type', spo[1]),
('object', spo[4]), ('subject', spo[3])]) for spo in refer_spo_list]
refer_spo_list.sort(key= lambda item:item['predicate'])
return refer_spo_list
def model_token_label_2_entity_sort_tuple_list(self, token_in_not_UNK_list, predicate_token_label_list):
def preprocessing_model_token_lable(predicate_token_label_list, token_in_list_lenth):
if predicate_token_label_list[0] == "[CLS]":
predicate_token_label_list = predicate_token_label_list[1:] # y_predict.remove('[CLS]')
if len(predicate_token_label_list) > token_in_list_lenth: # 只取输入序列长度即可
predicate_token_label_list = predicate_token_label_list[:token_in_list_lenth]
return predicate_token_label_list
predicate_token_label_list = preprocessing_model_token_lable(predicate_token_label_list, len(token_in_not_UNK_list))
entity_sort_list = []
entity_part_list = []
for idx, token_label in enumerate(predicate_token_label_list):
if token_label == "O":
if len(entity_part_list) > 0:
entity_sort_list.append(entity_part_list)
entity_part_list = []
if token_label.startswith("B-"):
if len(entity_part_list) > 0:
entity_sort_list.append(entity_part_list)
entity_part_list = []
entity_part_list.append(token_label)
entity_part_list.append(token_in_not_UNK_list[idx])
if idx == len(predicate_token_label_list) - 1:
entity_sort_list.append(entity_part_list)
if token_label.startswith("I-") or token_label == "[##WordPiece]":
if len(entity_part_list) > 0:
entity_part_list.append(token_in_not_UNK_list[idx])
if idx == len(predicate_token_label_list) - 1:
entity_sort_list.append(entity_part_list)
if token_label == "[SEP]":
break
entity_sort_tuple_list = self._merge_WordPiece_and_single_word(entity_sort_list)
return entity_sort_tuple_list
def produce_relationship_and_entity_sort_list(self):
text_sentence_list, token_in_not_NUK_list, token_label_list, reference_spo_list = self.get_input_list()
for [text_sentence, token_in_not_UNK, token_label, refer_spo_str] in\
zip(text_sentence_list, token_in_not_NUK_list, token_label_list, reference_spo_list):
text = text_sentence.split("\t")[0]
text_predicate = text_sentence.split("\t")[1]
token_in = token_in_not_UNK.split("\t")[0].split(" ")
token_in_predicate = token_in_not_UNK.split("\t")[1]
assert text_predicate == token_in_predicate
token_label_out = token_label.split(" ")
entity_sort_tuple_list = self.model_token_label_2_entity_sort_tuple_list(token_in, token_label_out)
if self.Competition_Mode:
yield text, text_predicate, entity_sort_tuple_list, None
else:
if refer_spo_str is not None:
refer_spo_list = self.preprocessing_reference_spo_list(refer_spo_str)
else:
refer_spo_list = []
yield text, text_predicate, entity_sort_tuple_list, refer_spo_list
def produce_output_file(self, OUT_RESULTS_DIR=None, keep_empty_spo_list=False):
filename = "subject_predicate_object_predict_output.json"
output_dict = dict()
for text, text_predicate, entity_sort_tuple_list, refer_spo_list in self.produce_relationship_and_entity_sort_list():
object_type, subject_type = schemas_dict_relation_2_object_subject_type[text_predicate][0]
subject_list = [value for name, value in entity_sort_tuple_list if name == "SUB"]
subject_list = list(set(subject_list))
subject_list = [value for value in subject_list if len(value) >= 2]
object_list = [value for name, value in entity_sort_tuple_list if name == "OBJ"]
object_list = list(set(object_list))
object_list = [value for value in object_list if len(value) >= 2]
if len(subject_list) == 0 or len(object_list) == 0:
output_dict.setdefault(text, [])
for subject_value in subject_list:
for object_value in object_list:
output_dict.setdefault(text, []).append({"object_type": object_type, "predicate": text_predicate,
"object": object_value, "subject_type": subject_type,
"subject": subject_value})
if keep_empty_spo_list:
filename = "keep_empty_spo_list_" + filename
if OUT_RESULTS_DIR is None:
out_path = filename
else:
out_path = os.path.join(OUT_RESULTS_DIR, filename)
if not os.path.exists(OUT_RESULTS_DIR):
os.makedirs(OUT_RESULTS_DIR)
result_json_write_f = open(out_path, "w", encoding='utf-8')
count_line_number = 0
count_empty_line_number = 0
for text, spo_list in output_dict.items():
count_line_number += 1
line_dict = dict()
line_dict["text"] = text
line_dict["spo_list"] = spo_list
line_json = json.dumps(line_dict, ensure_ascii=False)
if len(spo_list) == 0:
count_empty_line_number += 1
if keep_empty_spo_list:
result_json_write_f.write(line_json + "\n")
else:
if len(spo_list) > 0:
result_json_write_f.write(line_json + "\n")
print("empty_line: {}, line: {}, percentage: {:.2f}%".format(count_empty_line_number, count_line_number,
if __name__=='__main__':
TEST_DATA_DIR = "openue/sequence_labeling/sequence_labeling_data/" + sys.argv[1] + "test"
# MODEL_OUTPUT_DIR = "output/sequnce_infer_out/epochs9/ckpt20000"
MODEL_OUTPUT_DIR = None
OUT_RESULTS_DIR = "output/predict_text_spo_list_result"
Competition_Mode = True
spo_list_manager = Sorted_relation_and_entity_list_Management(TEST_DATA_DIR, MODEL_OUTPUT_DIR, Competition_Mode=Competition_Mode)
spo_list_manager.produce_output_file(OUT_RESULTS_DIR=OUT_RESULTS_DIR, keep_empty_spo_list=True)