def worker():
"""
预测模块，对主队列传入的文本进行处理及事件匹配预测，通过子队列将结果返回
:return: None
"""
try:
from jdqd.a01.event_match.algor.predict.execute import load_match_model, get_events, get_predict_result
# 加载事件匹配模型
model = load_match_model()
# 获取事件列表
event_list = get_events()
except:
trac = traceback.format_exc()
logger.error(trac)
raise trac
while True:
# 获取数据和子队列
logger.info("从主队列获取信息。。。")
content, sample_type, sub_queue = main_queue.get()
try:
logger.info("开始进行事件识别。。。")
content_pred = get_predict_result(model, event_list, content,
sample_type)
sub_queue.put((True, content_pred))
except:
# 通过子队列发送异常信息
trace = traceback.format_exc()
sub_queue.put((False, trace))
continue
def test_extract_model_train():
"""
调用事件抽取模块训练函数，测试训练流程是否成功
:return: status
"""
# 训练后模型路径
trained_model_path = cat_path(extract_train_config.trained_model_dir,
f"extract_model_{0}_{0}")
# 模型训练
model_train(version="0",
model_id="0",
all_steps=10,
trained_model_dir=extract_train_config.trained_model_dir,
data_dir=extract_train_config.supplement_data_dir,
maxlen=extract_train_config.maxlen,
epoch=1,
batch_size=extract_train_config.batch_size,
max_learning_rate=extract_train_config.learning_rate,
min_learning_rate=extract_train_config.min_learning_rate,
model_type="roberta")
# 判断是否有模型生成
assert os.path.exists(trained_model_path) == True
# 将生成的模型删除
os.remove(trained_model_path)
logger.info(f"event_extract_train demo is OK!")
return {"status": "success"}
def get_event_info(subject, verb, object, event_negaword):
"""
根据主语、谓语、宾语查询“事件信息表”。
:param subject: array.主语
:param verb: array.谓语
:param object: array.宾语
:param event_negaword: string.同义词
:return 事件信息表数据。
"""
db = DatabaseWrapper()
try:
# cursor = connect.cursor(cursor_factory=psycopg2.extras.RealDictCursor)
sql = f"SELECT * FROM ebm_event_info WHERE subject IN %s AND verb IN %s " \
f"AND object IN %s AND event_negaword = %s"
logger.info(sql)
result = db.query(
sql, (tuple(subject), tuple(verb), tuple(object), event_negaword),
QueryResultType.JSON)
# cursor.execute(sql)
# result = cursor.fetchall()
# result = json.loads(json.dumps(result))
except Exception as e:
raise RuntimeError(e)
finally:
db.close()
return result
def model_predict():
"""
模型预测接口。该接口完成了三个主要步骤：构建数据集、模型预测。
1、构建数据集。获取数据库中指定的预测数据，并且对数据集进行转换；
2、模型预测。使用指定的模型及数据集进行预测，预测结果记录到数据库。
返回数据，如：{"status":"success"}
"""
task_id = request.form.get("task_id")
logger.info(f"开始根据模型预测id<{task_id}>数据进行预测")
try:
# 查询t_event_task表，获取模型任务信息，该方法返回的是EventTask实体
event_task = query_teventtask_by_id(task_id)
# 1、构建数据集。获取数据库中指定的预测数据，并且对数据集进行转换；
dates, data = combine_data(event_task.tables_name)
data = data.values
events_set, events_p_oh = get_event(dates, event_task.event_type)
# 2、模型预测。使用指定的模型及数据集进行预测，预测结果记录到数据库。
update_task_status(task_id, ModelStatus.PROCESSING.value) # 任务状态修改为运行中
last_date_data_pred = predict_by_model(event_task.model_id, data, dates, events_set, task_id,
event_task.sample_start_date, event_task.model_type, event_task.event)
predict_task_finish(task_id, last_date_data_pred, ModelStatus.SUCCESS.value) # 任务状态修改为完成/成功
logger.info(f"模型id<{event_task.model_id}>的预测完成")
return {"success": True}
except Exception as e:
logger.error(f"表 id {task_id} 预测发生异常：{traceback.format_exc()}")
predict_task_finish(task_id, "", ModelStatus.FAILD.value) # 任务状态修改为失败
return {"success": False, "exception": e}
finally:
K.clear_session()
def get_cameo_model():
"""
加载事件类别（CAMEO）模型，返回事件类别模型对象
使用bert模型输出融合动词下标，预测事件cameo
:return: 事件cameo模型
"""
with cameo_sess.as_default():
with cameo_sess.graph.as_default():
# 搭建bert模型主体
bert_model = build_transformer_model(
config_path=bert_config.config_path,
return_keras_model=False,
model=bert_config.model_type
)
# 取出[CLS]对应的向量用来做分类
t = Lambda(lambda x: x[:, 0])(bert_model.model.output)
# 预测事件cameo
cameo_out_put = Dense(len(ID2CAMEO), activation='softmax')(t)
# cameo模型
cameo_model = Model(bert_model.model.inputs, cameo_out_put)
# 加载模型参数
logger.info("开始加载事件cameo模型参数。。。")
cameo_model.load_weights(pre_config.event_cameo_model_path)
logger.info("事件cameo模型参数加载完成！")
return cameo_model
def model_train():
"""
进行模型训练
:return: None
"""
# 训练集输入输出
input_train_labels, input_train_types = PreProcessInputData(input_train)
result_train_pro = PreProcessOutputData(result_train)
# 测试集输入输出
input_test_labels, input_test_types = PreProcessInputData(input_test)
result_test_pro = PreProcessOutputData(result_test)
# 构造callback模块的评估类
evaluator = Evaluate()
# 模型训练
with ss0.as_default():
with ss0.graph.as_default():
model.fit(x=[input_train_labels, input_train_types],
y=result_train_pro,
batch_size=CONFIG.batch_size,
epochs=CONFIG.epochs,
validation_data=[[input_test_labels, input_test_types],
result_test_pro],
verbose=1,
shuffle=True,
callbacks=[evaluator])
model_test(input_test)
f1, precision, recall = evaluator.evaluate()
logger.info(f"f1:{f1}, precision:{precision}, recall:{recall}")
def infer():
"""
从前端接受文章标题、内容、样本类型
:return: 事件相似度列表
"""
if request.method == "POST":
title = request.form.get("title", type=str, default=None)
content = request.form.get("content", type=str, default=None)
sample_type = request.form.get("sample_type", type=str, default='all')
else:
title = request.args.get("title", type=str, default=None)
content = request.args.get("content", type=str, default=None)
sample_type = request.args.get("sample_type", type=str, default='all')
if not content or content is None:
return error_resp('文章内容为空')
# 创建子队列，从预测模块获取处理信息
sub_queue = Queue()
# 使用主队列将请求内容以及子队列传入预测模块
logger.info("接收到前端请求开始进行事件识别。。。")
main_queue.put((content, sample_type, sub_queue))
# 使用子队列从预测模块获取请求信息以及预测数据
success, pred = sub_queue.get()
if success:
return success_resp([{'title_pred': []}, {'content_pred': pred}])
else:
return error_resp(pred)
def test_model(version,
model_id,
trained_model_dir="",
data_dir=extract_train_config.supplement_data_dir,
maxlen=160):
"""
进行模型训练的主函数，搭建模型，加载模型数据，根据传入的参数进行模型测试，测试模型是否达标
:param data_dir: 补充数据的文件夹路径
:param trained_model_dir: 训练后模型存放路径
:param maxlen: 最大长度
:param version: 模型版本号
:param model_id: 模型id
:return: status, F1, precision, recall, corpus_num
"""
# 训练后模型路径
if version and model_id:
trained_model_path = cat_path(trained_model_dir,
f"extract_model_{version}_{model_id}")
else:
trained_model_path = extract_pred_config.event_extract_model_path
# 获取训练集、验证集
train_data, dev_data = get_data(extract_train_config.train_data_path,
extract_train_config.dev_data_path,
data_dir)
# 搭建模型
trigger_model, object_model, subject_model, loc_model, time_model, negative_model, train_model = build_model(
)
with SESS.as_default():
with SESS.graph.as_default():
# 构造callback模块的评估类
evaluator = Evaluate(dev_data, maxlen, trained_model_path,
trigger_model, object_model, subject_model,
loc_model, time_model, negative_model,
train_model)
# 重载模型参数
train_model.load_weights(trained_model_path)
# 将验证集预测结果保存到文件中，暂时注释掉
f1, precision, recall = evaluator.evaluate()
assert f1 >= 0.8
assert precision >= 0.8
assert recall >= 0.8
logger.info(f"f1:{f1}, precision:{precision}, recall:{recall}")
logger.info(f"model is OK!")
return {
"status": "success",
"version": version,
"model_id": model_id,
"results": {
"f1": f1,
"precison": precision,
"recall": recall
}
}
def test_loguru():
os.environ['LOGURU_LEVEL'] = "INFO"
from loguru import logger
logger.debug(f"调试 {datetime.datetime.now()}")
logger.info(f"信息提示 {datetime.datetime.now()}")
logger.warning(f"警告 ======= {datetime.datetime.now()}")
logger.error(f"错误 !!!!!! {datetime.datetime.now()}")
def execute(raw_dir, target_dir):
"""
传入原始标注数据文件夹路径和解析后文件存放的路径，按照时间生成json文件名称，将解析好的数据保存到目标文件夹
:param raw_dir: 存放原始标注数据的文件夹
:param target_dir: 存放解析后数据的文件夹
:return: status--解析状态， corpus_num--数据量
"""
# 存放所有解析后的数据
all_datas = []
# 语料中的句子数量
all_sentence_num = 0
# 语料中的事件数量
all_event_num = 0
try:
# 判断数据路径是否正确
if valid_dir(raw_dir):
# 判断目标文件夹路径是否存在，不存在则创建
if not valid_dir(target_dir):
os.makedirs(target_dir)
file_name = f"{time.strftime('%Y-%m-%d', time.localtime(time.time()))}.json"
target_file_path = cat_path(target_dir, file_name)
# 获取文件夹下所有文件的名称
file_names = os.listdir(raw_dir)
file_names = list(
set(file_name.split(".")[0] for file_name in file_names))
# 遍历文件进行解析
for file_name in tqdm(file_names):
file_path = os.path.join(raw_dir, file_name)
# 判断两个文件是否都同时存在
if valid_file(f"{file_path}.ann") and valid_file(
f"{file_path}.txt"):
# 解析文件获取事件和文件中的句子以及事件数量
data, sentence_num, event_num = data_process(file_path)
all_datas.extend(data)
all_sentence_num += sentence_num
all_event_num += event_num
logger.info(f"总共有句子：{all_sentence_num}，总共有事件：{all_event_num}")
# 将解析后的数据保存到目标文件
save_json(all_datas, target_file_path)
return {
"status": "success",
"results": {
"sentences": all_sentence_num,
"events": all_event_num
}
}
else:
logger.error(f"存放原始标注数据的文件夹：{raw_dir}没有找到")
raise FileNotFoundError
except:
trace = traceback.format_exc()
logger.error(trace)
return {"status": "failed", "results": trace}
def test_mylog():
os.environ['HR_RUN_PROD'] = "y"
os.environ['HR_LOG_FILE'] = "/tmp/logger-test.log"
os.environ['HR_LOG_FILE_LEVEL'] = "ERROR"
os.environ['HR_LOG_CONSOLE'] = "y"
from feedwork.utils import logger
logger.debug(f"调试 {datetime.datetime.now()}")
logger.info(f"信息提示 {datetime.datetime.now()}")
logger.warning(f"警告 ======= {datetime.datetime.now()}")
logger.error(f"错误 !!!!!! {datetime.datetime.now()}")
def on_epoch_end(self, epoch, logs=None):
"""
选择所有训练次数中，f1最大时的模型
:param epoch: 训练次数
return None
"""
p, r, f1 = self.evaluate()
if f1 > self.F:
self.F = f1
model.save(TRAINED_MODEL_PATH, include_optimizer=True)
logger.info(f'epoch: {epoch}, p: {p}, best: {self.F}\n')
def on_epoch_end(self, epoch, logs=None):
"""
每个循环结束时判断指标是否最好，是则将模型保存下来
:param epoch: (int)循环次数
:param logs: 日志信息
:return: None
"""
accuracy, f1, precision, recall = self.evaluate()
if accuracy > self.best:
self.best = accuracy
MODEL.save(TRAINED_MODEL_PATH, include_optimizer=True)
logger.info(f'accuracy: {accuracy}.4f, best accuracy: {self.best}.4f\n')
def on_epoch_end(self, epoch, logs=None):
"""
选择所有训练次数中，f1最大时的模型
:param epoch: 训练次数
return None
"""
score_summary = self.evaluate()
if score_summary > self.best:
logger.info(f'{score_summary} better than old: {self.best}')
self.best = score_summary
model_path = cat_path(self.model_dir, 'cnn_model.h5')
self.model.save(model_path, include_optimizer=True)
def __setup_connection(self):
"""
创建数据库连接。当数据库连接无法构建时抛出RuntimeError异常。
"""
# 当连接不为空时，意味着使用者通过构造器构造了连接，导致重复调用该方法，所以此处直接返回，不抛出异常
if self.conn is not None:
logger.trace(f"{self.id} reuse autocommit={self.conn.autocommit}")
return
dbinfo = self.dbinfo
try:
start_time = time.time()
if ConnWay.DIRECTLY.value == dbinfo.way:
dsn = dbinfo.host + ":" + str(
dbinfo.port) + "/" + dbinfo.dbname
self.conn = self.database_manager.create_connection(
dbinfo.host, dbinfo.port, dbinfo.dbname, dbinfo.username,
dbinfo.password, dsn)
self.__conn_origin = self.conn
elif ConnWay.POOL.value == dbinfo.way:
from feedwork.database.init_database_pool import database_pool
self.conn = database_pool[dbinfo.name].connection()
# 连接池会对数据库连接进行封装，以下代码能拿到原始的数据库连接，从而能知道数据库连接的相关状态。该连接池没有提供状态查询接口
# 因为存储的是对象引用，所以可以直接修改该变量，从而修改数据库连接对象
self.__conn_origin = self.conn._con._con
else:
raise RuntimeError(
f"{dbinfo.way} connection way is not support!")
self.cursor = self.conn.cursor()
logger.info(
f"{self.id} new connection by {dbinfo.way} {self.desc} autocommit -> {dbinfo.autocommit}"
)
if dbinfo.show_conn_time:
logger.info(
f"{self.id} database connection spend {time.time() - start_time}s"
)
except Exception as e:
raise RuntimeError(
f"{dbinfo.name} can not connect to database! {e}")
if dbinfo.autocommit:
self.database_manager.get_or_set_autocommit(
self.__conn_origin, True)
else:
self.begin_transaction()
logger.debug(f"{self.id} connection setup success")
def on_epoch_end(self, epoch, logs=None):
"""
选择所有训练次数中，f1最大时的模型
:param epoch: 训练次数
return None
"""
score_summary = self.evaluate()
if score_summary > self.best:
logger.info(f'{score_summary} better than old: {self.best}')
self.best = score_summary
encoder_path = cat_path(self.model_dir, 'encoder.h5')
decoder_path = cat_path(self.model_dir, 'decoder.h5')
self.encoder.save(encoder_path)
self.decoder.save(decoder_path)
def on_epoch_end(self, epoch, logs=None):
"""
选择所有训练次数中，f1最大时的模型
:param epoch: 训练次数
return None
"""
f1, precision, recall = self.evaluate()
self.F1.append(f1)
if f1 > self.best:
self.best = f1
model.save(TRAINED_MODEL_PATH, include_optimizer=True)
logger.info(
f'epoch: {epoch}, f1: {f1}, precision: {precision}, recall: {recall}, best f1: {self.best}\n'
)
def evaluate(data: iter):
"""
传入经过ids化的数据，进行预测
:param data: (iter) ids化后的数据
:return: results(list)相似度列表
"""
results = []
logger.info("开始对传入的内容进行事件识别。。。")
for x_true, y_true in data:
# 调用模型，预测样本相似度[batch, 2]
y_pred = model.predict(x_true)
# 获取1维度的分数作为相似度，并重新reshape为一行，转化为列表
results.extend(np.reshape(y_pred[:, 1], (-1, )).tolist())
return results
def on_epoch_end(self, epoch, logs=None):
"""
每个循环结束时判断指标是否最好，是则将模型保存下来。
:param epoch: 循环次数
:param logs: 日志信息
:return: None
"""
f1, precision, recall = self.evaluate()
if f1 > self.best:
self.best = f1
self.train_model.save(self.trained_mdoel_path,
include_optimizer=True)
logger.info(
f'f1: {f1}.4f, precision: {precision}.4f, recall: {recall}.4f, best f1: {self.best}.4f\n'
)
def on_epoch_end(self, epoch, loggers=None):
"""
每个训练批次结束时运行，如果得到的f_score是最大的则进行模型保存
:param epoch: 循环次数
:param loggers: 日志信息
:return: None
"""
precision, recall, f_score, accuracy = self.evaluate()
if f_score > self.best:
self.best = f_score
MATCH_MODEL.save(search_config.trained_model_path,
include_optimizer=True)
logger.info(
f'precision: {precision}.4f, recall: {recall}.4f, f_score:{f_score}.4f, accuracy:{accuracy}.4f, best f_score: {self.best}.4f\n'
)
def load_bert_model():
"""
加载bert模型，用于向量生成
:return: bert_model(模型对象)
"""
logger.info("开始加载bert模型。。。")
# 在新的图上构建模型
with SESS_1.as_default():
with SESS_1.graph.as_default():
# 建立bert模型，加载权重
bert_model = build_transformer_model(bert_config.config_path, bert_config.checkpoint_path,
return_keras_model=True)
logger.info("bert模型加载完成!")
return bert_model
def rollback(self):
"""
数据库操作回滚。当数据库连接没有构建或者已经关闭时抛出RuntimeError异常。
该方法在该次连接为非自动提交时才会调用rollback()方法。
"""
if self.is_closed():
raise RuntimeError("Status error, Connection alredy closed!")
if self.dbinfo.autocommit is False:
self.conn.rollback()
# self.is_rollbacked = True
logger.info(f"{self.id} Transaction is rollback")
else:
logger.warning(
"The connection is autocommit, Please do not rollback")
def get_spacy():
"""
加载指代消解模型，返回模型对象。
:return: nlp指代模型对象
"""
# 加载spacy模型
logger.info("开始加载spacy模型。。。")
# spacy加载参数
nlp = spacy.load('en_core_web_sm')
# 加载字典，加载指代网络参数
coref = neuralcoref.NeuralCoref(nlp.vocab)
# 构建管道，整合spacy和指代网络
nlp.add_pipe(coref, name='neuralcoref')
logger.info("spacy模型加载完成!")
return nlp
def model_train():
"""
进行模型训练。
:return: None
"""
with SESS.as_default():
with SESS.graph.as_default():
# 构造评估对象
evaluator = Evaluate()
# 模型训练
MODEL.fit_generator(TRAIN_D.__iter__(), steps_per_epoch=TRAIN_D.__len__(), epochs=40, callbacks=[evaluator])
# 模型参数重载
MODEL.load_weights(TRAINED_MODEL_PATH)
accuracy, f1, precision, recall = evaluator.evaluate()
logger.info(f"accuracy:{accuracy}")
return accuracy, f1, precision, recall
def commit(self):
"""
提交事务，适用于已经开启事务的情况下使用。当数据库连接已经关闭时抛出RuntimeError异常。
"""
if self.is_closed():
raise RuntimeError(
"Status error, Connection is closed before commit!")
if self.dbinfo.autocommit is False:
self.conn.commit()
self.is_commited = True
# self.commit_count += 1
if self.dbinfo.show_sql:
logger.info(f"{self.id} Trans commit")
else:
logger.warning("")
def close(self):
"""
关闭数据库连接。当数据库连接没有构建或者已经关闭时抛出RuntimeError异常。
该方法在开启事务且没有提交事务的情况下会自动执行回滚操作。
"""
if self.is_closed():
raise RuntimeError("Status error, Connection already closed!")
# 意味着在开启事务状态下（默认开启）使用者必须手动提交事务
# # FIXME 这个处理代码没有意义。代码中的多段事务就失效了。如果想有用，需要结合execute中设置的标志
# if self.is_autocommit() is False and self.is_commited is False:
# logger.error(f"{self.id} Transaction has unhandled, auto rollback before close")
# self.conn.rollback()
self.cursor.close()
self.conn.close()
logger.info(f"{self.id} close success")
def insert_graph_db(graph_db, rst):
event_tag = "Event"
relation_type = "causality"
for i in range(len(rst)):
n = rst[i]['event_id_pair'][0]
n1 = rst[i]['event_id_pair'][1]
results = graph_db.run(
"MATCH (n:Event{event_id:'" + n + "'}),(n1:Event{event_id:'" + n1 +
"'}) "
"RETURN CASE WHEN (n)-[]-(n1) THEN '1' ELSE '0' END AS result"
).data()
logger.info(results)
if not results:
insert(graph_db, event_tag, i, relation_type, rst)
else:
if results[0]['result'] == '0':
logger.info(results[0]['result'])
insert(graph_db, event_tag, i, relation_type, rst)
def file_list_indir(root_dir, index_mdfile):
# root_dir目录下必须存在被检查的索引文件
if not os.path.isfile(os.path.join(root_dir, index_mdfile)):
logger.error(f"{index_mdfile} is not real file")
return None
file_list = []
for cur_root_dir, sub_dirs, filenames in os.walk(root_dir):
# for file in glob.glob(f"{root_dir}/*"):
for filename in filenames:
if filename == index_mdfile:
continue
cur_file = os.path.join(cur_root_dir,
filename) # 每个文件名，都是 root_dir 开头的
file_list.append(os.path.join(cur_root_dir, filename))
logger.info(f"file={filename}")
return file_list
def model_train():
"""
进行模型训练
:return: None
"""
# 构建模型评估对象
evaluator = Evaluate()
# 模型训练
MATCH_MODEL.fit_generator(TRAIN_D.__iter__(),
steps_per_epoch=TRAIN_D.__len__(),
epochs=100,
callbacks=[evaluator])
# 模型参数重载
MATCH_MODEL.load_weights(search_config.trained_model_path)
# 调用评估方法，测试模型效果
precision, recall, f_score, accuracy = evaluator.evaluate()
logger.info(
f'precision: {precision}.4f, recall: {recall}.4f, f_score:{f_score}.4f, accuracy:{accuracy}.4f'
)
def translate_any_2_anyone(article, target="en"):
"""
临时用的，将任意语言的文章翻译为英文
:param article: String.文章
:param target: String.可选值有en、zh等
"""
url = config.translate_url
article_t = ""
try:
article_detect = detect(article)
if article_detect == 'ja' or article_detect == 'zh-cn':
article_array = article.split("。")
for sentence in article_array:
data = {
"from": "auto",
"to": target,
"apikey": config.translate_user_key,
"src_text": sentence
}
cur_time = date_util.sys_date("%Y-%m-%d %H:%M:%S")
logger.info(f"-----------翻译句子开始----------- : {cur_time}")
res = http_post(data, url)
cur_time = date_util.sys_date("%Y-%m-%d %H:%M:%S")
logger.info(f"-----------翻译句子结束----------- : {cur_time}")
res_dict = json.loads(res)
if "tgt_text" in res_dict:
content = res_dict['tgt_text']
article_t += content + ". "
else:
article_array = article.split(".")
for sentence in article_array:
data = {
"from": "auto",
"to": target,
"apikey": config.translate_user_key,
"src_text": sentence
}
cur_time = date_util.sys_date("%Y-%m-%d %H:%M:%S")
logger.info(f"-----------翻译句子开始----------- : {cur_time}")
res = http_post(data, url)
cur_time = date_util.sys_date("%Y-%m-%d %H:%M:%S")
logger.info(f"-----------翻译句子结束----------- : {cur_time}")
res_dict = json.loads(res)
if "tgt_text" in res_dict:
content = res_dict['tgt_text']
article_t += content + ". "
return article_t
except HTTPError as e:
logger.error('翻译时发生的错误：', e)
return ''