def patch(self: Resource, args: typing.Dict, model_id: int,
model_evaluate_id: int) -> typing.Tuple[typing.Dict, int]:
"""
更新一条评估记录
"""
update_params = {}
if args.get("model_evaluate_state"):
update_params.update(evaluate_task_status=status_str2int_mapper()[
args["model_evaluate_state"]])
if args.get("model_evaluate_result"):
update_params.update(
evaluate_task_result=args["model_evaluate_result"])
if args.get("model_evaluate_name"):
update_params.update(
evaluate_task_name=args["model_evaluate_name"])
if args.get("model_evaluate_desc"):
update_params.update(
evaluate_task_desc=args["model_evaluate_desc"])
evaluate_task = ModelEvaluateService().update_evaluate_task_by_id(
evaluate_task_id=model_evaluate_id, args=update_params)
result = EvaluateTaskSchema().dump(evaluate_task)
return {
"message": "更新成功",
"result": result,
}, 200
def get(self: Resource, args: typing.Dict, model_id: int,
model_evaluate_id: int) -> typing.Tuple[typing.Dict, int]:
"""
获取单条模型评估记录
"""
evaluate_task = ModelEvaluateService().get_evaluate_task_by_id(
model_evaluate_id)
result = EvaluateTaskSchema().dump(evaluate_task)
return {
"message": "请求成功",
"result": result,
}, 200
def put(self, args):
"""
更新一条评估记录
"""
update_params = {}
if args.get("model_evaluate_state"):
update_params.update(evaluate_task_status=status_str2int_mapper()[
args["model_evaluate_state"]])
if args.get("model_evaluate_result"):
update_params.update(
evaluate_task_result=args["model_evaluate_result"])
evaluate_task = ModelEvaluateService().update_evaluate_task_by_id(
evaluate_task_id=args["model_evaluate_id"], args=update_params)
result = EvaluateTaskSchema().dump(evaluate_task)
return {
"message": "更新成功",
"result": result,
}, 201
def post(
self: Resource,
args: typing.Dict,
model_id: int,
) -> typing.Tuple[typing.Dict, int]:
"""
创建一条评估记录
"""
evaluate_task = ModelEvaluateService(
).create_evaluate_task_by_train_job_id(
train_job_id=model_id,
evaluate_task_name=args["model_evaluate_name"],
evaluate_task_desc=args["model_evaluate_desc"],
mark_job_ids=args["mark_job_ids"],
doc_term_ids=args["doc_term_ids"],
doc_relation_ids=args["doc_relation_ids"],
use_rule=args["use_rule"])
result = EvaluateTaskSchema().dump(evaluate_task)
return {"message": "创建成功", "result": result}, 201
def get(self: Resource, args: typing.Dict,
model_id: int) -> typing.Tuple[typing.Dict, int]:
"""
获取模型评估记录，分页
"""
order_by = args["order_by"][1:]
order_by_desc = True if args["order_by"][0] == "-" else False
count, evaluate_task_list = ModelEvaluateService(
).get_evaluate_task_list_by_train_job_id(train_job_id=model_id,
order_by=order_by,
order_by_desc=order_by_desc,
offset=args["offset"],
limit=args["limit"])
result = EvaluateTaskSchema(many=True).dump(evaluate_task_list)
return {
"message": "请求成功",
"result": result,
"count": count,
}, 200
def get(self, args):
"""
查看抽取文档类型下的最新上线模型信息
"""
data = ModelService().get_online_model_info_by_doc_type_id(
doc_type_id=args["doc_type_id"],
current_user=self.get_current_user())
if not data:
abort(400, message="未查询到数据")
# assign
train_task, evaluate_task, train_job, doc_type = data
result = {
"doc_type": DocTypeSchema().dump(doc_type),
"model": TrainJobSchema().dump(train_job),
"train": TrainTaskSchema().dump(train_task),
"evaluate": EvaluateTaskSchema().dump(evaluate_task),
}
return {
"message": "请求成功",
"result": result,
}, 200
def get_doc_type_info_by_nlp_task_by_user(nlp_task_id, current_user: CurrentUser):
"""
获取管理大厅首页的doc_type信息
"""
result = []
# get doc_type list by user
_, doc_type_list = DocTypeModel().get_by_nlp_task_id_by_user(nlp_task_id=nlp_task_id, current_user=current_user)
for doc_type, terms in doc_type_list:
doc_type.doc_terms = [int(t) for t in terms.split(",")] if terms is not None else []
doc_type_list = [d[0] for d in doc_type_list]
doc_type_list = [{"doc_type": DocTypeSchema().dump(doc_type)} for doc_type in doc_type_list]
# get all job count and approved job count
all_status, all_marked_status = MarkTaskModel().count_status_by_user(nlp_task_id=nlp_task_id, current_user=current_user)
# calculate marked mark_job count and all mark_job for each doc_type
all_status_dict = Common().tuple_list2dict(all_status)
all_marked_status_dict = Common().tuple_list2dict(all_marked_status)
for doc_type in doc_type_list:
doc_type_id = doc_type["doc_type"]["doc_type_id"]
mark_job_count = len(all_status_dict.get(doc_type_id, {}))
marked_mark_job_count = 0
for _mark_job_id, _count_sum in all_status_dict.get(doc_type_id, {}).items():
if _count_sum == all_marked_status_dict.get(doc_type_id, {}).get(_mark_job_id, 0):
marked_mark_job_count += 1
doc_type.update(progress_state={"job_num": mark_job_count,
"labeled_job_number": marked_mark_job_count,
"progress_rate": round(marked_mark_job_count / mark_job_count, 2) if mark_job_count > 0 else 0})
# get latest evaluation result if exists
latest_evaluate = EvaluateTaskModel().get_latest_evaluate_by_doc_type_id(nlp_task_id=nlp_task_id,
doc_type_id=doc_type_id)
if latest_evaluate:
doc_type.update(evaluate=EvaluateTaskSchema().dump(latest_evaluate))
result.append(doc_type)
return result