def get(
self: Resource,
args: typing.Dict,
model_id: int,
model_train_id: int
) -> typing.Tuple[typing.Dict, int]:
"""
获取单条模型训练记录
"""
# get single task task by id
train_task = ModelTrainService().get_train_task_by_id(train_task_id=model_train_id)
result = TrainTaskSchema().dump(train_task)
# add extra algorithm information for extract and relation.
if args["model_type"] in ["extract", "relation"]:
result = ModelTrainService().add_algo_dict_for_extract_relation(result)
return {
"message": "请求成功",
"result": result,
}, 200
def delete(
self: Resource,
args: typing.Dict,
model_id: int,
model_train_id: int
) -> typing.Tuple[typing.Dict, int]:
"""
删除模型
"""
ModelTrainService().delete_train_task_by_id(train_task_id=model_train_id)
return {
"message": "删除成功",
}, 200
def put(self, args):
"""
修改模型的状态
"""
update_params = {}
if args.get("model_train_state"):
# 后端返回结果转换，失败时后端目前还返回failed
update_params.update(train_status=status_str2int_mapper()[args["model_train_state"]])
train_task = ModelTrainService().update_train_task_by_model_version(model_version=args["model_version"], is_check_train_terms=args["check_train_terms"], args=update_params)
result = TrainTaskSchema().dump(train_task)
return {
"message": "更新成功",
"result": result,
}, 201
def get(
self: Resource,
args: typing.Dict,
model_id: int,
model_train_id: int,
) -> typing.Tuple[typing.Dict, int]:
"""
获取模型训练的所有字段
"""
count, train_term_list = ModelTrainService().get_train_term_list_by_train_task_id(train_task_id=model_train_id)
result = TrainTermTaskSchema(many=True).dump(train_term_list)
return {
"message": "请求成功",
"result": result,
"count": count,
}, 200
def get(
self: Resource,
args: typing.Dict,
model_id: int
) -> typing.Tuple[typing.Dict, int]:
"""
获取模型训练记录，分页
"""
order_by = args["order_by"][1:]
order_by_desc = True if args["order_by"][0] == "-" else False
count, train_task_list = ModelTrainService().get_train_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 = TrainTaskSchema(many=True).dump(train_task_list)
return {
"message": "请求成功",
"result": result,
"count": count,
}, 200
def put(self, args):
"""
修改模型训练的一个字段状态
"""
update_params = {}
if args.get("train_term_state"):
update_params.update(train_term_status=status_str2int_mapper()[args["train_term_state"]])
if args.get("train_term_result"):
update_params.update(train_term_result=args["train_term_result"])
train_term_task = ModelTrainService().update_train_term_by_model_version_and_doc_term_id(model_version=args["model_version"],
doc_term_id=args["doc_term_id"],
args=update_params)
result = TrainTermTaskSchema().dump(train_term_task)
return {
"message": "更新成功",
"result": result,
}, 201
def patch(
self: Resource,
args: typing.Dict,
model_id: int,
model_train_id: int
) -> typing.Tuple[typing.Dict, int]:
"""
修改模型的状态和结果
"""
update_params = {}
if args.get("model_train_state"): # 这里不考虑model_train_result因为新的表结构里没有这个列了
update_params.update(train_status=status_str2int_mapper()[args["model_train_state"]])
train_task = ModelTrainService().update_train_task_by_id(train_job_id=model_id, train_task_id=model_train_id,
is_check_train_terms=args["check_train_terms"], model_type = args["model_type"],
args=update_params)
result = TrainTaskSchema().dump(train_task)
return {
"message": "更新成功",
"result": result,
}, 200
def patch(
self: Resource,
args: typing.Dict,
model_id: int,
model_train_id: int,
train_term_id: int,
) -> typing.Tuple[typing.Dict, int]:
"""
修改模型训练的一个字段状态
"""
update_params = {}
if args.get("train_term_state"):
update_params.update(train_term_status=status_str2int_mapper()[args["train_term_state"]])
if args.get("train_term_result"):
update_params.update(train_term_result=args["train_term_result"])
train_term_task = ModelTrainService().update_train_task_term_by_id(train_term_task_id=train_term_id, args=update_params)
result = TrainTermTaskSchema().dump(train_term_task)
return {
"message": "更新成功",
"result": result,
}, 200