lychiyu/freqtrade_origin
1
0
Fork 0
mirror of https://github.com/freqtrade/freqtrade.git synced 2024-11-10 10:21:59 +00:00
Code Issues Actions 2 Packages Projects Releases Wiki Activity

fix typing in TensorboardCallback

Browse Source
This commit is contained in:
robcaulk 2023-04-26 10:54:54 +02:00
parent 0a05099713
commit e29ce218eb
2 changed files with 11 additions and 6 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

8
freqtrade/freqai/RL/BaseEnvironment.py
View File

@ -127,6 +127,14 @@ class BaseEnvironment(gym.Env):
self.history: dict = {}
self.trade_history: list = []
def get_attr(self, attr: str):
"""
Returns the attribute of the environment
:param attr: attribute to return
:return: attribute
"""
return getattr(self, attr)
@abstractmethod
def set_action_space(self):
"""

9
freqtrade/freqai/RL/TensorboardCallback.py
View File

@ -3,7 +3,7 @@ from typing import Any, Dict, Type, Union
from stable_baselines3.common.callbacks import BaseCallback
from stable_baselines3.common.logger import HParam
from stable_baselines3.common.vec_env import SubprocVecEnv
from stable_baselines3.common.vec_env import SubprocVecEnv, VecMonitor
from freqtrade.freqai.RL.BaseEnvironment import BaseActions, BaseEnvironment
@ -17,7 +17,7 @@ class TensorboardCallback(BaseCallback):
super().__init__(verbose)
self.model: Any = None
self.logger = None # type: Any
self.training_env: Type[BaseEnvironment] = None # type: ignore
self.training_env: Union[BaseEnvironment, SubprocVecEnv, VecMonitor] = None
self.actions: Type[Enum] = actions
def _on_training_start(self) -> None:
@ -45,10 +45,7 @@ class TensorboardCallback(BaseCallback):
def _on_step(self) -> bool:
local_info = self.locals["infos"][0]
if isinstance(self.training_env, SubprocVecEnv):
tensorboard_metrics = self.training_env.get_attr("tensorboard_metrics")[0]
else:
tensorboard_metrics = self.training_env.tensorboard_metrics
tensorboard_metrics = self.training_env.get_attr("tensorboard_metrics")[0]
for metric in local_info:
if metric not in ["episode", "terminal_observation"]: