adelorenzo/stable
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

custom info to tensorboard_metrics

Browse Source
This commit is contained in:
initrv
2022-12-11 15:37:45 +03:00
parent 58604c747e
commit cb8fc3c8c7
5 changed files with 10 additions and 15 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
freqtrade/freqai/RL/Base4ActionRLEnv.py
View File

@@ -46,9 +46,9 @@ class Base4ActionRLEnv(BaseEnvironment):
self._done = True
self._update_unrealized_total_profit()
step_reward = self.calculate_reward(action)
self.total_reward += step_reward
self.tensorboard_metrics[self.actions._member_names_[action]] += 1
trade_type = None
if self.is_tradesignal(action):

1
freqtrade/freqai/RL/Base5ActionRLEnv.py
View File

@@ -49,6 +49,7 @@ class Base5ActionRLEnv(BaseEnvironment):
self._update_unrealized_total_profit()
step_reward = self.calculate_reward(action)
self.total_reward += step_reward
self.tensorboard_metrics[self.actions._member_names_[action]] += 1
trade_type = None
if self.is_tradesignal(action):

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

@@ -77,7 +77,7 @@ class BaseEnvironment(gym.Env):
# set here to default 5Ac, but all children envs can override this
self.actions: Type[Enum] = BaseActions
self.custom_info: dict = {}
self.tensorboard_metrics: dict = {}
def reset_env(self, df: DataFrame, prices: DataFrame, window_size: int,
reward_kwargs: dict, starting_point=True):
@@ -136,10 +136,10 @@ class BaseEnvironment(gym.Env):
"""
Reset is called at the beginning of every episode
"""
# custom_info is used for episodic reports and tensorboard logging
self.custom_info: dict = {}
# tensorboard_metrics is used for episodic reports and tensorboard logging
self.tensorboard_metrics: dict = {}
for action in self.actions:
self.custom_info[action.name] = 0
self.tensorboard_metrics[action.name] = 0
self._done = False

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

@@ -44,16 +44,16 @@ class TensorboardCallback(BaseCallback):
def _on_step(self) -> bool:
local_info = self.locals["infos"][0]
custom_info = self.training_env.get_attr("custom_info")[0]
tensorboard_metrics = self.training_env.get_attr("tensorboard_metrics")[0]
for info in local_info:
if info not in ["episode", "terminal_observation"]:
self.logger.record(f"_info/{info}", local_info[info])
for info in custom_info:
for info in tensorboard_metrics:
if info in [action.name for action in self.actions]:
self.logger.record(f"_actions/{info}", custom_info[info])
self.logger.record(f"_actions/{info}", tensorboard_metrics[info])
else:
self.logger.record(f"_custom/{info}", custom_info[info])
self.logger.record(f"_custom/{info}", tensorboard_metrics[info])
return True