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add multiproc fix flake8

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smarmau 2022-12-03 22:30:04 +11:00 committed by GitHub
parent 469aa0d43f
commit d6f45a12ae
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2 changed files with 6 additions and 4 deletions
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6
freqtrade/freqai/prediction_models/ReinforcementLearner.py
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@ -102,7 +102,7 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
for action in Actions: for action in Actions:
self.custom_info[f"{action.name}"] = 0 self.custom_info[f"{action.name}"] = 0
return super().reset() return super().reset()
def step(self, action: int): def step(self, action: int):
observation, step_reward, done, info = super().step(action) observation, step_reward, done, info = super().step(action)
info = dict( info = dict(
@ -134,7 +134,7 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
factor = 100. factor = 100.
# reward agent for entering trades # reward agent for entering trades
if (action ==Actions.Long_enter.value if (action == Actions.Long_enter.value
and self._position == Positions.Neutral): and self._position == Positions.Neutral):
self.custom_info[f"{Actions.Long_enter.name}"] += 1 self.custom_info[f"{Actions.Long_enter.name}"] += 1
return 25 return 25
@ -174,6 +174,6 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2) factor *= self.rl_config['model_reward_parameters'].get('win_reward_factor', 2)
self.custom_info[f"{Actions.Short_exit.name}"] += 1 self.custom_info[f"{Actions.Short_exit.name}"] += 1
return float(pnl * factor) return float(pnl * factor)
self.custom_info["Unknown"] += 1 self.custom_info["Unknown"] += 1
return 0. return 0.

4
freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py
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@ -8,7 +8,7 @@ from stable_baselines3.common.vec_env import SubprocVecEnv
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
from freqtrade.freqai.RL.BaseReinforcementLearningModel import make_env from freqtrade.freqai.RL.BaseReinforcementLearningModel import TensorboardCallback, make_env
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
@ -49,3 +49,5 @@ class ReinforcementLearner_multiproc(ReinforcementLearner):
self.eval_callback = EvalCallback(self.eval_env, deterministic=True, self.eval_callback = EvalCallback(self.eval_env, deterministic=True,
render=False, eval_freq=len(train_df), render=False, eval_freq=len(train_df),
best_model_save_path=str(dk.data_path)) best_model_save_path=str(dk.data_path))
self.tensorboard_callback = TensorboardCallback()