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58 lines
2.0 KiB
Python
58 lines
2.0 KiB
Python
from enum import Enum
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from typing import Any, Dict, Type, Union
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from stable_baselines3.common.callbacks import BaseCallback
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from stable_baselines3.common.logger import HParam
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from freqtrade.freqai.RL.BaseEnvironment import BaseActions, BaseEnvironment
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class TensorboardCallback(BaseCallback):
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"""
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Custom callback for plotting additional values in tensorboard and
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episodic summary reports.
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"""
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def __init__(self, verbose=1, actions: Type[Enum] = BaseActions):
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super(TensorboardCallback, self).__init__(verbose)
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self.model: Any = None
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self.logger = None # type: Any
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self.training_env: BaseEnvironment = None # type: ignore
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self.actions: Type[Enum] = actions
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def _on_training_start(self) -> None:
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hparam_dict = {
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"algorithm": self.model.__class__.__name__,
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"learning_rate": self.model.learning_rate,
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# "gamma": self.model.gamma,
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# "gae_lambda": self.model.gae_lambda,
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# "batch_size": self.model.batch_size,
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# "n_steps": self.model.n_steps,
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}
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metric_dict: Dict[str, Union[float, int]] = {
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"eval/mean_reward": 0,
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"rollout/ep_rew_mean": 0,
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"rollout/ep_len_mean": 0,
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"train/value_loss": 0,
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"train/explained_variance": 0,
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}
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self.logger.record(
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"hparams",
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HParam(hparam_dict, metric_dict),
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exclude=("stdout", "log", "json", "csv"),
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)
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def _on_step(self) -> bool:
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local_info = self.locals["infos"][0]
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tensorboard_metrics = self.training_env.get_attr("tensorboard_metrics")[0]
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for metric in local_info:
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if metric not in ["episode", "terminal_observation"]:
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self.logger.record(f"info/{metric}", local_info[metric])
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for category in tensorboard_metrics:
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for metric in tensorboard_metrics[category]:
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self.logger.record(f"{category}/{metric}", tensorboard_metrics[category][metric])
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return True
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