reorganize/generalize tensorboard callback

freqtrade/freqai/RL/TensorboardCallback.py

@@ -0,0 +1,61 @@
+from enum import Enum
+from typing import Any, Dict, Type, Union
+
+from stable_baselines3.common.callbacks import BaseCallback
+from stable_baselines3.common.logger import HParam
+
+from freqtrade.freqai.RL.BaseEnvironment import BaseActions
+
+
+class TensorboardCallback(BaseCallback):
+    """
+    Custom callback for plotting additional values in tensorboard and
+    episodic summary reports.
+    """
+    def __init__(self, verbose=1, actions: Type[Enum] = BaseActions):
+        super(TensorboardCallback, self).__init__(verbose)
+        self.model: Any = None
+        # An alias for self.model.get_env(), the environment used for training
+        self.logger = None  # type: Any
+        # self.training_env = None  # type: Union[gym.Env, VecEnv]
+        self.actions: Type[Enum] = actions
+
+    def _on_training_start(self) -> None:
+        hparam_dict = {
+            "algorithm": self.model.__class__.__name__,
+            "learning_rate": self.model.learning_rate,
+            # "gamma": self.model.gamma,
+            # "gae_lambda": self.model.gae_lambda,
+            # "batch_size": self.model.batch_size,
+            # "n_steps": self.model.n_steps,
+        }
+        metric_dict: Dict[str, Union[float, int]] = {
+            "eval/mean_reward": 0,
+            "rollout/ep_rew_mean": 0,
+            "rollout/ep_len_mean": 0,
+            "train/value_loss": 0,
+            "train/explained_variance": 0,
+        }
+        self.logger.record(
+            "hparams",
+            HParam(hparam_dict, metric_dict),
+            exclude=("stdout", "log", "json", "csv"),
+        )
+
+    def _on_step(self) -> bool:
+        custom_info = self.training_env.get_attr("custom_info")[0]  # type: ignore
+        self.logger.record("_state/position", self.locals["infos"][0]["position"])
+        self.logger.record("_state/trade_duration", self.locals["infos"][0]["trade_duration"])
+        self.logger.record("_state/current_profit_pct", self.locals["infos"]
+                           [0]["current_profit_pct"])
+        self.logger.record("_reward/total_profit", self.locals["infos"][0]["total_profit"])
+        self.logger.record("_reward/total_reward", self.locals["infos"][0]["total_reward"])
+        self.logger.record_mean("_reward/mean_trade_duration", self.locals["infos"]
+                                [0]["trade_duration"])
+        self.logger.record("_actions/action", self.locals["infos"][0]["action"])
+        self.logger.record("_actions/_Invalid", custom_info["Invalid"])
+        self.logger.record("_actions/_Unknown", custom_info["Unknown"])
+        self.logger.record("_actions/Hold", custom_info["Hold"])
+        for action in self.actions:
+            self.logger.record(f"_actions/{action.name}", custom_info[action.name])
+        return True