add tensorboard category

docs/freqai-reinforcement-learning.md

@@ -248,13 +248,13 @@ FreqAI also provides a built in episodic summary logger called `self.tensorboard
             """
             def calculate_reward(self, action: int) -> float:
                 if not self._is_valid(action):
-                    self.tensorboard_log("is_valid")
+                    self.tensorboard_log("invalid")
                     return -2
 
 ```
 
 !!! Note
-    The `self.tensorboard_log()` function is designed for tracking incremented objects only i.e. events, actions inside the training environment. If the event of interest is a float, the float can be passed as the second argument e.g. `self.tensorboard_log("float_metric1", 0.23)` would add 0.23 to `float_metric`. In this case you can also disable incrementing using `inc=False` parameter.
+    The `self.tensorboard_log()` function is designed for tracking incremented objects only i.e. events, actions inside the training environment. If the event of interest is a float, the float can be passed as the second argument e.g. `self.tensorboard_log("float_metric1", 0.23)`. In this case the metric values are not incremented.
 
 ### Choosing a base environment
 

freqtrade/freqai/RL/Base3ActionRLEnv.py

@@ -47,7 +47,7 @@ class Base3ActionRLEnv(BaseEnvironment):
         self._update_unrealized_total_profit()
         step_reward = self.calculate_reward(action)
         self.total_reward += step_reward
-        self.tensorboard_log(self.actions._member_names_[action])
+        self.tensorboard_log(self.actions._member_names_[action], category="actions")
 
         trade_type = None
         if self.is_tradesignal(action):

freqtrade/freqai/RL/Base4ActionRLEnv.py

@@ -48,7 +48,7 @@ class Base4ActionRLEnv(BaseEnvironment):
         self._update_unrealized_total_profit()
         step_reward = self.calculate_reward(action)
         self.total_reward += step_reward
-        self.tensorboard_log(self.actions._member_names_[action])
+        self.tensorboard_log(self.actions._member_names_[action], category="actions")
 
         trade_type = None
         if self.is_tradesignal(action):

freqtrade/freqai/RL/Base5ActionRLEnv.py

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

freqtrade/freqai/RL/BaseEnvironment.py

@@ -137,7 +137,8 @@ class BaseEnvironment(gym.Env):
         self.np_random, seed = seeding.np_random(seed)
         return [seed]
 
-    def tensorboard_log(self, metric: str, value: Union[int, float] = 1, inc: bool = True):
+    def tensorboard_log(self, metric: str, value: Optional[Union[int, float]] = None,
+                        category: str = "custom"):
         """
         Function builds the tensorboard_metrics dictionary
         to be parsed by the TensorboardCallback. This
@@ -149,17 +150,23 @@ class BaseEnvironment(gym.Env):
 
         def calculate_reward(self, action: int) -> float:
             if not self._is_valid(action):
-                self.tensorboard_log("is_valid")
+                self.tensorboard_log("invalid")
                 return -2
 
         :param metric: metric to be tracked and incremented
-        :param value: value to increment `metric` by
+        :param value: `metric` value
-        :param inc: sets whether the `value` is incremented or not
+        :param category: `metric` category
         """
-        if not inc or metric not in self.tensorboard_metrics:
+        increment = True if not value else False
-            self.tensorboard_metrics[metric] = value
+        value = 1 if increment else value
+
+        if category not in self.tensorboard_metrics:
+            self.tensorboard_metrics[category] = {}
+
+        if not increment or metric not in self.tensorboard_metrics[category]:
+            self.tensorboard_metrics[category][metric] = value
         else:
-            self.tensorboard_metrics[metric] += value
+            self.tensorboard_metrics[category][metric] += value
 
     def reset_tensorboard_log(self):
         self.tensorboard_metrics = {}

freqtrade/freqai/RL/TensorboardCallback.py

@@ -46,14 +46,12 @@ class TensorboardCallback(BaseCallback):
         local_info = self.locals["infos"][0]
         tensorboard_metrics = self.training_env.get_attr("tensorboard_metrics")[0]
 
-        for info in local_info:
+        for metric in local_info:
-            if info not in ["episode", "terminal_observation"]:
+            if metric not in ["episode", "terminal_observation"]:
-                self.logger.record(f"_info/{info}", local_info[info])
+                self.logger.record(f"info/{metric}", local_info[metric])
 
-        for info in tensorboard_metrics:
+        for category in tensorboard_metrics:
-            if info in [action.name for action in self.actions]:
+            for metric in tensorboard_metrics[category]:
-                self.logger.record(f"_actions/{info}", tensorboard_metrics[info])
+                self.logger.record(f"{category}/{metric}", tensorboard_metrics[category][metric])
-            else:
-                self.logger.record(f"_custom/{info}", tensorboard_metrics[info])
 
         return True

freqtrade/freqai/prediction_models/ReinforcementLearner.py

@@ -100,7 +100,7 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
             """
             # first, penalize if the action is not valid
             if not self._is_valid(action):
-                self.tensorboard_log("is_valid")
+                self.tensorboard_log("invalid", category="actions")
                 return -2
 
             pnl = self.get_unrealized_profit()