Merge pull request #772 from macrocosm-os/fix/formatting

Fix formatting

Author: dbobrenko

prompting/weight_setting/weight_setter.py

@@ -1,7 +1,7 @@
 import asyncio
-from collections import deque
 import datetime
 import json
+from collections import deque
 from pathlib import Path
 from typing import Any
 
@@ -42,9 +42,7 @@ async def set_weights(
                 # If weights will not be set on chain, we should not synchronize.
                 augmented_weights = weights
             else:
-                augmented_weights = await weight_syncer.get_augmented_weights(
-                    weights=weights, uid=shared_settings.UID
-                )
+                augmented_weights = await weight_syncer.get_augmented_weights(weights=weights, uid=shared_settings.UID)
         except BaseException as ex:
             logger.exception(f"Issue with setting weights: {ex}")
             augmented_weights = weights
@@ -60,8 +58,7 @@ async def set_weights(
 
         # Convert to uint16 weights and uids.
         uint_uids, uint_weights = bt.utils.weight_utils.convert_weights_and_uids_for_emit(
-            uids=processed_weight_uids,
-            weights=processed_weights
+            uids=processed_weight_uids, weights=processed_weights
         )
     except Exception as ex:
         logger.exception(f"Skipping weight setting: {ex}")
@@ -167,9 +164,7 @@ async def _load_rewards(self):
                     if payload is None:
                         raise ValueError(f"Malformed weight history file: {data}")
 
-                    self.reward_history.append(
-                        {int(uid): {"reward": float(reward)} for uid, reward in payload.items()}
-                    )
+                    self.reward_history.append({int(uid): {"reward": float(reward)} for uid, reward in payload.items()})
         except BaseException as exc:
             self.reward_history: deque[dict[int, dict[str, Any]]] | None = deque(maxlen=self.reward_history_len)
             logger.error(f"Couldn't load rewards from file, resetting weight history: {exc}")
@@ -217,8 +212,7 @@ async def merge_task_rewards(cls, reward_events: list[list[WeightedRewardEvent]]
                 processed_rewards = task_rewards / max(1, (np.sum(task_rewards[task_rewards > 0]) + 1e-10))
             else:
                 processed_rewards = cls.apply_steepness(
-                    raw_rewards=task_rewards,
-                    steepness=shared_settings.REWARD_STEEPNESS
+                    raw_rewards=task_rewards, steepness=shared_settings.REWARD_STEEPNESS
                 )
             processed_rewards *= task_config.probability
 
@@ -238,11 +232,11 @@ def apply_steepness(cls, raw_rewards: npt.NDArray[np.float32], steepness: float
             p > 0.5 makes the function more exponential (winner takes all).
         """
         # 6.64385619 = ln(100)/ln(2) -> this way if p = 0.5, the exponent is exactly 1.
-        exponent = (steepness ** 6.64385619) * 100
+        exponent = (steepness**6.64385619) * 100
         raw_rewards = np.array(raw_rewards) / max(1, (np.sum(raw_rewards[raw_rewards > 0]) + 1e-10))
         positive_rewards = np.clip(raw_rewards, 1e-10, np.inf)
         normalised_rewards = positive_rewards / np.max(positive_rewards)
-        post_func_rewards = normalised_rewards ** exponent
+        post_func_rewards = normalised_rewards**exponent
         all_rewards = post_func_rewards / (np.sum(post_func_rewards) + 1e-10)
         all_rewards[raw_rewards <= 0] = raw_rewards[raw_rewards <= 0]
         return all_rewards
@@ -251,13 +245,13 @@ async def run_step(self):
         await asyncio.sleep(0.01)
         try:
             if self.reward_events is None:
-                logger.error(f"No rewards events were found, skipping weight setting")
+                logger.error("No rewards events were found, skipping weight setting")
                 return
 
             final_rewards = await self.merge_task_rewards(self.reward_events)
 
             if final_rewards is None:
-                logger.error(f"No rewards were found, skipping weight setting")
+                logger.error("No rewards were found, skipping weight setting")
                 return
 
             await self._save_rewards(final_rewards)