Deleted long deprecated functionality, removed unused warning module

There's better ways to deal with deprecations that we shall use in the future

tianshou/env/worker/base.py

@@ -6,7 +6,6 @@ import gymnasium as gym
 import numpy as np
 
 from tianshou.env.utils import gym_new_venv_step_type
-from tianshou.utils import deprecation
 
 
 class EnvWorker(ABC):
@@ -27,6 +26,7 @@ class EnvWorker(ABC):
     def set_env_attr(self, key: str, value: Any) -> None:
         pass
 
+    @abstractmethod
     def send(self, action: np.ndarray | None) -> None:
         """Send action signal to low-level worker.
 
@@ -34,17 +34,6 @@ class EnvWorker(ABC):
         it indicates "step" signal. The paired return value from "recv"
         function is determined by such kind of different signal.
         """
-        if hasattr(self, "send_action"):
-            deprecation(
-                "send_action will soon be deprecated. "
-                "Please use send and recv for your own EnvWorker.",
-            )
-            if action is None:
-                self.is_reset = True
-                self.result = self.reset()
-            else:
-                self.is_reset = False
-                self.send_action(action)
 
     def recv(self) -> gym_new_venv_step_type | tuple[np.ndarray, dict]:
         """Receive result from low-level worker.
@@ -54,13 +43,6 @@ class EnvWorker(ABC):
         info) or (obs, rew, terminated, truncated, info), based on whether
         the environment is using the old step API or the new one.
         """
-        if hasattr(self, "get_result"):
-            deprecation(
-                "get_result will soon be deprecated. "
-                "Please use send and recv for your own EnvWorker.",
-            )
-            if not self.is_reset:
-                self.result = self.get_result()
         return self.result
 
     @abstractmethod

tianshou/trainer/base.py

@@ -26,7 +26,6 @@ from tianshou.utils import (
     DummyTqdm,
     LazyLogger,
     MovAvg,
-    deprecation,
     tqdm_config,
 )
 from tianshou.utils.logging import set_numerical_fields_to_precision
@@ -76,7 +75,7 @@ class BaseTrainer(ABC):
         signature ``f(num_epoch: int, step_idx: int) -> None``.
     :param save_best_fn: a hook called when the undiscounted average mean
         reward in evaluation phase gets better, with the signature
-        ``f(policy: BasePolicy) -> None``. It was ``save_fn`` previously.
+        ``f(policy: BasePolicy) -> None``.
     :param save_checkpoint_fn: a function to save training process and
         return the saved checkpoint path, with the signature ``f(epoch: int,
         env_step: int, gradient_step: int) -> str``; you can save whatever you want.
@@ -173,16 +172,7 @@ class BaseTrainer(ABC):
         verbose: bool = True,
         show_progress: bool = True,
         test_in_train: bool = True,
-        save_fn: Callable[[BasePolicy], None] | None = None,
     ):
-        if save_fn:
-            deprecation(
-                "save_fn in trainer is marked as deprecated and will be "
-                "removed in the future. Please use save_best_fn instead.",
-            )
-            assert save_best_fn is None
-            save_best_fn = save_fn
-
         self.policy = policy
 
         if buffer is not None:

tianshou/utils/__init__.py

@@ -6,7 +6,6 @@ from tianshou.utils.logger.wandb import WandbLogger
 from tianshou.utils.lr_scheduler import MultipleLRSchedulers
 from tianshou.utils.progress_bar import DummyTqdm, tqdm_config
 from tianshou.utils.statistics import MovAvg, RunningMeanStd
-from tianshou.utils.warning import deprecation
 
 __all__ = [
     "MovAvg",
@@ -17,6 +16,5 @@ __all__ = [
     "TensorboardLogger",
     "LazyLogger",
     "WandbLogger",
-    "deprecation",
     "MultipleLRSchedulers",
 ]

tianshou/utils/warning.py

@@ -1,8 +0,0 @@
-import warnings
-
-warnings.simplefilter("once", DeprecationWarning)
-
-
-def deprecation(msg: str) -> None:
-    """Deprecation warning wrapper."""
-    warnings.warn(msg, category=DeprecationWarning, stacklevel=2)