Fix exception cause in base_class.py (#940)

docs/misc/changelog.rst

@@ -32,6 +32,7 @@ Bug Fixes:
 - Fixed a bug where ``EvalCallback`` would crash when trying to synchronize ``VecNormalize`` stats when observation normalization was disabled
 - Added a check for unbounded actions
 - Fixed issues due to newer version of protobuf (tensorboard) and sphinx
+- Fix exception causes all over the codebase (@cool-RR)
 
 Deprecations:
 ^^^^^^^^^^^^^
@@ -978,4 +979,4 @@ And all the contributors:
 @wkirgsn @AechPro @CUN-bjy @batu @IljaAvadiev @timokau @kachayev @cleversonahum
 @eleurent @ac-93 @cove9988 @theDebugger811 @hsuehch @Demetrio92 @thomasgubler @IperGiove @ScheiklP
 @simoninithomas @armandpl @manuel-delverme @Gautam-J @gianlucadecola @buoyancy99 @caburu @xy9485
-@Gregwar @ycheng517 @quantitative-technologies @bcollazo @git-thor @TibiGG
+@Gregwar @ycheng517 @quantitative-technologies @bcollazo @git-thor @TibiGG @cool-RR

stable_baselines3/common/base_class.py

@@ -628,11 +628,11 @@ class BaseAlgorithm(ABC):
             attr = None
             try:
                 attr = recursive_getattr(self, name)
-            except Exception:
+            except Exception as e:
                 # What errors recursive_getattr could throw? KeyError, but
                 # possible something else too (e.g. if key is an int?).
                 # Catch anything for now.
-                raise ValueError(f"Key {name} is an invalid object name.")
+                raise ValueError(f"Key {name} is an invalid object name.") from e
 
             if isinstance(attr, th.optim.Optimizer):
                 # Optimizers do not support "strict" keyword...

stable_baselines3/common/callbacks.py

@@ -380,12 +380,12 @@ class EvalCallback(EventCallback):
             if self.model.get_vec_normalize_env() is not None:
                 try:
                     sync_envs_normalization(self.training_env, self.eval_env)
-                except AttributeError:
+                except AttributeError as e:
                     raise AssertionError(
                         "Training and eval env are not wrapped the same way, "
                         "see https://stable-baselines3.readthedocs.io/en/master/guide/callbacks.html#evalcallback "
                         "and warning above."
-                    )
+                    ) from e
 
             # Reset success rate buffer
             self._is_success_buffer = []

stable_baselines3/common/env_checker.py

@@ -147,7 +147,7 @@ def _check_returned_values(env: gym.Env, observation_space: spaces.Space, action
             try:
                 _check_obs(obs[key], observation_space.spaces[key], "reset")
             except AssertionError as e:
-                raise AssertionError(f"Error while checking key={key}: " + str(e))
+                raise AssertionError(f"Error while checking key={key}: " + str(e)) from e
     else:
         _check_obs(obs, observation_space, "reset")
 
@@ -166,7 +166,7 @@ def _check_returned_values(env: gym.Env, observation_space: spaces.Space, action
             try:
                 _check_obs(obs[key], observation_space.spaces[key], "step")
             except AssertionError as e:
-                raise AssertionError(f"Error while checking key={key}: " + str(e))
+                raise AssertionError(f"Error while checking key={key}: " + str(e)) from e
 
     else:
         _check_obs(obs, observation_space, "step")

stable_baselines3/common/noise.py

@@ -105,8 +105,8 @@ class VectorizedActionNoise(ActionNoise):
         try:
             self.n_envs = int(n_envs)
             assert self.n_envs > 0
-        except (TypeError, AssertionError):
-            raise ValueError(f"Expected n_envs={n_envs} to be positive integer greater than 0")
+        except (TypeError, AssertionError) as e:
+            raise ValueError(f"Expected n_envs={n_envs} to be positive integer greater than 0") from e
 
         self.base_noise = base_noise
         self.noises = [copy.deepcopy(self.base_noise) for _ in range(n_envs)]

stable_baselines3/common/off_policy_algorithm.py

@@ -157,8 +157,10 @@ class OffPolicyAlgorithm(BaseAlgorithm):
 
             try:
                 train_freq = (train_freq[0], TrainFrequencyUnit(train_freq[1]))
-            except ValueError:
-                raise ValueError(f"The unit of the `train_freq` must be either 'step' or 'episode' not '{train_freq[1]}'!")
+            except ValueError as e:
+                raise ValueError(
+                    f"The unit of the `train_freq` must be either 'step' or 'episode' not '{train_freq[1]}'!"
+                ) from e
 
             if not isinstance(train_freq[0], int):
                 raise ValueError(f"The frequency of `train_freq` must be an integer and not {train_freq[0]}")

stable_baselines3/common/save_util.py

@@ -206,8 +206,8 @@ def open_path(path: Union[str, pathlib.Path, io.BufferedIOBase], mode: str, verb
     mode = mode.lower()
     try:
         mode = {"write": "w", "read": "r", "w": "w", "r": "r"}[mode]
-    except KeyError:
-        raise ValueError("Expected mode to be either 'w' or 'r'.")
+    except KeyError as e:
+        raise ValueError("Expected mode to be either 'w' or 'r'.") from e
     if ("w" == mode) and not path.writable() or ("r" == mode) and not path.readable():
         e1 = "writable" if "w" == mode else "readable"
         raise ValueError(f"Expected a {e1} file.")
@@ -441,7 +441,7 @@ def load_from_zip_file(
                         # State dicts. Store into params dictionary
                         # with same name as in .zip file (without .pth)
                         params[os.path.splitext(file_path)[0]] = th_object
-    except zipfile.BadZipFile:
+    except zipfile.BadZipFile as e:
         # load_path wasn't a zip file
-        raise ValueError(f"Error: the file {load_path} wasn't a zip-file")
+        raise ValueError(f"Error: the file {load_path} wasn't a zip-file") from e
     return data, params, pytorch_variables

stable_baselines3/her/her_replay_buffer.py

@@ -28,13 +28,13 @@ def get_time_limit(env: VecEnv, current_max_episode_length: Optional[int]) -> in
             if current_max_episode_length is None:
                 raise AttributeError
         # if not available check if a valid value was passed as an argument
-        except AttributeError:
+        except AttributeError as e:
             raise ValueError(
                 "The max episode length could not be inferred.\n"
                 "You must specify a `max_episode_steps` when registering the environment,\n"
                 "use a `gym.wrappers.TimeLimit` wrapper "
                 "or pass `max_episode_length` to the model constructor"
-            )
+            ) from e
     return current_max_episode_length