Changed .keys() to get_keys() in batch class (#1105)

Solves the inconsistency that iter(Batch) is not the same as Batch.keys() by "deprecating" the implicit .keys() method

Closes: #922

test/base/test_batch.py

@@ -556,7 +556,7 @@ def test_batch_standard_compatibility() -> None:
     batch = Batch(a=np.array([[1.0, 2.0], [3.0, 4.0]]), b=Batch(), c=np.array([5.0, 6.0]))
     batch_mean = np.mean(batch)
     assert isinstance(batch_mean, Batch)  # type: ignore  # mypy doesn't know but it works, cf. `batch.rst`
-    assert sorted(batch_mean.keys()) == ["a", "b", "c"]  # type: ignore
+    assert sorted(batch_mean.get_keys()) == ["a", "b", "c"]  # type: ignore
     with pytest.raises(TypeError):
         len(batch_mean)
     assert np.all(batch_mean.a == np.mean(batch.a, axis=0))

test/base/test_buffer.py

@@ -1379,11 +1379,14 @@ def test_custom_key() -> None:
     buffer.add(batch)
     sampled_batch, _ = buffer.sample(1)
     # Check if they have the same keys
-    assert set(batch.keys()) == set(
-        sampled_batch.keys(),
-    ), "Batches have different keys: {} and {}".format(set(batch.keys()), set(sampled_batch.keys()))
+    assert set(batch.get_keys()) == set(
+        sampled_batch.get_keys(),
+    ), "Batches have different keys: {} and {}".format(
+        set(batch.get_keys()),
+        set(sampled_batch.get_keys()),
+    )
     # Compare the values for each key
-    for key in batch.keys():
+    for key in batch.get_keys():
         if isinstance(batch.__dict__[key], np.ndarray) and isinstance(
             sampled_batch.__dict__[key],
             np.ndarray,

tianshou/data/batch.py

@@ -1,6 +1,6 @@
 import pprint
 import warnings
-from collections.abc import Collection, Iterable, Iterator, Sequence
+from collections.abc import Collection, Iterable, Iterator, KeysView, Sequence
 from copy import deepcopy
 from numbers import Number
 from types import EllipsisType
@@ -185,8 +185,8 @@ def alloc_by_keys_diff(
 
     This mainly is an internal method, use it only if you know what you are doing.
     """
-    for key in batch.keys():
-        if key in meta.keys():
+    for key in batch.get_keys():
+        if key in meta.get_keys():
             if isinstance(meta[key], Batch) and isinstance(batch[key], Batch):
                 alloc_by_keys_diff(meta[key], batch[key], size, stack)
             elif isinstance(meta[key], Batch) and meta[key].is_empty():
@@ -441,6 +441,9 @@ class Batch(BatchProtocol):
             result[k] = v
         return result
 
+    def get_keys(self) -> KeysView:
+        return self.__dict__.keys()
+
     def to_list_of_dicts(self) -> list[dict[str, Any]]:
         return [entry.to_dict() for entry in self]
 

tianshou/data/buffer/base.py

@@ -253,12 +253,12 @@ class ReplayBuffer:
         """
         # preprocess batch
         new_batch = Batch()
-        for key in batch.keys():
+        for key in batch.get_keys():
             new_batch.__dict__[key] = batch[key]
         batch = new_batch
         batch.__dict__["done"] = np.logical_or(batch.terminated, batch.truncated)
         assert {"obs", "act", "rew", "terminated", "truncated", "done"}.issubset(
-            batch.keys(),
+            batch.get_keys(),
         )  # important to do after preprocess batch
         stacked_batch = buffer_ids is not None
         if stacked_batch:

tianshou/data/buffer/manager.py

@@ -127,11 +127,11 @@ class ReplayBufferManager(ReplayBuffer):
         """
         # preprocess batch
         new_batch = Batch()
-        for key in set(self._reserved_keys).intersection(batch.keys()):
+        for key in set(self._reserved_keys).intersection(batch.get_keys()):
             new_batch.__dict__[key] = batch[key]
         batch = new_batch
         batch.__dict__["done"] = np.logical_or(batch.terminated, batch.truncated)
-        assert {"obs", "act", "rew", "terminated", "truncated", "done"}.issubset(batch.keys())
+        assert {"obs", "act", "rew", "terminated", "truncated", "done"}.issubset(batch.get_keys())
         if self._save_only_last_obs:
             batch.obs = batch.obs[:, -1]
         if not self._save_obs_next:

tianshou/policy/multiagent/mapolicy.py

@@ -225,7 +225,7 @@ class MultiAgentPolicyManager(BasePolicy):
                 results.append((False, np.array([-1]), Batch(), Batch(), Batch()))
                 continue
             tmp_batch = batch[agent_index]
-            if "rew" in tmp_batch.keys() and isinstance(tmp_batch.rew, np.ndarray):
+            if "rew" in tmp_batch.get_keys() and isinstance(tmp_batch.rew, np.ndarray):
                 # reward can be empty Batch (after initial reset) or nparray.
                 tmp_batch.rew = tmp_batch.rew[:, self.agent_idx[agent_id]]
             if not hasattr(tmp_batch.obs, "mask"):