feat(datasets): warn when skipping stats for zero-width features

Per review, log a warning when compute_episode_stats skips a feature with a
zero-width shape, so users know stats were intentionally not computed for it.

src/lerobot/datasets/compute_stats.py

@@ -520,6 +520,9 @@ def compute_episode_stats(
             continue
 
         if any(d == 0 for d in features[key].get("shape", ())):
+            logging.warning(
+                f"Skipping stats for feature '{key}' with a zero-width shape {features[key]['shape']}."
+            )
             continue
 
         if features[key]["dtype"] in ["image", "video"]:

tests/datasets/test_compute_stats.py

@@ -13,6 +13,7 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+import logging
 from unittest.mock import patch
 
 import numpy as np
@@ -687,8 +688,8 @@ def test_compute_episode_stats_string_features_skipped():
     assert "q01" in stats["action"]
 
 
-def test_compute_episode_stats_zero_width_features_skipped():
-    """Test that features with a zero-width dim (e.g. shape=(0,)) are skipped."""
+def test_compute_episode_stats_zero_width_features_skipped(caplog):
+    """Test that features with a zero-width dim (e.g. shape=(0,)) are skipped with a warning."""
     episode_data = {
         "empty": np.zeros((100, 0), dtype=np.float32),  # Zero-width feature
         "action": np.random.normal(0, 1, (100, 5)),
@@ -698,13 +699,12 @@ def test_compute_episode_stats_zero_width_features_skipped():
         "action": {"dtype": "float32", "shape": (5,)},
     }
 
-    stats = compute_episode_stats(
-        episode_data,
-        features,
-    )
+    with caplog.at_level(logging.WARNING):
+        stats = compute_episode_stats(episode_data, features)
 
-    # Zero-width features should be skipped
+    # Zero-width features should be skipped with a warning, others computed as usual
     assert "empty" not in stats
+    assert "empty" in caplog.text
     assert "action" in stats
     assert "q01" in stats["action"]
     assert stats["action"]["mean"].shape == (5,)