Feat/expand add features

examples/dataset/use_dataset_tools.py

@@ -30,9 +30,10 @@
 import numpy as np
 
 from lerobot.datasets.dataset_tools import (
-    add_feature,
+    add_features,
     delete_episodes,
     merge_datasets,
+    modify_features,
     remove_feature,
     split_dataset,
 )
@@ -57,50 +58,56 @@ def main():
     print(f"Train split: {splits['train'].meta.total_episodes} episodes")
     print(f"Val split: {splits['val'].meta.total_episodes} episodes")
 
-    print("\n3. Adding a reward feature...")
+    print("\n3. Adding features...")
 
     reward_values = np.random.randn(dataset.meta.total_frames).astype(np.float32)
-    dataset_with_reward = add_feature(
-        dataset,
-        feature_name="reward",
-        feature_values=reward_values,
-        feature_info={
-            "dtype": "float32",
-            "shape": (1,),
-            "names": None,
-        },
-        repo_id="lerobot/pusht_with_reward",
-    )
 
     def compute_success(row_dict, episode_index, frame_index):
         episode_length = 10
         return float(frame_index >= episode_length - 10)
 
-    dataset_with_success = add_feature(
-        dataset_with_reward,
-        feature_name="success",
-        feature_values=compute_success,
-        feature_info={
-            "dtype": "float32",
-            "shape": (1,),
-            "names": None,
+    dataset_with_features = add_features(
+        dataset,
+        features={
+            "reward": (
+                reward_values,
+                {"dtype": "float32", "shape": (1,), "names": None},
+            ),
+            "success": (
+                compute_success,
+                {"dtype": "float32", "shape": (1,), "names": None},
+            ),
         },
-        repo_id="lerobot/pusht_with_reward_and_success",
+        repo_id="lerobot/pusht_with_features",
     )
 
-    print(f"New features: {list(dataset_with_success.meta.features.keys())}")
+    print(f"New features: {list(dataset_with_features.meta.features.keys())}")
 
     print("\n4. Removing the success feature...")
     dataset_cleaned = remove_feature(
-        dataset_with_success, feature_names="success", repo_id="lerobot/pusht_cleaned"
+        dataset_with_features, feature_names="success", repo_id="lerobot/pusht_cleaned"
     )
     print(f"Features after removal: {list(dataset_cleaned.meta.features.keys())}")
 
-    print("\n5. Merging train and val splits back together...")
+    print("\n5. Using modify_features to add and remove features simultaneously...")
+    dataset_modified = modify_features(
+        dataset_with_features,
+        add_features={
+            "discount": (
+                np.ones(dataset.meta.total_frames, dtype=np.float32) * 0.99,
+                {"dtype": "float32", "shape": (1,), "names": None},
+            ),
+        },
+        remove_features="reward",
+        repo_id="lerobot/pusht_modified",
+    )
+    print(f"Modified features: {list(dataset_modified.meta.features.keys())}")
+
+    print("\n6. Merging train and val splits back together...")
     merged = merge_datasets([splits["train"], splits["val"]], output_repo_id="lerobot/pusht_merged")
     print(f"Merged dataset: {merged.meta.total_episodes} episodes")
 
-    print("\n6. Complex workflow example...")
+    print("\n7. Complex workflow example...")
 
     if len(dataset.meta.camera_keys) > 1:
         camera_to_remove = dataset.meta.camera_keys[0]