Format dataset creation script

create_dataset.py

@@ -14,7 +14,6 @@ import numpy as np
 from datasets import Dataset, DatasetDict, Features, Value, load_dataset
 from huggingface_hub import HfApi
 
-
 SOURCE_URL = (
     "https://static-content.springer.com/esm/"
     "art%3A10.1038%2Fs41586-024-08070-z/"
@@ -87,7 +86,9 @@ def parse_args() -> argparse.Namespace:
     )
     parser.add_argument(
         "--source",
-        default=str(DEFAULT_LOCAL_SOURCE if DEFAULT_LOCAL_SOURCE.exists() else SOURCE_URL),
+        default=str(
+            DEFAULT_LOCAL_SOURCE if DEFAULT_LOCAL_SOURCE.exists() else SOURCE_URL
+        ),
         help="Local source TSV path or public source URL.",
     )
     parser.add_argument(
@@ -104,7 +105,9 @@ def parse_args() -> argparse.Namespace:
     )
     parser.add_argument("--num-proc", type=int, default=8)
     parser.add_argument("--push", action="store_true", help="Push dataset/card/script.")
-    parser.add_argument("--private", action="store_true", help="Create/update as private.")
+    parser.add_argument(
+        "--private", action="store_true", help="Create/update as private."
+    )
     parser.add_argument(
         "--save-local",
         action="store_true",
@@ -182,7 +185,9 @@ def preprocess_split(dataset: Dataset, split: str, num_proc: int) -> Dataset:
         target_arrays = [
             np.asarray(batch[column], dtype=np.float64) for column in TARGET_COLUMNS
         ]
-        se_arrays = [np.asarray(batch[column], dtype=np.float64) for column in SE_COLUMNS]
+        se_arrays = [
+            np.asarray(batch[column], dtype=np.float64) for column in SE_COLUMNS
+        ]
         for column, values in zip(TARGET_COLUMNS, target_arrays):
             output[column] = values.astype(np.float32).tolist()
         for column, values in zip(SE_COLUMNS, se_arrays):
@@ -227,11 +232,13 @@ def add_train_zscores(dataset: Dataset, stats: dict[str, dict[str, float]]) -> D
             values = np.asarray(batch[column], dtype=np.float64)
             spec = stats[column]
             output[f"{column}_train_zscore"] = (
-                (values - spec["mean"]) / spec["std"]
-            ).astype(np.float32).tolist()
+                ((values - spec["mean"]) / spec["std"]).astype(np.float32).tolist()
+            )
         return output
 
-    return dataset.map(add_batch, batched=True, desc=f"Adding z-scores to {dataset[0]['split']}")
+    return dataset.map(
+        add_batch, batched=True, desc=f"Adding z-scores to {dataset[0]['split']}"
+    )
 
 
 def load_source_dataset(args: argparse.Namespace) -> Dataset:
@@ -277,7 +284,9 @@ def build_dataset(args: argparse.Namespace) -> tuple[DatasetDict, dict[str, Any]
 
 
 def collect_metadata(
-    dataset: DatasetDict, zscore_stats: dict[str, dict[str, float]], args: argparse.Namespace
+    dataset: DatasetDict,
+    zscore_stats: dict[str, dict[str, float]],
+    args: argparse.Namespace,
 ) -> dict[str, Any]:
     split_stats: dict[str, dict[str, Any]] = {}
     for split, split_dataset in dataset.items():
@@ -423,9 +432,7 @@ def write_artifacts(args: argparse.Namespace, metadata: dict[str, Any]) -> None:
     (args.output_dir / "metadata.json").write_text(
         json.dumps(metadata, indent=2, sort_keys=True) + "\n", encoding="utf-8"
     )
-    (args.output_dir / "README.md").write_text(
-        render_card(metadata), encoding="utf-8"
-    )
+    (args.output_dir / "README.md").write_text(render_card(metadata), encoding="utf-8")
     shutil.copy2(Path(__file__), args.output_dir / "create_dataset.py")