Upload create_pq.py

create_pq.py

@@ -0,0 +1,54 @@
+#!/usr/bin/env python3
+# /// script
+# requires-python = ">=3.10"
+# dependencies = [
+#     "fastparquet",
+#     "pandas",
+#     "pathlib",
+#     "pyarrow",
+# ]
+# ///
+"""
+Create parquet files for config subsets of the VSI-Bench dataset.
+* debiased: all examples not pruned by Iterative Bias Pruning (aka VSI-Bench-Debiased)
+* pruned: all examples pruned by Iterative Bias Pruning
+
+> [!NOTE]
+> If you do not pass `index=False`, the parquet files will have a `__index_level_0__` column
+"""
+
+import pandas as pd
+from pathlib import Path
+
+script_dir = Path(__file__).parent
+pruned_ids_path = script_dir / "pruned_ids.txt"
+test_jsonl_path = script_dir / "test.jsonl"
+pq_debiased_path = script_dir / "test_debiased.parquet"
+pq_pruned_path = script_dir / "test_pruned.parquet"
+
+print("Creating parquet files...")
+
+print(f"Loading pruned ids from '{pruned_ids_path}'...")
+with open(pruned_ids_path, "r") as f:
+    pruned_ids = f.read().splitlines()
+print(f"    -> Loaded {len(pruned_ids)} pruned ids.")
+
+print(f"Loading test data from '{test_jsonl_path}'...")
+df = pd.read_json(str(test_jsonl_path), lines=True)
+print(f"    -> Loaded {len(df)} examples.")
+df["pruned"] = df["id"].astype(str).isin(pruned_ids)
+print(f"    -> Added pruned column.")
+
+# save the debiased and pruned subsets separately to parquet files
+df_debiased = df[~df["pruned"]]
+df_pruned = df[df["pruned"]]
+
+print(f"Saving debiased examples to '{pq_debiased_path}'...")
+df_debiased.to_parquet(pq_debiased_path, index=False)
+print(f"    -> Saved {len(df_debiased)} debiased examples.")
+
+print(f"Saving pruned examples to '{pq_pruned_path}'...")
+df_pruned.to_parquet(pq_pruned_path, index=False)
+print(f"    -> Saved {len(df_pruned)} pruned examples.")
+
+print("Done.")