Upload examples/benchmark/run_evaluation.py with huggingface_hub

examples/benchmark/run_evaluation.py

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+"""Example: Run benchmark evaluation with baseline."""
+import json, sys
+
+# Use the benchmark evaluate script
+sys.path.insert(0, "benchmark")
+from evaluate import load_dataset, load_split, generate_baseline, evaluate_predictions, per_family_metrics
+
+# Load dataset and split
+entries = load_dataset()
+split = load_split("random_80_10_10")
+print(f"Loaded {len(entries):,} entries")
+print(f"Split: train={len(split['train']):,}  val={len(split['val']):,}  test={len(split['test']):,}")
+
+# Generate mean baseline
+predictions = generate_baseline(entries, split, "mean")
+print(f"\nGenerated mean baseline predictions")
+
+# Evaluate
+overall = evaluate_predictions(entries, split, predictions)
+print(f"\nOverall Results:")
+for target, metrics in overall.items():
+    print(f"  {target}: MAE={metrics['mae']:.4f}  R²={metrics['r2']:.4f}  RMSE={metrics['rmse']:.4f}")
+
+# Per-family
+family_results = per_family_metrics(entries, split, predictions)
+print(f"\nPer-Family FE MAE:")
+for fam in sorted(family_results.keys()):
+    fe = family_results[fam].get("FE", {})
+    mae = fe.get("mae", float("nan"))
+    print(f"  {fam:25s}: {mae:.4f}")