File size: 1,127 Bytes
6714b41 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | """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}")
|