File size: 4,685 Bytes
68442cd | 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 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 | #!/usr/bin/env python
"""
Evaluate all Phase A4 hyperparameter configs.
Find best configuration for achieving 40%+ success.
"""
from __future__ import annotations
import argparse
import json
import subprocess
import sys
from pathlib import Path
def main(argv: list[str] | None = None) -> int:
parser = argparse.ArgumentParser(
description="Evaluate all Phase A4 hyperparameter configs"
)
parser.add_argument("--config-dir", type=Path,
default=Path("/scratch/knguy52/dovla/experiments/phase_a4_hparam_sweep"),
help="Phase A4 configs directory")
parser.add_argument("--dataset", type=Path,
default=Path("/scratch/knguy52/dovla/experiments/maniskill_presuccess_six_task_collection"),
help="Dataset directory")
parser.add_argument("--out", type=Path,
default=Path("reports/phase_a4_results.json"),
help="Output JSON path")
args = parser.parse_args(argv)
print("=" * 70)
print("Phase A4 Hyperparameter Evaluation")
print("=" * 70)
print(f"Config dir: {args.config_dir}")
print(f"Dataset: {args.dataset}")
print()
configs = sorted([d for d in args.config_dir.iterdir() if d.is_dir()])
print(f"Found {len(configs)} configs to evaluate")
print()
results = {}
for config_dir in configs:
config_name = config_dir.name
checkpoint = config_dir / "best.pt"
eval_out = config_dir / "lattice_eval.json"
print(f"Evaluating: {config_name}")
if not checkpoint.exists():
print(f" ⚠️ Checkpoint not found: {checkpoint}")
results[config_name] = {"status": "missing_checkpoint"}
continue
# Run evaluation
cmd = [
sys.executable, "scripts/eval_lattice_checkpoint.py",
"--checkpoint", str(checkpoint),
"--dataset", str(args.dataset),
"--out", str(eval_out),
"--all-groups",
"--device", "cpu"
]
try:
subprocess.run(cmd, check=True, capture_output=True, timeout=600)
print(f" ✓ Evaluation complete")
if eval_out.exists():
with open(eval_out) as f:
data = json.load(f)
# Extract key metrics
results[config_name] = {
"status": "complete",
"selected_success_rate": data.get("selected_success_rate",
data.get("policy_rollout_success_rate", 0)),
"top1_action_selection": data.get("top1_action_selection", 0),
"pairwise_ranking_accuracy": data.get("pairwise_ranking_accuracy", 0),
"selection_regret": data.get("selection_regret", 0),
"oracle_success_rate": data.get("oracle_success_rate", 0)
}
print(f" Success: {results[config_name]['selected_success_rate']:.4f}")
else:
results[config_name] = {"status": "no_output"}
except subprocess.CalledProcessError as e:
print(f" ✗ Evaluation failed: {e}")
results[config_name] = {"status": "failed", "error": str(e)}
except subprocess.TimeoutExpired:
print(f" ⏰ Timeout")
results[config_name] = {"status": "timeout"}
print()
# Summary
print("=" * 70)
print("SUMMARY")
print("=" * 70)
print()
successful = [r for r in results.values() if r.get("status") == "complete"]
if successful:
# Sort by selected success rate
sorted_results = sorted(successful,
key=lambda x: x["selected_success_rate"],
reverse=True)
print("Top configurations:")
for i, r in enumerate(sorted_results[:5], 1):
print(f" {i}. {r['selected_success_rate']:.4f} (top1: {r['top1_action_selection']:.4f})")
best = sorted_results[0]
print()
print(f"Best config: {best['selected_success_rate']:.4f}")
print(f"Target 40%: {'✅ ACHIEVED' if best['selected_success_rate'] >= 0.40 else '❌ NOT YET'}")
# Save results
args.out.parent.mkdir(parents=True, exist_ok=True)
with open(args.out, "w") as f:
json.dump({
"configs": results,
"best_by_success": max(successful, key=lambda x: x["selected_success_rate"]) if successful else None
}, f, indent=2)
print()
print(f"✅ Results saved to: {args.out}")
return 0
if __name__ == "__main__":
sys.exit(main())
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