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5000a45 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 | #!/usr/bin/env python3
from __future__ import annotations
import argparse
import json
import sys
from pathlib import Path
from typing import Any, Dict, List
import numpy as np
ROOT = Path(__file__).resolve().parents[1]
if str(ROOT) not in sys.path:
sys.path.append(str(ROOT))
from scripts.run_random_baseline import run_random_baseline
from scripts.run_surrogate_baseline import run_surrogate_baseline
def _average_metric_dict(records: List[Dict[str, float]]) -> Dict[str, float]:
if not records:
return {}
keys = sorted({key for record in records for key in record.keys()}, key=lambda value: int(value))
return {
key: float(np.mean(np.asarray([record[key] for record in records if key in record], dtype=np.float32)))
for key in keys
}
def _summarize_runs(runs: List[Dict[str, Any]]) -> Dict[str, Any]:
mean_regret_records = [run["aggregate_metrics"].get("mean_regret_at", {}) for run in runs]
median_regret_records = [run["aggregate_metrics"].get("median_regret_at", {}) for run in runs]
auc_values = [run["aggregate_metrics"].get("mean_auc_regret") for run in runs]
oracle_hit_values = [run["aggregate_metrics"].get("oracle_hit_rate_final") for run in runs]
return {
"mean_regret_at": _average_metric_dict(mean_regret_records),
"median_regret_at": _average_metric_dict(median_regret_records),
"mean_best_so_far_auc": float(np.mean(np.asarray(auc_values, dtype=np.float32))) if auc_values else None,
"mean_oracle_hit_rate_final": float(np.mean(np.asarray(oracle_hit_values, dtype=np.float32))) if oracle_hit_values else None,
}
def _evaluate_section(
section_name: str,
split: Dict[str, Any],
measurement_path: str,
episodes: int,
budget: int,
seed: int,
acquisition: str,
beta: float,
xi: float,
) -> Dict[str, Any]:
train_tasks = split["train_tasks"]
test_tasks = split["test_tasks"]
random_runs: List[Dict[str, Any]] = []
surrogate_runs: List[Dict[str, Any]] = []
for idx, task in enumerate(test_tasks):
task_seed = seed + idx * 1000
random_runs.append(
run_random_baseline(
task=task,
episodes=episodes,
budget=budget,
seed=task_seed,
measurement_path=measurement_path,
)
)
surrogate_runs.append(
run_surrogate_baseline(
task=task,
episodes=episodes,
budget=budget,
seed=task_seed,
measurement_path=measurement_path,
train_task_ids=train_tasks,
acquisition=acquisition,
beta=beta,
xi=xi,
)
)
return {
"section": section_name,
"train_tasks": train_tasks,
"test_tasks": test_tasks,
"random_summary": _summarize_runs(random_runs),
"surrogate_summary": _summarize_runs(surrogate_runs),
"task_runs": {
"random": random_runs,
"surrogate": surrogate_runs,
},
}
def main() -> None:
parser = argparse.ArgumentParser(description="Evaluate random vs surrogate on shape and family holdout splits.")
parser.add_argument("--measurement-path", type=str, default="data/autotune_measurements.csv")
parser.add_argument("--splits", type=Path, default=Path("data/benchmark_splits.json"))
parser.add_argument("--episodes", type=int, default=20)
parser.add_argument("--budget", type=int, default=6)
parser.add_argument("--seed", type=int, default=2)
parser.add_argument("--acquisition", choices=("mean", "ucb", "ei"), default="ucb")
parser.add_argument("--beta", type=float, default=2.0)
parser.add_argument("--xi", type=float, default=0.0)
parser.add_argument("--output", type=Path, default=Path("outputs/generalization_eval.json"))
args = parser.parse_args()
splits = json.loads(args.splits.read_text(encoding="utf-8"))
sections = {
"shape_generalization": splits["shape_generalization"],
"family_holdout": splits["family_holdout"],
}
results = {
name: _evaluate_section(
section_name=name,
split=section,
measurement_path=args.measurement_path,
episodes=args.episodes,
budget=args.budget,
seed=args.seed,
acquisition=args.acquisition,
beta=args.beta,
xi=args.xi,
)
for name, section in sections.items()
}
summary = {
"measurement_path": args.measurement_path,
"splits_path": str(args.splits),
"episodes": args.episodes,
"budget": args.budget,
"acquisition": args.acquisition,
"beta": args.beta,
"xi": args.xi,
"results": results,
}
args.output.parent.mkdir(parents=True, exist_ok=True)
with args.output.open("w", encoding="utf-8") as handle:
json.dump(summary, handle, indent=2)
print(json.dumps(summary, indent=2))
if __name__ == "__main__":
main()
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