| |
| """Aggregate all fold/seed/checkpoint metrics for paper analysis.""" |
|
|
| import json |
| import argparse |
| from pathlib import Path |
|
|
| import pandas as pd |
|
|
| from run_experiment import METHODS, atomic_json |
| from release_support import add_path_arguments |
|
|
|
|
| def main() -> None: |
| parser = argparse.ArgumentParser(description="Summarize existing acquisition diagnostics; no MolAS training or figure reproduction.") |
| add_path_arguments(parser, inputs=False) |
| parser.add_argument("--methods", nargs="+", choices=METHODS, default=list(METHODS)) |
| parser.add_argument("--folds", nargs="+", type=int, choices=range(5), default=list(range(5))) |
| parser.add_argument("--seeds", nargs="+", type=int, default=[0, 1, 2]) |
| parser.add_argument("--smoke-test", action="store_true") |
| args = parser.parse_args() |
| if "plain_mean_ucb" not in args.methods: |
| parser.error("The historical ratio/delta summaries require plain_mean_ucb in --methods") |
| records = [] |
| shares = [] |
| for method in args.methods: |
| for fold in args.folds: |
| for seed in args.seeds: |
| root = args.output_dir / ("smoke" if args.smoke_test else "runs") / method / f"fold{fold}" / f"seed{seed}" |
| for percentage in range(1, 101): |
| label = f"{percentage:03d}pct" |
| reconstruction = json.loads((root / "reconstruction_metrics" / f"metrics_{label}.json").read_text()) |
| runtime = json.loads((root / "runtime_prediction_metrics" / f"metrics_{label}.json").read_text()) |
| records.append({"method": method, "fold": fold, "seed": seed, **reconstruction, **runtime}) |
| if percentage in {10, 20, 30, 40}: |
| distribution = pd.read_csv(root / "algorithm_query_distributions" / f"distribution_{label}.csv") |
| shares.append({ |
| "method": method, "fold": fold, "seed": seed, |
| "percentage": percentage, |
| "max_algorithm_share": distribution["cumulative_share"].max(), |
| "min_algorithm_share": distribution["cumulative_share"].min(), |
| }) |
| frame = pd.DataFrame(records) |
| summary = frame.groupby(["method", "percentage"]).agg( |
| runtime_mean=("cumulative_true_runtime", "mean"), |
| runtime_std=("cumulative_true_runtime", "std"), |
| unqueried_mae_mean=("unqueried_mae", "mean"), |
| unqueried_mae_std=("unqueried_mae", "std"), |
| ndcg_at_3_mean=("ndcg_at_3", "mean"), |
| ndcg_at_3_std=("ndcg_at_3", "std"), |
| top1_mean=("top1_agreement", "mean"), |
| top1_std=("top1_agreement", "std"), |
| vbs_regret_mean=("vbs_regret", "mean"), |
| vbs_regret_std=("vbs_regret", "std"), |
| ).reset_index() |
| baseline = summary[summary.method == "plain_mean_ucb"].set_index("percentage") |
| summary["runtime_ratio_to_plain"] = summary.apply( |
| lambda row: row.runtime_mean / baseline.loc[row.percentage, "runtime_mean"], axis=1 |
| ) |
| summary["mae_delta_to_plain"] = summary.apply( |
| lambda row: row.unqueried_mae_mean - baseline.loc[row.percentage, "unqueried_mae_mean"], axis=1 |
| ) |
| summary["ndcg_delta_to_plain"] = summary.apply( |
| lambda row: row.ndcg_at_3_mean - baseline.loc[row.percentage, "ndcg_at_3_mean"], axis=1 |
| ) |
| output = args.output_dir / ("smoke_summary" if args.smoke_test else "summary") |
| output.mkdir(parents=True, exist_ok=True) |
| atomic_json(output / "summary_scope.json", {"methods": args.methods, "folds": args.folds, |
| "seeds": args.seeds, "smoke_test": args.smoke_test, "records": len(frame), |
| "aggregation": "Descriptive means and standard deviations over the selected fold-seed records; not independent confidence intervals."}) |
| frame.to_csv(output / "all_checkpoint_metrics.csv", index=False) |
| summary.to_csv(output / "method_checkpoint_summary.csv", index=False) |
| pd.DataFrame(shares).to_csv(output / "algorithm_share_key_checkpoints.csv", index=False) |
| print(summary[summary.percentage.isin([10, 20, 30, 40])].to_string(index=False)) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
