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"""结合原始样本元数据分析不确定性评估结果。
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在 `analyze_uq_results.py` 的全局统计之外，本脚本会把 UQ CSV 与预处理数据中的
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参数、流量制度等元数据对齐，按制度族、参数区间等维度分组，帮助定位代理模型
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在哪类数值试井样本上更容易给出高误差或高不确定性。
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"""
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from __future__ import annotations
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import argparse
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import csv
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import json
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import sys
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from pathlib import Path
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import joblib
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import numpy as np
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ROOT = Path(__file__).resolve().parents[1]
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sys.path.append(str(ROOT))
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from src.common.experiment_paths import normalize_tag, processed_path_for_tag
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def parse_args() -> argparse.Namespace:
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"""解析 UQ 指标 CSV、processed 数据和输出路径，用于合并样本元数据分析。"""
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parser = argparse.ArgumentParser(description="Join UQ sample metrics with saved metadata")
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parser.add_argument("--processed", type=str, default=None, help="Processed dataset path")
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parser.add_argument("--uq-csv", type=str, default=None, help="sample_uncertainty_metrics.csv path")
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parser.add_argument("--tag", type=str, default="family_random_50k", help="Experiment tag")
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parser.add_argument("--output-dir", type=str, default=None, help="Output directory")
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parser.add_argument("--high-error-rmse", type=float, default=1.0)
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return parser.parse_args()
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def default_uq_csv(tag: str) -> Path:
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"""根据实验标签定位默认的不确定性指标 CSV。"""
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return Path("results") / f"evaluation_{tag}_ensemble_uq" / "sample_uncertainty_metrics.csv"
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def default_output_dir(tag: str) -> Path:
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"""根据实验标签生成当前分析脚本默认的输出目录。"""
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return Path("results") / f"evaluation_{tag}_ensemble_uq_metadata_analysis"
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def save_csv(path: Path, rows: list[dict]) -> None:
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"""把字典行写入 CSV；当没有行时仍写出表头，方便后续脚本读取。"""
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if not rows:
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return
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with open(path, "w", encoding="utf-8-sig", newline="") as f:
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writer = csv.DictWriter(f, fieldnames=list(rows[0].keys()))
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writer.writeheader()
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writer.writerows(rows)
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def summarize_group(rows: list[dict], group_key: str) -> list[dict]:
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"""对一个样本分组计算误差、不确定性和样本数量等汇总指标。"""
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groups: dict[str, list[dict]] = {}
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for row in rows:
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groups.setdefault(str(row[group_key]), []).append(row)
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out = []
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for key, group_rows in sorted(groups.items(), key=lambda item: len(item[1]), reverse=True):
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rmse = np.array([float(r["overall_rmse"]) for r in group_rows], dtype=np.float64)
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unc = np.array([float(r["unc_mean_std"]) for r in group_rows], dtype=np.float64)
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out.append(
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{
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group_key: key,
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"n_samples": len(group_rows),
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"rmse_mean": float(np.mean(rmse)),
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"rmse_median": float(np.median(rmse)),
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"unc_mean": float(np.mean(unc)),
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"unc_median": float(np.median(unc)),
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}
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)
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return out
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def main() -> None:
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"""把 UQ 结果与参数/制度元数据拼接，按工况区域统计误差和不确定性。"""
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args = parse_args()
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tag = normalize_tag(args.tag)
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processed_path = Path(args.processed) if args.processed is not None else processed_path_for_tag(tag)
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uq_csv = Path(args.uq_csv) if args.uq_csv is not None else default_uq_csv(tag or "family_random_50k")
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output_dir = Path(args.output_dir) if args.output_dir is not None else default_output_dir(tag or "family_random_50k")
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output_dir.mkdir(parents=True, exist_ok=True)
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data = joblib.load(processed_path)
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if "schedule_meta_test" not in data or "family_name_test" not in data:
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raise RuntimeError(
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"Processed dataset does not contain schedule metadata. Re-run preprocess on a metadata-rich raw HDF5 first."
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)
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schedule_meta_test = np.asarray(data["schedule_meta_test"], dtype=np.float32)
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family_name_test = np.asarray(data["family_name_test"]).astype(str)
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# source 字段只在混合数据集里存在；普通数据集没有时保持可选。
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source_name_test = np.asarray(data["source_name_test"]).astype(str) if "source_name_test" in data else None
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source_id_test = np.asarray(data["source_id_test"]) if "source_id_test" in data else None
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meta_names = data["meta"].get("schedule_meta_names") or []
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name_to_col = {name: i for i, name in enumerate(meta_names)}
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with open(uq_csv, "r", encoding="utf-8-sig", newline="") as f:
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uq_rows = list(csv.DictReader(f))
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enriched_rows = []
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for row in uq_rows:
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idx = int(row["idx"])
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meta_row = schedule_meta_test[idx]
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enriched = dict(row)
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# UQ CSV 的 idx 对应 processed 测试集下标，因此可直接取测试集元数据补列。
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enriched["family_name"] = family_name_test[idx]
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if source_name_test is not None:
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enriched["source_name"] = source_name_test[idx]
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if source_id_test is not None:
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enriched["source_id"] = int(source_id_test[idx])
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for name, col in name_to_col.items():
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enriched[name] = float(meta_row[col])
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enriched_rows.append(enriched)
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save_csv(output_dir / "uq_samples_with_metadata.csv", enriched_rows)
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family_summary = summarize_group(enriched_rows, "family_name")
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save_csv(output_dir / "summary_by_family.csv", family_summary)
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if source_name_test is not None:
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source_summary = summarize_group(enriched_rows, "source_name")
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save_csv(output_dir / "summary_by_source.csv", source_summary)
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if "n_prod" in name_to_col:
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for row in enriched_rows:
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row["n_prod_group"] = int(round(float(row["n_prod"])))
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# 生产段数常对应不同制度复杂度，单独分组有助于定位 UQ 失效区域。
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n_prod_summary = summarize_group(enriched_rows, "n_prod_group")
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save_csv(output_dir / "summary_by_n_prod.csv", n_prod_summary)
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unc_values = np.array([float(row["unc_mean_std"]) for row in enriched_rows], dtype=np.float64)
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low_unc_threshold = float(np.median(unc_values))
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# 筛出“高误差但低不确定性”的样本，后续可作为困难样本补采样或诊断入口。
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high_error_low_unc = [
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row
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for row in enriched_rows
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if float(row["overall_rmse"]) >= float(args.high_error_rmse)
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and float(row["unc_mean_std"]) <= low_unc_threshold
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]
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high_error_low_unc.sort(key=lambda row: float(row["overall_rmse"]), reverse=True)
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save_csv(output_dir / "high_error_low_unc_with_metadata.csv", high_error_low_unc)
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summary = {
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"processed_path": str(processed_path),
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"uq_csv": str(uq_csv),
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"n_samples": len(enriched_rows),
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"meta_names": meta_names,
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"has_source_name": bool(source_name_test is not None),
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"high_error_low_unc_count": len(high_error_low_unc),
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}
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with open(output_dir / "metadata_join_summary.json", "w", encoding="utf-8") as f:
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json.dump(summary, f, ensure_ascii=False, indent=2)
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print("UQ metadata join analysis complete.")
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print(f"Output dir: {output_dir}")
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print(f"High-error low-unc count={len(high_error_low_unc)}")
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if __name__ == "__main__":
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main()
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