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"""验证正演代理模型是否保留自动拟合局部排序。
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脚本围绕一个目标参数点采样局部候选，对每个候选同时计算真实数值求解器目标函数和
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代理模型目标函数，并统计 Pearson/Spearman 相关性、Top-K 命中和保留比例表现。
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它直接回答“代理模型能否用于 PSO 候选预筛选”这个问题。
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"""
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# pylint: disable=import-error,wrong-import-position,invalid-name,too-many-arguments,too-many-positional-arguments,too-many-locals,too-many-branches,too-many-statements,broad-exception-caught,no-member
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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 math
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import sys
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from pathlib import Path
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ROOT = Path(__file__).resolve().parents[1]
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sys.path.append(str(ROOT))
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import joblib
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import matplotlib.pyplot as plt
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import numpy as np
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import torch
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from src.common.config import Config
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from src.common.experiment_paths import config_for_stage, model_checkpoint_for_tag, normalize_tag, processed_path_for_tag
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from src.data.curve_processing import clean_curve_for_dataset, is_valid_curve, resample_curve_to_features
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from src.data.param_features import param_feature_transform_from_meta, transform_param_features
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from src.data.params import Params, Schedule
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from src.data.runner_client import CppRunner, read_result_bin
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from src.data.schedule_features import build_schedule_model_vector
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from src.evaluation.autofit_objective import dual_log_objective
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from src.models.forward_surrogate import ForwardSurrogate
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DEFAULT_CASE = {
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"config": "configs/data_gen_family_random.yaml",
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"tag": "family_random_mixed_50k",
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"params": {
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"k": 0.025,
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"skin": 0.0,
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"wellboreC": 0.01,
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"phi": 0.0245,
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"h": 9.144,
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"Cf": 0.0004315,
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},
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}
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def parse_args() -> argparse.Namespace:
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"""解析单目标局部排序验证所需的扰动范围、候选数量、模型和输出 CSV。"""
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parser = argparse.ArgumentParser(
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description="Validate whether surrogate preserves local autofit objective ranking around a target case"
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)
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parser.add_argument("--config", type=str, default=DEFAULT_CASE["config"])
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parser.add_argument("--stage", choices=["fixed_case", "case_neighborhood", "family_random", "family_random_v2_q"], default=None)
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parser.add_argument("--processed", type=str, default=None)
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parser.add_argument("--model", type=str, default=None)
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parser.add_argument("--output-dir", type=str, default=None)
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parser.add_argument("--tag", type=str, default=DEFAULT_CASE["tag"])
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parser.add_argument("--well-index", type=int, default=0)
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parser.add_argument("--solver-timeout", type=int, default=120)
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parser.add_argument("--n-candidates", type=int, default=64)
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parser.add_argument("--seed", type=int, default=42)
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parser.add_argument("--max-attempts-factor", type=int, default=4)
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parser.add_argument(
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"--span-frac",
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type=float,
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default=0.08,
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help="Local box width as a fraction of each parameter range in transformed space",
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)
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parser.add_argument(
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"--reuse-runner",
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action="store_true",
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help="Reuse one long-lived runner server across candidates; faster but less robust",
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)
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parser.add_argument("--k", type=float, default=DEFAULT_CASE["params"]["k"])
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parser.add_argument("--skin", type=float, default=DEFAULT_CASE["params"]["skin"])
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parser.add_argument("--wellboreC", type=float, default=DEFAULT_CASE["params"]["wellboreC"])
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parser.add_argument("--phi", type=float, default=DEFAULT_CASE["params"]["phi"])
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parser.add_argument("--h", type=float, default=DEFAULT_CASE["params"]["h"])
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parser.add_argument("--Cf", type=float, default=DEFAULT_CASE["params"]["Cf"])
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return parser.parse_args()
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def infer_curve_layout(meta: dict, curve_dim: int) -> dict:
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"""从元数据读取曲线分段布局；旧数据没有布局时按压力/导数/斜率三等分回退。"""
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curve_layout = meta.get("curve_layout")
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if curve_layout is not None:
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return curve_layout
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n_time_points = curve_dim // 3
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return {
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"n_time_points": int(n_time_points),
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"parts": [
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{"name": "log_pressure", "start": 0, "end": n_time_points},
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{"name": "log_derivative", "start": n_time_points, "end": 2 * n_time_points},
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{"name": "slope", "start": 2 * n_time_points, "end": 3 * n_time_points},
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],
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}
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def resolve_case_schedule(cfg: Config) -> Schedule:
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"""解析局部排序验证目标案例的流量制度，支持命令行覆盖配置默认值。"""
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schedule_cfg = cfg.raw["schedule"]["case_schedule"]
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time_q = list(map(float, schedule_cfg["timeQ"]))
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q = list(map(float, schedule_cfg["q"]))
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policy = cfg.raw["schedule"].get("section_policy", {}) or {}
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mode = str(policy.get("mode", "fixed_last")).lower()
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n_sections = len(time_q)
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if mode == "fixed_last":
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section_index = n_sections
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elif mode == "fixed_value":
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section_index = int(np.clip(int(policy.get("fixed_value", n_sections)), 1, n_sections))
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else:
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section_index = int(np.clip(int(schedule_cfg.get("default_section_index", n_sections)), 1, n_sections))
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schedule = Schedule(sectionIndex=section_index, timeQ=time_q, q=q)
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if not schedule.validate():
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raise ValueError("Invalid case_schedule in config")
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return schedule
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def resolve_paths(args: argparse.Namespace) -> tuple[Config, Path, Path, Path]:
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"""解析局部排序验证所需的配置、模型、预处理数据和输出目录。"""
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tag = normalize_tag(args.tag)
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config_path = args.config
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if config_path is None:
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config_path = str(config_for_stage(args.stage) or Path(DEFAULT_CASE["config"]))
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processed_path = (Path(args.processed) if args.processed is not None else processed_path_for_tag(tag)).resolve()
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model_path = (Path(args.model) if args.model is not None else model_checkpoint_for_tag(tag, use_schedule=True)).resolve()
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if args.output_dir is not None:
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output_dir = Path(args.output_dir).resolve()
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else:
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output_dir = (Path("results") / f"autofit_local_validation_{tag}").resolve()
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return Config(config_path), processed_path, model_path, output_dir
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def build_params_from_args(args: argparse.Namespace, schedule: Schedule) -> Params:
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"""根据命令行参数和默认值构造目标 Params 对象。"""
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return Params(
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k=float(args.k),
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skin=float(args.skin),
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wellboreC=float(args.wellboreC),
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phi=float(args.phi),
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h=float(args.h),
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Cf=float(args.Cf),
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schedule=schedule,
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)
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def build_schedule_vector(cfg: Config, schedule: Schedule) -> np.ndarray:
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"""把 Schedule 编码成正演代理模型可直接接收的流量制度特征向量。"""
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return build_schedule_model_vector(cfg, schedule)
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def load_model(checkpoint_path: Path) -> tuple[ForwardSurrogate, bool, torch.device]:
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"""加载模型检查点，按保存的维度和超参数重建网络并切换到评估模式。"""
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checkpoint = torch.load(checkpoint_path, map_location="cpu")
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use_schedule = bool(checkpoint.get("use_schedule", True))
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model = ForwardSurrogate(
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param_dim=int(checkpoint["param_dim"]),
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schedule_dim=int(checkpoint["schedule_dim"]),
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curve_dim=int(checkpoint["curve_dim"]),
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hidden_dim=int(checkpoint["hidden_dim"]),
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dropout=float(checkpoint["dropout"]),
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use_schedule=use_schedule,
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)
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model.load_state_dict(checkpoint["model_state_dict"])
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model.eval()
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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model = model.to(device)
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return model, use_schedule, device
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def predict_surrogate_curve(
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processed: dict,
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model: ForwardSurrogate,
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device: torch.device,
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use_schedule: bool,
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params: Params,
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schedule: Schedule,
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cfg: Config,
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) -> np.ndarray:
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"""使用正演代理模型预测单个参数和流量制度对应的完整曲线。"""
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scaler_params = processed["scaler_params"]
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scaler_schedule = processed["scaler_schedule"]
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scaler_curve = processed["scaler_curve"]
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param_transform = param_feature_transform_from_meta(processed.get("meta", {}))
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params_vec = np.asarray(
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[params.k, params.skin, params.wellboreC, params.phi, params.h, params.Cf],
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dtype=np.float32,
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).reshape(1, -1)
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schedule_vec = build_schedule_vector(cfg, schedule).reshape(1, -1)
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params_x = scaler_params.transform(transform_param_features(params_vec, param_transform)).astype(np.float32)
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schedule_x = scaler_schedule.transform(schedule_vec).astype(np.float32)
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with torch.no_grad():
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params_t = torch.tensor(params_x, dtype=torch.float32, device=device)
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schedule_t = torch.tensor(schedule_x, dtype=torch.float32, device=device) if use_schedule else None
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pred_scaled = model(params_t, schedule_t).cpu().numpy()
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return scaler_curve.inverse_transform(pred_scaled)[0].astype(np.float32)
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def run_solver_and_extract_curve(
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runner: CppRunner,
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cfg: Config,
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params: Params,
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well_index: int,
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timeout: int,
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) -> tuple[np.ndarray, dict]:
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"""调用 C++ 求解器运行一次正演，并把双对数输出重采样为模型曲线向量。"""
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ok = runner.run_simulation(params, timeout=timeout, override_schedule=params.schedule, include_schedule=True)
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result = read_result_bin(runner.result_bin) if runner.result_bin.exists() else None
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if not ok and result is None:
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raise RuntimeError("Solver failed and result.bin is unavailable")
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if result is None or not result["loglog"]:
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raise RuntimeError("Solver produced no loglog data")
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if well_index < 0 or well_index >= len(result["loglog"]):
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raise IndexError(f"well-index={well_index} out of range; n_wells={len(result['loglog'])}")
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loglog = result["loglog"][well_index]
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t = np.asarray(loglog["t"], dtype=np.float64)
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p = np.asarray(loglog["p"], dtype=np.float64)
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d = np.asarray(loglog["deriv"], dtype=np.float64)
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cleaned = clean_curve_for_dataset(cfg, t, p, d)
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if cleaned is None:
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raise RuntimeError("Curve invalid after cleaning")
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t_clean, p_clean, d_clean = cleaned
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valid, reason = is_valid_curve(cfg, t_clean, p_clean, d_clean)
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if not valid:
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raise RuntimeError(f"Curve failed validation: {reason}")
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curve_feat = resample_curve_to_features(cfg, t_clean, p_clean, d_clean)
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raw = {
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"t": t_clean.tolist(),
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"p": p_clean.tolist(),
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"d": d_clean.tolist(),
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"n_steps": int(result["nSteps"]),
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"n_wells": int(result["nWells"]),
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}
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return curve_feat, raw
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def _corr_pearson(x: np.ndarray, y: np.ndarray) -> float:
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"""计算 Pearson 相关系数，用于评估代理分数与真实分数的线性相关。"""
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if x.size < 2:
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return float("nan")
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if np.std(x) < 1e-12 or np.std(y) < 1e-12:
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return float("nan")
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return float(np.corrcoef(x, y)[0, 1])
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def _corr_spearman(x: np.ndarray, y: np.ndarray) -> float:
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"""计算 Spearman 秩相关，用于评估候选排序是否一致。"""
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if x.size < 2:
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return float("nan")
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xr = np.argsort(np.argsort(x))
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yr = np.argsort(np.argsort(y))
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return _corr_pearson(xr.astype(np.float64), yr.astype(np.float64))
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def _rank_positions(values: np.ndarray) -> np.ndarray:
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"""把目标函数值转换成排名位置，用于比较代理排序和真实排序。"""
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order = np.argsort(values)
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rank = np.empty_like(order)
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rank[order] = np.arange(order.size)
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return rank
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def _fixed_param_value(cfg: Config, name: str) -> float | None:
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"""读取固定参数值；优先使用命令行覆盖，其次使用配置默认值。"""
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fixed_cfg = ((cfg.raw["params"].get("fixed_params", {}) or {}).get(name, {}) or {})
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if bool(fixed_cfg.get("enabled", False)):
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return float(fixed_cfg["value"])
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return None
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def _search_param_names(cfg: Config) -> list[str]:
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"""确定局部邻域中需要扰动搜索的物理参数名称。"""
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active_names = list(cfg.raw["params"].get("active_param_names", cfg.raw["params"]["all_physical_param_names"]))
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names = [name for name in active_names if _fixed_param_value(cfg, name) is None]
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if not names:
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raise ValueError("No searchable parameters: active_param_names is empty after removing fixed_params")
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return names
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def _sample_local_candidates(cfg: Config, base: Params, n: int, seed: int, span_frac: float) -> list[Params]:
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"""围绕目标参数按扰动比例随机生成局部候选参数。"""
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rng = np.random.RandomState(seed)
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all_names = list(cfg.raw["params"]["all_physical_param_names"])
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names = _search_param_names(cfg)
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log_params = set(cfg.raw["params"]["log_params"])
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base_dict = {
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"k": float(base.k),
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"skin": float(base.skin),
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"wellboreC": float(base.wellboreC),
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"phi": float(base.phi),
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"h": float(base.h),
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"Cf": float(base.Cf),
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}
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samples: list[Params] = []
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for _ in range(n):
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cand = dict(base_dict)
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for name in all_names:
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fixed_value = _fixed_param_value(cfg, name)
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if fixed_value is not None:
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cand[name] = fixed_value
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continue
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lo, hi = map(float, cfg.raw["params"]["ranges"][name])
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base_val = float(base_dict[name])
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if name not in names:
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cand[name] = base_val
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continue
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if name in log_params:
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# 跨数量级参数在 log 空间扰动，保持相对扰动尺度一致。
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lo_t = math.log10(max(lo, 1e-30))
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hi_t = math.log10(max(hi, 1e-30))
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base_t = math.log10(max(base_val, 1e-30))
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span = span_frac * (hi_t - lo_t)
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v_t = np.clip(rng.uniform(base_t - span, base_t + span), lo_t, hi_t)
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cand[name] = float(10 ** v_t)
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else:
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# 线性参数直接在物理空间扰动，并裁剪回配置范围。
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span = span_frac * (hi - lo)
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v = np.clip(rng.uniform(base_val - span, base_val + span), lo, hi)
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cand[name] = float(v)
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samples.append(
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Params(
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k=cand["k"],
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skin=cand["skin"],
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wellboreC=cand["wellboreC"],
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phi=cand["phi"],
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h=cand["h"],
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Cf=cand["Cf"],
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schedule=base.schedule,
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)
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)
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return samples
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def _params_to_dict(params: Params) -> dict:
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"""把 Params 对象转换为可写入 CSV 的普通字典。"""
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return {
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"k": float(params.k),
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|
"skin": float(params.skin),
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"wellboreC": float(params.wellboreC),
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"phi": float(params.phi),
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"h": float(params.h),
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"Cf": float(params.Cf),
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}
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def _plot_scatter(csv_rows: list[dict], output_path: Path) -> None:
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"""绘制局部候选的代理目标和真实目标散点图。"""
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solver_obj = np.asarray([float(r["solver_objective"]) for r in csv_rows], dtype=np.float64)
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surrogate_obj = np.asarray([float(r["surrogate_objective"]) for r in csv_rows], dtype=np.float64)
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plt.figure(figsize=(7, 6))
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plt.scatter(solver_obj, surrogate_obj, alpha=0.75)
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if solver_obj.size >= 2:
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lo = min(float(np.min(solver_obj)), float(np.min(surrogate_obj)))
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hi = max(float(np.max(solver_obj)), float(np.max(surrogate_obj)))
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plt.plot([lo, hi], [lo, hi], linestyle="--", linewidth=1)
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plt.xlabel("Solver objective to target")
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plt.ylabel("Surrogate objective to target")
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plt.title("Local Candidate Ranking Validation")
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plt.grid(True, alpha=0.3)
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plt.tight_layout()
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plt.savefig(output_path, dpi=150, bbox_inches="tight")
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plt.close()
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def main() -> None:
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"""围绕一个目标样本生成候选参数，比较真实目标函数排序与代理排序。"""
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args = parse_args()
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cfg, processed_path, model_path, output_dir = resolve_paths(args)
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output_dir.mkdir(parents=True, exist_ok=True)
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if not processed_path.exists():
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raise FileNotFoundError(f"Processed dataset not found: {processed_path}")
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if not model_path.exists():
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raise FileNotFoundError(f"Model checkpoint not found: {model_path}")
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processed = joblib.load(processed_path)
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curve_layout = infer_curve_layout(processed["meta"], int(processed["meta"]["curve_dim"]))
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schedule = resolve_case_schedule(cfg)
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target_params = build_params_from_args(args, schedule)
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search_names = _search_param_names(cfg)
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model, use_schedule, device = load_model(model_path)
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shared_runner = None
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def make_runner(tag: str) -> CppRunner:
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|
"""创建 C++ 求解器客户端，并根据参数决定是否启用常驻服务。"""
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|
temp_dir = output_dir / "_runner_temp" / tag
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|
temp_dir.mkdir(parents=True, exist_ok=True)
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return CppRunner(cfg=cfg, auto_init=False, temp_dir=temp_dir)
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try:
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if args.reuse_runner:
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|
# 复用 runner 可以显著减少大量候选逐个启动 C++ 进程的开销。
|
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|
shared_runner = make_runner("shared")
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|
target_runner = shared_runner
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|
else:
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|
target_runner = make_runner("target")
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|
|
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|
print("Running target solver case...")
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|
# 目标曲线只计算一次，后面所有候选都以它为拟合对象。
|
|
|
target_curve, target_raw = run_solver_and_extract_curve(
|
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|
runner=target_runner,
|
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|
cfg=cfg,
|
|
|
params=target_params,
|
|
|
well_index=args.well_index,
|
|
|
timeout=args.solver_timeout,
|
|
|
)
|
|
|
|
|
|
if not args.reuse_runner:
|
|
|
target_runner.close()
|
|
|
|
|
|
max_attempts = max(int(args.n_candidates), int(args.n_candidates) * int(args.max_attempts_factor))
|
|
|
candidates = _sample_local_candidates(
|
|
|
cfg=cfg,
|
|
|
base=target_params,
|
|
|
n=max_attempts,
|
|
|
seed=int(args.seed),
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|
|
span_frac=float(args.span_frac),
|
|
|
)
|
|
|
|
|
|
rows: list[dict] = []
|
|
|
failed_rows: list[dict] = []
|
|
|
for attempt_idx, cand in enumerate(candidates):
|
|
|
if len(rows) >= int(args.n_candidates):
|
|
|
break
|
|
|
|
|
|
runner = shared_runner if shared_runner is not None else make_runner(f"cand_{attempt_idx:04d}")
|
|
|
try:
|
|
|
# 对每个候选同时拿到真实求解器曲线和代理预测曲线，比较二者目标函数排序。
|
|
|
solver_curve, _ = run_solver_and_extract_curve(
|
|
|
runner=runner,
|
|
|
cfg=cfg,
|
|
|
params=cand,
|
|
|
well_index=args.well_index,
|
|
|
timeout=args.solver_timeout,
|
|
|
)
|
|
|
pred_curve = predict_surrogate_curve(
|
|
|
processed=processed,
|
|
|
model=model,
|
|
|
device=device,
|
|
|
use_schedule=use_schedule,
|
|
|
params=cand,
|
|
|
schedule=schedule,
|
|
|
cfg=cfg,
|
|
|
)
|
|
|
|
|
|
solver_obj = dual_log_objective(target_curve, solver_curve, curve_layout)
|
|
|
surrogate_obj = dual_log_objective(target_curve, pred_curve, curve_layout)
|
|
|
|
|
|
rows.append(
|
|
|
{
|
|
|
"candidate_id": len(rows),
|
|
|
"attempt_id": attempt_idx,
|
|
|
"k": cand.k,
|
|
|
"skin": cand.skin,
|
|
|
"wellboreC": cand.wellboreC,
|
|
|
"phi": cand.phi,
|
|
|
"h": cand.h,
|
|
|
"Cf": cand.Cf,
|
|
|
"solver_objective": solver_obj["dual_log_objective"],
|
|
|
"solver_p_obj": solver_obj["log_pressure_objective"],
|
|
|
"solver_d_obj": solver_obj["log_derivative_objective"],
|
|
|
"surrogate_objective": surrogate_obj["dual_log_objective"],
|
|
|
"surrogate_p_obj": surrogate_obj["log_pressure_objective"],
|
|
|
"surrogate_d_obj": surrogate_obj["log_derivative_objective"],
|
|
|
"objective_gap": surrogate_obj["dual_log_objective"] - solver_obj["dual_log_objective"],
|
|
|
}
|
|
|
)
|
|
|
except Exception as exc:
|
|
|
failed_row = {"attempt_id": attempt_idx, "reason": str(exc)}
|
|
|
failed_row.update(_params_to_dict(cand))
|
|
|
failed_rows.append(failed_row)
|
|
|
print(
|
|
|
"[warn] candidate attempt "
|
|
|
f"{attempt_idx} skipped: {exc} | "
|
|
|
f"k={cand.k:.6g}, skin={cand.skin:.6g}, C={cand.wellboreC:.6g}, "
|
|
|
f"phi={cand.phi:.6g}, h={cand.h:.6g}, Cf={cand.Cf:.6g}"
|
|
|
)
|
|
|
finally:
|
|
|
if shared_runner is None:
|
|
|
runner.close()
|
|
|
|
|
|
if not rows:
|
|
|
raise RuntimeError("No valid candidate evaluations completed")
|
|
|
|
|
|
solver_obj = np.asarray([float(r["solver_objective"]) for r in rows], dtype=np.float64)
|
|
|
surrogate_obj = np.asarray([float(r["surrogate_objective"]) for r in rows], dtype=np.float64)
|
|
|
solver_rank = _rank_positions(solver_obj)
|
|
|
surrogate_rank = _rank_positions(surrogate_obj)
|
|
|
|
|
|
# rank_gap > 0 表示该候选在代理排序中比真实排序更靠后，可能被代理筛掉。
|
|
|
for i, row in enumerate(rows):
|
|
|
row["solver_rank"] = int(solver_rank[i])
|
|
|
row["surrogate_rank"] = int(surrogate_rank[i])
|
|
|
row["rank_gap"] = int(surrogate_rank[i] - solver_rank[i])
|
|
|
|
|
|
top5_solver = set(np.argsort(solver_obj)[: min(5, solver_obj.size)].tolist())
|
|
|
top5_sur = set(np.argsort(surrogate_obj)[: min(5, surrogate_obj.size)].tolist())
|
|
|
top10_solver = set(np.argsort(solver_obj)[: min(10, solver_obj.size)].tolist())
|
|
|
top10_sur = set(np.argsort(surrogate_obj)[: min(10, surrogate_obj.size)].tolist())
|
|
|
|
|
|
best_solver_idx = int(np.argmin(solver_obj))
|
|
|
best_surrogate_idx = int(np.argmin(surrogate_obj))
|
|
|
|
|
|
# summary 聚焦排序质量，而不是单纯曲线 RMSE，因为自动拟合筛选更关心候选相对优劣。
|
|
|
summary = {
|
|
|
"config_path": str(cfg.path),
|
|
|
"processed_path": str(processed_path),
|
|
|
"model_path": str(model_path),
|
|
|
"target_params": {
|
|
|
"k": target_params.k,
|
|
|
"skin": target_params.skin,
|
|
|
"wellboreC": target_params.wellboreC,
|
|
|
"phi": target_params.phi,
|
|
|
"h": target_params.h,
|
|
|
"Cf": target_params.Cf,
|
|
|
},
|
|
|
"schedule": {
|
|
|
"sectionIndex": schedule.sectionIndex,
|
|
|
"timeQ": schedule.timeQ,
|
|
|
"q": schedule.q,
|
|
|
},
|
|
|
"n_requested": int(args.n_candidates),
|
|
|
"n_valid": int(len(rows)),
|
|
|
"span_frac": float(args.span_frac),
|
|
|
"search_param_names": search_names,
|
|
|
"pearson_objective": _corr_pearson(solver_obj, surrogate_obj),
|
|
|
"spearman_objective": _corr_spearman(solver_obj, surrogate_obj),
|
|
|
"mean_objective_gap": float(np.mean(surrogate_obj - solver_obj)),
|
|
|
"mae_objective_gap": float(np.mean(np.abs(surrogate_obj - solver_obj))),
|
|
|
"top5_overlap": int(len(top5_solver & top5_sur)),
|
|
|
"top10_overlap": int(len(top10_solver & top10_sur)),
|
|
|
"best_solver_candidate_id": best_solver_idx,
|
|
|
"best_solver_candidate_surrogate_rank": int(surrogate_rank[best_solver_idx]),
|
|
|
"best_surrogate_candidate_id": best_surrogate_idx,
|
|
|
"best_surrogate_candidate_solver_rank": int(solver_rank[best_surrogate_idx]),
|
|
|
"target_solver_raw_loglog": target_raw,
|
|
|
}
|
|
|
|
|
|
csv_path = output_dir / "candidate_objectives.csv"
|
|
|
with open(csv_path, "w", newline="", encoding="utf-8") as f:
|
|
|
writer = csv.DictWriter(
|
|
|
f,
|
|
|
fieldnames=[
|
|
|
"candidate_id",
|
|
|
"attempt_id",
|
|
|
"k",
|
|
|
"skin",
|
|
|
"wellboreC",
|
|
|
"phi",
|
|
|
"h",
|
|
|
"Cf",
|
|
|
"solver_objective",
|
|
|
"solver_p_obj",
|
|
|
"solver_d_obj",
|
|
|
"surrogate_objective",
|
|
|
"surrogate_p_obj",
|
|
|
"surrogate_d_obj",
|
|
|
"objective_gap",
|
|
|
"solver_rank",
|
|
|
"surrogate_rank",
|
|
|
"rank_gap",
|
|
|
],
|
|
|
)
|
|
|
writer.writeheader()
|
|
|
writer.writerows(rows)
|
|
|
|
|
|
failed_csv_path = output_dir / "failed_candidates.csv"
|
|
|
with open(failed_csv_path, "w", newline="", encoding="utf-8") as f:
|
|
|
writer = csv.DictWriter(
|
|
|
f,
|
|
|
fieldnames=["attempt_id", "reason", "k", "skin", "wellboreC", "phi", "h", "Cf"],
|
|
|
)
|
|
|
writer.writeheader()
|
|
|
writer.writerows(failed_rows)
|
|
|
|
|
|
with open(output_dir / "summary.json", "w", encoding="utf-8") as f:
|
|
|
json.dump(summary, f, ensure_ascii=False, indent=2)
|
|
|
|
|
|
_plot_scatter(rows, output_dir / "objective_scatter.png")
|
|
|
|
|
|
print("\nLocal autofit validation complete.")
|
|
|
print(f"Output dir: {output_dir}")
|
|
|
print(f"Valid candidates={len(rows)}/{args.n_candidates}")
|
|
|
print(
|
|
|
f"Objective Pearson={summary['pearson_objective']:.6f}, "
|
|
|
f"Spearman={summary['spearman_objective']:.6f}, "
|
|
|
f"Top5 overlap={summary['top5_overlap']}, Top10 overlap={summary['top10_overlap']}"
|
|
|
)
|
|
|
print(
|
|
|
f"Best solver candidate id={summary['best_solver_candidate_id']}, "
|
|
|
f"its surrogate rank={summary['best_solver_candidate_surrogate_rank']}"
|
|
|
)
|
|
|
print(
|
|
|
f"Best surrogate candidate id={summary['best_surrogate_candidate_id']}, "
|
|
|
f"its solver rank={summary['best_surrogate_candidate_solver_rank']}"
|
|
|
)
|
|
|
print(f"CSV saved: {csv_path}")
|
|
|
print(f"Failed candidates saved: {failed_csv_path}")
|
|
|
print(f"Summary saved: {output_dir / 'summary.json'}")
|
|
|
print(f"Plot saved: {output_dir / 'objective_scatter.png'}")
|
|
|
finally:
|
|
|
if shared_runner is not None:
|
|
|
shared_runner.close()
|
|
|
|
|
|
|
|
|
if __name__ == "__main__":
|
|
|
main()
|