# -*- coding: utf-8 -*-
"""Ti-2 配方优化问题建模 自检脚本（Issue #78）。

不依赖 unittest，直接加载模板资产并做能力点断言，便于 CI / 部署期一键核对。
"""
import os
import sys

sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
from problem import ConstraintKind, OptimizationProblem, load_problem  # noqa: E402
from solver import SolverConfig, solve  # noqa: E402
from cross_process import CrossProcessModel, CrossProcessModelConfig, CrossProcessSample  # noqa: E402
from advisor import generate_advice  # noqa: E402

CONFIG = os.path.join(os.path.dirname(os.path.abspath(__file__)),
                      "config", "recipe_optim.template.yaml")


def main() -> int:
    failures = []

    # 1) 模板能加载
    p = load_problem(CONFIG)
    if not isinstance(p, OptimizationProblem):
        failures.append("load_problem 未返回 OptimizationProblem")

    # 2) 4 类约束齐全
    kinds = {c.kind for c in p.constraints}
    expected = {ConstraintKind.BOX, ConstraintKind.LINEAR,
                ConstraintKind.RATIO, ConstraintKind.FORBIDDEN}
    if kinds != expected:
        failures.append(f"约束种类不齐: {kinds} != {expected}")

    # 3) 静态校验通过
    errs = p.validate()
    if errs:
        failures.append(f"validate 未通过: {errs}")

    # 4) 可行性判定：合法取值可行、禁止组合不可行
    ok = p.is_feasible({"clf_temp": 850, "cl2_ratio": 1.0,
                        "feed_rate": 450, "catalyst": "A"})
    bad = p.is_feasible({"clf_temp": 950, "cl2_ratio": 1.0,
                         "feed_rate": 450, "catalyst": "A"})
    if not ok:
        failures.append("合法取值被判为不可行")
    if bad:
        failures.append("越界取值(950℃)未被识别为不可行")

    # 5) 序列化往返无损
    rt = OptimizationProblem.from_dict(p.to_dict())
    if [v.name for v in rt.variables] != [v.name for v in p.variables]:
        failures.append("序列化往返丢失变量")

    # 6) 求解器（#79）端到端：加载模板后能求出可行解
    sol = solve(p, SolverConfig(grid_steps=7, max_combinations=200000))
    if not sol.feasible:
        failures.append(f"求解器未求出可行解: {sol.message}")
    if sol.strategy != "grid":
        failures.append(f"求解策略非 grid: {sol.strategy}")

    # 7) 跨工序关联模型（#80）端到端：合成线性数据训练 + R² 评估
    cfg = CrossProcessModelConfig(
        upstream_features=["up"], downstream_targets=["down"],
        alpha=0.0, min_samples=8)
    samples = [CrossProcessSample(upstream={"up": float(i)},
                                  downstream={"down": 2.0 * float(i) + 1.0})
               for i in range(12)]
    cm = CrossProcessModel(cfg).fit(samples)
    report = cm.evaluate(samples)
    if not cm.fitted:
        failures.append("跨工序模型未训练成功")
    if not (report.get("r2_down", 0.0) > 0.99):
        failures.append(f"跨工序模型 R² 过低: {report}")

    # 8) 优化建议生成（#81）端到端：可解释、可溯源建议
    advice = generate_advice(p, sol, cross_process_weights={
        "Ti_purity": {"clf_temp": 0.8, "cl2_ratio": 1.2}})
    if not advice.feasible:
        failures.append("建议生成器标记不可行")
    if len(advice.items) != len(p.variables):
        failures.append("建议条目数与变量数不一致")
    if not all(it.evidence for it in advice.items):
        failures.append("存在无依据的建议条目（违反可溯源要求）")
    if not advice.trace:
        failures.append("溯源链路为空")

    if failures:
        print("❌ recipe-optim 自检失败:")
        for f in failures:
            print("  -", f)
        return 1
    print("✅ recipe-optim 自检通过（8 能力点）")
    return 0


if __name__ == "__main__":
    sys.exit(main())