chore(DE): add debug mode and detailed log regarding cost changes

unilabos/layout_optimizer/optimizer.py

@@ -13,7 +13,12 @@ from typing import Any, Callable
 
 import numpy as np
 
-from .constraints import evaluate_constraints, evaluate_default_hard_constraints
+from .constraints import (
+    evaluate_constraints,
+    evaluate_constraints_breakdown,
+    evaluate_default_hard_constraints,
+    evaluate_default_hard_constraints_breakdown,
+)
 from .mock_checkers import MockCollisionChecker, MockReachabilityChecker
 from .models import Constraint, Device, Lab, Placement
 from .pencil_integration import generate_initial_layout
@@ -34,6 +39,7 @@ def _run_de(
     seed: int | None,
     n_devices: int,
     strategy: str = "currenttobest1bin",
+    progress_callback: Callable[[int, np.ndarray, float], None] | None = None,
 ) -> tuple[np.ndarray, float, int]:
     """自定义差分进化循环。
 
@@ -56,6 +62,7 @@ def _run_de(
         seed: 随机种子
         n_devices: 设备数量（用于 per-device crossover）
         strategy: 变异策略，"currenttobest1bin" 或 "best1bin"
+        progress_callback: 每 10 代调用一次 (gen, best_vector, best_cost)
 
     Returns:
         (best_vector, best_cost, n_generations)
@@ -125,6 +132,10 @@ def _run_de(
         # 更新 best_idx（种群可能整体更新）
         best_idx = int(np.argmin(costs))
 
+        # 进度回调：每 10 代报告最优个体状态
+        if progress_callback and gen % 10 == 0:
+            progress_callback(gen, best_vector, best_cost)
+
         # Early stopping：最近 patience 代改善 < 0.1%
         best_cost_history.append(best_cost)
         if len(best_cost_history) >= patience:
@@ -296,6 +307,36 @@ def optimize(
         n, 3 * n, pop_count, maxiter, strategy,
     )
 
+    # DEBUG 模式进度回调：每 10 代输出完整约束分项表格
+    def _progress_cb(gen: int, best_vec: np.ndarray, best_cost_val: float) -> None:
+        if not logger.isEnabledFor(logging.DEBUG):
+            return
+        pls = _vector_to_placements(best_vec, devices)
+        hard_bd = evaluate_default_hard_constraints_breakdown(
+            devices, pls, lab, collision_checker,
+        )
+        lines = [f"=== DE Gen {gen} | best_cost={best_cost_val:.4f} ==="]
+        lines.append(f"  {'Constraint':<45} {'Type':<6} {'Weight':>8} {'Cost':>10}")
+        lines.append(f"  {'─' * 71}")
+        lines.append(
+            f"  {'[predefined] collision':<45} {'hard':<6} {hard_bd['collision_weight']:>8.0f} {hard_bd['collision']:>10.4f}"
+        )
+        lines.append(
+            f"  {'[predefined] boundary':<45} {'hard':<6} {hard_bd['boundary_weight']:>8.0f} {hard_bd['boundary']:>10.4f}"
+        )
+        if constraints:
+            user_bd = evaluate_constraints_breakdown(
+                devices, pls, lab, constraints,
+                collision_checker, reachability_checker,
+            )
+            for item in user_bd:
+                lines.append(
+                    f"  {item['name']:<45} {item['type']:<6} {item['weight']:>8.1f} {item['cost']:>10.4f}"
+                )
+        lines.append(f"  {'─' * 71}")
+        lines.append(f"  {'TOTAL':<45} {'':6} {'':>8} {best_cost_val:>10.4f}")
+        logger.debug("\n".join(lines))
+
     best_vector, best_cost, n_generations = _run_de(
         cost_fn=cost_function,
         bounds=bounds_array,
@@ -308,15 +349,52 @@ def optimize(
         seed=seed,
         n_devices=n,
         strategy=strategy,
+        progress_callback=_progress_cb,
     )
 
     # 评估次数估算：每代 pop_count 次（初始 + 每代 trial）
     n_evaluations = pop_count + n_generations * pop_count
 
-    logger.info(
-        "DE optimization complete: success=%s, cost=%.4f, iterations=%d, evaluations=%d",
-        best_cost < 1e17, best_cost, n_generations, n_evaluations,
+    # 最终布局分项明细（INFO 级别）
+    final_placements = _vector_to_placements(best_vector, devices)
+    hard_bd = evaluate_default_hard_constraints_breakdown(
+        devices, final_placements, lab, collision_checker,
     )
+    # success = 所有 hard 约束均满足（predefined + 用户 hard）
+    all_hard_met = hard_bd["total"] == 0.0
+    # 所有约束的 top violators 候选池（predefined + user）
+    all_violators: list[dict] = [
+        {"name": "[predefined] collision", "cost": hard_bd["collision"]},
+        {"name": "[predefined] boundary", "cost": hard_bd["boundary"]},
+    ]
+    if constraints:
+        user_bd = evaluate_constraints_breakdown(
+            devices, final_placements, lab, constraints,
+            collision_checker, reachability_checker,
+        )
+        user_total = sum(item["cost"] for item in user_bd)
+        for c_item in user_bd:
+            all_violators.append({"name": c_item["name"], "cost": c_item["cost"]})
+            if c_item["type"] == "hard" and c_item["cost"] > 0:
+                all_hard_met = False
+    else:
+        user_bd = []
+        user_total = 0.0
+
+    summary = [
+        "DE complete: success=%s, cost=%.4f, %d gens, %d evals"
+        % (all_hard_met, best_cost, n_generations, n_evaluations),
+        "  Predefined: subtotal=%.4f" % hard_bd["total"],
+    ]
+    if constraints:
+        summary.append(f"  User: subtotal={user_total:.4f}")
+    top_violators = sorted(all_violators, key=lambda x: x["cost"], reverse=True)[:3]
+    top_violators = [v for v in top_violators if v["cost"] > 0]
+    if top_violators:
+        summary.append("  Top violators:")
+        for v in top_violators:
+            summary.append(f"    {v['name']} = {v['cost']:.4f}")
+    logger.info("\n".join(summary))
 
     return _vector_to_placements(best_vector, devices)