93 lines
4.2 KiB
Python
93 lines
4.2 KiB
Python
import matplotlib.pyplot as plt
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import numpy as np
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from chromosome_utils import ChromosomeUtils
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class ResultVisualizer:
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"""结果可视化工具"""
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def __init__(self, utils: ChromosomeUtils):
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self.utils = utils
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self.figsize = (12, 8)
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self.supplier_names = ["RiskEnterprise"] + self.utils.supplier.names
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def plot_pareto_front(self, all_objectives: list[tuple[float, float]],
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non_dominated_objectives: list[tuple[float, float]]):
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"""绘制帕累托前沿"""
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plt.figure(figsize=self.figsize)
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if all_objectives:
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c_all = [obj[0] for obj in all_objectives]
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t_all = [obj[1] for obj in all_objectives]
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plt.scatter(c_all, t_all, color='blue', alpha=0.3, label='All Solutions', zorder=1)
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if non_dominated_objectives:
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c_nd = [obj[0] for obj in non_dominated_objectives]
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t_nd = [obj[1] for obj in non_dominated_objectives]
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plt.scatter(c_nd, t_nd, color='red', s=50, label='Pareto Front', zorder=5)
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plt.title('Pareto Front: Change Cost vs Tardiness')
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plt.xlabel('Change Cost')
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plt.ylabel('Tardiness')
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plt.legend()
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plt.grid(True, alpha=0.5)
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plt.savefig('pareto_front.png', dpi=300, bbox_inches='tight')
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plt.show()
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def plot_convergence(self, convergence_history: list[tuple[float, float]]):
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"""绘制收敛趋势图"""
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if len(convergence_history) == 0:
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return
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plt.figure(figsize=self.figsize)
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generations = list(range(len(convergence_history)))
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costs = [h[0] for h in convergence_history]
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tardiness = [h[1] for h in convergence_history]
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plt.subplot(2, 1, 1)
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plt.plot(generations, costs, 'b-')
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plt.title('Convergence History')
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plt.ylabel('Average Change Cost')
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plt.grid(True, alpha=0.5)
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plt.subplot(2, 1, 2)
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plt.plot(generations, tardiness, 'r-')
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plt.xlabel('Generation')
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plt.ylabel('Average Tardiness')
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plt.grid(True, alpha=0.5)
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plt.tight_layout()
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plt.savefig('convergence_history.png', dpi=300, bbox_inches='tight')
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plt.show()
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def print_solution_details(self, solution: np.ndarray, objectives: tuple[float, float]):
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"""打印单个解的详细信息（含数量校验）"""
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enterprise_layer, capacity_layer, quantity_layer = self.utils._split_chromosome(solution)
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split_points = np.cumsum(self.utils.material_enterprise_count)
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print(f"\n变更成本: {objectives[0]:.2f}, 交付延期: {objectives[1]:.2f}")
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print("=" * 80)
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total_q_check = []
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start = 0
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for i in range(self.utils.I):
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end = split_points[i]
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ents = self.utils.material_optional_enterprises[i]
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e_segment = enterprise_layer[start:end]
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c_segment = capacity_layer[start:end]
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q_segment = quantity_layer[start:end]
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demand_q = self.utils.order.Q[i]
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print(f"物料 {i} - 需求数量: {demand_q}, 分配总量: {np.sum(q_segment[e_segment==1]):.2f}")
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total_q_check.append(abs(np.sum(q_segment[e_segment==1]) - demand_q) < 1e-2)
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print(f" 选择的企业及其分配:")
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for idx, ent in enumerate(ents):
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if e_segment[idx] == 1:
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ent_name = self.supplier_names[ent]
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cap = c_segment[idx]
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qty = q_segment[idx]
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print(f" 企业: {ent_name}, 产能: {cap:.2f}, 分配数量: {qty:.2f}")
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start = end
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print("-" * 80)
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if all(total_q_check):
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print("✅ 所有物料数量满足需求约束")
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else:
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print("❌ 部分物料数量未满足需求约束")
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def print_pareto_solutions(self, population: np.ndarray, objectives: list[tuple[float, float]]):
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"""打印帕累托前沿解的详细信息"""
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print("\n" + "=" * 100)
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print("帕累托前沿解详细方案")
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print("=" * 100)
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for i in range(min(5, len(population))):
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print(f"\n===== 最优解 {i+1} =====")
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self.print_solution_details(population[i], objectives[i]) |