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# plot_pareto_fronts.py
import re
from pathlib import Path
import matplotlib.pyplot as plt
from itertools import cycle
def load_fronts(path: Path):
"""Parse text file and return list[list[tuple[float,float]]] = fronts."""
number = r'[-+]?(?:\d*\.\d+|\d+\.?)(?:[eE][-+]?\d+)?' # float regex
pair_re = re.compile(rf'\(\s*({number})\s*,\s*({number})\s*\)')
fronts = []
with path.open(encoding='utf-8') as f:
for line in f:
line = line.strip()
if line.startswith('Front'):
fronts.append([
(float(x), float(y))
for x, y in pair_re.findall(line)
])
return fronts
def plot_fronts(fronts, *, max_fronts=None, figsize=(8, 6),
alpha=0.8, marker_size=20, logy=True, save=None):
"""Scatter-plot fronts with different colours."""
if max_fronts is not None:
fronts = fronts[:max_fronts]
colors = cycle(plt.cm.tab20.colors) # 20 distinct colours then repeat
fig, ax = plt.subplots(figsize=figsize)
xs, ys = zip(*fronts[0])
ax.scatter(xs, ys, s=marker_size,
color=next(colors), alpha=alpha, label=f'Front {0}')
# for idx, front in enumerate(fronts):
# if not front: # skip empty layers
# continue
# xs, ys = zip(*front)
# ax.scatter(xs, ys, s=marker_size,
# color=next(colors), alpha=alpha, label=f'Front {idx}')
ILP_solutions = [(604.26, 1050886.072227), (655.5, 406775.197227), (586.37,1532352.572227),(574.73,2481330.572227),(573.65,2481330.572227),(568.33,2656832.572227),(560.43,3081204.572227),(559.89,2959894.572227),(538.3,4289528.572227),(517.33,11502856.572227),(500.17,16204696.572227),(492.73,17253496.572227),(492.73,17253496.572227),(488.76,24360536.572227),(479.16,31356888.572227),(478.08,31356888.572227)]
ILP_solutions = [(478.62, 3.13569e+07), (478.62, 3.13569e+07), (478.62, 3.13569e+07), (478.62, 3.13569e+07), (478.62, 3.13569e+07), (479.16, 3.13569e+07), (479.16, 3.13569e+07), (479.16, 3.13569e+07), (479.16, 3.13569e+07), (479.16, 3.13569e+07), (479.16, 3.13569e+07), (479.16, 3.13569e+07), (479.16, 3.13569e+07), (479.16, 3.13569e+07), (488.76, 2.43605e+07), (492.73, 1.72535e+07), (492.73, 1.72535e+07), (492.73, 1.72535e+07), (493.27, 1.72535e+07), (493.27, 1.72535e+07), (500.17, 1.62047e+07), (500.17, 1.62047e+07), (500.17, 1.62047e+07), (500.17, 1.62047e+07), (500.17, 1.62047e+07), (500.71, 1.62047e+07), (500.71, 1.62047e+07), (500.71, 1.62047e+07), (500.71, 1.62047e+07), (500.71, 1.62047e+07), (500.71, 1.62047e+07), (500.71, 1.62047e+07), (500.71, 1.62047e+07), (500.71, 1.62047e+07), (500.71, 1.62047e+07), (517.87, 1.15112e+07), (538.3, 4.28953e+06), (559.89, 2.95989e+06), (560.43, 2.9747e+06), (568.33, 2.65683e+06), (573.65, 2.48133e+06), (574.73, 2.48133e+06), (578.66, 0), (604.26, 1.03273e+06), (655.5, 412849)]
ILP_solutions = [(478.62, 3.13569e+07), (478.62, 3.13569e+07), (478.62, 3.13569e+07), (478.62, 3.13569e+07), (478.62, 3.13569e+07), (479.16, 3.13569e+07), (479.16, 3.13569e+07), (479.16, 3.13569e+07), (479.16, 3.13569e+07), (479.16, 3.13569e+07), (479.16, 3.13569e+07), (479.16, 3.13569e+07), (479.16, 3.13569e+07), (479.16, 3.13569e+07), (488.76, 2.43605e+07), (492.73, 1.72535e+07), (492.73, 1.72535e+07), (492.73, 1.72535e+07), (493.27, 1.72535e+07), (493.27, 1.72535e+07), (500.17, 1.62047e+07), (500.17, 1.62047e+07), (500.17, 1.62047e+07), (500.17, 1.62047e+07), (500.17, 1.62047e+07), (500.71, 1.62047e+07), (500.71, 1.62047e+07), (500.71, 1.62047e+07), (500.71, 1.62047e+07), (500.71, 1.62047e+07), (500.71, 1.62047e+07), (500.71, 1.62047e+07), (500.71, 1.62047e+07), (500.71, 1.62047e+07), (500.71, 1.62047e+07), (517.87, 1.15112e+07), (538.3, 4.28953e+06), (559.89, 2.95989e+06), (560.43, 2.9747e+06), (568.33, 2.65683e+06), (573.65, 2.48133e+06), (574.73, 2.48133e+06), (578.66, 1.53235e+06), (604.26, 1.04829e+06), (655.5, 416374)]
ILP_solutions = set(ILP_solutions) # 去重
xs, ys = zip(*ILP_solutions)
ax.scatter(xs, ys,
marker='*', # 五角星
s=160, # 更醒目
alpha=0.4,
color='red',
edgecolors='red',
linewidths=0.6,
label=f'ILP_solution (CAOE)')
# ilp_x, ilp_y = 646.94, 448863.400352
# ax.scatter(ilp_x, ilp_y,
# marker='*', # 五角星
# s=160, # 更醒目
# color='red',
# edgecolors='black',
# linewidths=0.6,
# label='ILP_solution (CoOpt=0.01)')
# ilp_x, ilp_y = 686.19, 441_681.357384
# ax.scatter(ilp_x, ilp_y,
# marker='*', # 五角星
# s=160, # 更醒目
# color='orange',
# edgecolors='black',
# linewidths=0.6,
# label='ILP_solution (CAOE=689)')
ax.set_xlabel('Area')
ax.set_ylabel('MED')
if logy:
ax.set_yscale('log') # 常见目标值跨度大时用对数轴
ax.legend(title='Pareto Layers', fontsize='small',
loc='best')
ax.set_title('Non-dominated Sorted Solutions')
fig.tight_layout()
if save:
fig.savefig(save, dpi=300)
print(f'Figure saved to {save}')
else:
plt.show()
def plot_fronts(fronts1, fronts2, *, max_fronts=None, figsize=(8, 6),
alpha=0.8, marker_size=20, logy=True, save=None):
"""Scatter-plot fronts with different colours."""
allSolutions1 = []
for front in fronts1:
allSolutions1.extend(front)
allSolutions2 = []
for front in fronts2:
allSolutions2.extend(front)
colors = cycle(plt.cm.tab20.colors) # 20 distinct colours then repeat
fig, ax = plt.subplots(figsize=figsize)
xs, ys = zip(*allSolutions1)
ax.scatter(xs, ys, s=marker_size,
color=next(colors), alpha=alpha, label=f'AC_EWs')
xs, ys = zip(*allSolutions2)
ax.scatter(xs, ys, s=marker_size,
color=next(colors), alpha=alpha, label=f'AC_General')
ax.set_xlabel('Area')
ax.set_ylabel('MED')
if logy:
ax.set_yscale('log') # 常见目标值跨度大时用对数轴
ax.legend(title='Pareto Layers', fontsize='small',
loc='best')
ax.set_title('Non-dominated Sorted Solutions')
fig.tight_layout()
if save:
fig.savefig(save, dpi=300)
print(f'Figure saved to {save}')
else:
plt.show()
if __name__ == '__main__':
txt_path = Path("/Users/fch/Python/OPACT-Extension/Training_log_0.0001_ste_counts/non-dominated-sorting.txt") # ← 改成自己的文件名
fronts1 = load_fronts(txt_path)
txt_path = Path("/Users/fch/Python/OPACT-Extension/Training_log_0.0001_ste_counts_general/non-dominated-sorting.txt") # ← 改成自己的文件名
fronts2 = load_fronts(txt_path)
# —— 根据需要自行调整参数 ——
plot_fronts(fronts1, fronts2,
max_fronts=10, # 只画前 10 层；改成 None 则画全部
marker_size=15,
logy=True,
save=None) # 改成 'pareto.png' 可直接保存