def cat_exp_main(data_dir):
m = 3
metric = Map_Dist()
fns_ = os.listdir(data_dir)
fns = list(filter(lambda s: s.endswith('.pkl'), fns_))
for fn in fns:
ofn = fn[:-3] + 'csv'
if ofn in fns_:
continue
with open(os.path.join(data_dir, fn), 'rb') as f:
data = pickle.load(f)
insts = data.keys()
s = min(insts, key=len)
print(s, end='')
seed = TSP(data[s])
with open(os.path.join(data_dir, ofn), 'w') as f:
print('Inst_a,Inst_b,Distance,Baseline', file=f)
for i in insts:
print('*', end='')
inst = TSP(data[i])
if s == i:
t = s + '_r0_#0'
else:
t = i
for _ in range(m):
res = (s, t, str(metric.dist(seed, inst)['dist']),
str(abs(seed.hardness_est() - inst.hardness_est())))
with open(os.path.join(data_dir, ofn), 'a') as f:
print(','.join(res), file=f)
print()
def acc_exp_main(data_dir):
m = 3
metric = Map_Dist()
ofn = 'dists.csv'
with open(os.path.join(data_dir, ofn), 'w') as f:
print('Inst_a,Inst_b,Distance,Baseline', file=f)
fns_ = os.listdir(data_dir)
fns = list(filter(lambda s: s.endswith('.pkl'), fns_))
data = {}
for fn in fns:
with open(os.path.join(data_dir, fn), 'rb') as f:
d = pickle.load(f)
keys = list(d.keys())
i = np.argmin([len(k) for k in keys])
data[keys[i]] = d[keys[i]]
# data.update()
idx = list(data.keys())
n = len(idx)
met_dist_mat = np.zeros((n, n))
har_dist_mat = np.zeros((n, n))
print('#instances:', n)
for i in range(n):
for j in range(n):
inst_i = TSP(data[idx[i]])
inst_j = TSP(data[idx[j]])
met_res = []
har_res = []
for k in range(m):
met_res.append(metric.dist(inst_i, inst_j)['dist'])
har_res.append(abs(inst_i.hardness_est() - inst_j.hardness_est()))
met_dist_mat[i][j] = np.mean(met_res)
har_dist_mat[i][j] = np.mean(har_res)
s = (idx[i], idx[j], str(met_dist_mat[i][j]), str(har_dist_mat[i][j]))
print(s)
with open(os.path.join(data_dir, ofn), 'a') as f:
print(','.join(s), file=f)