"""Generates and solves the special class of j-UFLP instances.
Instances and the experiment as discussed in the paper, in Section 4.2
(for Figure 11): investigating the effects of different linking methods
on the proposed heuristic efficiency (vs. the align-to-A baseline).
Instances are generated using :py:func:`UFLP_2_cav.gen_special_jUFLP`,
the code for different linking methods during instance generation
is implemented in :py:func:`UFLP_2_cav.make_cluster_reverse_custom_matching`.
The experiment itself is implemented in :py:func:`experiments.jUFLP_w_simscores.main`.
"""
import numpy as np
from time import time
from jUFLP_cavemen import solve_cm_jUFLP_MIP, solve_cm_jUFLP_CPPMIP_fullDDs
from jUFLP_cavemen import solve_cm_jUFLP_fullDDs
from jUFLP_utils import save_inst
from BDD import simscore
from UFLP_2_cav import gen_special_jUFLP, make_cluster_reverse_custom_matching
from UFLP_2_cav import COL_caves
from UFLP_fullDD import create_cover_DD
from UFLPOrder import UFLP_greedy_order
[docs]def main():
"""The j-UFLP experiment involving instances w/different simscores."""
print("experiment, n, M, L, N, A, inst_type, linking, param, CPP_simscore, tMIP, tMIP_CPP, tDD_VS, tDD_toA, int_VS, int_VS_toA")
M = 13
L = 0.35
n = 2
linking = "cluster-reverse-custom"
inst_type = "cavemen"
k = 0
for i in range(1, 100+1):
i1, i2, jm = gen_special_jUFLP(n, M, L, linking, inst_type, 0.0)
for param in [0.00, 0.25, 0.50, 0.75, 1.00]:
k += 1
ca1 = [S for S in i1[COL_caves]]
ca2 = [S for S in i2[COL_caves]]
jm = make_cluster_reverse_custom_matching(ca1, ca2, param)
save_inst(i1, i2, jm, f"instances/jUFLP_ss/inst_{param}_{k}.json")
print("---")
t0 = time()
objMIP = solve_cm_jUFLP_MIP(i1, i2, jm)
tMIP = time() - t0
print(f"✅ MIP in {tMIP:.2f} sec", flush=True)
# solve with CPP MIP
t0 = time()
objMIP_CPP = solve_cm_jUFLP_CPPMIP_fullDDs(i1, i2, jm)
tMIP_CPP = time() - t0
print(f"✅ CPP MIP in {tMIP_CPP:.2f} sec", flush=True)
t0 = time()
objDD_VS, int_VS = solve_cm_jUFLP_fullDDs(i1, i2, jm, "VS", True)
tDD_VS = time() - t0
print(f"✅ Full DDs VS in {tDD_VS:.2f} sec", flush=True)
t0 = time()
objDD_toA, int_VS_toA = solve_cm_jUFLP_fullDDs(i1, i2, jm, "toA", True)
tDD_toA = time() - t0
print(f"✅ Full DDs toA in {tDD_toA:.2f} sec", flush=True)
assert abs(objMIP - objMIP_CPP) < 0.01, f"objMIP = {objMIP:.2f}, objMIP_CPP={objMIP_CPP:.2f}"
assert abs(objMIP - objDD_VS) < 0.01, f"objMIP = {objMIP:.2f}, objDD_VS={objDD_VS:.2f}"
assert abs(objMIP - objDD_toA) < 0.01, f"objMIP = {objMIP:.2f}, objDD_toA={objDD_toA:.2f}"
A = sum([len(s)-1 for s in i1[0]])/2+sum([len(s)-1 for s in i2[0]])/2
# check the resulting CPP BDDs simscore
S, f, c, caves = i1
S2, f2, c2, caves2 = i2
B1, _ = create_cover_DD(S, f, c, UFLP_greedy_order(S, True))
B2, _ = create_cover_DD(S2, f2, c2, UFLP_greedy_order(S2, False))
B1.make_reduced()
B2.make_reduced()
B1.rename_vars(jm)
print(f"{k}, {n}, {M}, {L}, {len(i1[0])+len(i2[0])}, {A}, " +
f"{inst_type}, {linking}, {param}, " +
f"{simscore(B1.vars, B2.vars)}, " +
f"{tMIP:.2f}, {tMIP_CPP:.2f}, {tDD_VS:.2f}, {tDD_toA:.2f}, {int_VS}, {int_VS_toA}",
flush=True)
if __name__ == '__main__':
main()