我正在try 将Cplex日志(log)文件解析为Pandas Dataframe的MILP问题. 分支和绑定的日志(log)文件部分如下所示:
[' Node Left Objective IInf Best Integer Best Bound ItCnt Ga',
'',
' 0+ 0 0.0000 -6819503.0000 ---',
' 0 0 -149597.6789 3202 0.0000 -149597.6789 37 ---',
' 0+ 0 -83655.0000 -149597.6789 78.83',
' 0+ 0 -125854.0000 -149597.6789 18.87',
' 0+ 0 -126474.0000 -149597.6789 18.28',
' 0+ 0 -130810.0000 -138528.6611 5.90',
' 0+ 0 -133740.0000 -135305.2732 1.17',
' 0 2 -135305.2732 3622 -133740.0000 -135305.2732 6307 1.17',
' 79 49 -135300.3578 3627 -133740.0000 -135304.7364 6743 1.17',
' 207 172 -135294.5414 3606 -133740.0000 -135304.7364 8033 1.17',
' 337 280 -135292.2563 3569 -133740.0000 -135304.7364 9140 1.17',
' 457 433 -135294.0213 3591 -133740.0000 -135304.7364 10532 1.17',
' 569 500 -135285.6607 3573 -133740.0000 -135304.7364 11117 1.17',
' 627 559 -135282.5594 3559 -133740.0000 -135304.7364 11710 1.17',
' 735 667 -135283.6653 3582 -133740.0000 -135304.7364 12619 1.17',
' 815 688 -135281.1259 3574 -133740.0000 -135304.7364 12786 1.17',
' 912 844 -135282.2331 3629 -133740.0000 -135304.7364 14043 1.17',
' 1244 1085 -135267.1649 3544 -133740.0000 -135304.7364 16446 1.17',
' 1458 1352 -135241.3260 3490 -133740.0000 -135304.7364 18874 1.17',
' 1569 1487 -135230.0718 3454 -133740.0000 -135304.7364 20190 1.17',
' 1607 1525 -135214.0255 3400 -133740.0000 -135304.7364 20599 1.17',
' 1707 1590 -135196.7871 3372 -133740.0000 -135304.7364 21195 1.17',
' 1818+ 1327 -133837.0000 -135304.7364 1.10',
...
' 11004 10458 -134792.2319 2747 -134481.0000 -135304.7364 106870 0.61',
' 11005+ 9743 -134482.0000 -135304.7364 0.61',
' 11005 10377 -135051.5669 3342 -134482.0000 -135304.7364 106128 0.61',
' 11006+ 9743 -134485.0000 -135304.7364 0.61',
' 11007 9697 -135085.5502 3356 -134485.0000 -135304.7364 100103 0.61']
我的问题是，如果我try 用Pandas读取它，缺少的值会将值移动到错误的单元格中.
Node Left Objective IInf BestInteger BestBound ItCnt Gap
0 0+ 0 0.0000 -6.819503e+06 --- NaN NaN NaN
1 0 0 -149597.6789 3.202000e+03 0.0000 -149597.6789 37.0 ---
2 0+ 0 -83655.0000 -1.495977e+05 78.83 NaN NaN NaN
3 0+ 0 -125854.0000 -1.495977e+05 18.87 NaN NaN NaN
4 0+ 0 -126474.0000 -1.495977e+05 18.28 NaN NaN NaN
5 0+ 0 -130810.0000 -1.385287e+05 5.90 NaN NaN NaN
6 0+ 0 -133740.0000 -1.353053e+05 1.17 NaN NaN NaN
7 0 2 -135305.2732 3.622000e+03 -133740.0000 -135305.2732 6307.0 1.17
8 79 49 -135300.3578 3.627000e+03 -133740.0000 -135304.7364 6743.0 1.17
9 207 172 -135294.5414 3.606000e+03 -133740.0000 -135304.7364 8033.0 1.17
我使用下面这行代码将我的列表解析给Pandas :
test = pd.read_csv(io.StringIO("\n".join(milp_log)), delim_whitespace=True)