mirror of
https://github.com/ANL-CEEESA/MIPLearn.git
synced 2025-12-09 02:48:52 -06:00
Implement SetPackPerturber and SetCoverPerturber
This commit is contained in:
@@ -665,9 +665,10 @@
|
||||
"\n",
|
||||
"Finally, the weight of set $j$ is set to $w_j + K | S_j |$, where $w_j$ and $k$ are sampled from `costs` and `K`, respectively, and where $|S_j|$ denotes the size of set $S_j$. The parameter $K$ is used to introduce some correlation between the size of the set and its weight, making the instance more challenging. Note that `K` is only sampled once for the entire instance.\n",
|
||||
"\n",
|
||||
"If `fix_sets=True`, then all generated instances have exactly the same sets and elements. The costs of the sets, however, are multiplied by random scaling factors sampled from the provided probability distribution `costs_jitter`.\n",
|
||||
"To create multiple instances with the same incidence matrix but different costs, you can use [SetCoverPerturber][SetCoverPerturber]. This class takes an existing SetCoverData instance and generates new instances by applying randomization factors to the existing costs while keeping the incidence matrix fixed.\n",
|
||||
"\n",
|
||||
"[SetCoverGenerator]: ../../api/problems/#miplearn.problems.setcover.SetCoverGenerator"
|
||||
"[SetCoverGenerator]: ../../api/problems/#miplearn.problems.setcover.SetCoverGenerator\n",
|
||||
"[SetCoverPerturber]: ../../api/problems/#miplearn.problems.setcover.SetCoverPerturber"
|
||||
]
|
||||
},
|
||||
{
|
||||
@@ -680,7 +681,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"execution_count": 8,
|
||||
"id": "3224845b-9afd-463e-abf4-e0e93d304859",
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
@@ -699,8 +700,8 @@
|
||||
" [0 1 1 1 1 0 1 0 0 1]\n",
|
||||
" [0 1 1 0 0 0 1 1 0 1]\n",
|
||||
" [1 1 1 0 1 0 1 0 0 1]]\n",
|
||||
"costs [1044.58 850.13 1014.5 944.83 697.9 971.87 213.49 220.98 70.23\n",
|
||||
" 425.33]\n",
|
||||
"costs [1021.32 811.25 1081.2 917.76 667.32 980.17 198.16 234.34 64.25\n",
|
||||
" 466.75]\n",
|
||||
"\n",
|
||||
"Gurobi Optimizer version 13.0.0 build v13.0.0rc1 (linux64 - \"Ubuntu 22.04.5 LTS\")\n",
|
||||
"\n",
|
||||
@@ -708,15 +709,15 @@
|
||||
"Thread count: 16 physical cores, 16 logical processors, using up to 16 threads\n",
|
||||
"\n",
|
||||
"Optimize a model with 5 rows, 10 columns and 28 nonzeros (Min)\n",
|
||||
"Model fingerprint: 0xe5c2d4fa\n",
|
||||
"Model fingerprint: 0x57e5c4ba\n",
|
||||
"Model has 10 linear objective coefficients\n",
|
||||
"Variable types: 0 continuous, 10 integer (10 binary)\n",
|
||||
"Coefficient statistics:\n",
|
||||
" Matrix range [1e+00, 1e+00]\n",
|
||||
" Objective range [7e+01, 1e+03]\n",
|
||||
" Objective range [6e+01, 1e+03]\n",
|
||||
" Bounds range [1e+00, 1e+00]\n",
|
||||
" RHS range [1e+00, 1e+00]\n",
|
||||
"Found heuristic solution: objective 213.4900000\n",
|
||||
"Found heuristic solution: objective 198.1600000\n",
|
||||
"Presolve removed 5 rows and 10 columns\n",
|
||||
"Presolve time: 0.00s\n",
|
||||
"Presolve: All rows and columns removed\n",
|
||||
@@ -724,10 +725,10 @@
|
||||
"Explored 0 nodes (0 simplex iterations) in 0.00 seconds (0.00 work units)\n",
|
||||
"Thread count was 1 (of 16 available processors)\n",
|
||||
"\n",
|
||||
"Solution count 1: 213.49 \n",
|
||||
"Solution count 1: 198.16 \n",
|
||||
"\n",
|
||||
"Optimal solution found (tolerance 1.00e-04)\n",
|
||||
"Best objective 2.134900000000e+02, best bound 2.134900000000e+02, gap 0.0000%\n",
|
||||
"Best objective 1.981600000000e+02, best bound 1.981600000000e+02, gap 0.0000%\n",
|
||||
"\n",
|
||||
"User-callback calls 181, time in user-callback 0.00 sec\n"
|
||||
]
|
||||
@@ -736,23 +737,26 @@
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"from scipy.stats import uniform, randint\n",
|
||||
"from miplearn.problems.setcover import SetCoverGenerator, build_setcover_model_gurobipy\n",
|
||||
"from miplearn.problems.setcover import SetCoverGenerator, SetCoverPerturber, build_setcover_model_gurobipy\n",
|
||||
"\n",
|
||||
"# Set random seed, to make example reproducible\n",
|
||||
"np.random.seed(42)\n",
|
||||
"\n",
|
||||
"# Build random instances with five elements, ten sets and costs\n",
|
||||
"# in the [0, 1000] interval, with a correlation factor of 25 and\n",
|
||||
"# an incidence matrix with 25% density.\n",
|
||||
"data = SetCoverGenerator(\n",
|
||||
"# Generate a reference instance with five elements and ten sets\n",
|
||||
"generator = SetCoverGenerator(\n",
|
||||
" n_elements=randint(low=5, high=6),\n",
|
||||
" n_sets=randint(low=10, high=11),\n",
|
||||
" costs=uniform(loc=0.0, scale=1000.0),\n",
|
||||
" costs_jitter=uniform(loc=0.90, scale=0.20),\n",
|
||||
" density=uniform(loc=0.5, scale=0.00),\n",
|
||||
" K=uniform(loc=25.0, scale=0.0),\n",
|
||||
" fix_sets=True,\n",
|
||||
").generate(10)\n",
|
||||
")\n",
|
||||
"reference_instance = generator.generate(1)[0]\n",
|
||||
"\n",
|
||||
"# Generate perturbed instances using the reference\n",
|
||||
"perturber = SetCoverPerturber(\n",
|
||||
" costs_jitter=uniform(loc=0.9, scale=0.2),\n",
|
||||
")\n",
|
||||
"data = perturber.perturb(reference_instance, 10)\n",
|
||||
"\n",
|
||||
"# Print problem data for one instance\n",
|
||||
"print(\"matrix\\n\", data[0].incidence_matrix)\n",
|
||||
@@ -810,15 +814,18 @@
|
||||
"\n",
|
||||
"The class [SetPackGenerator][SetPackGenerator] can generate random instances of this problem. It accepts exactly the same arguments, and generates instance data in exactly the same way as [SetCoverGenerator][SetCoverGenerator]. For more details, please see the documentation for that class.\n",
|
||||
"\n",
|
||||
"To create multiple instances with the same incidence matrix but different costs, you can use [SetPackPerturber][SetPackPerturber]. This class takes an existing SetPackData instance and generates new instances by applying randomization factors to the existing costs while keeping the incidence matrix fixed.\n",
|
||||
"\n",
|
||||
"[SetPackGenerator]: ../../api/problems/#miplearn.problems.setpack.SetPackGenerator\n",
|
||||
"[SetCoverGenerator]: ../../api/problems/#miplearn.problems.setcover.SetCoverGenerator\n",
|
||||
"[SetPackPerturber]: ../../api/problems/#miplearn.problems.setpack.SetPackPerturber\n",
|
||||
"\n",
|
||||
"### Example"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"execution_count": 9,
|
||||
"id": "cc797da7",
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
@@ -841,8 +848,8 @@
|
||||
" [0 1 1 1 1 0 1 0 0 1]\n",
|
||||
" [0 1 1 0 0 0 1 1 0 1]\n",
|
||||
" [1 1 1 0 1 0 1 0 0 1]]\n",
|
||||
"costs [1044.58 850.13 1014.5 944.83 697.9 971.87 213.49 220.98 70.23\n",
|
||||
" 425.33]\n",
|
||||
"costs [1021.32 811.25 1081.2 917.76 667.32 980.17 198.16 234.34 64.25\n",
|
||||
" 466.75]\n",
|
||||
"\n",
|
||||
"Gurobi Optimizer version 13.0.0 build v13.0.0rc1 (linux64 - \"Ubuntu 22.04.5 LTS\")\n",
|
||||
"\n",
|
||||
@@ -850,15 +857,15 @@
|
||||
"Thread count: 16 physical cores, 16 logical processors, using up to 16 threads\n",
|
||||
"\n",
|
||||
"Optimize a model with 5 rows, 10 columns and 28 nonzeros (Min)\n",
|
||||
"Model fingerprint: 0x4ee91388\n",
|
||||
"Model fingerprint: 0x75cd8328\n",
|
||||
"Model has 10 linear objective coefficients\n",
|
||||
"Variable types: 0 continuous, 10 integer (10 binary)\n",
|
||||
"Coefficient statistics:\n",
|
||||
" Matrix range [1e+00, 1e+00]\n",
|
||||
" Objective range [7e+01, 1e+03]\n",
|
||||
" Objective range [6e+01, 1e+03]\n",
|
||||
" Bounds range [1e+00, 1e+00]\n",
|
||||
" RHS range [1e+00, 1e+00]\n",
|
||||
"Found heuristic solution: objective -1265.560000\n",
|
||||
"Found heuristic solution: objective -1255.660000\n",
|
||||
"Presolve removed 5 rows and 10 columns\n",
|
||||
"Presolve time: 0.00s\n",
|
||||
"Presolve: All rows and columns removed\n",
|
||||
@@ -866,11 +873,11 @@
|
||||
"Explored 0 nodes (0 simplex iterations) in 0.00 seconds (0.00 work units)\n",
|
||||
"Thread count was 1 (of 16 available processors)\n",
|
||||
"\n",
|
||||
"Solution count 2: -1986.37 -1265.56 \n",
|
||||
"No other solutions better than -1986.37\n",
|
||||
"Solution count 2: -2061.37 -1255.66 \n",
|
||||
"No other solutions better than -2061.37\n",
|
||||
"\n",
|
||||
"Optimal solution found (tolerance 1.00e-04)\n",
|
||||
"Best objective -1.986370000000e+03, best bound -1.986370000000e+03, gap 0.0000%\n",
|
||||
"Best objective -2.061370000000e+03, best bound -2.061370000000e+03, gap 0.0000%\n",
|
||||
"\n",
|
||||
"User-callback calls 242, time in user-callback 0.00 sec\n"
|
||||
]
|
||||
@@ -879,23 +886,26 @@
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"from scipy.stats import uniform, randint\n",
|
||||
"from miplearn.problems.setpack import SetPackGenerator, build_setpack_model_gurobipy\n",
|
||||
"from miplearn.problems.setpack import SetPackGenerator, SetPackPerturber, build_setpack_model_gurobipy\n",
|
||||
"\n",
|
||||
"# Set random seed, to make example reproducible\n",
|
||||
"np.random.seed(42)\n",
|
||||
"\n",
|
||||
"# Build random instances with five elements, ten sets and costs\n",
|
||||
"# in the [0, 1000] interval, with a correlation factor of 25 and\n",
|
||||
"# an incidence matrix with 25% density.\n",
|
||||
"data = SetPackGenerator(\n",
|
||||
"# Generate a reference instance with five elements and ten sets\n",
|
||||
"generator = SetPackGenerator(\n",
|
||||
" n_elements=randint(low=5, high=6),\n",
|
||||
" n_sets=randint(low=10, high=11),\n",
|
||||
" costs=uniform(loc=0.0, scale=1000.0),\n",
|
||||
" costs_jitter=uniform(loc=0.90, scale=0.20),\n",
|
||||
" density=uniform(loc=0.5, scale=0.00),\n",
|
||||
" K=uniform(loc=25.0, scale=0.0),\n",
|
||||
" fix_sets=True,\n",
|
||||
").generate(10)\n",
|
||||
")\n",
|
||||
"reference_instance = generator.generate(1)[0]\n",
|
||||
"\n",
|
||||
"# Generate perturbed instances using the reference\n",
|
||||
"perturber = SetPackPerturber(\n",
|
||||
" costs_jitter=uniform(loc=0.9, scale=0.2),\n",
|
||||
")\n",
|
||||
"data = perturber.perturb(reference_instance, 10)\n",
|
||||
"\n",
|
||||
"# Print problem data for one instance\n",
|
||||
"print(\"matrix\\n\", data[0].incidence_matrix)\n",
|
||||
|
||||
Reference in New Issue
Block a user