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Implement BinPackPerturber

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Alinson S. Xavier
2025-12-08 13:16:23 -06:00
parent a4cb46f73e
commit 9192bb02eb
3 changed files with 111 additions and 100 deletions
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69
docs/guide/problems.ipynb
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@@ -87,11 +87,12 @@
"\n",
"Random instances of the bin packing problem can be generated using the class [BinPackGenerator][BinPackGenerator].\n",
"\n",
"If `fix_items=False`, the class samples the user-provided probability distributions `n`, `sizes` and `capacity` to decide, respectively, the number of items, the sizes of the items and capacity of the bin. All values are sampled independently.\n",
"The class samples the user-provided probability distributions `n`, `sizes` and `capacity` to decide, respectively, the number of items, the sizes of the items and capacity of the bin. All values are sampled independently.\n",
"\n",
"If `fix_items=True`, the class creates a reference instance, using the method previously described, then generates additional instances by perturbing its item sizes and bin capacity. More specifically, the sizes of the items are set to $s_i \\gamma_i$, where $s_i$ is the size of the $i$-th item in the reference instance and $\\gamma_i$ is sampled from `sizes_jitter`. Similarly, the bin size is set to $B \\beta$, where $B$ is the reference bin size and $\\beta$ is sampled from `capacity_jitter`. The number of items remains the same across all generated instances.\n",
"To create multiple instances with the same items but different sizes and capacities, you can use [BinPackPerturber][BinPackPerturber]. This class takes an existing BinPackData instance and generates new instances by perturbing its item sizes and bin capacity. More specifically, the sizes of the items are set to $s_i \\gamma_i$, where $s_i$ is the size of the $i$-th item in the reference instance and $\\gamma_i$ is sampled from `sizes_jitter`. Similarly, the bin size is set to $B \\beta$, where $B$ is the reference bin size and $\\beta$ is sampled from `capacity_jitter`. The number of items remains the same across all generated instances.\n",
"\n",
"[BinPackGenerator]: ../../api/problems/#miplearn.problems.binpack.BinPackGenerator"
"[BinPackGenerator]: ../../api/problems/#miplearn.problems.binpack.BinPackGenerator\n",
"[BinPackPerturber]: ../../api/problems/#miplearn.problems.binpack.BinPackPerturber"
]
},
{
@@ -104,7 +105,7 @@
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 2,
"id": "f14e560c-ef9f-4c48-8467-72d6acce5f9f",
"metadata": {
"ExecuteTime": {
@@ -118,25 +119,24 @@
"name": "stdout",
"output_type": "stream",
"text": [
"0 [ 8.47 26. 19.52 14.11 3.65 3.65 1.4 21.76 14.82 16.96] 102.24\n",
"1 [ 8.69 22.78 17.81 14.83 4.12 3.67 1.46 22.05 13.66 18.08] 93.41\n",
"2 [ 8.55 25.9 20. 15.89 3.75 3.59 1.51 21.4 13.89 17.68] 90.69\n",
"3 [10.13 22.62 18.89 14.4 3.92 3.94 1.36 23.69 15.85 19.26] 107.9\n",
"4 [ 9.55 25.77 16.79 14.06 3.55 3.76 1.42 20.66 16.02 17.19] 95.62\n",
"5 [ 9.44 22.06 19.41 13.69 4.28 4.11 1.36 19.51 15.98 18.43] 104.58\n",
"6 [ 9.87 21.74 17.78 13.82 4.18 4. 1.4 19.76 14.46 17.08] 104.59\n",
"7 [ 9.62 25.61 18.2 13.83 4.07 4.1 1.47 22.83 15.01 17.78] 98.55\n",
"8 [ 8.47 21.9 16.58 15.37 3.76 3.91 1.57 20.57 14.76 18.61] 94.58\n",
"9 [ 8.57 22.77 17.06 16.25 4.14 4. 1.56 22.97 14.09 19.09] 100.79\n",
"0 [ 8.46 26. 19.52 14.11 3.65 3.65 1.39 21.76 14.83 16.96] 102.24\n",
"1 [ 8.69 22.78 17.81 14.84 4.12 3.67 1.45 22.05 13.67 18.08] 93.41\n",
"2 [ 8.55 25.9 20. 15.89 3.75 3.59 1.5 21.39 13.89 17.68] 90.69\n",
"3 [10.13 22.62 18.89 14.41 3.92 3.94 1.36 23.68 15.86 19.26] 107.9\n",
"4 [ 9.54 25.78 16.79 14.06 3.55 3.76 1.42 20.66 16.02 17.19] 95.62\n",
"5 [ 9.44 22.06 19.41 13.7 4.28 4.11 1.36 19.51 15.98 18.43] 104.58\n",
"6 [ 9.87 21.75 17.78 13.82 4.18 4. 1.4 19.76 14.46 17.08] 104.59\n",
"7 [ 9.62 25.61 18.2 13.83 4.07 4.1 1.47 22.82 15.01 17.78] 98.55\n",
"8 [ 8.47 21.91 16.59 15.38 3.76 3.91 1.57 20.56 14.76 18.6 ] 94.58\n",
"9 [ 8.57 22.77 17.06 16.26 4.14 4. 1.56 22.96 14.09 19.09] 100.79\n",
"\n",
"Set parameter LicenseID to value 389975\n",
"Gurobi Optimizer version 13.0.0 build v13.0.0rc1 (linux64 - \"Ubuntu 22.04.5 LTS\")\n",
"\n",
"CPU model: AMD Ryzen 9 7950X 16-Core Processor, instruction set [SSE2|AVX|AVX2|AVX512]\n",
"Thread count: 16 physical cores, 16 logical processors, using up to 16 threads\n",
"\n",
"Optimize a model with 20 rows, 110 columns and 210 nonzeros (Min)\n",
"Model fingerprint: 0x1ff9913f\n",
"Model fingerprint: 0xde862da6\n",
"Model has 10 linear objective coefficients\n",
"Variable types: 0 continuous, 110 integer (110 binary)\n",
"Coefficient statistics:\n",
@@ -149,18 +149,18 @@
"Presolved: 20 rows, 110 columns, 210 nonzeros\n",
"Variable types: 0 continuous, 110 integer (110 binary)\n",
"\n",
"Root relaxation: objective 1.274844e+00, 38 iterations, 0.00 seconds (0.00 work units)\n",
"Root relaxation: objective 1.274746e+00, 39 iterations, 0.00 seconds (0.00 work units)\n",
"\n",
" Nodes | Current Node | Objective Bounds | Work\n",
" Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time\n",
"\n",
" 0 0 1.27484 0 4 5.00000 1.27484 74.5% - 0s\n",
"H 0 0 4.0000000 1.27484 68.1% - 0s\n",
"H 0 0 3.0000000 1.27484 57.5% - 0s\n",
"H 0 0 2.0000000 1.27484 36.3% - 0s\n",
" 0 0 1.27484 0 4 2.00000 1.27484 36.3% - 0s\n",
" 0 0 1.27475 0 4 5.00000 1.27475 74.5% - 0s\n",
"H 0 0 4.0000000 1.27475 68.1% - 0s\n",
"H 0 0 3.0000000 1.27475 57.5% - 0s\n",
"H 0 0 2.0000000 1.27475 36.3% - 0s\n",
" 0 0 1.27475 0 4 2.00000 1.27475 36.3% - 0s\n",
"\n",
"Explored 1 nodes (38 simplex iterations) in 0.00 seconds (0.00 work units)\n",
"Explored 1 nodes (39 simplex iterations) in 0.01 seconds (0.00 work units)\n",
"Thread count was 16 (of 16 available processors)\n",
"\n",
"Solution count 4: 2 3 4 5 \n",
@@ -170,33 +170,30 @@
"\n",
"User-callback calls 149, time in user-callback 0.00 sec\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/axavier/Notebooks/[2025-12] Parallel BB Datasets/venv/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
" from .autonotebook import tqdm as notebook_tqdm\n"
]
}
],
"source": [
"import numpy as np\n",
"from scipy.stats import uniform, randint\n",
"from miplearn.problems.binpack import BinPackGenerator, build_binpack_model_gurobipy\n",
"from miplearn.problems.binpack import BinPackGenerator, BinPackPerturber, build_binpack_model_gurobipy\n",
"\n",
"# Set random seed, to make example reproducible\n",
"np.random.seed(42)\n",
"\n",
"# Generate random instances of the binpack problem with ten items\n",
"data = BinPackGenerator(\n",
"# Generate a reference instance with ten items\n",
"generator = BinPackGenerator(\n",
" n=randint(low=10, high=11),\n",
" sizes=uniform(loc=0, scale=25),\n",
" capacity=uniform(loc=100, scale=0),\n",
")\n",
"reference_instance = generator.generate(1)[0]\n",
"\n",
"# Generate perturbed instances using the reference\n",
"perturber = BinPackPerturber(\n",
" sizes_jitter=uniform(loc=0.9, scale=0.2),\n",
" capacity_jitter=uniform(loc=0.9, scale=0.2),\n",
" fix_items=True,\n",
").generate(10)\n",
")\n",
"data = perturber.perturb(reference_instance, 10)\n",
"\n",
"# Print sizes and capacities\n",
"for i in range(10):\n",