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211 lines
7.3 KiB
211 lines
7.3 KiB
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "3371f072-be1e-4c47-b765-b5d30fdbfae6",
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"metadata": {},
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"source": [
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"# Solvers\n",
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"\n",
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"## LearningSolver\n",
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"\n",
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"### Example"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64)\n",
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"Thread count: 6 physical cores, 12 logical processors, using up to 12 threads\n",
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"Optimize a model with 10 rows, 45 columns and 90 nonzeros\n",
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"Model fingerprint: 0x6ddcd141\n",
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"Coefficient statistics:\n",
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" Matrix range [1e+00, 1e+00]\n",
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" Objective range [4e+01, 1e+03]\n",
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" Bounds range [1e+00, 1e+00]\n",
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" RHS range [2e+00, 2e+00]\n",
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"Presolve time: 0.00s\n",
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"Presolved: 10 rows, 45 columns, 90 nonzeros\n",
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"\n",
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"Iteration Objective Primal Inf. Dual Inf. Time\n",
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" 0 6.3600000e+02 1.700000e+01 0.000000e+00 0s\n",
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" 15 2.7610000e+03 0.000000e+00 0.000000e+00 0s\n",
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"\n",
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"Solved in 15 iterations and 0.01 seconds (0.00 work units)\n",
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"Optimal objective 2.761000000e+03\n",
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"Set parameter LazyConstraints to value 1\n",
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"Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64)\n",
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"Thread count: 6 physical cores, 12 logical processors, using up to 12 threads\n",
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"Optimize a model with 10 rows, 45 columns and 90 nonzeros\n",
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"Model fingerprint: 0x74ca3d0a\n",
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"Variable types: 0 continuous, 45 integer (45 binary)\n",
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"Coefficient statistics:\n",
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" Matrix range [1e+00, 1e+00]\n",
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" Objective range [4e+01, 1e+03]\n",
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" Bounds range [1e+00, 1e+00]\n",
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" RHS range [2e+00, 2e+00]\n",
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"\n",
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"User MIP start produced solution with objective 2796 (0.01s)\n",
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"Loaded user MIP start with objective 2796\n",
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"\n",
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"Presolve time: 0.00s\n",
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"Presolved: 10 rows, 45 columns, 90 nonzeros\n",
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"Variable types: 0 continuous, 45 integer (45 binary)\n",
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"\n",
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"Root relaxation: objective 2.761000e+03, 14 iterations, 0.00 seconds (0.00 work units)\n",
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"\n",
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" Nodes | Current Node | Objective Bounds | Work\n",
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" Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time\n",
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"\n",
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" 0 0 cutoff 0 2796.00000 2796.00000 0.00% - 0s\n",
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"\n",
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"Cutting planes:\n",
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" Lazy constraints: 3\n",
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"\n",
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"Explored 1 nodes (15 simplex iterations) in 0.02 seconds (0.00 work units)\n",
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"Thread count was 12 (of 12 available processors)\n",
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"\n",
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"Solution count 1: 2796 \n",
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"\n",
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"Optimal solution found (tolerance 1.00e-04)\n",
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"Best objective 2.796000000000e+03, best bound 2.796000000000e+03, gap 0.0000%\n",
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"\n",
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"User-callback calls 103, time in user-callback 0.00 sec\n",
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"Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64)\n",
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"Thread count: 6 physical cores, 12 logical processors, using up to 12 threads\n",
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"Optimize a model with 10 rows, 45 columns and 90 nonzeros\n",
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"Model fingerprint: 0x74ca3d0a\n",
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"Variable types: 0 continuous, 45 integer (45 binary)\n",
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"Coefficient statistics:\n",
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" Matrix range [1e+00, 1e+00]\n",
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" Objective range [4e+01, 1e+03]\n",
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" Bounds range [1e+00, 1e+00]\n",
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" RHS range [2e+00, 2e+00]\n",
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"Presolved: 10 rows, 45 columns, 90 nonzeros\n",
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"\n",
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"Continuing optimization...\n",
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"\n",
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"\n",
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"Cutting planes:\n",
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" Lazy constraints: 3\n",
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"\n",
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"Explored 1 nodes (15 simplex iterations) in 0.01 seconds (0.00 work units)\n",
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"Thread count was 12 (of 12 available processors)\n",
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"\n",
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"Solution count 1: 2796 \n",
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"\n",
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"Optimal solution found (tolerance 1.00e-04)\n",
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"Best objective 2.796000000000e+03, best bound 2.796000000000e+03, gap 0.0000%\n",
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"\n",
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"User-callback calls 27, time in user-callback 0.00 sec\n"
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]
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}
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],
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"source": [
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"import random\n",
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"\n",
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"import numpy as np\n",
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"from scipy.stats import uniform, randint\n",
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"from sklearn.linear_model import LogisticRegression\n",
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"\n",
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"from miplearn.classifiers.minprob import MinProbabilityClassifier\n",
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"from miplearn.classifiers.singleclass import SingleClassFix\n",
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"from miplearn.collectors.basic import BasicCollector\n",
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"from miplearn.components.primal.actions import SetWarmStart\n",
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"from miplearn.components.primal.indep import IndependentVarsPrimalComponent\n",
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"from miplearn.extractors.AlvLouWeh2017 import AlvLouWeh2017Extractor\n",
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"from miplearn.io import save\n",
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"from miplearn.problems.tsp import (\n",
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" TravelingSalesmanGenerator,\n",
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" build_tsp_model,\n",
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")\n",
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"from miplearn.solvers.learning import LearningSolver\n",
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"\n",
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"# Set random seed to make example reproducible.\n",
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"random.seed(42)\n",
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"np.random.seed(42)\n",
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"\n",
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"# Generate a few instances of the traveling salesman problem.\n",
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"data = TravelingSalesmanGenerator(\n",
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" n=randint(low=10, high=11),\n",
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" x=uniform(loc=0.0, scale=1000.0),\n",
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" y=uniform(loc=0.0, scale=1000.0),\n",
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" gamma=uniform(loc=0.90, scale=0.20),\n",
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" fix_cities=True,\n",
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" round=True,\n",
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").generate(50)\n",
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"\n",
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"# Save instance data to data/tsp/00000.pkl.gz, data/tsp/00001.pkl.gz, ...\n",
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"all_data = save(data, \"data/tsp\")\n",
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"\n",
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"# Split train/test data\n",
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"train_data = all_data[:40]\n",
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"test_data = all_data[40:]\n",
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"\n",
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"# Collect training data\n",
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"bc = BasicCollector(time_limit_sec=3600)\n",
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"bc.collect(train_data, build_tsp_model, n_jobs=4)\n",
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"\n",
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"# Build learning solver\n",
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"solver = LearningSolver(\n",
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" components=[\n",
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" IndependentVarsPrimalComponent(\n",
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" base_clf=SingleClassFix(\n",
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" MinProbabilityClassifier(\n",
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" base_clf=LogisticRegression(),\n",
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" thresholds=[0.95, 0.95],\n",
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" ),\n",
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" ),\n",
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" extractor=AlvLouWeh2017Extractor(),\n",
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" action=SetWarmStart(),\n",
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" )\n",
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" ]\n",
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")\n",
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"\n",
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"# Train ML models\n",
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"solver.fit(train_data)\n",
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"\n",
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"# Solve a test instance\n",
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"solver.optimize(test_data[0], build_tsp_model);"
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],
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"metadata": {
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"collapsed": false
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}
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "e27d2cbd-5341-461d-bbc1-8131aee8d949",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.8.13"
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"nbformat": 4,
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"nbformat_minor": 5
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}
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