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    "# Solvers\n",
    "\n",
    "## LearningSolver\n",
    "\n",
    "### Example"
   ]
  },
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     "text": [
      "Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64)\n",
      "Thread count: 6 physical cores, 12 logical processors, using up to 12 threads\n",
      "Optimize a model with 10 rows, 45 columns and 90 nonzeros\n",
      "Model fingerprint: 0x6ddcd141\n",
      "Coefficient statistics:\n",
      "  Matrix range     [1e+00, 1e+00]\n",
      "  Objective range  [4e+01, 1e+03]\n",
      "  Bounds range     [1e+00, 1e+00]\n",
      "  RHS range        [2e+00, 2e+00]\n",
      "Presolve time: 0.00s\n",
      "Presolved: 10 rows, 45 columns, 90 nonzeros\n",
      "\n",
      "Iteration    Objective       Primal Inf.    Dual Inf.      Time\n",
      "       0    6.3600000e+02   1.700000e+01   0.000000e+00      0s\n",
      "      15    2.7610000e+03   0.000000e+00   0.000000e+00      0s\n",
      "\n",
      "Solved in 15 iterations and 0.01 seconds (0.00 work units)\n",
      "Optimal objective  2.761000000e+03\n",
      "Set parameter LazyConstraints to value 1\n",
      "Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64)\n",
      "Thread count: 6 physical cores, 12 logical processors, using up to 12 threads\n",
      "Optimize a model with 10 rows, 45 columns and 90 nonzeros\n",
      "Model fingerprint: 0x74ca3d0a\n",
      "Variable types: 0 continuous, 45 integer (45 binary)\n",
      "Coefficient statistics:\n",
      "  Matrix range     [1e+00, 1e+00]\n",
      "  Objective range  [4e+01, 1e+03]\n",
      "  Bounds range     [1e+00, 1e+00]\n",
      "  RHS range        [2e+00, 2e+00]\n",
      "\n",
      "User MIP start produced solution with objective 2796 (0.01s)\n",
      "Loaded user MIP start with objective 2796\n",
      "\n",
      "Presolve time: 0.00s\n",
      "Presolved: 10 rows, 45 columns, 90 nonzeros\n",
      "Variable types: 0 continuous, 45 integer (45 binary)\n",
      "\n",
      "Root relaxation: objective 2.761000e+03, 14 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     cutoff    0      2796.00000 2796.00000  0.00%     -    0s\n",
      "\n",
      "Cutting planes:\n",
      "  Lazy constraints: 3\n",
      "\n",
      "Explored 1 nodes (15 simplex iterations) in 0.02 seconds (0.00 work units)\n",
      "Thread count was 12 (of 12 available processors)\n",
      "\n",
      "Solution count 1: 2796 \n",
      "\n",
      "Optimal solution found (tolerance 1.00e-04)\n",
      "Best objective 2.796000000000e+03, best bound 2.796000000000e+03, gap 0.0000%\n",
      "\n",
      "User-callback calls 103, time in user-callback 0.00 sec\n",
      "Gurobi Optimizer version 9.5.2 build v9.5.2rc0 (linux64)\n",
      "Thread count: 6 physical cores, 12 logical processors, using up to 12 threads\n",
      "Optimize a model with 10 rows, 45 columns and 90 nonzeros\n",
      "Model fingerprint: 0x74ca3d0a\n",
      "Variable types: 0 continuous, 45 integer (45 binary)\n",
      "Coefficient statistics:\n",
      "  Matrix range     [1e+00, 1e+00]\n",
      "  Objective range  [4e+01, 1e+03]\n",
      "  Bounds range     [1e+00, 1e+00]\n",
      "  RHS range        [2e+00, 2e+00]\n",
      "Presolved: 10 rows, 45 columns, 90 nonzeros\n",
      "\n",
      "Continuing optimization...\n",
      "\n",
      "\n",
      "Cutting planes:\n",
      "  Lazy constraints: 3\n",
      "\n",
      "Explored 1 nodes (15 simplex iterations) in 0.01 seconds (0.00 work units)\n",
      "Thread count was 12 (of 12 available processors)\n",
      "\n",
      "Solution count 1: 2796 \n",
      "\n",
      "Optimal solution found (tolerance 1.00e-04)\n",
      "Best objective 2.796000000000e+03, best bound 2.796000000000e+03, gap 0.0000%\n",
      "\n",
      "User-callback calls 27, time in user-callback 0.00 sec\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "import numpy as np\n",
    "from scipy.stats import uniform, randint\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "from miplearn.classifiers.minprob import MinProbabilityClassifier\n",
    "from miplearn.classifiers.singleclass import SingleClassFix\n",
    "from miplearn.collectors.basic import BasicCollector\n",
    "from miplearn.components.primal.actions import SetWarmStart\n",
    "from miplearn.components.primal.indep import IndependentVarsPrimalComponent\n",
    "from miplearn.extractors.AlvLouWeh2017 import AlvLouWeh2017Extractor\n",
    "from miplearn.io import save\n",
    "from miplearn.problems.tsp import (\n",
    "    TravelingSalesmanGenerator,\n",
    "    build_tsp_model,\n",
    ")\n",
    "from miplearn.solvers.learning import LearningSolver\n",
    "\n",
    "# Set random seed to make example reproducible.\n",
    "random.seed(42)\n",
    "np.random.seed(42)\n",
    "\n",
    "# Generate a few instances of the traveling salesman problem.\n",
    "data = TravelingSalesmanGenerator(\n",
    "    n=randint(low=10, high=11),\n",
    "    x=uniform(loc=0.0, scale=1000.0),\n",
    "    y=uniform(loc=0.0, scale=1000.0),\n",
    "    gamma=uniform(loc=0.90, scale=0.20),\n",
    "    fix_cities=True,\n",
    "    round=True,\n",
    ").generate(50)\n",
    "\n",
    "# Save instance data to data/tsp/00000.pkl.gz, data/tsp/00001.pkl.gz, ...\n",
    "all_data = save(data, \"data/tsp\")\n",
    "\n",
    "# Split train/test data\n",
    "train_data = all_data[:40]\n",
    "test_data = all_data[40:]\n",
    "\n",
    "# Collect training data\n",
    "bc = BasicCollector(time_limit_sec=3600)\n",
    "bc.collect(train_data, build_tsp_model, n_jobs=4)\n",
    "\n",
    "# Build learning solver\n",
    "solver = LearningSolver(\n",
    "    components=[\n",
    "        IndependentVarsPrimalComponent(\n",
    "            base_clf=SingleClassFix(\n",
    "                MinProbabilityClassifier(\n",
    "                    base_clf=LogisticRegression(),\n",
    "                    thresholds=[0.95, 0.95],\n",
    "                ),\n",
    "            ),\n",
    "            extractor=AlvLouWeh2017Extractor(),\n",
    "            action=SetWarmStart(),\n",
    "        )\n",
    "    ]\n",
    ")\n",
    "\n",
    "# Train ML models\n",
    "solver.fit(train_data)\n",
    "\n",
    "# Solve a test instance\n",
    "solver.optimize(test_data[0], build_tsp_model);"
   ]
  },
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