{
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   "id": "6f01675f-cd1a-4712-9ca4-3cc1dbbd9056",
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    "tags": []
   },
   "source": [
    "## Multiple Objective Optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "567ea5b7-f871-4ce3-8970-6f82728de9bf",
   "metadata": {
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   "source": [
    "An example that computes a Pareto-optimal point of a benchmark function using Chebyshev weights is given in the\n",
    "following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cae5fecd-f30c-4a5c-966e-c14008a1af8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from entmoot import Enting, ProblemConfig, GurobiOptimizer, PyomoOptimizer\n",
    "from entmoot.benchmarks import (\n",
    "    build_multi_obj_categorical_problem,\n",
    "    eval_multi_obj_cat_testfunc,\n",
    ")\n",
    "\n",
    "# define problem\n",
    "problem_config = ProblemConfig(rnd_seed=73)\n",
    "# number of objectives\n",
    "number_objectives = 2\n",
    "build_multi_obj_categorical_problem(problem_config, n_obj=number_objectives)\n",
    "\n",
    "# sample data\n",
    "rnd_sample = problem_config.get_rnd_sample_list(num_samples=20)\n",
    "testfunc_evals = eval_multi_obj_cat_testfunc(rnd_sample, n_obj=number_objectives)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "180e8e4b",
   "metadata": {},
   "source": [
    "Model parameters can be defined in a few ways:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "63286741",
   "metadata": {},
   "outputs": [],
   "source": [
    "from entmoot import EntingParams, UncParams, TrainParams, TreeTrainParams\n",
    "\n",
    "# all three of the below `params` are valid arguments for Enting\n",
    "params = {\"unc_params\": {\"dist_metric\": \"l1\", \"acq_sense\": \"exploration\"}}\n",
    "params = EntingParams(**params)\n",
    "params = EntingParams(unc_params=UncParams(dist_metric=\"l1\", acq_sense=\"exploration\"))\n",
    "\n",
    "enting = Enting(problem_config, params=params)\n",
    "# fit tree ensemble\n",
    "enting.fit(rnd_sample, testfunc_evals)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b88056d-e265-4097-9711-6bdcec723b44",
   "metadata": {},
   "source": [
    "### Gurobi Version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "311c88fa-f757-44f3-8ae5-555f715fc1b4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Build GurobiOptimizer object and solve optimization problem\n",
    "params_gurobi = {\"MIPGap\": 0}\n",
    "opt_gur = GurobiOptimizer(problem_config, params=params_gurobi)\n",
    "res_gur = opt_gur.solve(enting)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab64ab28-9578-43ea-8943-821637681acf",
   "metadata": {},
   "source": [
    "### Pyomo Version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbf5ea4c-fe6b-4f29-8b35-ae88cfdb2379",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Build PyomoOptimizer object with GLPK as solver and solve optimization problem\n",
    "params_pyo = {\"solver_name\": \"gurobi\"}\n",
    "opt_pyo = PyomoOptimizer(problem_config, params=params_pyo)\n",
    "res_pyo = opt_pyo.solve(enting)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "772c3b0f-c885-43bf-9962-042a3da71756",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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