Feat: Multi-Objective API Integration (BiEGO, MF-BiEGO) & Advanced Profiling

docs/source/api/acquisition_strategies.rst

@@ -8,4 +8,8 @@ Acquisition Strategies
 
 .. automodule:: smt_optim.acquisition_strategies.mfsego
 
+.. automodule:: smt_optim.acquisition_strategies.biego
+
+.. automodule:: smt_optim.acquisition_strategies.mosego
+
 .. automodule:: smt_optim.acquisition_strategies.vfpi

docs/source/benchmark_registry.ipynb

@@ -1,55 +1,45 @@
 {
  "cells": [
   {
-   "metadata": {},
    "cell_type": "markdown",
-   "source": "# Benchmark problem registry",
-   "id": "255f316d1ab23dbc"
+   "id": "255f316d1ab23dbc",
+   "metadata": {},
+   "source": "# Benchmark problem registry"
   },
   {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c9b79250d8aa49a6",
    "metadata": {
-    "tags": [
-     "hide-cell"
-    ],
     "ExecuteTime": {
      "end_time": "2026-06-25T14:22:03.907234043Z",
      "start_time": "2026-06-25T14:22:02.446925336Z"
-    }
+    },
+    "tags": [
+     "hide-cell"
+    ]
    },
-   "cell_type": "code",
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import itables\n",
-    "itables.init_notebook_mode()\n",
-    "\n",
-    "from smt_optim.benchmarks.registry import list_problems, get_problem"
-   ],
-   "id": "c9b79250d8aa49a6",
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ],
       "text/html": [
        "<script type=\"module\">\n",
        "    const { set_or_remove_dark_class } = await import(window._itables_2_8_0);\n",
        "    set_or_remove_dark_class();\n",
        "</script>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
       ]
      },
-     "metadata": {},
-     "output_type": "display_data",
      "jetTransient": {
       "display_id": null
-     }
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
      "data": {
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ],
       "text/html": [
        "<script>\n",
        "function injectCSS(base64CSS) {\n",
@@ -173,71 +163,56 @@
        "(you should not see this message - is your notebook <it>trusted</it>?)\n",
        "</noscript>\n",
        "</div>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
       ]
      },
-     "metadata": {},
-     "output_type": "display_data",
      "jetTransient": {
       "display_id": null
-     }
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
-   "execution_count": 1
+   "source": [
+    "import pandas as pd\n",
+    "import itables\n",
+    "from smt_optim.benchmarks.registry import list_problems, get_problem\n",
+    "\n",
+    "\n",
+    "itables.init_notebook_mode()\n",
+    "\n"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "id": "33d48929fba08de1",
+   "metadata": {},
    "source": [
     "\n",
     "## Benchmark problems collection\n",
     "\n",
     "The table below lists the available problems in SMT-optim, along with some of their properties.\n"
-   ],
-   "id": "33d48929fba08de1"
+   ]
   },
   {
    "cell_type": "code",
+   "execution_count": 2,
    "id": "initial_id",
    "metadata": {
-    "collapsed": true,
-    "tags": [
-     "hide-input"
-    ],
     "ExecuteTime": {
      "end_time": "2026-06-25T14:22:03.971420624Z",
      "start_time": "2026-06-25T14:22:03.909394524Z"
-    }
+    },
+    "collapsed": true,
+    "tags": [
+     "hide-input"
+    ]
    },
-   "source": [
-    "\n",
-    "problems = list_problems(\n",
-    "    num_dim=None,\n",
-    "    num_obj=None,\n",
-    "    num_cstr=None,\n",
-    "    num_fidelity=None,\n",
-    ")\n",
-    "\n",
-    "rows = []\n",
-    "\n",
-    "for prob in problems:\n",
-    "    rows.append({\n",
-    "        \"Problem\": prob.__class__.__name__,\n",
-    "        \"Dimensions\": prob.num_dim,\n",
-    "        \"Constraints\": prob.num_cstr,\n",
-    "        \"Objectives\": prob.num_obj,\n",
-    "        \"Fidelity\": prob.num_fidelity,\n",
-    "        \"Tags\": prob.tags,\n",
-    "    })\n",
-    "\n",
-    "df = pd.DataFrame(data=rows).sort_values(by=[\"Objectives\", \"Fidelity\", \"Problem\", \"Dimensions\", \"Constraints\"])\n",
-    "itables.show(df, showIndex=False)"
-   ],
    "outputs": [
     {
      "data": {
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ],
       "text/html": [
        "<!--| quarto-html-table-processing: none -->\n",
        "<table id=\"itables_f383dbc6_a5e6_412f_803e_8825d80e373b\">\n",
@@ -370,20 +345,50 @@
        "        }\n",
        "    })();\n",
        "</script>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
       ]
      },
-     "metadata": {},
-     "output_type": "display_data",
      "jetTransient": {
       "display_id": null
-     }
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
    ],
-   "execution_count": 2
+   "source": [
+    "problems = list_problems(\n",
+    "    num_dim=None,\n",
+    "    num_obj=None,\n",
+    "    num_cstr=None,\n",
+    "    num_fidelity=None,\n",
+    ")\n",
+    "\n",
+    "rows = []\n",
+    "\n",
+    "for prob in problems:\n",
+    "    rows.append(\n",
+    "        {\n",
+    "            \"Problem\": prob.__class__.__name__,\n",
+    "            \"Dimensions\": prob.num_dim,\n",
+    "            \"Constraints\": prob.num_cstr,\n",
+    "            \"Objectives\": prob.num_obj,\n",
+    "            \"Fidelity\": prob.num_fidelity,\n",
+    "            \"Tags\": prob.tags,\n",
+    "        }\n",
+    "    )\n",
+    "\n",
+    "df = pd.DataFrame(data=rows).sort_values(\n",
+    "    by=[\"Objectives\", \"Fidelity\", \"Problem\", \"Dimensions\", \"Constraints\"]\n",
+    ")\n",
+    "itables.show(df, showIndex=False)"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "id": "c53ef42e82e3b7",
+   "metadata": {},
    "source": [
     "### Obtaining benchmark problems\n",
     "\n",
@@ -394,27 +399,18 @@
     "### Variable dimension\n",
     "\n",
     "The dimension of some problem can be set by the user. These problems can be identified with the `n_variable` tag. The dimension can be set with the `set_dim` class method. The code snippet below fetches the Ackley test problem, sets the number of variable to 5, and then to 10.\n"
-   ],
-   "id": "c53ef42e82e3b7"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "d57c1411c00173e1",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2026-06-25T14:22:04.029692248Z",
      "start_time": "2026-06-25T14:22:03.973644159Z"
     }
    },
-   "cell_type": "code",
-   "source": [
-    "ackley = get_problem(\"Ackley\")\n",
-    "\n",
-    "ackley.set_dim(5)\n",
-    "print(f\"bounds.shape[0] = {ackley.bounds.shape[0]}\")\n",
-    "\n",
-    "ackley.set_dim(10)\n",
-    "print(f\"bounds.shape[0] = {ackley.bounds.shape[0]}\")"
-   ],
-   "id": "d57c1411c00173e1",
    "outputs": [
     {
      "name": "stdout",
@@ -425,11 +421,20 @@
      ]
     }
    ],
-   "execution_count": 3
+   "source": [
+    "ackley = get_problem(\"Ackley\")\n",
+    "\n",
+    "ackley.set_dim(5)\n",
+    "print(f\"bounds.shape[0] = {ackley.bounds.shape[0]}\")\n",
+    "\n",
+    "ackley.set_dim(10)\n",
+    "print(f\"bounds.shape[0] = {ackley.bounds.shape[0]}\")"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "id": "b6bf846c205b6a58",
+   "metadata": {},
    "source": [
     "### Multi-fidelity problems\n",
     "\n",
@@ -456,35 +461,29 @@
     "value = problem.constraints[constraint_id: int][fidelity_level: int](x: np.ndarray)\n",
     "```\n",
     "\n"
-   ],
-   "id": "b6bf846c205b6a58"
+   ]
   },
   {
-   "metadata": {},
    "cell_type": "markdown",
+   "id": "2830f66c10eaa8a9",
+   "metadata": {},
    "source": [
     "\n",
     "### Mixed-variable problems\n",
     "\n",
     "The `bounds` attribute of mixed-variable problems is set to `None`. Users should instead refer to the `design_space` attribute which provides the SMT's `DesignSpace` object with respect to the problem. The code snippet below illustrates this for the mixed-variable Branin test problem.\n"
-   ],
-   "id": "2830f66c10eaa8a9"
+   ]
   },
   {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "f754bceff4736a52",
    "metadata": {
     "ExecuteTime": {
      "end_time": "2026-06-25T14:22:04.079269561Z",
      "start_time": "2026-06-25T14:22:04.031613341Z"
     }
    },
-   "cell_type": "code",
-   "source": [
-    "mixvar_branin = get_problem(\"MixVarBranin\")\n",
-    "\n",
-    "print(f\"------- bounds -------\\n{mixvar_branin.bounds}\")\n",
-    "print(f\"---- design_space ----\\n{mixvar_branin.design_space}\")"
-   ],
-   "id": "f754bceff4736a52",
    "outputs": [
     {
      "name": "stdout",
@@ -501,7 +500,12 @@
      ]
     }
    ],
-   "execution_count": 4
+   "source": [
+    "mixvar_branin = get_problem(\"MixVarBranin\")\n",
+    "\n",
+    "print(f\"------- bounds -------\\n{mixvar_branin.bounds}\")\n",
+    "print(f\"---- design_space ----\\n{mixvar_branin.design_space}\")"
+   ]
   }
  ],
  "metadata": {
@@ -525,4 +529,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 5
-}
+}