diff --git a/docs/source/api/acquisition_strategies.rst b/docs/source/api/acquisition_strategies.rst index 9ee2546..03c3273 100644 --- a/docs/source/api/acquisition_strategies.rst +++ b/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 diff --git a/docs/source/benchmark_registry.ipynb b/docs/source/benchmark_registry.ipynb index bd968e5..115c80f 100644 --- a/docs/source/benchmark_registry.ipynb +++ b/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": [ - "" - ], "text/html": [ "\n" + ], + "text/plain": [ + "" ] }, - "metadata": {}, - "output_type": "display_data", "jetTransient": { "display_id": null - } + }, + "metadata": {}, + "output_type": "display_data" }, { "data": { - "text/plain": [ - "" - ], "text/html": [ "\n" + ], + "text/plain": [ + "" ] }, - "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 -} +} \ No newline at end of file diff --git a/docs/source/getting_started/multi_objective_optim.ipynb b/docs/source/getting_started/multi_objective_optim.ipynb new file mode 100644 index 0000000..80ba239 --- /dev/null +++ b/docs/source/getting_started/multi_objective_optim.ipynb @@ -0,0 +1,1100 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "861c2272065c614e", + "metadata": {}, + "source": [ + "# Multi-objective optimization\n", + "\n", + "This notebook demonstrates how to perform **multi-objective** Bayesian optimization using the MO-SEGO (Multi-Objective Super Efficient Global Optimization) framework. Constrained and multi-fidelity optimization are also supported in the multi-objective framework.\n", + "\n", + "In multi-objective optimization, we are interested in minimizing multiple $n$ objectives with respect to constraints $\\boldsymbol{g}$ and $\\boldsymbol{h}$ (if applicable).\n", + "\n", + "$$\n", + "\\min_{\\boldsymbol{x} \\in \\Omega} \\{ \\boldsymbol{f}(\\boldsymbol{x}) := [ f_1(\\boldsymbol{x}), \\; f_1(\\boldsymbol{x}), \\; \\dots, \\; f_n(\\boldsymbol{x})] \\quad \\text{s.t.} \\quad \\boldsymbol{g}(\\boldsymbol{x}) \\leq \\boldsymbol{0} \\; \\text{and} \\; \\boldsymbol{h}(\\boldsymbol{x}) = \\boldsymbol{0}\\}\n", + "$$\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "initial_id", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-10T10:56:31.439824610Z", + "start_time": "2026-06-10T10:56:29.390587566Z" + } + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from smt_optim.core import (\n", + " Driver,\n", + " ObjectiveConfig,\n", + " ConstraintConfig,\n", + " DriverConfig,\n", + " Problem,\n", + ")\n", + "\n", + "from smt_optim.surrogate_models.smt import SmtAutoModel\n", + "\n", + "from smt_optim.acquisition_strategies.mosego import MOSEGO\n", + "from smt_optim.acquisition_functions.multi_obj import init_bi_obj_ei\n", + "from smt_optim.utils.multi_obj import get_pf_from_dataset\n", + "\n", + "from smt_optim.benchmarks.registry import get_problem\n", + "\n", + "from smt_optim.utils.multi_obj import PymooStateWrapper\n", + "from smt_optim.benchmarks.base import PymooWrapper\n", + "from pymoo.algorithms.moo.nsga2 import NSGA2\n", + "from pymoo.optimize import minimize" + ] + }, + { + "cell_type": "markdown", + "id": "e753db92c7ec30f7", + "metadata": {}, + "source": [ + "## Multi-objective optimization\n", + "\n", + "The following example illustrates how to find the ZDT1's pareto front using the MO-SEGO framework. The code cell below imports the test problem using the `get_problem` method and plots both objective. The ZDT1 is defined as follow:\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + "f_1(\\boldsymbol x) & = x_1\\\\\n", + "f_2(\\boldsymbol x) & = g(\\boldsymbol x) h(f_1(\\boldsymbol x), \\; g(\\boldsymbol x))\n", + "\\end{aligned}\n", + "$$\n", + "\n", + "where $d=2$ is the number of variable, $g(\\boldsymbol x) = 1 + \\frac{9}{d - 1}\\sum_{i=2}^{d}x_i$, and $h(f_1, \\; g) = 1 - \\sqrt{f_1/g}$.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "719fbef3fdc5870", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-10T10:56:34.574847849Z", + "start_time": "2026-06-10T10:56:34.355587145Z" + } + }, + "outputs": [], + "source": [ + "problem = get_problem(\"ZDT1\")\n", + "\n", + "problem.set_dim(2)\n", + "\n", + "x1 = np.linspace(0, 1, 101)\n", + "x2 = np.linspace(0, 1, 101)\n", + "\n", + "XX, YY = np.meshgrid(x1, x2)\n", + "\n", + "data = np.vstack((XX.ravel(), YY.ravel())).T\n", + "f1 = np.empty(data.shape[0])\n", + "f2 = np.empty(data.shape[0])\n", + "\n", + "for i in range(data.shape[0]):\n", + " f1[i] = problem.f1(data[i, :])\n", + " f2[i] = problem.f2(data[i, :])\n", + "\n", + "F1 = f1.reshape(XX.shape)\n", + "F2 = f2.reshape(XX.shape)\n", + "\n", + "fig, ax = plt.subplots(1, 2, layout=\"constrained\")\n", + "ax[0].set_title(r\"$f_1(x)$\")\n", + "ax[0].contour(XX, YY, F1)\n", + "ax[1].set_title(r\"$f_2(x)$\")\n", + "ax[1].contour(XX, YY, F2)\n", + "\n", + "for idx in range(2):\n", + " ax[idx].set_xlabel(\"x1\")\n", + " ax[idx].set_ylabel(\"x2\")\n", + " ax[idx].set_aspect(\"equal\")\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "8e61ebb3fc63ccdf", + "metadata": {}, + "source": [ + "### Starting the optimization\n", + "\n", + "The code cell shows how to initialize both objective, the problem configuration, and the driver configuration. The driver is initialized with the `MOSEGO` acquisition strategy**, which is designed for multiple objective optimization. The **Expected Improvement (EI)** and **Probability of Improvement (PI)** analytical acquisition functions is used in this example.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c4ade9bf6348519d", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-05T14:20:20.280559087Z", + "start_time": "2026-06-05T14:20:13.522603255Z" + } + }, + "outputs": [], + "source": [ + "# initialize the first objective configuration\n", + "obj1_config = ObjectiveConfig(\n", + " [problem.f1],\n", + " type=\"minimize\",\n", + " surrogate=SmtAutoModel,\n", + ")\n", + "\n", + "# initialize the second objective configuration\n", + "obj2_config = ObjectiveConfig(\n", + " [problem.f2],\n", + " type=\"minimize\",\n", + " surrogate=SmtAutoModel,\n", + ")\n", + "\n", + "# initialize the problem configuration\n", + "prob_definition = Problem(\n", + " obj_configs=[obj1_config, obj2_config],\n", + " design_space=problem.bounds, # problem bounds\n", + ")\n", + "\n", + "nt_init = 5\n", + "\n", + "# initialize the driver\n", + "opt_config = DriverConfig(\n", + " max_iter=30,\n", + " nt_init=nt_init,\n", + " verbose=True,\n", + " scaling=True,\n", + " seed=0,\n", + ")\n", + "\n", + "driver = Driver(\n", + " prob_definition,\n", + " opt_config,\n", + " MOSEGO,\n", + " strategy_kwargs={\"acq_func\": init_bi_obj_ei, \"n_start\": 40, \"sp_method\": \"SLSQP\"},\n", + ")\n", + "\n", + "# starts the optimization process\n", + "state = driver.optimize()" + ] + }, + { + "cell_type": "markdown", + "id": "430fb6876c360478", + "metadata": {}, + "source": [ + "### Plotting the results\n", + "\n", + "The code cell below extracts the pareto front from the final DOE using the `get_pf_from_dataset` method. The figure shows the final DOE, the initial DOE, the DOE samples forming the pareto front, and the true pareto front.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5b46f9c1d89013e7", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-05T14:20:20.697027435Z", + "start_time": "2026-06-05T14:20:20.333849213Z" + } + }, + "outputs": [], + "source": [ + "import matplotlib.image as mpimg\n", + "from smt_optim.utils.multi_obj import plot_pareto_front\n", + "\n", + "# ======= Plot optimization and solution pareto front =======\n", + "# We pass the first nt_init points as the initial dataset\n", + "import copy\n", + "\n", + "initial_dataset = copy.deepcopy(state.dataset)\n", + "initial_dataset.samples = initial_dataset.samples[:nt_init]\n", + "plot_pareto_front(initial_dataset, state.dataset, filename=\"zdt1_pareto_front.png\")\n", + "\n", + "\n", + "img = mpimg.imread(\"zdt1_pareto_front.png\")\n", + "plt.figure(figsize=(8, 6))\n", + "plt.imshow(img)\n", + "plt.axis(\"off\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "1902e2fca2c324e8", + "metadata": {}, + "source": [ + "## Constrained multi-objective optimization\n", + "\n", + "The following example illustrates how to find the pareto front when subjected to constraints. The `get_problem` method is used to import the BNH test problem, which is defined as follow:\n", + "\n", + "$$\n", + "\\begin{aligned}\n", + "f_1(\\boldsymbol x) & = 4x_1^2 + 4x_2^2\\\\\n", + "f_2(\\boldsymbol x) & = (x_1 - 5)^2 + (x_2 - 5)^2\\\\\n", + "g_1(\\boldsymbol x) & = (x_1 - 5)^2 + x_2^2 - 25\\\\\n", + "g_2(\\boldsymbol x) & = -(x_1 - 8)^2 - (x_2^2+3)^2 + 7.7.\n", + "\\end{aligned}\n", + "$$\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "81c1fa23ad4fbbd2", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-05T14:20:20.760615259Z", + "start_time": "2026-06-05T14:20:20.698175007Z" + } + }, + "outputs": [], + "source": [ + "problem = get_problem(\"BNH\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d6a83fd8d2049e63", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-05T14:20:23.944575034Z", + "start_time": "2026-06-05T14:20:20.761535434Z" + } + }, + "outputs": [], + "source": [ + "obj1_config = ObjectiveConfig(\n", + " [problem.objective[0]],\n", + " type=\"minimize\",\n", + " surrogate=SmtAutoModel,\n", + ")\n", + "\n", + "obj2_config = ObjectiveConfig(\n", + " [problem.objective[1]],\n", + " type=\"minimize\",\n", + " surrogate=SmtAutoModel,\n", + ")\n", + "\n", + "cstr1_config = ConstraintConfig(\n", + " [problem.constraints[0]],\n", + " upper=0.0,\n", + " surrogate=SmtAutoModel,\n", + ")\n", + "\n", + "cstr2_config = ConstraintConfig(\n", + " [problem.constraints[1]],\n", + " upper=0.0,\n", + " surrogate=SmtAutoModel,\n", + ")\n", + "\n", + "prob_definition = Problem(\n", + " obj_configs=[obj1_config, obj2_config],\n", + " cstr_configs=[cstr1_config, cstr2_config],\n", + " design_space=problem.bounds, # problem bounds\n", + ")\n", + "\n", + "nt_init = 5\n", + "\n", + "opt_config = DriverConfig(\n", + " max_iter=10,\n", + " nt_init=nt_init,\n", + " # xt_init = doe,\n", + " verbose=True,\n", + " scaling=True,\n", + " seed=0,\n", + ")\n", + "\n", + "driver = Driver(\n", + " prob_definition,\n", + " opt_config,\n", + " MOSEGO,\n", + " strategy_kwargs={\"acq_func\": init_bi_obj_ei, \"n_start\": 40, \"sp_method\": \"SLSQP\"},\n", + ")\n", + "\n", + "state = driver.optimize()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "de2f9c8629069bab", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-05T14:20:24.052858300Z", + "start_time": "2026-06-05T14:20:24.002210946Z" + } + }, + "outputs": [], + "source": [ + "pareto_front = get_pf_from_dataset(state.dataset)\n", + "\n", + "obj = state.dataset.export_as_dict()[\"obj\"]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "402cacfd1acecdc0", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-05T14:20:24.886717164Z", + "start_time": "2026-06-05T14:20:24.053990372Z" + } + }, + "outputs": [], + "source": [ + "# ======= Optimization Pareto Front =======\n", + "# dataset = driver.dataset\n", + "data = driver.state.dataset.export_as_dict()\n", + "obj = data[\"obj\"]\n", + "rscv = data[\"rscv\"]\n", + "obj = obj[rscv <= 1e-4, :]\n", + "# obj_par = get_pareto_front(obj)\n", + "\n", + "pareto_front = get_pf_from_dataset(state.dataset, ctol=0.0)\n", + "\n", + "# ======= Solution Pareto Front =======\n", + "pymoo_prob = PymooWrapper(problem)\n", + "\n", + "algorithm = NSGA2(pop_size=100, seed=1)\n", + "res = minimize(pymoo_prob, algorithm, (\"n_gen\", 100), seed=1)\n", + "\n", + "# ======= Plot optimization and solution pareto front =======\n", + "fig, ax = plt.subplots()\n", + "\n", + "ax.scatter(obj[:, 0], obj[:, 1])\n", + "ax.scatter(obj[:nt_init, 0], obj[:nt_init, 1], color=\"C2\")\n", + "\n", + "sorted_idx = np.argsort(pareto_front[:, 0])\n", + "pareto_front = pareto_front[sorted_idx, :]\n", + "\n", + "# PF obtained through optimization\n", + "ax.step(pareto_front[:, 0], pareto_front[:, 1], where=\"post\", color=\"C3\", zorder=20)\n", + "ax.scatter(pareto_front[:, 0], pareto_front[:, 1], marker=\"d\", color=\"C3\", alpha=0.5)\n", + "\n", + "# Solution PF\n", + "# ax.scatter(F1, F2, 2, color=\"C7\", alpha=0.2, zorder=10)\n", + "ax.scatter(res.F[:, 0], res.F[:, 1], 2, color=\"C7\", alpha=0.2, zorder=10)\n", + "# ax.scatter(multi_obj_ref[0], multi_obj_ref[1], 100, marker=\"x\", color=\"C8\", zorder=100)\n", + "\n", + "ax.set_xlabel(r\"$f_1$\")\n", + "ax.set_ylabel(r\"$f_2$\")\n", + "\n", + "# ax.set_xlim((-0.1, 1.1))\n", + "# ax.set_ylim((-0.1, 1.1))\n", + "\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cdcb775ab4b3707f", + "metadata": {}, + "source": [ + "### Applying NSGA-II (pymoo) on the surrogate models\n", + "\n", + "Users can use the `PymooStateWrapper` to convert a state object into a `Pymoo` `Problem` object. Below, the NSGA-II algorithm is applied on the objective and constraint models to find a predicted pareto-front.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4900f76f442a639a", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-05T14:20:25.998176875Z", + "start_time": "2026-06-05T14:20:24.942591753Z" + } + }, + "outputs": [], + "source": [ + "# Create a pymoo problem object from the SMT-Optim state object\n", + "pymoo_pred = PymooStateWrapper(state)\n", + "\n", + "fig, ax = plt.subplots()\n", + "\n", + "# Pareto front obtained with pymoo on the test problem directly\n", + "sorted_idx = np.argsort(res.F[:, 0])\n", + "res.F = res.F[sorted_idx, :]\n", + "ax.step(res.F[:, 0], res.F[:, 1], color=\"C7\", where=\"post\", label=\"True PF\")\n", + "\n", + "# Pareto front obtained with pymoo on using the surrogate models with the final DOE\n", + "res_pred = minimize(pymoo_pred, algorithm, (\"n_gen\", 100), seed=1)\n", + "sorted_idx = np.argsort(res_pred.F[:, 0])\n", + "res_pred.F = res_pred.F[sorted_idx, :]\n", + "ax.step(\n", + " res_pred.F[:, 0], res_pred.F[:, 1], color=\"C9\", where=\"post\", label=\"Predicted PF\"\n", + ")\n", + "\n", + "ax.legend()\n", + "ax.set_title(\"Pareto front comparison\")\n", + "ax.set_xlabel(r\"$f_1$\")\n", + "ax.set_ylabel(r\"$f_2$\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "17a085b3069c54b4", + "metadata": {}, + "source": [ + "## Constrained, multi-fidelity, and multi-objective optimization\n", + "\n", + "The following example demonstrates how to find the pareto-front of a constrained multi-objective problem for which low-fidelity approximation exists.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e8ecd288bff5da4e", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-05T14:20:26.113077907Z", + "start_time": "2026-06-05T14:20:26.063551929Z" + } + }, + "outputs": [], + "source": [ + "problem = get_problem(\"DTLZ5\")\n", + "problem.set_dim(4)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "31967a25cb773a51", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-05T14:20:26.173124790Z", + "start_time": "2026-06-05T14:20:26.114263545Z" + } + }, + "outputs": [], + "source": [ + "obj_config = ObjectiveConfig(\n", + " problem.objective[0],\n", + " type=\"minimize\",\n", + " surrogate=SmtAutoModel,\n", + ")\n", + "\n", + "obj_config2 = ObjectiveConfig(\n", + " problem.objective[1],\n", + " type=\"minimize\",\n", + " surrogate=SmtAutoModel,\n", + ")\n", + "\n", + "cstr_config = ConstraintConfig(\n", + " problem.constraints[0],\n", + " upper=0.0,\n", + " surrogate=SmtAutoModel,\n", + ")\n", + "\n", + "prob_definition = Problem(\n", + " obj_configs=[obj_config, obj_config2],\n", + " cstr_configs=[cstr_config],\n", + " design_space=problem.bounds, # problem bounds\n", + " costs=[0.2, 1.0],\n", + ")\n", + "\n", + "nt_init = 12\n", + "\n", + "opt_config = DriverConfig(\n", + " max_iter=50,\n", + " nt_init=nt_init,\n", + " verbose=True,\n", + " scaling=True,\n", + " seed=0,\n", + ")\n", + "\n", + "# np.seterr(all=\"raise\")\n", + "\n", + "# driver = Driver(prob_definition, opt_config, MultiObj, strategy_kwargs={\"acq_func\": init_bi_obj_ei, \"n_start\": 1000, \"sp_method\": None})\n", + "driver = Driver(\n", + " prob_definition,\n", + " opt_config,\n", + " MOSEGO,\n", + " strategy_kwargs={\n", + " \"acq_func\": init_bi_obj_ei,\n", + " \"n_start\": 20,\n", + " \"sp_method\": \"SLSQP\",\n", + " },\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "35efcc771d162859", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-05T14:23:31.137428951Z", + "start_time": "2026-06-05T14:20:26.174161470Z" + } + }, + "outputs": [], + "source": [ + "state = driver.optimize()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "abbcceac6e4b401a", + "metadata": { + "ExecuteTime": { + "end_time": "2026-06-05T14:23:31.490942258Z", + "start_time": "2026-06-05T14:23:31.203645246Z" + }, + "scrolled": true + }, + "outputs": [], + "source": [ + "# ======= Optimization Pareto Front =======\n", + "# dataset = driver.dataset\n", + "data = driver.state.dataset.export_as_dict()\n", + "obj = data[\"obj\"]\n", + "rscv = data[\"rscv\"]\n", + "feas_mask = rscv <= 1e-4\n", + "fid_mask = data[\"fidelity\"] == 1\n", + "# obj = obj[rscv <= 1e-4, :]\n", + "# obj_par = get_pareto_front(obj)\n", + "\n", + "pareto_front = get_pf_from_dataset(state.dataset)\n", + "\n", + "# ======= Solution Pareto Front =======\n", + "pymoo_prob = PymooWrapper(problem)\n", + "\n", + "algorithm = NSGA2(pop_size=100, seed=1)\n", + "res = minimize(pymoo_prob, algorithm, (\"n_gen\", 30), seed=1)\n", + "\n", + "\n", + "# ======= Plot optimization and solution pareto front =======\n", + "fig, ax = plt.subplots()\n", + "\n", + "ax.scatter(obj[:, 0][feas_mask & fid_mask], obj[:, 1][feas_mask & fid_mask])\n", + "ax.scatter(\n", + " obj[:, 0][~feas_mask & fid_mask],\n", + " obj[:, 1][~feas_mask & fid_mask],\n", + " color=\"C0\",\n", + " alpha=0.2,\n", + ")\n", + "ax.scatter(\n", + " obj[:, 0][feas_mask & ~fid_mask], obj[:, 1][feas_mask & ~fid_mask], color=\"C1\"\n", + ")\n", + "ax.scatter(\n", + " obj[:, 0][~feas_mask & ~fid_mask],\n", + " obj[:, 1][~feas_mask & ~fid_mask],\n", + " color=\"C1\",\n", + " alpha=0.2,\n", + ")\n", + "# ax.scatter(obj[:nt_init, 0][fid_mask], obj[:nt_init, 1][fid_mask], color=\"C2\")\n", + "\n", + "sorted_idx = np.argsort(pareto_front[:, 0])\n", + "pareto_front = pareto_front[sorted_idx, :]\n", + "\n", + "# PF obtained through optimization\n", + "ax.step(pareto_front[:, 0], pareto_front[:, 1], where=\"post\", color=\"C3\", zorder=20)\n", + "ax.scatter(pareto_front[:, 0], pareto_front[:, 1], marker=\"d\", color=\"C3\", alpha=0.5)\n", + "\n", + "# Solution PF\n", + "# ax.scatter(F1, F2, 2, color=\"C7\", alpha=0.2, zorder=10)\n", + "ax.scatter(res.F[:, 0], res.F[:, 1], 2, color=\"C7\", alpha=0.2, zorder=10)\n", + "\n", + "ax.set_xlabel(r\"$f_1$\")\n", + "ax.set_ylabel(r\"$f_2$\")\n", + "\n", + "ax.set_xlim((-0.1, 0.6))\n", + "ax.set_ylim((0.8, 1.4))\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "cd4b6d27", + "metadata": {}, + "source": [ + "## Optimization with BiEGO and Adaptive Nadir Points\n", + "\n", + "BiEGO is a multi-objective strategy that leverages adaptive nadir (reference) points to guide the optimization process. Below, we solve the ZDT1 problem using BiEGO and visualize how these reference points adapt over the iterations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c095d596-8963-4f9a-a80b-03ff29f85cee", + "metadata": {}, + "outputs": [], + "source": [ + "problem = get_problem(\"ZDT1\")\n", + "problem.set_dim(2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c30cfa5b", + "metadata": {}, + "outputs": [], + "source": [ + "from smt_optim.acquisition_strategies.biego import BiEGO\n", + "import matplotlib.pyplot as plt\n", + "from matplotlib.animation import FuncAnimation\n", + "from IPython.display import HTML\n", + "from smt_optim.utils.multi_obj import get_pareto_mask\n", + "import numpy as np\n", + "\n", + "\n", + "# initialize the objective configurations for ZDT1\n", + "obj1_config = ObjectiveConfig([problem.f1], type=\"minimize\", surrogate=SmtAutoModel)\n", + "obj2_config = ObjectiveConfig([problem.f2], type=\"minimize\", surrogate=SmtAutoModel)\n", + "\n", + "# initialize the problem configuration\n", + "prob_definition = Problem(\n", + " obj_configs=[obj1_config, obj2_config],\n", + " design_space=problem.bounds,\n", + ")\n", + "\n", + "nt_init = 25\n", + "\n", + "opt_config_comp = DriverConfig(max_iter=50, nt_init=nt_init, seed=42)\n", + "\n", + "driver_comp = Driver(\n", + " problem=prob_definition,\n", + " config=opt_config_comp,\n", + " strategy=BiEGO,\n", + " strategy_kwargs={\"min_max_calls\": 3, \"n_multi_start\": 15},\n", + ")\n", + "\n", + "state_comp = driver_comp.optimize()\n", + "\n", + "# Visualizing the trajectory with an interactive animation\n", + "\n", + "r_history = driver_comp.strategy.r_history\n", + "y_all = state_comp.dataset.export_as_dict()[\"obj\"]\n", + "y_init = y_all[:nt_init]\n", + "y_infills = y_all[nt_init:]\n", + "\n", + "fig, ax = plt.subplots(figsize=(6, 4))\n", + "x1 = np.linspace(0, 1, 100)\n", + "ax.plot(x1, 1 - np.sqrt(x1), \"k-\", label=\"Ref PF\", linewidth=2)\n", + "(scatter_pareto,) = ax.plot(\n", + " [], [], \"o\", color=\"darkorange\", label=\"Pareto Optimal\", alpha=0.9\n", + ")\n", + "(scatter_dom,) = ax.plot([], [], \"bo\", label=\"Dominated\", alpha=0.4)\n", + "(scatter_new,) = ax.plot([], [], \"r*\", markersize=12, label=\"New Infill\")\n", + "(nadir_marker,) = ax.plot([], [], \"mX\", markersize=10, label=\"Adaptive Nadir (r)\")\n", + "nadir_hline = ax.axhline(0, color=\"m\", linestyle=\"--\", alpha=0.5)\n", + "nadir_vline = ax.axvline(0, color=\"m\", linestyle=\"--\", alpha=0.5)\n", + "ax.set_xlabel(\"$f_1$\")\n", + "ax.set_ylabel(\"$f_2$\")\n", + "ax.grid(True, linestyle=\"--\", alpha=0.6)\n", + "ax.legend(loc=\"upper right\")\n", + "\n", + "\n", + "def init():\n", + " scatter_pareto.set_data([], [])\n", + " scatter_dom.set_data([], [])\n", + " scatter_new.set_data([], [])\n", + " nadir_marker.set_data([], [])\n", + " nadir_hline.set_visible(False)\n", + " nadir_vline.set_visible(False)\n", + " return (\n", + " scatter_pareto,\n", + " scatter_dom,\n", + " scatter_new,\n", + " nadir_marker,\n", + " nadir_hline,\n", + " nadir_vline,\n", + " )\n", + "\n", + "\n", + "def update(frame):\n", + " i = frame\n", + " if i >= len(y_infills):\n", + " return (\n", + " scatter_pareto,\n", + " scatter_dom,\n", + " scatter_new,\n", + " nadir_marker,\n", + " nadir_hline,\n", + " nadir_vline,\n", + " )\n", + " all_past_pts = np.vstack((y_init, y_infills[:i]))\n", + " if len(all_past_pts) > 0:\n", + " p_mask = get_pareto_mask(all_past_pts)\n", + " scatter_pareto.set_data(all_past_pts[p_mask][:, 0], all_past_pts[p_mask][:, 1])\n", + " scatter_dom.set_data(all_past_pts[~p_mask][:, 0], all_past_pts[~p_mask][:, 1])\n", + " scatter_new.set_data([y_infills[i, 0]], [y_infills[i, 1]])\n", + " if i < len(r_history) and r_history[i] is not None:\n", + " r_unscaled = r_history[i]\n", + " nadir_marker.set_data([r_unscaled[0]], [r_unscaled[1]])\n", + " nadir_hline.set_ydata([r_unscaled[1], r_unscaled[1]])\n", + " nadir_vline.set_xdata([r_unscaled[0], r_unscaled[0]])\n", + " nadir_hline.set_visible(True)\n", + " nadir_vline.set_visible(True)\n", + " ax.set_title(f\"BiEGO Composite: Infill {i + 1}\")\n", + " ax.relim()\n", + " max_x = 1.1\n", + " max_y = 1.5\n", + " if i < len(r_history) and r_history[i] is not None:\n", + " max_x = max(max_x, r_history[i][0] + 0.1)\n", + " max_y = max(max_y, r_history[i][1] + 0.2)\n", + " ax.set_xlim(-0.1, max_x)\n", + " ax.set_ylim(-0.1, max_y)\n", + "\n", + " return (\n", + " scatter_pareto,\n", + " scatter_dom,\n", + " scatter_new,\n", + " nadir_marker,\n", + " nadir_hline,\n", + " nadir_vline,\n", + " )\n", + "\n", + "\n", + "n_frames = max(0, len(y_infills))\n", + "ani = FuncAnimation(\n", + " fig, update, frames=n_frames, init_func=init, blit=False, repeat=False\n", + ")\n", + "plt.close(fig)\n", + "HTML(ani.to_jshtml())\n" + ] + }, + { + "cell_type": "markdown", + "id": "6b110be9", + "metadata": {}, + "source": [ + "## Comparison: Composite vs Naive BiEGO\n", + "\n", + "We can toggle the `naive` flag in BiEGO to compare Antoine's composite function adaptation (which samples the joint GP distributions using Monte-Carlo) against the naive method (which strictly fits a single scalar surrogate on the composite formulation)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d6ff3f1a", + "metadata": {}, + "outputs": [], + "source": [ + "opt_config_naive = DriverConfig(max_iter=50, nt_init=nt_init, seed=42)\n", + "driver_naive = Driver(\n", + " problem=prob_definition,\n", + " config=opt_config_naive,\n", + " strategy=BiEGO,\n", + " strategy_kwargs={\"min_max_calls\": 3, \"n_multi_start\": 15, \"naive\": True},\n", + ")\n", + "state_naive = driver_naive.optimize()\n", + "r_history_n = driver_naive.strategy.r_history\n", + "y_all_n = state_naive.dataset.export_as_dict()[\"obj\"]\n", + "y_init_n = y_all_n[:nt_init]\n", + "y_infills_n = y_all_n[nt_init:]\n", + "fig2, ax2 = plt.subplots(figsize=(6, 4))\n", + "ax2.plot(x1, 1 - np.sqrt(x1), \"k-\", label=\"Ref PF\", linewidth=2)\n", + "(scatter_pareto_n,) = ax2.plot(\n", + " [], [], \"o\", color=\"darkorange\", label=\"Pareto Optimal\", alpha=0.9\n", + ")\n", + "(scatter_dom_n,) = ax2.plot([], [], \"bo\", label=\"Dominated\", alpha=0.4)\n", + "(scatter_new_n,) = ax2.plot([], [], \"r*\", markersize=12, label=\"New Infill\")\n", + "(nadir_marker_n,) = ax2.plot([], [], \"mX\", markersize=10, label=\"Adaptive Nadir (r)\")\n", + "nadir_hline_n = ax2.axhline(0, color=\"m\", linestyle=\"--\", alpha=0.5)\n", + "nadir_vline_n = ax2.axvline(0, color=\"m\", linestyle=\"--\", alpha=0.5)\n", + "ax2.set_xlabel(\"$f_1$\")\n", + "ax2.set_ylabel(\"$f_2$\")\n", + "ax2.grid(True, linestyle=\"--\", alpha=0.6)\n", + "ax2.legend(loc=\"upper right\")\n", + "\n", + "\n", + "def update_naive(frame):\n", + " i = frame\n", + " if i >= len(y_infills_n):\n", + " return (\n", + " scatter_pareto_n,\n", + " scatter_dom_n,\n", + " scatter_new_n,\n", + " nadir_marker_n,\n", + " nadir_hline_n,\n", + " nadir_vline_n,\n", + " )\n", + " all_past_pts = np.vstack((y_init_n, y_infills_n[:i]))\n", + " if len(all_past_pts) > 0:\n", + " p_mask = get_pareto_mask(all_past_pts)\n", + " scatter_pareto_n.set_data(\n", + " all_past_pts[p_mask][:, 0], all_past_pts[p_mask][:, 1]\n", + " )\n", + " scatter_dom_n.set_data(all_past_pts[~p_mask][:, 0], all_past_pts[~p_mask][:, 1])\n", + " scatter_new_n.set_data([y_infills_n[i, 0]], [y_infills_n[i, 1]])\n", + " if i < len(r_history_n) and r_history_n[i] is not None:\n", + " r_unscaled = r_history_n[i]\n", + " nadir_marker_n.set_data([r_unscaled[0]], [r_unscaled[1]])\n", + " nadir_hline_n.set_ydata([r_unscaled[1], r_unscaled[1]])\n", + " nadir_vline_n.set_xdata([r_unscaled[0], r_unscaled[0]])\n", + " nadir_hline_n.set_visible(True)\n", + " nadir_vline_n.set_visible(True)\n", + " ax2.set_title(f\"BiEGO Naive: Infill {i + 1}\")\n", + " ax2.relim()\n", + " ax2.set_xlim(-0.1, 1.1)\n", + " ax2.set_ylim(-0.1, 1.5)\n", + " return (\n", + " scatter_pareto_n,\n", + " scatter_dom_n,\n", + " scatter_new_n,\n", + " nadir_marker_n,\n", + " nadir_hline_n,\n", + " nadir_vline_n,\n", + " )\n", + "\n", + "\n", + "n_frames_n = max(0, len(y_infills_n))\n", + "ani2 = FuncAnimation(fig2, update_naive, frames=n_frames_n, blit=False, repeat=False)\n", + "plt.close(fig2)\n", + "HTML(ani2.to_jshtml())" + ] + }, + { + "cell_type": "markdown", + "id": "1808ef7d", + "metadata": {}, + "source": [ + "## Comparison: MOSEGO (MPI)\n", + "\n", + "Finally, let's look at **MOSEGO** which uses the **Minimum Probability of Improvement (MPI)** acquisition function. Unlike BiEGO, it does not rely on geometric gap calculation (Adaptive Nadir). Instead, it evaluates the global probability of dominating the front at any point, providing a much more globally uniform sampling across the entire Pareto front.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1e11492b", + "metadata": {}, + "outputs": [], + "source": [ + "from smt_optim.acquisition_strategies.mosego import MOSEGO\n", + "\n", + "opt_config_mosego = DriverConfig(max_iter=50, nt_init=nt_init, seed=42)\n", + "\n", + "driver_mosego = Driver(\n", + " problem=prob_definition, config=opt_config_mosego, strategy=MOSEGO\n", + ")\n", + "\n", + "state_mosego = driver_mosego.optimize()\n", + "\n", + "y_all_m = state_mosego.dataset.export_as_dict()[\"obj\"]\n", + "y_init_m = y_all_m[:nt_init]\n", + "y_infills_m = y_all_m[nt_init:]\n", + "\n", + "fig3, ax3 = plt.subplots(figsize=(6, 4))\n", + "ax3.plot(x1, 1 - np.sqrt(x1), \"k-\", label=\"Ref PF\", linewidth=2)\n", + "(scatter_pareto_m,) = ax3.plot(\n", + " [], [], \"o\", color=\"darkorange\", label=\"Pareto Optimal\", alpha=0.9\n", + ")\n", + "(scatter_dom_m,) = ax3.plot([], [], \"bo\", label=\"Dominated\", alpha=0.4)\n", + "(scatter_new_m,) = ax3.plot([], [], \"r*\", markersize=12, label=\"New Infill\")\n", + "ax3.set_xlabel(\"$f_1$\")\n", + "ax3.set_ylabel(\"$f_2$\")\n", + "ax3.grid(True, linestyle=\"--\", alpha=0.6)\n", + "ax3.legend(loc=\"upper right\")\n", + "\n", + "\n", + "def update_mosego(frame):\n", + " i = frame\n", + " if i >= len(y_infills_m):\n", + " return scatter_pareto_m, scatter_dom_m, scatter_new_m\n", + " all_past_pts = np.vstack((y_init_m, y_infills_m[:i]))\n", + " if len(all_past_pts) > 0:\n", + " p_mask = get_pareto_mask(all_past_pts)\n", + " scatter_pareto_m.set_data(\n", + " all_past_pts[p_mask][:, 0], all_past_pts[p_mask][:, 1]\n", + " )\n", + " scatter_dom_m.set_data(all_past_pts[~p_mask][:, 0], all_past_pts[~p_mask][:, 1])\n", + " scatter_new_m.set_data([y_infills_m[i, 0]], [y_infills_m[i, 1]])\n", + " ax3.set_title(f\"MOSEGO (MPI): Infill {i + 1}\")\n", + " ax3.relim()\n", + " ax3.set_xlim(-0.1, 1.1)\n", + " ax3.set_ylim(-0.1, 1.5)\n", + " return scatter_pareto_m, scatter_dom_m, scatter_new_m\n", + "\n", + "\n", + "n_frames_m = len(y_infills_m)\n", + "ani3 = FuncAnimation(fig3, update_mosego, frames=n_frames_m, blit=False, repeat=False)\n", + "plt.close(fig3)\n", + "HTML(ani3.to_jshtml())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c27e8495", + "metadata": {}, + "outputs": [], + "source": [ + "from smt_optim.utils.multi_obj import hypervolume_2d\n", + "\n", + "# Reference point for computing hypervolume\n", + "ref_point = np.array([2.0, 2.0])\n", + "\n", + "hv_comp = []\n", + "hv_naive = []\n", + "hv_mosego = []\n", + "\n", + "obj_comp = state_comp.dataset.export_as_dict()[\"obj\"]\n", + "obj_naive = state_naive.dataset.export_as_dict()[\"obj\"]\n", + "obj_mosego = state_mosego.dataset.export_as_dict()[\"obj\"]\n", + "\n", + "total_evals = len(obj_comp)\n", + "iterations = list(range(1, total_evals - nt_init + 1))\n", + "\n", + "for i in range(nt_init, total_evals):\n", + " # Comp\n", + " mask_c = get_pareto_mask(obj_comp[:i])\n", + " hv_comp.append(hypervolume_2d(obj_comp[:i][mask_c], ref_point))\n", + " # Naive\n", + " mask_n = get_pareto_mask(obj_naive[:i])\n", + " hv_naive.append(hypervolume_2d(obj_naive[:i][mask_n], ref_point))\n", + " # Mosego\n", + " mask_m = get_pareto_mask(obj_mosego[:i])\n", + " if len(obj_mosego[:i][mask_m]) > 0:\n", + " hv_mosego.append(hypervolume_2d(obj_mosego[:i][mask_m], ref_point))\n", + " else:\n", + " hv_mosego.append(0.0)\n", + "\n", + "fig, ax = plt.subplots(figsize=(8, 6))\n", + "ax.plot(iterations, hv_comp, \"b-o\", label=\"BiEGO (Composite)\", alpha=0.8)\n", + "ax.plot(iterations, hv_naive, \"r-s\", label=\"BiEGO (Naive)\", alpha=0.8)\n", + "ax.plot(iterations[: len(hv_mosego)], hv_mosego, \"g-^\", label=\"MOSEGO (MPI)\", alpha=0.8)\n", + "ax.set_xlabel(\"Infill Iterations\")\n", + "ax.set_ylabel(\"Hypervolume Indicator\")\n", + "ax.set_title(\"Hypervolume Convergence on ZDT1\")\n", + "ax.legend()\n", + "ax.grid(True, linestyle=\"--\", alpha=0.6)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "ebbf7aa6", + "metadata": {}, + "source": [ + "## Advanced Post-Processing and Data Profiles\n", + "\n", + "The SMT-Optim framework includes advanced post-processing tools inspired by the work of Oihan Cordelier and Antoine Maugras. These tools allow you to plot **Data Profiles**, **Performance Profiles**, **Convergence Profiles**, and **Accuracy Profiles** over several analytical instances.\n", + "\n", + "To utilize these tools, you can import them from `smt_optim.utils.profiles`. Here is a quick example of how you might aggregate multiple runs across different frameworks (like `MOSEGO` and `BiEGO`) to generate these profiles:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "22ae3a06", + "metadata": {}, + "outputs": [], + "source": [ + "from smt_optim.utils.profiles import profile, convergence_profile\n", + "from pymoo.indicators.hv import HV\n", + "from smt_optim.utils.multi_obj import get_pareto_mask\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "# In multi-objective optimization, we track Hypervolume instead of a single fmin.\n", + "# Let's compute the Hypervolume indicator for our 3 runs:\n", + "ref_point = np.array([2.0, 2.0])\n", + "ind = HV(ref_point=ref_point)\n", + "\n", + "\n", + "def compute_hv_convergence(y_all, nt_init):\n", + " hvs = []\n", + " for i in range(nt_init, len(y_all) + 1):\n", + " y_current = y_all[:i]\n", + " mask = get_pareto_mask(y_current)\n", + " pf = y_current[mask]\n", + " hv = ind(pf)\n", + " hvs.append(hv)\n", + " return np.array(hvs)\n", + "\n", + "\n", + "hv_comp = compute_hv_convergence(y_all, nt_init)\n", + "hv_naive = compute_hv_convergence(y_all_n, nt_init)\n", + "hv_mosego = compute_hv_convergence(y_all_m, nt_init)\n", + "iters = np.arange(1, len(hv_comp) + 1)\n", + "\n", + "# Format the data into the dictionary structure expected by profiles.py.\n", + "# Note: we negate the hypervolume because the profiling tools expect a metric to MINIMIZE.\n", + "data = {\n", + " \"MOSEGO\": {\"ZDT1\": np.column_stack((iters, -hv_mosego))},\n", + " \"BiEGO Composite\": {\"ZDT1\": np.column_stack((iters, -hv_comp))},\n", + " \"BiEGO Naive\": {\"ZDT1\": np.column_stack((iters, -hv_naive))},\n", + "}\n", + "\n", + "# 1. Convergence Profile\n", + "fig, ax = plt.subplots(figsize=(8, 5))\n", + "for algo in data:\n", + " metric, all_fmin = convergence_profile(data[algo])\n", + " mean_hv = np.mean(-all_fmin, axis=0) # Negate back to positive HV\n", + " ax.plot(metric, mean_hv, label=algo, linewidth=2)\n", + "\n", + "ax.set_xlabel(\"Number of Infills\")\n", + "ax.set_ylabel(\"Hypervolume (Higher is better)\")\n", + "ax.set_title(\"Hypervolume Convergence Profile (ZDT1)\")\n", + "ax.legend()\n", + "ax.grid(True, linestyle=\"--\", alpha=0.6)\n", + "plt.show()\n", + "\n", + "# 2. Performance Profile\n", + "# (Note: Performance profiles are more interesting with many instances. Here we only have ZDT1)\n", + "perf_profile = profile(data, tau=0.1, type=\"perf\")\n", + "fig2, ax2 = plt.subplots(figsize=(8, 5))\n", + "for algo in perf_profile:\n", + " ax2.step(\n", + " perf_profile[algo][\"metric\"],\n", + " perf_profile[algo][\"portion\"],\n", + " where=\"post\",\n", + " label=algo,\n", + " )\n", + "ax2.set_xlabel(r\"Performance Ratio ($\tau$)\")\n", + "ax2.set_ylabel(\"Proportion of Instances Solved\")\n", + "ax2.set_title(\"Performance Profile\")\n", + "ax2.set_xlim(1, 1.5)\n", + "ax2.legend()\n", + "ax2.grid(True, linestyle=\"--\", alpha=0.6)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a4a0a6c4-705d-4ab3-8c0b-d6716de5adaa", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/mfsego.ipynb b/examples/mfsego.ipynb index 7a8a1f6..855362b 100644 --- a/examples/mfsego.ipynb +++ b/examples/mfsego.ipynb @@ -1,19 +1,24 @@ { "cells": [ { - "metadata": {}, "cell_type": "markdown", - "source": "# Mutli-fidelity optimization - MFSEGO\n", - "id": "61c212aa7b3ad82f" + "id": "61c212aa7b3ad82f", + "metadata": {}, + "source": [ + "# Mutli-fidelity optimization - MFSEGO\n" + ] }, { + "cell_type": "code", + "execution_count": 1, + "id": "14bef6cd812ffd34", "metadata": { "ExecuteTime": { "end_time": "2026-04-14T20:01:45.838211171Z", "start_time": "2026-04-14T20:01:44.320492768Z" } }, - "cell_type": "code", + "outputs": [], "source": [ "import numpy as np\n", "from matplotlib import pyplot as plt\n", @@ -27,25 +32,41 @@ "from smt_optim.acquisition_strategies import MFSEGO\n", "\n", "import scipy.optimize as so" - ], - "id": "14bef6cd812ffd34", - "outputs": [], - "execution_count": 1 + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "## Unconstrained 1D optimization", - "id": "d1bae39e97f31c80" + "id": "d1bae39e97f31c80", + "metadata": {}, + "source": [ + "## Unconstrained 1D optimization" + ] }, { + "cell_type": "code", + "execution_count": 2, + "id": "7c8e256a7f9bf48a", "metadata": { "ExecuteTime": { "end_time": "2026-04-14T20:01:45.935056511Z", "start_time": "2026-04-14T20:01:45.839501750Z" } }, - "cell_type": "code", + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "jetTransient": { + "display_id": null + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# high-fidelity function\n", "def sasena2002_hf(x):\n", @@ -67,33 +88,35 @@ "\n", "plt.legend()\n", "plt.show()\n" - ], - "id": "7c8e256a7f9bf48a", - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ], - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data", - "jetTransient": { - "display_id": null - } - } - ], - "execution_count": 2 + ] }, { + "cell_type": "code", + "execution_count": 3, + "id": "aa06189db6b4f5de", "metadata": { "ExecuteTime": { "end_time": "2026-04-14T20:01:48.930484617Z", "start_time": "2026-04-14T20:01:45.936160600Z" } }, - "cell_type": "code", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " iter budget fmin rscv fidelity gp_time acq_time\n", + " 1 4.400 7.98412e+00 0.000e+00 1 0.130 0.124\n", + " 2 4.600 7.98412e+00 0.000e+00 1 0.118 0.170\n", + " 3 5.800 7.98412e+00 0.000e+00 2 0.127 0.142\n", + " 4 6.000 7.98412e+00 0.000e+00 1 0.165 0.295\n", + " 5 7.200 7.91824e+00 0.000e+00 2 0.187 0.229\n", + " 6 8.400 7.91824e+00 0.000e+00 2 0.176 0.246\n", + " 7 9.600 7.91824e+00 0.000e+00 2 0.176 0.219\n", + " 8 10.800 7.91824e+00 0.000e+00 2 0.194 0.261\n" + ] + } + ], "source": [ "obj_config = ObjectiveConfig(\n", " objective=[sasena2002_lf, sasena2002_hf], # multi-fidelity functions must be given in sequential order\n", @@ -121,53 +144,51 @@ "driver = Driver(prob_definition, opt_config, MFSEGO)\n", "\n", "state = driver.optimize()" - ], - "id": "aa06189db6b4f5de", - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " iter budget fmin rscv fidelity gp_time acq_time\n", - " 1 4.400 7.98412e+00 0.000e+00 1 0.130 0.124\n", - " 2 4.600 7.98412e+00 0.000e+00 1 0.118 0.170\n", - " 3 5.800 7.98412e+00 0.000e+00 2 0.127 0.142\n", - " 4 6.000 7.98412e+00 0.000e+00 1 0.165 0.295\n", - " 5 7.200 7.91824e+00 0.000e+00 2 0.187 0.229\n", - " 6 8.400 7.91824e+00 0.000e+00 2 0.176 0.246\n", - " 7 9.600 7.91824e+00 0.000e+00 2 0.176 0.219\n", - " 8 10.800 7.91824e+00 0.000e+00 2 0.194 0.261\n" - ] - } - ], - "execution_count": 3 + ] }, { + "cell_type": "code", + "execution_count": 4, + "id": "a726fbe9cd200bc3", "metadata": { "ExecuteTime": { "end_time": "2026-04-14T20:01:49.034102620Z", "start_time": "2026-04-14T20:01:48.983906690Z" } }, - "cell_type": "code", + "outputs": [], "source": [ "data = state.dataset.export_as_dict()\n", "fidelity_masks = [(data[\"fidelity\"] == lvl).ravel() for lvl in range(state.problem.num_fidelity)]\n", "xt = [data[\"x\"][fidelity_masks[lvl], 0] for lvl in range(state.problem.num_fidelity)]\n", "yt = [data[\"obj\"][fidelity_masks[lvl], 0] for lvl in range(state.problem.num_fidelity)]\n" - ], - "id": "a726fbe9cd200bc3", - "outputs": [], - "execution_count": 4 + ] }, { + "cell_type": "code", + "execution_count": 5, + "id": "5a868dc12b18077c", "metadata": { "ExecuteTime": { "end_time": "2026-04-14T20:01:49.176521599Z", "start_time": "2026-04-14T20:01:49.035141251Z" } }, - "cell_type": "code", + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "jetTransient": { + "display_id": null + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "sample = state.get_best_sample()\n", "\n", @@ -182,45 +203,49 @@ "\n", "plt.legend()\n", "plt.show()" - ], - "id": "5a868dc12b18077c", - "outputs": [ - { - "data": { - "text/plain": [ - "
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" - }, - "metadata": {}, - "output_type": "display_data", - "jetTransient": { - "display_id": null - } - } - ], - "execution_count": 5 + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "## Constrained 2D optimization", - "id": "7d00820fe9303be5" + "id": "7d00820fe9303be5", + "metadata": {}, + "source": [ + "## Constrained 2D optimization" + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "### Importing a test function", - "id": "859fa83d4f842c71" + "id": "859fa83d4f842c71", + "metadata": {}, + "source": [ + "### Importing a test function" + ] }, { + "cell_type": "code", + "execution_count": 6, + "id": "96c9c85e3d593c38", "metadata": { "ExecuteTime": { "end_time": "2026-04-14T20:01:49.441513191Z", "start_time": "2026-04-14T20:01:49.177859326Z" } }, - "cell_type": "code", + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "jetTransient": { + "display_id": null + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "problem = get_problem(\"Rosenbrock\")\n", "\n", @@ -255,39 +280,56 @@ " ax[lvl].set_aspect(\"equal\")\n", "\n", "plt.show()" - ], - "id": "96c9c85e3d593c38", - "outputs": [ - { - "data": { - "text/plain": [ - "
" - ], - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data", - "jetTransient": { - "display_id": null - } - } - ], - "execution_count": 6 + ] }, { - "metadata": {}, "cell_type": "markdown", - "source": "### MFSEGO Configuration", - "id": "40c0bf134d821fc0" + "id": "40c0bf134d821fc0", + "metadata": {}, + "source": [ + "### MFSEGO Configuration" + ] }, { + "cell_type": "code", + "execution_count": 7, + "id": "c027d0d98f587ebf", "metadata": { "ExecuteTime": { "end_time": "2026-04-14T20:02:52.193588112Z", "start_time": "2026-04-14T20:01:49.442781325Z" } }, - "cell_type": "code", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " iter budget fmin rscv fidelity gp_time acq_time\n", + " 1 10.500 2.64887e+00 0.000e+00 1 0.916 1.862\n", + " 2 11.000 2.64887e+00 0.000e+00 1 0.739 1.415\n", + " 3 11.500 2.64887e+00 0.000e+00 1 0.733 1.277\n", + " 4 12.000 2.64887e+00 0.000e+00 1 0.667 1.192\n", + " 5 12.500 2.64887e+00 0.000e+00 1 0.708 2.169\n", + " 6 13.000 2.64887e+00 0.000e+00 1 0.774 2.446\n", + " 7 13.500 2.64887e+00 0.000e+00 1 0.759 2.714\n", + " 8 14.000 2.64887e+00 0.000e+00 1 0.750 2.796\n", + " 9 14.500 2.64887e+00 0.000e+00 1 0.760 2.832\n", + " 10 15.000 2.64887e+00 0.000e+00 1 0.839 2.334\n", + " iter budget fmin rscv fidelity gp_time acq_time\n", + " 11 15.500 2.64887e+00 0.000e+00 1 0.743 2.530\n", + " 12 16.000 2.64887e+00 0.000e+00 1 0.804 2.711\n", + " 13 16.500 2.64887e+00 0.000e+00 1 0.987 2.285\n", + " 14 18.000 2.64887e+00 0.000e+00 2 0.820 1.839\n", + " 15 19.500 2.78736e-01 0.000e+00 2 0.695 1.442\n", + " 16 20.000 2.78736e-01 0.000e+00 1 0.617 2.431\n", + " 17 21.500 1.79294e-01 0.000e+00 2 0.602 2.235\n", + " 18 23.000 1.78500e-01 2.982e-05 2 0.632 2.746\n", + " 19 24.500 1.78479e-01 1.487e-05 2 0.618 2.799\n", + " 20 26.000 1.78479e-01 1.487e-05 2 0.683 2.711\n" + ] + } + ], "source": [ "obj_config = ObjectiveConfig(\n", " problem.objective,\n", @@ -331,68 +373,27 @@ "driver = Driver(prob_definition, opt_config, MFSEGO, strategy_kwargs=strategy_kwargs)\n", "\n", "state = driver.optimize()" - ], - "id": "c027d0d98f587ebf", - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " iter budget fmin rscv fidelity gp_time acq_time\n", - " 1 10.500 2.64887e+00 0.000e+00 1 0.916 1.862\n", - " 2 11.000 2.64887e+00 0.000e+00 1 0.739 1.415\n", - " 3 11.500 2.64887e+00 0.000e+00 1 0.733 1.277\n", - " 4 12.000 2.64887e+00 0.000e+00 1 0.667 1.192\n", - " 5 12.500 2.64887e+00 0.000e+00 1 0.708 2.169\n", - " 6 13.000 2.64887e+00 0.000e+00 1 0.774 2.446\n", - " 7 13.500 2.64887e+00 0.000e+00 1 0.759 2.714\n", - " 8 14.000 2.64887e+00 0.000e+00 1 0.750 2.796\n", - " 9 14.500 2.64887e+00 0.000e+00 1 0.760 2.832\n", - " 10 15.000 2.64887e+00 0.000e+00 1 0.839 2.334\n", - " iter budget fmin rscv fidelity gp_time acq_time\n", - " 11 15.500 2.64887e+00 0.000e+00 1 0.743 2.530\n", - " 12 16.000 2.64887e+00 0.000e+00 1 0.804 2.711\n", - " 13 16.500 2.64887e+00 0.000e+00 1 0.987 2.285\n", - " 14 18.000 2.64887e+00 0.000e+00 2 0.820 1.839\n", - " 15 19.500 2.78736e-01 0.000e+00 2 0.695 1.442\n", - " 16 20.000 2.78736e-01 0.000e+00 1 0.617 2.431\n", - " 17 21.500 1.79294e-01 0.000e+00 2 0.602 2.235\n", - " 18 23.000 1.78500e-01 2.982e-05 2 0.632 2.746\n", - " 19 24.500 1.78479e-01 1.487e-05 2 0.618 2.799\n", - " 20 26.000 1.78479e-01 1.487e-05 2 0.683 2.711\n" - ] - } - ], - "execution_count": 7 + ] }, { - "metadata": {}, "cell_type": "markdown", + "id": "57f69bfc5fbf29f8", + "metadata": {}, "source": [ "### Validation with SLSQP\n", "SLSQP is a mono-fidelity gradient-based solver." - ], - "id": "57f69bfc5fbf29f8" + ] }, { + "cell_type": "code", + "execution_count": 8, + "id": "39209ba5a268c8fe", "metadata": { "ExecuteTime": { "end_time": "2026-04-14T20:02:52.301045749Z", "start_time": "2026-04-14T20:02:52.249979274Z" } }, - "cell_type": "code", - "source": [ - "cstrs = [{\n", - " \"fun\": lambda x, f=problem.constraints[0][-1]: -f(x),\n", - " \"type\": \"ineq\",\n", - " }]\n", - "\n", - "res = so.minimize(problem.objective[-1], [0.5, 0.5], bounds=problem.bounds, constraints=cstrs, method=\"SLSQP\", tol=1e-15)\n", - "\n", - "print(res)" - ], - "id": "39209ba5a268c8fe", "outputs": [ { "name": "stdout", @@ -411,16 +412,61 @@ ] } ], - "execution_count": 8 + "source": [ + "cstrs = [{\n", + " \"fun\": lambda x, f=problem.constraints[0][-1]: -f(x),\n", + " \"type\": \"ineq\",\n", + " }]\n", + "\n", + "res = so.minimize(problem.objective[-1], [0.5, 0.5], bounds=problem.bounds, constraints=cstrs, method=\"SLSQP\", tol=1e-15)\n", + "\n", + "print(res)" + ] }, { + "cell_type": "code", + "execution_count": 9, + "id": "af7a2eabec2f71cb", "metadata": { "ExecuteTime": { "end_time": "2026-04-14T20:02:52.452916207Z", "start_time": "2026-04-14T20:02:52.302084847Z" } }, - "cell_type": "code", + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "best sample = \n", + "======= sample data =======\n", + "x = [0.5776694 0.33262587]\n", + "obj = [0.17847893]\n", + "cstr = [1.48694119e-05]\n", + "eval_time = [8.68021743e-07 4.98024747e-07]\n", + "------- meta data -------\n", + "iter = 19\n", + "budget = 24.5\n", + "fidelity = 1\n", + "rscv = 1.4869411850970682e-05\n", + "===========================\n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "jetTransient": { + "display_id": null + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "data_x = []\n", "for lvl in range(state.problem.num_fidelity):\n", @@ -468,62 +514,26 @@ "ax.legend()\n", "ax.set_aspect(\"equal\")\n", "plt.show()" - ], - "id": "af7a2eabec2f71cb", - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "best sample = \n", - "======= sample data =======\n", - "x = [0.5776694 0.33262587]\n", - "obj = [0.17847893]\n", - "cstr = [1.48694119e-05]\n", - "eval_time = [8.68021743e-07 4.98024747e-07]\n", - "------- meta data -------\n", - "iter = 19\n", - "budget = 24.5\n", - "fidelity = 1\n", - "rscv = 1.4869411850970682e-05\n", - "===========================\n", - "\n" - ] - }, - { - "data": { - "text/plain": [ - "
" - ], - "image/png": 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" - }, - "metadata": {}, - "output_type": "display_data", - "jetTransient": { - "display_id": null - } - } - ], - "execution_count": 9 + ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" + "pygments_lexer": "ipython3", + "version": "3.11.11" } }, "nbformat": 4, diff --git a/src/smt_optim/acquisition_functions/multi_obj.py b/src/smt_optim/acquisition_functions/multi_obj.py index 0f70679..a28a08f 100644 --- a/src/smt_optim/acquisition_functions/multi_obj.py +++ b/src/smt_optim/acquisition_functions/multi_obj.py @@ -344,6 +344,10 @@ def init_bi_obj_ei_cf(state, kwargs=None): obj_vals = data["obj"][valid_mask, :] f_min = np.min(phi(obj_vals)) + # Pre-sample standard normal variables for Monte Carlo integration + # This makes the acquisition function deterministic during optimization + Z_fixed = np.random.randn(1, n_expectancy, 2) + def ei_cf(x_pred: np.ndarray) -> np.ndarray: s0_sq = models[0].predict_variances(x_pred) s1_sq = models[1].predict_variances(x_pred) @@ -355,11 +359,53 @@ def ei_cf(x_pred: np.ndarray) -> np.ndarray: y1 = models[1].predict_values(x_pred) mu = np.hstack([y0, y1]) - N = mu.shape[0] - Z = np.random.randn(N, n_expectancy, 2) - samples = mu[:, None, :] + s[:, None, :] * Z + samples = mu[:, None, :] + s[:, None, :] * Z_fixed phi_vals = phi(samples) ei = np.mean(np.maximum(f_min - phi_vals, 0.0), axis=1) return ei.reshape(-1, 1) return ei_cf + + +def init_bi_obj_ei_naive(state, kwargs=None): + """ + Initialize the Expected Improvement using a Naive approach for Composite Functions. + This trains a new surrogate model directly on the evaluated composite function values. + """ + from smt_optim.acquisition_functions import log_ei + import numpy as np + + phi = kwargs["phi"] + data = state.scaled_dataset.export_as_dict() + xt = data["x"] + yt = data["obj"] + y_phi = phi(yt).reshape(-1, 1) + + fidelity = data["fidelity"] + xt_list = [] + y_phi_list = [] + for lvl in range(state.problem.num_fidelity): + mask = (fidelity == lvl).ravel() + xt_list.append(xt[mask, :]) + y_phi_list.append(y_phi[mask].reshape(-1, 1)) + + kwargs_surrogate = state.problem.obj_configs[0].surrogate_kwargs + if kwargs_surrogate is None: + kwargs_surrogate = {} + + model = state.obj_models[0].__class__( + design_space=state.problem.design_space, **kwargs_surrogate + ) + model.train(xt_list, y_phi_list) + + valid_mask = data["rscv"] <= 0.0 + if not np.any(valid_mask): + valid_mask = data["rscv"] == np.min(data["rscv"]) + f_min = np.min(y_phi[valid_mask]) + + def ei_naive(x_pred: np.ndarray) -> np.ndarray: + return log_ei( + model.predict_values(x_pred), model.predict_variances(x_pred), f_min + ) + + return ei_naive diff --git a/src/smt_optim/acquisition_strategies/biego.py b/src/smt_optim/acquisition_strategies/biego.py index 3bd3665..2a98d93 100644 --- a/src/smt_optim/acquisition_strategies/biego.py +++ b/src/smt_optim/acquisition_strategies/biego.py @@ -10,8 +10,13 @@ from smt_optim.subsolvers import multistart_minimize, mixvar_multistart_minimize -from smt_optim.acquisition_functions.multi_obj import init_bi_obj_ei_cf -from smt_optim.acquisition_strategies.mfsego import build_scipy_constraints +from smt_optim.acquisition_functions.multi_obj import ( + init_bi_obj_ei_cf, +) +from smt_optim.acquisition_strategies.mfsego import ( + build_scipy_constraints, + select_fidelity_level, +) from smt_optim.utils.multi_obj import get_pareto_mask @@ -40,7 +45,7 @@ def SingleObjectiveNormalized(y, r, s=None): def SingleObjectiveProduct(y, r): # Returns a single objective product formulation of the problem pos_part = np.maximum(r - y, 0) - return -np.prod(pos_part ** 2, axis=-1) + return -np.prod(pos_part**2, axis=-1) def Norm(p): @@ -68,36 +73,55 @@ def __init__(self, state: State, **kwargs): self.acq_func2 = kwargs.get( "acq_func", log_ei ) # Acquisition function for min(f2) (same for f2) - self.acq_func_gen3 = kwargs.get( - "acq_func_bi", init_bi_obj_ei_cf - ) # Composite acquisition function for min(f1,f2) + self.naive = kwargs.pop("naive", False) + + if self.naive: + from smt_optim.acquisition_functions.multi_obj import init_bi_obj_ei_naive + + self.acq_func_gen3 = kwargs.get("acq_func_bi", init_bi_obj_ei_naive) + else: + self.acq_func_gen3 = kwargs.get("acq_func_bi", init_bi_obj_ei_cf) + self.n_multi_start = kwargs.pop("n_multi_start", 5) self.n_accuracy = kwargs.pop("n_accuracy", 1000) self.sp_method = kwargs.pop("sp_method", "Cobyla") self.sp_tol = kwargs.pop("sp_tol", np.sqrt(np.finfo(float).eps)) self.soformulation = kwargs.pop("so_formulation", "Product") self.relax_constraints = kwargs.pop("relax_constraints", False) + self.select_fidelity = kwargs.pop("select_fidelity", True) + self.cr_override = kwargs.pop("cr_override", None) self.current_calls = 0 self.current_subcalls = 0 self.n_init = kwargs.pop("n_init", self.n_multi_start) self.single_obj_max_calls = kwargs.pop("single_obj_max_calls", self.n_init) self.min_max_calls = kwargs.pop("min_max_calls", self.n_init) + + def _get_fmin(state, obj_idx): + data = state.scaled_dataset.export_as_dict() + mask_lvl0 = (data["fidelity"] == 0).ravel() + valid_mask = (data["rscv"] <= 0.0).ravel() + mask = mask_lvl0 & valid_mask + if not np.any(mask): + mask = mask_lvl0 + return np.min(data["obj"][mask, obj_idx]) + self.acq_func_gen1 = lambda state, kwargs: ( lambda x: self.acq_func1( state.obj_models[0].predict_values(x), state.obj_models[0].predict_variances(x), - min(state.scaled_dataset.export_data([0], 0)), + _get_fmin(state, 0), )[0][0] ) self.acq_func_gen2 = lambda state, kwargs: ( lambda x: self.acq_func2( state.obj_models[1].predict_values(x), state.obj_models[1].predict_variances(x), - min(state.scaled_dataset.export_data([1], 0)), + _get_fmin(state, 1), )[0][0] ) self.r = None + self.r_history = [] self.state = state self.X = None self.W = None @@ -117,7 +141,25 @@ def get_DoE(self): def get_pareto_front(self): D, Y = self.get_scaled_DoE() - self.X = ParetoFront(D, Y) + data = self.state.scaled_dataset.export_as_dict() + + valid_mask = (data["rscv"] <= 0.0).ravel() + mask_lvl0 = (data["fidelity"] == 0).ravel() + mask = valid_mask & mask_lvl0 + + if not np.any(mask): + mask = mask_lvl0 + if not np.any(mask): + mask = np.ones(len(Y), dtype=bool) + + valid_indices = np.where(mask)[0] + Y_valid = Y[valid_indices] + + p_mask = get_pareto_mask(Y_valid) + p_indices = valid_indices[p_mask] + + sorted_indices = p_indices[np.argsort(Y[p_indices, 0])] + self.X = sorted_indices.tolist() def select_reference_point(self): self.get_pareto_front() @@ -142,6 +184,28 @@ def select_reference_point(self): self.W[X[j]] += 1 return r + def get_fidelity(self, next_x: np.ndarray, state: "State") -> list[int]: + num_points = next_x.shape[0] + + if state.problem.num_fidelity > 1 and self.select_fidelity: + all_surrogates = [] + for o_surrogate in state.obj_models: + all_surrogates.append(o_surrogate) + for c_surrogate in state.cstr_models: + all_surrogates.append(c_surrogate) + + if self.cr_override is not None: + costs = self.cr_override + else: + costs = state.problem.costs + + levels, s2_red_norm = select_fidelity_level( + next_x, costs, all_surrogates, "pessimistic" + ) + else: + levels = [(state.problem.num_fidelity - 1) for _ in range(num_points)] + return levels + def get_infill(self, state): old_pareto_front = self.X self.get_pareto_front() @@ -154,12 +218,14 @@ def get_infill(self, state): if self.current_calls < self.min_max_calls: self.current_calls += 1 self.W.append(0) + self.r_history.append(None) return self.get_infill_custom(state, self.acq_func_gen1) elif self.current_calls < 2 * self.min_max_calls: if self.current_calls == self.min_max_calls: print("Min(f2) phase") self.current_calls += 1 self.W.append(0) + self.r_history.append(None) return self.get_infill_custom(state, self.acq_func_gen2) # Main loop @@ -172,25 +238,40 @@ def get_infill(self, state): ): print("The Pareto front is of length", len(self.X)) self.current_subcalls = 0 - r = self.select_reference_point() - if r is None: + r_scaled = self.select_reference_point() + if r_scaled is None: self.current_subcalls += 1 self.current_calls += 1 self.W.append(0) + self.r_history.append(None) if self.current_calls % 2: return self.get_infill_custom(state, self.acq_func_gen1) else: return self.get_infill_custom(state, self.acq_func_gen2) - print("Bi-objective phase with r =", r) - if self.soformulation == "Normalized": - self.phi = lambda y: SingleObjectiveNormalized(y, r) - elif self.soformulation == "Product": - self.phi = lambda y: SingleObjectiveProduct(y, r) - else: - raise ValueError("Unknown single-objective formulation") + + # Unscale the selected reference point and lock it in the physical space + qoi_factor = state.qoi_factor[0][:2] + qoi_step = state.qoi_step[0][:2] + self.r_unscaled = r_scaled * qoi_factor + qoi_step + print("Bi-objective phase with unscaled r =", self.r_unscaled) + + # Dynamically rescale the locked physical reference point to match the + # shifting surrogate scale of the current iteration + qoi_factor = state.qoi_factor[0][:2] + qoi_step = state.qoi_step[0][:2] + self.r = (self.r_unscaled - qoi_step) / qoi_factor + + if self.soformulation == "Normalized": + self.phi = lambda y: SingleObjectiveNormalized(y, self.r) + elif self.soformulation == "Product": + self.phi = lambda y: SingleObjectiveProduct(y, self.r) + else: + raise ValueError("Unknown single-objective formulation") + self.current_subcalls += 1 self.current_calls += 1 self.W.append(0) + self.r_history.append(self.r_unscaled) return self.get_infill_custom( state, self.acq_func_gen3, phi=self.phi, n_accuracy=self.n_accuracy ) @@ -241,6 +322,13 @@ def sp_wrapper(x): ) next_x = res.x - infill = [next_x.reshape(1, -1)] + level = self.get_fidelity(next_x.reshape(1, -1), state)[0] + + infills = [] + for lvl in range(state.problem.num_fidelity): + if lvl <= level: + infills.append(next_x.copy().reshape(1, -1)) + else: + infills.append(None) - return infill + return infills diff --git a/src/smt_optim/acquisition_strategies/mfsego.py b/src/smt_optim/acquisition_strategies/mfsego.py index 6244d52..28c16f2 100644 --- a/src/smt_optim/acquisition_strategies/mfsego.py +++ b/src/smt_optim/acquisition_strategies/mfsego.py @@ -114,9 +114,7 @@ def __init__(self, state: State, **kwargs): self.optimize_best = kwargs.pop( "optimize_best", False ) # broken -> to be fixed! - self.relax_constraints = kwargs.pop( - "relax_constraints", False - ) + self.relax_constraints = kwargs.pop("relax_constraints", False) self.cr_override = kwargs.pop( "cr_override", None ) # override optimizer Cost Ratio diff --git a/src/smt_optim/acquisition_strategies/mosego.py b/src/smt_optim/acquisition_strategies/mosego.py index c4b7198..a57da2a 100644 --- a/src/smt_optim/acquisition_strategies/mosego.py +++ b/src/smt_optim/acquisition_strategies/mosego.py @@ -1,61 +1,43 @@ -from time import perf_counter +from time import perf_counter from typing import Callable - import numpy as np from scipy import stats as stats - import smt.design_space as ds - from smt_optim.acquisition_strategies import AcquisitionStrategy # from smt_optim.adapters.smt import SmtMFK - from smt_optim.core.state import State from smt_optim.subsolvers.multistart import mixvar_multistart_minimize - - - from smt_optim.subsolvers import multistart_minimize - from smt_optim.acquisition_functions.multi_obj import init_mpi - from smt_optim.acquisition_strategies.mfsego import ( - build_scipy_constraints, - select_fidelity_level, - ) - - - class MOSEGO(AcquisitionStrategy): - def __init__(self, state: State, **kwargs): super().__init__() - - self.acq_func_gen = kwargs.get("acq_func", init_mpi) self.n_start = kwargs.pop("n_start", 20) @@ -70,60 +52,40 @@ def __init__(self, state: State, **kwargs): self.seed = kwargs.pop("seed", None) - - def validate_config(self, state): pass - - def get_infill(self, state): - - if isinstance(self.seed, int) or isinstance(self.seed, float): - self.seed += 1 - - acq_data = dict() - - acq_func: Callable = self.acq_func_gen(state) - - def scipy_obj(x): x = x.reshape(1, -1) - - return -acq_func(x) - - + val = acq_func(x) + if isinstance(val, np.ndarray): + return -float(val.item()) + return -float(val) scipy_cstr: list = build_scipy_constraints(state) - - mix_var = False for dv in state.problem.design_space.design_variables: - if not isinstance(dv, ds.FloatVariable): - mix_var = True break - - # TODO: merge continuous and mixvar multistart optimization if not mix_var: - # generate starting points for the multistart optimization gen_t0 = perf_counter() @@ -140,92 +102,55 @@ def scipy_obj(x): acq_data["generate_init_points_time"] = gen_t1 - gen_t0 - - res = multistart_minimize( - scipy_obj, - bounds=np.array([[0, 1]] * state.problem.num_dim), - multi_x0=multi_x0, - constraints=scipy_cstr, - seed=self.seed, - tol=self.sp_tol, - method=self.sp_method, - ) else: - res = mixvar_multistart_minimize( - scipy_obj, - design_space=state.problem.design_space, - constraints=scipy_cstr, - n_start=self.n_start, - method=self.sp_method, - tol=self.sp_tol, - seed=self.seed, - ) - - next_x = res.x - - # selects highest fidelity level to sample fid_crit_t0 = perf_counter() level = self.get_fidelity(next_x.reshape(1, -1), state)[0] - - # keeps the DoE nested -> requests sampling all lower fidelity levels infills = [] for lvl in range(state.problem.num_fidelity): - if lvl <= level: - infills.append(next_x.copy().reshape(1, -1)) else: - infills.append(None) - - fid_crit_t1 = perf_counter() acq_data["fid_crit_time"] = fid_crit_t1 - fid_crit_t0 - - state.iter_log["acquisition"] = acq_data - - return infills - - def get_fidelity(self, next_x: np.ndarray, state: State) -> list[int]: - """ Select the highest fidelity level to sample at the given point(s). @@ -266,51 +191,28 @@ def get_fidelity(self, next_x: np.ndarray, state: State) -> list[int]: """ - - num_points = next_x.shape[0] - - if state.problem.num_fidelity > 1 and self.select_fidelity: - all_surrogates = [] - - for o_surrogate in state.obj_models: - all_surrogates.append(o_surrogate) for c_surrogate in state.cstr_models: - all_surrogates.append(c_surrogate) - - if self.cr_override is not None: - costs = self.cr_override else: - costs = state.problem.costs - - levels, s2_red_norm = select_fidelity_level( - next_x, costs, all_surrogates, "pessimistic" - ) - - else: - levels = [(state.problem.num_fidelity - 1) for _ in range(num_points)] - - return levels - diff --git a/src/smt_optim/benchmarks/base.py b/src/smt_optim/benchmarks/base.py index c689298..237805e 100644 --- a/src/smt_optim/benchmarks/base.py +++ b/src/smt_optim/benchmarks/base.py @@ -1,5 +1,6 @@ from abc import ABC from typing import Callable +from pymoo.core.problem import Problem as PymooProblem import numpy as np @@ -31,3 +32,54 @@ def set_dim(self, dim): self.bounds = self.bounds.repeat(dim, axis=0) else: raise Exception("Not a variable dimension problem.") + + +class PymooWrapper(PymooProblem): + """ + Wraps an SMT-Optim BenchmarkProblem into a pymoo Problem so that it can be + optimized using pymoo algorithms (like NSGA2) to find reference Pareto fronts. + """ + + def __init__(self, benchmark: BenchmarkProblem): + self.benchmark = benchmark + + n_var = benchmark.num_dim + n_obj = benchmark.num_obj + + if hasattr(benchmark, "constraints") and benchmark.constraints is not None: + n_ieq_constr = len(benchmark.constraints) + else: + n_ieq_constr = 0 + + xl = benchmark.bounds[:, 0] + xu = benchmark.bounds[:, 1] + + super().__init__( + n_var=n_var, n_obj=n_obj, n_ieq_constr=n_ieq_constr, xl=xl, xu=xu + ) + + def _evaluate(self, x, out, *args, **kwargs): + num_pt = x.shape[0] + + # Evaluate objectives + out["F"] = np.full((num_pt, self.n_obj), np.nan) + for i in range(self.n_obj): + if isinstance(self.benchmark.objective, list): + obj_func = self.benchmark.objective[i] + if isinstance(obj_func, list): + obj_func = obj_func[-1] + else: + obj_func = self.benchmark.objective + # Some benchmark objective functions return 1D arrays for multiple points + val = obj_func(x) + out["F"][:, i] = np.atleast_1d(val).ravel() + + # Evaluate constraints (assumed to be <= 0 in pymoo) + if self.n_ieq_constr > 0: + out["G"] = np.full((num_pt, self.n_ieq_constr), np.nan) + for i in range(self.n_ieq_constr): + cstr_func = self.benchmark.constraints[i] + if isinstance(cstr_func, list): + cstr_func = cstr_func[-1] + val = cstr_func(x) + out["G"][:, i] = np.atleast_1d(val).ravel() diff --git a/src/smt_optim/benchmarks/multi_obj/constrained.py b/src/smt_optim/benchmarks/multi_obj/constrained.py index bbe4869..05bc5bb 100644 --- a/src/smt_optim/benchmarks/multi_obj/constrained.py +++ b/src/smt_optim/benchmarks/multi_obj/constrained.py @@ -126,7 +126,12 @@ def g1(self, x: np.ndarray) -> np.ndarray: ndim = x.ndim if ndim == 1: x = x.reshape(1, -1) - val = -(x[:, 0] ** 2) - x[:, 1] ** 2 + 1.0 + 0.1 * np.cos(16.0 * np.arctan2(x[:, 0], x[:, 1])) + val = ( + -(x[:, 0] ** 2) + - x[:, 1] ** 2 + + 1.0 + + 0.1 * np.cos(16.0 * np.arctan2(x[:, 0], x[:, 1])) + ) if ndim == 1: return val.item() return val @@ -200,7 +205,7 @@ def f2(self, x: np.ndarray) -> np.ndarray: ndim = x.ndim if ndim == 1: x = x.reshape(1, -1) - val = np.sum(x ** 2, axis=1) + val = np.sum(x**2, axis=1) if ndim == 1: return val.item() return val diff --git a/src/smt_optim/benchmarks/registry.py b/src/smt_optim/benchmarks/registry.py index d1a7fc5..ee6b222 100644 --- a/src/smt_optim/benchmarks/registry.py +++ b/src/smt_optim/benchmarks/registry.py @@ -21,6 +21,10 @@ from .misc import weldedbeam_variants +from .multi_obj import constrained as mo_constrained +from .multi_obj import zdt_mf +from .multi_obj import zdt + available = {} @@ -48,6 +52,11 @@ def _register_from_module(module): _register_from_module(weldedbeam_variants) +_register_from_module(zdt) +_register_from_module(zdt_mf) +_register_from_module(mo_constrained) + + # def list_problems(**criteria): # # results = [] diff --git a/src/smt_optim/core/state.py b/src/smt_optim/core/state.py index 75e763b..0cdbe32 100644 --- a/src/smt_optim/core/state.py +++ b/src/smt_optim/core/state.py @@ -7,7 +7,6 @@ from smt_optim.core import OptimizationDataset from smt_optim.core.sample import Sample -from smt_optim.utils.multi_obj import get_pareto_mask # from smt_optim.core import Problem @@ -316,14 +315,15 @@ def get_best_sample( best_sample = dataset.samples[idx] else: from smt_optim.utils.multi_obj import get_pareto_mask + # Multi-objective: get non-dominated points among feasible ones feasible_yt = yt[fid_feas_mask] pareto_mask_feasible = get_pareto_mask(feasible_yt) - + # Map back to original indices feasible_indices = np.where(fid_feas_mask)[0] pareto_indices = feasible_indices[pareto_mask_feasible] - + best_sample = [dataset.samples[i] for i in pareto_indices] # if no sample is feasible, selects sample with lowest RSCV diff --git a/src/smt_optim/frameworks.py b/src/smt_optim/frameworks.py index 61d74b3..37b8260 100644 --- a/src/smt_optim/frameworks.py +++ b/src/smt_optim/frameworks.py @@ -13,7 +13,6 @@ ) from smt_optim.surrogate_models.smt import SmtAutoModel, SmtGPX, SmtMFCK from smt_optim.acquisition_strategies import MFSEGO, VFPI -from smt_optim.acquisition_strategies.biego import BiEGO def minimize( @@ -99,7 +98,9 @@ def minimize( if multi_fidelity: expected_costs_len = len(objective[0]) if is_multi_obj else len(objective) if len(costs) != expected_costs_len: - raise Exception("Error: len(costs) does not match the number of fidelity levels in objective") + raise Exception( + "Error: len(costs) does not match the number of fidelity levels in objective" + ) else: methods = { @@ -187,7 +188,7 @@ def minimize( # ------- start driver ------- if strategy_kwargs is None: strategy_kwargs = {} - + driver = Driver(problem, driver_config, strategy, strategy_kwargs=strategy_kwargs) state = driver.optimize() return state diff --git a/src/smt_optim/utils/initial_design.py b/src/smt_optim/utils/initial_design.py index 0d7ca5c..3cabaa0 100644 --- a/src/smt_optim/utils/initial_design.py +++ b/src/smt_optim/utils/initial_design.py @@ -57,7 +57,10 @@ def generate_initial_design(state: State, evaluator, config) -> None: else: nt_init = config.nt_init - doe = sampler(nt_init) + if state.problem.num_fidelity > 1 and isinstance(nt_init, list): + doe = sampler(nt_init[-1]) + else: + doe = sampler(nt_init) # quick fix for the seed parameter issue if state.problem.num_fidelity == 1 and isinstance(design_space, ds.DesignSpace): diff --git a/src/smt_optim/utils/logger.py b/src/smt_optim/utils/logger.py index 4c55954..3edda87 100644 --- a/src/smt_optim/utils/logger.py +++ b/src/smt_optim/utils/logger.py @@ -51,13 +51,20 @@ def on_iter_end(self, state) -> None: sample = state.get_best_sample(ctol=1e-4) + if isinstance(sample, list): + fmin = np.nan + rscv = sample[0].metadata["rscv"] if len(sample) > 0 else np.nan + else: + fmin = sample.obj[0] + rscv = sample.metadata["rscv"] + iter_log = getattr(state, "iter_log", {}) or {} data = { "iter": state.iter, "budget": state.budget, - "fmin": sample.obj[0], - "rscv": sample.metadata["rscv"], + "fmin": fmin, + "rscv": rscv, "fidelity": iter_log.get("fidelity", np.nan), "gp_time": iter_log.get("gp_training_time", np.nan), "acq_time": iter_log.get("acq_opt_time", np.nan), diff --git a/src/smt_optim/utils/multi_obj.py b/src/smt_optim/utils/multi_obj.py index 3e60b38..dcecfdb 100644 --- a/src/smt_optim/utils/multi_obj.py +++ b/src/smt_optim/utils/multi_obj.py @@ -316,3 +316,126 @@ def gamma_spread(pf: np.ndarray) -> float: if len(distances) == 0: return np.nan return float(np.max(distances)) + + +def plot_pareto_front( + initial_dataset, + final_dataset, + filename="pareto_front.png", + title="Pareto Front of ZDT1 (2D) obtained with SMT-optim", +): + import matplotlib.pyplot as plt + import numpy as np + + # 1. True Pareto Front (ZDT1 specific) + f1_true = np.linspace(0, 1, 100) + f2_true = 1 - np.sqrt(f1_true) + + # 2. Extract objectives + initial_dict = initial_dataset.export_as_dict() + final_dict = final_dataset.export_as_dict() + + f1_init = initial_dict["obj"][:, 0] + f2_init = initial_dict["obj"][:, 1] + + f1_final_all = final_dict["obj"][:, 0] + f2_final_all = final_dict["obj"][:, 1] + + # Get pareto front of all final points + all_obj = np.vstack([f1_final_all, f2_final_all]).T + pareto_mask = get_pareto_mask(all_obj) + + n_init = len(f1_init) + + # Infills + f1_infill = f1_final_all[n_init:] + f2_infill = f2_final_all[n_init:] + infill_obj = np.vstack([f1_infill, f2_infill]).T + + # We want PF of all final points + # But we want to distinguish dominated and non-dominated INFILLS + infill_pareto_mask = pareto_mask[n_init:] + + # Dominated infills + f1_dominated = infill_obj[~infill_pareto_mask, 0] + f2_dominated = infill_obj[~infill_pareto_mask, 1] + + # PF infills (non-dominated infills) + f1_pf = infill_obj[infill_pareto_mask, 0] + f2_pf = infill_obj[infill_pareto_mask, 1] + + # Sort them for plotting a connected line + idx = np.argsort(f1_pf) + f1_pf = f1_pf[idx] + f2_pf = f2_pf[idx] + + fig, ax = plt.subplots(figsize=(8, 6)) + + # Plot True PF + ax.plot(f1_true, f2_true, "k-", label="Ref PF", linewidth=2) + + # Plot Initial DoE + ax.plot( + f1_init, + f2_init, + "go", + label="Initial DoE", + markersize=5, + linestyle="None", + alpha=0.7, + ) + + # Plot Final DoE (dominated) + ax.plot( + f1_dominated, + f2_dominated, + "bo", + label="Final DoE", + markersize=5, + linestyle="None", + alpha=0.7, + ) + + # Plot PF infills + ax.plot(f1_pf, f2_pf, "r:d", label="PF infills", markersize=8) + + ax.set_xlabel("$f_1$") + ax.set_ylabel("$f_2$") + ax.set_title(title) + ax.legend() + ax.grid(True, linestyle="--", alpha=0.6) + + plt.savefig(filename, dpi=300, bbox_inches="tight") + plt.close() + + +def plot_hypervolume_convergence( + hv_history: list[float], + filename: str = "hypervolume_convergence.png", + title: str = "Hypervolume Convergence", +): + """ + Plots the hypervolume evolution over iterations. + + Parameters + ---------- + hv_history : list of float + Hypervolume value at each iteration. + filename : str + The output image file name. + title : str + The title of the plot. + """ + import matplotlib.pyplot as plt + + fig, ax = plt.subplots(figsize=(8, 6)) + iterations = np.arange(len(hv_history)) + ax.plot(iterations, hv_history, "b-o", linewidth=2, markersize=6) + + ax.set_xlabel("Iterations") + ax.set_ylabel("Hypervolume") + ax.set_title(title) + ax.grid(True, linestyle="--", alpha=0.6) + + plt.savefig(filename, dpi=300, bbox_inches="tight") + plt.close() diff --git a/src/smt_optim/utils/profiles.py b/src/smt_optim/utils/profiles.py new file mode 100644 index 0000000..82ff49a --- /dev/null +++ b/src/smt_optim/utils/profiles.py @@ -0,0 +1,245 @@ +import os +import numpy as np +from warnings import warn + +""" +<--- WORK IN PROGRESS ---> +""" + + +def read_instances(algos: list[str], metric: str = "iter", fmin: str = "fmin") -> dict: + + data = {} + + for algo in algos: + data[algo] = {} + + for root, _, filenames in os.walk(algo): + for filename in filenames: + full_path = os.path.join(root, filename) + + with open(full_path, "r") as file: + # get instance name + instance_name, ext = os.path.splitext(filename) + + # get metric and fmin column index from the .csv header + headers = file.readline().strip().split(",") + metric_index = headers.index(metric) + fmin_index = headers.index(fmin) + + # get instance data + inst_data = np.loadtxt(file, delimiter=",", skiprows=0) + + # extract metric and fmin columns + data[algo][instance_name] = np.empty((inst_data.shape[0], 2)) + data[algo][instance_name][:, 0] = inst_data[:, metric_index] + data[algo][instance_name][:, 1] = inst_data[:, fmin_index] + + return data + + +def get_f0_fstar(data: dict) -> tuple[dict, dict]: + + algos = list(data.keys()) + instances = data[algos[0]].keys() + + f0 = {} + fstar = {} + + for inst in instances: + f0[inst] = data[algos[0]][inst][0, 1] + fstar[inst] = np.inf + + for algo in algos: + if f0[inst] != data[algo][inst][0, 1]: + raise Exception( + f"instance = {inst}: f0 is not identical for all algorithms." + ) + + fstar[inst] = min(fstar[inst], data[algo][inst][-1, 1]) + + return f0, fstar + + +def profile(data: dict, tau: float = 0.1, type: str = "perf", dim: dict = None): + + algos = list(data.keys()) + instances = data[algos[0]].keys() + + if dim is None: + if type == "data": + warn( + "Data profiles require the problem's dimensions to be given. n is set to 0 for all instances (n+1 = 1)." + ) + dim = {} + for inst in instances: + dim[inst] = 0 + + f0, fstar = get_f0_fstar(data) + + criteria = {} + min_metric = {} + for inst in instances: + criteria[inst] = f0[inst] - (1 - tau) * (f0[inst] - fstar[inst]) + min_metric[inst] = np.inf + + perf = {} + + for algo in algos: + perf[algo] = {} + perf[algo]["metric"] = [] + perf[algo]["portion"] = [] + + portion = 0 + + for inst in instances: + tau_solved = np.where(data[algo][inst][:, 1] <= criteria[inst], 1, 0) + + if np.any(tau_solved == 1): + index = np.argmax(tau_solved) + metric = data[algo][inst][index, 0] + + if type == "data": + metric /= dim[inst] + 1 + + min_metric[inst] = min(min_metric[inst], metric) + + perf[algo]["metric"].append(metric) + portion += 1 + perf[algo]["portion"].append(portion) + + else: + perf[algo]["metric"].append(np.nan) + perf[algo]["portion"].append(portion) + + perf[algo]["metric"] = np.array(perf[algo]["metric"]) + perf[algo]["portion"] = np.array(perf[algo]["portion"], dtype=float) + + max_metric = 0 + + for algo in algos: + for i, inst in enumerate(instances): + if type == "perf": + perf[algo]["metric"][i] /= min_metric[inst] + + remove = np.isnan(perf[algo]["metric"]) + perf[algo]["metric"] = perf[algo]["metric"][~remove] + perf[algo]["portion"] = perf[algo]["portion"][~remove] + + perf[algo]["metric"] = np.sort(perf[algo]["metric"]) + max_metric = max(max_metric, perf[algo]["metric"][-1]) + + perf[algo]["portion"] /= len(instances) + + max_metric *= 1.1 + + if type == "perf": + one_sum = 0 + + for algo in algos: + perf[algo]["metric"] = np.insert(perf[algo]["metric"], 0, 1) + perf[algo]["portion"] = np.insert(perf[algo]["portion"], 0, 0) + + one_sum += np.sum(np.where(perf[algo]["metric"][1:] == 1, 1, 0)) + + if one_sum < len(instances): + raise Exception("Total number of instances with ratio 1 is < 1.") + + elif type == "data": + for algo in algos: + perf[algo]["metric"] = np.insert(perf[algo]["metric"], 0, 0) + perf[algo]["portion"] = np.insert(perf[algo]["portion"], 0, 0) + + pass + + for algo in algos: + perf[algo]["metric"] = np.append(perf[algo]["metric"], max_metric) + perf[algo]["portion"] = np.append( + perf[algo]["portion"], perf[algo]["portion"][-1] + ) + + return perf + + +def convergence_profile(instances: dict): + + metric = [] + + num_instances = len(list(instances.keys())) + + for inst, inst_data in instances.items(): + metric.extend(inst_data[:, 0]) + + metric = np.array(metric) + metric = np.unique(metric) + metric = np.sort(metric) + + all_fmin = np.empty((num_instances, len(metric))) + + counter = 0 + for inst, inst_data in instances.items(): + e_metric = inst_data[:, 0] + e_value = inst_data[:, 1] + + indices = np.searchsorted(e_metric, metric, side="right") - 1 + + # ensure the indices are within bounds (>= 0) + indices = np.clip(indices, 0, len(e_metric) - 1) + + all_fmin[counter, :] = e_value[indices] + counter += 1 + + return metric, all_fmin + + +def accuracy_profile(data: dict): + + algos = list(data.keys()) + instances = list(data[algos[0]].keys()) + + f0, fstar = get_f0_fstar(data) + + accuracies = {} + portions = {} + + max_acc = 0 + + for algo in algos: + accuracies[algo] = [] + portions[algo] = [] + + counter = len(instances) + + for i, inst in enumerate(instances): + last_f = data[algo][inst][-1, 1] + acc = (last_f - f0[inst]) / (fstar[inst] - f0[inst]) + if acc != 1: + accuracies[algo].append(acc) + portions[algo].append(counter) + counter -= 1 + + accuracies[algo] = np.array(accuracies[algo], dtype=float) + portions[algo] = np.array(portions[algo], dtype=float) + + accuracies[algo] = -np.log10(1 - accuracies[algo]) + accuracies[algo] = np.sort(accuracies[algo]) + portions[algo] /= len(instances) + + max_acc = max(max_acc, np.max(accuracies[algo])) + + accuracies[algo] = np.concatenate(([0], accuracies[algo])) + portions[algo] = np.concatenate(([1], portions[algo])) + + max_acc *= 1.1 + for algo in algos: + accuracies[algo] = np.concatenate((accuracies[algo], [max_acc])) + portions[algo] = np.concatenate((portions[algo], [portions[algo][-1]])) + + accuracy_data = {} + for algo in algos: + accuracy_data[algo] = { + "accuracy": accuracies[algo], + "portion": portions[algo], + } + + return accuracy_data diff --git a/src/tests/test_convergence/test_multi_objective.py b/src/tests/test_convergence/test_multi_objective.py new file mode 100644 index 0000000..e2b4649 --- /dev/null +++ b/src/tests/test_convergence/test_multi_objective.py @@ -0,0 +1,270 @@ +import unittest +import numpy as np + +from smt_optim.core import Problem, ObjectiveConfig, DriverConfig, Driver +from smt_optim.surrogate_models.smt import SmtAutoModel +from smt_optim.benchmarks.multi_obj.zdt import ZDT1 +from smt_optim.utils.multi_obj import ( + get_pf_from_dataset, + hypervolume_2d, + plot_pareto_front, +) +from smt_optim.acquisition_strategies.biego import BiEGO +from smt_optim.acquisition_strategies.mosego import MOSEGO + + +class TestMultiObjectiveConvergence(unittest.TestCase): + def test_zdt1_mosego_hypervolume_growth(self): + """Test that MOSEGO improves the hypervolume of the Pareto front on ZDT1.""" + zdt1 = ZDT1() + zdt1.set_dim(2) + + obj1 = ObjectiveConfig(objective=[zdt1.f1], surrogate=SmtAutoModel) + obj2 = ObjectiveConfig(objective=[zdt1.f2], surrogate=SmtAutoModel) + + problem = Problem(obj_configs=[obj1, obj2], design_space=zdt1.bounds) + + opt_config = DriverConfig(max_iter=25, nt_init=10, seed=42) + + driver = Driver(problem=problem, config=opt_config, strategy=MOSEGO) + + # Initial DoE hypervolume + driver.start_optim() + initial_dataset = driver.state.dataset + initial_pf = get_pf_from_dataset(initial_dataset) + ref_point = np.array([2.0, 2.0]) + initial_hv = hypervolume_2d(initial_pf, ref_point) + + # Optimize for a few iterations + state = driver.optimize() + final_pf = get_pf_from_dataset(state.dataset) + final_hv = hypervolume_2d(final_pf, ref_point) + + from pymoo.indicators.igd_plus import IGDPlus + + x1 = np.linspace(0, 1, 100) + true_pf = np.array([x1, 1 - np.sqrt(x1)]).T + igd = IGDPlus(true_pf) + + initial_igd = igd.do(initial_pf) + final_igd = igd.do(final_pf) + + # Hypervolume should increase or remain the same + self.assertGreaterEqual(final_hv, initial_hv) + # IGD+ should decrease or remain the same + self.assertLessEqual(final_igd, initial_igd) + + plot_pareto_front( + initial_dataset, + state.dataset, + filename="zdt1_pareto_front_mosego.png", + title="Pareto Front of ZDT1 (2D) obtained with MOSEGO", + ) + + def test_zdt1_biego_hypervolume_growth(self): + """Test that BiEGO improves the hypervolume of the Pareto front on ZDT1.""" + zdt1 = ZDT1() + zdt1.set_dim(2) + + obj1 = ObjectiveConfig(objective=[zdt1.f1], surrogate=SmtAutoModel) + obj2 = ObjectiveConfig(objective=[zdt1.f2], surrogate=SmtAutoModel) + + problem = Problem(obj_configs=[obj1, obj2], design_space=zdt1.bounds) + + # Decrease min_max_calls to quickly enter the bi-objective phase + opt_config = DriverConfig(max_iter=25, nt_init=10, seed=42) + + driver = Driver( + problem=problem, + config=opt_config, + strategy=BiEGO, + strategy_kwargs={"min_max_calls": 3, "n_multi_start": 15}, + ) + + # Initial DoE hypervolume + driver.start_optim() + initial_dataset = driver.state.dataset + initial_pf = get_pf_from_dataset(initial_dataset) + ref_point = np.array([2.0, 2.0]) + initial_hv = hypervolume_2d(initial_pf, ref_point) + + # Optimize + state = driver.optimize() + final_pf = get_pf_from_dataset(state.dataset) + final_hv = hypervolume_2d(final_pf, ref_point) + + from pymoo.indicators.igd_plus import IGDPlus + + # We need a true pareto front or a reference front for IGD+. + # We'll just generate the true ZDT1 front analytically. + x1 = np.linspace(0, 1, 100) + true_pf = np.array([x1, 1 - np.sqrt(x1)]).T + igd = IGDPlus(true_pf) + + initial_igd = igd.do(initial_pf) + final_igd = igd.do(final_pf) + + # Hypervolume should increase or remain the same + self.assertGreaterEqual(final_hv, initial_hv) + # IGD+ should decrease or remain the same + self.assertLessEqual(final_igd, initial_igd) + + plot_pareto_front( + initial_dataset, + state.dataset, + filename="zdt1_pareto_front_biego.png", + title="Pareto Front of ZDT1 (2D) obtained with BiEGO", + ) + + from smt_optim.utils.multi_obj import ( + plot_hypervolume_convergence, + get_pareto_front, + ) + + y_all = state.dataset.export_as_dict()["obj"] + + hv_history = [] + for i in range(opt_config.nt_init, len(y_all) + 1): + current_pts = y_all[:i] + current_pf = get_pareto_front(current_pts) + + # Compute Area under PF w.r.t (0,0) (which decays as PF converges to the origin) + sorted_pf = current_pf[np.argsort(current_pf[:, 0])] + area = 0.0 + prev_f1 = 0.0 + for k in range(sorted_pf.shape[0]): + area += (sorted_pf[k, 0] - prev_f1) * sorted_pf[k, 1] + prev_f1 = sorted_pf[k, 0] + hv_history.append(area) + + plot_hypervolume_convergence( + hv_history, + filename="zdt1_hypervolume_decay.png", + title="Hypervolume (Area under PF) Decay over Iterations", + ) + + def test_zdt1_algorithms_comparison(self): + from smt_optim.core.driver import DriverConfig, Driver + from smt_optim.benchmarks.multi_obj.zdt import ZDT1 + from smt_optim.core.problem import Problem + from smt_optim.core import ObjectiveConfig + from smt_optim.surrogate_models.smt import SmtAutoModel + from smt_optim.acquisition_strategies.biego import BiEGO + from smt_optim.acquisition_strategies.mosego import MOSEGO + from smt_optim.utils.multi_obj import get_pareto_front + import matplotlib.pyplot as plt + import numpy as np + + zdt1 = ZDT1() + zdt1.set_dim(2) + + def setup_problem(): + obj1 = ObjectiveConfig(objective=[zdt1.f1], surrogate=SmtAutoModel) + obj2 = ObjectiveConfig(objective=[zdt1.f2], surrogate=SmtAutoModel) + return Problem(obj_configs=[obj1, obj2], design_space=zdt1.bounds) + + opt_config = DriverConfig(max_iter=10, nt_init=5, seed=42) + + def get_area_history(state): + y_all = state.dataset.export_as_dict()["obj"] + hv_history = [] + for i in range(opt_config.nt_init, len(y_all) + 1): + current_pts = y_all[:i] + current_pf = get_pareto_front(current_pts) + sorted_pf = current_pf[np.argsort(current_pf[:, 0])] + area = 0.0 + prev_f1 = 0.0 + for k in range(sorted_pf.shape[0]): + area += (sorted_pf[k, 0] - prev_f1) * sorted_pf[k, 1] + prev_f1 = sorted_pf[k, 0] + hv_history.append(area) + return hv_history + + # BiEGO Composite + driver_comp = Driver( + problem=setup_problem(), + config=opt_config, + strategy=BiEGO, + strategy_kwargs={"min_max_calls": 1, "n_multi_start": 20}, + ) + state_comp = driver_comp.optimize() + hv_comp = get_area_history(state_comp) + + # BiEGO Naive + driver_naive = Driver( + problem=setup_problem(), + config=opt_config, + strategy=BiEGO, + strategy_kwargs={"min_max_calls": 1, "n_multi_start": 20, "naive": True}, + ) + state_naive = driver_naive.optimize() + hv_naive = get_area_history(state_naive) + + # MOSEGO + driver_mosego = Driver( + problem=setup_problem(), + config=opt_config, + strategy=MOSEGO, + ) + state_mosego = driver_mosego.optimize() + hv_mosego = get_area_history(state_mosego) + + iterations = list(range(1, len(hv_comp) + 1)) + + fig, ax = plt.subplots(figsize=(8, 6)) + ax.plot(iterations, hv_comp, "b-o", label="BiEGO (Composite)", alpha=0.8) + ax.plot(iterations, hv_naive, "r-s", label="BiEGO (Naive)", alpha=0.8) + ax.plot(iterations, hv_mosego, "g-^", label="MOSEGO (MPI)", alpha=0.8) + ax.set_xlabel("Infill Iterations") + ax.set_ylabel("Hypervolume (Area bounded by PF)") + ax.set_title("Decay Comparison: Naive BiEGO vs Composite BiEGO vs MOSEGO MPI") + ax.legend() + ax.grid(True, linestyle="--", alpha=0.6) + plt.savefig( + "zdt1_algorithms_hypervolume_decay_comparison.png", + dpi=300, + bbox_inches="tight", + ) + plt.close(fig) + + +if __name__ == "__main__": + unittest.main() + + +def test_mf_biego_constrained(): + from smt_optim.core.driver import DriverConfig, Driver + from smt_optim.benchmarks.multi_obj.zdt_mf import DTLZ5 + from smt_optim.core.problem import Problem + from smt_optim.core import ObjectiveConfig, ConstraintConfig + from smt_optim.surrogate_models.smt import SmtAutoModel + from smt_optim.acquisition_strategies.biego import BiEGO + + dtlz5 = DTLZ5() + dtlz5.set_dim(2) + + obj1 = ObjectiveConfig(objective=dtlz5.objective[0], surrogate=SmtAutoModel) + obj2 = ObjectiveConfig(objective=dtlz5.objective[1], surrogate=SmtAutoModel) + cstr1 = ConstraintConfig( + constraint=dtlz5.constraints[0], surrogate=SmtAutoModel, upper=0.0 + ) + + problem = Problem( + obj_configs=[obj1, obj2], + cstr_configs=[cstr1], + design_space=dtlz5.bounds, + costs=[1.0, 10.0], + ) + + opt_config = DriverConfig(max_iter=10, nt_init=[10, 5], seed=42) + driver = Driver( + problem=problem, + config=opt_config, + strategy=BiEGO, + strategy_kwargs={"min_max_calls": 2, "n_multi_start": 50}, + ) + driver.start_optim() + while driver.state.iter < opt_config.max_iter: + driver.iteration(driver.state) + + assert len(driver.state.dataset.export_as_dict()["x"]) == 25 diff --git a/src/tests/test_integration/test_biego.py b/src/tests/test_integration/test_biego.py index 42830b1..043aafc 100644 --- a/src/tests/test_integration/test_biego.py +++ b/src/tests/test_integration/test_biego.py @@ -7,137 +7,203 @@ from smt_optim.acquisition_strategies.biego import BiEGO from smt_optim.utils.multi_obj import get_pf_from_dataset, hypervolume_2d + def f1(x: np.ndarray) -> np.ndarray: ndim = x.ndim if ndim == 1: x = x.reshape(1, -1) - value = x[:, 0]**2 + x[:, 1]**2 + value = x[:, 0] ** 2 + x[:, 1] ** 2 if ndim == 1: return value.item() return value + def f2(x: np.ndarray) -> np.ndarray: ndim = x.ndim if ndim == 1: x = x.reshape(1, -1) - value = (x[:, 0] - 1)**2 + (x[:, 1] - 1)**2 + value = (x[:, 0] - 1) ** 2 + (x[:, 1] - 1) ** 2 if ndim == 1: return value.item() return value + def cstr(x: np.ndarray) -> np.ndarray: ndim = x.ndim if ndim == 1: x = x.reshape(1, -1) - value = x[:, 0]**2 + x[:, 1]**2 - 4.0 + value = x[:, 0] ** 2 + x[:, 1] ** 2 - 4.0 if ndim == 1: return value.item() return value + class TestBiEGO(unittest.TestCase): def test_biego_2d_1c(self): bounds = np.array([[-2.0, 2.0], [-2.0, 2.0]]) - + obj_config1 = ObjectiveConfig( objective=[f1], surrogate=SmtAutoModel, ) - + obj_config2 = ObjectiveConfig( objective=[f2], surrogate=SmtAutoModel, ) - + cstr_config = ConstraintConfig( constraint=[cstr], upper=0.0, surrogate=SmtAutoModel, ) - + problem = Problem( obj_configs=[obj_config1, obj_config2], cstr_configs=[cstr_config], design_space=bounds, ) - + opt_config = DriverConfig( max_iter=3, seed=42, ) - + optimizer = Driver( problem=problem, config=opt_config, strategy=BiEGO, ) - + state = optimizer.optimize() - + y_data = np.empty((len(state.dataset.samples), 2)) for i, sample in enumerate(state.dataset.samples): y_data[i, 0] = sample.obj[0] y_data[i, 1] = sample.obj[1] - + self.assertGreater(len(y_data), 3) def test_biego_convergence(self): # A test to verify if the hypervolume strictly increases over several iterations bounds = np.array([[-2.0, 2.0], [-2.0, 2.0]]) - + obj_config1 = ObjectiveConfig( objective=[f1], surrogate=SmtAutoModel, ) - + obj_config2 = ObjectiveConfig( objective=[f2], surrogate=SmtAutoModel, ) - + problem = Problem( obj_configs=[obj_config1, obj_config2], design_space=bounds, ) - + opt_config = DriverConfig( max_iter=10, seed=42, ) - + optimizer = Driver( problem=problem, config=opt_config, strategy=BiEGO, ) - + # Run optimization state = optimizer.optimize() - + # Get initial dataset num_iters = opt_config.max_iter num_total = len(state.scaled_dataset.export_as_dict()["obj"]) num_init = num_total - num_iters - + # We construct the pareto front of the initial design points initial_objs = state.scaled_dataset.export_as_dict()["obj"][:num_init] # Quick pareto filtering for initial points is_efficient = np.ones(num_init, dtype=bool) for i, c in enumerate(initial_objs): if is_efficient[i]: - is_efficient[is_efficient] = np.any(initial_objs[is_efficient] < c, axis=1) + is_efficient[is_efficient] = np.any( + initial_objs[is_efficient] < c, axis=1 + ) is_efficient[i] = True - + initial_pf = initial_objs[is_efficient] - + ref_point = np.array([10.0, 10.0]) initial_hv = hypervolume_2d(initial_pf, ref_point) - + final_pf = get_pf_from_dataset(state.scaled_dataset) final_hv = hypervolume_2d(final_pf, ref_point) - - # Hypervolume of the Pareto front relative to [10, 10] should INCREASE + + # Hypervolume of the Pareto front relative to [10, 10] should INCREASE # as we minimize and push the front towards (0,0) / (1,1) self.assertGreater(final_hv, initial_hv) + if __name__ == "__main__": unittest.main() + + def test_mf_biego_2d_1c(self): + def f1_lf(x: np.ndarray) -> np.ndarray: + return f1(x) * 1.1 + 0.1 + + def f2_lf(x: np.ndarray) -> np.ndarray: + return f2(x) * 1.1 + 0.1 + + def cstr_lf(x: np.ndarray) -> np.ndarray: + return cstr(x) * 0.9 + + bounds = np.array([[-2.0, 2.0], [-2.0, 2.0]]) + + obj_config1 = ObjectiveConfig( + objective=[f1, f1_lf], + surrogate=SmtAutoModel, + surrogate_kwargs={"name": "MFK"}, + ) + + obj_config2 = ObjectiveConfig( + objective=[f2, f2_lf], + surrogate=SmtAutoModel, + surrogate_kwargs={"name": "MFK"}, + ) + + cstr_config = ConstraintConfig( + constraint=[cstr, cstr_lf], + upper=0.0, + surrogate=SmtAutoModel, + surrogate_kwargs={"name": "MFK"}, + ) + + problem = Problem( + obj_configs=[obj_config1, obj_config2], + cstr_configs=[cstr_config], + design_space=bounds, + costs=[100.0, 1.0], + ) + + opt_config = DriverConfig( + max_iter=3, + seed=42, + ) + + optimizer = Driver( + problem=problem, + config=opt_config, + strategy=BiEGO, + strategy_kwargs={"sp_method": "SLSQP"}, + ) + + state = optimizer.optimize() + + y_data = np.empty((len(state.dataset.samples), 2)) + for i, sample in enumerate(state.dataset.samples): + y_data[i, 0] = sample.obj[0] + y_data[i, 1] = sample.obj[1] + + self.assertGreater(len(y_data), 3) diff --git a/src/tests/test_integration/test_zdt1_hv.py b/src/tests/test_integration/test_zdt1_hv.py index 7b504e1..16d60fa 100644 --- a/src/tests/test_integration/test_zdt1_hv.py +++ b/src/tests/test_integration/test_zdt1_hv.py @@ -1,4 +1,3 @@ -import sys import numpy as np from smt_optim.core import Problem, ObjectiveConfig, DriverConfig, Driver from smt_optim.surrogate_models.smt import SmtAutoModel @@ -8,9 +7,10 @@ from smt_optim.acquisition_strategies.biego import BiEGO from smt_optim.acquisition_strategies.mosego import MOSEGO + def main(): print("Setting up ZDT1 benchmark (dim=2)...") - + zdt1 = ZDT1() zdt1.set_dim(2) @@ -22,17 +22,17 @@ def main(): objective=[zdt1.f2], surrogate=SmtAutoModel, ) - + problem = Problem( obj_configs=[obj_config1, obj_config2], design_space=zdt1.bounds, ) - + opt_config = DriverConfig( max_iter=15, seed=42, ) - + ref_point = np.array([2.0, 2.0]) print("\n--- Testing Classic MOSEGO ---") @@ -42,7 +42,7 @@ def main(): hv_mosego = hypervolume_2d(pf_mosego, ref_point) print(f"MOSEGO Finished. Total evaluations: {len(state_mosego.dataset.samples)}") print(f"MOSEGO Hypervolume (ref [2, 2]): {hv_mosego:.4f}") - + print("\n--- Testing BiEGO ---") driver_biego = Driver(problem=problem, config=opt_config, strategy=BiEGO) state_biego = driver_biego.optimize() @@ -51,5 +51,6 @@ def main(): print(f"BiEGO Finished. Total evaluations: {len(state_biego.dataset.samples)}") print(f"BiEGO Hypervolume (ref [2, 2]): {hv_biego:.4f}") + if __name__ == "__main__": main() diff --git a/src/tests/test_unit/test_constraints_relaxation.py b/src/tests/test_unit/test_constraints_relaxation.py index c15a64b..3e648f9 100644 --- a/src/tests/test_unit/test_constraints_relaxation.py +++ b/src/tests/test_unit/test_constraints_relaxation.py @@ -4,27 +4,31 @@ from smt_optim.acquisition_strategies.mfsego import build_scipy_constraints from dataclasses import dataclass + @dataclass class DummyConfig: equal: float | None = None lower: float | None = None upper: float | None = None + class DummyProblem: def __init__(self, configs): self.cstr_configs = configs + class DummyModel: def __init__(self, mu, var): self.mu = np.array([[mu]]) self.var = np.array([[var]]) - + def predict_values(self, x): return self.mu - + def predict_variances(self, x): return self.var + class DummyState: def __init__(self, configs, equal_vals, lower_vals, upper_vals, models): self.problem = DummyProblem(configs) @@ -33,12 +37,13 @@ def __init__(self, configs, equal_vals, lower_vals, upper_vals, models): self.cstr_upper = upper_vals self.cstr_models = models + class TestConstraintsRelaxation(unittest.TestCase): def test_scipy_constraints_relaxation(self): # We test a point with mu=2.0 and var=4.0 (so s=2.0) - m = DummyModel(2.0, 4.0) + m = DummyModel(2.0, 4.0) x = np.array([[0.0]]) - + # Upper bound: g(x) <= 1.0 # Relaxed should be: g(x) - 3s <= 1.0 => 2.0 - 3*2.0 = -4.0 <= 1.0 (True!) # Scipy ineq: 1.0 - (-4.0) = 5.0 >= 0 @@ -56,7 +61,7 @@ def test_scipy_constraints_relaxation(self): self.assertEqual(cstrs[0]["type"], "ineq") val = cstrs[0]["fun"](x) self.assertAlmostEqual(val, -2.0) - + # Equal bound: g(x) == 1.0 # Relaxed should be: |g(x) - 1.0| <= 3s => |2.0 - 1.0| = 1.0 <= 6.0 (True!) # Scipy ineq: -1.0 + 6.0 = 5.0 >= 0 @@ -67,16 +72,16 @@ def test_scipy_constraints_relaxation(self): self.assertAlmostEqual(val, 5.0) def test_scipy_constraints_no_relaxation(self): - m = DummyModel(2.0, 4.0) + m = DummyModel(2.0, 4.0) x = np.array([[0.0]]) - + # Upper bound: g(x) <= 1.0 # Normal: 2.0 <= 1.0 (False) => Scipy ineq: 1.0 - 2.0 = -1.0 >= 0 state_upper = DummyState([DummyConfig(upper=1.0)], [None], [None], [1.0], [m]) cstrs = build_scipy_constraints(state_upper, relax=False) self.assertEqual(cstrs[0]["type"], "ineq") self.assertAlmostEqual(cstrs[0]["fun"](x), -1.0) - + # Equal bound: g(x) == 1.0 # Normal: 2.0 == 1.0 (False) => Scipy eq: 2.0 - 1.0 = 1.0 == 0 state_eq = DummyState([DummyConfig(equal=1.0)], [1.0], [None], [None], [m]) @@ -84,5 +89,6 @@ def test_scipy_constraints_no_relaxation(self): self.assertEqual(cstrs[0]["type"], "eq") self.assertAlmostEqual(cstrs[0]["fun"](x), 1.0) + if __name__ == "__main__": unittest.main() diff --git a/src/tests/test_unit/test_mo_benchmarks.py b/src/tests/test_unit/test_mo_benchmarks.py index 699faaa..61b29ef 100644 --- a/src/tests/test_unit/test_mo_benchmarks.py +++ b/src/tests/test_unit/test_mo_benchmarks.py @@ -3,106 +3,98 @@ from smt_optim.benchmarks.multi_obj import BNH, TNK, OSY, DTLZ5 + class TestMOBenchmarks(unittest.TestCase): def test_bnh(self): prob = BNH() - x2d = np.array([ - [0.0, 0.0], - [1.0, 1.0] - ]) + x2d = np.array([[0.0, 0.0], [1.0, 1.0]]) x1d = np.array([0.0, 0.0]) - + np.testing.assert_allclose(prob.f1(x2d), [0.0, 8.0]) np.testing.assert_allclose(prob.f1(x1d), 0.0) - + np.testing.assert_allclose(prob.f2(x2d), [50.0, 32.0]) np.testing.assert_allclose(prob.f2(x1d), 50.0) - + np.testing.assert_allclose(prob.g1(x2d), [0.0, -8.0]) np.testing.assert_allclose(prob.g1(x1d), 0.0) - + np.testing.assert_allclose(prob.g2(x2d), [-65.3, -57.3]) np.testing.assert_allclose(prob.g2(x1d), -65.3) def test_tnk(self): prob = TNK() - x2d = np.array([ - [1.0, 1.0] - ]) + x2d = np.array([[1.0, 1.0]]) x1d = np.array([1.0, 1.0]) - + np.testing.assert_allclose(prob.f1(x2d), [1.0]) np.testing.assert_allclose(prob.f1(x1d), 1.0) - + np.testing.assert_allclose(prob.f2(x2d), [1.0]) np.testing.assert_allclose(prob.f2(x1d), 1.0) - + expected_g1 = -1.0 - 1.0 + 1.0 + 0.1 * np.cos(16.0 * np.arctan(1.0)) np.testing.assert_allclose(prob.g1(x2d), [expected_g1]) np.testing.assert_allclose(prob.g1(x1d), expected_g1) - + np.testing.assert_allclose(prob.g2(x2d), [0.0]) np.testing.assert_allclose(prob.g2(x1d), 0.0) def test_osy(self): prob = OSY() - x2d = np.array([ - [0.0, 0.0, 0.0, 0.0, 0.0, 0.0], - [1.0, 1.0, 1.0, 1.0, 1.0, 1.0] - ]) + x2d = np.array([[0.0, 0.0, 0.0, 0.0, 0.0, 0.0], [1.0, 1.0, 1.0, 1.0, 1.0, 1.0]]) x1d = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0]) - + np.testing.assert_allclose(prob.f1(x2d), [-122.0, -35.0]) np.testing.assert_allclose(prob.f1(x1d), -122.0) - + np.testing.assert_allclose(prob.f2(x2d), [0.0, 6.0]) np.testing.assert_allclose(prob.f2(x1d), 0.0) - + np.testing.assert_allclose(prob.g1(x2d), [2.0, 0.0]) np.testing.assert_allclose(prob.g1(x1d), 2.0) - + np.testing.assert_allclose(prob.g2(x2d), [-6.0, -4.0]) np.testing.assert_allclose(prob.g2(x1d), -6.0) - + np.testing.assert_allclose(prob.g3(x2d), [-2.0, -2.0]) np.testing.assert_allclose(prob.g3(x1d), -2.0) - + np.testing.assert_allclose(prob.g4(x2d), [-2.0, -4.0]) np.testing.assert_allclose(prob.g4(x1d), -2.0) - + np.testing.assert_allclose(prob.g5(x2d), [5.0, 1.0]) np.testing.assert_allclose(prob.g5(x1d), 5.0) - + np.testing.assert_allclose(prob.g6(x2d), [-5.0, -1.0]) np.testing.assert_allclose(prob.g6(x1d), -5.0) def test_dtlz5(self): prob = DTLZ5() - x2d = np.array([ - [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] - ]) + x2d = np.array([[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]]) x1d = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]) - + np.testing.assert_allclose(prob.f1(x2d), [2.75]) np.testing.assert_allclose(prob.f1(x1d), 2.75) - + np.testing.assert_allclose(prob.f2(x2d), [0.0]) np.testing.assert_allclose(prob.f2(x1d), 0.0) - + np.testing.assert_allclose(prob.g(x2d), [2.25]) np.testing.assert_allclose(prob.g(x1d), 2.25) - + np.testing.assert_allclose(prob.f1_lf(x2d), [2.4]) np.testing.assert_allclose(prob.f1_lf(x1d), 2.4) - + np.testing.assert_allclose(prob.f2_lf(x2d), [0.0]) np.testing.assert_allclose(prob.f2_lf(x1d), 0.0) - + np.testing.assert_allclose(prob.g_lf(x2d), [2.4]) np.testing.assert_allclose(prob.g_lf(x1d), 2.4) - + np.testing.assert_allclose(prob.u(x1d[3:]), 1.75) np.testing.assert_allclose(prob.u(x2d[:, 3:]), [1.75]) + if __name__ == "__main__": unittest.main() diff --git a/src/tests/test_unit/test_multi_objective.py b/src/tests/test_unit/test_multi_objective.py index ae6f5e4..cee72ca 100644 --- a/src/tests/test_unit/test_multi_objective.py +++ b/src/tests/test_unit/test_multi_objective.py @@ -55,15 +55,14 @@ def test_vectorization_single_objective_product(self): self.assertAlmostEqual(val_1d, -0.1225) # Shape: (N, M, 2) - x_3d = np.array([ - [[0.5, 0.3], [1.2, 0.9]], - [[1.1, 1.1], [0.8, 0.2]] - ]) + x_3d = np.array([[[0.5, 0.3], [1.2, 0.9]], [[1.1, 1.1], [0.8, 0.2]]]) val_3d = SingleObjectiveProduct(x_3d, r) - expected = np.array([ - [-0.1225, 0.0], - [0.0, -0.0256] # [0.8, 0.2] => [0.2, 0.8] => 0.04 * 0.64 = 0.0256 - ]) + expected = np.array( + [ + [-0.1225, 0.0], + [0.0, -0.0256], # [0.8, 0.2] => [0.2, 0.8] => 0.04 * 0.64 = 0.0256 + ] + ) np.testing.assert_allclose(val_3d, expected) def test_vectorization_single_objective_normalized(self): @@ -82,6 +81,7 @@ def test_vectorization_single_objective_normalized(self): # row2: max((0.2-0.1)/2, (0.9-0.1)/1) = max(0.05, 0.8) = 0.8 expected = np.array([0.2, 0.8]) np.testing.assert_allclose(val_2d, expected) + def test_mpi_zero_variance(self): state = MockState() # Initialize MPI diff --git a/src/tests/test_unit/test_zdt.py b/src/tests/test_unit/test_zdt.py index 77249dd..39355a4 100644 --- a/src/tests/test_unit/test_zdt.py +++ b/src/tests/test_unit/test_zdt.py @@ -3,25 +3,23 @@ from smt_optim.benchmarks.multi_obj import ZDT1, ZDT2, ZDT3, ZDT4 + class TestZDTBenchmarks(unittest.TestCase): def test_zdt1(self): zdt = ZDT1() zdt.set_dim(5) # Vectorized test (2D) - x2d = np.array([ - [0.0, 0.0, 0.0, 0.0, 0.0], - [1.0, 1.0, 1.0, 1.0, 1.0] - ]) + x2d = np.array([[0.0, 0.0, 0.0, 0.0, 0.0], [1.0, 1.0, 1.0, 1.0, 1.0]]) # 1D test x1d = np.array([0.0, 0.0, 0.0, 0.0, 0.0]) - + np.testing.assert_allclose(zdt.f1(x2d), [0.0, 1.0]) np.testing.assert_allclose(zdt.f1(x1d), 0.0) - + expected_f2 = [1.0, 10.0 * (1.0 - np.sqrt(0.1))] np.testing.assert_allclose(zdt.f2(x2d), expected_f2) np.testing.assert_allclose(zdt.f2(x1d), expected_f2[0]) - + # Test individual functions for 1D coverage np.testing.assert_allclose(zdt.g(x1d), 1.0) np.testing.assert_allclose(zdt.g(x2d), [1.0, 10.0]) @@ -29,57 +27,51 @@ def test_zdt1(self): def test_zdt2(self): zdt = ZDT2() zdt.set_dim(5) - x2d = np.array([ - [0.0, 0.0, 0.0, 0.0, 0.0], - [1.0, 1.0, 1.0, 1.0, 1.0] - ]) + x2d = np.array([[0.0, 0.0, 0.0, 0.0, 0.0], [1.0, 1.0, 1.0, 1.0, 1.0]]) x1d = np.array([0.0, 0.0, 0.0, 0.0, 0.0]) - + np.testing.assert_allclose(zdt.f1(x2d), [0.0, 1.0]) np.testing.assert_allclose(zdt.f1(x1d), 0.0) - + expected_f2 = [1.0, 9.9] np.testing.assert_allclose(zdt.f2(x2d), expected_f2) np.testing.assert_allclose(zdt.f2(x1d), expected_f2[0]) - + np.testing.assert_allclose(zdt.g(x1d), 1.0) np.testing.assert_allclose(zdt.g(x2d), [1.0, 10.0]) def test_zdt3(self): zdt = ZDT3() zdt.set_dim(5) - x2d = np.array([ - [0.0, 0.0, 0.0, 0.0, 0.0] - ]) + x2d = np.array([[0.0, 0.0, 0.0, 0.0, 0.0]]) x1d = np.array([0.0, 0.0, 0.0, 0.0, 0.0]) - + np.testing.assert_allclose(zdt.f1(x2d), [0.0]) np.testing.assert_allclose(zdt.f1(x1d), 0.0) - + expected_f2 = [1.0] np.testing.assert_allclose(zdt.f2(x2d), expected_f2) np.testing.assert_allclose(zdt.f2(x1d), expected_f2[0]) - + np.testing.assert_allclose(zdt.g(x1d), 1.0) np.testing.assert_allclose(zdt.g(x2d), [1.0]) def test_zdt4(self): zdt = ZDT4() zdt.set_dim(10) - x2d = np.array([ - [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0] - ]) + x2d = np.array([[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]]) x1d = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]) - + np.testing.assert_allclose(zdt.f1(x2d), [0.0]) np.testing.assert_allclose(zdt.f1(x1d), 0.0) - + expected_f2 = [1.0] np.testing.assert_allclose(zdt.f2(x2d), expected_f2) np.testing.assert_allclose(zdt.f2(x1d), expected_f2[0]) - + np.testing.assert_allclose(zdt.g(x1d), 1.0) np.testing.assert_allclose(zdt.g(x2d), [1.0]) + if __name__ == "__main__": unittest.main() diff --git a/tests/test_multi_obj_integration.py b/tests/test_multi_obj_integration.py new file mode 100644 index 0000000..ace8885 --- /dev/null +++ b/tests/test_multi_obj_integration.py @@ -0,0 +1,68 @@ +import unittest +import numpy as np + +from smt_optim.benchmarks.registry import get_problem +from smt_optim.benchmarks.base import PymooWrapper +from smt_optim.core import Driver, ObjectiveConfig, DriverConfig, Problem +from smt_optim.surrogate_models.smt import SmtAutoModel +from smt_optim.acquisition_strategies.mosego import MOSEGO +from smt_optim.acquisition_functions.multi_obj import init_bi_obj_ei + + +class TestMultiObjIntegration(unittest.TestCase): + def test_mosego_zdt1(self): + problem = get_problem("ZDT1") + problem.set_dim(2) + + obj1_config = ObjectiveConfig( + [problem.f1], + type="minimize", + surrogate=SmtAutoModel, + ) + obj2_config = ObjectiveConfig( + [problem.f2], + type="minimize", + surrogate=SmtAutoModel, + ) + prob_definition = Problem( + obj_configs=[obj1_config, obj2_config], + design_space=problem.bounds, + ) + opt_config = DriverConfig( + max_iter=1, + nt_init=5, + verbose=False, + scaling=True, + seed=0, + ) + driver = Driver( + prob_definition, + opt_config, + MOSEGO, + strategy_kwargs={ + "acq_func": init_bi_obj_ei, + "n_start": 5, + "sp_method": "SLSQP", + }, + ) + # Should run without issues (logger crash etc.) + state = driver.optimize() + self.assertGreater(len(state.dataset.samples), 5) + + def test_pymoo_wrapper_multi_fidelity(self): + # DTLZ5 has multi-fidelity objectives + problem = get_problem("DTLZ5") + pymoo_prob = PymooWrapper(problem) + + # Test evaluating points + x = np.random.rand(5, problem.num_dim) + out = {} + # Should not raise TypeError: 'list' object is not callable + pymoo_prob._evaluate(x, out) + + self.assertIn("F", out) + self.assertEqual(out["F"].shape, (5, 2)) + + +if __name__ == "__main__": + unittest.main()