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sk notebooks
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‎notebooks/spot_sk_ackley_100d.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# SPOT Kriging in 100 Dimensions: Nyström Approximation vs. Exact (Ackley Function)\n",
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"\n",
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"This notebook demonstrates how to use the `Spot` class from `spotpython` with and without the Nyström approximation for Kriging surrogates on the 100-dimensional Ackley function.\n",
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"\n",
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"We use a maximum of 500 function evaluations."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Seed set to 123\n"
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]
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}
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],
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"source": [
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"import warnings\n",
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"warnings.filterwarnings(\"ignore\")\n",
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"import numpy as np\n",
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"from spotpython.fun.objectivefunctions import Analytical\n",
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"from spotpython.spot import Spot\n",
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"from spotpython.utils.init import fun_control_init, design_control_init, surrogate_control_init"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Define the 100D Ackley Function"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"# do not change, these are given by the organizers\n",
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"dim = 100\n",
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"lower = np.full(dim, -32.768)\n",
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"upper = np.full(dim, 32.768)\n",
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"fun = Analytical().fun_ackley\n",
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"fun_evals = 500\n",
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"max_time = 600"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Set up SPOT Controls"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Regression and y as Infill Criterion"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"init_size = 10\n",
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"use_nystrom = False\n",
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"method = \"regression\"\n",
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"infill_criterion = \"y\"\n",
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"tolerance_x = 1e-9\n",
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"seed = 321\n",
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"max_surrogate_points = fun_evals\n",
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"min_Lambda = -4\n",
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"max_Lambda = 3\n",
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"min_theta = -3\n",
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"max_theta = 2\n",
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"isotropic = False\n",
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"kernel = \"matern\"\n",
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"kernel_params = {\"nu\": 1.5}\n",
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"selection_method = \"distance\"\n",
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"min_success_rate = 0.2\n",
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"max_time = 60"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Seed set to 321\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Moving TENSORBOARD_PATH: runs/ to TENSORBOARD_PATH_OLD: runs_OLD/runs_2025_11_04_22_21_30_0\n",
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"Created spot_tensorboard_path: runs/spot_logs/000_p040025_2025-11-04_22-21-30 for SummaryWriter()\n"
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]
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}
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],
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"source": [
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"fun_control = fun_control_init(\n",
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" lower=lower,\n",
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" upper=upper,\n",
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" fun_evals=fun_evals,\n",
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" seed=seed,\n",
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" show_progress=True,\n",
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" infill_criterion=infill_criterion,\n",
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" tolerance_x=tolerance_x,\n",
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" TENSORBOARD_CLEAN=True,\n",
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" tensorboard_log=True,\n",
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" kernel=kernel,\n",
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" kernel_params=kernel_params,\n",
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" selection_method=selection_method,\n",
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" min_success_rate=min_success_rate,\n",
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" max_time=max_time\n",
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")\n",
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"design_control = design_control_init(init_size=init_size)\n",
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"surrogate_control_exact = surrogate_control_init(use_nystrom=use_nystrom, method=method, max_surrogate_points=max_surrogate_points, min_Lambda=min_Lambda, max_Lambda=max_Lambda, min_theta=min_theta, max_theta=max_theta, isotropic=isotropic)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Sklearn Gaussian Process Regressor as Surrogate"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"spotpython tuning: 20.99221801489919 [----------] 2.20%. Success rate: 0.00% \n"
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]
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}
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],
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"source": [
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"# Needed for the sklearn surrogates:\n",
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"from sklearn.gaussian_process import GaussianProcessRegressor\n",
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"from sklearn.gaussian_process.kernels import RBF\n",
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"from sklearn.tree import DecisionTreeRegressor\n",
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"from sklearn.ensemble import RandomForestRegressor\n",
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"from sklearn import linear_model\n",
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"from sklearn import tree\n",
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"import pandas as pd\n",
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"from xgboost import XGBRegressor\n",
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"\n",
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"S_Tree = DecisionTreeRegressor(random_state=0)\n",
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"S_LM = linear_model.LinearRegression()\n",
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"S_Ridge = linear_model.Ridge()\n",
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"S_RF = RandomForestRegressor(max_depth=100, random_state=0)\n",
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"kernel = 1 * RBF(length_scale=1.0, length_scale_bounds=(1e-2, 1e2))\n",
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"S_GP = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=100)\n",
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"\n",
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"\n",
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"spot_exact_sk = Spot(\n",
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" fun=fun,\n",
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" fun_control=fun_control,\n",
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" design_control=design_control,\n",
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" surrogate_control=surrogate_control_exact,\n",
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" surrogate=S_GP,\n",
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")\n",
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"spot_exact_sk.run()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"spot_exact_sk.plot_progress(log_y=True, title=\"Exact sklearn Kriging Progress with y\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"print(f\"[100D] Exact Kriging y: min y = {spot_exact_sk.min_y:.4f} at x = {spot_exact_sk.min_X}\")\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "spot313",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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