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‎notebooks/00_spotPython_tests.ipynb‎

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"plt.show()"
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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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"from spotpython.utils.compare import selectNew\n",
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"import numpy as np\n",
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"A = np.array([[1,2,3],[4,5,7]])\n",
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"X = np.array([[1,2,3],[4,5,6]])\n",
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"B, ind = selectNew(A, X)\n",
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"assert B.shape[0] == 1\n",
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"assert np.equal(ind, np.array([False, True])).all()"
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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,

‎notebooks/spot_aquisition_random_rosenbrock_2d.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 2 Dimensions: Nyström Approximation vs. Exact (Rosenbrock 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 2-dimensional Rosenbrock function.\n",
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"\n",
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"We use a maximum of 100 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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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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 Rosenbrock 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": null,
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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 = 6\n",
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"lower = np.full(dim, -2)\n",
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"upper = np.full(dim, 2)\n",
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"fun = Analytical().fun_rosenbrock\n",
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"fun_evals = 100"
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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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"## Aquisition Random and 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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"init_size = 12\n",
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"use_nystrom = False\n",
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"method = \"regression\"\n",
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"infill_criterion = \"ei\"\n",
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"tolerance_x = 1e-6 # minimum distance in x for accepting new points\n",
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"seed = 321\n",
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"max_surrogate_points = 24\n",
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"min_Lambda = -6\n",
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"max_Lambda = 1\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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"optim_p = True\n",
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"min_success_rate = 0.10 # percent\n",
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"acquisition_failure_strategy = \"random\""
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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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"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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" max_time=60,\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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" min_success_rate=min_success_rate,\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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" acquisition_failure_strategy=acquisition_failure_strategy,\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, \n",
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" optim_p=optim_p,\n",
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" 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)\n",
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"spot_exact_random = 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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" )\n",
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"spot_exact_random.run()\n",
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"spot_exact_random.plot_progress(log_y=True, title=\"spotpython: Random Kriging\")"
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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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"## Aquisition MM and 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": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"acquisition_failure_strategy = \"mm\""
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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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"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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" max_time=60,\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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" min_success_rate=min_success_rate,\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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" acquisition_failure_strategy=acquisition_failure_strategy,\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, optim_p=optim_p, 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)\n",
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"spot_exact_mm = 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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")\n",
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"spot_exact_mm.run()\n",
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"spot_exact_mm.plot_progress(log_y=True, title=\"spotpython: Morris-Mitchell Kriging.\")"
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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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"## Compare Minimum Found"
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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\"[2D] spotpython Kriging Random:\\n min y = {spot_exact_random.min_y:.4f} at x = {spot_exact_random.min_X}\")\n",
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"print(f\"[2D] spotpython Kriging Morris-Mitchell:\\n min y = {spot_exact_mm.min_y:.4f} at x = {spot_exact_mm.min_X}\")"
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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_random.plot_important_hyperparameter_contour(max_imp=3)\n"
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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_mm.plot_important_hyperparameter_contour(max_imp=3)"
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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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}

‎pyproject.toml‎

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@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
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[project]
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name = "spotpython"
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version = "0.34.6"
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version = "0.34.7"
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authors = [
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{ name="T. Bartz-Beielstein", email="tbb@bartzundbartz.de" }
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]

‎src/spotpython/spot/spot.py‎

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@@ -215,7 +215,7 @@ def __init__(
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self.success_rate = 0.0
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self.success_counter = 0
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self.window_size = 100
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self.min_success_rate = 0.01
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self.min_success_rate = 0.0
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# small value:
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self.eps = sqrt(spacing(1))
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self.n_points = self.fun_control["n_points"]
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self.progress_file = self.fun_control["progress_file"]
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self.tkagg = self.fun_control["tkagg"]
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self.min_success_rate = self.fun_control["min_success_rate"]
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# self.success_counter = 0
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if self.tkagg:
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matplotlib.use("TkAgg")
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# the last window_size evaluations:
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self._update_success_rate(y_new=y0)
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# Append New Solutions (only if they are not nan):
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if (y0.shape[0] > 0) and (self._get_success_rate() >= self.min_success_rate):
1718+
if y0.shape[0] > 0:
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self.X = np.append(self.X, X0, axis=0)
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self.y = np.append(self.y, y0)
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else:

‎src/spotpython/utils/init.py‎

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max_surrogate_points=30,
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metric_sklearn=None,
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metric_sklearn_name=None,
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min_success_rate=0.0,
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noise=False,
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n_points=1,
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n_samples=None,
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The metric object from the scikit-learn library. Default is None.
229230
metric_sklearn_name (str):
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The name of the metric object from the scikit-learn library. Default is None.
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min_success_rate (float):
233+
The minimum success rate for the Spot optimization to continue.
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If the success rate falls below this value, a random point is proposed based on the acquisition_failure_strategy.
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Default is 0.0.
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noise (bool):
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Whether the objective function is noiy or not. Default is False.
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Affects the repeat of the function evaluations.
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'metric_sklearn_name': None,
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'metric_torch': None,
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'metric_params': {},
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'min_success_rate': 0.0,
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'model_dict': {},
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'noise': False,
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'n_points': 1,
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"metric_sklearn_name": metric_sklearn_name,
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"metric_torch": None,
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"metric_params": {},
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"min_success_rate": min_success_rate,
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"model_dict": {},
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"noise": noise,
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"n_points": n_points,

‎test/test_select_new_x.py‎

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import numpy as np
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import pytest
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from spotpython.utils.compare import selectNew
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def test_selectNew_all_known():
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A = np.array([[1, 2, 3], [4, 5, 6]])
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X = np.array([[1, 2, 3], [4, 5, 6]])
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B, ind = selectNew(A, X)
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assert B.shape[0] == 0
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assert np.all(ind == np.array([False, False]))
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def test_selectNew_one_new():
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A = np.array([[1, 2, 3], [4, 5, 7]])
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X = np.array([[1, 2, 3], [4, 5, 6]])
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B, ind = selectNew(A, X)
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assert B.shape[0] == 1
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assert np.all(ind == np.array([False, True]))
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assert np.all(B == np.array([[4, 5, 7]]))
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def test_selectNew_all_new():
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A = np.array([[7, 8, 9], [10, 11, 12]])
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X = np.array([[1, 2, 3], [4, 5, 6]])
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B, ind = selectNew(A, X)
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assert B.shape[0] == 2
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assert np.all(ind == np.array([True, True]))
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assert np.all(B == A)
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def test_selectNew_with_tolerance():
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A = np.array([[1.0, 2.0, 3.0], [4.01, 5.0, 6.0]])
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X = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
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B, ind = selectNew(A, X, tolerance=0.05)
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assert B.shape[0] == 0
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assert np.all(ind == np.array([False, False]))
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def test_selectNew_with_tolerance_one_new():
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A = np.array([[1.0, 2.0, 3.0], [4.1, 5.0, 6.0]])
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X = np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]])
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B, ind = selectNew(A, X, tolerance=0.05)
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assert B.shape[0] == 1
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assert np.all(ind == np.array([False, True]))
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assert np.allclose(B, np.array([[4.1, 5.0, 6.0]]))

‎test/test_update_success_rate.py‎

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@pytest.fixture
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def spot_instance():
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# Minimal Spot instance with required attributes
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fun_control = {"lower": np.array([-1, -1]), "upper": np.array([1, 1]), "var_type": ["num", "num"], "fun_evals": 10, "fun_repeats": 1, "max_time": 1e6, "noise": False, "tolerance_x": 0, "ocba_delta": 0, "log_level": 50, "show_models": False, "show_progress": False, "infill_criterion": "ei", "n_points": 1, "seed": 1, "progress_file": None, "tkagg": False, "verbosity": 0, "acquisition_failure_strategy": "random", "fun_mo2so": None, "penalty_NA": 0, "PREFIX": "test", "tensorboard_log": False, "spot_tensorboard_path": None, "save_experiment": False, "db_dict_name": None, "save_result": False, "var_name": ["x0", "x1"]}
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fun_control = {"lower": np.array([-1, -1]), "upper": np.array([1, 1]), "var_type": ["num", "num"], "fun_evals": 10, "fun_repeats": 1, "max_time": 1e6, "noise": False, "tolerance_x": 0, "ocba_delta": 0, "log_level": 50, "show_models": False, "show_progress": False, "infill_criterion": "ei", "n_points": 1, "seed": 1, "progress_file": None, "tkagg": False, "verbosity": 0, "acquisition_failure_strategy": "random", "fun_mo2so": None, "penalty_NA": 0, "PREFIX": "test", "tensorboard_log": False, "spot_tensorboard_path": None, "save_experiment": False, "db_dict_name": None, "save_result": False, "var_name": ["x0", "x1"], "min_success_rate": 0.0}
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design_control = {"init_size": 5, "repeats": 1}
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from spotpython.utils.init import surrogate_control_init, optimizer_control_init
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surrogate_control = surrogate_control_init()

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