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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "0d7b66e2-e07e-41b6-a9da-b9d127d283b6",
+ "metadata": {},
+ "source": [
+ "# Plot Rastrigin Heatmaps SO PGPE\n",
+ "\n",
+ "In diesem Notebook wird der 2D Rastrigin als Image dargestellt.\n",
+ "Von dne einzelnen PGPGE Versionen wird dann der Verlauf der Baseline eingezeichnet. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "83250d4e-c586-4aba-8598-72dd369e2aae",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from matplotlib import cm\n",
+ "import matplotlib\n",
+ "import math\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "from benchmarks.functions import rastrigin\n",
+ "from black_box_optimizers.pgpe_spielplatz import SyS_PGPE, PGPE, RandomSearch, Learner, SyS_PGPE_MS\n",
+ "from black_box_optimizers.hillclimber import HillClimber, SmartHillClimber\n",
+ "from optimization.optimizer import OptimizeableParameterDict as OP\n",
+ "from scipy import stats\n",
+ "import pickle as cp"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "fb48615a-3d98-4332-8fdc-dd7e0cf7d8b2",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# parameters\n",
+ "\n",
+ "# shared parameters\n",
+ "dim = 2\n",
+ "opt_iter = 100\n",
+ "trainings = 20\n",
+ "prob = 'rastrigin'\n",
+ "\n",
+ "# standard pgpe parameters\n",
+ "mue_alpha_s = 0.1 \n",
+ "sigma_alpha_s = 0.05\n",
+ "\n",
+ "# norm pgpe parameters\n",
+ "mue_alpha_n = mue_alpha_s \n",
+ "sigma_alpha_n = sigma_alpha_s \n",
+ "\n",
+ "# new pgpge parameters\n",
+ "mue_alpha_ms = mue_alpha_s\n",
+ "sigma_alpha_ms = sigma_alpha_s\n",
+ "\n",
+ "\n",
+ "# combined version parameters\n",
+ "mue_alpha_c = 0.1\n",
+ "sigma_alpha_c = 0.05\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ed5441d0-806e-4a44-aa56-1fa18b226565",
+ "metadata": {},
+ "source": [
+ "## Hier werden die 2D PGPGE Versionen trainiert"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "636195be-c02a-4db3-8b6b-521a41386586",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Creating the Optimizable Parameter Dict\n",
+ "def get_para_dict(dimension):\n",
+ " para_dict = OP()\n",
+ " for i in range(dimension):\n",
+ " para_dict.add_parameter(\n",
+ " key=f\"var_{i}\",\n",
+ " min=-10.0,\n",
+ " max=10.0,\n",
+ " type=\"f\", # f=float, i=int, b=1-float\n",
+ " scale=\"linear\", # linear, exp=exponential, ixp=inverse exponential\n",
+ " init=8.0, # if not provided uses center of search intervall\n",
+ " id=i, # if not provided uses internal counter\n",
+ " )\n",
+ " return para_dict"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "09ee3682-d5e9-4f03-8d82-18411c97d4bd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def get_algo(algo_kind, para_dict, mue_alpha, sigma_alpha):\n",
+ " seed = np.array([para_dict.get_init_parameters_array()])\n",
+ " para_dict = para_dict.parameter_dict\n",
+ "\n",
+ " if algo_kind == \"sys_pgpe\" or algo_kind == 'sys_pgpe_norm':\n",
+ " algorithm = SyS_PGPE(2,\n",
+ " paras=para_dict,\n",
+ " plot_paras = True,\n",
+ " seed=seed,\n",
+ " mue_alpha = mue_alpha, \n",
+ " sigma_alpha = sigma_alpha\n",
+ " )\n",
+ " elif algo_kind == \"sys_pgpe_ms\" or algo_kind == 'sys_pgpe_ms_norm':\n",
+ " algorithm = SyS_PGPE_MS(2,\n",
+ " paras=para_dict,\n",
+ " plot_paras = True,\n",
+ " seed=seed,\n",
+ " mue_alpha = mue_alpha, \n",
+ " sigma_alpha = sigma_alpha\n",
+ " )\n",
+ " return algorithm"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "84cc5da7-072c-432a-b984-503502d841f0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def evaluate_problem(pop, problem):\n",
+ " if problem == 'rastrigin':\n",
+ " return - rastrigin(pop).reshape(-1,)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "7ef7c619-4b3d-4cf0-a46a-c7f8fc1ab9b8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def create_sample(optimizer, dim, trainings, opt_iter, mue_alpha, sigma_alpha, problem='rastrigin'): \n",
+ " #print(\"dim: \", dim)\n",
+ " samples = []\n",
+ " for n in range(trainings):\n",
+ " sample = [] # samples of specific dimension\n",
+ " para_dict = get_para_dict(dim) #initialize the parameters for the current problem dimension\n",
+ " # get the algorithm object\n",
+ " algorithm = get_algo(algo_kind=optimizer, \n",
+ " para_dict=para_dict,\n",
+ " mue_alpha = mue_alpha,\n",
+ " sigma_alpha = sigma_alpha\n",
+ " )\n",
+ " for i in range(opt_iter):\n",
+ " sample.append(algorithm.mue[0].tolist())\n",
+ " population = algorithm.ask() # gibt mir das Sample\n",
+ " rewards = evaluate_problem(pop=population, \n",
+ " problem=problem\n",
+ " )\n",
+ " if \"norm\" in optimizer:\n",
+ " # tell with reward normalization \n",
+ " algorithm.tell_norm(pop=population, \n",
+ " rewards=rewards, \n",
+ " cfg=None,\n",
+ " logger=None\n",
+ " )\n",
+ " else: \n",
+ " algorithm.tell(pop=population, \n",
+ " rewards=rewards, \n",
+ " cfg=None,\n",
+ " logger=None\n",
+ " )\n",
+ " samples.append(sample)\n",
+ " return np.array(samples)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "id": "36a89ca6-44cc-467e-bc86-ae726eb4d0fe",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "original_samples = create_sample(\"sys_pgpe\", dim, trainings, opt_iter, mue_alpha_s, sigma_alpha_s)\n",
+ "#print(original_sample)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "e642cbd6-1149-41d2-a0a6-200611cec815",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "norm_samples = create_sample(\"sys_pgpe_norm\", dim, trainings, opt_iter, mue_alpha_n, sigma_alpha_n)\n",
+ "#print(norm_sample)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "552bd5e7-834d-45a2-ad7f-c02b01734f16",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "ms_samples = create_sample(\"sys_pgpe_ms\", dim, trainings, opt_iter, mue_alpha_ms, sigma_alpha_ms)\n",
+ "#print(ms_sample)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "34bd905a-d78e-4939-ab39-9caa9a3673c4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "comb_samples = create_sample(\"sys_pgpe_ms_norm\", dim, trainings, opt_iter, mue_alpha_c, sigma_alpha_c)\n",
+ "#print(comb_sample)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "1256975f-811d-4c34-a1de-664b5aa949c0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "all_samples = []"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "4c5a738b-d4d9-4f2b-8590-370ce81347af",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "all_samples.extend(original_samples)\n",
+ "all_samples.extend(norm_samples)\n",
+ "all_samples.extend(ms_samples)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ff8d24ec-ad02-4efd-8e4a-29862ba50653",
+ "metadata": {},
+ "source": [
+ "## Hier werden die PGPE Versionen in den Rastrigin geplottet"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "4164da95-38e2-427e-8101-7b3f216e3050",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "0\n",
+ "1\n",
+ "2\n",
+ "3\n",
+ "4\n",
+ "5\n",
+ "6\n",
+ "7\n",
+ "8\n",
+ "9\n",
+ "10\n",
+ "11\n",
+ "12\n",
+ "13\n",
+ "14\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/tmp/ipykernel_384943/1197428567.py:39: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.\n",
+ " plt.show()\n"
+ ]
+ }
+ ],
+ "source": [
+ "import datetime\n",
+ "# Generate data for each heatmap\n",
+ "\n",
+ "X = np.linspace(-5.12, 5.12, 100) \n",
+ "Y = np.linspace(-5.12, 5.12, 100) \n",
+ "X, Y = np.meshgrid(X, Y) \n",
+ "Z = (X**2 - 10 * np.cos(2 * np.pi * X)) + (Y**2 - 10 * np.cos(2 * np.pi * Y)) + 20\n",
+ "\n",
+ "# Create a grid of subplots with 3 rows and 5 columns\n",
+ "fig, axs = plt.subplots(nrows=3, ncols=5, figsize=(12, 8), layout=\"constrained\")\n",
+ "\n",
+ "# Set titles for each row\n",
+ "#axs[0, 0].set_title(\"Standard SyS PGPE\", fontsize=14)\n",
+ "#axs[1, 0].set_title(\"Normalized SyS PGPE\", fontsize=14)\n",
+ "#axs[2, 0].set_title(\"MS PGPE\", fontsize=14)\n",
+ "# Set titles for each row of subplots\n",
+ "\n",
+ "\n",
+ "# Plot each heatmap in a separate subplot\n",
+ "for i, ax in enumerate(axs.flat):\n",
+ " print(i)\n",
+ " im = ax.imshow(Z, cmap='plasma', extent=[-10.0, 10.0, -10.0, 10.0])\n",
+ " samp = all_samples[i]\n",
+ " x_points, y_points = zip(*samp)\n",
+ " ax.plot(x_points, y_points, '.', color='white')\n",
+ "\n",
+ "\n",
+ "# Add colorbar to the last subplot\n",
+ "fig.colorbar(im, ax=axs, shrink=0.6)\n",
+ "\n",
+ "# Set the plot title\n",
+ "plt.suptitle('Rastrigin Function Heatmaps, 100 iterations, 2D', fontsize=14)\n",
+ "\n",
+ "# save the plots\n",
+ "timestamp = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')\n",
+ "plt.savefig(f'./sopgpe_plots/rastrigin_heatmaps/{prob}_{opt_iter}_iterations_{dim}D_{timestamp}.png')\n",
+ "\n",
+ "# Display the plot\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7d987ea5-dcc5-4fb5-adab-29f7815cb29c",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c1e33757-bd90-47cc-a774-77c310fbd4a6",
+ "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",
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