comparisons.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A simple notebook to compare variant of MAP-Elites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "#-- Necessary Imports\n",
    "import numpy as np\n",
    "from os.path import exists\n",
    "import os\n",
    "import subprocess\n",
    "from tqdm import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from glob import glob\n",
    "\n",
    "%matplotlib inline\n",
    "font = {'size'   : 10}\n",
    "mpl.rc('font', **font)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compilation\n",
    "!  clang++ -DFILLED_SELECTION -DUSE_BOOST -DUSE_TBB -O3 -march=native -std=c++14 -I /usr/local/include/eigen3 map_elites.cpp -o map_elites_1 -ltbb\n",
    "!  clang++  -DFILLED_SELECTION -DUSE_TBB -O3 -march=native -std=c++14 -I /usr/local/include/eigen3 map_elites.cpp -o map_elites_2 -ltbb\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 20/20 [00:08<00:00,  2.28it/s]\n",
      "100%|██████████| 20/20 [00:09<00:00,  2.11it/s]\n"
     ]
    }
   ],
   "source": [
    "n_replicates = 20\n",
    "for i in tqdm(range(n_replicates)):\n",
    "    subprocess.run(f\"mkdir -p v1_{i} &&  ./map_elites_1 && cp *.dat v1_{i}\", shell=True, stdout=subprocess.DEVNULL)\n",
    "for i in tqdm(range(n_replicates)):\n",
    "    subprocess.run(f\"mkdir -p v2_{i} &&  ./map_elites_2 && cp *.dat v2_{i}\", shell=True, stdout=subprocess.DEVNULL)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.00000e+00 1.28000e+02 2.56000e+02 ... 4.99712e+05 4.99840e+05\n",
      " 4.99968e+05]\n",
      "(20, 3907) (20, 3907)\n"
     ]
    }
   ],
   "source": [
    "def load_data(path):\n",
    "    l = glob(path)\n",
    "    d = []\n",
    "    x = []\n",
    "    for i in l:\n",
    "        data = np.loadtxt(i)\n",
    "        x = data[:,0]\n",
    "        d += [data[:,1]]\n",
    "    return np.array(d), x\n",
    "\n",
    "print(x)\n",
    "v1, x = load_data(\"v1*/qd.dat\")\n",
    "v2, x = load_data(\"v2*/qd.dat\")\n",
    "print(v1.shape, v2.shape)\n",
    "\n",
    "def stats(d):\n",
    "    med = np.median(d, axis=0)\n",
    "    p25 = np.percentile(d, 25, axis=0)\n",
    "    p75 = np.percentile(d, 75, axis=0)\n",
    "    return med, p25, p75\n",
    "\n",
    "v1_s = stats(v1)\n",
    "v2_s = stats(v2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x166367790>"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10,5))\n",
    "\n",
    "ax = fig.add_subplot(111)\n",
    "ax.fill_between(x, v1_s[1], v1_s[2], color='red', alpha=0.5)\n",
    "ax.plot(x, v1_s[0],color='red', label='v1')\n",
    "\n",
    "ax.fill_between(x, v2_s[1], v2_s[2], color='blue', alpha=0.5)\n",
    "ax.plot(x, v2_s[0],color='blue', label=\"v2\")\n",
    "ax.legend()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.10.8"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "aee8b7b246df8f9039afb4144a1f6fd8d2ca17a180786b69acc140d282b71a49"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}