example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# nmoo quick example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also available on Google Colab [![Launch Google Colab\n",
    "notebook](https://img.shields.io/badge/launch-colab-blue?logo=googlecolab)](https://colab.research.google.com/github/altaris/noisy-moo/blob/main/example.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we consider a ZDT1 problem with Gaussian noise, and benckmark two \"denoising\" methods:\n",
    "* a naive average method,\n",
    "* the KNN-Avg algorithm."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Specifying the benchmark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install nmoo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nmoo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first step is to construct our problem pipelines. We start with a `ZDT1`\n",
    "instance, that we wrap in a `WrappedProblem`. In nmoo, `WrappedProblem` is the\n",
    "base class to modify problems, in our case adding and removing noise.\n",
    "Additionally, `WrappedProblem` and classes deriving from it maintain a history\n",
    "of every call made to their `_evaluate` method (see the [pymoo\n",
    "documentation](https://pymoo.org/getting_started.html#By-Class)).\n",
    "\n",
    "Next, we add a Gaussian noise of type `N(0, 0.25)` and the averaging algorithm.\n",
    "\n",
    "While it is possible to feed `zdt1` directly into a `GaussianNoise`, the point\n",
    "of using an intermediate `wrapped_zdt1` is to keep a history of all calls to\n",
    "the non-noisy ZDT1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pymoo.config import Config\n",
    "Config.show_compile_hint = False\n",
    "\n",
    "from pymoo.problems.multi import ZDT1\n",
    "import numpy as np\n",
    "\n",
    "zdt1 = ZDT1()\n",
    "wrapped_zdt1 = nmoo.WrappedProblem(zdt1)\n",
    "\n",
    "mean = np.array([0, 0])\n",
    "covariance = np.array([[1., -.5], [-.5, 1]])\n",
    "noisy_zdt1 = nmoo.GaussianNoise(wrapped_zdt1, mean, covariance)\n",
    "\n",
    "avg_zdt1 = nmoo.ResampleAverage(noisy_zdt1, n_evaluations=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We construct a similar pipeline for the KNN-Avg algorithm (`max_distance` is\n",
    "just a parameter of the algorithm). Note that parts of an already existing\n",
    "pipeline can be reused. Behind the scene, `noisy_zdt1` will be deepcopied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "knnavg_zdt1 = nmoo.KNNAvg(noisy_zdt1, max_distance=1.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we setup an algorithm that will try and solve our `avg_zdt1` and `knnavg_zdt1` problems."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pymoo.algorithms.moo.nsga2 import NSGA2\n",
    "\n",
    "nsga2 = NSGA2()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we setup our benchmark. It will run NSGA2 against `avg_zdt1` and\n",
    "`knnavg_zdt1` tree times each. Additionally, we specify a Pareto front\n",
    "population to measure the performance. It no Pareto front is specified (or\n",
    "known), performance indicators will use one automatically calculated based on\n",
    "the results of the benchmark.\n",
    "\n",
    "Since the `avg` problem evaluates the underlying noisy `ZDT1` problem 10 times,\n",
    "we apply a penalty of 10, meaning that every call to `avg.eval` will count as\n",
    "10 calls.\n",
    "\n",
    "For more information on how to specify the problems and the algorithms, refer\n",
    "to [the documentation](https://altaris.github.io/noisy-moo/nmoo/benchmark.html#Benchmark.__init__)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pymoo.factory import get_termination\n",
    "\n",
    "pareto_front = zdt1.pareto_front(100)\n",
    "\n",
    "benchmark = nmoo.Benchmark(\n",
    "    output_dir_path=\"./out\",\n",
    "    problems={\n",
    "        \"knnavg\": {\n",
    "            \"problem\": knnavg_zdt1,\n",
    "            \"pareto_front\": pareto_front,\n",
    "        },\n",
    "        \"avg\": {\n",
    "            \"problem\": avg_zdt1,\n",
    "            \"pareto_front\": pareto_front,\n",
    "            \"evaluator\": nmoo.PenalizedEvaluator(10),\n",
    "        },\n",
    "    },\n",
    "    algorithms={\n",
    "        \"nsga2\": {\n",
    "            \"algorithm\": nsga2,\n",
    "        },\n",
    "        \"nsga2_100\": {\n",
    "            \"algorithm\": nsga2,\n",
    "            \"termination\": get_termination(\"n_gen\", 100),\n",
    "        },\n",
    "    },\n",
    "    n_runs=3,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running the benchmark"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(this might take some time, especially if you are running the notebook on\n",
    "Google colab)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 10 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Compiled modules for significant speedup can not be used!\n",
      "https://pymoo.org/installation.html#installation\n",
      "\n",
      "To disable this warning:\n",
      "from pymoo.config import Config\n",
      "Config.show_compile_hint = False\n",
      "\n",
      "\n",
      "Compiled modules for significant speedup can not be used!\n",
      "https://pymoo.org/installation.html#installation\n",
      "\n",
      "To disable this warning:\n",
      "from pymoo.config import Config\n",
      "Config.show_compile_hint = False\n",
      "\n",
      "\n",
      "Compiled modules for significant speedup can not be used!\n",
      "https://pymoo.org/installation.html#installation\n",
      "\n",
      "To disable this warning:\n",
      "from pymoo.config import Config\n",
      "Config.show_compile_hint = False\n",
      "\n",
      "\n",
      "Compiled modules for significant speedup can not be used!\n",
      "https://pymoo.org/installation.html#installation\n",
      "\n",
      "To disable this warning:\n",
      "from pymoo.config import Config\n",
      "Config.show_compile_hint = False\n",
      "\n",
      "\n",
      "Compiled modules for significant speedup can not be used!\n",
      "https://pymoo.org/installation.html#installation\n",
      "\n",
      "To disable this warning:\n",
      "from pymoo.config import Config\n",
      "Config.show_compile_hint = False\n",
      "\n",
      "\n",
      "Compiled modules for significant speedup can not be used!\n",
      "https://pymoo.org/installation.html#installation\n",
      "\n",
      "To disable this warning:\n",
      "from pymoo.config import Config\n",
      "Config.show_compile_hint = False\n",
      "\n",
      "\n",
      "Compiled modules for significant speedup can not be used!\n",
      "https://pymoo.org/installation.html#installation\n",
      "\n",
      "To disable this warning:\n",
      "from pymoo.config import Config\n",
      "Config.show_compile_hint = False\n",
      "\n",
      "\n",
      "Compiled modules for significant speedup can not be used!\n",
      "https://pymoo.org/installation.html#installation\n",
      "\n",
      "To disable this warning:\n",
      "from pymoo.config import Config\n",
      "Config.show_compile_hint = False\n",
      "\n",
      "\n",
      "Compiled modules for significant speedup can not be used!\n",
      "https://pymoo.org/installation.html#installation\n",
      "\n",
      "To disable this warning:\n",
      "from pymoo.config import Config\n",
      "Config.show_compile_hint = False\n",
      "\n",
      "\n",
      "Compiled modules for significant speedup can not be used!\n",
      "https://pymoo.org/installation.html#installation\n",
      "\n",
      "To disable this warning:\n",
      "from pymoo.config import Config\n",
      "Config.show_compile_hint = False\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed:   17.2s\n",
      "[Parallel(n_jobs=-1)]: Done   3 out of  12 | elapsed:   17.3s remaining:   52.0s\n",
      "[Parallel(n_jobs=-1)]: Done   5 out of  12 | elapsed:   28.5s remaining:   39.9s\n",
      "[Parallel(n_jobs=-1)]: Done   7 out of  12 | elapsed:   32.1s remaining:   22.9s\n",
      "[Parallel(n_jobs=-1)]: Done   9 out of  12 | elapsed:   32.8s remaining:   10.9s\n",
      "[Parallel(n_jobs=-1)]: Done  12 out of  12 | elapsed:  1.4min finished\n",
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=2)]: Done   1 tasks      | elapsed:    1.8s\n",
      "[Parallel(n_jobs=2)]: Done   2 out of   4 | elapsed:    1.8s remaining:    1.8s\n",
      "[Parallel(n_jobs=2)]: Done   4 out of   4 | elapsed:    1.9s remaining:    0.0s\n",
      "[Parallel(n_jobs=2)]: Done   4 out of   4 | elapsed:    1.9s finished\n",
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=2)]: Done   1 tasks      | elapsed:    2.4s\n",
      "[Parallel(n_jobs=2)]: Done   4 tasks      | elapsed:    6.2s\n",
      "[Parallel(n_jobs=2)]: Done   9 tasks      | elapsed:    9.3s\n",
      "[Parallel(n_jobs=2)]: Done  12 out of  12 | elapsed:   10.9s finished\n"
     ]
    }
   ],
   "source": [
    "benchmark.run(verbose=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Benchmark results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Results of the benchmark are automatically saved. Refer to\n",
    "[the documentation](https://altaris.github.io/noisy-moo/nmoo/benchmark.html) for a precise\n",
    "explanation of the role of each artefact."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "avg.nsga2.1.1-resample_avg.npz\n",
      "avg.nsga2.1.2-gaussian_noise.npz\n",
      "avg.nsga2.1.3-wrapped_problem.npz\n",
      "avg.nsga2.1.csv\n",
      "avg.nsga2.1.pi-igd.csv\n",
      "avg.nsga2.1.pp.npz\n",
      "avg.nsga2.2.1-resample_avg.npz\n",
      "avg.nsga2.2.2-gaussian_noise.npz\n",
      "avg.nsga2.2.3-wrapped_problem.npz\n",
      "avg.nsga2.2.csv\n"
     ]
    }
   ],
   "source": [
    "! ls ./out | head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now visualize the results. The final result of all runs can be found using the `Benchmark.final_results` method:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>algorithm</th>\n",
       "      <th>problem</th>\n",
       "      <th>n_run</th>\n",
       "      <th>n_gen</th>\n",
       "      <th>n_eval</th>\n",
       "      <th>timedelta</th>\n",
       "      <th>perf_igd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>nsga2</td>\n",
       "      <td>avg</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>100000</td>\n",
       "      <td>29841</td>\n",
       "      <td>0.201766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>nsga2</td>\n",
       "      <td>avg</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>100000</td>\n",
       "      <td>129711</td>\n",
       "      <td>0.119726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>nsga2</td>\n",
       "      <td>avg</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
       "      <td>100000</td>\n",
       "      <td>144749</td>\n",
       "      <td>0.131315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>nsga2</td>\n",
       "      <td>knnavg</td>\n",
       "      <td>1</td>\n",
       "      <td>165</td>\n",
       "      <td>16500</td>\n",
       "      <td>698527</td>\n",
       "      <td>0.190680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>nsga2</td>\n",
       "      <td>knnavg</td>\n",
       "      <td>2</td>\n",
       "      <td>225</td>\n",
       "      <td>22500</td>\n",
       "      <td>108498</td>\n",
       "      <td>0.150406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>nsga2</td>\n",
       "      <td>knnavg</td>\n",
       "      <td>3</td>\n",
       "      <td>262</td>\n",
       "      <td>26200</td>\n",
       "      <td>803547</td>\n",
       "      <td>0.173940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>nsga2_100</td>\n",
       "      <td>avg</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>100000</td>\n",
       "      <td>102262</td>\n",
       "      <td>0.315564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>nsga2_100</td>\n",
       "      <td>avg</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>100000</td>\n",
       "      <td>849192</td>\n",
       "      <td>0.259745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>nsga2_100</td>\n",
       "      <td>avg</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
       "      <td>100000</td>\n",
       "      <td>817981</td>\n",
       "      <td>0.337144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>nsga2_100</td>\n",
       "      <td>knnavg</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>10000</td>\n",
       "      <td>429142</td>\n",
       "      <td>0.274805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>nsga2_100</td>\n",
       "      <td>knnavg</td>\n",
       "      <td>2</td>\n",
       "      <td>100</td>\n",
       "      <td>10000</td>\n",
       "      <td>54942</td>\n",
       "      <td>0.321031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>nsga2_100</td>\n",
       "      <td>knnavg</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
       "      <td>10000</td>\n",
       "      <td>187767</td>\n",
       "      <td>0.731495</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    algorithm problem  n_run  n_gen  n_eval  timedelta  perf_igd\n",
       "0       nsga2     avg      1    100  100000      29841  0.201766\n",
       "1       nsga2     avg      2    100  100000     129711  0.119726\n",
       "2       nsga2     avg      3    100  100000     144749  0.131315\n",
       "3       nsga2  knnavg      1    165   16500     698527  0.190680\n",
       "4       nsga2  knnavg      2    225   22500     108498  0.150406\n",
       "5       nsga2  knnavg      3    262   26200     803547  0.173940\n",
       "6   nsga2_100     avg      1    100  100000     102262  0.315564\n",
       "7   nsga2_100     avg      2    100  100000     849192  0.259745\n",
       "8   nsga2_100     avg      3    100  100000     817981  0.337144\n",
       "9   nsga2_100  knnavg      1    100   10000     429142  0.274805\n",
       "10  nsga2_100  knnavg      2    100   10000      54942  0.321031\n",
       "11  nsga2_100  knnavg      3    100   10000     187767  0.731495"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "benchmark.final_results()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following plot depicts the IGD performance of averaging vs. KNN-averaging."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='problem', ylabel='perf_igd'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEGCAYAAABo25JHAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAAAdnUlEQVR4nO3dfXxU1b3v8c+PAAKCCIL0mIBBofIgqBAUj70qWlrwVrEtKqgv4dUqB648eC09tdqq+FRpe2sB7fWih+LjoVqPFhTE01pr5YAmPAtoiVQhqcciYoSS8Pi7f8xOmIRAMsns7Mns7/v1yiuz116z92/CML9Za+29lrk7IiISXy2iDkBERKKlRCAiEnNKBCIiMadEICISc0oEIiIx1zLqABqiS5cunp+fH3UYIiLNysqVKz919641y5tlIsjPz6eoqCjqMEREmhUz+6i2cnUNiYjEnBKBiEjMKRGIiMRcsxwjqM3+/fspKSmhoqIi6lCyVps2bcjLy6NVq1ZRhyIiaZQ1iaCkpIQOHTqQn5+PmUUdTtZxd3bs2EFJSQk9e/aMOhwRSaOs6RqqqKjgpJNOUhIIiZlx0kknqcUlkoWyJhEASgIh099XJDtlTdeQiEhNc+bMobi4uFHHKC0tBSA3N7fBx+jVqxdTpkxpVBxhyqoWQVPKz8/n008/TcuxHn30UZ588kkA5s+fz9/+9rdQziMiqSsvL6e8vDzqMEKlFkHEDhw4wMSJE6u258+fz5lnnskpp5wSYVQi2SEd38KnTZsGwKxZsxp9rEylRFAPV155Jdu2baOiooJp06YxYcKEavvvvfdenn76abp27Ur37t0ZPHgw06dPZ82aNUycOJE9e/Zw+umnM2/ePDp16sTFF1/M2WefzVtvvcXYsWPZtWsX7du3r5o647rrrqNt27YsX74cSDRvFy1axP79+3n++efp06cPd999N3/961/ZsmULW7du5aGHHmLFihUsWbKE3NxcFi1apMs8RaRe1DVUD/PmzWPlypUUFRUxe/ZsduzYUbWvsLCQF154gbVr17JkyZJqcyDdcMMNzJw5k3Xr1jFgwABmzJhRtW/fvn0UFRXxve99r6ps9OjRFBQU8Mwzz7BmzRratm0LQJcuXVi1ahWTJk3i5z//eVX9Dz74gNdff52FCxdy/fXXM2zYMNavX0/btm155ZVXwvyTiEgWUSKoh9mzZ3PWWWcxdOhQtm3bxubNm6v2LVu2jFGjRtGmTRs6dOjA5ZdfDkBZWRmff/45F110EQDjxo3jzTffrHreNddcU+/zf+tb3wJg8ODBfPjhh1XlI0eOpFWrVgwYMICDBw8yYsQIAAYMGFCtnojIsahrqA5vvPEGv//971m+fDnt2rXj4osvTsu19Mcff3y96x533HEA5OTkcODAgSPKW7RoQatWraou72zRokW1eiIix6IWQR3Kysro1KkT7dq147333mPFihXV9l9wwQUsWrSIiooKdu/ezcsvvwxAx44d6dSpE3/+858BeOqpp6paB8fSoUMHdu3alf4XIiJyFKG3CMxsBDALyAEed/cHa+x/CBgWbLYDTnb3E8OOq75GjBjBo48+St++fTnjjDMYOnRotf1DhgzhiiuuYODAgXTr1o0BAwbQsWNHAJ544omqweLTTjuNX//613Web/z48UycOLHaYLGISJjM3cM7uFkO8BdgOFACFAJj3X3jUepPAc5x9+8c67gFBQVec2GaTZs20bdv37TEnardu3fTvn179uzZw4UXXsjcuXMZNGhQJLGELcq/s0gUsunyUTNb6e4FNcvDbhGcCxS7+5YgiAXAKKDWRACMBe4KOaa0mzBhAhs3bqSiooJx48ZlbRIQkewUdiLIBbYlbZcA59VW0cxOBXoCrx9l/wRgAkCPHj3SG2UjPfvss1GHICLSYJk0WDwG+K27H6xtp7vPdfcCdy/o2vWItZdFRKSBwk4EpUD3pO28oKw2Y4B/DzkeERGpIexEUAj0NrOeZtaaxIf9wpqVzKwP0AnQZTIiIk0s1ETg7geAycBSYBPwnLtvMLN7zOyKpKpjgAUe5iVMIiJSq9DvI3D3xcDiGmV31ti+O93nnXzr9/n7p5+l7Xgnd+nMw7/4WdqOl6o9e/Zw1VVX8cEHH5CTk8Pll1/Ogw8+WPcTRUTqkLVTTPz908/4oFvdd/LW2yd/St+xGmj69OkMGzaMffv2cemll7JkyRJGjhwZdVgi0sxl0lVDzd6HH35I3759uemmm+jfvz9f+9rXKC8vZ/bs2fTr14+BAwcyZswYALZv387w4cPp378/N954I6eeemrVAjRXXnklgwcPpn///sydOxeAdu3aMWxY4gbs1q1bM2jQIEpKSqJ5oSKSVZQI0mzz5s3cfPPNbNiwgRNPPJEXXniBBx98kNWrV7Nu3ToeffRRAGbMmMEll1zChg0bGD16NFu3bq06xrGmvQb4/PPPWbRoEZdeemmTvjYRyU5KBGnWs2dPzj77bODwtNEDBw7kuuuu4+mnn6Zly0Rv3FtvvVXVOhgxYgSdOnWqOsaxpr0+cOAAY8eOZerUqZx22mlN98JEJGspEaRZ5dTQcHja6FdeeYWbb76ZVatWMWTIkGNOEZ087fXatWs555xzqk17PWHCBHr37s0tt9wS5ssQkRhRIgjZoUOH2LZtG8OGDWPmzJmUlZWxe/duLrjgAp577jkAXnvtNXbu3Akce9rrH/3oR5SVlfHLX/4yipciIlkqa68aOrlL57Re6XNyl84Net7Bgwe5/vrrKSsrw92ZOnUqJ554InfddRdjx47lqaee4vzzz+dLX/oSHTp0OOq01yUlJdx///306dOnalK7yZMnc+ONN6btNYpIPGVtIojimv/8/Hzefffdqu3p06cftW7Hjh1ZunQpLVu2ZPny5RQWFlZ1Ky1ZsqTW5+h+OxEJQ9Ymgky3detWrr76ag4dOkTr1q157LHHog5JRGJKiSAivXv3ZvXq1VGHISKiwWIRkbhTIhARiTklAhGRmFMiEBGJuawdLL79e5Mp+/STtB2vY5duPPB/Hk7b8UREMkXWJoKyTz/hB6e/l7bjzfwgbYdqkFTXI3jzzTe55ZZbWLduHQsWLGD06NFV+5544gnuu+8+IHG38rhx4wBYuXIl48ePp7y8nMsuu4xZs2ZhZuG+MBGJnLqGmpHp06fz3nvvsXr1apYtW3bUG88AevTowfz587n22murlX/22WfMmDGDt99+m3feeYcZM2ZUTW8xadIkHnvsMTZv3szmzZt59dVXQ309IpIZlAjSKJPWI8jPz2fgwIG0aFH9n3jp0qUMHz6czp0706lTJ4YPH86rr77Kxx9/zBdffMHQoUMxM2644QZeeumlEP5KIpJplAjSLNPXIygtLaV79+5V23l5eZSWllJaWkpeXt4R5SKS/ZQI0kzrEYhIc6NEkGaZvh5Bbm4u27Ztq9ouKSkhNzeX3Nzcal1NleUikv2y9qqhjl26pfVKn45dujXoecnrEXzlK19hwYIF1dYj+MEPfpDyegSPP/54g1/H17/+dW6//faq87322mv85Cc/oXPnzpxwwgmsWLGC8847jyeffJIpU6Y0+Dwi0nxkbSLIlGv+o1qPoLCwkG9+85vs3LmTRYsWcdddd7FhwwY6d+7Mj3/8Y4YMGQLAnXfeSefOibUWfvWrX1VdPjpy5EhGjhzZBH8hEYmaNcc57gsKCryoqKha2aZNm+jbt29EEaVu79695OTkVK1HMGnSJNasWRN1WHVqbn9nkcaaNm0aALNmzYo4ksYzs5XuXlCzPPQWgZmNAGYBOcDj7n7EXVBmdjVwN+DAWne/tmadbKP1CEQkU4SaCMwsB3gEGA6UAIVmttDdNybV6Q38ELjA3Xea2clhxpQp0rUewf3338/zzz9freyqq67ijjvuaPSxRSQewm4RnAsUu/sWADNbAIwCNibVuQl4xN13Arj73xt6MneP3ZQId9xxR5N96DfHbkQRqVvYl4/mAtuStkuCsmRfBr5sZsvMbEXQlXQEM5tgZkVmVrR9+/Yj9rdp04YdO3bowyok7s6OHTto06ZN1KGISJplwlVDLYHewMVAHvCmmQ1w98+TK7n7XGAuJAaLax4kLy+PkpISaksSkh5t2rSpdvexiGSHsBNBKdA9aTsvKEtWArzt7vuBv5rZX0gkhsJUTtSqVSt69uzZmFhFRGIp7K6hQqC3mfU0s9bAGGBhjTovkWgNYGZdSHQVbQk5LhERCYSaCNz9ADAZWApsAp5z9w1mdo+ZXRFUWwrsMLONwB+B77v7jtqPKCIi6Rb6GIG7LwYW1yi7M+mxA7cGPyIi0sQ06ZyISMwpEYiIxJwSgYhIzCkRiIjEnBKBiEjMKRGIiMScEoGISMwpEYiIxJwSgYhIzCkRiIjEnBKBiEjMKRGIiMScEoGISMwpEYiIxJwSgYhIzCkRiIjEnBKBiEjMhb5CmYhIQ82ZM4fi4uJIY6g8/7Rp0yKLoVevXkyZMiW04ysRiEjGKi4uZvOG1fRofzCyGFrvT3Sc7P2oKJLzb92dE/o5lAhEJKP1aH+Q2wd9EXUYkXlg1Qmhn0NjBCIiMadEICISc0oEIiIxF3oiMLMRZva+mRWb2W217B9vZtvNbE3wc2PYMYmIyGGhDhabWQ7wCDAcKAEKzWyhu2+sUfU37j45zFhERKR2YbcIzgWK3X2Lu+8DFgCjQj6niIikIOxEkAtsS9ouCcpq+raZrTOz35pZ99oOZGYTzKzIzIq2b98eRqwiIrGUCYPFi4B8dx8I/CfwRG2V3H2uuxe4e0HXrl2bNEARkWwWdiIoBZK/4ecFZVXcfYe77w02HwcGhxyTiIgkCTsRFAK9zaynmbUGxgALkyuY2T8lbV4BbAo5JhERSRLqVUPufsDMJgNLgRxgnrtvMLN7gCJ3XwhMNbMrgAPAZ8D4MGMSEZHqQp9ryN0XA4trlN2Z9PiHwA/DjkNERGqXCYPFIiISoTpbBGa2HvCj7Q+u9hERkWaqPl1D3wh+3xz8fir4fV36wxERkaZWZyJw948AzGy4u5+TtOs2M1sFHDF/kIiINB+pjBGYmV2QtPHPKT5fREQyUCpXDX0XmGdmHQEDdgLfCSUqERFpMvVOBO6+EjgrSAS4e1loUYmISJOpdyIws1trbAOUASvdfU16wxIRkaaSSh9/ATCRxOyhucC/ACOAx8zsX0OITUREmkAqYwR5wCB33w1gZncBrwAXAiuBn6Y/PBERCVsqLYKTgb1J2/uBbu5eXqNcRESakVRaBM8Ab5vZ74Lty4Fnzex4oObSkyIi0kykctXQvWa2BKi8l2CiuxcFj3WXsYhIM1WfuYZOcPcvzKwzsCX4qdzX2d0/CzNAEREJV31aBM+SmG9oJdUnn7Ng+7QQ4hIRkSZSn7mGvhH87nmsembW3903pCswERFpGumcK+ipuquIiEimSWcisDQeS0REmkg6E8FRF68REZHMpWmkRURirs5EULkGgZkdV0fVfWmJSEREmlR9WgSzg9/Lj1XJ3Yc2PhwREWlq9bmPYL+ZzQXyzGx2zZ3uPjX9YYmISFOpT4vgG8DrQDmJm8pq/hyTmY0ws/fNrNjMjrq+sZl928zczArqF7qIiKRDfW4o+9TMngdOcfcnUjm4meUAjwDDgRKg0MwWuvvGGvU6ANOAt1M5voiINF69rhpy94PAmAYc/1yg2N23uPs+YAEwqpZ69wIzgYoGnENERBohlctHl5nZw2b2P8xsUOVPHc/JBbYlbZcEZVWCY3R391eOdSAzm2BmRWZWtH379hTCFhGRY0llPYKzg9/3JJU5cElDT25mLYBfAOPrquvuc4G5AAUFBbp5TUQkTVJZj2BYA45fCnRP2s4Lyip1AM4E3jAzgC8BC83siqS1DkREJET17hoys25m9m/B4jSYWT8z+24dTysEeptZTzNrTWKcYWHlTncvc/cu7p7v7vnACkBJQESkCaUyRjAfWAqcEmz/BbjlWE9w9wPA5OB5m4Dn3H2Dmd1jZlekHK2IiKRdKmMEXdz9OTP7ISQ+5M3sYF1PcvfFwOIaZXcepe7FKcQjIiJpkEqL4B9mdhLBLKNmNhQoCyUqERFpMqm0CG4l0b9/mpktA7oCo0OJSkREmkwqiWAj8CKwB9gFvERinEBERJqxVLqGngT6AA8Ac4Avo+UpRUSavVRaBGe6e7+k7T+a2caj1hYRkWYhlRbBqmCAGAAzOw/Q9f4iIs1cKi2CwcB/mdnWYLsH8L6ZrQfc3QemPToREQldKolgRGhRiIhIZFKZa+ijMAMREZFopDJGICIiWUiJQEQk5pQIRERiTolARCTmlAhERGIulctHRWo1Z84ciouLG/z80tLEonW5ubl11Dy2Xr16MWXKlEYdQySOlAgkcuXl5VGHIBJrSgTSaI39Fj5t2jQAZs2alY5wRCRFSgQx19hunXSoPH9lQoiKupYkrpQIYq64uJjNG1bTo32dq46GpvX+xDULez+Kbg7DrbtzIju3SNSUCIQe7Q9y+6Avog4jUg+sOiHqEKQWpaWl/GNXTqz/fT7alcPxwQUVYdHloyIiMacWQczpG1dCU3zrktTl5uay98DHsW6xPrDqBI5r5KXVdVGLQEQk5tQiiDl940poim9dIpkq9BaBmY0ws/fNrNjMbqtl/0QzW29ma8zsLTPrV9txREQkHKEmAjPLAR4BRgL9gLG1fNA/6+4D3P1s4KfAL8KMSUREqgu7a+hcoNjdtwCY2QJgFLCxsoK7J/dJHA94yDGJSDOydXe0FzN8sifxfblbu0ORnH/r7hx6h3yOsBNBLrAtabsEOK9mJTO7GbgVaA1cUtuBzGwCMAGgR48eaQ9URDJPr169og6BfcGd78edGk0svQn/75ARg8Xu/gjwiJldC/wIGFdLnbnAXICCggK1GkRiIBOm/IjDXFhhDxaXAt2TtvOCsqNZAFwZZkAiIlJd2C2CQqC3mfUkkQDGANcmVzCz3u6+Odj8n8BmpEnFvQ8WmqYfViRThZoI3P2AmU0GlgI5wDx332Bm9wBF7r4QmGxmXwX2AzuppVtIwqM+2ISm6IcVyVShjxG4+2JgcY2yO5MeRzv3cMypD1ZENMWEiEjMZcRVQyIiYUjHwkvpWDgp0xc9UiIQETmGtm3bRh1C6JQIRCRrZfK38EyiMQIRkZhTIhARiTl1DUmjNXZALh2DcZD5A3IimUqJQCIXh8E4kUymRCCNpm/hIs2bxghERGJOiUBEJOaUCEREYk6JQEQk5pQIRERiTolARCTmlAhERGJOiUBEJOaUCEREYk6JQEQk5pQIRERiTolARCTmlAhERGJOiUBEJOZCTwRmNsLM3jezYjO7rZb9t5rZRjNbZ2Z/MLNTw45JREQOC3U9AjPLAR4BhgMlQKGZLXT3jUnVVgMF7r7HzCYBPwWuCTOudGjsqlylpaUA5ObmNioOrcolIo0VdovgXKDY3be4+z5gATAquYK7/9Hd9wSbK4C8kGPKCOXl5ZSXl0cdhohI6CuU5QLbkrZLgPOOUf+7wJLadpjZBGACQI8ePdIVX4M19lt45fq8s2bNSkc4IiINljGDxWZ2PVAA/Ky2/e4+190L3L2ga9euTRuciEgWC7tFUAp0T9rOC8qqMbOvAncAF7n73pBjEslqjR2/gvSMYWn8qvkIOxEUAr3NrCeJBDAGuDa5gpmdA/w/YIS7/z3keESkHjR+FS+hJgJ3P2Bmk4GlQA4wz903mNk9QJG7LyTRFdQeeN7MALa6+xVhxiWSzdLxLVxjWPESdosAd18MLK5RdmfS46+GHYNIc5KOrp3Gqjx/ZUKIgrqWmk7oiUBEUlNcXMyadzdxsF3nyGJosc8BWLnlk0jOn7Pns0jOG1dKBCIZJjFQ65HGcKjNCZGeH7xqwFrClzGXj4qISDTUIhDJMLm5ufz33paU97ks6lAi0/a9xeTmdos6jNiIZSLQYNxhGpATkVgmAg3GJWhALnPl7PmMtu8trrtiSFpUfAFEN1aQeG+qRdBUYpkIAA626xzrpjcQ6QeNHF2vXr2iDoHi4l0A9Dotqg/jbhnxd4iL2CYCkUyVCV11uqEsXnTVkIhIzKlFIJJl0nExRDouZtCFCM2HEoGIHKFt27ZRhyBNSIlAJMvoW7ikSmMEIiIxp0QgIhJzSgQiIjEXyzGC0tJScvaUxf6Gqpw9OygtPRB1GCISMbUIRERiLpaJILEgt0UaQ4uKL6rmc4mONWpxchHJDrHsGsqEOUyin8sFNJ+LiEBME0E6rrPOhKmsQXdvikjjxTIRZALduSkimUKJoIH0LVxEskUsB4tFROSw0BOBmY0ws/fNrNjMbqtl/4VmtsrMDpjZ6LDjERGR6kJNBGaWAzwCjAT6AWPNrF+NaluB8cCzYcYiIiK1C3uM4Fyg2N23AJjZAmAUsLGygrt/GOw7FHIsIiJSi7C7hnKBbUnbJUFZysxsgpkVmVnR9u3b0xKciIg0o8Fid5/r7gXuXtC1a9eowxERyRphJ4JSoHvSdl5QJiIiGSLsRFAI9DaznmbWGhgDLAz5nCIikgJz93BPYHYZ8EsgB5jn7veb2T1AkbsvNLMhwItAJ6AC+G9371/HMbcDH4UaeLx0AT6NOgiRWui9mV6nuvsRfeuhJwLJfGZW5O4FUcchUpPem02j2QwWi4hIOJQIRERiTolAAOZGHYDIUei92QQ0RiAiEnNqEYiIxJwSgYhIzCkRiEgozCzfzN6NOg6pmxKBiEjMKRHEgJm9ZGYrzWxDMIvrRDP7WdL+8Wb2cPD4x8FCQm+Z2b+b2fToIpdsYWanmdlqM/u+mf2Hmb1qZpvN7KdJdXab2f1mttbMVphZt6D8cjN7O3j+782sm5m1MLMPzezEpOdvDvadHjx/vZndZ2a7I3jJzYoSQTx8x90HAwXAVBJTenwzaf81wIJguo9vA2eRWExId3RKo5nZGcALJBag2g6cTeI9NwC4xswqJ6Y8Hljh7mcBbwI3BeVvAUPd/RxgAfCv7n4I+B3B+9jMzgM+cvdPgFnALHcfQGLqe6mDEkE8TDWztcAKErPB9gS2mNlQMzsJ6AMsAy4AfufuFe6+C1gUWcSSLbqS+MC+zt3XBmV/cPcyd68gsUjVqUH5PuDl4PFKID94nAcsNbP1wPeByrnIfkMioUBiQsvfBI/PB54PHmvlw3pQIshyZnYx8FXg/OCb1mqgDYlvVleTaAG86LqhRMJRRmI52q8kle1NenyQwysl7k96HyaXzwEeDr7h/wuJ9y/AcqCXmXUFrgT+I+3Rx4QSQfbrCOx09z1m1gcYGpS/SGLZ0LEkkgIkWgWXm1kbM2sPfKPJo5Vss49E980NZnZtA4/RkcPrmIyrLAySxovAL4BN7r4j2LWCxBccSLQUpA5KBNnvVaClmW0CHiTxnwR33wlsIjEt7TtBWSGJ9SLWAUuA9SS+0Yk0mLv/g8SXiv8NnNCAQ9wNPG9mKzlySurfANdzuFsI4BbgVjNbB/RC7+E6aYoJqcbM2rv7bjNrR2LAboK7r4o6LpH6Ct675e7uZjYGGOvuo6KOK5O1rLuKxMxcM+tHoh/2CSUBaYYGAw+bmQGfA9+JNpzMpxaBiEjMaYxARCTmlAhERGJOiUBEJOaUCEQa6Whz2ZjZfDMb3dTxiKRKiUCkHswsJ+oYRMKiRCCxF8yb/56ZPWNmm8zst2bWLpjdcqaZrQKuMrOxwYyW75rZzBrHeCiY3fUPwZQHNc8x2Mz+FMwCu9TM/ikofyN4blFw7iHB7Jybzey+JvoTSMwpEYgknAH8yt37Al8A/yso3+Hug0jcXDcTuITE7JlDzOzKoM7xQJG79wf+BNyVfGAza0VivpzRwSyw84D7k6rsc/cC4FESE7TdDJwJjA8mBRQJlW4oE0nY5u7LgsdPk5iuGw5PXTAEeMPdtwOY2TPAhcBLwKGkek9z5ORnZ5D4YP/PxD1O5AAfJ+1fGPxeD2xw94+Dc2whMVvsDkRCpEQgklDzzsrK7X+k4VhG4gP+/KPUr5yN8xDVZ+Y8hP6PShNQ15BIQg8zq/ygvpbEYijJ3gEuMrMuwcDxWBLdQJD4fzT6GM99H+haeXwza2Vm/RHJEEoEIgnvAzcHs7R2Av5v8s6gu+Y24I/AWmClu/8u2P0P4NxgofZLgHtqPHcfiUQxM1ggaA3wz+G9FJHUaK4hiT0zywdedvczo45FJApqEYiIxJxaBCIiMacWgYhIzCkRiIjEnBKBiEjMKRGIiMScEoGISMz9fywvtMaRcm/8AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.boxplot(\n",
    "    x=\"problem\", \n",
    "    y=\"perf_igd\", \n",
    "    hue=\"algorithm\",\n",
    "    data=benchmark.final_results(),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following boxplot depicts the runtimes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='problem', ylabel='timedelta'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(\n",
    "    x=\"problem\", \n",
    "    y=\"timedelta\", \n",
    "    hue=\"algorithm\",\n",
    "    data=benchmark.final_results(),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`nmoo` also offers a few plotting primitives, see [the documentation](https://altaris.github.io/noisy-moo/nmoo/plotting.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x16c133f10>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 306.375x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nmoo.plot_performance_indicators(benchmark, row=\"algorithm\")"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "1556a16cf37fb4214f8861bf943a2d99c2b3956d871fdd1a2a66e1a032ac6deb"
  },
  "kernelspec": {
   "display_name": "Python 3.8.2 64-bit ('venv': venv)",
   "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": "3.9.13"
  },
  "orig_nbformat": 2
 },
 "nbformat": 4,
 "nbformat_minor": 2
}