process_perf_profile.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import optperfprofpy\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format='retina'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "problems = pd.Series([1, 1, 2, 2, 3, 3], dtype=int, name='problem')\n",
    "methods = pd.Series(['A', 'B', 'A', 'B', 'A', 'B'], dtype=str, name='method')\n",
    "objective_vals = pd.Series([2, 20, 25, 5, 30, 4], dtype=float, name='obj')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "example_df = pd.DataFrame([problems, methods, objective_vals]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of problems per solver:  3\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 261,
       "width": 392
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "taus, solver_vals, solvers, transformed_data = optperfprofpy.calc_perprof(\n",
    "    example_df, ['problem'], ['obj'], ['method'])\n",
    "optperfprofpy.draw_simple_pp(taus, solver_vals, solvers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def my_draw_simple_pp(taus, solver_vals, solvers, ax,\n",
    "        fname=\"performance_profiles.pdf\"):\n",
    "    \"\"\"Simple step plotter for performance profiles.\n",
    "    Parameters\n",
    "    ----------\n",
    "    taus : numpy.ndarray\n",
    "        x values of plot.\n",
    "    solver_vals : numpy.ndarray\n",
    "        y values of plot.\n",
    "    solvers : list\n",
    "        Labels of curves.\n",
    "    \"\"\"\n",
    "\n",
    "    # Add lines individually to support labels\n",
    "    for n, solver in enumerate(solvers):\n",
    "        ax.step(taus, solver_vals[n, :], alpha=0.5, label=solver)\n",
    "\n",
    "    plt.legend(loc=4, frameon=False)\n",
    "    plt.xlim(1, taus.max())\n",
    "    #ax.set_xscale(\"log\")\n",
    "    ax.set_xlabel(r\"$\\tau$\")\n",
    "    ax.set_ylabel(r\"$\\rho_{s}(\\tau)$\")\n",
    "\n",
    "    plt.savefig(fname, bbox_inches='tight')\n",
    "    plt.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of problems per solver:  12\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 207,
       "width": 331
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "problems = pd.Series(\n",
    "        np.arange(12).tolist() + \\\n",
    "        np.arange(12).tolist() + \\\n",
    "        np.arange(12).tolist(),\n",
    "        # np.arange(12).tolist(), \n",
    "        dtype=int, name='problem')\n",
    "methods = pd.Series(\n",
    "        [r'GOGGLES'] * 12 + \\\n",
    "        [r'Snuba, $D=1$'] * 12 + \\\n",
    "        [r'Interactive WS'] * 12, \n",
    "        # [r'Supervised'] * 12, \n",
    "        dtype=str, name='method')\n",
    "goggles_perfs = [\n",
    "        0.3633, 0.4613, 0.2281,\n",
    "        0.4441, 0.4524, 0.2951,\n",
    "        0.2846, 0.3365, 0.2190,\n",
    "        0.2880, 0.3523, 0.3009]\n",
    "snuba_perfs = [\n",
    "        0.2726, 0.2793, 0.1826, \n",
    "        0.3715, 0.1414, 0.1309,\n",
    "        0.2834, 0.3311, 0.2409,\n",
    "        0.3244, 0.3135, 0.2499,]\n",
    "iws_perfs = [\n",
    "        0.2363, 0.2759, 0.1100,\n",
    "        0.3848, 0.4546, 0.1878,\n",
    "        0.3612, 0.3934, 0.1808,\n",
    "        0.2738, 0.3509, 0.2047,]\n",
    "# sup_perfs = [\n",
    "#         0.9, 0.8, 0.8,\n",
    "#         0.8, 0.6, 0.7,\n",
    "#         0.6, 0.7, 0.8,\n",
    "#         0.6, 0.6, 0.7,]\n",
    "objective_vals = pd.Series(\n",
    "        1.0 - np.array(goggles_perfs + snuba_perfs + iws_perfs), \n",
    "        dtype=float, name='obj')\n",
    "\n",
    "df = pd.DataFrame([problems, methods, objective_vals]).T\n",
    "\n",
    "taus, solver_vals, solvers, transformed_data = optperfprofpy.calc_perprof(\n",
    "    df, ['problem'], ['obj'], ['method'])\n",
    "\n",
    "fig = plt.figure(figsize=(5, 3))\n",
    "ax = fig.add_subplot(111)\n",
    "my_draw_simple_pp(taus, solver_vals, solvers, ax,\n",
    "        fname=\"performance_profiles_search.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of problems per solver:  9\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 207,
       "width": 331
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "problems = pd.Series(\n",
    "        np.arange(3).tolist() + \\\n",
    "        np.arange(3).tolist() + \\\n",
    "        np.arange(3).tolist() + \\\n",
    "        np.arange(3).tolist() + \\\n",
    "        np.arange(3, 6).tolist() + \\\n",
    "        np.arange(3, 6).tolist() + \\\n",
    "        np.arange(3, 6).tolist() + \\\n",
    "        np.arange(3, 6).tolist() + \\\n",
    "        np.arange(6, 9).tolist() + \\\n",
    "        np.arange(6, 9).tolist() + \\\n",
    "        np.arange(6, 9).tolist() + \\\n",
    "        np.arange(6, 9).tolist(),\n",
    "        dtype=int, name='problem')\n",
    "methods = pd.Series(\n",
    "        ([r'Raw features'] * 3 + \\\n",
    "        [r'PCA'] * 3 + \\\n",
    "        [r'ResNet-18'] * 3 + \\\n",
    "        [r'VAE'] * 3) * 3, \n",
    "        dtype=str, name='method')\n",
    "goggles_perfs = [\n",
    "        0.3633, 0.4613, 0.2281,\n",
    "        0.4441, 0.4524, 0.2951,\n",
    "        0.2846, 0.3365, 0.2190,\n",
    "        0.2880, 0.3523, 0.3009]\n",
    "snuba_perfs = [\n",
    "        0.2726, 0.2793, 0.1826, \n",
    "        0.3715, 0.1414, 0.1309,\n",
    "        0.2834, 0.3311, 0.2409,\n",
    "        0.3244, 0.3135, 0.2499,]\n",
    "iws_perfs = [\n",
    "        0.2363, 0.2759, 0.1100,\n",
    "        0.3848, 0.4546, 0.1878,\n",
    "        0.3612, 0.3934, 0.1808,\n",
    "        0.2738, 0.3509, 0.2047,]\n",
    "# sup_perfs = [\n",
    "#         0.9, 0.8, 0.8,\n",
    "#         0.8, 0.6, 0.7,\n",
    "#         0.6, 0.7, 0.8,\n",
    "#         0.6, 0.6, 0.7,]\n",
    "objective_vals = pd.Series(\n",
    "        1.0 - np.array(goggles_perfs + snuba_perfs + iws_perfs), \n",
    "        dtype=float, name='obj')\n",
    "\n",
    "df = pd.DataFrame([problems, methods, objective_vals]).T\n",
    "\n",
    "taus, solver_vals, solvers, transformed_data = optperfprofpy.calc_perprof(\n",
    "    df, ['problem'], ['obj'], ['method'])\n",
    "\n",
    "fig = plt.figure(figsize=(5, 3))\n",
    "ax = fig.add_subplot(111)\n",
    "my_draw_simple_pp(taus, solver_vals, solvers, ax,\n",
    "        fname=\"performance_profiles_emb.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>problem</th>\n",
       "      <th>method</th>\n",
       "      <th>obj</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Raw features</td>\n",
       "      <td>0.6367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Raw features</td>\n",
       "      <td>0.5387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Raw features</td>\n",
       "      <td>0.7719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>PCA</td>\n",
       "      <td>0.5559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>PCA</td>\n",
       "      <td>0.5476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>PCA</td>\n",
       "      <td>0.7049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td>ResNet-18</td>\n",
       "      <td>0.7154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>ResNet-18</td>\n",
       "      <td>0.6635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>ResNet-18</td>\n",
       "      <td>0.781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0</td>\n",
       "      <td>VAE</td>\n",
       "      <td>0.712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1</td>\n",
       "      <td>VAE</td>\n",
       "      <td>0.6477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>VAE</td>\n",
       "      <td>0.6991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3</td>\n",
       "      <td>Raw features</td>\n",
       "      <td>0.7274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4</td>\n",
       "      <td>Raw features</td>\n",
       "      <td>0.7207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>5</td>\n",
       "      <td>Raw features</td>\n",
       "      <td>0.8174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3</td>\n",
       "      <td>PCA</td>\n",
       "      <td>0.6285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4</td>\n",
       "      <td>PCA</td>\n",
       "      <td>0.8586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5</td>\n",
       "      <td>PCA</td>\n",
       "      <td>0.8691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3</td>\n",
       "      <td>ResNet-18</td>\n",
       "      <td>0.7166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4</td>\n",
       "      <td>ResNet-18</td>\n",
       "      <td>0.6689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5</td>\n",
       "      <td>ResNet-18</td>\n",
       "      <td>0.7591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3</td>\n",
       "      <td>VAE</td>\n",
       "      <td>0.6756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4</td>\n",
       "      <td>VAE</td>\n",
       "      <td>0.6865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5</td>\n",
       "      <td>VAE</td>\n",
       "      <td>0.7501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>6</td>\n",
       "      <td>Raw features</td>\n",
       "      <td>0.7637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>7</td>\n",
       "      <td>Raw features</td>\n",
       "      <td>0.7241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>8</td>\n",
       "      <td>Raw features</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>6</td>\n",
       "      <td>PCA</td>\n",
       "      <td>0.6152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>7</td>\n",
       "      <td>PCA</td>\n",
       "      <td>0.5454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>8</td>\n",
       "      <td>PCA</td>\n",
       "      <td>0.8122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>6</td>\n",
       "      <td>ResNet-18</td>\n",
       "      <td>0.6388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>7</td>\n",
       "      <td>ResNet-18</td>\n",
       "      <td>0.6066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>8</td>\n",
       "      <td>ResNet-18</td>\n",
       "      <td>0.8192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>6</td>\n",
       "      <td>VAE</td>\n",
       "      <td>0.7262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>7</td>\n",
       "      <td>VAE</td>\n",
       "      <td>0.6491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>8</td>\n",
       "      <td>VAE</td>\n",
       "      <td>0.7953</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   problem        method     obj\n",
       "0        0  Raw features  0.6367\n",
       "1        1  Raw features  0.5387\n",
       "2        2  Raw features  0.7719\n",
       "3        0           PCA  0.5559\n",
       "4        1           PCA  0.5476\n",
       "5        2           PCA  0.7049\n",
       "6        0     ResNet-18  0.7154\n",
       "7        1     ResNet-18  0.6635\n",
       "8        2     ResNet-18   0.781\n",
       "9        0           VAE   0.712\n",
       "10       1           VAE  0.6477\n",
       "11       2           VAE  0.6991\n",
       "12       3  Raw features  0.7274\n",
       "13       4  Raw features  0.7207\n",
       "14       5  Raw features  0.8174\n",
       "15       3           PCA  0.6285\n",
       "16       4           PCA  0.8586\n",
       "17       5           PCA  0.8691\n",
       "18       3     ResNet-18  0.7166\n",
       "19       4     ResNet-18  0.6689\n",
       "20       5     ResNet-18  0.7591\n",
       "21       3           VAE  0.6756\n",
       "22       4           VAE  0.6865\n",
       "23       5           VAE  0.7501\n",
       "24       6  Raw features  0.7637\n",
       "25       7  Raw features  0.7241\n",
       "26       8  Raw features    0.89\n",
       "27       6           PCA  0.6152\n",
       "28       7           PCA  0.5454\n",
       "29       8           PCA  0.8122\n",
       "30       6     ResNet-18  0.6388\n",
       "31       7     ResNet-18  0.6066\n",
       "32       8     ResNet-18  0.8192\n",
       "33       6           VAE  0.7262\n",
       "34       7           VAE  0.6491\n",
       "35       8           VAE  0.7953"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def my_draw_simple_pp(taus, solver_vals, solvers, ax,\n",
    "        fname=\"performance_profiles.pdf\"):\n",
    "    \"\"\"Simple step plotter for performance profiles.\n",
    "    Parameters\n",
    "    ----------\n",
    "    taus : numpy.ndarray\n",
    "        x values of plot.\n",
    "    solver_vals : numpy.ndarray\n",
    "        y values of plot.\n",
    "    solvers : list\n",
    "        Labels of curves.\n",
    "    \"\"\"\n",
    "\n",
    "    # Add lines individually to support labels\n",
    "    for n, solver in enumerate(solvers):\n",
    "        ax.step(taus, solver_vals[n, :], alpha=0.5, label=solver)\n",
    "\n",
    "    ax.legend(loc=4, frameon=False)\n",
    "    ax.set_xlim(1, taus.max())\n",
    "    #ax.set_xscale(\"log\")\n",
    "    ax.set_xlabel(r\"$\\tau$\")\n",
    "    ax.set_ylabel(r\"$\\rho_{s}(\\tau)$\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "#axs[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of problems per solver:  12\n",
      "Number of problems per solver:  9\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 360x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 279,
       "width": 350
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 1, figsize=(5, 4))\n",
    "\n",
    "problems = pd.Series(\n",
    "        np.arange(12).tolist() + \\\n",
    "        np.arange(12).tolist() + \\\n",
    "        np.arange(12).tolist(),\n",
    "        # np.arange(12).tolist(), \n",
    "        dtype=int, name='problem')\n",
    "methods = pd.Series(\n",
    "        [r'GOGGLES'] * 12 + \\\n",
    "        [r'Snuba, $D=1$'] * 12 + \\\n",
    "        [r'Interactive WS'] * 12, \n",
    "        # [r'Supervised'] * 12, \n",
    "        dtype=str, name='method')\n",
    "goggles_perfs = [\n",
    "        0.3633, 0.4613, 0.2281,\n",
    "        0.4441, 0.4524, 0.2951,\n",
    "        0.2846, 0.3365, 0.2190,\n",
    "        0.2880, 0.3523, 0.3009]\n",
    "snuba_perfs = [\n",
    "        0.2726, 0.2793, 0.1826, \n",
    "        0.3715, 0.1414, 0.1309,\n",
    "        0.2834, 0.3311, 0.2409,\n",
    "        0.3244, 0.3135, 0.2499,]\n",
    "iws_perfs = [\n",
    "        0.2363, 0.2759, 0.1100,\n",
    "        0.3848, 0.4546, 0.1878,\n",
    "        0.3612, 0.3934, 0.1808,\n",
    "        0.2738, 0.3509, 0.2047,]\n",
    "# sup_perfs = [\n",
    "#         0.9, 0.8, 0.8,\n",
    "#         0.8, 0.6, 0.7,\n",
    "#         0.6, 0.7, 0.8,\n",
    "#         0.6, 0.6, 0.7,]\n",
    "objective_vals = pd.Series(\n",
    "        1.0 - np.array(goggles_perfs + snuba_perfs + iws_perfs), \n",
    "        dtype=float, name='obj')\n",
    "\n",
    "df = pd.DataFrame([problems, methods, objective_vals]).T\n",
    "\n",
    "taus, solver_vals, solvers, transformed_data = optperfprofpy.calc_perprof(\n",
    "    df, ['problem'], ['obj'], ['method'])\n",
    "\n",
    "\n",
    "my_draw_simple_pp(taus, solver_vals, solvers, axs[1])\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "problems = pd.Series(\n",
    "        np.arange(3).tolist() + \\\n",
    "        np.arange(3).tolist() + \\\n",
    "        np.arange(3).tolist() + \\\n",
    "        np.arange(3).tolist() + \\\n",
    "        np.arange(3, 6).tolist() + \\\n",
    "        np.arange(3, 6).tolist() + \\\n",
    "        np.arange(3, 6).tolist() + \\\n",
    "        np.arange(3, 6).tolist() + \\\n",
    "        np.arange(6, 9).tolist() + \\\n",
    "        np.arange(6, 9).tolist() + \\\n",
    "        np.arange(6, 9).tolist() + \\\n",
    "        np.arange(6, 9).tolist(),\n",
    "        dtype=int, name='problem')\n",
    "methods = pd.Series(\n",
    "        ([r'Raw features'] * 3 + \\\n",
    "        [r'PCA'] * 3 + \\\n",
    "        [r'ResNet-18'] * 3 + \\\n",
    "        [r'VAE'] * 3) * 3, \n",
    "        dtype=str, name='method')\n",
    "goggles_perfs = [\n",
    "        0.3633, 0.4613, 0.2281,\n",
    "        0.4441, 0.4524, 0.2951,\n",
    "        0.2846, 0.3365, 0.2190,\n",
    "        0.2880, 0.3523, 0.3009]\n",
    "snuba_perfs = [\n",
    "        0.2726, 0.2793, 0.1826, \n",
    "        0.3715, 0.1414, 0.1309,\n",
    "        0.2834, 0.3311, 0.2409,\n",
    "        0.3244, 0.3135, 0.2499,]\n",
    "iws_perfs = [\n",
    "        0.2363, 0.2759, 0.1100,\n",
    "        0.3848, 0.4546, 0.1878,\n",
    "        0.3612, 0.3934, 0.1808,\n",
    "        0.2738, 0.3509, 0.2047,]\n",
    "# sup_perfs = [\n",
    "#         0.9, 0.8, 0.8,\n",
    "#         0.8, 0.6, 0.7,\n",
    "#         0.6, 0.7, 0.8,\n",
    "#         0.6, 0.6, 0.7,]\n",
    "objective_vals = pd.Series(\n",
    "        1.0 - np.array(goggles_perfs + snuba_perfs + iws_perfs), \n",
    "        dtype=float, name='obj')\n",
    "\n",
    "df = pd.DataFrame([problems, methods, objective_vals]).T\n",
    "\n",
    "taus, solver_vals, solvers, transformed_data = optperfprofpy.calc_perprof(\n",
    "    df, ['problem'], ['obj'], ['method'])\n",
    "\n",
    "my_draw_simple_pp(taus, solver_vals, solvers, axs[0])\n",
    "\n",
    "\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"performance_profiles.pdf\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "6fc900d93f77a9b4c6b924fbd3bb6f1112414aed80d2fca60bc4c561a035ee7c"
  },
  "kernelspec": {
   "display_name": "Python 3.7.11 ('FWRENCH')",
   "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": "3.7.11"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}