process_perf_profile_neurips.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "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": 2,
   "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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "example_df = pd.DataFrame([problems, methods, objective_vals]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 17,
   "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, where=\"post\")\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": 18,
   "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": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of problems per solver:  9\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAApcAAAGeCAYAAADBiyrEAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAABYlAAAWJQFJUiTwAABDd0lEQVR4nO3dd5gW5b3/8fcXWBAsNDUoGsGCaIxBUDRiAxSNOSK2eGIJWKPYY34nwRLASDQEC1FJogioMXosQaNRMSKgwY4ajxEsSNGIoogVC+X+/fE8u+4uu7DszpZn9/26ruea3Sn3fGd2YD875Z5IKSFJkiRloVl9FyBJkqTGw3ApSZKkzBguJUmSlBnDpSRJkjJjuJQkSVJmDJeSJEnKjOFSkiRJmTFcSpIkKTOGS0mSJGXGcClJkqTMGC4lSZKUmRb1XUBTERHzgI2A+fVciiRJ0tp0AT5JKXVd1wUNl3Vno9atW3fYYYcdOtR3IZIkSWsye/Zsvvjii2ota7isO/N32GGHDrNmzarvOiRJktaoV69ePP/88/Ors6z3XEqSJCkzhktJkiRlxnApSZKkzBguJUmSlBnDpSRJkjJjuJQkSVJmDJeSJEnKjOFSkiRJmSn4cBkRR0bENRHxeER8EhEpIv5czba2iIgJEfFORHwVEfMj4uqIaJ913ZIkSY1RY3hDz0XA94DPgLeB7tVpJCK2AZ4ANgXuBeYAvYFzgIMiok9KaUkmFUuSJDVSBX/mEjgP6AZsBJxeg3bGkQuWZ6eUBqWUfplS6gdcBWwPjKpxpZIkSY1cwYfLlNK0lNLrKaVU3TbyZy0HAPOB68pNHg58DhwfEetXu1BJkqQmoDFcFs9C3/zw4ZTSqtITUkqfRsRMcuFzD2DqmhqKiFmVTKrW5XpJkrTulj3/AsueeYa0fHnJuEUff8HbS79g5aqy56M+W/kBn61YTGJV+WaarKULXq32sgV/5jIj2+eHr1Uy/fX8sFsd1CJJkmqofLAEKgyWgMEyY565zGmbH35cyfTi8e3W1lBKqVdF4/NnNHuuc2WSJGmdlQ+WQIXBEjBYZsxwKUmSGrVNzjoTgDf+8c0FyvMO+OZi5LgXx5V8PbTH0LorrAH70yOdmbfk82ot62XxnOIzk20rmV48/qPaL0WSJKlwGS5ziu9areyeyu3yw8ruyZQkSRKGy2LT8sMBEVFmn0TEhkAfYBnwVF0XJkmSVEiaVLiMiKKI6J7v17JESmku8DDQBTij3GIjgfWBW1JK1bv5QJIkqYko+Ad6ImIQMCj/baf88PsRMSn/9QcppZ/nv+4MzAYWkAuSpQ0l9/rH30dE//x8u5PrA/M14MLsq5ckSWpcCj5cAj2AweXGbZ3/QC5I/py1SCnNjYhdgUuAg4CDgUXAWGBkSmlpVgVLkiQ1VgUfLlNKI4ARVZx3PhBrmP4WcEIWdUmSJDVFTeqeS0mSJNUuw6UkSZIyY7iUJElSZgyXkiRJyozhUpIkSZkxXEqSJCkzhktJkiRlxnApSZKkzBguJUmSlBnDpSRJkjJjuJQkSVJmDJeSJEnKjOFSkiRJmTFcSpIkKTOGS0mSJGXGcClJkqTMGC4lSZKUGcOlJEmSMmO4lCRJUmYMl5IkScqM4VKSJEmZMVxKkiQpM4ZLSZIkZcZwKUmSpMwYLiVJkpQZw6UkSZIy06K+C5AkVWzWgg956s0P+XrFqvoupcFr9+ZsOrz+Ms1WrqjvUmrks5Uf8NmKxSSq9zNvueIzWq38jCBlXFlh+9fER1YbN25y64pnXvpxLVfT+HnmUpIaKINl1TWGYAnUKFgCBssKrGweq41rtvooAIqieS1X0zQYLiWpgTJYVl1jCJZAjYIlYLAsZ2Xz4L0t25QZ1yxgo/WKVpu3KJqz23qb1lVpjZqXxSWpAJx3QLf6LqFBe39Ox5KvNznrzHqspGbGvTiu5OuhPYauewPTLvvm677DMqhITdaGdwHvVGtRz1xKkiQpM4ZLSZIkZcZwKUmSpMwYLiVJkpQZw6UkSZIyY7iUJElSZgyXkiRJyozhUpIkSZkxXEqSJCkzhktJkiRlxnApSZKkzBguJUmSlBnDpSRJkjJjuJQkSVJmDJeSJEnKjOFSkiRJmTFcSpIkKTOGS0mSJGXGcClJkqTMGC4lSZKUGcOlJEmSMtMowmVEbBEREyLinYj4KiLmR8TVEdF+HdvZKyLuzS//ZUQsjIgHIuKg2qpdkiSpMSn4cBkR2wCzgBOAZ4CrgDeBc4AnI6JjFds5HXgc6J8fXgXMAPYFHoyIC7OvXpIkqXFpUd8FZGAcsClwdkrpmuKREXElcB4wCjhtTQ1ERBFwGfAl0Cul9Gqpab8BXgAujIgxKaWvst8ESZKkxqGgz1zmz1oOAOYD15WbPBz4HDg+ItZfS1MdgLbAa6WDJUBKaTbwGtAa2CCDsiVJkhqtgg6XQN/88OGU0qrSE1JKnwIzgTbAHmtpZzHwPtAtIrYrPSEiugHbAS+mlJZkUrUkSVIjVeiXxbfPD1+rZPrr5M5sdgOmVtZISilFxBnAn4FZETEZeAfoDBwG/Bv476oUFBGzKpnUvSrLS5IkFbJCD5dt88OPK5lePL7d2hpKKd0ZEe8AtwE/KTXpPWAiuYeEJEmStAaFflk8MxFxHPAIuSfFdyB3OX0Hcmc8rwVur0o7KaVeFX2AObVUuiRJUoNR6OGy+Mxk20qmF4//aE2N5O+rnEDu8vfxKaU5KaUvUkpzgOPJdXV0VETsV9OCJUmSGrNCD5fFT3Z3q2R68cM5ld2TWWwAUATMqODBoFXAY/lve1WnSEmSpKai0MPltPxwQESU2ZaI2BDoAywDnlpLO63yw00qmV48/uvqFClJktRUFHS4TCnNBR4GugBnlJs8ElgfuCWl9HnxyIjoHhHln9x+PD88MiJ2Lj0hInoARwIJeDSz4iVJkhqhQn9aHGAo8ATw+4joD8wGdifXB+ZrQPnXNs7OD6N4RErpmYiYSO4Vks/muyJaQC60DgJaAlenlP5de5tRt2Yt+JCn3vyQr1esWvvMUhP23tdzePerf7MyraizdW741bts9NUigm/+fY6b3LrO1l/bNlrwOe3f/IxYWTv//8yfPLNW2q1zSyvrCEVq2Ao+XKaU5kbErsAlwEHAwcAiYCwwMqW0tIpNnUTu3sohwIHAhsAnwD+BG1JKVXpavFAYLKWqqetgCawWLJvFGmYuQLUZLFPzgr4gV6IomtesgeZF2RQiVUPBh0uAlNJb5M46VmXeCv+bTiklYFL+0+gZLKWqqetgCawWLDdar3EFhdoMlku3Lvy39BZFc3Zbb9PqN9C8CLrsnV1B0jpqFOFSNXPeAZU9bC9p3IsdS74e2mNo3ax02mXffN13WN2ssw69//a1JV9vctaZ9ViJpNrQOK4fSJIkqUEwXEqSJCkzhktJkiRlxnApSZKkzBguJUmSlBnDpSRJkjJjuJQkSVJmDJeSJEnKjOFSkiRJmTFcSpIkKTOGS0mSJGXGcClJkqTMGC4lSZKUGcOlJEmSMmO4lCRJUmYMl5IkScqM4VKSJEmZMVxKkiQpM4ZLSZIkZcZwKUmSpMwYLiVJkpQZw6UkSZIyY7iUJElSZgyXkiRJyozhUpIkSZkxXEqSJCkzhktJkiRlxnApSZKkzBguJUmSlBnDpSRJkjJjuJQkSVJmDJeSJEnKjOFSkiRJmWlR3wWodsxa8CFPvfkhX69YVd+lSGv14uIXefbdZ1m+anl9l1I9C5+G+Y/DygKtfx0te/4Flj3zDGl509heSevGM5eNVFWDZcsWHgKqf4UQLIuaFVU+sbaCZfM1rLMeZRUso6hhbp+kmjFZNFJVDZZ7bN2hDqqR1qwQguVunXarfIbaCpZd9s6+3QxkFSzb9O6dQTWSGhovizcB5x3Qrb5LkKpsaI+h9V1CzfQdVt8V1KlNzjqzvkuQ1MB45lKSJEmZMVxKkiQpM4ZLSZIkZcZwKUmSpMwYLiVJkpQZw6UkSZIyY7iUJElSZgyXkiRJyozhUpIkSZkxXEqSJCkzhktJkiRlxnApSZKkzBguJUmSlBnDpSRJkjKTabiMiAMiYnREPBUR70TE1xHxcUS8HhF3RsTpEdE5y3Xm17tFREzIr/OriJgfEVdHRPtqtNUzIv4SEW/n23ovImZExE+yrluSJKmxaVHTBiKiDXA28FPg20DkJ30JLAZaA1sD2wBHAGMj4j5gTErpyQzWvw3wBLApcC8wB+gNnAMcFBF9UkpLqtjWmcBYYCnwd+A/QAdgJ+Bg4Oaa1itJktSY1ShcRsSJwK+BzciFupHATODZlNInpeYLYHtgD+BA4FBgUETcBfy/lNLCGpQxjlywPDuldE2pdV4JnAeMAk6rwrYMAH4P/AM4MqX0abnpRTWoUZIkqUmo6WXx8cDTwO4ppR1TSpeklKaWDpYAKWdOSmlSSunHQCfgXGAvYEh1V54/azkAmA9cV27ycOBz4PiIWL8Kzf0O+AI4pnywzG/D8urWKUmS1FTU9LL4riml59d1oXz4vCYibgC61GD9ffPDh1NKq8qt49OImEkufO4BTK2skYjYCdgZuAf4MCL6Ar2ABLwITCvfviRJklZXo3BZPlhGRMuU0tfrsPyX5C6nV9f2+eFrlUx/nVy47MYawiWwW364GJgO7FNu+v9FxOEppTfWVlBEzKpkUve1LStJklTosu6K6OOIWOv9jRlqW7zeSqYXj2+3lnY2zQ9PIncm9Yf5trsBfwa+C/w9IlpWt1BJkqSmoMZPi5fTim+CWiEpDtnNgf8u9RT7J/kuiLoDu5J72v22NTWUUupV0fj8Gc2e2ZQrSZLUMBV6J+rFZybbVjK9ePxHa2mnePq75btHSiklcl0cQa6LI0mSJFWizsNlRPx3RPwuo+ZezQ+7VTJ9u/ywsnsyy7fzUSXTl+aHratWliRJUtNUG+HyvyLiZxHRLyI6VjB9N3L9T2ZhWn44ICLKbEtEbAj0AZYBT62lnafIdVvUpZJui3bKD+fVoFZJkqRGrzbC5a7AGHKdkS+OiLci4v6IuDwiLgB+AryXxYpSSnOBh8k9hHNGuckjgfWBW1JKnxePjIjuEVHmye2U0jLgRmA94NJ8p+/F83+XXF+cK4C7sqhbkiSpscr6gR6ASeTe0tMD+B65/iMPzn+KXZjh+oaSe/3j7yOiPzAb2J1cH5ivVbCu2flhlBt/MbkuiM4Fvp/vI/NbwOHkQue5+TArSZKkStRGuJyfUrqx9IiI6EruieuNgNdSSi9ktbKU0tyI2BW4BDiIXIhdRO4d4SNTSkvXtHypdj6JiL2BYcBRwJnk3tjzT3LvQX84q5olSZIaq9oIl6tJKc2jFu9XTCm9BZxQxXnLn7EsPe0zcmc6szyzKkmS1GRkfc9lyrg9SZIkFZCsz1xuCmyQcZuSJEkqEJmGy5TSEmBJlm1KkiSpcBT6G3okSZLUgNQoXEZEjd9Yk0UbkiRJahhqell8XkRcBvwxpfTVuiwYEd8j133Qc8Cva1hHozVrwYc89eaHfL1iVX2XohpY9vwLLHvmGdLy5fVdSp179/N3eeez/7AyVX4Mdyn19fv3L4SPFkJaWeu11YqXr63vCiSpXtX0svgU4EpgUUT8ISL6rulMZERsHRGnR8STwPPkOlmfVtn8osbBsmUL73xoCJpqsATWGixLax7NCjtYRvP6rqBORVFRfZcgqQGq0ZnLlNLgiLgWGAWcmv+sjIjZ5DoyX0ru7TYdge2Bjcm9GWcxub4kr1rXM55NTU2D5R5bd8iwGlVXUw2WwDoFy8036AxfvF7LFdWSaA7tvl3fVdSZKCqiTe/e9V2GpAaoxk+Lp5SeBQZExHbASUB/cq9+/G65Wd8H/grcDdydUmq6v22r6bwDutV3CcrAJmedWd8l1Kn5L44r+Xpoj6FrX2DaZd983XdYLVQkSapNmXVFlFJ6HfglQES0ATqTO2P5BbA4pbQoq3VJkiSpYaqV1z+mlJYBr+c/kiRJaiJ82kOSJEmZqZNwGRF3RsSppb7fPiKOiohN6mL9kiRJqht1deZyH+BFgIjoCDwNjAf+HRHlH/yRJElSgaqrcLkhua6JAI4A5gEdgBvIdWMkSZKkRqCuwuVCYJv810cCt6SUVgKTgD3qqAZJkiTVslp5WrwCE4BrI+JBoC9wWqn1t6mjGiRJklTL6iRcppRGRwTAgcDPU0pv5if1BhbURQ2SJEmqfbUWLiNiS2BlSukdyAVMYHS52b4F3F5bNUiSJKluZX7PZURcGBFLgPnAWxHxSUTcExEHl583pTQ6pfTrrGuQJElS/cg0XEbEhcCvgfbAq8DzwGfAQOC+iLg/ItpmuU5JkiQ1HFmfuTwFWAr0TCntmFLaLaW0ObA7cCdwMDAlIlpmvF5JkiQ1AFmHy87A7SmlF0uPTCk9m1L6b+Ascg/x/Czj9UqSJKkByDpcLgM+rWxiSuk6YCbwk4zXK0mSpAYg63D5AjBgLfM8DnTNeL2SJElqALIOlxcB34uI36xhnk2ADzJeryRJkhqATMNlSumfwOXALyPi8Yg4LCLWK54eET8AjgFuznK9kiRJahgy70Q9pXRhRHwAXALcBazKf98SaAfcl58mSZKkRibzTtQBUkpXAd2A/wGeJBcq2wMBHAJ8FBHPRsSfIuK0iOhdG3VIkiSpbtXa6x9TSouAK4ArIqI58B2gV6nPzvkhQAKa11YtkiRJqhu1Fi5LSymtBF7KfyYCREQzYEdgV6BnXdQhSZKk2lUn4bIiKaVVwMv5z6T6qkOSJEnZqZV7LiVJktQ0GS4lSZKUGcOlJEmSMmO4lCRJUmYMl5IkScqM4VKSJEmZMVxKkiQpM4ZLSZIkZcZwKUmSpMwYLiVJkpSZenv9Y1O0+JOvuOofr9V3GVW27PkXWPbMM6TlyzNr893P3+Wdz/7DyrQqszYLzfwXx9V3CdX38dvw0UJYtbJ6yy/9ONt6JEkNjmcuC0TLFnX/o8o6WAJNPlimevg5ZqoGwbIomq/bAs2LqrUeSVL9KvDfdE1DyxbN2GPrDnW+3qyDJdDkg+XSHbeo7zJqpgbBcrf1Nq36As2LoMve1VqXJKl+eVm8Dm26USvOO6BbfZdRLZucdWYm7ZS+JDy0x9BM2lQdKn1Zu++w+qtDktRgeeZSkiRJmTFcSpLUiEREmU/z5s3ZeOON6devH3/5y18qXW7OnDmcddZZ7LTTTrRt25aWLVuy+eab88Mf/pAbb7yRr776qtJlR40aVbK+V199tTY2SwXEy+KSJDVCw4cPB2D58uXMmTOHe++9l2nTpvHcc89x5ZVXlpn3kksuYeTIkaxatYrvf//7DB48mA022ID33nuP6dOnc/LJJ/OHP/yB5557brX1pJQYP348EUFKiRtuuIExY8bUyTaqYTJcSpLUCI0YMaLM91OnTuWAAw7g6quv5uyzz6ZLly4A/OY3v2H48OFsueWW3Hnnney+++6rtXX//fdzxRVXVLiehx9+mPnz5zNkyBAeeughbrrpJn7zm9/QsmXLrDdJBcLL4pIkNQH9+/ene/fupJR49tlnAZg/fz4jRoygqKiIBx54oMJgCfBf//VfPPTQQxVOu+GGGwA45ZRTOPbYY/nggw+YPHly7WyECoLhUpKkJiKlBOTuywSYOHEiy5cv54gjjmCnnXZa47KtWrVabdx7773H3/72N7p168aee+7JkCFDALj++uuzLVwFpVFcFo+ILYBLgIOAjsAi4B5gZEppaTXb3AeYRi6Aj0opXZRNtZKkulZIb0errS7rHnnkEV599VUigt122w2Af/7zn0DurGZ1FIfT4lC500470atXL6ZNm8Ybb7zBtttum0ntKiwFHy4jYhvgCWBT4F5gDtAbOAc4KCL6pJSWrGObGwI3AcuADbKtWJKk2ld8z+Xy5ct59dVXueeee0gpcd5557HVVlsBsGjRIgC22GLdX/BQ/CBPs2bN+MlPflIyfsiQIcyaNYsbbriB3/72tzXfEBWcgg+XwDhywfLslNI1xSMj4krgPGAUcNo6tjkWaAtcll9ekqSCMnLkSCB3Cbxdu3bsvffenHTSSRx33HGZtP/oo48yd+5cDjzwQDp37lwy/phjjuH8889n0qRJXHrppRQV+SrXpqagw2X+rOUAYD5wXbnJw4FTgeMj4vyU0udVbPNQ4ATgeAp8/0iScgr17Wg1UXx/5ZpsttlmzJ49m//85z/r3H7xfZXFl8SLdejQgUMOOYS7776be++9lyOPPHKd21ZhK/QHevrmhw+nVPal1SmlT4GZQBtgj6o0FhGbAjcA96SU/lydgiJiVkUfoHt12pMkqbbstddeQK6bonXx/vvvc8899wDw4x//eLWO2++++27AB3uaqkI/M7d9fljZndqvkzuz2Q2oyr+cG8gF7nW9jC5JUsE54YQTuOyyy7j77rt55ZVX2HHHHSud96uvvip5Yvymm27i66+/plevXvTo0aPC+f/2t7/xyCOPMG/ePLp27Vob5auBKvRw2TY//LiS6cXj262toYg4ERgIHJ1Seq+6BaWUelXS/iygZ3XblSQpa126dGHEiBFceOGF/PCHP+TOO+9k1113XW2+hx56iNGjR/Poo48C3/RtOW7cOHr37l1h2xdffDGXXnop48ePZ9QoH19oSgr9sngmIqILcDVwZ0rpjvqtRpKkunPBBRcwcuRIFi5cyG677UafPn0455xzuPDCCzn55JPp1q0bP/jBD/j0008BmD59Oq+99hrf/e53Kw2WACeddBIRwcSJE1mxYkVdbY4agEIPl8VnJttWMr14/EdraWcC8AUwNIOaJEkqKL/61a94+eWXOfPMM/n444+ZOHEiv/vd7/j73//ONttsw/jx40v6xCw+a3nyySevsc0uXbqw//77s2jRIu67775a3wY1HIV+WfzV/LCyxwC3yw/X1ntuT3JB9P3itxaUc2FEXAjcm1IatK5FSpJUV6rylHhFdthhB6655pq1znfrrbdy6623VqnNhx9+uFq1qLAVericlh8OiIhmpZ8Yz3eE3odcR+hPraWdm8k9VV7edsA+wIvALOCFmhYsSZLUmBV0uEwpzY2Ih8k9EX4GUPpPrpHA+sCfSvdxGRHd88vOKdXO2RW1HxFDyIXLv/v6R0mSpLUr6HCZN5Tc6x9/HxH9gdnA7uT6wHwNuLDc/LPzwwqvf0uSJKn6Cv2BHlJKc4FdgUnkQuX5wDbkXuG4x7q+V1ySJEnV1xjOXJJSeovcKxurMm+Vz1imlCaRC62SJEmqgoI/cylJkqSGw3ApSZKkzBguJUmSlBnDpSRJkjJjuJQkSVJmDJeSJEnKjOFSkiRJmTFcSpIkKTOGS0mS1KC8/vrrHHbYYXTq1ImIoF27dvVdktZBo3hDjyRJyoko+yK6Zs2a0bZtW3beeWeGDBnC4MGDV5unIVm5ciWDBg3ijTfe4Pjjj2eLLbZgvfXWq7P177fffsyYMYOUUp2ts7ExXEqS1AgNHz4cgOXLl/PGG28wefJkZsyYwXPPPce1115bz9VVbt68ebzyyiuccsopXH/99fVdjqrBcClJUiM0YsSIMt/PnDmTffbZh3HjxnH++efTtWvX+ilsLd555x0ANt9883quRNVluKxDKxa/z/vXNNy/FqvqxcUv8uy7z7J81fL6LkWlLXwa5j8OK/25SFpdnz596N69O6+88gqzZs0qEy5nzZrFzTffzPTp03nrrbdYtmwZW265JQMHDuSiiy6iffv2JfNOmTKFgw46iAsuuIBRo0aVjJ82bRr9+vUDYOHChWy55ZYl044++mjuuOMO5s6dy9Zbb11pjaUv148cOZKRI0cCubOwxWF5xYoVXH/99dx888288sorrFixgu23356TTjqJoUOH0qxZ2cdJJk2axH333ccLL7zAokWLKCoq4rvf/S6nn346xx13XMl88+fPL7NPStey7777Mn369JLxpb8vbciQIdx0003MmzePLl26lGl38ODBXHDBBVx88cVMmzaNDz74gEcffZT99tuvZL+OHTuWZ555hk8//ZQtttiCww8/nAsvvHC1e05feuklLrvsMp588kkWLVrERhttxJZbbsk+++zD7373O4qKiirdx3XBcKm1inIHaRbBsqhZ/R74jVJdBsvm/vykQlY+fNxwww1MnjyZfffdl/33359Vq1Yxa9YsrrzySh588EGefvppNtxwQwD23ntvWrZsydSpU8uEy6lTp5b5esiQIQCklJg2bRpdunRZY7CEXIicP38+N910E/vuu29J8CoeLl++nEMOOYQpU6aw/fbbc8wxx7Deeusxbdo0zjrrLJ5++mluueWWMm2efvrpfOc732GfffZhs802Y8mSJTzwwAMcf/zxvPrqq/z6178GoF27dgwfPpxJkyaxYMGCktsKgJKgWBNz585l9913p1u3bhx77LF88cUXbLTRRkAuSI8YMYIOHTrwX//1X2y66aa89NJLjBkzhgceeIAnn3yyZN6XXnqJ3XffnYhg4MCBdO3alU8++YQ33niDcePGcemllxou1bBFURFtevcuMy6LYLlbp91q1IYqUJfBssvedbMuKSvTLqvvCqqu77Baafaxxx5jzpw5tGzZkt7l/l8fNmwY1113Hc2bNy8z/sYbb+Tkk09m3Lhx/OIXvwCgTZs27L777jzxxBN8/PHHtG3bFsgFyl122YWFCxeWCZcvvfQS77//PocccshaaxwxYgTTp0/npptuYr/99lvt0v6oUaOYMmUKZ555JldffXVJvStXruTUU09lwoQJHHnkkRx66KEly7z88stss802Zdr5+uuv+cEPfsDll1/OaaedRufOnWnXrl3J+hcsWLDaumvqn//8J8OGDeM3v/lNmfHTpk1jxIgRfP/73+eBBx4oc5Zy0qRJnHDCCQwfPpyrrroKgJtuuokvv/ySe+65p8x2AixdupQ2bdpkWnd1GC7rUItNN2GTs86s7zIyNbTH0PouQRWppV9OkgpHcTgq/UBPSokxY8aw2WablZl3q622qrCNE088kZ/97GdMmTKlJFwC9O/fn8cff5wZM2YwcOBAPv30U5577jl+9rOf8eabb/Loo4+WzFt8RrN///412p5Vq1ZxzTXX0KlTJ6666qoyQbh58+ZcccUVTJw4kVtvvbVM6CofLAFatmzJGWecwaOPPsrUqVP5yU9+UqPaquJb3/pWmbOhxX7/+98DubPH5S9/DxkyhLFjx3LrrbeWhMtirVu3Xq2t0rcv1CfDpSRJjVDx/YrFIoIbb7yRE044YbV5ly9fzp/+9Cduv/12XnnlFT7++GNWrVpVMv0///lPmfn79evHiBEjmDp1KgMHDmTGjBmsWLGC/v3706VLF+666y5mz57NDjvsUBI0i+/HrK7XXnuNDz/8kO22245LL720wnlat27N7Nmzy4xbuHAhv/3tb5k6dSoLFy7kiy++KDO9/LbVlu9973u0atVqtfFPPvkkRUVF3Hnnndx5552rTf/66695//33WbJkCR07duToo49m7NixDBo0iCOPPJL999+fPn36VBii64vhUpLU+DXBs/nF/TR+/vnnPPnkk5x00kmcdtppbLXVVqsFvaOPPprJkyez9dZbc+ihh9KpU6eSIHT11Vfz1VdflZl/jz32YP311y85Kzl16lRatmzJXnvtVXJ/4tSpU9luu+147LHH2HHHHenUqVONtmfJkiVAroP18sG5tM8++6zk6zfffJPevXuzdOlS9t57bwYMGEDbtm1p3rx5yb2d5bettlS2/UuWLGHFihVr3CbIbVfHjh3p3bs3jz/+OKNGjeKuu+4qucd0++23Z/jw4fz4xz/OvPZ1ZbiUJKkRW3/99dl///2577776NmzJ4MHD+bVV18tuTfvueeeY/Lkyey///48+OCDtGjxTTRYtWoVo0ePXq3NoqIi9tprL6ZMmcK7777L1KlT+f73v0+bNm3o1q0bW2yxBY888gg9e/bk008/rfFZS6Dk3s7DDjuMv/71r1Va5sorr2TJkiVMnDix5B7QYrfddhs33XTTOtcREaxYsaLCaR999NEal6tI27ZtWbVqFR9++GGVa/j+97/P/fffz1dffcWsWbN46KGHuOaaazjmmGPYZJNN2H///avcVm3w9Y+SJDUBO++8M6eccgpvv/12mfv33njjDQAGDhxYJlgCPPPMM6tdRi5WfA/lbbfdxssvv1zmnsp+/foxffp0/vGPf5SZtya6d+9Ou3bteOqpp1i+vGoPMBZv2xFHHLHatBkzZlS4TOmHhCrSvn173nrrrdXGr1y5khdffLFKdZW2xx57sHTpUv7973+v87KtWrVizz335JJLLim5d/Pee+9d53ayZriUJKmJuOiii2jVqhVjxoxh6dKlwDfd7JTvt3Hx4sWcccYZlbZVfDby8ssvJ6W0Wrj8+OOPGTduHM2aNSvpSqgmWrRowVlnncWiRYs4++yzKwy9ixYt4pVXXin5vrJtmzJlCuPHj69wPR07dgRy92pWpHfv3ixcuJCHH364zPhLL72UBQsWVHVzSpx33nkAnHLKKSUdyJf2+eef89RTT5V8/8QTT1S47e+99x6AT4tLkqS607lzZ0477TTGjh3L6NGjueyyy9htt93o06cPf/3rX9lzzz3Za6+9eO+993jwwQfZfvvtK31Tzi677EL79u1ZvHgxG264YZnujYqD5uLFi9l1111Xewq6ui6++GL+9a9/8cc//pH77ruPfv360blzZxYvXszrr7/OzJkzGTVqFDvuuCMAQ4cOZeLEiRx11FEceeSRbL755rz88ss89NBD/OhHP+J///d/V1tH//79ufPOOzn88MM5+OCDad26NVtttRXHH388AD//+c+ZMmUKhx56KEcffTQdOnTgiSeeYN68eey3334Vdq6+Jv379+fyyy9n2LBhbLfddhx88MF07dqVzz77jAULFjBjxgz22msvHnroIQBGjx7No48+yt57703Xrl3ZYIMN+Pe//82DDz5I+/btOfXUU2u2k7OQUvJTBx9gVs+ePVNjcN0L15V81IA8+ptvPpKaLCDlfr1X7N13301t2rRJbdq0Se+++25KKaUlS5ak008/PW211VapVatWaeutt07Dhg1Ln3/+edpqq63SVlttVWFbhx9+eALSwQcfvNq0bt26JSD9z//8zzrVP23atASk4cOHVzh91apV6eabb079+vVL7du3T0VFRWnzzTdPffr0SaNGjUoLFy4sM//MmTNT3759U7t27dIGG2yQ+vTpkyZPnlzpelasWJGGDRuWunbtmlq0aJGAtO+++5aZ59577029evVKrVq1Sh06dEhHH310mj9/fho8eHAC0rx580rmnTdvXgLS4MGD17jdjz/+eDrqqKPSZpttloqKitLGG2+cvve976XzzjsvPfvssyXzTZkyJQ0ZMiTtsMMOaaONNkpt2rRJ3bp1S2eddVaaP3/+2nZvlfXs2TMBs1I1Mk+k/NNkql0RMatnz549Z82aVd+l1Ni4F8eVfG0/lw1I6U6im+CTsZKk7PTq1Yvnn3/++ZRSr3Vd1nsuJUmSlBnDpSRJkjJjuJQkSVJmDJeSJEnKjOFSkiRJmTFcSpIkKTOGS0mSJGXGcClJkqTMGC4lSZKUGcOlJEmSMmO4lCRJUmYMl5IkScqM4VKSJEmZMVxKkiQpM4ZLSZIakYgo82nevDkdOnRgv/32Y9KkSaSU6qyW6dOnl9Txox/9qMJ55s+fT0Sw11571Xh9EcF+++1XrWWfeeYZhg0bxg9+8AM6depERLDFFlusdbm///3vDBgwgC222ILWrVuz9dZbc9RRR/Hkk09Wq47GoEV9FyBJkrI3fPhwAJYvX84bb7zB5MmTmTFjBs899xzXXnttnddz55138tRTT7HHHnvU+bqr4i9/+Qtjx46lqKiIHXfckffee2+ty/ziF79g9OjRdOzYkUGDBrHxxhvzxhtvcO+993L33Xdz8803c9xxx9VB9Q2L4VKSpEZoxIgRZb6fOXMm++yzD+PGjeP888+na9eudVbLNttsw9y5c/n5z3/OP//5zzpb77oYMmQIgwcP5jvf+Q4tW7YkItY4/7vvvsuYMWP41re+xUsvvcSmm25aMm3atGn069ePX/3qV00yXHpZXJKkJqBPnz50796dlBKzZs1abfrTTz/NkUceSadOnWjZsiVbbrklP/3pT3nnnXdWm/fNN9/k1FNPZdttt6V169Z06NCB7373u5x22mksWbJktfn32GMPDj30UGbOnMndd9+9TnXfdttt9O3bl3bt2rHeeuuxww47cOmll/LVV1+VzDNp0qSSMDhjxowytwWUD9mV6dGjB7vssgstW7as0vwLFixg1apV7L777mWCJUDfvn3ZcMMNef/996u2kY2MZy4lSWpiioqKynw/YcIETj31VFq1asXAgQPZcsstef311xk/fjz33XcfTz31FN/+9rcBWLRoEbvtthuffPIJBx98MEcccQRffvkl8+bN45ZbbuHMM8+kY8eOq61z9OjR/P3vf+eXv/wlAwcOXK2Gipx44olMnDiRLbbYgiOOOIJ27drx1FNPcfHFFzN16lT+8Y9/0KJFC3r06MHw4cMZOXIkW221FUOGDClpo7r3YK7NdtttR8uWLXnmmWf44IMP2HjjjUumPfbYY3z66acMGjSoVtbd0BkuJUmN3rgXx9V3CVU2tMfQWmn3scceY86cObRs2ZLevXuXjH/ttdc47bTT6NKlCzNmzKBz584l06ZOncqAAQM455xzmDx5MgB33XUXH374IVdffTXnnHNOmXV8/vnnNGtW8UXRbt268dOf/pTrrruOP/zhD5x99tlrrHfSpElMnDiRww47jFtvvZXWrVuXTBsxYgQjR47kuuuu45xzzqFHjx706NGDkSNH0qVLlyqfrayJDh068Nvf/paf/exn7LjjjgwaNIiOHTsyd+5c/va3v3HAAQfwpz/9qdbraIgMl5IkNULFAav0Az0pJcaMGcNmm21WMt8f/vAHli9fztixY8sES4D+/fszcOBA7rvvPj799FM23HDDkmmlw16x9ddff401DR8+nFtuuYVLLrmEwYMH07Zt20rnHTt2LC1atGDChAmrreviiy/m2muv5dZbb10t4Nalc889ly5dunDiiSdyww03lIzfdtttGTJkyGqXy5sKw6UkSY3QyJEjy3wfEdx4442ccMIJZcYXd5kzY8YMnn322dXaWbx4MStXruS1116jV69eDBw4kAsuuIAzzjiDKVOmcOCBB9KnTx923HHHtT4Es8kmm/DLX/6SCy64gFGjRjF69OgK51u2bBn/+te/2Hjjjbn66qsrnKdVq1bMnj17jesrraKzmUOGDKFLly5VbqO80aNHc8EFF3D22Wdz5pln0qlTJ+bMmcOwYcM49thjefHFFyvdxkYtpeSnDj7ArJ49e6bG4LoXriv5qAF59DfffCQ1WUDK/XrP+eyzz9I//vGP9O1vfzu1bNkyTZ06tcz82267bckya/pMnz69ZJlXXnkl/ehHP0obbbRRyfQtt9wyjR07tkzb06ZNS0A69thjS8Z98cUXacstt0ytWrVK8+fPT/PmzUtA6tOnT8k8b7/9dpVqKr2dxdu+7777rnG/lP5MmzZtjfuxc+fOlU4v3rbDDjtstWmff/556ty5c2rWrFmaO3dupW00ZD179kzArFSNzOPT4pIkNWLrr78++++/P/fddx8rV65k8ODBLFu2rGR68aXpjz/+eI2BYd999y1ZZocdduB///d/WbJkCc899xyXX345q1at4pxzzuHGG29cYz3rrbdeydPeF1xwQYXzFNe0yy67VOXkTZVUtGxNHva5//77gdyT4eW1adOG3r17s2rVKl544YVqr6NQGS4lSWoCdt55Z0455RTefvttrrrqqpLxxZ2aP/744+vcZosWLejVqxe/+MUvuO222wC455571rrc8ccfzy677MJtt93Gc889t9r0DTbYgO985zv8+9//5sMPP6xyPc2aNWPlypVVnr8mirtCqqy7oeLxVe3aqDHxnssC9uLiF3n23WdZvmp5fZeidbXwaZj/OKz0Zyep7lx00UVMnDiRMWPGMHToUNq3b8+ZZ57J9ddfz3nnncd2221Ht27dyizz9ddf8/TTT7P33nsDMGvWLLbddtvVHsYpfqNNmzZt1lpHRDBmzBj69+/PsGHDKpznZz/7GSeddBInnngikyZNol27dmWmL126lHnz5tGzZ8+ScR07duStt95a6/qzsPfee3Pttddy/fXX89Of/rTMw1APPvggM2fOZL311mPPPfesk3oaEsNlAavvYFnUbO19lKkStRksm/tzkVSxzp07c9pppzF27FhGjx7NZZddRvfu3ZkwYQInnngi3/nOdzjooIPo1q0by5cvZ+HChTz++ONssskmzJkzB4BbbrmFP/3pT+y1115ss802tG/fnrlz53LffffRqlUrzj333CrV0q9fPw4++GAeeOCBCqefeOKJzJo1i3HjxrHNNttw4IEH8u1vf5sPP/yQefPm8dhjj3HCCSfwxz/+sWSZ/v37c/vtt3PIIYfQs2dPioqK2Geffdhnn33WWs+cOXO4/PLLy4xbunRpmT4zx4wZU9Kf5ZFHHsn+++/PI488wg477MBhhx1Gp06dmD17Nvfffz8pJS6//PIK+/xs7GJd7ldQ9UXErJ49e/as6K0I1VWf/bYVNStit0670WPTHvVWQ0GbdlnttNu8CLrsDd/evXbal9TgFT+xXdnv9/fee4+tt94ayL1p51vf+hYA//d//8cVV1zBtGnTePfdd1l//fXZfPPN6dOnD0cffTT9+vUDcm/ymTRpEk888QRvvfUWX3zxBZ07d2bvvffm/PPPZ6eddipZ1/Tp0+nbty/HHnssf/7zn1er5ZVXXmHnnXdm5cqV9OnTp8JXQ95///388Y9/5JlnnuGjjz6iQ4cOfPvb32bAgAEcd9xxdO/evWTexYsXc+655zJ16lQ++OADVq1axfDhw6vU72VxrWsyb968Mk+XL1++nOuuu47bb7+dV155hWXLltGhQwd69+7N2WefzYABA9a63oaqV69ePP/888+nlHqt67KNIlxGxBbAJcBBQEdgEXAPMDKltLQKy68PDAJ+CPQEtgRWAa8CtwHXpJS+rmGNtRoua6vTXdWS0uGyb8WXhCRJqi81CZcFf1k8IrYBngA2Be4F5gC9gXOAgyKiT0pp9RedlrU38GfgQ2AauWDaHhgIjAEOj4j+KaUva2UjJEmSGomCD5fAOHLB8uyU0jXFIyPiSuA8YBRw2lraeBc4Driz9BnKiPg5MB3YEzgDuCLTyiVJkhqZgu6KKH/WcgAwH7iu3OThwOfA8fnL3pVKKb2YUrq1/KXvlNKnfBMo98uiZkmSpMas0M9cFt95+3BKaVXpCSmlTyNiJrnwuQcwtZrrKH6kd0VVZo6Iym6q7F7JeEmSpEajoM9cAtvnh69VMv31/LBbJdOr4sT88KEatCFJktQkFPqZy+IeXD+uZHrx+HbVaTwiziT3BPqLwISqLFPZU1X5M5o9K5omSZLUWBT6mctaExGHA1eTe9jniJSSr1KRJElai0IPl8VnJttWMr14/Efr0mhEDAJuBxYD+6WU3qxOcZIkSU1NoYfLV/PDyu6p3C4/rOyezNVExFHAncB7wL4ppVfXsogkSZLyCj1cTssPB0REmW2JiA2BPsAy4KmqNBYRx5J7I8875ILl62tZRJIkSaUUdLhMKc0FHga6kOvkvLSRwPrALSmlz4tHRkT3iFitW6CIGAzcDCwE9vFSuCRJ0ror9KfFAYaSe/3j7yOiPzAb2J1cH5ivAReWm392fhjFIyKiL7mnwZuROxt6QkSUW4yPUkpXZ128JElSY1Lw4TKlNDcidgUuIddt0MHAImAsMDKltLQKzWzFN2dxT6xkngXknh6XJElSJQo+XAKklN4CTqjivKudkkwpTQImZVuVJElS01PQ91xKkqScY489lohg3Lhxa513wIABRASTJ08uGffWW2/RvHlzIoILLrhgjctHxFo/06dPr+kmqUA1ijOXkiQ1daeccgp/+ctfGD9+PEOHDq10vvnz5/PII4+w2Wabccghh5SMHz9+PKtWrSIimDhxIpdccgktWqw5JgwfPrzSaV26dFnnbVDjYLiUJKkR2G+//ejWrRsvvPACzz//PD17VvzG4RtvvJGUEieccEJJeFy5ciUTJkxgo4024rjjjmPcuHH87W9/4/DDD1/jOkeMGJH1ZqgR8LK4JEmNxCmnnALADTfcUOH0lStXMnHiRCKCk08+uWT8gw8+yNtvv83RRx/N6aefvsY2pLUxXEqS1EgMHjyYli1bctttt7Fs2bLVpj/44IP85z//Yf/996dr164l46+//noAhgwZwk477USvXr14+OGHWbBgQZ3VrsbDy+KSpEbv/Wuure8SqmyTs86s/rKbbMKgQYO44447uOOOOxgyZEiZ6cVnI0899dSScf/5z3944IEH6NatG3vuuSeQC5mzZs1i/Pjx/PrXv650fZVdFl9vvfX45S9/We3tUGEzXEqS1Iiceuqp3HHHHYwfP75MuFy0aBEPPPAAm266KYceemjJ+AkTJrBy5coy8x5zzDGcf/75TJgwgREjRtC8efMK1zVy5MgKx7dt29Zw2YR5WVySpEakX79+bLPNNsycOZPZs2eXjJ84cSIrVqxgyJAhFBUVAbBq1SpuvPFGmjVrxk9+8pOSeTt06MAhhxzCO++8w9///vdK15VSqvDz0Ucf1dr2qeHzzKUkqdGryaXmQlP8sM6wYcMYP348V1xxBSklbrzxRiKi5KEfgClTprBgwQIOPPBAOnfuXKadIUOGcPfdd3P99dczcODAut4MFTDPXEqS1MiccMIJFBUVcfPNN/P111/z6KOP8uabb9K3b1+23XbbkvmKH+SZMmXKap2gF/eB+dBDD/HWW2/Vy3aoMHnmUpKkRuZb3/oWAwcO5O677+aee+4peRNP6Qd53n33Xe6//3422mgjjjrqqArbmTNnDjNnzmTChAlr7DBdKs1wKUlSI3TKKadw9913c8UVV/Cvf/2LjTfemMMOO6xk+oQJE1ixYgXHHntspa+MfOONN+jWrRs33ngjF198Mc2aecFTa2e4lCSpERowYABdunThmWeeAeDMM8+kZcuWQO5BnPHjxwOU6Uy9vG233ZZ9992X6dOn8+CDD/LDH/6wzPQ1vaFn0KBB9OjRo2YboYJkuJQkqREqfrDnoosuAijzIM8jjzzCvHnz2GWXXSp9TWSxU045henTp3P99devFi4r64oIcu8WN1w2TYZLSZIaqQsvvJALL7xwtfEHHHAAKaUqtXHMMcdwzDHHlBlX1WXVNHnzhCRJkjJjuJQkSVJmDJeSJEnKjOFSkiRJmfGBnjr0/kfzGDf5x7XT+NKPa6ddSZKkdeCZy0agKJrXdwmqruZF9V2BJEmZMlwWuKJozm7rbVrfZag6mhdBl73ruwpJkjLlZfE6tEm7rgw97Lb6LkOSJKnWeOZSkiRJmTFcSpIkKTOGS0mSJGXGcClJkqTMGC4lSZKUGcOlJEmSMmO4lCRJUmYMl5IkScqM4VKSJEmZMVxKkiQpM4ZLSZIkZSZSSvVdQ5MQEUtat27dYYcddqjvUiRJktZo9uzZfPHFFx+mlDqu67KGyzoSEV8BzYF/1XctjUj3/HBOvVbR+Lhfs+c+zZ77NHvu0+wV8j7tAnySUuq6rgu2yL4WVeJlgJRSr/oupLGIiFngPs2a+zV77tPsuU+z5z7NXlPdp95zKUmSpMwYLiVJkpQZw6UkSZIyY7iUJElSZgyXkiRJyoxdEUmSJCkznrmUJElSZgyXkiRJyozhUpIkSZkxXEqSJCkzhktJkiRlxnApSZKkzBguJUmSlBnDpSRJkjJjuFyDiDgyIq6JiMcj4pOISBHx52q2tUVETIiIdyLiq4iYHxFXR0T7NSyzY0TcERGLI+LLiHg1IkZGROvqb1X9qs99ml9XZZ+narZl9SerfVrddiJiz4h4ICI+jIgvIuKliDg3IppXb4sahvrarxHRZS3H6u0127L6k8U+jYiOEXFyREyOiDfyx9zHEfHPiDgpIir9vdYYj9X62qcep1Vq57cRMTUi3srv0w8j4oWIGB4RHdewXMEfpy3qu4AG7iLge8BnwNtA9+o0EhHbAE8AmwL3AnOA3sA5wEER0SeltKTcMrsDjwJFwF3AW0A/4FdA/4jon1L6qjr11LN626d5C4BJFYx/uzp1NBCZ7NPqtBMRhwJ3A18C/wt8CBwCXAX0AY6qZi0NQb3t17x/AfdUMP7latbREGSxT48C/gAsAqYBC4FvAYcD44EfRMRRqdzr5xrxsVpv+zTP47Ry5wHPA/8AFgPrA3sAI4BTI2KPlNJbpRdoNMdpSslPJR+gL7AdEMB+QAL+XI12puSXPavc+Cvz4/9Ybnxz4JX8tIGlxjcjFzQT8Mv63j+FtE/z0xIwvb73QQPep+vUDrARuf8wvwJ2LTV+PXLBPwH/Xd/7pwD3a5f8PJPqex80xH1K7o/sQ4Bm5cZ3IheKEnBEuWmN9litx33qcbr2dtarZPyofJvjyo1vNMepl8XXIKU0LaX0esr/dKsjf4ZtADAfuK7c5OHA58DxEbF+qfH7AjsAj6WU/laqnlXA/+S/PS0iorp11Zd63KeNVhb7tJrtHAlsAtyeUnquVDtfkvvLH+D0mtRUn+pxvzZaWeyLlNKjKaX78v8flh7/LvDH/Lf7lVus0R6r9bhPG60M/+1/WcmkO/LD7cqNbzTHqZfFa1/f/PDhCv7hfhoRM8kFpT2AqflJ/fLDh8o3llJ6MyJeA7oBWwNza6Xqhq06+7RYu4g4kdxf5B8Ds1JKBXu/ZT2r9DgFHgOWAXtGRKtUmLdw1LfNI+KnQEdgCfBkSumleq6poVueH64oN95jtfoq26fFPE7X3SH5Yfn91GiOU8Nl7ds+P3ytkumvkwtC3fgmCFVlmW75T1MMl9XZp8W+B9xYekRE/As4PqX0f1kW2QRU+nNIKa2IiHnAd8j9ETS7LgtrJA7If0pExHRgcEppYb1U1IBFRAvgJ/lvy/9y9lithrXs02Iep2sRET8HNgDaArsCe5ELlpeXm7XRHKdeFq99bfPDjyuZXjy+XQ2XaUqqu3+uJHdD9CbAhsBu5O5h/R7waER0zrbMRs/jtHYsA34N9ALa5z/7knvQYj9galO55WMdXQ7sBDyQUppSbprHavWsaZ96nFbdz8ndsnUuuWD5EDAgpfR+ufkazXFquFSTkVI6P6X0RErpg5TSZyml51JKR5F7Mm9jcv8BSPUqpbQ4pfSrlNLzKaWP8p/HyJ2NfxrYFji5fqtsWCLibOB8cr1GHF/P5TQKa9unHqdVl1LqlFIKcrdjHU7uzOMLEdGzfiurPYbL2lf8l0bbSqYXj/+ohss0JVnvn+Ib1vepbkFNlMdpHUoprSDXLQx4rJaIiDOBseR62OibUvqwgtk8VtdBFfdphTxOK5dSei+lNJlcAO8I3FxulkZznBoua9+r+WG3SqYXPy1W+h6L6izTlGS9f4ovTXgJZ91U+nPI36vVldxDAG/WZVGNnMdqKRFxLnANuT4V++afbq6Ix2oVrcM+XROP0zVIKS0gF9y/ExEbl5rUaI5Tw2Xtm5YfDij/loOI2JDcPYDLgNJPLD+aHx5UvrGI2JrcgbeAAjjAakl19uma7JEfNtX9WV2VHqfkzli0AZ5o6E81FhiP1byI+AW5jqVfJBeCFq9hdo/VKljHfbomHqdrt3l+uLLUuEZznBouMxIRRRHRPd8HY4mU0lzgYXIdzp5RbrGR5P6yuyWl9Hmp8TPIPQm2T0QMLLWOZsBv89/+sbH3m5flPo2InSOiqIJ17EyuQ1uAar2GspBUtk+r6S7gA+C/I2LXUutYD7g0/+0fMlhPg5flfo2InpW8bq8/uTd+QBM/ViPiYnIPm8wC+qeUPlhLcx6rZLtPPU5zKtunEdEtIla7vB0RzSJiFLm3yz2RUlpaanKjOU6jkeeTGomIQcCg/LedgAPJ/SX2eH7cBymln+fn7QLMAxaklLqUa6f8qwpnA7uT66/xNWDPtPbXPy4E+pPrxmAmuX/8Df6vl/Lqa59GxCRyfYs9Tu5Vml+Re6XXQeTeiHQD8NNCDOwZ7tMqt1NumbvIvarsdnKvKhtIrkuNu4AfFeI+hfrbr/luXLYjd3wXv5Z0Z77pA+/ilFLxL5qCksU+jYjB5F7hupLc5duKnqydn1KaVMG6G92xWl/71ON0rfv0XOAy4J/56UvIvVJzX3IP9LxL7vf4KxWsu/CP09QAXhPUUD/k3v+Z1vCZX2reLuXHlWtrS2AiuXe3fk3usvbVQPs1rH9H4E5yf8l8RS40jQRa1/e+KbR9Su4/ir8CbwCf5OdfBNxHqVdsFuInq326Lu2UW64P8ACwFPgC+D9yZy6a1/e+KcT9CpwE3E/uDVSf5f/tLyT3nuG963u/1Pc+rUIbiUpe89oYj9X62qcep2vdpzsB15K7xeADcvdKfgw8m2+/wxrWX/DHqWcuJUmSlBnvuZQkSVJmDJeSJEnKjOFSkiRJmTFcSpIkKTOGS0mSJGXGcClJkqTMGC4lSZKUGcOlJEmSMmO4lCRJUmYMl5IkScqM4VKSJEmZMVxKkiQpM4ZLSZIkZcZwKUmSpMwYLiVJkpSZFvVdgCSp6iJiY2AxEGuZ9WugbUrpy9qvSpK+YbiUpMKyAXBJqe+7AIOBWcD9pca/b7CUVB8ipVTfNUiSqikiTgZuAP5fSmlMfdcjSd5zKUmFrUd++GI91iBJJQyXklTYvpcf/qteq5CkPC+LS1KBiogAPgI+Syl1rudyJAnwzKUkFbKuwEZ41lJSA2K4lKTC1T0/fLleq5CkUgyXklS4NsoPP6nXKiSpFPu5lKTC9Vp+eG5EdACeTSndVp8FSZJnLiWpQKWUngcuApYD5wK71mtBkoRPi0uSJClDnrmUJElSZgyXkiRJyozhUpIkSZkxXEqSJCkzhktJkiRlxnApSZKkzBguJUmSlBnDpSRJkjJjuJQkSVJmDJeSJEnKjOFSkiRJmTFcSpIkKTOGS0mSJGXGcClJkqTMGC4lSZKUGcOlJEmSMvP/Add9xL1TY0r+AAAAAElFTkSuQmCC",
      "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": 20,
   "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": 20,
     "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": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def my_draw_simple_pp(taus, solver_vals, solvers, ax,\n",
    "        fname=\"performance_profiles.pdf\", xlim=None, log=False, frameon=False, \n",
    "        colors=None, legend=True):\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",
    "    if colors is None:\n",
    "        colors = [f\"C{i}\" for i in range(10)]\n",
    "\n",
    "    # Add lines individually to support labels\n",
    "    for n, solver in enumerate(solvers):\n",
    "        ax.step(taus, \n",
    "            solver_vals[n, :], alpha=0.5, linewidth=3, \n",
    "            label=solver, color=colors[n], where='post')\n",
    "\n",
    "    if legend:\n",
    "        ax.legend(frameon=frameon, loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "    if xlim == None:\n",
    "        ax.set_xlim(1, taus.max())\n",
    "    else:\n",
    "        ax.set_xlim(1, xlim)\n",
    "    if log:\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": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "#axs[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of problems per solver:  16\n",
      "Number of problems per solver:  20\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 378x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 247,
       "width": 369
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 1, figsize=(5.25, 3.5))\n",
    "\n",
    "\n",
    "\n",
    "ntotal_task = 7\n",
    "nrows = 4\n",
    "rowlist = [0, 1, 2, 3]\n",
    "tasklist = [0, 1, 2, 3] #[0, 1, 2, 3, 4, 5, 6]\n",
    "assert len(tasklist) <= 7\n",
    "ntask = len(tasklist)\n",
    "inds = np.array([[t + (ntotal_task * row) for t in tasklist] for row in rowlist]).reshape(-1).tolist()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "goggles_perfs = [\n",
    "        0.3778, 0.1697, 0.0897, 0.3778, 0.2710, 0.5284, 0.5283,\n",
    "        0.4563, 0.1989, 0.1074, 0.4561, 0.2688, 0.4863, 0.6728,\n",
    "        0.3258, 0.1768, 0.1192, 0.1894, 0.3127, 0.9353, 0.5312,\n",
    "        0.3309, 0.5436, 0.1884, 0.1405, 0.0000, 0.7505, 0.0000,\n",
    "]\n",
    "iws_perfs = [\n",
    "        0.4128, 0.1482, 0.1154, 0.3862, 0.2615, 0.5403, 0.4997,\n",
    "        0.3503, 0.1047, 0.1283, 0.4970, 0.2687, 0.5155, 0.4777,\n",
    "        0.0822, 0.2120, 0.1352, 0.5101, 0.4235, 0.7995, 0.7343,\n",
    "        0.6885, 0.7892, 0.1108, 0.3495, 0.0000, 0.0000, 0.0000,\n",
    "\n",
    "        ]\n",
    "snuba_perfs = [\n",
    "        0.2694, 0.1624, 0.0805, 0.2308, 0.2648, 0.5235, 0.7082,\n",
    "        0.2313, 0.0924, 0.1526, 0.2640, 0.2598, 0.8811, 0.4905,\n",
    "        0.4219, 0.1520, 0.1554, 0.1804, 0.2506, 0.9774, 0.6994,\n",
    "        0.6617, 0.7550, 0.3103, 0.2419, 0.0000, 0.8414, 0.0000,\n",
    "]\n",
    "snub_multi_perfs = [\n",
    "        0.2374, 0.1386, 0.0991, 0.2343, 0.2596, 0.5421, 0.7264,\n",
    "        0.5625, 0.1390, 0.2354, 0.5625, 0.2644, 0.8968, 0.5143,\n",
    "        0.3866, 0.2036, 0.1588, 0.2283, 0.3702, 0.9742, 0.7559,\n",
    "        0.3541, 0.6788, 0.2479, 0.2785, 0.0000, 0.7916, 0.0000,\n",
    "]\n",
    "sup_perfs = [\n",
    "        0.7295, 0.2315, 0.1496, 0.7295, 0.2583, 0.6316, 0.6517,\n",
    "        0.7306, 0.2262, 0.1483, 0.7305, 0.2548, 0.7863, 0.7488,\n",
    "        0.7966, 0.3335, 0.2544, 0.3485, 0.4700, 0.9900, 0.5329,\n",
    "        0.3579, 0.6857, 0.1011, 0.1009, 0.0000, 0.7658, 0.0000,\n",
    "]\n",
    "\n",
    "goggles_perfs = np.array(goggles_perfs)[inds].tolist()\n",
    "iws_perfs = np.array(iws_perfs)[inds].tolist()\n",
    "snuba_perfs = np.array(snuba_perfs)[inds].tolist()\n",
    "snub_multi_perfs = np.array(snub_multi_perfs)[inds].tolist()\n",
    "sup_perfs = np.array(sup_perfs)[inds].tolist()\n",
    "\n",
    "problems = pd.Series(\n",
    "        np.arange(len(goggles_perfs)).tolist() + \\\n",
    "        np.arange(len(iws_perfs)).tolist() + \\\n",
    "        np.arange(len(snuba_perfs)).tolist() + \\\n",
    "        np.arange(len(snub_multi_perfs)).tolist() + \\\n",
    "        np.arange(len(sup_perfs)).tolist(),\n",
    "        dtype=int, name='problem')\n",
    "methods = pd.Series(\n",
    "        [r'GOGGLES'] * len(goggles_perfs) + \\\n",
    "        [r'Interactive WS'] * len(iws_perfs) + \\\n",
    "        [r'Snuba'] * len(snuba_perfs) + \\\n",
    "        [r'Snuba (multipolar)'] * len(snub_multi_perfs) + \\\n",
    "        [r'Few-Shot'] * len(sup_perfs),\n",
    "        dtype=str, name='method')\n",
    "\n",
    "\n",
    "objective_vals = pd.Series(\n",
    "        1.0 - np.array(\n",
    "                goggles_perfs + \\\n",
    "                iws_perfs + \\\n",
    "                snuba_perfs + \\\n",
    "                snub_multi_perfs + \\\n",
    "                sup_perfs\n",
    "                ), \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(\n",
    "        taus, solver_vals, solvers, axs[0], \n",
    "        xlim=None, log=True, frameon=False)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# TODO index into each of the above by task\n",
    "\n",
    "problems = pd.Series(\n",
    "        np.arange(0*ntask, 0*ntask+ntask).tolist() * 4 + \\\n",
    "        np.arange(1*ntask, 1*ntask+ntask).tolist() * 4 + \\\n",
    "        np.arange(2*ntask, 2*ntask+ntask).tolist() * 4 + \\\n",
    "        np.arange(3*ntask, 3*ntask+ntask).tolist() * 4 + \\\n",
    "        np.arange(4*ntask, 4*ntask+ntask).tolist() * 4,\n",
    "        dtype=int, name='problem')\n",
    "methods = pd.Series(\n",
    "        ([r'Raw features'] * ntask + \\\n",
    "        [r'PCA'] * ntask + \\\n",
    "        [r'ResNet-18'] * ntask + \\\n",
    "        [r'CLIP'] * ntask) * 5,\n",
    "        dtype=str, name='method')\n",
    "\n",
    "\n",
    "objective_vals = pd.Series(\n",
    "        1 - np.array(\n",
    "                goggles_perfs + \\\n",
    "                iws_perfs + \\\n",
    "                snuba_perfs + \\\n",
    "                snub_multi_perfs + \\\n",
    "                sup_perfs\n",
    "                ), \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(\n",
    "        taus, solver_vals, solvers, axs[1], \n",
    "        xlim=None, log=True, frameon=False, colors=[\n",
    "                f\"C{i}\" for i in [5, 6, 8, 9]])\n",
    "\n",
    "\n",
    "plt.suptitle('Accuracy on image tasks')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"neurips/perf_all_image_tasks.pdf\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of problems per solver:  12\n",
      "Number of problems per solver:  15\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nick11roberts/github.com/nick11roberts/Kaylee0501/AutoWS-Bench-101/optperfprofpy.py:183: UserWarning: Solvers do not solve 100% of problems. Total amount of problems solved: 166.66666666666669\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 378x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 247,
       "width": 362
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 1, figsize=(5.25, 3.5))\n",
    "\n",
    "\n",
    "\n",
    "ntotal_task = 7\n",
    "nrows = 4\n",
    "rowlist = [0, 1, 2, 3]\n",
    "tasklist = [4, 5, 6] #[0, 1, 2, 3, 4, 5, 6]\n",
    "assert len(tasklist) <= 7\n",
    "ntask = len(tasklist)\n",
    "inds = np.array([[t + (ntotal_task * row) for t in tasklist] for row in rowlist]).reshape(-1).tolist()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "goggles_perfs = [\n",
    "        0.3778, 0.1697, 0.0897, 0.3778, 0.2710, 0.5284, 0.5283,\n",
    "        0.4563, 0.1989, 0.1074, 0.4561, 0.2688, 0.4863, 0.6728,\n",
    "        0.3258, 0.1768, 0.1192, 0.1894, 0.3127, 0.9353, 0.5312,\n",
    "        0.3309, 0.5436, 0.1884, 0.1405, 0.0000, 0.7505, 0.0000,\n",
    "]\n",
    "iws_perfs = [\n",
    "        0.4128, 0.1482, 0.1154, 0.3862, 0.2615, 0.5403, 0.4997,\n",
    "        0.3503, 0.1047, 0.1283, 0.4970, 0.2687, 0.5155, 0.4777,\n",
    "        0.0822, 0.2120, 0.1352, 0.5101, 0.4235, 0.7995, 0.7343,\n",
    "        0.6885, 0.7892, 0.1108, 0.3495, 0.0000, 0.0000, 0.0000,\n",
    "\n",
    "        ]\n",
    "snuba_perfs = [\n",
    "        0.2694, 0.1624, 0.0805, 0.2308, 0.2648, 0.5235, 0.7082,\n",
    "        0.2313, 0.0924, 0.1526, 0.2640, 0.2598, 0.8811, 0.4905,\n",
    "        0.4219, 0.1520, 0.1554, 0.1804, 0.2506, 0.9774, 0.6994,\n",
    "        0.6617, 0.7550, 0.3103, 0.2419, 0.0000, 0.8414, 0.0000,\n",
    "]\n",
    "snub_multi_perfs = [\n",
    "        0.2374, 0.1386, 0.0991, 0.2343, 0.2596, 0.5421, 0.7264,\n",
    "        0.5625, 0.1390, 0.2354, 0.5625, 0.2644, 0.8968, 0.5143,\n",
    "        0.3866, 0.2036, 0.1588, 0.2283, 0.3702, 0.9742, 0.7559,\n",
    "        0.3541, 0.6788, 0.2479, 0.2785, 0.0000, 0.7916, 0.0000,\n",
    "]\n",
    "sup_perfs = [\n",
    "        0.7295, 0.2315, 0.1496, 0.7295, 0.2583, 0.6316, 0.6517,\n",
    "        0.7306, 0.2262, 0.1483, 0.7305, 0.2548, 0.7863, 0.7488,\n",
    "        0.7966, 0.3335, 0.2544, 0.3485, 0.4700, 0.9900, 0.5329,\n",
    "        0.3579, 0.6857, 0.1011, 0.1009, 0.0000, 0.7658, 0.0000,\n",
    "]\n",
    "\n",
    "goggles_perfs = np.array(goggles_perfs)[inds].tolist()\n",
    "iws_perfs = np.array(iws_perfs)[inds].tolist()\n",
    "snuba_perfs = np.array(snuba_perfs)[inds].tolist()\n",
    "snub_multi_perfs = np.array(snub_multi_perfs)[inds].tolist()\n",
    "sup_perfs = np.array(sup_perfs)[inds].tolist()\n",
    "\n",
    "problems = pd.Series(\n",
    "        np.arange(len(goggles_perfs)).tolist() + \\\n",
    "        np.arange(len(iws_perfs)).tolist() + \\\n",
    "        np.arange(len(snuba_perfs)).tolist() + \\\n",
    "        np.arange(len(snub_multi_perfs)).tolist() + \\\n",
    "        np.arange(len(sup_perfs)).tolist(),\n",
    "        dtype=int, name='problem')\n",
    "methods = pd.Series(\n",
    "        [r'GOGGLES'] * len(goggles_perfs) + \\\n",
    "        [r'Interactive WS'] * len(iws_perfs) + \\\n",
    "        [r'Snuba'] * len(snuba_perfs) + \\\n",
    "        [r'Snuba (multipolar)'] * len(snub_multi_perfs) + \\\n",
    "        [r'Few-Shot'] * len(sup_perfs),\n",
    "        dtype=str, name='method')\n",
    "\n",
    "\n",
    "objective_vals = pd.Series(\n",
    "        1.0 - np.array(\n",
    "                goggles_perfs + \\\n",
    "                iws_perfs + \\\n",
    "                snuba_perfs + \\\n",
    "                snub_multi_perfs + \\\n",
    "                sup_perfs\n",
    "                ), \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(\n",
    "        taus, solver_vals, solvers, axs[0], \n",
    "        xlim=None, log=True, frameon=False)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# TODO index into each of the above by task\n",
    "\n",
    "problems = pd.Series(\n",
    "        np.arange(0*ntask, 0*ntask+ntask).tolist() * 4 + \\\n",
    "        np.arange(1*ntask, 1*ntask+ntask).tolist() * 4 + \\\n",
    "        np.arange(2*ntask, 2*ntask+ntask).tolist() * 4 + \\\n",
    "        np.arange(3*ntask, 3*ntask+ntask).tolist() * 4 + \\\n",
    "        np.arange(4*ntask, 4*ntask+ntask).tolist() * 4,\n",
    "        dtype=int, name='problem')\n",
    "methods = pd.Series(\n",
    "        ([r'Raw features'] * ntask + \\\n",
    "        [r'PCA'] * ntask + \\\n",
    "        [r'ResNet-18'] * ntask + \\\n",
    "        [r'CLIP'] * ntask) * 5,\n",
    "        dtype=str, name='method')\n",
    "\n",
    "\n",
    "objective_vals = pd.Series(\n",
    "        1 - np.array(\n",
    "                goggles_perfs + \\\n",
    "                iws_perfs + \\\n",
    "                snuba_perfs + \\\n",
    "                snub_multi_perfs + \\\n",
    "                sup_perfs\n",
    "                ), \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(\n",
    "        taus, solver_vals, solvers, axs[1], \n",
    "        xlim=None, log=True, frameon=False, colors=[\n",
    "                f\"C{i}\" for i in [5, 6, 8, 9]])\n",
    "\n",
    "\n",
    "plt.suptitle('Accuracy on non-image tasks')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"neurips/perf_all_nonimage_tasks.pdf\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Coverage analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of problems per solver:  16\n",
      "Number of problems per solver:  12\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 378x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 247,
       "width": 362
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 1, figsize=(5.25, 3.5))\n",
    "\n",
    "\n",
    "\n",
    "ntotal_task = 7\n",
    "nrows = 4\n",
    "rowlist = [0, 1, 2, 3]\n",
    "tasklist = [0, 1, 2, 3] #[0, 1, 2, 3, 4, 5, 6]\n",
    "assert len(tasklist) <= 7\n",
    "ntask = len(tasklist)\n",
    "inds = np.array([[t + (ntotal_task * row) for t in tasklist] for row in rowlist]).reshape(-1).tolist()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "iws_perfs = [\n",
    "        0.3617, 0.4616, 0.3666, 0.3656, 0.5125, 0.8163, 0.9999,\n",
    "        0.5107, 0.2731, 0.2696, 0.4797, 0.5448, 0.9874, 0.8873,\n",
    "        0.7422, 0.3580, 0.0917, 0.1631, 0.6109, 0.9999, 0.6142,\n",
    "        0.2758, 0.7239, 0.0993, 0.1757, 0.0000, 0.0000, 0.0000,\n",
    "]\n",
    "snuba_perfs = [\n",
    "        0.6842, 0.6409, 0.8387, 0.8105, 0.9090, 0.5379, 0.9999,\n",
    "        0.9608, 0.7183, 0.9557, 0.9716, 0.9956, 0.9999, 0.8072,\n",
    "        0.8761, 0.9772, 0.9230, 0.9320, 0.8998, 0.9999, 0.5912 ,\n",
    "        0.7667, 0.7262, 0.1521, 0.1323, 0.0000, 0.4116, 0.0000,\n",
    "        ]\n",
    "snub_multi_perfs = [\n",
    "        0.7422, 0.8129, 0.4470, 0.5407, 0.9999, 0.9999, 0.9999,\n",
    "        0.3472, 0.5807, 0.4763, 0.3472, 0.9999, 0.9999, 0.9999,\n",
    "        0.6185, 0.6337, 0.8232, 0.7193, 0.9999, 0.9999, 0.9999,\n",
    "        0.5027, 0.3372, 0.2033, 0.7235, 0.0000, 0.9999, 0.0000,\n",
    "]\n",
    "\n",
    "\n",
    "iws_perfs = np.array(iws_perfs)[inds].tolist()\n",
    "snuba_perfs = np.array(snuba_perfs)[inds].tolist()\n",
    "snub_multi_perfs = np.array(snub_multi_perfs)[inds].tolist()\n",
    "\n",
    "problems = pd.Series(\n",
    "        np.arange(len(iws_perfs)).tolist() + \\\n",
    "        np.arange(len(snuba_perfs)).tolist() + \\\n",
    "        np.arange(len(snub_multi_perfs)).tolist(),\n",
    "        dtype=int, name='problem')\n",
    "methods = pd.Series(\n",
    "        [r'Interactive WS'] * len(iws_perfs) + \\\n",
    "        [r'Snuba'] * len(snuba_perfs) + \\\n",
    "        [r'Snuba (multipolar)'] * len(snub_multi_perfs),\n",
    "        dtype=str, name='method')\n",
    "\n",
    "\n",
    "objective_vals = pd.Series(\n",
    "        1.0 - np.array(\n",
    "                iws_perfs + \\\n",
    "                snuba_perfs + \\\n",
    "                snub_multi_perfs,\n",
    "                ), \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(\n",
    "        taus, solver_vals, solvers, axs[0], \n",
    "        xlim=None, log=True, frameon=False)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# TODO index into each of the above by task\n",
    "\n",
    "problems = pd.Series(\n",
    "        np.arange(0*ntask, 0*ntask+ntask).tolist() * 4 + \\\n",
    "        np.arange(1*ntask, 1*ntask+ntask).tolist() * 4 + \\\n",
    "        np.arange(2*ntask, 2*ntask+ntask).tolist() * 4,\n",
    "        dtype=int, name='problem')\n",
    "methods = pd.Series(\n",
    "        ([r'Raw features'] * ntask + \\\n",
    "        [r'PCA'] * ntask + \\\n",
    "        [r'ResNet-18'] * ntask + \\\n",
    "        [r'CLIP'] * ntask) * 3,\n",
    "        dtype=str, name='method')\n",
    "\n",
    "\n",
    "objective_vals = pd.Series(\n",
    "        1 - np.array(\n",
    "                iws_perfs + \\\n",
    "                snuba_perfs + \\\n",
    "                snub_multi_perfs,\n",
    "                ), \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(\n",
    "        taus, solver_vals, solvers, axs[1], \n",
    "        xlim=None, log=True, frameon=False, colors=[\n",
    "                f\"C{i}\" for i in [5, 6, 8, 9]])\n",
    "\n",
    "\n",
    "plt.suptitle('Coverage on image tasks')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"neurips/perf_coverage_all_image_tasks.pdf\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of problems per solver:  12\n",
      "Number of problems per solver:  9\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nick11roberts/github.com/nick11roberts/Kaylee0501/AutoWS-Bench-101/optperfprofpy.py:183: UserWarning: Solvers do not solve 100% of problems. Total amount of problems solved: 175.0\n",
      "  warnings.warn(\n",
      "/Users/nick11roberts/github.com/nick11roberts/Kaylee0501/AutoWS-Bench-101/optperfprofpy.py:183: UserWarning: Solvers do not solve 100% of problems. Total amount of problems solved: 188.88888888888889\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 378x252 with 2 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 247,
       "width": 369
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 1, figsize=(5.25, 3.5))\n",
    "\n",
    "\n",
    "\n",
    "ntotal_task = 7\n",
    "nrows = 4\n",
    "rowlist = [0, 1, 2, 3]\n",
    "tasklist = [4, 5, 6] #[0, 1, 2, 3, 4, 5, 6]\n",
    "assert len(tasklist) <= 7\n",
    "ntask = len(tasklist)\n",
    "inds = np.array([[t + (ntotal_task * row) for t in tasklist] for row in rowlist]).reshape(-1).tolist()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "iws_perfs = [\n",
    "        0.3617, 0.4616, 0.3666, 0.3656, 0.5125, 0.8163, 0.9999,\n",
    "        0.5107, 0.2731, 0.2696, 0.4797, 0.5448, 0.9874, 0.8873,\n",
    "        0.7422, 0.3580, 0.0917, 0.1631, 0.6109, 0.9999, 0.6142,\n",
    "        0.2758, 0.7239, 0.0993, 0.1757, 0.0000, 0.0000, 0.0000,\n",
    "]\n",
    "snuba_perfs = [\n",
    "        0.6842, 0.6409, 0.8387, 0.8105, 0.9090, 0.5379, 0.9999,\n",
    "        0.9608, 0.7183, 0.9557, 0.9716, 0.9956, 0.9999, 0.8072,\n",
    "        0.8761, 0.9772, 0.9230, 0.9320, 0.8998, 0.9999, 0.5912 ,\n",
    "        0.7667, 0.7262, 0.1521, 0.1323, 0.0000, 0.4116, 0.0000,\n",
    "        ]\n",
    "snub_multi_perfs = [\n",
    "        0.7422, 0.8129, 0.4470, 0.5407, 0.9999, 0.9999, 0.9999,\n",
    "        0.3472, 0.5807, 0.4763, 0.3472, 0.9999, 0.9999, 0.9999,\n",
    "        0.6185, 0.6337, 0.8232, 0.7193, 0.9999, 0.9999, 0.9999,\n",
    "        0.5027, 0.3372, 0.2033, 0.7235, 0.0000, 0.9999, 0.0000,\n",
    "]\n",
    "\n",
    "\n",
    "iws_perfs = np.array(iws_perfs)[inds].tolist()\n",
    "snuba_perfs = np.array(snuba_perfs)[inds].tolist()\n",
    "snub_multi_perfs = np.array(snub_multi_perfs)[inds].tolist()\n",
    "\n",
    "problems = pd.Series(\n",
    "        np.arange(len(iws_perfs)).tolist() + \\\n",
    "        np.arange(len(snuba_perfs)).tolist() + \\\n",
    "        np.arange(len(snub_multi_perfs)).tolist(),\n",
    "        dtype=int, name='problem')\n",
    "methods = pd.Series(\n",
    "        [r'Interactive WS'] * len(iws_perfs) + \\\n",
    "        [r'Snuba'] * len(snuba_perfs) + \\\n",
    "        [r'Snuba (multipolar)'] * len(snub_multi_perfs),\n",
    "        dtype=str, name='method')\n",
    "\n",
    "\n",
    "objective_vals = pd.Series(\n",
    "        1.0 - np.array(\n",
    "                iws_perfs + \\\n",
    "                snuba_perfs + \\\n",
    "                snub_multi_perfs,\n",
    "                ), \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(\n",
    "        taus, solver_vals, solvers, axs[0], \n",
    "        xlim=None, log=True, frameon=False)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# TODO index into each of the above by task\n",
    "\n",
    "problems = pd.Series(\n",
    "        np.arange(0*ntask, 0*ntask+ntask).tolist() * 4 + \\\n",
    "        np.arange(1*ntask, 1*ntask+ntask).tolist() * 4 + \\\n",
    "        np.arange(2*ntask, 2*ntask+ntask).tolist() * 4,\n",
    "        dtype=int, name='problem')\n",
    "methods = pd.Series(\n",
    "        ([r'Raw features'] * ntask + \\\n",
    "        [r'PCA'] * ntask + \\\n",
    "        [r'ResNet-18'] * ntask + \\\n",
    "        [r'CLIP'] * ntask) * 3,\n",
    "        dtype=str, name='method')\n",
    "\n",
    "\n",
    "objective_vals = pd.Series(\n",
    "        1 - np.array(\n",
    "                iws_perfs + \\\n",
    "                snuba_perfs + \\\n",
    "                snub_multi_perfs,\n",
    "                ), \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(\n",
    "        taus, solver_vals, solvers, axs[1], \n",
    "        xlim=None, log=True, frameon=False, colors=[\n",
    "                f\"C{i}\" for i in [5, 6, 8, 9]])\n",
    "\n",
    "\n",
    "plt.suptitle('Coverage on non-image tasks')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"neurips/perf_coverage_all_non_image_tasks.pdf\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cardinality analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of problems per solver:  5\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 306x252 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 243,
       "width": 296
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 1, figsize=(4.25, 3.5))\n",
    "\n",
    "\n",
    "\n",
    "ntotal_task = 7\n",
    "nrows = 4\n",
    "rowlist = [0]\n",
    "tasklist = [0, 1, 2, 3, 4]\n",
    "assert len(tasklist) <= 5\n",
    "ntask = len(tasklist)\n",
    "inds = np.array([[t + (ntotal_task * row) for t in tasklist] for row in rowlist]).reshape(-1).tolist()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "snuba1_perfs = [\n",
    "        0.7667, 0.7262, 0.1521, 0.1323, 0.4116,\n",
    "        ]\n",
    "\n",
    "snuba2_perfs = [\n",
    "        0.7033, 0.7025, 0.0714, 0.3016, 0.79157\n",
    "        ]\n",
    "\n",
    "snuba4_perfs = [\n",
    "        0.6788, 0.79610, 0.3506, 0.2164, 0.7878\n",
    "        ]\n",
    "\n",
    "snuba8_perfs = [\n",
    "        0.7051, 0.79479, 0.3577, 0.2678, 0.7915\n",
    "        ]\n",
    "\n",
    "snuba10_perfs = [\n",
    "        0.68718, 0.79479, 0.3605, 0.2321, 0.79157\n",
    "        ]\n",
    "\n",
    "\n",
    "\n",
    "snuba1_perfs = np.array(snuba1_perfs)[inds].tolist()\n",
    "snuba2_perfs = np.array(snuba2_perfs)[inds].tolist()\n",
    "snuba4_perfs = np.array(snuba4_perfs)[inds].tolist()\n",
    "snuba8_perfs = np.array(snuba8_perfs)[inds].tolist()\n",
    "#snuba10_perfs = np.array(snuba10_perfs)[inds].tolist()\n",
    "\n",
    "problems = pd.Series(\n",
    "        np.arange(len(snuba1_perfs)).tolist() + \\\n",
    "        np.arange(len(snuba2_perfs)).tolist() + \\\n",
    "        np.arange(len(snuba4_perfs)).tolist() + \\\n",
    "        np.arange(len(snuba8_perfs)).tolist(),\n",
    "        dtype=int, name='problem')\n",
    "methods = pd.Series(\n",
    "        [r'Snuba D=1'] * len(snuba1_perfs) + \\\n",
    "        [r'Snuba D=2'] * len(snuba2_perfs) + \\\n",
    "        [r'Snuba D=4'] * len(snuba4_perfs) + \\\n",
    "        [r'Snuba D=8'] * len(snuba8_perfs),\n",
    "        dtype=str, name='method')\n",
    "\n",
    "\n",
    "objective_vals = pd.Series(\n",
    "        1.0 - np.array(\n",
    "                snuba1_perfs + \\\n",
    "                snuba2_perfs + \\\n",
    "                snuba4_perfs + \\\n",
    "                snuba8_perfs,\n",
    "                ), \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(\n",
    "        taus, solver_vals, solvers, axs, \n",
    "        xlim=2, log=False, frameon=False)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#plt.suptitle('Coverage on all tasks')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"neurips/snuba_cardinality.pdf\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Polarity analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of problems per solver:  16\n",
      "Number of problems per solver:  16\n",
      "Number of problems per solver:  12\n",
      "Number of problems per solver:  12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nick11roberts/github.com/nick11roberts/Kaylee0501/AutoWS-Bench-101/optperfprofpy.py:183: UserWarning: Solvers do not solve 100% of problems. Total amount of problems solved: 116.66666666666667\n",
      "  warnings.warn(\n",
      "/Users/nick11roberts/github.com/nick11roberts/Kaylee0501/AutoWS-Bench-101/optperfprofpy.py:183: UserWarning: Solvers do not solve 100% of problems. Total amount of problems solved: 141.66666666666669\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 576x252 with 4 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 240,
       "width": 567
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2, 2, figsize=(8, 3.5))\n",
    "\n",
    "\n",
    "\n",
    "ntotal_task = 7\n",
    "nrows = 4\n",
    "rowlist = [0, 1, 2, 3]\n",
    "tasklist = [0, 1, 2, 3]\n",
    "assert len(tasklist) <= 7\n",
    "ntask = len(tasklist)\n",
    "inds = np.array([[t + (ntotal_task * row) for t in tasklist] for row in rowlist]).reshape(-1).tolist()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "snuba_perfs = [\n",
    "        0.2694, 0.1624, 0.0805, 0.2308, 0.2648, 0.5235, 0.7082,\n",
    "        0.2313, 0.0924, 0.1526, 0.2640, 0.2598, 0.8811, 0.4905,\n",
    "        0.4219, 0.1520, 0.1554, 0.1804, 0.2506, 0.9774, 0.6994,\n",
    "        0.6617, 0.7550, 0.3103, 0.2419, 0.0000, 0.8414, 0.0000,\n",
    "]\n",
    "snub_multi_perfs = [\n",
    "        0.2374, 0.1386, 0.0991, 0.2343, 0.2596, 0.5421, 0.7264,\n",
    "        0.5625, 0.1390, 0.2354, 0.5625, 0.2644, 0.8968, 0.5143,\n",
    "        0.3866, 0.2036, 0.1588, 0.2283, 0.3702, 0.9742, 0.7559,\n",
    "        0.3541, 0.6788, 0.2479, 0.2785, 0.0000, 0.7916, 0.0000,\n",
    "]\n",
    "\n",
    "snuba_perfs = np.array(snuba_perfs)[inds].tolist()\n",
    "snub_multi_perfs = np.array(snub_multi_perfs)[inds].tolist()\n",
    "\n",
    "problems = pd.Series(\n",
    "        np.arange(len(snuba_perfs)).tolist() + \\\n",
    "        np.arange(len(snub_multi_perfs)).tolist(),\n",
    "        dtype=int, name='problem')\n",
    "methods = pd.Series(\n",
    "        [r'Snuba'] * len(snuba_perfs) + \\\n",
    "        [r'Snuba (multipolar)'] * len(snub_multi_perfs),\n",
    "        dtype=str, name='method')\n",
    "\n",
    "\n",
    "objective_vals = pd.Series(\n",
    "        1.0 - np.array(\n",
    "                snuba_perfs + \\\n",
    "                snub_multi_perfs,\n",
    "                ), \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(\n",
    "        taus, solver_vals, solvers, axs[0, 0], \n",
    "        xlim=None, log=False, frameon=False, legend=False)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "snuba_perfs = [\n",
    "        0.6842, 0.6409, 0.8387, 0.8105, 0.9090, 0.5379, 0.9999,\n",
    "        0.9608, 0.7183, 0.9557, 0.9716, 0.9956, 0.9999, 0.8072,\n",
    "        0.8761, 0.9772, 0.9230, 0.9320, 0.8998, 0.9999, 0.5912 ,\n",
    "        0.7667, 0.7262, 0.1521, 0.1323, 0.0000, 0.4116, 0.0000,\n",
    "        ]\n",
    "snub_multi_perfs = [\n",
    "        0.7422, 0.8129, 0.4470, 0.5407, 0.9999, 0.9999, 0.9999,\n",
    "        0.3472, 0.5807, 0.4763, 0.3472, 0.9999, 0.9999, 0.9999,\n",
    "        0.6185, 0.6337, 0.8232, 0.7193, 0.9999, 0.9999, 0.9999,\n",
    "        0.5027, 0.3372, 0.2033, 0.7235, 0.0000, 0.9999, 0.0000,\n",
    "]\n",
    "\n",
    "snuba_perfs = np.array(snuba_perfs)[inds].tolist()\n",
    "snub_multi_perfs = np.array(snub_multi_perfs)[inds].tolist()\n",
    "\n",
    "problems = pd.Series(\n",
    "        np.arange(len(snuba_perfs)).tolist() + \\\n",
    "        np.arange(len(snub_multi_perfs)).tolist(),\n",
    "        dtype=int, name='problem')\n",
    "methods = pd.Series(\n",
    "        [r'Snuba'] * len(snuba_perfs) + \\\n",
    "        [r'Snuba (multipolar)'] * len(snub_multi_perfs),\n",
    "        dtype=str, name='method')\n",
    "\n",
    "\n",
    "objective_vals = pd.Series(\n",
    "        1.0 - np.array(\n",
    "                snuba_perfs + \\\n",
    "                snub_multi_perfs,\n",
    "                ), \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(\n",
    "        taus, solver_vals, solvers, axs[1, 0], \n",
    "        xlim=None, log=True, frameon=False, legend=False)\n",
    "\n",
    "        \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "ntotal_task = 7\n",
    "nrows = 4\n",
    "rowlist = [0, 1, 2, 3]\n",
    "tasklist = [4, 5, 6]\n",
    "assert len(tasklist) <= 7\n",
    "ntask = len(tasklist)\n",
    "inds = np.array([[t + (ntotal_task * row) for t in tasklist] for row in rowlist]).reshape(-1).tolist()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "snuba_perfs = [\n",
    "        0.2694, 0.1624, 0.0805, 0.2308, 0.2648, 0.5235, 0.7082,\n",
    "        0.2313, 0.0924, 0.1526, 0.2640, 0.2598, 0.8811, 0.4905,\n",
    "        0.4219, 0.1520, 0.1554, 0.1804, 0.2506, 0.9774, 0.6994,\n",
    "        0.6617, 0.7550, 0.3103, 0.2419, 0.0000, 0.8414, 0.0000,\n",
    "]\n",
    "snub_multi_perfs = [\n",
    "        0.2374, 0.1386, 0.0991, 0.2343, 0.2596, 0.5421, 0.7264,\n",
    "        0.5625, 0.1390, 0.2354, 0.5625, 0.2644, 0.8968, 0.5143,\n",
    "        0.3866, 0.2036, 0.1588, 0.2283, 0.3702, 0.9742, 0.7559,\n",
    "        0.3541, 0.6788, 0.2479, 0.2785, 0.0000, 0.7916, 0.0000,\n",
    "]\n",
    "\n",
    "snuba_perfs = np.array(snuba_perfs)[inds].tolist()\n",
    "snub_multi_perfs = np.array(snub_multi_perfs)[inds].tolist()\n",
    "\n",
    "problems = pd.Series(\n",
    "        np.arange(len(snuba_perfs)).tolist() + \\\n",
    "        np.arange(len(snub_multi_perfs)).tolist(),\n",
    "        dtype=int, name='problem')\n",
    "methods = pd.Series(\n",
    "        [r'Snuba'] * len(snuba_perfs) + \\\n",
    "        [r'Snuba (multipolar)'] * len(snub_multi_perfs),\n",
    "        dtype=str, name='method')\n",
    "\n",
    "\n",
    "objective_vals = pd.Series(\n",
    "        1.0 - np.array(\n",
    "                snuba_perfs + \\\n",
    "                snub_multi_perfs,\n",
    "                ), \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(\n",
    "        taus, solver_vals, solvers, axs[0, 1], \n",
    "        xlim=None, log=False, frameon=False)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "snuba_perfs = [\n",
    "        0.6842, 0.6409, 0.8387, 0.8105, 0.9090, 0.5379, 0.9999,\n",
    "        0.9608, 0.7183, 0.9557, 0.9716, 0.9956, 0.9999, 0.8072,\n",
    "        0.8761, 0.9772, 0.9230, 0.9320, 0.8998, 0.9999, 0.5912 ,\n",
    "        0.7667, 0.7262, 0.1521, 0.1323, 0.0000, 0.4116, 0.0000,\n",
    "        ]\n",
    "snub_multi_perfs = [\n",
    "        0.7422, 0.8129, 0.4470, 0.5407, 0.9999, 0.9999, 0.9999,\n",
    "        0.3472, 0.5807, 0.4763, 0.3472, 0.9999, 0.9999, 0.9999,\n",
    "        0.6185, 0.6337, 0.8232, 0.7193, 0.9999, 0.9999, 0.9999,\n",
    "        0.5027, 0.3372, 0.2033, 0.7235, 0.0000, 0.9999, 0.0000,\n",
    "]\n",
    "\n",
    "snuba_perfs = np.array(snuba_perfs)[inds].tolist()\n",
    "snub_multi_perfs = np.array(snub_multi_perfs)[inds].tolist()\n",
    "\n",
    "problems = pd.Series(\n",
    "        np.arange(len(snuba_perfs)).tolist() + \\\n",
    "        np.arange(len(snub_multi_perfs)).tolist(),\n",
    "        dtype=int, name='problem')\n",
    "methods = pd.Series(\n",
    "        [r'Snuba'] * len(snuba_perfs) + \\\n",
    "        [r'Snuba (multipolar)'] * len(snub_multi_perfs),\n",
    "        dtype=str, name='method')\n",
    "\n",
    "\n",
    "objective_vals = pd.Series(\n",
    "        1.0 - np.array(\n",
    "                snuba_perfs + \\\n",
    "                snub_multi_perfs,\n",
    "                ), \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(\n",
    "        taus, solver_vals, solvers, axs[1, 1], \n",
    "        xlim=None, log=True, frameon=False, legend=False)\n",
    "\n",
    "        \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "axs[0, 0].set_title('Accuracy on image tasks')\n",
    "axs[1, 0].set_title('Coverage on image tasks')\n",
    "\n",
    "axs[0, 1].set_title('Accuracy on non-image tasks')\n",
    "axs[1, 1].set_title('Coverage on non-image tasks')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"neurips/snuba_polarity.pdf\", bbox_inches='tight')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.5 ('py39')",
   "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": "3.9.5"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "61b4062b24dfb1010f420dad5aa3bd73a4d2af47d0ec44eafec465a35a9d7239"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}