aup.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import optperfprofpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 640,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_step_area(y, x, prestep=True):\n",
    "    area = 0.0\n",
    "    for j in range(1, len(x)):\n",
    "        width = x[j] - x[j-1]\n",
    "        height = y[j] if prestep else y[j-1]\n",
    "        area += width * height\n",
    "    return area\n",
    "\n",
    "def draw_shaded_perfprof(taus, solver_vals, solvers):\n",
    "    \"\"\"Shaded step plotter for performance profiles with AUPP\n",
    "\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",
    "    fig = plt.figure()\n",
    "    ax = fig.add_subplot(111)\n",
    "    \n",
    "    # Add lines individually to support labels\n",
    "    aupps = []\n",
    "    solver_order = []\n",
    "    for i, solver in enumerate(solvers):\n",
    "        aupp = compute_step_area(solver_vals[i, :], taus)\n",
    "        aupps.append(aupp)\n",
    "        solver_order.append(solver)\n",
    "        \n",
    "        ax.fill_between(\n",
    "            x=taus, \n",
    "            y1=solver_vals[i, :],\n",
    "            step=\"pre\",\n",
    "            alpha=0.1)\n",
    "\n",
    "        ax.step(taus, solver_vals[i, :], \n",
    "            label=f\"{solver}: {aupp:.2f}\", linewidth=5, alpha=0.5)\n",
    "        \n",
    "    plt.legend(loc=4)\n",
    "    plt.xlim(taus.min(), taus.max())\n",
    "    ax.set_xlabel(r\"$\\tau$\")\n",
    "    ax.set_ylabel(r\"$\\rho_s(\\tau)$\")\n",
    "    \n",
    "    plt.plot()\n",
    "\n",
    "    return aupps, solver_order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 641,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of problems per solver:  3\n"
     ]
    },
    {
     "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>1</td>\n",
       "      <td>A</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>B</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>A</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>B</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>A</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>B</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  problem method   obj\n",
       "0       1      A   1.0\n",
       "1       1      B  10.0\n",
       "2       2      A   5.0\n",
       "3       2      B   1.0\n",
       "4       3      A   7.5\n",
       "5       3      B   1.0"
      ]
     },
     "execution_count": 641,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "M = 1\n",
    "\n",
    "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*M, 25, 5*M, 30, 4*M], dtype=float, name='obj')\n",
    "\n",
    "example_df = pd.DataFrame([problems, methods, objective_vals]).T\n",
    "taus, solver_vals, solvers, transformed_data = optperfprofpy.calc_perprof(\n",
    "    example_df, ['problem'], ['obj'], ['method'])\n",
    "\n",
    "taus = np.array(taus, dtype=float)\n",
    "solver_vals = np.array(solver_vals, dtype=float)\n",
    "\n",
    "draw_shaded_perfprof(taus, solver_vals, solvers)\n",
    "transformed_data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AUPP with simulated data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 642,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of problems per solver:  10\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "([162.25966390104145, 179.12925169431173, 164.94568057261898],\n",
       " ['C. Elegans connectome', 'MEGA NAS', 'random guessing'])"
      ]
     },
     "execution_count": 642,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_probs = 10\n",
    "n_methods = 3\n",
    "\n",
    "problems_array = np.repeat(np.arange(n_probs), 3)\n",
    "methods = [\"MEGA NAS\", \"random guessing\", \"C. Elegans connectome\"]\n",
    "method_labels = methods * n_probs\n",
    "r0 = np.random.rand(10) * 0.15\n",
    "r1 = np.random.rand(10) * 0.45\n",
    "r2 = np.random.rand(10) * 0.65\n",
    "simulated_obj_vals = np.vstack([r0, r1, r2]).T.reshape(-1)\n",
    "\n",
    "problems = pd.Series(problems_array, dtype=int, name='problem')\n",
    "methods = pd.Series(method_labels, dtype=str, name='method')\n",
    "objective_vals = pd.Series(simulated_obj_vals, dtype=float, name='obj')\n",
    "\n",
    "df = pd.DataFrame([problems, methods, objective_vals]).T\n",
    "taus, solver_vals, solvers, transformed_data = optperfprofpy.calc_perprof(\n",
    "    df, ['problem'], ['obj'], ['method'])\n",
    "\n",
    "taus = np.array(taus, dtype=float)\n",
    "solver_vals = np.array(solver_vals, dtype=float)\n",
    "\n",
    "draw_shaded_perfprof(taus, solver_vals, solvers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 673,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 30)\n",
      "Number of problems per solver:  10\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_probs = 10\n",
    "n_methods = 30\n",
    "\n",
    "def sigmoid(x):\n",
    "    return 1 / (1 + np.exp(-x))\n",
    "\n",
    "problems_array = np.repeat(np.arange(n_probs), n_methods)\n",
    "methods = [i for i in range(n_methods)]\n",
    "method_labels = methods * n_probs\n",
    "rmax = np.random.rand(n_methods)\n",
    "perfs = np.vstack(\n",
    "    [sigmoid(\n",
    "        (np.random.randn(n_probs) * 0.1) + rmax[i])  for i in range(n_methods)]).T\n",
    "\n",
    "print(perfs.shape)\n",
    "\n",
    "simulated_obj_vals = perfs.reshape(-1)\n",
    "\n",
    "problems = pd.Series(problems_array, dtype=int, name='problem')\n",
    "methods = pd.Series(method_labels, dtype=str, name='method')\n",
    "objective_vals = pd.Series(simulated_obj_vals, dtype=float, name='obj')\n",
    "\n",
    "df = pd.DataFrame([problems, methods, objective_vals]).T\n",
    "taus, solver_vals, solvers, transformed_data = optperfprofpy.calc_perprof(\n",
    "    df, ['problem'], ['obj'], ['method'])\n",
    "\n",
    "taus = np.array(taus, dtype=float)\n",
    "solver_vals = np.array(solver_vals, dtype=float)\n",
    "\n",
    "aupps, solvers = draw_shaded_perfprof(taus, solver_vals, solvers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'solvers' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m/app/codalab/github.com/cxxz/automl_decathlon_starting_kit/aupp.ipynb Cell 7\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell://attached-container%2B7b22636f6e7461696e65724e616d65223a222f657069635f6a657073656e222c2273657474696e6773223a7b22686f7374223a227373683a2f2f636173746c652d67726579736b756c6c2d667265642d6465736b746f702d32227d7d/app/codalab/github.com/cxxz/automl_decathlon_starting_kit/aupp.ipynb#ch0000006vscode-remote?line=0'>1</a>\u001b[0m \u001b[39m# Re-sort these because they lost their ordering due to string sorting\u001b[39;00m\n\u001b[0;32m----> <a href='vscode-notebook-cell://attached-container%2B7b22636f6e7461696e65724e616d65223a222f657069635f6a657073656e222c2273657474696e6773223a7b22686f7374223a227373683a2f2f636173746c652d67726579736b756c6c2d667265642d6465736b746f702d32227d7d/app/codalab/github.com/cxxz/automl_decathlon_starting_kit/aupp.ipynb#ch0000006vscode-remote?line=1'>2</a>\u001b[0m solvers_ints \u001b[39m=\u001b[39m [\u001b[39mint\u001b[39m(x) \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m solvers]\n\u001b[1;32m      <a href='vscode-notebook-cell://attached-container%2B7b22636f6e7461696e65724e616d65223a222f657069635f6a657073656e222c2273657474696e6773223a7b22686f7374223a227373683a2f2f636173746c652d67726579736b756c6c2d667265642d6465736b746f702d32227d7d/app/codalab/github.com/cxxz/automl_decathlon_starting_kit/aupp.ipynb#ch0000006vscode-remote?line=2'>3</a>\u001b[0m inds \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39margsort(solvers_ints)\n\u001b[1;32m      <a href='vscode-notebook-cell://attached-container%2B7b22636f6e7461696e65724e616d65223a222f657069635f6a657073656e222c2273657474696e6773223a7b22686f7374223a227373683a2f2f636173746c652d67726579736b756c6c2d667265642d6465736b746f702d32227d7d/app/codalab/github.com/cxxz/automl_decathlon_starting_kit/aupp.ipynb#ch0000006vscode-remote?line=3'>4</a>\u001b[0m aupps \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39marray(aupps)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'solvers' is not defined"
     ]
    }
   ],
   "source": [
    "# Re-sort these because they lost their ordering due to string sorting\n",
    "solvers_ints = [int(x) for x in solvers]\n",
    "inds = np.argsort(solvers_ints)\n",
    "aupps = np.array(aupps)\n",
    "aupps = aupps[inds]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.10 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
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