examples/prof-plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "from math import sqrt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/shane/miniconda3/envs/cvxpylayers/lib/python3.7/site-packages/numpy/core/fromnumeric.py:3257: RuntimeWarning: Mean of empty slice.\n",
      "  out=out, **kwargs)\n",
      "/home/shane/miniconda3/envs/cvxpylayers/lib/python3.7/site-packages/numpy/core/_methods.py:161: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = pd.read_csv('results.csv')\n",
    "\n",
    "for qp in sorted(results.qp.unique()):\n",
    "    for nz in results.nz.unique():\n",
    "        for nbatch in results.nbatch.unique():\n",
    "            x = 0\n",
    "            xs, forwards, backwards, overall = [], [], [], []\n",
    "            canons, dcanons = [], []\n",
    "            for mode, cuda in [('qpth', 'True'), ('qpth', 'False'), ('cvxpylayers', 'False')]:\n",
    "                for direction in ['forward', 'backward']:\n",
    "                    a = results.query(\n",
    "                        f\"qp == '{qp}' & mode == '{mode}' & cuda == {cuda} & \" + \\\n",
    "                        f\"direction == '{direction}' & nz == {nz} & nbatch == {nbatch}\"\n",
    "                    )\n",
    "                    t = np.array(a.time)\n",
    "                    \n",
    "                    if mode == 'cvxpylayers':\n",
    "                        if direction == 'forward':\n",
    "                            t -= np.array(a.canon_time)\n",
    "                            canons.append(a.canon_time.mean())\n",
    "                        else:\n",
    "                            t -= np.array(a.dcanon_time)\n",
    "                            dcanons.append(a.dcanon_time.mean())\n",
    "                    if direction == 'forward':\n",
    "                        forwards.append(np.mean(t))\n",
    "                        if mode == 'cvxpylayers':\n",
    "                            overall.append(t + np.array(a.canon_time))\n",
    "                        else:\n",
    "                            overall.append(t)\n",
    "                    else:\n",
    "                        backwards.append(np.mean(t))\n",
    "                        if mode == 'cvxpylayers':\n",
    "                            overall[-1] += t + np.array(a.dcanon_time)\n",
    "                        else:\n",
    "                            overall[-1] += t\n",
    "                x += 1\n",
    "                xs.append(x)\n",
    "            \n",
    "            if np.all(np.isnan(forwards)):\n",
    "                continue\n",
    "                \n",
    "            xlabels = ['qpth GPU', 'qpth CPU', 'cvxpylayers']\n",
    "            I = (~np.isnan(forwards))\n",
    "            forwards = np.array(forwards)[I]\n",
    "            backwards = np.array(backwards)[I]\n",
    "            overall = np.array(overall)[I]\n",
    "            xs = np.array(xs)[I]\n",
    "            xlabels = np.array(xlabels)[I]\n",
    "                \n",
    "            fig, ax = plt.subplots(1, 1, figsize=(2*len(xs),4))\n",
    "            ax.bar(xs, forwards, label='forward', color='#7293cb')\n",
    "            ax.bar(xs[:-1], backwards[:-1], yerr=[o.std() for o in overall[:-1]], bottom = forwards[:-1], label='backward', color='#e1974c')\n",
    "            ax.bar(xs[-1], backwards[-1], bottom=forwards[-1], color='#e1974c')\n",
    "            ax.bar(xs[-1], canons[0], bottom=backwards[-1]+forwards[-1], color='#ab6857', label='canon')\n",
    "            ax.bar(xs[-1], dcanons[0], yerr=overall[-1].std(), bottom=backwards[-1]+forwards[-1]+canons[-1], color='#d35e60', label='retrieval')\n",
    "            ax.set_xticks(xs)\n",
    "            ax.set_xticklabels(xlabels, rotation=0, ha='center')\n",
    "            ax.set_ylabel('time (s)')\n",
    "            ax.legend()\n",
    "            fig.tight_layout()\n",
    "            fig.savefig(f\"{qp}_results.pdf\")\n",
    "            plt.show(fig)\n",
    "            plt.close(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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