implementations_speed.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "from src import em_algorithm, random_graph\n",
    "from src.utils import sort_parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def benchmark(n, Q, implementations, runs=10, iterations=10):\n",
    "    X, _, alpha, pi = random_graph(n, Q)\n",
    "    alpha, pi = sort_parameters(alpha, pi)\n",
    "    times = []\n",
    "\n",
    "    for implementation in implementations:\n",
    "        times.append([])\n",
    "        for i in range(runs):\n",
    "            print(\"Run\", i + 1, \"with\", implementation, \"implementation\", end=\"\\r\")\n",
    "            start_time = time.time()\n",
    "            em_algorithm(\n",
    "                X,\n",
    "                Q,\n",
    "                n_init=1,\n",
    "                iterations=iterations,\n",
    "                implementation=implementation,\n",
    "                verbose=False,\n",
    "            )\n",
    "            times[-1].append(time.time() - start_time)\n",
    "    return times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run 10 with numpy implementationtation\r"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Q = 3\n",
    "n = 100\n",
    "times = benchmark(n, Q, implementations=[\"pytorch\", \"pytorch_log\", \"numpy\"])\n",
    "\n",
    "plt.boxplot(times, labels=[\"pytorch\", \"pytorch_log\", \"numpy\"])\n",
    "plt.yscale(\"log\")\n",
    "plt.xlabel(\"Implementation\")\n",
    "plt.ylabel(\"Time (seconds)\")\n",
    "plt.show()"
   ]
  }
 ],
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