numpy_numba_research.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numba import jit\n",
    "import numpy as np\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "SAMPLES = 1_000_000 * 64\n",
    "tempo_decorrido = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def for_operation(nsamples):\n",
    "    result = 0.00\n",
    "    a = [x for x in range(nsamples)]\n",
    "    b = [x for x in range(nsamples)]\n",
    "    for i in range(nsamples):\n",
    "        result += a[i] * b[i]\n",
    "    return result\n",
    "\n",
    "@jit(nopython=True)\n",
    "def for_numba_operation(nsamples):\n",
    "    result = 0.00\n",
    "    a = [x for x in range(nsamples)]\n",
    "    b = [x for x in range(nsamples)]\n",
    "    for i in range(nsamples):\n",
    "        result += a[i] * b[i]\n",
    "    return result\n",
    "    \n",
    "def numpy_operation(nsamples):\n",
    "    a = np.arange(nsamples).astype(np.float64)\n",
    "    b = np.arange(nsamples).astype(np.float64)\n",
    "    return np.sum(a * b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pure Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.738133128538303e+22\n",
      "Tempo decorrido: 13.377476930618286\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "print(str(for_operation(SAMPLES)))\n",
    "tempo_decorrido.append(time.time() - start_time)\n",
    "print(\"Tempo decorrido: {}\".format(str(tempo_decorrido[-1])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# First Numba function call"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.738133128538303e+22\n",
      "Tempo decorrido: 2.3002991676330566\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "print(for_numba_operation(SAMPLES))\n",
    "tempo_decorrido.append(time.time() - start_time)\n",
    "print(\"Tempo decorrido: {}\".format(str(tempo_decorrido[-1])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Numpy vetorized operation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.738133128533233e+22\n",
      "Tempo decorrido: 1.0772738456726074\n"
     ]
    }
   ],
   "source": [
    "start_time = time.time()\n",
    "print(str(numpy_operation(SAMPLES)))\n",
    "tempo_decorrido.append(time.time() - start_time)\n",
    "print(\"Tempo decorrido: {}\".format(str(tempo_decorrido[-1])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plotagem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 7))\n",
    "plt.bar(np.arange(len(tempo_decorrido)), tempo_decorrido)\n",
    "plt.title(\"Tempo decorrido para cada modo de processamento (64M operações)\")\n",
    "plt.xticks(np.arange(len(tempo_decorrido)), ('Pure Python', 'Numba', 'NumPy'))\n",
    "plt.ylabel(\"Tempo decorrido\")\n",
    "plt.savefig(\"/Users/gabriel/Downloads/tempo_decorrido_numba.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = list([1, 10, 100, 10_000, 100_000, 1_000_000, 10_000_000])\n",
    "plt.figure(figsize=(10, 7))\n",
    "y1, y2, y3 = [], [], []\n",
    "for sample in x:\n",
    "    #pure python\n",
    "    start_time = time.time()\n",
    "    for_operation(sample)\n",
    "    y1.append(time.time() - start_time)\n",
    "    #numba\n",
    "    start_time = time.time()\n",
    "    for_numba_operation(sample)\n",
    "    y2.append(time.time() - start_time)\n",
    "    #numpy\n",
    "    start_time = time.time()\n",
    "    numpy_operation(sample)\n",
    "    y3.append(time.time() - start_time)\n",
    "\n",
    "plt.plot(x, y1, 'bo--', linewidth=2, label='Pure Python')\n",
    "plt.plot(x, y2, 'gs--', linewidth=2, label='Numba')\n",
    "plt.plot(x, y3, 'r+--', linewidth=2, label='NumPy')\n",
    "\n",
    "plt.xlabel(\"Operaçōes\")\n",
    "plt.grid(False)\n",
    "plt.ylabel(\"Tempo\")\n",
    "plt.legend()\n",
    "plt.savefig(\"/Users/gabriel/Downloads/tempo_decorrido_numba_2.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}