plot_results.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the CPU utilization and runtime analysis from each dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from time import time\n",
    "import psutil\n",
    "import warnings\n",
    "import random\n",
    "warnings.filterwarnings('ignore', category=FutureWarning)\n",
    "warnings.filterwarnings('ignore', category=DeprecationWarning)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "phone_train_cpu = pd.read_csv('phone/phone_cpu.csv').values.flatten()\n",
    "mushroom_train_cpu = pd.read_csv('mushroom/mushroom_cpu.csv').values.flatten()\n",
    "income_train_cpu = pd.read_csv('income/income_cpu.csv').values.flatten()\n",
    "crime_train_cpu = pd.read_csv('crime/crime_cpu.csv').values.flatten()\n",
    "\n",
    "phone_runtime = pd.read_csv('phone/phone_runtime.csv').values.flatten()\n",
    "mushroom_runtime = pd.read_csv('mushroom/mushroom_runtime.csv').values.flatten()\n",
    "income_runtime = pd.read_csv('income/income_runtime.csv').values.flatten()\n",
    "crime_runtime = pd.read_csv('crime/crime_runtime.csv').values.flatten()\n",
    "\n",
    "phone_traintime = pd.read_csv('phone/phone_train_times.csv').values.flatten()\n",
    "mushroom_traintime = pd.read_csv('mushroom/mushroom_train_times.csv').values.flatten()\n",
    "income_traintime = pd.read_csv('income/income_train_times.csv').values.flatten()\n",
    "crime_traintime = pd.read_csv('crime/crime_train_times.csv').values.flatten()\n",
    "\n",
    "phone_acc = pd.read_csv('phone/phone_accuracy.csv').values.flatten()\n",
    "mushroom_acc = pd.read_csv('mushroom/mushroom_accuracy.csv').values.flatten()\n",
    "income_acc = pd.read_csv('income/income_accuracy.csv').values.flatten()\n",
    "crime_acc = pd.read_csv('crime/crime_accuracy.csv').values.flatten()\n",
    "\n",
    "phone_mem = pd.read_csv('phone/phone_peak_mem.csv').values.flatten() / 10**3\n",
    "mushroom_mem = pd.read_csv('mushroom/mushroom_peak_mem.csv').values.flatten() / 10**3\n",
    "income_mem = pd.read_csv('income/income_peak_mem.csv').values.flatten() / 10**3\n",
    "crime_mem = pd.read_csv('crime/crime_peak_mem.csv').values.flatten() / 10**3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# using example from https://python-graph-gallery.com/11-grouped-barplot/\n",
    "plt.figure(figsize=(8,5))\n",
    "\n",
    "# set width of bar\n",
    "barWidth = 0.20\n",
    " \n",
    "# Set position of bar on X axis\n",
    "r1 = np.arange(len(phone_train_cpu))\n",
    "r2 = [x + barWidth for x in r1]\n",
    "r3 = [x + barWidth for x in r2]\n",
    "r4 = [x + barWidth for x in r3]\n",
    " \n",
    "# Make the plot\n",
    "plt.bar(r1, phone_train_cpu,    width=barWidth, edgecolor='white', label='phone dataset')\n",
    "plt.bar(r2, mushroom_train_cpu, width=barWidth, edgecolor='white', label='mushroom dataset')\n",
    "plt.bar(r3, income_train_cpu,   width=barWidth, edgecolor='white', label='income dataset')\n",
    "plt.bar(r4, crime_train_cpu,    width=barWidth, edgecolor='white', label='crime dataset')\n",
    " \n",
    "# Add xticks on the middle of the group bars\n",
    "plt.xlabel('Algorithm', fontweight='bold')\n",
    "plt.ylabel('CPU Utilization (%)', fontweight='bold')\n",
    "plt.xticks([r + barWidth for r in range(len(phone_cpu))], ['Nominal', 'KNN', 'Logistic Regression', 'SVM', 'Random Forest', 'MLP'])\n",
    " \n",
    "# Create legend & Show graphic\n",
    "plt.legend()\n",
    "plt.title('Model Training CPU Utilization')\n",
    "plt.savefig('train_cpu_utilization_all_data.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# using example from https://python-graph-gallery.com/11-grouped-barplot/\n",
    "plt.figure(figsize=(8,5))\n",
    "\n",
    "# set width of bar\n",
    "barWidth = 0.20\n",
    " \n",
    "# Set position of bar on X axis\n",
    "r1 = np.arange(len(phone_mem))\n",
    "r2 = [x + barWidth for x in r1]\n",
    "r3 = [x + barWidth for x in r2]\n",
    "r4 = [x + barWidth for x in r3]\n",
    " \n",
    "# Make the plot\n",
    "plt.bar(r1, phone_mem,    width=barWidth, edgecolor='white', label='phone dataset')\n",
    "plt.bar(r2, mushroom_mem, width=barWidth, edgecolor='white', label='mushroom dataset')\n",
    "plt.bar(r3, income_mem,   width=barWidth, edgecolor='white', label='income dataset')\n",
    "plt.bar(r4, crime_mem,    width=barWidth, edgecolor='white', label='crime dataset')\n",
    " \n",
    "# Add xticks on the middle of the group bars\n",
    "plt.xlabel('Algorithm', fontweight='bold')\n",
    "plt.ylabel('Memory (Kb)', fontweight='bold')\n",
    "plt.xticks([r + barWidth for r in range(len(phone_mem))], ['KNN', 'Logistic Regression', 'SVM', 'Random Forest', 'MLP'])\n",
    " \n",
    "# Create legend & Show graphic\n",
    "plt.legend()\n",
    "plt.title('Model Training Peak Memory Usage')\n",
    "plt.savefig('train_memory_all_data.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# using example from https://python-graph-gallery.com/11-grouped-barplot/\n",
    "plt.figure(figsize=(8,5))\n",
    "\n",
    "# set width of bar\n",
    "barWidth = 0.20\n",
    " \n",
    "# Set position of bar on X axis\n",
    "r1 = np.arange(len(phone_runtime))\n",
    "r2 = [x + barWidth for x in r1]\n",
    "r3 = [x + barWidth for x in r2]\n",
    "r4 = [x + barWidth for x in r3]\n",
    " \n",
    "# Make the plot\n",
    "plt.bar(r1, phone_runtime,    width=barWidth, edgecolor='white', label='phone dataset')\n",
    "plt.bar(r2, mushroom_runtime, width=barWidth, edgecolor='white', label='mushroom dataset')\n",
    "plt.bar(r3, income_runtime,   width=barWidth, edgecolor='white', label='income dataset')\n",
    "plt.bar(r4, crime_runtime,    width=barWidth, edgecolor='white', label='crime dataset')\n",
    " \n",
    "# Add xticks on the middle of the group bars\n",
    "plt.xlabel('Algorithm', fontweight='bold')\n",
    "plt.ylabel('Runtime (seconds)', fontweight='bold')\n",
    "plt.xticks([r + barWidth for r in range(len(phone_runtime))], ['KNN', 'Logistic Regression', 'SVM', 'Random Forest', 'MLP'])\n",
    " \n",
    "# Create legend & Show graphic\n",
    "plt.legend()\n",
    "plt.title('Model Predictions Runtime (500 entries)')\n",
    "plt.savefig('prediction_runtimes_all_data.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# using example from https://python-graph-gallery.com/11-grouped-barplot/\n",
    "plt.figure(figsize=(8,5))\n",
    "\n",
    "# set width of bar\n",
    "barWidth = 0.20\n",
    " \n",
    "# Set position of bar on X axis\n",
    "r1 = np.arange(len(phone_traintime))\n",
    "r2 = [x + barWidth for x in r1]\n",
    "r3 = [x + barWidth for x in r2]\n",
    "r4 = [x + barWidth for x in r3]\n",
    " \n",
    "# Make the plot\n",
    "plt.bar(r1, phone_traintime,    width=barWidth, edgecolor='white', label='phone dataset')\n",
    "plt.bar(r2, mushroom_traintime, width=barWidth, edgecolor='white', label='mushroom dataset')\n",
    "plt.bar(r3, income_traintime,   width=barWidth, edgecolor='white', label='income dataset')\n",
    "plt.bar(r4, crime_traintime,    width=barWidth, edgecolor='white', label='crime dataset')\n",
    " \n",
    "# Add xticks on the middle of the group bars\n",
    "plt.xlabel('Algorithm', fontweight='bold')\n",
    "plt.ylabel('Time (seconds)', fontweight='bold')\n",
    "plt.yscale('log')\n",
    "plt.xticks([r + barWidth for r in range(len(phone_cpu))], ['KNN', 'Logistic Regression', 'SVM', 'Random Forest', 'MLP'])\n",
    " \n",
    "# Create legend & Show graphic\n",
    "plt.legend()\n",
    "plt.title('Model Training Times (log scale)')\n",
    "plt.savefig('train_times_all_data.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# using example from https://python-graph-gallery.com/11-grouped-barplot/\n",
    "plt.figure(figsize=(8,5))\n",
    "\n",
    "# set width of bar\n",
    "barWidth = 0.20\n",
    " \n",
    "# Set position of bar on X axis\n",
    "r1 = np.arange(len(phone_acc))\n",
    "r2 = [x + barWidth for x in r1]\n",
    "r3 = [x + barWidth for x in r2]\n",
    "r4 = [x + barWidth for x in r3]\n",
    " \n",
    "# Make the plot\n",
    "plt.bar(r1, phone_acc,    width=barWidth, edgecolor='white', label='phone dataset')\n",
    "plt.bar(r2, mushroom_acc, width=barWidth, edgecolor='white', label='mushroom dataset')\n",
    "plt.bar(r3, income_acc,   width=barWidth, edgecolor='white', label='income dataset')\n",
    "plt.bar(r4, crime_acc,    width=barWidth, edgecolor='white', label='crime dataset')\n",
    " \n",
    "# Add xticks on the middle of the group bars\n",
    "plt.xlabel('Algorithm', fontweight='bold')\n",
    "plt.ylabel('Accuracy (%)', fontweight='bold')\n",
    "plt.xticks([r + barWidth for r in range(len(phone_cpu))], ['KNN', 'Logistic Regression', 'SVM', 'Random Forest', 'MLP'])\n",
    " \n",
    "# Create legend & Show graphic\n",
    "plt.legend()\n",
    "plt.title('Model Accuracy')\n",
    "plt.savefig('accuracy_all_data.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.5.2 64-bit",
   "language": "python",
   "name": "python35264bitfe733a0a6abe4f6faea0dd7487b23bcb"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}