sorting_algorithm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "killing-charter",
   "metadata": {},
   "source": [
    "# Sorting algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "resistant-breed",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "friendly-botswana",
   "metadata": {},
   "source": [
    "## Import modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ecological-greeting",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Module for math operations\n",
    "import math\n",
    "# Module for random number\n",
    "from random import randint\n",
    "# Module for timing\n",
    "import time\n",
    "# Module for dataframe manipulation\n",
    "import pandas as pd\n",
    "# Module for linear algebra calculation\n",
    "import numpy as np\n",
    "# Module for data visualization with plotnine\n",
    "from plotnine import *\n",
    "import plotnine\n",
    "# Module for system\n",
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fixed-village",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3000\n"
     ]
    }
   ],
   "source": [
    "# Set the recursive limit (3000)\n",
    "print(sys.getrecursionlimit())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "important-forward",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set new recursive limit (15000)\n",
    "sys.setrecursionlimit(3000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "interracial-virtue",
   "metadata": {},
   "source": [
    "## Buble sort algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "sixth-carolina",
   "metadata": {},
   "outputs": [],
   "source": [
    "def BubbleSort(array):\n",
    "    swapped = True\n",
    "    while swapped:\n",
    "        swapped = False\n",
    "        for i in range(len(array) - 1):\n",
    "            if array[i] > array[i + 1]:\n",
    "                array[i], array[i + 1] = array[i + 1], array[i]\n",
    "                swapped = True"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "specific-surgeon",
   "metadata": {},
   "source": [
    "## Quick sort algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "applicable-folder",
   "metadata": {},
   "outputs": [],
   "source": [
    "def partition(array, start, end):\n",
    "    pivot = array[start]\n",
    "    low = start + 1\n",
    "    high = end\n",
    "\n",
    "    while True:\n",
    "        while low <= high and array[high] >= pivot:\n",
    "            high = high - 1\n",
    "        while low <= high and array[low] <= pivot:\n",
    "            low = low + 1\n",
    "        if low <= high:\n",
    "            array[low], array[high] = array[high], array[low]\n",
    "        else:\n",
    "            break\n",
    "    \n",
    "    array[start], array[high] = array[high], array[start]\n",
    "    return high\n",
    "\n",
    "def QuickSort(array, start, end):\n",
    "    if start >= end:\n",
    "        return\n",
    "\n",
    "    p = partition(array, start, end)\n",
    "    QuickSort(array, start, p - 1)\n",
    "    QuickSort(array, p + 1, end)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "decent-stations",
   "metadata": {},
   "source": [
    "## Random number"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "exterior-mumbai",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function for random number\n",
    "def random_with_N_digits(n:str):\n",
    "    range_start = 10 ** (n-1)\n",
    "    range_end = (10 ** n) - 1\n",
    "    return randint(range_start, range_end)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "secure-boston",
   "metadata": {},
   "source": [
    "## Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "primary-tourist",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate the random number\n",
    "list_random = []\n",
    "for i in range(200):\n",
    "    random = random_with_N_digits(16)\n",
    "    list_random.append(random)\n",
    "# Create a copy of random numbers\n",
    "list_random_twin = list_random.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "alternative-directive",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200\n",
      "200\n"
     ]
    }
   ],
   "source": [
    "print(len(list_random))\n",
    "print(len(list_random_twin))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "young-harassment",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python script is ran in 0.0100078583 seconds\n"
     ]
    }
   ],
   "source": [
    "# Buble sort algorithm\n",
    "start = time.time()\n",
    "output = BubbleSort(array = list_random)\n",
    "end = time.time()\n",
    "dur = round(end - start, ndigits = 10)\n",
    "print('Python script is ran in {} seconds'.format(dur))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "continuous-compatibility",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python script is ran in 0.0009999275 seconds\n"
     ]
    }
   ],
   "source": [
    "# Quick sort algorithm\n",
    "start = time.time()\n",
    "output = QuickSort(array = list_random_twin, start = 0, end = len(list_random) - 1)\n",
    "end = time.time()\n",
    "dur = round(end - start, ndigits = 10)\n",
    "print('Python script is ran in {} seconds'.format(dur))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "optional-genre",
   "metadata": {},
   "source": [
    "## Simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "swedish-ladder",
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 iteration 9 subiteration with 500 data -> Bubble sort 0.06084702014 and Quick sort 0.0014992475500000002\n",
      "1 iteration 9 subiteration with 600 data -> Bubble sort 0.10272243023000001 and Quick sort 0.00250179769\n",
      "2 iteration 9 subiteration with 700 data -> Bubble sort 0.10737774372000002 and Quick sort 0.00260221959\n",
      "3 iteration 9 subiteration with 800 data -> Bubble sort 0.15075690745 and Quick sort 0.0029522419199999998\n",
      "4 iteration 9 subiteration with 900 data -> Bubble sort 0.18543181419 and Quick sort 0.00350232122\n",
      "5 iteration 9 subiteration with 1000 data -> Bubble sort 0.27514510154 and Quick sort 0.00440340042\n",
      "6 iteration 9 subiteration with 1100 data -> Bubble sort 0.32888560296 and Quick sort 0.0046512841999999995\n",
      "7 iteration 9 subiteration with 1200 data -> Bubble sort 0.42565231321999997 and Quick sort 0.00520198344\n",
      "8 iteration 9 subiteration with 1300 data -> Bubble sort 0.42605595589 and Quick sort 0.00560417176\n",
      "9 iteration 9 subiteration with 1400 data -> Bubble sort 0.5132644891699999 and Quick sort 0.00575397013\n",
      "10 iteration 9 subiteration with 1500 data -> Bubble sort 0.66557605267 and Quick sort 0.0057541608799999994\n",
      "11 iteration 9 subiteration with 1600 data -> Bubble sort 0.8313398122800001 and Quick sort 0.008006382000000001\n",
      "12 iteration 9 subiteration with 1700 data -> Bubble sort 0.82227971554 and Quick sort 0.00865786076\n",
      "13 iteration 9 subiteration with 1800 data -> Bubble sort 0.8600605487799999 and Quick sort 0.00880789757\n",
      "14 iteration 9 subiteration with 1900 data -> Bubble sort 0.94947135448 and Quick sort 0.00770549775\n",
      "15 iteration 9 subiteration with 2000 data -> Bubble sort 1.10103001594 and Quick sort 0.0091581106\n",
      "16 iteration 9 subiteration with 2100 data -> Bubble sort 1.99926912785 and Quick sort 0.014060425749999998\n",
      "17 iteration 9 subiteration with 2200 data -> Bubble sort 1.53383703232 and Quick sort 0.01280899047\n",
      "18 iteration 9 subiteration with 2300 data -> Bubble sort 1.52558312415 and Quick sort 0.010257220269999999\n",
      "19 iteration 9 subiteration with 2400 data -> Bubble sort 1.51877965928 and Quick sort 0.01050567627\n",
      "20 iteration 9 subiteration with 2500 data -> Bubble sort 1.6796408891599999 and Quick sort 0.01245892047\n",
      "21 iteration 9 subiteration with 2600 data -> Bubble sort 1.8541663646600004 and Quick sort 0.010909557349999998\n",
      "22 iteration 9 subiteration with 2700 data -> Bubble sort 2.1026912689299997 and Quick sort 0.012759613999999999\n",
      "23 iteration 9 subiteration with 2800 data -> Bubble sort 2.23153641224 and Quick sort 0.01226048469\n",
      "24 iteration 9 subiteration with 2900 data -> Bubble sort 2.2904265642 and Quick sort 0.016911649699999996\n"
     ]
    }
   ],
   "source": [
    "# List for saving the result\n",
    "list_length = []\n",
    "list_algorithm = []\n",
    "list_time = []\n",
    "list_repetition = []\n",
    "\n",
    "# Length of data\n",
    "length = 500\n",
    "\n",
    "for i in range(25):\n",
    "    list_time_bubble = []\n",
    "    list_time_quick = []\n",
    "    for j in range(10):\n",
    "        # Generate dummy data\n",
    "        list_random = []\n",
    "        for _ in range(length):\n",
    "            random = random_with_N_digits(16)\n",
    "            list_random.append(random)\n",
    "        list_random_twin = list_random.copy()\n",
    "        # Buble sort\n",
    "        start_bubble = time.time()\n",
    "        output_bubble = BubbleSort(\n",
    "            array = list_random\n",
    "        )\n",
    "        end_bubble = time.time()\n",
    "        dur_bubble = round(end_bubble - start_bubble, ndigits = 10)\n",
    "        # Quick Sort list\n",
    "        start_quick = time.time()\n",
    "        output_quick = QuickSort(\n",
    "            array = list_random_twin,\n",
    "            start = 0,\n",
    "            end = len(list_random_twin) - 1\n",
    "        )\n",
    "        end_quick = time.time()\n",
    "        dur_quick = round(end_quick - start_quick, ndigits = 10)\n",
    "        # Append the result into list\n",
    "        list_length += [length, length]\n",
    "        list_algorithm += ['Bubble Sort', 'Quick Sort']\n",
    "        list_time += [dur_bubble, dur_quick]\n",
    "        list_repetition += [j, j]\n",
    "        list_time_bubble.append(dur_bubble)\n",
    "        list_time_quick.append(dur_quick)\n",
    "    # Status\n",
    "    avg_time_bubble = np.mean(list_time_bubble)\n",
    "    avg_time_quick = np.mean(list_time_quick)\n",
    "    print('{} iteration {} subiteration with {} data -> Bubble sort {} and Quick sort {}'\n",
    "          .format(i, j, length, avg_time_bubble, avg_time_quick))\n",
    "    length += 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "peaceful-crystal",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dataframe\n",
    "df = pd.DataFrame(\n",
    "        {\n",
    "            'length': list_length,\n",
    "            'algorithm': list_algorithm,\n",
    "            'repetition': list_repetition,\n",
    "            'time': list_time\n",
    "        }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "extensive-shield",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimension: 500 rows and 4 columns\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\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>length</th>\n",
       "      <th>algorithm</th>\n",
       "      <th>repetition</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>500</td>\n",
       "      <td>Bubble Sort</td>\n",
       "      <td>0</td>\n",
       "      <td>0.092566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>500</td>\n",
       "      <td>Quick Sort</td>\n",
       "      <td>0</td>\n",
       "      <td>0.002002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>500</td>\n",
       "      <td>Bubble Sort</td>\n",
       "      <td>1</td>\n",
       "      <td>0.099071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>500</td>\n",
       "      <td>Quick Sort</td>\n",
       "      <td>1</td>\n",
       "      <td>0.001500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>500</td>\n",
       "      <td>Bubble Sort</td>\n",
       "      <td>2</td>\n",
       "      <td>0.060544</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   length    algorithm  repetition      time\n",
       "0     500  Bubble Sort           0  0.092566\n",
       "1     500   Quick Sort           0  0.002002\n",
       "2     500  Bubble Sort           1  0.099071\n",
       "3     500   Quick Sort           1  0.001500\n",
       "4     500  Bubble Sort           2  0.060544"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Dimension: {} rows and {} columns'.format(len(df), len(df.columns)))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "reduced-sweden",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data aggregation\n",
    "df_agg = df.groupby(['algorithm', 'length']).agg(\n",
    "    {\n",
    "        'time': ['mean', 'std']\n",
    "    }\n",
    ")\n",
    "# Multi index to single index\n",
    "df_agg.columns = df_agg.columns.droplevel(0)\n",
    "df_agg.reset_index(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "registered-combination",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimension: 50 rows and 4 columns\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>algorithm</th>\n",
       "      <th>length</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Quick Sort</td>\n",
       "      <td>2500</td>\n",
       "      <td>0.012459</td>\n",
       "      <td>0.005148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Quick Sort</td>\n",
       "      <td>2600</td>\n",
       "      <td>0.010910</td>\n",
       "      <td>0.001648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Quick Sort</td>\n",
       "      <td>2700</td>\n",
       "      <td>0.012760</td>\n",
       "      <td>0.002939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Quick Sort</td>\n",
       "      <td>2800</td>\n",
       "      <td>0.012260</td>\n",
       "      <td>0.001702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Quick Sort</td>\n",
       "      <td>2900</td>\n",
       "      <td>0.016912</td>\n",
       "      <td>0.006257</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     algorithm  length      mean       std\n",
       "45  Quick Sort    2500  0.012459  0.005148\n",
       "46  Quick Sort    2600  0.010910  0.001648\n",
       "47  Quick Sort    2700  0.012760  0.002939\n",
       "48  Quick Sort    2800  0.012260  0.001702\n",
       "49  Quick Sort    2900  0.016912  0.006257"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Dimension: {} rows and {} columns'.format(len(df_agg), len(df_agg.columns)))\n",
    "df_agg.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "still-oasis",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (135450088665)>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Time-series plot\n",
    "plotnine.options.figure_size = (8, 4.8)\n",
    "(\n",
    "    ggplot(data = df_agg)+\n",
    "    geom_line(aes(x = 'length',\n",
    "                  y = 'mean',\n",
    "                  color = 'algorithm',\n",
    "                  group = 'algorithm'),\n",
    "              size = 1.5)+\n",
    "    geom_point(aes(x = 'length',\n",
    "                   y = 'mean'),\n",
    "               size = 2)+\n",
    "    scale_color_manual(name = 'Algorithm', \n",
    "                      values = ['#80797c', '#981220'], \n",
    "                      labels = ['Bubble Sort', 'Quick Sort'])+\n",
    "    labs(title = 'Sorting Algorithm Performance')+\n",
    "    xlab('Number of Observations')+\n",
    "    ylab('Time (second)')+\n",
    "    theme_minimal()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "premium-facing",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['length'] = df['length'].astype('category')\n",
    "df['length'] = df['length'].cat.reorder_categories(list(map(str, df['length'].unique())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fitting-thickness",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (135450103025)>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Box and whisker plot\n",
    "plotnine.options.figure_size = (8, 4.8)\n",
    "(\n",
    "    ggplot(data = df)+\n",
    "    geom_boxplot(aes(x = 'length',\n",
    "                     y = 'time',\n",
    "                     fill = 'algorithm'))+\n",
    "    labs(title = 'Standard Deviation of Sorting Algorithm Performance')+\n",
    "    scale_fill_manual(name = 'Algorithm',\n",
    "                      values = ['#80797c', '#981220'],\n",
    "                      labels = ['Bubble Sort', 'Quick Sort'])+\n",
    "    xlab('Number of Observations')+\n",
    "    ylab('Time (second)')+\n",
    "    coord_flip()+\n",
    "    theme_minimal()\n",
    ")"
   ]
  }
 ],
 "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}