Pre Class Work 2.2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> build a schedule-based simulation of an M/D/1 queue, and show simulation result (class 2.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import heapq\n",
    "import numpy as np\n",
    "import scipy.stats as sts\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import heapq\n",
    "\n",
    "class Event:\n",
    "    '''\n",
    "    Store the properties of one event in the Schedule class defined below. Each\n",
    "    event has a time at which it needs to run, a function to call when running\n",
    "    the event, along with the arguments and keyword arguments to pass to that\n",
    "    function.\n",
    "    '''\n",
    "    def __init__(self, timestamp, function, *args, **kwargs):\n",
    "        self.timestamp = timestamp\n",
    "        self.function = function\n",
    "        self.args = args\n",
    "        self.kwargs = kwargs\n",
    "\n",
    "    def __lt__(self, other):\n",
    "        '''\n",
    "        This overloads the less-than operator in Python. We need it so the\n",
    "        priority queue knows how to compare two events. We want events with\n",
    "        earlier (smaller) times to go first.\n",
    "        '''\n",
    "        return self.timestamp < other.timestamp\n",
    "\n",
    "    def run(self, schedule):\n",
    "        '''\n",
    "        Run an event by calling the function with its arguments and keyword\n",
    "        arguments. The first argument to any event function is always the\n",
    "        schedule in which events are being tracked. The schedule object can be\n",
    "        used to add new events to the priority queue.\n",
    "        '''\n",
    "        self.function(schedule, *self.args, **self.kwargs)\n",
    "\n",
    "\n",
    "class Schedule:\n",
    "    '''\n",
    "    Implement an event schedule using a priority queue. You can add events and\n",
    "    run the next event.\n",
    "    \n",
    "    The `now` attribute contains the time at which the last event was run.\n",
    "    '''\n",
    "    \n",
    "    def __init__(self):\n",
    "        self.now = 0  # Keep track of the current simulation time\n",
    "        self.priority_queue = []  # The priority queue of events to run\n",
    "    \n",
    "    def add_event_at(self, timestamp, function, *args, **kwargs):\n",
    "        # Add an event to the schedule at a particular point in time.\n",
    "        heapq.heappush(\n",
    "            self.priority_queue,\n",
    "            Event(timestamp, function, *args, **kwargs))\n",
    "    \n",
    "    def add_event_after(self, interval, function, *args, **kwargs):\n",
    "        # Add an event to the schedule after a specified time interval.\n",
    "        self.add_event_at(self.now + interval, function, *args, **kwargs)\n",
    "    \n",
    "    def next_event_time(self):\n",
    "        return self.priority_queue[0].timestamp\n",
    "\n",
    "    def run_next_event(self):\n",
    "        # Get the next event from the priority queue and run it.\n",
    "        event = heapq.heappop(self.priority_queue)\n",
    "        self.now = event.timestamp\n",
    "        event.run(self)\n",
    "        \n",
    "    def __repr__(self):\n",
    "        return (\n",
    "            f'Schedule() at time {self.now} ' +\n",
    "            f'with {len(self.priority_queue)} events in the queue')\n",
    "    \n",
    "    def print_events(self):\n",
    "        print(repr(self))\n",
    "        for event in sorted(self.priority_queue):\n",
    "            print(f'   {event.timestamp}: {event.function.__name__}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# the Queue objects will model that part of the people are queueing, \n",
    "# whereas the GroceryStore object models how people enter the market. \n",
    "class Queue:\n",
    "    def __init__(self, service_rate):\n",
    "        self.ppl_queue = 0 # numbers of people in the queue\n",
    "        self.ppl_served = 0 # if people is being served(1) or not(0)\n",
    "        self.service_time = 1/ service_rate # constant service time - assumption of M/D/1 queue \n",
    "        \n",
    "    def add_customer(self, schedule):\n",
    "        self.ppl_queue += 1\n",
    "        if self.ppl_served < 1: \n",
    "            schedule.add_event_after(0, self.serve_customer)\n",
    "            # if the ppl_served is 0, then serve the customer immediately \n",
    "    \n",
    "    def serve_customer(self, schedule):\n",
    "        self.ppl_queue -= 1\n",
    "        self.ppl_served += 1\n",
    "        schedule.add_event_after(self.service_time, self.serve_finished)\n",
    "        # add a event \"serve_finished\" after service_time\n",
    "    \n",
    "    def serve_finished(self, schedule):\n",
    "        self.ppl_served -= 1\n",
    "        if self.ppl_queue > 0:\n",
    "            schedule.add_event_after(0, self.serve_customer)\n",
    "            # if the there's people in the queue, serve it immediately \n",
    "            \n",
    "\n",
    "class GroceryStore:\n",
    "    def __init__(self, service_rate, arrival_rate):\n",
    "        self.queue = Queue(service_rate)\n",
    "        self.arrival_dist = sts.expon(scale=1/arrival_rate) \n",
    "        # the distribution of arrival rate \n",
    "    \n",
    "    def add_customer(self, schedule):\n",
    "        self.queue.add_customer(schedule)\n",
    "        arrival_interval = self.arrival_dist.rvs()\n",
    "        schedule.add_event_after(arrival_interval, self.add_customer)\n",
    "        # add a event that pushing customer into the queue \n",
    "        \n",
    "    def run(self, schedule):\n",
    "        schedule.add_event_after(self.arrival_dist.rvs(), self.add_customer)\n",
    "        \n",
    "def run_sim(arrival_rate, service_rate, run_until):\n",
    "    schedule = Schedule()\n",
    "    grocery_store = GroceryStore(arrival_rate, service_rate)\n",
    "    grocery_store.run(schedule)\n",
    "    \n",
    "    while schedule.next_event_time() < run_until:\n",
    "        schedule.run_next_event()\n",
    "        \n",
    "    return grocery_store"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The simulation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "def experiment(trials, arrival_rate, service_rate, run_until):\n",
    "    \"\"\"\n",
    "    run simulation based on the param \n",
    "    \"\"\"\n",
    "    queue_record = []\n",
    "\n",
    "    for i in range(trials):\n",
    "        gs = run_sim(arrival_rate, service_rate, run_until)\n",
    "        queue_record.append(gs.queue.ppl_queue)\n",
    "        \n",
    "    return np.array(queue_record)\n",
    "\n",
    "def mean_conf_int_95(data):\n",
    "    m = np.mean(data)\n",
    "    print('// Sample mean:', m)\n",
    "    t = sts.sem(data)\n",
    "    print('// Standard error of the mean:', round(t,3))\n",
    "    conf = [round(m + 1.96*t,3), round(m - 1.96*t,3)]\n",
    "    print('// 95% confidence interval of population mean:', conf)\n",
    "    return m, conf \n",
    "\n",
    "def show_result(trials, arrival_rate, service_rate, run_until):\n",
    "    # data \n",
    "    data = experiment(trials, arrival_rate, service_rate, run_until)\n",
    "    mean, conf = mean_conf_int_95(data)\n",
    "    binn = [i for i in range(0,max(data)+1,1)]\n",
    "    title_string = 'Histogram for customers in queue.'\n",
    "    subtitle_string =f'Arrival rate = {arrival_rate}, Service rate = {service_rate}. Trials = {trials}. Units of Time = {run_until}. Mean = {mean}. 98% conf. interval = {conf}'\n",
    "    \n",
    "    # plot \n",
    "    plt.figure(num=None, figsize=(14, 6), dpi=80, facecolor='w', edgecolor='k')\n",
    "    plt.hist(data, binn)\n",
    "    plt.xlabel('Numbers of Customers in Queue')\n",
    "    plt.ylabel('Frequency')\n",
    "    plt.xticks(binn)\n",
    "    plt.axvline(mean, color='grey', linestyle='--', label = 'Mean') # Make a vertical red dashed line\n",
    "    plt.axvline(conf[0], color='red', linestyle='--', label = '95% interval') # Make a vertical red dashed line\n",
    "    plt.axvline(conf[1], color='red', linestyle='--') # Make a vertical red dashed line\n",
    "    plt.legend()\n",
    "\n",
    "    plt.suptitle(title_string, fontsize=16)\n",
    "    plt.title(subtitle_string, fontsize=12)              \n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "// Sample mean: 20.9\n",
      "// Standard error of the mean: 0.963\n",
      "// 95% confidence interval of population mean: [22.787, 19.013]\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 1120x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# a experiment of 0.8 arrival_rate and 1 service_rate\n",
    "show_result(trials=100, arrival_rate = 0.8, service_rate = 1, run_until = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "// Sample mean: 21.716\n",
      "// Standard error of the mean: 0.288\n",
      "// 95% confidence interval of population mean: [22.28, 21.152]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1120x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# a experiment of 0.8 arrival_rate and 1 service_rate\n",
    "show_result(trials=1000, arrival_rate = 0.8, service_rate = 1, run_until = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "// Sample mean: 21.51\n",
      "// Standard error of the mean: 0.13\n",
      "// 95% confidence interval of population mean: [21.764, 21.256]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1120x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# a experiment of 0.8 arrival_rate and 1 service_rate\n",
    "show_result(trials=5000, arrival_rate = 0.8, service_rate = 1, run_until = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Arrival Rate: 1. Service Rate = 0.85\n",
      "// Sample mean: 2.66\n",
      "// Standard error of the mean: 0.321\n",
      "// 95% confidence interval of population mean: [3.29, 2.03]\n",
      "Arrival Rate: 1. Service Rate = 0.8681818181818182\n",
      "// Sample mean: 3.29\n",
      "// Standard error of the mean: 0.374\n",
      "// 95% confidence interval of population mean: [4.024, 2.556]\n",
      "Arrival Rate: 1. Service Rate = 0.8863636363636364\n",
      "// Sample mean: 3.51\n",
      "// Standard error of the mean: 0.438\n",
      "// 95% confidence interval of population mean: [4.368, 2.652]\n",
      "Arrival Rate: 1. Service Rate = 0.9045454545454545\n",
      "// Sample mean: 3.6\n",
      "// Standard error of the mean: 0.39\n",
      "// 95% confidence interval of population mean: [4.363, 2.837]\n",
      "Arrival Rate: 1. Service Rate = 0.9227272727272727\n",
      "// Sample mean: 4.35\n",
      "// Standard error of the mean: 0.43\n",
      "// 95% confidence interval of population mean: [5.192, 3.508]\n",
      "Arrival Rate: 1. Service Rate = 0.9409090909090909\n",
      "// Sample mean: 4.48\n",
      "// Standard error of the mean: 0.448\n",
      "// 95% confidence interval of population mean: [5.358, 3.602]\n",
      "Arrival Rate: 1. Service Rate = 0.9590909090909091\n",
      "// Sample mean: 5.52\n",
      "// Standard error of the mean: 0.552\n",
      "// 95% confidence interval of population mean: [6.601, 4.439]\n",
      "Arrival Rate: 1. Service Rate = 0.9772727272727273\n",
      "// Sample mean: 6.27\n",
      "// Standard error of the mean: 0.611\n",
      "// 95% confidence interval of population mean: [7.467, 5.073]\n",
      "Arrival Rate: 1. Service Rate = 0.9954545454545455\n",
      "// Sample mean: 6.43\n",
      "// Standard error of the mean: 0.576\n",
      "// 95% confidence interval of population mean: [7.559, 5.301]\n",
      "Arrival Rate: 1. Service Rate = 1.0136363636363637\n",
      "// Sample mean: 8.08\n",
      "// Standard error of the mean: 0.59\n",
      "// 95% confidence interval of population mean: [9.236, 6.924]\n",
      "Arrival Rate: 1. Service Rate = 1.0318181818181817\n",
      "// Sample mean: 9.59\n",
      "// Standard error of the mean: 0.779\n",
      "// 95% confidence interval of population mean: [11.116, 8.064]\n",
      "Arrival Rate: 1. Service Rate = 1.05\n",
      "// Sample mean: 9.58\n",
      "// Standard error of the mean: 0.754\n",
      "// 95% confidence interval of population mean: [11.058, 8.102]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Numbers of Customers in Queue')"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "conf_arr = []\n",
    "mean_arr = []\n",
    "space = np.linspace(0.85,1.05, 12)\n",
    "\n",
    "for sr in space:\n",
    "    data = experiment(trials=100, arrival_rate= 1, service_rate = sr, run_until=100)\n",
    "    print(f'Arrival Rate: 1. Service Rate = {sr}' )\n",
    "    mean, conf = mean_conf_int_95(data)\n",
    "    mean_arr.append(mean)\n",
    "    conf_arr.append(conf)\n",
    "    \n",
    "err = [i[0] - i[1] for i in conf_arr]\n",
    "# plt.plot(space, np.array(mean_arr),error=conf_arr)\n",
    "plt.errorbar(space, mean_arr, err,\n",
    "            color='black', marker='o', capsize=5, linestyle='--', linewidth=1)\n",
    "plt.axhline(5, color='red', linestyle='--', label = '95% interval')\n",
    "plt.xlabel('Service/Arrival Rate Ratio')\n",
    "plt.ylabel('Numbers of Customers in Queue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reflection \n",
    "Based on previous pre class work, I develop a function that run the experiment parameters and return an array with result. After experimenting 100 and 100 trials, we the sample mean of queue legnth is around 21, and the more time we run the trails, the narrower the confidence interval is. I use a plot function to plot informative plots, and finally experiment different ratio to see when does the mean queue legnth exceed 5 people."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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