auctions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Auction Theory\n",
    "> Auctions are bayesian games of incomplete information. The code below analyses auctions from a game theory perspective\n",
    "\n",
    "![Auctions](https://qph.ec.quoracdn.net/main-qimg-ffdfa32fb01c75477ef67c7a3a05daa1-c)\n",
    "Source: https://www.quora.com/What-are-the-best-books-to-start-learning-about-Auction-Theory\n",
    "\n",
    "### About this Notebook\n",
    "> The following is an experimental code to study different types of auctions. This is a part of CS6283 - Computational Social Choice. The code is written using Python 3.x and requires Ipython, numpy, math, matplotlib, decimal and itertools modules. \n",
    "\n",
    "> Author: _Sivam Pillai_\n",
    "\n",
    "Running the code is simple. Just run each cell sequentially.\n",
    "* Run the first cell, which generates an html file where you can enter the game specifications\n",
    "* The following cells go through a number of computation separated into modules for easy flow and understanding\n",
    "* The last cell shows optimal allocation and individual player's payments in a tabular form\n",
    "\n",
    "***\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">Tip: In this notebook the terms Players and Bidders are used interchangeably.</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<div style=\"background-color:gainsboro; border:solid black;\">\n",
       "<div><b>Enter the number of items and bidders for the Auction:</b></div>\n",
       "<div style=\"display:inline\">\n",
       "Players: <input type=\"text\" id=\"var_players\" value=\"1\">\n",
       "Items  : <input type=\"text\" id=\"var_items\" value=\"2\">\n",
       "</div>\n",
       "<button onclick=\"gen_table()\">Generate Table</button>\n",
       "</div>\n",
       "<style>\n",
       "    table{\n",
       "        width:100%;\n",
       "        height:10)%;\n",
       "    }\n",
       "    table td{\n",
       "        padding:5px;\n",
       "        margin:5px;\n",
       "        border:1px solid #ccc;\n",
       "    }\n",
       "</style>\n",
       "<div id=\"box\">\n",
       "<table id=\"basicTable\">\n",
       "</table>\n",
       "</div>\n",
       "<button onclick=\"rescale()\">Scale Values</button>\n",
       "<button onclick=\"set_valuations()\">Set Values</button>\n",
       "<button onclick=\"reset_valuations()\">Reset Values</button>\n",
       "\n",
       "<script type=\"text/Javascript\">\n",
       "    function gen_table(){\n",
       "        var players = document.getElementById('var_players').value;\n",
       "        var items = document.getElementById('var_items').value;\n",
       "        \n",
       "        var parent = document.getElementById('box');\n",
       "        var child = document.getElementById('basicTable');\n",
       "        parent.removeChild(child);\n",
       "        mytable = $('<table></table>').attr({ id: \"basicTable\" });\n",
       "        var rows = players;\n",
       "        var cols = items;\n",
       "        var tr = [];\n",
       "        var row = $('<tr><td></td></tr>').attr({ class: [\"class1\"].join(' ') }).appendTo(mytable);\n",
       "        for (var j = 0; j < cols; j++) {\n",
       "                $(\"<td>Item \"+j+\"</td>\").appendTo(row); \n",
       "            }\n",
       "        for (var i = 0; i < rows; i++) {\n",
       "            var row = $('<tr></tr>').attr({ class: [\"class1\"].join(' ') }).appendTo(mytable);\n",
       "            $(\"<td>Player \"+i+\"</td>\").appendTo(row)\n",
       "            for (var j = 0; j < cols; j++) {\n",
       "                $(\"<td><input type='text' id='val_\"+i+\"_\"+j+\"' value='1'></td>\").appendTo(row); \n",
       "            }\n",
       "\n",
       "        }\n",
       "        console.log(\"TTTTT:\"+mytable.html());\n",
       "        mytable.appendTo(\"#box\"); \n",
       "    }\n",
       "\n",
       "    function set_valuations(){\n",
       "        var players = document.getElementById('var_players').value;\n",
       "        var items = document.getElementById('var_items').value;\n",
       "        var val = [];\n",
       "        var x;\n",
       "        for (var i = 0; i < players; i++) {\n",
       "            val[i] = [];\n",
       "            for (var j = 0; j < items; j++) {\n",
       "                x = 'val_'+i+'_'+j;\n",
       "                val[i][j] = document.getElementById(x).value;\n",
       "            }\n",
       "        }\n",
       "        \n",
       "        var kernel = IPython.notebook.kernel;\n",
       "        var cmd1 = \"players = \" + players;\n",
       "        var cmd2 = \"items = \" + items;\n",
       "        var cmd3 = \"my_val = \" + val;\n",
       "        \n",
       "        console.log(\"Executing Command: \" + cmd1);     \n",
       "        kernel.execute(cmd1);\n",
       "        \n",
       "        console.log(\"Executing Command: \" + cmd2);\n",
       "        kernel.execute(cmd2);  \n",
       "        \n",
       "        console.log(\"Executing Command: \" + cmd3);\n",
       "        kernel.execute(cmd3); \n",
       "\n",
       "    }\n",
       "    \n",
       "    function rescale(){\n",
       "        var players = document.getElementById('var_players').value;\n",
       "        var items = document.getElementById('var_items').value;\n",
       "        var val = [];\n",
       "        var sum = 0;\n",
       "        var x;\n",
       "        for (var i = 0; i < players; i++) {\n",
       "            val[i] = [];\n",
       "            for (var j = 0; j < items; j++) {\n",
       "                x = 'val_'+i+'_'+j;\n",
       "                val[i][j] = parseInt(document.getElementById(x).value);\n",
       "                sum = sum + val[i][j];  \n",
       "            }\n",
       "            for (var j = 0; j < items; j++) {\n",
       "                val[i][j] = val[i][j]*100/sum;\n",
       "                x = 'val_'+i+'_'+j;\n",
       "                document.getElementById(x).value = val[i][j];\n",
       "            }\n",
       "            sum = 0\n",
       "        }        \n",
       "    }\n",
       "    function reset_valuations(){\n",
       "        var players = document.getElementById('var_players').value;\n",
       "        var items = document.getElementById('var_items').value;\n",
       "        var val = [];\n",
       "        var x;\n",
       "        for (var i = 0; i < players; i++) {\n",
       "            val[i] = [];\n",
       "            for (var j = 0; j < items; j++) {\n",
       "                x = 'val_'+i+'_'+j;\n",
       "                document.getElementById(x).value = 1;\n",
       "            }\n",
       "        }\n",
       "    \n",
       "    }\n",
       "</script>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "\n",
    "input_form = \"\"\"\n",
    "<div style=\"background-color:gainsboro; border:solid black;\">\n",
    "<div><b>Enter the number of items and bidders for the Auction:</b></div>\n",
    "<div style=\"display:inline\">\n",
    "Players: <input type=\"text\" id=\"var_players\" value=\"1\">\n",
    "Items  : <input type=\"text\" id=\"var_items\" value=\"2\">\n",
    "</div>\n",
    "<button onclick=\"gen_table()\">Generate Table</button>\n",
    "</div>\n",
    "<style>\n",
    "    table{\n",
    "        width:100%;\n",
    "        height:10)%;\n",
    "    }\n",
    "    table td{\n",
    "        padding:5px;\n",
    "        margin:5px;\n",
    "        border:1px solid #ccc;\n",
    "    }\n",
    "</style>\n",
    "<div id=\"box\">\n",
    "<table id=\"basicTable\">\n",
    "</table>\n",
    "</div>\n",
    "<button onclick=\"rescale()\">Scale Values</button>\n",
    "<button onclick=\"set_valuations()\">Set Values</button>\n",
    "<button onclick=\"reset_valuations()\">Reset Values</button>\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "\n",
    "javascript = \"\"\"\n",
    "<script type=\"text/Javascript\">\n",
    "    function gen_table(){\n",
    "        var players = document.getElementById('var_players').value;\n",
    "        var items = document.getElementById('var_items').value;\n",
    "        \n",
    "        var parent = document.getElementById('box');\n",
    "        var child = document.getElementById('basicTable');\n",
    "        parent.removeChild(child);\n",
    "        mytable = $('<table></table>').attr({ id: \"basicTable\" });\n",
    "        var rows = players;\n",
    "        var cols = items;\n",
    "        var tr = [];\n",
    "        var row = $('<tr><td></td></tr>').attr({ class: [\"class1\"].join(' ') }).appendTo(mytable);\n",
    "        for (var j = 0; j < cols; j++) {\n",
    "                $(\"<td>Item \"+j+\"</td>\").appendTo(row); \n",
    "            }\n",
    "        for (var i = 0; i < rows; i++) {\n",
    "            var row = $('<tr></tr>').attr({ class: [\"class1\"].join(' ') }).appendTo(mytable);\n",
    "            $(\"<td>Player \"+i+\"</td>\").appendTo(row)\n",
    "            for (var j = 0; j < cols; j++) {\n",
    "                $(\"<td><input type='text' id='val_\"+i+\"_\"+j+\"' value='1'></td>\").appendTo(row); \n",
    "            }\n",
    "\n",
    "        }\n",
    "        console.log(\"TTTTT:\"+mytable.html());\n",
    "        mytable.appendTo(\"#box\"); \n",
    "    }\n",
    "\n",
    "    function set_valuations(){\n",
    "        var players = document.getElementById('var_players').value;\n",
    "        var items = document.getElementById('var_items').value;\n",
    "        var val = [];\n",
    "        var x;\n",
    "        for (var i = 0; i < players; i++) {\n",
    "            val[i] = [];\n",
    "            for (var j = 0; j < items; j++) {\n",
    "                x = 'val_'+i+'_'+j;\n",
    "                val[i][j] = document.getElementById(x).value;\n",
    "            }\n",
    "        }\n",
    "        \n",
    "        var kernel = IPython.notebook.kernel;\n",
    "        var cmd1 = \"players = \" + players;\n",
    "        var cmd2 = \"items = \" + items;\n",
    "        var cmd3 = \"my_val = \" + val;\n",
    "        \n",
    "        console.log(\"Executing Command: \" + cmd1);     \n",
    "        kernel.execute(cmd1);\n",
    "        \n",
    "        console.log(\"Executing Command: \" + cmd2);\n",
    "        kernel.execute(cmd2);  \n",
    "        \n",
    "        console.log(\"Executing Command: \" + cmd3);\n",
    "        kernel.execute(cmd3); \n",
    "\n",
    "    }\n",
    "    \n",
    "    function rescale(){\n",
    "        var players = document.getElementById('var_players').value;\n",
    "        var items = document.getElementById('var_items').value;\n",
    "        var val = [];\n",
    "        var sum = 0;\n",
    "        var x;\n",
    "        for (var i = 0; i < players; i++) {\n",
    "            val[i] = [];\n",
    "            for (var j = 0; j < items; j++) {\n",
    "                x = 'val_'+i+'_'+j;\n",
    "                val[i][j] = parseInt(document.getElementById(x).value);\n",
    "                sum = sum + val[i][j];  \n",
    "            }\n",
    "            for (var j = 0; j < items; j++) {\n",
    "                val[i][j] = val[i][j]*100/sum;\n",
    "                x = 'val_'+i+'_'+j;\n",
    "                document.getElementById(x).value = val[i][j];\n",
    "            }\n",
    "            sum = 0\n",
    "        }        \n",
    "    }\n",
    "    function reset_valuations(){\n",
    "        var players = document.getElementById('var_players').value;\n",
    "        var items = document.getElementById('var_items').value;\n",
    "        var val = [];\n",
    "        var x;\n",
    "        for (var i = 0; i < players; i++) {\n",
    "            val[i] = [];\n",
    "            for (var j = 0; j < items; j++) {\n",
    "                x = 'val_'+i+'_'+j;\n",
    "                document.getElementById(x).value = 1;\n",
    "            }\n",
    "        }\n",
    "    \n",
    "    }\n",
    "</script>\n",
    "\"\"\"\n",
    "\n",
    "HTML(input_form + javascript)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# First compute all different one-to-one allocations of items to bidders. \n",
    "import numpy as np\n",
    "import itertools\n",
    "\n",
    "k = 0\n",
    "allocs = np.zeros((items, players, 2),dtype=int)\n",
    "valuations = np.zeros((players, items),dtype=int)\n",
    "for i in range(0,players):\n",
    "    for j in range(0,items):\n",
    "        allocs[j,i] = [j,i]\n",
    "        valuations[i,j] = my_val[k]\n",
    "        k += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Identify all possible allocations of items to bidders\n",
    "########\n",
    "# Uncomment the following lines to use the default values for items, players and valuations\n",
    "# items = 4\n",
    "# players = 3\n",
    "# allocs = np.array([[[0,0],[0,1],[0,2]],\n",
    "#                    [[1,0],[1,1],[1,2]],\n",
    "#                    [[2,0],[2,1],[2,2]],\n",
    "#                    [[3,0],[3,1],[3,2]],])\n",
    "# valuations = np.array([[10,20,30,40],\n",
    "#                        [20,20,20,40],\n",
    "#                        [40,10,10,40]])\n",
    "########\n",
    "\n",
    "x = list(itertools.product(*allocs))\n",
    "r = len(x)\n",
    "c = len(x[0])\n",
    "\n",
    "p = np.zeros((r,items+players+1),dtype=int)\n",
    "\n",
    "for i in range(0,r):\n",
    "    for j in range(0,c):\n",
    "        plid = x[i][j][1]+items # initial columns are items\n",
    "        p[i,plid] = p[i,plid] + valuations[x[i][j][1], x[i][j][0]]\n",
    "        p[i,x[i][j][0]] = x[i][j][1]\n",
    "    p[i,-1] = np.sum(p[i,-players-1:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Store the data in the form of a Dataframe\n",
    "import pandas as pd\n",
    "\n",
    "auctions_df = pd.DataFrame(p)\n",
    "auctions_df.columns = ['i'+ str(i) for i in range(0,items)] + \\\n",
    "                      ['v'+ str(i) for i in range(0,players)] + \\\n",
    "                      ['v_tot']\n",
    "#auctions_df.columns = ['i1','i2','i3','i4','v1','v2','v3','v_tot']\n",
    "v_max_without = np.zeros(players,dtype=int)\n",
    "for i in range(0,players):\n",
    "    v_max_without[i] = max(auctions_df.v_tot[(auctions_df.filter(regex='i') != i).all(1)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Vickrey–Clarke–Groves Auction (VCG)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "vcg = auctions_df[auctions_df.v_tot == max(auctions_df.v_tot)].copy().reset_index(drop=True)\n",
    "for i in range(0,players):\n",
    "    vcg.loc[:,'p'+str(i)] = v_max_without[i] - (vcg.v_tot - vcg['v'+str(i)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Items:  ['i0', 'i1', 'i2', 'i3']\n",
      "Valuation of Player to their allocation:  ['v0', 'v1', 'v2']\n",
      "Payment of Player for their allocation:  ['p0', 'p1', 'p2']\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>i0</th>\n",
       "      <th>i1</th>\n",
       "      <th>i2</th>\n",
       "      <th>i3</th>\n",
       "      <th>v0</th>\n",
       "      <th>v1</th>\n",
       "      <th>v2</th>\n",
       "      <th>v_tot</th>\n",
       "      <th>p0</th>\n",
       "      <th>p1</th>\n",
       "      <th>p2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>130</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>50</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>130</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>50</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>130</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>20</td>\n",
       "      <td>40</td>\n",
       "      <td>130</td>\n",
       "      <td>60</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>60</td>\n",
       "      <td>40</td>\n",
       "      <td>130</td>\n",
       "      <td>20</td>\n",
       "      <td>60</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>20</td>\n",
       "      <td>80</td>\n",
       "      <td>130</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_names = list(vcg)\n",
    "print('Items: ',[s for s in col_names if 'i' in s[0]])\n",
    "print('Valuation of Player to their allocation: ',[s for s in col_names if 'v' in s[0] and '_' not in s])\n",
    "print('Payment of Player for their allocation: ',[s for s in col_names if 'p' in s[0]])\n",
    "HTML(vcg.to_html())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VCG Payments:\n",
      "\n",
      "VCG Cost for Player  0 :  10\n",
      "VCG Cost for Player  1 :  0\n",
      "VCG Cost for Player  2 :  20\n"
     ]
    }
   ],
   "source": [
    "print('VCG Payments:\\n')\n",
    "for i in range(0,players):\n",
    "    print('VCG Cost for Player ', i, ': ', vcg['v'+str(i)][0] - vcg['p'+str(i)][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Part 2 - Seller Revenue for Uniform Distribution Bidder Valuation in [0,1]\n",
    "### Single Item, Second Price Auction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "def second_largest(numbers):\n",
    "    count = 0\n",
    "    m1 = m2 = float('-inf')\n",
    "    for x in numbers:\n",
    "        count += 1\n",
    "        if x > m2:\n",
    "            if x >= m1:\n",
    "                m1, m2 = x, m1            \n",
    "            else:\n",
    "                m2 = x\n",
    "    return m2 if count >= 2 else None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def get_revenue(pnum,players, items, steps, trials, second_price):\n",
    "    rev = np.zeros((steps,trials))\n",
    "    thresh = np.zeros(steps)\n",
    "      \n",
    "    for i in range(0,steps):\n",
    "        for j in range(0,trials):\n",
    "            valuations_scale = np.random.rand(players,items)\n",
    "            if np.max(valuations_scale) >= thresh[i]:\n",
    "                if second_largest(valuations_scale) >= thresh[i]:\n",
    "                    rev[i,j] = second_largest(valuations_scale)\n",
    "                else:\n",
    "                    if second_price:\n",
    "                        rev[i,j] = thresh[i]\n",
    "                    else:\n",
    "                        rev[i,j] = np.max(valuations_scale)\n",
    "            else:\n",
    "                rev[i,j] = 0\n",
    "            f_part.value = j + i*trials\n",
    "            f_full.value = pnum*steps*trials + j + i*trials\n",
    "            \n",
    "        if i<steps-1:\n",
    "            thresh[i+1] = thresh[i] + 1/steps\n",
    "    \n",
    "    return rev, thresh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Set of Players:  [2, 4, 8, 10, 20, 40, 80, 100, 500, 1000]\n",
      "Current Players Set:  2\n",
      "Current Players Set:  4\n",
      "Current Players Set:  8\n",
      "Current Players Set:  10\n",
      "Current Players Set:  20\n",
      "Current Players Set:  40\n",
      "Current Players Set:  80\n",
      "Current Players Set:  100\n",
      "Current Players Set:  500\n",
      "Current Players Set:  1000\n",
      "\n",
      "All Runs Complete...\n"
     ]
    },
    {
     "data": {
      "image/png": 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4+2w/cE2mKPFLINTkCsJKsEJVWlq6xUXRY9O57lI5dOhQhg0bxrDobPrY9NBk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\nx4nkKn33RUKlPzDX3Re6+wbgceCo6BXcfYa7l0YezgQ6pjlGERGpJ1T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GVE7Hm4ffokruvyy+HFF2MvSq1bBx07Ri+zZg3cey9cckniuuKlpcbHFrRlHY2xYQN89FH85V9/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa6f44a8160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from decimal import *\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "from ipywidgets import FloatProgress\n",
    "from IPython.display import display\n",
    "%matplotlib inline\n",
    "\n",
    "#players = [2,4,8,10]\n",
    "players = [2,4,8,10,20,40,80,100,500,1000]\n",
    "select_flag = [1,0,0,1,0,0,0,1,0,1]\n",
    "steps = 10\n",
    "trials = 1000\n",
    "items = 1\n",
    "subplotnum = 1\n",
    "    \n",
    "print('Set of Players: ',players)\n",
    "ax = plt.figure(figsize=(15,10))\n",
    "ax.suptitle('Box plot showing mean and median over 1000 trials')\n",
    "trend_data = np.zeros((len(players),2,steps))\n",
    "trend_rev = np.zeros((len(players)))\n",
    "\n",
    "f_part = FloatProgress(min=0, max=steps*trials,description='Current Progress:')\n",
    "f_full = FloatProgress(min=0, max=len(players)*steps*trials,description='Overall Progress:')\n",
    "\n",
    "display(f_part)\n",
    "display(f_full)\n",
    "\n",
    "for i in range(0,len(players)):\n",
    "    \n",
    "    print('Current Players Set: ', players[i])\n",
    "    rev, thresh = get_revenue(i,players[i],items,steps,trials,second_price=True)\n",
    "    #print(str(players[i]),\" Players\\nMedian: \",[float(Decimal(\"%.2f\" % e)) for e in np.median(rev,axis=1)],\n",
    "    #      \"\\nMean  : \",[float(Decimal(\"%.2f\" % e)) for e in np.mean(rev,axis=1)] )\n",
    "    \n",
    "\n",
    "    n = players[i]\n",
    "    n_fact = math.factorial(players[i])\n",
    "    theo_rev_0 = (n-1)/(n+1)\n",
    "\n",
    "    #theo_thresh = 1/(1+1/(n-1/math.factorial(n-2)))\n",
    "    theo_thresh = 1/2\n",
    "\n",
    "    k = (theo_thresh+0.1)*steps\n",
    "\n",
    "\n",
    "    theo_rev = theo_rev_0 + \\\n",
    "                np.power(theo_thresh,n) - \\\n",
    "                (2*n/(n+1))*np.power(theo_thresh,n+1)\n",
    "\n",
    "    #l2, = plt.plot(k,theo_rev, 'og')\n",
    "    if select_flag[i] == 1:\n",
    "        l0 = plt.subplot(2,2,subplotnum)\n",
    "        subplotnum += 1\n",
    "        l1 = plt.boxplot(np.transpose(rev),notch=True,showmeans=True)\n",
    "        l2 = plt.plot(k,theo_rev, 'og',label='Theoretical best')\n",
    "        x = np.zeros((2,len(l1['means'])))\n",
    "        for j in range(0,len(l1['means'])):\n",
    "            x[0,j] = l1['means'][j].get_xdata()\n",
    "            x[1,j] = l1['means'][j].get_ydata()\n",
    "        plt.plot(x[0],x[1],'g--',label='Mean Line')\n",
    "        plt.xticks([i for i in range(1,steps+1)],thresh)\n",
    "        plt.xlabel('Reserve Price')\n",
    "        plt.ylabel('Seller Revenue')\n",
    "        plt.title(str(players[i])+' Players - IID')\n",
    "        handles, labels = l0.get_legend_handles_labels()\n",
    "        l0.legend(handles[::-1], labels[::-1],loc='lower left')\n",
    "        \n",
    "    trend_data[i,0,:] = np.mean(rev,axis=1)\n",
    "    trend_data[i,1,:] = np.median(rev,axis=1)\n",
    "    trend_rev[i] = theo_rev\n",
    "print('\\nAll Runs Complete...')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fa70a2752b0>"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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w8SI4OPB6587g65v79fBwRtWqBR4euZdHjwSLBRwceKhixfwJ7d4wXMxmsGvDinnz6GWx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HJknj7+mP7Jl9no3JS/ug6mefOcT6ekp1Dh3QpceeMKOrUcVarAlStgmCkvCQlQtSokJspoFyFs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\nTkxM1MePH9d169bVLi4uxX7tA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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa738732f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.pyplot import gca\n",
    "\n",
    "ax = plt.figure(figsize=(15,5))\n",
    "a0 = plt.subplot(1,2,1)\n",
    "plt.plot(trend_data[:,0,:])\n",
    "plt.plot(trend_rev,'r^-.',linewidth=3.0,label='Theoretical best')\n",
    "plt.xticks([i for i in range(0,len(players))],players)\n",
    "plt.xlabel('Number of Players')\n",
    "plt.ylabel('Seller Revenue')\n",
    "plt.title('Mean Revenue wrt Number of Players')\n",
    "\n",
    "handles, labels = a0.get_legend_handles_labels()\n",
    "a2 = plt.legend(thresh,loc='lower right',title='Reserve Price')\n",
    "a0.legend(handles[::-1], labels[::-1],loc='lower left')\n",
    "gca().add_artist(a2)\n",
    "\n",
    "a1 = plt.subplot(1,2,2)\n",
    "plt.plot(trend_data[:,1,:])\n",
    "plt.plot(trend_rev,'r^-.',linewidth=3.0,label='Theoretical best')\n",
    "plt.xticks([i for i in range(0,len(players))],players)\n",
    "plt.xlabel('Number of Players')\n",
    "plt.ylabel('Seller Revenue')\n",
    "plt.title('Median Revenue wrt Number of Players')\n",
    "\n",
    "a2 = plt.legend(thresh,loc='lower right',title='Reserve Price')\n",
    "handles, labels = a1.get_legend_handles_labels()\n",
    "a1.legend(handles[::-1], labels[::-1],loc='lower left')\n",
    "gca().add_artist(a2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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