plot_user_similarity_graph.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>.container { width:100% !important; }</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import cross_validation\n",
    "from sklearn.metrics import mean_squared_error,r2_score\n",
    "import fileinput\n",
    "import operator\n",
    "import math\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#Fit the code to the screen\n",
    "from IPython.core.display import HTML\n",
    "HTML(\"<style>.container { width:100% !important; }</style>\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading User Movie and Ratings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572, 3.69248291572]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f9686387f50>]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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k3KnQN4EBA2DhQujQIQy7vOkm+GCnIxYiIoWhQt9E9toLbrkF5syB1auhVy+4/Xb45JOk\nk4lIuVGhb2IHHRQuJv7Xv8JTT8Hhh8PEibBtW9LJRKRcaNRNgWWzYXrjrVvhhz+E009POpGIpJ2G\nVxYhd/jDH0Lf/SGHhC6eo49OOpWIpJWGVxYhM7jwQvjnP+Gcc2DwYLj4Yli2LOlkIlKKVOgT1LIl\nXH01LFoE3brBccfB9dfDv/6VdDIRKSUq9CnQrl24gMkrr8CGDWEahZkzk04lIqVCffQp9Kc/wTXX\nwPPPh1E7IlLeGttHH9d89BKjL34RliwJ/ffTpsEeeySdSESKmVr0KeUOV10FK1bAlClhagURKU8a\ndVOizODOO8N4+2uv1cXHRaThVOhTrEULmDw5nGT1058mnUZEipU6BFKufXt47DE4+WTo3h0+//mk\nE4lIsVEffZF46aVwcHbq1DDeXkTKh/roy0TfvvDLX8LQobByZdJpRKSYqOumiJx//vZhl3//u4Zd\nikh+8mrRm9kQM1tgZovMbHSO5e3MbIqZzTGz+WZ2aexJBYBRo+CEE2DYMNiyJek0IlIM6u2jN7Nm\nwCLgdOAtYAbwFXdfUG2dG4F27n6jme0DLAT2d/ctNbalPvoYbN4MZ50V5rbXaByR0leIPvq+wGJ3\nX+bum4EHgC/UWMeBqo6EPYB1NYu8xKdFC3joIXj6afjZz5JOIyJpl08f/YHAimqPVxKKf3V3AlPM\n7C2gLfDleOJJbWoOuzz77KQTiUhaxXUwdjAw291PM7MewFNm1sfdN9RcsbKy8rP7mUyGTCYTU4Ty\n061bmADt7LPhy18Oc9r37RvOqhWR4pXNZslms7FtL58++hOBSncfEj2+AXB3v6XaOn8Bxrr7P6LH\nTwOj3f3lGttSH30TWL4c7r8/3Jo1CwX/oos086VIqShEH/0M4BAz62pmLYGvAFNqrLMMOCMKtD/Q\nC1ja0FCya7p0ge9+FxYuDBciX7ECjjkmXI/2/vvDHPciUr7yOjPWzIYAdxC+GCa4+zgzu5LQsh9v\nZp2A3wCdoh8Z6+6TcmxHLfoC2bQJ/vIXuO++MOZ+6FD4+tdhwICkk4nIrtLFwaVe77wDEyfC7bfD\nkCFw663Qpk3SqUQkX5oCQeq1335hquO5c+Hjj+HYY8PcOSJSHtSiL0N/+EO4KPnIkXDzzbqoiUja\nqetGGmTVKhgxAj78EH77W+jZM+lEIlIbdd1Igxx4YJjyePhwOOkkuPtuXcVKpFSpRS+8+ip87Wth\nmOb994ezbkUkPdSil0br3RtefBE6dYIzz4T33ks6kYjESYVeAGjZMlzYpH9/OO00WLs26UQiEhcV\nevmMWRhjP2RIKPbvvJN0IhGJgwbWyQ7M4H//F3bbDTKZMBVyp071/piIpJgKvezEDCorQ3fOwIHw\nzDPQuXPSqUSkoVTopVY33RRa9gMHhpZ9t25JJxKRhlChlzp9+9uhZZ/JhLH2p58epkIWkeKhcfSS\nl8mT4fvfD7NiXnklXHopdOiQdCqR8qBx9FIQF14YJkX7zW9gzhzo0SNc4OT553VGrUjaqUUvDbJ2\nbbjIyV13wQEHhG6dQw9NOpVIaVKLXhKxzz4wahQsWgQXXACnnAI//jFs3Zp0MhGpSS16icWSJXDF\nFbBxI/z613DEEUknEikdatFLKvToEYZgXnIJnHoqjB2ra9WKpIVa9BK7N9+Eb30rFP4+fcLQzEwG\nTj4Z2rZNOJxIESrIhUeii4PfzvaLg99SY/n1wFcBB1oAhwP7uPv7NdZToS8jH38ML7wA2Wy4zZoF\ne+wRrmhVUQHNm4cpkSdNgl69kk4rkl5NXujNrBmwCDgdeAuYAXzF3RfUsv45wLXufkaOZSr0ZWzj\nxjAF8pYt4aDtli3wxBNw553hC2HvvZNOKJJOjS30+ZwZ2xdY7O7Loh0+AHwByFnogWHApIYGktLV\nqlW4VdezJyxbFkbuPPkktGiRTDaRQtu8Gd59N8wSW9+tsfJp0Z8PDHb3b0SPvwb0dfdv5li3FbAS\n6FGz2yZarha97GTrVjj33DBL5l13hUnVRIqNO7z//o4Fes2a2ov3+vVhmPJ++9V/69696Vv0u+Lz\nwLRcRb5KZWXlZ/czmQyZTCbmCFJsmjeHiRPDWPzbb4frrtt5HXdYuhRmzAgna33wQbgNGACf/3zh\nM0t52Lgxvxb3O++E1nnr1rkLde/eMGjQjs/ttVft80Zls1my2Wxs/498WvQnApXuPiR6fAPgNQ/I\nRsv+CEx29wdq2ZZa9FKrZcvChcqPPRb23DMcqG3bFl5/HaZPDwdw+/WDjh23L5swAT73Objttp27\nhaQ8bdkCH30UhvdW/Vt1q+/xhg2wbt324v3pp7D//vm1uvfdN8z22hQKcTC2ObCQcDB2NfASMMzd\nX6uxXntgKdDZ3TfWsi0VeqnT8uVhLp0PPoAPPwy3bt3C0MwuXXbu1vngA7jqKpg/H370o9ACW706\nfEBPPTV8aTRrBtu2hb8I9tsP2rVL5L+WmKVLw5dg1fGPqt9hY/6NYxvu4fXalUKcz+NPP4U2bUJD\noG3bHe/X97hNmzBZX1XxbtcuHV2JhRxeeQfbh1eOM7MrCS378dE6lxD68ofXsR0Veomde5h35557\nQquqU6fw4fzb30Kr7OCD4bXXwp/KZvC734UvgVL16afw9tthDqIpU+Cyy0JLc+vW7RPQNebfOLZR\npXXr/IpvvoW6bVvYffd0FOc4FaTQx0WFXgpt1apwAlfv3qE76PHH4fLL4RvfgO9+tzRG+axbF2YW\nbd0aPvkkHONYtSoU1ObN4dFH4fjjk04pjaFCL7KLVq+GESPCv3ffDX37Jp2oYVavhnHjwtTRRx8d\ninyzZjBsGFxzTTi20bFjOElNilshxtGLlJROncKJWhMnwtCh4SIqP/hBaP1W98knMHs2vPhi6Pq4\n4IIwHK4269eHM4BnzgzD6tq1g0MOCf8ee2zjzv5duhRefjkckP773+H3vw9dNMOGhWW5LgLTs2fD\n9yelRS16KWtr18KXvxwulzhpUjg4OHVq6O54+ukwWVu/fuGg7+OPw2GHQdeu4UBd27ahBf3xx/DS\nS6H7pF+/cOC4Y8cwpnrp0nBA+bnn4Mgjw4HlioqwbMaM8OXQpUv4C+Oqq8KyKtu2hX1Onw7jx4dr\n927dCgcdFC7x2LnzjutL6VLXjUgjbdkSCud994XCfcYZcM45cNZZO7bgN2wII4KWLw9fEOvXh37w\n3XeHY44JBb5169z72LQpfIG8+24o1m3bhn7zvfcOI4bGjg1/QfTsGfL06AEPPRS2feaZYVZQXdil\nfKnQi8Rk6dLQuk6ilbxtWzgr2D2MGFmyJHzRDBpUeiNIZNep0IuIlDhdeEREROqkQi8iUuJU6EVE\nSpwKvYhIiVOhFxEpcSr0IiIlToVeRKTEqdCLiJQ4FXoRkRKnQi8iUuJU6EVESpwKvYhIicur0JvZ\nEDNbYGaLzGx0LetkzGy2mb1iZs/GG1NERBqq3kJvZs2AO4HBQG9gmJkdVmOd9sDPgXPc/UjgS02Q\ntUlks9mkI+SUxlzKlB9lyl8ac6UxU2Pl06LvCyx292Xuvhl4APhCjXWGAw+7+yoAd18bb8ymk9YX\nNY25lCk/ypS/NOZKY6bGyqfQHwisqPZ4ZfRcdb2Avc3sWTObYWYXxRWwqb355ptJR8gpjbmUKT/K\nlL805kpjpsaK61o6FcBxwGlAG+B5M3ve3V+PaftNJq0vahpzKVN+lCl/acyVxkyNlU+hXwV0qfa4\nc/RcdSuBte6+CdhkZs8BRwM7FXpL4XXR0pgJ0plLmfKjTPlLY640ZmqMfAr9DOAQM+sKrAa+Agyr\nsc4jwM/MrDmwG9APuK3mhhpzKSwREWmYegu9u281s6uB/yP06U9w99fM7Mqw2Me7+wIzexKYB2wF\nxrv7P5s0uYiI5KWgFwcXEZHCK8iZsWY2wczWmNm8QuwvH2bW2cyeMbNXzWy+mX0z6UxVzKyZmc0y\nsylJZwEws+uiE+HmmdnvzaxlQjlyvo/M7Bozey16HccVONNuZvZidLLgfDMbEz3/wyjTHDN72Mza\nFThXezP7Q5ThVTPrV23Zt81sm5nt3cQZdnq9avu9mFmFmf0meo+9amY3NFGmmp/7a6Lnx5jZyuhz\nN8vMhlT7mT5mNj36DMxtive/mb0ZbXu2mb0UPXdBtM+tZnZctXXPMLOXo/VnmNmgenfg7k1+A/oD\nxwDzCrG/PDN1BI6J7rcFFgKHJZ0rynMd8DtgSgqyHAAsBVpGjx8ELk4oy07vIyBD6FasiB7vk0Cu\n1tG/zYEXCOeenAE0i54fB4wtcKbfACOi+xVAu+h+Z2Aq8AawdwKvV87fC+G438TofqsoX5cmyJTz\ncw+MAf4rx/rNgbnAkdHjvYh6QmLOtRTYq8ZzhwI9gWeA46o9fzTQMbrfG1hZ3/YL0qJ392nAe4XY\nV77c/W13nxPd3wC8xs7nBxScmXUGzgLuSTpLNc2BNmZWAbQG3koiRC3vo/8Axrn7lmidgp+s5+4f\nR3d3IxRVd/e/uvu26PkXCAW2IKJW8gB3vzfKt8XdP4wW/wQYVYgcuV6vOn4vTniPNSe8xz4BPiRm\n9Xzucw0W+Rww191fiX7mPY8qbMyMGj0s7r7Q3RfXzOXuc9397ej+q8DuZtairo1rUjPAzLoRWh4v\nJpsE2P5BTMXBE3d/C7gVWE4YVvu+u/812VQ76AWcamYvRCfsHV/oAFFX22zgbeApd59RY5XLgCcK\nGOlgYK2Z3Rt1Q4w3s9ZmNhRY4e7zC5ilLtV/Lw8BHxNG9r0J/Njd32/Knef43F8ddSndE03rAuH9\nhZlNjbpLmupL0oGnoq6Yr+f7Q2Z2ATDLw6wFtSr7Qm9mbQlvsm9F3/BJZjkbWBO1OIzcLYyCMrM9\nCVNedCV047Q1s+HJptpBBeFP3hOB7wCTCx3A3be5+7GE1mk/MzuiapmZ3QxsdveJBYxUdQLjz939\nOOAjoBK4idBF8Vm8AmbaQY7fS19gC6FrpTtwfVSIm2r/NT/3vwC6u/sxhC/squHhFcAphK6lAcAX\n8+oT33WnRK/VWcB/mln/PP4PvYGxwDfqW7esC33UFfEQ8Ft3fyTpPIQ31FAzWwpMAgaZ2f0JZzoD\nWOru/3L3rcAfgZMTzlTdCkImopb0NjPrkESQqHvkWWAIgJldSvjgFvqLcSWh5f5y9PhhQuHvBsw1\nszcIX0ozzWy/Amer7fcyHJgafWm+C/wDaJK/znJ97t393WpdMncDJ0T3VwLPRV02G4HHCb/LWLn7\n6qocwJ8IX3x1/R86E973F7n7m/Vtv5CFPhUt1Bp+DfzT3e9IOgiAu9/k7l3cvTvhxLRn3P3ihGMt\nB040s93NzIDTCf2aSan5PvozYeoNzKwX0MLd1xUsjNk+VX/mm1kr4ExgQTRqYxQw1N0/KVQeAHdf\nA6yIfh8QXrOZ7t7R3bu7+8GEAnasu7/TxHF2eL3q+L0sZ/vr2AY4EVjQRJl2+tybWcdqy88DXonu\nPwkcFb3/K4CBQKznCEXdam2j+20IxwVeqblatfXbA38BRrv7C3ntJO6jx7UcUZ5IOID3CeEFHVGI\n/daT6RTCyV1zgNnALGBI0rmq5RtICkbdRFnGEIr7POA+QjFNIsdO7yPCn9a/BeYDLwMDC5zpqOi9\nMyf6/dwcPb8YWBYtmwX8osC5jiac1T6H0PJrX2P5Upp+1E2u1yvn74UwR9bkqMC9Qo4RMDFlyvm5\nB+6PXr85hMbD/tV+ZniUaR5NMHqKcEylKs984Ibo+XMJf7FuJBy7eCJ6/mZgfZS96v9Q52gznTAl\nIlLiyrqPXkSkHKjQi4iUOBV6EZESp0IvIlLiVOhFREqcCr2ISIlToRcRKXEq9CIiJe7/AfVB2cGe\n0PvzAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9686812710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Reading the movies data\n",
    "my_path = !pwd\n",
    "\n",
    "\n",
    "rand_mse = 3.69248291572\n",
    "\n",
    "\n",
    "users = [1,\n",
    "2,\n",
    "3,\n",
    "4,\n",
    "5,\n",
    "6,\n",
    "7,\n",
    "8,\n",
    "9,\n",
    "10,\n",
    "11,\n",
    "12,\n",
    "13,\n",
    "14,\n",
    "15,\n",
    "16,\n",
    "17,\n",
    "18,\n",
    "19,\n",
    "20,\n",
    "21,\n",
    "22,\n",
    "23,\n",
    "24,\n",
    "25,\n",
    "26,\n",
    "27,\n",
    "28,\n",
    "29,\n",
    "30,\n",
    "31,\n",
    "32,\n",
    "33,\n",
    "34,\n",
    "35,\n",
    "36,\n",
    "37,\n",
    "38,\n",
    "39,\n",
    "40,\n",
    "41,\n",
    "42,\n",
    "43,\n",
    "44,\n",
    "45,\n",
    "46,\n",
    "47,\n",
    "48,\n",
    "49,\n",
    "50,\n",
    "51,\n",
    "52,\n",
    "53,\n",
    "54,\n",
    "55,\n",
    "56,\n",
    "57,\n",
    "58,\n",
    "59,\n",
    "60,\n",
    "61,\n",
    "62,\n",
    "63,\n",
    "64,\n",
    "128,\n",
    "256,\n",
    "512,\n",
    "1024,\n",
    "2048,\n",
    "4096,\n",
    "]\n",
    "\n",
    "rand = [rand_mse for i in x]\n",
    "print rand\n",
    "\n",
    "\n",
    "mse=[1.3006833713,\n",
    "1.01480637813,\n",
    "0.878005568211,\n",
    "0.847949886105,\n",
    "0.797813211845,\n",
    "0.784864591243,\n",
    "0.764120682442,\n",
    "0.739108769932,\n",
    "0.729829297787,\n",
    "0.716445063135,\n",
    "0.711854050502,\n",
    "0.711609578447,\n",
    "0.69329629846,\n",
    "0.692505042355,\n",
    "0.695557459039,\n",
    "0.6962886426,\n",
    "0.687780215487,\n",
    "0.688859857869,\n",
    "0.686094838334,\n",
    "0.687877535095,\n",
    "0.681048367589,\n",
    "0.675382570958,\n",
    "0.668391419977,\n",
    "0.670716810548,\n",
    "0.673086944691,\n",
    "0.674682550627,\n",
    "0.67210651967,\n",
    "0.671294680268,\n",
    "0.670974951972,\n",
    "0.671141396386,\n",
    "0.672789773185,\n",
    "0.674449428745,\n",
    "0.674986119261,\n",
    "0.674128191215,\n",
    "0.673887609387,\n",
    "0.671526905044,\n",
    "0.671269332783,\n",
    "0.671375158686,\n",
    "0.672515063985,\n",
    "0.672393490576,\n",
    "0.670605419525,\n",
    "0.66709770909,\n",
    "0.668256195311,\n",
    "0.666946650279,\n",
    "0.668301863905,\n",
    "0.670484961609,\n",
    "0.671064422617,\n",
    "0.673901896597,\n",
    "0.673992838935,\n",
    "0.673732354076,\n",
    "0.675962792167,\n",
    "0.676225888332,\n",
    "0.677510067268,\n",
    "0.679815654756,\n",
    "0.680883565507,\n",
    "0.680803767539,\n",
    "0.680484190667,\n",
    "0.681225302865,\n",
    "0.683999124417,\n",
    "0.683852736399,\n",
    "0.68257433325,\n",
    "0.684323141816,\n",
    "0.687912868425,\n",
    "0.687378742474,\n",
    "0.687114877944,\n",
    "0.693227623867,\n",
    "0.704717151869,\n",
    "0.710002812248,\n",
    "0.714395885942,\n",
    "0.7149141051]\n",
    "fig, ax = plt.subplots()\n",
    "plt.xlim(xmin=1,xmax=512)   \n",
    "ax.set_xscale('log', basex=2)\n",
    "plt.xticks([1,2,4,8,16,32,64,128,256,512],[\"1\",\"2\",\"4\",\"8\",\"16\",\"32\",\"64\",\"128\",\"256\",\"512\"])\n",
    "           \n",
    "    \n",
    "plt.plot(users,mse)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Ibr75Zpo1a0avXr349ttvARgwYAADBgyga9eudOrUiYiIiJDuASVd+1dffcUV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Hq9NLzYWT12ZO5crM9H48udUl2HZ8/GlpS4I7wHp0/H+nw0u9H5uTvw3ntYr5cWI0cCzgiyv7iY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\nskLyliBNGgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f96815fced0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_path = !pwd\n",
    "errors = dict()\n",
    "num_users_set = set()\n",
    "for line in fileinput.input(\"user_similarity_errors_per_user.txt\"):\n",
    "    fields = line.split(' ')\n",
    "    num_users = int(fields[0])\n",
    "    userId  = int(fields[1])\n",
    "    error = math.sqrt(float(fields[3]))\n",
    "    num_users_set.add(num_users)\n",
    "    if userId not in errors:\n",
    "        errors[userId]=[]\n",
    "    errors[userId].append(error)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(10,7.5)\n",
    "x = list(num_users_set)\n",
    "x = sorted(x)\n",
    "all_errors = []\n",
    "for userId in errors:\n",
    "    for i in range(len(errors[userId])):\n",
    "        while i>= len(all_errors):\n",
    "            all_errors.append([])\n",
    "        all_errors[i].append(errors[userId][i])\n",
    "average = []        \n",
    "std = []\n",
    "max_err = []\n",
    "min_err = []\n",
    "improvement = []\n",
    "for i in range(len(all_errors)):    \n",
    "    average.append(np.mean(all_errors[i]))\n",
    "    std.append(np.std(all_errors[i]))\n",
    "    max_err.append(max(all_errors[i]))\n",
    "    min_err.append(min(all_errors[i]))\n",
    "    if i==0:\n",
    "        improvement.append(0)\n",
    "    else:\n",
    "        improvement.append(average[i-1]-average[i])\n",
    "plt.xlim(xmin=min(x),xmax=16) \n",
    "\n",
    "plt.ylim(ymin=0,ymax=1.5) \n",
    "plt.plot(x,average,linewidth=4,linestyle = \"--\")\n",
    "plt.plot(x[1:],improvement[1:],linewidth=3,linestyle = \"--\")\n",
    "colors = [\"#8000ff\",\"#c00000\",\"#008080\",\"#000000\",\"#c0ff00\",\"#ff00ff\",\"#800080\",\"#ff0000\",\"#40ff40\",\"#ffc040\"]\n",
    "i=0\n",
    "for userId in errors:\n",
    "    plt.plot(x,errors[userId],color = colors[i],linestyle=\":\")\n",
    "    ax.text(16,errors[userId][16],\"user{0}\".format(userId),color=colors[i])\n",
    "    i+=1\n",
    "plt.legend(labels =[\"RMSE(mean)\",\"Improvement(mean)\"])    \n",
    "plt.title(\"Rating Prediction using N Most Similar Users\")\n",
    "plt.xlabel(\"# Similar Users\")\n",
    "plt.ylabel(\"Prediction RMSE\")\n",
    "plt.savefig(\"user_similarity.pdf\")\n",
    "#plt.plot(x,std)\n",
    "#plt.plot(x,max_err)\n",
    "#plt.plot(x,min_err)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "global_similarity_dict = dict()\n",
    "\n",
    "def set_global_similarity_dict(userId,user_features,similarity_features):\n",
    "    #print user_features[user_features[\"userId\"]==5530]\n",
    "    #print user_features[user_features[\"userId\"]==2106]\n",
    "    similarity_dict=dict()\n",
    "    users_to_chose_from = user_features\n",
    "    for simliarity_userId in users_to_chose_from[\"userId\"].unique():\n",
    "        if userId!=simliarity_userId:\n",
    "            similarity_dict[simliarity_userId] = cosine_similarity(user_features,userId,simliarity_userId,similarity_features)\n",
    "    sorted_dict = sorted(similarity_dict.items(), key=operator.itemgetter(1)) \n",
    "    sorted_dict.reverse()\n",
    "    ids =[]\n",
    "    for items in sorted_dict:\n",
    "        ids.append(items[0])\n",
    "    global_similarity_dict[userId] = ids\n",
    "tested_userIds = [457,2768,1980,402,4126,3418,1112,3119,1737,1322]\n",
    "for tested_userId in tested_userIds:\n",
    "    set_global_similarity_dict(tested_userId,user_features,similarity_features)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "print global_similarity_dict "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#USER SIMILARITY_FUNCTIONS\n",
    "\n",
    "N_MOST_SIMILAR=10\n",
    "def cosine_similarity(user_features,userId1,userId2,similarity_features):\n",
    "    user1 = user_features[user_features[\"userId\"] == userId1][similarity_features]\n",
    "    user2 = user_features[user_features[\"userId\"] == userId2][similarity_features]\n",
    "    sim = np.dot(user1,user2.T)/np.linalg.norm(user1)/np.linalg.norm(user2)\n",
    "    return sim[0][0]\n",
    "    \n",
    "global_similarity_dict = dict()\n",
    "#returns the 'n' most similar users to 'userId' according to 'similarity_features'. \n",
    "#If 'predicted_movieId' is not 'None' we restrict the users to chose from only to users that rated the movie \n",
    "def n_most_similar(n,userId,user_features,similarity_features,predicted_movieId=None):\n",
    "    #print user_features[user_features[\"userId\"]==5530]\n",
    "    #print user_features[user_features[\"userId\"]==2106]\n",
    "    users_to_chose_from = user_features\n",
    "    if predicted_movieId!=None:\n",
    "        users_to_chose_from = dfratings[dfratings[\"movieId\"]==predicted_movieId]\n",
    "    similarity_dict = dict()\n",
    "    for simliarity_userId in users_to_chose_from[\"userId\"].unique():\n",
    "        if userId!=simliarity_userId:\n",
    "            similarity_dict[simliarity_userId] = cosine_similarity(user_features,userId,simliarity_userId,similarity_features)\n",
    "            #print simliarity_userId, similarity_dict[simliarity_userId]\n",
    "    sorted_dict = sorted(similarity_dict.items(), key=operator.itemgetter(1)) \n",
    "    sorted_dict.reverse()\n",
    "    ids =[]\n",
    "    for items in sorted_dict[:min(n,len(sorted_dict)-1)]:\n",
    "        ids.append(items[0])\n",
    "    return ids\n",
    "\n",
    "\n",
    "user_genre_pref_filename = '/explore/user_genre_pref.pik'\n",
    "user_genre_pref = pickle.load(open(my_path[0]+user_genre_pref_filename, 'rb'))\n",
    "similarity_features = list(user_genre_pref.columns)\n",
    "similarity_features.append(\"rate_num\")\n",
    "similarity_features.append(\"rate_avg\")\n",
    "similarity_features.append(\"rate_std\")  \n",
    "similarity_features.append(\"avg_score_diff\")\n",
    "random_seed = 42\n",
    "#print user_features[\"userId\"][0]\n",
    "MAX_SIMILAR_USERS=10\n",
    "predictions = []\n",
    "\n",
    "tested_userIds = [457,2768,1980,402,4126,3418,1112,3119,1737,1322]\n",
    "#tested_userIds = [457,2768,1980]\n",
    "ratings_pred = []\n",
    "ratings_real = []\n",
    "for n in range(1,MAX_SIMILAR_USERS):\n",
    "    ratings_pred.append([])\n",
    "    ratings_real.append([])    \n",
    "\n",
    "       \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def n_most_similar_global(n,userId,user_features,similarity_features,predicted_movieId=None):\n",
    "    #print user_features[user_features[\"userId\"]==5530]\n",
    "    #print user_features[user_features[\"userId\"]==2106]\n",
    "    all_similar_ids = global_similarity_dict[userId]\n",
    "    users_to_chose_from = user_features\n",
    "    if predicted_movieId!=None:\n",
    "        users_to_chose_from = dfratings[dfratings[\"movieId\"]==predicted_movieId]\n",
    "    valid_user_ids = users_to_chose_from[\"userId\"].unique()        \n",
    "    ids =[]\n",
    "    i=0\n",
    "    for similar_id in all_similar_ids:\n",
    "        if similar_id in valid_user_ids:\n",
    "            i+=1\n",
    "            if i>n:\n",
    "                break\n",
    "            ids.append(similar_id)\n",
    "    return ids\n",
    "ratings_pred = []\n",
    "for n in range(33,64):\n",
    "    while n >= len(ratings_pred):\n",
    "        ratings_pred.append([])\n",
    "    ratings_pred[n-1]=[]\n",
    "    for tested_userId in tested_userIds:\n",
    "            user_ratings = dfratings[dfratings[\"userId\"]==tested_userId]\n",
    "            user_ratings.reset_index(inplace=True)\n",
    "            user_ratings_shuffle_indices = cross_validation.ShuffleSplit(len(user_ratings), n_iter=1, test_size=0.1, random_state=random_seed)\n",
    "            for dummy_train_indices,user_ratings_test_indices in user_ratings_shuffle_indices:\n",
    "                predicted_movieIds = (user_ratings[\"movieId\"])[user_ratings_test_indices]\n",
    "                for predicted_movieId in predicted_movieIds:\n",
    "                    n_similar_userIds = n_most_similar_global(n,tested_userId,user_features,similarity_features,predicted_movieId)\n",
    "                    similar_ratings = []\n",
    "                    for similar_userId in n_similar_userIds:\n",
    "                        similar_user_ratings = dfratings[dfratings[\"userId\"]==similar_userId]\n",
    "                        similar_movie_rating = similar_user_ratings[similar_user_ratings[\"movieId\"] == predicted_movieId][\"rating\"]\n",
    "                        similar_ratings.append(similar_movie_rating.item())\n",
    "                    user_rating = dfratings[dfratings[\"userId\"]==tested_userId]\n",
    "                    user_movie_rating = user_rating[user_rating[\"movieId\"] == predicted_movieId][\"rating\"]                    \n",
    "                    predict = float(sum(similar_ratings))/float(len(similar_ratings))\n",
    "                    #print n,len(ratings_pred)\n",
    "                    ratings_pred[n-1].append(predict)\n",
    "                    #ratings_real[n-1].append(user_movie_rating.item())\n",
    "    with open(my_path[0]+'/ratings_pred{0}.pik'.format(n), 'wb') as handle:\n",
    "        pickle.dump(ratings_pred, handle, protocol=pickle.HIGHEST_PROTOCOL)                    \n",
    "        print n,mean_squared_error(ratings_pred[n-1],ratings_real[0]),r2_score(ratings_pred[n-1],ratings_real[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ratings_rand=[]\n",
    "for i in range(len(ratings_real[0])):\n",
    "    ratings_rand.append(random.randint(1,5))\n",
    "print \"random\",mean_squared_error(ratings_rand,ratings_real[0]),r2_score(ratings_rand,ratings_real[0])    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "ratings_pred = pickle.load(open(my_path[0]+\"/ratings_pred9.pik\", 'rb'))\n",
    "ratings_real = pickle.load(open(my_path[0]+\"/ratings_real1.pik\", 'rb'))\n",
    "#print ratings_real,ratings_pred\n",
    "for n in range(1,10):\n",
    "    while n >= len(ratings_pred):\n",
    "        ratings_pred.append([])\n",
    "    ratings_pred[n-1]=[]\n",
    "    for tested_userId in tested_userIds:\n",
    "            user_ratings = dfratings[dfratings[\"userId\"]==tested_userId]\n",
    "            user_ratings.reset_index(inplace=True)\n",
    "            user_ratings_shuffle_indices = cross_validation.ShuffleSplit(len(user_ratings), n_iter=1, test_size=0.1, random_state=random_seed)\n",
    "            for dummy_train_indices,user_ratings_test_indices in user_ratings_shuffle_indices:\n",
    "                predicted_movieIds = (user_ratings[\"movieId\"])[user_ratings_test_indices]\n",
    "                for predicted_movieId in predicted_movieIds:\n",
    "                    n_similar_userIds = n_most_similar(n,tested_userId,user_features,similarity_features,predicted_movieId)\n",
    "                    similar_ratings = []\n",
    "                    for similar_userId in n_similar_userIds:\n",
    "                        similar_user_ratings = dfratings[dfratings[\"userId\"]==similar_userId]\n",
    "                        similar_movie_rating = similar_user_ratings[similar_user_ratings[\"movieId\"] == predicted_movieId][\"rating\"]\n",
    "                        similar_ratings.append(similar_movie_rating.item())\n",
    "                    user_rating = dfratings[dfratings[\"userId\"]==tested_userId]\n",
    "                    user_movie_rating = user_rating[user_rating[\"movieId\"] == predicted_movieId][\"rating\"]                    \n",
    "                    predict = float(sum(similar_ratings))/float(len(similar_ratings))\n",
    "                    #print n,len(ratings_pred)\n",
    "                    ratings_pred[n-1].append(predict)\n",
    "                    #ratings_real[n-1].append(user_movie_rating.item())\n",
    "    with open(my_path[0]+'/ratings_pred{0}.pik'.format(n), 'wb') as handle:\n",
    "        pickle.dump(ratings_pred, handle, protocol=pickle.HIGHEST_PROTOCOL)                    \n",
    "        print n,mean_squared_error(ratings_pred[n-1],ratings_real[0]),r2_score(ratings_pred[n-1],ratings_real[0])\n",
    "      "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading ratings data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Reading the ratings data\n",
    "ratings_filename = \"/ml-1m/ratings.dat\"\n",
    "dfratings = pd.read_csv(my_path[0]+ratings_filename, index_col=None, sep='::', header=None)\n",
    "dfratings.columns = [\"userId\", \"movieId\", \"rating\", \"timestamp\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating genres vector for each movie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Disable false positive Pandas warning for 'SettingWithCopyWarning'\n",
    "pd.options.mode.chained_assignment = None\n",
    "\n",
    "#Adding genres vector to each movie\n",
    "all_genres = ['Mystery', 'Sci-Fi', 'Crime', 'Drama', 'Animation', 'IMAX', 'Action', 'Comedy', 'Documentary', 'War', 'Romance', 'Horror', \n",
    "              'Film-Noir', 'Musical', 'Fantasy', 'Adventure', 'Children', 'Thriller', 'Western']\n",
    "for genre in all_genres:\n",
    "    dfmovies[genre] = dfmovies['genres'].apply(lambda x: x.find(genre)>=0).astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create users genre preference vectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "users = (dfratings[\"userId\"]).unique()\n",
    "genres_pref = []\n",
    "for userId in users:\n",
    "    user_ratings = dfratings[dfratings[\"userId\"] == userId]\n",
    "    movies_total = 0\n",
    "    geners_rating_score = [0] * len(all_genres)\n",
    "    for index, rating in user_ratings.iterrows():\n",
    "        movieId = rating[\"movieId\"]\n",
    "        score = rating[\"rating\"]\n",
    "        movie_data = dfmovies.loc[dfmovies[\"movieId\"] == movieId]\n",
    "        for genre in all_genres:\n",
    "            if (movie_data[genre].item() == 1):\n",
    "                geners_rating_score[all_genres.index(genre)] += score\n",
    "    genres_pref.append(geners_rating_score)\n",
    "    if (userId % 1000 == 0):\n",
    "        print userId"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Save the genres prefrence to dataframe and to pickle\n",
    "user_pref_df = pd.DataFrame(genres_pref)\n",
    "user_pref_df.columns = all_genres\n",
    "with open(my_path[0]+'/explore/user_genre_pref.pik', 'wb') as handle:\n",
    "    pickle.dump(user_pref_df, handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with open(my_path[0]+'/explore/user_genre_pref.pik', 'rb') as handle:\n",
    "    user_pref_df = pickle.load(handle)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unify all the users features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading the users data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "users_filename = \"/ml-1m/users.dat\"\n",
    "dfusers = pd.read_csv(my_path[0]+users_filename, sep='::', header=None)\n",
    "dfusers.columns = [\"userId\",\"gender\", \"age\", \"occupation\", \"zip-code\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Extracting gender, age and occupation features\n",
    "del dfusers[\"zip-code\"]\n",
    "user_features = dfusers.join(user_pref_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Calculating number of ratings and average rating per user\n",
    "users = (dfratings[\"userId\"]).unique()\n",
    "user_rate_num = []\n",
    "user_rate_avg = []\n",
    "user_rate_std = []\n",
    "for userId in users:\n",
    "    user_ratings = dfratings[dfratings[\"userId\"] == userId]\n",
    "    user_rate_num.append(user_ratings.shape[0])\n",
    "    user_rate_avg.append(np.average(user_ratings[\"rating\"]))\n",
    "    user_rate_std.append(np.std(user_ratings[\"rating\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "user_features[\"rate_num\"] = user_rate_num\n",
    "user_features[\"rate_avg\"] = user_rate_avg\n",
    "user_features[\"rate_std\"] = user_rate_std"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "with open(my_path[0]+'/features/user_features.pik', 'wb') as handle:\n",
    "    pickle.dump(user_features, handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open(my_path[0]+'/features/user_features.pik', 'rb') as handle:\n",
    "    user_features = pickle.load(handle)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Calculate movies features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Calculate average rating and number of ratings\n",
    "all_movies = dfmovies[\"movieId\"]\n",
    "avg_rate = []\n",
    "num_rate = []\n",
    "std_rate = []\n",
    "for movie in all_movies:\n",
    "    movie_data = dfratings[dfratings[\"movieId\"] == movie]\n",
    "    num_rate.append(movie_data.shape[0])\n",
    "    avg_rate.append(np.average(movie_data[\"rating\"]))\n",
    "    std_rate.append(np.std(movie_data[\"rating\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Removing movies with 0 reviews\n",
    "movies_features = dfmovies.copy(deep=True)\n",
    "exclude_movies = []\n",
    "for i in sorted(all_movies.index, reverse=True):\n",
    "    if (num_rate[i] == 0):\n",
    "        exclude_movies.append(all_movies[i])\n",
    "        del avg_rate[i]\n",
    "        del num_rate[i]\n",
    "        movies_features.drop(movies_features.index[i], inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unify all movies features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "del movies_features[\"genres\"]\n",
    "movies_features[\"avg_rate\"] = avg_rate\n",
    "movies_features[\"num_rate\"] = num_rate\n",
    "movies_features[\"std_rate\"] = std_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "with open(my_path[0]+'/features/movie_features.pik', 'wb') as handle:\n",
    "    pickle.dump(movies_features, handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cross_validation.ShuffleSplit(len(user_features), n_iter=1, test_size=.01, random_state=random_seed)\n",
    "user_features[\"userId\"][userId_shuffle_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#ratings_pred = pickle.load(open(my_path[0]+\"/ratings_pred63.pik\", 'rb'))\n",
    "#ratings_real = pickle.load(open(my_path[0]+\"/ratings_real1.pik\", 'rb'))\n",
    "\n",
    "for i in range(len(ratings_pred)):\n",
    "    if len(ratings_pred[i]) ==len(ratings_real[0]):\n",
    "        print i+1,mean_squared_error(ratings_pred[i],ratings_real[0]),r2_score(ratings_pred[i],ratings_real[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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