voting_pattern.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of members : 440\n",
      "Number of bills : 808\n",
      "(440, 808)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Eigenmembers')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/9GmDwBXDOzl5zXdYtPpOTl7zncwT+PC2EVbdtqOs+t60eU/bjDp2asjM2sJMvY8q6Z10xfBObtq85/DziQhu2ryHHzz6LAdfOsRTo2P0z+lldGycSvrVtMuoY/caqqFOnZDKrJOk/08bcfZ7fJqeSo3mXkN11skTUpl1isL/00Z9ZqtzIKgRr0xm1vpm6mJar89sdQ4ENeIJ08xaXzP+H9thyUwHghrxymQzq8d6tWblaNb/Y6sf6w4ENeIJ06bnNhRrBauWL54yWri3R3z4LQsPjyieVYc5kT/5zR/W/k1ryIGgRrwy2fTchmIto7CVOGDwxHmHB4790lG9Nf/Ig+OHav6eteRxBDXklcmKcxuKtYJ1G3cxfmhyJBg/FJP6+z8/Nv36A53IgaCGPI6gOC86Y62glAuS/jm9My5G02mcGqqRrBz45Ru2s8gNo4DbUKw1lNKpox5jbOe0+LoFrV27NpKVA88fT6U0jHZ6jxq3oVgrKOWCZLQOqaHPrnhjzd+zlpwaqpGZct3TrZCUtWj2mm/mJr7qpBOl21Cs2UqZj6hHmrLCWa0+t1U5ENRIKcvcFQsW0/WoafUDyKzdzHRBUusg0A6cGqqRrFvOQsXyk+5RY9Y6BmrcgaGnDuMSas13BDWSvuUcGR2bsqSdgLf+6vzM17pHjVlzpXv8zTli+gu6cn3x/Utr+n714EBQQ+lbziuGd3Lz5j2Hg0EA39g6wuCJ86bclq5avjhz0ewXXnyZ4W0jLZsecndZq1SrHDvD20a4+o6HJnUXfeGl2k1K96WLlrbF/4RTQ3XyvYf3l7zIdb5HzasKrkRGx8ZbdhoGTxlhlWqVYydfj3qNGRjo72uLIAAOBHVTSd7/YMaVSKXTMNS7O6qnjLBKtcqxU88pqdttjIxTQ3VSbt5/3cZdRRfKKLfRuBHdUd3AbZVq5LEzXQqq1tNDH9EjxieiLdOkVd0RSJonaZOk3cn3uUXKrUzK7Ja0MmP/7ZIerKYurabUkbT5K/fpDspyG40bccXlabetUo06dmZKQdWyM89ZJ8/jx595J4+tvYD7Vp/TVkEAqk8NrQbujohTgLuT55NImgdcCZwBvBm4Mh0wJK0Afl5lPVpOKSNp0wdqMYKybzEbccXlKSOsUo06dopdEF2WTP1S6WiBI2e/ctrs7+vlSxct5ebfO7OKmjZftamhC4Gzk8c3AvcCnygosxzYFBEHACRtAs4Hvibp1cDHgUuBW6qsS8uZaeDKTDlKAR96y8Kyry4a0R21lBGaZlkadezUOtU0S/BfzljIdUNLavq+raDaQHBcROxLHv8UOC6jzADwZOr53mQbwLXAF4GDM32QpEvJBQwWLlxYaX1bynQH6kCF/xzD20Z44cWXp2xPX3HVquuep4ywStX62Mk6pksZ7T+Tvt6erpgTa8ZAIOku4LUZuz6VfhIRIankuy1JS4GTI+JySYtmKh8R64H1AIODgy0/BryUk22xA3Wgv4/7Vp9T0WdmjUeYO6eXK991GkPLBrpmXiPrHsWO6fe8aYBvbB2puGdQpRdj7WjGQBAR5xbbJ+lpScdHxD5JxwPPZBQb4ZX0EcACcimkM4FBSY8n9XiNpHsj4mzaXKkn26yBZNPlSmcKLsVSTXOOmD3pdtzzGlk7K/w/eOHFlzOP6e89vJ/rVyw5PNq/FP19vVz17tO67n+h2tTQ7cBKYG3y/dsZZTYCn001EJ8HrEnaDP43QHJH8PedEARg+kaqdRt3HT6Bl5MrLSW4lNJI7G6f1s6y/g+KeWp07PD/2fC2ES7bsD2zXH9fL9uvPK8u9W0X1QaCtcAtki4GngDeDyBpEPhoRFwSEQckXQvcn7zmmnzDcaea7qRaeAIvNVdaypV8KY3EntfI2kGxu99yBoGlj+mhZQNseeIAN23eM6lM7yxx1btPq2nd21FV3Ucj4tmIeFtEnBIR5+ZP8BGxJSIuSZX7akS8Pvn6m4z3eTwi3lBNXVrJTCfVSvr0l3IlX0q3PHf7tFY2vG2EpVd/l8s2bM/s/1/qnWvWMX3d0BK+dNHSSV26173v9K5LA2XxyOI6WLV8Matu3TFlkey0rAO6cAKsdL6ylCv5UlJN7vZprapYZwd45eKp2P/B3Dm9zDli9ozHtHu6ZXMgqJcZhi3OkibNLDq8bYRVt+1gfOKV4DE6Ns6qW3cApTcsl3Kg+5/BWtFMaZ+nRse44aKlmf8H+V5xVhkHgjpYt3HXpBN6lomISW0FxV4zfihYt3HX4e6kvpK3TjVT2ueE1Gye/j+oLQeCOig1jzk2PsEf3JK74p+p9wP4St4623QDwNJ3v/4/qD1PQ10H5fTAmYg4nP6pxfuZtatiy73OndPbFaN7m8l3BHVQbMWxYqZrVO6dpab26GmVlaSs8znt0zwOBHVQuH5xNZrZvc3TUVijOe3THA4EdZI/oE+qYrpb4PB4g8Lpqxtx1VTL6Sh8Z2HWutxGUGfV5vdHRse4bMN2ll79XYa3jWQutnH5hu1cMbyzNhVOqdV0FK2yRq2ZZXMgqLNiDWDlyi9kf/UdD025Sg/g5s17an5irdVKUq2yRq2ZZXMgqKN8OmRsfIIe5UaY5b9XYmx84vCo40IBNT+x1mo6Ck90Z9ba3EZQJ4UNrRMR9M4SR8yexQsvVTY/+kxqfWKtVS8OT3Rn1tocCOokKx0yfigYrzII9Pf18vzYeGYDdOG0FbVQi14c5a67YGaN5dRQndQz7fEbJ8/LnMooP21FqzXCDi0b4PoVSybN+ugBQmatw3cEdVLteqk9EhMRzBKkx5uNjo3zwJ7n+dBbFvK1HzzJREy+N2jV1cbcP9ysdfmOoE6q6S3UI/GT69/J42sv4Pijp+bRx8Yn+Psd+zgU2SMU3AhrZuXwHUGdFDa0HtU7i7HxQyW99i2/PJez1t7DU0m/+yyjY+P09/UyOja1F5EbYc2sHA4EdZROhyy75rslB4IH9jxf0jxFz/9inN4eTZq+2o2wZlYuB4IGGN42UrT/f6FZouTJ6iKAyM3OOHpwvGWmbvB0EmbtxYGgAcoZ6DXNRKSZxg8Fc46YzbZPn1dmrerDE9WZtR83FjdAvRtvR0bHWqbLqKeTMGs/DgQNUE7jbX9fb0W9jVpl/ICnkzBrPw4EDVBOV1IJ3vOmAfr7esv6jLHxCa6+46FKqldTtZqozswax4GgAdIjayF3si/muYPjfGPryLRlpntts+8KajVRnZk1jqLIoKRWNjg4GFu2bGl2NaoyvG2EVbftmNT1sxYG+vu4b/U5NX3PcrnXkFlrkrQ1IgYLt7vXUJNcfcdDNQ8C0Bq5eE8nYdZenBpqkpnGFfT39dLbU35+yLl4MytXVYFA0jxJmyTtTr7PLVJuZVJmt6SVqe1HSFov6ceSHpb0nmrq00lefHmC3lnlBYLeWXIu3szKVm1qaDVwd0SslbQ6ef6JdAFJ84ArgUFyC2ltlXR7RDwHfAp4JiJ+RdIsYF6V9WkbgmkXtS91Ooq8/r5ernr3aVWlZJzbN+tO1aaGLgRuTB7fCAxllFkObIqIA8nJfxNwfrLvd4HrASLiUET8rMr6tI1atg4IeNWR1cV0LzBv1r2qDQTHRcS+5PFPgeMyygwAT6ae7wUGJPUnz6+V9ICkWyVlvR4ASZdK2iJpy/79+6usdvMN1DCXX4sTt0cEm3WvGQOBpLskPZjxdWG6XER+CrSSzQYWAP8cEb8G/AvwhWKFI2J9RAxGxOD8+fPL+JjWVM16BcVUc+L2iGCz7jVjPiEizi22T9LTko6PiH2SjgeeySg2Apyder4AuBd4FjgIfDPZfitwcWnVbn/53PvlG7bXNE2Un3eo3Fy/F5g3617VpoZuB/K9gFYC384osxE4T9LcpFfRecDG5A7iDl4JEm8D/q3K+rSdegznW3XrjrJz/R4RbNa9qg0Ea4G3S9oNnJs8R9KgpK8ARMQB4Frg/uTrmmQb5HoYXSXph8BvA39QZX3aSr3y7+OHstcxno4XmDfrXp5ioolOWn1nXe4Isgh4bO0FDfo0M2tFnmKiBRXLy9dDAItW31mT8QbtoFFjIjz2wjqBA0ETrVq+mMs2bG/oZ46OjbPq1h1seeIA33t4/6QTGNARJ7VGrZLm1disUzg11GRXDO/kps17Gv65M41shlxjcTu2E5y19p7MO61az8zaqM8xq5ViqSFPOtdk1w0t4UsXLa3pALNSlBL+23VAWaPGRHjshXUKB4IWMLRsgPtWn8Pjay+gr7e1/iTteFJr1CppXo3NOkVrnXWMX5Q52Vy9ZZ3UhreNcNbaezhp9Z2ctfaelpuPqFFjIjz2wjqFG4tbTCN7Es0k66TWDg2k+XrUu+G7UZ9jVm9uLG4xhSfaZinWzbTVG0jdndOsOI8jaBPpq8xm3Rn0ztKUIJA/wRarUyvcxbTD3YpZK3IbQQsaWjbAquWL6e/rbcrnjx+KSb2F0msVTGfZNd9taruBp9I2q4wDQQvKn3hHx6Zf17ie8rOYQvYJNstzB8ebuqiNu3OaVcaBoAWVeuKtt8s3bOeK4Z0VnUibcSXu7pxmlXEbQQtqlSvYAG7evIf+Ob08d7D8u5OR0THOWnvPjA236faHHomJCAZS5UttAF61fPGUhnZ35zSbmQNBC2qlLqQBROROqOXepYhXGpFHRse4fMN2tjxxgOuGlhwuU9jAO5H0Ysunl7Y8cYBvbB0pqQHY3TnNKuPuoy2oVbqQ5gm44aKlk06wb/3V+YcnrTu6r5cXXnqZ8YmY9JqsIyv/XvmTc7HuqHn5O4RCrdJd1aydeK6hNpJeJAZyJ8NmysqxD544j1XLF3NCfx/Pj43zqiNmM3dO7+FFbYpdXgSTF+SZKQ2WFQRKeZ2Zlc6poRY1tGxgUkpjpivnenrm38cmTZc9MjrGqtt2QLyyGtro2Dh9vT2Hr/anq2/6JD5TGqzYHYEbgM1qx3cEbSJrXptGyZr+aHwipl0Sc9XyxRS7j0mfxKf7ufp6e/jgGa/zfD5mdeZA0Cby6aJWl7/aH1o2wIfesnBKMCg8iRdLg+XXTL5uaInXUjarMzcWt5lmpohKUdiI67l/zFqH5xrqEFl95VtFVsqmsK3DzFqPA0GbKewrL8GhFripG/DVvlnbciBoQ+mr7OFtI6y6bcekPvw9s8QsmNKYmzeQjAO4efOeKd08S1nLOK1d1zU2s1e4sbjNDS0bYN17T5/UmPrF953Ouvedztw5U2cvzadvrhtawmNrLzi8XnL+tTeUsH5yj+SGW7MO4sbiDldJY+10I5t9B2DWvtxY3KUqaawtXBwnayI4M+scDgSWyb19zLpHVW0EkuZJ2iRpd/J9bpFyK5MyuyWtTG3/oKSdkn4o6R8kHVtNfczMrHzVNhavBu6OiFOAu5Pnk0iaB1wJnAG8GbhS0lxJs4EvA2+NiDcCPwQ+VmV9zMysTNUGgguBG5PHNwJDGWWWA5si4kBEPAdsAs4n11NRwKskCfgl4Kkq62NmZmWqNhAcFxH7ksc/BY7LKDMAPJl6vhcYiIhx4L8DO8kFgFOBvy72QZIulbRF0pb9+/dXWW0zM8ubMRBIukvSgxlfF6bLRa4fasl9USX1kgsEy4ATyKWG1hQrHxHrI2IwIgbnz59f6seYmdkMZuw1FBHnFtsn6WlJx0fEPknHA89kFBsBzk49XwDcCyxN3v8nyXvdQkYbg5mZ1Ve1qaHbgXwvoJXAtzPKbATOSxqI5wLnJdtGgFMl5S/v3w78qMr6mJlZmaodR7AWuEXSxcATwPsBJA0CH42ISyLigKRrgfuT11wTEQeSclcD35c0nrz+I1XWx8zMyuQpJszMuoQXrzczs0wOBGZmXc6BwMysyzkQmJl1OQcCM7Mu50BgZtblHAjMzLqcA4GZWZdzIDAz63IOBGZmXc6BwMysyzkQmJl1OQcCM7Mu50BgZtblHAjMzLqcA4GZWZdzIDAz63IOBGZmXc6BwMysyzkQmJl1OQcCM7Mup4hodh3KJmk/8EQZLzkW+FmdqtMIrn9zuf7N5frXzokRMb9wY1sGgnJJ2hIRg82uR6Vc/+Zy/ZvL9a8/p4bMzLqcA4GZWZfrlkCwvtkVqJLr31yuf3O5/nXWFW0EZsMUo/kAAARpSURBVGZWXLfcEZiZWREOBGZmXa5jAoGkeZI2SdqdfJ9bpNzKpMxuSStT24+QtF7SjyU9LOk9jat99fVP7b9d0oP1r/GUz624/pLmSLoz+b0/JGltg+p8vqRdkh6RtDpj/5GSNiT7fyBpUWrfmmT7LknLG1HfjPpVVH9Jb5e0VdLO5Ps5ja57Uo+Kf//J/oWSfi7pDxtV54LPr+b4eaOkf0mO952Sjmpk3aeIiI74Aj4PrE4erwY+l1FmHvBo8n1u8nhusu9q4Lrk8Szg2Haqf7J/BfB3wIPt9PsH5gBvTcocAfwj8I4617cH+Anwy8ln7gBOLSjzP4C/SB5/ANiQPD41KX8kcFLyPj0N/n1XU/9lwAnJ4zcAI004Xiquf2r/bcCtwB+2U/2B2cAPgdOT58c0+viZ8vM088Nr/IfZBRyfPD4e2JVR5oPAX6ae/yXwweTxk8Cr2rj+rwb+KTlJNSMQVFX/gnJfBn6vzvU9E9iYer4GWFNQZiNwZvJ4NrnRoSosmy7XwN93xfUvKCPgAHBkO9UfGALWAVc1KRBUc/y8E7ip0XWe7qtjUkPAcRGxL3n8U+C4jDID5E74eXuBAUn9yfNrJT0g6VZJWa+vp4rrnzy+FvgicLBuNZxetfUHIPlbvAu4ux6VLKcu6TIR8TLwPLmrt1JeW2/V1D/tPcADEfFinepZTMX1l/Rq4BPk7uKbpZrf/68AIWljcr75owbUd1qzm12Bcki6C3htxq5PpZ9EREgqp1/sbGAB8M8R8XFJHwe+APx2xZXNUK/6S1oKnBwRlxfmUWupjr///PvPBr4G/ElEPFpZLa1Ukk4DPgec1+y6lOkq4IaI+LmkZtelErOB3wR+ndyF292StkZEvS9+pq1Q24iIc4vtk/S0pOMjYp+k44FnMoqNAGenni8A7gWeJfcH+Way/Vbg4lrUOa2O9T8TGJT0OLm/6Wsk3RsRZ1NDdax/3npgd0R8qQbVnckI8LqCuowUKbM3CVJHkztWSnltvVVTfyQtAL4F/E5E/KT+1Z2imvqfAbxX0ueBfuCQpF9ExJ/Wv9pT6pZXTv33At+PiJ8BSPoO8GvU/y64uGbnpmqYs1vH5MbKz2eUmQc8Rq6Bcm7yeF6y7+vAOcnjjwC3tlP9U2UW0Zw2gmp//9cB3wBmNai+s8k1Vp/EK419pxWU+X0mN/bdkjw+jcmNxY/S+Mbiaurfn5Rf0ejjpBb1LyhzFc1pI6jm9z8XeIBcJ4nZwF3ABc36W0R0VmPxMeQi6u7kF5s/wQwCX0mV+13gkeTrv6a2nwh8n1xr/t3Awnaqf2r/IpoTCCquP7mrqQB+BGxPvi5pQJ3fCfyYXO+PTyXbrgHenTw+itzd4SPAvwK/nHrtp5LX7aLOPZxqXX/gCuCF1O96O/Cadql/wXtcRRMCQQ2Onw8DDwEPknHR1OgvTzFhZtblOqnXkJmZVcCBwMysyzkQmJl1OQcCM7Mu50BgZtblHAjMzLqcA4GZWZf7/4pMqvL8+Y6tAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import csv\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from matplotlib.lines import Line2D\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "import sys\n",
    "import math\n",
    "\n",
    "if len(sys.argv) == 2:\n",
    "    congress = sys.argv[1]\n",
    "else:\n",
    "    congress = 'H115'\n",
    "congress = 'H116'\n",
    "\n",
    "data_folder = 'data/'+congress+'/'\n",
    "output_folder = 'output/raw/'+congress+'/'+congress+'_'\n",
    "hd_file = 'output/house_details.csv'\n",
    "trunc = 2\n",
    "\n",
    "passed_indices = []\n",
    "failed_indices = []\n",
    "unknown_indices = []\n",
    "\n",
    "member_count = -1\n",
    "member_icpsr = {}\n",
    "with open(data_folder+congress+'_members.csv') as csv_file:\n",
    "    csv_reader = csv.reader(csv_file, delimiter=',')\n",
    "    for row in csv_reader:\n",
    "        if not (member_count == -1):\n",
    "            member_icpsr[row[2]] = member_count\n",
    "        member_count = member_count + 1\n",
    "bill_count = -1\n",
    "bill_roll = {}\n",
    "with open(data_folder+congress+'_rollcalls.csv') as csv_file:\n",
    "    csv_reader = csv.reader(csv_file, delimiter=',')\n",
    "    for row in csv_reader:\n",
    "        if bill_count == -1:\n",
    "            bill_count = bill_count+1\n",
    "            continue\n",
    "        if not bill_count == -1:\n",
    "            bill_roll[row[2]] = bill_count\n",
    "        if row[14].lower() == 'passed':\n",
    "            passed_indices.append(int(row[2])-1)\n",
    "        elif row[14].lower() == 'failed':\n",
    "            failed_indices.append(int(row[2])-1)\n",
    "        else:\n",
    "            unknown_indices.append(int(row[2])-1)\n",
    "        bill_count = bill_count + 1\n",
    "print(\"Number of members : \" + str(member_count))\n",
    "print(\"Number of bills : \" + str(bill_count))\n",
    "\n",
    "A = np.zeros((member_count, bill_count))\n",
    "I = np.eye(bill_count)\n",
    "\n",
    "vote_count = -1\n",
    "abstention_count = 0\n",
    "with open(data_folder+congress+'_votes.csv') as csv_file:\n",
    "    csv_reader = csv.reader(csv_file, delimiter=',')\n",
    "    for row in csv_reader:\n",
    "        if not vote_count == -1:\n",
    "            if int(row[4]) == 1:\n",
    "                A[member_icpsr[row[3]]][bill_roll[row[2]]] = 1\n",
    "            elif int(row[4]) == 6:\n",
    "                A[member_icpsr[row[3]]][bill_roll[row[2]]] = -1\n",
    "            else:\n",
    "                A[member_icpsr[row[3]]][bill_roll[row[2]]] = 0\n",
    "                if int(row[4]) == 9:\n",
    "                    abstention_count = abstention_count + 1\n",
    "        vote_count = vote_count+1\n",
    "\n",
    "u, s, vh = np.linalg.svd(A)\n",
    "print(A.shape)\n",
    "\n",
    "#plt.scatter(vh[0,:], vh[1,:])\n",
    "plt.scatter(vh[0,passed_indices], vh[1,passed_indices], c='g')\n",
    "plt.scatter(vh[0,failed_indices], vh[1,failed_indices], c='r')\n",
    "plt.title('Eigenbills')\n",
    "plt.show()\n",
    "#plt.scatter(u[:n_p,0], u[:n_p,1], c='r')\n",
    "#plt.scatter(u[n_p+1:,0], u[n_p+1:,1], c='b')\n",
    "plt.scatter(u[:,0], u[:,1])\n",
    "plt.title('Eigenmembers')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def std_vot_mat(a):\n",
    "    a_sorted = np.copy(a)\n",
    "    [u,s,v]=np.linalg.svd(a);\n",
    "    a1 = 1;\n",
    "    a2 = 0;\n",
    "    b1 = 1;\n",
    "    b2 = 0;\n",
    "    mem_inds = sorted(range(u.shape[0]), key=lambda k: b1*u[k,0]+b2*u[k,1])\n",
    "    bill_inds = sorted(range(v.shape[0]), key=lambda k: a1*v[0,k]+a2*v[1,k])\n",
    "    for i in range(a.shape[0]):\n",
    "        for j in range(a.shape[1]):\n",
    "            a_sorted[i,j] = a[mem_inds[i], bill_inds[j]]\n",
    "    return a_sorted, mem_inds, bill_inds\n",
    "\n",
    "a_sorted, m_ind, b_ind = std_vot_mat(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "'''\n",
    "mem_inds = sorted(range(len(mem)), key=lambda k: mem[k])\n",
    "bill_inds = sorted(range(len(bill_params)), key=lambda k: 10*np.exp(bill_params[k][0])+0.1*bill_params[k][1])\n",
    "a_sorted = np.copy(a)\n",
    "for i in range(a.shape[0]):\n",
    "    for j in range(a.shape[1]):\n",
    "        a_sorted[i,j] = a[mem_inds[i], bill_inds[j]]\n",
    "plt.imshow(a_sorted)\n",
    "plt.show()\n",
    "'''\n",
    "plt.imshow(A)\n",
    "plt.show()\n",
    "plt.imshow(a_sorted)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Tokens collected:\n",
      "1303\n",
      "(808, 1303, 1)\n"
     ]
    },
    {
     "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": [
    "tokens = dict()\n",
    "bill_details = []\n",
    "with open(output_folder+'eigenbills.csv') as csvfile:\n",
    "    csv_reader = csv.reader(csvfile, delimiter=',')\n",
    "    for row in csv_reader:\n",
    "        bill_details.append(row)\n",
    "        for token in bill_details[-1][-1].split(' '):\n",
    "            if token.lower() not in tokens:\n",
    "                tokens[token.lower()] = 1\n",
    "            else:\n",
    "                tokens[token.lower()] = tokens[token.lower()]+1\n",
    "\n",
    "all_b = []\n",
    "for i in range(len(b_ind)):\n",
    "    all_b.append(bill_details[b_ind[i]][-1])\n",
    "                \n",
    "print('Number of Tokens collected:')\n",
    "#print(tokens.keys())\n",
    "print(len(tokens.keys()))\n",
    "\n",
    "def vectorize(sent_list):\n",
    "    vec_list = []\n",
    "    for sent in sent_list:\n",
    "        vec = np.zeros((len(tokens.keys()),1))\n",
    "        if len(sent) == 0:\n",
    "            vec_list.append(vec)\n",
    "            break\n",
    "        vec_dict = dict()\n",
    "        for token in tokens.keys():\n",
    "            vec_dict[token] = 0\n",
    "        for token in sent.split(' '):\n",
    "            vec_dict[token.lower()] = vec_dict[token.lower()]+1\n",
    "        i = 0\n",
    "        for token in tokens.keys():\n",
    "            vec[i] = vec_dict[token]\n",
    "            i = i + 1\n",
    "        vec = vec/sum(vec)\n",
    "        vec_list.append(vec)\n",
    "    return vec_list\n",
    "\n",
    "import operator\n",
    "sorted_tokens = sorted(tokens.items(), key=operator.itemgetter(1), reverse=True)\n",
    "\n",
    "stopwords = [word[0] for word in sorted_tokens[:5]]\n",
    "for i in range(len(all_b)):\n",
    "    #print(all_b[i])\n",
    "    querywords = all_b[i].split()\n",
    "    resultwords  = [word for word in querywords if word.lower() not in stopwords]\n",
    "    result = ' '.join(resultwords)\n",
    "    all_b[i] = result\n",
    "    #print(all_b[i])\n",
    "vectorized = vectorize(all_b)\n",
    "vectorized = np.array(vectorized)\n",
    "#print(vectorized)\n",
    "print(vectorized.shape)\n",
    "plt.imshow(vectorized[:,:,0].T)\n",
    "vectorized = vectorized[:,:,0].T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1303, 808)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PL: 1\n",
      "PR: 419\n",
      "FL: 0\n",
      "FR: 177\n"
     ]
    },
    {
     "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": [
    "u, s, vh = np.linalg.svd(vectorized)\n",
    "print(vectorized.shape)\n",
    "\n",
    "'''\n",
    "passed_indices = []\n",
    "failed_indices = []\n",
    "for i in range(A.shape[1]):\n",
    "    if np.sum(A[:,i]) > 0:\n",
    "        passed_indices.append(i)\n",
    "    else:\n",
    "        failed_indices.append(i)\n",
    "'''\n",
    "#plt.scatter(vh[0,:], vh[1,:])\n",
    "plt.scatter(vh[:,0],[0 for i in range(vh.shape[0])])\n",
    "#plt.scatter(vh[0,passed_indices], vh[1,passed_indices], c='g')\n",
    "#plt.scatter(vh[0,failed_indices], vh[1,failed_indices], c='r')\n",
    "plt.title('Eigentokens')\n",
    "plt.show()\n",
    "#plt.scatter(u[:n_p,0], u[:n_p,1], c='r')\n",
    "#plt.scatter(u[n_p+1:,0], u[n_p+1:,1], c='b')\n",
    "#plt.scatter(u[:,0], u[:,1])\n",
    "plt.scatter(u[passed_indices,0],[0 for i in range(len(passed_indices))],c='g')\n",
    "plt.scatter(u[failed_indices,0],[0 for i in range(len(failed_indices))],c='r')\n",
    "plt.title('Eigenbills')\n",
    "#plt.show()\n",
    "plt.savefig('Eigen-BT')\n",
    "plt.show()\n",
    "plt.scatter(u[passed_indices,1],[0 for i in range(len(passed_indices))],c='g')\n",
    "plt.scatter(u[failed_indices,1],[0 for i in range(len(failed_indices))],c='r')\n",
    "plt.title('Eigenbills')\n",
    "#plt.show()\n",
    "plt.savefig('Eigen-BT')\n",
    "plt.show()\n",
    "\n",
    "u_passed = u[passed_indices,0]\n",
    "u_failed = u[failed_indices,0]\n",
    "passed_left = []\n",
    "passed_right = []\n",
    "for i in range(len(passed_indices)):\n",
    "    if u_passed[i] < (min(u[:,0])+max(u[:,0]))/2.0:\n",
    "        passed_left.append(i)\n",
    "    else:\n",
    "        passed_right.append(i)\n",
    "failed_left = []\n",
    "failed_right = []\n",
    "for i in range(len(failed_indices)):\n",
    "    if u_failed[i] < (min(u[:,0])+max(u[:,0]))/2.0:\n",
    "        failed_left.append(i)\n",
    "    else:\n",
    "        failed_right.append(i)\n",
    "\n",
    "u_passed_left = u_passed[passed_left]\n",
    "u_passed_right = u_passed[passed_right]\n",
    "u_failed_left = u_failed[failed_left]\n",
    "u_failed_right = u_failed[failed_right]\n",
    "plt.scatter(u_passed[passed_left],[0 for i in range(len(passed_left))],c='g')\n",
    "plt.scatter(u_failed[failed_left],[0 for i in range(len(failed_left))],c='r')\n",
    "plt.title('Eigenbills zoomed in left')\n",
    "plt.show()\n",
    "plt.scatter(u_passed[passed_right],[0 for i in range(len(passed_right))],c='g')\n",
    "plt.scatter(u_failed[failed_right],[0 for i in range(len(failed_right))],c='r')\n",
    "plt.title('Eigenbills zoomed in right')\n",
    "plt.show()\n",
    "print('PL: ' + str(len(passed_left)))\n",
    "print('PR: ' + str(len(passed_right)))\n",
    "print('FL: ' + str(len(failed_left)))\n",
    "print('FR: ' + str(len(failed_right)))\n",
    "\n",
    "plt.plot(s[:20])\n",
    "plt.show()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
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