visualization-ratio.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from plotting import *\n",
    "\n",
    "def load_grid(file):\n",
    "    # grid_results: [gold_agreement, expected_agreement, sampled_agreement, no_possible_coaltions, entropy]\n",
    "    grid_results = None\n",
    "    with open('model_runs/ratio/{}_issues.pickle'.format(file), 'rb') as handle:\n",
    "        grid_results = pickle.load(handle)\n",
    "        \n",
    "    E = file + 1\n",
    "\n",
    "    avg_gold_regrets = [None] * E\n",
    "    avg_expected_regrets = [None] * E\n",
    "    avg_possible_coalitions = [None] * E\n",
    "    avg_entropies = [None] * E\n",
    "    normalized_expected_regrets = [None] * E\n",
    "    expected_regrets_std = [None] * E\n",
    "    sampled_regrets_std = [None] * E\n",
    "\n",
    "    labels = [n for n in range(E)]\n",
    "\n",
    "    no_results = len(grid_results)\n",
    "    print(\"Results in grid: \", no_results)\n",
    "    print(\"Values per setting: \", no_results/E)\n",
    "\n",
    "    for e in range(E):          \n",
    "        gold_regrets = [None] * int(no_results / E)\n",
    "        expected_regrets = [None] * int(no_results / E)\n",
    "        no_possible_coalitions = [None] * int(no_results / E)\n",
    "        entropies = [None] * int(no_results / E)\n",
    "\n",
    "        for i, index in enumerate(range(e, len(grid_results), E)):\n",
    "            gold_regrets[i], expected_regrets[i], no_possible_coalitions[i], entropies[i] = grid_results[index]\n",
    "\n",
    "        avg_gold_regrets[e] = np.sum(gold_regrets) / len(gold_regrets)\n",
    "\n",
    "        avg_expected_regrets[e] = np.sum(expected_regrets) / len(expected_regrets)\n",
    "\n",
    "        avg_possible_coalitions[e] = np.sum(no_possible_coalitions) / len(no_possible_coalitions)\n",
    "\n",
    "        avg_entropies[e] = np.sum(entropies) / len(entropies)\n",
    "\n",
    "        normalized_expected_regrets[e] = avg_expected_regrets[e] / avg_gold_regrets[e]\n",
    "\n",
    "        expected_regrets_std[e] = np.std(expected_regrets)\n",
    "        \n",
    "    return [avg_gold_regrets, avg_expected_regrets, avg_possible_coalitions, \n",
    "            avg_entropies, normalized_expected_regrets, expected_regrets_std, [n for n in range(E)]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# grid[0] = avg_gold_regrets\n",
    "# grid[1] = avg_expected_regrets\n",
    "# grid[2] = avg_possible_coalitions\n",
    "# grid[3] = avg_entropies\n",
    "# grid[4] = normalized_expected_regrets\n",
    "# grid[5] = expected_regrets_std\n",
    "# grid[6] = labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Entropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results in grid:  3000\n",
      "Values per setting:  1000.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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7EUl9JTW3PUvS/5NUR5JCCCMkvSjpJ5KmS1oh6T8rq1gAAIDqrMJgFUIYUMH0IOlX0SoC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\nrXcfr169ekGSCgoKVLt27VC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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f94baa46160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results in grid:  5000\n",
      "Values per setting:  1000.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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CAACIhGAFAAAQCcEKAAAgEoIVAABAJAQrAACASAhWAAAAkRCsAAAAIiFYAQAAREKwAgAA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\naNAgNGjQoGLfffdd+Prrr29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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f94ba8c9cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results in grid:  7000\n",
      "Values per setting:  1000.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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AEAjBCgAAIBCCFQAAQCAEKwAAgEAIVgAAAIEQrAAAAAIhWAEAAARCsAIAAAiEYAUAABAI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\nAAAgEIIVAABAIP8f8XHb++w4CKAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f94ba8c9518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results in grid:  9000\n",
      "Values per setting:  1000.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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CsAIAAAiEYAUAABAIwQoAACAQghUAAEAgBCsAAIBACFYAAACBEKwAAAACIVgBAAAEQrAC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\nhkiiy4G+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f94ba8d95f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results in grid:  11000\n",
      "Values per setting:  1000.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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WAAAAgRCsAAAAAiFYAQAABEKwAgAACIRgBQAAEAjBCgAAIBCCFQAAQCAEKwAAgEAIVgAA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f94bab61128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results in grid:  13000\n",
      "Values per setting:  1000.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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wqDGMXK8x1+tzp8ZQqLFySZrkO+kmzM8pvrGsmR0oaZXzk2wAAPADVOapQDN7XFI/SU3N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\nsAIAAAjk/wMl8yILzPAnnwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f94f5575080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results in grid:  15000\n",
      "Values per setting:  1000.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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AAAiEwgoAACAQCisAAIBAKKwAAAAC4XILALawcM0KXTt2dNbj/773gEpMAwBV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\nUbPk3e+1UJ4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f94ba9c7da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results in grid:  17000\n",
      "Values per setting:  1000.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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x4TpWQDmyfNM63TphTInr/b573wyUBgCQisQKQIWV/gVKuTgpgPTQFQgAABAT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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f94ba6a1278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Results in grid:  19000\n",
      "Values per setting:  1000.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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CVyCASpfbvQkl7k8IoKqjxQoAACASEisAAIBISKwAAAAiIbECAACIhMQKAAAg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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f94ba6a1ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(2, 20, 2):\n",
    "    grid = load_grid(i)\n",
    "    labels = grid[6]\n",
    "    \n",
    "    title = \"Normalized Voter Agreement vs. Entropy for {} Issues\".format(i)\n",
    "    xlabel = \"Number of Breaking Points\"\n",
    "    xticklabels = labels\n",
    "    ylabel = \"\"\n",
    "    legend = [\"Normalized Voter agreement\", \"Entropy\"]\n",
    "    colours = ['#F78F1E', '#078C62', '#0000FF']\n",
    "    name = \"graphs/ratio/entropy_{}.png\".format(i)\n",
    "\n",
    "    data = pd.DataFrame([grid[4], grid[3]]).T\n",
    "    plot_bar_chart_2(data, title, xlabel, xticklabels, ylabel, legend, colours, name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}