Experiment 1 Figure 1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dfc7613d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import combinations, permutations\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.utils import shuffle\n",
    "from rules import divisiveness, win_rate, bootstrap, bootstrap2, bootstrap3, copeland\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "677fdaf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>number_proposals</th>\n",
       "      <th>number_individuals</th>\n",
       "      <th>simulation</th>\n",
       "      <th>RANK(WR)</th>\n",
       "      <th>RANK(COP)</th>\n",
       "      <th>RANK(STD)</th>\n",
       "      <th>RANK(DIV(WR))</th>\n",
       "      <th>RANK(DIV(COP))</th>\n",
       "      <th>KT(WR,STD)</th>\n",
       "      <th>KT(WR,COP)</th>\n",
       "      <th>KT(WR,RANK(DIV(WR)))</th>\n",
       "      <th>KT(WR,RANK(DIV(COP)))</th>\n",
       "      <th>KT(RANK(DIV(WR)),STD)</th>\n",
       "      <th>KT(RANK(DIV(WR)),COP)</th>\n",
       "      <th>KT(RANK(DIV(WR)),RANK(DIV(COP)))</th>\n",
       "      <th>KT(RANK(DIV(COP)),STD)</th>\n",
       "      <th>KT(RANK(DIV(COP)),COP)</th>\n",
       "      <th>KT(RANK(DIV(COP)),RANK(DIV(WR)))</th>\n",
       "      <th>METHOD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>[1, 2, 0]</td>\n",
       "      <td>[1, 2, 0]</td>\n",
       "      <td>[2, 0, 1]</td>\n",
       "      <td>[2, 0, 1]</td>\n",
       "      <td>[0, 1, 2]</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>IC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>200</td>\n",
       "      <td>0</td>\n",
       "      <td>[0, 1, 2]</td>\n",
       "      <td>[0, 1, 2]</td>\n",
       "      <td>[1, 2, 0]</td>\n",
       "      <td>[1, 2, 0]</td>\n",
       "      <td>[0, 1, 2]</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>IC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>300</td>\n",
       "      <td>0</td>\n",
       "      <td>[0, 2, 1]</td>\n",
       "      <td>[0, 2, 1]</td>\n",
       "      <td>[0, 2, 1]</td>\n",
       "      <td>[0, 2, 1]</td>\n",
       "      <td>[0, 1, 2]</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>IC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>400</td>\n",
       "      <td>0</td>\n",
       "      <td>[2, 1, 0]</td>\n",
       "      <td>[2, 0, 1]</td>\n",
       "      <td>[1, 2, 0]</td>\n",
       "      <td>[1, 2, 0]</td>\n",
       "      <td>[0, 1, 2]</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>IC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>[0, 1, 2]</td>\n",
       "      <td>[0, 1, 2]</td>\n",
       "      <td>[0, 1, 2]</td>\n",
       "      <td>[0, 1, 2]</td>\n",
       "      <td>[0, 1, 2]</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>IC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   number_proposals  number_individuals  simulation   RANK(WR)  RANK(COP)  \\\n",
       "0                 3                 100           0  [1, 2, 0]  [1, 2, 0]   \n",
       "1                 3                 200           0  [0, 1, 2]  [0, 1, 2]   \n",
       "2                 3                 300           0  [0, 2, 1]  [0, 2, 1]   \n",
       "3                 3                 400           0  [2, 1, 0]  [2, 0, 1]   \n",
       "4                 3                 500           0  [0, 1, 2]  [0, 1, 2]   \n",
       "\n",
       "   RANK(STD) RANK(DIV(WR)) RANK(DIV(COP))  KT(WR,STD)  KT(WR,COP)  \\\n",
       "0  [2, 0, 1]     [2, 0, 1]      [0, 1, 2]   -0.333333    1.000000   \n",
       "1  [1, 2, 0]     [1, 2, 0]      [0, 1, 2]   -0.333333    1.000000   \n",
       "2  [0, 2, 1]     [0, 2, 1]      [0, 1, 2]    1.000000    1.000000   \n",
       "3  [1, 2, 0]     [1, 2, 0]      [0, 1, 2]    0.333333    0.333333   \n",
       "4  [0, 1, 2]     [0, 1, 2]      [0, 1, 2]    1.000000    1.000000   \n",
       "\n",
       "   KT(WR,RANK(DIV(WR)))  KT(WR,RANK(DIV(COP)))  KT(RANK(DIV(WR)),STD)  \\\n",
       "0             -0.333333              -0.333333                    1.0   \n",
       "1             -0.333333               1.000000                    1.0   \n",
       "2              1.000000               0.333333                    1.0   \n",
       "3              0.333333              -1.000000                    1.0   \n",
       "4              1.000000               1.000000                    1.0   \n",
       "\n",
       "   KT(RANK(DIV(WR)),COP)  KT(RANK(DIV(WR)),RANK(DIV(COP)))  \\\n",
       "0              -0.333333                         -0.333333   \n",
       "1              -0.333333                         -0.333333   \n",
       "2               1.000000                          0.333333   \n",
       "3              -0.333333                         -0.333333   \n",
       "4               1.000000                          1.000000   \n",
       "\n",
       "   KT(RANK(DIV(COP)),STD)  KT(RANK(DIV(COP)),COP)  \\\n",
       "0               -0.333333               -0.333333   \n",
       "1               -0.333333                1.000000   \n",
       "2                0.333333                0.333333   \n",
       "3               -0.333333               -0.333333   \n",
       "4                1.000000                1.000000   \n",
       "\n",
       "   KT(RANK(DIV(COP)),RANK(DIV(WR))) METHOD  \n",
       "0                         -0.333333     IC  \n",
       "1                         -0.333333     IC  \n",
       "2                          0.333333     IC  \n",
       "3                         -0.333333     IC  \n",
       "4                          1.000000     IC  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "N_PROFILE = 100\n",
    "plot = None\n",
    "for METHOD in ['IC','UM10','UM50']:\n",
    "    df = pd.read_csv(\"data/experiment_1_method_%s_profile_%s.csv\"%(METHOD,N_PROFILE))\n",
    "    df['METHOD'] = METHOD\n",
    "    if type(plot) == None:\n",
    "        plot = pd.DataFrame(columns=df.columns)\n",
    "    plot = pd.concat([plot,df])  \n",
    "    \n",
    "plot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9df4776c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>METHOD</th>\n",
       "      <th>number_proposals</th>\n",
       "      <th>number_individuals</th>\n",
       "      <th>simulation</th>\n",
       "      <th>variable</th>\n",
       "      <th>value</th>\n",
       "      <th>Comparison</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>IC</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
       "      <td>0</td>\n",
       "      <td>KT(RANK(DIV(WR)),RANK(DIV(COP)))</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>KT($\\mathit{Div}^\\mathit{Borda}$,$\\mathit{Div}...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IC</td>\n",
       "      <td>3</td>\n",
       "      <td>200</td>\n",
       "      <td>0</td>\n",
       "      <td>KT(RANK(DIV(WR)),RANK(DIV(COP)))</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>KT($\\mathit{Div}^\\mathit{Borda}$,$\\mathit{Div}...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>IC</td>\n",
       "      <td>3</td>\n",
       "      <td>300</td>\n",
       "      <td>0</td>\n",
       "      <td>KT(RANK(DIV(WR)),RANK(DIV(COP)))</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>KT($\\mathit{Div}^\\mathit{Borda}$,$\\mathit{Div}...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IC</td>\n",
       "      <td>3</td>\n",
       "      <td>400</td>\n",
       "      <td>0</td>\n",
       "      <td>KT(RANK(DIV(WR)),RANK(DIV(COP)))</td>\n",
       "      <td>-0.333333</td>\n",
       "      <td>KT($\\mathit{Div}^\\mathit{Borda}$,$\\mathit{Div}...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IC</td>\n",
       "      <td>3</td>\n",
       "      <td>500</td>\n",
       "      <td>0</td>\n",
       "      <td>KT(RANK(DIV(WR)),RANK(DIV(COP)))</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>KT($\\mathit{Div}^\\mathit{Borda}$,$\\mathit{Div}...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  METHOD  number_proposals  number_individuals  simulation  \\\n",
       "0     IC                 3                 100           0   \n",
       "1     IC                 3                 200           0   \n",
       "2     IC                 3                 300           0   \n",
       "3     IC                 3                 400           0   \n",
       "4     IC                 3                 500           0   \n",
       "\n",
       "                           variable     value  \\\n",
       "0  KT(RANK(DIV(WR)),RANK(DIV(COP))) -0.333333   \n",
       "1  KT(RANK(DIV(WR)),RANK(DIV(COP))) -0.333333   \n",
       "2  KT(RANK(DIV(WR)),RANK(DIV(COP)))  0.333333   \n",
       "3  KT(RANK(DIV(WR)),RANK(DIV(COP))) -0.333333   \n",
       "4  KT(RANK(DIV(WR)),RANK(DIV(COP)))  1.000000   \n",
       "\n",
       "                                          Comparison  \n",
       "0  KT($\\mathit{Div}^\\mathit{Borda}$,$\\mathit{Div}...  \n",
       "1  KT($\\mathit{Div}^\\mathit{Borda}$,$\\mathit{Div}...  \n",
       "2  KT($\\mathit{Div}^\\mathit{Borda}$,$\\mathit{Div}...  \n",
       "3  KT($\\mathit{Div}^\\mathit{Borda}$,$\\mathit{Div}...  \n",
       "4  KT($\\mathit{Div}^\\mathit{Borda}$,$\\mathit{Div}...  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subp = plot.melt(id_vars=['METHOD','number_proposals','number_individuals','simulation'],\\\n",
    "                 value_vars=[\"KT(RANK(DIV(WR)),RANK(DIV(COP)))\",\n",
    "                             \"KT(RANK(DIV(WR)),STD)\",\n",
    "                             \"KT(RANK(DIV(COP)),STD)\"])\n",
    "subp['Comparison']= None\n",
    "subp.loc[subp['variable']=='KT(RANK(DIV(WR)),RANK(DIV(COP)))','Comparison']= \"KT($\\mathit{Div}^\\mathit{Borda}$,$\\mathit{Div}^\\mathit{Copeland}$)\"\n",
    "subp.loc[subp['variable']=='KT(RANK(DIV(WR)),STD)','Comparison']= \"KT($\\mathit{Div}^\\mathit{Borda}$,$\\mathit{Variance}$)\"\n",
    "subp.loc[subp['variable']=='KT(RANK(DIV(COP)),STD)', 'Comparison']= \"KT($\\mathit{Div}^\\mathit{Copeland}$,$\\mathit{Variance}$)\"\n",
    "subp.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "816a1a4d",
   "metadata": {},
   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure 1\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(4,4), dpi=150)\n",
    "\n",
    "g = sns.lineplot(data=subp[(subp['number_individuals'].isin([100])) & \n",
    "                          (subp['METHOD'].isin([\"UM10\"]))], \\\n",
    "             x='number_proposals', \n",
    "                 y='value',\\\n",
    "                 hue='Comparison',\n",
    "             linewidth=2, \n",
    "                 style='Comparison',\\\n",
    "                 markers = True,\n",
    "                 alpha=0.8, \n",
    "                 palette='Dark2', legend=True,\n",
    "              ax=ax)\n",
    "leg = g.legend(title=\"\", frameon=False, fontsize=8, loc='lower left')\n",
    "leg.get_title().set_position((-10, 0))\n",
    "for line in leg.get_lines():\n",
    "    line.set_linewidth(2)\n",
    "\n",
    "\n",
    "ax.spines[\"left\"].set_linewidth(1.2)\n",
    "ax.spines[\"bottom\"].set_linewidth(1.2)\n",
    "ax.spines[\"right\"].set_visible(False)\n",
    "ax.spines[\"top\"].set_visible(False)\n",
    "ax.set_ylim(-0.25,1.05)\n",
    "plt.title(\"UM10\")\n",
    "import matplotlib.ticker as mticker\n",
    "ax.xaxis.set_major_locator(mticker.MultipleLocator(3))\n",
    "ax.set_ylabel(\"KT\")\n",
    "ax.set_xlabel(\"# issues\")\n",
    "plt.tight_layout()\n",
    "plt.savefig(\"figures/figure_1.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9dfddf53",
   "metadata": {},
   "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": "3.10.8 (tags/v3.10.8:aaaf517, Oct 11 2022, 16:50:30) [MSC v.1933 64 bit (AMD64)]"
  },
  "vscode": {
   "interpreter": {
    "hash": "369f2c481f4da34e4445cda3fffd2e751bd1c4d706f27375911949ba6bb62e1c"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}