CWI2018_Subj.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b54cfc14",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas\n",
    "from math import sqrt\n",
    "import matplotlib.pyplot as plt\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6c84db53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'prob_comp'}>]], dtype=object)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "columns=[\"id\", \"context\", \"begin\", \"end\", \"token\", \"native_annotators\", \"non-native_annotators\", \"native_comp\", \"non-native_comp\", \"bin_comp\", \"prob_comp\"]\n",
    "\n",
    "dfs = []\n",
    "for file in os.listdir(\"CWI_2018\"):\n",
    "    df_tmp = pandas.read_csv(\"CWI_2018/\" +file ,sep='\\t',names=columns)\n",
    "    dfs.append(df_tmp)\n",
    "\n",
    "df = pandas.concat(dfs,ignore_index=True)\n",
    "\n",
    "df.hist(column=\"prob_comp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ee8d5f57",
   "metadata": {},
   "outputs": [],
   "source": [
    "def subj(x):\n",
    "    zeros = [0] * (20 - int(x*20))\n",
    "    ones  = [1] * int(x*20)\n",
    "    subj = sum([abs(x - i) for i in zeros])\n",
    "    subj += sum([abs(x - i) for i in ones])\n",
    "    subj /= 20.0\n",
    "    return subj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "35cccfb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['subjectivity'] = df.prob_comp.apply(subj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "cf2b2d14",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bgeOSHALsX1XXV1UBF/W1kSQtkn2XaL8FfDlJAX9eVZuBsaq6F6Cq7k3y/LbtocANfW13tNrjbXl6fTdJNtKbuTA2NsbExMS8Oj22As48enJebUfVzp075/1+jSrHvDw45oWzVEHymqq6p4XFV5J8Zw/bznTeo/ZQ373YC6rNAOPj47V27dq97G7PeZdcydnbluotWxpb1q1kvu/XqJqYmHDMy4BjXjhLcmirqu5pf+8HPg8cB9zXDlfR/t7fNt8BHNbXfBVwT6uvmqEuSVpEix4kSVYmec7UMvAbwG3AVcApbbNTgCvb8lXAhiT7JTmc3kn1m9phsEeTHN+u1jq5r40kaZEsxXGaMeDz7UrdfYFPV9UXk9wMXJ7kNOCHwJsAqur2JJcDdwCTwBlVtau91unAFmAFcE17SJIW0aIHSVV9D3jZDPWfACfM0mYTsGmG+lbgJQvdR0nS4J5Kl/9KkkaQQSJJ6sQgkSR1YpBIkjoxSCRJnRgkkqRODBJJUicGiSSpE4NEktSJQSJJ6mR53RNdkpptP36YU8/6wlJ3Y1FtWbdyKK/rjESS1IlBIknqxCCRJHXiORLtkceRJc3FGYkkqRODRJLUiUEiSerEcySSluW5sDOPXuoePH04I5EkdWKQSJI68dCWNI2HeaS944xEktSJQSJJ6sQgkSR1YpBIkjoxSCRJnRgkkqRODBJJUicGiSSpE4NEktTJyAdJknVJ7kqyPclZS90fSVpuRjpIkuwD/G/gN4EjgbcmOXJpeyVJy8tIBwlwHLC9qr5XVf8CXAqsX+I+SdKykqpa6j7MW5KTgHVV9Tvt+duAV1bVO6dttxHY2J6+ELhrnrs8GHhgnm1HlWNeHhzz8tBlzP+2qp4304pRv/tvZqjtloxVtRnY3HlnydaqGu/6OqPEMS8Pjnl5GNaYR/3Q1g7gsL7nq4B7lqgvkrQsjXqQ3AysSXJ4kmcCG4CrlrhPkrSsjPShraqaTPJO4EvAPsCnqur2Ie6y8+GxEeSYlwfHvDwMZcwjfbJdkrT0Rv3QliRpiRkkkqRODJIZzHXblfSc29bfmuTlS9HPhTTAmF+U5PokjyV571L0caENMObfbp/vrUm+meRlS9HPhTTAmNe38d6SZGuSX1+Kfi6UQW+hlOQVSXa176aNtAE+47VJHm6f8S1JPtB5p1Xlo+9B76T9/wN+BXgm8A/AkdO2eT1wDb3vsRwP3LjU/V6EMT8feAWwCXjvUvd5kcb8auCAtvyby+RzfjZPnDt9KfCdpe73MMfbt911wNXASUvd70X4jNcC/2ch9+uMZHeD3HZlPXBR9dwA/Oskhyx2RxfQnGOuqvur6mbg8aXo4BAMMuZvVtVD7ekN9L6nNMoGGfPOav+3AVYywxd8R8igt1B6F/BZ4P7F7NyQLMltowyS3R0K/Kjv+Y5W29ttRsnTbTyD2Nsxn0ZvFjrKBhpzkjcm+Q7wBeDti9S3YZhzvEkOBd4IfHwR+zVMg/53/aok/5DkmiRHdd2pQbK7QW67MtCtWUbI0208gxh4zEleSy9I/nioPRq+QW8p9PmqehFwIvChYXdqiAYZ78eAP66qXcPvzqIYZMzfpnffrJcB5wF/3XWnBsnuBrntytPt1ixPt/EMYqAxJ3kp8AlgfVX9ZJH6Nix79TlX1deBf5fk4GF3bEgGGe84cGmSu4GTgPOTnLgovRuOOcdcVY9U1c62fDXwjK6fsUGyu0Fuu3IVcHK7eut44OGqunexO7qAluOtZuYcc5J/A3wOeFtV/eMS9HGhDTLmI5KkLb+c3gnbUQ3QOcdbVYdX1eqqWg1cAfxeVf31ovd04QzyGf9y32d8HL0c6PQZj/QtUoahZrntSpL/1tZ/nN7VHa8HtgM/A/7rUvV3IQwy5iS/DGwF9gd+keTd9K4GeWSp+t3FgJ/zB4CD6P0rFWCyRvhusQOO+b/Q+0fS48A/A2/pO/k+UgYc79PKgGM+CTg9ySS9z3hD18/YW6RIkjrx0JYkqRODRJLUiUEiSerEIJEkdWKQSJI6MUikPUjy/iS3990R95V72PaDM90ZOckLklwxz/2fmuQFfc8/keTIOdp8s/1dneS35rNfaW/4PRJpFkleBbwBeHlVPda+/fvMvX2dqrqH3rX783EqcBvt28lV9TsD7O/VbXE18FvAp+e5b2kgzkik2R0CPFBVjwFU1QNVdU+Su6duKZFkPMlEX5uXJbkuyXeT/G7bZnWS29ryPkn+Z5Kb2yznHVMNk/xRkm3tZnofbr+NMQ5c0mZDK5JMtH2enuR/9LU9Ncl5bXlnK38Y+Pet7XuS/F2SY/rafKPdAkbqxBmJNLsvAx9I8o/A3wKXVdXX5mjzUnq/UbMS+L9JvjBt/Wn0bqnziiT7Ad9I8mVg6iaJr6yqnyU5sKoebN9Sfm9VbQVo37CH3u08rgf+qD1/C73fiul3Vmv7htb2QXoznHcn+VVgv6q6dcD3QpqVMxJpFu3GdscCG4F/Ai5Lcuocza6sqn+uqgeAr9L7fYh+v0HvFiS3ADfSuwXLGuA/AX9ZVT9r+35wjr79E/C9JMcnOQh4IfCNOfr2V8AbkjyD3u3ht8yxvTQQZyTSHrTbi08AE0m2AacAkzzxj7BnTW8yx/MA76qqLz2pmKybYdu5XAa8GfgO8Pm57pfUZjpfofdDR2+md9hM6swZiTSLJC9MsqavdAzwA+BuejMV6N3ksN/6JM9qs4S19O7G2u9L9G6Y94y2j19NspLeYbS3J/lXrX5g2/5R4DmzdPFz9A6HvZVeqEw3U9tPAOcCN88165EGZZBIs3s2cGGSO5LcChwJfBD478CfJfk7YPoPIt1E75cFbwA+1K7YgidmG58A7gC+3U7A/zmwb1V9kd7tvre2w15TlxFvAT4+dbK9f0ftZ4DvoPcjRTfN0P9bgcl28v49rc23gEeAv9zbN0OajXf/lYYsybHAR6vqPzwF+vICeofqXlRVv1ji7uhpwhmJNERJxoHPAH/2FOjLyfRO8L/fENFCckYiSerEGYkkqRODRJLUiUEiSerEIJEkdWKQSJI6+f9tNr3qp1800gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = df.subjectivity.hist(bins=5)\n",
    "plot.set_xlabel(\"Subjectivity\")\n",
    "plot.set_ylabel(\"Count\")\n",
    "plot.figure.savefig(\"Figures/CWI_2018_Hist.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5a51a3a0",
   "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"
    }
   ],
   "source": [
    "plot = df.plot.scatter(x='prob_comp',y='subjectivity')\n",
    "plot.set_xlabel(\"Complexity\")\n",
    "plot.set_ylabel(\"Subjectivity\")\n",
    "plot.figure.savefig(\"Figures/CWI_2018_Scatter.pdf\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4dc13dbc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.1\n"
     ]
    }
   ],
   "source": [
    "alpha = 0.0025\n",
    "L = 2\n",
    "N = 20\n",
    "T_subj = alpha * L * N\n",
    "print(T_subj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "85d71985",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['subjective'] = df.subjectivity > T_subj"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a1daabf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "alphas = [] \n",
    "xs = []\n",
    "ys = []\n",
    "for alpha in range(0,250):\n",
    "    alpha /= 10000\n",
    "    T_subj = alpha * L * N\n",
    "    y = sum(df.subjectivity > T_subj)/len(df.subjectivity)\n",
    "    alphas.append(alpha)\n",
    "    xs.append(T_subj)\n",
    "    ys.append(y) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "83e9d765",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax2 = ax1.twiny()\n",
    "\n",
    "\n",
    "ax1.plot(xs,ys)\n",
    "ax1.set_xlabel('Subjectivty Threshold')\n",
    "ax1.set_ylabel(\"Subjectivity Proportion\")\n",
    "\n",
    "ax2.set_xlim(ax1.get_xlim())\n",
    "ax2.set_xticks([0,0.2,0.4,0.6,0.8,1])\n",
    "ax2.set_xticklabels([0.0,0.005,0.01,0.015,0.02,0.025])\n",
    "ax2.set_xlabel(\"alpha\")\n",
    "\n",
    "\n",
    "plt.show()\n",
    "fig.savefig(\"Figures/CWI_2018_threshold.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8dac8321",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_pickle(\"cwi_2018.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3298739",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}