CAT_data_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import editdistance\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# CAT에서 사용한 translation 데이터를 읽어온다 (mutation 포함)\n",
    "f = open(\"./data/LookUpTable.txt\")\n",
    "lines = f.readlines()\n",
    "f.close()\n",
    "\n",
    "trans = {}\n",
    "for t in range(0, len(lines), 2):\n",
    "    trans_results = lines[t + 1].strip().split('\\t')\n",
    "    res = []\n",
    "    for i in range(0, len(trans_results), 2):\n",
    "        res.append((trans_results[i], float(trans_results[i+1])))\n",
    "    trans[lines[t].strip()] = res\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'旧金山 商场 28 岁 的 老 厨师'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def translate(s):\n",
    "    return trans[s][0][0]\n",
    "\n",
    "translate('28 - Year - Old Chef Found Dead at San Francisco Mall')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9056603773584906"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def is_mutated(orig, s):\n",
    "    orig_ = orig.split()\n",
    "    s_ = s.split()\n",
    "\n",
    "    diff_count = 0\n",
    "    for i in range(0, min(len(orig_), len(s_))):\n",
    "        if orig_[i] != s_[i]:\n",
    "            diff_count += 1\n",
    "\n",
    "    if diff_count == 1:\n",
    "        return True\n",
    "    \n",
    "    else:\n",
    "        return False\n",
    "\n",
    "def get_sentence_similarity(s1, s2):\n",
    "    max_len = max(len(s1), len(s2))\n",
    "    if max_len == 0:\n",
    "        return 0\n",
    "    return 1 - (editdistance.eval(s1, s2) / max(len(s1), len(s2)))\n",
    "\n",
    "\n",
    "is_mutated('28 - Year - Old Chef Shot Dead at San Francisco Mall', '28 - Year - Old Chef Found Dead at San Francisco Mall')\n",
    "get_sentence_similarity('28 - Year - Old Chef Shot Dead at San Francisco Mall', '28 - Year - Old Chef Found Dead at San Francisco Mall')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "eng_sentences = list(trans.keys())\n",
    "original_mutants_map = defaultdict(list)\n",
    "\n",
    "original_sentence = eng_sentences[0]\n",
    "for s in eng_sentences[1:]:\n",
    "    if is_mutated(original_sentence, s):\n",
    "        original_mutants_map[original_sentence].append(s)\n",
    "    else:\n",
    "        original_sentence = s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25952"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(original_mutants_map) # translation에 사용된 문장의 갯수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "gender_male = [\"he\", \"him\", \"his\", \"male\", \"men\", \"man\", \"boy\"]\n",
    "gender_female = [\"she\", \"her\", \"hers\", \"female\", \"women\", \"woman\", \"girl\"]\n",
    "religion = [\"confucianism\", \"confucianist\", \"taoism\", \"shamanism\", \"shamanist\", \"shamanistic\",\n",
    "            \"buddhism\", \"hinduism\", \"hindu\", \"muslim\", \"islam\", \"ismalic\", \"islamism\",\n",
    "            \"christianity\", \"christian\", \"catholic\", \"catholism\", \"jewish\", \"jews\", \"judaism\"]\n",
    "race = [\"asian\", \"african\", \"american\", \"caucasian\", \"chinese\", \"europian\", \"indian\", \"korean\", \"japanese\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3282\n",
      "2515\n"
     ]
    }
   ],
   "source": [
    "sentence_about_male = []\n",
    "sentence_about_female = []\n",
    "\n",
    "for sentence in original_mutants_map:\n",
    "    for gender_word in gender_female:\n",
    "        if gender_word in sentence.split() and \"he\" not in sentence.split():\n",
    "            sentence_about_female.append(sentence)\n",
    "            break\n",
    "    for gender_word in gender_male:\n",
    "        if gender_word in sentence.split() and \"she\" not in sentence.split():\n",
    "            sentence_about_male.append(sentence)\n",
    "            break\n",
    "\n",
    "print(len(sentence_about_male))\n",
    "print(len(sentence_about_female))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8666666666666667"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def wdiff_sentences(s1, s2):\n",
    "    f = open(\"memory_1.txt\", \"w\")\n",
    "    f.write(s1.strip())\n",
    "    f.close()\n",
    "    f = open(\"memory_2.txt\", \"w\")\n",
    "    f.write(s2.strip())\n",
    "    f.close()\n",
    "    diff = os.popen(\"wdiff memory_1.txt memory_2.txt\")\n",
    "    lines = diff.readlines()\n",
    "    diff.close()\n",
    "\n",
    "    if len(lines) == 1:\n",
    "        return lines[0]\n",
    "    else:\n",
    "        return \"\"\n",
    "\n",
    "def calc_consistency_score(s1, s2):\n",
    "    wdiff = wdiff_sentences(s1, s2)\n",
    "    diff_slices_o = re.findall(r\"\\[\\-([^\\-]+)\\-\\]\", wdiff)\n",
    "\n",
    "    diff_slices_m = re.findall(r\"\\{\\+([^\\+]+)\\+\\}\", wdiff)\n",
    "\n",
    "    T_o = []\n",
    "    wdiff_o = re.sub(r\"\\{\\+[^\\+]+\\+\\}\", \"\", wdiff)\n",
    "    for s in diff_slices_o:\n",
    "        subsequence = re.sub(rf\"\\[\\-{re.escape(s)}\\-\\]\", \"\", wdiff_o)\n",
    "        subsequence = subsequence.replace(\"[-\", \"\").replace(\"-]\", \"\")\n",
    "        T_o.append(\" \".join(subsequence.split()))\n",
    "\n",
    "    T_m = []\n",
    "    wdiff_m = re.sub(r\"\\[\\-[^\\-]+\\-\\]\", \"\", wdiff)\n",
    "    for s in diff_slices_m:\n",
    "        subsequence = wdiff_m.replace(\"{+\" + s + \"+}\", \"\")\n",
    "        subsequence = subsequence.replace(\"{+\", \"\").replace(\"+}\", \"\")\n",
    "        T_m.append(\" \".join(subsequence.split()))\n",
    "\n",
    "    consistency_score = -1\n",
    "\n",
    "    for s_o in T_o:\n",
    "        for s_m in T_m:\n",
    "            s = get_sentence_similarity(s_o, s_m)\n",
    "            if s > consistency_score:\n",
    "                consistency_score = s\n",
    "\n",
    "    return consistency_score\n",
    "\n",
    "\n",
    "\n",
    "calc_consistency_score(translate('28 - Year - Old Chef Found Dead at San Francisco Mall'), translate('28 - Year - Old Chef Shot Dead at San Francisco Mall'))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
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    {
     "name": "stderr",
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     "text": [
      "100%|██████████| 3282/3282 [11:32<00:00,  4.74it/s]\n"
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       " [1.0],\n",
       " ...]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_scores_male = []\n",
    "for s in tqdm(sentence_about_male):\n",
    "    scores = []\n",
    "    for s_m in original_mutants_map[s]:\n",
    "        scores.append(calc_consistency_score(translate(s), translate(s_m)))\n",
    "    total_scores_male.append(scores)\n",
    "\n",
    "total_scores_male"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 2515/2515 [08:18<00:00,  5.05it/s]\n"
     ]
    },
    {
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       "  1.0],\n",
       " ...]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_scores_female = []\n",
    "for s in tqdm(sentence_about_female):\n",
    "    scores = []\n",
    "    for s_m in original_mutants_map[s]:\n",
    "        scores.append(calc_consistency_score(translate(s), translate(s_m)))\n",
    "    total_scores_female.append(scores)\n",
    "\n",
    "total_scores_female"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "with open('scores_male.npy', 'wb') as f:\n",
    "    np.save(f, np.array(total_scores_male, dtype=object))\n",
    "\n",
    "with open('scores_female.npy', 'wb') as f:\n",
    "    np.save(f, np.array(total_scores_female, dtype=object))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('scores_male.npy', 'rb') as f:\n",
    "    total_scores_male = np.load(f, allow_pickle=True)\n",
    "\n",
    "with open('scores_female.npy', 'rb') as f:\n",
    "    total_scores_female = np.load(f, allow_pickle=True)\n",
    "\n",
    "total_scores_male = list(total_scores_male)\n",
    "total_scores_female = list(total_scores_female)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3081\n",
      "2366\n",
      "0.9208583838285019\n",
      "0.919364486456791\n"
     ]
    }
   ],
   "source": [
    "average_male = []\n",
    "average_female = []\n",
    "\n",
    "for scores in total_scores_male:\n",
    "    scores = [s for s in scores if s > 0]\n",
    "    if len(scores) == 0:\n",
    "        continue\n",
    "    average_male.append(np.mean(scores))\n",
    "\n",
    "for scores in total_scores_female:\n",
    "    scores = [s for s in scores if s > 0]\n",
    "    if len(scores) == 0:\n",
    "        continue\n",
    "    average_female.append(np.mean(scores))\n",
    "\n",
    "print(len(average_male))\n",
    "print(len(average_female))\n",
    "print(np.mean(average_male))\n",
    "print(np.mean(average_female))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "ax = sns.boxplot(data=[average_male, average_female])\n",
    "ax.set_xticklabels([\"Male Subject\",\"Female Subject\"])\n",
    "plt.tight_layout()\n",
    "plt.savefig('ConScore_gender_CAT_dataset.png',bbox_inches='tight', dpi=150)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "878eaed65604c1104097ccdf5de881d73204f55acb592f52cbb5bd6ae899bb77"
  },
  "kernelspec": {
   "display_name": "Python 3.8.2 64-bit ('tensorflow-env')",
   "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": "3.8.2"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}