visualize.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "initial_id",
   "metadata": {
    "collapsed": true,
    "ExecuteTime": {
     "end_time": "2024-05-12T04:33:03.667707Z",
     "start_time": "2024-05-12T04:33:03.638116Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from pathlib import Path\n",
    "from scipy.special import softmax\n",
    "import numpy as np\n",
    "def read_tsv(file_name):\n",
    "    normalized_results = []\n",
    "    results = Path(file_name).read_text().strip().split('\\n')[1:]\n",
    "    for result in results:\n",
    "        result_sent = [float(x) for x in result.split('\\t')]\n",
    "        normalized = softmax(result_sent)\n",
    "        normalized_results.append(normalized)\n",
    "    return normalized_results\n",
    "\n",
    "verb_m1_mbert = read_tsv('verb_m1_mbert.tsv')\n",
    "verb_f1_mbert = read_tsv('verb_f1_mbert.tsv')\n",
    "\n",
    "verb_m1_xlm = read_tsv('verb_m1_xlm.tsv')\n",
    "verb_f1_xlm = read_tsv('verb_f1_xlm.tsv')\n",
    "\n",
    "verb_m1_glm = read_tsv('verb_m1_glm.tsv')\n",
    "verb_f1_glm = read_tsv('verb_f1_glm.tsv')\n",
    "\n",
    "verb_m1_bert = read_tsv('verb_m1_bert.tsv')\n",
    "verb_f1_bert = read_tsv('verb_f1_bert.tsv')\n",
    "\n",
    "\n",
    "amb_m1_mbert = read_tsv('amb_m1_mbert.tsv')\n",
    "amb_f1_mbert = read_tsv('amb_f1_mbert.tsv')\n",
    "\n",
    "amb_f1_bert = read_tsv('amb_f1_bert.tsv')\n",
    "amb_m1_bert = read_tsv('amb_f1_bert.tsv')\n",
    "\n",
    "amb_f1_glm = read_tsv('amb_f1_glm.tsv')\n",
    "amb_m1_glm = read_tsv('amb_m1_glm.tsv')\n",
    "\n",
    "amb_f1_xlm = read_tsv('amb_f1_xlm.tsv')\n",
    "amb_m1_xlm = read_tsv('amb_m1_xlm.tsv')\n",
    "\n",
    "\n",
    "subj_m1_mbert = read_tsv('subj_m1_mbert.tsv')\n",
    "subj_f1_mbert = read_tsv('subj_f1_mbert.tsv')\n",
    "\n",
    "subj_f1_bert = read_tsv('subj_f1_bert.tsv')\n",
    "subj_m1_bert = read_tsv('subj_f1_bert.tsv')\n",
    "\n",
    "subj_f1_glm = read_tsv('subj_f1_glm.tsv')\n",
    "subj_m1_glm = read_tsv('subj_m1_glm.tsv')\n",
    "\n",
    "subj_f1_xlm = read_tsv('subj_f1_xlm.tsv')\n",
    "subj_m1_xlm = read_tsv('subj_m1_xlm.tsv')"
   ]
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as pl\n",
    "pl\n",
    "# there are 4 individuals, each one tested under 3 different settings\n",
    "\n",
    "# --- Random data, e.g. results per algorithm:\n",
    "\n",
    "# Invidual 1\n",
    "d1_1 = list(np.array(verb_m1_bert).transpose())[0]\n",
    "d1_2 = list(np.array(verb_m1_bert).transpose())[1]\n",
    "d1_3 = list(np.array(verb_f1_bert).transpose())[0]\n",
    "d1_4 = list(np.array(verb_f1_bert).transpose())[1]\n",
    "# Individual 2\n",
    "d2_1 = list(np.array(verb_m1_mbert).transpose())[0]\n",
    "d2_2 = list(np.array(verb_m1_mbert).transpose())[1]\n",
    "d2_3 = list(np.array(verb_f1_mbert).transpose())[0]\n",
    "d2_4 = list(np.array(verb_f1_mbert).transpose())[1]\n",
    "# Individual 3\n",
    "\n",
    "d3_1 = list(np.array(verb_m1_xlm).transpose())[0]\n",
    "d3_2 = list(np.array(verb_m1_xlm).transpose())[1]\n",
    "d3_3 = list(np.array(verb_f1_xlm).transpose())[0]\n",
    "d3_4 = list(np.array(verb_f1_xlm).transpose())[1]\n",
    "# Individual 4\n",
    "d4_1 = list(np.array(verb_m1_glm).transpose())[0]\n",
    "d4_2 = list(np.array(verb_m1_glm).transpose())[1]\n",
    "d4_3 = list(np.array(verb_f1_glm).transpose())[0]\n",
    "d4_4 = list(np.array(verb_f1_glm).transpose())[1]\n",
    "\n",
    "\n",
    "d11_1 = list(np.array(amb_m1_bert).transpose())[0]\n",
    "d11_2 = list(np.array(amb_m1_bert).transpose())[1]\n",
    "d11_3 = list(np.array(amb_f1_bert).transpose())[0]\n",
    "d11_4 = list(np.array(amb_f1_bert).transpose())[1]\n",
    "# Individual 2\n",
    "d21_1 = list(np.array(amb_m1_mbert).transpose())[0]\n",
    "d21_2 = list(np.array(amb_m1_mbert).transpose())[1]\n",
    "d21_3 = list(np.array(amb_f1_mbert).transpose())[0]\n",
    "d21_4 = list(np.array(amb_f1_mbert).transpose())[1]\n",
    "# Individual 3\n",
    "\n",
    "d31_1 = list(np.array(amb_m1_xlm).transpose())[0]\n",
    "d31_2 = list(np.array(amb_m1_xlm).transpose())[1]\n",
    "d31_3 = list(np.array(amb_f1_xlm).transpose())[0]\n",
    "d31_4 = list(np.array(amb_f1_xlm).transpose())[1]\n",
    "# Individual 4\n",
    "d41_1 = list(np.array(amb_m1_glm).transpose())[0]\n",
    "d41_2 = list(np.array(amb_m1_glm).transpose())[1]\n",
    "d41_3 = list(np.array(amb_f1_glm).transpose())[0]\n",
    "d41_4 = list(np.array(amb_f1_glm).transpose())[1]\n",
    "\n",
    "d1_11 = list(np.array(subj_m1_bert).transpose())[0]\n",
    "d1_21 = list(np.array(subj_m1_bert).transpose())[1]\n",
    "d1_31 = list(np.array(subj_f1_bert).transpose())[0]\n",
    "d1_41 = list(np.array(subj_f1_bert).transpose())[1]\n",
    "# Individual 2\n",
    "d2_11 = list(np.array(subj_m1_mbert).transpose())[0]\n",
    "d2_21 = list(np.array(subj_m1_mbert).transpose())[1]\n",
    "d2_31 = list(np.array(subj_f1_mbert).transpose())[0]\n",
    "d2_41 = list(np.array(subj_f1_mbert).transpose())[1]\n",
    "# Individual 3\n",
    "\n",
    "d3_11 = list(np.array(subj_m1_xlm).transpose())[0]\n",
    "d3_21 = list(np.array(subj_m1_xlm).transpose())[1]\n",
    "d3_31 = list(np.array(subj_f1_xlm).transpose())[0]\n",
    "d3_41 = list(np.array(subj_f1_xlm).transpose())[1]\n",
    "# Individual 4\n",
    "d4_11 = list(np.array(subj_m1_glm).transpose())[0]\n",
    "d4_21 = list(np.array(subj_m1_glm).transpose())[1]\n",
    "d4_31 = list(np.array(subj_f1_glm).transpose())[0]\n",
    "d4_41 = list(np.array(subj_f1_glm).transpose())[1]\n",
    "\n",
    "# --- Combining your data:\n",
    "data_group1 = [d1_1, d2_1, d3_1, d4_1]\n",
    "data_group2 = [d1_2, d2_2, d3_2, d4_2]\n",
    "data_group3 = [d1_3, d3_2, d3_3, d4_3]\n",
    "data_group4 = [d1_4, d4_2, d3_4, d4_4]\n",
    "\n",
    "data_group11 = [d11_1, d21_1, d31_1, d41_1]\n",
    "data_group12 = [d11_2, d21_2, d31_2, d41_2]\n",
    "data_group13 = [d11_3, d31_2, d31_3, d41_3]\n",
    "data_group14 = [d11_4, d41_2, d31_4, d41_4]\n",
    "\n",
    "data_group111 = [d1_11, d2_11, d3_11, d4_11]\n",
    "data_group21 = [d1_21, d2_21, d3_21, d4_21]\n",
    "data_group31 = [d1_31, d3_21, d3_31, d4_31]\n",
    "data_group41 = [d1_41, d4_21, d3_41, d4_41]\n",
    "\n",
    "colors = ['#2C7BB6','#D7191C',  '#2C7BB6', '#D7191C']\n",
    "\n",
    "# we compare the performances of the 4 individuals within the same set of 3 settings \n",
    "data_groups = [data_group1, data_group2, data_group3, data_group4]\n",
    "data_groups1 = [data_group11, data_group12, data_group13, data_group14]\n",
    "data_groups11 = [data_group111, data_group21, data_group31, data_group41]\n",
    "# --- Labels for your data:\n",
    "labels_list = ['Chinese BERT','mBERT', 'XLM-R', 'Chat-GLM']\n",
    "width       = 1/len(labels_list)\n",
    "xlocations  = [ x*((1+ len(data_groups))*width) for x in range(len(data_group1)) ]\n",
    "\n",
    "ymin        = min ( [ val  for dg in data_groups  for data in dg for val in data ] )\n",
    "ymax        = max ( [ val  for dg in data_groups  for data in dg for val in data ])\n",
    "\n",
    "space = len(data_groups)/2\n",
    "offset = len(data_groups)/2\n",
    "\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-12T05:33:37.608237Z",
     "start_time": "2024-05-12T05:33:37.572561Z"
    }
   },
   "id": "75d9af31094a6d23",
   "execution_count": 132
  },
  {
   "cell_type": "code",
   "outputs": [
    {
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   "source": [
    "verb_m1_bert"
   ],
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    "ExecuteTime": {
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   },
   "id": "1a33c5211aa1b8c7",
   "execution_count": 105
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    {
     "data": {
      "text/plain": "<Figure size 1500x500 with 3 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "xlocations = range(len(data_groups))\n",
    "labels_list = ['Chinese BERT', 'mBERT', 'XLM-R', 'Chat-GLM']\n",
    "colors = ['#2C7BB6', '#F1948A', '#ABD9E9', '#E6B0AA']\n",
    "space = 0.2\n",
    "width = 0.15\n",
    "\n",
    "# Calculate group positions\n",
    "group_positions = []\n",
    "for num, dg in enumerate(data_groups):    \n",
    "    _off = (0 - space + (0.5 + num))\n",
    "    group_positions.append([x + _off * (width + 0.01) for x in xlocations])\n",
    "\n",
    "fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(15, 5))\n",
    "\n",
    "for dg, pos, c in zip(data_groups, group_positions, colors):\n",
    "    boxes = axes[0].boxplot(dg,\n",
    "                       sym='.',  # Red plusses as fliers\n",
    "                       positions=pos, \n",
    "                       widths=width, \n",
    "                       boxprops=dict(linestyle='-', linewidth=1.5, color=c),\n",
    "                       medianprops=dict(color='grey', linestyle='-', linewidth=1.5),\n",
    "                       patch_artist=False,  # Do not fill with color\n",
    "                       whiskerprops={'linestyle': '-', 'color': 'black'},  # Custom whisker style\n",
    "                       capprops={'color': 'black'},  # Custom cap style\n",
    "                       flierprops={'marker': 'o', 'color': 'red', 'alpha': 0.5},  # Custom flier style\n",
    "                       )\n",
    "axes[0].set_xticks(xlocations)\n",
    "axes[0].set_xticklabels(labels_list, rotation=45)\n",
    "axes[0].set_title('Predicted Probabilities of Pronouns: Masculine vs. Feminine \\n Across Two Verb Orientation Groups',fontsize='small')\n",
    "\n",
    "\n",
    "for dg, pos, c in zip(data_groups1, group_positions, colors):\n",
    "    boxes = axes[1].boxplot(dg,\n",
    "                       sym='.',  # Red plusses as fliers\n",
    "                       positions=pos, \n",
    "                       widths=width, \n",
    "                       boxprops=dict(linestyle='-', linewidth=1.5, color=c),\n",
    "                       medianprops=dict(color='grey', linestyle='-', linewidth=1.5),\n",
    "                       patch_artist=False,  # Do not fill with color\n",
    "                       whiskerprops={'linestyle': '-', 'color': 'black'},  # Custom whisker style\n",
    "                       capprops={'color': 'black'},  # Custom cap style\n",
    "                       flierprops={'marker': 'o', 'color': 'red', 'alpha': 0.5},  # Custom flier style\n",
    "                       )\n",
    "axes[1].set_xticks(xlocations)\n",
    "axes[1].set_xticklabels(labels_list, rotation=45)\n",
    "axes[1].set_title('Predicted Probabilities of Pronouns: Masculine vs. Feminine \\n Across Two Ambiguous Binding Groups',fontsize='small')\n",
    "\n",
    "\n",
    "for dg, pos, c in zip(data_groups1, group_positions, colors):\n",
    "    boxes = axes[2].boxplot(dg,\n",
    "                       sym='.',  # Red plusses as fliers\n",
    "                       positions=pos, \n",
    "                       widths=width, \n",
    "                       boxprops=dict(linestyle='-', linewidth=1.5, color=c),\n",
    "                       medianprops=dict(color='grey', linestyle='-', linewidth=1.5),\n",
    "                       patch_artist=False,  # Do not fill with color\n",
    "                       whiskerprops={'linestyle': '-', 'color': 'black'},  # Custom whisker style\n",
    "                       capprops={'color': 'black'},  # Custom cap style\n",
    "                       flierprops={'marker': 'o', 'color': 'red', 'alpha': 0.5},  # Custom flier style\n",
    "                       )\n",
    "axes[2].set_xticks(xlocations)\n",
    "axes[2].set_xticklabels(labels_list, rotation=45)\n",
    "axes[2].set_title('Predicted Probabilities of Pronouns: Masculine vs. Feminine \\n Across Two Subject Orientation '\n",
    "                  'Groups', fontsize='small')\n",
    "\n",
    "# Customize x-ticks\n",
    "# \n",
    "# ax.set_xticklabels(labels_list, rotation=0)\n",
    "\n",
    "# Custom legend for colors\n",
    "plt.plot([], c='#2C7BB6', label='Masc pronoun (Masc First group)')\n",
    "plt.plot([], c='#F1948A', label='Fem pronoun (Masc First group)')\n",
    "plt.plot([], c='#ABD9E9', label='Masc pronoun (Fem First)')\n",
    "plt.plot([], c='#E6B0AA', label='Fem pronoun (Fem First)')\n",
    "plt.legend(title=\"Categories\", loc='upper right', fontsize='smaller')\n",
    "\n",
    "# plt.title('Predicted Probabilities of Masculine and Feminine Pronouns \\n in Two Groups of the Verb Orientation Setting')\n",
    "plt.ylabel('Value Distribution')\n",
    "# plt.grid(True, linestyle='--', alpha=0.6)  # Add grid lines for better readability\n",
    "# plt.tight.replace(\"grid(True, linestyle='--', alpha=0.6)\", \"grid(True, linestyle='--', alpha=0.5)\")\n",
    "fig.savefig('mascfem.pdf')\n",
    "plt.show()\n"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-05-12T04:46:46.007064Z",
     "start_time": "2024-05-12T04:46:45.395186Z"
    }
   },
   "id": "795ed4819796ee1",
   "execution_count": 131
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 10000/10000 [00:23<00:00, 432.19it/s]\n"
     ]
    }
   ],
   "source": [
    "import spacy\n",
    "from spacy.lang.zh.examples import sentences \n",
    "from pathlib import Path\n",
    "from tqdm import tqdm\n",
    "from collections import Counter\n",
    "sents = Path('data/Exm-2024-08-21-5497dfb69516b7f1.txt').read_text().strip().split('\\n')\n",
    "nlp = spacy.load(\"zh_core_web_sm\")\n",
    "sent_collection = []\n",
    "with open('data/filtered_sents2.txt', 'w') as fil:\n",
    "    for sent in tqdm(sents):\n",
    "        try:\n",
    "            doc = nlp(sent)\n",
    "            for sub_sent in doc.sents:\n",
    "                sub_text = [x.text for x in sub_sent]\n",
    "                if '自己' in sub_text:\n",
    "                    sub_poses = [token.pos_ for token in sub_sent]\n",
    "                    deps = [token.dep_ for token in sub_sent]\n",
    "                    ziji_index = sub_text.index('自己')\n",
    "                    # print(ziji_index)\n",
    "                    if sub_poses[ziji_index-1]!='PRON' and 'ccomp' in deps and Counter(deps)['nsubj']>0 and 'dobj' in deps[ziji_index]:\n",
    "                        ccomp_index = sorted([abs(i-ziji_index) for i, c in enumerate(deps) if c =='ccomp' and i<ziji_index])\n",
    "                        if ccomp_index and ccomp_index[0]<3:\n",
    "                            sub_text = ''.join(sub_text)\n",
    "                            sent_collection.append(sub_text)\n",
    "                            fil.write(f'{sub_text}\\n')\n",
    "                    elif sub_poses[ziji_index-1]!='PRON' and 'iobj' in deps and 'obj'in deps and Counter(deps)['nsubj']>0:\n",
    "                        \n",
    "                        sub_text = ''.join(sub_text)\n",
    "                        if sub_text not in sent_collection:\n",
    "                            fil.write(f'{sub_text}\\n')\n",
    "        except ValueError as e:\n",
    "            continue"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-21T13:17:33.781241Z",
     "start_time": "2024-08-21T13:17:09.783606Z"
    }
   },
   "id": "fdd1a705eefc8438",
   "execution_count": 7
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  2%|▏         | 91/3978 [00:00<00:08, 451.68it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "不管他是否原谅他自己.\n",
      "赫尔曼问他自己。\n",
      "原始的欲又来了，一种百战不死的老兵；他唤醒他自己。\n",
      "他没有看见他自己。\n",
      "王德好象问他自己。\n",
      "他嘲笑他自己。\n",
      "不过这事全都怪他自己。”\n",
      "他只爱他自己。”\n",
      "他问他自己。\n",
      "雅夏问他自己。\n",
      "斯捷潘·阿尔卡季奇想到他自己。\n",
      "倘使有这种男人，也是他在欺骗他自己。\n",
      "这要看他自己。\n",
      "他不爱他自己。\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  5%|▍         | 182/3978 [00:00<00:09, 412.94it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "雅夏问他自己。\n",
      "他问他自己。\n",
      "雅夏问他自己。\n",
      "他问他自己。\n",
      "他回答他自己。\n",
      "瑞宣认识他自己。\n",
      "他恨他自己，他轻视他自己。\n",
      "他问他自己。\n",
      "你不信问他自己。\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  7%|▋         | 267/3978 [00:00<00:08, 414.82it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "以同样的方式，拥有认识自我的知识的人会认识他自己。\n",
      "每个人只能代表他自己。\n",
      "每个人只能代表他自己。\n",
      "林乃久问他自己。\n",
      "别这么心慌，雅夏尔，他劝他自己。\n",
      "雅夏问他自己。\n",
      "雅夏问他自己。\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  9%|▉         | 351/3978 [00:00<00:09, 402.82it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "是相信我胜过相信他自己。\n",
      "“无端绪的想象，使他自己不免有点吓怕起来了。\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 13%|█▎        | 522/3978 [00:01<00:08, 393.01it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "一开口说话，声音让他自己也吃了一惊。\n",
      "这声音使他自己也吃了一惊，他几乎没听出自己的声音陌生人友善地微微一笑，掏出一\n",
      "让他自己也吃惊的是，泪水刺痛了他的眼睛。\n",
      "他的这种感情使他自己也很惊讶；对于他这样一个人来说，能如此深刻地打动他，这是很不寻\n",
      "这使他自己也无法相信，甚至还拒绝承认，那就是：费尔米纳的虚幻的爱情，\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 15%|█▌        | 614/3978 [00:01<00:07, 427.52it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "这使他自己也觉得年轻了，如果还有什么事情使他不高兴，那就是怕自己就要衰老\n",
      "不过，令他自己也觉惊讶的是，他竟然开始g享受其中的新鲜好玩。\n",
      "接下来，只等着大下巴的人介绍他自己了。\n",
      "王子在打扮他自己了。”\n",
      "那末，这十个美分就归他自己了。\n",
      "不过都因为事情太关乎他自己了。\n",
      "这时候他说到他自己了。\n",
      "既然家有信来，还是要好好替他打算一下，把利害说给他，取决当然在于他自己了，我离得这样远，关于他的情形，我总不能十分知道，上次你的信是\n",
      "谁叫他自己他妈的那么傻，都不知道这是星期六晚上，大伙儿不是外出度周末，就\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 19%|█▉        | 748/3978 [00:01<00:07, 423.16it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "杰夫劝他自己保管，或者早点回家，以免那些不怀好意的庄稼汉钻进来骗了去。\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 22%|██▏       | 885/3978 [00:02<00:07, 434.08it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "宰　相您问他自己去，听他回答您什么吧。\n",
      "接下来的六个星期都属于他自己可是女主人说：“噢，别急！\n",
      "他的空虚令他自己吃惊。\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 24%|██▍       | 972/3978 [00:02<00:07, 417.12it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "“假如他说到他自己呢？”\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 28%|██▊       | 1107/3978 [00:02<00:06, 413.92it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "我还告诉他自己在这里有几个亲戚——我现在不敢与他们相认，因他们不久便会知道我\n",
      "酒还让他自己堆起他一贯厌恶的笑脸，那种怀揣明白的功利目的与人瞎聊胡扯的笑脸\n",
      "这种想法可以使他自己好过一些，所以它有些好作用。\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 33%|███▎      | 1310/3978 [00:03<00:07, 365.17it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "大家齐声请他自己开始用晚餐，莉莉拿着三个她专为他留下的土豆走过来。\n",
      "这使他自己很恐慌，也有头有尾的告诉别人，于是班中的空气时时紧张起来，而先\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 35%|███▍      | 1386/3978 [00:03<00:07, 365.26it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "她不但告诉他自己想再度回到健治身边，还跟他提到有关健治有音乐才能的事。\n",
      "这能使他自己感着满足吗？\n",
      "可是想不到是这人回到他自己房里时，就告给校工即刻应当为Ａ教授找寻蜗牛的话。\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 38%|███▊      | 1505/3978 [00:03<00:06, 377.26it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "这是盛特拉姆让他自己所率领的农民来战斗了。\n",
      "她让他自己找出去的路。\n",
      "可能照理他的地方，照理他一点儿，不能的地方就让他自己找路走，至于“被迫”，我也想不出来被什么所迫。\n",
      "格林先生让他自己担当起这里的一切任务，对各种事物、人等发号施令。\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 45%|████▌     | 1806/3978 [00:04<00:05, 408.90it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "“我打算让他自己权衡一下利弊，一方面是偷窃的行为给他带来的一点点小利益，另一方\n",
      "一个新的疑问要求他自己来解答：为什么他这顾全各方的办法不为大家所信服？\n",
      "我让他自己来说吧，我如实地引用他所讲的一个故事。\n",
      "，伟大的，祥子，不知陪着人家送了多少回殡；不知道何时何地会埋起他自己来，埋起这堕落的，自私的，不幸的，社会病胎里的产儿，个人主义的\n",
      "王琦瑶让他自己泡茶，问他这些日子做什么，打不打桥牌，有没有分配工作的消息。\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 50%|████▉     | 1984/3978 [00:04<00:04, 425.77it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "使善的事物成为他自己的。\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 53%|█████▎    | 2114/3978 [00:05<00:04, 408.57it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "不过都因为事情太关乎他自己了。\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 75%|███████▌  | 2994/3978 [00:07<00:02, 446.66it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "有时候他会好气他自己，用根棍于敲他自己的脚，或是拿他的头去撞石头什么的。\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 81%|████████▏ | 3233/3978 [00:07<00:01, 466.30it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "“没有一分钟留给他自己的，”魏德尔太太叹道，“如果你知道他扛在肩膀上的东西有多少……\n",
      "通过使美的东西成为他自己的，他将得到什么呢？\n",
      "使善的事物成为他自己的。\n",
      "不过，这三天假日是属于他自己的，他要干自己想干的事。\n",
      "让他捡，如果他拿他自己的，而且心满意足，你就把另外两把全赠给他。\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 85%|████████▍ | 3377/3978 [00:08<00:01, 455.46it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "他应该写他自己而不是克里斯塔尔……我辜负了他。\n",
      "去年5月8日的下午，蒙蒂教授曾记载他自己要在多尼咖啡店与罗伯特?莱布朗会面。\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 90%|████████▉ | 3563/3978 [00:08<00:00, 449.21it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "你让他自己说，他会帮着我们说话；要是当面和他说破，他反而不敢帮我们——因\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 92%|█████████▏| 3654/3978 [00:08<00:00, 438.76it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "有时他会把玩他自己送给我作礼物的蒙古墨水瓶。\n",
      "这让他自己都觉得不好意思了。\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 93%|█████████▎| 3698/3978 [00:08<00:00, 432.82it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "他恨他自己！\n",
      "他只想到他自己！\n",
      "他只想到他自己！\n",
      "他问他自己，“怎么！\n",
      "“由于贫困，”陀思妥耶夫斯基说他自己，“我被迫为金钱而匆忙写作，所以接二连三地失败。”\n",
      "他问他自己，“说到头来，这是我的，我的……\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 95%|█████████▍| 3778/3978 [00:09<00:00, 230.33it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "有保留在他印象中的完全，对于美人迟暮自不免兴一种感伤，但他若想想他自己，也到了一礼拜不修脸就不成样子的人，他就觉得未来生活渺茫，把自\n",
      "他恨他自己，什么本事也没有，连点力气都没有，到底是干什么的呢？\n",
      "振保突然提醒他自己，他正在挖空心思想出各种的理由，证明他为什么应当同这女人睡觉。\n",
      "同时，他更相信他自己，他的才识，他的胆气，他的体力以至容貌，毕竟胜于常人，好运\n",
      "“他到处炫示他自己，他的时髦衣服，他的四轮敞篷马车，通过这些，去教导人们懂得什么\n",
      "陪审员们以及仅仅从民谣和历史小说里吸收历史知识的一般人，他想起他自己，他自己也不是从布道词和法律里，而是从寓言、小说、诗歌里汲取生\n",
      "你剩下他自己，他觉得凄凉黯淡。\n",
      "他恨他自己，他轻视他自己。\n",
      "他问他自己，但是他没法回答。\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 98%|█████████▊| 3881/3978 [00:09<00:00, 293.45it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "这也怪他自己，因为他拖拖拉拉，没有给安娜上紧宗教信仰这根弦。\n",
      "这些事全不在乎他自己，她！\n",
      "这要怨他自己，小狮子不平地说，我们的满腹苦水对谁诉?办理结婚登记手续，\n",
      "我父亲不爱谈他自己，我们也不问。\n",
      "听到大师兄这么贬低他自己，楚玄玄自然大感不满。\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3978/3978 [00:09<00:00, 402.54it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "他问他自己，脑子里一片混乱。\n",
      "阿希礼用语言来禁闭他自己，这禁闭比任何监狱都牢固。\n",
      "克雷默尔立刻述说他自己，述说关于他自己的一切。\n",
      "其实所有人，包括他自己，都是男女欢好后的产物。\n",
      "但是，一个作者只能写他自己，难道不是真的吗？\n",
      "他说他自己：“我这人我问他一天能卖多少钱。\n",
      "他说他自己：“我这人我问他一天能卖多少钱。\n",
      "不起陛下，太对不起他的母亲，也太对不起他的夫人了，可是他尤其对不起他自己；他所失去的这位妻子，她的美貌足以使人间粉黛一齐失色，\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "sents = Path('data/Exm-2024-08-09-89447889aa3c7b2a.txt').read_text().strip().split('\\n')\n",
    "with open('data/filtered_sents_local_m_binding.txt', 'w') as fil:\n",
    "    senttt =[]\n",
    "    for sent in tqdm(sents):\n",
    "        try:\n",
    "            doc = nlp(sent)\n",
    "            for sub_sent in doc.sents:\n",
    "                sub_text = [x.text for x in sub_sent]\n",
    "                if '自己' in sub_text:\n",
    "                    sub_poses = [token.pos_ for token in sub_sent]\n",
    "                    deps = [token.dep_ for token in sub_sent]\n",
    "                    ziji_index = sub_text.index('自己')\n",
    "                    if sub_text[ziji_index-1]=='他' and deps[ziji_index]=='dobj':\n",
    "                        if 'nsubj' in deps and 0<ziji_index-deps.index('nsubj')<5:\n",
    "                            text = ''.join(sub_text)\n",
    "                            print(text)\n",
    "                            if text not in senttt:\n",
    "                                fil.write(f'{text}\\n')\n",
    "                            senttt.append(text)       \n",
    "                            \n",
    "        except:\n",
    "            continue\n",
    "            \n",
    "          "
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-09T10:46:37.362945Z",
     "start_time": "2024-08-09T10:46:27.471915Z"
    }
   },
   "id": "133e49f48febb71a",
   "execution_count": 60
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "bd170d24d2bf193e"
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "female_words = [\"丈夫\", \"手\", \"胳膊\", \"女儿\", \"头发\", \"脸\", \"病\", \"眼睛\", \"身体\", \"父亲\", \"声音\", \"母亲\", \"心\", \"儿子\", \"学校\", \"头\", \"时候\", \"话\", \"人\",\"丈夫\", \"乳房\", \"美貌\", \"裙子\", \"胸脯\", \"面颊\", \"脑子\", \"卧室\", \"肚子\", \"笑容\", \"身材\", \"肉体\", \"容貌\", \"处境\", \"妈妈\", \"脸庞\", \"笑声\", \"举止\", \"形象\", \"脸蛋\", \"愿望\", \"照片\", \"关系\", \"姐姐\", \"爱人\", \"胸部\", \"欢心\", \"前额\"]\n",
    "male_words = [\"球\", \"妻子\", \"父亲\", \"同伙\", \"目光\", \"心\", \"母亲\", \"妈\", \"老人家\", \"家人\", \"脸\", \"媳妇\", \"手\", \"心目\", \"样子\", \"记忆\", \"生命\", \"爹\", \"屋子\", \"左手\", \"身子\", \"话\", \"双手\", \"烟\", \"诗\", \"意见\", \"头\", \"声音\", \"腰\", \"儿子\", \"印象\", \"作品\", \"山\", \"脚\", \"床\", \"衣服\", \"艺术\", \"病\", \"朋友\", \"眼睛\", \"父母\", \"时候\", \"大学\", \"事业\", \"车\", \"信\", \"记者\", \"钱\", \"门\", \"妻子\", \"儿子\", \"女儿\", \"父亲\", \"母亲\", \"父母\", \"作品\", \"房间\", \"马\", \"书\", \"太太\", \"帽子\", \"女人\", \"老婆\", \"情妇\", \"财产\", \"夫人\", \"哥哥\", \"兄弟\", \"诗\", \"狗\", \"伙伴\", \"敌人\", \"家庭\"]\n",
    "\n",
    "\n",
    "overlap = [x for x in female_words if x in male_words]\n",
    "\n",
    "female_exclusive = [x for x in female_words if x not in overlap]\n",
    "male_exclusive = [x for x in male_words if x not in overlap]"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-07T03:28:17.587065Z",
     "start_time": "2024-08-07T03:28:17.551799Z"
    }
   },
   "id": "dac34a34c9f8f478",
   "execution_count": 28
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'钱', '帽子', '诗', '马', '山', '父母', '记忆', '记者', '脚', '情妇', '财产', '印象', '生命', '目光', '衣服', '敌人', '事业', '妈', '伙伴', '大学', '屋子', '双手', '艺术', '老人家', '家人', '朋友', '球', '媳妇', '身子', '床', '信', '书', '左手', '太太', '家庭', '同伙', '妻子', '意见', '女人', '样子', '爹', '狗', '夫人', '作品', '兄弟', '车', '腰', '烟', '哥哥', '老婆', '心目', '门', '房间'}\n"
     ]
    }
   ],
   "source": [
    "print(set(male_exclusive))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-07T03:36:37.307816Z",
     "start_time": "2024-08-07T03:36:37.296954Z"
    }
   },
   "id": "867e6be2a3dcb77c",
   "execution_count": 32
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25\n"
     ]
    }
   ],
   "source": [
    "print(len(['钱',  '情妇', '财产', '印象', '生命', '目光', '敌人', '事业', '伙伴', '球', '媳妇', '床',  '太太', '家庭', '同伙', '妻子', '意见', '女人', '样子', '夫人', '作品', '兄弟', '车', '烟',  '老婆']))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-07T09:41:25.576196Z",
     "start_time": "2024-08-07T09:41:25.565806Z"
    }
   },
   "id": "71a81f302ec04e5b",
   "execution_count": 39
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25\n"
     ]
    }
   ],
   "source": [
    "print(len(['丈夫', '胳膊', '头发', '身体',  '美貌', '裙子', '胸脯', '面颊', '笑容', '身材', '肉体', '容貌', '处境', '脸庞', '笑声', '举止', '形象', '脸蛋', '愿望', '照片', '关系', '姐姐', '爱人',  '欢心', '前额']))"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-07T09:35:10.897809Z",
     "start_time": "2024-08-07T09:35:10.893201Z"
    }
   },
   "id": "1662401679dd261c",
   "execution_count": 37
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "lb = Path('data/subject_orientation_m1.txt').read_text().strip().split('\\n')\n",
    "with open('data/subject_orientation_m1.txt', 'w') as rd:\n",
    "    for s in lb:\n",
    "        s+='。'\n",
    "        rd.write(f'{s}\\n')\n",
    "        "
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-08T13:39:34.780741Z",
     "start_time": "2024-08-08T13:39:34.774595Z"
    }
   },
   "id": "ef74832ee74dbb57",
   "execution_count": 46
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [
    "annos = Path('data/subject_orientation_f1.txt').read_text().strip().split('\\n')\n",
    "sentences = [x.split()[0]+'。' for x in annos]\n",
    "annos2 = [x.split()[0] for x in Path('data/real_data_lb_name.txt').read_text().strip().split('\\n')]\n",
    "\n",
    "\n",
    "with open('data/subject_orientation_f1.txt', 'w') as in_verb:\n",
    "    for i, x in enumerate(sentences):\n",
    "        in_verb.write(x)\n",
    "        in_verb.write('\\n')\n",
    "    "
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-12T09:40:49.544965Z",
     "start_time": "2024-08-12T09:40:49.514759Z"
    }
   },
   "id": "4efc4eca415cf7ce",
   "execution_count": 69
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "data": {
      "text/plain": "Counter({'h': 1, 'a': 1, 'p': 2, 'y': 1})"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "Counter('happy')"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2024-08-21T16:31:51.316815Z",
     "start_time": "2024-08-21T16:31:51.257977Z"
    }
   },
   "id": "2e67963e575292b2",
   "execution_count": 8
  },
  {
   "cell_type": "code",
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   },
   "id": "3504e8cde9de8c18"
  }
 ],
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