Reproduce_semantic_change_plot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<frozen importlib._bootstrap>:219: RuntimeWarning: numpy.ufunc size changed, may indicate binary incompatibility. Expected 216 from C header, got 232 from PyObject\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reproduce figure from paper\n",
    "This is to reproduce the figure from:\n",
    "Temporal Attention for Language Models\n",
    "Guy D. Rosin and and Kira Radinsky\n",
    "Findings of NAACL 2022\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>word_pos</th>\n",
       "      <th>score</th>\n",
       "      <th>word</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chairman_nn</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>chairman</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>risk_nn</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>risk</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>fiction_nn</td>\n",
       "      <td>0.020723</td>\n",
       "      <td>fiction</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>relationship_nn</td>\n",
       "      <td>0.056218</td>\n",
       "      <td>relationship</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>tree_nn</td>\n",
       "      <td>0.070839</td>\n",
       "      <td>tree</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           word_pos     score          word  rank\n",
       "4       chairman_nn  0.000000      chairman     0\n",
       "28          risk_nn  0.000000          risk     1\n",
       "10       fiction_nn  0.020723       fiction     2\n",
       "27  relationship_nn  0.056218  relationship     3\n",
       "34          tree_nn  0.070839          tree     4"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true_scores = pd.read_csv('data/semeval_eng/truth/graded.txt', sep='\\t', header=None)\n",
    "true_scores.columns = ['word_pos', 'score']\n",
    "true_scores['word'] = [s.split('_')[0] for s in true_scores['word_pos']]\n",
    "true_scores = true_scores.sort_values('score')\n",
    "true_scores['rank'] = range(len(true_scores))\n",
    "true_scores.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>word</th>\n",
       "      <th>score</th>\n",
       "      <th>rank</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fiction</td>\n",
       "      <td>7.801256</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>multitude</td>\n",
       "      <td>8.101946</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>stab</td>\n",
       "      <td>8.114007</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>pin</td>\n",
       "      <td>8.361450</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>donkey</td>\n",
       "      <td>8.532033</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         word     score  rank\n",
       "2     fiction  7.801256     0\n",
       "8   multitude  8.101946     1\n",
       "30       stab  8.114007     2\n",
       "20        pin  8.361450     3\n",
       "15     donkey  8.532033     4"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_scores = pd.read_csv('data/semeval_eng/predicted_shifts.tsv', sep='\\t', header=None)\n",
    "pred_scores.columns = ['word', 'score']\n",
    "pred_scores = pred_scores.sort_values('score')\n",
    "pred_scores['rank'] = range(len(pred_scores))\n",
    "pred_scores.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='rank_true', ylabel='rank_pred'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores_merged = pred_scores.merge(true_scores, on='word', suffixes=('_pred', '_true'))\n",
    "scores_merged.plot(kind='scatter', x='rank_true', y='rank_pred')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.scatter(x=scores_merged['rank_true'], \n",
    "         y=scores_merged['rank_pred'])\n",
    "for i in range(len(scores_merged)):\n",
    "    plt.text(x=scores_merged['rank_true'][i],\n",
    "         y=scores_merged['rank_pred'][i],\n",
    "         s=scores_merged['word'][i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SpearmanrResult(correlation=0.0521574205784732, pvalue=0.7591625205473088)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.stats import spearmanr\n",
    "spearmanr(scores_merged['rank_true'], scores_merged['rank_pred'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.13 ('tempo_att')",
   "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": "3.8.13"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "13de6c7718113c92d8f75f925fea2af1ddf999e0dc4ea26e4b8fffb1d196338e"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}