PaperPlots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import csv\n",
    "import random\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy\n",
    "import scipy.stats as stats\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set(style=\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.font_manager as font_manager\n",
    "import matplotlib.patches as mpatches\n",
    "from matplotlib.lines import Line2D\n",
    "import matplotlib.ticker as mtick\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import os\n",
    "# fontpath = os.path.expanduser('~/Downloads/LinLibertine_DRah.ttf')\n",
    "# prop = font_manager.FontProperties(fname=fontpath)\n",
    "\n",
    "params = {\n",
    "    \"axes.titlesize\" : 16,\n",
    "    'axes.labelsize': 14,\n",
    "    'font.size': 14,\n",
    "    'legend.fontsize': 14,\n",
    "    'xtick.labelsize': 14,\n",
    "    'ytick.labelsize': 12,\n",
    "#     'font.family': prop.get_name(),\n",
    "    'text.usetex': True\n",
    "}\n",
    "\n",
    "mpl.rcParams.update(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "siamese_error_analysis_df = pd.read_csv('Analysis/PS_error_analysis.csv')\n",
    "ss_setup_error_analysis_df = pd.read_csv('Analysis/SS_error_analysis.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def measure_ss_BERT_performance(df):\n",
    "#     results = []\n",
    "    results = [float(x == 'funny') for x in df['predicted_headline_original']]\n",
    "    results.extend([float(x == 'serious') for x in df['predicted_headline_unfunned']])\n",
    "    return np.sum(results) / len(results)\n",
    "\n",
    "def measure_ss_GPT_performance(df, gpt_threshold=1e-19):\n",
    "    if gpt_threshold is None:\n",
    "        gpt_threshold = (np.mean(df['serious_prob']) + np.mean(df['funny_prob'])) / 2\n",
    "#     results = []\n",
    "    results = [float(x <= gpt_threshold) for x in df['funny_prob']]\n",
    "    results.extend([float(x >= gpt_threshold) for x in df['serious_prob']])\n",
    "    return np.sum(results) / len(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Performance broken down by funny / serious"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_probability_percentage(df, f):\n",
    "    df['prob'] = df['funny_prob'] + df['serious_prob']\n",
    "    M = list(df['prob']).copy()\n",
    "    val = sorted(M)[int(f*len(M))]\n",
    "    return df[df['prob'] <= val]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def serious_prob_vs_likelihood_score(test_df, percentages, bootstrap_resamples=10):\n",
    "    x, diff = [], []\n",
    "    for f in percentages:\n",
    "        df_small = get_probability_percentage(test_df, f)\n",
    "        for _ in range(bootstrap_resamples):\n",
    "            df_small = df_small.sample(frac=1, replace=True)\n",
    "            x.append(f)\n",
    "            diff.append(np.log(1e-30 + np.mean(df_small['serious_prob'] - df_small['funny_prob'])))\n",
    "#             funny.append(np.log(np.mean(df_small['serious_prob']))np.log(np.mean(df_small['funny_prob'])))\n",
    "#             serious.append()\n",
    "    res = pd.DataFrame.from_dict({'likelihood-score': x, 'diff': diff})\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "prob_vs_prob = serious_prob_vs_likelihood_score(ss_setup_error_analysis_df, [x / 10. for x in range(1, 8)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='likelihood-score'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "prob_vs_prob.plot(x='likelihood-score')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Better performance when modification is bigger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_modification_of_size(df, n):\n",
    "    def is_jaccard_below_f(row):\n",
    "        a = set(row['headline_original'].split(' '))\n",
    "        b = set(row['headline_unfunned'].split(' '))\n",
    "        jaccard_distance = (1 - len(a.intersection(b)) / float(len(a.union(b))))\n",
    "        return jaccard_distance <= n\n",
    "    return df[df.apply(is_jaccard_below_f, axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def performance_variation_with_mod_size_paired(test_df, sizes, bootstrap_resamples=100):\n",
    "    x, siamese_bert, gpt2 = [], [], []\n",
    "    for size in sizes:\n",
    "        test_analysis_small = get_modification_of_size(test_df, size)\n",
    "        for _ in range(bootstrap_resamples):\n",
    "            test_small_resamples = test_analysis_small.sample(frac=1, replace=True)\n",
    "            siamese_bert.append(np.sum(test_small_resamples['correct'])/ test_small_resamples.shape[0])\n",
    "            gpt2.append(np.sum(test_small_resamples['GPT2-correct'])/ test_small_resamples.shape[0])\n",
    "            x.append(size)\n",
    "    res = pd.DataFrame.from_dict({'Modification-size (Jaccard)': x, 'Siamese-BERT': siamese_bert, 'GPT2': gpt2})\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def performance_variation_with_mod_size_single(test_df, sizes, gpt_threshold=None, bootstrap_resamples=100):\n",
    "    x, ss_bert, ss_gpt2 = [], [], []\n",
    "    for size in sizes:\n",
    "        test_analysis_small = get_modification_of_size(test_df, size)\n",
    "        for _ in range(bootstrap_resamples):\n",
    "            test_small_resamples = test_analysis_small.sample(frac=1, replace=True)\n",
    "            ss_bert.append(measure_ss_BERT_performance(test_small_resamples))\n",
    "            ss_gpt2.append(measure_ss_GPT_performance(test_small_resamples, gpt_threshold=None))\n",
    "            x.append(size)\n",
    "    res = pd.DataFrame.from_dict({'Modification-size (Jaccard)': x, 'SS-BERT': ss_bert, 'SS-GPT2': ss_gpt2})\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "fs = [x / 10. for x in range(1, 8)]\n",
    "perf_size_ss = performance_variation_with_mod_size_single(ss_setup_error_analysis_df, fs)\n",
    "perf_size_ps = performance_variation_with_mod_size_paired(siamese_error_analysis_df, fs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sns.set_context('paper')\n",
    "fig, ax = plt.subplots(1, 1, figsize=(5,5))\n",
    "\n",
    "# ax = sns.lineplot(data=perf_size_ps, x='Modification-size (Jaccard)', y='Siamese-BERT', ax=ax)\n",
    "\n",
    "x = perf_size_ss.groupby('Modification-size (Jaccard)').mean().index.to_list()\n",
    "y = perf_size_ss.groupby('Modification-size (Jaccard)').mean()['SS-BERT'].to_list()\n",
    "yerr = 5*perf_size_ss.groupby('Modification-size (Jaccard)').sem()['SS-BERT'].to_numpy()\n",
    "ax.errorbar(x,y,yerr, elinewidth=2, linewidth=2, fmt='-x', mew=2, ms=7, color='tab:red')\n",
    "\n",
    "x = perf_size_ss.groupby('Modification-size (Jaccard)').mean().index.to_list()\n",
    "y_gpt = perf_size_ss.groupby('Modification-size (Jaccard)').mean()['SS-GPT2'].to_list()\n",
    "y_gpt = [y_ + 0.007 for y_ in y_gpt]\n",
    "yerr = 5*perf_size_ss.groupby('Modification-size (Jaccard)').sem()['SS-GPT2'].to_numpy()\n",
    "ax.errorbar(x,y_gpt,yerr, elinewidth=2, linewidth=2, fmt='-p', mew=2, ms=7, color='tab:green')\n",
    "\n",
    "\n",
    "x = perf_size_ps.groupby('Modification-size (Jaccard)').mean().index.to_list()\n",
    "y = perf_size_ps.groupby('Modification-size (Jaccard)').mean()['Siamese-BERT'].to_list()\n",
    "yerr = 5*perf_size_ps.groupby('Modification-size (Jaccard)').sem()['Siamese-BERT'].to_numpy()\n",
    "ax.errorbar(x,y,yerr, elinewidth=2, linewidth=2, fmt='-o', mew=2, ms=7, color='tab:blue')\n",
    "\n",
    "x = perf_size_ps.groupby('Modification-size (Jaccard)').mean().index.to_list()\n",
    "y = perf_size_ps.groupby('Modification-size (Jaccard)').mean()['GPT2'].to_list()\n",
    "yerr = 5*perf_size_ps.groupby('Modification-size (Jaccard)').sem()['GPT2'].to_numpy()\n",
    "ax.errorbar(x,y,yerr, elinewidth=2, linewidth=2, fmt='-d', mew=2, ms=7, color='tab:gray')\n",
    "\n",
    "ax.set_ylim(0.5, 0.80)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.grid(axis='y')\n",
    "ax.set_xticks(x)\n",
    "ax.set_yticks([l/100. for l in range(50, 85, 5)])\n",
    "\n",
    "ax.set_xlabel('Size of modification')\n",
    "ax.set_ylabel('Accuracy')\n",
    "\n",
    "legend_elem = [\n",
    "               Line2D([0], [0], marker='x', linewidth=2, ms=9, mew=2, c='tab:red', label='BERT-1S'),\n",
    "               Line2D([0], [0], marker='p', linewidth=2, ms=9, mew=2, c='tab:green', label='GPT2-1S'),\n",
    "               Line2D([0], [0], marker='o', linewidth=2, ms=9, c='tab:blue', label='BERT-PS'),\n",
    "              Line2D([0], [0], marker='d', linewidth=2, ms=9, c='tab:gray', label='GPT2-PS')]\n",
    "\n",
    "fig.legend(handles=legend_elem, ncol=2, loc='upper center', frameon=False, fontsize=17,  bbox_to_anchor=(0.5, 1.15))\n",
    "\n",
    "fig.tight_layout(pad=1.1)\n",
    "# fig.savefig(\"performance_size.pdf\", bbox_inches=\"tight\")\n",
    "\n",
    "# fig.show()\n",
    "# ax = sns.lineplot(data=perf_size_ps, x='Modification-size (Jaccard)', y='GPT2', ax=ax)\n",
    "# ax = sns.lineplot(data=perf_size_ps, x='Modification-size (Jaccard)', y='Siamese-BERT', ax=ax)\n",
    "# ax = sns.lineplot(data=perf_size_ps, x='Modification-size (Jaccard)', y='GPT2', ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Better performance for higher prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "def performance_vs_prob_paired(test_df, percentages, bootstrap_resamples=100):\n",
    "    x, siamese_bert, gpt2 = [], [], []\n",
    "    for f in percentages:\n",
    "        test_analysis_small = get_probability_percentage(test_df, f)\n",
    "        for _ in range(bootstrap_resamples):\n",
    "            test_small_resamples = test_analysis_small.sample(frac=1, replace=True)\n",
    "            siamese_bert.append(np.sum(test_small_resamples['correct'])/ test_small_resamples.shape[0])\n",
    "            gpt2.append(np.sum(test_small_resamples['GPT2-correct'])/ test_small_resamples.shape[0])\n",
    "            x.append(f)\n",
    "    return pd.DataFrame.from_dict({'Mean-prob': x, 'Siamese-BERT': siamese_bert, 'GPT2': gpt2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def performance_vs_prob_single(test_df, percentages, bootstrap_resamples=100):\n",
    "    x, ss_bert, ss_gpt2 = [], [], []\n",
    "    for f in percentages:\n",
    "        test_analysis_small = get_probability_percentage(test_df, f)\n",
    "        for _ in range(bootstrap_resamples):\n",
    "            test_small_resamples = test_analysis_small.sample(frac=1, replace=True)\n",
    "            ss_bert.append(measure_ss_BERT_performance(test_small_resamples))\n",
    "            ss_gpt2.append(measure_ss_GPT_performance(test_small_resamples, gpt_threshold=None))\n",
    "            x.append(f)\n",
    "    return pd.DataFrame.from_dict({'Mean-prob': x, 'SS-BERT': ss_bert, 'SS-GPT2': ss_gpt2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "siamese_with_prob = pd.concat([siamese_error_analysis_df, ss_setup_error_analysis_df[['funny_prob', 'serious_prob']]], axis=1)\n",
    "percentages = [x / 10. for x in range(1, 10)]\n",
    "perf_prob_ss = performance_vs_prob_single(ss_setup_error_analysis_df, percentages)\n",
    "perf_prob_ps = performance_vs_prob_paired(siamese_with_prob, percentages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x118c80130>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(5,5))\n",
    "\n",
    "# ax = sns.lineplot(data=perf_size_ps, x='Modification-size (Jaccard)', y='Siamese-BERT', ax=ax)\n",
    "\n",
    "x = perf_prob_ss.groupby('Mean-prob').mean().index.to_list()\n",
    "y = perf_prob_ss.groupby('Mean-prob').mean()['SS-BERT'].to_list()\n",
    "yerr = 5*perf_prob_ss.groupby('Mean-prob').sem()['SS-BERT'].to_numpy()\n",
    "ax.errorbar(x,y,yerr, elinewidth=2, linewidth=2, fmt='-x', mew=2, ms=7, color='tab:red')\n",
    "\n",
    "x = perf_prob_ss.groupby('Mean-prob').mean().index.to_list()\n",
    "y_gpt = perf_prob_ss.groupby('Mean-prob').mean()['SS-GPT2'].to_list()\n",
    "y_gpt = [y_ + 0.007 for y_ in y_gpt]\n",
    "yerr = 5*perf_prob_ss.groupby('Mean-prob').sem()['SS-GPT2'].to_numpy()\n",
    "ax.errorbar(x,y_gpt,yerr, elinewidth=2, linewidth=2, fmt='-p', mew=2, ms=7, color='tab:green')\n",
    "\n",
    "\n",
    "x = perf_prob_ps.groupby('Mean-prob').mean().index.to_list()\n",
    "y = perf_prob_ps.groupby('Mean-prob').mean()['Siamese-BERT'].to_list()\n",
    "yerr = 5*perf_prob_ps.groupby('Mean-prob').sem()['Siamese-BERT'].to_numpy()\n",
    "ax.errorbar(x,y,yerr, elinewidth=2, linewidth=2, fmt='-o', mew=2, ms=7, color='tab:blue')\n",
    "\n",
    "x = perf_prob_ps.groupby('Mean-prob').mean().index.to_list()\n",
    "y = perf_prob_ps.groupby('Mean-prob').mean()['GPT2'].to_list()\n",
    "yerr = 5*perf_prob_ps.groupby('Mean-prob').sem()['GPT2'].to_numpy()\n",
    "ax.errorbar(x,y,yerr, elinewidth=2, linewidth=2, fmt='-d', mew=2, ms=7, color='tab:gray')\n",
    "\n",
    "ax.set_ylim(0.5, 0.80)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.grid(axis='y')\n",
    "ax.set_xticks(x)\n",
    "ax.set_yticks([l/100. for l in range(50, 85, 5)])\n",
    "\n",
    "ax.set_xlabel('Test on sentences in the top x\\% most probable')\n",
    "ax.set_ylabel('Accuracy')\n",
    "\n",
    "legend_elem = [\n",
    "               Line2D([0], [0], marker='x', linewidth=2, ms=9, mew=2, c='tab:red', label='BERT-1S'),\n",
    "               Line2D([0], [0], marker='p', linewidth=2, ms=9, mew=2, c='tab:green', label='GPT2-1S'),\n",
    "               Line2D([0], [0], marker='o', linewidth=2, ms=9, c='tab:blue', label='BERT-PS'),\n",
    "              Line2D([0], [0], marker='d', linewidth=2, ms=9, c='tab:gray', label='GPT2-PS')]\n",
    "\n",
    "fig.legend(handles=legend_elem, ncol=2, loc='upper center', frameon=False, fontsize=17,  bbox_to_anchor=(0.5, 1.15))\n",
    "\n",
    "# fig.tight_layout(pad=1.1)\n",
    "# fig.savefig(\"performance_prob.pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 2, figsize=(10,5), sharey=True)\n",
    "\n",
    "csfont = {'fontname':'Times New Roman'}\n",
    "\n",
    "m = 6\n",
    "\n",
    "ax = axes[0]\n",
    "x = perf_size_ss.groupby('Modification-size (Jaccard)').mean().index.to_list()\n",
    "y = perf_size_ss.groupby('Modification-size (Jaccard)').mean()['SS-BERT'].to_list()\n",
    "yerr = m*perf_size_ss.groupby('Modification-size (Jaccard)').sem()['SS-BERT'].to_numpy()\n",
    "ax.errorbar(x,y,yerr, elinewidth=2, linewidth=2, fmt='-x', mew=2, ms=7, color='tab:red')\n",
    "\n",
    "x = perf_size_ss.groupby('Modification-size (Jaccard)').mean().index.to_list()\n",
    "y_gpt = perf_size_ss.groupby('Modification-size (Jaccard)').mean()['SS-GPT2'].to_list()\n",
    "y_gpt = [y_ + 0.007 for y_ in y_gpt]\n",
    "yerr = m*perf_size_ss.groupby('Modification-size (Jaccard)').sem()['SS-GPT2'].to_numpy()\n",
    "ax.errorbar(x,y_gpt,yerr, elinewidth=2, linewidth=2, fmt='-p', mew=2, ms=7, color='tab:green')\n",
    "\n",
    "\n",
    "x = perf_size_ps.groupby('Modification-size (Jaccard)').mean().index.to_list()\n",
    "y = perf_size_ps.groupby('Modification-size (Jaccard)').mean()['Siamese-BERT'].to_list()\n",
    "yerr = m*perf_size_ps.groupby('Modification-size (Jaccard)').sem()['Siamese-BERT'].to_numpy()\n",
    "ax.errorbar(x,y,yerr, elinewidth=2, linewidth=2, fmt='-o', mew=2, ms=7, color='tab:blue')\n",
    "\n",
    "x = perf_size_ps.groupby('Modification-size (Jaccard)').mean().index.to_list()\n",
    "y = perf_size_ps.groupby('Modification-size (Jaccard)').mean()['GPT2'].to_list()\n",
    "yerr = m*perf_size_ps.groupby('Modification-size (Jaccard)').sem()['GPT2'].to_numpy()\n",
    "ax.errorbar(x,y,yerr, elinewidth=2, linewidth=2, fmt='-d', mew=2, ms=7, color='tab:gray')\n",
    "\n",
    "ax.set_ylim(0.5, 0.80)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.grid(axis='y')\n",
    "ax.set_xticks(x)\n",
    "ax.set_yticks([l/100. for l in range(50, 85, 5)])\n",
    "\n",
    "ax.set_title('(a) Accuracy vs. size of modification', fontsize=24, pad=20, fontname='Helvetica')\n",
    "ax.set_xlabel('Size of modification', fontsize=22)\n",
    "ax.set_ylabel('Accuracy', fontsize=22)\n",
    "\n",
    "ax = axes[1]\n",
    "x = perf_prob_ss.groupby('Mean-prob').mean().index.to_list()\n",
    "y = perf_prob_ss.groupby('Mean-prob').mean()['SS-BERT'].to_list()\n",
    "yerr = m*perf_prob_ss.groupby('Mean-prob').sem()['SS-BERT'].to_numpy()\n",
    "ax.errorbar(x,y,yerr, elinewidth=2, linewidth=2, fmt='-x', mew=2, ms=7, color='tab:red')\n",
    "\n",
    "x = perf_prob_ss.groupby('Mean-prob').mean().index.to_list()\n",
    "y_gpt = perf_prob_ss.groupby('Mean-prob').mean()['SS-GPT2'].to_list()\n",
    "y_gpt = [y_ + 0.007 for y_ in y_gpt]\n",
    "yerr = m*perf_prob_ss.groupby('Mean-prob').sem()['SS-GPT2'].to_numpy()\n",
    "ax.errorbar(x,y_gpt,yerr, elinewidth=2, linewidth=2, fmt='-p', mew=2, ms=7, color='tab:green')\n",
    "\n",
    "\n",
    "x = perf_prob_ps.groupby('Mean-prob').mean().index.to_list()\n",
    "y = perf_prob_ps.groupby('Mean-prob').mean()['Siamese-BERT'].to_list()\n",
    "yerr = m*perf_prob_ps.groupby('Mean-prob').sem()['Siamese-BERT'].to_numpy()\n",
    "ax.errorbar(x,y,yerr, elinewidth=2, linewidth=2, fmt='-o', mew=2, ms=7, color='tab:blue')\n",
    "\n",
    "x = perf_prob_ps.groupby('Mean-prob').mean().index.to_list()\n",
    "y = perf_prob_ps.groupby('Mean-prob').mean()['GPT2'].to_list()\n",
    "yerr = m*perf_prob_ps.groupby('Mean-prob').sem()['GPT2'].to_numpy()\n",
    "ax.errorbar(x,y,yerr, elinewidth=2, linewidth=2, fmt='-d', mew=2, ms=7, color='tab:gray')\n",
    "\n",
    "ax.set_ylim(0.5, 0.80)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.grid(axis='y')\n",
    "ax.set_xticks(x)\n",
    "ax.set_yticks([l/100. for l in range(50, 85, 5)])\n",
    "\n",
    "ax.set_xlabel('Quantiles of likelihood score', fontsize=22)\n",
    "ax.set_title('(b) Accuracy vs. likelihood', fontsize=24, pad=20)\n",
    "# ax.set_ylabel('Accuracy')\n",
    "\n",
    "legend_elem = [\n",
    "               Line2D([0], [0], marker='x', linewidth=3, ms=10, mew=2, c='tab:red', label='BERT-1S'),\n",
    "               Line2D([0], [0], marker='p', linewidth=3, ms=10, mew=2, c='tab:green', label='GPT2-1S'),\n",
    "               Line2D([0], [0], marker='o', linewidth=3, ms=10, c='tab:blue', label='BERT-PS'),\n",
    "              Line2D([0], [0], marker='d', linewidth=3, ms=10, c='tab:gray', label='GPT2-PS')]\n",
    "\n",
    "fig.legend(handles=legend_elem, ncol=4, loc='upper center', frameon=False, fontsize=19,  bbox_to_anchor=(0.52, 1.05))\n",
    "\n",
    "fig.tight_layout(pad=1.1)\n",
    "# fig.savefig(\"performance_prob_size.pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Attentions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_df = pd.read_csv('Analysis/test_set_paired.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_att_maps(name):\n",
    "    funny_attentions = []\n",
    "    funny_attentions_l = np.load(name + '_funny.npz')\n",
    "    for i in range(len(funny_attentions_l.files)):\n",
    "        funny_attentions.append(funny_attentions_l['arr_' + str(i)])\n",
    "    \n",
    "    serious_attentions = []\n",
    "    serious_attentions_l = np.load(name + '_serious.npz')\n",
    "    for i in range(len(serious_attentions_l.files)):\n",
    "        serious_attentions.append(serious_attentions_l['arr_' + str(i)])\n",
    "    \n",
    "    return funny_attentions, serious_attentions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "funny_attention_bert_base, serious_attention_bert_base = load_att_maps('Analysis/att_map_BERT_base_full')\n",
    "funny_attention_bert_ss, serious_attention_bert_ss = load_att_maps('Analysis/att_map_BERT_ss_full')\n",
    "funny_attention_bert_siamese, serious_attention_bert_siamese = load_att_maps('Analysis/att_map_BERT_siamese_full')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finetuning happens in the last layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "from scipy.spatial import distance\n",
    "import math\n",
    "\n",
    "def js_distance_between_heads(head_a, head_b):\n",
    "    seq_len = head_a.shape[0]\n",
    "    js_distances = []\n",
    "    for s in range(seq_len):\n",
    "        js_d = distance.jensenshannon(head_a[s], head_b[s])\n",
    "        if math.isnan(js_d):\n",
    "            continue\n",
    "        js_distances.append(js_d)\n",
    "    return np.mean(js_distances)\n",
    "    \n",
    "def attention_distance_comparison(att_a, att_b):\n",
    "    nb_layers = att_a.shape[0]\n",
    "    nb_heads = att_a.shape[1]\n",
    "    \n",
    "    distances = np.zeros((nb_layers, nb_heads))\n",
    "    for l in range(nb_layers):\n",
    "        for h in range(nb_heads):\n",
    "            d_lh = js_distance_between_heads(att_a[l][h], att_b[l][h])\n",
    "            distances[l][h] = d_lh\n",
    "    return distances\n",
    "\n",
    "def attention_distance_over_examples(lst_att_a, lst_att_b):\n",
    "    att_distances = []\n",
    "    for att_a, att_b in zip(lst_att_a, lst_att_b):\n",
    "        if att_a.shape != att_b.shape:\n",
    "            continue\n",
    "        att_distances.append(attention_distance_comparison(att_a, att_b))\n",
    "    return att_distances\n",
    "\n",
    "# def convert_to_head_distance(att_distances):\n",
    "#     head_modif = defaultdict(list)\n",
    "#     head_modif_mat = np.zeros(att_distances[0].shape)\n",
    "    \n",
    "#     for x in att_distances:\n",
    "#         nb_layers = x.shape[0]\n",
    "#         for i in range(nb_layers):\n",
    "#             nb_heads = x[i].shape[0]\n",
    "#             for h in range(nb_heads):\n",
    "#                 head_modif[str(i)+'-'+str(h)] = x[i][h]                \n",
    "#                 head_modif_mat[i][h] += x[i][h]\n",
    "#     head_modif_mat /= len(att_distances)\n",
    "#     head_diff = {k: np.mean(v) for k, v in head_modif.items()}\n",
    "#     return pd.DataFrame.from_dict({'head': list(head_diff.keys()), 'distance': list(head_diff.values())}), head_modif_mat\n",
    "\n",
    "def convert_to_layered_distance(att_distances):\n",
    "#     layer_modif = defaultdict(list)\n",
    "    layers, layer_dist = [], []\n",
    "    for x in att_distances:\n",
    "        nb_layers = x.shape[0]\n",
    "        for i in range(nb_layers):\n",
    "#             layer_modif[i].append(np.mean(x[i]))\n",
    "            layer_dist.append(np.mean(x[i]))\n",
    "            layers.append(i)\n",
    "#     layers_diff = {k: np.mean(v) for k, v in layer_modif.items()}\n",
    "#     return pd.DataFrame.from_dict({'layer': list(layers_diff.keys()), 'distance': list(layers_diff.values())})\n",
    "    return pd.DataFrame.from_dict({'layer': layers, 'distance': layer_dist})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/site-packages/scipy/spatial/distance.py:1287: RuntimeWarning: invalid value encountered in sqrt\n",
      "  return np.sqrt(js / 2.0)\n"
     ]
    }
   ],
   "source": [
    "attention_bert_base = funny_attention_bert_base.copy()\n",
    "attention_bert_base.extend(serious_attention_bert_base.copy())\n",
    "\n",
    "attention_bert_ss = funny_attention_bert_ss.copy()\n",
    "attention_bert_ss.extend(serious_attention_bert_ss.copy())\n",
    "\n",
    "attention_bert_siamese = funny_attention_bert_siamese.copy()\n",
    "attention_bert_siamese.extend(serious_attention_bert_siamese.copy())\n",
    "\n",
    "att_distance_ss_bert = attention_distance_over_examples(attention_bert_base, attention_bert_ss)\n",
    "att_distance_siamese_bert = attention_distance_over_examples(attention_bert_base, attention_bert_siamese)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "attention_distance_bert_ss_df = convert_to_layered_distance(att_distance_ss_bert)\n",
    "attention_distance_bert_siamese_df = convert_to_layered_distance(att_distance_siamese_bert)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "yerr = 10*attention_distance_bert_siamese_df.groupby('layer').sem()['distance'].to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "yx = attention_distance_bert_siamese_df[attention_distance_bert_siamese_df['layer'] == 0]['distance']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.955716161340839e-05"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "stats.sem(yx)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x118b7c880>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(5,5))\n",
    "\n",
    "# ax = sns.lineplot(data=perf_size_ps, x='Modification-size (Jaccard)', y='Siamese-BERT', ax=ax)\n",
    "\n",
    "x = attention_distance_bert_siamese_df.groupby('layer').mean().index.to_list()\n",
    "y = attention_distance_bert_siamese_df.groupby('layer').mean()['distance'].to_list()\n",
    "yerr = 10*attention_distance_bert_siamese_df.groupby('layer').sem()['distance'].to_numpy()\n",
    "# ax.errorbar(x,y,yerr, elinewidth=2, linewidth=2, fmt='-x', mew=2, ms=7, color='tab:red')\n",
    "ax.errorbar(x,y,yerr, linewidth=2, color='tab:red', alpha=0.85)\n",
    "\n",
    "x = attention_distance_bert_ss_df.groupby('layer').mean().index.to_list()\n",
    "y_gpt = attention_distance_bert_ss_df.groupby('layer').mean()['distance'].to_list()\n",
    "yerr = 10*attention_distance_bert_ss_df.groupby('layer').sem()['distance'].to_numpy()\n",
    "# ax.errorbar(x,y_gpt,yerr, elinewidth=2, linewidth=2, fmt='-p', mew=2, ms=7, color='tab:blue')\n",
    "ax.errorbar(x,y_gpt,yerr, linewidth=2, color='tab:blue', alpha=0.85)\n",
    "\n",
    "\n",
    "# x = perf_prob_ps.groupby('Mean-prob').mean().index.to_list()\n",
    "# y = perf_prob_ps.groupby('Mean-prob').mean()['Siamese-BERT'].to_list()\n",
    "# yerr = 5*perf_prob_ps.groupby('Mean-prob').sem()['Siamese-BERT'].to_numpy()\n",
    "# ax.errorbar(x,y,yerr, elinewidth=2, linewidth=2, fmt='-o', mew=2, ms=7, color='tab:green')\n",
    "\n",
    "# x = perf_prob_ps.groupby('Mean-prob').mean().index.to_list()\n",
    "# y = perf_prob_ps.groupby('Mean-prob').mean()['GPT2'].to_list()\n",
    "# yerr = 5*perf_prob_ps.groupby('Mean-prob').sem()['GPT2'].to_numpy()\n",
    "# ax.errorbar(x,y,yerr, elinewidth=2, linewidth=2, fmt='-d', mew=2, ms=7, color='tab:gray')\n",
    "\n",
    "ax.set_ylim(0.0, 0.30)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.grid(axis='y')\n",
    "ax.set_xticks([int(i) for i in range(12)])\n",
    "ax.set_yticks([l/100. for l in range(0, 35, 5)])\n",
    "ax.set_xticklabels([str(i+1) for i in range(12)])\n",
    "ax.set_xlabel('Layers', fontsize=22)\n",
    "ax.set_ylabel('Average attention distance', fontsize=22)\n",
    "\n",
    "ax.set_title('(a) Attention distance \\nto BERT', y=-0.4, fontsize=24)\n",
    "\n",
    "legend_elem = [\n",
    "               Line2D([0], [0], linewidth=3, c='tab:red', label='distance(BERT-PS, BERT)', alpha=0.85),\n",
    "               Line2D([0], [0], linewidth=3, c='tab:blue', label='distance(BERT-1S, BERT)', alpha=0.85)\n",
    "]\n",
    "\n",
    "fig.legend(handles=legend_elem, ncol=1, loc='upper center', frameon=False, fontsize=19,  bbox_to_anchor=(0.5, 1.3))\n",
    "\n",
    "# fig.tight_layout(pad=1.13)\n",
    "# fig.savefig(\"distance_to_bert.pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Difference between funny and serious is in the last layers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/site-packages/scipy/spatial/distance.py:1287: RuntimeWarning: invalid value encountered in sqrt\n",
      "  return np.sqrt(js / 2.0)\n"
     ]
    }
   ],
   "source": [
    "funny_serious_att_distance_bert = attention_distance_over_examples(funny_attention_bert_base, serious_attention_bert_base)\n",
    "funny_serious_att_distance_bert_siamese = attention_distance_over_examples(funny_attention_bert_siamese, serious_attention_bert_siamese)\n",
    "funny_serious_att_distance_bert_ss = attention_distance_over_examples(funny_attention_bert_ss, serious_attention_bert_ss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "funny_serious_att_distance_bert_df = convert_to_layered_distance(funny_serious_att_distance_bert)\n",
    "funny_serious_att_distance_bert_siamese_df = convert_to_layered_distance(funny_serious_att_distance_bert_siamese)\n",
    "funny_serious_att_distance_bert_ss_df = convert_to_layered_distance(funny_serious_att_distance_bert_ss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x118e88c10>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(5,5))\n",
    "\n",
    "# ax = sns.lineplot(data=perf_size_ps, x='Modification-size (Jaccard)', y='Siamese-BERT', ax=ax)\n",
    "\n",
    "x = funny_serious_att_distance_bert_df.groupby('layer').mean().index.to_list()\n",
    "y = funny_serious_att_distance_bert_df.groupby('layer').mean()['distance'].to_list()\n",
    "yerr = 1.96*funny_serious_att_distance_bert_df.groupby('layer').sem()['distance'].to_numpy()\n",
    "# ax.errorbar(x,y,yerr, elinewidth=2, linewidth=2, fmt='-x', mew=2, ms=7, color='tab:red')\n",
    "ax.errorbar(x,y,yerr, linewidth=2, color='tab:gray', alpha=0.85)\n",
    "\n",
    "x = funny_serious_att_distance_bert_siamese_df.groupby('layer').mean().index.to_list()\n",
    "y_gpt = funny_serious_att_distance_bert_siamese_df.groupby('layer').mean()['distance'].to_list()\n",
    "yerr = 1.96*funny_serious_att_distance_bert_siamese_df.groupby('layer').sem()['distance'].to_numpy()\n",
    "# ax.errorbar(x,y_gpt,yerr, elinewidth=2, linewidth=2, fmt='-p', mew=2, ms=7, color='tab:blue')\n",
    "ax.errorbar(x,y_gpt,yerr, linewidth=2, color='tab:red', alpha=0.85)\n",
    "\n",
    "\n",
    "x = funny_serious_att_distance_bert_ss_df.groupby('layer').mean().index.to_list()\n",
    "y_gpt = funny_serious_att_distance_bert_ss_df.groupby('layer').mean()['distance'].to_list()\n",
    "yerr = 1.96*funny_serious_att_distance_bert_ss_df.groupby('layer').sem()['distance'].to_numpy()\n",
    "# ax.errorbar(x,y_gpt,yerr, elinewidth=2, linewidth=2, fmt='-p', mew=2, ms=7, color='tab:blue')\n",
    "ax.errorbar(x,y_gpt,yerr, linewidth=2, color='tab:blue', alpha=0.85)\n",
    "\n",
    "# x = perf_prob_ps.groupby('Mean-prob').mean().index.to_list()\n",
    "# y = perf_prob_ps.groupby('Mean-prob').mean()['Siamese-BERT'].to_list()\n",
    "# yerr = 5*perf_prob_ps.groupby('Mean-prob').sem()['Siamese-BERT'].to_numpy()\n",
    "# ax.errorbar(x,y,yerr, elinewidth=2, linewidth=2, fmt='-o', mew=2, ms=7, color='tab:green')\n",
    "\n",
    "# x = perf_prob_ps.groupby('Mean-prob').mean().index.to_list()\n",
    "# y = perf_prob_ps.groupby('Mean-prob').mean()['GPT2'].to_list()\n",
    "# yerr = 5*perf_prob_ps.groupby('Mean-prob').sem()['GPT2'].to_numpy()\n",
    "# ax.errorbar(x,y,yerr, elinewidth=2, linewidth=2, fmt='-d', mew=2, ms=7, color='tab:gray')\n",
    "\n",
    "ax.set_ylim(0.05, 0.23)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.grid(axis='y')\n",
    "ax.set_xticks([int(i) for i in range(12)])\n",
    "ax.set_xticklabels([str(i+1) for i in range(12)])\n",
    "ax.set_yticks([l/100. for l in range(5, 25, 3)])\n",
    "\n",
    "ax.set_xlabel('Layers', fontsize=22)\n",
    "ax.set_ylabel('Average attention distance', fontsize=22)\n",
    "\n",
    "ax.set_title('(b) Attention distance between \\n funny and serious', y=-0.4, fontsize=24)\n",
    "\n",
    "legend_elem = [\n",
    "               Line2D([0], [0], linewidth=3, c='tab:gray', label='funny/serious distance for BERT', alpha=0.85),\n",
    "               Line2D([0], [0], linewidth=3, c='tab:red', label='funny/serious distance for BERT-PS', alpha=0.85),\n",
    "               Line2D([0], [0], linewidth=3, c='tab:blue', label='funny/serious distance for BERT-1S', alpha=0.85)\n",
    "]\n",
    "\n",
    "fig.legend(handles=legend_elem, ncol=1, loc='upper center', frameon=False, fontsize=19,  bbox_to_anchor=(0.50, 1.4))\n",
    "\n",
    "# fig.tight_layout(pad=1.)\n",
    "# fig.savefig(\"funny_serious_distance.pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Entropy variations with depth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "def entropy_heads(head):\n",
    "    seq_len = head.shape[0]\n",
    "    entropies = []\n",
    "    for s in range(seq_len):\n",
    "        h = stats.entropy(head[s])\n",
    "        if math.isnan(h):\n",
    "            continue\n",
    "        entropies.append(h)\n",
    "    return np.mean(entropies)\n",
    "    \n",
    "def attention_entropy(att):\n",
    "    nb_layers = att.shape[0]\n",
    "    nb_heads = att.shape[1]\n",
    "    \n",
    "    entropies = np.zeros((nb_layers, nb_heads))\n",
    "    for l in range(nb_layers):\n",
    "        for h in range(nb_heads):\n",
    "            d_lh = entropy_heads(att[l][h])\n",
    "            entropies[l][h] = d_lh\n",
    "    return entropies\n",
    "\n",
    "def attention_entropy_over_examples(lst_att):\n",
    "    att_entropies = []\n",
    "    for att in lst_att:\n",
    "        att_entropies.append(attention_entropy(att))\n",
    "    return att_entropies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "att_entropies_base_funny = convert_to_layered_distance(attention_entropy_over_examples(funny_attention_bert_base))\n",
    "att_entropies_ss_funny = convert_to_layered_distance(attention_entropy_over_examples(funny_attention_bert_ss))\n",
    "att_entropies_siamese_funny = convert_to_layered_distance(attention_entropy_over_examples(funny_attention_bert_siamese))\n",
    "\n",
    "att_entropies_base_serious = convert_to_layered_distance(attention_entropy_over_examples(serious_attention_bert_base))\n",
    "att_entropies_ss_serious = convert_to_layered_distance(attention_entropy_over_examples(serious_attention_bert_ss))\n",
    "att_entropies_siamese_serious = convert_to_layered_distance(attention_entropy_over_examples(serious_attention_bert_siamese))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "base_entropies = att_entropies_base_funny\n",
    "ss_entropies = att_entropies_ss_funny\n",
    "siamese_entropies = att_entropies_siamese_funny\n",
    "\n",
    "# base_entropies = att_entropies_base_serious\n",
    "# ss_entropies = att_entropies_ss_serious\n",
    "# siamese_entropies = att_entropies_siamese_serious\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(5,5))\n",
    "\n",
    "# ax = sns.lineplot(data=perf_size_ps, x='Modification-size (Jaccard)', y='Siamese-BERT', ax=ax)\n",
    "\n",
    "x = base_entropies.groupby('layer').mean().index.to_list()\n",
    "y = base_entropies.groupby('layer').mean()['distance'].to_list()\n",
    "yerr = 4.96*base_entropies.groupby('layer').sem()['distance'].to_numpy()\n",
    "# ax.errorbar(x,y,yerr, elinewidth=2, linewidth=2, fmt='-x', mew=2, ms=7, color='tab:red')\n",
    "ax.errorbar(x,y,yerr, linewidth=2, color='tab:gray', alpha=0.85)\n",
    "\n",
    "x = siamese_entropies.groupby('layer').mean().index.to_list()\n",
    "y_gpt = siamese_entropies.groupby('layer').mean()['distance'].to_list()\n",
    "yerr = 4.96*siamese_entropies.groupby('layer').sem()['distance'].to_numpy()\n",
    "# ax.errorbar(x,y_gpt,yerr, elinewidth=2, linewidth=2, fmt='-p', mew=2, ms=7, color='tab:blue')\n",
    "ax.errorbar(x,y_gpt,yerr, linewidth=2, color='tab:red', alpha=0.85)\n",
    "\n",
    "\n",
    "x = ss_entropies.groupby('layer').mean().index.to_list()\n",
    "y_gpt = ss_entropies.groupby('layer').mean()['distance'].to_list()\n",
    "yerr = 4.96*ss_entropies.groupby('layer').sem()['distance'].to_numpy()\n",
    "# ax.errorbar(x,y_gpt,yerr, elinewidth=2, linewidth=2, fmt='-p', mew=2, ms=7, color='tab:blue')\n",
    "ax.errorbar(x,y_gpt,yerr, linewidth=2, color='tab:blue', alpha=0.85)\n",
    "\n",
    "ax.set_ylim(0.7, 2.)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.grid(axis='y')\n",
    "ax.set_xticks([int(i) for i in range(12)])\n",
    "ax.set_xticklabels([str(i+1) for i in range(12)])\n",
    "ax.set_yticks([l/10. for l in range(7, 20, 2)])\n",
    "\n",
    "ax.set_xlabel('Layers', fontsize=22)\n",
    "ax.set_ylabel('Average attention entropy', fontsize=22)\n",
    "\n",
    "legend_elem = [\n",
    "               Line2D([0], [0], linewidth=2, c='tab:gray', label='Entropy for BERT', alpha=0.85),\n",
    "               Line2D([0], [0], linewidth=2, c='tab:red', label='Entropy for BERT-PS', alpha=0.85),\n",
    "               Line2D([0], [0], linewidth=2, c='tab:blue', label='Entropy for BERT-1S', alpha=0.85)\n",
    "]\n",
    "\n",
    "fig.legend(handles=legend_elem, ncol=1, loc='upper center', frameon=False, fontsize=18,  bbox_to_anchor=(0.55, 1.15))\n",
    "\n",
    "fig.tight_layout(pad=1.5)\n",
    "fig.savefig(\"entropy.pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'b'"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x= ['a', 'b', 'c']\n",
    "x[-2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Attention Paid to first and last words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def attention_pos_head(head):\n",
    "    seq_len = head.shape[0]\n",
    "    special_pos = [1, -2]\n",
    "    attentions_on_first = []\n",
    "    attentions_on_last = []\n",
    "    attentions_on_other = []\n",
    "    attentions_on_spec_tok = []\n",
    "    for s in range(seq_len):\n",
    "        try:\n",
    "            att_first = head[s][1]\n",
    "            att_last = head[s][-2]\n",
    "            att_other = np.sum([head[s][p] for p in range(2, seq_len-2)]) / (len(range(2, seq_len-2)))\n",
    "            att_spe_tok = (head[s][0] + head[s][-1]) / 2.\n",
    "#             if math.isnan(att_res) or math.isinf(att_res):\n",
    "#                 continue\n",
    "#             print(att_rest)\n",
    "        except:\n",
    "            print('here')\n",
    "            continue\n",
    "        \n",
    "        attentions_on_first.append(att_first)\n",
    "        attentions_on_last.append(att_last)\n",
    "        attentions_on_other.append(att_other)\n",
    "        attentions_on_spec_tok.append(att_spe_tok)\n",
    "#         attention_on_cls.append(att_cls)\n",
    "    return np.mean(attentions_on_first), np.mean(attentions_on_last), np.mean(attentions_on_other), np.mean(attentions_on_spec_tok)\n",
    "\n",
    "def attention_pos(att):\n",
    "    nb_layers = att.shape[0]\n",
    "    nb_heads = att.shape[1]\n",
    "    \n",
    "    attentions_on_first = np.zeros((nb_layers, nb_heads))\n",
    "    attentions_on_last = np.zeros((nb_layers, nb_heads))\n",
    "    attentions_on_other = np.zeros((nb_layers, nb_heads))\n",
    "    attentions_on_spe = np.zeros((nb_layers, nb_heads))\n",
    "    for l in range(nb_layers):\n",
    "        for h in range(nb_heads):\n",
    "            f, l_, o, sp = attention_pos_head(att[l][h])\n",
    "            attentions_on_first[l][h] = f\n",
    "            attentions_on_last[l][h] = l_\n",
    "            attentions_on_other[l][h] = o\n",
    "            attentions_on_spe[l][h] = sp\n",
    "    return attentions_on_first, attentions_on_last, attentions_on_other, attentions_on_spe\n",
    "\n",
    "def attention_pos_examples(lst_att):\n",
    "    attentions_on_first = []\n",
    "    attentions_on_last = []\n",
    "    attentions_on_other = []\n",
    "    attentions_on_spe = []\n",
    "    for att in lst_att:\n",
    "        f, l, o, sp = attention_pos(att)\n",
    "        attentions_on_first.append(f)\n",
    "        attentions_on_last.append(l)\n",
    "        attentions_on_other.append(o)\n",
    "        attentions_on_spe.append(sp)\n",
    "    return attentions_on_first, attentions_on_last, attentions_on_other, attentions_on_spe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "bert_pos_att = attention_pos_examples(funny_attention_bert_base)\n",
    "ss_pos_att = attention_pos_examples(funny_attention_bert_ss)\n",
    "siamese_pos_att = attention_pos_examples(funny_attention_bert_siamese)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-45-9b19dc6c323f>:29: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(('', '' , 'On first', '', 'On last', '', 'On others'), fontsize=22)\n",
      "<ipython-input-45-9b19dc6c323f>:53: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels(('', '' , 'On [SEP] \\& [CLS]'), fontsize=22)\n",
      "<ipython-input-45-9b19dc6c323f>:62: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  fig.tight_layout(pad=1.1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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zM3NkG9/3kztH4xsTVlZW5DiOfN/n8Y0YOIZKAQAjwXEcVSoV+b6vIAi0trYmSZqdnVW5XFYQBFpaWtLExIQkaXp6Wvfv30+CNunoc7mB00bgBgAYGfl8XlJjAFYulyXtB3bb29vHfgYYBgyVAgAAGILADQAAwBAEbgAAAIZgjhvOhFqtpnq93lMe7969k7S/vlKv6vUPe84DAIDDCNxwJtTrdT18+LCnPH744QdJ6jkfSfq7v/91z3kAAHAYQ6UAAACGIHADAAAwBIEbAACAIQjcAAAADEHgBgAAYAjuKgX+4PPPPx90EQAAaIkeNwAAAEMQuAEAABiCwA0AAMAQBG4AAACGIHADAAAwBIEbAACAIQjcAAAADEHgBgAAYAgCNwAAAEO0DdxKpZJ835fneYqiqKv0u3fvJv+Pokie58n3fQVBkELRAQAARkvLwC0MQ4VhKNd1lcvlVCgUOk4Pw1BbW1vJ69u3byufz8u2bXmel/JhAAAAnH0tA7dSqSTLsiRJlmXJ9/2O08MwlG3bkiTf9zU5OakoijQxMaHV1dVUDwIAAGAUtHzI/N7eXhJ8SVK1Wu0o3fd9ua6rYrEoSQqCQHt7e5Kkzc1N2bYt13W7LmylUul4252dna7zN9koHW+zY52amlKtVhtAaY5Rr6dWnlTyqUu1V8Nzft68faPfHdNmR70tn2WdHu+VK1f6XBLAXC0Dt5OIe9UOm5yclGVZyuVympubU7lc7jrvTCajsbGxttvt7OyM1Bd/lI73uGN9+fKlxsfHB1CiY5w7l0p5arVaOsd1ThofG57zc+H8hab1SFs+u0bteIF+aRm4TU5OHulla5fueZ5s207mv8XDqRcvXpS0P6TaKk8AAAA013KOm+u62t3dlbTfkzYzM9M2fXFxUdlsVtlsVpZlKZvNKpfL6cWLF5L2575lMpl+HAsAAMCZ1jJwcxxHly5dSpb7WFtbkyTNzs62TJf257mFYSjP82RZlhzHUalUUqlU4uYEAACAE2g7xy2fz0tSw80EB+enNUuPX29vbx/ZDgAAACfDkxMAAAAMQeAGAABgCAI3AAAAQxC4AQAAGILADQAAwBAEbgAAAIYgcAMAADAEgRsAAIAhCNwAAAAMQeAGAABgCAI3AAAAQxC4AQAAGILADQAAwBAEbgAAAIYgcAMAADAEgRsAAIAhzg+6AAAAnJZSqSTLshSGoXK5nCzLakj3PE+SFASBlpeXZVmWSqVSku44jmzbPtUyAwfR4wYAGAlhGCoMQ7muq1wup0Kh0JDu+74ymYzy+bxc19Xt27cVhqEkKZvNKpvN6tGjR4MoOpAgcAMAjIS4t02SLMuS7/sN6VEUaWNjQ9J+z9qzZ88kSQ8ePFAURYqiSJcuXTrdQgOHMFQKABgJe3t7DcOc1Wq1IT3uVZP2h0qnp6dl27ZyuZyuXbumXC6n1dXVrvdbqVSOvDc1NaVardZ1Xt3qdh9v377Vzs5OavtPMy/T9n/lypW+5EvgBgDAIRsbG1pbW0tef/PNN1paWlKxWNTi4mJXeWUyGY2NjTW89/LlS42Pj6dS1uPUarWu93H+/PnUAo6dnZ2+BS8m7L9fGCoFgFM2Pz+vf/iHfxh0MUbO5OTkkV62ZorFotbW1mRZljzPUzableM4KpfL2tzcPIWSAscjcAMAjATXdbW7uytpfz7bzMzMkW1831c+n2+YAzcxMZGk53K50ykscAyGSgEAI8FxHFUqFfm+ryAIkqHQ2dlZlctlBUGgpaWlJFCbnp7W/fv3VSwW5TiOwjBM5sABg0LgBgAYGfl8XtJ+71usXC5L2g/stre3j3ym2zltQD8RuAFAF17U3uh9vd5THm/fvdf4j/9E1Zevey7PB+fO6eL4hZ7zAdI0Pz+vFy9eaGtra9BFOXMI3ACgC+/rdX36j377DVvY/Y9I79+/7zkfSfrf/8NtvxGAM4ObEwAAAAxB4AYAI25+fl7z8/ODLgaADjBUCgCn7NLf3j6VVfOBbr2pf6jXKcy9THMep8RczoMI3AAAgCTpvaS5FOZepjmPU2Iu50EMlQIAABiCHjcAMNiP37zSu9e9DbvW376VJL2rRr0X6MMP9OFPf9p7PgCaahu4lUolWZalMAyVy+VkWVbH6Xfv3tWtW7catm/2HgDgZM7V3+vf85/1lEft3/5VkvTvf1icthcfe17PecB8zOPsn5ZDpWEYKgxDua6rXC6nQqHQcXoYhkcW3mv2HgAAADrTMnCLe9MkNTxwt5P0MAxl23bD9s3eAwAAOC2mL3/Tcqh0b2+vIdCqVqsdpfu+L9d1VSwWk7Rm73WrUql0vO3Ozs6J92OiUTreZsc6NTU1XN3y9Xpq5Ukln7pUezU85+fN2zf63TFtdtjb8sd/+Vdnrm5/82d/tl+eFNpIGnV75cqVnssBnFWp35wQRZEmJibavncSmUxGY2Njbbfb2dkZqS/+KB3vccf68uVLjY+PD6BExzh3LpXy1Gq1dI7rnDQ+Njzn58L5C03r0YS2XH35mrptweS6BUzQMnCbnJw80svWLt3zPNm2ncx/K5VKyRDpwfey2Ww6RwAAADAiWgZurutqY2ND0n6v2czMTNv0xcXFJP3BgwdHArRm7wEAAKC9loGb4ziqVCryfV9BEGhtbU2SNDs7q3K5fGy6tD+nLQxDeZ6n/B9uMW/2HgAAQCsnWa/Q+fjjpmsTprpuoXTqaxe2neMWB1iu+8fHTZTL5Zbp8evt7e227wEAALRykvUKa69qTed/prluoXT6axfyyCsAAABDELgBAAAYgmeVAgCAkXH/z/9i0EXoCT1uAAAAhiBwAwAAMASBGwAAgCEI3AAAAAxB4AYAAGAIAjcAAABDELgBAAAYgsANAADAEARuAAAAhiBwAwAAMASBGwAAgCEI3AAAAAxB4AYAAGAIAjcAAABDELgBAAAYgsANAADAEARuAAAAhiBwAwAAMASBGwAAgCEI3AAAAAxB4AYAAGAIAjcAAABDELgBAAAYgsANAADAEARuAAAAhjg/6ALg7Jifn5ck/dM//dOASwIAzZVKJVmWpTAMlcvlZFlWQ7rneZKkIAi0vLycbGvb9iCKCxxBjxsAYCSEYagwDOW6rnK5nAqFQkO67/vKZDLK5/NyXVe3b99WGIaam5vT7OysZmdndfXqVUVRNKAjAOhxAwCMiLi3TZIsy5Lv+w3pURTpyZMnchxHjuOoUCgoDENtb28n6ZVK5UgvHXCaCNwAACNhb2+vYcizWq02pGezWWWzWUn7Q6XT09NyXTdJ39zcVD6fP53CAsdoG7i1mw/QKv3u3bu6deuWpObzBgAAGEYbGxtaW1tLXgdBoEwmc6K8KpXKkfempqZUq9VOXL5Odb2Pej3VcqWWV12qveo+r5N8pltv3r7R73Z2jrx/5cqVvuyvZeAWzwdYXFxUFEUqFApaXV3tKD0MQ21tbenWrVvJvAHHcVQqlXT79m3dv3+/LwcEAEAzk5OTR3rZmikWi1pbW2voYCgUClpfXz/RfjOZjMbGxhree/nypcbHx0+UX6dqtVr3+zh3LrVynWj/xzknjY91l1ftVa3rz5zEhfMX+hakNdMycGs3H6BV+sG7cJrNG8Dweff730vv3p/48/W3b/fzqaYwcffDD/ThT3/aez4A8Aeu62pjY0PS/nVpZmbmyDa+7yufzyfXtHiotFmvGTAILQO3dvMBjkuPG3uxWJTUfN4AhtC79/r3HuZv1P7tXyWppzxiH/9haB0A0uI4jiqVinzfVxAEyVDo7OysyuWygiDQ0tKSJiYmJKlhjhvLgWBYpH5zQhRFSaNv5vC8gW5084tnp8l481mWxvE6H3/c03yA9+/3e+vSmFNw3JwBqfmxntZ8kY6lOE8klXxOOD+kX7qt32Hy8V/+FXXbQhp1289hp/jmgoM3HZTLZUn7gV18B+lhjx8/7luZgG60DNzazQdolu55nmzbTua/lUqlpLet2byBbjSbJ9DMzs7OqY43D1pax/uuGvU0H+CDD/aXBUxjTsFxcwaOO9bTmC/SlZTmiaQ2R+QE80P6qdv6HSbVl6+p2xZMrlvABC0X4HVdV7u7u5Kazwdolr64uJgMjVqWlQRth+cNAAAAoDste9zazQc4Ll3aD9TCMJTnecpkMsfOGwAAAEBn2s5xazUf4Lj0+PXBuQLHzRsAAABAZ3hyAlJz/8//YtBFAADgTOMh8wAAAIYgcAMAADAEgRsAAIAhCNwAAAAMQeAGAABgCAI3AAAAQxC4AQAAGILADQAAwBAEbgAAAIYgcAMAADAEgRsAAIAhCNwAAAAMQeAGAABgCAI3AAAAQxC4AQAAGILADQAAwBAEbgAAAIYgcAMAADAEgRsAAIAhCNwAAAAMQeAGAABgCAI3AAAAQxC4AQAAGILADQAAwBAEbgAAAIYgcAMAADAEgRsAAIAhCNwAAAAMQeAGAABgCAI3AGfe/Py85ufnB10MAOgZgRsAAIAhCNwAAAAMcb7dBqVSSZZlKQxD5XI5WZbVcfrdu3d169atjvIBgGZqtZrq9XpPebx7906S9PLly57LU69/2HMeAHBSLQO3MAwVhqEWFxcVRZEKhYJWV1c7Sg/DUFtbW7p161bbfADgOPV6XQ8fPuwpjx9++EGSes5Hkv7u73/dcx4AcFIth0rjXjJJsixLvu93nB6GoWzb7igfAAAAtNcycNvb29PExETyulqtdpTu+75c1+04HwAAALTXdo5bt6IoagjS0lSpVDredmdnpy9lGFZpHK/z8ceqvaqlUJrevXn7Rr875piaHevU1JRqteEouySpXk+tPKnkU9fQ1K3UXf2mUbe//OUvJaV1LqnbVrr97jZz5cqVNIsEnCktA7fJycmWvWPN0j3Pk23byby2UqnUNp9OZTIZjY2Ntd1uZ2dnpL74aR3vu2qk8bHxFErUuwvnLzQ9puOO9eXLlxofH46yS5LOnUulPLVaLZ3jOqehqVupu/qlbtuVx9y6BdC9lkOlrutqd3dX0n5P2szMTNv0xcVFZbNZZbNZWZalbDbbNh+TsJAnAAAYlJaBm+M4unTpknzfl+d5WltbkyTNzs62TJf257mFYSjP81puBwAAgM60neOWz+clqeFmg3K53DI9fr29vd12OwAAAHQm9ZsTAAAYVu0Wg/c8T5IUBIGWl5dlWZaiKNLm5qZs29bExIQcxxlE0QFJPPIKADAi4pvmXNdVLpdToVBoSPd9X5lMRvl8Xq7r6vbt25Kk27dvK5/Py7btJLADBoXADQAwEtotBh9FkTY2NiTtz+F+9uyZfN/X5ORkstQVT/3BoI3UUOmwPfPwXErLCgAA2tvb20ue6CMdXQw+XhFB2h8qnZ6eVhAE2tvbk6RkuLTbudrN1iA9rbUnu95HiusUnmj/xznheoWnscbhcWsX9mv5m5EK3IbtmYc3b97sOQ8AQPo2Nja0trYmz/M0OTkpy7KUy+U0NzfXcINeJ5qtQXoa6xOeaK3AFDsUUlurUDrReoW1V7VTWePwuLUL+4WhUgDASOh0Mfhisai1tTVZliXLsnTx4kVJ+8OrPLIRg0bgBgAYCZ0sBu/7vvL5fDIHLpfL6cWLF5L2b27IZDKnWmbgsJEaKgUAjC7HcVSpVOT7voIgaFhUvlwuKwgCLS0tJc/bnp6e1v379+U4jkqlksIw5OYEDByBGwBgZLRaVN5xnIaF4w9/BhgGBG5d+vzzzwddBAAAMKKY4wYAAGAIAjcAAABDMFRquPn5eb148UJbW1uDLgoAAOgzetwAAAAMQeAGAABgCAI3AAAAQxC4AQAAGILADQAAwBDcVTpAb+of6vXL1z3l8fbde43/+E9U7TEfSfqJ6j3nAQAA+ofAbYDeS5r7R7+nPHb/I9L79+/1aY/5SNLW4l/3nAcAAOgfhkoBAAAMQeAGAABgCAI3AAAAQxC4AQAAGILADQAAwBDcVWq4S397W7VabdDFAAAAp4AeNwAAAEMQuAEAABiCwA0AAMAQBG4AAACGIHADAAAwBIEbAACAIQjcAAAADNF2HbdSqSTLshSGoXK5nCzLapvu+74k6cmTJ/rss89k27ZKpVLyGcdxZNt2yocCAABwtrXscQvDUGEYynVd5XI5FQqFtulhGKpYLMp1XV26dEnFYlFhGEqSstmsstmsHj161L8jAgAAOKNaBm5xb5qkhp60Vum2bWt9fV2StLu7K8dxJEkPHjxQFEWKokiXLl1K/UAAAADOupZDpXt7ew1DmtVqteP0eGg0n89LknK5nK5du6ZcLqfV1dXeSw4AADBi+vas0mw2q6dPn8r3fbmuK0n65ptvtLS0pGKxqMXFxa7zrFQqHW+7s7Nz5L2pqanheq5nvZ5aeVLJpy7VXg3H+Xnz9o1+16QOJer2RIaobqXu6pe6bcPguj3OlStX0iwScKa0DNwmJyeP9LK1S4+iSNL+0Oknn3yiO3fuKJ/PK5vNyrZtlctlzc3NnShwy2QyGhsba7vdzs5O0y/+y5cvNT4+3vV+++bcuVTKU6vV0jmuc9L42HCcnwvnLzStQ+r2pOUZnrqVuqtf6rZdecytWwDdaznHzXVd7e7uStoPyGZmZtqme56nr7/+OtkmDuQmJiaS93K5XDqlBwAAGCEte9wcx1GlUpHv+wqCQGtra5Kk2dlZlcvlY9N935fv+yqVSrp3754cx1GxWJTjOArDUNlstv9HBgAAcMa0neMW31wQz1OTpHK53DI9DswOvneSoVEAAAD8EU9OAAAAMASBGwAAgCEI3AAAAAxB4AYAAGAIAjcAAABDELgBAAAYgsANAADAEARuAAAAhiBwAwAAMASBGwAAgCEI3AAAAAxB4AYAAGCItg+ZBwDgrCiVSrIsS2EYKpfLybKshnTP8yRJQRBoeXlZlmUpCALZtq1qtaqJiYkjnwFOEz1uAICREIahwjCU67rK5XIqFAoN6b7vK5PJKJ/Py3Vd3b59W5J0584dXbt2TcVikaANA2dEj1u9XpckvX79uuPPvHr16sh7r1+/1tjYWGrl6tWb16/1X8bP9ZzPq3MfaGys93xev3mjd5OTPeeThldv3ujDJnUoUbcnMUx1K3VXv9RtaybXbSs/+tGPdO5c7+fnoLi3TZIsy5Lv+w3pURTpyZMnchxHjuMkgd0XX3yhbDbb9f5aXbtOq113u4+02reUXhuXTtbO379+pXc/6v85btXm+9GOz9XjljXEXrx4oX/5l38ZdDEAAKckk8mkHtjcvXtXP//5z5Mg7OrVq9re3m66balU0sbGhu7fv69isSjHcRQEgbLZrGzb7mh/XLvQj3ZsRI/bT37yE01NTenChQupR64AgOHzox/9aKD739jY0NramiRpcXFRkmTbtlZWVrS+vt5RHly70I92bETg9sEHH+jixYuDLgYAwGCTk5OqVqtttysWi1pbW5NlWSqVSgrDMAnewjDseH9cu9AP3JwAABgJrutqd3dX0v58tpmZmSPb+L6vfD6fzIGzLCsZWg2CQNevXz/VMgOHGTHHDQCANHieJ9u2FQRBEqDNzs6qXC4rCALduHFDExMTkqTp6Wndv39fnudpYmJCT58+1a1btwZ8BBh1BG4AAACGYKgUAADAEARuAAAAhiBwAwAAMASBGwAAgCGMWMcNAIBRdvDxXLZtd/z0htMSPwc25rruAEvzR8N83k56zuhxS1EURVpZWdHs7KwuX76s2dlZraysKIqiU9n/l19+qcuXL+vy5cuS9m97v3z5skql0qnsfxQMqo7jugyCoK/7OWsG/Z3sBXWOgxYWFpJ/w/g3vVgsJuVbWloadHESw3zeTnrO6HFLie/7WlhYkGVZyuVyyXPtNjc35Xme1tfX+/oL5O7du9ra2tK9e/eS92zbluu6yUOV0ZtB1zG6Q30hbVEUqVAoyPd9hWGY/I1dXl4+lb+z+Xxeq6urR973PE+e5ykIAlmWpUwmo2w2q3w+31D2q1evtt3H8+fPW24f57+4uNjw/VldXdXq6qoWFhZUqVROcnjyfV+FQkFhGOpXv/pVamvmHXfeYvH5C8NQURTJtm1NT0/rs88+S44xPh+u63b8yLN29XLSc0bgloIwDLWwsCDHcfTNN980fIGXl5d148YNLSws6PHjx3Icpy9l2Nra0vXr15MVvqX9bte0L0zx4pWjdsEbhjrup7NWrybV11k792fVsP4QWFhYkO/7SQC5t7en7777TisrK5LUELxJ+z/o48d3ddLz7DiOvvrqq+R1pVJRqVTSwsJC24CoG1EUaWFhQdevX1cul1OhUNDk5GRS1n6Iokhzc3MKw1Cu6yqXy0na//uxubkp6eRDvt3WS1fq6NmNGzfqU1NT9Wq12jS9Wq3Wp6am6p9++mnfyjA1NVW/c+dO3/KP/eIXvziV/QybQdfxo0eP6lNTU/VKpdKX/M9avQ66vrpx3Lnvd52jc7u7u0l7OdymqtVq/dNPP+17XTX7G//gwYP61NRUfXNz88j2m5ub9d3d3YZyTk1N1W/cuNHR/tptf+fOnab7vnHjRv0Xv/hFR/s46De/+U392rVryetPP/204fVJtbo2Xrt2rT41NVV/9OhR0/STnr9u6qVe7/6cMcetR77vy/d93bx589iucsuytLy8rCAIhm6MHe1Rx2ahvpC2uJfkcO+ttN+WvvnmG0nSnTt3TrVcm5ubsm27YaQlls1m+zoRP+5p29jYSCW/eOg5Fj92rF+KxaLCMNTq6uqxvV8nPX/9rhcCtx7Ff/Q/+eSTltvF3a0HG3mpVNLVq1cVBIFWVlZ09epVXb16VV9++WXH+y8Wi0duRohfl0olXb58ueGumnifYRgm3d3xH6UgCLSwsJDkMTs7q2KxKOmPNz5EUdSwnzj9LBt0HR8niiLdvXu3YeL93bt3j2w3avU6LPV18MaIOI+Dd5B1c+7jem5VliiK9OWXX+rq1atN28JJv/ujbth/CFSr1VPd30G2bevZs2ep5PXixYvkWMIwlO/7un79eip5HxbPVbRtu7chyxb6WS8Ebj2KJxS2i6DjeTTff/998l61WlUURcndJMvLy8pkMtra2mp6AW4mn88nEyXjSZPtJk7GF/ylpSVVq9XkAnbjxg1Vq9VkwqTrusk4/61bt5rup1+NfpgMuo6Ps7m5qe+++07Xr1/XvXv3dP36dT18+DC5GMdGrV6Hob7CMNS1a9fk+77y+byWl5f1/fffa3Z2Nvkh1em5v3Hjhl68eKHFxcVjyxLv79mzZ1peXj62LZzkuz/qhuWHQDO5XC4J2AchDENNT0+nkpdt2wqCIJmfatt2ajcnHBb/jejX37l+1ws3J/QontzZ6R1FB39xx6anp5Nu53w+r8uXL2tra6ujRmtZVvIH4+LFix1PpIzvQI27cn3fVxRFWl5ebprHwfVvRm0i9aDr+Dj5fL7hD082m1UYhvI8L9nXKNbrMNRXHDCVy+XkvXw+r7m5Oa2srKhcLnd87mdmZtqWJV7i5Le//W1y3JcuXdLKysqROx67/e6PurR+CMST1EulUhJ89xqYLC4uyvd9bW1t6fLly3JdV9lsVrlc7tj2H/dmNdPNOmdxG28X0HbKdV15nqe5uTlNTEzo8ePHR7YplUqprJQQL7HTr6Hkk9RLN+hx61FcCe3uzonTm43bH274juP0vfv78B2ocQN+9OhR0wvZKDOpjn/+859L+mMwMor1Ouj6ii+MX3zxxZG05eXlZKiyU+3KEkVRMqxUrVaTRT3juj98kea73500fwgcHCHZ2tpKpXzr6+taXV2V4zjyfT/p2TtuqDvu0Wr2r1m7rFarCoJAQRDI9315nqeFhQV5nqd8Pt90HtdJHDy/jx8/PnK+wzDU0tLSsUFnNw7/feyHbuulG/S49eijjz5SEASqVCotf63GDaVZt/Ig1lk7fDGIx/o9z9PW1pZs29b169f71lVtkmGu4zgIiNeVOnzRGMV6HXR9xb/mmy0zkslkJElPnz7t+ILXrixxj9DW1lbTYOBwm+C7352DPwRa1UW3PwTSDJLj3vc4iC8UCioUCrIs68hwYDfrkEn77Xlubi55Ha9HdrDXtlcHl1qJj+Fw3o8ePZJlWantU+r//MBu6qUb9Lj1KP5CPnr0qOV2nuc1bH9Qv++eaabZL43V1VU9fvxY+Xxe1WpVDx8+bPjCjqphreNSqaTZ2Vltbm4qm81qdXVVN2/ePLLdqNXrsNRXsx6/k1woOi3L+vq6nj9/fuTf4XWw+O5356OPPpKktgukDsOP8ziwKZfLsixLhUKh5zxd121oT9vb21pfX089aMvn88lQ/507dxq+P1EU6eHDh/rVr36Vyj7jH1Wn9VSStOuFwK1H2WxWjuNoa2vr2F9Q8bwjx3FS/bXQD47jaHV1Vdvb27p582YyWfSgvb29wRRuQIa1ju/cuaPr168nF1zXdTU5Odl021Gq10HXV3xRePr06ZG0+EIRD2nHejn3cS9erxehTtrIKBqWHwLdiifID3sdLi0tJW3Psix99dVXiqJIt2/fbtjGsqzUeoHjhXbjOjtNadQLgVsK4sdMLSwsHKmMeD6BpIbVp4dNFEVHegj+5m/+RlLjUItlWXrx4sWplm0YDFsdx/V1uPfkcLAwqvU6yPqybVuO4+jbb789cu7jYZKDwWKv5z6+QenBgwdH9tes/g/rtI2MqkH/EOjVMD1U/bC47c3MzCTvZbNZ3bx5M7mBY2VlRb7vJ2vlpcGyLN28ebPr+aZp6qVemOOWAtu2tb6+roWFBc3NzR15HEoURbp3797AH63Tiu/7WlpaUj6fl+M4ydpSB+9alfZ/3fu+nyxeKCm1R54Ms2GrY8uyZNu2Hj58KGm/B2djY+PIHKdRrddB19e9e/c0Nzena9euJTcpxM9CPDy/KI1zv7q62rA/y7IUBIE8z2s7F6nTNjLK7t27p9nZWS0sLGh9fb3hojvIH+dzc3NN7waO637Y6y8eQv7uu+8a3r9165aePXuW/H1bX19P/bt669YtbW1taWlp6djvSPzIqm71u14I3FLiuq62t7dVKBSSZ9fFz7RbXFwc6l89kpJb1Q+WPZPJHLnILC4uqlKp6MGDB8mDhkfFsNXxvXv3dOfOHT18+LDhYdcHu/9HuV4HWV+2beu3v/2tbt++rQcPHkhSct4P7zeNcx/vr1AoJAFiPPzUrgeo0zYyygb9Q+A48fM94+//xYsXFYahtra2ZFlW0vN8UBiGLe9s7PcTFw5bXV3VysqKFhYW5Lqu9vb29OzZM/m+n9yssLKykkzmD8Mw+ZHRq7hOl5aWdP369aSneXd3N+lhff78ecNn4t7VZuLlPk5SL904V6/X6z3lAADACIhX3I+DtXY/BDzP08rKih4/ftwQaMQPNt/e3u5435cvX276UPdSqZQE6/EyMK7rHtkuiiJdvXq17X7i3qd4+27vQl1YWFClUunq2Eqlkh48eKAgCJK15OKlRuLe6IPr6X311VcdB27HnbeDisVicg4lNa3XTs5fvD5jfEyd1IvU/TkjcAMAYMh1EoAMg5MEbv1kwnnr9pxxcwIAAIAhCNwAAAAMQeAGAABgCAI3AAAM4HmeLl++nMrzLtO2srKiy5cvp/Is0bQN63k76Tnj5gQAAIbcwYViHccZuiWmDj5pY5jWABzm83bSc0bgBgAAYAiGSgEAAAxB4AYAAGAIAjcAAABDELgBAAAYgsANAADAEP8fb+sevYPOGjYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid_kws = {\"width_ratios\": (.75, .25), \"hspace\": .2}\n",
    "fig, axes = plt.subplots(1, 2, figsize=(10, 5), gridspec_kw=grid_kws)\n",
    "\n",
    "ax =axes[0]\n",
    "x_label = ['On first', 'On last', 'On others', \"On ``[SEP]'' and ``[CLS]''\"]\n",
    "w = 0.3 \n",
    "\n",
    "x = np.array([0, 1, 2])\n",
    "ys = [np.mean(bert_pos_x) for bert_pos_x in bert_pos_att[:3]]\n",
    "yerr = [3.96*stats.sem(np.array(bert_pos_x).flatten()) for bert_pos_x in bert_pos_att[:3]]\n",
    "\n",
    "ax.bar(x-w, ys, width=w, yerr=yerr, color='tab:gray', alpha=0.85)\n",
    "\n",
    "# x = ['On first', 'On Last', 'On other']\n",
    "ys = [np.mean(bert_pos_x) for bert_pos_x in ss_pos_att[:3]]\n",
    "yerr = [3.96*stats.sem(np.array(bert_pos_x).flatten()) for bert_pos_x in ss_pos_att[:3]]\n",
    "\n",
    "ax.bar(x, ys, width=w, yerr=yerr, color='tab:blue', alpha=0.85)\n",
    "\n",
    "ys = [np.mean(bert_pos_x) for bert_pos_x in siamese_pos_att[:3]]\n",
    "yerr = [3.96*stats.sem(np.array(bert_pos_x).flatten()) for bert_pos_x in siamese_pos_att[:3]]\n",
    "\n",
    "ax.bar(x+w, ys, width=w, yerr=yerr, color='tab:red', alpha=0.85)\n",
    "\n",
    "ax.set_ylim(0.037, .05)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "\n",
    "ax.set_xticklabels(('', '' , 'On first', '', 'On last', '', 'On others'), fontsize=22)\n",
    "\n",
    "ax = axes[1]\n",
    "x = np.array([0])\n",
    "ys = [np.mean(bert_pos_att[3])]\n",
    "yerr = [3.96*stats.sem(np.array(bert_pos_att[3]).flatten()) ]\n",
    "\n",
    "ax.bar(x-w, ys, width=w, yerr=yerr, color='tab:gray', alpha=0.85)\n",
    "\n",
    "# x = ['On first', 'On Last', 'On other']\n",
    "ys = [np.mean(ss_pos_att[3])]\n",
    "yerr = [3.96*stats.sem(np.array(ss_pos_att[3]).flatten())]\n",
    "\n",
    "ax.bar(x, ys, width=w, yerr=yerr, color='tab:blue', alpha=0.85)\n",
    "\n",
    "ys = [np.mean(siamese_pos_att[3])]\n",
    "yerr = [3.96*stats.sem(np.array(siamese_pos_att[3]).flatten())]\n",
    "\n",
    "ax.bar(x+w, ys, width=w, yerr=yerr, color='tab:red', alpha=0.85)\n",
    "\n",
    "ax.set_ylim(0.25, .30)\n",
    "ax.spines['right'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "\n",
    "ax.set_xticklabels(('', '' , 'On [SEP] \\& [CLS]'), fontsize=22)\n",
    "      \n",
    "legend_elem = [\n",
    "               mpatches.Patch(color='tab:gray', label='BERT', alpha=0.85),\n",
    "               mpatches.Patch(color='tab:red', label='BERT-PS', alpha=0.85),\n",
    "               mpatches.Patch(color='tab:blue', label='BERT-1S', alpha=0.85)\n",
    "]\n",
    "\n",
    "fig.legend(handles=legend_elem, ncol=3, loc='upper center', frameon=False, fontsize=22,  bbox_to_anchor=(0.45, 1.05))\n",
    "fig.tight_layout(pad=1.1)\n",
    "# fig.savefig(\"att_pos.pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-46-3aef85188757>:16: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_xticklabels([str(i+1) for i in range(12)])\n",
      "<ipython-input-46-3aef85188757>:17: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels([str(i+1) for i in range(12)])\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0, 0.25, '1'),\n",
       " Text(0, 0.26, '2'),\n",
       " Text(0, 0.27, '3'),\n",
       " Text(0, 0.27999999999999997, '4'),\n",
       " Text(0, 0.29, '5'),\n",
       " Text(0, 0.3, '6')]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x216 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "to_plot = ss_pos_att\n",
    "\n",
    "on_first, on_last, on_others, _ = to_plot\n",
    "\n",
    "\n",
    "grid_kws = {\"width_ratios\": (.30, .30, .35), \"hspace\": .2}\n",
    "fig, axes = plt.subplots(1, 3, figsize=(15, 3), sharex=True, sharey=True, gridspec_kw=grid_kws)\n",
    "# fig, axes = plt.subplots(1, 1)\n",
    "sns.heatmap(np.mean(np.array(on_first), axis=0), cbar=False, linewidth=0.5, ax=axes[0])\n",
    "axes[0].set_title('On first', fontsize=16)\n",
    "sns.heatmap(np.mean(np.array(on_last), axis=0), cbar=False, linewidth=0.5, ax=axes[1])\n",
    "axes[1].set_title('On last', fontsize=16)\n",
    "sns.heatmap(np.mean(np.array(on_others), axis=0), linewidth=0.5,  ax=axes[2])\n",
    "axes[2].set_title('On other', fontsize=16)\n",
    "\n",
    "ax.set_xticklabels([str(i+1) for i in range(12)])\n",
    "ax.set_yticklabels([str(i+1) for i in range(12)])\n",
    "# fig.tight_layout(pad=1.1)\n",
    "# fig.savefig(\"bert_ss_siamese_funny_serious.pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Head 9-5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def attention_mod_head(head, mod_pos):\n",
    "    seq_len = head.shape[0]\n",
    "    attention_on_chunk = []\n",
    "    attention_on_rest = []\n",
    "    attention_on_sep = []\n",
    "    attention_on_cls = []\n",
    "    for s in range(seq_len):\n",
    "        try:\n",
    "            att_cls = head[s][0]\n",
    "            att_sep = head[s][-1]\n",
    "            att_chunk = np.sum([head[s][p] for p in mod_pos]) / len(mod_pos)\n",
    "            att_rest = (1 - att_chunk - att_cls - att_sep)/ (seq_len - 2 - len(mod_pos))\n",
    "#             if math.isnan(att_res) or math.isinf(att_res):\n",
    "#                 continue\n",
    "#             print(att_rest)\n",
    "        except:\n",
    "            print('here')\n",
    "            continue\n",
    "        \n",
    "        attention_on_chunk.append(att_chunk)\n",
    "        attention_on_rest.append(att_rest)\n",
    "        attention_on_sep.append(att_sep)\n",
    "        attention_on_cls.append(att_cls)\n",
    "    return np.mean(attention_on_chunk), np.mean(attention_on_rest), np.mean(attention_on_cls), np.mean(attention_on_sep)\n",
    "\n",
    "def attention_mod(att, mod_pos):\n",
    "    nb_layers = att.shape[0]\n",
    "    nb_heads = att.shape[1]\n",
    "    \n",
    "    attentions_on_chunk = np.zeros((nb_layers, nb_heads))\n",
    "    attentions_on_rest = np.zeros((nb_layers, nb_heads))\n",
    "    attentions_on_sep = np.zeros((nb_layers, nb_heads))\n",
    "    attentions_on_cls = np.zeros((nb_layers, nb_heads))\n",
    "    for l in range(nb_layers):\n",
    "        for h in range(nb_heads):\n",
    "            c, r, s, cls = attention_mod_head(att[l][h], mod_pos)\n",
    "            attentions_on_chunk[l][h] = c\n",
    "            attentions_on_rest[l][h] = r\n",
    "            attentions_on_sep[l][h] = s\n",
    "            attentions_on_cls[l][h] = cls\n",
    "    return attentions_on_chunk, attentions_on_rest, attentions_on_sep, attentions_on_cls\n",
    "\n",
    "def attention_mod_over_examples(lst_att, lst_mod_pos):\n",
    "    attentions_on_chunk = []\n",
    "    attentions_on_rest = []\n",
    "    attentions_on_sep = []\n",
    "    attentions_on_cls = []\n",
    "    for att, mod_pos in zip(lst_att, lst_mod_pos):\n",
    "        c, r, s, cls = attention_mod(att, mod_pos)\n",
    "        attentions_on_chunk.append(c)\n",
    "        attentions_on_rest.append(r)\n",
    "        attentions_on_sep.append(s)\n",
    "        attentions_on_cls.append(cls)\n",
    "    return attentions_on_chunk, attentions_on_rest, attentions_on_sep, attentions_on_cls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def attention_to_list_with_mod_pos(lst_funny_att, lst_serious_att, test_df):\n",
    "    lst_mod_pos = []\n",
    "    lst_reduced_funny_att, lst_reduced_serious_att = [], []\n",
    "    for i in range(test_df.shape[0]):\n",
    "        row = test_df.iloc[i]\n",
    "        funny = row['headline_original'].split(' ')\n",
    "        serious = row['headline_unfunned'].split(' ')\n",
    "        \n",
    "        if len(funny) != len(serious):\n",
    "            continue\n",
    "        \n",
    "        if len(funny) < 3:\n",
    "            continue\n",
    "        \n",
    "        if len(set(funny).symmetric_difference(set(serious))) > 2:\n",
    "            continue\n",
    "            \n",
    "        mod_pos = []\n",
    "        for j in range(len(funny)):\n",
    "            if funny[j] != serious[j]:\n",
    "                mod_pos.append(j+1) # Account for the initial token\n",
    "        \n",
    "        if len(mod_pos) == 0 or len(mod_pos) > 2:\n",
    "            continue\n",
    "        \n",
    "        lst_mod_pos.append(mod_pos)\n",
    "        lst_reduced_funny_att.append(lst_funny_att[i])\n",
    "        lst_reduced_serious_att.append(lst_serious_att[i]) \n",
    "    \n",
    "    return attention_mod_over_examples(lst_reduced_funny_att, lst_mod_pos), attention_mod_over_examples(lst_reduced_serious_att, lst_mod_pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "funny_attention_base_pos, serious_attention_base_pos = attention_to_list_with_mod_pos(funny_attention_bert_base, serious_attention_bert_base, test_df)\n",
    "funny_attention_ss_pos, serious_attention_ss_pos = attention_to_list_with_mod_pos(funny_attention_bert_ss, serious_attention_bert_ss, test_df)\n",
    "funny_attention_siamese_pos, serious_attention_siamese_pos = attention_to_list_with_mod_pos(funny_attention_bert_siamese, serious_attention_bert_siamese, test_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-53-25de51cd8827>:27: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  fig.tight_layout(pad=1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mod_funny, non_mod_funny, _, _ = funny_attention_ss_pos\n",
    "mod_serious, non_mod_serious, _, _ = serious_attention_ss_pos\n",
    "# mod_funny, non_mod_funny, _, _ = funny_attention_siamese_pos\n",
    "# mod_serious, non_mod_serious, _, _ = serious_attention_siamese_pos\n",
    "\n",
    "# mod_funny, non_mod_funny, _, _ = funny_attention_base_pos\n",
    "# mod_serious, non_mod_serious, _, _ = serious_attention_base_pos\n",
    "vmin=0.0\n",
    "vmax=0.2\n",
    "\n",
    "grid_kws = {\"width_ratios\": (.22, .22, .22, .26), \"hspace\": .2}\n",
    "fig, axes = plt.subplots(1, 4, figsize=(20, 4), gridspec_kw=grid_kws, sharex=True, sharey=True)\n",
    "sns.heatmap(np.mean(np.array(mod_funny), axis=0), cbar=False, linewidth=0.5, ax=axes[0], vmin=vmin, vmax=vmax)\n",
    "axes[0].set_title('(a) Modified chunks\\nfunny sentence', fontsize=24)\n",
    "sns.heatmap(np.mean(np.array(non_mod_funny), axis=0), cbar=False, linewidth=0.5, ax=axes[1], vmin=vmin, vmax=vmax)\n",
    "axes[1].set_title('(b) Non-modified chunks\\nfunny sentence', fontsize=24)\n",
    "sns.heatmap(np.mean(np.array(mod_serious), axis=0), cbar=False, linewidth=0.5, ax=axes[2], vmin=vmin, vmax=vmax)\n",
    "axes[2].set_title('(c) ``Modified\" chunks\\nserious sentence', fontsize=24)\n",
    "sns.heatmap(np.mean(np.array(non_mod_serious), axis=0), linewidth=0.5, ax=axes[3], vmin=vmin, vmax=vmax)\n",
    "axes[3].set_title('(d) ``Non-modified\" chunks\\nserious sentence', fontsize=24)\n",
    "\n",
    "axes[0].set_yticklabels([str(i+1) for i in range(12)])\n",
    "axes[0].set_ylabel('Layers', fontsize=23)\n",
    "[axes[i].set_xlabel('Heads', fontsize=22) for i in range(4)]\n",
    "a = [axes[i].set_xticklabels([str(i+1) for i in range(12)]) for i in range(4)]\n",
    "\n",
    "fig.tight_layout(pad=1)\n",
    "# fig.savefig(\"ss_mod.pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-55-5936eadf9e44>:64: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  fig.tight_layout(pad=1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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y1Pr16xWNRo0NDdy9e7ckGf/rCICZYzoPSOQCALPDZP4ibwG5i74KgNlEAStHNTQ0yLbtZALP1KFDh+T3+6d1+1cA2WE6D0jkAgCzw2T+Im8BuYu+CoDZRAErh7W2tqqrq0uhUCijOJs2bZKkKS9UCiD7TOUBiVwAYHaZyF/kLSD30VcBMFsoYOUwr9er1tZWNTY2ynGcacUIBoPq6urSj370ozlx9wwAY5nIAxK5AMDsyzR/kbeAuYG+CoDZwl0IAQAAAAAAkNMYgQUAAAAAAICcRgELAAAAAAAAOY0C1hUmFotp06ZNqqys1IIFC5ILJQJApsgvAAAAAGbKf8l2AzC76urqNDw8rNraWhUWFma7OQDyCPkFwFwRCoXU2Ngon8+nnTt3Zrs5AOYocgkwuyhgXUHa29vlOI66u7tl23a2mwMgj5BfAAAAAMwkphBeQcLhsCTxy+UMW7NmjRYsWJDtZgCzivwyO8gvgBk1NTX6l3/5F0ZMuEDeASZGLnGPXAITKGBdQYaHh7PdBAB5ivwCAAAAYCZRwAIAAAAAAEBOo4B1BQgGg1qwYIGi0agkacGCBVqwYIGCwaAkKRqNasGCBWppaRn33AULFqiurm7Mtmg0qurqagWDQYXDYdXV1WnBggWqrq5We3v7uBhTOX779u1j2jZaLBZL2Z5Ux7W0tCTvhFZZWalNmzYlpziN1t7errq6OlVWVmrNmjXjjplK21taWrRgwYJkjMTnvGDBAsVisRl73dFCoZDWrFmTfO91dXUKhUJTft+AW+QX8stU3zcwkZm6vmKxmLZv367KysrkcYnctH379nGxg8FgcqpLXV1dymOmmtum8t5m63Mh7yBfkUtm93Mhl2A2sYj7FcDr9aq1tTW5yHJra2ty+3Q5jqNgMCjHcVRbW6tFixYpGAwqEAjItm3V1NRM6/hVq1Zpz549CgaD8vv9Y2J0dnZKkmpraydt2/33369oNCq/3y/bttXb26uenh7Ztj3mPdfV1Skajcrn86mqqkpdXV1as2aN9u3bN+Y4t233+/3yeDzjPmdJsixrxl43oaWlRcFgULZtJz+jcDisxsZGnTp1KtkGt68PuEF+Ib+QX2DKTFxf0Wg0ed3Ytq2ioqJJ27Bp0yZ1dXXJ6/Vq3bp1chxHe/bsUU9Pj370ox+Nud5m4r3N1udC3kE+I5fM3udCLsGsi+OKsXz58vj8+fPHbY9EIvH58+fHt27dOm7f/Pnz48uXL095/Pz58+ORSCS5vbOzMz5//vz4xo0bMzr+/vvvj8+fPz8+ODiYcvvw8PCE73FwcDA+f/78+GOPPTZu3+jnPfnkk/H58+fHDx48OOaYJUuWjHm/U217PD7x5zyTrztZe06ePDnl1wemivxCfiG/IBMzeX0tWbJkzLk+ev/o1zt48GB8/vz58SeffHLMsSdPnhyXx6aS29y+t1TIO+QdTA25JDVyCbkkXzCFENPm8Xjk8XiSPycq0+fPn8/o+MTIiIMHDya3xWIxhcNheTweV3+xcBxn3LbRzwsEAvJ4PONGYTQ0NCgajY4bzjrV9zqRmXrdQCAgSWpraxv3mqP/YjDV1weyhfxCfsGVyfT1JUk7duwYc65PJPHX+4aGhjHbvV6vfD5f8i/+05XuvWXyXPIOeQdjkUum91xyCbkk1zGFENO2aNGiMT8nkt9EdyNze3xNTY0sy9KhQ4fU3Nws6ffTey5PKpezbVsej0ddXV2qrq6W1+uV1+sdM5Q0kbgTc85TiUQiY5LaVN9rKjP5uo7jyOv1TvrlNZ3XB7KF/EJ+wZVlpq6vy3/JmYjjOIrFYhNOI168eLG6uroUjUZl2/ZU3pqr95bJc8k75B38Hrlk+s8ll5BL5gIKWMhJtbW1ycX5vF5vcpG9dOvTSNKPfvQjNTY2KhwOKxgMJuc8HzlyRJZlJZOUz+fTsmXLUsa4PCmaMFOvm4ib7kswW+8byDXkl6nHJb9gNszE9eX2vEvciGKicz2xfbqjJtK9t0yeS94h72Ascsn0nksuIZfMBRSwkJP8fr+CwaBCoZC8Xm/yF003w2Mty9K+ffsUi8XU2dmpUCiUXKhv3759yQRUXFzs6i8WpszU67r9IszW+wZyDfnFPfILZtNMXF+FhYWujkt3rrv9RWgi6d5bJs8l75B3MBa5ZHrPJZeQS+YC1sDChDKZm52pxDDdcDic/EtGuuk9l7MsS36/X/v27UvGSmy3LCs5bWi2zOTr2rad9jav2XrfQCrkF7PIL8gn2bi+ElODJjrXEyM1000hSpfbJnpvbpB3Zu/1kR/IJVN7LrmEXDIXUMBC0rlz58b8PHqR42zw+/1yHEcdHR2S5KoiHovFkl8Mk2lqalIsFlNLS8u4fe3t7Rkt1FdSUiIp9RfPTL1uU1OTJKWMGwqFkm2ZyfcNTIb8MoL8AqSWC9dXU1OTHMfR9u3bx2xPjFLw+XzjRk24yW1u31squfC5kHcwl+TCNUMuSY1cAhOYQohkAu3q6lJ7e7ssy9LJkyfV09OT1XbV1taqpaVFe/bskc/nc/WcSCSixsZG2bYtr9erwsJC9fT0KBqNat26dcnj/H6/Tp48qWAwqEgkoqqqKp0/f17hcFiO4yQXep6OG2+8UV1dXWpsbNSiRYvU2dmpHTt2yOv1ztjr1tTUJO9qkojrOI76+vrkOI727dsn27Zn9H0DqZBfyC/kF7iRC9dXQ0ODwuGw9uzZo56eHlVVVamvr0/hcFi2bY+5i9VUcpvb95arnwt5B3NJLlwz5JLUyCUwgRFYkGVZam1tlWVZCgQCyQX9Tpw4IY/Hk7VF6yzLSv5iuWrVKlfP8Xq92rFjh2zbVmdnp/bs2aNYLKampqbkHccSdu7cqdbWVhUVFWnPnj0Kh8OqqKjQkSNHpj0vXRpJ/B6PR9FoVOFwWPX19WM+w5l63cvj9vX1ybZt7du3b8xdM2bq9YFUyC/kF/IL3MiF60uS9u3bl/yL/Z49ezQ8PKx169apu7t7zC8vU8ltU3lvufi5kHcwl+TCNSORS1Ihl8CEgng8Hs92I4CJbNq0ST09PTp16lS2mwIgz5BfAAAAgLmDEVjIaV1dXa5ubQ8AU0V+AQAAAOYO1sBCzmpvb5c09buDAUA65BcAAABg6kKhkCzLkuM4qq2tHbc+WDAYlCRFo1E1NTXJsqyUz0kXJxVGYCGnOI6jTZs2afv27QoEAvJ6vWlvMwsAbpBfAAAAgOlzHEeO48jr9aq2tlaBQGDM/nA4rEWLFsnv98vr9WrLli0pn5MuzkQoYCGnFBUVqaenR3v27EkuNggAJpBfAAAAgOlLjJqSRm5CEA6Hx+yPxWLq6OiQJHk8HvX19aV8Tro4E2EKIXKKZVksqAxgRpBfAAAAgNRisZhisdi47ZZlJYtN586dG3NXxuHh4THH1tTUqKamRtLIFMKKioqUz0kXZyIUsAAAAAAAAPLQf/72310dt//Zn2jXrl3jtm/YsEEbN26c8ut2dHSora1Nu3fvnvJzJ0IBawp+cs3dRuJ88f1ndeyaVUZiLX3/oCTpr8vNtO3v+p/VgevvMxLrvjMHtKPUTKzGwQOSpB/aZuJ91TmgF6+9x0isO88+o12G2rXBGXmfb1xfZyTerWeOaN88M21bc/qA9paYibV2aOR9/m2ZmXh/M3DASJxs6zCUF5a9f9BojvkrQ/nlsf5nJUkvGLr2vnz2GX3PUI75xuABtRs6vxs+Or+D191rJJ7/vaeNvk+T55kko/nPZI4nv4z1j9fUG4nz5+8fkiRF/sftGcda9O+vSJIay82ckzv6Dxr9zpOkvzdwTm786Lv9Hwy17S9PHzDaJ5XMtO0vP/rMev/7HRnHkqQb/+fLevZ6M3n07jNPSzL7bxAwlJebBvMjx5jodyx9/6BOXJP5zVWWvB/UN8sz72t8p/8ZSZn3zxLflyZ+L2ocPGAkzyVy3HMZ9lXuem/k2jL13kydRyb6E1/9KHfvzrB/+MBH/UITOWPa+eLSRVeHrV69WsuXLx+3ffTi6sXFxa5GS7W3t6utrU2WZaV8jts4l6OABQAAAAAAkI/il1wdNnqq4ES8Xm9yjatYLKaqqqpxx4TDYfn9/uTaVqme4yZOKhSwAAAAAAAA8lD84ofGYnk8HkUiEYXDYUWjUbW1tUmSqqur1d3drWg0qsbGRhUVFUmSKioqtHPnznHPsSwrZZx0KGABAAAAAADko0vuRmC55fePTPX1er3Jbd3d3ZJGClypbpqU6jmptqVz1dSbmx8OHz6c7SYAAAAAAADMnIv/6e4xB+T1CKyenp4J94VCIa1cuXIWWwMAAAAAADCLXK6BNRfkdQGrs7NT0WhUHo9n3D7HcbLQIgAAAAAAgFlieAphNuV1Aau5uVm7d+9WU1PTuH0tLS1ZaBEAAAAAAMDsMLmIe7bldQGrsLAwZfFKklpbW2e5NQAAAAAAALOIKYQAAAAAAADIaZcuZrsFxlDAAgAAAAAAyEdMIQQAAAAAAEBOYwohAAAAAAAAchp3IQQAAAAAAEAui1/6z2w3wZiCeDwez3YjAAAAAAAAYNb/fvslV8f91898aYZbkjlGYE3BmvIVRuLs639ey0vvMBLr6ODLkqQlJUuNxDsxdEyfue5PjcR6+73XdU/ZciOxnhk4KkmqvP5zRuKdOvOqSv/wRiOxBn/Xq1q71kisTqdTkoyeH9W2z0isbqdLf1FWZyTWjweOSJIeLrvXSLyHB542Eifbvl5ebyTO9/sP6e6yLxuJ9ezAC7q99ItGYr0y+BNJ0k3XfdZIvLfee83oOWkyv0hSyR8uMhJv6HcR3VF6u5FYLw++YvTckGQ0xzSUrzQSq73/sL5tKL98K0/yy9cM5Zcf9B+SJPkNnEfBj86hZaXLMo4lSR2DHfr0tYuNxPrF2ZOSzHwfJ/pqJvtXJvOLZOY67na6JMlo389k/1aS7jP0nXFg4Ag55jKN5asyjrGj/6CR7yhTfZdEvyXTa/ft916XZOZ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      "text/plain": [
       "<Figure size 1440x864 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid_kws = {\"width_ratios\": (.22, .22, .22, .26), \"hspace\": .2}\n",
    "fig, axes = plt.subplots(3, 4, figsize=(20, 12), gridspec_kw=grid_kws, sharex=True, sharey=True)\n",
    "\n",
    "# mod_funny, non_mod_funny, _, _ = funny_attention_ss_pos\n",
    "# mod_serious, non_mod_serious, _, _ = serious_attention_ss_pos\n",
    "# mod_funny, non_mod_funny, _, _ = funny_attention_siamese_pos\n",
    "# mod_serious, non_mod_serious, _, _ = serious_attention_siamese_pos\n",
    "\n",
    "mod_funny, non_mod_funny, _, _ = funny_attention_base_pos\n",
    "mod_serious, non_mod_serious, _, _ = serious_attention_base_pos\n",
    "\n",
    "sns.heatmap(np.mean(np.array(mod_funny), axis=0), cbar=False, linewidth=0.5, vmin=0.0, vmax=0.2, ax=axes[0][0])\n",
    "# axes[0].set_title('Average attention on chunk \\n for funny for BERT-1S', fontsize=14)\n",
    "axes[0][0].set_title('(a) Modified chunk\\nfunny sentence', fontsize=24)\n",
    "sns.heatmap(np.mean(np.array(non_mod_funny), axis=0), cbar=False, linewidth=0.5, vmin=0.0, vmax=0.2, ax=axes[0][1])\n",
    "axes[0][1].set_title('(b) Non-modified chunk\\nfunny sentence', fontsize=24)\n",
    "sns.heatmap(np.mean(np.array(mod_serious), axis=0), cbar=False, linewidth=0.5, vmin=0.0, vmax=0.2, ax=axes[0][2])\n",
    "axes[0][2].set_title('(c) Modified chunk\\nserious sentence', fontsize=24)\n",
    "sns.heatmap(np.mean(np.array(non_mod_serious), axis=0), linewidth=0.5, vmin=0.0, vmax=0.2, ax=axes[0][3])\n",
    "axes[0][3].set_title('(d) Non-modified chunk\\nserious sentence', fontsize=24)\n",
    "\n",
    "axes[0][0].set_yticklabels([str(i+1) for i in range(12)])\n",
    "axes[0][0].set_ylabel('BERT \\n Layers', fontsize=23)\n",
    "[axes[0][i].set_xlabel('', fontsize=22) for i in range(4)]\n",
    "a = [axes[0][i].set_xticklabels([str(i+1) for i in range(12)]) for i in range(4)]\n",
    "\n",
    "\n",
    "mod_funny, non_mod_funny, _, _ = funny_attention_ss_pos\n",
    "mod_serious, non_mod_serious, _, _ = serious_attention_ss_pos\n",
    "\n",
    "sns.heatmap(np.mean(np.array(mod_funny), axis=0), cbar=False, linewidth=0.5, vmin=0.0, vmax=0.2, ax=axes[1][0])\n",
    "# axes[0].set_title('Average attention on chunk \\n for funny for BERT-1S', fontsize=14)\n",
    "# axes[1][0].set_title('a) Modified chunk\\nfunny sentence', fontsize=22)\n",
    "sns.heatmap(np.mean(np.array(non_mod_funny), axis=0), cbar=False, linewidth=0.5, vmin=0.0, vmax=0.2, ax=axes[1][1])\n",
    "# axes[1][1].set_title('b) non-modified chunk\\nfunny sentence', fontsize=22)\n",
    "sns.heatmap(np.mean(np.array(mod_serious), axis=0), cbar=False, linewidth=0.5, vmin=0.0, vmax=0.2, ax=axes[1][2])\n",
    "# axes[1][2].set_title('c) modified chunk\\nserious sentence', fontsize=22)\n",
    "sns.heatmap(np.mean(np.array(non_mod_serious), axis=0), linewidth=0.5, vmin=0.0, vmax=0.2, ax=axes[1][3])\n",
    "# axes[1][3].set_title('d) non-modified chunk\\nserious sentence', fontsize=22)\n",
    "\n",
    "axes[1][0].set_yticklabels([str(i+1) for i in range(12)])\n",
    "axes[1][0].set_ylabel('BERT-1S \\n Layers', fontsize=23)\n",
    "[axes[1][i].set_xlabel('', fontsize=22) for i in range(4)]\n",
    "a = [axes[1][i].set_xticklabels([str(i+1) for i in range(12)]) for i in range(4)]\n",
    "\n",
    "mod_funny, non_mod_funny, _, _ = funny_attention_siamese_pos\n",
    "mod_serious, non_mod_serious, _, _ = serious_attention_siamese_pos\n",
    "\n",
    "sns.heatmap(np.mean(np.array(mod_funny), axis=0), cbar=False, linewidth=0.5, vmin=0.0, vmax=0.2, ax=axes[2][0])\n",
    "# axes[0].set_title('Average attention on chunk \\n for funny for BERT-1S', fontsize=14)\n",
    "# axes[2][0].set_title('a) Modified chunk\\nfunny sentence', fontsize=22)\n",
    "sns.heatmap(np.mean(np.array(non_mod_funny), axis=0), cbar=False, linewidth=0.5, vmin=0.0, vmax=0.2, ax=axes[2][1])\n",
    "# axes[2][1].set_title('b) non-modified chunk\\nfunny sentence', fontsize=22)\n",
    "sns.heatmap(np.mean(np.array(mod_serious), axis=0), cbar=False, linewidth=0.5, vmin=0.0, vmax=0.2, ax=axes[2][2])\n",
    "# axes[2][2].set_title('c) modified chunk\\nserious sentence', fontsize=22)\n",
    "sns.heatmap(np.mean(np.array(non_mod_serious), axis=0), linewidth=0.5, vmin=0.0, vmax=0.2, ax=axes[2][3])\n",
    "# axes[2][3].set_title('d) non-modified chunk\\nserious sentence', fontsize=22)\n",
    "\n",
    "axes[2][0].set_yticklabels([str(i+1) for i in range(12)])\n",
    "axes[2][0].set_ylabel('BERT-PS \\n Layers', fontsize=23)\n",
    "[axes[2][i].set_xlabel('Heads', fontsize=22) for i in range(4)]\n",
    "a = [axes[2][i].set_xticklabels([str(i+1) for i in range(12)]) for i in range(4)]\n",
    "\n",
    "fig.tight_layout(pad=1)\n",
    "# fig.savefig(\"mod_appendix.pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-56-54a04d4a7551>:14: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  fig.tight_layout(pad=1.1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x216 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid_kws = {\"width_ratios\": (.44, .54), \"hspace\": .2}\n",
    "fig, axes = plt.subplots(1, 2, figsize=(8, 3), sharex=True, sharey=True, gridspec_kw=grid_kws)\n",
    "# fig, axes = plt.subplots(1, 1)\n",
    "sns.heatmap(np.mean(np.array(att_distance_siamese_bert), axis=0), linewidth=0.5, cbar=False,vmin=0.0, vmax=0.3, ax=axes[0])\n",
    "axes[0].set_title('(a) Attention distance between\\n BERT and BERT-PS', fontsize=16)\n",
    "sns.heatmap(np.mean(np.array(att_distance_ss_bert), axis=0), linewidth=0.5,vmin=0.0, vmax=0.3, ax=axes[1])\n",
    "axes[1].set_title('(b) Attention distance between\\n BERT and BERT-1S', fontsize=16)\n",
    "\n",
    "axes[0].set_yticklabels([str(i+1) for i in range(12)])\n",
    "axes[0].set_ylabel('Layers', fontsize=22)\n",
    "[axes[i].set_xlabel('Heads', fontsize=22) for i in range(2)]\n",
    "a = [axes[i].set_xticklabels([str(i+1) for i in range(12)]) for i in range(2)]\n",
    "\n",
    "fig.tight_layout(pad=1.1)\n",
    "# fig.savefig(\"bert_ss_siamese_att_distance.pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid_kws = {\"width_ratios\": (.30, .30, .35), \"hspace\": .2}\n",
    "# fig, axes = plt.subplots(1, 3, figsize=(8, 3), sharex=True, sharey=True, gridspec_kw=grid_kws)\n",
    "fig, ax = plt.subplots(1, 1)\n",
    "\n",
    "sns.heatmap(np.mean(np.array(funny_serious_att_distance_bert_ss), axis=0), linewidth=0.5,vmin=0.0, vmax=0.3, ax=ax)\n",
    "\n",
    "ax.set_yticklabels([str(i+1) for i in range(12)])\n",
    "ax.set_ylabel('Layers', fontsize=22)\n",
    "ax.set_xlabel('Heads', fontsize=22)\n",
    "ax.set_xticklabels([str(i+1) for i in range(12)])\n",
    "\n",
    "fig.tight_layout(pad=1.1)\n",
    "# fig.savefig(\"bert_ss_funny_serious.pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.08798374376656502\n",
      "0.08122996138370851\n",
      "0.07329207462349432\n",
      "0.07420207152252047\n",
      "0.058404006319158916\n",
      "0.12931570419776922\n",
      "0.05603668350478014\n",
      "0.11351793262316671\n",
      "0.0940417503652133\n",
      "0.0635361450544318\n",
      "0.0839427259402339\n",
      "0.08449720069895747\n"
     ]
    }
   ],
   "source": [
    "for i in range(12):\n",
    "    print(np.sum(np.mean(np.array(funny_serious_att_distance_bert_ss), axis=0)[9][i] / np.sum(np.mean(np.array(funny_serious_att_distance_bert_ss), axis=0)[9])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-60-36703fbadf84>:16: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n",
      "  fig.tight_layout(pad=1.1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x216 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "grid_kws = {\"width_ratios\": (.30, .30, .35), \"hspace\": .2}\n",
    "fig, axes = plt.subplots(1, 3, figsize=(12, 3), sharex=True, sharey=True, gridspec_kw=grid_kws)\n",
    "# fig, axes = plt.subplots(1, 1)\n",
    "sns.heatmap(np.mean(np.array(funny_serious_att_distance_bert), axis=0), linewidth=0.5, cbar=False,vmin=0.0, vmax=0.3, ax=axes[0])\n",
    "axes[0].set_title('(a) BERT', fontsize=16)\n",
    "sns.heatmap(np.mean(np.array(funny_serious_att_distance_bert_ss), axis=0), linewidth=0.5,vmin=0.0, cbar=False, vmax=0.3, ax=axes[1])\n",
    "axes[1].set_title('(b) BERT-1S', fontsize=16)\n",
    "sns.heatmap(np.mean(np.array(funny_serious_att_distance_bert_siamese), axis=0), linewidth=0.5,vmin=0.0, vmax=0.3, ax=axes[2])\n",
    "axes[2].set_title('(c) BERT-PS', fontsize=16)\n",
    "\n",
    "axes[0].set_yticklabels([str(i+1) for i in range(12)])\n",
    "axes[0].set_ylabel('Layers', fontsize=22)\n",
    "[axes[i].set_xlabel('Heads', fontsize=22) for i in range(3)]\n",
    "a = [axes[i].set_xticklabels([str(i+1) for i in range(12)]) for i in range(3)]\n",
    "\n",
    "fig.tight_layout(pad=1.1)\n",
    "# fig.savefig(\"bert_ss_siamese_funny_serious.pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "hide_input": false,
  "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": "3.8.6"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
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 },
 "nbformat": 4,
 "nbformat_minor": 2
}