Citation Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "99fec4b3",
   "metadata": {},
   "source": [
    "# SciCite"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "e99ec899",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from utils_package.util_funcs import load_json\n",
    "\n",
    "LED_LARGE_STATS_FILE = \"outputs/predictions/led-large-10200/acl_arc/stats.csv\"\n",
    "LED_BASE_STATS_FILE = \"outputs/predictions/led-base-13800/acl_arc/stats.csv\"\n",
    "ALLENAI_LED_BASE_FILE = \"outputs/predictions/allenai_led-base-16384/acl_arc/stats.csv\"\n",
    "ALLENAI_LED_LARGE_FILE = \"outputs/predictions/allenai_led-large-16384/acl_arc/stats.csv\"\n",
    "ALLENAI_LED_LARGE_ARXIV_FILE = \"outputs/predictions/allenai_led-large-16384-arxiv/acl_arc/stats.csv\"\n",
    "LEAD_FILE = \"outputs/predictions/lead/acl_arc/stats.csv\"\n",
    "TEXTRANK_FILE = \"outputs/predictions/textrank/acl_arc/stats.csv\"\n",
    "LEXRANK_FILE = \"outputs/predictions/lexrank/acl_arc/stats.csv\"\n",
    "TEST_STATS_FILE = \"data/acl_arc/stats-test.csv\"\n",
    "\n",
    "led_large_stats_df = pd.read_csv(LED_LARGE_STATS_FILE)\n",
    "led_base_stats_df = pd.read_csv(LED_BASE_STATS_FILE)\n",
    "allenai_led_base_stats_df = pd.read_csv(ALLENAI_LED_BASE_FILE)\n",
    "allenai_led_large_stats_df = pd.read_csv(ALLENAI_LED_LARGE_FILE)\n",
    "allenai_led_large_arxiv_stats_df = pd.read_csv(ALLENAI_LED_LARGE_ARXIV_FILE)\n",
    "lead_stats_df = pd.read_csv(LEAD_FILE)\n",
    "textrank_stats_df = pd.read_csv(TEXTRANK_FILE)\n",
    "lexrank_stats_df = pd.read_csv(LEXRANK_FILE)\n",
    "\n",
    "test_stats_df = pd.read_csv(TEST_STATS_FILE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "45eac895",
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(\"images\"):\n",
    "    os.mkdir(\"images\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "9d9b7119",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_label_col_ds_index_df(df):\n",
    "    data = {}\n",
    "    for label in labels:\n",
    "        data[label] = []\n",
    "\n",
    "    for _, row in df.iterrows():\n",
    "        data[row[\"label\"]].append(row[\"count\"])\n",
    "\n",
    "    new_df = pd.DataFrame(data, index=datasets)\n",
    "    new_df = new_df.div(new_df.sum(axis=1), axis=0)\n",
    "    return new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "e03b5cb8",
   "metadata": {},
   "outputs": [],
   "source": [
    "datasets = test_stats_df[\"dataset\"].unique().tolist()\n",
    "labels = test_stats_df[\"label\"].unique().tolist()\n",
    "\n",
    "test_stats_df = create_label_col_ds_index_df(test_stats_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "2b2bf6e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = {}\n",
    "for label in labels:\n",
    "    data[label] = []\n",
    "\n",
    "ds_names = [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\"]\n",
    "ds_name_mappings = [\"Aburaed\", \"Chen Delve\", \"Chen S2ORC\", \"Lu\",\n",
    "                    \"Total Macro\", \"Total Micro\", \"Xing\"]\n",
    "\n",
    "df = test_stats_df.copy()\n",
    "df.index = ds_names\n",
    "\n",
    "ax = df.plot.bar(stacked=True, rot=0)\n",
    "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n",
    "\n",
    "ax.figure.savefig(\"images/acl-arc-test-data-vert.png\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "3506b995",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_name_mappings = [\"ABURAED\", \"CHEN Delve\", \"CHEN S2ORC\", \"LU\",\n",
    "                    \"Total\", \"Total Micro\", \"XING\"]\n",
    "\n",
    "# Dict for holding the sorting order\n",
    "sort_dict = {\"ABURAED\": 0, \"CHEN Delve\": 1, \"CHEN S2ORC\": 2, \"LU\": 3, \n",
    "             \"XING\": 4, \"Total Macro\": 5} \n",
    "\n",
    "dataset_df = test_stats_df.copy()\n",
    "\n",
    "dataset_df.index = ds_name_mappings\n",
    "dataset_df.drop(\"Total Micro\", inplace=True)\n",
    "dataset_df.sort_index(key=lambda x: x.map(sort_dict), inplace=True, ascending=False)\n",
    "\n",
    "ax = dataset_df.plot.barh(stacked=True)\n",
    "plt.legend(loc='center left', bbox_to_anchor=(0.25, -0.25))\n",
    "\n",
    "ax.figure.savefig(\"images/acl-arc-test-data-hor.png\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "8de9d4e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_stats_df = pd.read_csv(TEST_STATS_FILE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "93e4c6f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "datasets = led_large_stats_df[\"dataset\"].unique().tolist()\n",
    "labels = led_large_stats_df[\"label\"].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "c9b9ff38",
   "metadata": {},
   "outputs": [],
   "source": [
    "led_large_stats_df = create_label_col_ds_index_df(led_large_stats_df)\n",
    "led_base_stats_df = create_label_col_ds_index_df(led_base_stats_df)\n",
    "allenai_led_base_stats_df = create_label_col_ds_index_df(allenai_led_base_stats_df)\n",
    "allenai_led_large_stats_df = create_label_col_ds_index_df(allenai_led_large_stats_df)\n",
    "allenai_led_large_arxiv_stats_df = create_label_col_ds_index_df(allenai_led_large_arxiv_stats_df)\n",
    "lead_stats_df = create_label_col_ds_index_df(lead_stats_df)\n",
    "textrank_stats_df = create_label_col_ds_index_df(textrank_stats_df)\n",
    "lexrank_stats_df = create_label_col_ds_index_df(lexrank_stats_df)\n",
    "\n",
    "test_stats_df = create_label_col_ds_index_df(test_stats_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "50105ad1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Test Data'}>"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(15,12))\n",
    "\n",
    "axes[0,0].set_title(\"Led Large\")\n",
    "axes[0,1].set_title(\"Led Base\")\n",
    "axes[1,0].set_title(\"Test Data\")\n",
    "\n",
    "led_large_stats_df.plot.bar(ax=axes[0,0], stacked=True)\n",
    "led_base_stats_df.plot.bar(ax=axes[0,1], stacked=True)\n",
    "test_stats_df.plot.bar(ax=axes[1,0], stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "1c66cb81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = {}\n",
    "for label in labels:\n",
    "    data[label] = []\n",
    "\n",
    "model_names = [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\"]\n",
    "model_name_mappings = [\"Led Large\", \"Led Base\", \"AllenAI Led Base\", \"AllenAI Led Large\", \n",
    "               \"AllenAI Led Large ArXiv\", \"Lead\", \"TextRank\", \"LexRank\", \"Test Data\"]\n",
    "\n",
    "dfs = [led_large_stats_df, led_base_stats_df, allenai_led_base_stats_df, \n",
    "       allenai_led_large_stats_df, allenai_led_large_arxiv_stats_df, \n",
    "       lead_stats_df, textrank_stats_df, lexrank_stats_df, test_stats_df]\n",
    "\n",
    "for df in dfs:\n",
    "    for idx, row in df.iterrows():\n",
    "        if idx == \"total_macro\":\n",
    "            for label in labels:\n",
    "                data[label].append(row[label])\n",
    "\n",
    "models_df = pd.DataFrame(data, index=model_names)\n",
    "models_df = models_df.div(models_df.sum(axis=1), axis=0)\n",
    "\n",
    "ax = models_df.plot.bar(stacked=True, rot=0)\n",
    "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n",
    "\n",
    "ax.figure.savefig(\"images/acl-arc-models-vert.png\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "ad47ff1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_name_mappings = [\"*led-large-arxiv\", \"*led-base\", \"led-base\", \"led-large\", \n",
    "               \"led-large-arxiv\", \"LEAD\", \"TextRank\", \"LexRank\", \"Test Data\"]\n",
    "models_df.index = model_name_mappings\n",
    "\n",
    "ax = models_df.plot.barh(stacked=True)\n",
    "plt.legend(loc='center left', bbox_to_anchor=(0.25, -0.25))\n",
    "\n",
    "ax.figure.savefig(\"images/acl-arc-models-hor.png\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "b1d689a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 450x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Dict for holding the sorting order\n",
    "sort_dict = {\n",
    "    \"LEAD\": 0,\n",
    "    \"TextRank\": 1,\n",
    "    \"LexRank\": 2,\n",
    "    \"led-base\": 3,\n",
    "    \"led-large\": 4,\n",
    "    \"led-large-arxiv\": 5,\n",
    "    \"*led-base\": 6,\n",
    "    \"*led-large-arxiv\": 7,\n",
    "    \"ABURAED\": 8, \n",
    "    \"CHEN Delve\": 9, \n",
    "    \"CHEN S2ORC\": 10, \n",
    "    \"LU\": 11, \n",
    "    \"XING\": 12, \n",
    "    \"Total\": 13\n",
    "} \n",
    "\n",
    "model_name_mappings = [\"*led-large-arxiv\", \"*led-base\", \"led-base\", \"led-large\", \n",
    "               \"led-large-arxiv\", \"LEAD\", \"TextRank\", \"LexRank\", \"Test Data\"]\n",
    "models_df.index = model_name_mappings\n",
    "\n",
    "comb_df = pd.concat([models_df, dataset_df])\n",
    "comb_df.drop([\"Test Data\"], inplace=True)\n",
    "comb_df.sort_index(key=lambda x: x.map(sort_dict), inplace=True, ascending=False)\n",
    "\n",
    "ax = comb_df.plot.barh(stacked=True, figsize=(4.5,5))\n",
    "plt.legend(loc='lower center', bbox_to_anchor=(0.35, -0.23), ncol=3)\n",
    "\n",
    "\n",
    "ax.figure.savefig(\"images/acl-arc-both-hor.png\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "3ba08d23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Models'}>"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(18,6))\n",
    "\n",
    "axes[0].set_title(\"Test Data\")\n",
    "axes[1].set_title(\"Models\")\n",
    "\n",
    "dataset_df.plot.barh(ax=axes[0], stacked=True, legend=False)\n",
    "\n",
    "model_name_mappings = [\"*Led Large\", \"*Led Base\", \"Led Base\", \"Led Large\", \n",
    "               \"Led Large ArXiv\", \"Lead\", \"TextRank\", \"LexRank\", \"Test Data\"]\n",
    "models_df.index = model_name_mappings\n",
    "models_df.plot.barh(ax=axes[1], stacked=True, legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68b41a8d",
   "metadata": {},
   "source": [
    "## KL Divergence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "e1bb03b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_kl_div_data(df):\n",
    "    datasets = [\"aburaed_et_al\", \"chen_et_al_delve\", \"chen_et_al_s2orc\",\n",
    "          \"lu_et_al\", \"xing_et_al\"]\n",
    "    data = []\n",
    "    for ds in datasets:\n",
    "        data.append(df.loc[ds].values.flatten().tolist())\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "9934abfb",
   "metadata": {},
   "outputs": [
    {
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       "#T_38a10_row4_col2 {\n",
       "  background-color: #fc9b7c;\n",
       "  color: #000000;\n",
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       "#T_38a10_row4_col3 {\n",
       "  background-color: #fca285;\n",
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       "</style>\n",
       "<table id=\"T_38a10\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_38a10_level0_col0\" class=\"col_heading level0 col0\" >aburaed</th>\n",
       "      <th id=\"T_38a10_level0_col1\" class=\"col_heading level0 col1\" >chen delve</th>\n",
       "      <th id=\"T_38a10_level0_col2\" class=\"col_heading level0 col2\" >chen s2orc</th>\n",
       "      <th id=\"T_38a10_level0_col3\" class=\"col_heading level0 col3\" >lu</th>\n",
       "      <th id=\"T_38a10_level0_col4\" class=\"col_heading level0 col4\" >xing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_38a10_level0_row0\" class=\"row_heading level0 row0\" >aburaed</th>\n",
       "      <td id=\"T_38a10_row0_col0\" class=\"data row0 col0\" >0.000000</td>\n",
       "      <td id=\"T_38a10_row0_col1\" class=\"data row0 col1\" >0.048176</td>\n",
       "      <td id=\"T_38a10_row0_col2\" class=\"data row0 col2\" >0.248256</td>\n",
       "      <td id=\"T_38a10_row0_col3\" class=\"data row0 col3\" >0.068410</td>\n",
       "      <td id=\"T_38a10_row0_col4\" class=\"data row0 col4\" >0.046705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_38a10_level0_row1\" class=\"row_heading level0 row1\" >chen delve</th>\n",
       "      <td id=\"T_38a10_row1_col0\" class=\"data row1 col0\" >0.048066</td>\n",
       "      <td id=\"T_38a10_row1_col1\" class=\"data row1 col1\" >0.000000</td>\n",
       "      <td id=\"T_38a10_row1_col2\" class=\"data row1 col2\" >0.197122</td>\n",
       "      <td id=\"T_38a10_row1_col3\" class=\"data row1 col3\" >0.011889</td>\n",
       "      <td id=\"T_38a10_row1_col4\" class=\"data row1 col4\" >0.066662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_38a10_level0_row2\" class=\"row_heading level0 row2\" >chen s2orc</th>\n",
       "      <td id=\"T_38a10_row2_col0\" class=\"data row2 col0\" >0.253106</td>\n",
       "      <td id=\"T_38a10_row2_col1\" class=\"data row2 col1\" >0.217094</td>\n",
       "      <td id=\"T_38a10_row2_col2\" class=\"data row2 col2\" >0.000000</td>\n",
       "      <td id=\"T_38a10_row2_col3\" class=\"data row2 col3\" >0.127562</td>\n",
       "      <td id=\"T_38a10_row2_col4\" class=\"data row2 col4\" >0.080835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_38a10_level0_row3\" class=\"row_heading level0 row3\" >lu</th>\n",
       "      <td id=\"T_38a10_row3_col0\" class=\"data row3 col0\" >0.061257</td>\n",
       "      <td id=\"T_38a10_row3_col1\" class=\"data row3 col1\" >0.012455</td>\n",
       "      <td id=\"T_38a10_row3_col2\" class=\"data row3 col2\" >0.121747</td>\n",
       "      <td id=\"T_38a10_row3_col3\" class=\"data row3 col3\" >0.000000</td>\n",
       "      <td id=\"T_38a10_row3_col4\" class=\"data row3 col4\" >0.040373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_38a10_level0_row4\" class=\"row_heading level0 row4\" >xing</th>\n",
       "      <td id=\"T_38a10_row4_col0\" class=\"data row4 col0\" >0.050513</td>\n",
       "      <td id=\"T_38a10_row4_col1\" class=\"data row4 col1\" >0.068434</td>\n",
       "      <td id=\"T_38a10_row4_col2\" class=\"data row4 col2\" >0.086569</td>\n",
       "      <td id=\"T_38a10_row4_col3\" class=\"data row4 col3\" >0.041648</td>\n",
       "      <td id=\"T_38a10_row4_col4\" class=\"data row4 col4\" >0.000000</td>\n",
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       "  </tbody>\n",
       "</table>\n"
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       "<pandas.io.formats.style.Styler at 0x7fdd8b6d9be0>"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy.special import rel_entr\n",
    "\n",
    "test_stats_df = pd.read_csv(TEST_STATS_FILE)\n",
    "\n",
    "datasets = test_stats_df[\"dataset\"].unique().tolist()\n",
    "labels = test_stats_df[\"label\"].unique().tolist()\n",
    "\n",
    "test_stats_df = create_label_col_ds_index_df(test_stats_df)\n",
    "dist_data = get_kl_div_data(test_stats_df)\n",
    "\n",
    "ds_names = [\"aburaed\", \"chen delve\", \"chen s2orc\", \"lu\", \"xing\"]\n",
    "res = []\n",
    "for i, d in enumerate(dist_data):\n",
    "    res_i = []\n",
    "    for j, e in enumerate(dist_data):\n",
    "        #calculate (P || Q)\n",
    "        res_i.append(sum(rel_entr(d, e)))\n",
    "    res.append(res_i)\n",
    "    \n",
    "df = pd.DataFrame(res, index=ds_names, columns=ds_names)\n",
    "df.style.background_gradient(cmap=\"Reds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "122f1459",
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_c113d_level0_col0\" class=\"col_heading level0 col0\" >aburaed test</th>\n",
       "      <th id=\"T_c113d_level0_col1\" class=\"col_heading level0 col1\" >chen delve test</th>\n",
       "      <th id=\"T_c113d_level0_col2\" class=\"col_heading level0 col2\" >chen s2orc test</th>\n",
       "      <th id=\"T_c113d_level0_col3\" class=\"col_heading level0 col3\" >lu test</th>\n",
       "      <th id=\"T_c113d_level0_col4\" class=\"col_heading level0 col4\" >xing test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_c113d_level0_row0\" class=\"row_heading level0 row0\" >aburaed model</th>\n",
       "      <td id=\"T_c113d_row0_col0\" class=\"data row0 col0\" >0.424975</td>\n",
       "      <td id=\"T_c113d_row0_col1\" class=\"data row0 col1\" >0.419048</td>\n",
       "      <td id=\"T_c113d_row0_col2\" class=\"data row0 col2\" >0.145346</td>\n",
       "      <td id=\"T_c113d_row0_col3\" class=\"data row0 col3\" >0.322574</td>\n",
       "      <td id=\"T_c113d_row0_col4\" class=\"data row0 col4\" >0.205080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c113d_level0_row1\" class=\"row_heading level0 row1\" >chen delve model</th>\n",
       "      <td id=\"T_c113d_row1_col0\" class=\"data row1 col0\" >0.437917</td>\n",
       "      <td id=\"T_c113d_row1_col1\" class=\"data row1 col1\" >0.250859</td>\n",
       "      <td id=\"T_c113d_row1_col2\" class=\"data row1 col2\" >0.197538</td>\n",
       "      <td id=\"T_c113d_row1_col3\" class=\"data row1 col3\" >0.220236</td>\n",
       "      <td id=\"T_c113d_row1_col4\" class=\"data row1 col4\" >0.239821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c113d_level0_row2\" class=\"row_heading level0 row2\" >chen s2orc model</th>\n",
       "      <td id=\"T_c113d_row2_col0\" class=\"data row2 col0\" >0.534609</td>\n",
       "      <td id=\"T_c113d_row2_col1\" class=\"data row2 col1\" >0.422974</td>\n",
       "      <td id=\"T_c113d_row2_col2\" class=\"data row2 col2\" >0.105285</td>\n",
       "      <td id=\"T_c113d_row2_col3\" class=\"data row2 col3\" >0.313206</td>\n",
       "      <td id=\"T_c113d_row2_col4\" class=\"data row2 col4\" >0.258143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c113d_level0_row3\" class=\"row_heading level0 row3\" >lu model</th>\n",
       "      <td id=\"T_c113d_row3_col0\" class=\"data row3 col0\" >0.337646</td>\n",
       "      <td id=\"T_c113d_row3_col1\" class=\"data row3 col1\" >0.255792</td>\n",
       "      <td id=\"T_c113d_row3_col2\" class=\"data row3 col2\" >0.027149</td>\n",
       "      <td id=\"T_c113d_row3_col3\" class=\"data row3 col3\" >0.169294</td>\n",
       "      <td id=\"T_c113d_row3_col4\" class=\"data row3 col4\" >0.129714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c113d_level0_row4\" class=\"row_heading level0 row4\" >xing model</th>\n",
       "      <td id=\"T_c113d_row4_col0\" class=\"data row4 col0\" >0.271760</td>\n",
       "      <td id=\"T_c113d_row4_col1\" class=\"data row4 col1\" >0.248596</td>\n",
       "      <td id=\"T_c113d_row4_col2\" class=\"data row4 col2\" >0.039664</td>\n",
       "      <td id=\"T_c113d_row4_col3\" class=\"data row4 col3\" >0.159324</td>\n",
       "      <td id=\"T_c113d_row4_col4\" class=\"data row4 col4\" >0.094208</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fdd8ba170a0>"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dist_led_large_data = get_kl_div_data(led_large_stats_df)\n",
    "dist_test_data = get_kl_div_data(test_stats_df)\n",
    "\n",
    "col_names = [\"aburaed test\", \"chen delve test\", \"chen s2orc test\", \"lu test\", \"xing test\"]\n",
    "index_names = [\"aburaed model\", \"chen delve model\", \"chen s2orc model\", \"lu model\", \"xing model\"]\n",
    "\n",
    "res = []\n",
    "for i, d in enumerate(dist_led_large_data):\n",
    "    res_i = []\n",
    "    for j, e in enumerate(dist_test_data):\n",
    "        #calculate (P || Q)\n",
    "        res_i.append(sum(rel_entr(d, e)))\n",
    "    res.append(res_i)\n",
    "    \n",
    "df = pd.DataFrame(res, index=index_names, columns=col_names)\n",
    "df.style.background_gradient(cmap=\"Reds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "1636cf49",
   "metadata": {},
   "outputs": [
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       "#T_d23f0_row4_col1 {\n",
       "  background-color: #fb6c4c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d23f0_row4_col3 {\n",
       "  background-color: #f6553c;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d23f0_row4_col4 {\n",
       "  background-color: #d01d1f;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d23f0_row5_col0, #T_d23f0_row5_col1, #T_d23f0_row5_col2, #T_d23f0_row5_col3, #T_d23f0_row5_col4 {\n",
       "  background-color: #67000d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_d23f0_row6_col0 {\n",
       "  background-color: #fff0e8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d23f0_row6_col1, #T_d23f0_row6_col3, #T_d23f0_row7_col0, #T_d23f0_row7_col2, #T_d23f0_row7_col4 {\n",
       "  background-color: #fff5f0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d23f0_row6_col2 {\n",
       "  background-color: #ffece4;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d23f0_row6_col4 {\n",
       "  background-color: #ffeee6;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d23f0_row7_col1 {\n",
       "  background-color: #feeae0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_d23f0_row7_col3 {\n",
       "  background-color: #fff1ea;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_d23f0\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_d23f0_level0_col0\" class=\"col_heading level0 col0\" >aburaed</th>\n",
       "      <th id=\"T_d23f0_level0_col1\" class=\"col_heading level0 col1\" >chen delve</th>\n",
       "      <th id=\"T_d23f0_level0_col2\" class=\"col_heading level0 col2\" >chen s2orc</th>\n",
       "      <th id=\"T_d23f0_level0_col3\" class=\"col_heading level0 col3\" >lu</th>\n",
       "      <th id=\"T_d23f0_level0_col4\" class=\"col_heading level0 col4\" >xing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_d23f0_level0_row0\" class=\"row_heading level0 row0\" >LEAD</th>\n",
       "      <td id=\"T_d23f0_row0_col0\" class=\"data row0 col0\" >0.475845</td>\n",
       "      <td id=\"T_d23f0_row0_col1\" class=\"data row0 col1\" >0.277821</td>\n",
       "      <td id=\"T_d23f0_row0_col2\" class=\"data row0 col2\" >0.172033</td>\n",
       "      <td id=\"T_d23f0_row0_col3\" class=\"data row0 col3\" >0.236995</td>\n",
       "      <td id=\"T_d23f0_row0_col4\" class=\"data row0 col4\" >0.263566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d23f0_level0_row1\" class=\"row_heading level0 row1\" >TextRank</th>\n",
       "      <td id=\"T_d23f0_row1_col0\" class=\"data row1 col0\" >0.553110</td>\n",
       "      <td id=\"T_d23f0_row1_col1\" class=\"data row1 col1\" >0.366244</td>\n",
       "      <td id=\"T_d23f0_row1_col2\" class=\"data row1 col2\" >0.205589</td>\n",
       "      <td id=\"T_d23f0_row1_col3\" class=\"data row1 col3\" >0.320067</td>\n",
       "      <td id=\"T_d23f0_row1_col4\" class=\"data row1 col4\" >0.305640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d23f0_level0_row2\" class=\"row_heading level0 row2\" >LexRank</th>\n",
       "      <td id=\"T_d23f0_row2_col0\" class=\"data row2 col0\" >0.529496</td>\n",
       "      <td id=\"T_d23f0_row2_col1\" class=\"data row2 col1\" >0.350822</td>\n",
       "      <td id=\"T_d23f0_row2_col2\" class=\"data row2 col2\" >0.199423</td>\n",
       "      <td id=\"T_d23f0_row2_col3\" class=\"data row2 col3\" >0.308436</td>\n",
       "      <td id=\"T_d23f0_row2_col4\" class=\"data row2 col4\" >0.292229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d23f0_level0_row3\" class=\"row_heading level0 row3\" >led-base</th>\n",
       "      <td id=\"T_d23f0_row3_col0\" class=\"data row3 col0\" >0.522122</td>\n",
       "      <td id=\"T_d23f0_row3_col1\" class=\"data row3 col1\" >0.302813</td>\n",
       "      <td id=\"T_d23f0_row3_col2\" class=\"data row3 col2\" >0.193281</td>\n",
       "      <td id=\"T_d23f0_row3_col3\" class=\"data row3 col3\" >0.261236</td>\n",
       "      <td id=\"T_d23f0_row3_col4\" class=\"data row3 col4\" >0.302472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d23f0_level0_row4\" class=\"row_heading level0 row4\" >led-large</th>\n",
       "      <td id=\"T_d23f0_row4_col0\" class=\"data row4 col0\" >0.569735</td>\n",
       "      <td id=\"T_d23f0_row4_col1\" class=\"data row4 col1\" >0.351181</td>\n",
       "      <td id=\"T_d23f0_row4_col2\" class=\"data row4 col2\" >0.210725</td>\n",
       "      <td id=\"T_d23f0_row4_col3\" class=\"data row4 col3\" >0.306665</td>\n",
       "      <td id=\"T_d23f0_row4_col4\" class=\"data row4 col4\" >0.324663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d23f0_level0_row5\" class=\"row_heading level0 row5\" >led-large-arxiv</th>\n",
       "      <td id=\"T_d23f0_row5_col0\" class=\"data row5 col0\" >0.661334</td>\n",
       "      <td id=\"T_d23f0_row5_col1\" class=\"data row5 col1\" >0.440567</td>\n",
       "      <td id=\"T_d23f0_row5_col2\" class=\"data row5 col2\" >0.294325</td>\n",
       "      <td id=\"T_d23f0_row5_col3\" class=\"data row5 col3\" >0.400391</td>\n",
       "      <td id=\"T_d23f0_row5_col4\" class=\"data row5 col4\" >0.391082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d23f0_level0_row6\" class=\"row_heading level0 row6\" >*led-base</th>\n",
       "      <td id=\"T_d23f0_row6_col0\" class=\"data row6 col0\" >0.367713</td>\n",
       "      <td id=\"T_d23f0_row6_col1\" class=\"data row6 col1\" >0.264103</td>\n",
       "      <td id=\"T_d23f0_row6_col2\" class=\"data row6 col2\" >0.072389</td>\n",
       "      <td id=\"T_d23f0_row6_col3\" class=\"data row6 col3\" >0.188373</td>\n",
       "      <td id=\"T_d23f0_row6_col4\" class=\"data row6 col4\" >0.153483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d23f0_level0_row7\" class=\"row_heading level0 row7\" >*led-large-arxiv</th>\n",
       "      <td id=\"T_d23f0_row7_col0\" class=\"data row7 col0\" >0.358103</td>\n",
       "      <td id=\"T_d23f0_row7_col1\" class=\"data row7 col1\" >0.276175</td>\n",
       "      <td id=\"T_d23f0_row7_col2\" class=\"data row7 col2\" >0.059718</td>\n",
       "      <td id=\"T_d23f0_row7_col3\" class=\"data row7 col3\" >0.193648</td>\n",
       "      <td id=\"T_d23f0_row7_col4\" class=\"data row7 col4\" >0.142114</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7fdd9274a640>"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "led_large = led_large_stats_df.loc[\"total_macro\"].values.flatten().tolist()\n",
    "led_base = led_base_stats_df.loc[\"total_macro\"].values.flatten().tolist()\n",
    "allenai_led_base = allenai_led_base_stats_df.loc[\"total_macro\"].values.flatten().tolist()\n",
    "allenai_led_large = allenai_led_large_stats_df.loc[\"total_macro\"].values.flatten().tolist()\n",
    "allenai_led_large_arxiv = allenai_led_large_arxiv_stats_df.loc[\"total_macro\"].values.flatten().tolist()\n",
    "lead = lead_stats_df.loc[\"total_macro\"].values.flatten().tolist()\n",
    "textrank = textrank_stats_df.loc[\"total_macro\"].values.flatten().tolist()\n",
    "lexrank = lexrank_stats_df.loc[\"total_macro\"].values.flatten().tolist()\n",
    "\n",
    "models_data = [lead, textrank, lexrank, allenai_led_base, allenai_led_large, \n",
    "        allenai_led_large_arxiv, led_base, led_large]\n",
    "\n",
    "col_names = [\"ABURAED\", \"CHEN Delve\", \"CHEN S2ORC\", \"LU\", \"XING\"]\n",
    "col_names = [\"aburaed\", \"chen delve\", \"chen s2orc\", \"lu\", \"xing\"]\n",
    "index_names = [\"LEAD\", \"TextRank\", \"LexRank\", \"led-base\", \"led-large\", \n",
    "               \"led-large-arxiv\", \"*led-base\", \"*led-large-arxiv\"]\n",
    "\n",
    "res = []\n",
    "for i, d in enumerate(models_data):\n",
    "    res_i = []\n",
    "    for j, e in enumerate(dist_test_data):\n",
    "        #calculate (P || Q)\n",
    "        res_i.append(sum(rel_entr(d, e)))\n",
    "    res.append(res_i)\n",
    "    \n",
    "df = pd.DataFrame(res, index=index_names, columns=col_names)\n",
    "df.style.background_gradient(cmap=\"Reds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "5dec91f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "heatmap = sns.heatmap(df, annot=True, cmap=\"Reds\")\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "fig = heatmap.get_figure()\n",
    "fig.savefig(\"images/acl-arc-kl-div-heatmap.png\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "609c78d1",
   "metadata": {},
   "source": [
    "# CORWA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "3f9dddfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "LED_LARGE_STATS_FILE = \"outputs/predictions/led-large-10200/corwa/outputs/stats.csv\"\n",
    "LED_BASE_STATS_FILE = \"outputs/predictions/led-base-13800/corwa/outputs/stats.csv\"\n",
    "ALLENAI_LED_BASE_FILE = \"outputs/predictions/allenai_led-base-16384/corwa/outputs/stats.csv\"\n",
    "ALLENAI_LED_LARGE_FILE = \"outputs/predictions/allenai_led-large-16384/corwa/outputs/stats.csv\"\n",
    "ALLENAI_LED_LARGE_ARXIV_FILE = \"outputs/predictions/allenai_led-large-16384-arxiv/corwa/outputs/stats.csv\"\n",
    "LEAD_FILE = \"outputs/predictions/lead/corwa/outputs/stats.csv\"\n",
    "LEXRANK_FILE = \"outputs/predictions/lexrank/corwa/outputs/stats.csv\"\n",
    "TEXTRANK_FILE = \"outputs/predictions/textrank/corwa/outputs/stats.csv\"\n",
    "TEST_STATS_FILE = \"data/corwa/outputs/test/stats.csv\"\n",
    "\n",
    "led_large_stats_df = pd.read_csv(LED_LARGE_STATS_FILE)\n",
    "led_base_stats_df = pd.read_csv(LED_BASE_STATS_FILE)\n",
    "allenai_led_base_stats_df = pd.read_csv(ALLENAI_LED_BASE_FILE)\n",
    "allenai_led_large_stats_df = pd.read_csv(ALLENAI_LED_LARGE_FILE)\n",
    "allenai_led_large_arxiv_stats_df = pd.read_csv(ALLENAI_LED_LARGE_ARXIV_FILE)\n",
    "lead_stats_df = pd.read_csv(LEAD_FILE)\n",
    "lexrank_stats_df = pd.read_csv(LEXRANK_FILE)\n",
    "textrank_stats_df = pd.read_csv(TEXTRANK_FILE)\n",
    "test_stats_df = pd.read_csv(TEST_STATS_FILE)\n",
    "\n",
    "df_list = [led_large_stats_df, led_base_stats_df, allenai_led_base_stats_df, \n",
    "           allenai_led_large_stats_df, allenai_led_large_arxiv_stats_df, \n",
    "           lead_stats_df, lexrank_stats_df, textrank_stats_df, test_stats_df]\n",
    "model_names = [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\"]\n",
    "model_name_mappings = [\"Led Large\", \"Led Base\", \"AllenAI Led Base\", \"AllenAI Led Large\", \n",
    "               \"AllenAI Led Large ArXiv\", \"Lead\", \"TextRank\", \"LexRank\", \"Test Data\"]\n",
    "    \n",
    "datasets = df_list[0][\"dataset\"].unique().tolist()\n",
    "labels = df_list[0][\"label\"].unique().tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "9c3add0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_label_col_ds_index_df(df):\n",
    "    data = {}\n",
    "    for label in labels:\n",
    "        data[label] = []\n",
    "\n",
    "    for _, row in df.iterrows():\n",
    "        data[row[\"label\"]].append(row[\"count\"])\n",
    "\n",
    "    new_df = pd.DataFrame(data, index=datasets)\n",
    "    new_df = new_df.div(new_df.sum(axis=1), axis=0)\n",
    "    return new_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "83c85292",
   "metadata": {},
   "outputs": [],
   "source": [
    "for idx in range(len(df_list)):\n",
    "    df_list[idx] = create_label_col_ds_index_df(df_list[idx])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "7960c545",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['aburaed_et_al',\n",
       " 'chen_et_al_delve',\n",
       " 'chen_et_al_s2orc',\n",
       " 'lu_et_al',\n",
       " 'total_macro',\n",
       " 'total_micro',\n",
       " 'xing_et_al']"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "7b4671bc",
   "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>Multi_summ</th>\n",
       "      <th>Narrative_cite</th>\n",
       "      <th>Other</th>\n",
       "      <th>Reflection</th>\n",
       "      <th>Single_summ</th>\n",
       "      <th>Transition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>aburaed_et_al</th>\n",
       "      <td>0.013228</td>\n",
       "      <td>0.240741</td>\n",
       "      <td>0.029101</td>\n",
       "      <td>0.050265</td>\n",
       "      <td>0.420635</td>\n",
       "      <td>0.246032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chen_et_al_delve</th>\n",
       "      <td>0.007726</td>\n",
       "      <td>0.137451</td>\n",
       "      <td>0.049891</td>\n",
       "      <td>0.133915</td>\n",
       "      <td>0.261458</td>\n",
       "      <td>0.409559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chen_et_al_s2orc</th>\n",
       "      <td>0.001286</td>\n",
       "      <td>0.164324</td>\n",
       "      <td>0.312963</td>\n",
       "      <td>0.204166</td>\n",
       "      <td>0.098664</td>\n",
       "      <td>0.218597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lu_et_al</th>\n",
       "      <td>0.015592</td>\n",
       "      <td>0.160143</td>\n",
       "      <td>0.015715</td>\n",
       "      <td>0.119687</td>\n",
       "      <td>0.394223</td>\n",
       "      <td>0.294641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_macro</th>\n",
       "      <td>0.007697</td>\n",
       "      <td>0.180549</td>\n",
       "      <td>0.086668</td>\n",
       "      <td>0.128958</td>\n",
       "      <td>0.278213</td>\n",
       "      <td>0.317916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_micro</th>\n",
       "      <td>0.007577</td>\n",
       "      <td>0.154291</td>\n",
       "      <td>0.141097</td>\n",
       "      <td>0.153538</td>\n",
       "      <td>0.240328</td>\n",
       "      <td>0.303168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xing_et_al</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.268868</td>\n",
       "      <td>0.037736</td>\n",
       "      <td>0.198113</td>\n",
       "      <td>0.191038</td>\n",
       "      <td>0.304245</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Multi_summ  Narrative_cite     Other  Reflection  \\\n",
       "aburaed_et_al       0.013228        0.240741  0.029101    0.050265   \n",
       "chen_et_al_delve    0.007726        0.137451  0.049891    0.133915   \n",
       "chen_et_al_s2orc    0.001286        0.164324  0.312963    0.204166   \n",
       "lu_et_al            0.015592        0.160143  0.015715    0.119687   \n",
       "total_macro         0.007697        0.180549  0.086668    0.128958   \n",
       "total_micro         0.007577        0.154291  0.141097    0.153538   \n",
       "xing_et_al          0.000000        0.268868  0.037736    0.198113   \n",
       "\n",
       "                  Single_summ  Transition  \n",
       "aburaed_et_al        0.420635    0.246032  \n",
       "chen_et_al_delve     0.261458    0.409559  \n",
       "chen_et_al_s2orc     0.098664    0.218597  \n",
       "lu_et_al             0.394223    0.294641  \n",
       "total_macro          0.278213    0.317916  \n",
       "total_micro          0.240328    0.303168  \n",
       "xing_et_al           0.191038    0.304245  "
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_list[idx].head(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "be47a1b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = {}\n",
    "for label in labels:\n",
    "    data[label] = []\n",
    "\n",
    "for df in df_list:\n",
    "    for idx, row in df.iterrows():\n",
    "        if idx == \"total_macro\":\n",
    "            for label in labels:\n",
    "                data[label].append(row[label])\n",
    "\n",
    "models_df = pd.DataFrame(data, index=model_names)\n",
    "models_df = models_df.div(models_df.sum(axis=1), axis=0)\n",
    "\n",
    "ax = models_df.plot.bar(stacked=True, rot=0)\n",
    "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n",
    "\n",
    "ax.figure.savefig(\"images/corwa-models-vert.png\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "3741e1b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_name_mappings = [\"*led-large-arxiv\", \"*led-base\", \"led-base\", \"led-large\", \n",
    "               \"led-large-arxiv\", \"LEAD\", \"TextRank\", \"LexRank\", \"Test Data\"]\n",
    "models_df.index = model_name_mappings\n",
    "\n",
    "ax = models_df.plot.barh(stacked=True)\n",
    "plt.legend(loc='center left', bbox_to_anchor=(0.25, -0.25))\n",
    "\n",
    "ax.figure.savefig(\"images/corwa-models-hor.png\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "5221e7c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5u7uX+8jOzrZ1iXaPD7cBAADcAvz9/ZWRkVHhflSM4AsAAHALcHJyUnBwsK3LuKUx1QEAAACGQPAFAACAIRB8AQAAYAgEXwAAABgCwRcAAACGQPAFAAC4DSUnJ8vLy8vWZUiSsrKyZDKZKlyOrSawnBkAALAfL3nW8PXOWNU8OjpaKSkpSkxM1OTJk83b165dqwEDBqikpKS6K6yUwMBAjR8/XuPHjzdvGzx4sPr06WOTeq4VEBCgnJwcNWjQQJKUnp6ubt266dSpUzUazhnxBQAAsIKLi4teeeUVnTp1qtrOWVhYWGpbSUmJLl++XOVzurq6qlGjRjdSVrVxdHSUr6+vnJxsO+ZK8AUAALBCjx495Ovrq8TExDL3//rrrxoyZIgaN24sNzc3hYWF6V//+pdFm65duyomJkbjx49XgwYNFBkZqfT0dJlMJm3cuFFt27aVs7Oztm3bpkOHDqlfv37y8fGRu7u77rnnHm3dutXiXD/99JMmTJggk8kkk8kkyXKqw4EDB2QymbRv3z6LOl5//XU1a9bM/Py7775T79695e7uLh8fHz366KM6ceJEpfqluLhYc+bMUXBwsJydnXXnnXdq5syZkiynOmRlZalbt26SJG9vb5lMJkVHR5vPkZiYqKCgILm6uio8PFzvv/9+pa5fGQRfAAAAKzg6OmrWrFl688039d///rfU/osXL6pt27basGGDvvvuOz3xxBN69NFHtXPnTot2KSkpql27trZv366kpCTz9smTJ2v27NnKzMxUq1atdP78efXp00dpaWnas2ePevXqpaioKGVnZ0uSVq9erTvuuEPTp09XTk6OcnJyStV01113qV27dlqxYoXF9hUrVmjo0KGSpNOnT6t79+6KiIjQrl27tGnTJuXl5emRRx6pVL/ExcVp9uzZmjp1qn744QetXLlSPj4+pdoFBATogw8+kCTt379fOTk5euONNyRJiYmJ+vvf/66kpCR9//33mjBhgoYPH65PP/20UjVcD3N8AQAArDRgwAC1bt1aCQkJWrZsmcW+xo0ba+LEiebnzzzzjDZv3qxVq1apffv25u0hISGaM2eO+fmVwDp9+nT17NnTvL1evXoKDw83P58xY4bWrFmjdevWKSYmRvXq1ZOjo6Pq1q0rX1/fcmseNmyYFi5cqBkzZkj6fRT466+/1j//+U9J0sKFCxUREaFZs2aZj1m+fLkCAgJ04MAB3XXXXeWe+9y5c3rjjTe0cOFCjRw5UpLUrFkz3XfffaXaOjo6ql69epKkRo0amUelCwoKNGvWLG3dulUdO3aUJDVt2lTbtm3TW2+9pS5dupR7/cpixBcAAKAKXnnlFaWkpCgzM9Nie1FRkWbMmKGwsDDVq1dP7u7u2rx5s3mE9oq2bduWed527dpZPD9//rwmTpyo0NBQeXl5yd3dXZmZmaXOdz1//vOflZWVpf/85z+Sfh/tbdOmjVq0aCFJ+uabb/TJJ5/I3d3d/Liy79ChQxWeOzMzUwUFBfrTn/5kVU1XO3jwoC5cuKCePXta1PD3v//9utevLEZ8AQAAquCPf/yjIiMjFRcXZ56jKkmvvvqq3njjDc2fP19hYWGqU6eOxo8fX+oDbHXq1CnzvNdunzhxorZs2aLXXntNwcHBcnV11aBBg8r8QFxFfH191b17d61cuVL33nuvVq5cqaefftq8//z584qKitIrr7xS6lg/P78Kz+3q6mpVLWU5f/68JGnDhg1q3LixxT5nZ+cbPr9UxRHfRYsWKTAwUC4uLurQoUOpOSvXOn36tMaNGyc/Pz85Ozvrrrvu0ocfflilggEAAOzF7Nmz9f/+3//Tjh07zNu2b9+ufv36afjw4QoPD1fTpk114MCBKl9j+/btio6O1oABAxQWFiZfX19lZWVZtKldu7aKioque65hw4YpNTVVO3bs0OHDh/XnP//ZvK9Nmzb6/vvvFRgYqODgYItHeSH9ipCQELm6uiotLa1Sr6l27dqSZFFzy5Yt5ezsrOzs7FLXDwgIqNR5r8fq4JuamqrY2FglJCRo9+7dCg8PV2RkpI4dO1Zm+8LCQvXs2VNZWVl6//33tX//fi1durRUkgcAALjVhIWFadiwYVqwYIF5W0hIiLZs2aIvvvhCmZmZevLJJ5WXl1fla4SEhGj16tXKyMjQN998o6FDh6q4uNiiTWBgoD777DP9/PPPFa7C8NBDD+ncuXN6+umn1a1bN/n7+5v3jRs3TidPntSQIUP01Vdf6dChQ9q8ebNGjRp13VDt4uKiSZMm6fnnnzdPTfjPf/5Tav7zFU2aNJHJZNL69et1/PhxnT9/XnXr1tXEiRM1YcIEpaSk6NChQ9q9e7fefPNNpaSkWNFj5bM6+M6bN09jxozRqFGj1LJlSyUlJcnNzU3Lly8vs/3y5ct18uRJrV27Vp07d1ZgYKC6dOliMUkbAADgVjV9+nSLIDplyhS1adNGkZGR6tq1q3x9fdW/f/8qn3/evHny9vZWp06dFBUVpcjISLVp06ZUDVlZWWrWrJkaNmxY7rnq1q2rqKgoffPNNxo2bJjFPn9/f23fvl1FRUV64IEHFBYWpvHjx8vLy0sODtePjFOnTtVf/vIXxcfHKzQ0VIMHDy53YLRx48aaNm2aJk+eLB8fH8XExEj6/YN7U6dOVWJiokJDQ9WrVy9t2LBBQUFB171+ZZhKrLjFSGFhodzc3PT+++9b/AOOHDlSp0+f1r///e9Sx/Tp00f16tWTm5ub/v3vf6thw4YaOnSoJk2aJEdHxzKvU1BQoIKCAvPzs2fPKiAgQGfOnJGHh4cVL8/Soqc+rvKxN8O4pO62LsFs7uAHbV2Chb+krrd1CWaZLUJtXYKF0H2Z129UQ+zpe8qevp8k+qYi9E35bre+OXv2rDw9Pcv8/X3x4kUdOXJEQUFBcnFxueFrwbiseS9ZNeJ74sQJFRUVlVqTzcfHR7m5uWUec/jwYb3//vsqKirShx9+qKlTp2ru3Ll6+eWXy71OYmKiPD09zY/qmtcBAAAA47rpy5kVFxerUaNG+tvf/qa2bdtq8ODBevHFFy0War5WXFyczpw5Y34cPXr0ZpcJAACAcmRnZ1ssMXbtw9ql1WzFquXMGjRoIEdHx1ITtPPy8spdMNnPz0+1atWymNYQGhqq3NxcFRYWmj/VdzVnZ+dqW7YCAAAAN8bf318ZGRkV7r8VWBV8a9eurbZt2yotLc08x7e4uFhpaWnmScnX6ty5s1auXKni4mLzxOgDBw7Iz8+vzNAL23DxjrV1CQAAwE45OTkpODjY1mXcMKunOsTGxmrp0qXmO5U8/fTTys/P16hRoyRJI0aMUFxcnLn9008/rZMnT+q5557TgQMHtGHDBs2aNUvjxo2rvlcBAAAAXIfVd24bPHiwjh8/rvj4eOXm5qp169batGmT+QNv2dnZFkteBAQEaPPmzZowYYJatWqlxo0b67nnntOkSZOq71UAAAAA11GlWxbHxMSUO7UhPT291LaOHTua7wsNAAAA2MJNX9UBAAAAsAcEXwAAABgCwRcAAKCGJCcny8vLy9ZlGFaV5vgCAADcDGEpYTV6vb0j91bpuKNHjyohIUGbNm3SiRMn5Ofnp/79+ys+Pl7169eXJAUGBmr8+PEaP358NVaMG8GILwAAgBUOHz6sdu3a6ccff9S//vUvHTx4UElJSUpLS1PHjh118uTJGq/p0qVLNX7NWxHBFwAAwArjxo1T7dq19dFHH6lLly6688471bt3b23dulU///yzXnzxRXXt2lU//fSTJkyYIJPJJJPJZHGOzZs3KzQ0VO7u7urVq5dycnIs9r/99tsKDQ2Vi4uLWrRoocWLF5v3ZWVlyWQyKTU1VV26dJGLi4tWrFhRI6/9VsdUBwAAgEo6efKkNm/erJkzZ8rV1dVin6+vr4YNG6bU1FT9+OOPat26tZ544gmNGTPGot2FCxf02muv6R//+IccHBw0fPhwTZw40RxeV6xYofj4eC1cuFARERHas2ePxowZozp16mjkyJHm80yePFlz585VRESEXFxcbv6Lvw0QfAEAACrpxx9/VElJiUJDQ8vcHxoaqlOnTqmoqEiOjo6qW7eufH19LdpcunRJSUlJatasmaTf748wffp08/6EhATNnTtXDz30kCQpKChIP/zwg9566y2L4Dt+/HhzG1QOwRcAAMBKJSUlVT7Wzc3NHHolyc/PT8eOHZMk5efn69ChQ3rssccsRoovX74sT09Pi/O0a9euyjUYFcEXAACgkoKDg2UymZSZmakBAwaU2p+ZmSlvb281bNiw3HPUqlXL4rnJZDIH6fPnz0uSli5dqg4dOli0c3R0tHhep06dKr0GI+PDbQAAAJVUv3599ezZU4sXL9Zvv/1msS83N1crVqzQ4MGDZTKZVLt2bRUVFVl1fh8fH/n7++vw4cMKDg62eAQFBVXnSzEkgi8AAIAVFi5cqIKCAkVGRuqzzz7T0aNHtWnTJvXs2VONGzfWzJkzJf2+ju9nn32mn3/+WSdOnKj0+adNm6bExEQtWLBABw4c0N69e/XOO+9o3rx5N+slGQbBFwAAwAohISHatWuXmjZtqkceeUTNmjXTE088oW7dumnHjh2qV6+eJGn69OnKyspSs2bNKpz6cK3HH39cb7/9tt555x2FhYWpS5cuSk5OZsS3GjDHFwAA2I2q3kmtpjVp0kTJyckVtrn33nv1zTffWGyLjo5WdHS0xbb+/fuX+rDc0KFDNXTo0DLPGxgYeEMfrjMyRnwBAABgCARfAAAAGALBFwAAAIZA8AUAAIAhEHwBAABgCARfAAAAGALBFwAAAIZA8AUAAIAhEHwBAABgCARfAACAGrR9+3aFhYWpVq1a6t+/v9LT02UymXT69Ombet2uXbtq/PjxN/Ua9o5bFgMAALuR2SK0Rq8Xui/TqvbR0dFKSUmRJDk5OemOO+7Qww8/rOnTp8vFxaVS54iNjVXr1q21ceNGubu7KyMjw9qyK5Senq5u3brp1KlT8vLyMm9fvXq1atWqVa3XutUQfAEAAKzQq1cvvfPOO7p06ZK+/vprjRw5UiaTSa+88kqljj906JCeeuop3XHHHTe5Ukv16tWr0evZI6Y6AAAAWMHZ2Vm+vr4KCAhQ//791aNHD23ZskWSVFxcrMTERAUFBcnV1VXh4eF6//33JUlZWVkymUz69ddfNXr0aJlMJiUnJ5d5jW3btun++++Xq6urAgIC9Oyzzyo/P9+8v6CgQJMmTVJAQICcnZ0VHBysZcuWKSsrS926dZMkeXt7y2QyKTo6WlLpqQ6nTp3SiBEj5O3tLTc3N/Xu3Vs//vijeX9ycrK8vLy0efNmhYaGyt3dXb169VJOTk419mbNYsQXAAA70T19nK1LuIp1UwCM6rvvvtMXX3yhJk2aSJISExP1z3/+U0lJSQoJCdFnn32m4cOHq2HDhrrvvvuUk5Oj5s2ba/r06Ro8eLA8PT315ZdfWpzz0KFD6tWrl15++WUtX75cx48fV0xMjGJiYvTOO+9IkkaMGKEdO3ZowYIFCg8P15EjR3TixAkFBATogw8+0MCBA7V//355eHjI1dW1zNqjo6P1448/at26dfLw8NCkSZPUp08f/fDDD+YpERcuXNBrr72mf/zjH3JwcNDw4cM1ceJErVix4ib26s1D8AUAALDC+vXr5e7ursuXL6ugoEAODg5auHChCgoKNGvWLG3dulUdO3aUJDVt2lTbtm3TW2+9pS5dusjX11cmk0menp7y9fUt8/yJiYkaNmyYeXQ2JCRECxYsUJcuXbRkyRJlZ2dr1apV2rJli3r06GG+zhVXpjQ0atTIYo7v1a4E3u3bt6tTp06SpBUrViggIEBr167Vww8/LEm6dOmSkpKS1KxZM0lSTEyMpk+ffmMdaEMEXwAAACt069ZNS5YsUX5+vl5//XU5OTlp4MCB+v7773XhwgX17NnTon1hYaEiIiIqff5vvvlG3377rcWoaklJiYqLi3XkyBHt3btXjo6O6tKlS5VfQ2ZmppycnNShQwfztvr166t58+bKzPzfaL+bm5s59EqSn5+fjh07VuXr2hrBFwAAwAp16tRRcHCwJGn58uUKDw/XsmXLdPfdd0uSNmzYoMaNG1sc4+zsXOnznz9/Xk8++aSeffbZUvvuvPNOHTx48Aaqt861q0CYTCaVlJTU2PWrG8EXAACgihwcHPTCCy8oNjZWBw4ckLOzs7Kzs29oNLZNmzb64YcfzOH6WmFhYSouLtann35qnupwtdq1a0uSioqKyr1GaGioLl++rC+//NI81eHXX3/V/v371bJlyyrXbu9Y1QEAAOAGPPzww3J0dNRbb72liRMnasKECUpJSdGhQ4e0e/duvfnmm+a1fytj0qRJ+uKLLxQTE6OMjAz9+OOP+ve//62YmBhJUmBgoEaOHKnRo0dr7dq1OnLkiNLT07Vq1SpJUpMmTWQymbR+/XodP35c58+fL3WNkJAQ9evXT2PGjNG2bdv0zTffaPjw4WrcuLH69etXPR1jhwi+AAAAN8DJyUkxMTGaM2eO4uLiNHXqVCUmJio0NFS9evXShg0bFBQUVOnztWrVSp9++qkOHDig+++/XxEREYqPj5e/v7+5zZIlSzRo0CCNHTtWLVq00JgxY8zLnTVu3FjTpk3T5MmT5ePjYw7M13rnnXfUtm1bPfjgg+rYsaNKSkr04Ycf3tY3uTCV3AITNc6ePStPT0+dOXNGHh4eVT7Poqc+rsaqbty4pO62LsGMvilfTd9F6HqsvcvQzWRP7xt7es9I9E1F6Jvy2dPPm+r4WVPR7++LFy/qyJEjCgoKqvQdz4CyWPNeYsQXAAAAhsCH24DreCTOvr5N9tq6AAAAblGM+AIAAMAQCL4AAAAwBIIvAAAADIHgCwAAAEMg+AIAAMAQCL4AAAAwBIIvAAAADMG+FigFANz2uqePs3UJV7GfOyHi9mEymbRmzRr179+/2s750ksvae3atcrIyKi2cxoRwRcAANiNmr6ldVVuW338+HHFx8drw4YNysvLk7e3t8LDwxUfH6/OnTsrJydH3t7eN6Fa3CiCLwAAgBUGDhyowsJCpaSkqGnTpsrLy1NaWpp+/fVXSZKvr6+NK0R5mOMLAABQSadPn9bnn3+uV155Rd26dVOTJk3Uvn17xcXF6f/+7/8k/T7VYe3atZKkrKwsmUwmrV69Wt26dZObm5vCw8O1Y8cOi/MuXbpUAQEBcnNz04ABAzRv3jx5eXlVWMvbb7+t0NBQubi4qEWLFlq8eHGlXkNhYaFiYmLk5+cnFxcXNWnSRImJiRb1Xj2l4vTp0zKZTEpPT5ckpaeny2QyafPmzYqIiJCrq6u6d++uY8eOaePGjQoNDZWHh4eGDh2qCxcumM/TtWtXPfPMMxo/fry8vb3l4+OjpUuXKj8/X6NGjVLdunUVHBysjRs3Vup1VAXBFwAAoJLc3d3l7u6utWvXqqCgoNLHvfjii5o4caIyMjJ01113aciQIbp8+bIkafv27Xrqqaf03HPPKSMjQz179tTMmTMrPN+KFSsUHx+vmTNnKjMzU7NmzdLUqVOVkpJy3VoWLFigdevWadWqVdq/f79WrFihwMDASr+WK1566SUtXLhQX3zxhY4ePapHHnlE8+fP18qVK7VhwwZ99NFHevPNNy2OSUlJUYMGDbRz504988wzevrpp/Xwww+rU6dO2r17tx544AE9+uijFoG5OhF8AQAAKsnJyUnJyclKSUmRl5eXOnfurBdeeEHffvtthcdNnDhRffv21V133aVp06bpp59+0sGDByVJb775pnr37q2JEyfqrrvu0tixY9W7d+8Kz5eQkKC5c+fqoYceUlBQkB566CFNmDBBb7311nVfQ3Z2tkJCQnTfffepSZMmuu+++zRkyJDKd8L/7+WXX1bnzp0VERGhxx57TJ9++qmWLFmiiIgI3X///Ro0aJA++eQTi2PCw8M1ZcoUhYSEKC4uTi4uLmrQoIHGjBmjkJAQxcfH69dff71uf1YVwRcAAMAKAwcO1C+//KJ169apV69eSk9PV5s2bZScnFzuMa1atTJ/7efnJ0k6duyYJGn//v1q3769Rftrn18tPz9fhw4d0mOPPWYegXZ3d9fLL7+sQ4cOXbf+6OhoZWRkqHnz5nr22Wf10UcfXfeYslz9mnx8fOTm5qamTZtabLvyGss6xtHRUfXr11dYWJjFMZJKHVddCL4AAABWcnFxUc+ePTV16lR98cUXio6OVkJCQrnta9WqZf7aZDJJkoqLi6t07fPnz0v6fV5wRkaG+fHdd9/pP//5z3WPb9OmjY4cOaIZM2bot99+0yOPPKJBgwZJkhwcfo+GJSUl5vaXLl2q1Gu6+vmVbde+xrLaVGffXA/BFwAA4Aa1bNlS+fn5VTq2efPm+uqrryy2Xfv8aj4+PvL399fhw4cVHBxs8QgKCqrUNT08PDR48GAtXbpUqamp+uCDD3Ty5Ek1bNhQkpSTk2NuezutHcxyZgAAAJX066+/6uGHH9bo0aPVqlUr1a1bV7t27dKcOXPUr1+/Kp3zmWee0R//+EfNmzdPUVFR+vjjj7Vx40bz6GdZpk2bpmeffVaenp7q1auXCgoKtGvXLp06dUqxsbEVXm/evHny8/NTRESEHBwc9N5778nX11deXl5ycHDQvffeq9mzZysoKEjHjh3TlClTqvS67BEjvgAAAJXk7u6uDh066PXXX9cf//hH3X333Zo6darGjBmjhQsXVumcnTt3VlJSkubNm6fw8HBt2rRJEyZMkIuLS7nHPP7443r77bf1zjvvKCwsTF26dFFycnKlRnzr1q2rOXPmqF27drrnnnuUlZWlDz/80DzNYfny5bp8+bLatm2r8ePH6+WXX67S67JHppKrJ3HYqbNnz8rT01NnzpyRh4dHlc9T03eDuZ6q3C3mZqFvyheWEnb9RjVo78i9ti7BzJ7eN/b0npHom4pktgi1dQlmofvs65bFt1vfVPT7++LFizpy5IiCgoIqDHhGNWbMGO3bt0+ff/65rUuxe9a8l5jqAAAAYGOvvfaaevbsqTp16mjjxo1KSUmp9A0pUHlMdQAAALCxnTt3qmfPngoLC1NSUpIWLFigxx9/vErnmjVrlsUyZ1c/rrc+8O2OEV8AAAAbW7VqVbWd66mnntIjjzxS5j5XV9dqu86tqEojvosWLVJgYKBcXFzUoUMH7dy5s1LHvfvuuzKZTOrfv39VLgsAAIDrqFevXqllzq48GjdubOvybMrq4JuamqrY2FglJCRo9+7dCg8PV2Rk5HXvsJGVlaWJEyfq/vvvr3KxAAAAQFVZHXznzZunMWPGaNSoUWrZsqWSkpLk5uam5cuXl3tMUVGRhg0bpmnTplncyg4AAACoKVbN8S0sLNTXX3+tuLg48zYHBwf16NFDO3bsKPe46dOnq1GjRnrssccqtSxHQUGBCgoKzM/Pnj1rTZkAYHPd08fZuoSr2NeSXQBgK1aN+J44cUJFRUXy8fGx2O7j46Pc3Nwyj9m2bZuWLVumpUuXVvo6iYmJ8vT0ND8CAgKsKRMAAAAo5aYuZ3bu3Dk9+uijWrp0qRo0aFDp4+Li4nTmzBnz4+jRozexSgAAABiBVVMdGjRoIEdHR+Xl5Vlsz8vLk6+vb6n2hw4dUlZWlqKioszbiouLf7+wk5P279+vZs2alTrO2dlZzs7O1pQGAABgSCaTSWvWrKlw1azo6GidPn1aa9eurbG67JFVwbd27dpq27at0tLSzJ1bXFystLQ0xcTElGrfokUL7d1reXvVKVOm6Ny5c3rjjTeYwgAAACzMHfxgjV7vL6nrK93WZDJVuD8hIUEvvfTSDVZkvZycHHl7e0v6fRWtoKAg7dmzR61btza3eeONN1RSUlLjtdkbq29gERsbq5EjR6pdu3Zq37695s+fr/z8fI0aNUqSNGLECDVu3FiJiYlycXHR3XffbXG8l5eXJJXaDgAAYM9ycnLMX6empio+Pl779+83b3N3dzd/XVJSoqKiIjk53fx7hZX1v+7X8vT0vOl13AqsnuM7ePBgvfbaa4qPj1fr1q2VkZGhTZs2mT/wlp2dbfHGAAAAuB34+vqaH56enjKZTObn+/btU926dbVx40a1bdtWzs7O2rZtmw4dOqR+/frJx8dH7u7uuueee7R161aL8wYGBmrWrFkaPXq06tatqzvvvFN/+9vfzPsLCwsVExMjPz8/ubi4qEmTJkpMTDTvN5lM5ikMQUFBkqSIiAiZTCZ17dpV0u9THa6eClFQUKBnn31WjRo1kouLi+677z599dVX5v3p6ekymUxKS0tTu3bt5Obmpk6dOlkE/VtRlT7cFhMTo59++kkFBQX68ssv1aFDB/O+9PR0JScnl3tscnKy4eeXAACA29PkyZM1e/ZsZWZmqlWrVjp//rz69OmjtLQ07dmzR7169VJUVJSys7Mtjps7d67atWunPXv2aOzYsXr66afNIXPBggVat26dVq1apf3792vFihUKDAws8/pX7qa7detW5eTkaPXq1WW2e/755/XBBx8oJSVFu3fvVnBwsCIjI3Xy5EmLdi+++KLmzp2rXbt2ycnJSaNHj77BHrKtmz/+DgAAYBDTp09Xz549zc/r1aun8PBw8/MZM2ZozZo1WrduncXno/r06aOxY8dKkiZNmqTXX39dn3zyiZo3b67s7GyFhITovvvuk8lkUpMmTcq9fsOGDSVJ9evXL3cKRH5+vpYsWaLk5GT17t1bkrR06VJt2bJFy5Yt01//+ldz25kzZ6pLly6Sfg/1ffv21cWLF+Xi4mJt19iFm7qcGQAAgJG0a9fO4vn58+c1ceJEhYaGysvLS+7u7srMzCw14tuqVSvz11emUBw7dkzS79MUMjIy1Lx5cz377LP66KOPbqjGQ4cO6dKlS+rcubN5W61atdS+fXtlZlre8Obquvz8/CTJXNetiOALAABQTerUqWPxfOLEiVqzZo1mzZqlzz//XBkZGQoLC1NhYaFFu1q1alk8N5lM5iVg27RpoyNHjmjGjBn67bff9Mgjj2jQoEE394WUUdeVVS2u1HUrIvgCAADcJNu3b1d0dLQGDBigsLAw+fr6Kisry+rzeHh4aPDgwVq6dKlSU1P1wQcflJqPK/2+9KwkFRUVlXuuZs2aqXbt2tq+fbt526VLl/TVV1+pZcuWVtd2K2GOLwAAwE0SEhKi1atXKyoqSiaTSVOnTrV6xHTevHny8/NTRESEHBwc9N5778nX19e8ROzVGjVqJFdXV23atEl33HGHXFxcSi1lVqdOHT399NP661//qnr16unOO+/UnDlzdOHCBT322GM38nLtHiO+AAAAN8m8efPk7e2tTp06KSoqSpGRkWrTpo1V56hbt67mzJmjdu3a6Z577lFWVpY+/PBDOTiUjnFOTk5asGCB3nrrLfn7+6tfv35lnnP27NkaOHCgHn30UbVp00YHDx7U5s2bzTfCuF2ZSm6B23icPXtWnp6eOnPmjDw8PKp8nkVPfVyNVd24cUndbV2CGX1TvrCUMFuXYGHvyL3Xb1RD7Ol9Y0/vGUnKbBFq6xLMQvdlXr9RDaJvyne79U1Fv78vXryoI0eOKCgo6JZdIQD2wZr3EiO+AAAAMASCLwAAAAyB4AsAAABDIPgCAADAEAi+AAAAMASCLwAAsJlbYHEp2Dlr3kMEXwAAUOMcHR0lqdStewFrXbhwQVLp2z6XhTu3AQCAGufk5CQ3NzcdP35ctWrVKvNmDEBFSkpKdOHCBR07dkxeXl7mP6YqQvAFAAA1zmQyyc/PT0eOHNFPP/1k63JwC/Py8pKvr2+l2hJ8AQCATdSuXVshISFMd0CV1apVq1IjvVcYKvh2Tx9n6xKuYV+3ygQAoKY5ODhwy2LUGEMFX5SPPwoAAMDtjpnkAAAAMASCLwAAAAyB4AsAAABDIPgCAADAEAi+AAAAMASCLwAAAAyB5cwAVJl9LYPHEngAgIoRfAEAsBOPxNnPr+W9ti4AuAmY6gAAAABDsJ8/LQEAhsCoJgBbYcQXAAAAhkDwBQAAgCEQfAEAAGAIBF8AAAAYAsEXAAAAhkDwBQAAgCHYz5oyNcCeltCRWEYHAACgJjHiCwAAAEMg+AIAAMAQ7Ov//gHgNmFPU6uYVgUAv2PEFwAAAIZgP0MSAAAY3N4j2bYuAbitMeILAAAAQyD4AgAAwBAIvgAAADAEgi8AAAAMwVAfbuNDAwAAAMbFiC8AAAAMgeALAAAAQyD4AgAAwBAIvgAAADAEgi8AAAAMgeALAAAAQyD4AgAAwBAIvgAAADAEgi8AAAAMgeALAAAAQyD4AgAAwBAIvgAAADAEgi8AAAAMgeALAAAAQ6hS8F20aJECAwPl4uKiDh06aOfOneW2Xbp0qe6//355e3vL29tbPXr0qLA9AAAAcDNYHXxTU1MVGxurhIQE7d69W+Hh4YqMjNSxY8fKbJ+enq4hQ4bok08+0Y4dOxQQEKAHHnhAP//88w0XDwAAAFSW1cF33rx5GjNmjEaNGqWWLVsqKSlJbm5uWr58eZntV6xYobFjx6p169Zq0aKF3n77bRUXFystLe2GiwcAAAAqy6rgW1hYqK+//lo9evT43wkcHNSjRw/t2LGjUue4cOGCLl26pHr16pXbpqCgQGfPnrV4AAAAADfCquB74sQJFRUVycfHx2K7j4+PcnNzK3WOSZMmyd/f3yI8XysxMVGenp7mR0BAgDVlAgAAAKXU6KoOs2fP1rvvvqs1a9bIxcWl3HZxcXE6c+aM+XH06NEarBIAAAC3IydrGjdo0ECOjo7Ky8uz2J6XlydfX98Kj33ttdc0e/Zsbd26Va1ataqwrbOzs5ydna0pDTfokTir3go33V5bFwAAAG47VqWd2rVrq23btkpLS1P//v0lyfxBtZiYmHKPmzNnjmbOnKnNmzerXbt2N1QwAPthT38w8ccSAOB6rP6tFRsbq5EjR6pdu3Zq37695s+fr/z8fI0aNUqSNGLECDVu3FiJiYmSpFdeeUXx8fFauXKlAgMDzXOB3d3d5e7uXo0vBQDsx94j2bYuAQBwDauD7+DBg3X8+HHFx8crNzdXrVu31qZNm8wfeMvOzpaDw/+mDi9ZskSFhYUaNGiQxXkSEhL00ksv3Vj1AAAAQCVV6f8pY2Jiyp3akJ6ebvE8KyurKpcAAAAAqlWNruoAAAAA2Ir9fDIFwC2HeawAgFsJI74AAAAwBIIvAAAADIHgCwAAAENgji9wHcxjBQDg9kDwBQDUKP6YBGArTHUAAACAIRB8AQAAYAgEXwAAABgCwRcAAACGQPAFAACAIbCqAyTxKWsAAHD7Y8QXAAAAhkDwBQAAgCEQfAEAAGAIBF8AAAAYAsEXAAAAhkDwBQAAgCEQfAEAAGAIBF8AAAAYAsEXAAAAhkDwBQAAgCEQfAEAAGAIBF8AAAAYAsEXAAAAhkDwBQAAgCEQfAEAAGAIBF8AAAAYAsEXAAAAhkDwBQAAgCEQfAEAAGAIBF8AAAAYAsEXAAAAhkDwBQAAgCEQfAEAAGAIBF8AAAAYAsEXAAAAhkDwBQAAgCEQfAEAAGAIBF8AAAAYAsEXAAAAhkDwBQAAgCEQfAEAAGAIBF8AAAAYAsEXAAAAhkDwBQAAgCEQfAEAAGAIBF8AAAAYAsEXAAAAhkDwBQAAgCEQfAEAAGAIBF8AAAAYAsEXAAAAhkDwBQAAgCEQfAEAAGAIBF8AAAAYAsEXAAAAhuBk6wIA3LoCL660dQlmWbYuAABg96oUfBctWqRXX31Vubm5Cg8P15tvvqn27duX2/69997T1KlTlZWVpZCQEL3yyivq06dPlYuuKnv6JS3xixoAAKAmWT3VITU1VbGxsUpISNDu3bsVHh6uyMhIHTt2rMz2X3zxhYYMGaLHHntMe/bsUf/+/dW/f3999913N1w8AAAAUFlWj/jOmzdPY8aM0ahRoyRJSUlJ2rBhg5YvX67JkyeXav/GG2+oV69e+utf/ypJmjFjhrZs2aKFCxcqKSnpBstHdWE0HKhe9vQ9lWXrAq5B3wCwFauCb2Fhob7++mvFxcWZtzk4OKhHjx7asWNHmcfs2LFDsbGxFtsiIyO1du3acq9TUFCggoIC8/MzZ85Iks6ePWtNuaUUF1y4oeOr242+nupE31SgoMTWFViyo76xp/eNXb1nRN9UhL6pgD39vKmGvrnSvyUldvS6YGhWBd8TJ06oqKhIPj4+Ftt9fHy0b9++Mo/Jzc0ts31ubm6510lMTNS0adNKbQ8ICLCmXLvnOd/WFdgv+qYCsz1tXYFd4j1TPvqmfPRNBarxZ825c+fk6cnPLtieXa7qEBcXZzFKXFxcrJMnT6p+/foymUw2rOz3v14DAgJ09OhReXh42LQWe0PflI++KR99Uz76pmz0S/nsrW9KSkp07tw5+fv727oUQJKVwbdBgwZydHRUXl6exfa8vDz5+vqWeYyvr69V7SXJ2dlZzs7OFtu8vLysKfWm8/DwsIsfKvaIvikffVM++qZ89E3Z6Jfy2VPfMNILe2LVqg61a9dW27ZtlZaWZt5WXFystLQ0dezYscxjOnbsaNFekrZs2VJuewAAAOBmsHqqQ2xsrEaOHKl27dqpffv2mj9/vvLz882rPIwYMUKNGzdWYmKiJOm5555Tly5dNHfuXPXt21fvvvuudu3apb/97W/V+0oAAACAClgdfAcPHqzjx48rPj5eubm5at26tTZt2mT+AFt2drYcHP43kNypUyetXLlSU6ZM0QsvvKCQkBCtXbtWd999d/W9ihrk7OyshISEUlMxQN9UhL4pH31TPvqmbPRL+egboGKmEtYYAQAAgAFYfec2AAAA4FZE8AUAAIAhEHwBAABgCARfAAAAGALB10o7duyQo6Oj+vbta+tS7EZ0dLRMJpP5Ub9+ffXq1UvffvutrUuzC7m5uXrmmWfUtGlTOTs7KyAgQFFRUaXWtzaSq98ztWrVko+Pj3r27Knly5eruLjY1uXZ3LXfU1cevXr1snVpNlde3xw8eNDWpdlcbm6unnvuOQUHB8vFxUU+Pj7q3LmzlixZogsXLti6PMAuEHyttGzZMj3zzDP67LPP9Msvv9i6HLvRq1cv5eTkKCcnR2lpaXJyctKDDz5o67JsLisrS23bttXHH3+sV199VXv37tWmTZvUrVs3jRs3ztbl2dSV90xWVpY2btyobt266bnnntODDz6oy5cv27o8m7v6e+rK41//+pety7ILZfVNUFCQrcuyqcOHDysiIkIfffSRZs2apT179mjHjh16/vnntX79em3dutXWJQJ2wep1fI3s/PnzSk1N1a5du5Sbm6vk5GS98MILti7LLjg7O5tvQ+3r66vJkyfr/vvv1/Hjx9WwYUMbV2c7Y8eOlclk0s6dO1WnTh3z9j/84Q8aPXq0DSuzvavfM40bN1abNm1077336k9/+pOSk5P1+OOP27hC27q6f2CJvilt7NixcnJy0q5duyx+1jRt2lT9+vUTK5cCv2PE1wqrVq1SixYt1Lx5cw0fPlzLly/nh0kZzp8/r3/+858KDg5W/fr1bV2OzZw8eVKbNm3SuHHjLH4RXeHl5VXzRdm57t27Kzw8XKtXr7Z1KcAt49dff9VHH31U7s8aSTKZTDVcFWCfCL5WWLZsmYYPHy7p9/9qO3PmjD799FMbV2Uf1q9fL3d3d7m7u6tu3bpat26dUlNTLe7iZzQHDx5USUmJWrRoYetSbiktWrRQVlaWrcuwuau/p648Zs2aZeuy7MK1ffPwww/buiSbuvKzpnnz5hbbGzRoYO6jSZMm2ag6wL4w1aGS9u/fr507d2rNmjWSJCcnJw0ePFjLli1T165dbVucHejWrZuWLFkiSTp16pQWL16s3r17a+fOnWrSpImNq7MN/jegakpKShidkuX31BX16tWzUTX25dq+KW+U0+h27typ4uJiDRs2TAUFBbYuB7ALBN9KWrZsmS5fvix/f3/ztpKSEjk7O2vhwoXy9PS0YXW2V6dOHQUHB5ufv/322/L09NTSpUv18ssv27Ay2wkJCZHJZNK+fftsXcotJTMz0/AfVJJKf0/hf+gbS8HBwTKZTNq/f7/F9qZNm0qSXF1dbVEWYJeM+//QVrh8+bL+/ve/a+7cucrIyDA/vvnmG/n7+/NJ6zKYTCY5ODjot99+s3UpNlOvXj1FRkZq0aJFys/PL7X/9OnTNV+Unfv444+1d+9eDRw40NalALeM+vXrq2fPnlq4cGGZP2sA/A8jvpWwfv16nTp1So899lipkd2BAwdq2bJleuqpp2xUnX0oKChQbm6upN+nOixcuFDnz59XVFSUjSuzrUWLFqlz585q3769pk+frlatWuny5cvasmWLlixZoszMTFuXaDNX3jNFRUXKy8vTpk2blJiYqAcffFAjRoywdXk2d/X31BVOTk5q0KCBjSqCPVu8eLE6d+6sdu3a6aWXXlKrVq3k4OCgr776Svv27VPbtm1tXSJgFwi+lbBs2TL16NGjzOkMAwcO1Jw5c/Ttt9+qVatWNqjOPmzatEl+fn6SpLp166pFixZ67733DD//uWnTptq9e7dmzpypv/zlL8rJyVHDhg3Vtm3bUvM3jebKe8bJyUne3t4KDw/XggULNHLkSEN/KPKKq7+nrmjevDlTZ1CmZs2aac+ePZo1a5bi4uL03//+V87OzmrZsqUmTpyosWPH2rpEwC6YSvgEDgAAAAyAYRUAAAAYAsEXAAAAhkDwBQAAgCEQfAEAAGAIBF8AAAAYAsEXAAAAhkDwBQAAgCEQfAEAAGAIBF8AAAAYAsEXAAAAhkDwBQAAgCEQfAEAAGAI/x+TiHpHAO8fCgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_stats_df = pd.read_csv(TEST_STATS_FILE)\n",
    "\n",
    "datasets = test_stats_df[\"dataset\"].unique().tolist()\n",
    "labels = test_stats_df[\"label\"].unique().tolist()\n",
    "\n",
    "test_stats_df = create_label_col_ds_index_df(test_stats_df)\n",
    "\n",
    "ds_names = [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\"]\n",
    "ds_name_mappings = [\"Aburaed\", \"Chen Delve\", \"Chen S2ORC\", \"Lu\"\n",
    "                    \"Total Macro\", \"Total Micro\", \"Xing\"]\n",
    "\n",
    "test_stats_df.index = ds_names\n",
    "\n",
    "ax = test_stats_df.plot.bar(stacked=True, rot=0)\n",
    "plt.legend(loc='center left', bbox_to_anchor=(1.0, 0.5))\n",
    "\n",
    "ax.figure.savefig(\"images/corwa-test-data-vert.png\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "8b0f24d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_name_mappings = [\"ABURAED\", \"CHEN Delve\", \"CHEN S2ORC\", \"LU\",\n",
    "                    \"Total\", \"Total Micro\", \"XING\"]\n",
    "\n",
    "# Dict for holding the sorting order\n",
    "sort_dict = {\"ABURAED\": 0, \"CHEN Delve\": 1, \"CHEN S2ORC\": 2, \"LU\": 3, \n",
    "             \"XING\": 4, \"Total\": 5} \n",
    "\n",
    "dataset_df = test_stats_df.copy()\n",
    "\n",
    "dataset_df.index = ds_name_mappings\n",
    "dataset_df.drop(\"Total Micro\", inplace=True)\n",
    "dataset_df.sort_index(key=lambda x: x.map(sort_dict), inplace=True, ascending=False)\n",
    "\n",
    "ax = dataset_df.plot.barh(stacked=True)\n",
    "plt.legend(loc='center left', bbox_to_anchor=(0.25, -0.25))\n",
    "\n",
    "ax.figure.savefig(\"images/corwa-test-data-hor.png\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "f0fad200",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fdd8b215400>"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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vIFAyly9flqenpy5duqSqVauWdzgAAMBO3DiG8PDw0OnTp3Xx4sXyDgs3KCws1KlTpxQYGCgnJ/tYvOPl5SU/Pz8ZDIbyDgUAAId1p7kiZgQCAAA4oOtJQF9fX1WuXJkkjo0oKCjQlStXFBwcLGdn5/IO55bMZrNycnJ09uxZSZK/v385RwQAAG6HRCAAAICDKSgosCQBq1WrVt7h4AYFBQWSJKPRaPOJQElyd3eXJJ09e1a+vr52ETMAAI7MPtYbAAAA4J65nmyqXLlyOUeCiuD694h7TQIAYPtIBAIAADgolgPjXuB7BACA/SARCAAAAAAAADgAEoEAAABwWAaDQWvXrr1lnbi4OMXGxpZJPAAAAPcTDwsBAACARfCo9WV6vvTJnUtUPy4uTsnJyXrhhRe0YMECq31DhgzRvHnz1K9fPyUlJZU8lvR0hYSE6ODBg2ratKmlfNasWTKbzSVuDwAAwNYwIxAAAAB2JTAwUMuXL9eVK1csZbm5uVq2bJnq1Klzz8/n6ekpLy+ve94uAABAWSMRCAAAALvSrFkzBQYGavXq1Zay1atXq06dOoqKirKUBQcHa+bMmVbHNm3aVG+99Vax7YaEhEiSoqKiZDAY1K5dO0klWxq8atUqRUZGyt3dXdWqVdNjjz2m7OxsSVK7du00fPhwq/qxsbGKi4uzbIeGhmrx4sWKi4uTyWRSUFCQ1q1bp3PnzqlLly4ymUxq3Lix9u3bZzkmKSlJXl5e+vTTT1W/fn1VrlxZ3bp1U05OjpKTkxUcHCxvb28NGzbM8sRoAADgmEgEAgAAwO70799fS5YssWx/8MEHev755++qza+//lqStHXrVmVmZlolGu9EZmamevXqpf79+ys1NVU7duzQ008/XeJlxf/+97/VqlUrHTx4UJ07d1afPn3Ut29f9e7dWwcOHFBoaKj69u1r1W5OTo5mz56t5cuXa9OmTdqxY4e6du2qDRs2aMOGDfroo4+0cOFCrVq1qkSxAACAioV7BAIAAMDu9O7dW6NHj9ZPP/0kSdq9e7eWL1+uHTt2lLrNGjVqSJKqVasmPz+/Eh+fmZmpa9eu6emnn1ZQUJAkKTIyssTttGrVSoMHD5azs7MSExM1f/58PfDAA+revbskaeTIkWrZsqXOnDljiTM/P1/z589XaGioJKlbt2766KOPdObMGZlMJjVs2FDt27fX9u3b1bNnzxLHBAAAKgYSgQAAALA7NWrUUOfOnZWUlCSz2azOnTurevXq5RpTkyZN9Le//U2RkZGKjo7W448/rm7dusnb27tE7YSHh1ve16xZU5J1QvF62dmzZy2JwMqVK1uSgNfrBAcHy2QyWZWdPXu25BcGAAAqDJYGAwAAwC71799fSUlJSk5OVv/+/Yvsd3JyKrIsNz8//77F4+zsrC1btmjjxo1q2LCh5syZo/r16ystLa1E8Tg7O1veGwwGSZKrq2uRssLCQkvZjfuv1ymu7MZjAACA4yERCAAAALvUsWNH5eXlKT8/X9HR0UX216hRQ5mZmZbty5cvW5JyxalUqZIk3dUDNQwGg1q3bq23335bBw8eVKVKlbRmzZpi4ykoKND3339f6nMBAACUFEuDAQAAYJecnZ2Vmppqef9njz76qJKSkhQTEyMvLy8lJiYWW+86X19fubu7a9OmTapdu7aMRqM8PT3vOJ6vvvpK27Zt0+OPPy5fX1999dVXOnfunCIiIizxjBgxQuvXr1doaKhmzJihixcvluyiAQAA7gKJQDt0fUnJ5cuXyzkSAABgT66PHUr6FFtbVrVq1ZvuGz16tNLS0vTkk0/K09NTEyZMuOWMQBcXF82ePVvjx49XYmKiHn744RI9fKRq1ar6/PPPNXPmTF2+fFlBQUGaPn26OnXqJOmPpczffvut+vbtKxcXF8XHx6t9+/bFtvXnWYmFhYWWshv/W1BQYFnue+MxxZWZzWaZzea7mvF4s1gLCwuVlZWlvLy8e9o2AAC4M3c6zjOYK9JI0EGcPHnS6mbQAAAAJZGWlqYrV64oJCRERqOxvMPBDa5evapDhw6Vdxgldv78eb344ouWpzgDAIDycerUKdWuXfum+5kRaId8fHwkSRkZGSVaroL75/LlywoMDNSpU6duOTMBZYc+sS30h+2hT2xPWfSJ2WzW77//Lh8fHxI2Nur60uXGjRvfchmzLcnNzVV6err27dtnuc9iRcLvS9tDn9gW+sP20Ce2pyzHeQEBAbesRyLQDjk5/fGMF09PT36obUzVqlXpExtDn9gW+sP20Ce25373iaenp3Jzc+9b+xVZRkaGGjZseNP9P/74o+rUqXNX57j+RGBnZ2e7SQQ6OzvLyclJJpOpQs8w5fel7aFPbAv9YXvoE9tTFuO82yERCAAAANyBgIAApaSk3HI/AACALSMRCAAAANwBFxcXhYWFlXcYAAAApeZU3gGg5Nzc3DRu3Di5ubmVdyj4P/SJ7aFPbAv9YXvoE9tDn0D6Y2lwQECAZYkwyh8/m7aHPrEt9IftoU9sjy31CU8NBgAAcDC5ublKS0vjqcG4J/g+AQBgP5gRCAAAAAAAADgAEoEAAAAAAACAAyARCAAAAAAAADgAEoEAAADAfZCUlCQvL6/yDkOSlJ6eLoPBoJSUlPIOBQAAlCMSgTbqn//8p4KDg2U0GvXQQw/p66+/vmX9jz/+WA0aNJDRaFRkZKQ2bNhQRpE6jpL0yaJFi/Twww/L29tb3t7eeuyxx27bhyi5kv6cXLd8+XIZDAbFxsbe3wAdTEn74+LFixoyZIj8/f3l5uamevXq8bvrHitpn8ycOVP169eXu7u7AgMDFR8fr9zc3DKKtmL7/PPPFRMTY3ka7Nq1a297zI4dO9SsWTO5ubkpLCxMSUlJ9z1OSdJbnmX7KqG4uDgZDAZNnjzZqnzt2rXl+qTd4OBgzZw506qsZ8+eOnr0aJG6Z8+e1Xfffaf9+/crNTVV2dnZt2z7woUL+v7777V//3798MMPunTpUonjCwwMVGZmpho1aiTpj++XwWDQxYsXS9xWRcQ4z/YwzrMtjPNsD+M822FX4zyRCLRJK1as0IgRIzRu3DgdOHBATZo0UXR0tM6ePVts/T179qhXr14aMGCADh48qNjYWMXGxur7778v48grrpL2yY4dO9SrVy9t375de/fuVWBgoB5//HH9/PPPZRx5xVXSPrkuPT1dCQkJevjhh8soUsdQ0v7Iy8tThw4dlJ6erlWrVunIkSNatGiRatWqVcaRV1wl7ZNly5Zp1KhRGjdunFJTU7V48WKtWLFCb7zxRhlHXjFlZ2erSZMm+uc//3lH9dPS0tS5c2e1b99eKSkpGj58uAYOHKjNmzff50jtg9Fo1JQpU/Tbb7/dszbz8vKKlJnNZl27dq3Ubbq7u8vX19eq7MKFCzp16pQCAgLUsGFDubu76+jRo8rPzy+2jaysLJ08eVLVq1dXw4YN5eXlpePHj+vKlSslisXZ2Vl+fn5ycXEp9fVUVIzzbA/jPNvCOM/2MM6zLXY3zjPD5jz44IPmIUOGWLYLCgrMAQEB5kmTJhVbv0ePHubOnTtblT300EPmF1544b7G6UhK2id/du3aNbOHh4c5OTn5foXocErTJ9euXTO3atXK/P7775v79etn7tKlSxlE6hhK2h/z5883161b15yXl1dWITqckvbJkCFDzI8++qhV2YgRI8ytW7e+r3E6IknmNWvW3LLO66+/bv7LX/5iVdazZ09zdHT0PYnhypUr5h9//NF85cqVojvHVS3bVwn169fP/OSTT5obNGhg/vvf/24pX7Nmjfn60Pb8+fPm//qv/zIHBASY3d3dzY0aNTIvW7bMqp22bduahwwZYn711VfN1apVM7dr1868fft2syTzhg0bzM2aNTO7urqat2/fbj5+/Lj5qaeeMvv6+pqrVKlibtGihXnLli1WbUmyepnNZvOSJUvMnp6eZrPZbD5y5IhZkvnTTz81//TTT5Zjp0+fbq5du7b5l19+MZvNZvOhQ4fMHTt2NFepUsXs6+tr7tKli/nLL7+0iv3HH380p6enF/lsCgoKzFOmTDGHhoaaK1WqZA4MDDS/8847ZrPZbE5LSzNLMh88eNDy/sZXv379LG1MnDjRHBwcbDYajebGjRubP/7441v2yS2/T3aAcZ7tYZxnWxjn2R7GebbLFsZ5t8OMQBuTl5en/fv367HHHrOUOTk56bHHHtPevXuLPWbv3r1W9SUpOjr6pvVRMqXpkz/LyclRfn6+fHx87leYDqW0fTJ+/Hj5+vpqwIABZRGmwyhNf6xbt04tW7bUkCFDVLNmTTVq1EgTJ05UQUFBWYVdoZWmT1q1aqX9+/dblpWcPHlSGzZs0BNPPFEmMcMa/7bfmrOzsyZOnKg5c+bof//3f4vsz83NVfPmzbV+/Xp9//33Gjx4sPr06VNk2VRycrIqVaqk3bt3a8GCBZbyUaNGafLkyUpNTVXjxo2VlZWlJ554Qtu2bdPBgwfVsWNHxcTEKCMjQ5K0evVq1a5dW+PHj1dmZqYyMzOLxFSvXj21aNFCq1evloeHh6V82bJlio2NVXZ2ti5evKhHH31UUVFR2rdvnzZt2qQzZ87otddes2qratWqysrKKnKO0aNHa/LkyRo7dqx+/PFHLVu2TDVr1ixSLzAwUJ988okk6ciRI8rMzNSsWbMkSZMmTdKHH36oBQsW6IcfflB8fLx69+6tnTt33rQ/7BnjPNvDOM+2MM6zPYzz7F95j/NYG2Bjzp8/r4KCgiKDtpo1a+rw4cPFHnP69Oli658+ffq+xelIStMnfzZy5EgFBAQU+WFH6ZSmT3bt2qXFixdzk/T7oDT9cfLkSf3nP//Rc889pw0bNuj48eN6+eWXlZ+fr3HjxpVF2BVaafrk2Wef1fnz59WmTRvLcsgXX3yRJSPl5Gb/tl++fFlXrlyRu7t7OUVmO7p27aqmTZtq3LhxWrx4sdW+WrVqKSEhwbL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      "text/plain": [
       "<Figure size 1500x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(15,8))\n",
    "\n",
    "axes[0].set_title(\"Test Data\")\n",
    "axes[1].set_title(\"Models\")\n",
    "\n",
    "dataset_df.plot.barh(ax=axes[0], stacked=True, legend=False)\n",
    "models_df.plot.barh(ax=axes[1], stacked=True, legend=False)\n",
    "\n",
    "handles, labels = axes[0].get_legend_handles_labels()\n",
    "fig.legend(handles, labels, loc = (0.5, -0.01))#loc='lower center')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "25ca900d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 450x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sort_dict = {\n",
    "    \"LEAD\": 0,\n",
    "    \"TextRank\": 1,\n",
    "    \"LexRank\": 2,\n",
    "    \"led-base\": 3,\n",
    "    \"led-large\": 4,\n",
    "    \"led-large-arxiv\": 5,\n",
    "    \"*led-base\": 6,\n",
    "    \"*led-large-arxiv\": 7,\n",
    "    \"ABURAED\": 8, \n",
    "    \"CHEN Delve\": 9, \n",
    "    \"CHEN S2ORC\": 10, \n",
    "    \"LU\": 11, \n",
    "    \"XING\": 13, \n",
    "    \"Total\": 14\n",
    "} \n",
    "\n",
    "model_name_mappings = [\"*led-large-arxiv\", \"*led-base\", \"led-base\", \"led-large\", \n",
    "               \"led-large-arxiv\", \"LEAD\", \"TextRank\", \"LexRank\", \"Test Data\"]\n",
    "models_df.index = model_name_mappings\n",
    "\n",
    "comb_df = pd.concat([models_df, dataset_df])\n",
    "comb_df.drop([\"Test Data\"], inplace=True)\n",
    "comb_df.sort_index(key=lambda x: x.map(sort_dict), inplace=True, ascending=False)\n",
    "\n",
    "ax = comb_df.plot.barh(stacked=True, figsize=(4.5,5))\n",
    "plt.legend(loc='lower center', bbox_to_anchor=(0.35, -0.23), ncol=3)\n",
    "\n",
    "ax.figure.savefig(\"images/corwa-both-hor.png\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e502d4bd",
   "metadata": {},
   "source": [
    "## KL Divergence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "84f4402e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.special import rel_entr\n",
    "\n",
    "test_stats_df = pd.read_csv(TEST_STATS_FILE)\n",
    "\n",
    "datasets = test_stats_df[\"dataset\"].unique().tolist()\n",
    "labels = test_stats_df[\"label\"].unique().tolist()\n",
    "\n",
    "test_stats_df = create_label_col_ds_index_df(test_stats_df)\n",
    "\n",
    "aburaed_test_data = test_stats_df.loc[\"aburaed_et_al\"].values.flatten().tolist()\n",
    "chen_delve_test_data = test_stats_df.loc[\"chen_et_al_delve\"].values.flatten().tolist()\n",
    "chen_s2orc_test_data = test_stats_df.loc[\"chen_et_al_s2orc\"].values.flatten().tolist()\n",
    "lu_test_data = test_stats_df.loc[\"lu_et_al\"].values.flatten().tolist()\n",
    "xing_test_data = test_stats_df.loc[\"xing_et_al\"].values.flatten().tolist()\n",
    "\n",
    "dist_test_data = [aburaed_test_data, chen_delve_test_data, \n",
    "             chen_s2orc_test_data, lu_test_data, xing_test_data]\n",
    "\n",
    "ds_names = [\"aburaed\", \"chen delve\", \"chen s2orc\", \"lu\", \"xing\"]\n",
    "res = []\n",
    "for i, d in enumerate(dist_test_data):\n",
    "    res_i = []\n",
    "    for j, e in enumerate(dist_test_data):\n",
    "        #calculate (P || Q)\n",
    "        res_i.append(sum(rel_entr(d, e)))\n",
    "    res.append(res_i)\n",
    "    \n",
    "df = pd.DataFrame(res, index=ds_names, columns=ds_names)\n",
    "# df.style.background_gradient(cmap=\"Reds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "46afd973",
   "metadata": {},
   "outputs": [],
   "source": [
    "led_large_stats_df = create_label_col_ds_index_df(led_large_stats_df)\n",
    "led_base_stats_df = create_label_col_ds_index_df(led_base_stats_df)\n",
    "allenai_led_base_stats_df = create_label_col_ds_index_df(allenai_led_base_stats_df)\n",
    "allenai_led_large_stats_df = create_label_col_ds_index_df(allenai_led_large_stats_df)\n",
    "allenai_led_large_arxiv_stats_df = create_label_col_ds_index_df(allenai_led_large_arxiv_stats_df)\n",
    "lead_stats_df = create_label_col_ds_index_df(lead_stats_df)\n",
    "textrank_stats_df = create_label_col_ds_index_df(textrank_stats_df)\n",
    "lexrank_stats_df = create_label_col_ds_index_df(lexrank_stats_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "ca47d29e",
   "metadata": {},
   "outputs": [],
   "source": [
    "led_large = led_large_stats_df.loc[\"total_macro\"].values.flatten().tolist()\n",
    "led_base = led_base_stats_df.loc[\"total_macro\"].values.flatten().tolist()\n",
    "allenai_led_base = allenai_led_base_stats_df.loc[\"total_macro\"].values.flatten().tolist()\n",
    "allenai_led_large = allenai_led_large_stats_df.loc[\"total_macro\"].values.flatten().tolist()\n",
    "allenai_led_large_arxiv = allenai_led_large_arxiv_stats_df.loc[\"total_macro\"].values.flatten().tolist()\n",
    "lead = lead_stats_df.loc[\"total_macro\"].values.flatten().tolist()\n",
    "textrank = textrank_stats_df.loc[\"total_macro\"].values.flatten().tolist()\n",
    "lexrank = lexrank_stats_df.loc[\"total_macro\"].values.flatten().tolist()\n",
    "\n",
    "models_data = [lead, textrank, lexrank, allenai_led_base, allenai_led_large, \n",
    "        allenai_led_large_arxiv, led_base, led_large]\n",
    "\n",
    "col_names = [\"aburaed\", \"chen delve\", \"chen s2orc\", \"lu\", \"xing\"]\n",
    "index_names = [\"LEAD\", \"TextRank\", \"LexRank\", \"led-base\", \"led-large\", \n",
    "               \"led-large-arxiv\", \"*led-base\", \"*led-large-arxiv\"]\n",
    "\n",
    "res = []\n",
    "for i, d in enumerate(models_data):\n",
    "    res_i = []\n",
    "    for j, e in enumerate(dist_test_data):\n",
    "        #calculate (P || Q)\n",
    "        res_i.append(sum(rel_entr(d, e)))\n",
    "    res.append(res_i)\n",
    "    \n",
    "df = pd.DataFrame(res, index=index_names, columns=col_names)\n",
    "# df.style.background_gradient(cmap=\"Reds\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "25c4a1d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "df.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
    "\n",
    "heatmap = sns.heatmap(df, annot=True, cmap=\"Reds\", mask=df.isnull())\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "fig = heatmap.get_figure()\n",
    "fig.savefig(\"images/corwa-kl-div-heatmap.png\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "aa4d8819",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WtwEAuLE1a9ZIki5evKi9e/cqPDz8pusUdL2yvxaW7OVqLhg1apSio6Pz7WNr4S0/zzzzjN5//32NGDFCkZGRCggIkMlk0mOPPUY+AgAAJQpFMhhGhQoVdObMGau2nJwcHTt2zKZxatasKbPZrPDwcNWpU+eGfYtylzLpyl3PTp06paVLl+ree++1tB88eLBI4wEAbPPTTz9p0qRJ6tevn3bs2KGBAwdq586dCggIuKVxAwMD5evrq3379l23LL+2G7n99tslXZnBHBUVdcO+Rc1HkvT555+rT58+mj59uqXt4sWL1+VUAACA4o5rksEwatasqQ0bNli1vfvuuzbPJOvRo4c8PT01ceLE62YGmM1mnTp1yvK8bNmyRToV5epMhL+On5OTozlz5tg8FgDANpcuXVLfvn1VtWpVvfHGG1qwYIHS09M1cuTIWx7b09NTUVFRWrZsmX7//XdL+759+7Rq1SqbxgoMDFS7du30zjvv5PuFz4kTJyw/ly1bVpKKVNjy9PS8Lt+9+eabNudPAAAAd8dMMhjGwIEDNXjwYD388MPq2LGjfvzxR61Zs0aVK1e2aZyaNWvqpZdeUlxcnA4dOqTu3burXLlyOnjwoL788ks9/fTTGjVqlCSpefPmWrJkiWJjY3XXXXfJz89P3bp1u+k2WrVqpQoVKqhPnz4aPny4TCaTPvzwQ4ecrgMAsPbSSy9px44dSk5OVrly5dS4cWPFx8dr3LhxeuSRR9S5c+dbGn/ChAn617/+pdatW2vIkCHKzc3VW2+9pYYNG2rHjh02jTV79mzdc889atSokQYNGqTbb79d6enp2rRpk/73v//pxx9/lCQ1bdpUnp6eevnll5WRkSEfHx916NBBgYGBN91G165d9eGHHyogIEANGjTQpk2b9M0336hSpUpFefkAAABuiyIZDGPQoEE6ePCg5s2bp9WrV6tNmzZau3at7rvvPpvHGjNmjOrUqaPXX39dEydOlCSFhobq/vvv14MPPmjpN3ToUO3YsUPvv/++Xn/9ddWoUaNQRbJKlSrp66+/1nPPPadx48apQoUKeuqpp3TfffcVeN0ZAMCt27Ztm6ZOnaqYmBi1b9/e0j5mzBj985//1KBBg/TLL7/c0jaaN2+uVatWadSoURo/frxCQ0M1adIk7dq1y+a7Fzdo0EBbtmzRxIkTtWDBAp06dUqBgYFq1qyZ4uPjLf2Cg4OVlJSkxMREDRgwQLm5uVq/fn2himRvvPGGPD099fHHH+vixYtq3bq1vvnmG/IRAAAocUxmpqYAAAC4XPfu3fXLL79o7969rg4FAADAkLgmGQAAgJNduHDB6vnevXu1cuVKtWvXzjUBAQAAgJlkwF+lpaXdcHnp0qVv+c5mJ06cuOHFjr29vVWxYsVb2gYAwL2FhISob9++uv3223X48GG9/fbbys7O1vbt21W7dm1lZGRcV0i7VnBw8C3FcO7cOZ07d+6GfapUqWK5mQwAAEBJR5EM+AuTyXTD5X369NGCBQtuaRthYWE6fPhwgcvbtm2rlJSUW9oGAMC99evXT+vXr1daWpp8fHwUGRmpqVOn6s4775Qk9e3bVwsXLrzhGLf6J9yECRMs19UsyMGDBxUWFnZL2wEAACguKJIBf/HNN9/ccHnVqlXVoEGDW9rG999/f8PZARUqVFDz5s1vaRsAgOLt119/1e+//37DPlFRUbe0jQMHDujAgQM37HPPPffI19f3lrYDuJsNGzbo1Vdf1datW3Xs2DF9+eWX6t69+w3XSUlJUWxsrH755ReFhoZq3Lhx6tu3r1PiBQA4D0UyAAAAAIaxatUqff/992revLl69Ohx0yLZwYMH1bBhQw0ePFgDBw5UcnKyRowYoRUrVnCXVwAoYSiSAQAAADAkk8l00yLZ6NGjtWLFCv3888+Wtscee0xnzpzR6tWrnRAlAMBZvFwdQGHk5eXp999/V7ly5W56zSgAwM2ZzWadPXtWVatWlYcHNzomzwCAfZWkPLNp06brTm+Ojo7WiBEjClwnOztb2dnZlud5eXk6ffq0KlWqRJ4BADtwVJ4pFkWy33//XaGhoa4OAwBKnCNHjui2225zdRguR54BAMcoCXkmLS1NQUFBVm1BQUHKzMzUhQsXVLp06evWSUxMvOmNMQAAt87eeaZYFMnKlSsn6cqL9/f3d3E0AFD8ZWZmKjQ01HJ8NTryDADYl9HzTFxcnGJjYy3PMzIyVL16dfIMANiJo/JMsSiSXZ2S7O/vT1IBADvilI8ryDMA4BglIc8EBwcrPT3dqi09PV3+/v75ziKTJB8fH/n4+FzXTp4BAPuyd54p3hcIAAAAAAAHioyMVHJyslXb2rVrFRkZ6aKIAACOQpEMAAAAgGGcO3dOO3bs0I4dOyRJBw8e1I4dO5SamirpyqmSvXv3tvQfPHiwDhw4oBdeeEG7d+/WnDlz9Omnn2rkyJGuCB8A4EAUyQAAAAAYxpYtW9SsWTM1a9ZMkhQbG6tmzZopPj5eknTs2DFLwUySwsPDtWLFCq1du1ZNmjTR9OnT9d577yk6Otol8QMAHKdYXJMMMKLc3FxdunTJ1WGgmCpVqpQ8PT1dHQYAN0aewa0oznmmXbt2MpvNBS5fsGBBvuts377dgVEBANwBRTLAzZjNZqWlpenMmTOuDgXFXPny5RUcHFwiLpoMwH7IM7AX8gwAoKShSAa4masfXAIDA1WmTBn+8ITNzGazzp8/r+PHj0uSQkJCXBwRAHdCnsGtIs8AAEoqimSAG8nNzbV8cKlUqZKrw0ExdvWW9MePH1dgYGCxPSUGgH2RZ2Av5BkAQEnEhfsBN3L12jBlypRxcSQoCa6+j7jmEICryDOwJ/IMAKCkoUgGuCFOfYE98D4CUBCOD7AH3kcAgJKGIhkAAAAAAAAMjyIZALdkMpm0bNmyG/bp27evunfv7pR4AAAlC3kGAABciwv3A8VE2JgVTt3eoWldbOrft29fLVy4UP/4xz+UlJRktWzYsGGaM2eO+vTpowULFtgey6FDCg8P1/bt29W0aVNL+xtvvCGz2WzzeACA65FnyDMAABgdM8kA2E1oaKgWL16sCxcuWNouXryoRYsWqXr16nbfXkBAgMqXL2/3cQEA7ok8AwAAHMnmItmGDRvUrVs3Va1atVDT1CUpJSVFd955p3x8fFSrVq0ifcMHwP3deeedCg0N1dKlSy1tS5cuVfXq1dWsWTNLW1hYmGbOnGm1btOmTTVhwoR8xw0PD5ckNWvWTCaTSe3atZNk22kwn3/+uRo1aqTSpUurUqVKioqKUlZWliSpXbt2GjFihFX/7t27q2/fvlYxv/TSS+rdu7f8/PxUo0YNLV++XCdOnNDf/vY3+fn5qXHjxtqyZYtlnQULFqh8+fL6+uuvVbduXZUpU0aPPPKIzp8/r4ULFyosLEwVKlTQ8OHDlZubW6jXYQTkGQAFIc+QZwAAcCSbi2RZWVlq0qSJZs+eXaj+Bw8eVJcuXdS+fXvt2LFDI0aM0MCBA7VmzRqbgwXg/vr376/333/f8nz+/Pnq16/fLY25efNmSdI333yjY8eOWX04Koxjx47p8ccfV//+/bVr1y6lpKSoR48eNp9C8/rrr6t169bavn27unTpol69eql379566qmntG3bNtWsWVO9e/e2Gvf8+fOaNWuWFi9erNWrVyslJUUPPfSQVq5cqZUrV+rDDz/UO++8o88//9ymWEoy8gyAGyHPkGcAAHAUm69J9sADD+iBBx4odP+kpCSFh4dr+vTpkqT69evru+++0+uvv67o6GhbNw/AzT311FOKi4vT4cOHJUnff/+9Fi9erJSUlCKPWaVKFUlSpUqVFBwcbPP6x44d0+XLl9WjRw/VqFFDktSoUSObx+ncubP+8Y9/SJLi4+P19ttv66677tLf//53SdLo0aMVGRmp9PR0S5yXLl3S22+/rZo1a0qSHnnkEX344YdKT0+Xn5+fGjRooPbt22v9+vXq2bOnzTGVROQZADdCniHPAADgKA6/cP+mTZsUFRVl1RYdHX3dlPO/ys7OVnZ2tuV5Zmamo8IDYGdVqlRRly5dtGDBApnNZnXp0kWVK1d2aUxNmjTRfffdp0aNGik6Olr333+/HnnkEVWoUMGmcRo3bmz5OSgoSJL1h6CrbcePH7d8eClTpozlg8vVPmFhYfLz87NqO378uO0vDJLIM4DRkGfIMwAAOIrDi2RpaWmWhH5VUFCQMjMzdeHCBZUuXfq6dRITEzVx4kSbtzW9Z9cix3mt55Z8bbexiMs2xFX89e/fX0MGD5YkJU6IV9r+vbqYdU4XPD2Utn+vzHm5yjhxXGn791rWuZCVpXOnT1m1/ZH2+5XnnqVuKR5PT0+tXbtWGzdu1NLFn+j16dMVN2aMVn7xmaqHhupydrbOnfnDatvnMs7I5//jlaTcy5d1IeOMVR9JKlXqz9hMJpMkKS8vL9/lV/vk13b+bOZ1YxdVcM3adhmnuHBmnpHc91hgr7jcMSaJuGxFniHPXNtGngEA4Obc8u6WcXFxysjIsDyOHDni6pAA2KBTp066dClHly9dUrs2ba5bXqliRR0/ccLy/OzZc0r93/8KHM/b21uSbumiwyaTSa1bt9bzI57V2uXL5F2qlFb+a+3/x1NBx4//GU9ubq52/2afDxJwT+QZoHgjzwAAAEdw+Eyy4OBgpaenW7Wlp6fL398/32/3JcnHx0c+Pj6ODg2Ag3h6emrD6tWWn6/V+u679enSL9WxQ3sF+PvrlZlv5NvvqsDAQJUuXVqrV6/WbbfdJl9fXwUEBBQ6nh9++EHJycm6//77pQtZ2r7jR506fVq1a105PaV1ZKQmTE3UN+vXq0b16npn/vucfleMkGcA4yHPAAAAR3B4kSwyMlIrV660alu7dq0iIyMdvWkALlSunF+By4YPHqwj//ufeg/6h8qVK6fRI5/VkRt8w+/l5aVZs2Zp0qRJio+PV5s2bWy6QLO/v782bNigmTNnKjMjQ7dVq6aEuDG6r21bSdLjjzysX3ft1vDnR8vT01NP9+urVndHFHp8uBZ5BjAm8gwAALA3k9nGe1OfO3dO+/btkyQ1a9ZMM2bMUPv27VWxYkVVr15dcXFxOnr0qD744ANJ0sGDB9WwYUMNGzZM/fv317p16zR8+HCtWLGi0Hcdy8zMVEBAgDIyMuTv719gP3e9lgdx2cbIcV28eFEHDx5UeHi4fH19CzWWva4vItn3GiPEZRtHxHWj91Nhj6uu4M55Rir5xyh3jEkiLluRZ2xDXLYp7nnGFdgfAGBfjjqu2nxNsi1btqhZs2Zq1qyZJCk2NlbNmjVTfHy8pCu3wE5NTbX0Dw8P14oVK7R27Vo1adJE06dP13vvvVfoDy4AAGMhzwAAAABwBZtPt2zXrp1uNPlswYIF+a6zfft2WzcFAIWSmpqqBg0aFLj8119/VfXq1Z0YEW4FeQaAuyHPAABgDA6/JhkAOFrVqlW1Y8eOGy4HAKCoyDMAABgDRTIAxZ6Xl5dq1arl6jAAACUUeQYAAGOw+ZpkAAAAAAAAQElDkQwAAAAAAACGR5EMAAAAAAAAhkeRDAAAAAAAAIZHkQwAAAAAAACGR5EMgOEs+WKp6jZr7uowJEmHDh2SyWTSz7/+6upQAAB2Qp4BAKB48nJ1AAAKaUJAvs3BDttehk3d+/btq4ULFyoxMVFjxoyxtK9au1b9hwzTsX2/2TvCQgkLC9OIESM0YsQIS9uDXTrrvnZtXRLPtUJDQ3Xs2DFdzvhDkrTxPz/o4ad6afe2LQrw93dxdAAMhTxTJOQZAABKDmaSAbAbX19fvfzyy/rjjz/sNmZOTs51bWazWZcvXy7ymKV9fVW5UqVbCctuPD09FRwcLC8vvrMAgJshz9iOPAMAQOFRJANgN1FRUQoODlZiYmK+y0//8YeGjBipZq3vUXjDxmrfuau+/Oprqz49nnhKL06YqPEvTVGDu1rq8X4DlJKSIpPJpFWrVql58+by8fHRd999p/379+tvf/ubgoKC5Ofnp7vuukvffPONZax27drp8OHDGjlypEwmk0wmkyTr02D2HzyokFp1tHf/fqs43pn/vu5uf5/l+e7fftMT/QeoZuOmahQRqZjnRunkyZOF2i95eXl65ZVXVKtWLfn4+Kh69eqaMmWKJOvTYI787396+KlekqR6d7ZQSK06evaF0ZYxZr2dpJbtOij8jka6r2s3fb1qdaG2DwAlBXkmf+QZAADsgyIZALvx9PTU1KlT9eabb+r3Y2nXLc/Ozlbjhg314dx3lbLyaz312KN6ZtTz2v7jj1b9Pv1ymbxLldLyJYv18uSJlvYxY8Zo2rRp2rVrlxo3bqxz586pc+fOSk5O1vbt29WpUyd169ZNqampkqSlS5fqtttu06RJk3Ts2DEdO3bsuphqhoerSaOGWrr8K6v2pcu/0kMPdpMkZWRm6pGneqthgwZa/eUXWjR/nk6cPKVHH320UPslLi5O06ZN0/jx4/Xrr79q0aJFCgoKuq5f1ZAQvTf7TUnSd2vX6MdN32vy+HGSpFlJ7+izZcv08qSJSlm1Qk/366eY50Zp4w+bCxUDAJQE5Jn8kWcAALAP5l0DsKuHHnpITZs21WtvzNKMaVOtloUEB2vIwAGW5wN691bKt99p+cpVatakiaU9vEYNjR/9guX57iNHJUmTJk1Sx44dLe0VK1ZUk7+sN3nyZH355Zdavny5YmJiVLFiRXl6eqpcuXIKDr5yVZ20/Wevi7nHgw/q/Q8/0uiRIyRd+db/p59/1lvTX5Ukzf/wIzVs0EAvjnrOss7r0xLVvM29+u2331SnTp0C98fZs2f1xhtv6K233lKfPn0kSTVr1tQ999xzXV9PT09VCCgvSapcqZLlWjHZ2Tma9XaSPl24QC3ubCZJqlG9ujZv2aKPFi9Wq4iWBW4fAEoa8ow18gwAAPZDkQyA3b388svq0KGDBg/sb9Wem5urN95O0lcrVyktPV05ly4pJydHpX1LW/Vr3PCOfMdt0aKF1fNz585pwoQJWrFixZWLEl++rAsXLli+4S+s7l27aNK0l7V1+w41b9ZUS/+5XI3uuEO1a9aUJP26a7c2/vCDajZuet26+/fvv+GHl127dik7O1v33XdfgX1u5tDhw7pw4YJ69u1n1X7p0iU1bFC/yOOiZPGtEOvqEACnIc/8iTwDAID9UCQDYHf33nuv2rW5R1Nfm66eD/ewtM+Z+57eW7hQk8aOVf26dVSmdBnFT5minEvWF00uU6ZMvuOWLVvW6vmoUaO0du1avfbaa6pVq5ZKly6tRx55JN+LMN9IYJUqan333fryq6/UvFlTffnV1+r9xOOW5Vnns9SxQ3uNe/55q/Wq1AhTSEjIDccuXbr0DZcXRtb585KkD+e+q5BrTp/x9va+5fEBoLghz/yJPAMAgP1QJAPgEGOfH6Wobn9Trdtvt7T9d+s2dbovSo90/5ukKxcJPnDwkGrXqlmkbXz//ffq27evHnroIUlXvvE/dOiQVR9vb2/l5ubedKyH/9ZNk19+Vd27dtXhI0fUvWsXy7JGd9yhFWvWKPS2alZ3BwuuWeum49auXVulS5dWcnKyBg4ceNP+pbxLSZJVzHVq1ZSPt7eO/v47p7wAwP8jz1xBngEAwH64cD8Ah6hft656PNhN8xZ+YGkLD6uhf3//vf67bZt+27dPz48brxOFvHNXfmrXrq2lS5dqx44d+vHHH/XEE08oLy/Pqk9YWJg2bNigo0eP3vAuYZ3vv1/nsrI0JiFBre+OUPBfvknv99STOnMmQ0NGxGrHTz/p0OFUrd/wrfr163fTD0a+vr4aPXq0XnjhBX3wwQfav3+//vOf/2jevHn59r+talWZTCZ9s369Tp46raysLPn5+WnwwAFKmDpVny5dqkOHU/XTz79o3gcf6NOlS23YYwBQcpBnriDPAABgPxTJADjMCyOetfowMWLYUDW6o4Ee7zdADz/ZS4FVqqhTx6gijz9jxgxVqFBBrVq1Urdu3RQdHa0777zTqs+kSZN06NAh1axZU1WqVClwLD8/P93fob1+2bVbPR580GpZcFCQln+6WLl5uXqsb3+179JV8VOmqHz58vLwuPlhdPz48XruuecUHx+v+vXrq2fPnjp+/Hi+fUOCgzXq2eGa8up0Nb47Ui9OnCRJGj1yhEYOG6ZZSe/o3k4P6In+A/TN+n8r9LbQm24fAEoq8swV5BkAAOzDZDabza4O4mYyMzMVEBCgjIwM+f//XXjyM71nV7tt87klX9ttLOKyjZHjunjxog4ePKjw8HD5+voWaqy0/XvtFldwzdp2G4u4bOOIuG70firscdUobNkf7nqMmj14nV3GGZbUwS7jSO67r4wcF3kmf8RlG/KM7dgfAGBfjjquMpMMAAAAAAAAhkeRDABuQWpqqvz8/Ap8pKamujpEAEAxRp4BAMB5uLslANyCqlWraseOHTdcDgBAUZFnAABwHopkAHALvLy8VKtWLVeHAQAoocgzAAA4D6dbAgAAAAAAwPAokgEAAAAAAMDwKJIBAAAAAADA8CiSAQAAAAAAwPAokgEAAAAAAMDwKJIBcLklXyxV3WbNXR0GAKAEI9cAAICb8XJ1AAAKp9HCRo7fyHd//rizz06bVz9y5IgSEhK0evVqnTx5UoFVqqhTxyjFxgxTxQoVJEl3tW2vQX376Ol+fe0UNADAHopDnpH+zDUrV6zQ6T/+INcAAAC7YSYZALs4cOCAWrRoob179+qTTz7RxuS1ennyRH23cZO6/b2n/jhzxukxXbp0yenbBAA4zl9zzZzXZ5BrAACAXVEkA2AXw4YNk7e3t/71r3+pbdu2uq1qVd3Xtq0+/WCBjqWna9qM19Xjiaf0v6NHlTBlqkJq1VFIrTpWY6zf8K3aRHdSzcZN9Xi/AUo/ftxq+Xvvvaf69evL19dX9erV05w5cyzLDh06JJPJpCVLlqht27by9fXVxx9/7JTXDgBwjr/mmlYRLck1AADArjjdEsAtO336tNasWaMpU6aodOnSVssCq1RRjwe7afmKlfr+m3+pY7e/6cnHeuqpno9a9btw8aKS5s3Tm6+9Kg+Th2KeG6WJ017WnBnTJUkff/yx4uPj9dZbb6lZs2bavn27Bg0apLJly6pPnz6WccaMGaPp06erWbNm8vX1dfyLBwA4xbW5JuMvy2411yz953JJ5BoAAIyOIhmAW7Z3716ZzWbVr18/3+W1a9bUmYwM5eXlycPTU35lyyqwShWrPpcuXdLLkyYprEZ1SVL/Xk9pxluzLcsTEhI0ffp09ejRQ5IUHh6uX3/9Ve+8847VB5cRI0ZY+gAASg5yDQAAcDSKZADsxmw2F3nd0qVLWz60SFJgYBWdPHVKkpSVlaX9+/drwIABGjRokKXP5cuXFRAQYDVOixYtihwDAMD9kWsAAICjlKgimW+FWFeHABhSrVq1ZDKZtGvXLj300EPXLd+7f7/KBwSoUsWKBY5Rysv6cGQymSwfhM6dOydJmjt3riIiIqz6eXp6Wj0vW7ZskV4DAMC9kWsAAICjlagiGQDXqFSpkjp27Kg5c+Zo5MiRVtclO37ihJYu/0p/f6i7TCaTvEuVUl5urk3jBwUFqWrVqjpw4ICefPJJe4cP2IQvZADXuDbX/BW5BgAA2AN3twRgF2+99Zays7MVHR2tDRs26Ojvx7Tu3xvUs08/hQQFaUzslQ80t1Wrpv/8d4uOpaXp1OnThR5/4sSJSkxM1KxZs/Tbb79p586dev/99zVjxgxHvSQAgJv5a67ZtPm/5BoAAGBXzCRzAmYdwAhq166tLVu2KCEhQY8++qhOnz6tKpUr64GOUYp9JkYVypeXJL0w4lm9MH68IjtEKTsnR8f2/Vao8QcOHKgyZcro1Vdf1fPPP6+yZcuqUaNGGjFihONeFADArfw11/xj+LM6k5FBrgEAAHZDkQwoJnb22Zlve9r+vXbbRnDN2re0fo0aNbRgwQJJBcfVvFlTJX/9lVVbz4d7qOfD1ncJe6Bjx+s+1DzxxBN64okn8h03LCzsli7mDABGVxzyjPRnrrlRXOQaAABQFJxuCQAAAAAAAMOjSAYAAAAAAADDo0gGAAAAAAAAw6NIBgAAAAAAAMOjSAYAAAAAAADDo0gGAAAAAAAAw6NIBgAAAAAAAMOjSAYAAAAAAADDo0gGAAAAAAAAw6NIBsBtbN66Ve07d1VovQbqO3iINv7nB4XUqqMzZ844dLs9nnhK41+a4tBtAABcjzwDAABuxMvVAQAonF316jt8G3/85ef6u3fZtG7fvn21cOFCSZKXl5dCgoPV7YFOen7Es/L18SnUGBOmJuqO+vX08bz3VLZsGf3yq20x3ExKSorat2+v3du2KMDf39I+b85bKuXF4RCAsZFnbh15BgCA4o2ZZADsplOnTjp27JgOHDigiWPj9OEni/XaG7MKvf6h1FTdExmpqiHBVh8uHK1C+fLy8/Nz2vYAAEVDngEAAI5EkQyA3fj4+Cg4OFihoaF6oGNHtWndSv/+7ntJUl5enma9naSW7Too/I5Guq9rN329arUk6cj//qeQWnX0xx9nNHJMnEJq1dGSL5bmu43vvvtObdq0UenSpRUaGqrhw4crKyvLsjw7O1ujR49WaGiofHx8VKtWLc2bN0+HDh1S+/btJUn17myhkFp19OwLoyVdfxrMmYwMPTPqedW7s4XCGzbWE/0H6MChQ5blS75YqrrNmmvNmjWqX7++/Pz8LB/cAACOQ54hzwAA4EgUyQA4xO7fftOWbdvlXaqUJGlW0jv6bNkyvTxpolJWrdDT/fop5rlR2vjDZlUNCdGPm75XOT8/TRo3Vj9u+l4Pdul83Zj79+9Xp06d9PDDD+unn37SkiVL9N133ykmJsbSp3fv3vrkk080a9Ys7dq1S++88478/PwUGhqqL774QpL03do1+nHT95o8fly+sY94YYx+3PmzFrzztr7+bInMZumpAYN06dIlS58LFy/qtdde04cffqgNGzYoNTVVo0aNsucuBADcAHkGt2L27NkKCwuTr6+vIiIitHnz5hv2nzlzpurWrWspno4cOVIXL150UrQAAGfh4ggA7Obrr7+Wn5+fLl++rOzsbHl4eGhKQryys3M06+0kfbpwgVrc2UySVKN6dW3eskUfLV6sVhEtFVilikwmk/zLlVNglSr5jp+YmKgnn3xSI0aMkCTVrl1bs2bNUtu2bfX2228rNTVVn376qdauXauoqChJ0u23325Zv2LFipKkypUqFXiazYFDh7QmOVnLP12su+68U5I0e8Zrat6mrVav/UbdOj8gSbp06ZKSkpJUs2ZNSVJMTIwmTZp0i3sQAHAj5BnyjD0sWbJEsbGxSkpKUkREhGbOnKno6Gjt2bNHgYGB1/VftGiRxowZo/nz56tVq1b67bff1LdvX5lMJs2YMcMFrwAA4CgUyQDYTfv27fX2228rKytLUydNlJenp7p2itae3/bqwoUL6tm3n1X/S5cuqWGDwl8o+scff9RPP/2kjz/+2NJmNpuVl5engwcPaufOnfL09FTbtm2L/Br27tsvLy8v3dmkiaWtYoUKqhUerr3791vaSpcubfngIkkhISE6fvx4kbcLALg58gx5xh5mzJihQYMGqV+/K++XpKQkrVixQvPnz9eYMWOu679x40a1bt1aTzzxhCQpLCxMjz/+uH744Qenxg0AcDyKZADspmzZsqpVq5Yk6fVpibqv64Na9OlnqlenjiTpw7nvKiQoyGodb2/vQo9/7tw5/eMf/9Dw4cOvW1a9enXt27fvFqK3zbV3KTOZTDKbzU7bPgAYEXmGPHOrcnJytHXrVsXFxVnaPDw8FBUVpU2bNuW7TqtWrfTRRx9p8+bNatmypQ4cOKCVK1eqV69eBW4nOztb2dnZlueZmZn2exEAAIehSAbAITw8PDR8yGBNmJqo79eukY+3t47+/rtaRbQs8ph33nmnfv31V8sHpGs1atRIeXl5+ve//205Deavrn5Qys3NLXAbtWvV1OXLl7Xtxx8tp8Gc/uMP7Tt4UHUK2C4AwPnIMyiKkydPKjc3V0HXFFODgoK0e/fufNd54okndPLkSd1zzz0ym826fPmyBg8erBdffLHA7SQmJmrixIl2jR0A4HhcuB+Aw3R7oJM8PTz04eIlGjxwgBKmTtWnS5fq0OFU/fTzL5r3wQf6dGn+dxfLz+jRo7Vx40bFxMRox44d2rt3r/75z39aLqgcFhamPn36qH///lq2bJkOHjyolJQUffrpp5KkGjVqyGQy6Zv163Xy1Gmru5VddXtYmKKj7tOoF8fphy1b9MuuXYp5bpRCgoIUHXWffXYMAMAuyDNwhpSUFE2dOlVz5szRtm3btHTpUq1YsUKTJ08ucJ24uDhlZGRYHkeOHHFixACAomImGdyOb4VYV4cAO/Hy8lK/Xk9p9rtztTllnSpVrKhZSe8o9ch4+Zcrp0Z33KHhQwYXerzGjRvr3//+t8aOHas2bdrIbDarZs2a6tmzp6XP22+/rRdffFFDhw7VqVOnVL16dcs3vdWqVdOoZ4dryqvTNWJ0nP7+UHe98crL121n5svTNH7yS+o96B/KuXRJd991lz6aN1el/v8OagAA90Cega0qV64sT09PpaenW7Wnp6crODg433XGjx+vXr16aeDAgZKuzCjMysrS008/rbFjx8rD4/p5Bz4+PvLx8bH/CwAAOJTJXAwubpCZmamAgABlZGTIv4A7BUnS7MHr7LbNYUkd7DaWu8Y1vWdXu4313JKv7TbWrnqFv8DuzdTfvctuYzljf128eFEHDx5UeHi4fH19CzVW2v69dosruGZtu41FXLZxRFw3ej8V9rhqFLbsD3c9ptsrLiPkGSPHRZ7JH3HZprjnmYiICLVs2VJvvvmmJCkvL0/Vq1dXTExMvhfub968uaKiovTyy38WPD/55BMNGDBAZ8+elaen50236c77AwCKI0cdV5lJBgAAAMAwYmNj1adPH7Vo0UItW7bUzJkzlZWVZbnbZe/evVWtWjUlJiZKkrp166YZM2aoWbNmioiI0L59+zR+/Hh169atUAUyAEDxQZEMAAAAgGH07NlTJ06cUHx8vNLS0tS0aVOtXr3acjH/1NRUq1Mox40bJ5PJpHHjxuno0aOqUqWKunXrpilTprjqJQAAHIQiGQAAAABDiYmJsdyQ4VopKSlWz728vJSQkKCEhAQnRAYAcCXubgkAAAAAAADDo0gGAAAAAAAAw6NIBgAAAAAAAMOjSAYAAAAAAADDo0gGAAAAAAAAwytSkWz27NkKCwuTr6+vIiIitHnz5hv2nzlzpurWravSpUsrNDRUI0eO1MWLF4sUMACg5CPPAAAAAHA2m4tkS5YsUWxsrBISErRt2zY1adJE0dHROn78eL79Fy1apDFjxighIUG7du3SvHnztGTJEr344ou3HDyA4iWkVh2tWrvWrmNOmDBBTZs2teuYcC3yDICiIs8AAIBb4WXrCjNmzNCgQYPUr18/SVJSUpJWrFih+fPna8yYMdf137hxo1q3bq0nnnhCkhQWFqbHH39cP/zwwy2GDhjL7MHrnLCVI5afhiV1sHntEydOKD4+XitWrFB6WpoCAgLUoF49xT4zTC2bN9ePm75XgH+APQNGCUSegaN1/nG/q0NwS+QZAABgdDYVyXJycrR161bFxcVZ2jw8PBQVFaVNmzblu06rVq300UcfafPmzWrZsqUOHDiglStXqlevXgVuJzs7W9nZ2ZbnmZmZtoQJwEUefvhh5eTkaOHChfLzNOnkyZP6duMm/fHHGUlSYJUqrg0Qbo88A+BGyDMAAMCRbDrd8uTJk8rNzVVQUJBVe1BQkNLS0vJd54knntCkSZN0zz33qFSpUqpZs6batWt3w9NgEhMTFRAQYHmEhobaEiYAFzhz5oy+/fZbvfzyy2rfvr1Cq1VTsyZNNHzIYEVH3SfJ+jSYI//7n0Jq1dGKNWv08JO9FN6wse7r2k1btm23GvejxUsUGhqqMmXK6KGHHtKMGTNUvnz5G8by3nvvqX79+vL19VW9evU0Z86cQr2GnJwcvThhoppEtlZYg4ZqcW87zXo7ySren3/91eo1m0wmpaSkSJJSUlJkMpm0Zs0aNWvWTKVLl1aHDh10/PhxrVq1SvXr15e/v7+eeOIJnT9/3jJOu3btNHbiJI1/aYrq3dlCjSIi9dHiJTp//rxGjB6jWk2aKbJDlJL//e9CvY7ijDwDoCDkGfIMAACO5vC7W6akpGjq1KmaM2eOtm3bpqVLl2rFihWaPHlygevExcUpIyPD8jhy5EiBfQG4Bz8/P/n5+WnZsmVWM3RuZtqM1zVkYH9989U/dXtYuIaMjNXly5clSZu3btXo+AQ9++yz2rFjhzp27KgpU6bccLyPP/5Y8fHxmjJlinbt2qWpU6dq/PjxWrhw4U1jmffBh1qTvE7vzJqpb9eu0ewZryn0ttsK/VqumjBhgt566y1t3LhRR44c0aOPPqqZM2dq0aJFWrFihf71r3/pzTfftFrn06VfqmKFClq59HP1791LYxImaNAzw9WiWTP9659fqu099+iZUc/r/IULNsdT0pFnAGMgz/yJPAMAgGPYdLpl5cqV5enpqfT0dKv29PR0BQcH57vO+PHj1atXLw0cOFCS1KhRI2VlZenpp5/W2LFj5eFxfZ3Ox8dHPj4+toQGwMW8vLy0YMECDRo0SElJSWrUoIHubnmXunftogb16hW43pABAxTVvr0kadSzw9Xugc46ePiwatesqfkffKgObe/VqFGjJEl16tTRxo0b9fXXXxc4XkJCgqZPn64ePXpIksLDw/Xrr7/qnXfeUZ8+fW74Go7+/rtuD6uhiBYtZDKZFFqtmq27QZL00ksvqXXr1pKkAQMGKC4uTvv379ftt98uSXrkkUe0fv16jR492rJOg/r1NHLYUEnS8MH/0FvvvKu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\n",
      "text/plain": [
       "<Figure size 1500x1200 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(15,12))\n",
    "\n",
    "filtered_datasets = [d for d in datasets if \"total\" not in d]\n",
    "model_indices = [\"A\", \"B\", \"C\", \"D\", \"E\", \"F\", \"G\", \"H\", \"I\"]\n",
    "\n",
    "plot_indices = [(0,0), (0,1), (0,2), (1,0), (1,1)]\n",
    "for dataset, plot_idx in zip(filtered_datasets, plot_indices):\n",
    "    data = {}\n",
    "    for label in labels:\n",
    "        data[label] = []\n",
    "\n",
    "    for df in df_list:\n",
    "        for idx, row in df.iterrows():\n",
    "            if idx == dataset:\n",
    "                for label in labels:\n",
    "                    data[label].append(row[label])\n",
    "       \n",
    "    models_df = pd.DataFrame(data, index=model_indices)\n",
    "    models_df = models_df.div(models_df.sum(axis=1), axis=0)\n",
    "\n",
    "    axes[plot_idx].set_title(dataset)\n",
    "    models_df.plot.bar(ax=axes[plot_idx], stacked=True)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "vscode": {
   "interpreter": {
    "hash": "4dbe9a16179a7a5dabaeea400cd2118eb95148fbb18ef137489d44400ff00a02"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}