pretrained_comparisons.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "sns.set_style(\"darkgrid\")\n",
    "sns.set_palette('colorblind')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        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>item</th>\n",
       "      <th>model</th>\n",
       "      <th>construction</th>\n",
       "      <th>condition</th>\n",
       "      <th>gap</th>\n",
       "      <th>gram</th>\n",
       "      <th>ungram</th>\n",
       "      <th>wh_effect</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>grnn</td>\n",
       "      <td>Wh-Movement</td>\n",
       "      <td>Simple</td>\n",
       "      <td>+gap</td>\n",
       "      <td>I know who the family from the big city saw la...</td>\n",
       "      <td>I know that the family from the big city saw l...</td>\n",
       "      <td>-1.910231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>grnn</td>\n",
       "      <td>Wh-Movement</td>\n",
       "      <td>Simple</td>\n",
       "      <td>-gap</td>\n",
       "      <td>I know that the family from the big city saw t...</td>\n",
       "      <td>I know who the family from the big city saw th...</td>\n",
       "      <td>0.583257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>grnn</td>\n",
       "      <td>Wh-Movement</td>\n",
       "      <td>Simple</td>\n",
       "      <td>+gap</td>\n",
       "      <td>I remember what the woman with the blue hat bo...</td>\n",
       "      <td>I remember that the woman with the blue hat bo...</td>\n",
       "      <td>-3.269071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>grnn</td>\n",
       "      <td>Wh-Movement</td>\n",
       "      <td>Simple</td>\n",
       "      <td>-gap</td>\n",
       "      <td>I remember that the woman with the blue hat bo...</td>\n",
       "      <td>I remember what the woman with the blue hat bo...</td>\n",
       "      <td>6.700210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>grnn</td>\n",
       "      <td>Wh-Movement</td>\n",
       "      <td>Simple</td>\n",
       "      <td>+gap</td>\n",
       "      <td>The newspaper reported who the man from far aw...</td>\n",
       "      <td>The newspaper reported that the man from far a...</td>\n",
       "      <td>-0.152476</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   item model construction condition   gap  \\\n",
       "0     0  grnn  Wh-Movement    Simple  +gap   \n",
       "1     0  grnn  Wh-Movement    Simple  -gap   \n",
       "2     1  grnn  Wh-Movement    Simple  +gap   \n",
       "3     1  grnn  Wh-Movement    Simple  -gap   \n",
       "4     2  grnn  Wh-Movement    Simple  +gap   \n",
       "\n",
       "                                                gram  \\\n",
       "0  I know who the family from the big city saw la...   \n",
       "1  I know that the family from the big city saw t...   \n",
       "2  I remember what the woman with the blue hat bo...   \n",
       "3  I remember that the woman with the blue hat bo...   \n",
       "4  The newspaper reported who the man from far aw...   \n",
       "\n",
       "                                              ungram  wh_effect  \n",
       "0  I know that the family from the big city saw l...  -1.910231  \n",
       "1  I know who the family from the big city saw th...   0.583257  \n",
       "2  I remember that the woman with the blue hat bo...  -3.269071  \n",
       "3  I remember what the woman with the blue hat bo...   6.700210  \n",
       "4  The newspaper reported that the man from far a...  -0.152476  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pretrained_df = pd.read_csv(\"data/pretrained_island_effects.csv\")\n",
    "pretrained_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>item</th>\n",
       "      <th>model</th>\n",
       "      <th>construction</th>\n",
       "      <th>condition</th>\n",
       "      <th>gap</th>\n",
       "      <th>gram</th>\n",
       "      <th>ungram</th>\n",
       "      <th>wh_effect</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>cleft</td>\n",
       "      <td>Wh-Movement</td>\n",
       "      <td>Simple</td>\n",
       "      <td>+gap</td>\n",
       "      <td>I know who the family from the big city saw la...</td>\n",
       "      <td>I know that the family from the big city saw l...</td>\n",
       "      <td>-3.157723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>cleft</td>\n",
       "      <td>Wh-Movement</td>\n",
       "      <td>Simple</td>\n",
       "      <td>-gap</td>\n",
       "      <td>I know that the family from the big city saw t...</td>\n",
       "      <td>I know who the family from the big city saw th...</td>\n",
       "      <td>3.212996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>cleft</td>\n",
       "      <td>Wh-Movement</td>\n",
       "      <td>Simple</td>\n",
       "      <td>+gap</td>\n",
       "      <td>I remember what the woman with the blue hat bo...</td>\n",
       "      <td>I remember that the woman with the blue hat bo...</td>\n",
       "      <td>-1.855624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>cleft</td>\n",
       "      <td>Wh-Movement</td>\n",
       "      <td>Simple</td>\n",
       "      <td>-gap</td>\n",
       "      <td>I remember that the woman with the blue hat bo...</td>\n",
       "      <td>I remember what the woman with the blue hat bo...</td>\n",
       "      <td>4.361690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2</td>\n",
       "      <td>cleft</td>\n",
       "      <td>Wh-Movement</td>\n",
       "      <td>Simple</td>\n",
       "      <td>+gap</td>\n",
       "      <td>The newspaper reported who the man from far aw...</td>\n",
       "      <td>The newspaper reported that the man from far a...</td>\n",
       "      <td>-0.475909</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   item  model construction condition   gap  \\\n",
       "0     0  cleft  Wh-Movement    Simple  +gap   \n",
       "1     0  cleft  Wh-Movement    Simple  -gap   \n",
       "2     1  cleft  Wh-Movement    Simple  +gap   \n",
       "3     1  cleft  Wh-Movement    Simple  -gap   \n",
       "4     2  cleft  Wh-Movement    Simple  +gap   \n",
       "\n",
       "                                                gram  \\\n",
       "0  I know who the family from the big city saw la...   \n",
       "1  I know that the family from the big city saw t...   \n",
       "2  I remember what the woman with the blue hat bo...   \n",
       "3  I remember that the woman with the blue hat bo...   \n",
       "4  The newspaper reported who the man from far aw...   \n",
       "\n",
       "                                              ungram  wh_effect  \n",
       "0  I know that the family from the big city saw l...  -3.157723  \n",
       "1  I know who the family from the big city saw th...   3.212996  \n",
       "2  I remember that the woman with the blue hat bo...  -1.855624  \n",
       "3  I remember what the woman with the blue hat bo...   4.361690  \n",
       "4  The newspaper reported that the man from far a...  -0.475909  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "augmented_df = pd.read_csv(\"data/augmented_island_effects.csv\")\n",
    "augmented_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "combined_effects = pd.concat([pretrained_df, augmented_df])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cleft-RNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/23/7hcv_qtd60j5mx97z8mfdjgh0000gn/T/ipykernel_8557/3767357994.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  cleft_data['model'] = cleft_data['model'].apply(lambda model: \"Pretrained\" if model == \"grnn\" else \"Cleft-RNN\")\n"
     ]
    }
   ],
   "source": [
    "cleft_data = combined_effects[combined_effects['model'].isin(['grnn', 'cleft'])]\n",
    "cleft_data['model'] = cleft_data['model'].apply(lambda model: \"Pretrained\" if model == \"grnn\" else \"Cleft-RNN\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "cleft_data.columns\n",
    "simple_cleft_data = cleft_data[cleft_data['condition'] == 'Simple']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Simple', 'Island'], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleft_data.condition.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filler-Gap Effects before and after training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1897.5x500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.65)\n",
    "g = sns.catplot(x=\"model\", y=\"wh_effect\", hue=\"gap\", data=simple_cleft_data, kind=\"bar\", col = \"construction\",\n",
    "            estimator=\"mean\", aspect = 0.7)\n",
    "g.set_axis_labels(\"\", \"Filler Effect\")\n",
    "g.set_xticklabels([\"Pretrained\" , \"Cleft-RNN\"])\n",
    "g.set_titles(template=\"{col_name}\")\n",
    "g.legend.set_title(\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_construction_islands(data, construction_name, shift = 1):\n",
    "    construction_results = data[data['construction'] == construction_name]\n",
    "    g = sns.catplot(data = construction_results, x = \"condition\", y = \"wh_effect\",\n",
    "                    hue = \"gap\", kind = \"bar\", col = \"model\")\n",
    "    g.set_axis_labels(\"\", \"Filler Effect\")\n",
    "    g.set_xticklabels([\"Simple\" , \"Island\"])\n",
    "    g.set_titles(template=\"{col_name}\")\n",
    "    g.legend.set_title(\"\")\n",
    "    plt.suptitle(construction_name, fontsize = 22, y = shift)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1147.5x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1147.5x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1147.5x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for construction in [\"Clefting\", \"Wh-Movement\", \"Tough Movement\"]:\n",
    "    plot_construction_islands(cleft_data, construction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Topic RNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/23/7hcv_qtd60j5mx97z8mfdjgh0000gn/T/ipykernel_8557/536196607.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  topic_data['model'] = topic_data['model'].apply(lambda model: \"Pretrained\" if model == \"grnn\" else \"Topic-RNN\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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Ek7taM2PGDHXp0kUNGzbU/fv3tXv3bsXGxkqKW7EmuTBBks6cOWO5nZlzNUyaNMkyYqN+/foaNWrUYx9j67q9vLxUpEgRSdLRo0cfO9In4c9OwhVgnqT4ZXTPnDmj4OBgnThxQlLiIKROnTqWFXiOHDliCUIaNmz42AlhAQDAk0UQAgDIEk2bNpWTk5Mkafny5SleBrF48WLLBJ3NmjVLtt3KlSuT3ff9999bbrdo0SJVNcbP7yDFTTYaGRlptV1YWJh2794tSapWrZo8PDwUGhpq2Z/wco9HnThxQsePH7fcj4mJSVVtj7Nu3TrLaipFihTRN99889jJZCVlWt0Jl91N64oy8f8+oaGhWrt2bbLtgoKCtHHjRklxc5/YamRFkyZNJMU9z6VLl1ou10lYT+7cuS2jnnbv3m157dJzWUxGXlsAAPB4BCEAgCxRqFAhvfLKK5LiLoF47733rM7DsX79ei1dulRS3KUq3bp1S7bPZcuWaefOnUm2+/r6Wv6grlevnurUqZOqGt3d3dW1a1dJcZfVjB07Nskf/FFRURo7dqxlks/4+ooXL25p8/PPP1sd2XD69GmNGDEi0R+zyYUtaXHixAl98sknkuIuJZo1a5by58+fqsdmVt0J51eJH4mTWr169bKEZAmX/E0oNDRUw4cPt7zuffv2TTG4yUqFChWyhBzLli2TFLdcc8JLq6T/D0b++OMPy1K96QlCMvLaAgCAx2OOEABAlhkzZowOHz6sa9euafv27WrTpo06deqkZ555RmFhYdqxY4f27NkjKe5b8AkTJqT4B31sbKyGDh2qdu3aqUmTJoqNjdXOnTu1Y8cOSXHBRnxAkFrDhw/Xrl27dPnyZa1fv16nTp3Sa6+9pqJFi+ratWtat26dZZ6Khg0b6tVXX5UUN3LF09NToaGhOnv2rNq3b68uXbqoaNGiCg4O1oEDB/T7778nCVYyOu9GcHCwhg0bZgkwevbsqaioKO3bt08RERHJXsLi6empOnXqZFrd+fLlk6urqyIiIuTr66vy5csrT548qlGjxmOXIi5Tpow++OADff755woPD1ffvn3l4+Ojxo0by83NTefPn9eqVassSzDXrFlTgwYNSu9LlileeOEFnTlzxhLMWBudUq9ePcsSxlLcpTzxlwGlRcLHzJgxQ1FRUYqNjVW7du1YJQYAgEzA2RQAkGXy5Mmj5cuXa9iwYTpx4oQCAwM1ffr0JO08PT311Vdf6fnnn0+xvw8//FCTJk3Sxo0bLZdMxPPy8tL8+fP17LPPpqlGd3d3ff/99xo8eLBOnjypCxcuaOrUqUnaNWrUSDNmzJDBYJAkeXh4aOrUqXrnnXcUERGhK1euWH2ct7e3Bg8erJEjR0qKm8skIy5cuKCgoCDL/QULFlgmP01JvXr1tGzZskyr22AwqHXr1vL19dWDBw8sAdQnn3yi7t27P7aenj17ymg0avLkyYqKitIvv/yiX375JUm7du3aafz48TYPAF544QXNmzfPct/aPDTx84TEj6RJ72oxlStXVunSpeXv768LFy7oP//5j6S4eXfKlSuXrj4BAMD/IwgBAGSpwoULa9WqVdqyZYs2b96skydPKiQkRJ6enipZsqRat26tDh06KE+ePI/tq1evXqpcubIWLFig48ePKzw8XKVLl9bLL7+s3r17K2/evOmq0cvLS6tWrdKGDRu0adMmnTlzRnfv3lWuXLlUpUoVvfbaa2rTpk2iuRukuD90161bp++++04HDhxQUFCQjEaj8ufPrwoVKqh169Zq06aNHBwc5OXlpVu3bunXX3/Ve++9l6SvJymz6v7000+VN29ebdu2Tbdv35arq2uaLuXo0aOHXnzxRf3www/au3evAgMDFRsbq6JFi6p69erq0qWLateunRUvQZrVqFFDuXPntoyMsTYiJG/evPL29tbZs2clpT8IcXJy0uLFizVlyhQdOnRI9+7dU968efXvv/8ShAAAkAkM5sycvh4AgEzWs2dPy8ooGR1NAQAAADBZKgAAAAAAsBsEIQAAAAAAwG4QhAAAAAAAALtBEAIAAAAAAOwGQQgAAAAAALAbrBoDAAAAAADsBiNCAAAAAACA3SAIAQAAAAAAdoMgBAAAAAAA2A2CEAAAAAAAYDcIQgAAeAqNHj1aFSpUUIUKFXTw4MEsO47ZbNaKFSt069atLDsGAADAk0QQAgAArPL399ebb76pzz77TJGRkbYuBwAAIFMQhAAAAKs2bNigI0eO2LoMAACATEUQAgAAAAAA7AZBCAAAAAAAsBsEIQAAAAAAwG442roAAACQ+SpUqCBJGjFihIYMGaKDBw9q+fLl+uuvvxQcHKx8+fKpbt266tmzp2rWrJnosTNmzNDMmTMTbWvRooUkqXjx4vrtt98StXNwcNDp06fl6+urefPmKSAgQPny5VPVqlX1xRdfyN3d3dLPzZs39cMPP2jv3r26du2aoqOjVbBgQdWqVUudOnVSw4YNs/JlAQAAIAgBACCn+/rrrzVv3jyZzWbLtn///VebN2/Wli1b9O6772rAgAEZOsbKlSv16aefJur/33//TRSCrFixQpMnT06yAk1gYKACAwO1ceNG+fj4aMqUKXJzc8tQPQAAAMkhCAEAIAfbsGGDrly5oly5cqlz586qVq2awsPDtWnTJh08eFBms1lfffWVnn/+eVWuXFmS1KZNG1WqVMkSlEjSuHHjVKBAAbm6uiY5hslk0sSJE5UnTx699dZbKlGihI4ePaqqVata2qxYsUKfffaZJMlgMMjHx0eNGzeWu7u7zp49q9WrVyskJETbtm1TSEiIlixZIgcHhyfwCgEAAHtDEAIAQA525coVFS9eXN9//71KlChh2d61a1eNGTNG69atk9ls1tq1ay1BSLly5VSuXDmdOXPG0v75559P9PiEzGazoqOjtWzZMtWoUUOS9Oqrr1r2BwQEaOLEiZIkd3d3zZo1S40aNbLsb9u2rfr27asBAwboxIkTOnTokBYuXKiBAwdm2usAAAAQj8lSAQDI4T788EOrIcaQIUMst0+ePJmhYzRo0MASgjxq0aJFio6OliS9//77iUKQePny5dPMmTPl4eEhSVq8eLEiIiIyVBMAAIA1BCEAAORgzs7OatasmdV9JUuWVK5cuSRJYWFhGTpOnTp1kt33xx9/SJI8PT3VqVOnZNsVLlxYHTp0kCSFhITo2LFjGaoJAADAGoIQAABysDJlysjRMfkrYeNHYMTExGToOGXLlrW6/datWwoMDJQk1a5dW87Ozin2k3DVmBMnTmSoJgAAAGsIQgAAyMHig47kxIckCVeUSY/cuXNb3X7nzh3L7ZIlSz62n4SX8Ny+fTtDNQEAAFhDEAIAQA6W0miQzJTcSI8HDx5YbqdmSdyEbR4+fJjxwgAAAB5BEAIAALKMu7u75XZqgo3w8HDL7dQEJwAAAGlFEAIAALJMwYIFLbevXbv22PZ+fn6W24UKFcqKkgAAgJ0jCAEAAFnGy8tLRYoUkSQdPXpUUVFRKbY/cOCA5XaFChWytDYAAGCfCEIAAIBVBoPBcjsjk6m2aNFCkhQaGqq1a9cm2y4oKEgbN26UJOXKlUt169ZN9zEBAACSQxACAACsSjhHx927d9PdT69eveTk5CRJmjp1qv78888kbUJDQzV8+HDL5Kp9+/ZNNL8IAABAZnkyU8kDwCNiY2Pl4OBg6zIApCD+khZJmjRpknr06CFJatOmTZr6KVOmjD744AN9/vnnCg8PV9++feXj46PGjRvLzc1N58+f16pVqxQcHCxJqlmzpgYNGpR5TwQAACABghAgh2jevLkCAwOT3e/s7Cx3d3cVL15c9evXV/fu3VWyZMknWGGcqKgozZs3Ty4uLhowYMATPXZAQIBliP7UqVP16quvPtHjp9fBgwfVq1cvSdLOnTtVokQJG1cEe9GoUSN5enoqNDRUR44c0ZEjR2QwGNSsWbM0j9bo2bOnjEajJk+erKioKP3yyy/65ZdfkrRr166dxo8f/8SW/cWTsW7dOo0ZMybdj//+++9Vv379TKzo8eLPq507d9aECROeyDFnzJihmTNnptjGaDTK1dVVXl5eqlSpkjp27KimTZtabTt69Gj5+vpKSv1576OPPtKaNWtUvHhx/fbbb4n29ezZU4cOHZLBYNAPP/ygOnXqPLa/t956S3/++afq1aunZcuWPbY9ADwJXBoD2ImoqCiFhobq1KlT+u6779S6dWvLtfhPUq9evTRz5kxFRkY+8WMDSJt8+fJp6dKlaty4sfLkySNHR0d5eXnpxo0b6eqvR48e2rZtm/r166cKFSrIw8NDbm5uKlu2rF577TWtWLFCX375JZfEACkwmUwKDw+Xv7+/tm7dqgEDBuijjz567OMmTpyo27dvZ0oNZrNZH330kSIiIjKlPwB40vi6BchhateurQULFiTZbjKZdO/ePe3bt09ffvmlQkJCNHr0aJUvX14VK1Z8YvX9+++/T+xYj3JyclKpUqUkxU3ECDzNJk+erMmTJye7/9y5c6nq59FvfB9VsWJFLVq0yOq+4cOHa/jw4ak6TrxixYrp/fff1/vvv5+mx+Hp1r59e/n4+Fjd165dO12/fj3Z85ckubq6ZmV5VhUvXlwODg4qUKDAEz+2JG3evFlFixZNst1kMikkJESHDx/W7NmzFRAQoDVr1ui5556zXL5mTWhoqD777DPNmDEjU+rz8/PTN998o9GjR2dKfwDwJBGEADmMg4NDsn/k586dW126dFG5cuXUvXt3xcTEaM6cOfr222+fcJW2UbhwYW3fvt3WZQCA3XF0dEz2cqf41YlSOn/Zgq0v43B1dU3xfF6qVCk1a9ZM7du31+3bt7Vo0SJ169ZNRmPyA75//fVXbdmyJc3z/CRn6dKl8vHxUc2aNTOlPwB4Urg0BrBDtWrVslxr/ccff2RoWUwAAGAbBQoUUJcuXSRJgYGByc4VVqBAAeXPn1+SNH78eMvExOlVqlQpubi4yGQy6cMPP1RUVFSG+gOAJ40gBLBTlSpVkiQ9fPhQISEhkuImhqtQoYJ+/vln7dq1S+3atVOVKlXUuHFjffrpp4keHxUVpR9++EHdu3dX/fr1VbVqVTVv3lxjxoyxOiR/9OjRqlChguVD2syZM1WhQgVVqFDB0qZnz56qUKGCZs+erRMnTqhz586qWrWqGjZsqKFDhyYKbG7duqUZM2bojTfeUMOGDVWlShXVqVNHr7zyiiZOnKjr168nqSEgIMByzJ9//jnRvvjtR44cUWhoqKZOnSofHx9Vq1ZN9evXV//+/bVr164UX9P79+9r7ty56tSpk+rUqaNq1arJx8dH48aNU0BAQIqPvXfvnubOnatXX31VNWvWVIMGDTR8+HCdPn06xccBgD2IiIjQsmXL1KNHD9WtW1dVqlRRs2bNNGrUKB09etTqYw4ePGj53R4TE6OjR4+qf//+ql+/vmrXrq2OHTtqyZIlevjwodXHx58Tk5t/4969e1q6dKlef/111a9fX1WqVNFLL72kTz/9VNeuXcu05/44hQsXttxObg6QXLly6eOPP5YkBQcHa/z48Rk6ZvHixTVs2DBJ0uXLlzV9+vQM9QcATxqXxgB2Kn4osqQkw2gPHz6sdevWKTY2VlJc6JA7d27L/sDAQA0YMEAXL15M9LjAwECtW7dO69ev1wcffKC33norXbVdvnxZCxYsUHh4uKS4D20uLi6Wmnfu3KlRo0Yl+fAaHR2te/fu6fz581q3bp2WLFmiKlWqpOnY/v7+GjlyZKK5TCIjI7Vnzx7t2bNHw4cPt3z4S+jMmTMaOHCggoKCEm338/OTn5+f1qxZo8mTJ1sdjuzn56d+/folCkvCw8P166+/6vfff1e3bt3S9BwAICe5fPmyhgwZoitXriTafuPGDW3atEmbNm1S7969NXr06GQvC/n111/13nvvKSYmxrLt1KlTOnXqlNatW6dFixbJy8sr1TWdPXtWQ4YMSTIC4+rVq7p69ap+/vlnzZgxQ40bN07DM02fhOfilJ5DmzZttGXLFm3fvt1yecxLL72U7uP269dP27Zt0z///KPvvvtOPj4+qlq1arr7A4AniREhgJ06efKkpLjhsp6enon2rV69WkWKFNGSJUu0b98+TZ8+XV27dpUkPXjwQH379tXFixfl7u6u999/X9u3b9eBAwe0fPlyvfjiizKZTJo0aVKiVWnGjRunY8eOqVixYpKkgQMH6tixYzp27FiS2jZu3CgnJyfNmjVL+/fv18KFC9W3b19JcWHLu+++q4cPH6ps2bKaPn26du7cqf3792v16tXq3LmzpLhv6lKaSDI548eP1/379/X+++9rx44d2r9/v77++mvLZHmzZ8+Wn59foscEBQWpT58+CgoKUsGCBTVu3Dj9/vvv+vPPP/Xdd9+pZs2aioyM1H/+8x8dPnw40WOjoqL09ttvKyAgQO7u7vrwww+1a9cu7d69W+PGjZO7u7u+//77ND8PAMgJgoOD1b9/f125ckUuLi4aPny4tm7dqgMHDmjZsmVq2LChpLi5Kr7++utk+/nwww/l4uKisWPHas+ePdqxY4cGDRoko9Goc+fOpWnS37t376pv374KDAxU7ty5NWbMGO3cuVN79uzRV199pSJFiig8PFwjR47UrVu3MvwapMTPz0/r16+XJHl7ez92efVPPvnEcs7/7LPPFBoamu5jOzg4aNKkSXJyclJsbCyXyAB4qhCEAHZo3759lj/Ik/s2aOrUqWrYsKEKFiwoHx8flSxZUpI0f/58+fn5ycnJSUuXLlW/fv1UqlQp5cuXT3Xq1NGcOXMsox4mTJhgWVrP2dlZuXLlsozqcHJyUq5cuZKdCO6jjz5Sy5YtVaBAATVp0sQysmP58uWKiIiQs7OzFi1aJB8fH5UoUUIFChRQtWrVNGHCBLVu3VqSdPTo0TQv7ffw4UPNnj1b/fr1U8mSJVWgQAG1adNG06ZNkyTFxsYmmXB12rRpCgkJkaenp3788Ue9/vrrKlasmPLnz6/nn39e33//vWrXrq3Y2FiNGzcu0WOXL1+uq1evymAwaPbs2erdu7eKFi2qwoUL6/XXX9fixYvl5OSUpucAADnFvHnzFBgYKKPRqNmzZ2vYsGF65plnlC9fPtWrV88yEkGSFixYoEuXLlntJzo6WgsWLFCPHj1UqFAhlSxZUiNHjtSHH34oSTp+/Li2bt2aqppmzJihO3fuyMnJSYsXL9Zbb72lEiVKqFChQmrbtq0WLFggJycnhYWFaeXKlel+7hEREXrw4EGS/27fvq0zZ87ou+++U7du3XT//n0ZjUaNGTPmsX16eXlZnvOtW7c0YcKEdNcnxYUvQ4YMkSSdP39es2fPzlB/APCkEIQAOUxsbKzVD07BwcE6ceKEvv76aw0ePFiSlCdPHg0aNChJH15eXqpTp06S7WazWatWrZIktW3bVtWqVUvSxmAw6IMPPpAkhYSEaOfOnWl+Dg4ODskGNN7e3nr99dc1YMAAy+iSR9WtW1dS3BKDd+/eTdOxq1SpYvmGMaEGDRpYlm9MOBT67t27lg/PPXr0sARGCTk7O+vdd9+VFPdB8a+//rLs27RpkySpadOmVo9buXJlderUKU3PAQByApPJpHXr1kmKu6zD2mUmRqNRn376qZydnWU2m/Xjjz9a7atTp06qXbt2ku1vvvmmypQpIyluudrU1PTLL79Ikl577TWrl4J4e3urXbt2qlWrVoaW/W3btq1q1aqV5L/nn39eHTp00JQpUxQcHCxPT099++23atSoUar6ffXVV9WsWTNJ0oYNGx47/9XjDBgwwDLv2MKFC3XmzJkM9QcATwJzhAA5zNGjR1WrVq3HtvP09NSMGTNUtGjRJPsSTmCa0MWLFy0zzVeqVEkPHjyw2i537twqXry4AgMDdfToUbVt2zYNzyBuNnp3d3er+zp06KAOHTok+9irV6/qwoULlvsJrwdPDWvhjhQXznh6eurmzZuKjIy0bD9+/Liio6MlSRUrVkz2NSlfvrycnZ0VFRWlo0ePqkaNGrp3755OnTolSWrSpEmyNbVs2TLZD/cAkFOdO3dOYWFhkpIfvShJ+fPnV7169bR3794klx/Gix8p+CiDwaBmzZppyZIlOnDgwGNrOn/+vGVC0vgwwZr0XJqZFk5OTmrRooUaNWqkNm3aJJrHKzXGjRuntm3b6t69exo7dqw2b96c5j7iOTo6atKkSercubOio6M1ZswYrVmzJtnlkgEgO+A3FGAnXF1dlSdPHnl7e6tRo0bq1KlTkrlB4uXLl8/q9oSz4E+aNEmTJk167HFv3ryZ5lqTO35CDx8+1P79+3X27FldvXpV/v7+unz5cpIRIGldGjilYzs7OyfpM+FrktprzONfk6CgIEtf1kaSxCtbtmyq+gWAnOTGjRuW2+XKlUuxbbly5bR3795Ej0nI29s72ceWKlVKkhQWFqbw8PBkg3hJiSbEjh9JkhoPHz6UyWSyus/Jyclyfklo586dljk/TCaTbt26pbVr12r27NmKjo5WcHCwmjVrlq4Ao3Dhwho9erQ++ugjBQUFadKkSZo4cWKa+4lXqVIlDRgwQLNnz9aZM2c0b948DR06NN39AUBWIwgBcph69epp2bJlGerDxcXF6vb79++nua/0PCa548dbunSppk+fnqRvR0dHVatWTQUKFNDvv/+e5uPG95EWGXlN4r/plCQ3N7dk26f3WzoAeJol/P2aUjgh/f/v0PjVxh6V0u/RhL9/79+/n+KxEobtabnspW3btklWmIk3bNiwxwbpRqNRhQsX1pAhQ+Tt7a3hw4fr0KFD6t27t1asWKH8+fOnupZ4nTt31i+//KK9e/dq7dq1yV5+lFqDBw/Wjh07dP78ec2ZM0cvvfRSigEUANgSQQiAVEv4YXHBggV64YUXnngNixYt0tSpUyVJxYsXV8uWLfXcc8+pbNmy8vb2lqurq9atW5fuICStEr4mW7Zseey3lgnlzZvXcvvRpYATYhZ+APYoYSCRXMARL/6yxORCjMjISKujLhL2bTAYkh0paa2mtE7GnVlatmypAQMGaO7cubpy5YreffddLV682DIZeVqMHz9e7dq104MHD/Txxx8nWu0trZydnTVhwgS98cYblktk4ucVA4DshslSAaRawvlEkvtmK15aL0lJjYcPH2rWrFmS4uby+OWXX/Thhx+qQ4cOqlatmuXbuZCQkEw/dnIy8poULlxYRmPcr+ErV64k+7iEl98AgL0oXry45XZyq8E8uj+5SbRT+j0avyR6wYIFkw1L4hUuXDhVfR4+fFgzZ87U+vXrZTab9dtvv+ncuXNW/0vL0r3xhg8frsqVK0uS/vzzTy1dujTNfUhxr9d7770nSbp+/brli4b0qlatmmW5+3/++UeLFi3KUH8AkFUIQgCkWsWKFS3L3f7222/Jtvv3339Vq1YttWjRIt0fzqy5ePGi5Vu/Dh06JHsJzZ9//mm5nRWBTEK1atWyfAuX0mty6tQpVa9eXT4+PpYVBzw8PFSzZk1J0o4dO5J97O7duzOxYgB4Onh7e1suaXl02fKEgoODLZOk1qhRw2qbPXv2WN1uMpksIwhTM8qxYsWKllEhe/fuTbbdmjVrNGPGDM2aNStdIzUex9HRURMmTLBczvntt98mOz/K47zxxhuqX7++JGnVqlU6duxYhmobPny4ZW6rGTNmKCAgIEP9AUBWIAgBkGqOjo7q2LGjpLg/zn/99Ver7aZOnarw8HAFBARYvrGK5+TkJEmWlVbSevx4yX07uHHjxkQfeLP6shIvLy/LygFr1qxJtDRuwhomT56syMhIBQQEqEqVKpZ98a/n4cOHrS7deO3aNX3//fdZUjsAZGcODg6W35FbtmzRvn37krQxmUwaN26coqOjZTAY9Nprr1nta/HixYkmOk24Pf4P9ZRWJIvn5OSkV155RZK0evVqq+eiS5cuWZZVb9OmzWP7TK9KlSqpV69ekuIu7xk/fny6+jEYDJowYYLc3NxkNpt1+fLlDNXl4uKiiRMnymg0KioqilGNALIlghAAaTJkyBAVKVJEkjRy5Eh9+eWXunjxokJCQvTXX39p+PDhlmuMW7VqpTp16iR6fPz113/88Ydu3LihO3fupPrY5cuXl5eXlyTpxx9/1Lx583T16lUFBwfr2LFj+u9//2sZ4hsvueVsM9Po0aOVK1cuRUdHq0+fPpo/f76lrj///FP9+vXToUOHJEm9e/dOtEJMp06dLKNCPvjgA82YMUPXrl3TnTt3tHHjRnXv3j3F+UMAICcbMmSIihYtKpPJpMGDB2vWrFny8/NTaGiojhw5orffftsyyq5Pnz7JLoEeEhKibt26adu2bQoODtaVK1c0ZcoUTZs2TZLUrl071atXL1U1DRs2TPnz51dERITefPNNrV69WkFBQbp+/brWr1+vPn36KCIiQl5eXpbLRLLK8OHDLZcD7dy5M8XRhSkpWbKk3n333Uyrq2bNmpaQBgCyIyZLBZAm+fPn13fffaeBAwfq2rVrmj9/vubPn5+kXePGja0uxdewYUP99ddfOnv2rGUkxZ49e1SoUKHHHtvR0VGfffaZ3nnnHcXExOirr77SV199laiNk5OT+vfvrzlz5kiS/P39k/1gnFnKlCmj+fPna/jw4QoODtaXX36pL7/8Mkm7Dh06JPmgaTAYNHv2bA0cOFAnTpzQzJkzNXPmTMt+o9Go999/X5MnT87S5wAA2ZGnp6flnHP16lVNnz5d06dPT9KuT58+GjVqVLL9tGvXTps3b9Y777yTZF/Lli01YcKEVNdUqFAhzZ8/X4MGDdLt27f13//+N0kbLy8vzZ8/P9Gk2FnB3d1d//3vfzVkyBBJ0oQJE9SoUaPHrrJjzZtvvqmtW7fq6NGjmVLbyJEj9fvvv8vf3z9T+gOAzMSIEABpVq5cOW3atEkffvih6tSpI09PTzk6Oipfvnxq3LixvvjiCy1cuNAyn0hCQ4YMUZ8+fVSoUCE5OTnJy8vrsZOMJtSiRQutXLlSPj4+KliwoBwcHOTu7q7y5curR48e+vnnn/U///M/ltVbUrquPDPVqVNH27Zt0zvvvKOqVasqT548cnR0lJeXl1q0aKH58+drypQpVpfnzZ8/v5YvX65PP/1U1apVU+7cuZU7d241btxYS5cuVbt27Z7IcwCA7Khs2bLauHGjxowZo1q1ailPnjxycXFR6dKl1bFjR61atUqjR49Ocfnzbt26afHixWrQoIFy5colT09P1a9fX998841mzJiRpqVwJalq1ar65ZdfNGzYMFWqVEnu7u5ydnZW2bJl9fbbb2vDhg167rnnMvrUU6VFixZq2bKlpLgJT2fMmJGufoxGoyZOnJjm1yI5rq6umjBhQpbMkQIAGWUwZ/VMggAAAMATdvDgQcvlGcuXL09yqSYAwH4xIgQAAAAAANgNghAAAAAAAGA3CEIAAAAAAIDdIAgBAAAAAAB2gyAEAAAAAADYDVaNAQAAAAAAdiP5BdeRbcTGmhQc/MDWZQAAnjJeXrmf+DE5ZwEA0sMW5yzYLy6NAQAAAAAAdoMgBAAAAAAA2A2CEAAAAAAAYDcIQgAAAAAAgN0gCAEAAAAAAHaDIAQAAAAAANgNghAAAAAAAGA3CEIAAAAAAIDdIAgBAAAAAAB2gyAEAAAAAADYDYIQAAAAAABgNwhCAAAAAACA3SAIAQAAAAAAdoMgBAAAAAAA2A2CEAB4SixdulDdu3fU0qULbV0KAAAA8NQiCAGAp0BkZKS2bt0ik8mkbdu2KDIy0tYlAQAAAE8lghAAeArExETLbDZJkkwmk2Jiom1cEQAAAPB0IggBAAAAAAB2w9HWBQBAdmI0GmQ0GmxdRhKOjsYk9x/dlh2YTGaZTGZblwEAAAAkiyAEAP6P0WiQp6e7HByyX8Dg5JQ4XPD0zCUPj1w2qiZ5sbEmhYaGE4YAAAAg2yIIAYD/YzQa5OBg1KyV+xT4711bl5NITHREovvj5v4qRydXG1VjXfFCeTW02/MyGg0EIQAAAMi2CEIA4BGB/96VX2CIrctIxBSTeJWYq9dDZXR0sVE1AAAAwNMr+43/BgAAAAAAyCIEIQDwFDAYHSTFT+Jq+L/7AAAAANKKIAQAngIGo6PcC1WSZJB7oUoyGLmyEQAAAEgPPkkDwFMid8kGyl2yga3LAAAAAJ5qjAgBAAAAAAB2gyAEAAAAAADYDYIQAAAAAABgNwhCAAAAAACA3SAIAQAAAAAAdoMgBAAAAAAA2A2CEAAAAAAAYDcIQgAAAAAAgN0gCAEAAAAAAHaDIAQAAAAAANgNghAAAAAAAGA3CEIAAAAAAIDdIAgBAAAAAAB2gyAEAAAAAADYDYIQAAAAAABgNwhCAAAAAACA3SAIAQAAAAAAdoMgBAAAAAAA2A2CEAAAAAAAYDcIQgAAAAAAgN0gCAEAAAAAAHaDIAQAAAAAANgNghAAAAAAAGA3CEIAAAAAAIDdIAgBAAAAAAB2gyAE2crSpQvVvXtHLV260NalAAAAAAByIIIQZBuRkZHaunWLTCaTtm3bosjISFuXBAAAAADIYQhCkG3ExETLbDZJkkwmk2Jiom1cEQAAAAAgpyEIAQAAAAAAdoMgBAAAAAAA2A2CEAAAAAAAYDcIQgAAAAAAgN0gCAEAAAAAAHaDIAQAAAAAANgNghAAAAAAAGA3HG1dwJMUGRmpxYsXa9OmTfL395erq6sqVKigbt26qW3bttmmTwAAAAAAkDXsJgiJiIhQ3759dfToUTk4OMjb21v379/X4cOHdfjwYe3fv18TJkyweZ8AAAAAACDr2M2lMZ9//rmOHj2qZ599Vtu2bdP69eu1Y8cOzZs3T25ublqzZo1Wr15t8z4BAAAAAEDWsYsgJCAgQL6+vjIYDPryyy9VsmRJy75mzZpp9OjRkqQZM2bIZDLZrE8AAAAAAJC17CII+fnnnxUTE6OqVauqYsWKSfZ37NhRrq6uCgoK0qFDh2zWJwAAAAAAyFp2EYQcP35cklSnTh2r+52dnVW1alVJSnVokRV9AgAAADnB0qUL1b17Ry1dutDWpQBAEnYRhPj5+UlSostXHlWiRIlEbW3RJwAAAPC0i4yM1NatW2QymbRt2xZFRkbauiQASMQuVo25c+eOJCl//vzJtvH09JQkhYSE2KzPlDg6Zl5mZTAYZDQaMq2/zBITk/jH0cXFUS4u2e9H1GyWDNnv5XtqmExmmc1mW5dhlYND3PuseKG8Nq7k6RT/ujk5OVheS6RNdn5/pIU9nLOeFpyzMia7vyez6/sjJiZCZnPcHHkmk0kODmY+0+VA2f39AaQk+/1GygIRERGSJBcXl2TbxO97+PChzfpMjtFoUL58uTLUR0ImkzlbnjSNxthE93PndpOHh5uNqkme2WSSwcgfeemV3V8/k8msod2et3UZTy2zySQPD1dbl/HUyu7vj9Swl3PW0yIn/EzZUnZ//bLr+4PPdPaB1w9PM7sIQhwcHFK9coshlbFwVvSZHJPJrLCw8Az1kbCW3LldJWW/k+bTwmA06sqmBXp454atS3nquBUoqmfava2wsIeKjc2eqyll12/XngZGo0EeHq68P9IpK94fmRlIpFZmnrMcHIzKk8dNs1buU+C/dzOlT3tSvUIxvd6qBu/JdMru56z494fvjpO6FfrA1uUkEhWZ+EvAJT8flrNL9gpCni1ZQM3rl+f9kU455ZwF+2UXQYi7u7vu3r2b4vWJ8fvc3FL3Szor+kxJTEzmnYBDQ8Oz5R96Dx4kPomHhj5QdHT2qjP+Q8fDOzf0MOiqrct5asXGmjL1ZxrZQ/zlELw/MiYnvD8yu/7Af+/KLzDjl5nam2JeeSTxnsyo7PqeNJnMio016bWWVW1dShL379+X73f/f/+tV+vKw8PDdgWlgPdHxmTX9wfwOHYRhOTLl093795VaGhosm3i5/FIac6PrO7zSTGZzDKZst/1fI/+Eo2J4RcrAACANSaTmS+3MiD+yy0A9skugpBy5crJz89PgYGBybYJCAiQJJUpU8ZmfQIAAACpxZdbAJA+djG7TfXq1SVJx44ds7o/KipK//zzjySpVq1aNusTAAAAAABkLbsIQlq3bi0pLrQ4d+5ckv1r165VRESEihcvrnr16tmsTwAAAOBp5+joJIMh7s8Mo9EoR0cnG1cEAInZRRBSqlQpvfrqqzKZTHrnnXd06dIly74//vhDU6dOlSQNHjxYjo6Jrxa6fv26Ll26pOvXr2danwAAAEBO5eLiolat2shoNMrHp41cXFxsXRIAJGI3f6F/9NFHunDhgk6fPq127dqpfPnyioiIkL+/vyTpjTfeUJcuXZI87oMPPtChQ4dUr149LVu2LFP6BAAAAHKy3r37q3fv/rYuAwCsspsgJG/evFq5cqUWL16sLVu2yM/PT0ajUTVq1FDXrl3VsWPHbNEnAAAAAADIOnYThEiSq6urBg8erMGDB6f6MY+OAsmMPgEAAAAAgG3YxRwhAAAAAAAAEkEIAAAAAACwIwQhAAAAAADAbhCEAAAAAAAAu0EQAgAAAAAA7AZBCAAAAAAAsBsEIQAAAAAAwG4QhCDbcHR0ksEQ9yNpNBrl6Ohk44oAAAAAADkNQQiyDRcXF7Vq1UZGo1E+Pm3k4uJi65IAAAAAADmMo60LABLq3bu/evfub+syAAAAAAA5FCNCAAAAAACA3SAIAQAAAAAAdoMgBAAAAAAA2A2CEAAAAAAAYDcIQgAAAAAAgN0gCAEAAAAAAHaDIAQAAAAAANgNghAAAAAAAGA3CEIAAAAAAIDdIAgBAAAAAAB2gyAEAAAAAADYDYIQAAAAAABgNwhCAAAAAACA3SAIAQAAAAAAduOJByExMTFP+pAAAAAAAACSMhCE9OrVS71791ZUVFSq2t+9e1etW7dW27Zt03tIAAAAAACADHFM7wMPHTokg8Gg2NjYVLU3m826cuWKXFxc0ntIAAAAAACADHlsEGIymXTs2DGZzWar+48ePfrYcCM6OlpbtmyRJLm6uqajTAAAAAAAgIx7bBBiNBq1YsUK/fLLL4m2GwwGSdLbb7+d6oMZDAbVr18/jSUCAAAAAABkjlTNETJmzBh5eHjIbDZb/ouXcFtK/0lSrVq19NFHH2XNMwEAAAAAAHiMVM0R4uXlpW3btunhw4eS4sKPli1bymAwaPPmzSle7mIwGOTo6ChPT085OztnTtUAAAAAAADpkOrJUvPnz5/ofrFixWQwGFSiRAkmQAUAAAAAAE+FdK8a89tvvyW6f//+fXl4eCTadv36dd28eVM1a9a0zCkCAAAAAABgK6maIyQlP/30k1q0aKGPP/44yb5du3apR48eatGihWXVGAAAAAAAAFtJ94gQSRo7dqxWr14ts9mcZDSIJAUEBMhsNuv69esaNWqUAgICNGDAgIwcEgAAAAAAIN3SPSLk999/16pVq2Q2m1WnTh2NHDkySZuRI0dqyZIlqlevnsxms7755hsdP348QwUDAAAAAACkV7qDkJUrV0qSXnzxRX3//fdq1qxZkjZOTk5q0KCBvv/+ezVp0kQmk0lLly5Nd7EAAAAAAAAZke4g5OTJkzIYDBo6dKiMxsd3M3jwYEnSkSNH0ntIAAAAAACADEl3EHL//n1JUsmSJVPV/plnnpEkhYaGpveQAAAAAAAAGZLuIMTLy0uSdO3atVS1v3XrliQpb9686T0kAAAAAMDOmExmW5eQKXLK88gJ0r1qTJUqVXTjxg0tW7ZMU6ZMeWz7+DlFqlSpkt5DAgAAAADsjNFo0KyV+xT4711bl5JuxQvl1dBuz9u6DPyfdAchnTp10q+//qoNGzYof/78GjlypJydnZO0i4mJ0bx587Ry5UoZDAZ16tQpQwUDAAAAAOxL4L935RcYYusykEOkOwhp2rSp2rZtq82bN2vJkiVau3atGjRooNKlS8vV1VURERG6du2aDh48aJkX5MUXX9TLL7+cWbUDAAAAAACkSbqDEEmaOHGi3NzctHbtWoWFhWn79u1J2pjNcddBvfLKKxo/fnxGDgcAAAAAAB6jZ8+eOnTokCZNmqSOHTvaupxsJ0NBiIuLiz7//HN169ZNmzdv1oEDBxQUFKS7d+/Kzc1NRYoUUc2aNdWpUydVr149s2oGAAAAAABIlwwFIfEqV66sypUrZ0ZXAAAAAADgCdmxY4d++OEHnT9/Xg8ePFC5cuXUpUsXvf7666pUqZIk6dy5c4keExsbq61bt2rz5s36559/FBISIqPRqIIFC6pOnTrq1atXkoxg9OjR8vX11fvvv6+XXnpJX375pQ4ePKiIiAiVLFlS7dq1U48ePeTh4ZHlzzlTghAAAAAAAPD0MJvNGjdunFasWCFJKlSokJ599lldvnxZn376qf7880+rj4uMjNTgwYO1b98+SVLx4sXl7e2tkJAQBQYGKiAgQJs3b9acOXPUpEmTJI+/ePGi5s6dq7CwMJUrV05Go1EXLlzQV199pU2bNmnhwoUqXLhw1j1xZVIQEhUVpR07dujYsWO6efOmHjx4oMWLF0uSli1bpsqVK6tWrVqZcSgAAAAAAJBBa9as0YoVK+Ts7Kzx48erQ4cOkqTw8HBNnjxZP/30k9XHLViwQPv27ZOnp6fmzZunGjVqWPadO3dO77zzjvz8/DRz5kyrQci6detUqFAhLVq0SNWqVbM8bujQoTp//rzGjh2refPmZfrzTSjDQcjmzZs1YcIEhYTELWVkNptlMBgs+5cuXarAwEC1bt1aEyZMkJubW0YPCQAAAAAAJM2dO1d//PFHom3nz5+XJM2bN0+rV69OtO/jjz9WxYoV9e2330qSRo0aZQlBJMnd3V3jxo1TYGCg9u7dm+R4+/fvl9Fo1LBhwxKFIJJUoUIF9e3bV2PHjrXU8CiDwaBZs2ZZQpD4x82YMUOvvfaadu3apZMnT6pq1aqpfg3SKkNByJIlSzRlyhTLyjDFixdXYGBgojZ37tyR2WzWL7/8onv37mnBggUZOSQAAAAAAPg/fn5+OnbsWLL7/Pz8Em27d++e/vrrL926dUuOjo7q0qWL1ce+9dZbVoOQFStWKDo6OtEAiITiBz88fPjQ6v66desmCkHiVapUSTVq1NDx48e1c+fO7BmEnD9/XlOnTpXZbFabNm30wQcfyMPDQ7Vr107U7tdff9WUKVO0adMm7d27V1u2bFGbNm0yXDgAAAAAAPZu8uTJmjx5cqJtj1s+N/6yl1KlSilXrlxW+61SpUqyx3RycrIEKleuXFFAQID8/Px09uxZBQUFSZJlwMSjrIUg8by9vXX8+PEk4U1mS3cQsmTJEplMJr344ov66quvJMVdS/QoLy8vffHFF4qMjNT27du1bt06ghAAAAAAAGwkfmoLd3f3ZNvkzp3b6vYHDx5o0qRJWr9+vaKjoy3bHR0dVaFCBVWrVk3bt29Ptt+8efMmuy8+lLl3716K9WdUuoOQAwcOyGAw6O23305V+/79+2v79u06ffp0eg8JAAAAAAAyKP7ylfv37yfb5sGDB1a3Dxs2TPv371fu3LnVo0cPVa9eXeXKldMzzzwjZ2dn7dy5M8UgJLlLZqT/D0Dy58+fmqeRbukOQm7fvi1JKleuXKraly5dWpIUFhaW3kMCAAAAAIAMqlChgiTp2rVrevDggdXLY86cOZNk24kTJ7R//35JcZO01qlTJ0mbmzdvpnjsCxcuJLsvfuBE+fLlU+wjo4zpfWB8gpTaYOPOnTuSJA8Pj/QeEgAAAAAAZFCtWrVUsGBBxcbGau3atVbb/Pjjj0m2Xb161XI7PkxJKCYmJlF/MTExSdrs3r3baljy999/69SpUzIYDPLx8UnV80ivdAchzz77rCRp586dqWr/888/S8r6ZAcAAAAAAHu2bNkynTt3zupEqZLk7OysIUOGSJK+/PJLbd682bIvKipKX3/9tX755Zckjytbtqzl9syZMxPNEeLv76/Bgwfr1KlTlm0RERFJ+oiMjNTgwYN17do1y7YTJ07onXfekSR17drVckVJVkn3pTGtW7fW0aNHNWPGDNWtW1eVK1dOtu2OHTv03XffyWAw6OWXX07vIQEAAAAAdqh4oeQn2HwaZMf6u3fvrn/++Ufr1q3Tu+++qylTpqhQoULy9/dXWFiYatSoob/++ksODg6Wxzz33HNq27atNm/erCVLlmj9+vUqUaKEwsLCLKNF6tevr4MHD0qKu0wmfhBFvDJlyujSpUvy8fFR+fLlFRUVpcuXL0uSmjRpog8++CDLn3u6g5DXX39dK1eu1KVLl9StWze1bdtWFStWtOzft2+fAgIC9Ntvv2n37t0ym80qVaqUXn/99UwpHAAAAACQ85lMZg3t9ryty8gwk8kso9Fg6zIsDAaDJk2apIYNG+qnn37S2bNndeHCBT377LPq3r27ypQpo+7du8vV1TXR46ZNm6ZGjRrpp59+0tWrV3Xu3Dl5enqqcePG6tSpk9q0aaNXXnlF58+f186dO5MEIc8995y+/fZbffvttzp8+LDMZrNq1qyprl27qkOHDjIa033hSqqlOwhxcnLS/Pnz1bdvX/n7+2v9+vWS4l5MKW6VmHhms1lFihTR3Llz5ezsnLGKAQAAAAB2IzuFBxmRXZ9H+/bt1b59+yTbd+3aJUny8vJKtN3BwUGdO3dW586dk+1z48aNKR6zYsWKmjNnTtqLzSQZilqKFy8uX19fDRw4UPny5ZPZbE7yn7u7u9588035+vomup4IAAAAAAA8eZGRkWrcuLG6deuWaK6OhOKDkCpVqjzByp6MVI0I2bRpk/Lnz6969erJ0THxQ9zd3TVy5EiNHDlSFy9e1LVr13T//n25ubmpaNGiqlixYqJrigAAAAAAgO24uLioUKFCOnbsmCZNmqQJEyYoX758kuJWelm9erVWrVolo9Go7t2727jazJeqIOSrr77SzZs3tWvXLhUqVEhS3AyxkjRo0CBLOPLss88muf4HAAAAAABkL//973/Vr18/7dy5U3v27FHp0qXl5OSkgIAAhYWFycHBQaNHj1bt2rVtXWqmS1UQcvv2bZnNZrm4uFi2zZw5UwaDQf369UsySgQAAAAAAGRftWrV0tatW7Vs2TLt3btX169fV2RkpAoVKqSXXnpJ3bp1U9WqVW1dZpZIVYLh5uam6OhonTlzRg0aNMjqmgAAAAAAQBYrXLiw/vOf/+g///lPlh9r8uTJmjx5cpYfJzVSFYR4e3vryJEjGj58uBo1aiR3d3fLvk8++SRNc4AYDAZNnDgx7ZUCAAAAgB3YcOaO9l8LU6OSedS+UgFblwPkOKkKQvr3768jR47o3r17+vXXXy3bzWbzY5fFSchsNhOEAAAAAEAyomJN2nc1TGZJ+6+FqZV3Pjk7ZGixTwCPSFUQ0rRpUy1YsEC+vr4KDg5WTEyMDh8+LIPBoFq1aslo5I0JAAAAABkVazLL/H+3Tea4+2IRTiBTpSoI8fPzU7169dS4cWPLtooVK0qSFi5cKDc3t6ypDgAAAAAAIBOlaijHwIEDVatWLQUFBVm2FStWTMWKFZPBYMiy4gAAAAAAADJTqkaE3Lp1S7GxsYkmSb1+/boMBoPMZnMKjwQAAAAAAMg+UjUiJD7suHHjRpYWAwAAAAAAkJVSNSKkZMmSunDhgkaPHq1OnTopV65cln0bN26Us7Nzmg7aoUOHNLUHAAAAgMzmVqCorUtIwhwVI+mq5b5roZJyc07Vn21PTHZ83YC0SNU7qlOnTpo0aZLOnDmjzz//3LLdbDbrk08+SdMBDQYDQQgAAAAAmzGZzDKZYvVMu7dtXUoS9+/fl3581XK/Yrf35eHhYcOKrDOZYmUyMU0Cnk6pCkJ69+6t4OBgrVu3TsHBwYqNjbXMD5LWOUKYUwQAAACALZlMZoWEPJTRmP0Wfnjw4EGi+6GhDxQdnf3qjAuT+NsOT6dUj7EaOXKkRo4cablfsWJFGQwGHTt2jOVzAQAAADxVsusf8jExpiT3H90GIGNSNVkqAAAAAAC2YDbljCAopzyPnCDds+7s3LlTkhgNAgAAAADIMgajUVc2LdDDO0/vKqZuBYpmyzlp7FW6g5DixYunqb3ZbNa5c+ckxV1WAwAAAABAajy8c0MPg64+viGQCqkKQnr16iWDwaAFCxakeanceA8fPlSHDh1kNBp1+vTpdPUBAAAAAACQEakKQg4dOiSDwaDY2Fir+8PDw1WrVq1UhRysGgMAAAAAAGwl3ZfGWJPdQ46goCDNmjVLe/bs0a1bt5Q3b17Vrl1b/fv3V7Vq1dLc38mTJ9W5c+cU25QvX16bNm1Kb8kAAAAA7Iijo5MMBqPMZpOMRqMcHZ1sXRJygMjISK1atUq+vr7y9/eXwWBQtWrV1L9/fzk4OKhXr16qV6+eli1bluhx9+/f108//aRdu3bpwoULun//vpydnVW8eHE1btxYffr0UaFChRI9pnnz5goMDNSaNWsUFhamOXPm6PTp03J0dFS5cuX0xhtvqH379jIYbLcsdKYGIdmZv7+/unfvrtu3b8vd3V3e3t66ceOGtm3bph07dujzzz9Xx44d09Tn2bNnJUn58uXTM888Y7VNyZIlM1w7AAAAAPvg4uKiVq3aaNu2LfLxaSMXFxdbl4Sn3N27dzVw4EAdP35ckvTMM8/IxcVFBw4c0L59+/Tyyy9bfdzVq1f11ltvKTAwUI6OjipRooSKFSummzdv6vz58zp//rw2btyodevWJQlDJOnnn3/WDz/8IEdHR3l7eyskJETHjh3TsWPHtGvXLk2bNk2OjraJJOwiCImNjdWgQYN0+/Zt+fj4aNKkScqVK5diYmI0b948TZ8+XWPHjlX16tVVrly5VPcbH4R07NhR77//flaVDwAAAMCO9O7dX71797d1Gcghxo8fr+PHj6tYsWKaPn26qlatKkm6fv263n33Xf36669WH/ff//5XgYGBqlatmmbOnKnChQtb9u3YsUMjR47UrVu3tGzZMo0aNSrJ45ctW6a6devqyy+/tDz2l19+0ejRo7VlyxZVqVJF/fr1y4Jn/HhGmxz1Cdu0aZMuX76sQoUKadq0acqVK5ckydHRUUOHDlXbtm0VHR2t2bNnp6nf+CCkQoUKmV4zAAAAAAAZcenSJW3cuFEGg0EzZ860hCCSVKxYMc2bN08FCxZM8rg7d+7o/PnzkqTPP/88UQgiSS1btrSMJIlv96jChQtr7ty5iR7bunVrS2gyb948RUdHZ+wJppNdBCHr1q2TJLVv397q0LLu3btLknbu3Knw8PBU9ZlwOWCCEAAAAABAdrN9+3ZJUvXq1VW5cuUk+/PmzWt1iogCBQrowIED+vvvv63+vWsymSwDDB4+fGj12F26dJGHh0eS7Z07d5ajo6Pu3r2rI0eOpOn5ZJYcf2mMyWTS33//LUmqU6eO1TbVq1eXo6OjHj58qBMnTqhBgwaP7TcgIED37t2Tk5NTmi6nAQAAAADgSbhw4YIkqWLFism2qVKlSrL7XF1ddePGDZ08eVL+/v66du2arly5ojNnzujevXuSkl80JbkFSdzd3VWyZElduXJFV65cUcOGDVP7dDJNjg9CgoKCLAlVqVKlrLZxcnJS4cKFFRgYKD8/v1QFIfGXxTzzzDO6cuWKNmzYoLNnz8psNqtMmTJq27atatWqlXlPBACQIRvO3NH+a2FqVDKP2lcqYOtyAAAAMuz06dMaP3681X1NmzZVSEiIpLjwITm5c+e2ut3Pz0+ffvqp/vzzz0Tb3d3dVb16dUVERFgmYLXG09Mz2X3x9cSHKU9ajg9C7ty5Y7mdL1++ZNt5enoqMDDQ8oPyOPFBSEBAgNq3b58oBdu7d69++OEHde7cWZ9++qmcnDK+5JWjo11cxZTtOTjw75AZeB1zpuz87xoVa9K+q2EyS9p/LUytvPPJOZvWm51fx9TKrHNWTngt8PTj5xBAdnbv3j0dO3bM6r7SpUvLzc1NkvTgwYNk+7C2Lzg4WG+++aZu3bql0qVL69VXX1XlypVVtmxZlShRQkajUZMmTUoxCElp2on79+9LkvLnz59sm6z0VAUho0ePlq+vb6rbz5o1K1EKldLSU/H7IiIiUtX3mTNnLO3feustvf766ypevLhu374tX19fzZkzR2vWrJGjo6M+++yzVNdsjdFoUL58uTLUB5Cd5MnjZusSYGdiTWbFx9Umc9x9Odi0pGQ97e8PzlnIaZ729ySAnK1+/fqWuSutmT59unbs2GH5+9Uaa/vWrl2rW7duydPTU2vWrFGePHmStLlx40aKtV24cMHqZS/37t3TtWvXJEnly5dPsY+skqYg5MaNG3J1dU2yPeHkKDdu3LB6jVByE6hkNaMxa1L8Jk2ayNPTU/Xr19err75q2V6sWDENHTpUJUuW1HvvvaeffvpJ3bp1S/GarMcxmcwKC0vdJK7IWg4ORj4QZYKwsIeKjTXZugxkMt4fmSMz3x+2CCQy85zFzxSyA85ZwJNBiJ41Xn75Zc2aNUsnT57UmTNnVKlSpUT7IyIitH79+iSPiw8qihYtajUEuX79uvbu3StJiomJsXrstWvXqkePHnJwSPzt048//iiTyaSiRYuqevXq6XlaGZamIKRt27bJ7jMYDJKk5s2bZ6yiFIwbN04ff/xxqtu7urpaJoeRpMjISMvMto+KjIyUJMvQocfp1q2bunXrluz+9u3ba9asWfLz89OOHTsyFIRIUkwMJ2DkHLGxJn6mgWTkhPfH014/kFBOeE8CsF8VK1ZUq1attHXrVg0bNkwzZszQc889JyluGokxY8YoMDAwyePKli0rSTp37py2bNmiNm3aWPYdPHhQY8eOtVxSk9xVFWfPntXo0aP1ySefWFaP8fX11bfffitJ+s9//mPJEZ60VAchyc0E+yQ5OzvL2dk5TY9JOC9IaGhostcgxc8NUqBA5k2gV6lSJfn5+Vn9wQIAAAAApI5bgaK2LiFDbFn/uHHjdPXqVZ0+fVqvvfaaypYtKzc3N50/f16xsbGqWrWqTp48mWjkRufOnbVy5Ur5+flp5MiRmjZtmvLnz6+goCDdunVLBoNB9erV06FDhxQUFGT1uN7e3tqwYYN27NihsmXL6tatW5a2gwYNUrt27Z7I87cmVUHIsGHDsrqOLFO4cGHlzp1b9+7dU2BgoCXZSigqKkr//vuvJKlMmTKp7jsqKkpOTk7JplgmU9y3B46OT9VULAAAAACQbZhNJj3T7m1bl5FhZpNJhiyauiElefPm1cqVK7V48WJt2bJFV69elaOjo+rXr6/Bgwfr6NGjOnnyZKJpMDw8PLRmzRotXLhQv/32mwICAnTnzh15eXnplVde0ZtvvqkSJUrohRde0J07d3T8+HHVrFkz0XG7d++uAgUKaNGiRTp79qw8PDzUvHlzvfXWW6pfv/6TfhkSyfFBiCRVr15de/fu1bFjx9SkSZMk+0+cOKGYmBg5OTmpcuXKj+3v8uXL6tq1q+7du6cffvhBdevWtdouftKZZ599NmNPAAAAAADslC3Cg6xgy+fh6uqqwYMHa/DgwUn27dy5U5JUqFChRNtz586tkSNHauTIkcn2e/r06RSP+/LLL+vll19OR8VZK2f8RD1G69atJcVdjxQ/F0hCy5cvt7RLaX3leKVKlbIsibt27VqrbbZu3aqrV6/KyclJPj4+6S0dAAAAAIB02bdvn1q0aKEhQ4ZYne7CbDZrz549kpSqQQE5hV1cs9G+fXstWLBAfn5+GjFihKZMmaK8efMqNjZW8+fP15YtW+Tk5KRBgwYleeylS5ckxc01Ej+/iKOjo/r376+pU6fK19dXZcqUUb9+/SzhyJYtW/TRRx9Jkvr166ciRYo8oWcKALaXHa/hNUfFSLpque9aqKTcnLPXKTA7vm4AAODpVqlSJQUFBSkgIECzZ8/W22+/bZl3MywsTNOmTdOFCxfk5eWlVq1a2bjaJyd7fQrMIs7Ozvrqq6/Up08f/f7772ratKnKli2rmzdv6s6dOzIYDJo8ebLKlSuX5LHxs+MOGzZMw4cPt2zv06ePLl26pLVr1+rrr7/WwoULVapUKf3777+6deuWJKlLly4aMWLEk3mSAGBjJpNZJlNstryG9/79+9KP/7/UecVu71tmL89OTKZYmUy2n5wcAADkDPnz59d7772niRMnavr06Vq8eLFKliypqKgoXbt2TZGRkfL09NTXX3+tvHnz2rrcJ8YughApbpjPhg0bNGfOHO3Zs0fnz5+Xu7u7mjVrpv79+yc7z0dyjEajJk6cqJYtW+rHH3/UiRMndO7cOeXNm1cvvviiunXrpqZNm2bRswGA7MdkMisk5KGMRtssg5aS+OXd4oWGPlB0dParMy5MIggBAACZp3fv3qpTp46+//57/f3337py5YocHR1VunRpvfjii+rRo4cKFy5s6zKfKLsJQiSpSJEi+uyzz9L0mHPnzqW4v3nz5mrevHlGygKAHCO7/iEfE2NKcv/RbQAAADlV5cqVNWXKlCdyrN9+++2JHCcj7GKyVAAAAAAAAIkgBAAAAAAA2BGCEAAAAAAAYDfSPUfI8OHD5ezsrH79+um5557LzJoAAAAAAACyRLpHhBw9elRbtmxRSEhIZtYDAAAAAACQZdIdhNy/f1+SVKlSpUwrBgAAAAAAICulOwjx9vaWJP3111+ZVQsAAAAAAECWSncQMnbsWLm5uWnMmDHatGmTIiIiMrMuAAAyjaOjkwyGuFOe0WiUo6OTjSsCAACAraR7stT9+/erRYsW2rhxo9577z05ODioVKlSypcvn1xcXJJ9nMFg0KJFi9J7WAAA0szFxUWtWrXRtm1b5OPTJsXzFAAAAHK2dAch33zzjQwGgyTJbDYrJiZGly9fTra9wWCQ2Wy2PAYAgCepd+/+6t27v63LAAAAgI2lOwipW7duZtYBAAAAAACQ5dIdhCx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79+5VQECAbt68qXz58unll19W165d1aRJk8f289xzz2nDhg2aNWuWjh07pqtXr8rJyUnFixdX48aN1atXL508eVLvvPOO/vzzT4WHh1tWnchI7StXrtTy5cu1ceNGXb16Va6urmrTpo1GjhyphQsX8oES2QrnLc5byDjOWwCymsGc3KLZAFKtefPmCgwM1Keffqpu3brZuhwAAFLEeQsAYM+YIwQAAAAAANgNghAAAAAAAGA3CEIAAAAAAIDdIAgBAAAAAAB2g8lSAQAAAACA3WBECAAAAAAAsBsEIQAAAAAAwG4QhAAAAAAAALtBEAIAAAAAAOwGQQgAAAAAALAbBCEAAAAAAMBuEIQAAAAAAAC7QRACAAAAAADsxv8CYIiivS6GY20AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1147.5x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1147.5x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "topic_data = combined_effects[combined_effects['model'].isin(['grnn', 'intro_topic'])]\n",
    "topic_data['model'] = topic_data['model'].apply(lambda model: \"Pretrained\" if model == \"grnn\" else \"Topic-RNN\")\n",
    "plot_construction_islands(topic_data, \"Topicalization with \\n Intro\", shift = 1.05)\n",
    "plot_construction_islands(topic_data, \"Topicalization without \\n Intro\", shift = 1.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "g = sns.catplot(x=\"condition\", y=\"wh_effect\", hue=\"gap\", data=topic_data, kind=\"bar\", col = \"construction\", row = \"model\",\n",
    "            estimator=\"mean\", aspect = 0.7)\n",
    "g.set_axis_labels(\"\", \"Filler Effect\", fontsize = 24)\n",
    "g.set_xticklabels([\"Simple\" , \"Island\"], size = 24)\n",
    "\n",
    "g.set_titles(template=\"{col_name} ({row_name})\", size = 20)\n",
    "g.fig.subplots_adjust(hspace=0.25, wspace=0.25)\n",
    "constructions = ['Wh-Movement', 'Clefting', 'Topicalization with Intro',\n",
    "       'Topicalization without Intro', 'Tough Movement']\n",
    "\n",
    "g.legend.set_title(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing Differences in Surprisal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>model</th>\n",
       "      <th>construction</th>\n",
       "      <th>condition</th>\n",
       "      <th>gap</th>\n",
       "      <th>gram</th>\n",
       "      <th>ungram</th>\n",
       "      <th>wh_effect</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>grnn</td>\n",
       "      <td>Wh-Movement</td>\n",
       "      <td>Simple</td>\n",
       "      <td>+gap</td>\n",
       "      <td>I know who the family from the big city saw la...</td>\n",
       "      <td>I know that the family from the big city saw l...</td>\n",
       "      <td>-1.910231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>grnn</td>\n",
       "      <td>Wh-Movement</td>\n",
       "      <td>Simple</td>\n",
       "      <td>-gap</td>\n",
       "      <td>I know that the family from the big city saw t...</td>\n",
       "      <td>I know who the family from the big city saw th...</td>\n",
       "      <td>0.583257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>grnn</td>\n",
       "      <td>Wh-Movement</td>\n",
       "      <td>Simple</td>\n",
       "      <td>+gap</td>\n",
       "      <td>I remember what the woman with the blue hat bo...</td>\n",
       "      <td>I remember that the woman with the blue hat bo...</td>\n",
       "      <td>-3.269071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>grnn</td>\n",
       "      <td>Wh-Movement</td>\n",
       "      <td>Simple</td>\n",
       "      <td>-gap</td>\n",
       "      <td>I remember that the woman with the blue hat bo...</td>\n",
       "      <td>I remember what the woman with the blue hat bo...</td>\n",
       "      <td>6.700210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>grnn</td>\n",
       "      <td>Wh-Movement</td>\n",
       "      <td>Simple</td>\n",
       "      <td>+gap</td>\n",
       "      <td>The newspaper reported who the man from far aw...</td>\n",
       "      <td>The newspaper reported that the man from far a...</td>\n",
       "      <td>-0.152476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27647</th>\n",
       "      <td>grnn</td>\n",
       "      <td>Tough Movement</td>\n",
       "      <td>Island</td>\n",
       "      <td>-gap</td>\n",
       "      <td>it is difficult to lose the house that contain...</td>\n",
       "      <td>her book is difficult to lose the house that c...</td>\n",
       "      <td>1.374783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27648</th>\n",
       "      <td>grnn</td>\n",
       "      <td>Tough Movement</td>\n",
       "      <td>Island</td>\n",
       "      <td>+gap</td>\n",
       "      <td>her book is difficult to lose the house that c...</td>\n",
       "      <td>it is difficult to lose the house that contain...</td>\n",
       "      <td>0.665067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27649</th>\n",
       "      <td>grnn</td>\n",
       "      <td>Tough Movement</td>\n",
       "      <td>Island</td>\n",
       "      <td>-gap</td>\n",
       "      <td>it is difficult to lose the house that contain...</td>\n",
       "      <td>her book is difficult to lose the house that c...</td>\n",
       "      <td>1.374783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27650</th>\n",
       "      <td>grnn</td>\n",
       "      <td>Tough Movement</td>\n",
       "      <td>Island</td>\n",
       "      <td>+gap</td>\n",
       "      <td>her book is difficult to lose the house that c...</td>\n",
       "      <td>it is difficult to lose the house that contain...</td>\n",
       "      <td>-1.218410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27651</th>\n",
       "      <td>grnn</td>\n",
       "      <td>Tough Movement</td>\n",
       "      <td>Island</td>\n",
       "      <td>-gap</td>\n",
       "      <td>it is difficult to lose the house that contain...</td>\n",
       "      <td>her book is difficult to lose the house that c...</td>\n",
       "      <td>1.374783</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27652 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      model    construction condition   gap  \\\n",
       "0      grnn     Wh-Movement    Simple  +gap   \n",
       "1      grnn     Wh-Movement    Simple  -gap   \n",
       "2      grnn     Wh-Movement    Simple  +gap   \n",
       "3      grnn     Wh-Movement    Simple  -gap   \n",
       "4      grnn     Wh-Movement    Simple  +gap   \n",
       "...     ...             ...       ...   ...   \n",
       "27647  grnn  Tough Movement    Island  -gap   \n",
       "27648  grnn  Tough Movement    Island  +gap   \n",
       "27649  grnn  Tough Movement    Island  -gap   \n",
       "27650  grnn  Tough Movement    Island  +gap   \n",
       "27651  grnn  Tough Movement    Island  -gap   \n",
       "\n",
       "                                                    gram  \\\n",
       "0      I know who the family from the big city saw la...   \n",
       "1      I know that the family from the big city saw t...   \n",
       "2      I remember what the woman with the blue hat bo...   \n",
       "3      I remember that the woman with the blue hat bo...   \n",
       "4      The newspaper reported who the man from far aw...   \n",
       "...                                                  ...   \n",
       "27647  it is difficult to lose the house that contain...   \n",
       "27648  her book is difficult to lose the house that c...   \n",
       "27649  it is difficult to lose the house that contain...   \n",
       "27650  her book is difficult to lose the house that c...   \n",
       "27651  it is difficult to lose the house that contain...   \n",
       "\n",
       "                                                  ungram  wh_effect  \n",
       "0      I know that the family from the big city saw l...  -1.910231  \n",
       "1      I know who the family from the big city saw th...   0.583257  \n",
       "2      I remember that the woman with the blue hat bo...  -3.269071  \n",
       "3      I remember what the woman with the blue hat bo...   6.700210  \n",
       "4      The newspaper reported that the man from far a...  -0.152476  \n",
       "...                                                  ...        ...  \n",
       "27647  her book is difficult to lose the house that c...   1.374783  \n",
       "27648  it is difficult to lose the house that contain...   0.665067  \n",
       "27649  her book is difficult to lose the house that c...   1.374783  \n",
       "27650  it is difficult to lose the house that contain...  -1.218410  \n",
       "27651  her book is difficult to lose the house that c...   1.374783  \n",
       "\n",
       "[27652 rows x 7 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pretrained_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "cleft_rnn = augmented_df[augmented_df['model'] == 'cleft']\n",
    "topic_rnn = augmented_df[augmented_df['model'] == 'intro_topic']\n",
    "\n",
    "pretrained_df['cleft_diff'] = cleft_rnn['wh_effect'] - pretrained_df['wh_effect']\n",
    "pretrained_df['topic_diff'] = topic_rnn['wh_effect'].values - pretrained_df['wh_effect'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1831.12x500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(x=\"condition\", y=\"cleft_diff\", hue = \"gap\", data=pretrained_df, kind=\"bar\", col = \"construction\",\n",
    "            estimator=\"mean\", aspect = 0.7)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1831.12x500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.catplot(x=\"condition\", y=\"topic_diff\", hue = \"gap\", data=pretrained_df, kind=\"bar\", col = \"construction\",\n",
    "            estimator=\"mean\", aspect = 0.7)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "lm-gen-env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}