scripts/exploratory-analysis-recall.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "30a9b1e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c98a6790",
   "metadata": {},
   "outputs": [],
   "source": [
    "dir_geoparsed_results = os.path.dirname(os.getcwd())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55fdcaff",
   "metadata": {},
   "source": [
    "## Read Toponym Recognition Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1133930c",
   "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>Unnamed: 0</th>\n",
       "      <th>geoNameId</th>\n",
       "      <th>toponym</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2324433.0</td>\n",
       "      <td>Rivers State</td>\n",
       "      <td>6.82766</td>\n",
       "      <td>4.74974</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2328926.0</td>\n",
       "      <td>Federal Republic of Nigeria</td>\n",
       "      <td>8.00000</td>\n",
       "      <td>10.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>4829764.0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>-86.75026</td>\n",
       "      <td>32.75041</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>2215636.0</td>\n",
       "      <td>Libya</td>\n",
       "      <td>17.00000</td>\n",
       "      <td>28.00000</td>\n",
       "      <td>0.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>130758.0</td>\n",
       "      <td>Islamic Republic of Iran</td>\n",
       "      <td>53.00000</td>\n",
       "      <td>32.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  geoNameId                      toponym  longitude  latitude  \\\n",
       "0           0  2324433.0                 Rivers State    6.82766   4.74974   \n",
       "1           1  2328926.0  Federal Republic of Nigeria    8.00000  10.00000   \n",
       "2           6  4829764.0                      Alabama  -86.75026  32.75041   \n",
       "3          10  2215636.0                        Libya   17.00000  28.00000   \n",
       "4          12   130758.0     Islamic Republic of Iran   53.00000  32.00000   \n",
       "\n",
       "     recall  \n",
       "0  0.500000  \n",
       "1  0.000000  \n",
       "2  0.666667  \n",
       "3  0.625000  \n",
       "4  0.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_geocorpora_spacy = pd.read_csv(dir_geoparsed_results+'\\\\geoparsed-results\\\\geocorpora_geotagged_results_spaCy.csv')\n",
    "df_geocorpora_spacy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "95520b5e",
   "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>Unnamed: 0</th>\n",
       "      <th>geoNameId</th>\n",
       "      <th>toponym</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>recall</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>2324433.0</td>\n",
       "      <td>Rivers State</td>\n",
       "      <td>6.82766</td>\n",
       "      <td>4.74974</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2328926.0</td>\n",
       "      <td>Federal Republic of Nigeria</td>\n",
       "      <td>8.00000</td>\n",
       "      <td>10.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>4829764.0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>-86.75026</td>\n",
       "      <td>32.75041</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>2215636.0</td>\n",
       "      <td>Libya</td>\n",
       "      <td>17.00000</td>\n",
       "      <td>28.00000</td>\n",
       "      <td>0.625000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12</td>\n",
       "      <td>130758.0</td>\n",
       "      <td>Islamic Republic of Iran</td>\n",
       "      <td>53.00000</td>\n",
       "      <td>32.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  geoNameId                      toponym  longitude  latitude  \\\n",
       "0           0  2324433.0                 Rivers State    6.82766   4.74974   \n",
       "1           1  2328926.0  Federal Republic of Nigeria    8.00000  10.00000   \n",
       "2           6  4829764.0                      Alabama  -86.75026  32.75041   \n",
       "3          10  2215636.0                        Libya   17.00000  28.00000   \n",
       "4          12   130758.0     Islamic Republic of Iran   53.00000  32.00000   \n",
       "\n",
       "     recall  \n",
       "0  0.500000  \n",
       "1  0.000000  \n",
       "2  0.666667  \n",
       "3  0.625000  \n",
       "4  0.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_geocorpora_neurotpr = pd.read_csv(dir_geoparsed_results+'\\\\geoparsed-results\\\\geocorpora_geotagged_results_NeuroTPR.csv')\n",
    "df_geocorpora_neurotpr.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f6db9c0d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(df_geocorpora_spacy, x=\"recall\", stat=\"probability\")\n",
    "plt.xlabel('Recall', fontsize=14)\n",
    "plt.ylabel('Normalized Frequency', fontsize=14)\n",
    "plt.title('GeoCorpora - spaCy', fontsize = 18)\n",
    "plt.ylim(0,1)\n",
    "plt.margins(x = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7f3df1fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.displot(df_geocorpora_neurotpr, x=\"recall\", stat=\"probability\")\n",
    "plt.xlabel('Recall', fontsize=14)\n",
    "plt.ylabel('Normalized Frequency', fontsize=14)\n",
    "plt.title('GeoCorpora - NeuroTPR', fontsize = 18)\n",
    "plt.ylim(0, 1)\n",
    "plt.margins(x = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb23f0e1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}