Notebooks to explore mismatch between catalog files and media manifest they should represent.

notebooks/ToL_media_mismatch.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load in full images to ease process."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"../data/predicted-catalog.csv\", low_memory = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>split</th>\n",
+       "      <th>treeoflife_id</th>\n",
+       "      <th>eol_content_id</th>\n",
+       "      <th>eol_page_id</th>\n",
+       "      <th>bioscan_part</th>\n",
+       "      <th>bioscan_filename</th>\n",
+       "      <th>inat21_filename</th>\n",
+       "      <th>inat21_cls_name</th>\n",
+       "      <th>inat21_cls_num</th>\n",
+       "      <th>kingdom</th>\n",
+       "      <th>phylum</th>\n",
+       "      <th>class</th>\n",
+       "      <th>order</th>\n",
+       "      <th>family</th>\n",
+       "      <th>genus</th>\n",
+       "      <th>species</th>\n",
+       "      <th>common</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>train</td>\n",
+       "      <td>f2f0aa29-e87b-469c-bf5b-51a3611ab001</td>\n",
+       "      <td>22131926.0</td>\n",
+       "      <td>269504.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Animalia</td>\n",
+       "      <td>Arthropoda</td>\n",
+       "      <td>Insecta</td>\n",
+       "      <td>Lepidoptera</td>\n",
+       "      <td>Lycaenidae</td>\n",
+       "      <td>Orthomiella</td>\n",
+       "      <td>rantaizana</td>\n",
+       "      <td>Chinese Straight-wing Blue</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>train</td>\n",
+       "      <td>5faa4f55-32e9-4872-953d-567e5d232e52</td>\n",
+       "      <td>22291283.0</td>\n",
+       "      <td>6101931.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Plantae</td>\n",
+       "      <td>Tracheophyta</td>\n",
+       "      <td>Polypodiopsida</td>\n",
+       "      <td>Polypodiales</td>\n",
+       "      <td>Woodsiaceae</td>\n",
+       "      <td>Woodsia</td>\n",
+       "      <td>subcordata</td>\n",
+       "      <td>Woodsia subcordata</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>train</td>\n",
+       "      <td>2282f2bf-2b52-4522-b588-dd6f356d5fd6</td>\n",
+       "      <td>21802775.0</td>\n",
+       "      <td>45513632.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Animalia</td>\n",
+       "      <td>Chordata</td>\n",
+       "      <td>Aves</td>\n",
+       "      <td>Passeriformes</td>\n",
+       "      <td>Laniidae</td>\n",
+       "      <td>Lanius</td>\n",
+       "      <td>minor</td>\n",
+       "      <td>Lesser Grey Shrike</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>train</td>\n",
+       "      <td>76b57c36-2181-4e6d-a5c4-b40e22a09449</td>\n",
+       "      <td>12784812.0</td>\n",
+       "      <td>51655800.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>tenuis</td>\n",
+       "      <td>Tenuis</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>train</td>\n",
+       "      <td>f57d3ab6-2cf5-484b-a590-e2a3d49a3ca2</td>\n",
+       "      <td>29713643.0</td>\n",
+       "      <td>45515896.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Animalia</td>\n",
+       "      <td>Chordata</td>\n",
+       "      <td>Aves</td>\n",
+       "      <td>Casuariiformes</td>\n",
+       "      <td>Casuariidae</td>\n",
+       "      <td>Casuarius</td>\n",
+       "      <td>casuarius</td>\n",
+       "      <td>Southern Cassowary</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   split                         treeoflife_id  eol_content_id  eol_page_id  \\\n",
+       "0  train  f2f0aa29-e87b-469c-bf5b-51a3611ab001      22131926.0     269504.0   \n",
+       "1  train  5faa4f55-32e9-4872-953d-567e5d232e52      22291283.0    6101931.0   \n",
+       "2  train  2282f2bf-2b52-4522-b588-dd6f356d5fd6      21802775.0   45513632.0   \n",
+       "3  train  76b57c36-2181-4e6d-a5c4-b40e22a09449      12784812.0   51655800.0   \n",
+       "4  train  f57d3ab6-2cf5-484b-a590-e2a3d49a3ca2      29713643.0   45515896.0   \n",
+       "\n",
+       "   bioscan_part bioscan_filename inat21_filename inat21_cls_name  \\\n",
+       "0           NaN              NaN             NaN             NaN   \n",
+       "1           NaN              NaN             NaN             NaN   \n",
+       "2           NaN              NaN             NaN             NaN   \n",
+       "3           NaN              NaN             NaN             NaN   \n",
+       "4           NaN              NaN             NaN             NaN   \n",
+       "\n",
+       "   inat21_cls_num   kingdom        phylum           class           order  \\\n",
+       "0             NaN  Animalia    Arthropoda         Insecta     Lepidoptera   \n",
+       "1             NaN   Plantae  Tracheophyta  Polypodiopsida    Polypodiales   \n",
+       "2             NaN  Animalia      Chordata            Aves   Passeriformes   \n",
+       "3             NaN       NaN           NaN             NaN             NaN   \n",
+       "4             NaN  Animalia      Chordata            Aves  Casuariiformes   \n",
+       "\n",
+       "        family        genus     species                      common  \n",
+       "0   Lycaenidae  Orthomiella  rantaizana  Chinese Straight-wing Blue  \n",
+       "1  Woodsiaceae      Woodsia  subcordata          Woodsia subcordata  \n",
+       "2     Laniidae       Lanius       minor          Lesser Grey Shrike  \n",
+       "3          NaN          NaN      tenuis                      Tenuis  \n",
+       "4  Casuariidae    Casuarius   casuarius          Southern Cassowary  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 10092530 entries, 0 to 10092529\n",
+      "Data columns (total 17 columns):\n",
+      " #   Column            Non-Null Count     Dtype  \n",
+      "---  ------            --------------     -----  \n",
+      " 0   split             10092530 non-null  object \n",
+      " 1   treeoflife_id     10092530 non-null  object \n",
+      " 2   eol_content_id    6277374 non-null   float64\n",
+      " 3   eol_page_id       6277374 non-null   float64\n",
+      " 4   bioscan_part      1128313 non-null   float64\n",
+      " 5   bioscan_filename  1128313 non-null   object \n",
+      " 6   inat21_filename   2686843 non-null   object \n",
+      " 7   inat21_cls_name   2686843 non-null   object \n",
+      " 8   inat21_cls_num    2686843 non-null   float64\n",
+      " 9   kingdom           9831721 non-null   object \n",
+      " 10  phylum            9833317 non-null   object \n",
+      " 11  class             9813548 non-null   object \n",
+      " 12  order             9807409 non-null   object \n",
+      " 13  family            9775447 non-null   object \n",
+      " 14  genus             8908268 non-null   object \n",
+      " 15  species           8749857 non-null   object \n",
+      " 16  common            10092530 non-null  object \n",
+      "dtypes: float64(4), object(13)\n",
+      "memory usage: 1.3+ GB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info(show_counts = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `train_small` is duplicates of `train`, so we will drop those to analyze the full training set plus val."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`predicted-catalog` doesn't have `train_small`, hence, it's a smaller file."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's add a column indicating the original data source so we can also get some stats by datasource, specifically focusing on EOL since we know licensing for BIOSCAN-1M and iNat21."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add data_source column for easier slicing\n",
+    "df.loc[df['inat21_filename'].notna(), 'data_source'] = 'iNat21'\n",
+    "df.loc[df['bioscan_filename'].notna(), 'data_source'] = 'BIOSCAN'\n",
+    "df.loc[df['eol_content_id'].notna(), 'data_source'] = 'EOL'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Get just EOL CSV for Media Manifest Merge"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eol_df = df.loc[df['data_source'] == 'EOL']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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>split</th>\n",
+       "      <th>treeoflife_id</th>\n",
+       "      <th>eol_content_id</th>\n",
+       "      <th>eol_page_id</th>\n",
+       "      <th>bioscan_part</th>\n",
+       "      <th>bioscan_filename</th>\n",
+       "      <th>inat21_filename</th>\n",
+       "      <th>inat21_cls_name</th>\n",
+       "      <th>inat21_cls_num</th>\n",
+       "      <th>kingdom</th>\n",
+       "      <th>phylum</th>\n",
+       "      <th>class</th>\n",
+       "      <th>order</th>\n",
+       "      <th>family</th>\n",
+       "      <th>genus</th>\n",
+       "      <th>species</th>\n",
+       "      <th>common</th>\n",
+       "      <th>data_source</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>train</td>\n",
+       "      <td>f2f0aa29-e87b-469c-bf5b-51a3611ab001</td>\n",
+       "      <td>22131926.0</td>\n",
+       "      <td>269504.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Animalia</td>\n",
+       "      <td>Arthropoda</td>\n",
+       "      <td>Insecta</td>\n",
+       "      <td>Lepidoptera</td>\n",
+       "      <td>Lycaenidae</td>\n",
+       "      <td>Orthomiella</td>\n",
+       "      <td>rantaizana</td>\n",
+       "      <td>Chinese Straight-wing Blue</td>\n",
+       "      <td>EOL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>train</td>\n",
+       "      <td>5faa4f55-32e9-4872-953d-567e5d232e52</td>\n",
+       "      <td>22291283.0</td>\n",
+       "      <td>6101931.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Plantae</td>\n",
+       "      <td>Tracheophyta</td>\n",
+       "      <td>Polypodiopsida</td>\n",
+       "      <td>Polypodiales</td>\n",
+       "      <td>Woodsiaceae</td>\n",
+       "      <td>Woodsia</td>\n",
+       "      <td>subcordata</td>\n",
+       "      <td>Woodsia subcordata</td>\n",
+       "      <td>EOL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>train</td>\n",
+       "      <td>2282f2bf-2b52-4522-b588-dd6f356d5fd6</td>\n",
+       "      <td>21802775.0</td>\n",
+       "      <td>45513632.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Animalia</td>\n",
+       "      <td>Chordata</td>\n",
+       "      <td>Aves</td>\n",
+       "      <td>Passeriformes</td>\n",
+       "      <td>Laniidae</td>\n",
+       "      <td>Lanius</td>\n",
+       "      <td>minor</td>\n",
+       "      <td>Lesser Grey Shrike</td>\n",
+       "      <td>EOL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>train</td>\n",
+       "      <td>76b57c36-2181-4e6d-a5c4-b40e22a09449</td>\n",
+       "      <td>12784812.0</td>\n",
+       "      <td>51655800.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>tenuis</td>\n",
+       "      <td>Tenuis</td>\n",
+       "      <td>EOL</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>train</td>\n",
+       "      <td>f57d3ab6-2cf5-484b-a590-e2a3d49a3ca2</td>\n",
+       "      <td>29713643.0</td>\n",
+       "      <td>45515896.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Animalia</td>\n",
+       "      <td>Chordata</td>\n",
+       "      <td>Aves</td>\n",
+       "      <td>Casuariiformes</td>\n",
+       "      <td>Casuariidae</td>\n",
+       "      <td>Casuarius</td>\n",
+       "      <td>casuarius</td>\n",
+       "      <td>Southern Cassowary</td>\n",
+       "      <td>EOL</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   split                         treeoflife_id  eol_content_id  eol_page_id  \\\n",
+       "0  train  f2f0aa29-e87b-469c-bf5b-51a3611ab001      22131926.0     269504.0   \n",
+       "1  train  5faa4f55-32e9-4872-953d-567e5d232e52      22291283.0    6101931.0   \n",
+       "2  train  2282f2bf-2b52-4522-b588-dd6f356d5fd6      21802775.0   45513632.0   \n",
+       "3  train  76b57c36-2181-4e6d-a5c4-b40e22a09449      12784812.0   51655800.0   \n",
+       "4  train  f57d3ab6-2cf5-484b-a590-e2a3d49a3ca2      29713643.0   45515896.0   \n",
+       "\n",
+       "   bioscan_part bioscan_filename inat21_filename inat21_cls_name  \\\n",
+       "0           NaN              NaN             NaN             NaN   \n",
+       "1           NaN              NaN             NaN             NaN   \n",
+       "2           NaN              NaN             NaN             NaN   \n",
+       "3           NaN              NaN             NaN             NaN   \n",
+       "4           NaN              NaN             NaN             NaN   \n",
+       "\n",
+       "   inat21_cls_num   kingdom        phylum           class           order  \\\n",
+       "0             NaN  Animalia    Arthropoda         Insecta     Lepidoptera   \n",
+       "1             NaN   Plantae  Tracheophyta  Polypodiopsida    Polypodiales   \n",
+       "2             NaN  Animalia      Chordata            Aves   Passeriformes   \n",
+       "3             NaN       NaN           NaN             NaN             NaN   \n",
+       "4             NaN  Animalia      Chordata            Aves  Casuariiformes   \n",
+       "\n",
+       "        family        genus     species                      common  \\\n",
+       "0   Lycaenidae  Orthomiella  rantaizana  Chinese Straight-wing Blue   \n",
+       "1  Woodsiaceae      Woodsia  subcordata          Woodsia subcordata   \n",
+       "2     Laniidae       Lanius       minor          Lesser Grey Shrike   \n",
+       "3          NaN          NaN      tenuis                      Tenuis   \n",
+       "4  Casuariidae    Casuarius   casuarius          Southern Cassowary   \n",
+       "\n",
+       "  data_source  \n",
+       "0         EOL  \n",
+       "1         EOL  \n",
+       "2         EOL  \n",
+       "3         EOL  \n",
+       "4         EOL  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eol_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We don't need the BIOSCAN or iNat21 columns, nor the taxa columns."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['treeoflife_id', 'eol_content_id', 'eol_page_id'], dtype='object')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eol_license_cols = eol_df.columns[1:4]\n",
+    "eol_license_cols"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eol_df = eol_df[eol_license_cols]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "treeoflife_id     6277374\n",
+       "eol_content_id    6277374\n",
+       "eol_page_id        504018\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eol_df.nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Number of unique `eol_content_id`s and `treeoflife_id`s match, and match with total number of `eol_content_id`s shown above in the info for the full dataset."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Merge with Media Manifest\n",
+    "Let's merge with the [media manifest](https://huggingface.co/datasets/imageomics/eol/blob/be7b7e6c372f6547e30030e9576d9cc638320099/data/interim/media_manifest.csv) from which all these images should have been downloaded from to get a clear picture of what is or isn't in the manifest."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 6574224 entries, 0 to 6574223\n",
+      "Data columns (total 6 columns):\n",
+      " #   Column                  Non-Null Count    Dtype \n",
+      "---  ------                  --------------    ----- \n",
+      " 0   EOL content ID          6574224 non-null  int64 \n",
+      " 1   EOL page ID             6574224 non-null  int64 \n",
+      " 2   Medium Source URL       6574222 non-null  object\n",
+      " 3   EOL Full-Size Copy URL  6574224 non-null  object\n",
+      " 4   License Name            6574224 non-null  object\n",
+      " 5   Copyright Owner         5942181 non-null  object\n",
+      "dtypes: int64(2), object(4)\n",
+      "memory usage: 300.9+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "media = pd.read_csv(\"../data/media_manifest (july 26).csv\", dtype = {\"EOL content ID\": \"int64\", \"EOL page ID\": \"int64\"}, low_memory = False)\n",
+    "media.info(show_counts = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We want to make sure the EOL content and page IDs have matching types, so we'll set them to `int64` in `eol_df` too."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 6277374 entries, 0 to 6277373\n",
+      "Data columns (total 3 columns):\n",
+      " #   Column          Dtype \n",
+      "---  ------          ----- \n",
+      " 0   treeoflife_id   object\n",
+      " 1   eol_content_id  int64 \n",
+      " 2   eol_page_id     int64 \n",
+      "dtypes: int64(2), object(1)\n",
+      "memory usage: 191.6+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "eol_df = eol_df.astype({\"eol_content_id\": \"int64\", \"eol_page_id\": \"int64\"})\n",
+    "eol_df.info()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Notice that we have about 300K more entries in the media manifest, which is about expected from the [comparison of predicted-catalog to the original full list](https://huggingface.co/datasets/imageomics/ToL-EDA/blob/main/notebooks/ToL_predicted-catalog_EDA.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Rename media columns for easier matching."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "media.rename(columns = {\"EOL content ID\": \"eol_content_id\", \"EOL page ID\": \"eol_page_id\"}, inplace = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Check consistency of merge when matching both `eol_content_id` and `eol_page_id`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "merge_cols = [\"eol_content_id\", \"eol_page_id\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 6163903 entries, 0 to 6163902\n",
+      "Data columns (total 7 columns):\n",
+      " #   Column                  Non-Null Count    Dtype \n",
+      "---  ------                  --------------    ----- \n",
+      " 0   treeoflife_id           6163903 non-null  object\n",
+      " 1   eol_content_id          6163903 non-null  int64 \n",
+      " 2   eol_page_id             6163903 non-null  int64 \n",
+      " 3   Medium Source URL       6163903 non-null  object\n",
+      " 4   EOL Full-Size Copy URL  6163903 non-null  object\n",
+      " 5   License Name            6163903 non-null  object\n",
+      " 6   Copyright Owner         5549428 non-null  object\n",
+      "dtypes: int64(2), object(5)\n",
+      "memory usage: 329.2+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "eol_df_media_cp = pd.merge(eol_df, media, how = \"inner\", left_on = merge_cols, right_on = merge_cols)\n",
+    "eol_df_media_cp.info(show_counts = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Okay, so we do have a mis-match of about 113K images where the content IDs and page IDs don't both match.\n",
+    "\n",
+    "Let's save this to a CSV."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eol_df_media_cp.to_csv(\"../data/eol_files/eol_cp_match_media.csv\", index = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Note that merging on just content IDs is going to give the same numbers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 6163903 entries, 0 to 6163902\n",
+      "Data columns (total 8 columns):\n",
+      " #   Column                  Non-Null Count    Dtype \n",
+      "---  ------                  --------------    ----- \n",
+      " 0   treeoflife_id           6163903 non-null  object\n",
+      " 1   eol_content_id          6163903 non-null  int64 \n",
+      " 2   eol_page_id_x           6163903 non-null  int64 \n",
+      " 3   eol_page_id_y           6163903 non-null  int64 \n",
+      " 4   Medium Source URL       6163903 non-null  object\n",
+      " 5   EOL Full-Size Copy URL  6163903 non-null  object\n",
+      " 6   License Name            6163903 non-null  object\n",
+      " 7   Copyright Owner         5549428 non-null  object\n",
+      "dtypes: int64(3), object(5)\n",
+      "memory usage: 376.2+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "eol_media_content = pd.merge(eol_df,\n",
+    "                             media,\n",
+    "                             how = \"inner\",\n",
+    "                             left_on = \"eol_content_id\",\n",
+    "                             right_on = \"eol_content_id\")\n",
+    "eol_media_content.info(show_counts = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The interesting thing is when we look at the uniqueness. There are less _**unique**_ `Medium Source URLs`, suggesting that there are duplicated images that have different content IDs and unique `EOL Full-Size Copy URL`s, so EOL presumably has them duplicated."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "treeoflife_id             6163903\n",
+       "eol_content_id            6163903\n",
+       "eol_page_id                503865\n",
+       "Medium Source URL         6153828\n",
+       "EOL Full-Size Copy URL    6163903\n",
+       "License Name                   16\n",
+       "Copyright Owner            345470\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eol_df_media_cp.nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We'll look into this a little further down. First, let's get a list of all the `treeoflife_id`s that do match to the media manifest so we can make a CSV with all the images that _**aren't**_ matching."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['f2f0aa29-e87b-469c-bf5b-51a3611ab001',\n",
+       " '5faa4f55-32e9-4872-953d-567e5d232e52',\n",
+       " '2282f2bf-2b52-4522-b588-dd6f356d5fd6',\n",
+       " '76b57c36-2181-4e6d-a5c4-b40e22a09449',\n",
+       " 'f57d3ab6-2cf5-484b-a590-e2a3d49a3ca2']"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tol_ids_in_media = list(eol_df_media_cp.treeoflife_id)\n",
+    "tol_ids_in_media[:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "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>treeoflife_id</th>\n",
+       "      <th>eol_content_id</th>\n",
+       "      <th>eol_page_id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>f2f0aa29-e87b-469c-bf5b-51a3611ab001</td>\n",
+       "      <td>22131926</td>\n",
+       "      <td>269504</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>5faa4f55-32e9-4872-953d-567e5d232e52</td>\n",
+       "      <td>22291283</td>\n",
+       "      <td>6101931</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2282f2bf-2b52-4522-b588-dd6f356d5fd6</td>\n",
+       "      <td>21802775</td>\n",
+       "      <td>45513632</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>76b57c36-2181-4e6d-a5c4-b40e22a09449</td>\n",
+       "      <td>12784812</td>\n",
+       "      <td>51655800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>f57d3ab6-2cf5-484b-a590-e2a3d49a3ca2</td>\n",
+       "      <td>29713643</td>\n",
+       "      <td>45515896</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                          treeoflife_id  eol_content_id  eol_page_id\n",
+       "0  f2f0aa29-e87b-469c-bf5b-51a3611ab001        22131926       269504\n",
+       "1  5faa4f55-32e9-4872-953d-567e5d232e52        22291283      6101931\n",
+       "2  2282f2bf-2b52-4522-b588-dd6f356d5fd6        21802775     45513632\n",
+       "3  76b57c36-2181-4e6d-a5c4-b40e22a09449        12784812     51655800\n",
+       "4  f57d3ab6-2cf5-484b-a590-e2a3d49a3ca2        29713643     45515896"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eol_df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's save a copy of the EOL section with content and page IDs that are mismatched."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 113471 entries, 126 to 6277290\n",
+      "Data columns (total 3 columns):\n",
+      " #   Column          Non-Null Count   Dtype \n",
+      "---  ------          --------------   ----- \n",
+      " 0   treeoflife_id   113471 non-null  object\n",
+      " 1   eol_content_id  113471 non-null  int64 \n",
+      " 2   eol_page_id     113471 non-null  int64 \n",
+      "dtypes: int64(2), object(1)\n",
+      "memory usage: 3.5+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "eol_df_missing_media = eol_df.loc[~eol_df.treeoflife_id.isin(tol_ids_in_media)]\n",
+    "eol_df_missing_media.info(show_counts = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How many pages are these distributed across?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "treeoflife_id     113471\n",
+       "eol_content_id    113471\n",
+       "eol_page_id         9762\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eol_df_missing_media.nunique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eol_df_missing_media.to_csv(\"../data/eol_files/eol_cp_not_media.csv\", index = False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Check out the Duplication of Medium Source URLs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Identify unique Medium Source URLs\n",
+    "eol_df_media_cp['duplicate'] = eol_df_media_cp.duplicated(subset = \"Medium Source URL\", keep = 'first')\n",
+    "eol_df_media_unique = eol_df_media_cp.loc[~eol_df_media_cp['duplicate']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 6153828 entries, 0 to 6163902\n",
+      "Data columns (total 8 columns):\n",
+      " #   Column                  Non-Null Count    Dtype \n",
+      "---  ------                  --------------    ----- \n",
+      " 0   treeoflife_id           6153828 non-null  object\n",
+      " 1   eol_content_id          6153828 non-null  int64 \n",
+      " 2   eol_page_id             6153828 non-null  int64 \n",
+      " 3   Medium Source URL       6153828 non-null  object\n",
+      " 4   EOL Full-Size Copy URL  6153828 non-null  object\n",
+      " 5   License Name            6153828 non-null  object\n",
+      " 6   Copyright Owner         5539739 non-null  object\n",
+      " 7   duplicate               6153828 non-null  bool  \n",
+      "dtypes: bool(1), int64(2), object(5)\n",
+      "memory usage: 381.5+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "eol_df_media_unique.info(show_counts = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It's about 10K images that are duplicated. Let's see how many `Medium Source URL`s it is."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "treeoflife_id             10075\n",
+       "eol_content_id            10075\n",
+       "eol_page_id                5391\n",
+       "Medium Source URL          5833\n",
+       "EOL Full-Size Copy URL    10075\n",
+       "License Name                  9\n",
+       "Copyright Owner             545\n",
+       "duplicate                     1\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eol_df_media_cp.loc[eol_df_media_cp['duplicate']].nunique()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are 5,833 unique `Medium Source URLs` that are duplicated."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Check how this compares to Catalog \n",
+    "Let's see if the missing images are all in TreeOfLife-10M, or a mix between it and Rare Species."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cat_df = pd.read_csv(\"../data/catalog.csv\", low_memory = False)\n",
+    "# Remove duplicates in train_small\n",
+    "cat_df = cat_df.loc[cat_df.split != 'train_small']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add data_source column for easier slicing\n",
+    "cat_df.loc[cat_df['inat21_filename'].notna(), 'data_source'] = 'iNat21'\n",
+    "cat_df.loc[cat_df['bioscan_filename'].notna(), 'data_source'] = 'BIOSCAN'\n",
+    "cat_df.loc[cat_df['eol_content_id'].notna(), 'data_source'] = 'EOL'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eol_cat_df = cat_df.loc[cat_df.data_source == \"EOL\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Reduce down to just relevant columns and recast the EOL content and page IDs as `int64`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eol_cat_df = eol_cat_df[eol_license_cols]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eol_cat_df = eol_cat_df.astype({\"eol_content_id\": \"int64\", \"eol_page_id\": \"int64\"})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 6250420 entries, 956715 to 11000930\n",
+      "Data columns (total 3 columns):\n",
+      " #   Column          Dtype \n",
+      "---  ------          ----- \n",
+      " 0   treeoflife_id   object\n",
+      " 1   eol_content_id  int64 \n",
+      " 2   eol_page_id     int64 \n",
+      "dtypes: int64(2), object(1)\n",
+      "memory usage: 190.7+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "eol_cat_df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 112575 entries, 956761 to 10998986\n",
+      "Data columns (total 3 columns):\n",
+      " #   Column          Non-Null Count   Dtype \n",
+      "---  ------          --------------   ----- \n",
+      " 0   treeoflife_id   112575 non-null  object\n",
+      " 1   eol_content_id  112575 non-null  int64 \n",
+      " 2   eol_page_id     112575 non-null  int64 \n",
+      "dtypes: int64(2), object(1)\n",
+      "memory usage: 3.4+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "eol_cat_df.loc[eol_cat_df[\"treeoflife_id\"].isin(list(eol_df_missing_media.treeoflife_id))].info(show_counts = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "They are _**almost**_ entirely in TreeOfLife-10M, but _some_ may be in Rare Species.\n",
+    "\n",
+    "#### Quick check for the duplicates here"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['e37fc4b8-73ef-4a8c-8a65-cf65f9f1174e',\n",
+       " '5e3edcd1-8150-4534-8f69-f63c447afd7d',\n",
+       " '776a596f-96a1-47d8-b510-db8fb41be44d',\n",
+       " '7ce491fa-7573-46e8-b11a-ebac6d702bda',\n",
+       " 'd4ca1530-685d-46e8-969c-44a74f0a00d4']"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tol_ids_duplicated = list(eol_df_media_cp.loc[eol_df_media_cp['duplicate'], \"treeoflife_id\"].values)\n",
+    "tol_ids_duplicated[:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "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>treeoflife_id</th>\n",
+       "      <th>eol_content_id</th>\n",
+       "      <th>eol_page_id</th>\n",
+       "      <th>Medium Source URL</th>\n",
+       "      <th>EOL Full-Size Copy URL</th>\n",
+       "      <th>License Name</th>\n",
+       "      <th>Copyright Owner</th>\n",
+       "      <th>duplicate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>33275</th>\n",
+       "      <td>e37fc4b8-73ef-4a8c-8a65-cf65f9f1174e</td>\n",
+       "      <td>13611057</td>\n",
+       "      <td>37146541</td>\n",
+       "      <td>https://pensoft.net/J_FILES/1/articles/5492/ex...</td>\n",
+       "      <td>https://content.eol.org/data/media/d4/f0/a9/58...</td>\n",
+       "      <td>cc-by-3.0</td>\n",
+       "      <td>James K. Liebherr</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36445</th>\n",
+       "      <td>5e3edcd1-8150-4534-8f69-f63c447afd7d</td>\n",
+       "      <td>13620019</td>\n",
+       "      <td>16355052</td>\n",
+       "      <td>https://pensoft.net/J_FILES/1/articles/7546/ex...</td>\n",
+       "      <td>https://content.eol.org/data/media/d5/13/ac/58...</td>\n",
+       "      <td>cc-by-3.0</td>\n",
+       "      <td>Jin-Kyung Choi, Jong-Wook Lee</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>52304</th>\n",
+       "      <td>776a596f-96a1-47d8-b510-db8fb41be44d</td>\n",
+       "      <td>13610902</td>\n",
+       "      <td>732357</td>\n",
+       "      <td>https://pensoft.net/J_FILES/1/articles/5352/ex...</td>\n",
+       "      <td>https://content.eol.org/data/media/d4/f0/11/58...</td>\n",
+       "      <td>cc-by-3.0</td>\n",
+       "      <td>Mary Liz Jameson, Alain Drumont</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67099</th>\n",
+       "      <td>7ce491fa-7573-46e8-b11a-ebac6d702bda</td>\n",
+       "      <td>14119729</td>\n",
+       "      <td>62672726</td>\n",
+       "      <td>https://live.staticflickr.com/4302/35924815981...</td>\n",
+       "      <td>https://content.eol.org/data/media/d7/93/6e/54...</td>\n",
+       "      <td>cc-publicdomain</td>\n",
+       "      <td>Biodiversity Heritage Library</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>73915</th>\n",
+       "      <td>d4ca1530-685d-46e8-969c-44a74f0a00d4</td>\n",
+       "      <td>13613433</td>\n",
+       "      <td>60227621</td>\n",
+       "      <td>https://pensoft.net/J_FILES/1/articles/5999/ex...</td>\n",
+       "      <td>https://content.eol.org/data/media/d4/f9/f4/58...</td>\n",
+       "      <td>cc-by-3.0</td>\n",
+       "      <td>Oleg Pekarsky</td>\n",
+       "      <td>True</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                              treeoflife_id  eol_content_id  eol_page_id  \\\n",
+       "33275  e37fc4b8-73ef-4a8c-8a65-cf65f9f1174e        13611057     37146541   \n",
+       "36445  5e3edcd1-8150-4534-8f69-f63c447afd7d        13620019     16355052   \n",
+       "52304  776a596f-96a1-47d8-b510-db8fb41be44d        13610902       732357   \n",
+       "67099  7ce491fa-7573-46e8-b11a-ebac6d702bda        14119729     62672726   \n",
+       "73915  d4ca1530-685d-46e8-969c-44a74f0a00d4        13613433     60227621   \n",
+       "\n",
+       "                                       Medium Source URL  \\\n",
+       "33275  https://pensoft.net/J_FILES/1/articles/5492/ex...   \n",
+       "36445  https://pensoft.net/J_FILES/1/articles/7546/ex...   \n",
+       "52304  https://pensoft.net/J_FILES/1/articles/5352/ex...   \n",
+       "67099  https://live.staticflickr.com/4302/35924815981...   \n",
+       "73915  https://pensoft.net/J_FILES/1/articles/5999/ex...   \n",
+       "\n",
+       "                                  EOL Full-Size Copy URL     License Name  \\\n",
+       "33275  https://content.eol.org/data/media/d4/f0/a9/58...        cc-by-3.0   \n",
+       "36445  https://content.eol.org/data/media/d5/13/ac/58...        cc-by-3.0   \n",
+       "52304  https://content.eol.org/data/media/d4/f0/11/58...        cc-by-3.0   \n",
+       "67099  https://content.eol.org/data/media/d7/93/6e/54...  cc-publicdomain   \n",
+       "73915  https://content.eol.org/data/media/d4/f9/f4/58...        cc-by-3.0   \n",
+       "\n",
+       "                       Copyright Owner  duplicate  \n",
+       "33275                James K. Liebherr       True  \n",
+       "36445    Jin-Kyung Choi, Jong-Wook Lee       True  \n",
+       "52304  Mary Liz Jameson, Alain Drumont       True  \n",
+       "67099    Biodiversity Heritage Library       True  \n",
+       "73915                    Oleg Pekarsky       True  "
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eol_df_media_cp.loc[eol_df_media_cp['duplicate']].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 10068 entries, 956913 to 10996963\n",
+      "Data columns (total 3 columns):\n",
+      " #   Column          Non-Null Count  Dtype \n",
+      "---  ------          --------------  ----- \n",
+      " 0   treeoflife_id   10068 non-null  object\n",
+      " 1   eol_content_id  10068 non-null  int64 \n",
+      " 2   eol_page_id     10068 non-null  int64 \n",
+      "dtypes: int64(2), object(1)\n",
+      "memory usage: 314.6+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "eol_cat_df.loc[eol_cat_df[\"treeoflife_id\"].isin(tol_ids_duplicated)].info(show_counts = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All but 7 of the duplicates are here too."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's save a version of the merged manifest with all duplicates (as in, _**every**_ image that's duplicated is listed, not just the 2nd through however many to appear)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 15908 entries, 1691 to 6163695\n",
+      "Data columns (total 8 columns):\n",
+      " #   Column                  Non-Null Count  Dtype \n",
+      "---  ------                  --------------  ----- \n",
+      " 0   treeoflife_id           15908 non-null  object\n",
+      " 1   eol_content_id          15908 non-null  int64 \n",
+      " 2   eol_page_id             15908 non-null  int64 \n",
+      " 3   Medium Source URL       15908 non-null  object\n",
+      " 4   EOL Full-Size Copy URL  15908 non-null  object\n",
+      " 5   License Name            15908 non-null  object\n",
+      " 6   Copyright Owner         15148 non-null  object\n",
+      " 7   duplicate               15908 non-null  bool  \n",
+      "dtypes: bool(1), int64(2), object(5)\n",
+      "memory usage: 1009.8+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Identify unique Medium Source URLs\n",
+    "eol_df_media_copies = eol_df_media_cp.copy()\n",
+    "eol_df_media_copies['duplicate'] = eol_df_media_copies.duplicated(subset = \"Medium Source URL\", keep = False)\n",
+    "eol_df_media_duplicates = eol_df_media_copies.loc[eol_df_media_copies['duplicate']]\n",
+    "eol_df_media_duplicates.info(show_counts = True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we'll save this to CSV (without the duplicate column since they're all duplicates)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "eol_df_media_duplicates[eol_df_media_duplicates.columns[:7]].to_csv(\"../data/eol_files/eol_media_duplicates.csv\", index = False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "formats": "ipynb,py:percent"
+  },
+  "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": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

notebooks/ToL_media_mismatch.py

@@ -0,0 +1,243 @@
+# ---
+# jupyter:
+#   jupytext:
+#     formats: ipynb,py:percent
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.16.0
+#   kernelspec:
+#     display_name: Python 3 (ipykernel)
+#     language: python
+#     name: python3
+# ---
+
+# %%
+import pandas as pd
+
+# %% [markdown]
+# Load in full images to ease process.
+
+# %%
+df = pd.read_csv("../data/predicted-catalog.csv", low_memory = False)
+
+# %%
+df.head()
+
+# %%
+df.info(show_counts = True)
+
+# %% [markdown]
+# The `train_small` is duplicates of `train`, so we will drop those to analyze the full training set plus val.
+
+# %% [markdown]
+# `predicted-catalog` doesn't have `train_small`, hence, it's a smaller file.
+
+# %% [markdown]
+# Let's add a column indicating the original data source so we can also get some stats by datasource, specifically focusing on EOL since we know licensing for BIOSCAN-1M and iNat21.
+
+# %%
+# Add data_source column for easier slicing
+df.loc[df['inat21_filename'].notna(), 'data_source'] = 'iNat21'
+df.loc[df['bioscan_filename'].notna(), 'data_source'] = 'BIOSCAN'
+df.loc[df['eol_content_id'].notna(), 'data_source'] = 'EOL'
+
+# %% [markdown]
+# #### Get just EOL CSV for Media Manifest Merge
+
+# %%
+eol_df = df.loc[df['data_source'] == 'EOL']
+
+# %%
+eol_df.head()
+
+# %% [markdown]
+# We don't need the BIOSCAN or iNat21 columns, nor the taxa columns.
+
+# %%
+eol_license_cols = eol_df.columns[1:4]
+eol_license_cols
+
+# %%
+eol_df = eol_df[eol_license_cols]
+
+# %%
+eol_df.nunique()
+
+# %% [markdown]
+# Number of unique `eol_content_id`s and `treeoflife_id`s match, and match with total number of `eol_content_id`s shown above in the info for the full dataset.
+
+# %% [markdown]
+# ### Merge with Media Manifest
+# Let's merge with the [media manifest](https://huggingface.co/datasets/imageomics/eol/blob/be7b7e6c372f6547e30030e9576d9cc638320099/data/interim/media_manifest.csv) from which all these images should have been downloaded from to get a clear picture of what is or isn't in the manifest.
+
+# %%
+media = pd.read_csv("../data/media_manifest (july 26).csv", dtype = {"EOL content ID": "int64", "EOL page ID": "int64"}, low_memory = False)
+media.info(show_counts = True)
+
+# %% [markdown]
+# We want to make sure the EOL content and page IDs have matching types, so we'll set them to `int64` in `eol_df` too.
+
+# %%
+eol_df = eol_df.astype({"eol_content_id": "int64", "eol_page_id": "int64"})
+eol_df.info()
+
+# %% [markdown]
+# Notice that we have about 300K more entries in the media manifest, which is about expected from the [comparison of predicted-catalog to the original full list](https://huggingface.co/datasets/imageomics/ToL-EDA/blob/main/notebooks/ToL_predicted-catalog_EDA.ipynb).
+
+# %% [markdown]
+# Rename media columns for easier matching.
+
+# %%
+media.rename(columns = {"EOL content ID": "eol_content_id", "EOL page ID": "eol_page_id"}, inplace = True)
+
+# %% [markdown]
+# Check consistency of merge when matching both `eol_content_id` and `eol_page_id`.
+
+# %%
+merge_cols = ["eol_content_id", "eol_page_id"]
+
+# %%
+eol_df_media_cp = pd.merge(eol_df, media, how = "inner", left_on = merge_cols, right_on = merge_cols)
+eol_df_media_cp.info(show_counts = True)
+
+# %% [markdown]
+# Okay, so we do have a mis-match of about 113K images where the content IDs and page IDs don't both match.
+#
+# Let's save this to a CSV.
+
+# %%
+eol_df_media_cp.to_csv("../data/eol_files/eol_cp_match_media.csv", index = False)
+
+# %% [markdown]
+# Note that merging on just content IDs is going to give the same numbers.
+
+# %%
+eol_media_content = pd.merge(eol_df,
+                             media,
+                             how = "inner",
+                             left_on = "eol_content_id",
+                             right_on = "eol_content_id")
+eol_media_content.info(show_counts = True)
+
+# %% [markdown]
+# The interesting thing is when we look at the uniqueness. There are less _**unique**_ `Medium Source URLs`, suggesting that there are duplicated images that have different content IDs and unique `EOL Full-Size Copy URL`s, so EOL presumably has them duplicated.
+
+# %%
+eol_df_media_cp.nunique()
+
+# %% [markdown]
+# We'll look into this a little further down. First, let's get a list of all the `treeoflife_id`s that do match to the media manifest so we can make a CSV with all the images that _**aren't**_ matching.
+
+# %%
+tol_ids_in_media = list(eol_df_media_cp.treeoflife_id)
+tol_ids_in_media[:5]
+
+# %%
+eol_df.head()
+
+# %% [markdown]
+# Let's save a copy of the EOL section with content and page IDs that are mismatched.
+
+# %%
+eol_df_missing_media = eol_df.loc[~eol_df.treeoflife_id.isin(tol_ids_in_media)]
+eol_df_missing_media.info(show_counts = True)
+
+# %% [markdown]
+# How many pages are these distributed across?
+
+# %%
+eol_df_missing_media.nunique()
+
+# %%
+eol_df_missing_media.to_csv("../data/eol_files/eol_cp_not_media.csv", index = False)
+
+# %% [markdown]
+# ### Check out the Duplication of Medium Source URLs
+
+# %%
+# Identify unique Medium Source URLs
+eol_df_media_cp['duplicate'] = eol_df_media_cp.duplicated(subset = "Medium Source URL", keep = 'first')
+eol_df_media_unique = eol_df_media_cp.loc[~eol_df_media_cp['duplicate']]
+
+# %%
+eol_df_media_unique.info(show_counts = True)
+
+# %% [markdown]
+# It's about 10K images that are duplicated. Let's see how many `Medium Source URL`s it is.
+
+# %%
+eol_df_media_cp.loc[eol_df_media_cp['duplicate']].nunique()
+
+# %% [markdown]
+# There are 5,833 unique `Medium Source URLs` that are duplicated.
+
+# %% [markdown]
+# ### Check how this compares to Catalog 
+# Let's see if the missing images are all in TreeOfLife-10M, or a mix between it and Rare Species.
+
+# %%
+cat_df = pd.read_csv("../data/catalog.csv", low_memory = False)
+# Remove duplicates in train_small
+cat_df = cat_df.loc[cat_df.split != 'train_small']
+
+# %%
+# Add data_source column for easier slicing
+cat_df.loc[cat_df['inat21_filename'].notna(), 'data_source'] = 'iNat21'
+cat_df.loc[cat_df['bioscan_filename'].notna(), 'data_source'] = 'BIOSCAN'
+cat_df.loc[cat_df['eol_content_id'].notna(), 'data_source'] = 'EOL'
+
+# %%
+eol_cat_df = cat_df.loc[cat_df.data_source == "EOL"]
+
+# %% [markdown]
+# Reduce down to just relevant columns and recast the EOL content and page IDs as `int64`.
+
+# %%
+eol_cat_df = eol_cat_df[eol_license_cols]
+
+# %%
+eol_cat_df = eol_cat_df.astype({"eol_content_id": "int64", "eol_page_id": "int64"})
+
+# %%
+eol_cat_df.info()
+
+# %%
+eol_cat_df.loc[eol_cat_df["treeoflife_id"].isin(list(eol_df_missing_media.treeoflife_id))].info(show_counts = True)
+
+# %% [markdown]
+# They are _**almost**_ entirely in TreeOfLife-10M, but _some_ may be in Rare Species.
+#
+# #### Quick check for the duplicates here
+
+# %%
+tol_ids_duplicated = list(eol_df_media_cp.loc[eol_df_media_cp['duplicate'], "treeoflife_id"].values)
+tol_ids_duplicated[:5]
+
+# %%
+eol_df_media_cp.loc[eol_df_media_cp['duplicate']].head()
+
+# %%
+eol_cat_df.loc[eol_cat_df["treeoflife_id"].isin(tol_ids_duplicated)].info(show_counts = True)
+
+# %% [markdown]
+# All but 7 of the duplicates are here too.
+
+# %% [markdown]
+# Let's save a version of the merged manifest with all duplicates (as in, _**every**_ image that's duplicated is listed, not just the 2nd through however many to appear).
+
+# %%
+# Identify unique Medium Source URLs
+eol_df_media_copies = eol_df_media_cp.copy()
+eol_df_media_copies['duplicate'] = eol_df_media_copies.duplicated(subset = "Medium Source URL", keep = False)
+eol_df_media_duplicates = eol_df_media_copies.loc[eol_df_media_copies['duplicate']]
+eol_df_media_duplicates.info(show_counts = True)
+
+# %% [markdown]
+# Now we'll save this to CSV (without the duplicate column since they're all duplicates).
+
+# %%
+eol_df_media_duplicates[eol_df_media_duplicates.columns[:7]].to_csv("../data/eol_files/eol_media_duplicates.csv", index = False)
+
+# %%