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ToL-EDA

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Tags:
EDA
imageomics
biology
rare-species
plants
animals
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 Community
12
egrace479 commited on
Commit
8438146
·
1 Parent(s): b5e8a68

Generate csv to store license files for all predicted images in dataset from EOL. This will include Rare Species as well.

Browse files
Files changed (3) hide show
  1. data/eol_licenses.csv +3 -0
  2. notebooks/ToL_license_check.ipynb +716 -0
  3. notebooks/ToL_license_check.py +76 -0
data/eol_licenses.csv ADDED
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+ size 406784620
notebooks/ToL_license_check.ipynb ADDED
@@ -0,0 +1,716 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "cells": [
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+ {
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+ "cell_type": "code",
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+ "execution_count": 1,
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+ "metadata": {},
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+ "outputs": [],
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+ "source": [
9
+ "import pandas as pd\n",
10
+ "import seaborn as sns\n",
11
+ "\n",
12
+ "sns.set_style(\"whitegrid\")\n",
13
+ "sns.set(rc = {'figure.figsize': (10,10)})"
14
+ ]
15
+ },
16
+ {
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+ "cell_type": "markdown",
18
+ "metadata": {},
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+ "source": [
20
+ "Load in full images to ease process."
21
+ ]
22
+ },
23
+ {
24
+ "cell_type": "code",
25
+ "execution_count": 2,
26
+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ "/var/folders/nv/f0fq1p1n1_3b11x579py_0q80000gq/T/ipykernel_88306/4112647395.py:1: DtypeWarning: Columns (5,6,7,14,15) have mixed types. Specify dtype option on import or set low_memory=False.\n",
33
+ " df = pd.read_csv(\"../data/predicted-catalog.csv\")\n"
34
+ ]
35
+ }
36
+ ],
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+ "source": [
38
+ "df = pd.read_csv(\"../data/predicted-catalog.csv\")"
39
+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": 3,
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "data": {
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+ "text/html": [
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+ "<div>\n",
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+ "<style scoped>\n",
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+ " .dataframe tbody tr th:only-of-type {\n",
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+ " vertical-align: middle;\n",
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+ " }\n",
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+ "\n",
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+ " .dataframe tbody tr th {\n",
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+ " vertical-align: top;\n",
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+ " }\n",
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+ "\n",
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+ " .dataframe thead th {\n",
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+ " text-align: right;\n",
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+ " }\n",
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+ "</style>\n",
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+ "<table border=\"1\" class=\"dataframe\">\n",
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+ " <thead>\n",
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+ " <tr style=\"text-align: right;\">\n",
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+ " <th></th>\n",
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+ " <th>split</th>\n",
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+ " <th>treeoflife_id</th>\n",
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+ " <th>eol_content_id</th>\n",
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+ " <th>eol_page_id</th>\n",
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+ " <th>bioscan_part</th>\n",
72
+ " <th>bioscan_filename</th>\n",
73
+ " <th>inat21_filename</th>\n",
74
+ " <th>inat21_cls_name</th>\n",
75
+ " <th>inat21_cls_num</th>\n",
76
+ " <th>kingdom</th>\n",
77
+ " <th>phylum</th>\n",
78
+ " <th>class</th>\n",
79
+ " <th>order</th>\n",
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+ " <th>family</th>\n",
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+ " <th>genus</th>\n",
82
+ " <th>species</th>\n",
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+ " <th>common</th>\n",
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+ " </tr>\n",
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+ " </thead>\n",
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+ " <tbody>\n",
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+ " <tr>\n",
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+ " <th>0</th>\n",
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+ " <td>train</td>\n",
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+ " <td>f2f0aa29-e87b-469c-bf5b-51a3611ab001</td>\n",
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+ " <td>22131926.0</td>\n",
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+ " <td>269504.0</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>Animalia</td>\n",
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+ " <td>Arthropoda</td>\n",
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+ " <td>Insecta</td>\n",
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+ " <td>Lepidoptera</td>\n",
102
+ " <td>Lycaenidae</td>\n",
103
+ " <td>Orthomiella</td>\n",
104
+ " <td>rantaizana</td>\n",
105
+ " <td>Chinese Straight-wing Blue</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>1</th>\n",
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+ " <td>train</td>\n",
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+ " <td>5faa4f55-32e9-4872-953d-567e5d232e52</td>\n",
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+ " <td>22291283.0</td>\n",
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+ " <td>6101931.0</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
117
+ " <td>NaN</td>\n",
118
+ " <td>Plantae</td>\n",
119
+ " <td>Tracheophyta</td>\n",
120
+ " <td>Polypodiopsida</td>\n",
121
+ " <td>Polypodiales</td>\n",
122
+ " <td>Woodsiaceae</td>\n",
123
+ " <td>Woodsia</td>\n",
124
+ " <td>subcordata</td>\n",
125
+ " <td>Woodsia subcordata</td>\n",
126
+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>2</th>\n",
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+ " <td>train</td>\n",
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+ " <td>2282f2bf-2b52-4522-b588-dd6f356d5fd6</td>\n",
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+ " <td>21802775.0</td>\n",
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+ " <td>45513632.0</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>Animalia</td>\n",
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+ " <td>Chordata</td>\n",
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+ " <td>Aves</td>\n",
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+ " <td>Passeriformes</td>\n",
142
+ " <td>Laniidae</td>\n",
143
+ " <td>Lanius</td>\n",
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+ " <td>minor</td>\n",
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+ " <td>Lesser Grey Shrike</td>\n",
146
+ " </tr>\n",
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+ " <tr>\n",
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+ " <th>3</th>\n",
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+ " <td>train</td>\n",
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+ " <td>76b57c36-2181-4e6d-a5c4-b40e22a09449</td>\n",
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+ " <td>12784812.0</td>\n",
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+ " <td>51655800.0</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>tenuis</td>\n",
165
+ " <td>Tenuis</td>\n",
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+ " </tr>\n",
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+ " <tr>\n",
168
+ " <th>4</th>\n",
169
+ " <td>train</td>\n",
170
+ " <td>f57d3ab6-2cf5-484b-a590-e2a3d49a3ca2</td>\n",
171
+ " <td>29713643.0</td>\n",
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+ " <td>45515896.0</td>\n",
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+ " <td>NaN</td>\n",
174
+ " <td>NaN</td>\n",
175
+ " <td>NaN</td>\n",
176
+ " <td>NaN</td>\n",
177
+ " <td>NaN</td>\n",
178
+ " <td>Animalia</td>\n",
179
+ " <td>Chordata</td>\n",
180
+ " <td>Aves</td>\n",
181
+ " <td>Casuariiformes</td>\n",
182
+ " <td>Casuariidae</td>\n",
183
+ " <td>Casuarius</td>\n",
184
+ " <td>casuarius</td>\n",
185
+ " <td>Southern Cassowary</td>\n",
186
+ " </tr>\n",
187
+ " </tbody>\n",
188
+ "</table>\n",
189
+ "</div>"
190
+ ],
191
+ "text/plain": [
192
+ " split treeoflife_id eol_content_id eol_page_id \n",
193
+ "0 train f2f0aa29-e87b-469c-bf5b-51a3611ab001 22131926.0 269504.0 \\\n",
194
+ "1 train 5faa4f55-32e9-4872-953d-567e5d232e52 22291283.0 6101931.0 \n",
195
+ "2 train 2282f2bf-2b52-4522-b588-dd6f356d5fd6 21802775.0 45513632.0 \n",
196
+ "3 train 76b57c36-2181-4e6d-a5c4-b40e22a09449 12784812.0 51655800.0 \n",
197
+ "4 train f57d3ab6-2cf5-484b-a590-e2a3d49a3ca2 29713643.0 45515896.0 \n",
198
+ "\n",
199
+ " bioscan_part bioscan_filename inat21_filename inat21_cls_name \n",
200
+ "0 NaN NaN NaN NaN \\\n",
201
+ "1 NaN NaN NaN NaN \n",
202
+ "2 NaN NaN NaN NaN \n",
203
+ "3 NaN NaN NaN NaN \n",
204
+ "4 NaN NaN NaN NaN \n",
205
+ "\n",
206
+ " inat21_cls_num kingdom phylum class order \n",
207
+ "0 NaN Animalia Arthropoda Insecta Lepidoptera \\\n",
208
+ "1 NaN Plantae Tracheophyta Polypodiopsida Polypodiales \n",
209
+ "2 NaN Animalia Chordata Aves Passeriformes \n",
210
+ "3 NaN NaN NaN NaN NaN \n",
211
+ "4 NaN Animalia Chordata Aves Casuariiformes \n",
212
+ "\n",
213
+ " family genus species common \n",
214
+ "0 Lycaenidae Orthomiella rantaizana Chinese Straight-wing Blue \n",
215
+ "1 Woodsiaceae Woodsia subcordata Woodsia subcordata \n",
216
+ "2 Laniidae Lanius minor Lesser Grey Shrike \n",
217
+ "3 NaN NaN tenuis Tenuis \n",
218
+ "4 Casuariidae Casuarius casuarius Southern Cassowary "
219
+ ]
220
+ },
221
+ "execution_count": 3,
222
+ "metadata": {},
223
+ "output_type": "execute_result"
224
+ }
225
+ ],
226
+ "source": [
227
+ "df.head()"
228
+ ]
229
+ },
230
+ {
231
+ "cell_type": "code",
232
+ "execution_count": 4,
233
+ "metadata": {},
234
+ "outputs": [
235
+ {
236
+ "name": "stdout",
237
+ "output_type": "stream",
238
+ "text": [
239
+ "<class 'pandas.core.frame.DataFrame'>\n",
240
+ "RangeIndex: 10092530 entries, 0 to 10092529\n",
241
+ "Data columns (total 17 columns):\n",
242
+ " # Column Non-Null Count Dtype \n",
243
+ "--- ------ -------------- ----- \n",
244
+ " 0 split 10092530 non-null object \n",
245
+ " 1 treeoflife_id 10092530 non-null object \n",
246
+ " 2 eol_content_id 6277374 non-null float64\n",
247
+ " 3 eol_page_id 6277374 non-null float64\n",
248
+ " 4 bioscan_part 1128313 non-null float64\n",
249
+ " 5 bioscan_filename 1128313 non-null object \n",
250
+ " 6 inat21_filename 2686843 non-null object \n",
251
+ " 7 inat21_cls_name 2686843 non-null object \n",
252
+ " 8 inat21_cls_num 2686843 non-null float64\n",
253
+ " 9 kingdom 9831721 non-null object \n",
254
+ " 10 phylum 9833317 non-null object \n",
255
+ " 11 class 9813548 non-null object \n",
256
+ " 12 order 9807409 non-null object \n",
257
+ " 13 family 9775447 non-null object \n",
258
+ " 14 genus 8908268 non-null object \n",
259
+ " 15 species 8749857 non-null object \n",
260
+ " 16 common 10092530 non-null object \n",
261
+ "dtypes: float64(4), object(13)\n",
262
+ "memory usage: 1.3+ GB\n"
263
+ ]
264
+ }
265
+ ],
266
+ "source": [
267
+ "df.info(show_counts = True)"
268
+ ]
269
+ },
270
+ {
271
+ "cell_type": "markdown",
272
+ "metadata": {},
273
+ "source": [
274
+ "The `train_small` is duplicates of `train`, so we will drop those to analyze the full training set plus val."
275
+ ]
276
+ },
277
+ {
278
+ "cell_type": "markdown",
279
+ "metadata": {},
280
+ "source": [
281
+ "`predicted-catalog` doesn't have `train_small`, hence, it's a smaller file."
282
+ ]
283
+ },
284
+ {
285
+ "cell_type": "markdown",
286
+ "metadata": {},
287
+ "source": [
288
+ "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."
289
+ ]
290
+ },
291
+ {
292
+ "cell_type": "code",
293
+ "execution_count": 3,
294
+ "metadata": {},
295
+ "outputs": [],
296
+ "source": [
297
+ "# Add data_source column for easier slicing\n",
298
+ "df.loc[df['inat21_filename'].notna(), 'data_source'] = 'iNat21'\n",
299
+ "df.loc[df['bioscan_filename'].notna(), 'data_source'] = 'BIOSCAN'\n",
300
+ "df.loc[df['eol_content_id'].notna(), 'data_source'] = 'EOL'"
301
+ ]
302
+ },
303
+ {
304
+ "cell_type": "markdown",
305
+ "metadata": {},
306
+ "source": [
307
+ "#### Get just EOL CSV for license addition"
308
+ ]
309
+ },
310
+ {
311
+ "cell_type": "code",
312
+ "execution_count": 4,
313
+ "metadata": {},
314
+ "outputs": [],
315
+ "source": [
316
+ "eol_df = df.loc[df['data_source'] == 'EOL']"
317
+ ]
318
+ },
319
+ {
320
+ "cell_type": "code",
321
+ "execution_count": 5,
322
+ "metadata": {},
323
+ "outputs": [
324
+ {
325
+ "data": {
326
+ "text/html": [
327
+ "<div>\n",
328
+ "<style scoped>\n",
329
+ " .dataframe tbody tr th:only-of-type {\n",
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+ " vertical-align: middle;\n",
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+ " }\n",
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+ "\n",
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+ " .dataframe tbody tr th {\n",
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+ " vertical-align: top;\n",
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+ " }\n",
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+ "\n",
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+ " .dataframe thead th {\n",
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+ " text-align: right;\n",
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+ " }\n",
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+ "</style>\n",
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+ "<table border=\"1\" class=\"dataframe\">\n",
342
+ " <thead>\n",
343
+ " <tr style=\"text-align: right;\">\n",
344
+ " <th></th>\n",
345
+ " <th>split</th>\n",
346
+ " <th>treeoflife_id</th>\n",
347
+ " <th>eol_content_id</th>\n",
348
+ " <th>eol_page_id</th>\n",
349
+ " <th>bioscan_part</th>\n",
350
+ " <th>bioscan_filename</th>\n",
351
+ " <th>inat21_filename</th>\n",
352
+ " <th>inat21_cls_name</th>\n",
353
+ " <th>inat21_cls_num</th>\n",
354
+ " <th>kingdom</th>\n",
355
+ " <th>phylum</th>\n",
356
+ " <th>class</th>\n",
357
+ " <th>order</th>\n",
358
+ " <th>family</th>\n",
359
+ " <th>genus</th>\n",
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+ " <th>species</th>\n",
361
+ " <th>common</th>\n",
362
+ " <th>data_source</th>\n",
363
+ " </tr>\n",
364
+ " </thead>\n",
365
+ " <tbody>\n",
366
+ " <tr>\n",
367
+ " <th>0</th>\n",
368
+ " <td>train</td>\n",
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+ " <td>f2f0aa29-e87b-469c-bf5b-51a3611ab001</td>\n",
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+ " <td>22131926.0</td>\n",
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+ " <td>269504.0</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
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+ " <td>NaN</td>\n",
376
+ " <td>NaN</td>\n",
377
+ " <td>Animalia</td>\n",
378
+ " <td>Arthropoda</td>\n",
379
+ " <td>Insecta</td>\n",
380
+ " <td>Lepidoptera</td>\n",
381
+ " <td>Lycaenidae</td>\n",
382
+ " <td>Orthomiella</td>\n",
383
+ " <td>rantaizana</td>\n",
384
+ " <td>Chinese Straight-wing Blue</td>\n",
385
+ " <td>EOL</td>\n",
386
+ " </tr>\n",
387
+ " <tr>\n",
388
+ " <th>1</th>\n",
389
+ " <td>train</td>\n",
390
+ " <td>5faa4f55-32e9-4872-953d-567e5d232e52</td>\n",
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+ " <td>22291283.0</td>\n",
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+ " <td>6101931.0</td>\n",
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+ " <td>NaN</td>\n",
394
+ " <td>NaN</td>\n",
395
+ " <td>NaN</td>\n",
396
+ " <td>NaN</td>\n",
397
+ " <td>NaN</td>\n",
398
+ " <td>Plantae</td>\n",
399
+ " <td>Tracheophyta</td>\n",
400
+ " <td>Polypodiopsida</td>\n",
401
+ " <td>Polypodiales</td>\n",
402
+ " <td>Woodsiaceae</td>\n",
403
+ " <td>Woodsia</td>\n",
404
+ " <td>subcordata</td>\n",
405
+ " <td>Woodsia subcordata</td>\n",
406
+ " <td>EOL</td>\n",
407
+ " </tr>\n",
408
+ " <tr>\n",
409
+ " <th>2</th>\n",
410
+ " <td>train</td>\n",
411
+ " <td>2282f2bf-2b52-4522-b588-dd6f356d5fd6</td>\n",
412
+ " <td>21802775.0</td>\n",
413
+ " <td>45513632.0</td>\n",
414
+ " <td>NaN</td>\n",
415
+ " <td>NaN</td>\n",
416
+ " <td>NaN</td>\n",
417
+ " <td>NaN</td>\n",
418
+ " <td>NaN</td>\n",
419
+ " <td>Animalia</td>\n",
420
+ " <td>Chordata</td>\n",
421
+ " <td>Aves</td>\n",
422
+ " <td>Passeriformes</td>\n",
423
+ " <td>Laniidae</td>\n",
424
+ " <td>Lanius</td>\n",
425
+ " <td>minor</td>\n",
426
+ " <td>Lesser Grey Shrike</td>\n",
427
+ " <td>EOL</td>\n",
428
+ " </tr>\n",
429
+ " <tr>\n",
430
+ " <th>3</th>\n",
431
+ " <td>train</td>\n",
432
+ " <td>76b57c36-2181-4e6d-a5c4-b40e22a09449</td>\n",
433
+ " <td>12784812.0</td>\n",
434
+ " <td>51655800.0</td>\n",
435
+ " <td>NaN</td>\n",
436
+ " <td>NaN</td>\n",
437
+ " <td>NaN</td>\n",
438
+ " <td>NaN</td>\n",
439
+ " <td>NaN</td>\n",
440
+ " <td>NaN</td>\n",
441
+ " <td>NaN</td>\n",
442
+ " <td>NaN</td>\n",
443
+ " <td>NaN</td>\n",
444
+ " <td>NaN</td>\n",
445
+ " <td>NaN</td>\n",
446
+ " <td>tenuis</td>\n",
447
+ " <td>Tenuis</td>\n",
448
+ " <td>EOL</td>\n",
449
+ " </tr>\n",
450
+ " <tr>\n",
451
+ " <th>4</th>\n",
452
+ " <td>train</td>\n",
453
+ " <td>f57d3ab6-2cf5-484b-a590-e2a3d49a3ca2</td>\n",
454
+ " <td>29713643.0</td>\n",
455
+ " <td>45515896.0</td>\n",
456
+ " <td>NaN</td>\n",
457
+ " <td>NaN</td>\n",
458
+ " <td>NaN</td>\n",
459
+ " <td>NaN</td>\n",
460
+ " <td>NaN</td>\n",
461
+ " <td>Animalia</td>\n",
462
+ " <td>Chordata</td>\n",
463
+ " <td>Aves</td>\n",
464
+ " <td>Casuariiformes</td>\n",
465
+ " <td>Casuariidae</td>\n",
466
+ " <td>Casuarius</td>\n",
467
+ " <td>casuarius</td>\n",
468
+ " <td>Southern Cassowary</td>\n",
469
+ " <td>EOL</td>\n",
470
+ " </tr>\n",
471
+ " </tbody>\n",
472
+ "</table>\n",
473
+ "</div>"
474
+ ],
475
+ "text/plain": [
476
+ " split treeoflife_id eol_content_id eol_page_id \n",
477
+ "0 train f2f0aa29-e87b-469c-bf5b-51a3611ab001 22131926.0 269504.0 \\\n",
478
+ "1 train 5faa4f55-32e9-4872-953d-567e5d232e52 22291283.0 6101931.0 \n",
479
+ "2 train 2282f2bf-2b52-4522-b588-dd6f356d5fd6 21802775.0 45513632.0 \n",
480
+ "3 train 76b57c36-2181-4e6d-a5c4-b40e22a09449 12784812.0 51655800.0 \n",
481
+ "4 train f57d3ab6-2cf5-484b-a590-e2a3d49a3ca2 29713643.0 45515896.0 \n",
482
+ "\n",
483
+ " bioscan_part bioscan_filename inat21_filename inat21_cls_name \n",
484
+ "0 NaN NaN NaN NaN \\\n",
485
+ "1 NaN NaN NaN NaN \n",
486
+ "2 NaN NaN NaN NaN \n",
487
+ "3 NaN NaN NaN NaN \n",
488
+ "4 NaN NaN NaN NaN \n",
489
+ "\n",
490
+ " inat21_cls_num kingdom phylum class order \n",
491
+ "0 NaN Animalia Arthropoda Insecta Lepidoptera \\\n",
492
+ "1 NaN Plantae Tracheophyta Polypodiopsida Polypodiales \n",
493
+ "2 NaN Animalia Chordata Aves Passeriformes \n",
494
+ "3 NaN NaN NaN NaN NaN \n",
495
+ "4 NaN Animalia Chordata Aves Casuariiformes \n",
496
+ "\n",
497
+ " family genus species common \n",
498
+ "0 Lycaenidae Orthomiella rantaizana Chinese Straight-wing Blue \\\n",
499
+ "1 Woodsiaceae Woodsia subcordata Woodsia subcordata \n",
500
+ "2 Laniidae Lanius minor Lesser Grey Shrike \n",
501
+ "3 NaN NaN tenuis Tenuis \n",
502
+ "4 Casuariidae Casuarius casuarius Southern Cassowary \n",
503
+ "\n",
504
+ " data_source \n",
505
+ "0 EOL \n",
506
+ "1 EOL \n",
507
+ "2 EOL \n",
508
+ "3 EOL \n",
509
+ "4 EOL "
510
+ ]
511
+ },
512
+ "execution_count": 5,
513
+ "metadata": {},
514
+ "output_type": "execute_result"
515
+ }
516
+ ],
517
+ "source": [
518
+ "eol_df.head()"
519
+ ]
520
+ },
521
+ {
522
+ "cell_type": "markdown",
523
+ "metadata": {},
524
+ "source": [
525
+ "We don't need the BIOSCAN or iNat21 columns, nor the taxa columns."
526
+ ]
527
+ },
528
+ {
529
+ "cell_type": "code",
530
+ "execution_count": 6,
531
+ "metadata": {},
532
+ "outputs": [
533
+ {
534
+ "data": {
535
+ "text/plain": [
536
+ "Index(['split', 'treeoflife_id', 'eol_content_id', 'eol_page_id'], dtype='object')"
537
+ ]
538
+ },
539
+ "execution_count": 6,
540
+ "metadata": {},
541
+ "output_type": "execute_result"
542
+ }
543
+ ],
544
+ "source": [
545
+ "eol_license_cols = eol_df.columns[:4]\n",
546
+ "eol_license_cols"
547
+ ]
548
+ },
549
+ {
550
+ "cell_type": "code",
551
+ "execution_count": 8,
552
+ "metadata": {},
553
+ "outputs": [
554
+ {
555
+ "name": "stderr",
556
+ "output_type": "stream",
557
+ "text": [
558
+ "/var/folders/nv/f0fq1p1n1_3b11x579py_0q80000gq/T/ipykernel_88306/3674968623.py:2: SettingWithCopyWarning: \n",
559
+ "A value is trying to be set on a copy of a slice from a DataFrame.\n",
560
+ "Try using .loc[row_indexer,col_indexer] = value instead\n",
561
+ "\n",
562
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
563
+ " eol_license_df[\"license\"] = None\n"
564
+ ]
565
+ }
566
+ ],
567
+ "source": [
568
+ "eol_license_df = eol_df[eol_license_cols]\n",
569
+ "eol_license_df[\"license\"] = None"
570
+ ]
571
+ },
572
+ {
573
+ "cell_type": "code",
574
+ "execution_count": 9,
575
+ "metadata": {},
576
+ "outputs": [
577
+ {
578
+ "data": {
579
+ "text/html": [
580
+ "<div>\n",
581
+ "<style scoped>\n",
582
+ " .dataframe tbody tr th:only-of-type {\n",
583
+ " vertical-align: middle;\n",
584
+ " }\n",
585
+ "\n",
586
+ " .dataframe tbody tr th {\n",
587
+ " vertical-align: top;\n",
588
+ " }\n",
589
+ "\n",
590
+ " .dataframe thead th {\n",
591
+ " text-align: right;\n",
592
+ " }\n",
593
+ "</style>\n",
594
+ "<table border=\"1\" class=\"dataframe\">\n",
595
+ " <thead>\n",
596
+ " <tr style=\"text-align: right;\">\n",
597
+ " <th></th>\n",
598
+ " <th>split</th>\n",
599
+ " <th>treeoflife_id</th>\n",
600
+ " <th>eol_content_id</th>\n",
601
+ " <th>eol_page_id</th>\n",
602
+ " <th>license</th>\n",
603
+ " </tr>\n",
604
+ " </thead>\n",
605
+ " <tbody>\n",
606
+ " <tr>\n",
607
+ " <th>0</th>\n",
608
+ " <td>train</td>\n",
609
+ " <td>f2f0aa29-e87b-469c-bf5b-51a3611ab001</td>\n",
610
+ " <td>22131926.0</td>\n",
611
+ " <td>269504.0</td>\n",
612
+ " <td>None</td>\n",
613
+ " </tr>\n",
614
+ " <tr>\n",
615
+ " <th>1</th>\n",
616
+ " <td>train</td>\n",
617
+ " <td>5faa4f55-32e9-4872-953d-567e5d232e52</td>\n",
618
+ " <td>22291283.0</td>\n",
619
+ " <td>6101931.0</td>\n",
620
+ " <td>None</td>\n",
621
+ " </tr>\n",
622
+ " <tr>\n",
623
+ " <th>2</th>\n",
624
+ " <td>train</td>\n",
625
+ " <td>2282f2bf-2b52-4522-b588-dd6f356d5fd6</td>\n",
626
+ " <td>21802775.0</td>\n",
627
+ " <td>45513632.0</td>\n",
628
+ " <td>None</td>\n",
629
+ " </tr>\n",
630
+ " <tr>\n",
631
+ " <th>3</th>\n",
632
+ " <td>train</td>\n",
633
+ " <td>76b57c36-2181-4e6d-a5c4-b40e22a09449</td>\n",
634
+ " <td>12784812.0</td>\n",
635
+ " <td>51655800.0</td>\n",
636
+ " <td>None</td>\n",
637
+ " </tr>\n",
638
+ " <tr>\n",
639
+ " <th>4</th>\n",
640
+ " <td>train</td>\n",
641
+ " <td>f57d3ab6-2cf5-484b-a590-e2a3d49a3ca2</td>\n",
642
+ " <td>29713643.0</td>\n",
643
+ " <td>45515896.0</td>\n",
644
+ " <td>None</td>\n",
645
+ " </tr>\n",
646
+ " </tbody>\n",
647
+ "</table>\n",
648
+ "</div>"
649
+ ],
650
+ "text/plain": [
651
+ " split treeoflife_id eol_content_id eol_page_id \n",
652
+ "0 train f2f0aa29-e87b-469c-bf5b-51a3611ab001 22131926.0 269504.0 \\\n",
653
+ "1 train 5faa4f55-32e9-4872-953d-567e5d232e52 22291283.0 6101931.0 \n",
654
+ "2 train 2282f2bf-2b52-4522-b588-dd6f356d5fd6 21802775.0 45513632.0 \n",
655
+ "3 train 76b57c36-2181-4e6d-a5c4-b40e22a09449 12784812.0 51655800.0 \n",
656
+ "4 train f57d3ab6-2cf5-484b-a590-e2a3d49a3ca2 29713643.0 45515896.0 \n",
657
+ "\n",
658
+ " license \n",
659
+ "0 None \n",
660
+ "1 None \n",
661
+ "2 None \n",
662
+ "3 None \n",
663
+ "4 None "
664
+ ]
665
+ },
666
+ "execution_count": 9,
667
+ "metadata": {},
668
+ "output_type": "execute_result"
669
+ }
670
+ ],
671
+ "source": [
672
+ "eol_license_df.head()"
673
+ ]
674
+ },
675
+ {
676
+ "cell_type": "code",
677
+ "execution_count": 11,
678
+ "metadata": {},
679
+ "outputs": [],
680
+ "source": [
681
+ "eol_license_df.to_csv(\"../data/eol_licenses.csv\", index = False)"
682
+ ]
683
+ },
684
+ {
685
+ "cell_type": "code",
686
+ "execution_count": null,
687
+ "metadata": {},
688
+ "outputs": [],
689
+ "source": []
690
+ }
691
+ ],
692
+ "metadata": {
693
+ "jupytext": {
694
+ "formats": "ipynb,py:percent"
695
+ },
696
+ "kernelspec": {
697
+ "display_name": "Python 3 (ipykernel)",
698
+ "language": "python",
699
+ "name": "python3"
700
+ },
701
+ "language_info": {
702
+ "codemirror_mode": {
703
+ "name": "ipython",
704
+ "version": 3
705
+ },
706
+ "file_extension": ".py",
707
+ "mimetype": "text/x-python",
708
+ "name": "python",
709
+ "nbconvert_exporter": "python",
710
+ "pygments_lexer": "ipython3",
711
+ "version": "3.11.3"
712
+ }
713
+ },
714
+ "nbformat": 4,
715
+ "nbformat_minor": 2
716
+ }
notebooks/ToL_license_check.py ADDED
@@ -0,0 +1,76 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # ---
2
+ # jupyter:
3
+ # jupytext:
4
+ # formats: ipynb,py:percent
5
+ # text_representation:
6
+ # extension: .py
7
+ # format_name: percent
8
+ # format_version: '1.3'
9
+ # jupytext_version: 1.15.2
10
+ # kernelspec:
11
+ # display_name: Python 3 (ipykernel)
12
+ # language: python
13
+ # name: python3
14
+ # ---
15
+
16
+ # %%
17
+ import pandas as pd
18
+ import seaborn as sns
19
+
20
+ sns.set_style("whitegrid")
21
+ sns.set(rc = {'figure.figsize': (10,10)})
22
+
23
+ # %% [markdown]
24
+ # Load in full images to ease process.
25
+
26
+ # %%
27
+ df = pd.read_csv("../data/predicted-catalog.csv")
28
+
29
+ # %%
30
+ df.head()
31
+
32
+ # %%
33
+ df.info(show_counts = True)
34
+
35
+ # %% [markdown]
36
+ # The `train_small` is duplicates of `train`, so we will drop those to analyze the full training set plus val.
37
+
38
+ # %% [markdown]
39
+ # `predicted-catalog` doesn't have `train_small`, hence, it's a smaller file.
40
+
41
+ # %% [markdown]
42
+ # 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.
43
+
44
+ # %%
45
+ # Add data_source column for easier slicing
46
+ df.loc[df['inat21_filename'].notna(), 'data_source'] = 'iNat21'
47
+ df.loc[df['bioscan_filename'].notna(), 'data_source'] = 'BIOSCAN'
48
+ df.loc[df['eol_content_id'].notna(), 'data_source'] = 'EOL'
49
+
50
+ # %% [markdown]
51
+ # #### Get just EOL CSV for license addition
52
+
53
+ # %%
54
+ eol_df = df.loc[df['data_source'] == 'EOL']
55
+
56
+ # %%
57
+ eol_df.head()
58
+
59
+ # %% [markdown]
60
+ # We don't need the BIOSCAN or iNat21 columns, nor the taxa columns.
61
+
62
+ # %%
63
+ eol_license_cols = eol_df.columns[:4]
64
+ eol_license_cols
65
+
66
+ # %%
67
+ eol_license_df = eol_df[eol_license_cols]
68
+ eol_license_df["license"] = None
69
+
70
+ # %%
71
+ eol_license_df.head()
72
+
73
+ # %%
74
+ eol_license_df.to_csv("../data/eol_licenses.csv", index = False)
75
+
76
+ # %%