{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import seaborn as sns\n", "\n", "sns.set_style(\"whitegrid\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Reading in our potential test sets from [here](https://huggingface.co/datasets/imageomics/lila-bc-camera/tree/d18307b285217d18b31d1a7b2c9091bb0873ade0/data/potential-test-sets):\n", " - [Ohio Small Animals](https://lila.science/datasets/ohio-small-animals/)\n", " - [Desert Lion Conservation Camera Traps](https://lila.science/datasets/desert-lion-conservation-camera-traps/)\n", " - [Orinoquia Camera Traps](https://lila.science/datasets/orinoquia-camera-traps/)\n", " - [Island Conservation Camera Traps](https://lila.science/datasets/island-conservation-camera-traps/)\n", " - [ENA24](https://lila.science/datasets/ena24detection)\n", "\n", "We'll clean them down to just the taxa and identifier columns, then further reduce to make balanced test sets for each.\n", "\n", "# Island Conservation Camera Traps Datasets\n", "upper/lower bounded and balanced" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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dataset_nameurl_gcpurl_awsurl_azureimage_idsequence_idlocation_idframe_numoriginal_labelscientific_name...superfamilyfamilysubfamilytribegenusspeciessubspeciesvarietymulti_speciesnum_species
0Island Conservation Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Island Conservation Camera Traps : dominicanre...Island Conservation Camera Traps : unknownIsland Conservation Camera Traps : dominicanre...-1cowbos taurus...NaNbovidaebovinaebovinibosbos taurusNaNNaNFalse1.0
1Island Conservation Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Island Conservation Camera Traps : dominicanre...Island Conservation Camera Traps : unknownIsland Conservation Camera Traps : dominicanre...-1donkeyequus asinus...NaNequidaeNaNNaNequusequus asinusNaNNaNFalse1.0
2Island Conservation Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Island Conservation Camera Traps : dominicanre...Island Conservation Camera Traps : unknownIsland Conservation Camera Traps : dominicanre...-1donkeyequus asinus...NaNequidaeNaNNaNequusequus asinusNaNNaNFalse1.0
3Island Conservation Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Island Conservation Camera Traps : dominicanre...Island Conservation Camera Traps : unknownIsland Conservation Camera Traps : dominicanre...-1iguanaiguana...NaNiguanidaeNaNNaNiguanaNaNNaNNaNFalse1.0
4Island Conservation Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Island Conservation Camera Traps : dominicanre...Island Conservation Camera Traps : unknownIsland Conservation Camera Traps : dominicanre...-1iguanaiguana...NaNiguanidaeNaNNaNiguanaNaNNaNNaNFalse1.0
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5 rows × 34 columns

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" ], "text/plain": [ " dataset_name \\\n", "0 Island Conservation Camera Traps \n", "1 Island Conservation Camera Traps \n", "2 Island Conservation Camera Traps \n", "3 Island Conservation Camera Traps \n", "4 Island Conservation Camera Traps \n", "\n", " url_gcp \\\n", "0 https://storage.googleapis.com/public-datasets... \n", "1 https://storage.googleapis.com/public-datasets... \n", "2 https://storage.googleapis.com/public-datasets... \n", "3 https://storage.googleapis.com/public-datasets... \n", "4 https://storage.googleapis.com/public-datasets... \n", "\n", " url_aws \\\n", "0 http://us-west-2.opendata.source.coop.s3.amazo... \n", "1 http://us-west-2.opendata.source.coop.s3.amazo... \n", "2 http://us-west-2.opendata.source.coop.s3.amazo... \n", "3 http://us-west-2.opendata.source.coop.s3.amazo... \n", "4 http://us-west-2.opendata.source.coop.s3.amazo... \n", "\n", " url_azure \\\n", "0 https://lilawildlife.blob.core.windows.net/lil... \n", "1 https://lilawildlife.blob.core.windows.net/lil... \n", "2 https://lilawildlife.blob.core.windows.net/lil... \n", "3 https://lilawildlife.blob.core.windows.net/lil... \n", "4 https://lilawildlife.blob.core.windows.net/lil... \n", "\n", " image_id \\\n", "0 Island Conservation Camera Traps : dominicanre... \n", "1 Island Conservation Camera Traps : dominicanre... \n", "2 Island Conservation Camera Traps : dominicanre... \n", "3 Island Conservation Camera Traps : dominicanre... \n", "4 Island Conservation Camera Traps : dominicanre... \n", "\n", " sequence_id \\\n", "0 Island Conservation Camera Traps : unknown \n", "1 Island Conservation Camera Traps : unknown \n", "2 Island Conservation Camera Traps : unknown \n", "3 Island Conservation Camera Traps : unknown \n", "4 Island Conservation Camera Traps : unknown \n", "\n", " location_id frame_num \\\n", "0 Island Conservation Camera Traps : dominicanre... -1 \n", "1 Island Conservation Camera Traps : dominicanre... -1 \n", "2 Island Conservation Camera Traps : dominicanre... -1 \n", "3 Island Conservation Camera Traps : dominicanre... -1 \n", "4 Island Conservation Camera Traps : dominicanre... -1 \n", "\n", " original_label scientific_name ... superfamily family subfamily \\\n", "0 cow bos taurus ... NaN bovidae bovinae \n", "1 donkey equus asinus ... NaN equidae NaN \n", "2 donkey equus asinus ... NaN equidae NaN \n", "3 iguana iguana ... NaN iguanidae NaN \n", "4 iguana iguana ... NaN iguanidae NaN \n", "\n", " tribe genus species subspecies variety multi_species num_species \n", "0 bovini bos bos taurus NaN NaN False 1.0 \n", "1 NaN equus equus asinus NaN NaN False 1.0 \n", "2 NaN equus equus asinus NaN NaN False 1.0 \n", "3 NaN iguana NaN NaN NaN False 1.0 \n", "4 NaN iguana NaN NaN NaN False 1.0 \n", "\n", "[5 rows x 34 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(\"../data/potential-test-sets/Island_Conservation_Camera_Traps_image_urls_and_labels.csv\", low_memory = False)\n", "df.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['dataset_name', 'url_gcp', 'url_aws', 'url_azure', 'image_id',\n", " 'sequence_id', 'location_id', 'frame_num', 'original_label',\n", " 'scientific_name', 'common_name', 'datetime', 'annotation_level',\n", " 'kingdom', 'phylum', 'subphylum', 'superclass', 'class', 'subclass',\n", " 'infraclass', 'superorder', 'order', 'suborder', 'infraorder',\n", " 'superfamily', 'family', 'subfamily', 'tribe', 'genus', 'species',\n", " 'subspecies', 'variety', 'multi_species', 'num_species'],\n", " dtype='object')" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Observe that we also now get multiple URL options; `url_aws` will likely be best/fastest for use with [`distributed-downloader`](https://github.com/Imageomics/distributed-downloader) to get the images." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 44438 entries, 0 to 44437\n", "Data columns (total 34 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 dataset_name 44438 non-null object \n", " 1 url_gcp 44438 non-null object \n", " 2 url_aws 44438 non-null object \n", " 3 url_azure 44438 non-null object \n", " 4 image_id 44438 non-null object \n", " 5 sequence_id 44438 non-null object \n", " 6 location_id 44438 non-null object \n", " 7 frame_num 44438 non-null int64 \n", " 8 original_label 44438 non-null object \n", " 9 scientific_name 44438 non-null object \n", " 10 common_name 44438 non-null object \n", " 11 datetime 44354 non-null object \n", " 12 annotation_level 44438 non-null object \n", " 13 kingdom 44438 non-null object \n", " 14 phylum 44438 non-null object \n", " 15 subphylum 44438 non-null object \n", " 16 superclass 79 non-null object \n", " 17 class 44438 non-null object \n", " 18 subclass 25269 non-null object \n", " 19 infraclass 25189 non-null object \n", " 20 superorder 25268 non-null object \n", " 21 order 44261 non-null object \n", " 22 suborder 18430 non-null object \n", " 23 infraorder 79 non-null object \n", " 24 superfamily 6945 non-null object \n", " 25 family 44190 non-null object \n", " 26 subfamily 16736 non-null object \n", " 27 tribe 11810 non-null object \n", " 28 genus 28006 non-null object \n", " 29 species 13202 non-null object \n", " 30 subspecies 0 non-null float64\n", " 31 variety 938 non-null object \n", " 32 multi_species 44438 non-null bool \n", " 33 num_species 44438 non-null float64\n", "dtypes: bool(1), float64(2), int64(1), object(30)\n", "memory usage: 11.2+ MB\n" ] } ], "source": [ "df.info(show_counts = True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Most of these are not labeled to the species level, so we'll need to take a closer look at the taxa. Perhaps subdivide?\n", "\n", "Everything is labeled to the class level, most make it to family, but then it's a steep drop-off." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "lin_taxa = ['kingdom', 'phylum', 'class', 'order', 'family', 'genus', 'species']\n", "taxa_cols = ['original_label', 'scientific_name', 'common_name', 'kingdom', 'phylum', 'class', 'order', 'family', 'genus', 'species']" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "original_label 45\n", "scientific_name 42\n", "common_name 45\n", "kingdom 1\n", "phylum 2\n", "class 6\n", "order 19\n", "family 25\n", "genus 25\n", "species 22\n", "dtype: int64" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[taxa_cols].nunique()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "42 scientific names, but 45 common and original. Only 22 species --more genera and families than species!\n", "\n", "Also, should check for duplicated images." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "number of unique images: 44007\n" ] }, { "data": { "text/plain": [ "multi_species\n", "False 43577\n", "True 861\n", "Name: count, dtype: int64" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(f\"number of unique images: {df[\"image_id\"].nunique()}\")\n", "\n", "df[\"multi_species\"].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We also have 430 duplicated images (multiple species per image)." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "num_species\n", "1.0 43577\n", "2.0 858\n", "3.0 3\n", "Name: count, dtype: int64" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[\"num_species\"].value_counts()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "scientific_name\n", "gallus gallus domesticus 308\n", "procellariidae 180\n", "asio flammeus 85\n", "aves 83\n", "rattus 36\n", "felis catus 29\n", "leporidae 27\n", "iguana 24\n", "calonectris 23\n", "calcinus tubularis 20\n", "corvus corax 13\n", "capra hircus 8\n", "insecta 5\n", "brachyura 4\n", "falco tinnunculus 3\n", "sus scrofa 3\n", "equus asinus 3\n", "columbidae 2\n", "zenaida asiatica 1\n", "bos taurus 1\n", "rallidae 1\n", "lepidoptera 1\n", "araneae 1\n", "Name: count, dtype: int64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[df[\"multi_species\"], \"scientific_name\"].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "That's a large variety, what happens if we drop these?" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "41" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[~df[\"multi_species\"], \"scientific_name\"].nunique()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hmm, we'll lose one." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['bos taurus' 'equus asinus' 'iguana' 'corvus corax' 'felis catus'\n", " 'canis familiaris' 'rattus' 'zenaida asiatica' 'aves' 'passeriformes'\n", " 'strigiformes' 'athene cunicularia' 'strix varia' 'butorides virescens'\n", " 'falco sparverius' 'mimidae' 'ardea herodias' 'gallus gallus domesticus'\n", " 'procellariidae' 'leporidae' 'falco tinnunculus' 'calonectris'\n", " 'nasua nasua' 'hydrobates pelagicus' 'columbidae' 'araneae'\n", " 'asio flammeus' 'insecta' 'turdus' 'phocidae' 'capra hircus' 'sus scrofa'\n", " 'nyctanassa violacea' 'chelonioidea' 'brachyura' 'megapodiidae'\n", " 'anous stolidus' 'caloenas nicobarica' 'rallidae' 'varanidae'\n", " 'lepidoptera' 'calcinus tubularis'] ['cow' 'donkey' 'iguana' 'raven' 'cat' 'dog' 'rat' 'white-winged_dove'\n", " 'bird' 'passerine' 'owl' 'burrowing_owl' 'barred_owl' 'green_heron'\n", " 'american_kestrel' 'mockingbird' 'great_blue_heron' 'chicken' 'petrel'\n", " 'rabbit' 'petrel_chick' 'kestrel' 'shearwater' 'coati' 'storm_petrel'\n", " 'pigeon' 'dove' 'spider' 'short-eared_owl' 'insect' 'zorzal' 'seal'\n", " 'goat' 'pig' 'yellow-crowned_night_heron' 'sea_turtle' 'rooster' 'crab'\n", " 'megapode' 'brown_noddy' 'nicobar_pigeon' 'rail' 'monitor_lizard' 'moth'\n", " 'hermit_crab']\n" ] } ], "source": [ "print(df.scientific_name.unique(), df.original_label.unique())" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "common_name\n", "rabbits and hares 7729\n", "rat 6880\n", "petrel 6629\n", "typical iguanas 6187\n", "shearwater 1722\n", "petrel chick 1097\n", "monitor lizard 354\n", "megapode 274\n", "bird 137\n", "rail 52\n", "insect 40\n", "songbirds 38\n", "pigeon 30\n", "crab 20\n", "thrush 15\n", "seal 15\n", "sea turtle 6\n", "spider 5\n", "mockingbird 3\n", "dove 1\n", "owl 1\n", "moth 1\n", "Name: count, dtype: int64" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[df[\"species\"].isna(), \"common_name\"].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Perhaps this one should be a common name task since they are provided for all images.\n", "\n", "\"typical iguanas\" and \"rabbits and hares\" may not be ideal.\n", "Maybe \"typical iguanas\" is meant to be \"iguana iguana\" like representative species? let's check it's scientific name." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "scientific_name\n", "iguana 6187\n", "Name: count, dtype: int64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[df[\"common_name\"] == \"typical iguanas\", \"scientific_name\"].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Just iguana. How about \"rabbits and hares\"?" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "scientific_name\n", "leporidae 7729\n", "Name: count, dtype: int64" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[df[\"common_name\"] == \"rabbits and hares\", \"scientific_name\"].value_counts()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['cattle', 'donkey', 'typical iguanas', 'raven', 'cat',\n", " 'domestic dog', 'rat', 'white-winged dove', 'bird', 'songbirds',\n", " 'owl', 'burrowing owl', 'barred owl', 'green heron',\n", " 'american kestrel', 'mockingbird', 'great blue heron',\n", " 'domestic chicken', 'petrel', 'rabbits and hares', 'petrel chick',\n", " 'kestrel', 'shearwater', 'coati', 'storm petrel', 'pigeon', 'dove',\n", " 'spider', 'short-eared owl', 'insect', 'thrush', 'seal', 'goat',\n", " 'pig', 'yellow-crowned night heron', 'sea turtle', 'rooster',\n", " 'crab', 'megapode', 'brown noddy', 'nicobar pigeon', 'rail',\n", " 'monitor lizard', 'moth', 'hermit crab'], dtype=object)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.common_name.unique()" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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dataset_nameurl_gcpurl_awsurl_azureimage_idsequence_idlocation_idframe_numoriginal_labelscientific_name...superfamilyfamilysubfamilytribegenusspeciessubspeciesvarietymulti_speciesnum_species
1369Island Conservation Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Island Conservation Camera Traps : dominicanre...Island Conservation Camera Traps : unknownIsland Conservation Camera Traps : dominicanre...-1iguanaiguana...NaNiguanidaeNaNNaNiguanaNaNNaNNaNFalse1.0
16627Island Conservation Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Island Conservation Camera Traps : chile_filip...Island Conservation Camera Traps : unknownIsland Conservation Camera Traps : chile_filip...-1petrelprocellariidae...NaNprocellariidaeNaNNaNNaNNaNNaNNaNFalse1.0
8315Island Conservation Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Island Conservation Camera Traps : chile_franc...Island Conservation Camera Traps : unknownIsland Conservation Camera Traps : chile_franc...-1rabbitleporidae...NaNleporidaeNaNNaNNaNNaNNaNNaNFalse1.0
7461Island Conservation Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Island Conservation Camera Traps : chile_franc...Island Conservation Camera Traps : unknownIsland Conservation Camera Traps : chile_franc...-1rabbitleporidae...NaNleporidaeNaNNaNNaNNaNNaNNaNFalse1.0
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4 rows × 34 columns

\n", "
" ], "text/plain": [ " dataset_name \\\n", "1369 Island Conservation Camera Traps \n", "16627 Island Conservation Camera Traps \n", "8315 Island Conservation Camera Traps \n", "7461 Island Conservation Camera Traps \n", "\n", " url_gcp \\\n", "1369 https://storage.googleapis.com/public-datasets... \n", "16627 https://storage.googleapis.com/public-datasets... \n", "8315 https://storage.googleapis.com/public-datasets... \n", "7461 https://storage.googleapis.com/public-datasets... \n", "\n", " url_aws \\\n", "1369 http://us-west-2.opendata.source.coop.s3.amazo... \n", "16627 http://us-west-2.opendata.source.coop.s3.amazo... \n", "8315 http://us-west-2.opendata.source.coop.s3.amazo... \n", "7461 http://us-west-2.opendata.source.coop.s3.amazo... \n", "\n", " url_azure \\\n", "1369 https://lilawildlife.blob.core.windows.net/lil... \n", "16627 https://lilawildlife.blob.core.windows.net/lil... \n", "8315 https://lilawildlife.blob.core.windows.net/lil... \n", "7461 https://lilawildlife.blob.core.windows.net/lil... \n", "\n", " image_id \\\n", "1369 Island Conservation Camera Traps : dominicanre... \n", "16627 Island Conservation Camera Traps : chile_filip... \n", "8315 Island Conservation Camera Traps : chile_franc... \n", "7461 Island Conservation Camera Traps : chile_franc... \n", "\n", " sequence_id \\\n", "1369 Island Conservation Camera Traps : unknown \n", "16627 Island Conservation Camera Traps : unknown \n", "8315 Island Conservation Camera Traps : unknown \n", "7461 Island Conservation Camera Traps : unknown \n", "\n", " location_id frame_num \\\n", "1369 Island Conservation Camera Traps : dominicanre... -1 \n", "16627 Island Conservation Camera Traps : chile_filip... -1 \n", "8315 Island Conservation Camera Traps : chile_franc... -1 \n", "7461 Island Conservation Camera Traps : chile_franc... -1 \n", "\n", " original_label scientific_name ... superfamily family \\\n", "1369 iguana iguana ... NaN iguanidae \n", "16627 petrel procellariidae ... NaN procellariidae \n", "8315 rabbit leporidae ... NaN leporidae \n", "7461 rabbit leporidae ... NaN leporidae \n", "\n", " subfamily tribe genus species subspecies variety multi_species \\\n", "1369 NaN NaN iguana NaN NaN NaN False \n", "16627 NaN NaN NaN NaN NaN NaN False \n", "8315 NaN NaN NaN NaN NaN NaN False \n", "7461 NaN NaN NaN NaN NaN NaN False \n", "\n", " num_species \n", "1369 1.0 \n", "16627 1.0 \n", "8315 1.0 \n", "7461 1.0 \n", "\n", "[4 rows x 34 columns]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.loc[df[\"species\"].isna()].sample(4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's remove the multi-species images and set this up as a common name task." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "image_id 43577\n", "scientific_name 41\n", "common_name 43\n", "species 22\n", "dtype: int64" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_single = df.loc[~df[\"multi_species\"]].copy()\n", "df_single[[\"image_id\",\"scientific_name\", \"common_name\", \"species\"]].nunique()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "common_name\n", "rabbits and hares 7702\n", "rat 6844\n", "petrel 6538\n", "typical iguanas 6163\n", "cat 4663\n", "goat 2506\n", "pig 2283\n", "shearwater 1699\n", "petrel chick 1008\n", "raven 858\n", "donkey 697\n", "rooster 533\n", "monitor lizard 354\n", "megapode 274\n", "white-winged dove 211\n", "Name: count, dtype: int64" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_single.common_name.value_counts()[:15]" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "common_name\n", "crab 16\n", "thrush 15\n", "seal 15\n", "storm petrel 12\n", "nicobar pigeon 10\n", "great blue heron 9\n", "american kestrel 9\n", "burrowing owl 6\n", "sea turtle 6\n", "green heron 6\n", "spider 4\n", "kestrel 4\n", "mockingbird 3\n", "barred owl 1\n", "owl 1\n", "Name: count, dtype: int64" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_single.common_name.value_counts()[28:]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The other owl is \"short-eared owl\", if we combine them all to \"owl\" then we could keep them all. short-eared owls do seem to be well-represented though (see below). They are least concern but decreasing [IUCN](https://www.iucnredlist.org/species/22689531/202226582). Same for burrowing owls [IUCN](https://www.iucnredlist.org/species/22689353/93227732). Barred owls are both least concern and increasing [IUCN](https://www.iucnredlist.org/species/22689094/264599155), so combining may not be an issue (at least from that perspective)." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "193" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_single.loc[df_single[\"common_name\"] == \"short-eared owl\"].shape[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "location_id\n", "Island Conservation Camera Traps : chile_filipiananbek 3647\n", "Island Conservation Camera Traps : chile_frances01 2906\n", "Island Conservation Camera Traps : chile_frances02 2800\n", "Island Conservation Camera Traps : chile_piedra01 2788\n", "Island Conservation Camera Traps : chile_filipianalamatris 2704\n", " ... \n", "Island Conservation Camera Traps : ecuador1_cam1613 5\n", "Island Conservation Camera Traps : ecuador2_ic1602 5\n", "Island Conservation Camera Traps : dominicanrepublic_camara23 4\n", "Island Conservation Camera Traps : palau_cam16a 4\n", "Island Conservation Camera Traps : ecuador2_ic1603 4\n", "Name: count, Length: 118, dtype: int64" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_single.location_id.value_counts()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['Island Conservation Camera Traps : dominicanrepublic_camara02',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara04',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara06',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara101',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara103',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara107',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara109',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara111',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara114',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara13',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara17',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara20',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara30',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara10',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara106',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara112',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara11',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara116',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara12',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara19',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara113',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara15',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara08',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara108',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara118',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara18',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara14',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara33',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara05',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara07',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara09',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara105',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara32',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara01',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara03',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara117',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara24',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara27',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara16',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara102',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara104',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara29',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara115',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara22',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara28',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara119',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara120',\n", " 'Island Conservation Camera Traps : dominicanrepublic_camara23',\n", " 'Island Conservation Camera Traps : ecuador1_ic1619',\n", " 'Island Conservation Camera Traps : ecuador1_ic1616',\n", " 'Island Conservation Camera Traps : ecuador1_cam1612',\n", " 'Island Conservation Camera Traps : ecuador1_cam1613',\n", " 'Island Conservation Camera Traps : ecuador1_ic1611',\n", " 'Island Conservation Camera Traps : chile_filipiananbek',\n", " 'Island Conservation Camera Traps : chile_frances01',\n", " 'Island Conservation Camera Traps : chile_frances02',\n", " 'Island Conservation Camera Traps : chile_filipianalamatris',\n", " 'Island Conservation Camera Traps : chile_piedra01',\n", " 'Island Conservation Camera Traps : chile_vaqueria',\n", " 'Island Conservation Camera Traps : puertorico_19',\n", " 'Island Conservation Camera Traps : puertorico_20',\n", " 'Island Conservation Camera Traps : puertorico_23',\n", " 'Island Conservation Camera Traps : puertorico_25',\n", " 'Island Conservation Camera Traps : puertorico_29',\n", " 'Island Conservation Camera Traps : puertorico_3a',\n", " 'Island Conservation Camera Traps : puertorico_4b',\n", " 'Island Conservation Camera Traps : puertorico_2a',\n", " 'Island Conservation Camera Traps : puertorico_7a',\n", " 'Island Conservation Camera Traps : puertorico_26',\n", " 'Island Conservation Camera Traps : puertorico_1a',\n", " 'Island Conservation Camera Traps : puertorico_8b',\n", " 'Island Conservation Camera Traps : puertorico_17b',\n", " 'Island Conservation Camera Traps : puertorico_9a',\n", " 'Island Conservation Camera Traps : puertorico_5a',\n", " 'Island Conservation Camera Traps : puertorico_6a',\n", " 'Island Conservation Camera Traps : puertorico_18',\n", " 'Island Conservation Camera Traps : palau_cam09a',\n", " 'Island Conservation Camera Traps : palau_cam02a',\n", " 'Island Conservation Camera Traps : palau_cam04a',\n", " 'Island Conservation Camera Traps : palau_cam05a',\n", " 'Island Conservation Camera Traps : palau_cam06a',\n", " 'Island Conservation Camera Traps : palau_cam10a',\n", " 'Island Conservation Camera Traps : palau_cam13a',\n", " 'Island Conservation Camera Traps : palau_cam14a',\n", " 'Island Conservation Camera Traps : palau_cam15a',\n", " 'Island Conservation Camera Traps : palau_cam16a',\n", " 'Island Conservation Camera Traps : palau_cam17a',\n", " 'Island Conservation Camera Traps : palau_cam01a',\n", " 'Island Conservation Camera Traps : palau_cam03a',\n", " 'Island Conservation Camera Traps : palau_cam07a',\n", " 'Island Conservation Camera Traps : palau_cam08a',\n", " 'Island Conservation Camera Traps : ecuador2_ic1618',\n", " 'Island Conservation Camera Traps : ecuador2_ic1602',\n", " 'Island Conservation Camera Traps : ecuador2_ic1603',\n", " 'Island Conservation Camera Traps : ecuador2_ic1604',\n", " 'Island Conservation Camera Traps : ecuador2_ic1605',\n", " 'Island Conservation Camera Traps : ecuador2_ic1607',\n", " 'Island Conservation Camera Traps : ecuador2_ic1609',\n", " 'Island Conservation Camera Traps : ecuador2_ic1614',\n", " 'Island Conservation Camera Traps : ecuador2_ic1621',\n", " 'Island Conservation Camera Traps : ecuador2_ic1621a',\n", " 'Island Conservation Camera Traps : micronesia_cam04',\n", " 'Island Conservation Camera Traps : micronesia_cam12',\n", " 'Island Conservation Camera Traps : micronesia_cam18',\n", " 'Island Conservation Camera Traps : micronesia_cam19',\n", " 'Island Conservation Camera Traps : micronesia_cam16',\n", " 'Island Conservation Camera Traps : micronesia_cam10',\n", " 'Island Conservation Camera Traps : micronesia_cam05',\n", " 'Island Conservation Camera Traps : micronesia_cam13',\n", " 'Island Conservation Camera Traps : micronesia_cam17',\n", " 'Island Conservation Camera Traps : micronesia_cam15',\n", " 'Island Conservation Camera Traps : micronesia_cam02',\n", " 'Island Conservation Camera Traps : micronesia_cam03',\n", " 'Island Conservation Camera Traps : micronesia_cam11',\n", " 'Island Conservation Camera Traps : micronesia_cam14',\n", " 'Island Conservation Camera Traps : micronesia_cam08',\n", " 'Island Conservation Camera Traps : micronesia_cam06',\n", " 'Island Conservation Camera Traps : micronesia_cam09'],\n", " dtype=object)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_single.location_id.unique()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The 6 countries are Dominican Republic, Ecuador, Chile, Puerto Rico, Palau, and Micronesia. It seems there are two islands in Ecuador." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looking at least-represented species:\n", " - mockingbirds have a variety of statuses, depending on the species, but some island mocking birds are vulnerable, endangered, or critically endangered [IUCN](https://www.iucnredlist.org/search?query=mockingbird&searchType=species).\n", " - Similarly for kestrels.\n", " - green heron not listed by IUCN\n", " - sea turtles all vulnerable, endangered, critically endangered, or data deficient [IUCN](https://www.iucnredlist.org/search?query=Sea%20Turtles&searchType=species)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `common_name` most prevalent in this dataset is \"rabbits and hares\", but these are two different species within the same family. Most of the images are classified down to the family level, so let's see what classification to family would look like as this may present a better set of classes, though we do have only 25 distinct families, compared to 45 common names." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "family\n", "procellariidae 9245\n", "leporidae 7702\n", "muridae 6844\n", "iguanidae 6163\n", "felidae 4663\n", "bovidae 2635\n", "suidae 2283\n", "corvidae 858\n", "equidae 697\n", "phasianidae 630\n", "varanidae 354\n", "megapodiidae 274\n", "columbidae 250\n", "strigidae 200\n", "canidae 137\n", "ardeidae 128\n", "procyonidae 106\n", "laridae 106\n", "rallidae 51\n", "calcinidae 39\n", "turdidae 15\n", "phocidae 15\n", "falconidae 13\n", "hydrobatidae 12\n", "mimidae 3\n", "Name: count, dtype: int64" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_single.family.value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this case, we would just lose mimidae (includes mocking birds) when creating a balanced set at 12 images per class. Perhaps we set up a balanced by-family task and a common name task that's imbalanced." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.histplot(df_single, y = 'family')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Common Name version\n", "Replace \"rabbits and hares\" with \"rabbit or hare\" and \"typical iguanas\" with \"iguana\", then create the imbalanced and lower-bounded subsets.\n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "df_common = df_single.copy()\n", "df_common.loc[df_common[\"common_name\"] == \"rabbits and hares\", \"common_name\"] = \"rabbit or hare\"\n", "df_common.loc[df_common[\"common_name\"] == \"typical iguanas\", \"common_name\"] = \"iguana\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Filter to Family classifications" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Index: 43423 entries, 0 to 44437\n", "Data columns (total 10 columns):\n", " # Column Non-Null Count Dtype \n", "--- ------ -------------- ----- \n", " 0 original_label 43423 non-null object\n", " 1 scientific_name 43423 non-null object\n", " 2 common_name 43423 non-null object\n", " 3 kingdom 43423 non-null object\n", " 4 phylum 43423 non-null object\n", " 5 class 43423 non-null object\n", " 6 order 43423 non-null object\n", " 7 family 43423 non-null object\n", " 8 genus 27449 non-null object\n", " 9 species 12728 non-null object\n", "dtypes: object(10)\n", "memory usage: 3.6+ MB\n" ] } ], "source": [ "df_filter = df_single.loc[df_single[\"family\"].notna()].copy()\n", "df_filter[taxa_cols].info(show_counts=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Remove extra columns\n", "\n", "Only need `taxa_cols` (Linnean taxonomy + `original_label`, `scientific_name`, and `common_name`) and `id_cols`." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "id_cols = ['dataset_name',\n", " 'url_gcp',\n", " 'url_aws',\n", " 'url_azure',\n", " 'image_id',\n", " 'sequence_id',\n", " 'location_id',\n", " 'frame_num']\n", "\n", "cols_to_keep = [col for col in list(df.columns) if (col in id_cols or col in taxa_cols)]" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['dataset_name',\n", " 'url_gcp',\n", " 'url_aws',\n", " 'url_azure',\n", " 'image_id',\n", " 'sequence_id',\n", " 'location_id',\n", " 'frame_num',\n", " 'original_label',\n", " 'scientific_name',\n", " 'common_name',\n", " 'kingdom',\n", " 'phylum',\n", " 'class',\n", " 'order',\n", " 'family',\n", " 'genus',\n", " 'species']" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cols_to_keep" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's add a number of images column (by `family` and by `common_name` for each subset)." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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dataset_nameurl_gcpurl_awsurl_azureimage_idsequence_idlocation_idframe_numoriginal_labelscientific_name...familysubfamilytribegenusspeciessubspeciesvarietymulti_speciesnum_speciesnum_fam_images
0Island Conservation Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Island Conservation Camera Traps : dominicanre...Island Conservation Camera Traps : unknownIsland Conservation Camera Traps : dominicanre...-1cowbos taurus...bovidaebovinaebovinibosbos taurusNaNNaNFalse1.02635.0
1Island Conservation Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Island Conservation Camera Traps : dominicanre...Island Conservation Camera Traps : unknownIsland Conservation Camera Traps : dominicanre...-1donkeyequus asinus...equidaeNaNNaNequusequus asinusNaNNaNFalse1.0697.0
2Island Conservation Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Island Conservation Camera Traps : dominicanre...Island Conservation Camera Traps : unknownIsland Conservation Camera Traps : dominicanre...-1donkeyequus asinus...equidaeNaNNaNequusequus asinusNaNNaNFalse1.0697.0
3Island Conservation Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Island Conservation Camera Traps : dominicanre...Island Conservation Camera Traps : unknownIsland Conservation Camera Traps : dominicanre...-1iguanaiguana...iguanidaeNaNNaNiguanaNaNNaNNaNFalse1.06163.0
4Island Conservation Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Island Conservation Camera Traps : dominicanre...Island Conservation Camera Traps : unknownIsland Conservation Camera Traps : dominicanre...-1iguanaiguana...iguanidaeNaNNaNiguanaNaNNaNNaNFalse1.06163.0
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5 rows × 35 columns

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" ], "text/plain": [ " dataset_name \\\n", "0 Island Conservation Camera Traps \n", "1 Island Conservation Camera Traps \n", "2 Island Conservation Camera Traps \n", "3 Island Conservation Camera Traps \n", "4 Island Conservation Camera Traps \n", "\n", " url_gcp \\\n", "0 https://storage.googleapis.com/public-datasets... \n", "1 https://storage.googleapis.com/public-datasets... \n", "2 https://storage.googleapis.com/public-datasets... \n", "3 https://storage.googleapis.com/public-datasets... \n", "4 https://storage.googleapis.com/public-datasets... \n", "\n", " url_aws \\\n", "0 http://us-west-2.opendata.source.coop.s3.amazo... \n", "1 http://us-west-2.opendata.source.coop.s3.amazo... \n", "2 http://us-west-2.opendata.source.coop.s3.amazo... \n", "3 http://us-west-2.opendata.source.coop.s3.amazo... \n", "4 http://us-west-2.opendata.source.coop.s3.amazo... \n", "\n", " url_azure \\\n", "0 https://lilawildlife.blob.core.windows.net/lil... \n", "1 https://lilawildlife.blob.core.windows.net/lil... \n", "2 https://lilawildlife.blob.core.windows.net/lil... \n", "3 https://lilawildlife.blob.core.windows.net/lil... \n", "4 https://lilawildlife.blob.core.windows.net/lil... \n", "\n", " image_id \\\n", "0 Island Conservation Camera Traps : dominicanre... \n", "1 Island Conservation Camera Traps : dominicanre... \n", "2 Island Conservation Camera Traps : dominicanre... \n", "3 Island Conservation Camera Traps : dominicanre... \n", "4 Island Conservation Camera Traps : dominicanre... \n", "\n", " sequence_id \\\n", "0 Island Conservation Camera Traps : unknown \n", "1 Island Conservation Camera Traps : unknown \n", "2 Island Conservation Camera Traps : unknown \n", "3 Island Conservation Camera Traps : unknown \n", "4 Island Conservation Camera Traps : unknown \n", "\n", " location_id frame_num \\\n", "0 Island Conservation Camera Traps : dominicanre... -1 \n", "1 Island Conservation Camera Traps : dominicanre... -1 \n", "2 Island Conservation Camera Traps : dominicanre... -1 \n", "3 Island Conservation Camera Traps : dominicanre... -1 \n", "4 Island Conservation Camera Traps : dominicanre... -1 \n", "\n", " original_label scientific_name ... family subfamily tribe genus \\\n", "0 cow bos taurus ... bovidae bovinae bovini bos \n", "1 donkey equus asinus ... equidae NaN NaN equus \n", "2 donkey equus asinus ... equidae NaN NaN equus \n", "3 iguana iguana ... iguanidae NaN NaN iguana \n", "4 iguana iguana ... iguanidae NaN NaN iguana \n", "\n", " species subspecies variety multi_species num_species num_fam_images \n", "0 bos taurus NaN NaN False 1.0 2635.0 \n", "1 equus asinus NaN NaN False 1.0 697.0 \n", "2 equus asinus NaN NaN False 1.0 697.0 \n", "3 NaN NaN NaN False 1.0 6163.0 \n", "4 NaN NaN NaN False 1.0 6163.0 \n", "\n", "[5 rows x 35 columns]" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "for sci_name in list(df_filter[\"family\"].unique()):\n", " df_filter.loc[df_filter[\"family\"] == sci_name, \"num_fam_images\"] = df_filter.loc[df_filter[\"family\"] == sci_name].shape[0]\n", "\n", "df_filter.head()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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dataset_nameurl_gcpurl_awsurl_azureimage_idsequence_idlocation_idframe_numoriginal_labelscientific_name...familysubfamilytribegenusspeciessubspeciesvarietymulti_speciesnum_speciesnum_cn_images
0Island Conservation Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Island Conservation Camera Traps : dominicanre...Island Conservation Camera Traps : unknownIsland Conservation Camera Traps : dominicanre...-1cowbos taurus...bovidaebovinaebovinibosbos taurusNaNNaNFalse1.0129.0
1Island Conservation Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Island Conservation Camera Traps : dominicanre...Island Conservation Camera Traps : unknownIsland Conservation Camera Traps : dominicanre...-1donkeyequus asinus...equidaeNaNNaNequusequus asinusNaNNaNFalse1.0697.0
2Island Conservation Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Island Conservation Camera Traps : dominicanre...Island Conservation Camera Traps : unknownIsland Conservation Camera Traps : dominicanre...-1donkeyequus asinus...equidaeNaNNaNequusequus asinusNaNNaNFalse1.0697.0
3Island Conservation Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Island Conservation Camera Traps : dominicanre...Island Conservation Camera Traps : unknownIsland Conservation Camera Traps : dominicanre...-1iguanaiguana...iguanidaeNaNNaNiguanaNaNNaNNaNFalse1.06163.0
4Island Conservation Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Island Conservation Camera Traps : dominicanre...Island Conservation Camera Traps : unknownIsland Conservation Camera Traps : dominicanre...-1iguanaiguana...iguanidaeNaNNaNiguanaNaNNaNNaNFalse1.06163.0
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5 rows × 35 columns

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" ], "text/plain": [ " dataset_name \\\n", "0 Island Conservation Camera Traps \n", "1 Island Conservation Camera Traps \n", "2 Island Conservation Camera Traps \n", "3 Island Conservation Camera Traps \n", "4 Island Conservation Camera Traps \n", "\n", " url_gcp \\\n", "0 https://storage.googleapis.com/public-datasets... \n", "1 https://storage.googleapis.com/public-datasets... \n", "2 https://storage.googleapis.com/public-datasets... \n", "3 https://storage.googleapis.com/public-datasets... \n", "4 https://storage.googleapis.com/public-datasets... \n", "\n", " url_aws \\\n", "0 http://us-west-2.opendata.source.coop.s3.amazo... \n", "1 http://us-west-2.opendata.source.coop.s3.amazo... \n", "2 http://us-west-2.opendata.source.coop.s3.amazo... \n", "3 http://us-west-2.opendata.source.coop.s3.amazo... \n", "4 http://us-west-2.opendata.source.coop.s3.amazo... \n", "\n", " url_azure \\\n", "0 https://lilawildlife.blob.core.windows.net/lil... \n", "1 https://lilawildlife.blob.core.windows.net/lil... \n", "2 https://lilawildlife.blob.core.windows.net/lil... \n", "3 https://lilawildlife.blob.core.windows.net/lil... \n", "4 https://lilawildlife.blob.core.windows.net/lil... \n", "\n", " image_id \\\n", "0 Island Conservation Camera Traps : dominicanre... \n", "1 Island Conservation Camera Traps : dominicanre... \n", "2 Island Conservation Camera Traps : dominicanre... \n", "3 Island Conservation Camera Traps : dominicanre... \n", "4 Island Conservation Camera Traps : dominicanre... \n", "\n", " sequence_id \\\n", "0 Island Conservation Camera Traps : unknown \n", "1 Island Conservation Camera Traps : unknown \n", "2 Island Conservation Camera Traps : unknown \n", "3 Island Conservation Camera Traps : unknown \n", "4 Island Conservation Camera Traps : unknown \n", "\n", " location_id frame_num \\\n", "0 Island Conservation Camera Traps : dominicanre... -1 \n", "1 Island Conservation Camera Traps : dominicanre... -1 \n", "2 Island Conservation Camera Traps : dominicanre... -1 \n", "3 Island Conservation Camera Traps : dominicanre... -1 \n", "4 Island Conservation Camera Traps : dominicanre... -1 \n", "\n", " original_label scientific_name ... family subfamily tribe genus \\\n", "0 cow bos taurus ... bovidae bovinae bovini bos \n", "1 donkey equus asinus ... equidae NaN NaN equus \n", "2 donkey equus asinus ... equidae NaN NaN equus \n", "3 iguana iguana ... iguanidae NaN NaN iguana \n", "4 iguana iguana ... iguanidae NaN NaN iguana \n", "\n", " species subspecies variety multi_species num_species num_cn_images \n", "0 bos taurus NaN NaN False 1.0 129.0 \n", "1 equus asinus NaN NaN False 1.0 697.0 \n", "2 equus asinus NaN NaN False 1.0 697.0 \n", "3 NaN NaN NaN False 1.0 6163.0 \n", "4 NaN NaN NaN False 1.0 6163.0 \n", "\n", "[5 rows x 35 columns]" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "for common_name in list(df_common[\"common_name\"].unique()):\n", " df_common.loc[df_common[\"common_name\"] == common_name, \"num_cn_images\"] = df_common.loc[df_common[\"common_name\"] == common_name].shape[0]\n", "\n", "df_common.head()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "cols_to_keep_common = [col for col in cols_to_keep]\n", "cols_to_keep_common.append(\"num_cn_images\")" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "cols_to_keep.append(\"num_fam_images\")" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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dataset_nameurl_gcpurl_awsurl_azureimage_idsequence_idlocation_idframe_numoriginal_labelscientific_namecommon_namekingdomphylumclassorderfamilygenusspeciesnum_fam_images
0Island Conservation Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Island Conservation Camera Traps : dominicanre...Island Conservation Camera Traps : unknownIsland Conservation Camera Traps : dominicanre...-1cowbos tauruscattleanimaliachordatamammaliaartiodactylabovidaebosbos taurus2635.0
1Island Conservation Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Island Conservation Camera Traps : dominicanre...Island Conservation Camera Traps : unknownIsland Conservation Camera Traps : dominicanre...-1donkeyequus asinusdonkeyanimaliachordatamammaliaperissodactylaequidaeequusequus asinus697.0
2Island Conservation Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Island Conservation Camera Traps : dominicanre...Island Conservation Camera Traps : unknownIsland Conservation Camera Traps : dominicanre...-1donkeyequus asinusdonkeyanimaliachordatamammaliaperissodactylaequidaeequusequus asinus697.0
3Island Conservation Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Island Conservation Camera Traps : dominicanre...Island Conservation Camera Traps : unknownIsland Conservation Camera Traps : dominicanre...-1iguanaiguanatypical iguanasanimaliachordatareptiliasquamataiguanidaeiguanaNaN6163.0
4Island Conservation Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Island Conservation Camera Traps : dominicanre...Island Conservation Camera Traps : unknownIsland Conservation Camera Traps : dominicanre...-1iguanaiguanatypical iguanasanimaliachordatareptiliasquamataiguanidaeiguanaNaN6163.0
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" ], "text/plain": [ " dataset_name \\\n", "0 Island Conservation Camera Traps \n", "1 Island Conservation Camera Traps \n", "2 Island Conservation Camera Traps \n", "3 Island Conservation Camera Traps \n", "4 Island Conservation Camera Traps \n", "\n", " url_gcp \\\n", "0 https://storage.googleapis.com/public-datasets... \n", "1 https://storage.googleapis.com/public-datasets... \n", "2 https://storage.googleapis.com/public-datasets... \n", "3 https://storage.googleapis.com/public-datasets... \n", "4 https://storage.googleapis.com/public-datasets... \n", "\n", " url_aws \\\n", "0 http://us-west-2.opendata.source.coop.s3.amazo... \n", "1 http://us-west-2.opendata.source.coop.s3.amazo... \n", "2 http://us-west-2.opendata.source.coop.s3.amazo... \n", "3 http://us-west-2.opendata.source.coop.s3.amazo... \n", "4 http://us-west-2.opendata.source.coop.s3.amazo... \n", "\n", " url_azure \\\n", "0 https://lilawildlife.blob.core.windows.net/lil... \n", "1 https://lilawildlife.blob.core.windows.net/lil... \n", "2 https://lilawildlife.blob.core.windows.net/lil... \n", "3 https://lilawildlife.blob.core.windows.net/lil... \n", "4 https://lilawildlife.blob.core.windows.net/lil... \n", "\n", " image_id \\\n", "0 Island Conservation Camera Traps : dominicanre... \n", "1 Island Conservation Camera Traps : dominicanre... \n", "2 Island Conservation Camera Traps : dominicanre... \n", "3 Island Conservation Camera Traps : dominicanre... \n", "4 Island Conservation Camera Traps : dominicanre... \n", "\n", " sequence_id \\\n", "0 Island Conservation Camera Traps : unknown \n", "1 Island Conservation Camera Traps : unknown \n", "2 Island Conservation Camera Traps : unknown \n", "3 Island Conservation Camera Traps : unknown \n", "4 Island Conservation Camera Traps : unknown \n", "\n", " location_id frame_num \\\n", "0 Island Conservation Camera Traps : dominicanre... -1 \n", "1 Island Conservation Camera Traps : dominicanre... -1 \n", "2 Island Conservation Camera Traps : dominicanre... -1 \n", "3 Island Conservation Camera Traps : dominicanre... -1 \n", "4 Island Conservation Camera Traps : dominicanre... -1 \n", "\n", " original_label scientific_name common_name kingdom phylum \\\n", "0 cow bos taurus cattle animalia chordata \n", "1 donkey equus asinus donkey animalia chordata \n", "2 donkey equus asinus donkey animalia chordata \n", "3 iguana iguana typical iguanas animalia chordata \n", "4 iguana iguana typical iguanas animalia chordata \n", "\n", " class order family genus species num_fam_images \n", "0 mammalia artiodactyla bovidae bos bos taurus 2635.0 \n", "1 mammalia perissodactyla equidae equus equus asinus 697.0 \n", "2 mammalia perissodactyla equidae equus equus asinus 697.0 \n", "3 reptilia squamata iguanidae iguana NaN 6163.0 \n", "4 reptilia squamata iguanidae iguana NaN 6163.0 " ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_reduced = df_filter[cols_to_keep].copy()\n", "df_reduced.head()" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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dataset_nameurl_gcpurl_awsurl_azureimage_idsequence_idlocation_idframe_numoriginal_labelscientific_namecommon_namekingdomphylumclassorderfamilygenusspeciesnum_cn_images
0Island Conservation Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Island Conservation Camera Traps : dominicanre...Island Conservation Camera Traps : unknownIsland Conservation Camera Traps : dominicanre...-1cowbos tauruscattleanimaliachordatamammaliaartiodactylabovidaebosbos taurus129.0
1Island Conservation Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Island Conservation Camera Traps : dominicanre...Island Conservation Camera Traps : unknownIsland Conservation Camera Traps : dominicanre...-1donkeyequus asinusdonkeyanimaliachordatamammaliaperissodactylaequidaeequusequus asinus697.0
2Island Conservation Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Island Conservation Camera Traps : dominicanre...Island Conservation Camera Traps : unknownIsland Conservation Camera Traps : dominicanre...-1donkeyequus asinusdonkeyanimaliachordatamammaliaperissodactylaequidaeequusequus asinus697.0
3Island Conservation Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Island Conservation Camera Traps : dominicanre...Island Conservation Camera Traps : unknownIsland Conservation Camera Traps : dominicanre...-1iguanaiguanaiguanaanimaliachordatareptiliasquamataiguanidaeiguanaNaN6163.0
4Island Conservation Camera Trapshttps://storage.googleapis.com/public-datasets...http://us-west-2.opendata.source.coop.s3.amazo...https://lilawildlife.blob.core.windows.net/lil...Island Conservation Camera Traps : dominicanre...Island Conservation Camera Traps : unknownIsland Conservation Camera Traps : dominicanre...-1iguanaiguanaiguanaanimaliachordatareptiliasquamataiguanidaeiguanaNaN6163.0
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" ], "text/plain": [ " dataset_name \\\n", "0 Island Conservation Camera Traps \n", "1 Island Conservation Camera Traps \n", "2 Island Conservation Camera Traps \n", "3 Island Conservation Camera Traps \n", "4 Island Conservation Camera Traps \n", "\n", " url_gcp \\\n", "0 https://storage.googleapis.com/public-datasets... \n", "1 https://storage.googleapis.com/public-datasets... \n", "2 https://storage.googleapis.com/public-datasets... \n", "3 https://storage.googleapis.com/public-datasets... \n", "4 https://storage.googleapis.com/public-datasets... \n", "\n", " url_aws \\\n", "0 http://us-west-2.opendata.source.coop.s3.amazo... \n", "1 http://us-west-2.opendata.source.coop.s3.amazo... \n", "2 http://us-west-2.opendata.source.coop.s3.amazo... \n", "3 http://us-west-2.opendata.source.coop.s3.amazo... \n", "4 http://us-west-2.opendata.source.coop.s3.amazo... \n", "\n", " url_azure \\\n", "0 https://lilawildlife.blob.core.windows.net/lil... \n", "1 https://lilawildlife.blob.core.windows.net/lil... \n", "2 https://lilawildlife.blob.core.windows.net/lil... \n", "3 https://lilawildlife.blob.core.windows.net/lil... \n", "4 https://lilawildlife.blob.core.windows.net/lil... \n", "\n", " image_id \\\n", "0 Island Conservation Camera Traps : dominicanre... \n", "1 Island Conservation Camera Traps : dominicanre... \n", "2 Island Conservation Camera Traps : dominicanre... \n", "3 Island Conservation Camera Traps : dominicanre... \n", "4 Island Conservation Camera Traps : dominicanre... \n", "\n", " sequence_id \\\n", "0 Island Conservation Camera Traps : unknown \n", "1 Island Conservation Camera Traps : unknown \n", "2 Island Conservation Camera Traps : unknown \n", "3 Island Conservation Camera Traps : unknown \n", "4 Island Conservation Camera Traps : unknown \n", "\n", " location_id frame_num \\\n", "0 Island Conservation Camera Traps : dominicanre... -1 \n", "1 Island Conservation Camera Traps : dominicanre... -1 \n", "2 Island Conservation Camera Traps : dominicanre... -1 \n", "3 Island Conservation Camera Traps : dominicanre... -1 \n", "4 Island Conservation Camera Traps : dominicanre... -1 \n", "\n", " original_label scientific_name common_name kingdom phylum class \\\n", "0 cow bos taurus cattle animalia chordata mammalia \n", "1 donkey equus asinus donkey animalia chordata mammalia \n", "2 donkey equus asinus donkey animalia chordata mammalia \n", "3 iguana iguana iguana animalia chordata reptilia \n", "4 iguana iguana iguana animalia chordata reptilia \n", "\n", " order family genus species num_cn_images \n", "0 artiodactyla bovidae bos bos taurus 129.0 \n", "1 perissodactyla equidae equus equus asinus 697.0 \n", "2 perissodactyla equidae equus equus asinus 697.0 \n", "3 squamata iguanidae iguana NaN 6163.0 \n", "4 squamata iguanidae iguana NaN 6163.0 " ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_reduced_common = df_common[cols_to_keep_common].copy()\n", "df_reduced_common.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Reduce to no more than 10K images per species" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "common_name\n", "rabbit or hare 7702\n", "rat 6844\n", "petrel 6538\n", "iguana 6163\n", "cat 4663\n", "Name: count, dtype: int64" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_reduced_common[\"common_name\"].value_counts()[:5]" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "family\n", "procellariidae 9245\n", "leporidae 7702\n", "muridae 6844\n", "iguanidae 6163\n", "felidae 4663\n", "Name: count, dtype: int64" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_reduced.family.value_counts()[:5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Both subsets are already under 10K." ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "df_reduced_common.to_csv(\"../data/potential-test-sets/filtered/island-imbalanced_common.csv\", index = False)\n", "df_reduced.to_csv(\"../data/potential-test-sets/filtered/island-imbalanced_family.csv\", index = False)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "df_reduced_common.loc[df_reduced_common[\"num_cn_images\"] >= 10].to_csv(\"../data/potential-test-sets/filtered/island-lower-bound_common.csv\", index = False)\n", "df_reduced.loc[df_reduced[\"num_fam_images\"] >= 10].to_csv(\"../data/potential-test-sets/filtered/island-lower-bound_family.csv\", index = False)" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.histplot(df_reduced_common.loc[df_reduced_common[\"num_cn_images\"] >= 10], y = \"common_name\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Randomly sample to balanced set (12 images per family)\n" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "288" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "balanced_set = []\n", "for sci_name in list(df_reduced[\"family\"].unique()):\n", " temp = df_reduced.loc[df_reduced[\"family\"] == sci_name].copy()\n", " if temp.shape[0] < 12:\n", " continue\n", " sample_set = list(temp.sample(12, random_state = 614)[\"image_id\"])\n", " balanced_set = balanced_set + sample_set\n", "\n", "len(balanced_set)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Filter to just balanced set and drop the number of species column since it's been balanced to 12 each." ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "df_balanced = df_reduced.loc[df_reduced[\"image_id\"].isin(balanced_set)].copy()\n", "df_balanced.drop(columns = [\"num_fam_images\"], inplace = True)\n", "df_balanced.to_csv(\"../data/potential-test-sets/filtered/island-balanced.csv\", index = False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "jupytext": { "formats": "ipynb,py:percent" }, "kernelspec": { "display_name": "data-dev", "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": "3.12.3" } }, "nbformat": 4, "nbformat_minor": 2 }