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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "96b34bd1",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import xgboost as xgb\n",
"import sklearn"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "37b99e9f",
"metadata": {},
"outputs": [],
"source": [
"model_name = 'models/xgboost_first_model.json'\n",
"xgb_model1 = xgb.XGBClassifier()\n",
"xgb_model1.load_model(model_name)\n",
"model_name = 'models/xgboost_second_model.json'\n",
"xgb_model2 = xgb.XGBClassifier()\n",
"xgb_model2.load_model(model_name)\n",
"model_name = 'models/xgboost_third_model.json'\n",
"xgb_model3 = xgb.XGBClassifier()\n",
"xgb_model3.load_model(model_name)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "40aa73e8",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2022\n",
"2023\n",
"2024\n",
"2025\n"
]
}
],
"source": [
"m3_pred = []\n",
"m2_pred = []\n",
"m1_pred = []\n",
"bird_list = []\n",
"year_list = []\n",
"dur_list = []\n",
"tim_list = []\n",
"fil_list = []\n",
"aug_list = []\n",
"\n",
"for year in [2022,2023,2024,2025]:\n",
"# for year in [2022,2023,2024]:\n",
" print(year)\n",
" birds = os.listdir(f'/mnt/d/acoustics-data/birdclef-{year}/train_audio')\n",
" for bird in birds:\n",
" file = f'/mnt/d/acoustics-data/processed/birdclef-{year}/{bird}_features.npy'\n",
" try:\n",
" X = np.load(file,)\n",
" n = X.shape[0]\n",
" bird_list += [bird for _ in range(n)]\n",
" year_list += [year for _ in range(n)]\n",
" y_pred_proba = xgb_model1.predict_proba(X)[:,1]\n",
" m1_pred += list(y_pred_proba)\n",
" y_pred_proba = xgb_model2.predict_proba(X)[:,1]\n",
" m2_pred += list(y_pred_proba)\n",
" y_pred_proba = xgb_model3.predict_proba(X)[:,1]\n",
" m3_pred += list(y_pred_proba)\n",
" tab = pd.read_csv(f'/mnt/d/acoustics-data/processed/birdclef-{year}/{bird}_slices.csv')\n",
" dur_list += tab['duration'].tolist()\n",
" tim_list += tab['start_time'].tolist()\n",
" fil_list += tab['file_name'].tolist()\n",
" aug_list += tab['is_augmented'].tolist()\n",
" except:\n",
" pass"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "876d3800",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>bird</th>\n",
" <th>year</th>\n",
" <th>model_1</th>\n",
" <th>model_2</th>\n",
" <th>model_3</th>\n",
" <th>duration</th>\n",
" <th>time</th>\n",
" <th>file_name</th>\n",
" <th>is_augmented</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>afrsil1</td>\n",
" <td>2022</td>\n",
" <td>0.520478</td>\n",
" <td>0.992990</td>\n",
" <td>0.997792</td>\n",
" <td>3.5004</td>\n",
" <td>0.0</td>\n",
" <td>XC207432.ogg</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>afrsil1</td>\n",
" <td>2022</td>\n",
" <td>0.512172</td>\n",
" <td>0.992990</td>\n",
" <td>0.998557</td>\n",
" <td>3.5004</td>\n",
" <td>0.0</td>\n",
" <td>XC207432.ogg</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>afrsil1</td>\n",
" <td>2022</td>\n",
" <td>0.374894</td>\n",
" <td>0.985676</td>\n",
" <td>0.996787</td>\n",
" <td>3.5004</td>\n",
" <td>0.0</td>\n",
" <td>XC207432.ogg</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>afrsil1</td>\n",
" <td>2022</td>\n",
" <td>0.348209</td>\n",
" <td>0.987380</td>\n",
" <td>0.994968</td>\n",
" <td>3.5004</td>\n",
" <td>0.0</td>\n",
" <td>XC207432.ogg</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>afrsil1</td>\n",
" <td>2022</td>\n",
" <td>0.469575</td>\n",
" <td>0.984070</td>\n",
" <td>0.995817</td>\n",
" <td>3.5004</td>\n",
" <td>0.0</td>\n",
" <td>XC207432.ogg</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" bird year model_1 model_2 model_3 duration time file_name \\\n",
"0 afrsil1 2022 0.520478 0.992990 0.997792 3.5004 0.0 XC207432.ogg \n",
"1 afrsil1 2022 0.512172 0.992990 0.998557 3.5004 0.0 XC207432.ogg \n",
"2 afrsil1 2022 0.374894 0.985676 0.996787 3.5004 0.0 XC207432.ogg \n",
"3 afrsil1 2022 0.348209 0.987380 0.994968 3.5004 0.0 XC207432.ogg \n",
"4 afrsil1 2022 0.469575 0.984070 0.995817 3.5004 0.0 XC207432.ogg \n",
"\n",
" is_augmented \n",
"0 0 \n",
"1 1 \n",
"2 1 \n",
"3 1 \n",
"4 1 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"D = dict()\n",
"D['bird'] = bird_list\n",
"D['year'] = year_list\n",
"D['model_1'] = m1_pred\n",
"D['model_2'] = m2_pred\n",
"D['model_3'] = m3_pred\n",
"D['duration'] = dur_list\n",
"D['time'] = tim_list\n",
"D['file_name'] = fil_list\n",
"D['is_augmented'] = aug_list\n",
"df = pd.DataFrame(D)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "b0aae25d",
"metadata": {},
"outputs": [],
"source": [
"# for 2022, 2023, 2024 data\n",
"ebird = pd.read_csv('/mnt/d/acoustics-data/birdclef-2022/eBird_Taxonomy_v2021.csv')\n",
"ebird = ebird[['SPECIES_CODE','ORDER1']]\n",
"ebird.columns = ['bird','order']\n",
"df = df.merge(ebird, on='bird', how='left')"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "c92d8d76",
"metadata": {},
"outputs": [],
"source": [
"# for 2025 included\n",
"nonbird = df[df['order'].isna()]\n",
"nonbird = nonbird[nonbird.columns[:8]]\n",
"taxo = pd.read_csv('/mnt/d/acoustics-data/birdclef-2025/taxonomy.csv')\n",
"taxo = taxo[['primary_label','class_name']]\n",
"taxo.columns = ['bird','order']\n",
"nonbird = nonbird.merge(taxo, on='bird', how='left')\n",
"nonnonbird = df[df['order'].notna()]\n",
"df = pd.concat((nonnonbird,nonbird))\n",
"del nonnonbird\n",
"del nonbird"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "2a60bc16",
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
"<Figure size 1200x800 with 9 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(3, 3, figsize=(12, 8), sharex=True, sharey=True)\n",
"\n",
"axes = axes.flatten()\n",
"\n",
"axes = axes.flatten()\n",
"\n",
"uniq_classes = df['order'].value_counts().index.tolist()\n",
"\n",
"classes = uniq_classes[0:9]\n",
"\n",
"colors = ['tab:blue', 'tab:orange', 'tab:green', \n",
" 'tab:red', 'tab:purple', 'tab:brown',\n",
" 'tab:pink', 'tab:gray', 'tab:olive']\n",
"\n",
"for i, clss in enumerate(classes):\n",
" x = df[(df['order'] == clss)].model_3\n",
" axes[i].hist(x, 50, edgecolor='k', density=True, alpha=0.7, color=colors[i])\n",
" axes[i].set_title(clss)\n",
" axes[i].set_xlabel('Predicted presence')\n",
" axes[i].set_ylabel('Density')\n",
" axes[i].grid(True)\n",
"plt.tight_layout()\n",
"plt.savefig('xgboost_third_model_orders_1_9.png')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "469a8f1e",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.7374329893469489"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df['model_3']>0.9].shape[0] / df.shape[0]"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "birdclef",
"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": "3.12.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|