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Updated imports and requirements.txt
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-102
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5 files changed

+23
-102
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‎src/experiments/OOD-adj-scores/0_Train_Models.ipynb

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"import sys\n",
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"sys.path.insert(0, \"../\")\n",
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"\n",
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"\n",
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"from autogluon.vision import ImagePredictor, ImageDataset\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import pickle\n",
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"import datetime\n",
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"from pathlib import Path\n",
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"from sklearn.ensemble import IsolationForest\n",
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"from sklearn.model_selection import StratifiedKFold\n",
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"from sklearn.metrics import roc_auc_score\n",
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"from sklearn.model_selection import train_test_split\n",
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"\n",
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"pd.set_option('display.max_rows', None)\n",
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"pd.set_option('display.max_columns', None)\n",
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" \n",
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" _ = train_ag_model(train_dataset, dataset_name=dataset, model=model, epochs=100)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ec1ed3e1",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {

‎src/experiments/OOD-adj-scores/1_Evaluate_All_OOD_Experiments.ipynb

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"from autogluon.vision import ImagePredictor, ImageDataset\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import pickle\n",
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"import datetime\n",
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"from pathlib import Path\n",
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"\n",
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"import seaborn as sns\n",
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"from sklearn.model_selection import StratifiedKFold\n",
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"from sklearn.metrics import roc_auc_score\n",
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"from sklearn.model_selection import train_test_split\n",
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"from IPython.display import HTML\n",
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"from matplotlib import pyplot as plt\n",
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"from cleanlab.count import get_confident_thresholds\n",
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"from cleanlab.internal.label_quality_utils import get_normalized_entropy\n",
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"\n",
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},
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"outputs": [],
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"source": [
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"# export to latex\n",
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"\n",
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"# write to Latex file\n",
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"with open(f\"{model}_ood_auroc.tex\", \"w\") as tf:\n",
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" tf.write(df_results[cols].rename(columns=cols_rename_dict).to_latex(index=False, float_format=\"%.4f\"))"

‎src/experiments/OOD-adj-scores/requirements.txt

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autogluon==0.4.0
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numpy==1.22.4
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pandas==1.3.5
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scikit-learn==1.0.2
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torch==1.10.1+cu113
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torchvision==0.11.2+cu113
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torchaudio==0.10.1+cu113
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cleanlab
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seaborn
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cifar2png
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‎src/experiments/OOD/0_Train_Models.ipynb

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"import sys\n",
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"sys.path.insert(0, \"../\")\n",
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"\n",
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"\n",
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"from autogluon.vision import ImagePredictor, ImageDataset\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import pickle\n",
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"import datetime\n",
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"from pathlib import Path\n",
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"from sklearn.ensemble import IsolationForest\n",
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"from sklearn.model_selection import StratifiedKFold\n",
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"from sklearn.metrics import roc_auc_score\n",
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"from sklearn.model_selection import train_test_split\n",
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"\n",
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"pd.set_option('display.max_rows', None)\n",
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"pd.set_option('display.max_columns', None)\n",
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"execution_count": null,
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"id": "5c9b59b4-c51c-4cdb-a958-46f227cdb5d8",
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"metadata": {},
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"outputs": [],
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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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"execution_count": null,
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"id": "cde63994-e833-4f87-93b6-e05b3c7ba479",
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"metadata": {},
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"outputs": [],
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"execution_count": null,
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"id": "1ae79a8d-bb68-46d5-b4b9-1f082da7d695",
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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>image</th>\n",
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" <th>label</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>/Data/cifar100_png/train/apple/0001.png</td>\n",
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" <td>0</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>/Data/cifar100_png/train/apple/0002.png</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>/Data/cifar100_png/train/apple/0003.png</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>/Data/cifar100_png/train/apple/0004.png</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>/Data/cifar100_png/train/apple/0005.png</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" image label\n",
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"0 /Data/cifar100_png/train/apple/0001.png 0\n",
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"1 /Data/cifar100_png/train/apple/0002.png 0\n",
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"2 /Data/cifar100_png/train/apple/0003.png 0\n",
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"3 /Data/cifar100_png/train/apple/0004.png 0\n",
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"4 /Data/cifar100_png/train/apple/0005.png 0"
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]
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},
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"execution_count": 7,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"outputs": [],
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"source": [
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"# Check out a dataset\n",
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"cifar_100_train_dataset.head()"

‎src/experiments/OOD/1_Evaluate_All_OOD_Experiments.ipynb

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"from autogluon.vision import ImagePredictor, ImageDataset\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import pickle\n",
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"import datetime\n",
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"from pathlib import Path\n",
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"import umap\n",
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"import seaborn as sns\n",
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"from sklearn.ensemble import IsolationForest\n",
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"from sklearn.model_selection import StratifiedKFold\n",
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"from sklearn.metrics import roc_auc_score\n",
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"from sklearn.model_selection import train_test_split\n",
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"from IPython.display import HTML\n",
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"from matplotlib import pyplot as plt\n",
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"from cleanlab.internal.label_quality_utils import get_normalized_entropy\n",
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"\n",

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