Updated imports and requirements.txt

Author: ulya-tkch

src/experiments/OOD-adj-scores/0_Train_Models.ipynb

@@ -13,17 +13,9 @@
     "import sys\n",
     "sys.path.insert(0, \"../\")\n",
     "\n",
-    "\n",
     "from autogluon.vision import ImagePredictor, ImageDataset\n",
     "import numpy as np\n",
     "import pandas as pd\n",
-    "import pickle\n",
-    "import datetime\n",
-    "from pathlib import Path\n",
-    "from sklearn.ensemble import IsolationForest\n",
-    "from sklearn.model_selection import StratifiedKFold\n",
-    "from sklearn.metrics import roc_auc_score\n",
-    "from sklearn.model_selection import train_test_split\n",
     "\n",
     "pd.set_option('display.max_rows', None)\n",
     "pd.set_option('display.max_columns', None)\n",
@@ -207,6 +199,14 @@
     "    \n",
     "    _ = train_ag_model(train_dataset, dataset_name=dataset, model=model, epochs=100)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ec1ed3e1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {

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

@@ -21,16 +21,9 @@
     "from autogluon.vision import ImagePredictor, ImageDataset\n",
     "import numpy as np\n",
     "import pandas as pd\n",
-    "import pickle\n",
-    "import datetime\n",
-    "from pathlib import Path\n",
     "\n",
     "import seaborn as sns\n",
-    "from sklearn.model_selection import StratifiedKFold\n",
     "from sklearn.metrics import roc_auc_score\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from IPython.display import HTML\n",
-    "from matplotlib import pyplot as plt\n",
     "from cleanlab.count import get_confident_thresholds\n",
     "from cleanlab.internal.label_quality_utils import get_normalized_entropy\n",
     "\n",
@@ -455,8 +448,6 @@
    },
    "outputs": [],
    "source": [
-    "# export to latex\n",
-    "\n",
     "# write to Latex file\n",
     "with open(f\"{model}_ood_auroc.tex\", \"w\") as tf:\n",
     "    tf.write(df_results[cols].rename(columns=cols_rename_dict).to_latex(index=False, float_format=\"%.4f\"))"

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

@@ -0,0 +1,11 @@
+autogluon==0.4.0
+numpy==1.22.4
+pandas==1.3.5
+scikit-learn==1.0.2
+torch==1.10.1+cu113
+torchvision==0.11.2+cu113
+torchaudio==0.10.1+cu113
+cleanlab
+seaborn
+cifar2png
+

src/experiments/OOD/0_Train_Models.ipynb

@@ -13,17 +13,9 @@
     "import sys\n",
     "sys.path.insert(0, \"../\")\n",
     "\n",
-    "\n",
     "from autogluon.vision import ImagePredictor, ImageDataset\n",
     "import numpy as np\n",
     "import pandas as pd\n",
-    "import pickle\n",
-    "import datetime\n",
-    "from pathlib import Path\n",
-    "from sklearn.ensemble import IsolationForest\n",
-    "from sklearn.model_selection import StratifiedKFold\n",
-    "from sklearn.metrics import roc_auc_score\n",
-    "from sklearn.model_selection import train_test_split\n",
     "\n",
     "pd.set_option('display.max_rows', None)\n",
     "pd.set_option('display.max_columns', None)\n",
@@ -40,7 +32,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "5c9b59b4-c51c-4cdb-a958-46f227cdb5d8",
    "metadata": {},
    "outputs": [],
@@ -63,7 +55,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "id": "cde63994-e833-4f87-93b6-e05b3c7ba479",
    "metadata": {},
    "outputs": [],
@@ -96,79 +88,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "1ae79a8d-bb68-46d5-b4b9-1f082da7d695",
    "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>image</th>\n",
-       "      <th>label</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>/Data/cifar100_png/train/apple/0001.png</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>/Data/cifar100_png/train/apple/0002.png</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>/Data/cifar100_png/train/apple/0003.png</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>/Data/cifar100_png/train/apple/0004.png</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>/Data/cifar100_png/train/apple/0005.png</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                                     image  label\n",
-       "0  /Data/cifar100_png/train/apple/0001.png      0\n",
-       "1  /Data/cifar100_png/train/apple/0002.png      0\n",
-       "2  /Data/cifar100_png/train/apple/0003.png      0\n",
-       "3  /Data/cifar100_png/train/apple/0004.png      0\n",
-       "4  /Data/cifar100_png/train/apple/0005.png      0"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Check out a dataset\n",
     "cifar_100_train_dataset.head()"

src/experiments/OOD/1_Evaluate_All_OOD_Experiments.ipynb

@@ -19,16 +19,12 @@
     "from autogluon.vision import ImagePredictor, ImageDataset\n",
     "import numpy as np\n",
     "import pandas as pd\n",
-    "import pickle\n",
-    "import datetime\n",
-    "from pathlib import Path\n",
     "import umap\n",
     "import seaborn as sns\n",
     "from sklearn.ensemble import IsolationForest\n",
     "from sklearn.model_selection import StratifiedKFold\n",
     "from sklearn.metrics import roc_auc_score\n",
     "from sklearn.model_selection import train_test_split\n",
-    "from IPython.display import HTML\n",
     "from matplotlib import pyplot as plt\n",
     "from cleanlab.internal.label_quality_utils import get_normalized_entropy\n",
     "\n",