diff --git a/notebooks/05_covid_anomaly_detection.ipynb b/notebooks/05_covid_anomaly_detection.ipynb
new file mode 100644
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+++ b/notebooks/05_covid_anomaly_detection.ipynb
@@ -0,0 +1,2350 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from ssl_tools.data.data_modules.covid_anomaly import CovidUserAnomalyDataModule\n",
+    "from ssl_tools.utils.data import get_full_data_split\n",
+    "from ssl_tools.models.nets.lstm_ae import LSTMAutoencoder\n",
+    "import lightning as L\n",
+    "import torch\n",
+    "import numpy as np\n",
+    "from torchmetrics import MeanSquaredError"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\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>datetime</th>\n",
+       "      <th>RHR-0</th>\n",
+       "      <th>RHR-1</th>\n",
+       "      <th>RHR-2</th>\n",
+       "      <th>RHR-3</th>\n",
+       "      <th>RHR-4</th>\n",
+       "      <th>RHR-5</th>\n",
+       "      <th>RHR-6</th>\n",
+       "      <th>RHR-7</th>\n",
+       "      <th>RHR-8</th>\n",
+       "      <th>...</th>\n",
+       "      <th>RHR-10</th>\n",
+       "      <th>RHR-11</th>\n",
+       "      <th>RHR-12</th>\n",
+       "      <th>RHR-13</th>\n",
+       "      <th>RHR-14</th>\n",
+       "      <th>RHR-15</th>\n",
+       "      <th>anomaly</th>\n",
+       "      <th>baseline</th>\n",
+       "      <th>label</th>\n",
+       "      <th>participant_id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2027-01-14 21:00:00</td>\n",
+       "      <td>1.170175</td>\n",
+       "      <td>0.653752</td>\n",
+       "      <td>-0.392374</td>\n",
+       "      <td>-1.431553</td>\n",
+       "      <td>-2.129013</td>\n",
+       "      <td>-2.755962</td>\n",
+       "      <td>-3.681322</td>\n",
+       "      <td>-4.674443</td>\n",
+       "      <td>-5.668570</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-6.937363</td>\n",
+       "      <td>-7.102118</td>\n",
+       "      <td>-6.975790</td>\n",
+       "      <td>-6.554774</td>\n",
+       "      <td>-6.112156</td>\n",
+       "      <td>-5.396099</td>\n",
+       "      <td>False</td>\n",
+       "      <td>True</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>P110465</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2027-01-15 05:00:00</td>\n",
+       "      <td>-5.668570</td>\n",
+       "      <td>-6.373289</td>\n",
+       "      <td>-6.937363</td>\n",
+       "      <td>-7.102118</td>\n",
+       "      <td>-6.975790</td>\n",
+       "      <td>-6.554774</td>\n",
+       "      <td>-6.112156</td>\n",
+       "      <td>-5.396099</td>\n",
+       "      <td>-4.415848</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-2.656756</td>\n",
+       "      <td>-1.305630</td>\n",
+       "      <td>-0.072756</td>\n",
+       "      <td>1.046195</td>\n",
+       "      <td>1.530467</td>\n",
+       "      <td>1.829053</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>P110465</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2027-01-15 13:00:00</td>\n",
+       "      <td>-4.415848</td>\n",
+       "      <td>-3.467073</td>\n",
+       "      <td>-2.656756</td>\n",
+       "      <td>-1.305630</td>\n",
+       "      <td>-0.072756</td>\n",
+       "      <td>1.046195</td>\n",
+       "      <td>1.530467</td>\n",
+       "      <td>1.829053</td>\n",
+       "      <td>1.223064</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.424000</td>\n",
+       "      <td>-1.145581</td>\n",
+       "      <td>-1.355121</td>\n",
+       "      <td>-2.321206</td>\n",
+       "      <td>-3.124961</td>\n",
+       "      <td>-3.928738</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>P110465</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2027-01-15 21:00:00</td>\n",
+       "      <td>1.223064</td>\n",
+       "      <td>0.472444</td>\n",
+       "      <td>-0.424000</td>\n",
+       "      <td>-1.145581</td>\n",
+       "      <td>-1.355121</td>\n",
+       "      <td>-2.321206</td>\n",
+       "      <td>-3.124961</td>\n",
+       "      <td>-3.928738</td>\n",
+       "      <td>-4.802627</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-6.067744</td>\n",
+       "      <td>-5.460156</td>\n",
+       "      <td>-4.671143</td>\n",
+       "      <td>-3.408943</td>\n",
+       "      <td>-2.237883</td>\n",
+       "      <td>-1.187843</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>P110465</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2027-01-16 05:00:00</td>\n",
+       "      <td>-4.802627</td>\n",
+       "      <td>-5.831013</td>\n",
+       "      <td>-6.067744</td>\n",
+       "      <td>-5.460156</td>\n",
+       "      <td>-4.671143</td>\n",
+       "      <td>-3.408943</td>\n",
+       "      <td>-2.237883</td>\n",
+       "      <td>-1.187843</td>\n",
+       "      <td>-0.062360</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.266944</td>\n",
+       "      <td>3.794465</td>\n",
+       "      <td>4.625745</td>\n",
+       "      <td>4.827756</td>\n",
+       "      <td>4.720000</td>\n",
+       "      <td>4.677464</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>normal</td>\n",
+       "      <td>P110465</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31732</th>\n",
+       "      <td>2024-12-13 00:00:00</td>\n",
+       "      <td>-0.180702</td>\n",
+       "      <td>-0.499793</td>\n",
+       "      <td>-0.749829</td>\n",
+       "      <td>-0.868485</td>\n",
+       "      <td>-0.966754</td>\n",
+       "      <td>-1.004670</td>\n",
+       "      <td>-0.888210</td>\n",
+       "      <td>-0.580762</td>\n",
+       "      <td>-0.467943</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.092000</td>\n",
+       "      <td>0.347840</td>\n",
+       "      <td>0.636395</td>\n",
+       "      <td>0.958195</td>\n",
+       "      <td>1.170514</td>\n",
+       "      <td>1.301841</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>recovered</td>\n",
+       "      <td>P992022</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31733</th>\n",
+       "      <td>2024-12-13 08:00:00</td>\n",
+       "      <td>-0.467943</td>\n",
+       "      <td>-0.162740</td>\n",
+       "      <td>0.092000</td>\n",
+       "      <td>0.347840</td>\n",
+       "      <td>0.636395</td>\n",
+       "      <td>0.958195</td>\n",
+       "      <td>1.170514</td>\n",
+       "      <td>1.301841</td>\n",
+       "      <td>1.477526</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.660344</td>\n",
+       "      <td>1.656600</td>\n",
+       "      <td>1.685652</td>\n",
+       "      <td>1.747252</td>\n",
+       "      <td>1.767329</td>\n",
+       "      <td>1.793616</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>recovered</td>\n",
+       "      <td>P992022</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31734</th>\n",
+       "      <td>2024-12-13 16:00:00</td>\n",
+       "      <td>1.477526</td>\n",
+       "      <td>1.657321</td>\n",
+       "      <td>1.660344</td>\n",
+       "      <td>1.656600</td>\n",
+       "      <td>1.685652</td>\n",
+       "      <td>1.747252</td>\n",
+       "      <td>1.767329</td>\n",
+       "      <td>1.793616</td>\n",
+       "      <td>1.728615</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.509833</td>\n",
+       "      <td>1.380749</td>\n",
+       "      <td>1.263744</td>\n",
+       "      <td>1.139997</td>\n",
+       "      <td>1.024205</td>\n",
+       "      <td>0.946663</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>recovered</td>\n",
+       "      <td>P992022</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31735</th>\n",
+       "      <td>2024-12-14 00:00:00</td>\n",
+       "      <td>1.728615</td>\n",
+       "      <td>1.616265</td>\n",
+       "      <td>1.509833</td>\n",
+       "      <td>1.380749</td>\n",
+       "      <td>1.263744</td>\n",
+       "      <td>1.139997</td>\n",
+       "      <td>1.024205</td>\n",
+       "      <td>0.946663</td>\n",
+       "      <td>1.136868</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.642153</td>\n",
+       "      <td>1.909381</td>\n",
+       "      <td>2.114439</td>\n",
+       "      <td>2.282238</td>\n",
+       "      <td>2.453691</td>\n",
+       "      <td>2.587843</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>recovered</td>\n",
+       "      <td>P992022</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31736</th>\n",
+       "      <td>2024-12-14 08:00:00</td>\n",
+       "      <td>1.136868</td>\n",
+       "      <td>1.380418</td>\n",
+       "      <td>1.642153</td>\n",
+       "      <td>1.909381</td>\n",
+       "      <td>2.114439</td>\n",
+       "      <td>2.282238</td>\n",
+       "      <td>2.453691</td>\n",
+       "      <td>2.587843</td>\n",
+       "      <td>2.437232</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.359840</td>\n",
+       "      <td>2.173400</td>\n",
+       "      <td>2.098140</td>\n",
+       "      <td>1.967669</td>\n",
+       "      <td>1.784512</td>\n",
+       "      <td>1.561848</td>\n",
+       "      <td>False</td>\n",
+       "      <td>False</td>\n",
+       "      <td>recovered</td>\n",
+       "      <td>P992022</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>31737 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                  datetime     RHR-0     RHR-1     RHR-2     RHR-3     RHR-4  \\\n",
+       "0      2027-01-14 21:00:00  1.170175  0.653752 -0.392374 -1.431553 -2.129013   \n",
+       "1      2027-01-15 05:00:00 -5.668570 -6.373289 -6.937363 -7.102118 -6.975790   \n",
+       "2      2027-01-15 13:00:00 -4.415848 -3.467073 -2.656756 -1.305630 -0.072756   \n",
+       "3      2027-01-15 21:00:00  1.223064  0.472444 -0.424000 -1.145581 -1.355121   \n",
+       "4      2027-01-16 05:00:00 -4.802627 -5.831013 -6.067744 -5.460156 -4.671143   \n",
+       "...                    ...       ...       ...       ...       ...       ...   \n",
+       "31732  2024-12-13 00:00:00 -0.180702 -0.499793 -0.749829 -0.868485 -0.966754   \n",
+       "31733  2024-12-13 08:00:00 -0.467943 -0.162740  0.092000  0.347840  0.636395   \n",
+       "31734  2024-12-13 16:00:00  1.477526  1.657321  1.660344  1.656600  1.685652   \n",
+       "31735  2024-12-14 00:00:00  1.728615  1.616265  1.509833  1.380749  1.263744   \n",
+       "31736  2024-12-14 08:00:00  1.136868  1.380418  1.642153  1.909381  2.114439   \n",
+       "\n",
+       "          RHR-5     RHR-6     RHR-7     RHR-8  ...    RHR-10    RHR-11  \\\n",
+       "0     -2.755962 -3.681322 -4.674443 -5.668570  ... -6.937363 -7.102118   \n",
+       "1     -6.554774 -6.112156 -5.396099 -4.415848  ... -2.656756 -1.305630   \n",
+       "2      1.046195  1.530467  1.829053  1.223064  ... -0.424000 -1.145581   \n",
+       "3     -2.321206 -3.124961 -3.928738 -4.802627  ... -6.067744 -5.460156   \n",
+       "4     -3.408943 -2.237883 -1.187843 -0.062360  ...  2.266944  3.794465   \n",
+       "...         ...       ...       ...       ...  ...       ...       ...   \n",
+       "31732 -1.004670 -0.888210 -0.580762 -0.467943  ...  0.092000  0.347840   \n",
+       "31733  0.958195  1.170514  1.301841  1.477526  ...  1.660344  1.656600   \n",
+       "31734  1.747252  1.767329  1.793616  1.728615  ...  1.509833  1.380749   \n",
+       "31735  1.139997  1.024205  0.946663  1.136868  ...  1.642153  1.909381   \n",
+       "31736  2.282238  2.453691  2.587843  2.437232  ...  2.359840  2.173400   \n",
+       "\n",
+       "         RHR-12    RHR-13    RHR-14    RHR-15  anomaly  baseline      label  \\\n",
+       "0     -6.975790 -6.554774 -6.112156 -5.396099    False      True     normal   \n",
+       "1     -0.072756  1.046195  1.530467  1.829053    False     False     normal   \n",
+       "2     -1.355121 -2.321206 -3.124961 -3.928738    False     False     normal   \n",
+       "3     -4.671143 -3.408943 -2.237883 -1.187843    False     False     normal   \n",
+       "4      4.625745  4.827756  4.720000  4.677464    False     False     normal   \n",
+       "...         ...       ...       ...       ...      ...       ...        ...   \n",
+       "31732  0.636395  0.958195  1.170514  1.301841    False     False  recovered   \n",
+       "31733  1.685652  1.747252  1.767329  1.793616    False     False  recovered   \n",
+       "31734  1.263744  1.139997  1.024205  0.946663    False     False  recovered   \n",
+       "31735  2.114439  2.282238  2.453691  2.587843    False     False  recovered   \n",
+       "31736  2.098140  1.967669  1.784512  1.561848    False     False  recovered   \n",
+       "\n",
+       "      participant_id  \n",
+       "0            P110465  \n",
+       "1            P110465  \n",
+       "2            P110465  \n",
+       "3            P110465  \n",
+       "4            P110465  \n",
+       "...              ...  \n",
+       "31732        P992022  \n",
+       "31733        P992022  \n",
+       "31734        P992022  \n",
+       "31735        P992022  \n",
+       "31736        P992022  \n",
+       "\n",
+       "[31737 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Read CSV data\n",
+    "data_path = \"/workspaces/hiaac-m4/data/Stanford-COVID/processed/windowed_16_overlap_8_df_scaled.csv\"\n",
+    "df = pd.read_csv(data_path)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CovidUserAnomalyDataModule (Data=/workspaces/hiaac-m4/data/Stanford-COVID/processed/windowed_16_overlap_8_df_scaled.csv, 1 participant selected)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dm = CovidUserAnomalyDataModule(\n",
+    "    data_path,\n",
+    "    participants=[\"P992022\"],\n",
+    "    batch_size=32,\n",
+    "    num_workers=0,\n",
+    "    reshape=(16, 1),\n",
+    ")\n",
+    "dm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LSTMAutoencoder(\n",
+       "  (backbone): _LSTMAutoEncoder(\n",
+       "    (lstm1): LSTM(1, 128, batch_first=True)\n",
+       "    (lstm2): LSTM(128, 64, batch_first=True)\n",
+       "    (repeat_vector): Linear(in_features=64, out_features=1024, bias=True)\n",
+       "    (lstm3): LSTM(64, 64, batch_first=True)\n",
+       "    (lstm4): LSTM(64, 128, batch_first=True)\n",
+       "    (time_distributed): Linear(in_features=128, out_features=1, bias=True)\n",
+       "  )\n",
+       "  (loss_fn): MSELoss()\n",
+       ")"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = LSTMAutoencoder(input_shape=(16, 1))\n",
+    "model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "GPU available: True (cuda), used: False\n",
+      "TPU available: False, using: 0 TPU cores\n",
+      "IPU available: False, using: 0 IPUs\n",
+      "HPU available: False, using: 0 HPUs\n",
+      "/usr/local/lib/python3.10/dist-packages/lightning/pytorch/trainer/setup.py:187: GPU available but not used. You can set it by doing `Trainer(accelerator='gpu')`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<lightning.pytorch.trainer.trainer.Trainer at 0x7f98860215a0>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trainer = L.Trainer(max_epochs=100, devices=1, accelerator=\"cpu\")\n",
+    "trainer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "  | Name     | Type             | Params\n",
+      "----------------------------------------------\n",
+      "0 | backbone | _LSTMAutoEncoder | 316 K \n",
+      "1 | loss_fn  | MSELoss          | 0     \n",
+      "----------------------------------------------\n",
+      "316 K     Trainable params\n",
+      "0         Non-trainable params\n",
+      "316 K     Total params\n",
+      "1.264     Total estimated model params size (MB)\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "122a71df981c48c183eb2b4e7585103d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Sanity Checking: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.10/dist-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'val_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=47` in the `DataLoader` to improve performance.\n",
+      "/usr/local/lib/python3.10/dist-packages/lightning/pytorch/trainer/connectors/data_connector.py:441: The 'train_dataloader' does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` to `num_workers=47` in the `DataLoader` to improve performance.\n",
+      "/usr/local/lib/python3.10/dist-packages/lightning/pytorch/loops/fit_loop.py:298: The number of training batches (5) is smaller than the logging interval Trainer(log_every_n_steps=50). Set a lower value for log_every_n_steps if you want to see logs for the training epoch.\n"
+     ]
+    },
+    {
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+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bb469f0d48b34328a5cb7f435929ed61",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2269ddb5c1af4af987be8989ff2691d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dc73bcce23bc4226b46c6256d79cb53c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b22316d5af8740cc8177f09d3ac361d7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4454f60681e148d0865da4f6bdaf480d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "70fda9becc084eb99b6097553177be28",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e1a8daf0ad0848cab71ef796786c17f8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Validation: |          | 0/? [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "`Trainer.fit` stopped: `max_epochs=100` reached.\n"
+     ]
+    }
+   ],
+   "source": [
+    "trainer.fit(model, dm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_losses(y, y_pred, loss_fn):\n",
+    "    losses = []\n",
+    "    for _y, _y_pred in zip(y, y_pred):\n",
+    "        loss = loss_fn(_y, _y_pred)\n",
+    "        loss = loss.detach().numpy().item()\n",
+    "        losses.append(loss)\n",
+    "    return losses"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Predict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dm.setup(\"fit\")\n",
+    "train_dataset = dm.train_dataloader().dataset\n",
+    "\n",
+    "dm.setup(\"test\")\n",
+    "test_dataset = dm.test_dataloader().dataset"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Defining Anomaly Threshold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_train = torch.stack([torch.Tensor(x) for x, y in train_dataset])\n",
+    "y_train = np.array([y for x, y in train_dataset])\n",
+    "x_train_hat = model(x_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.3742748498916626"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mse = MeanSquaredError()\n",
+    "losses = compute_losses(x_train, x_train_hat, mse)\n",
+    "\n",
+    "anomaly_threshold = max(losses)\n",
+    "anomaly_threshold"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Predicting on Test set"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x_test = torch.stack([torch.Tensor(x) for x, y in test_dataset])\n",
+    "y_test = np.array([y for x, y in test_dataset])\n",
+    "\n",
+    "x_test_hat = model(x_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mse = MeanSquaredError()\n",
+    "losses = compute_losses(x_test, x_test_hat, mse)\n",
+    "\n",
+    "y_test_hat = [1 if loss > anomaly_threshold else 0 for loss in losses]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "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>true</th>\n",
+       "      <th>predicted</th>\n",
+       "      <th>loss</th>\n",
+       "      <th>anomaly_threshold</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.023700</td>\n",
+       "      <td>0.374275</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.091413</td>\n",
+       "      <td>0.374275</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.054299</td>\n",
+       "      <td>0.374275</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.007486</td>\n",
+       "      <td>0.374275</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.024601</td>\n",
+       "      <td>0.374275</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>89</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.089833</td>\n",
+       "      <td>0.374275</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>90</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.051562</td>\n",
+       "      <td>0.374275</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>91</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.132748</td>\n",
+       "      <td>0.374275</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>92</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.158610</td>\n",
+       "      <td>0.374275</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>93</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.025522</td>\n",
+       "      <td>0.374275</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>94 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    true  predicted      loss  anomaly_threshold\n",
+       "0      0          0  0.023700           0.374275\n",
+       "1      0          0  0.091413           0.374275\n",
+       "2      0          0  0.054299           0.374275\n",
+       "3      0          0  0.007486           0.374275\n",
+       "4      0          0  0.024601           0.374275\n",
+       "..   ...        ...       ...                ...\n",
+       "89     1          0  0.089833           0.374275\n",
+       "90     1          0  0.051562           0.374275\n",
+       "91     1          0  0.132748           0.374275\n",
+       "92     1          0  0.158610           0.374275\n",
+       "93     1          0  0.025522           0.374275\n",
+       "\n",
+       "[94 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results_dataframe = pd.DataFrame(\n",
+    "    {\n",
+    "        \"true\": y_test,\n",
+    "        \"predicted\": y_test_hat,\n",
+    "        \"loss\": losses,\n",
+    "        \"anomaly_threshold\": anomaly_threshold,\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "results_dataframe"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "F1-score: 0.0\n",
+      "Recall: 0.0\n",
+      "Balanced Accuracy: 0.5\n",
+      "ROC AUC: 0.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import f1_score, recall_score, balanced_accuracy_score, roc_auc_score\n",
+    "\n",
+    "# Extract true and predicted labels from the results_dataframe\n",
+    "true_labels = results_dataframe['true']\n",
+    "predicted_labels = results_dataframe['predicted']\n",
+    "\n",
+    "# Calculate the F1-score\n",
+    "f1 = f1_score(true_labels, predicted_labels)\n",
+    "\n",
+    "# Calculate the recall\n",
+    "recall = recall_score(true_labels, predicted_labels)\n",
+    "\n",
+    "# Calculate the balanced accuracy\n",
+    "balanced_acc = balanced_accuracy_score(true_labels, predicted_labels)\n",
+    "\n",
+    "# Calculate the ROC AUC\n",
+    "roc_auc = roc_auc_score(true_labels, predicted_labels)\n",
+    "\n",
+    "# Print the results\n",
+    "print(\"F1-score:\", f1)\n",
+    "print(\"Recall:\", recall)\n",
+    "print(\"Balanced Accuracy:\", balanced_acc)\n",
+    "print(\"ROC AUC:\", roc_auc)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Get the true and predicted labels from the results_dataframe\n",
+    "true_labels = results_dataframe['true']\n",
+    "predicted_labels = results_dataframe['predicted']\n",
+    "\n",
+    "# Compute the confusion matrix\n",
+    "cm = confusion_matrix(true_labels, predicted_labels)\n",
+    "\n",
+    "# Define the class labels\n",
+    "class_labels = ['Normal', 'Anomaly']\n",
+    "\n",
+    "# Plot the confusion matrix\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "plt.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n",
+    "plt.title('Confusion Matrix')\n",
+    "plt.colorbar()\n",
+    "tick_marks = np.arange(len(class_labels))\n",
+    "plt.xticks(tick_marks, class_labels, rotation=45)\n",
+    "plt.yticks(tick_marks, class_labels)\n",
+    "plt.xlabel('Predicted Label')\n",
+    "plt.ylabel('True Label')\n",
+    "\n",
+    "# Add the values to the confusion matrix plot\n",
+    "thresh = cm.max() / 2.\n",
+    "for i in range(cm.shape[0]):\n",
+    "    for j in range(cm.shape[1]):\n",
+    "        plt.text(j, i, format(cm[i, j], 'd'),\n",
+    "                 horizontalalignment=\"center\",\n",
+    "                 color=\"white\" if cm[i, j] > thresh else \"black\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}