From 517d252a5e9d774067984bbb5e648a93ec174312 Mon Sep 17 00:00:00 2001
From: Faried Abu Zaid <fariedaz@gmail.com>
Date: Tue, 4 Jun 2024 15:15:45 +0200
Subject: [PATCH 1/3] adopt to pandas api change

---
 src/tfl_training_anomaly_detection/exercise_tools.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/tfl_training_anomaly_detection/exercise_tools.py b/src/tfl_training_anomaly_detection/exercise_tools.py
index 366d03e..28a235d 100755
--- a/src/tfl_training_anomaly_detection/exercise_tools.py
+++ b/src/tfl_training_anomaly_detection/exercise_tools.py
@@ -702,7 +702,7 @@ def perform_rkde_experiment(
                 if epsilon != 0:
                     model.compute_anomaly_roc(y)
                 new_scores = model.get_score()
-                total_scores = total_scores.append(new_scores, ignore_index=True)
+                total_scores = total_scores._append(new_scores, ignore_index=True)
     return total_scores
 
 

From 2bd2fa4412f2a6c44b5798a4831496d3404c1671 Mon Sep 17 00:00:00 2001
From: Faried Abu Zaid <fariedaz@gmail.com>
Date: Tue, 4 Jun 2024 15:17:09 +0200
Subject: [PATCH 2/3] Adopt to pandas api change, Fix error in KDE definition

---
 ...aly_detection_via_density_estimation.ipynb | 340 ++++++++++++++++--
 1 file changed, 304 insertions(+), 36 deletions(-)

diff --git a/notebooks/nb_02_anomaly_detection_via_density_estimation.ipynb b/notebooks/nb_02_anomaly_detection_via_density_estimation.ipynb
index c22aa54..36443d9 100644
--- a/notebooks/nb_02_anomaly_detection_via_density_estimation.ipynb
+++ b/notebooks/nb_02_anomaly_detection_via_density_estimation.ipynb
@@ -404,8 +404,8 @@
     "**Definition:**\n",
     "\n",
     "---\n",
-    "Let $D = \\{x_1,\\ldots,x_N\\}\\subset \\mathbb{R}^p$. The KDE with kernel $K$ and bandwidth $h$ is\n",
-    "$KDE_h(x, D) = \\frac{1}{N}\\sum_{i=1}^N \\frac{1}{h^p}K\\left(\\frac{|x-x_i|}{h}\\right)$\n",
+    "Let $D = \\{x_1,\\ldots,x_N\\}\\subset \\mathbb{R}^p$. The KDE with symmetric kernel $K$ and bandwidth $h$ is\n",
+    "$KDE_h(x, D) = \\frac{1}{N}\\sum_{i=1}^N \\frac{1}{h^p}K\\left(\\frac{x-x_i}{h}\\right)$\n",
     "\n",
     "---\n",
     "\n",
@@ -536,7 +536,7 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1b867c01b0fe43baafd4884bcf92b680",
+       "model_id": "83d957ba3af3428bbb3bcd6db94adfb7",
        "version_major": 2,
        "version_minor": 0
       },
@@ -762,7 +762,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/opt/homebrew/Caskroom/miniconda/base/envs/tfl-training-ad/lib/python3.10/site-packages/sklearn/model_selection/_search.py:1051: UserWarning: One or more of the test scores are non-finite: [-583.82004189 -460.99529797 -481.91244569 -500.00511672 -545.60663629\n",
+      "/Users/fariedabuzaid/.pyenv/versions/3.10.11/lib/python3.10/site-packages/sklearn/model_selection/_search.py:1051: UserWarning: One or more of the test scores are non-finite: [-583.82004189 -460.99529797 -481.91244569 -500.00511672 -545.60663629\n",
       " -561.06553923 -393.29051279 -436.60580108 -508.03454897          -inf\n",
       " -452.96132239 -473.5478597  -460.29104963 -522.20259792 -598.90918215\n",
       " -467.81178514 -648.98516413 -484.85010317 -534.20785976 -353.24010639\n",
@@ -783,7 +783,7 @@
       " -531.0443066  -496.60353839 -603.09910009 -518.27158361 -485.81926434\n",
       " -446.02585033 -617.26185348          -inf -492.11710758 -509.52974428]\n",
       "  warnings.warn(\n",
-      "/opt/homebrew/Caskroom/miniconda/base/envs/tfl-training-ad/lib/python3.10/site-packages/sklearn/model_selection/_search.py:1062: RuntimeWarning: invalid value encountered in subtract\n",
+      "/Users/fariedabuzaid/.pyenv/versions/3.10.11/lib/python3.10/site-packages/sklearn/model_selection/_search.py:1062: RuntimeWarning: invalid value encountered in subtract\n",
       "  (array - array_means[:, np.newaxis]) ** 2, axis=1, weights=weights\n"
      ]
     },
@@ -2603,9 +2603,9 @@
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       },
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        "                            \n",
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      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|███████████████████████████████████████████████████████████████████████████████| 14/14 [00:15<00:00,  1.12s/it]\n"
+      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 14/14 [00:15<00:00,  1.10s/it]\n"
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@@ -5874,7 +5874,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|███████████████████████████████████████████████████████████████████████████████| 14/14 [00:00<00:00, 23.22it/s]\n"
+      "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 14/14 [00:00<00:00, 20.21it/s]\n"
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@@ -6107,7 +6107,7 @@
     "- Advantage: Scalar product is computable\n",
     "- Call this the reproducing kernel Hilbert space (RKHS) of $K$.\n",
     "- $\\mathrm{KDE}_h(\\cdot, D) = \\frac{1}{N}\\sum_{i=1}^N K_h(\\cdot, x_i) = \\frac{1}{N}\\sum_{i=1}^N\\phi_{x_i}$\n",
-    "    - where $K_h(x,y) = \\frac{1}{h}K\\left(\\frac{|x-y|}{h}\\right)$\n",
+    "    - where $K_h(x,y) = \\frac{1}{h}K\\left(\\frac{x-y}{h}\\right)$\n",
     "\n",
     "\n",
     "\n",
@@ -6162,6 +6162,7 @@
    "execution_count": 31,
    "id": "da628e2f",
    "metadata": {
+    "scrolled": true,
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@@ -8105,9 +8106,9 @@
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        "                            \n",
-       "var gd = document.getElementById('383afb22-22bb-42e6-a51f-956df091e653');\n",
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@@ -8143,20 +8144,122 @@
       "Outlier prop: 0.01 (1 / 10)\n",
       "downsample outliers\n",
       "Finding best bandwidth...\n",
-      "Algo:  kde\n"
-     ]
-    },
-    {
-     "ename": "AttributeError",
-     "evalue": "'DataFrame' object has no attribute 'append'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m/var/folders/6x/yjqm1szs7f98bfmhnyrvk_hw0000gn/T/ipykernel_33372/4010907263.py\u001b[0m in \u001b[0;36m?\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m auc_scores = perform_rkde_experiment(\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0malgos\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mdataset_options\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/code/tfl-training-practical-anomaly-detection/src/tfl_training_anomaly_detection/exercise_tools.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(algos, dataset, dataset_options, outlierprop_range, kernel)\u001b[0m\n\u001b[1;32m    701\u001b[0m                 \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnorm_mom\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    702\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mepsilon\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    703\u001b[0m                     \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_anomaly_roc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    704\u001b[0m                 \u001b[0mnew_scores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 705\u001b[0;31m                 \u001b[0mtotal_scores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtotal_scores\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_scores\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mignore_index\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    706\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mtotal_scores\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/opt/homebrew/Caskroom/miniconda/base/envs/tfl-training-ad/lib/python3.10/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m?\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   6289\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessors\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6290\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6291\u001b[0m         ):\n\u001b[1;32m   6292\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6293\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'append'"
+      "Algo:  kde\n",
+      "Algo:  mom-kde\n",
+      "Algo:  rkde-huber\n",
+      "Stop at 4 iterations\n",
+      "Stop at 2 iterations\n",
+      "Algo:  rkde-hampel\n",
+      "Stop at 4 iterations\n",
+      "Stop at 100 iterations\n",
+      "\n",
+      "Outlier prop: 0.02 (2 / 10)\n",
+      "downsample outliers\n",
+      "Finding best bandwidth...\n",
+      "Algo:  kde\n",
+      "Algo:  mom-kde\n",
+      "Algo:  rkde-huber\n",
+      "Stop at 4 iterations\n",
+      "Stop at 2 iterations\n",
+      "Algo:  rkde-hampel\n",
+      "Stop at 4 iterations\n",
+      "Stop at 10 iterations\n",
+      "\n",
+      "Outlier prop: 0.03 (3 / 10)\n",
+      "downsample outliers\n",
+      "Finding best bandwidth...\n",
+      "Algo:  kde\n",
+      "Algo:  mom-kde\n",
+      "Algo:  rkde-huber\n",
+      "Stop at 3 iterations\n",
+      "Stop at 2 iterations\n",
+      "Algo:  rkde-hampel\n",
+      "Stop at 3 iterations\n",
+      "Stop at 100 iterations\n",
+      "\n",
+      "Outlier prop: 0.05 (4 / 10)\n",
+      "downsample outliers\n",
+      "Finding best bandwidth...\n",
+      "Algo:  kde\n",
+      "Algo:  mom-kde\n",
+      "Algo:  rkde-huber\n",
+      "Stop at 5 iterations\n",
+      "Stop at 3 iterations\n",
+      "Algo:  rkde-hampel\n",
+      "Stop at 5 iterations\n",
+      "Stop at 13 iterations\n",
+      "\n",
+      "Outlier prop: 0.07 (5 / 10)\n",
+      "downsample outliers\n",
+      "Finding best bandwidth...\n",
+      "Algo:  kde\n",
+      "Algo:  mom-kde\n",
+      "Algo:  rkde-huber\n",
+      "Stop at 4 iterations\n",
+      "Stop at 2 iterations\n",
+      "Algo:  rkde-hampel\n",
+      "Stop at 4 iterations\n",
+      "Stop at 100 iterations\n",
+      "\n",
+      "Outlier prop: 0.1 (6 / 10)\n",
+      "downsample outliers\n",
+      "Finding best bandwidth...\n",
+      "Algo:  kde\n",
+      "Algo:  mom-kde\n",
+      "Algo:  rkde-huber\n",
+      "Stop at 4 iterations\n",
+      "Stop at 3 iterations\n",
+      "Algo:  rkde-hampel\n",
+      "Stop at 4 iterations\n",
+      "Stop at 100 iterations\n",
+      "\n",
+      "Outlier prop: 0.2 (7 / 10)\n",
+      "downsample outliers\n",
+      "Finding best bandwidth...\n",
+      "Algo:  kde\n",
+      "Algo:  mom-kde\n",
+      "Algo:  rkde-huber\n",
+      "Stop at 5 iterations\n",
+      "Stop at 3 iterations\n",
+      "Algo:  rkde-hampel\n",
+      "Stop at 5 iterations\n",
+      "Stop at 100 iterations\n",
+      "\n",
+      "Outlier prop: 0.3 (8 / 10)\n",
+      "downsample outliers\n",
+      "Finding best bandwidth...\n",
+      "Algo:  kde\n",
+      "Algo:  mom-kde\n",
+      "Algo:  rkde-huber\n",
+      "Stop at 4 iterations\n",
+      "Stop at 3 iterations\n",
+      "Algo:  rkde-hampel\n",
+      "Stop at 4 iterations\n",
+      "Stop at 15 iterations\n",
+      "\n",
+      "Outlier prop: 0.4 (9 / 10)\n",
+      "downsample outliers\n",
+      "Finding best bandwidth...\n",
+      "Algo:  kde\n",
+      "Algo:  mom-kde\n",
+      "Algo:  rkde-huber\n",
+      "Stop at 4 iterations\n",
+      "Stop at 2 iterations\n",
+      "Algo:  rkde-hampel\n",
+      "Stop at 4 iterations\n",
+      "Stop at 100 iterations\n",
+      "\n",
+      "Outlier prop: 0.5 (10 / 10)\n",
+      "downsample inliers\n",
+      "Finding best bandwidth...\n",
+      "Algo:  kde\n",
+      "Algo:  mom-kde\n",
+      "Algo:  rkde-huber\n",
+      "Stop at 3 iterations\n",
+      "Stop at 2 iterations\n",
+      "Algo:  rkde-hampel\n",
+      "Stop at 3 iterations\n",
+      "Stop at 100 iterations\n"
      ]
     }
    ],
@@ -8172,15 +8275,180 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "280ed959",
+   "execution_count": 33,
+   "id": "86124f7a",
    "metadata": {},
-   "outputs": [],
+   "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>algo</th>\n",
+       "      <th>bandwidth</th>\n",
+       "      <th>outlier_prop</th>\n",
+       "      <th>n_block</th>\n",
+       "      <th>auc_anomaly</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>kde</td>\n",
+       "      <td>0.031623</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0.960000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>kde</td>\n",
+       "      <td>0.031623</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0.608889</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>kde</td>\n",
+       "      <td>0.031623</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0.733333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>kde</td>\n",
+       "      <td>0.056645</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0.683810</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32</th>\n",
+       "      <td>kde</td>\n",
+       "      <td>0.035533</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0.851515</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>40</th>\n",
+       "      <td>kde</td>\n",
+       "      <td>0.044864</td>\n",
+       "      <td>0.10</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0.909167</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>48</th>\n",
+       "      <td>kde</td>\n",
+       "      <td>0.060046</td>\n",
+       "      <td>0.20</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0.856036</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>kde</td>\n",
+       "      <td>0.056645</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0.828125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>64</th>\n",
+       "      <td>kde</td>\n",
+       "      <td>0.056645</td>\n",
+       "      <td>0.40</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0.822133</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>72</th>\n",
+       "      <td>kde</td>\n",
+       "      <td>0.047557</td>\n",
+       "      <td>0.50</td>\n",
+       "      <td>None</td>\n",
+       "      <td>0.801000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   algo  bandwidth  outlier_prop n_block  auc_anomaly\n",
+       "0   kde   0.031623          0.01    None     0.960000\n",
+       "8   kde   0.031623          0.02    None     0.608889\n",
+       "16  kde   0.031623          0.03    None     0.733333\n",
+       "24  kde   0.056645          0.05    None     0.683810\n",
+       "32  kde   0.035533          0.07    None     0.851515\n",
+       "40  kde   0.044864          0.10    None     0.909167\n",
+       "48  kde   0.060046          0.20    None     0.856036\n",
+       "56  kde   0.056645          0.30    None     0.828125\n",
+       "64  kde   0.056645          0.40    None     0.822133\n",
+       "72  kde   0.047557          0.50    None     0.801000"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "algo_data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "280ed959",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Comparison of rKDE against contamination')"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "fig, ax = plt.subplots(figsize=(7, 5))\n",
     "for algo, algo_data in auc_scores.groupby('algo'):\n",
-    "    x = algo_data.groupby('outlier_prop').mean().index\n",
-    "    y = algo_data.groupby('outlier_prop').mean()['auc_anomaly']\n",
+    "    x = algo_data.drop(\"algo\", axis=1).groupby('outlier_prop').mean().index\n",
+    "    y = algo_data.drop(\"algo\", axis=1).groupby('outlier_prop').mean()['auc_anomaly']\n",
     "    ax.plot(x, y, 'o-', label=algo)\n",
     "plt.legend()\n",
     "plt.xlabel('outlier_prop')\n",
@@ -8261,7 +8529,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.13"
+   "version": "3.10.11"
   },
   "toc": {
    "base_numbering": 1,

From 9365dd25765da626c835374d8c0909a7f56ad6bf Mon Sep 17 00:00:00 2001
From: Faried Abu Zaid <fariedaz@gmail.com>
Date: Tue, 4 Jun 2024 15:17:22 +0200
Subject: [PATCH 3/3] Remove stale reference

---
 README.md | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/README.md b/README.md
index 9e4ce47..7d8c615 100755
--- a/README.md
+++ b/README.md
@@ -60,6 +60,3 @@ Finally, clone repository, change into the directory of the cloned repository an
 ```bash
 pip install -e .
 ```
-
-## References
-You can find the main references for the workshop [here](docs/references.md).