initiaö commit

1 files changed, 403 insertions, 0 deletions

diff --git a/evaluation/adept/evaluation_savi.ipynb b/evaluation/adept/evaluation_savi.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import seaborn as sns\n",
+    "import warnings\n",
+    "import scipy.stats as stats\n",
+    "import os\n",
+    "\n",
+    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
+    "pd.options.mode.chained_assignment = None \n",
+    "plt.style.use('ggplot')\n",
+    "sns.color_palette(\"Paired\");\n",
+    "sns.set_theme();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Data Loading"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# setting path to results folder\n",
+    "root_path = '../../out/pretrained/adept/savi/results/'\n",
+    "\n",
+    "# list all folders in root path that don't stat with a dot\n",
+    "nets = [f for f in os.listdir(root_path) if not f.startswith('.')]\n",
+    "\n",
+    "# read pickle file\n",
+    "sf = pd.DataFrame()\n",
+    "af = pd.DataFrame()\n",
+    "\n",
+    "# load statistics files from nets\n",
+    "for net in nets:\n",
+    "    path = os.path.join(root_path, net, 'control', 'statistics',)\n",
+    "    with open(os.path.join(path, 'slotframe.csv'), 'rb') as f:\n",
+    "        sf_temp = pd.read_csv(f, index_col=0)\n",
+    "    sf_temp['net'] = net\n",
+    "    sf = pd.concat([sf,sf_temp])\n",
+    "\n",
+    "    with open(os.path.join(path, 'accframe.csv'), 'rb') as f:\n",
+    "        af_temp = pd.read_csv(f, index_col=0)\n",
+    "    af_temp['net'] = net\n",
+    "    af = pd.concat([af,af_temp])\n",
+    "\n",
+    "# cast variables\n",
+    "sf['visible'] = sf['visible'].astype(bool)\n",
+    "sf['bound'] = sf['bound'].astype(bool)\n",
+    "sf['occluder'] = sf['occluder'].astype(bool)\n",
+    "sf['inimage'] = sf['inimage'].astype(bool)\n",
+    "sf['alpha_pos'] = 1-sf['alpha_pos']\n",
+    "sf['alpha_ges'] = 1-sf['alpha_ges']\n",
+    "\n",
+    "# scale to percentage\n",
+    "sf['TE'] = sf['TE'] * 100\n",
+    "\n",
+    "# add surprise as dummy code\n",
+    "sf['control'] = [('control' in set)  for set in sf['set']]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Calculate Tracking Error (TE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Tracking Error when visible: M: 26.7 , STD: 12.6, Count: 1100\n",
+      "Tracking Error when occluded: M: 19.1 , STD: 9.74, Count: 220\n"
+     ]
+    }
+   ],
+   "source": [
+    "grouping = (sf.inimage & sf.bound & ~sf.occluder & sf.control)\n",
+    "\n",
+    "def get_stats(col):\n",
+    "    return f' M: {col.mean():.3} , STD: {col.std():.3}, Count: {col.count()}'\n",
+    "\n",
+    "# When Visible\n",
+    "temp = sf[grouping & sf.visible]\n",
+    "print(f'Tracking Error when visible:' + get_stats(temp['TE']))\n",
+    "\n",
+    "# When Occluded\n",
+    "temp = sf[grouping & ~sf.visible]\n",
+    "print(f'Tracking Error when occluded:' + get_stats(temp['TE']))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Calculate Succesfull Trackings (TE)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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>set</th>\n",
+       "      <th>evalmode</th>\n",
+       "      <th>tracked_pos</th>\n",
+       "      <th>tracked_neg</th>\n",
+       "      <th>tracked_pos_pro</th>\n",
+       "      <th>tracked_neg_pro</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>control</td>\n",
+       "      <td>control</td>\n",
+       "      <td>1</td>\n",
+       "      <td>30</td>\n",
+       "      <td>0.032258</td>\n",
+       "      <td>0.967742</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       set evalmode  tracked_pos  tracked_neg  tracked_pos_pro  \\\n",
+       "0  control  control            1           30         0.032258   \n",
+       "\n",
+       "   tracked_neg_pro  \n",
+       "0         0.967742  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# succesfull trackings: In the last visible moment of the target, the slot was less than 10% away from the target\n",
+    "# determine last visible frame numeric\n",
+    "grouping_factors = ['net','set','evalmode','scene','slot']\n",
+    "ff = sf[sf.visible].groupby(grouping_factors).max()\n",
+    "ff.rename(columns = {'frame':'last_visible'}, inplace = True)\n",
+    "ff = ff[['last_visible']]\n",
+    "\n",
+    "# add dummy variable to sf\n",
+    "sf = sf.merge(ff, on=grouping_factors, how='left')\n",
+    "sf['last_visible'] = (sf['last_visible'] == sf['frame'])\n",
+    "\n",
+    "# same for first bound frame\n",
+    "ff = sf[sf.visible & sf.bound & sf.inimage].groupby(grouping_factors).min()\n",
+    "ff.rename(columns = {'frame':'first_visible'}, inplace = True)\n",
+    "ff = ff[['first_visible']]\n",
+    "\n",
+    "# add dummy variable to sf\n",
+    "sf = sf.merge(ff, on=grouping_factors, how='left')\n",
+    "\n",
+    "# extract the trials where the target was last visible and threshold the TE\n",
+    "ff = sf[sf['last_visible']] \n",
+    "ff['tracked_pos'] = (ff['TE'] < 10)\n",
+    "ff['tracked_neg'] = (ff['TE'] >= 10)\n",
+    "\n",
+    "# fill NaN with 0\n",
+    "sf = sf.merge(ff[grouping_factors + ['tracked_pos', 'tracked_neg']], on=grouping_factors, how='left')\n",
+    "sf['tracked_pos'].fillna(False, inplace=True)\n",
+    "sf['tracked_neg'].fillna(False, inplace=True)\n",
+    "\n",
+    "# Aggreagte over all scenes\n",
+    "temp = sf[(sf['frame']== 15) & ~sf.occluder & sf.control & (sf.first_visible < 20)]\n",
+    "temp = temp.groupby(['set', 'evalmode']).sum()\n",
+    "temp = temp[['tracked_pos', 'tracked_neg']]\n",
+    "temp = temp.reset_index()\n",
+    "\n",
+    "temp['tracked_pos_pro'] = temp['tracked_pos'] / (temp['tracked_pos'] + temp['tracked_neg'])\n",
+    "temp['tracked_neg_pro'] = temp['tracked_neg'] / (temp['tracked_pos'] + temp['tracked_neg'])\n",
+    "\n",
+    "temp"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Mostly Tracked stats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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NoT4th4/khOQ8NWrUZPDgYQwdOoijR3OIj4/niSdGEBsbS+PGF3HXXbdQsWJFmjRpymWXXc7OnTuYOnUySUkvUq9e/QLn6tDhWhYvXkRsrOekr1mzZi3i4uLp1+9Bhg59ssC+jl268/prE5n73kzOOqsy551/Iam7U7iqfRc2b/qZ4X97EJ/Py3Wd/0JCYt3jz4tP+AMdOt7I9ORJPPq3UWzbuoknh/bB6/VyUbOWtG13HS6Xi45dujNiSB8qV65CrCcuJO+hiISGy/+/1EUxxgwBKltrh+dv9wRaWWt7FXFsJWAp8KK1dnoAr18f2JyWlonXWzDHrl1biY+vF9BvwklycnIYNWoEV1/dgSuvvDro50dGutn4297QBwuxzP0pTHhvR8kHimPMe+5GUlPDcdeI87jdLmrVqgJwDrCl8P5ARuxuwL91XcAJy/IZY6oB7wHrAiz14/IDFrB7t5vIyDProh2fz0fXrp1o2fIyrrrqatxuN2vXruG558YWefyECS/i8Zx8VC9Slng8MeGOcEYIpNi3A239tuOBnf4HGGMSgI+BRcCjwYYoasTu9XrJyTnzlnWdN+9TALzevPegceNmvP76zGKPL/wenWn/GEr5ohF7aPiN2IsUSLEvBEYaYzzAQaA7cHwaxhgTAcwDZllrnz69uCIicrpKLHZr7Q5jzDDgcyAamGKtXWGMmQ+MAOoCFwORxpib8p+2ylrbs7RCi4hI8QK6jt1aOxOYWeixYxdZr0J3sIqIlBkqZBERhyk3xV6jWjQeT0zIf9WoVvo/ySczM5MhQ/IW4NqzJ5VBg/qf9Pjk5FdJTn4VgDZtLjml15z9n2n89OP6U3quvy8+X8CrL/3zlJ6bunsXA/r89bQziEhwys1aMZHRFfh1dPeSDwxSg2GzgdL9gdn+y+vGxnoYP35Sqb4ewI8b1nFB42al/joiUvaUm2IPtzVrVjF9ejIREZGkpOygUaMLefzxv/P666+xevVK9u/fT2xsLE89NYaaNWtx/fUdMKYRaWl7qFUrtsDyuv36Pcg778zj11838vzz48jKyiIjI50777yHbt1uOuG1vV4vN998IxMmvMTZZ9cjKyuL22+/iTfffLfIBcWWLvmEzZssU5LG8+jgp5g+9UWqVIlh+29b6PfoCOxP37Hsi085cuQwEZGRPPzIcBLrnM3361cz840kvF4fsZ44+jwyrMB5Z7z+Evv2ZdCn31A2b/6Ff0/7F0eyDxMTU437ej1G7bgEtmz+hdeSxgFwdr1zS+cPQ0ROqtxMxZQF3323ngEDBjJz5myOHMnm7bffZNu2LbzyylTeeutd4uLi+fjjjwDYu3cvt99+F9OmzWTQoCcKLK97zLx573P33fczZcobTJr0Ci+/XPRI3u1206nT9XzySd65Fy/+jMsvb1PsKpFtr7yWc8419Ow9iLr1GgBQt14Dxk96A09cAqtXLmPYyOd5dsJUml/8Jz5dMIejR7P516RnePDhJ3h2QjJ1zz6HL5d8fPycs2dNIyN9D737DcXr9ZL8ynj6PDKM0WMn07nrzSS/+hwAr7z4LLfe3ovRYydTOy7x9N5wETklGrEHoVmz5px9dn0AOnbszNy57/Hoo39j3rw5bNu2lR9++I46df5w/PiSltzt23cA33zzNTNmvM6mTRvJyjpU7LGdO3dlwIA+9Oz5EAsWfEivXg8Hlb3heRcAcNZZlenzyDC+/moRu3ZuZ/3alZxd/1x+27aZGjVrUe+chgDccvsDQN4c+7q1Kziwfy9PjUkiIiKC33Zs47+7djLhn8OPnz8r6xAH9u9jb0YaTZrmfS5wRbvrWLJoflA5ReT0qdiD4L9Mb96dsi4efbQvt956G1dd1Z6ICHeB5WwrVKh40vONGPEEMTFVad26Le3bX8vChR8Xe2xCQiLx8QksWbKI9PS0oNZpB4iKzhvdp+3ZzeiRj3JNx240bd6KatVrsnXzRiIiInHxv/V8Dx3MJOtw3g/59njiuPmv9zMt+QWefPpFvF4vteMSj6/l7s3NZd++DHBR4Pfv9nu/ROT3o6mYIKxfv5bU1N14vV4WLPiQiy5qSvPmLejW7Sbq1j2br776Eq/3xGUQilted+XKFfTs+RBt27Zj+fKvAIpdhhegS5cbmDhxPNddV/I67REREXiLONevm34iLr4Ona7vQYNz/8iqFV/i9eaSkFiX/fv3suO3LQB88P5/WPTJXAAS69SjXfvOVKhQkYUfzyGxTl0yM/cfv+pmyecf8fILo4mJqUasJ45vVy8H4OsvPysxp4iEnoo9CLGxHp5++knuuKMHHo+H667rzMaNP3PXXbfQr9+DGHPB8fXY/fkvr+vvvvseoHfvntxxRw/WrfuWhITEIp9/zJVXXsX+/ftKXKcd4KJmLZk6+Xl+tt8XeLzJRS3xeX38bcC9DH/8QRLrnE3q7l1ER0fTu99QXnnpWYYM7MmO7Vvo2u22As+994EBvPf2DPbv30f/x55k5vQkhgzsydLFn/BA/s9T7d1/CO+9PZ1hg3vx313F/15EpPSUuGxvKatPgMv21qgWTWR00R8Wno6c7CNk7Cv5csc1a1YxdepkXnqp+J9+VJp8Ph/Lly9jzpzZ/POfzxd7nJbtlbJKy/aGTiiW7S0T8sq3dK83L8smTZrAsmVfFLgGvl+/BzlwoOA3issFbdp1pv21N/zeEUWkjCg3I3YJjEbsUlZpxB46JY3YNccuIuIwKnYREYdRsYuIOIyKXUTEYcrNVTEx1StQMSr0S+wePprNgb1HQn5eEZFwKTfFXjEqmpv/0zvk5511SxIH+P2LfcOG71m8eBF9+vRn/vx5fPvtaoYNGxnUOU712vpDBzN59eV/8ujfRgX1PBEpHzQVEyZbtmwmIyM9LK998GAmW7dsDMtri0jpKzcj9nBbs2YVb7wxlaioKFJSdtK69RVUqlSJpUuX4PP5GD/+BX78cQOvvZaEz+clMbEOgwcPpWbNWrz00kRWrvwGt9tF27bt6NHjr0yZ8gpZWVlMn56Mx1MbgFWrVpCc/ApJSVMBmD9/Hhs2fM+gQUNKzLdt21bGjh3NgQP7cbmjufO+vpzb8I98tfQzPnj/LdxuN57aCfTuP5Q3pr5IRnoaz4/9u0btIg6kEXsQNmz4gUGDhjBlygzefXcW1avXIDl5Bg0bnsecObMZN+4ZxowZz/Tpb9GkSVMmTBjLrl0pLF/+FdOnv0lS0lS2bNlMdHQ0PXs+RJs2V3D33fcfP3+LFi3ZsyeNHTu2A7BgwYd06tQ1oGyjRv2dHj1u5d//nsUd9/Rh0nMjOXo0m7ffmsrjfx/L02NfxVM7npQd27jrvn7UqFlLpS7iUCr2IDRocC5xcfFUrFiRatWqc8klrQCIi4tn2bKlXHDBhSQk5P1wiRtu+AurV68kNtZDhQoV6N37Pt5++0169+5X7A/IcLlcdOrUhY8/ns+uXbtIT08PaHneQ4cOsX37dq688moAGp7fiCpVqpKy8zeat/gTTw3vz5szXqXVZVccX29dRJxLxR6EyMiCM1f+67P7fAWX6/X5fOTm5hIZGcnkydPo2bM3+/bt46GH7mXbtq3Fvkbnzl357LNPWLhwAR07lrw8b1Gv7f/6d93Xl0cGjaRylRj+NekZvvzi04DOKSLll4o9RBo1asyGDd8dX3Z37tx3ufjiFvz880/07duLpk2b07fvAOrXb8C2bVuLXaM9Pj4Bj6c2c+bMDmh5XoDKlauQmFiHJUsWAbDx5w3s25tO3brnMLDfncTEVOOGP99G2yuvZevmjbiLeW0RcYZy8+Hp4aPZzLolqVTOGwo1atRk8OBhDB06iKNHc4iPj+eJJ0YQGxtL48YXcdddt1CxYkWaNGnKZZddzs6dO5g6dTJJSS9Sr179Aufq0OFaFi9eRGysJ+DXHzFiFOPGPcPUqZPx+tw8MvgfREZF0f2We3h21GCioytQtVp1ej38OJUrxxAbW5vRIx9j2MgJIfn9i0jZodUdy5icnBxGjRrB1Vd3OD5nHgyt7ihllVZ3DB3HrMd+JvD5fHTr1omWLS+lbdt2AKxb9y3PPz+uyOPHj38hqFG9iJwZVOxliMvl4oMPCn642bRpc6ZNmxmmRCJSHpXpD0/DPE0kpcXnQ3+0IqWnzBa72x1Bbm5OuGNIqfBy5OiJl2iKSGiU2WKvVKkKBw7sLfIabSnHfD6OHN7Pmo36EE2ktJTZOfYqVaqRkZHKf/+7HdD/2wPldrvJ3H8o3DGK5fPB1t2HWflLZrijiDhWmS12l8tFzZq1wx2j3PF4Ynhi4PvhjiEiYRRQsRtjbgOGA1HARGvty4X2NwOmAFWBL4CHrLWaIBcRCYMS59iNMXWA0UAboBnQyxjTqNBh/wf0tdaeD7iAB0KcU0REAhTIiL0DsMhamw5gjHkHuAl4Kn+7HlDJWrs8//hpwD+AQO7/j4C8u6gkdGrXqBTuCCJF0vd6aPi9jxFF7Q+k2BOBFL/tFKBVCfv/EGC+BIAaNSoHeLgEInn4teGOIFKk/NvgJXQSgE2FHwyk2N0UvCzFBXiD2H8yK4G25P1joOUGRUQCE0Feqa8samcgxb6dvPI9Jh7YWWh/wkn2n8wR4MsAjxURkf85YaR+TCA3KC0E2htjPMaYs4DuwIJjO621W4HDxpjW+Q/dCXx0GmFFROQ0lFjs1todwDDgc2AtMNNau8IYM98Yc0n+YbcDzxtjfgKqAJNKKa+IiJQg3Ouxi4hIiJXZtWJEROTUqNhFRBxGxS4i4jAqdhERh1Gxi4g4jIpdRMRhyux67CK/J2PMiJPtt9Y+9XtlETldKnaRPFp2UBxDNyiJFGKM8QCXkjfw+dpa+98wRxIJiubYRfwYY64jb+mMe4G7gfXGmOvDGkokSJqKESloNNDGWrsZwBjTAHgX+CCsqUSCoBG7SEFRx0odwFr7K/o+kXJGI3aRgrYZYwYAyfnbPYGt4YsjEjyNREQKuh/4E/ArsDn/615hTSQSJI3YRQrqb629JdwhRE6HRuwiBXU1xuiadinXNGIXKSgN+MkYswbIOvagtfa+8EUSCY6KXaSg6UU8prv4pFxRsYsUlGitHeP/gDHmmXCFETkVWlJABDDGPAvUBm4A5vrtigQus9aeH5ZgIqdAI3aRPLOBRkB7YInf4znAqLAkEjlFGrGL+DHGVLPW7gt3DpHToRG7SEHdjDHPATXyt12Az1obEcZMIkFRsYsUNAJoZ639PtxBRE6VblASKWinSl3KO43YRQpabYx5B/gEOHzsQWvtG+GLJBIcFbtIQdWAA+Qt/nWMD1CxS7mhq2JECjHGRAGGvIHP99banDBHEgmKil3EjzGmBXnXtKeR9xlUHPBna+03YQ0mEgRNxYgUNAm45ViRG2MuA14EWoU1lUgQdFWMSEFV/Efn1trlQMUw5hEJmopdpKB0Y8yNxzaMMd3Im5YRKTc0FSNSUC/gA2NMMvl3nQKXhzeSSHA0YhcpqBNwCKgHXAWkAu3CGUgkWCp2kYJ6Aa2ttQetteuBFkC/MGcSCYqKXaSgKCDbbzsb/QQlKWc0xy5S0BxgkTFmFnmF3h14P6yJRIKkG5RECjHG3ARcCRwFvrDWzglvIpHgqNhFRBxGc+wiIg6jYhcRcRgVu0gQjDHnGGNmhzuHyMmo2EWCU4+8JX1Fyix9eCpnNGNMFeB14DzAC6wGHgS6AMOBaPLuRB0ErAAsUIe8q2WuC0dmkZJoxC5nuj8DMdbaZkDL/MfOBZ4BOltrm5N3N+q75K3y2BPYpFKXskw3KMmZ7kvgGWPMYuBTYCJwDZAAfGbM8VkXL9AwDPlEgqYRu5zRrLWbySvsMUBVYCEQAXxmrW127BdwGfB92IKKBEHFLmc0Y0xv8ubYP7HWPg58DNQErjXG/DH/mM7AeqASkEPeejIiZZY+PJUzmjGmMjAVuIi8D0m3AfcBHYBh5K3JngMMsNYuNcbUBD4HjgCXWmv1DSRljopdRMRhNBUjIuIwKnYREYdRsYuIOIyKXUTEYVTsIiIOo2IXEXEYFbuIiMOo2EVEHOb/ATOom5O2pBONAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "temp = af[af.index == 'OVERALL']\n",
+    "temp['mostly_tracked'] = temp['mostly_tracked'] / temp['num_unique_objects']\n",
+    "temp['partially_tracked'] = temp['partially_tracked'] / temp['num_unique_objects']\n",
+    "temp['mostly_lost'] = temp['mostly_lost'] / temp['num_unique_objects']\n",
+    "g = temp[['mostly_tracked', 'partially_tracked', 'mostly_lost','set']].set_index(['set']).groupby(['set']).mean().plot(kind='bar', stacked=True);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# MOTA "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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>idf1</th>\n",
+       "      <th>idp</th>\n",
+       "      <th>idr</th>\n",
+       "      <th>recall</th>\n",
+       "      <th>precision</th>\n",
+       "      <th>num_unique_objects</th>\n",
+       "      <th>mostly_tracked</th>\n",
+       "      <th>partially_tracked</th>\n",
+       "      <th>mostly_lost</th>\n",
+       "      <th>num_false_positives</th>\n",
+       "      <th>num_misses</th>\n",
+       "      <th>num_switches</th>\n",
+       "      <th>num_fragmentations</th>\n",
+       "      <th>mota</th>\n",
+       "      <th>motp</th>\n",
+       "      <th>num_transfer</th>\n",
+       "      <th>num_ascend</th>\n",
+       "      <th>num_migrate</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>set</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>control</th>\n",
+       "      <td>0.462403</td>\n",
+       "      <td>0.327823</td>\n",
+       "      <td>0.78443</td>\n",
+       "      <td>0.867853</td>\n",
+       "      <td>0.362687</td>\n",
+       "      <td>86.0</td>\n",
+       "      <td>54.0</td>\n",
+       "      <td>31.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>8482.0</td>\n",
+       "      <td>735.0</td>\n",
+       "      <td>76.0</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>-0.670802</td>\n",
+       "      <td>0.082861</td>\n",
+       "      <td>16.0</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             idf1       idp      idr    recall  precision  num_unique_objects  \\\n",
+       "set                                                                             \n",
+       "control  0.462403  0.327823  0.78443  0.867853   0.362687                86.0   \n",
+       "\n",
+       "         mostly_tracked  partially_tracked  mostly_lost  num_false_positives  \\\n",
+       "set                                                                            \n",
+       "control            54.0               31.0          1.0               8482.0   \n",
+       "\n",
+       "         num_misses  num_switches  num_fragmentations      mota      motp  \\\n",
+       "set                                                                         \n",
+       "control       735.0          76.0                21.0 -0.670802  0.082861   \n",
+       "\n",
+       "         num_transfer  num_ascend  num_migrate  \n",
+       "set                                             \n",
+       "control          16.0        58.0          0.0  "
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "af[af.index == 'OVERALL'].groupby(['set']).mean()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "loci23",
+   "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": "3.9.16"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}