{
 "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": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '../../../out/pretrained/adept_ablations/lambda/results/adept_level1_ablation_lambda.run311/trialframe.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m/Users/fredericbecker/Documents/Master/Masterarbeit/code/local/loci_public/evaluation/adept_ablation/lambda/evaluation_loci_looped_abl1.ipynb Cell 3\u001b[0m line \u001b[0;36m1\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/fredericbecker/Documents/Master/Masterarbeit/code/local/loci_public/evaluation/adept_ablation/lambda/evaluation_loci_looped_abl1.ipynb#W2sZmlsZQ%3D%3D?line=12'>13</a>\u001b[0m \u001b[39mfor\u001b[39;00m net \u001b[39min\u001b[39;00m nets:\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/fredericbecker/Documents/Master/Masterarbeit/code/local/loci_public/evaluation/adept_ablation/lambda/evaluation_loci_looped_abl1.ipynb#W2sZmlsZQ%3D%3D?line=13'>14</a>\u001b[0m     path \u001b[39m=\u001b[39m os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mjoin(root_path, \u001b[39m'\u001b[39m\u001b[39mresults\u001b[39m\u001b[39m'\u001b[39m, net)\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/fredericbecker/Documents/Master/Masterarbeit/code/local/loci_public/evaluation/adept_ablation/lambda/evaluation_loci_looped_abl1.ipynb#W2sZmlsZQ%3D%3D?line=14'>15</a>\u001b[0m     \u001b[39mwith\u001b[39;00m \u001b[39mopen\u001b[39;49m(os\u001b[39m.\u001b[39;49mpath\u001b[39m.\u001b[39;49mjoin(path, \u001b[39m'\u001b[39;49m\u001b[39mtrialframe.csv\u001b[39;49m\u001b[39m'\u001b[39;49m), \u001b[39m'\u001b[39;49m\u001b[39mrb\u001b[39;49m\u001b[39m'\u001b[39;49m) \u001b[39mas\u001b[39;00m f:\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/fredericbecker/Documents/Master/Masterarbeit/code/local/loci_public/evaluation/adept_ablation/lambda/evaluation_loci_looped_abl1.ipynb#W2sZmlsZQ%3D%3D?line=15'>16</a>\u001b[0m         tf_temp \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mread_csv(f, index_col\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m)\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/fredericbecker/Documents/Master/Masterarbeit/code/local/loci_public/evaluation/adept_ablation/lambda/evaluation_loci_looped_abl1.ipynb#W2sZmlsZQ%3D%3D?line=16'>17</a>\u001b[0m     tf_temp[\u001b[39m'\u001b[39m\u001b[39mnet\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m net\n",
      "File \u001b[0;32m~/miniconda3/envs/loci23/lib/python3.9/site-packages/IPython/core/interactiveshell.py:282\u001b[0m, in \u001b[0;36m_modified_open\u001b[0;34m(file, *args, **kwargs)\u001b[0m\n\u001b[1;32m    275\u001b[0m \u001b[39mif\u001b[39;00m file \u001b[39min\u001b[39;00m {\u001b[39m0\u001b[39m, \u001b[39m1\u001b[39m, \u001b[39m2\u001b[39m}:\n\u001b[1;32m    276\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m    277\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mIPython won\u001b[39m\u001b[39m'\u001b[39m\u001b[39mt let you open fd=\u001b[39m\u001b[39m{\u001b[39;00mfile\u001b[39m}\u001b[39;00m\u001b[39m by default \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    278\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mas it is likely to crash IPython. If you know what you are doing, \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    279\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39myou can use builtins\u001b[39m\u001b[39m'\u001b[39m\u001b[39m open.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    280\u001b[0m     )\n\u001b[0;32m--> 282\u001b[0m \u001b[39mreturn\u001b[39;00m io_open(file, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '../../../out/pretrained/adept_ablations/lambda/results/adept_level1_ablation_lambda.run311/trialframe.csv'"
     ]
    }
   ],
   "source": [
    "# setting path to results folder\n",
    "root_path = '../../../out/pretrained/adept_ablations/lambda'\n",
    "\n",
    "# list all folders in root path that don't stat with a dot\n",
    "nets = ['adept_level1_ablation_lambda.run311']\n",
    "\n",
    "# read pickle file\n",
    "tf = pd.DataFrame()\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, 'results')\n",
    "    with open(os.path.join(path, 'trialframe.csv'), 'rb') as f:\n",
    "        tf_temp = pd.read_csv(f, index_col=0)\n",
    "    tf_temp['net'] = net\n",
    "    tf = pd.concat([tf,tf_temp])\n",
    "\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['vanishing'] = sf['vanishing'].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",
    "tf['control'] = [('control' in set) for set in tf['set']]\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": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tracking Error when visible: M: 11.6 , STD: 13.5, Count: 2281\n",
      "Tracking Error when occluded: M: 7.1 , STD: 4.02, Count: 564\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": null,
   "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>open</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       set evalmode  tracked_pos  tracked_neg  tracked_pos_pro  \\\n",
       "0  control     open            0           54              0.0   \n",
       "\n",
       "   tracked_neg_pro  \n",
       "0              1.0  "
      ]
     },
     "execution_count": 5,
     "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 & sf.bound & sf.inimage].groupby(grouping_factors).max()\n",
    "ff.rename(columns = {'frame':'last_visible'}, inplace = True)\n",
    "sf = sf.merge(ff[['last_visible']], on=grouping_factors, how='left')\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",
    "sf = sf.merge(ff[['first_visible']], on=grouping_factors, how='left')\n",
    "\n",
    "# add dummy variable to sf\n",
    "sf['last_visible'] = (sf['last_visible'] == sf['frame'])\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']== 1) & ~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": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "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": null,
   "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>0.722042</td>\n",
       "      <td>0.879472</td>\n",
       "      <td>0.612416</td>\n",
       "      <td>0.658158</td>\n",
       "      <td>0.945161</td>\n",
       "      <td>86.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>187.0</td>\n",
       "      <td>1674.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>0.614662</td>\n",
       "      <td>0.033957</td>\n",
       "      <td>5.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>surprise</th>\n",
       "      <td>0.738122</td>\n",
       "      <td>0.923004</td>\n",
       "      <td>0.614946</td>\n",
       "      <td>0.646612</td>\n",
       "      <td>0.970532</td>\n",
       "      <td>33.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>558.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.623813</td>\n",
       "      <td>0.031067</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              idf1       idp       idr    recall  precision  \\\n",
       "set                                                           \n",
       "control   0.722042  0.879472  0.612416  0.658158   0.945161   \n",
       "surprise  0.738122  0.923004  0.614946  0.646612   0.970532   \n",
       "\n",
       "          num_unique_objects  mostly_tracked  partially_tracked  mostly_lost  \\\n",
       "set                                                                            \n",
       "control                 86.0            35.0               22.0         29.0   \n",
       "surprise                33.0            11.0                9.0         13.0   \n",
       "\n",
       "          num_false_positives  num_misses  num_switches  num_fragmentations  \\\n",
       "set                                                                           \n",
       "control                 187.0      1674.0          26.0                38.0   \n",
       "surprise                 31.0       558.0           5.0                 6.0   \n",
       "\n",
       "              mota      motp  num_transfer  num_ascend  num_migrate  \n",
       "set                                                                  \n",
       "control   0.614662  0.033957           5.0        21.0          0.0  \n",
       "surprise  0.623813  0.031067           0.0         5.0          0.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "af[af.index == 'OVERALL'].groupby(['set']).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gate Openings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percept gate openings when visible: M: 1.02 , STD: 0.681, Count: 2281\n",
      "Percept gate openings when occluded: M: 0.216 , STD: 0.48, Count: 564\n"
     ]
    }
   ],
   "source": [
    "grouping = (sf.inimage & sf.bound & ~sf.occluder & sf.control)\n",
    "temp = sf[grouping & sf.visible]\n",
    "print(f'Percept gate openings when visible:' + get_stats(temp['alpha_pos'] + temp['alpha_ges']))\n",
    "temp = sf[grouping & ~sf.visible]\n",
    "print(f'Percept gate openings when occluded:' + get_stats(temp['alpha_pos'] + temp['alpha_ges']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "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
}