{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "from tqdm.notebook import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import cv2 as cv\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "from py.PlotUtils import get_percentiles\n",
    "from py.Dataset import Dataset\n",
    "from py.ImageUtils import display_images\n",
    "from py.Labels import LABELS\n",
    "\n",
    "DIR = '/home/kleinsteuber/vscode/ResizedSessions_NoBackup' # dataset directory\n",
    "CONFIGS_BEAVER_01 = [\n",
    "    {\n",
    "        \"session\": \"beaver_01\",\n",
    "        \"clusters\": 1024,\n",
    "        \"step\": 20,\n",
    "        \"random\": True,\n",
    "    },\n",
    "    {\n",
    "        \"session\": \"beaver_01\",\n",
    "        \"clusters\": 2048,\n",
    "        \"step\": 20,\n",
    "        \"random\": True,\n",
    "    },\n",
    "    {\n",
    "        \"session\": \"beaver_01\",\n",
    "        \"clusters\": 4096,\n",
    "        \"step\": 20,\n",
    "        \"random\": True,\n",
    "    },\n",
    "    {\n",
    "        \"session\": \"beaver_01\",\n",
    "        \"clusters\": 512,\n",
    "        \"random\": True,\n",
    "    },\n",
    "    {\n",
    "        \"session\": \"beaver_01\",\n",
    "        \"clusters\": 1024,\n",
    "        \"random\": True,\n",
    "    },\n",
    "    {\n",
    "        \"session\": \"beaver_01\",\n",
    "        \"clusters\": 2048,\n",
    "        \"random\": True,\n",
    "    },\n",
    "    {\n",
    "        \"session\": \"beaver_01\",\n",
    "        \"clusters\": 4096,\n",
    "        \"random\": True,\n",
    "    },\n",
    "    {\n",
    "        \"session\": \"beaver_01\",\n",
    "        \"clusters\": 8192,\n",
    "        \"random\": True,\n",
    "    },\n",
    "    {\n",
    "        \"session\": \"beaver_01\",\n",
    "        \"clusters\": 1024,\n",
    "        \"step\": 40,\n",
    "        \"random\": True,\n",
    "    },\n",
    "    {\n",
    "        \"session\": \"beaver_01\",\n",
    "        \"clusters\": 2048,\n",
    "        \"step\": 40,\n",
    "        \"random\": True,\n",
    "    },\n",
    "    {\n",
    "        \"session\": \"beaver_01\",\n",
    "        \"clusters\": 4096,\n",
    "        \"step\": 40,\n",
    "        \"random\": True,\n",
    "    },\n",
    "]\n",
    "\n",
    "CONFIGS_COMPARE_SESSIONS = [\n",
    "    {\n",
    "        \"session\": \"beaver_01\",\n",
    "        \"clusters\": 4096,\n",
    "        \"step\": 20,\n",
    "        \"random\": True,\n",
    "    },\n",
    "    {\n",
    "        \"session\": \"marten_01\",\n",
    "        \"clusters\": 4096,\n",
    "        \"step\": 20,\n",
    "        \"random\": True,\n",
    "    },\n",
    "    {\n",
    "        \"session\": \"gfox_03\",\n",
    "        \"clusters\": 4096,\n",
    "        \"step\": 20,\n",
    "        \"random\": True,\n",
    "    },\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 3 sessions\n"
     ]
    }
   ],
   "source": [
    "ds = Dataset(DIR)\n",
    "\n",
    "def read_results(clusters: int, session: str, step: int = 30, size: int = None, random: bool = False, motion: bool = False):\n",
    "    # size = step by default\n",
    "    if size is None:\n",
    "        size = step\n",
    "    \n",
    "    # Get filename suffix\n",
    "    suffix = \"\"\n",
    "    if random:\n",
    "        suffix += \"_random\"\n",
    "    if motion:\n",
    "        suffix += \"_motion\"\n",
    "\n",
    "    # correct name (e.g. from beaver_01 to Beaver_01)\n",
    "    session = ds.create_session(session).name\n",
    "\n",
    "    # Read CSV\n",
    "    test_labels = []\n",
    "    test_df = []\n",
    "    with open(f\"./bow_train_NoBackup/{session}/bow_eval_{step}_{size}_{clusters}{suffix}.csv\", \"r\") as f:\n",
    "        for line in f:\n",
    "            entries = line.split(\",\")\n",
    "            # Get label\n",
    "            filename = entries[0]\n",
    "            img_number = int(filename[-9:-4])\n",
    "            if img_number > LABELS[session][\"max\"] or img_number in LABELS[session][\"not_annotated\"]:\n",
    "                continue\n",
    "            is_normal = (img_number in LABELS[session][\"normal\"])\n",
    "            test_labels.append(1 if is_normal else -1)\n",
    "            # Get decision function values\n",
    "            test_df.append([float(df) for df in entries[1:]])\n",
    "    test_labels = np.array(test_labels)\n",
    "    test_df = np.array(test_df)\n",
    "    print(f\"{len(test_df)} test results with {len(test_labels)} labels\")\n",
    "    return test_labels, test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_aucs(test_labels, test_dfs):\n",
    "    aucs = []\n",
    "    for i in range(test_dfs.shape[1]):\n",
    "        fpr, tpr, thresholds = roc_curve(test_labels, test_dfs[:,i])\n",
    "        aucs.append(auc(fpr, tpr))\n",
    "    return aucs\n",
    "\n",
    "def plot_scores(configs, scores, savefile=None, label_below=[], color=\"darkgoldenrod\", figsize=(15, 10)):\n",
    "    plt.figure(figsize=figsize)\n",
    "    bp_dict = plt.boxplot(scores, medianprops={\"linewidth\": 1.5, \"color\": color})\n",
    "\n",
    "    for i, line in enumerate(bp_dict['medians']):\n",
    "        # get position data for median line\n",
    "        x, y = line.get_xydata()[1] # top of median line\n",
    "        # overlay median value\n",
    "        if i in label_below:\n",
    "            plt.text(x - 0.25, y - 0.0007, f\"{y:.4f}\"[1:], verticalalignment=\"top\", horizontalalignment=\"center\", color=color) # draw below, centered\n",
    "        else:\n",
    "            plt.text(x - 0.25, y + 0.0005, f\"{y:.4f}\"[1:], verticalalignment=\"bottom\", horizontalalignment=\"center\", color=color) # draw above, centered\n",
    "\n",
    "    plt.xticks(np.arange(1, len(configs) + 1), [f\"k={config['clusters']},s={config['step'] if 'step' in config else 30}\" for config in configs])\n",
    "    plt.grid(True, ls=\"dotted\", lw=0.5)\n",
    "    # plt.ylim((0.7, 0.9))\n",
    "    # plt.legend()\n",
    "    if savefile is not None:\n",
    "        plt.savefig(savefile, bbox_inches=\"tight\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare configurations on Beaver_01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n",
      "695 test results with 695 labels\n",
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n",
      "695 test results with 695 labels\n",
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n",
      "695 test results with 695 labels\n",
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n",
      "695 test results with 695 labels\n",
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n",
      "695 test results with 695 labels\n",
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n",
      "695 test results with 695 labels\n",
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n",
      "695 test results with 695 labels\n",
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n",
      "695 test results with 695 labels\n",
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n",
      "695 test results with 695 labels\n",
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n",
      "695 test results with 695 labels\n",
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n",
      "695 test results with 695 labels\n"
     ]
    }
   ],
   "source": [
    "beaver_01_scores = [get_aucs(*read_results(**config)) for config in CONFIGS_BEAVER_01]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scores(CONFIGS_BEAVER_01, beaver_01_scores, savefile=\"plots/approach3/boxplot_random.pdf\", label_below=[2,8,9])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n",
      "695 test results with 695 labels\n",
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n",
      "695 test results with 695 labels\n",
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n",
      "695 test results with 695 labels\n",
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n",
      "695 test results with 695 labels\n",
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n",
      "695 test results with 695 labels\n",
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n",
      "695 test results with 695 labels\n",
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n",
      "695 test results with 695 labels\n",
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n",
      "695 test results with 695 labels\n",
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n",
      "695 test results with 695 labels\n",
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n",
      "695 test results with 695 labels\n",
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n",
      "695 test results with 695 labels\n"
     ]
    }
   ],
   "source": [
    "def get_elimination_rate_vals(test_labels, test_dfs, percentile=0.95):\n",
    "    percentiles = []\n",
    "    for i in range(test_dfs.shape[1]):\n",
    "        fpr, tpr, thresholds = roc_curve(test_labels, test_dfs[:,i])\n",
    "        percentiles.append(get_percentiles(fpr, tpr, thresholds, percentiles=[percentile], verbose = False)[0])\n",
    "    return percentiles\n",
    "\n",
    "beaver_01_tnr_scores = [get_elimination_rate_vals(*read_results(**config)) for config in CONFIGS_BEAVER_01]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scores(CONFIGS_BEAVER_01, beaver_01_tnr_scores, savefile=\"plots/approach3/boxplot_random_tnr95.pdf\", label_below=[2], color=\"red\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare sessions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n",
      "695 test results with 695 labels\n",
      "Session 'Marten_01' at folder: /home/kleinsteuber/vscode/ResizedSessions_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Marten_01\n",
      "Loaded scans.\n",
      "3105 test results with 3105 labels\n",
      "Session 'GFox_03' at folder: /home/kleinsteuber/vscode/ResizedSessions_NoBackup/VIELAAS_Spring_Session03-VIELAAS_GFox_03\n",
      "Loaded scans.\n",
      "2738 test results with 2738 labels\n"
     ]
    }
   ],
   "source": [
    "session_scores = [get_aucs(*read_results(**config)) for config in CONFIGS_COMPARE_SESSIONS]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scores(CONFIGS_COMPARE_SESSIONS, session_scores, savefile=\"plots/approach3/boxplot_random_sessions.pdf\", figsize=(8, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Session 'Beaver_01' at folder: /home/kleinsteuber/vscode/ResizedSessions_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Beaver_01\n",
      "Loaded scans.\n",
      "695 test results with 695 labels\n",
      "Session 'Marten_01' at folder: /home/kleinsteuber/vscode/ResizedSessions_NoBackup/VIELAAS_Spring_Session01-VIELAAS_Marten_01\n",
      "Loaded scans.\n",
      "3105 test results with 3105 labels\n",
      "Session 'GFox_03' at folder: /home/kleinsteuber/vscode/ResizedSessions_NoBackup/VIELAAS_Spring_Session03-VIELAAS_GFox_03\n",
      "Loaded scans.\n",
      "2738 test results with 2738 labels\n"
     ]
    }
   ],
   "source": [
    "session_tnr_scores = [get_elimination_rate_vals(*read_results(**config)) for config in CONFIGS_COMPARE_SESSIONS]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZmYm6BEkYZUpCU6b8qRmLiIhETM045rT5R0JTpiQ0ZcqfmnHM6cf0EpoyJaEpU/7UjGNOYzESmjIloSlT/tSMY845F3UJkjDKlISmTPlTM465np6eqEuQhFGmJDRlyp+accxpLEZCU6YkNGXKn5pxzBWLxahLkIRRpiQ0ZcqfmnHM1ev1qEuQhFGmJDRlyp+acczp1GQSmjIloSlT/tpqxmZ2mZk9bGb7zWzbMyzzn81sn5k9ZGafDVtm9+rt7Y26BEkYZUpCU6b8LftxxsxWALcAvwEcAHaZ2Xbn3L4Fy5wNXAe80jl32Mye16mCu02xWFTQJShlSkJTpvy18834QmC/c+5R51wVuAO4atEy1wC3OOcOAzjnfh62zO6lsRgJTZmS0JQpf+004zOAxxZMH2jOW+gc4Bwz+wcz+yczuyxUgd0uldKwvoSlTEloypS/UKPuaeBs4BKgH/iWmb3cOXdk4UJmthnYDNDf30+5XKZarVKtVgHI5/PUajUqlUprul6vUyqVAMjlcsDR3ej7+vpIpVLMzs4Cjd+6pdPp1nQmkyGTybQO1ZbJZMhms8zMzOCco6enh2w2S7FYpF6vk06n6e3tbU2nUilyuRzlcplardaarlQqzM3NYWYUCgUqlUprHQqFQtB1MjNKpVKi1imJr9PJtE7lcpmpqalErVMSX6eTaZ16enqYnp5O1Dp14nU6FlvuMGZmdjFwg3Pu0ub0dQDOuZsWLPPXwHedc59qTn8N2Oac2/VMjzs4OOgmJiaO+dwCR44cYeXKlVGXIQmiTEloylR7zGy3c25wqdva2bawCzjbzM40swxwNbB90TJ30fhWjJmtobHZ+tETLViOqtVqUZcgCaNMSWjKlL9lm7FzrgZcC9wHTAKfd849ZGY3mtmVzcXuAw6a2T5gB/DHzrmDnSq6m2gsRkJTpiQ0ZcrfspupO0WbqdtTLpf1kwEJSpmS0JSp9vhuppYIze8sIBKKMiWhKVP+1Ixjbm5uLuoSJGGUKQlNmfKnZhxzZhZ1CZIwypSEpkz5UzOOuUKhEHUJkjDKlISmTPlTM445jcVIaMqUhKZM+VMzjrn5o7+IhKJMSWjKlD81YxERkYipGcecxmIkNGVKQlOm/IU6UYQch07veRjVgVzk5FCtVnWABglKmfKnZhyB42mWZqbmKkFpfE9CU6b8aTO1iIhIxNSMRbpMPp+PugRJGGXKn5qxSJfR6e4kNGXKn5rxiXrb2+DMM2HDhsZl797G/C99Cc47rzFvcBB27mzM37sXLr4YXvayxu2f+9zRx3rLW+ClL4X16+Ed7wAd51U6SAdokNCUKX9qxj4+9KFGk927t9F8AV7zGvje9xrzbrsN3vnOxvxcDj7zGXjoIfjyl2HrVjhypHHbW94CP/gBfP/7UCrBrbc+22siIiIR0t7UoS38vd3sLMz/jOmcc47O/+Vfhuc9D554AlauhCuuOHrbhRfCgQPPSqnSnTS+J6EpU/70zdjHyEhjk/N73gMLN9P83d/BuefC617X+Ha82P33Q7UKL37xU+fPzcHtt8Nll3W2bulq9Xo96hIkYZQpf2rGJ+qmmxqblnftgkOH4AMfOHrb61/fuO2uu+BP//Sp9/vpT+F3fxc+9SlILfrz/+Efwq//Ovzar3W8fOlepVIp6hIkYZQpf2rGJ+r00xuboLNZePvbG992F/v1X4dHH4Wpqcb09HTj2/LoKLziFU9d9n3va2y2/vCHO1+7iIjEisaMT9RPf9poyM7BXXfx0a9+lfeY8WLgkeYiFwB/D/Q/97n0APc2p2/+7d9+ykMNA+8AXgOUV6x42lN16vCZq1at4tChQx15bImvXC4XdQmSMMqUP30zPl5XXAE/+UljD+iXv7xxmZrivZUKzjn2v//9uHXrcOefz/97xSs449vfxjlH9fbbeU06zUfPPx83f9mzB+cct65Ywa+edRal+fnvex/OOZxzzM7Otq6Hvhw+fDjqv6aIiAAW1XGPBwcH3cTERCTPHdzWrXzj5pu55FWvCv7Qc3Nz9PT0BH9cgI9+85ts1XGvu87U1BRr1qyJugxJEGWqPWa22zk3uNRt+mYsIiISMX0zDqRTZ1eanZ3t2G/4dEao7tTJTEl3Uqbao2/GJ7HU4p8/iXhSpiQ0Zcqf/oIxNzs7G3UJkjDKlISmTPlTMxYREYmYfmccUKd+D9wpq1atiroEiUA2m426BEkYZcqfmnEgx7MjVKebtnbKkmNJp/XfXsJSpvxpM3UEjufAHE888cRxH8xD5Fg0viehKVP+1IxFREQipmYcc5lMJuoSJGGUKQlNmfLXVjM2s8vM7GEz229m25a4/W1m9oSZ7W1e3hm+1O6kkEtoypSEpkz5W7YZm9kK4BbgcmAdMGRm65ZY9HPOuQ3Ny62B6+xaMzMzUZcgCaNMSWjKlL92vhlfCOx3zj3qnKsCdwBXdbYsERGR7tFOMz4DeGzB9IHmvMXeaGYPmNmdZvaCINWJNv9IcMqUhKZM+Qv147C/B8adcxUz+z3gb4BXL17IzDYDmwH6+/spl8tUq1Wq1SoA+XyeWq1GpVJpTdfrdUqlEnD0BNbFYhGAvr4+UqlUa7f6bDZLOp1uTWcyGTKZTGsTSiaTIZvNMjMzg3OOnp4estksxWKRer1OOp2mt7e3NZ1KpcjlcpTLZWq1Wmu6UqkwNzeHmVEoFKhUKq11KBQKQdfJzCiVSolapyS+TifTOs3MzFCtVhO1Tkl8nU6mdcpkMkxPTydqnTrxOh3LsmdtMrOLgRucc5c2p68DcM7d9AzLrwAOOeeec6zHTdpZmzrl4MGDrF69OuoyJEGUKQlNmWqP71mbdgFnm9mZZpYBrga2L3qC0xdMXglMnmix8lQ6iIeEpkxJaMqUv2U3UzvnamZ2LXAfsAK4zTn3kJndCEw457YD/9XMrgRqwCHgbR2suav09PREXYIkjDIloSlT/pbdTN0p2kzdnnK5TG9vb9RlSIIoUxKaMtUe383UEqH5nQFEQlGmJDRlyp+acczV6/WoS5CEUaYkNGXKn5pxzOnUZBKaMiWhKVP+1IxjTuMwEpoyJaEpU/7UjGNOYzESmjIloSlT/tSMY05jMRKaMiWhKVP+1IxjLpXSSyRhKVMSmjLlT3/BmJs/3qlIKMqUhKZM+VMzjrlyuRx1CZIwypSEpkz5UzOOuVqtFnUJkjDKlISmTPlTM445jcVIaMqUhKZM+dNfMOY0FiOhKVMSmjLlT8045uZPZC0SijIloSlT/tSMY25ubi7qEiRhlCkJTZnyp2Ycc2YWdQmSMMqUhKZM+VMzjrlCoRB1CZIwypSEpkz5UzOOOY3FSGjKlISmTPlTM465arUadQmSMMqUhKZM+VMzFhERiZiaccxpLEZCU6YkNGXKn5pxzGnzj4SmTEloypQ/NeOYU8glNGVKQlOm/KkZi4iIREzNOOby+XzUJUjCKFMSmjLlT8045nRqMglNmZLQlCl/asYxpx/TS2jKlISmTPlTMxYREYmYmnFMjY+Ps379ek477TTWr1/P+Ph41CVJQmh8T0JTpvyloy5Anm58fJyRkRHGxsa44IIL2LNnD8PDwwAMDQ1FXJ2c7Or1etQlSMIoU/70zTiGRkdHGRsbY9OmTdRqNTZt2sTY2Bijo6NRlyYJUCqVoi5BEkaZ8qdmHEOTk5Ns3LjxKfM2btzI5ORkRBWJiEgnqRnH0MDAADt37gQgl8sBsHPnTgYGBqIsSxJiPlMioShT/tpqxmZ2mZk9bGb7zWzbMZZ7o5k5MxsMV2L3GRkZYXh4mB07djA3N8eOHTsYHh5mZGQk6tJERKQDlt2By8xWALcAvwEcAHaZ2Xbn3L5Fy/0S8G7gu50otJsMDQ3xne98h8svv5xKpUI2m+Waa67RzlsSRLFY1DcZCUqZ8tfON+MLgf3OuUedc1XgDuCqJZb7M+ADQDlgfV1pfHycu+++m3vvvZef/OQn3Hvvvdx99936eZOISEK104zPAB5bMH2gOa/FzH4FeIFz7u6AtXWthXtTn3LKKdqbWoLq6+uLugRJGGXKn/fvjM0sBXwYeFsby24GNgP09/dTLpepVqut02/l83lqtVrr0Gr5fJ56vd7abX5+M0ixWAQaAUilUszOzgKQzWZJp9Ot6UwmQyaTYWZmpjWdzWaZmZnBOUdPTw/ZbJZisUi9XiedTtPb29uaTqVS5HI5yuUytVqtNV2pVJibm8PMKBQKVCqV1joUCgXvdZqcnOTcc89lamoKgFQqxbnnnsvk5CTT09Mn5Tol8XU6WdfpyJEjlEqlRK1TEl+nk2md0uk009PTiVqnTrxOx2LOuWMvYHYxcINz7tLm9HUAzrmbmtPPAR4BZpp3eT5wCLjSOTfxTI87ODjoJiae8eautn79ej72sY+xadMmpqamWLNmDTt27GDLli08+OCDUZcnJ7n5TImEoky1x8x2O+eW3MG5nc3Uu4CzzexMM8sAVwPb5290zj3pnFvjnFvrnFsL/BPLNGI5Nu1NLSLSXZbdTO2cq5nZtcB9wArgNufcQ2Z2IzDhnNt+7EeQ4zW/1/SWLVuYnJxkYGCA0dFR7U0tQSy3uUzkeClT/pbdTN0p2kzdnlKppJ0jJChlSkJTptrju5laIjQ/+C8SijIloSlT/tSMRUREIqZmHHOZTCbqEiRhlCkJTZnyp2Yccwq5hKZMSWjKlD8145ib/5G7SCjKlISmTPlTMxYREYmYmnHMafOPhKZMSWjKlD8145jTj+klNGVKQlOm/KkZx5zGYiQ0ZUpCU6b8qRnHXFRHSJPkUqYkNGXKn5pxzPX09ERdgiSMMiWhKVP+1IxjTmMxEpoyJaEpU/7UjGNu/kTVIqEoUxKaMuVPzTjm6vV61CVIwihTEpoy5U/NOObS6WVPOS1yXJQpCU2Z8qdmHHO9vb1RlyAJo0xJaMqUPzXjmNNYjISmTEloypQ/NeOY01iMhKZMSWjKlD8145hLpfQSSVjKlISmTPnTXzDmcrlc1CVIwihTEpoy5U/NOObK5XLUJUjCKFMSmjLlT8045mq1WtQlSMIoUxKaMuVPzTjmNBYjoSlTEpoy5U9/wZjTWIyEpkxJaMqUPzXjmKtUKlGXIAmjTEloypQ/NeOYm5ubi7oESRhlSkJTpvypGcecmUVdgiSMMiWhKVP+1IxjrlAoRF2CJIwyJaEpU/7UjGNOYzESmjIloSlT/tSMY65arUZdgiSMMiWhKVP+1IxFREQipmYccxqLkdCUKQlNmfLXVjM2s8vM7GEz229m25a4/ffN7PtmttfMdprZuvCldidt/pHQlCkJTZnyt2wzNrMVwC3A5cA6YGiJZvtZ59zLnXMbgA8CHw5daLdSyCU0ZUpCU6b8tfPN+EJgv3PuUedcFbgDuGrhAs656QWTecCFK1FERCTZ0m0scwbw2ILpA8BFixcys3cBfwRkgFcHqU7I5/NRlyAJo0xJaMqUv3aacVucc7cAt5jZm4H3Am9dvIyZbQY2A/T391Mul6lWq61NHPl8nlqt1vrNWj6fp16vUyqVgKMHIy8WiwD09fWRSqWYnZ0FIJvNkk6nW9OZTIZMJsPMzExrOpvNMjMzg3OOnp4estksxWKRer1OOp2mt7e3NZ1KpcjlcpTLZWq1Wmu6UqkwNzeHmVEoFKhUKq11KBQKQddp/tRkSVqnJL5OJ9M6HTx4kFwul6h1SuLrdDKtk5kxNzeXqHXqxOt0LObcsbcom9nFwA3OuUub09cBOOdueoblU8Bh59xzjvW4g4ODbmJi4pjPLTA1NcWaNWuiLkMSRJmS0JSp9pjZbufc4FK3tTNmvAs428zONLMMcDWwfdETnL1g8nXAj060WBERkW6z7GZq51zNzK4F7gNWALc55x4ysxuBCefcduBaM3stMAccZolN1HJiNBYjoSlTEpoy5a+tMWPn3D3APYvmXb/g+rsD1yVN9Xo96hIkYZQpCU2Z8qcjcMXc/M4BIqEoUxKaMuVPzVhERCRiasYxN7+7vEgoypSEpkz5UzMWERGJmJpxzM3/gFwkFGVKQlOm/KkZi4iIREzNOOb6+vqiLkESRpmS0JQpf2rGMZdK6SWSsJQpCU2Z8qe/YMzNH2RcJBRlSkJTpvypGYuIiERMzTjmljvtlsjxUqYkNGXKn5pxzKXTwU45LQIoUxKeMuVPzTjmNBYjoSlTEpoy5U/NWKRLjI+Ps379ek477TTWr1/P+Ph41CWJSJO2LcRcJpOJugRJgPHxcUZGRhgbG+O8887jgQceYHh4GIChoaGIq5OTnd6n/Ombccwp5BLC6OgoY2NjbNq0iXw+z6ZNmxgbG2N0dDTq0iQB9D7lT8045mZmZqIuQRJgcnKSjRs3AkcztXHjRiYnJ6MsSxJC71P+1IxFusDAwAA7d+58yrydO3cyMDAQUUUispCaccxp84+EMDIywvDwMDt27MDM2LFjB8PDw4yMjERdmiSA3qf8aQeumNOP6SWE+Z20tmzZwuTkJAMDA4yOjmrnLQlC71P+1IxjbmZmRkGXIIaGhhgaGuLgwYOsXr066nIkQfQ+5U+bqWPOORd1CZIwypSEpkz5UzOOuZ6enqhLkIRRpiQ0ZcqfmnHMadOPhKZMSWjKlD8145grFotRlyAJo0xJaMqUPzXjmKvX61GXIAmjTEloypQ/NeOY06nJJDRlSkJTpvypGcdcb29v1CVIQsyftWn16tU6a5MEpfcpf/o4E3PFYlFBF28Lz9q0bt069u3bp7M2STB6n/Knb8Yxp7EYCWHhWZtWrFihszZJUHqf8qdmHHOplF6irvHxj8NLXgJmMDV1dP7hw/D618N558GFF8KDDx697ctfhpe+tHG/97//6HznYGQEzjkHBgZ4zb59rbM2zWdKZ22SUPQ+5U9/wZjL5XJRlyDPlle+Er76VXjRi546/8//HDZsgAcegM98Bt797sb8X/wC3vUuuPde2LcPxscb/wJ8+tPw2GPwgx/A5CR7zjmnddam+UzprE0Sit6n/LXVjM3sMjN72Mz2m9m2JW7/IzPbZ2YPmNnXzOxFSz2OHL9yuRx1CfJsueACWLv26fP37YNXv7px/dxz4V/+BX72M7j//sY34rPOgkwGrr4avvSlxnJ/9Vdw/fXQ/MbyB//jf7TO2vTv//7vOmuTBKX3KX/LNmMzWwHcAlwOrAOGzGzdosX2AIPOufOAO4EPhi60W9VqtahLkKidfz588YuN6/ffDz/+MRw4AI8/Di94wdHl+vsb8wAeeQQ+9zkYHITLL2docJDR0VG2bNnC85//fLZs2aKzNkkwep/y187e1BcC+51zjwKY2R3AVcC++QWcczsWLP9PwO+ELLKbaSxG2LatsWl6wwZ4+csb36BXrDj2fSoV6O2FiYlGI3/HOxj69rcZGhri0KFDnHrqqc9K6dId9D7lr51mfAbw2ILpA8BFx1h+GLjXpyg5SmMxwimnwKc+1bjuHJx5ZmPTdKnUGBeed+AAnHFG43p/P7zhDY3rr389vP3trcWUKQlNmfIX9HfGZvY7wCDwqme4fTOwGaC/v59yuUy1WqVarQKQz+ep1WpUKpXWdL1ep1QqAUdf8PnjoPb19ZFKpZidnQUaBytPp9Ot6UwmQyaTYWZmpjWdzWaZmZnBOUdPTw/ZbJZisUi9XiedTtPb29uaTqVS5HI5yuUytVqtNV2pVJibm8PMKBQKVCqV1joUCoWg6zQ3N8fKlSsTtU5JfJ1CrtNz6nWmDx+mr1CgXC7zi4MHsXye3MqV1D/xCVIXXcTs3ByF886j5+GHObJ7N/XTT+fU8XGKn/wk5akpcr/5m6Tvu4/KW95C/etfJ3fWWdRKJer1Ok888QSFQkGvk9Yp2Do55xK3Tp14nY7FljsPpZldDNzgnLu0OX1d849/06LlXgt8DHiVc+7nx3xQYHBw0E1MTCy3WNebmppizZo1UZchnXTFFXDrrXDnnfDBD8K//Rs873lH5//jP8Jb39r4ydPLXgZjY7BqVeO+99wDW7fyyI9+xG3Anzcf8jnA3wIvBGaA3wceOMHydK5aWY7ep9pjZrudc4NL3tZGM04DPwReAzwO7ALe7Jx7aMEyF9DYcesy59yP2ilKzbg9Bw8eZPXq1VGXIRE49dRTOXz4cNRlHJdVq1Zx6NChqMuQZ5nep9pzrGa87GZq51zNzK4F7gNWALc55x4ysxuBCefcduBDQAH4gpkB/Ktz7spga9DFCoVC1CVIFLZu5YuHD3PJq5Yc8fEyv9muEz76zW925HEl3vQ+5a+tMWPn3D3APYvmXb/g+msD1yVNlUpFJ+6WoOrO6Wg/EpTep/wtu5m6U7SZuj0ai+leZtaR8dpOZqpTNUu86X2qPV6bqUUkOs1hn5PGqvkdy0TkuKgZx5zGYrpXp75h6turhKb3KX8aOoq5+d+4iYjEld6n/KkZx5xCLiJxp/cpf9pMLZIAxzu2fDzLa5O2SOepGcdcPp+PugQ5CRxPwyyVSvT19XWwGuk2ep/yp83UMadTk0loypSEpkz5UzOOufmDkouEokxJaMqUPzVjERGRiKkZx5zGYiSU8fFx1q9fz2mnncb69esZHx+PuiRJCL1P+dMOXDFXr9ejLkESYHx8nJGREcbGxrjgggvYs2cPw8PDAAwNDUVcnZzs9D7lT9+MY27+xNUiPkZHRxkbG2PTpk3UajU2bdrE2NgYo6OjUZcmCaD3KX9qxiJdYHJyko0bNz5l3saNG5mcnIyoIhFZSM045nK5XNQlSAIMDAywc+dO4Gimdu7cycDAQJRlSULofcqfmrFIFxgZGWF4eJgdO3YwNzfHjh07GB4eZmRkJOrSRATtwBV7xWJRnzrF2/xOWlu2bGFycpKBgQFGR0e185YEofcpf2rGIl1iaGiIoaEhnQheJIa0mTrmdAxhCU2ZktCUKX9qxjGXSuklkrCUKQlNmfKnv2DMzc7ORl2CJIwyJaEpU/7UjEVERCKmZhxz2Ww26hIkYZQpCU2Z8qdmHHPptHZ4l7CUKQlNmfKnZhxzGouR0JQpCU2Z8qdmLCIiEjE145jLZDJRlyAJo0xJaMqUPzXjmFPIJTRlSkJTpvyZcy6aJzZ7AvhxJE9+clkDTEVdhCSKMiWhKVPteZFz7rlL3RBZM5b2mNmEc24w6jokOZQpCU2Z8qfN1CIiIhFTMxYREYmYmnH8fSLqAiRxlCkJTZnypDFjERGRiOmbsYiISMTUjI/BzNaa2YMe99++8P5mdqqZfcXMftT8d1Vz/ioz+zsze8DM7jez9Qvus9LM7jSzH5jZpJld7LdWrcfNmdndzcd9yMzev+C2rJl9zsz2m9l3zWxtiOcUZUqZCk+ZSkam1Iw7xMzeAMwsmr0N+Jpz7mzga81pgP8O7HXOnQf8F+DmBfe5Gfiyc+5c4HxgMmCZf9F83AuAV5rZ5c35w8Bh59xLgI8AHwj4nHKClCkJTZmKEeecLs9wAdYCDzavnwXsAf5jG/crADuBdfP3b85/GDi9ef104OHm9buBX1uw3CPAacBzgH+mObZ/jOf7beBB4HvAt05wXW8Grmlevw+4uHk9TePH/MesQRdlSplSppSpE7/ovFdtMLOXAncAbwPKZrb3GRa9xDl3BPgz4C+B4qLbT3PO/bR5/d9oBBka4XwD8G0zuxB4EdAP/AJ4AviUmZ0P7Abe7ZxbfIqU64FLnXOPm9nKBTV/bpk659dvJfCfOPpJ9wzgMQDnXM3MngRWoyPsBKNMKVOhKVMneaai/jQQ5wuNT5w/A34ArGvzPhuA7Qvuv/AT55FFyx5u/nsK8ClgL3A7sKv5OINADbioudzNwJ8t8Zx/DXwFuAZYfZzrmAbuBbYumPcg0L9g+hFgTdSvRxIuypQypUwpU0td9M14eU8C/wpsBPYt90kOuBgYNLN/oRGg55nZN5xzlwA/M7PTnXM/NbPTgZ8DOOemgbcDmJnR2OTzKJADDjjnvtt8/Ds5On7T4pz7fTO7CHgdsNvM/gONY8W284nzE8CPnHMfXXD748ALgANmlqaxGergMzyWHD9lSpkKTZk62TMV9aeBOF9ofmIE8jTGVt58IvdfMP0hYFvz+jbgg83rK4FM8/o1wGcW3OfbwEub128APtS8fi1wbfP6ixcsvwvY0GZ9/xP430Bq0fx3AX/dvH418PmoX4ukXJQpZUqZUqaWXM+oC4jzhafuGLGyGaArT+T+zenVNPZO/BHwVeDU5vyLgR/S2HHii8CqBffZAEwADwB3zd8GfBwYal7/IvD95n/Im2ljJwYaYz2Oxl6Pe5uXdzZv6wW+AOwH7gfOivq1SMpFmVKmlCllaqmLjsB1kjKz/wO8wTlXjboWSQZlSkJTptqnZiwiIhIxHfRDREQkYmrGIiIiEVMzFhERiZiasYiISMTUjEVERCKmZiwiIhIxNWMREZGI/X9nfc4J8y/ebQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_scores(CONFIGS_COMPARE_SESSIONS, session_tnr_scores, savefile=\"plots/approach3/boxplot_random_sessions_tnr95.pdf\", figsize=(8, 6), color=\"red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6.9 64-bit",
   "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": "3.6.9"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
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