{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "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": 2,
   "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": 18,
   "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.rcParams['text.usetex'] = True\n",
    "    plt.rcParams.update({\"font.size\": 18})\n",
    "    plt.figure(figsize=figsize)\n",
    "    bp_dict = plt.boxplot(scores, medianprops={\"linewidth\": 1.5, \"color\": color}, widths=0.68)\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 - 0.335, y - 0.0007, f\"{y:.4f}\", verticalalignment=\"top\", horizontalalignment=\"center\", color=color) # draw below, centered\n",
    "        else:\n",
    "            plt.text(x - 0.335, y + 0.0005, f\"{y:.4f}\", verticalalignment=\"bottom\", horizontalalignment=\"center\", color=color) # draw above, centered\n",
    "\n",
    "    plt.xticks(np.arange(1, len(configs) + 1), [f\"k={config['clusters']}\\ns={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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "beaver_01_scores = [get_aucs(*read_results(**config)) for config in CONFIGS_BEAVER_01]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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": 20,
   "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": 22,
   "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": 23,
   "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": 24,
   "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": 25,
   "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_tnr_scores, savefile=\"plots/approach3/boxplot_random_sessions_tnr95.pdf\", figsize=(8, 6), color=\"red\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright &copy; 2023 Felix Kleinsteuber and Computer Vision Group, Friedrich Schiller University Jena"
   ]
  }
 ],
 "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"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}