df_results_graphs.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import pickle\n",
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.metrics import roc_curve, auc, roc_auc_score\n",
    "from itertools import cycle, islice\n",
    "import sklearn.metrics as metrics\n",
    "import seaborn as sns\n",
    "import pprint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pf_datapath = 'logs/08-Mar-2021_18_56_12'\n",
    "pf_test_metadata = 'saved/DeepFakeDetectModel/Test/video_classi_0.8_0.6_0.3'\n",
    "\n",
    "mri_datapath = 'logs/08-Mar-2021_07_53_22' # MRI-GAN from logs/23-Feb-2021_21_22_21, tau=0.3\n",
    "mri_test_metadata = 'saved/DeepFakeDetectModel/Test/video_classi_0.7_0.6_0.3'\n",
    "\n",
    "#mri_datapath = 'logs/07-Mar-2021_19_12_06' # MRI_GAN from 04-Mar-2021_09_12_48 (tau=0.7)\n",
    "#mri_test_metadata = 'saved/DeepFakeDetectModel/Test/video_classi_0.55_0.6_0.5'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pf_train_acc_path = os.path.join(pf_datapath, 'latest_epoch/DeepFakeDetectModel/Train/model_acc.log')\n",
    "pf_valid_acc_path = os.path.join(pf_datapath, 'latest_epoch/DeepFakeDetectModel/Validation/model_acc.log')\n",
    "pf_train_loss_path = os.path.join(pf_datapath, 'latest_epoch/DeepFakeDetectModel/Train/model_losses.log')\n",
    "pf_valid_loss_path = os.path.join(pf_datapath, 'latest_epoch/DeepFakeDetectModel/Validation/model_losses.log')\n",
    "\n",
    "mri_train_acc_path = os.path.join(mri_datapath, 'latest_epoch/DeepFakeDetectModel/Train/model_acc.log')\n",
    "mri_valid_acc_path = os.path.join(mri_datapath, 'latest_epoch/DeepFakeDetectModel/Validation/model_acc.log')\n",
    "mri_train_loss_path = os.path.join(mri_datapath, 'latest_epoch/DeepFakeDetectModel/Train/model_losses.log')\n",
    "mri_valid_loss_path = os.path.join(mri_datapath, 'latest_epoch/DeepFakeDetectModel/Validation/model_losses.log')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(pf_train_acc_path, \"rb\") as f:\n",
    "    pf_train_acc = pickle.load(f)\n",
    "with open(pf_valid_acc_path, \"rb\") as f:\n",
    "    pf_valid_acc = pickle.load(f)\n",
    "with open(pf_train_loss_path, \"rb\") as f:\n",
    "    pf_train_loss = pickle.load(f)\n",
    "with open(pf_valid_loss_path, \"rb\") as f:\n",
    "    pf_valid_loss = pickle.load(f)\n",
    "    \n",
    "with open(mri_train_acc_path, \"rb\") as f:\n",
    "    mri_train_acc = pickle.load(f)\n",
    "with open(mri_valid_acc_path, \"rb\") as f:\n",
    "    mri_valid_acc = pickle.load(f)\n",
    "with open(mri_train_loss_path, \"rb\") as f:\n",
    "    mri_train_loss = pickle.load(f)\n",
    "with open(mri_valid_loss_path, \"rb\") as f:\n",
    "    mri_valid_loss = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "epoch = len(pf_train_acc)\n",
    "fig = plt.figure(figsize=(8, 8))\n",
    "\n",
    "plt.xlabel('Epoch', fontsize=13)\n",
    "plt.ylabel('Loss',fontsize=13)\n",
    "plt.xlim(0, epoch + 2)\n",
    "\n",
    "plt.plot(pf_train_loss, marker='o', label='Plain frames : Train loss')\n",
    "plt.plot(pf_valid_loss, marker='o', label='Plain frames : Val loss')\n",
    "\n",
    "plt.plot(mri_train_loss, marker='s', label='MRI : Train loss')\n",
    "plt.plot(mri_valid_loss, marker='s', label='MRI : Val loss')\n",
    "\n",
    "plt.gca().legend(loc='center left', bbox_to_anchor=(1, 0.5),fontsize=13)\n",
    "plt.grid(True)\n",
    "plt.savefig('Acc_plot.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "epoch = len(pf_train_acc)\n",
    "fig = plt.figure(figsize=(8, 8))\n",
    "\n",
    "plt.xlabel('Epoch', fontsize=13)\n",
    "plt.ylabel('Accuracy',fontsize=13)\n",
    "matplotlib.rc('xtick', labelsize=13) \n",
    "matplotlib.rc('ytick', labelsize=13)\n",
    "\n",
    "plt.xlim(0, epoch + 2)\n",
    "plt.plot(pf_train_acc, marker='o', label='Plain frames : Train acc')\n",
    "plt.plot(pf_valid_acc, marker='o', label='Plain frames : Val acc')\n",
    "\n",
    "plt.plot(mri_train_acc, marker='s', label='MRI : Train acc')\n",
    "plt.plot(mri_valid_acc, marker='s', label='MRI : Val acc')\n",
    "\n",
    "plt.gca().legend(loc='center left', bbox_to_anchor=(1, 0.5),fontsize=13)\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pf_all_samples_pred_csv = os.path.join(pf_datapath,pf_test_metadata ,'all_samples_pred.csv')\n",
    "pf_final_df = pd.read_csv(pf_all_samples_pred_csv)\n",
    "\n",
    "pf_ground_truth = pf_final_df['ground_truth'].to_numpy()\n",
    "pf_probability = pf_final_df['fake_prob'].to_numpy()\n",
    "\n",
    "pf_fpr = dict()\n",
    "pf_tpr = dict()\n",
    "pf_roc_auc = dict()\n",
    "for i in range(2):\n",
    "    pf_fpr[i], pf_tpr[i], _ = roc_curve(pf_ground_truth, pf_probability)\n",
    "    pf_roc_auc[i] = auc(pf_fpr[i], pf_tpr[i])\n",
    "\n",
    "\n",
    "mri_all_samples_pred_csv = os.path.join(mri_datapath, mri_test_metadata,'all_samples_pred.csv')\n",
    "mri_final_df = pd.read_csv(mri_all_samples_pred_csv)\n",
    "\n",
    "mri_ground_truth = mri_final_df['ground_truth'].to_numpy()\n",
    "mri_probability = mri_final_df['fake_prob'].to_numpy()\n",
    "\n",
    "mri_fpr = dict()\n",
    "mri_tpr = dict()\n",
    "mri_roc_auc = dict()\n",
    "for i in range(2):\n",
    "    mri_fpr[i], mri_tpr[i], _ = roc_curve(mri_ground_truth, mri_probability)\n",
    "    mri_roc_auc[i] = auc(mri_fpr[i], mri_tpr[i])\n",
    "\n",
    "#fig = plt.figure()\n",
    "fig, ax = plt.subplots()\n",
    "plt.plot(pf_fpr[1], pf_tpr[1], label='Plain-frames method')\n",
    "plt.plot(mri_fpr[1], mri_tpr[1], label='MRI-based method')\n",
    "\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.xlabel('False Positive Rate',fontsize=18)\n",
    "plt.ylabel('True Positive Rate',fontsize=18)\n",
    "#plt.gca().legend(loc='center left', bbox_to_anchor=(1, 0.5),fontsize=13)\n",
    "plt.gca().legend(fontsize=18)\n",
    "\n",
    "\n",
    "# We change the fontsize of minor ticks label \n",
    "ax.tick_params(axis='both', which='major', labelsize=15)\n",
    "ax.tick_params(axis='both', which='minor', labelsize=15)\n",
    "\n",
    "\n",
    "mri_roc_auc = round(roc_auc_score(mri_ground_truth, mri_probability),2)\n",
    "pf_roc_auc = round(roc_auc_score(pf_ground_truth, pf_probability),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RcyVNi4i+knqTpLztn2ldoKPPSVr7pj592bIegi2BHl3LO+a83mn3lvWbffWCIZn9SeoLTAW2iIhnC8qnA4dGxN8y2j4CvBgRx7ZXp0Wl5yQ/K/jzdEn907L2z5Y2s6qW15xkREwj2SNdcDG8pMEkWeSEdpohaSNgR+CPpYy/0kFyPNByUerDJEehbiLjA5lZdZPK20pUDwyXNEhSHTASGBsRUzLaHA08ERHPldJBpYPkkUDLwE4G3gDmAj+s8DjMrEJqalTWVqIRwF3A08A7QBfgEABJQyXNKKwsqSdwKCVmkVDhAzcR8U7Bnz+h9aSsmXVCeV5hGBFNwKnpVvzaGGBMUdlsYOXF6aPiS6VJ2gE4DFgjIr4taStgxYhof/kUM6taHek67HJUdHdb0veBvwFzgJazbgM4p5LjMLPKyXlOMneVziTPBL4REeMkHZqWvQB03IuxzWyJVHsmWekguWZEtFzL1XLu1HySyVYz64SqPUhW+uj2REnbFZVtR3KRuZl1Qt7dXjznAndK+h3QTdIpwEnAURUeh5lVSLVnkpU+BeivkmYCPyE5R3JXkovM16zkOMyscqo8RlYuSKaXC20OvBoR307L9iFZ9GJ14PpKjcXMKseZZAkkHUiy2kZXICQdCewG7A1cDPyuEuMws8qr8hhZsQM3ZwI/A3oDw4E/AD2BdSLi3IhYeA19M7MOoFJBciDJfSRmAb8nWcftRxExtUL9m9kyUu13S6zUnGSXiGgGiIh5khrSpdbNrJPrQPGuLJUKkiukdypr0b3oectNesysk+lIWWE5KhUkn+DzdSQBnip6HhTcxczMOo8qj5GVCZIRsUsl+jGzjseZpJlZhiqPkQ6SZpYvZ5JmZhmqPEY6SJpZvpxJmpllcJA0M8tQ5THSQdLM8lXtmWSlVyY3s+VMniuTS+oiaZSkjyQ1SrpNUr+M+v0l/VnSJ5IaJD0rKXM9WwdJM8tVzgtcnA7sC2wLrJ2WjW5nHD2AfwLzgPWBvsBQYEZWB97dNrNc5by3PQw4JyImJX3pNOB1SQMjYkpR3R+QBMbjIuKztOzFRXXgTNLMclUjlbUtiqQ+wABgfEtZREwEGoBN22iyK/AScGW6u/1fSScvcvylflAzs0qSNEzSuIJtWFGVuvSxeNnFaQWvFeoHfAN4DlgDOAQ4Q9LQrHF4d9vMclXu7nZE1AP1GVVa7mjQp6i8L0k22Vb9dyKi5XYx4yTdQDKnOaa9TpxJmlmu8jpwExHTgDeBLQv6GkySRU5oo8mzJMsyLvRWWf04SJpZrmpU3laiemC4pEGS6oCRwNg2DtoAXAesIun49NShzUiObt+eOf6Sh2JmVoacTwEaAdwFPA28A3QhmWtE0lBJC07viYg3SO7QeiTJ7vitwNkRcXNWB56TNLNc5XkKUEQ0AaemW/FrYyiaa4yIh4EtFqcPB0kzy5Wo7ssSHSTNLFeLMb/YITlImlmuqn2BCwdJM8tVlcdIB0kzy1cplxh2ZA6SZparKo+RDpJmli/PSZqZZajyGOkgaWb56rRzkpKyVt9YICKKly8yM1ugukNkdibZrWKjMLNOq9POSUbEDys5EDOzjshzkmaWq2q/LLGkpdIkrSppjKT3JTUVbnkP0MyqW85LpeWu1PUkfw+sBfwImAnsAzwOnJjPsMyss8jzvtuVUOru9m7AJhHxoaTmiPiHpOdJFq28NL/hmVm160hZYTlKDZLdgI/SP8+WtGJEvClpg5zGZWadRLXPSZYaJF8ludnOeJLbMZ4haTrwQV4DM7POYXnJJM8Auhf8+SagFvCJ5GaWqbpDZIlBMiIeLPjzM8B6uY3IzDqVTntZYiFJa7b3WkS8u/SGY2adTZXHyJJ3t9+m/Rt4d1lKYzGzTqja5yRLPU9yEDC4YNsRGAscns+wzKyzyPM8SUldJI2S9JGkRkm3SerXTt1dJIWkGQXb44vqo9Q5yTeKit6Q9APgQWB0Ke9hZsunnOckTwf2BbYFPgGuIYlJe7VTvykiei9OB0ty7fYs4ItL0N7MlgM5720PA86JiElJXzoNeF3SwIiYsjQ6KPXAzfeLilYEvgc8uTQGsTg+/PfvK92lLSUr7TB8WQ/BlsDsJ0aW1S6vOUlJfYABJOdvAxAREyU1AJsCU9po1kXSWyQXyIwHzoiI57L6KTWTPK/o+QxgHHBWie3NbDlV6oGPYpKG0fpc7PqIKFwMvC59nF7UdFrBa4X+C2wOvAj0BoYDD0raJOssnVLnJAeVUs/MrFi5mWQaELPukNCYPvYpKu8LNLTxfu8D76dPpwE/l3Qgyfzl1e11UupSaW0OVNIVpbQ3M1vaImIa8CbJJdMASBpMkkVOKPFtmlnERUGlZsIHt1P+nRLbm9lyqkblbSWqB4ZLGiSpDhgJjG3roI2k3SStI6lGUm9JZwOrkZzO2K7M3W1J2y34nNLXaB1x1yVZW9LMrF05rwI0AlgJeJpkfYn7gUMAJA0Friw45Wcz4FqgH0nsegbYIyLeyupgUXOSj6WPAfyroDyA94AzS/0kZrZ8yvOKm4hoAk5Nt+LXxgBjCp5fDFy8uH1kBsmIqAGQ9GxEbL64b25mtlysJ+kAaWblqvJLt0s+uj1W0m5FZbtJujefYZlZZ1EjlbV1FKUe3d4KeKSo7BFg66U7HDPrbGrK3DqKUq+4aSa5jGd+QVk3qn/RYTPLWQdKCstSasAeD/y4qOwEkkPoZmbtqvbd7VIzyeHAw5IOILkp2HrptktO4zKzTqIDxbuylJRJRsQEYCPgNpJrIm8hWcPtR/kNzcw6g5yvuMldyfOj6cXhvwOeAvYDHqLgmkkzs7YsF7vbkjYiWbLoUKAXSXAdEhH35zg2M+sEOlC8K0tmJinpEEmPAi8AOwNnA2sBnwKZC1WamUH1724vKpO8nuS+Ed+MiHtaCqv97mdmVjmq8jMFFzUn+b8kC1v+VdIdkr4tqSOd52lmHVy1Z5KZAS8izgW+RHKgBpKj2++QrPy7Zp4DMzPrCBaZFUbinojYn+TuiJcDHwBPS/q/vAdoZtWtU2eSxSLivYj4NTCI5DzJFXIZlZl1GpLK2jqKsu67HREB3J1uZmbt6khZYTnKCpJmZqXqQElhWRwkzSxXHenqmXI4SJpZrry7bWaWocoTSQdJM8tXTSe/4sbMbIlI5W2lvbe6SBol6SNJjZJuk9SvhHbHSgpJZy2qroOkmeUq55PJTyc5Z3tbYO20bHRWA0lfBE4Bni9p/CUPxcysDDmvJzkMGBkRkyJiOnAaMETSwIw2VwNnkqxmtujxlzoSM7Ny5LW7LakPMIDkHlwARMREkrsnbNpOm6OBWRFxc6nj94EbM8tVuedJShpGkim2qI+I+oLndenj9KKm0wpeK3y/AcBZwFcXZxwOkmaWq3JPAUoDYn1Glcb0sU9ReV+SbLLYVcC5EfHO4ozDQdLMcpXXnF5ETJP0Jsm9tp4FkDSYJIuc0EaTPYCtJJ2XPu8DbCNpz4jYsb1+HCTNLFc5r+hTDwyX9BDJXRRGAmMjYkobdb9Q9PwW4FHgwqwOHCTNLFc5n0o+AlgJeBroDtwPHAIgaShwZUT0BoiIt1uNS5oLNETEB1kdOEiaWdWKiCbg1HQrfm0MMCaj7S6l9OEgaWa58ipAZmYZqjtEOkiaWc6qPJF0kDSzfHWk+9WUw0HSzHJV7dc+O0iaWa6cSZqZZajuEOkgaWY5cyZpZpbBc5JmZhmcSZqZZajuEOkgaWY5q/JE0kHSzPJV7beUdZA0s1w5kzQzyyBnkmZm7av2TLLaT2EyM8uVM0kzy5UP3JiZZaj23W0HSTPLlYOkmVkGH902M8tQU90x0ke3zSxfKvO/kt5b6iJplKSPJDVKuk1Sv3bq7ijpGUmfSpqe/vl/FtWHM8kKmTTxdUaNOI8JE56ltraW/fY/kKOOOZ4uXbq02+azz+Zx+aW/4/kJz/HySy8wd+5cxj33cgVHbS02GNifi07Zl203GcD0xjlc+7enOO/qB2hujsx2Gw5ajQtO/BbbbTaQWXM+4/YHJ3DGpXczc/a8Co182ct5TvJ0YF9gW+AT4BpgNLBXG3VfAfYH3kyf7wjcK2mriGj3h+VMsgIaGqZz3NFHIMGFl1zGkcOO44brr+PKKy7NbDdn9hz+evut9OjRg00326JCo7VifWt7cvelRxEEB512Pedf8wA//f5O/OKoPTLb1a3Yg3v+cBQ9u3fj0LNu5OeX/oP9dtmEa88+uEIj7xjyzCSBYcDIiJgUEdOB04AhkgYWV4yIDyPijYgIksWJmkli4DpZHTiTrIDbbrmZuXPmcsFFl9K7d2/4GsycOYP6P/6Bww4/MilrQ21dHQ8++gSSuPkvY3j6qScqPHIDOHL/benRvSsHDx9N46y5PEgSAM88cncuGv3/aJw1t812ww74Kj27d+OAU69j+ow5AExtmMWtow5nyw3W4pn/vlPBT7Hs5DUnKakPMAAY31IWERMlNQCbAlPaaTcNWJEk/j0C3JfVjzPJCnj8sUf46nbbtwqGew7Zm7lz5vDMuKcz21b7gqWdwZ5fW58HnnytVTC85f7n6NVjBXbccnC77TZbb02eefntBQES4IEnX6O5uZkh22+Y65g7knIzSUnDJI0r2IYVvXVd+ji9qHxawWsLiYi+QG+SXe+7gflZ43eQrIApkyczcFDrH9Pqa6xJjx49mTJl0jIalZVqvS/255U3PmxV9tYH05g5ex7rf3HVdtt1X6Er8z5ralU2v6mZ5uZgg4H9cxlrRySVt0VEfURsXbDVF711Y/rYp6i8L9CQNaaImBsRfwV2Bo7MqlvxIJkejdpO0nfT570k9az0OCqpobGB2trahcrr6upobMj8Lq0DWKmuJ9Mb5yxUPq1xNn1r2/+rO+ntT9hk3TXo2uXzn9mWG6xF165dWKmuU/+Vb0VlbosSEdNIDsJsuaAvaTBJFjmhxOF1BdbNqlDRICnpS8ALJCnu1WnxN4A/VXIcy0Jbu80RUf2XIywnkrn+1gRkHdu+5s6nWLXvilx0yr6stnJvNhy0Gpf8bD/mz2+iaRFHxTuTGqmsrUT1wHBJgyTVASOBsRExpbiipAMkbSKpq6Qeko4CdgPGZo5/8T7uErsUuAlYGfgsLXuY5FB8uwrnJq69ujjj7vjqautobGxcqHzGjBltZpjWsUxtmE2fNjLGut49mN44u912r77xEcePuJ3vfGNzptz9C56+4UTGvfQWz732Hh9+uvDfh84qr0wyNQK4C3gaeAfoAhwCIGmopBkFddcAbieZs3wXOAL4XkTcn9VBpY9ufwXYJyKaJQUkKbOkvlmN0rmIeoDGOdX3T/DAQYOYMrn13OP777/H7NmzGDiw/Yl/6xhefePDheYe1+7fh969uvPKGx9ltr3+7+O4+b5nWecL/fho6gw+njaTd8b+kuv+9lSeQ+5YctxZiogm4NR0K35tDDCm4PllwGWL20elM8kGkknVBSStCXxQ4XFU1HY77MQTj/+LmTNnLii7f+w9dO/Rgy233mYZjsxKMfbfr7D7tuvRu9cKC8oO3H0zZs2Zx6PPLPrA29x583lx4vt8+OkMvjdkC2pqxG3/LHXKrPrlfJ5k7iodJG8HrpG0NoCkVYBLgJsrPI6KOuCg79JthW787OQf8+QTj3P7rf9H/RV/YOihP2h1WtB+39qTc355Zqu2/3rsER64fyyvvpJcEPDA/WN54P6xvPfu8nGOXUdw1R1PMvez+dw04jB23WYdjtj3K5x55O78/i+Ptjot6IVbfsYVZxy44Hltr+6ce/xeDNluA3bfdj1+fdwQrjjjQE656G9MbWh/N906lkrvbv+C5IBNy2VBHwI3AudVeBwVVVfXhyvqr+WC35zLyT85jt61tXz/kMMYduwJreo1Nc2nubm5VdmI837Fe+++u+D56aeeCMAvzzmfb++7f+5jt+Qo9t4n/ImLT92X20YdzrQZs7n0psc496rWU1ldu9bQpcvnGVBTczObrbcmP9znK/Ts3o2XJr3P0DNu4K5HXqr0R1imqv3YpNo6apdbZ1LPiJidZpCDgDci4iNJa0fE26W8RzXOSVqi/y4/X9ZDsCUw+4mRZYW7pydNL+s3u83gPh0ivFZ6d/v/JCkiPomIcWmA7A9kHl0ysyqW8+HtvFU6SDaRnAYEQHpU+z6S8ybNrBPygZvF833gK5JOldQLuAd4KiJOqfA4zKxCyr0ssaOo6IGbiJgl6dvA48BhwPMRUXzRupl1Ih0o3pUl9yAp6fttFN8E/BAY2/J6RNyY91jMbBmo8ihZiUyyvdN75gK/Sv8cJKcCmVkn05HmF8uRe5CMiEF592FmHVdHml8sh1cmN7NcVXmMrGyQTNeNPAv4OrAqBf//IsIrPZh1RlUeJSt9CtDFJHc2Gw2sBlxIMjd5TYXHYWYV4vMkF8+3SZZK+wMwP308ANi1wuMwswrxeZKLp3dEtKwtNU/SChHxkiSvF2bWSXWgeFeWSgfJyZI2TG8E/l/giPT2jsV3OzOzzqLKo2RFgqSk+vTKmt8AX5RUC5wD/BXoDhxbiXGYWeV1pPnFclQqkzwYGBYRNwNI+jQiVpa0ErBCRMzMbm5mtmxUKkgW/1MigIj4jM9vCGZmnVBHOghTjkoFyeJFN71wrtlyospjZMWC5AqSzih43qPoORFxfoXGYmaVVOVRslLnST4B7FGwPVn0fPcKjcPMKizPk8kldZE0StJHkhol3SapXzt195b0oKSPJU2V9KikHRfVR0UyyYjYpRL9mFnHk/Oc5OkkV/FtC3xCcvXeaGCvNuquRHJnhIeAGcBRwD3paYlvtddBpa+4MbPlTM63uBkGjIyISRExHTgNGCJpYHHFiBgTEXdExLSImB8RVwCzga2zOnCQNLN85RQlJfUBBgDjW8oiYiLQAGxaQvtNgVWAF7LqOUiaWa7KnZOUNEzSuIKt+FYvdelj8RV70wpea3tMyV1abwUuiIjXsup6PUkzy1W5c5IRUQ/UZ1RpTB/7FJX3Jckm2xmP1iS5jfV9wCJvBu9M0sxyldecZERMA94EtlzQlzSYJIuc0OZYkrnKR4F7IuKEiFjkOdsOkmaWr3yP3NQDwyUNklQHjATGRsSUhYYhbQA8BvwlIk4ttQMHSTPLVc6L7o4A7gKeBt4BugCHAEgaKmlGQd3hwFrAiZJmFGxDM8dfQrbZoTTOaa6uAdsC/XdZ5PSPdWCznxhZ1uzi5I/nlPWbHdSvR4e4VscHbswsVx0i0i0BB0kzy1eVR0kHSTPLVbUvuusDN2ZmGZxJmlmuvOiumVmGKo+RDpJmli9nkmZmmao7SjpImlmunEmamWWo8hjpIGlm+XImaWaWodpPJneQNLN8VXeMdJA0s3xVeYx0kDSzfHlO0swsg+ckzcyyVHeMdJA0s3xVeYx0kDSzfHlO0swsg+ckzcwyVHsm6ZXJzcwyOEiaWdWS1EXSKEkfSWqUdJukfu3UXUvSnZLekBSSDimlDwdJM8uVVN5WotOBfYFtgbXTstHt1G0G7gO+D7xdageekzSzXOV84GYYcE5ETAKQdBrwuqSBETGlsGJEvAf8Ia3XVGoHziTNLFd5ZZKS+gADgPEtZRExEWgANl1a43eQNLNcqdxNGiZpXME2rOit69LH6UXl0wpeW2Le3TazfJW5tx0R9UB9RpXG9LFPUXlfkmxyqXAmaWa5Upn/LUpETAPeBLZc0Jc0mCSLnLC0xu8gaWa5yvnodj0wXNIgSXXASGBs8UGbz8eiHpJ6kOS33dLnmXvUDpJmlqty5yRLNAK4C3gaeAfoAhwCIGmopBlF9Wen2wDgmvTPZ2V14DlJM8tXjmcARUQTcGq6Fb82BhhTVLbYo3GQNLNceYELM7MM1b7AhSJiWY/BCkgalp76YFXI31/n4wM3HU/xCbNWXfz9dTIOkmZmGRwkzcwyOEh2PJ7Pqm7+/joZH7gxM8vgTNLMLIODZJWSdLik15f1OOxzkqaUeksAqx4OkjmS9LCkuZJmSJou6VlJBy3rcdmiFX13LdtVy3pcVnkOkvn7dUT0BlYBrgNulLTOsh2SlejXEdG7YDtyWQ/IKs9BskIiYj7wJ5JLQTcHkLSfpPGSpkl6WdLQlvqS1pZ0b3oXuOmSHpW01bIZvQFIOljSc5IaJL0n6UpJK7ZTt1d6Z75/SFoxff5bSZMlfZp+t/7Hsgo4SFaIpBWAY9Onr0raA7gaOBFYGfgBcJmkndI6NcDlwBeB1YFngNsldavkuK2V6SR32usL7JhuCy2zJWl14P8B7wL7RMRM4CpgA+CrJN/nk8Df/X12fD4FKEeSHia51eVcoBb4DDg+Iq6W9HfgqYg4p6D+pUDPtnbrJNWSLEm/cUS8JOlw4KyIcDaSg6LvrsWQiHiioM4JwGER8ZX0+RTgRuBg4MqIGJmW9wM+Ar4YEW+mZTXAVOCbEfFY7h/IyuZMMn/nRURfoB9wN7BbWj6IZEXlaS0bcDiwJiQ/LEnXS3pTUgPwVtpu1UoOfjl3XkT0bdmA2nTa46P0OxnJwt/HEcAs0luXpgaljxMKvutPgW7AF3L9BLbEHCQrJCKmAkcCe0vaF3gDOLvwRxgRtRGxd9rkN8AawLYRUcfnP6YqX3iqOqXTJX8FbgIGpN/JcBb+Pk4HngcekLRSWvZG+rhu0ffdKyL+UoHh2xJwkKygiPgUuAg4H7gEOFHSjpK6SFpB0laStk6r15FkJFMl9SbJWmzZWQHoAUyNiNmSNgJOaKPefGAoSaB8WNJqEfEhyW745ZLWApDUV9L+6XdrHZiDZOX9jiRDXJNkWa1RwMfAe8DFQMuP5pdAf+ATkju/PQ40VXqwloiIGSQH3i5I75vyB5LA11bd5og4Cvgn8IikAcBRwCskgbORJIgeBPigQAfnAzdmZhmcSZqZZXCQNDPL4CBpZpbBQdLMLIODpJlZBgdJM7MMDpK2xCTtICkKnv9R0mVL8H5nSLpr6YzObMn4PMnlQLpYw9dIFthoAiYB50bEbUvp/XcAHo2Ixb5kMh3bAxFx7tIYi9nS5kxy+VG4+O9fgJslrVdYwct2mS3MQXI5ky7+eznQBdhE0nxJh0qaRLIyDZIGSLo1XVj2PUn16VJtpK+vm97eoFHSc8DWhX1Iuq7wVgeSVpV0dcuKRulCw+unu+Q7Ar9Ib4/wSlr/bEkPFLRfJV0R6T1J70v6s6SVC16fku6i/zN9nxckbVfw+u6S/pP2/XHhe5stioPkciZdzeZ4kl3vlUiC5V7AFsBqknoADwIvAYOBjYC1Sa45R1JX4C7gRZJryw8Ejsnorwa4k2Sh2m3Sxx8CjRFxAvAon98mYf123mZMOtaNgA1Jlp0bXVTnCOAnQB/gfuDPBa9dD/w+fW0t4Lz2xmtWzEFy+XFmuo7h28C+wAFAy90WT4+I6RExC/gWyVz1/0bE7HSJt18AQyV1IVmIdhDws/T114ALM/rdmiQ4HhERH6SLP0yIiHdLGbSkNYE9gZMjYmo6npNJlpxbo6DqlRHxYkQ0kawCvo6kPulr84AvAatFxNyIeKiUvs3AQXJ50rKAbP+I2C4iWo4eN/P5gr6QBMABRYsB/5NktZrVSbLKD9OA2mJyRr8D0/rTyxx3yzqahX1MLHoNklWUWsxMH1umCPYF1gWel/SSpBPLHIsth7ou6wHYMhfR+hSHN4BXI2LjtipLegfoL6lXQaAc1Fbd1JS0fl1ENLTxevMixtcSwAfyeeY7uOi1TBHxHPBdSQJ2AO6TNCEiHiylvS3fnElasb8D3dIDIbVKrCVp//T1J0gC6QhJPSV9CTgp4/3GAeOBqyT1l1QjaZOCXeX3gXbv05Pult8HXJguVLsSye79PRHxXnvtWqSLGf9AUr/0H4OpJIF5/qLamoGDpBVJs8Ovkxwk+S/JHQL/SXob3PTo+D7AZsCHwO1Afcb7Naf1ZwPPAtOAa/l8V/hiYOt01/7Fdt7mEKAxHc9/0/c4bDE+1neB/6aL5f4N+GVEPLIY7W055pPJzcwyOJM0M8vgIGlmlsFB0swsg4OkmVkGB0kzswwOkmZmGRwkzcwyOEiamWVwkDQzy/D/AYO6kjehN2ZkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "target_class_names = ['Real', 'Fake']\n",
    "cmap = plt.cm.Blues\n",
    "plot_x_label = \"Predictions\"\n",
    "plot_y_label = \"Actual\"\n",
    "plt.rcParams['font.size'] = 15\n",
    "pf_cm = metrics.confusion_matrix(pf_final_df['ground_truth'], pf_final_df['prediction'], normalize='true')\n",
    "df_confusion = pd.DataFrame(pf_cm)\n",
    "df_confusion.index = target_class_names\n",
    "df_confusion.columns = target_class_names\n",
    "df_confusion = df_confusion.round(4)\n",
    "fig = plt.figure(figsize=(5, 5))\n",
    "sns.heatmap(df_confusion, annot=True, cmap=cmap)\n",
    "plt.xlabel(plot_x_label,fontsize=13)\n",
    "plt.ylabel(plot_y_label, fontsize=13)\n",
    "fig.savefig('confusion_matrix_pf.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mri_cm = metrics.confusion_matrix(mri_final_df['ground_truth'], mri_final_df['prediction'], normalize='true')\n",
    "df_confusion = pd.DataFrame(mri_cm)\n",
    "df_confusion.index = target_class_names\n",
    "df_confusion.columns = target_class_names\n",
    "df_confusion = df_confusion.round(4)\n",
    "fig = plt.figure(figsize=(5, 5))\n",
    "sns.heatmap(df_confusion, annot=True, cmap=cmap)\n",
    "plt.xlabel(plot_x_label,fontsize=13)\n",
    "plt.ylabel(plot_y_label, fontsize=13)\n",
    "\n",
    "\n",
    "fig.savefig('confusion_matrix_mri.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ol0I+Li7261SXHvLJC+nnhE7SP2O0+WOgvX+V+m/N829ZNeK43DBGm6HP4d75tMUcrwDqikojFQusg/yE3uUx21lf0gdmtl/sHiUkNb/A6/IBiVx+R+rJj6S6pwDdqnZSmaafyZeKy9e+8uUu42gi6XxJLyc531ZqZOqD8jdeokbGhXSQ9JaZbZjW3ibyF0obBreKdpikR1PlSfNiZn+RT3LId97nPpKeN7OzctyvmdlNkm5V/O8K04qlDUvCzPpJ+lAZRrlGOFo+iF6xrfqSHpBPnMnlGGsnf7xvlmMfKu67vXzSxHXK772pL+l4+e/v9KzlasXMOsq/Z4fmuGlDSdeZ2YUx9r2R/GfvoDybaCPpHjN7MFVhIp8+NJI0WP57sn4um8rf4HounzlkANRar0v6LW1Z+ujVqhysypVk7su7R4UVVXWgquosNZaZNTezx+Tf652UuZJcK/lqDmPMLH2ESbb762Zmj8oHUZ+QD16uluXm7eWrhXxoZm+bWc98+pDqR1nqN3+0fOLlKjls3lG+msxt8tVBcj43MrNyM7tVPnB0Tpb7X1P+9/0HMzsrdf2St9T0Ge/LJ6ttXMXTO0v6l6RvzWz3OPstJjM7Tj4IeqoyVyBpKv/d9q2Z7RnR1kpm9pqk5+UrN2V6/deQdL+ZvZkaNRmLmXUxs8clDZevpNS5qk3kR+NeIel7MxsYtw/Fkrr+Gi6fEFjVd0NvSf+Vf9/WSmDf20v6Vv6abVtVfS7dRL66zStm9n6qclbcPtSKz2XqfkZ6n5bKz++OumtThasLvhWjzbcDy7aL0V4pdYhYPjFGm+8Elu2RT0MEXgHUFe0ilhd0nrgIs+VPTj+W/7H8WH9mHIU0lfSYmW1RnO5lVF8+Q7F/2vKF8n/TUPm/J/2GU0VHZVkysyZrKT/Je5e05Qvkb1YMlQ/K5nsy4OTn+fwq1daQ1P/PyrDNTpIeiROgTHONfNCzooXyF+pD5IOoUfPMNpX0jPm5gWVmfeRHDJSnPW+i/Os0JNVu1NzH+0s6LpfOL2dmf5N0v6JLZS+SP7bfTz2WZ9dXakrSNanSzNk6Xf5GQJTp8kGpofLfFZ/Lf1dUy1JDqXLAL6ry+/ib/PEwRP61jPquO9DMKr4eN8qXXq6o4jH2iaK/w5tLesj8XJ05MbNO8he4/TM8baL8e/O2/GdvZsTz1pC/2dkt134USXP5G8drpy2fKz838zvy712mcuUXp0oU5yRVqv5NRd8IWyL/eXtX/tj/I0Nzh0t6PM8bqg/Ij8SIMlH+NXhffi7h9ON3N/kbXQAg58sBPpi2eFszS5+vNZP0QO1Pqr7zu0Ul9eVaWrlGSJX3fUe5Jww1kXSzmd2cx7n4avLB+KY5bpduW0mfmdlWuW6YSm56Qj7hMqoUf7bqK8dpz1IJm1/LV4nJJ9mplfy1y+OphKucmdl68ucDm+e4aQf5JK30eRurHTO7QtId8qOrs9Vc0mAzW+GmdKqs76fy16C52F4+KJfzOXyFfe8sH4g8UPkl4HeSdJ+Z3ZDgtXNBmNm+8teKfXLctJekd1Pn4/nu+5/yFaN659nEFvLfSXkPMqhln8v15OelrOhL51ymezyo/UJTTCyRvweRr9B0OetX9++7CFsGlv3snMt0T7oqocBr+jyyWSE7G0BdEZX5lvfclzmYIuml1GOYpJ9cqn5BRakL+R3lLyjTR96VyQfN1nXOzShsdzO6UH6O0eU+lC/19JZLm7vKzPrKl17cJ9DOdWY2uIgnkftoxdGAneQDyBVNUriv+bhSK2ZhfyqfPftG4HXqo6qzy5x8kOl5+YDTCBeY1zB1orS6fED0RFXOUt5N0t8k/S/bPyTCjlrx4uYb+Rsxrzrn5lboTz35v+1/qjxvaXdJZ5vZlfKjzcpTyxdJulnSPc65FeZrMD+P1BmSzlLlEZD/MbMnXIb5Y9OZ2W6Srg2scvIBxJskve+cm5+2XSf50YHnq/JrfKGZve+ce7OKfbeXL3OUbrL86/WUc+6HiG0by2fwbyufebeVchupVygP6c8kl0XypfXuDryPHfXn+5h+LnqZ+Xlad5MfIbLccPnXK3SM9ZcfmZo+ynZl+RERF2f7B6RGLj4p/zlKN0n+vXnGpc2PlBqdu4V8ebL0k/Jukh40s+2cc1HJAxVH5+4u6YK09S9LuiyLPyHX79RbtOLr9rL8Z+J9V2EOk9R3y2by32PbpLVhku40s9Uz/H0rbuArJzyu8A37P+Tf68edc1PStttEPlkhdJNkb/lj6ups+pBq71j5m3LpnKS75Oda/zptm66SjpT//C+/CX6SfGAWACQ/OvWcCv82+e+NKr/HzWx1+dENFQ0KXTtUEytHLP+9qL0onkdV+SboYvkylb/JB6F6KDrp92T53+rzE+rP9NR+Z8rPo9hC/sZ9VKC/naSXzGyDqPPMCFcr83XSZPnXYK78b2gr+XPkHopZeSoVKH5Z/rUNcfKJUZPlX4P28kGoUCWM/SWVm9muLoe5d1MjhV9T9MiaisdAC/m/u22F9fXlK3T8lO0+i83MTpU/j61oeRLc7/KvZy9VDgxJflDPw2a2hnPuN/Nzub4lfw5c0RT55OGZ8q/PGgpXtdlS/r5Den+y+TsOkPSIou93L5VPZJ0i/751lD9eQtdSp8kfywNz7UcxpEabPqrKx/pvqcds+dd59cBzJP8ZfcTM1nPOLcxx35fKjxyNMlN+judp8p+bngonjzSVT4g42Dn3VI596Kna9bncObCsuiZdoXhClcR+cM4tiNHmN4FlreQ/I+NitFtUqftifw2sejhOu6nfsZ+04jluFzNbxzkXeu0yNsaDBw8e1e6hBOd4lQ+ALQu0N6qK7YYEtumf5T57ywfKDlQec2vKZ1HPD+z//BzbGRdoo2eW24b+/uWPJZJOybKdSyPaOL6Ex1fPQH/G5dlWptfJyc9Pmde8tvLluK6X1COPbTvIj7pN78905TZvzcVV/H3/reoYl79R8lFg28nyQerl//5F0ppZ9Gn/iM/0iTn8XV1S+09vY5KkbbNso73Cc/COl9Sqim1PDGw3WlLnPN7rbvLlePOexyKPfWY6Jn6VtHYWbRwQ8T5ekfbeXC2pXhVtNYs4xn6r6vhMa+fKiL9pkKSmWbZxlMLzvZ2V5fYDQ/tP4D0bl+E9mydp3yzaqKfw77OTtHMOfbk9oo03lcU8bPIJB6G5bhZKWjfLPnSUvxmU3sZsSTtksX1vSWOq+CxcXIzPIw8ePIr7iPgevD3tOem/ST8qi/NB+d/zitstk9S9wvr+oe+bHPoe3F75z/F6V0R7u+fYTs+IdvpXo/f43bR//yGfeNM2bVuTT8Z6KuJvWpbL3yWfkLt82/HyU1QMUIZ5Y+UDKkcqer7DzyU1yHL/ays8b+M4+UBylwzbNpNPJLhA/px5+XlfVsebpK7yAbLQ3/ClfLJppfMG+WuPA/VnpZX0x6U5vP4WeO+zOQa2kk8urfj87+QraSTy+cvz2B4Y2P9n8udQy/89NvW88sD2m8iPsAy9HoNSz3m9wrKl8nPBbqi070D5G/1nSpoTaGuRpD45/m3ryVeYCvVtqHySXsuIz8ux8tcwoW2PzrDP9qljfPljWGD7k9KeE/UIzn+s8Pf2qNTxt/zfs+Xvu6wW8Xn4q3wAPfT35TS3o/wo5tA1nJOvBrSLpLK0bZrI3+MaFbHdbEmr1NXPZap/zwX6MDCPdkLHSyEfFxfp9ema5eeo0I/1i3xcvB94zV+I2Wb7iPdyu2L+bTH/BpP/bUn/G6ZI6pBA+88E2v5rzu2U+oXiwYMHj9BDyQZe74/4Ubmxiu2GBLbpn+U+8wq0pbWxR2D/vyrtJLaKNsYF2uiZ5bahvz+vE0CFL84+KuHx1TPQn3F5tpXpdTonZj9jHUfyo3xDJ2on5NDGxRn+vhtyaGc1+QvoqLamKYcAs3x50LyPqYgTqd8l9crxNW4maWSgrX9Usd3gfL9fqsMjw/s4PZfXUNHfz8sf1yZwjGUVEJS/WRO6iXBNHq/PwEA7UyQ1yXPbQQm8Z+MiXuOlkrbPoZ1G8jco0tt5NIf3KfQ6f6rckkL2UPhG8OtZbn9LYNslknbKoQ89FL5Rs/xxcdz3jQcPHtXvoewCr8cFnrN1Fe3Wl08YqrjNm2nP6R/6vsmh78HtlccNZvkEllASzDwFghtVtNUzol/9q9F7XPHxriKCJGntHBHxW/WdpMZZ9mV7+XKeWSc4VdjW5Ke2WBrow6FZtpGeDODkR2Hl9B6n2lpdPrG0ykTD1OchFFhZJOn0LPdXJl86N/R7v2mWbRwf8xgYGHEMxPr8xTi2B1bRl0clNcridR0c8d6cW+Hf01XF916qvS0VTjq/Koe/q7nCCXGzJR2UZRtN5StMhdrI6vpGMe4hZWizfxXv2TBlSICo0E4v+VGo6duPV5aJqvJB6gkR/fibqrh/IV+qPCph58Mc+lGrPpepPoVe14InVof+9mL+3Tn08+Iq3q9iPcYV+e8OJYTclkC7CwPtRiaZVKeHpFXlR7un93+JpD0LeLzdmWs7zPEKoFZLlRH8S8TqRwu1X5f6po7ZxovyJS8r6qrKZR6L7THn3KActwmVVtsw3zl2aoiP5OcSylvc48j5kkHHB1alz82aj2/lR/Nm25cx8mXCopzqnPs5h/1fF1i2UarcSEZmtqqkvQKrDnTOjc2hD3K+7O2hqjz/7Cmp8rNR0ktvzZVPUKjpTs/xNQy9j8uNVA4lxlLH2CuBVaF5P0L+ocrl8N7SiuUis+3LIFUuZ95WyXz2kna1c+6tbJ+c+l4JlfPNdh7yU1T5dV4k6XBXoYx0Fv14UX7UT7odzGy1TNum5pdOnz9Y8qWFX8+hDz/Ll80GgHSPywcSKkqfuzXdLvLTYVR0X2I9SpCZNZAv5Raah/FJV7vnpBshaYBzrsopa5xzDyp8Lt5HK06pkMnbzrkdnXOv5dDH5ft3zrkb5EumpgstC0mfo9NJOjKf99g5N9o5d6LLbu61k+VHp1W0TNL+qb8pm/0tcc4dLx9Ir6i+MpdJlfT/ZQyvCKzK5RgYJP+31ATPSjrMVVF21vkyzcfKB1YraiBfPUby53Y7OuferWqnzrn3Fb5uPjyHOQcvlb8ZX9F8+RFcj2fTgPNTAu0jf/5fUXP56Syqo5Hywe0JVT0xdY0W+h3qospTXUX5h6TOgeVnO+euq+r+hfPlUY9T+F7cZspi/uza+LlMTcUTel1HBZahbmkfWPZHAu2GpoQI7atozKylmW0aeGxvZgeY2SVmNlS+Ulz6uckcSQc7555PqDuhksIb5toIgVcAtVLqC/tqSXdGPOUl59xHxexTnh4ILOtX9F78ycnP5ZmrtyVNTVvWQOH5CmqLS12W8x0WknNupKQv0hZvmJrLMo5rqrooD4g6CfpJOSZCOOe+lM/+q6hMvhxaVc5U5XOgp5xzeQU+nZ8D8oW0xd3lRyhEaZn279lJJGyU2Fj5eV6z5pz7SpXfx+WucRXmGc1S6Bhbv6qNzKybKs/1uUzS32N8jkMJJ0fn2VahzFN+CSLLyydW1C01d3GkVMAzlAx1i3Puuzz6caGkGem7UdU3sw9S5c/gHEkX5dqB1M28T3LdDkDt5pybKT8irKL9zSxqnkqp8g3xWaqcxFNyZraGfLJY6DxnvrIIaNVgTtJRqfc3uw2cu0fSi4FVx2W5fRLnh7fKV5aoqJ+ZRc3RW1F6suC3zrmCzomYuk75e2DVJXneVD1FfkqTinZNJWNmsr/8CL+K8jkG7lA4ObA6mSzp2GzPe51zUxW+V7Hcpc65z3PY/43yI7Mr6qzoeaT/n5mVKzzP3wnOuc9y6IOcc0vlz1XnpK06MrWf6mSJfKAhva+RnHNvS/ogsKrKBMpU0vyxgVVDnHNZX0+kvtOOk6/wkO6ULJqojZ/LPoFlU5xz6QlcqENS186hwSpZH+cZhJKn0j9XxbaB/ACW9Mebkp6Qv/bfWismcTv58+31XI7zRFchdI+qd66NEHgFUJNEZb9samabmdkuZnaMmd0rf2EVGr0k+czM0IVcdRS6WNm06L3404fOudG5bpS6gPsysCp0glkbTJCf26a6SD+Omihe0HuhpMfy2C5qIvoH8wxuhdrLeAMllTV9QGDVTXnsv6JQwDE9S7+iGWn/7mhm3WP2odSSfB8Xyo8WytXwwLKqbqpJ0n7ygfuKhjrnQu1lJRVUHpm2OKtR2UU02Dk3LdeNnHMz5OcrTFfVd/qmqhzwlHzJsZyl+hE6TtIzYNPtElj2rHMufeRGtqrliDQAJTco7d/N5W8YV2JmbeXn7azosSLedF0/w3VWfzPb28wuMLO35H/bNoto5xjnXHqAqzZ5NMdg0nKh0XKrmVlRKhmlAh0PBlZlc12Z/rtdjNHMB8iX869ovMIVN6qUqqiRXmXF5MuNZhIK5uV7DJyZxzbFdFsqmJqLqCD4bPlAatacc1Pkb7CnqzKBUtKJ8t+vFX2m8DGfTV9+k3RP2uJmqpykWWpPOedG5LHdE4Fl2YzkOkBSu8DyM3LtQCpYfH5g1WZmVtV7Xhs/l+nfd5K/p4S6LSpZL+sqURmE2miWQLvF9KikVZ1z+zrnQvcm4gh9/lqaWetcGiHwCqAmWV/h7JeP5OeDeEXS3fLZ4q0i2lgoaa88R9aUQqhMSjaZyYXyXoxtfwgsi3qfarp3q8No1wqSPo6+yPNG4LiI5e/n2Y9Qe1UdU+tISj9Z+iNGH5YLbb95huenlw0ySXeYWcOY/SilJN/Hz1OlqJJoK5vvma0Dy57OY//p0l+TBpI2TqDdpBT7Oz10o364c+7bGP14JLBsNTPLlLG7SWBZnPf7SVUeAQwAb6lyxvzAiOceJin9HGBQwv3J5BlFX2e9Iz+a4DJJ2ymc2LpA0hHOuYJN5VJN3JvPRqnpED4MrAolAhVKvgm9M9L+vVZqFE4hhcqN3pXnueFyDweWRSZJmlkzhUcB5nsMfCvp43y2LZJBeWwTlaD4jHNudkLtZZNAGTpebo45WjzXpNpSSA8OZyu9GpaUXUJ8KLHx01T1p3w8rsrfL1H7kVSrP5ehBOyJRe9FNeacu9g5Z9Xg0bOIf3aDiOVLEmh7cWBZTbsXdYikt83svAJUJJik8PV9KEkiEoFXAHXJSEmbOefi3GjOm5mtZGZHm9kNZvaOmf1gZr+b2Xwzc6GHwj+o5UXuekXpo7dyEcqODo18qg2GFaJRM6uXGnVwvpk9ZmZfm9mvZjbDzJZmOI4uCDRXHqMr+QZJosogJdleVYGf0AXzsLiBcufcJPmbjhWtk2GTlwLLdpH0tZkdXs1GRWYryfexmMeEFJ4HNp+s6XTjAssyHRfFVuzv9FCp/PTSh7n6TJVL01nEvpQqh9wzsCrv9zs1anhcvtsDqJ1S5xb3py3eOqK8a3qZ4TE1ZFqUpfKJK32dczlNN1ADTZMPQufrycCyUCJQoeSbiJmeLNhC0o05zL2Zk1S7ofOyWAlxqXkfx6Qt3ig1X3HIRvJzwVYU9xgIjTSsDn5zfv7PnKRGqc4LrCpaUm3qZnv6ufUSRY/GzdaXqjwiLFNSbbEtUXiEcDbyTYgPJVDmUwVL0v/P95pekj9qP8vV1s9laBRdPskLqF3Sj/Xl0q998xFqI+50ZKXQTX7O59FmtmdSjabKzocGm5Tn0k5NfEEBIFejJd0hXz4n1zkpYzOzHeTLHm+v6B/OXJQn0Ea+8i3DKIV/tCKDS2bWUtKaee5rlHOuGGWwooxPsjEzayXpHPn5Zrok1Gx5jG1zLk0qSc65+RH3aPI9rkLHVGgOjIpCc8DOMbMkSnjP1YrHdKYyJM9I+k6Vs7hXly+LdbuZvS5/EfmupG+q2SjqkLyOC4Xfx7yOiYhjLOMxYWYdJYXmJi1P4LgoDywr9dwpFRXtOz0llCH6VYw+LH/Px6jy70VU6e5QH6Y55+J+b38tqVfMNgDUPoO0YgKcSTpSFeaUNrN1Ja2Xtl1NKGG+SNLJzrm7S92RIvky5rlYaJTZRmZmuY7MM7Ne8gl7feWDTp3kk59aKrcRK+VZPOclSeklkY+RtLaZXSnpJedcEqNvlltTUtu0ZfNUOQCcj3GSVqvw78byI/1CbYeC4oU4BqqDOJVH5khKHwFdzATKLVR5UNEPqeko8uacW2pm47Xi8bKymTV1zoWCzcX2c6qEdj5yTp5MlcMPzW8YN4HyY1VOPMoUeK2tn8tQFYE4I/xRO0T9tiYRzwu1ERoFWzTOuSFKq6piZvXkfwfayp8rbyVfJSb9PKGjpGfN7BTn3K0JdWm+Kn82c6r4QeAVQG3h5E/UZ8rfSP5WfhTMB6XKFk9lX96h5OcCKWXd/STmEqgoU6b0Bso/c3FbSUPy3DYJiQV9zWwfSbdKWimpNlPiHEeJXmwmfPFaVfZ9+gma5OdbC865FlOZmbUMJQGkLuYPks8ID70XzSTtk3pI0mwz+1jS25LezHMOm4JK+H0s5g2N0DEhSa8WaH/VKfBazO90KZyMkEQZrd9UOfAalfhQHlj2RwJ9SKINALWMc+5HM3tPK1bcONLMLq4QbEu/6bxUec5NWGQNJd1lZjtKGljE+Wj/n5mtqfwq6MxyzuUayMu3nOZyoTKqLeV/r7JKXjOzAyWdJj/yLokRp+VZPOdOSeeq8vlLP0nPSppuZq/IX3u9Jz9aO06J11CS5CRJ/RIYZBuq+hd1XhZK1Ip7DMTdvlDyTZ6UEkygjGgrn6TaqQkl1YaCEG1U3OuUKHknT0YkqlaVPBlKaHSKf0x/GVjWwcwaRQyaqK2fyyaBZQReETVwKImSwKE2ij5QqSqppIrpqccPkp4ys7Ml/V0+ibHib4RJusXMfnXOvZDA7uep8r0iAq8Aaq2hzrn+pe5ENlJB1zfkS6Gg7knkYszM/iI/4qEQUwMUpDxYDVDsoFcrRQTinXNfmdmWkp6StEoV7bSQtGPqcaWZ/SjpAfn5i+LcLEFpjom6qjywLIlElVAbuQRekyjlVcoqCwCqt0FaMfDaQz5J8O1UmdPD0p7/unPutyL1bblezrlx6QtTc8+3kU9u2UU+SNwu7WkHSmpnZruVoLrQrao8GjMbQyX1z3GbWAk2zrmZZrZIlW92Vhl4NbNu8tcE28fpQ0CViZipfh8u6QWFqze1lnRo6iFJ08zsA/lkwdfzCHCHEuJWVv5lVasSdR5YHlhWqGOg1JIOJJY6qXYLFfZ4SbS6VZ6STp6sSui8eo5zLmoqoWxF/da1lk+4SFceWFYbPpehkY3EbBB1jdo8gbZbBJbViOvZ1LnmlWb2rqTXVPlc5m4z65NAFcTQVAQ5jQrmQwwAhfGgooOuMyR9KF9e8Vf50T7z5TPaFgWeXxPmd0LCzGxjRQddnfzcjB9K+lH+OJopfwzNV+VJ4I+VLwmG4pfqzlhePBV8XUvSiZJOVXZzbUk+UHuJpDPN7HJJ/405uqAuKy/y/pIoOV9ThbK5kxghFWojKhs1NHIi9Nubq2qXIQyg2nhC0o1a8cbQUfKBqQGqHMisNmWGnXOL5G9+T5IPFP9bfgRkekWf7eSDoLX5fDOpJJ3097s80wZm1l2+ClC254i5yCoR0zn3ipntIulhSR2qeHob+eN6gCSZ2XD55IM7syyNWl0S4soDywp1DCB/1eV4qe3KA8sKlTwp5RZ4rQ2fy9B3Y+i6CXWIc26Rmc1R5UBreQLNh9qoUQn9zrkPzOwYVZ5ruoOkv0q6NuYuQvcTckosIvAKAAkzs10l7RFY9YOkf0p6OjVRdzZt8T1dB5mv/XOTKgddnaTbJF0TGpWQob3Q8VhXJTkHVSJSGXvXm9kNkraUH1GyrXzyRijLrqKWkq6WtKWZ7ZfwHFt1Ba9Z8cxS5RtkhcrYnRnx3NDy0Pa5yqfUJYA6wDk3x8yelvSXCov3NbOTVLnM8DRJzxetczlKjQw6OPXP9ODr0Wb2mnPuiWL3q0gKlaQTWUrVzOpLek7RQdc/5KetGCGfiPm7fCLmAlUeldFJ0jM59vf/OefeNLPeks6UdLyynwalr6T/STrXzM52zt1fxfPL8+1jnqIS4kjUqhnKi7y/uppAWczkSanuJVASeK2CmXWV1LXU/ZC00DkXKpFdKJNUeX7lWNOQpc4t2kfsq0Zxzj1uZmdISi8vf5jiB15Dn8Gcqg1wQx8AkndKYNlwSds452bk2FZUqcRaLzSxeh2ykfy8SemOcc7lMwqizh5HAaGs2hOdc7cXvSdpUiNW30s9ZGZN5Y+DbVKPLRV97ran/Inl6YXvaa0TOiZ+d84lPa8yfMWH9MBrEiMHQkHPqLmvZmS5fRJ9AIDl7tOKgdem8nN17pL2vEdLUK43J845Z2ZHSVpX0mppq282s9fzuOapCQqVpBOVKCRJx0laL7D8C0kXSHot24onqaBpLM652ZIuNrPLJO0gaSf5ZMF1VfXUKB0kDTKzfs65kzI8r7okxJGoVTNUl+OltgtdLxUqeVKqewmUEwPLGBm/omPl5/QstZ8l9Szi/sapcuA1NOdyLroo/Js9Nma7pfK4Kgde1zWzlvmWGzazlgoPgshpKpBCzBkHAHVWKlCyXWDVsXnegOBkq24aEFj2cp5BV4njqKJfA8uqml+1JJxz85xz7zjnLnbObSuflXi0pKgMy5PNbNXi9bDWCB0THc0siZsJWFEoGNorgXZDn+GowOvUwLKuZtY4Zh/47AHIZKgq39C6RJUTqqpNmeFMnHPz5Ec9pmuvIt4Ydc71d85ZHo/+eewu1o15M6uncKBiRobNQgm9r0vawjn3ao7TTCSWiOmcW+qce805d6ZzbgP5pKrdJF0l6RNJyzJsfqKZ/S3D+tBN0sfzfJ+zeQyK6MeMwLJCHQPIX+h4uaqAx8uQYv+B1cSMwLJCJU9KRUygrCafy58Dy7oUvReojr4PLOsTs82oRKwfYrZbKp8HltVTvBHSoc/fUuU4xzeBVwBI1pqS0m/efu+c+yzP9taP2R/UTBsElj0Uoz2Ooz+NCizrW/Re5ME5NyMVfN9Q0nmBp9SXdHBgOTIbr/BNm3WK3ZE64NvAsljfT2bWVlK3wKrQZ13yF6/pZc3KJK0dow8mjhcAGaQCZOklVtNLVn7jnPuiSF2KzTk3VNLTgVUnpeYlrW3i3uhcRZWr+ThFzKlmZr3kry0rWizpqDxHRRcsEdM5N9M594pz7lzn3KaSOsuP6I66iXuRmUUFgqtLkmQoUSuJm911taJToVSX46W2CwVCm5hZx5jtht4rp+iElNr6uQyNNOycusZA3fZVYFn3DL+h2Qhdf09wzk2O0WYpRfU7zmsUCrxOyHVqLwKvAJCs0Iln6EZztraMsS1qrtBxNDqfhlIXQ3EvRmqTjwPL+ptZEiWKisJ5/1F4Dji+M3KUuhn+aWBVaOR5MeQyeqWm+SiwbMtUpnm+tgksW6iIkeGpi6VhgVWhahXZ6qfSZ8oDqP7uV+bv+EFF6keSLpAfAVBRQ0n/KkFfCm29Amz/vXMuao7DDQPL3nHO5VTmroKiJWI65353zt0kHzi+OfCUVoo+zwolTq1uZg2T6l+WhgeWrRezzbjbo7Iam1Rbw/ygyvNGS/G/V0Lbf+ecS/9dWa62fi6/V+V5ZhsqfknZbES91qgePolYvlmMNkPbhu6H1BTNIpbHmf85VM3qm1wbIfAKAMkqDyzLafLt5cysiaSDYvUGNVV5YFlex5Gkgfl3o1b6WJUzZRtKOrAEfYnricAy5iXNz0uBZQebWWhej0ILjWIpRT8KIRR47SKpf4w2jwgsG1bFaKBQPw6N0YfDYmwLoI5wzo2TNCRi9RLFq25SEs650ZIeC6w60sx6Frk7hbaGmcU5zwol+ETdUJUSTMRMKXpynnNusaTTFR6xE9Wf4apcmaK54p0r5CP03hTiGEA8ofdp1RJOv1IrEyidcwsU/hyHEiBzEdo+dJ6+XK38XKa+K0NBnWJU1JmTvsDMGhVhvzlJTb9UqBLiuTx6FvlPH67wiM6d8mnMzMrk52ZP92Y+7VUTUSWFf4/RZiiBJ1TSOCMCrwCQrBmBZZ3zbOsY+flyUPfMCCzL+ThKzTl8cuze1CKp7NlHAqsurI4XGFWYEliWXrYQ2XlMlbO4e0k6tgR9mR1YFpXFWdN8JenHwPLT82nMzPpI2j2wKlT6sqJQcGNdM9sxjz50lPSXXLcDUGdFzeH6knPuj6L2JDmXq/Kcng0k/bMEfSmkespzSodUItcBgVWZAgzlgWX5JvR2kZTzb1wSnHPLJD0VWBUMlKQCEK8HVoUSrQrGOfeDKt/sLsQxgBicc+MlfR1YVdTjpYK6lkB5SL7lcM2snaRdstyPpFr/uQwFdYoxejt07Ukln2oiVZ0rlCS+f55Vo3ZU5fvMUfuoKXYOLJutHOdjTUPgFQCqoYmBZZuaWatcGklliF+RSI9QE4WOo9BFSVWuUnjuw7ruRvmRJRV1Ty2vSVYOLMu3/Fyd5pybpHBA/gozW6PI3ZkRWNajyH0oiNSF422BVXua2a55NHmjKt/Mmifp3ir68bWk0NzrN+SRgHGNpJY5bgOg7npKvsRq+uNvpexUHKlRr08GVtXGUa+n5Zmod7SktmnLFin8ui03I7As34Tev8nPZ14quSYLPhBYdqiZ5T0fe55C54b5HgPHiqTqQgkdL2eYWYei96R2J1CGEht7SNo7z/ZOVuXz+MUKT6dTUW39XA4JLNu4CPsdF1hWG+dpr8lCScNdJO2bR1unBJZ94Jz7OY+2Ss7MyhWuXvduKvErnzYbSFo3bfESSe/l2haBVwBI1nBJs9KWNZJ0YbYNpC4QnpNUY+acROJCP+gnprLVs2JmJ4rRrkGpTNlQ8Oc4M7si5nyTMrPWZhZ5A9XMWpjZLWYWCpxmu4+Gkk4IrMo5Cw//71+qPJKkXNLLSQRfzWwrM9s+i6eG5qpay8xqS3DvXoVvSj1gZqtl24iZXaZwQsr9zrnpWTRxbWDZGpIeNbOsRo6b2Tkq3YgKADWQc26+c+7FwGNsqfsW02WqXGKzNo567SXpnFw2MLO28qOC0z3jnAsFJJcLJWLukO1vVIX9byLpjFy2KYBckwWfU+UKGfXkzxWKGcS6M7Asn2OgvfxnBIVxryonKjSXNCjXz0sC0vsh1Z4EyncVLof7v9Q0WVlLJeWEPkdPpRJiM6mtn8vXVXm+1a3zHVGcg9C8uf0KvE/k5m2Fpxq4NJepkcxsK0m7BVaF5mLP1M44M3NpjyG5tJGg/0lqF1geStDIVj9JTdOWfeScm5lrQwReASBBzrklCpdo+LuZnVXV9ma2uXzQbXlZAya6r5teUOWbVy0lvVrVyAEza2pm/5F0q6TlJ+kcR5WdJ+m7iOVvmFlOZX3MrF4qsHaLpF8lXZnh6fUlnSTpOzN70sz2zCVDN5XV94TC5U/inGDWac65XxUecdRT0udmdmoeNxU6mtlfzexzSe8qi6zlVNBwXNriBpLOzmXf1VXq7wu9zu0kvWVmO2Ta3syamNl1ki4IrB4v6fws+/G4pFcDq/aR9LqZRd4kM7PmZnarpP9UWDwvm/0CQG3knBsp6ZnAqto46vUiM9s/myempv14UZVvCi6TdF0Vm7+vytcDXeTPIbOSKsn/lPKcisLMVk0lJYbmm822jdYKJylFJgumpgYJnfesL+mZXKtJBfpUZmaHVpUE6ZwbpfC5Qi7HQHP5+wPpI56REOfcDIUDaLtKujfudDKp6+sTsjzuQgmUm8XZfzVzQ2BZT0mP5JC4WC4/qjV0XRVqfwW19XOZukb6NG1xGxW+3HAo6f+EVKI3qoFU1ahQRcQ1JF2dTRtm1kbS/YFVYxSeDqAgzGxNM7vdzGKNqk79jt8k6ajA6p+UuaJIVfoHlr2ST0MEXgEgeVeo8kWyJF1jZh+a2V/MrJeZNTazZma2SurC71n5C+xVK2yTKXiDWipVsi1UymdtScPN7L9mtmVqZGVZKrizaWoE2BitmO05RdLdReh2jeKcmytpT4VLn20n6Wsze9vMzjKzzcysS+qiu4GZtUt9bncws7+b2YPyoxLelb8Zlm0mfn1J+8tn9f+eCsKebWbbmdnKqfe3fmq/PcxsVzO7XtJYSXsF2nvAORfKQkaWnHN3KZzx2VS+rO0vZnaTmR1oZn3MrE3qM9g8dYysZ2aHmdnVZvaB/GiOOyVtmGNXQp//f5rZm2Z2ipntbGabpz73FR9r5rifknDO3SN/3KfrIp/48FzqNe6VCrS2Sb22/5Q0UuGRO07Skambb9k6QZWrVEj+O2CMmT1tZsemXu/tzOxwM7tD0s+STqzw/F8l3Z7DfgGgNqrto16XJ9jUkw8y/MvMIsv3ppL4PpC0aWD1rc659BvsK0jN+ftxYNW1ZnZIVZ01sz3kz02XTzuSTyJmU/mkxJ/N7B4z2z7b4EqqD13kA8+d0lYtVhU3ep1zzyicULiTpK/MbHez3EaCmdkaZnaBfILbw5KyKUV7kionV2V7DKwnfwwsT7xbJmlBLn1G1q5XOID0F0kfm9mWuTZoZhuaT2j+Vb5aUjYJmMMCy/ay4pfJLpT7JA0NLN9bPikiY5JG6lrlbUnrBFbf7Jz7JMt+1NbPZegacKcC7/N5Va5GtK6kIWa2t5mtlOn1RNE8pPA5wRlmdqVlqNpmZt0kvSk/MrzS9qlkp2JpKOl4ST+Y2aNmtq/lkNyeuh+3p6SvFC6bLEknpuaLz9eOgWWhz2aV+OAAQMKccyPM7BqFs3Q3U/YZj69LukThUT2o/c6UtI2k9mnLW6TWnZlFG4slHSxpq2S7Vjs458aY2Xby2WuhMs7bph7F0Eo+CJtVlm7AKJW+jFxtcZr8zcnTA+vayZ/gR53kJ+UOSafKX5hUtH3qEWWowhma1dGRkt5SOCi9Z+qRi1Odc2/nsoFz7mcz20/+pnD6aIhG8vPmVDV3znz5z22obBMA1BnOua/N7AVV/v4+0sz+7ZwbV4JuJekqSWfJn4s3kHSppOPN7DH50ZsT5cu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\n",
      "text/plain": [
       "<Figure size 2160x1800 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "PF_cm_dir = os.path.join(pf_datapath, pf_test_metadata)\n",
    "MRI_cm_dir = os.path.join(mri_datapath, mri_test_metadata)\n",
    "\n",
    "PF_cm_file = os.path.join(PF_cm_dir,'confusion_matrix_normalized.csv')\n",
    "MRI_cm_file = os.path.join(MRI_cm_dir,'confusion_matrix_normalized.csv')\n",
    "\n",
    "\n",
    "PF_cm_df = pd.read_csv(PF_cm_file,index_col=0)\n",
    "MRI_cm_df = pd.read_csv(MRI_cm_file,index_col=0)\n",
    "\n",
    "plot_x_label = \"Predictions\"\n",
    "plot_y_label = \"Actual\"\n",
    "class_names = ['Real', 'Fake']\n",
    "\n",
    "greek_letterz = [chr(code) for code in range(945, 970)]\n",
    "tau_str = greek_letterz[19]\n",
    "mri_cm_str = 'MRI-based method (' + tau_str + ' = 0.3)'\n",
    "cmap = plt.cm.Blues\n",
    "\n",
    "fontsize_text = 55\n",
    "fontsize_num = 70\n",
    "fig, ax = plt.subplots(1,2, figsize=(30,25))\n",
    "g = sns.heatmap(PF_cm_df, annot=True, cmap=cmap,ax=ax[0],cbar=False,annot_kws={\"fontsize\":fontsize_num})\n",
    "ax[0].set_title('Plain-frames method', fontsize = fontsize_text, y=1.1)\n",
    "ax[0].set_xlabel(plot_x_label,fontsize=fontsize_text)\n",
    "ax[0].set_ylabel(plot_y_label,fontsize=fontsize_text)\n",
    "g.set_yticklabels(g.get_yticklabels(), rotation = 0, fontsize = fontsize_text)\n",
    "g.set_xticklabels(g.get_xticklabels(), rotation = 45, fontsize = fontsize_text)\n",
    "    \n",
    "g= sns.heatmap(MRI_cm_df, annot=True, cmap=cmap,ax=ax[1],cbar=False,annot_kws={\"fontsize\":fontsize_num})\n",
    "ax[1].set_title(mri_cm_str, fontsize = fontsize_text,y=1.1)\n",
    "ax[1].set_xlabel(plot_x_label,fontsize=fontsize_text)\n",
    "#ax[1].set_ylabel(plot_y_label,fontsize=fontsize_text)\n",
    "ax[1].set_ylabel('',fontsize=fontsize_text)\n",
    "\n",
    "#g.set_yticklabels(g.get_yticklabels(), rotation = 0, fontsize = fontsize_text)\n",
    "g.set_yticklabels('', rotation = 0, fontsize = fontsize_text)\n",
    "g.set_xticklabels(g.get_xticklabels(), rotation = 45, fontsize = fontsize_text)\n",
    "mappable = g.get_children()[0]\n",
    "cb = plt.colorbar(mappable, ax = [ax[0],ax[1]],orientation = 'horizontal',aspect=50)\n",
    "cb.ax.tick_params(labelsize=50) \n",
    "\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "fig.savefig('confusion_matrix_combined.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "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>Plain-frames method</th>\n",
       "      <th>MRI method</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>TPR</th>\n",
       "      <td>0.90</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FNR</th>\n",
       "      <td>0.10</td>\n",
       "      <td>0.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FPR</th>\n",
       "      <td>0.08</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TNR</th>\n",
       "      <td>0.92</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ACC</th>\n",
       "      <td>0.91</td>\n",
       "      <td>0.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BAL ACC</th>\n",
       "      <td>0.91</td>\n",
       "      <td>0.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1</th>\n",
       "      <td>0.91</td>\n",
       "      <td>0.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>0.92</td>\n",
       "      <td>0.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Specificity</th>\n",
       "      <td>0.92</td>\n",
       "      <td>0.81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUC ROC</th>\n",
       "      <td>0.95</td>\n",
       "      <td>0.76</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Plain-frames method  MRI method\n",
       "TPR                         0.90        0.69\n",
       "FNR                         0.10        0.31\n",
       "FPR                         0.08        0.19\n",
       "TNR                         0.92        0.81\n",
       "ACC                         0.91        0.74\n",
       "BAL ACC                     0.91        0.75\n",
       "F1                          0.91        0.77\n",
       "Precision                   0.92        0.85\n",
       "Specificity                 0.92        0.81\n",
       "AUC ROC                     0.95        0.76"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_rates(conf):\n",
    "    tp = conf[0,0]\n",
    "    fp = conf[0,1]\n",
    "    fn = conf[1,0]\n",
    "    tn = conf[1,1]\n",
    "    dict_metric = dict()\n",
    "    dict_metric[\"TPR\"] = round(tp / (tp + fn),2)\n",
    "    dict_metric[\"FNR\"] = round(fn / (tp + fn),2)\n",
    "    dict_metric[\"FPR\"] = round(fp / (fp + tn),2)\n",
    "    dict_metric[\"TNR\"] = round(tn / (fp + tn),2)\n",
    "    dict_metric[\"ACC\"] = round((tp+tn)/(tp+tn+fp+fn),2)\n",
    "    dict_metric[\"BAL ACC\"] = round((dict_metric[\"TPR\"] + dict_metric[\"TNR\"] )/2,2)\n",
    "    dict_metric[\"F1\"] = round(2*tp/(2*tp+fp+fn),2)\n",
    "    dict_metric[\"Precision\"] = round(tp/(tp+fp),2)\n",
    "    dict_metric[\"Specificity\"] = round(tn/(tn+fp),2)\n",
    "    return dict_metric\n",
    "\n",
    "pf_rates=get_rates(pf_cm)\n",
    "pf_rates['AUC ROC'] = pf_roc_auc\n",
    "mri_rates=get_rates(mri_cm)\n",
    "mri_rates['AUC ROC'] = mri_roc_auc\n",
    "pf_rates_df = pd.DataFrame.from_dict(pf_rates,orient='index',\n",
    "                       columns=['Plain-frames method'])\n",
    "mri_rates_df = pd.DataFrame.from_dict(mri_rates,orient='index',\n",
    "                       columns=['MRI method'])\n",
    "\n",
    "df = pd.concat([pf_rates_df,mri_rates_df], axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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