validating_approaches.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d14209bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from joblib import load\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "cwd = os.getcwd()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2bd2247b",
   "metadata": {},
   "source": [
    "# Autoencoder Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0c097600",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Validation Loss: 0.9113189010201274\n"
     ]
    },
    {
     "data": {
      "image/png": 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44YWp8tXU1HDVVVcxduxYRowYwauvvppxXWfOnJm6Evjyyy8HoKmpiWnTpjF8+HBGjBjBjTfeCMCMGTM49NBDGTlyJOecc04n9+rOcj1IW85VlCXYtEoHd0U67Ykr4MN/9Gyae42Az1zf7uIBAwZw5JFH8uSTT3Laaacxa9Yszj77bMyM6667jv79+9PU1MTEiRN5+eWXGTlyZJvpLFiwgFmzZvH3v/+dxsZGxo4dy+GHHw7A6aefzle+8hUAfvjDH3LnnXdyySWXMGnSJE499VTOPPPMHdLaunUr06ZNY86cORx44IGcf/753HrrrXzrW98CYODAgSxcuJCf//zn3HDDDdxxxx273A3Lly/n8ssvZ8GCBVRVVfGpT32K2bNns88++/D++++zaFFw48Jkt9X111/PO++8Q1lZWZtdWZ0VgRZ/XF09IgUkvbsnvZvngQceYOzYsYwZM4bFixfv0C3T2l/+8he+8IUv0Lt3b/r27cukSZNSyxYtWsTxxx/PiBEjuP/++1m8eHGH5XnttdcYOnQoBx54IABTp05l7ty5qeXJIZoPP/xwli5dmlEd582bx/jx46muriaRSDBlyhTmzp3LsGHDePvtt7nkkkt48skn6du3LxCMJzRlyhTuu+++du9A1hnF3+Iv1cFdkS7poGWeTZ///Oe59NJLWbhwIVu2bGHs2LG888473HDDDcybN4+qqiqmTZvG1q1bO0ynvdutTps2jdmzZzNq1Cjuuecenn322Q7T2dV4Zsnhnzsz9HN7aVZVVfHSSy/x1FNPccstt/DAAw9w11138fjjjzN37lweffRRrr32WhYvXtytH4Cib/FXlCXYvL2JpubdfzA6EYHKykrGjx/PhRdemGrtr1+/noqKCvbYYw9WrFjBE0880WEaJ5xwAo888ghbtmxhw4YN/O53v0st27BhA4MGDaKhoYH7778/Nb9Pnz5s2LBhp7QOPvhgli5dyptvvgnAvffeyyc+8Ylu1XHcuHH8+c9/ZtWqVTQ1NTFz5kw+8YlPsGrVKpqbmznjjDO49tprWbhwIc3Nzbz33ntMmDCBn/3sZ6xdu5aNGzd2K/+ib/H3Ccfr2bS9kb7lu77tmojk3+TJkzn99NNTXT6jRo1izJgxHHbYYQwbNoxjjz22w+3Hjh3L2WefzejRo9lvv/04/vjjU8uuvfZaxo0bx3777ceIESNSwf6cc87hK1/5CjNmzEgd1AUoLy/n7rvv5otf/CKNjY0cccQRfPWrX+1UfebMmbPD3b9++9vf8tOf/pQJEybg7pxyyimcdtppvPTSS1xwwQU0Nwf3Cf/pT39KU1MT5557LuvWrcPd+fa3v93lM5eSinpYZoCZL7zLlQ//g+euPJFBe/Tq4ZKJFBcNy1y4NCxzGg3NLCKyo6IP/JXhXbg0Xo+ISCCbt168y8xWhrdZbL3su2bmZjYwW/knJYdmVotfJDOF0P0rO+rse5bNFv89wMmtZ5rZPsAngXezmHdKsqtH5/KL7Fp5eTmrV69W8C8g7s7q1aspLy/PeJusndXj7nPNrKaNRTcClwH/k62801Wqj18kY0OGDKGuro76+vp8F0U6oby8fIezhnYlp6dzmtkk4H13f6m9iyvS1r0IuAhg33337XKeOrgrkrmSkhKGDh2a72JIluXs4K6Z9QZ+APw4k/Xd/XZ3r3X32urq6i7nW5nq6tHBXRERyO1ZPfsDQ4GXzGwpMARYaGZ7ZTPT8pIYMVOLX0QkKWddPe7+D2DP5Osw+Ne6+6ps5mtmGpNfRCRNNk/nnAk8BxxkZnVm9uVs5bUrlboLl4hISjbP6unwljnuXpOtvFurVItfRCSl6K/cBd1+UUQkXSQCv7p6RERaRCLwV5TF2aTTOUVEgMgEfnX1iIgkRSLwV5Yl2LRdgV9EBCIS+CvUxy8ikhKJwF9ZlqChydnWqH5+EZFIBP6K0uBmLDrAKyISlcCvETpFRFIiEfgrdTMWEZGUSAR+tfhFRFpEIvBXlgeBf4MCv4hIRAK/WvwiIimRCPzq6hERaRGJwF9ZqtsviogkRSLwV5Qlz+NXi19EJBKBPxGPUZaIKfCLiJDdWy/eZWYrzWxR2rx/M7NXzexlM3vEzPplK//WdBcuEZFANlv89wAnt5r3R2C4u48EXgeuzGL+O9BAbSIigawFfnefC3zUat4f3D0Zff8PGJKt/FsLxuTXwV0RkXz28V8IPNHeQjO7yMzmm9n8+vr6bmdWWRZXi19EhDwFfjP7AdAI3N/eOu5+u7vXunttdXV1t/Os0M1YRESADAK/mR1rZhXh9Llm9h9mtl9XMzSzqcCpwBR3966m01kVZQk2blXgFxHJpMV/K7DZzEYBlwHLgF91JTMzOxm4HJjk7pu7kkZX9dFZPSIiQGaBvzFsmZ8G3OzuNwN9drWRmc0EngMOMrM6M/sy8F/htn80sxfN7LZulL1TdFaPiEggkcE6G8zsSuBc4AQziwMlu9rI3Se3MfvOTpavxwR9/E00NzuxmOWrGCIieZdJi/9sYBvwZXf/EBgM/FtWS5UFleGwDZsbdEqniERbRi1+gi6eJjM7EDgYmJndYvW89BE6k8M0i4hEUSYt/rlAmZkNBuYAFxBclVtQdPtFEZFAJoHfwjNwTgf+092/AByW3WL1vIpSjckvIgIZBn4zOxqYAjwezotnr0jZUaEWv4gIkFng/xbBYGqPuPtiMxsGPJPVUmVBy+0XdXBXRKJtl0c53f3PwJ/NrI+ZVbr728D07BetZ+lmLCIigUyGbBhhZn8HFgGvmNkCMyu4Pn4d3BURCWTS1fPfwKXuvp+77wt8B/hFdovV89THLyISyCTwV7h7qk/f3Z8FKrJWoizpXRrHTF09IiKZXMn0tpn9CLg3fH0u8E72ipQdZkZlqQZqExHJpMV/IVANPBw+BgLTslimrNFAbSIimZ3Vs4ZWZ/GY2W8IxvApKBVlcZ3OKSKR19U7cB3do6XIkUqNyS8iktd77uacunpERDro6jGzse0tIoPx+HdHFWUJPtqU0xt/iYjsdjrq4//3Dpa9uquEzewugnvrrnT34eG8/sBvgBpgKXBWeAwhJyp1w3URkfYDv7tP6Gba9xDcajH9/rxXAHPc/XozuyJ8fXk388mYDu6KiGSxj9/d5wIftZp9GvDLcPqXwOezlX9bKnRwV0Qk5wd3P+buHwCEz3vmMvPK0gTbG5vZ3ticy2xFRHYru+1ZPWZ2kZnNN7P59fX1PZJm+u0XRUSiqt3Ab2bnpk0f22rZN7qY3wozGxSmMQhY2d6K7n67u9e6e211dXUXs9tRZbkGahMR6ajFf2na9H+2WnZhF/N7FJgaTk8F/qeL6XRJ6mYsOrNHRCKso8Bv7Uy39Xrnjc1mAs8BB5lZnZl9Gbge+KSZvQF8MnydM+rqERHp+Dx+b2e6rdc7b+w+uZ1FE3e1bbZUhnfh2qhTOkUkwjoK/Aeb2csErfv9w2nC18OyXrIsUItfRKTjwH9IzkqRIxWlOrgrItLRlbvL0l+b2QDgBOBdd1+Q7YJlQ6Va/CIiHZ7O+ZiZJcfYGURws/ULgXvN7Fu5KV7PUlePiEjHZ/UMdfdF4fQFwB/d/XPAOLp+OmdelSZilMZjOrgrIpHWUeBvSJueCPwewN03AAU75kEwUJta/CISXR0d3H3PzC4B6oCxwJMAZtaLAh2PHzRQm4hIRy3+LwOHEdxY/Wx3XxvOPwq4O7vFyh7dflFEoq6js3pWAl9tY/4zwDPZLFQ2Ver2iyIScR3devHRjjZ090k9X5zsqyhLsHbz9nwXQ0Qkbzrq4z8aeA+YCTxPBuPzFILKsgR1a3TfXRGJro4C/14EA6lNBr4EPA7MdPfFuShYtuj2iyISde0e3HX3Jnd/0t2nEhzQfRN4NjzTp2BVqI9fRCKuoxY/ZlYGfJag1V8DzAAezn6xsqeyLMGm7Y24O2ZF0XslItIpHR3c/SUwHHgC+EnaVbwFraIsQbPDloYmepd2+LsnIlKUOop85wGbgAOB6WmtYwPc3ftmuWxZ0bc8uPZs3ZYGBX4RiaSOzuPfbW/E3h1VvYPAv2ZTA4P26JXn0oiI5F5egruZfdvMFpvZIjObaWblucq7qqIUQOfyi0hk5Tzwm9lgYDpQ6+7DgThwTq7y7x8G/o8U+EUkovLVnZMAeplZAugNLM9VxlW9g8C/ZpMCv4hEU84Dv7u/D9wAvAt8AKxz9z+0Xs/MLjKz+WY2v76+vsfy7xf28X+0qWEXa4qIFKeO7sC1wczWt/foaoZmVgWcBgwF9gYqzOzc1uu5++3uXuvutdXV1V3Nbicl8Rh9yhOsUVePiERUR2f19AEws2uAD4F7CU7lnAL06UaeJwHvuHt9mP7DwDHAfd1Is1P6V5Qq8ItIZGXS1fNpd/+5u29w9/XufitwRjfyfBc4ysx6W3BxwERgSTfS67Sq3qV8pD5+EYmoTAJ/k5lNMbO4mcXMbArQ5VHO3P154EFgIfCPsAy3dzW9rlCLX0SiLJPA/yXgLGBF+PhiOK/L3P0qdz/Y3Ye7+3nuvq076XVWVe9S1ujgrohE1C7HLHD3pQQHY4tGVe8SdfWISGTtssVvZgea2RwzWxS+HmlmP8x+0bKnqqKULQ1NbG3QuPwiEj2ZdPX8ArgSaABw95fJ4ZW22ZC8elf9/CISRZkE/t7u/kKreQV9J5Pk1bvq7hGRKMok8K8ys/0BBzCzMwmuuC1YqRa/DvCKSARlMiD91wlOtzzYzN4H3iG4iKtgJYdm1kBtIhJFmQT+Knc/ycwqgJi7bzCzzwHLsly2rNHQzCISZRkd3DWzEe6+KQz65wAFfVZPv17JgdoU+EUkejJp8Z8JPBhesXsccD7wqayWKssS8Rh79CrR0MwiEkmZXMD1dtjKnw28B3zK3bdku2DZ1r+ilI826+CuiERPu4HfzP5BeCZPqD/B3bKeNzPcfWS2C5dN/XqrxS8i0dRRi//UnJUiD/r3LuWDdVvzXQwRkZxr9+Cuuy9z92UEPw4fhtNDCcbtWZej8mVNVUWpzuoRkUjK5KyehwiGZv44cCdB8P91VkuVA0EfvwK/iERPJoG/2d0bgdOBm9z928Cg7BYr+6p6l7K1oZkt2zVQm4hESyaBv8HMJhOcxvlYOK8ke0XKDV29KyJRlUngvwA4GrjO3d8xs6Hk8P642VKVGq9HgV9EoiWT8/hfAaanvX4HuL47mZpZP+AOYDjBKaMXuvtz3UmzszQ0s4hE1S4Dv5kdAPwUOBQoT85392HdyPdm4El3P9PMSoHe3UirSzQ0s4hEVSZdPXcDtxKMwT8B+BVwb1czNLO+wAkEZwjh7tvdfW1X0+uq/urqEZGIyiTw93L3OYCF5/ZfDZzYjTyHAfXA3Wb2dzO7Ixz5cwdmdpGZzTez+fX19d3Irm179CrBDA3bICKRk0ng32pmMeANM/uGmX0B2LMbeSaAscCt7j4G2ARc0Xold7/d3Wvdvba6urob2bUtHjMN1CYikZRJ4P8WQR/8dOBw4DxgajfyrAPq3P358PWDBD8EOde/d6kO7opI5GRyVs+8cHIjwamd3eLuH5rZe2Z2kLu/BkwEXuluul1RVaHALyLR09HonI92tKG7T+pGvpcA94dn9LxND/ygdEVV71LeX1vwI0yLiHRKRy3+ownG358JPA9YT2Xq7i8CtT2VXldV9S5h0fsFP96ciEindBT49wI+CUwGvgQ8Dsx098W5KFguJAdqc3fMeux3TURkt9bRsMxN7v6ku08FjgLeBJ41s0tyVrosq6ooZXtjM5s1UJuIREiHB3fNrAz4LEGrvwaYATyc/WLlRv/eLcM2VJRlcvthEZHC19HB3V8SjKXzBPATd1+Us1LlSMtAbQ0MqcpzYUREcqSjZu55BBdXHQhMT+sDN8DdvW+Wy5Z1GppZRKKo3cDv7plc3FXQNDSziERR0Qf3jvTXCJ0iEkGRDvx9e5UQM3TTdRGJlEgH/njM6NdbN10XkWiJdOAH6Ne7hDWbNDSziERH5AN//96l6uMXkUiJfODXCJ0iEjWRD/wak19Eoibygb+qopQ1mxpw93wXRUQkJxT4e5ewvamZTRqoTUQiQoFfV++KSMTkLfCbWdzM/m5mj+WrDKCrd0UkevLZ4v8msCSP+QNpLX4d4BWRiMhL4DezIQTj/N+Rj/zT9VfgF5GIyVeL/ybgMqC5vRXM7CIzm29m8+vr67NWkNTQzLp6V0QiIueB38xOBVa6+4KO1nP329291t1rq6urs1aevuXBQG06uCsiUZGPFv+xwCQzWwrMAk40s/vyUA4AYjGjSgO1iUiE5Dzwu/uV7j7E3WuAc4A/ufu5uS5HuuAiLgV+EYmGyJ/HDxq2QUSiJa+B392fdfdT81kGgKoKDc0sItGhFj+oj19EIkWBn5Y+fg3UJiJRoMBP0Mff2Oxs2NaY76KIiGSdAj8twzasVT+/iESAAj/QvyK4enfVpm15LomISPYp8AMfr+4DwOL31+W5JCIi2afAD+zTvxcf61vGC0vX5LsoIiJZp8APmBm1Nf2Z985HOrNHRIqeAn/oyJr+fLh+K3VrtuS7KCIiWaXAHzqipj8A85Z+lOeSiIhklwJ/6KC9+tCnLKHALyJFT4E/FI8Zh9dUMU8HeEWkyCnwpzmipj9vrtyoG6+LSFFT4E9z5FD184tI8VPgTzNi8B6UxmPMV+AXkSKmwJ+mvCTOqH320IVcIlLUFPhbOaKmP4vfX8fm7RqpU0SKU84Dv5ntY2bPmNkSM1tsZt/MdRk6ckRNfxqbnRffXZvvooiIZEU+WvyNwHfc/RDgKODrZnZoHsrRprH7VWEGL6ifX0SKVM4Dv7t/4O4Lw+kNwBJgcK7L0Z49epVw8F59dWaPiBStvPbxm1kNMAZ4vo1lF5nZfDObX19fn9NyHVlTxcJla2loas5pviIiuZC3wG9mlcBDwLfcfX3r5e5+u7vXunttdXV1TstWW9OfLQ1NvLJ8p2KJiBS8vAR+MyshCPr3u/vD+ShDR3Qhl4gUs3yc1WPAncASd/+PXOefiY/1LWff/r154R0FfhEpPvlo8R8LnAecaGYvho9T8lCODh1R05/5y9boxiwiUnQSuc7Q3f8KWK7z7awjaqp4aGEdb9Vv4uN7Vua7OCIiPab4r9xtburSZuOGDQDgxj++zrbGrqUhIrI7Ku7A/9zP4b7TYeu6Tm86dGAFV3zmYB7/xwdMu2se67c2ZKGAIiK5V9yBv1cVLP0r3HUyrKvr9OZf/cT+3Hj2KOYt/YizbnuOD9bpfrwiUviKO/CPngznPhQE/V9MhA9e6nQSXxgzhHsuOJK6NVs4/ed/4/UVG7JQUBGR3CnuwA8wbDxc+BTEEnD3KfDGHzudxHEHDOQ3/3QUTc3OGbf+jVueeZNnX1vJyg1be768IiJZZoVwumJtba3Pnz+/e4ms/wB+fRasWAyfvg5GnRN0BXVC3ZrNfO3+hbxc13LMYGBlKYcM6suhg/py0F59OHivvuy/ZwVliXj3yisi0k1mtsDda3eaH5nAD7BtIzx4Abzxh+B19SGwz5Gwz7jguf/+ENv1n6B1mxtY8uF6Xlm+niUfrGfx8vW8uXIj28OxfRIxY1h1Bfv2r6CyLE5FWYLKsgS9SxNUlMUpS8QoS8QpKwmfEzFK4jEScaMkHqMkbiRiweuYGYmYEY8ZsVgwHbPgddyMeDx4NiM1P2YQXCcnIlGmwJ/U3ATL/hfefR7eex7qXmg566ekAvY8BD52GHxseDBdVgkWh1g8eLYYJEohUQ6JsvC5nIZmZ+mqTSz5cAOvfrCeVz/cwPK1W9i0vZHN25rYuK2RbY25HfTNLLhgwswwgh+G5A9ELHzGWi6qMCBuzcRoxjAcwy0WLAm3bVmzJX0A82YS1kScZuI4WLC9WQwPM4nhYdrBcwwP03GMsGx4UDZ3LFzHUut6mIanFyMoZ/iIpdJuIo4TJ1mmZmLeFL5uSqURliAoY9rO8NS8tIzCZ0/faa3WSC9NUoxmLNglYf3BwjKYB/ub1PqWergZMQ/qEtSpGfOmHbJ2J632vsM+BoLaWpwmS4R7xIJ9QVOYdjNxmiijgRLfTikNlHgDCW+gyeI0UMJ2K6GBEhqsZMe9Fb5HJdYUbOsNlLKNEm8gTnPLHvDkvg62bLJY2jtpLfvTWvazp/Z16/1sqd0Uw4l5M3EaSXhD8N56I47RSIIGEjRaggZKACNGE4m0R4xmmsNPV3NaWeI0kfDG1Ppxb0q+66nSEX62Et5EgkbiNFLijTRjNFoJjam8EzQTS76jqRq1fE6T74Xv8DkwPFX5XidfxSG1E+iK9gJ/zi/gyrtYHIaeEDwAmpth1etQNy/oBlqxCJY8Cgt/2alkS2IlHBAv4YBYCZPiCYiVBMcVSix49Am+yO4WvL0OjofPAfeWj0ZL4PHk9yZYz5ux5iZi3hgEAW8Kv1hh+mlfGvPgy200Yd6MefDD0xKUHHMnFi6P0bzzNy3tdTJkA2FeseBr4y2BVIpDAyXEaez0+7rdSmmwUprZuavTwk+QefLHqeUzt8Nnso082/v/mvxha7SScDoIaQkPfgwSBD9iMZxGEsGPYLhe8vNr4Q9IMvA2h8uT6zZb8FOZXhIHmi1OUxjggx/WBOBU+Obgx8AbUz8gnhb6kz9szdYS+t1afnxSYsnGU89/t6IX+FuLxWDPg4NHkjts+BDql0DDVvCm4J+CNwePpu3QsAUat0Hj1uDRtB2aGqC5MXxuCLcJQ7kHrbuW0MmO+SU/7Gnr78zDfx+J8BH+Ewl/IIJfkbTWY/o/lVj4byVsvaeYBWkl14klWpZ7WpoetrrT8/DmNsoTbp+qR3PLdPIfk8Vayp3Wymt5T+Lh34lYWpnDabMdt0vfX97cknaqPuF0vCRtfiJMl7R9nb7PW02n8kkua+Ot2aEK1lJOaCl3+v5PvSdpdduhHGE+sVg772PrvNvYX3jwGWxubHmk3rNkerFgf6T/g42XUpJ8D5sbg8950/bgOZlX6j2xYN+G25WaUdrO7smGGFCSyYruJMwU8ELaD20xg76DgodIVCWDejyj0Lp70zGvHRT/6ZwiIrIDBX4RkYhR4BcRiRgFfhGRiFHgFxGJGAV+EZGIUeAXEYkYBX4RkYgpiLF6zKweWJbBqgOBVVkuTi4VU32KqS5QXPUpprqA6pNuP3evbj2zIAJ/psxsflsDEhWqYqpPMdUFiqs+xVQXUH0yoa4eEZGIUeAXEYmYYgv8t+e7AD2smOpTTHWB4qpPMdUFVJ9dKqo+fhER2bVia/GLiMguKPCLiERMUQR+MzvZzF4zszfN7Ip8l6ezzOwuM1tpZovS5vU3sz+a2Rvhc1U+y9gZZraPmT1jZkvMbLGZfTOcX3B1MrNyM3vBzF4K6/KTcH7B1SXJzOJm9nczeyx8Xch1WWpm/zCzF81sfjivkOvTz8weNLNXw+/P0dmoT8EHfjOLA7cAnwEOBSab2aH5LVWn3QOc3GreFcAcdz8AmBO+LhSNwHfc/RDgKODr4XtSiHXaBpzo7qOA0cDJZnYUhVmXpG8CS9JeF3JdACa4++i0c90LuT43A0+6+8HAKIL3qefr4+4F/QCOBp5Ke30lcGW+y9WFetQAi9JevwYMCqcHAa/lu4zdqNv/AJ8s9DoBvYGFwLhCrQswJAweJwKPhfMKsi5heZcCA1vNK8j6AH2BdwhPuslmfQq+xQ8MBt5Le10Xzit0H3P3DwDC5z3zXJ4uMbMaYAzwPAVap7Br5EVgJfBHdy/YugA3AZcBzWnzCrUuENyS/g9mtsDMLgrnFWp9hgH1wN1hV9wdZlZBFupTDIG/rbso6xzV3YCZVQIPAd9y9/X5Lk9XuXuTu48maC0faWbD81ykLjGzU4GV7r4g32XpQce6+1iCrt6vm9kJ+S5QNySAscCt7j4G2ESWuqmKIfDXAfukvR4CLM9TWXrSCjMbBBA+r8xzeTrFzEoIgv797v5wOLug6+Tua4FnCY7HFGJdjgUmmdlSYBZwopndR2HWBQB3Xx4+rwQeAY6kcOtTB9SF/ygBHiT4Iejx+hRD4J8HHGBmQ82sFDgHeDTPZeoJjwJTw+mpBP3kBcHMDLgTWOLu/5G2qODqZGbVZtYvnO4FnAS8SgHWxd2vdPch7l5D8D35k7ufSwHWBcDMKsysT3Ia+BSwiAKtj7t/CLxnZgeFsyYCr5CN+uT7gEYPHRQ5BXgdeAv4Qb7L04XyzwQ+ABoIfvW/DAwgOAj3RvjcP9/l7ER9jiPobnsZeDF8nFKIdQJGAn8P67II+HE4v+Dq0qpe42k5uFuQdSHoE38pfCxOfvcLtT5h2UcD88PP22ygKhv10ZANIiIRUwxdPSIi0gkK/CIiEaPALyISMQr8IiIRo8AvIhIxCvwSaWbWFI7smHz02JWSZlaTPuKqyO4ike8CiOTZFg+GYxCJDLX4RdoQjvP+r+FY/C+Y2cfD+fuZ2Rwzezl83jec/zEzeyQct/8lMzsmTCpuZr8Ix/L/Q3j1L2Y23cxeCdOZladqSkQp8EvU9WrV1XN22rL17n4k8F8Eo1oSTv/K3UcC9wMzwvkzgD97MG7/WIIrSQEOAG5x98OAtcAZ4fwrgDFhOl/NTtVE2qYrdyXSzGyju1e2MX8pwQ1Y3g4HnPvQ3QeY2SqCsdEbwvkfuPtAM6sHhrj7trQ0agiGcT4gfH05UOLu/2xmTwIbCS7Ln+3uG7NcVZEUtfhF2uftTLe3Tlu2pU030XJc7bMEd447HFhgZjreJjmjwC/SvrPTnp8Lp/9GMLIlwBTgr+H0HOBiSN24pW97iZpZDNjH3Z8huClKP2Cnfx0i2aJWhkRdr/DuWklPunvylM4yM3ueoIE0OZw3HbjLzL5HcLekC8L53wRuN7MvE7TsLyYYcbUtceA+M9uD4EZCN3ow1r9ITqiPX6QNYR9/rbuvyndZRHqaunpERCJGLX4RkYhRi19EJGIU+EVEIkaBX0QkYhT4RUQiRoFfRCRi/j/Gvg1HCNmBeQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "losses_400 = load(os.path.join(cwd, 'saved_models', 'losses_check_point_400.pkl'))\n",
    "\n",
    "train_losses = [np.mean(loss_values) for loss_values in losses_400['train_loss']]\n",
    "validation_losses = [np.mean(loss_values) for loss_values in losses_400['evaluation_loss']]\n",
    "\n",
    "print(f'Mean Validation Loss: {validation_losses[-1]}')\n",
    "\n",
    "x = [epoch for epoch in range(1, 61)]\n",
    "plt.plot(x, train_losses, label = \"Train Loss\")\n",
    "plt.plot(x, validation_losses, label = \"Validation Loss\")\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Masked MSE Loss')\n",
    "plt.title('First Model Train and Validation Loss')\n",
    "plt.legend()\n",
    "plt.savefig(os.path.join(cwd, 'first_ae_loss.png'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f1323ec9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Validation Loss: 0.8212220032472868\n"
     ]
    },
    {
     "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": [
    "losses_400_200_400 = load(os.path.join(cwd, 'saved_models', 'losses_check_point_400_200_400.pkl'))\n",
    "\n",
    "train_losses = [np.mean(loss_values) for loss_values in losses_400_200_400['train_loss']]\n",
    "validation_losses = [np.mean(loss_values) for loss_values in losses_400_200_400['evaluation_loss']]\n",
    "\n",
    "print(f'Mean Validation Loss: {validation_losses[-1]}')\n",
    "\n",
    "x = [epoch for epoch in range(1, 61)]\n",
    "plt.plot(x, train_losses, label = \"Train Loss\")\n",
    "plt.plot(x, validation_losses, label = \"Validation Loss\")\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Masked MSE Loss')\n",
    "plt.title('Second Model Train and Validation Loss')\n",
    "plt.legend()\n",
    "plt.savefig(os.path.join(cwd, 'second_ae_loss.png'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ff5fc2f6",
   "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": [
    "validation_400_losses = [np.mean(loss_values) for loss_values in losses_400['evaluation_loss']]\n",
    "validation_400_200_400_losses = [np.mean(loss_values) for loss_values in losses_400_200_400['evaluation_loss']]\n",
    "\n",
    "x = [epoch for epoch in range(1, 61)]\n",
    "plt.plot(x, validation_400_losses, label = \"First Model Validation Loss\")\n",
    "plt.plot(x, validation_400_200_400_losses, label = \"Second Model Validation Loss\")\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Masked MSE Loss')\n",
    "plt.title('Autoencoders Validation Loss Comparison')\n",
    "plt.legend()\n",
    "plt.savefig(os.path.join(cwd, 'ae_loss_comparison.png'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb83794b",
   "metadata": {},
   "source": [
    "As can be seen from the charts above, the second model has a better performance and it will be used for the recommendation system."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eff33a76",
   "metadata": {},
   "source": [
    "# Loading the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7df47be9",
   "metadata": {},
   "source": [
    "We will use the evaluation dataset we created (the models has not seen some ratings in this dataset). This dataset contains all movies and users presented in the training dataset, but for each user, some ratings are new and the train dataset does not contain them.\n",
    "\n",
    "We will use those users not used in the training phase of the models (5850 users) for evaluations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5659c01c",
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.exists(os.path.join(os.path.join(cwd, 'data', 'processed', 'evaluation_users.csv'))):\n",
    "    all_users = pd.read_csv(os.path.join(cwd, 'data', 'processed', 'users.csv'))\n",
    "\n",
    "    users = []\n",
    "    for (_, user) in all_users.iterrows():\n",
    "        print(f'Checking User {user[\"id\"]}')\n",
    "        train_ratings = pd.read_csv(os.path.join(cwd, 'data', 'processed', 'train_user_ratings', f'{user[\"id\"]}.csv'))\n",
    "        evaluation_ratings = pd.read_csv(os.path.join(cwd, 'data', 'processed', 'evaluation_user_ratings', f'{user[\"id\"]}.csv'))\n",
    "        if len(evaluation_ratings[~evaluation_ratings['movieId'].isin(train_ratings['movieId'])]) > 0:\n",
    "            users.append(user['id'])\n",
    "    \n",
    "    users_df = pd.DataFrame(users, columns=['id'])\n",
    "    users_df.to_csv(os.path.join(cwd, 'data', 'processed', 'evaluation_users.csv'), index=False)\n",
    "\n",
    "    del all_users\n",
    "    del users_df\n",
    "else:\n",
    "    users_df = pd.read_csv(os.path.join(cwd, 'data', 'processed', 'evaluation_users.csv'))\n",
    "    users = users_df['id'].values\n",
    "    \n",
    "    del users_df\n",
    "\n",
    "user_unseen_ratings = {}\n",
    "\n",
    "for user in users:\n",
    "    train_ratings = pd.read_csv(os.path.join(cwd, 'data', 'processed', 'train_user_ratings', f'{user}.csv'))\n",
    "    evaluation_ratings = pd.read_csv(os.path.join(cwd, 'data', 'processed', 'evaluation_user_ratings', f'{user}.csv'))\n",
    "    user_unseen_ratings[user] = evaluation_ratings[~evaluation_ratings['movieId'].isin(train_ratings['movieId'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6e87c314",
   "metadata": {},
   "outputs": [],
   "source": [
    "movie_actors_genre = pd.read_csv(os.path.join(cwd, 'data', 'processed', 'movie_actors_genres.csv'))\n",
    "average_movie_ratings = pd.read_csv(os.path.join(cwd, 'data', 'processed', 'movies_average_ratings.csv'))\n",
    "movies_genres = {}\n",
    "movies_casts = {}\n",
    "movies_average_rating = {}\n",
    "for (_, movie) in movie_actors_genre.iterrows():\n",
    "    movies_genres[movie['movieId']] = movie['genres'].split('|')\n",
    "    movies_casts[movie['movieId']] = movie['casts'].split('|')\n",
    "for (_, movie) in average_movie_ratings.iterrows():\n",
    "    movies_average_rating[movie['id']] = movie['rating']\n",
    "movie_mappings = pd.read_csv(os.path.join(cwd, 'data', 'processed', 'movie_mappings.csv'))\n",
    "train_average_movie_ratings = pd.read_csv(os.path.join(cwd, 'data', 'processed', 'train_average_movie_ratings.csv'))\n",
    "genres = [' '.join(genre.split('_')[1:-1]).title() for genre in train_average_movie_ratings.drop(['userId'], axis=1).columns.tolist()]\n",
    "\n",
    "del movie_actors_genre\n",
    "del average_movie_ratings\n",
    "\n",
    "clustering_predictions = {}\n",
    "kd_tree_predictions = {}\n",
    "autoencoder_predictions = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e108e466",
   "metadata": {},
   "source": [
    "## KMeans Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e49d18d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "KMeans Clustering Done!\n"
     ]
    }
   ],
   "source": [
    "clustering_data = train_average_movie_ratings[train_average_movie_ratings['userId'].isin(users)]\n",
    "clustering_data_users_index = clustering_data['userId']\n",
    "clustering_data = clustering_data.drop(['userId'], axis=1)\n",
    "\n",
    "clustering_model = load(os.path.join(cwd, 'saved_models', 'k_means_model.pkl'))\n",
    "cluster_for_all_trained_users = clustering_model.predict(train_average_movie_ratings.drop(['userId'], axis=1).values)\n",
    "clusters_meta = {}\n",
    "for index, cluster in enumerate(cluster_for_all_trained_users):\n",
    "    if not cluster in clusters_meta:\n",
    "        clusters_meta[cluster] = []\n",
    "    clusters_meta[cluster].append(index)\n",
    "del cluster_for_all_trained_users\n",
    "predicted_clusters = clustering_model.predict(clustering_data.values)\n",
    "\n",
    "cluster_genre_score = {}\n",
    "\n",
    "for user_index, cluster in enumerate(predicted_clusters):\n",
    "    clustering_predictions[clustering_data_users_index.iloc[user_index]] = {}\n",
    "    if not cluster in cluster_genre_score:\n",
    "        users_in_cluster = clusters_meta[cluster]\n",
    "        users_in_cluster = train_average_movie_ratings[train_average_movie_ratings['userId'].isin(users_in_cluster)].drop(['userId'], axis=1)\n",
    "        average_genre_ratings_in_cluster = {' '.join(k.split('_')[1:-1]).title():v for k, v in dict(users_in_cluster.mean(axis=0)).items()}\n",
    "        average_genre_ratings_in_cluster = {k: (v/10) for k, v in average_genre_ratings_in_cluster.items()}\n",
    "        cluster_genre_score[cluster] = average_genre_ratings_in_cluster\n",
    "    for (_, movie) in user_unseen_ratings[clustering_data_users_index.iloc[user_index]].iterrows():\n",
    "        movie_genres = movies_genres[int(movie['movieId'])]\n",
    "        movie_score = sum([score for genre, score in cluster_genre_score[cluster].items() if genre in movie_genres])\n",
    "        predicted_rating = movies_average_rating[int(movie['movieId'])] * movie_score\n",
    "        actual_rating = movie['rating']\n",
    "        clustering_predictions[clustering_data_users_index.iloc[user_index]][int(movie['movieId'])] = {\n",
    "            'actual_rating': actual_rating,\n",
    "            'predicted_rating': predicted_rating,\n",
    "            'movie_average_rating': movies_average_rating[int(movie['movieId'])],\n",
    "            'clustering_score': movie_score,\n",
    "        }\n",
    "\n",
    "print(\"KMeans Clustering Done!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecbc9794",
   "metadata": {},
   "source": [
    "## KD-Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d20bbddb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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    {
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    {
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     "output_type": "stream",
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      "Processing User 270835\n",
      "Processing User 270854\n",
      "Processing User 270871\n",
      "KD-Tree Done!\n"
     ]
    }
   ],
   "source": [
    "encoded_movies_actors_genres_data = pd.read_csv(os.path.join(cwd, 'data', 'processed', 'genre_cast_encoded_movies.csv'))\n",
    "movies_index = encoded_movies_actors_genres_data['movieId']\n",
    "total_number_of_movies = len(encoded_movies_actors_genres_data)\n",
    "\n",
    "tree = load(os.path.join(cwd, 'saved_models', 'kd_tree_model.pkl'))\n",
    "\n",
    "movies_distance_score = {}\n",
    "calculated_movies = []\n",
    "\n",
    "print('Creating Movie Distances For Each User')\n",
    "for user in users:\n",
    "    print(f'Processing User {user}')\n",
    "    user_unseen_movies = user_unseen_ratings[user]\n",
    "    user_unseen_movies = user_unseen_movies[~user_unseen_movies['movieId'].isin(calculated_movies)]\n",
    "    user_unseen_movies = user_unseen_movies['movieId'].unique().tolist()\n",
    "    if len(user_unseen_movies):\n",
    "        tree_data = encoded_movies_actors_genres_data[encoded_movies_actors_genres_data['movieId'].isin(user_unseen_movies)]\n",
    "        tree_data_movies_index = tree_data['movieId']\n",
    "        tree_data = tree_data.drop(['imdbId', 'tmdbId', 'movieId', 'genres', 'casts'], axis=1)\n",
    "        tree_data = tree_data.values\n",
    "\n",
    "        dist, ind = tree.query(tree_data, k=total_number_of_movies)\n",
    "\n",
    "        for source_movie_index, distances in enumerate(dist):\n",
    "            source_movie_id = tree_data_movies_index.iloc[source_movie_index]\n",
    "            movies_distance_score[source_movie_id] = {}\n",
    "            calculated_movies.append(source_movie_id)\n",
    "            maximum_distance = max(distances)\n",
    "            for index_in_results, distance in enumerate(distances):\n",
    "                target_movie_index = ind[source_movie_index][index_in_results]\n",
    "                target_movie_id = movies_index.iloc[target_movie_index]\n",
    "                movie_distance = distance\n",
    "                # A distance of zero means a hit, so we use (1 - distance/maximum_distance) to calculate the distance score\n",
    "                movies_distance_score[source_movie_id][target_movie_id] = 1 - (movie_distance/maximum_distance)\n",
    "\n",
    "    train_ratings = pd.read_csv(os.path.join(cwd, 'data', 'processed', 'train_user_ratings', f'{user}.csv'))\n",
    "    kd_tree_predictions[user] = {}\n",
    "    for (_, movie) in user_unseen_ratings[user].iterrows():\n",
    "        distance_based_rating_sum = 0\n",
    "        sum_of_distances = 0\n",
    "        for (__, seen_movie) in train_ratings.iterrows():\n",
    "            distance_based_rating_sum += (movies_distance_score[movie['movieId']][seen_movie['movieId']] * movies_average_rating[int(movie['movieId'])])\n",
    "            sum_of_distances += movies_distance_score[movie['movieId']][seen_movie['movieId']]\n",
    "        predicted_rating = distance_based_rating_sum / sum_of_distances\n",
    "        actual_rating = movie['rating']\n",
    "        kd_tree_predictions[user][int(movie['movieId'])] = {\n",
    "            'actual_rating': actual_rating,\n",
    "            'predicted_rating': predicted_rating,\n",
    "        }\n",
    "\n",
    "print(\"KD-Tree Done!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c26b387",
   "metadata": {},
   "source": [
    "## Autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d894cfef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from models import AutoEncoder\n",
    "import torch\n",
    "\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "model = AutoEncoder(num_features=len(movie_mappings), dimensions=[400, 200, 400])\n",
    "model = model.to(device)\n",
    "model.load_state_dict(torch.load(os.path.join(cwd, 'saved_models', 'final_model_400_200_400.pth')))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2539eb7",
   "metadata": {},
   "source": [
    "We load the data used for training and for evaluations, we check the predicted value of the model for those items available in the evaluation ratings file (previously loaded in the `user_unseen_ratings` variable)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "91f26f16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Autoencoder Done!\n"
     ]
    }
   ],
   "source": [
    "from data_loader import Dataset\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "movie_mappings_dict = {movie_info['rating_id']:movie_info['id'] for (_, movie_info) in movie_mappings.iterrows()}\n",
    "\n",
    "evaluation_dat = Dataset(os.path.join(cwd, 'data', 'processed'), 'train_user_ratings', users, movie_mappings_dict)\n",
    "batch_size = 128\n",
    "evaluation_dl = DataLoader(dataset=evaluation_dat, batch_size=batch_size, shuffle=False, num_workers=1)\n",
    "\n",
    "batch_number = 0\n",
    "for i, data in enumerate(evaluation_dl, 0):\n",
    "    batch_number += 1\n",
    "    model.eval()\n",
    "    inputs = data\n",
    "    inputs = inputs.to(device)\n",
    "\n",
    "    inputs = inputs.float()\n",
    "\n",
    "    outputs = model(inputs)\n",
    "    user_number_in_batch = 0\n",
    "    for output in outputs:\n",
    "        user_number_in_batch += 1\n",
    "        predicted_values = output.detach().cpu().numpy()\n",
    "        user_id = users[((batch_number - 1) * batch_size) + (user_number_in_batch - 1)]\n",
    "        user_true_ratings = user_unseen_ratings[user_id]\n",
    "        autoencoder_predictions[user_id] = {}\n",
    "        for (_, movie) in user_unseen_ratings[user_id].iterrows():\n",
    "            autoencoder_predictions[user_id][int(movie['movieId'])] = {\n",
    "                'actual_rating': movie['rating'],\n",
    "                'predicted_rating': predicted_values[movie_mappings_dict[movie['movieId']]],\n",
    "            }\n",
    "print(\"Autoencoder Done!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ccf7c52",
   "metadata": {},
   "source": [
    "## Hybrid Approach\n",
    "In this method, we want to combine the Autoencoder approach with the KD-Tree approach so that we have both a content-based and a collaborative filtering based method. To do so, we will use 0.7 as coefficient for the collaborative filtering rating, and 0.3 for the content-based filtering method (Sum of the coefficients sum is 1). For each movie, the rating produced by each method will be multiplied by its corresponding method's coefficient and summed together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "eaa5d9bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hybrid Done!\n"
     ]
    }
   ],
   "source": [
    "hybrid_predictions = {}\n",
    "\n",
    "for user in users:\n",
    "    hybrid_predictions[user] = {}\n",
    "    kd_tree_user_predictions = kd_tree_predictions[user]\n",
    "    autoencoder_user_predictions = autoencoder_predictions[user]\n",
    "    for (movie_id, rating_values) in kd_tree_user_predictions.items():\n",
    "        kd_value = kd_tree_user_predictions[movie_id]['predicted_rating']\n",
    "        ae_value = autoencoder_user_predictions[movie_id]['predicted_rating']\n",
    "        hybrid_predictions[user][movie_id] = {\n",
    "                'actual_rating': rating_values['actual_rating'],\n",
    "                'predicted_rating': (ae_value * 0.7) + (kd_value * 0.3)\n",
    "            }\n",
    "\n",
    "print(\"Hybrid Done!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89ee14fc",
   "metadata": {},
   "source": [
    "# Evaluations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "b842cf10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculations Done!\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, accuracy_score, precision_score, recall_score, f1_score\n",
    "\n",
    "all_clustering_predicted_ratings = []\n",
    "all_clustering_actual_ratings = []\n",
    "all_kd_tree_predicted_ratings = []\n",
    "all_kd_tree_actual_ratings = []\n",
    "all_autoencoder_predicted_ratings = []\n",
    "all_autoencoder_actual_ratings = []\n",
    "all_hybrid_predicted_ratings = []\n",
    "all_hybrid_actual_ratings = []\n",
    "for user_id in users:\n",
    "    kd_tree_user_predictions = kd_tree_predictions[user_id]\n",
    "    for (movie_id, rating_values) in kd_tree_user_predictions.items():\n",
    "        all_clustering_predicted_ratings.append(clustering_predictions[user_id][int(movie_id)]['predicted_rating'])\n",
    "        all_clustering_actual_ratings.append(clustering_predictions[user_id][int(movie_id)]['actual_rating'])\n",
    "        all_kd_tree_predicted_ratings.append(kd_tree_predictions[user_id][int(movie_id)]['predicted_rating'])\n",
    "        all_kd_tree_actual_ratings.append(kd_tree_predictions[user_id][int(movie_id)]['actual_rating'])\n",
    "        all_autoencoder_predicted_ratings.append(autoencoder_predictions[user_id][int(movie_id)]['predicted_rating'])\n",
    "        all_autoencoder_actual_ratings.append(autoencoder_predictions[user_id][int(movie_id)]['actual_rating'])\n",
    "        all_hybrid_predicted_ratings.append(hybrid_predictions[user_id][int(movie_id)]['predicted_rating'])\n",
    "        all_hybrid_actual_ratings.append(hybrid_predictions[user_id][int(movie_id)]['actual_rating'])\n",
    "mse = {\n",
    "    'clustering': mean_squared_error(all_clustering_actual_ratings, all_clustering_predicted_ratings),\n",
    "    'kd_tree': mean_squared_error(all_kd_tree_actual_ratings, all_kd_tree_predicted_ratings),\n",
    "    'autoencoder': mean_squared_error(all_autoencoder_actual_ratings, all_autoencoder_predicted_ratings),\n",
    "    'hybrid': mean_squared_error(all_hybrid_actual_ratings, all_hybrid_predicted_ratings),\n",
    "}\n",
    "cl_true_classes = [1 if rating >= 3.5 else 0 for rating in all_clustering_actual_ratings]\n",
    "cl_pred_classes = [1 if rating >= 3.5 else 0 for rating in all_clustering_predicted_ratings]\n",
    "kd_true_classes = [1 if rating >= 3.5 else 0 for rating in all_kd_tree_actual_ratings]\n",
    "kd_pred_classes = [1 if rating >= 3.5 else 0 for rating in all_kd_tree_predicted_ratings]\n",
    "ae_true_classes = [1 if rating >= 3.5 else 0 for rating in all_autoencoder_actual_ratings]\n",
    "ae_pred_classes = [1 if rating >= 3.5 else 0 for rating in all_autoencoder_predicted_ratings]\n",
    "hb_true_classes = [1 if rating >= 3.5 else 0 for rating in all_hybrid_actual_ratings]\n",
    "hb_pred_classes = [1 if rating >= 3.5 else 0 for rating in all_hybrid_predicted_ratings]\n",
    "accuracy = {\n",
    "    'clustering': accuracy_score(cl_true_classes, cl_pred_classes),\n",
    "    'kd_tree': accuracy_score(kd_true_classes, kd_pred_classes),\n",
    "    'autoencoder': accuracy_score(ae_true_classes, ae_pred_classes),\n",
    "    'hybrid': accuracy_score(hb_true_classes, hb_pred_classes),\n",
    "}\n",
    "precision = {\n",
    "    'clustering': precision_score(cl_true_classes, cl_pred_classes),\n",
    "    'kd_tree': precision_score(kd_true_classes, kd_pred_classes),\n",
    "    'autoencoder': precision_score(ae_true_classes, ae_pred_classes),\n",
    "    'hybrid': precision_score(hb_true_classes, hb_pred_classes),\n",
    "}\n",
    "recall = {\n",
    "    'clustering': recall_score(cl_true_classes, cl_pred_classes),\n",
    "    'kd_tree': recall_score(kd_true_classes, kd_pred_classes),\n",
    "    'autoencoder': recall_score(ae_true_classes, ae_pred_classes),\n",
    "    'hybrid': recall_score(hb_true_classes, hb_pred_classes),\n",
    "}\n",
    "f1_measure = {\n",
    "    'clustering': f1_score(cl_true_classes, cl_pred_classes),\n",
    "    'kd_tree': f1_score(kd_true_classes, kd_pred_classes),\n",
    "    'autoencoder': f1_score(ae_true_classes, ae_pred_classes),\n",
    "    'hybrid': f1_score(hb_true_classes, hb_pred_classes),\n",
    "}\n",
    "\n",
    "print(\"Calculations Done!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "89334f82",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------+-------+----------+-----------+--------+------------+\n",
      "|             |  MSE  | Accuracy | Precision | Recall | F1 Measure |\n",
      "+-------------+-------+----------+-----------+--------+------------+\n",
      "| Clustering  | 2.618 |  0.515   |   0.672   | 0.377  |   0.483    |\n",
      "+-------------+-------+----------+-----------+--------+------------+\n",
      "|   KD Tree   | 0.921 |  0.662   |   0.742   | 0.672  |   0.706    |\n",
      "+-------------+-------+----------+-----------+--------+------------+\n",
      "| Autoencoder | 0.912 |  0.699   |   0.782   | 0.694  |   0.735    |\n",
      "+-------------+-------+----------+-----------+--------+------------+\n",
      "|   Hybrid    | 0.824 |  0.704   |   0.783   | 0.702  |    0.74    |\n",
      "+-------------+-------+----------+-----------+--------+------------+\n"
     ]
    }
   ],
   "source": [
    "from beautifultable import BeautifulTable\n",
    "\n",
    "headers = ['Clustering', 'KD_Tree', 'Autoencoder', 'Hybrid']\n",
    "\n",
    "table = BeautifulTable()\n",
    "for header in headers:\n",
    "    table.rows.append([mse[header.lower()], accuracy[header.lower()], precision[header.lower()], recall[header.lower()], f1_measure[header.lower()]])\n",
    "table.columns.header = [\"MSE\", \"Accuracy\", \"Precision\", \"Recall\", \"F1 Measure\"]\n",
    "table.rows.header = [' '.join(header.split('_')) for header in headers]\n",
    "print(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "edbcd76c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = ['MSE', 'Accuracy', 'Precision', 'Recall', 'F1 Measure']\n",
    "clustering_res = [mse['clustering'], accuracy['clustering'], precision['clustering'], recall['clustering'], f1_measure['clustering']]\n",
    "kd_res = [mse['kd_tree'], accuracy['kd_tree'], precision['kd_tree'], recall['kd_tree'], f1_measure['kd_tree']]\n",
    "ae_res = [mse['autoencoder'], accuracy['autoencoder'], precision['autoencoder'], recall['autoencoder'], f1_measure['autoencoder']]\n",
    "hybrid_res = [mse['hybrid'], accuracy['hybrid'], precision['hybrid'], recall['hybrid'], f1_measure['hybrid']]\n",
    "\n",
    "clustering_res = [float(f\"{res:.4f}\") for res in clustering_res]\n",
    "kd_res = [float(f\"{res:.4f}\") for res in kd_res]\n",
    "ae_res = [float(f\"{res:.4f}\") for res in ae_res]\n",
    "hybrid_res = [float(f\"{res:.4f}\") for res in hybrid_res]\n",
    "\n",
    "x = np.arange(len(labels))  # the label locations\n",
    "width = 0.35  # the width of the bars\n",
    "\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"]=(18, 5)\n",
    "fig, ax = plt.subplots()\n",
    "rects1 = ax.bar(x - (3*width/4), clustering_res, width/2, label='Clustering')\n",
    "rects2 = ax.bar(x - width/4, kd_res, width/2, label='KD Tree')\n",
    "rects3 = ax.bar(x + width/4, ae_res, width/2, label='Autoencoder')\n",
    "rects4 = ax.bar(x + (3*width/4), hybrid_res, width/2, label='Hybrid')\n",
    "\n",
    "# Add some text for labels, title and custom x-axis tick labels, etc.\n",
    "ax.set_ylabel('Metrics')\n",
    "ax.set_title('Metrics Grouped by Method')\n",
    "ax.set_xticks(x)\n",
    "ax.set_xticklabels(labels)\n",
    "ax.legend()\n",
    "\n",
    "ax.bar_label(rects1, padding=3)\n",
    "ax.bar_label(rects2, padding=3)\n",
    "ax.bar_label(rects3, padding=3)\n",
    "ax.bar_label(rects4, padding=3)\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.savefig(os.path.join(cwd, 'methods_comparison.png'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da55de77",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "As can be seen by the above table and chart, the Hybrid approach has the lowest Mean Squared Error and the highest Accuracy, Precision, and Recall. Therefore, the hybrid approach would be a better option.\n",
    "\n",
    "The KD-Tree approach is only based on the genres and casts. It is a content-based only approach. The Clustering approach takes the ratings and genere into account. As a result, it is using both the collaborative filtering and content-based filtering approaches. However, it fails to produce more accurate results compared to the other methods. The autoencoder is a collaborative filtering only approach. The input is the user ratings. The movies use has not yet rated will be set to zero. Then the model tries to recreate the input ratings in the output. In the process of learning, the weights will be updated based on the training users ratings. As a result, the optimization of the network's weights is done using the collaboration of the previous users' ratings. When the model generates the outputs, it also predicts ratings for the unseen movies and these ratings will be used as predictions.\n",
    "\n",
    "Finally, the hybrid approach uses both the collaborative filtering approach (the autoencoder) and the content-based filtering approach (KD-Tree). We have assigned a coefficient of 0.7 for the autoencoder results and a coefficient of 0.3 for the KD-Tree results, and added the final values together. The experiments show that the final Hybrid results are better than the other three approaches."
   ]
  }
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