Baseline - OCSVM.ipynb

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   "source": [
    "import random\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "\n",
    "from evaluator import evaluate\n",
    "\n",
    "from data_loader import load_kdd_cup_urc, load_yahoo_A1, load_yahoo_A2, load_yahoo_A3, load_yahoo_A4, load_power_demand # Univariate Datasets\n",
    "from data_loader import load_nasa, load_ecg, load_gesture, load_smd # Multivariate Datasets\n",
    "\n",
    "from tqdm.notebook import tqdm\n",
    "from matplotlib import pyplot as plt\n",
    "from sklearn.svm import OneClassSVM as OC_SVM\n",
    "\n",
    "# THESE LINES ARE FOR REPRODUCIBILITY\n",
    "random.seed(0)\n",
    "np.random.seed(0)\n",
    "# tf.random.set_seed(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bfcdb8c-20d2-4378-b8b9-78dbe39b2150",
   "metadata": {},
   "source": [
    "### Yahoo S5"
   ]
  },
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   "execution_count": 2,
   "id": "7120f818-ae90-427c-8f14-f0f66dd8f0e9",
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   "source": [
    "total_scores = {'dataset': [], 'f1': [], 'pr_auc': [], 'roc_auc': []}"
   ]
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      "yahoo_A3 0.6666666000000033 0.28238489471634765 0.42799998921440013\n",
      "yahoo_A3 0.1333333084444478 0.019230768121301835 0.47310754597149385\n",
      "yahoo_A3 0.4999999500000025 0.18121935042738538 0.4427710768689216\n",
      "yahoo_A3 0.9999998500000123 0.0 0.9840953288302094\n",
      "yahoo_A3 0.8571427836734729 0.5205564361764399 0.48649998774020015\n",
      "yahoo_A3 0.0 0.0 -0.0\n",
      "yahoo_A3 0.03773584172303435 0.009976224382582919 0.5782499854281005\n",
      "yahoo_A3 0.39999995200000177 0.008961690073875784 0.44199998886160025\n",
      "yahoo_A3 0.12499995156251753 0.025225889856636523 0.512048184134127\n",
      "yahoo_A3 0.7142856551020442 0.4590052328316895 0.47076612305276905\n",
      "yahoo_A3 0.39999995200000177 0.0008397945716561782 0.22874999423550013\n",
      "yahoo_A3 0.16666664861111138 0.045454540495868265 0.510934342443159\n",
      "yahoo_A3 0.39999993600000633 0.08333332638888935 0.48107567306154\n",
      "yahoo_A3 0.9999999000000052 0.4999999375000063 0.49203184781035364\n",
      "yahoo_A3 0.014018690190191375 0.004176847580832417 0.3413173538240887\n",
      "yahoo_A3 0.013888886817612123 0.0027727051723101786 0.26480371700192457\n",
      "yahoo_A3 0.03225806027286437 0.00975230408485427 0.5972499849493003\n",
      "yahoo_A3 0.6666666000000033 0.2535667358153059 0.5659999857368003\n",
      "yahoo_A3 0.9999999250000032 0.7499999447916703 0.5119999870976002\n",
      "yahoo_A3 0.5714285061224537 0.1489780170115033 0.4584164849606182\n",
      "yahoo_A3 0.9999998500000123 0.0 0.5009939855862884\n",
      "yahoo_A3 0.09523805804990017 0.006882022918877858 0.18376753135796248\n",
      "yahoo_A3 0.6666666000000033 0.2738756139702038 0.5737499855415003\n",
      "yahoo_A3 0.9999999000000052 0.4999999375000063 0.49203184781035364\n",
      "yahoo_A3 0.7999999200000041 0.3923076538113117 0.5332667818518656\n",
      "yahoo_A3 0.04255318714350386 0.010869564106805403 0.9105366880893655\n",
      "yahoo_A3 0.33333329444444565 0.009502745719805675 0.5374749390425905\n",
      "yahoo_A3 0.3636363074380234 0.0762515891932147 0.361523038789274\n",
      "yahoo_A3 0.4999999500000025 0.24113909486293472 0.434404276474573\n",
      "yahoo_A3 0.4999999250000064 0.152116388162514 0.4492031647013552\n",
      "yahoo_A3 0.8571427959183704 0.5206692057828746 0.4624495909003379\n",
      "yahoo_A3 0.33333329444444565 0.050534766827294644 0.7246492839589884\n",
      "yahoo_A3 0.005917159169846993 0.0014836793764143531 0.20576538693722565\n",
      "yahoo_A3 0.666666577777783 0.0 0.4999999749003998\n",
      "yahoo_A3 0.012987011691122815 0.003942782930432491 0.3063872152748398\n",
      "yahoo_A3 0.33333329444444565 0.04968267017135721 0.36152303878927405\n",
      "yahoo_A3 0.13333329244445566 0.019056656976769247 0.5174999869590002\n",
      "yahoo_A3 0.9999998500000123 0.0 0.570576483584384\n",
      "yahoo_A3 0.005865102051065952 0.0014705880878027827 0.2783299919923824\n",
      "yahoo_A3 0.666666577777783 0.1333333242777783 0.4820716889477958\n",
      "yahoo_A3 0.9999999166666703 0.6666666055555603 0.48502992385528404\n",
      "yahoo_A3 0.666666577777783 0.006777231432245979 0.405378465706101\n",
      "yahoo_A3 0.33333327777778377 0.084505230105092 0.3229999918604002\n",
      "yahoo_A3 0.7499999343750033 0.409397244790842 0.6787575013188865\n",
      "yahoo_A3 0.8571427836734729 0.5047665298437921 0.5379999864424002\n",
      "yahoo_A3 0.49999993750000526 0.10720163310090385 0.49424998754490024\n",
      "yahoo_A3 0.9999999000000052 0.4999999375000063 0.5199202926255152\n",
      "yahoo_A3 0.9999999000000052 0.4999999375000063 0.46613543476770336\n",
      "yahoo_A3 0.8571427836734729 0.5562499565651072 0.4799999879040003\n",
      "yahoo_A3 0.006329112662233805 0.0007961783038865695 0.11205178720377484\n",
      "yahoo_A3 0.024193544416623886 0.007500368348774917 0.3833667257227723\n",
      "yahoo_A3 0.0051413866416430065 0.00043177891468376527 0.05721889362353144\n",
      "yahoo_A3 0.07692306213017959 0.011949369270714721 0.4651394188814476\n",
      "yahoo_A3 0.08695649829868218 0.018011562939011586 0.5249500821972827\n",
      "yahoo_A3 0.3999999555555591 0.13508704478846398 0.8542499784729005\n"
     ]
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yahoo_A4 0.7999999200000041 0.3374306962665778 0.28176978429727406\n",
      "yahoo_A4 0.009523808566893424 0.0023923442572285672 0.5228627706714042\n",
      "yahoo_A4 0.04255318315980203 0.007351247807714425 0.5418326421231424\n",
      "yahoo_A4 0.010050249256332211 0.002758116778856883 0.3047808611942676\n",
      "yahoo_A4 0.8571427836734729 0.2525092463411058 0.6514999835822004\n",
      "yahoo_A4 0.8888888197530895 0.20515598349244885 0.723446879173658\n",
      "yahoo_A4 0.4999999375000028 0.026864800914764585 0.4328010500245025\n",
      "yahoo_A4 0.23529408581315192 0.06947197351556658 0.40485693918510335\n",
      "yahoo_A4 0.1999999725000028 0.04288068020561243 0.6294078298588459\n",
      "yahoo_A4 0.4999999250000064 0.004891489841093674 0.8481075271467937\n",
      "yahoo_A4 0.14285710000001145 0.03701619764722695 0.42149998937820016\n",
      "yahoo_A4 0.04545453657024923 0.007587556705594939 0.5044820463885508\n",
      "yahoo_A4 0.009592325179856162 0.002752077489294918 0.1499003908814944\n",
      "yahoo_A4 0.09090906652893123 0.022225801519414532 0.31803059479125617\n",
      "yahoo_A4 0.0 0.0 -0.0\n",
      "yahoo_A4 0.10526314681440453 0.027777774845679302 0.39363813153208377\n",
      "yahoo_A4 0.2857142367346995 0.057743573820128236 0.4555110128425831\n",
      "yahoo_A4 0.03773583495906505 0.005559084777696058 0.21174999466390013\n",
      "yahoo_A4 0.33333329444444565 0.05107241114872103 0.5370741374474803\n",
      "yahoo_A4 0.7999999200000041 0.46031741802721443 0.9753825354895009\n",
      "yahoo_A4 0.09999998950000007 0.02631578670360138 0.9274352947460459\n",
      "yahoo_A4 0.010752687096774247 0.0027553367200710164 0.29133464672981463\n",
      "yahoo_A4 0.4999999375000028 0.028555715499716455 0.7498336408572085\n",
      "yahoo_A4 0.15384613727810673 0.0 0.9761430433447127\n",
      "yahoo_A4 0.9999999000000052 0.4999999375000063 0.4940238795828652\n",
      "yahoo_A4 0.017391299236296225 0.0027216544622632164 0.24051895401054207\n",
      "yahoo_A4 0.5454544958677713 0.29471205051126814 0.29044858502051196\n",
      "yahoo_A4 0.01351351215303141 0.003401360201767816 0.2932405272776887\n",
      "yahoo_A4 0.3636363074380234 0.09848119223190305 0.5304609111281643\n",
      "yahoo_A4 0.3999999420000064 0.11054701131348299 0.48416832689295236\n",
      "yahoo_A4 0.9999999000000052 0.0 0.6693226755638817\n",
      "yahoo_A4 0.15384613224852298 0.033973271788784246 0.4377910698172522\n",
      "yahoo_A4 0.004073319347439243 0.0010204080610162535 -0.0\n",
      "yahoo_A4 0.666666577777783 0.011660561059400476 0.615039809762942\n",
      "yahoo_A4 0.6666666055555593 0.37335119041135456 0.38554216217157794\n",
      "yahoo_A4 0.28571425215419793 0.10052849375829692 0.7327499815347005\n",
      "yahoo_A4 0.7999999200000041 0.33333329722222554 0.4457751014856519\n",
      "yahoo_A4 0.4999999375000028 0.0036685699018782596 0.24683964574802517\n",
      "yahoo_A4 0.018348622023959944 0.005113390059830217 0.45309379718168497\n",
      "yahoo_A4 0.666666577777783 0.0030446995077699954 0.42778882314685596\n",
      "yahoo_A4 0.7999999200000041 0.3535713917365396 0.4893546076756671\n",
      "yahoo_A4 0.011428570279183673 0.0028735629293830385 0.11232603248264007\n",
      "yahoo_A4 0.2499999437500102 0.040960702864413306 0.5439999862912003\n",
      "yahoo_A4 0.047619038321996814 0.011162710121335916 0.4322708946350069\n",
      "yahoo_A4 0.032786878796023516 0.004237287916546981 0.2499999874501999\n",
      "yahoo_A4 0.9999998500000123 0.0 0.37574548918971634\n",
      "yahoo_A4 0.06249998789062671 0.0 0.37549798911842763\n",
      "yahoo_A4 0.11764704636678214 0.03124999667968783 0.4572564154160568\n",
      "yahoo_A4 0.01398601259067282 0.004247438453683861 0.3592814250779881\n",
      "yahoo_A4 0.1333333084444478 0.019230768121301835 0.48804778426533046\n",
      "yahoo_A4 0.7999999360000031 0.5259037353248331 0.41633198762278034\n",
      "yahoo_A4 0.5714285142857172 0.26936651761116226 0.6525049968391775\n",
      "yahoo_A4 0.00557103008201364 0.0013966479046378217 0.10139163994008221\n",
      "yahoo_A4 0.9999998500000123 0.0 0.717693765066072\n",
      "yahoo_A4 0.3076922674556251 0.07566137226009924 0.10113106779973\n",
      "yahoo_A4 0.666666577777783 0.113095230725624 0.5049800543316787\n",
      "yahoo_A4 0.666666577777783 0.0017361296727142856 0.5039840384454228\n",
      "yahoo_A4 0.043010748387096995 0.011358702444499838 0.4392430058387974\n",
      "yahoo_A4 0.28571425306122533 0.0 0.9622265437450936\n",
      "yahoo_A4 0.02197801979229578 0.005902589703090964 0.19424999510490007\n",
      "yahoo_A4 0.008368200000000041 0.0021272967710377847 0.07420318352605534\n",
      "yahoo_A4 0.0 0.0 -0.0\n",
      "yahoo_A4 0.09523807755102323 0.01626775601257302 0.5827499853147002\n",
      "yahoo_A4 0.33333329444444565 0.024284878702036147 0.5589178243809787\n",
      "yahoo_A4 0.0 0.0 -0.0\n",
      "yahoo_A4 0.4999999375000028 0.005893381382656544 0.3093812271504898\n",
      "yahoo_A4 0.9999999000000052 0.4999999375000063 0.4282868310899839\n",
      "yahoo_A4 0.005586591618551232 0.0014005600836413136 0.28628227747787915\n",
      "yahoo_A4 0.307692253254446 0.06848026273490612 0.14390896678279383\n",
      "yahoo_A4 0.39999995200000177 0.04627050430209136 0.37049999066340017\n",
      "yahoo_A4 0.0 0.0 -0.0\n",
      "yahoo_A4 0.6666666074074105 0.3375258548442968 0.3509036085389753\n",
      "yahoo_A4 0.4999999472222262 0.18675594366452342 0.45699998848360024\n",
      "yahoo_A4 0.307692253254446 0.10434433418164099 0.5582329223109306\n",
      "yahoo_A4 0.009615383653846201 0.001813056254021248 0.23705178092887474\n",
      "yahoo_A4 0.041666660525174315 0.009541815322373623 0.562541564635998\n",
      "yahoo_A4 0.2222221827160542 0.03571428341836747 0.3057768770805233\n",
      "yahoo_A4 0.027397256211297148 0.00523969140098962 0.2807717803387241\n",
      "yahoo_A4 0.04255318315980203 0.007879940280119515 0.46713145065395917\n",
      "yahoo_A4 0.04285713590816426 0.004345021622935951 0.5505009908836673\n",
      "yahoo_A4 0.02217294679967176 0.007082901385009641 0.3663326579305949\n",
      "yahoo_A4 0.006329112662233805 0.0007961783038865695 0.10159362039808917\n"
     ]
    }
   ],
   "source": [
    "for loader in [load_yahoo_A1, load_yahoo_A2, load_yahoo_A3, load_yahoo_A4]:\n",
    "    datasets = loader()\n",
    "    x_trains, x_tests, y_tests = datasets['x_train'], datasets['x_test'], datasets['y_test']\n",
    "    \n",
    "    for i in tqdm(range(len(x_trains))):\n",
    "        X_train = x_trains[i]\n",
    "        X_test = x_tests[i]\n",
    "        \n",
    "        clf = OC_SVM().fit(X_train)\n",
    "        # 1 -> 0 for inliers, -1 -> 1 for outliers.\n",
    "        X_test_rec = -clf.decision_function(X_test)\n",
    "        X_test_rec = X_test_rec.reshape(y_tests[i].shape)\n",
    "\n",
    "        \n",
    "        scores = evaluate(X_test, X_test_rec, y_tests[i], is_reconstructed=False)\n",
    "\n",
    "        total_scores['dataset'].append(loader.__name__.replace('load_', ''))\n",
    "        total_scores['f1'].append(np.max(scores['f1']))\n",
    "        total_scores['pr_auc'].append(scores['pr_auc'])\n",
    "        total_scores['roc_auc'].append(scores['roc_auc'])\n",
    "        print(loader.__name__.replace('load_', ''), np.max(scores['f1']), scores['pr_auc'], scores['roc_auc'])   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cccf779a-730b-4703-8e6c-97724ab860f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "yahoo_results = pd.DataFrame(total_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ea7039b9-d931-49ba-8e6f-a56d20f94a49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>f1</th>\n",
       "      <th>pr_auc</th>\n",
       "      <th>roc_auc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>yahoo_A1</th>\n",
       "      <td>0.729201</td>\n",
       "      <td>0.216275</td>\n",
       "      <td>0.475218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yahoo_A2</th>\n",
       "      <td>0.433420</td>\n",
       "      <td>0.053706</td>\n",
       "      <td>0.629472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yahoo_A3</th>\n",
       "      <td>0.397581</td>\n",
       "      <td>0.149353</td>\n",
       "      <td>0.430581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yahoo_A4</th>\n",
       "      <td>0.306565</td>\n",
       "      <td>0.077984</td>\n",
       "      <td>0.420363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                f1    pr_auc   roc_auc\n",
       "dataset                               \n",
       "yahoo_A1  0.729201  0.216275  0.475218\n",
       "yahoo_A2  0.433420  0.053706  0.629472\n",
       "yahoo_A3  0.397581  0.149353  0.430581\n",
       "yahoo_A4  0.306565  0.077984  0.420363"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yahoo_results.groupby('dataset').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ddd82095-b636-4390-bc21-b072f0751558",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1ef9799a-dc1c-4402-823b-73686adb75fe",
   "metadata": {},
   "source": [
    "### NASA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "aadd094c-c63d-4d7c-b5d8-68fab9affcf6",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_scores = {'dataset': [], 'f1': [], 'pr_auc': [], 'roc_auc': []}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4fe4df3d-d02a-4b9b-a5c1-aaf26a8300ae",
   "metadata": {},
   "outputs": [
    {
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D1 0.13237733345371766 0.017229667008321563 0.023491769262070215\n",
      "D2 0.239109990245366 0.07736669558433912 0.09214854523418256\n"
     ]
    }
   ],
   "source": [
    "for loader in [load_nasa]:\n",
    "    datasets = loader()\n",
    "    x_trains, x_tests, y_tests = datasets['x_train'], datasets['x_test'], datasets['y_test']\n",
    "    \n",
    "    for i in tqdm(range(len(x_trains))):\n",
    "        X_train = x_trains[i]\n",
    "        X_test = x_tests[i]\n",
    "        \n",
    "        clf = OC_SVM().fit(X_train)\n",
    "        # 1 -> 0 for inliers, -1 -> 1 for outliers.\n",
    "        X_test_rec = -clf.decision_function(X_test)\n",
    "        X_test_rec = X_test_rec.reshape(y_tests[i].shape)\n",
    "\n",
    "        \n",
    "        scores = evaluate(X_test, X_test_rec, y_tests[i], is_reconstructed=False)\n",
    "\n",
    "        total_scores['dataset'].append(f'D{i+1}')\n",
    "        total_scores['f1'].append(np.max(scores['f1']))\n",
    "        total_scores['pr_auc'].append(scores['pr_auc'])\n",
    "        total_scores['roc_auc'].append(scores['roc_auc'])\n",
    "        print(f'D{i+1}', np.max(scores['f1']), scores['pr_auc'], scores['roc_auc'])   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "44ae8daa-b8bd-4c69-96b3-38185b1e5dc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "nasa_results = pd.DataFrame(total_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "678bf9be-364d-419b-bbb4-4f6fd194a659",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>f1</th>\n",
       "      <th>pr_auc</th>\n",
       "      <th>roc_auc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>D1</th>\n",
       "      <td>0.132377</td>\n",
       "      <td>0.017230</td>\n",
       "      <td>0.023492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D2</th>\n",
       "      <td>0.239110</td>\n",
       "      <td>0.077367</td>\n",
       "      <td>0.092149</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               f1    pr_auc   roc_auc\n",
       "dataset                              \n",
       "D1       0.132377  0.017230  0.023492\n",
       "D2       0.239110  0.077367  0.092149"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nasa_results.groupby('dataset').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23288b5a-ff7b-4bc3-8a66-5c61ef843e1c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "58c6a2d1-049b-41e2-9945-880812f374ed",
   "metadata": {},
   "source": [
    "### SMD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "db3a6273-3f92-41b3-bafa-d217a421f5fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_scores = {'dataset': [], 'f1': [], 'pr_auc': [], 'roc_auc': []}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "efa87945-2e91-4dc7-8162-1e2ed2dbc41d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f2084ffb7a234c19a30a89b0087ba1db",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/28 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4cf45683883c4713a78b5149f4a99cd4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/28 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "smd 0.45610564765178774 0.29044377899549245 0.7568291575896886\n",
      "smd 0.5374553642724784 0.4281995155907894 0.1873317312855692\n",
      "smd 0.13177100695673052 0.05076801258074222 0.4343943173273155\n",
      "smd 0.183730677530404 0.070025784907337 0.5841973783862764\n",
      "smd 0.3728813151840193 0.01191899480453335 0.6056449625367526\n",
      "smd 0.8591906329457708 0.5832235568773836 0.8123091667137141\n",
      "smd 0.17318469934796973 0.08547074465164277 0.14237526607757378\n",
      "smd 0.26966289682085814 0.057775044739286074 0.41348534477040916\n",
      "smd 0.4535103869952418 0.3407342450239448 0.353122006959464\n",
      "smd 0.24340570102914458 0.11113982108651937 0.22302670919608544\n",
      "smd 0.4812833723723604 0.41451944192119394 0.6188243452414239\n",
      "smd 0.32922619221106897 0.09942958116670797 0.39353887445890345\n",
      "smd 0.24761900708310183 0.07884986110262711 0.519802002387287\n",
      "smd 0.23891397981074217 0.05049525098673728 0.6860021971737942\n",
      "smd 0.7861885293870144 0.050454763876202485 0.7363370180960099\n",
      "smd 0.04961831561936049 0.0006313143055188895 0.6996885699040141\n",
      "smd 0.5811087815537342 0.316138281525452 0.6206212256304704\n",
      "smd 0.4090908589580935 0.011173962725669109 0.5814222405720787\n",
      "smd 0.7765486237316113 0.7069752310772635 0.5016284862631574\n",
      "smd 0.05162972117496042 0.0015589052527618852 0.7374874256431438\n",
      "smd 0.17991499675162634 0.06693796717040185 0.6028538182320663\n",
      "smd 0.10424023334698733 0.035750543733866026 0.2057237385742343\n",
      "smd 0.08840433415248374 0.00022891732149000376 0.067955670867605\n",
      "smd 0.07408258586913781 0.014488348442581563 0.5105834590829256\n",
      "smd 0.414547142593672 0.2957733373177821 0.7347235474520598\n",
      "smd 0.06550327743431901 0.0012435789336326015 0.17983442669702446\n",
      "smd 0.470358764556866 0.34915443152267206 0.6776512431198423\n",
      "smd 0.34482753618847334 0.01634110813708189 0.6153228942806155\n"
     ]
    }
   ],
   "source": [
    "for loader in [load_smd]:\n",
    "    datasets = loader()\n",
    "    x_trains, x_tests, y_tests = datasets['x_train'], datasets['x_test'], datasets['y_test']\n",
    "    \n",
    "    for i in tqdm(range(len(x_trains))):\n",
    "        X_train = x_trains[i]\n",
    "        X_test = x_tests[i]\n",
    "        \n",
    "        clf = OC_SVM().fit(X_train)\n",
    "        # 1 -> 0 for inliers, -1 -> 1 for outliers.\n",
    "        X_test_rec = -clf.decision_function(X_test)\n",
    "        X_test_rec = X_test_rec.reshape(y_tests[i].shape)\n",
    "\n",
    "        \n",
    "        scores = evaluate(X_test, X_test_rec, y_tests[i], is_reconstructed=False)\n",
    "\n",
    "        total_scores['dataset'].append(loader.__name__.replace('load_', ''))\n",
    "        total_scores['f1'].append(np.max(scores['f1']))\n",
    "        total_scores['pr_auc'].append(scores['pr_auc'])\n",
    "        total_scores['roc_auc'].append(scores['roc_auc'])\n",
    "        print(loader.__name__.replace('load_', ''), np.max(scores['f1']), scores['pr_auc'], scores['roc_auc'])   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9dc39b15-12b3-4c10-a4c4-507b07f08c6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "smd_results = pd.DataFrame(total_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5c893abe-8a44-4eee-8da5-5a5a36bcb8ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>f1</th>\n",
       "      <th>pr_auc</th>\n",
       "      <th>roc_auc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>smd</th>\n",
       "      <td>0.334786</td>\n",
       "      <td>0.162137</td>\n",
       "      <td>0.50724</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               f1    pr_auc  roc_auc\n",
       "dataset                             \n",
       "smd      0.334786  0.162137  0.50724"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smd_results.groupby('dataset').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35288ba6-925a-43e7-87ba-05813d0c0fa1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "60435faf-40e6-4a37-b8df-59a3c195e6b8",
   "metadata": {},
   "source": [
    "### ECG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1477439e-db02-4e2b-9a7b-66f950a45cec",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_scores = {'dataset': [], 'f1': [], 'pr_auc': [], 'roc_auc': []}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "76d2746b-d736-4294-9cf0-a264b8f3ac68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "621293bf62e54f49a3213af1f7166761",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f432525b5b384ae1af50bce8d5b9a893",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D1 0.4471403313559403 0.3428974367560506 0.26087573415811255\n",
      "D2 0.40531556450812417 0.33026908925879145 0.30935255787605564\n",
      "D3 0.1976284168687303 0.07664542535069907 0.10992214421704366\n",
      "D4 0.21345703452286288 0.0766606276030171 0.16408398960369924\n",
      "D5 0.4771241331133376 0.36229251001572227 0.7257551750983611\n",
      "D6 0.09964408400098196 0.026521753636017255 0.11996061190488712\n",
      "D7 0.06432159003374155 0.019136653169685942 0.22569809689621562\n",
      "D8 0.12636692364855745 0.026324812533934323 0.11753871192397448\n",
      "D9 0.2594523747560351 0.07872649396852109 0.15792236952012004\n"
     ]
    }
   ],
   "source": [
    "for loader in [load_ecg]:\n",
    "    datasets = loader()\n",
    "    x_trains, x_tests, y_tests = datasets['x_train'], datasets['x_test'], datasets['y_test']\n",
    "    \n",
    "    for i in tqdm(range(len(x_trains))):\n",
    "        X_train = x_trains[i]\n",
    "        X_test = x_tests[i]\n",
    "        \n",
    "        clf = OC_SVM().fit(X_train)\n",
    "        # 1 -> 0 for inliers, -1 -> 1 for outliers.\n",
    "        X_test_rec = -clf.decision_function(X_test)\n",
    "        X_test_rec = X_test_rec.reshape(y_tests[i].shape)\n",
    "\n",
    "        \n",
    "        scores = evaluate(X_test, X_test_rec, y_tests[i], is_reconstructed=False)\n",
    "\n",
    "        total_scores['dataset'].append(f'D{i+1}')\n",
    "        total_scores['f1'].append(np.max(scores['f1']))\n",
    "        total_scores['pr_auc'].append(scores['pr_auc'])\n",
    "        total_scores['roc_auc'].append(scores['roc_auc'])\n",
    "        print(f'D{i+1}', np.max(scores['f1']), scores['pr_auc'], scores['roc_auc'])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "12d7acd4-7787-4f4d-a887-1a5a8b503038",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>f1</th>\n",
       "      <th>pr_auc</th>\n",
       "      <th>roc_auc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>D1</th>\n",
       "      <td>0.447140</td>\n",
       "      <td>0.342897</td>\n",
       "      <td>0.260876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D2</th>\n",
       "      <td>0.405316</td>\n",
       "      <td>0.330269</td>\n",
       "      <td>0.309353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D3</th>\n",
       "      <td>0.197628</td>\n",
       "      <td>0.076645</td>\n",
       "      <td>0.109922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D4</th>\n",
       "      <td>0.213457</td>\n",
       "      <td>0.076661</td>\n",
       "      <td>0.164084</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D5</th>\n",
       "      <td>0.477124</td>\n",
       "      <td>0.362293</td>\n",
       "      <td>0.725755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D6</th>\n",
       "      <td>0.099644</td>\n",
       "      <td>0.026522</td>\n",
       "      <td>0.119961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D7</th>\n",
       "      <td>0.064322</td>\n",
       "      <td>0.019137</td>\n",
       "      <td>0.225698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D8</th>\n",
       "      <td>0.126367</td>\n",
       "      <td>0.026325</td>\n",
       "      <td>0.117539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D9</th>\n",
       "      <td>0.259452</td>\n",
       "      <td>0.078726</td>\n",
       "      <td>0.157922</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               f1    pr_auc   roc_auc\n",
       "dataset                              \n",
       "D1       0.447140  0.342897  0.260876\n",
       "D2       0.405316  0.330269  0.309353\n",
       "D3       0.197628  0.076645  0.109922\n",
       "D4       0.213457  0.076661  0.164084\n",
       "D5       0.477124  0.362293  0.725755\n",
       "D6       0.099644  0.026522  0.119961\n",
       "D7       0.064322  0.019137  0.225698\n",
       "D8       0.126367  0.026325  0.117539\n",
       "D9       0.259452  0.078726  0.157922"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ecg_results = pd.DataFrame(total_scores)\n",
    "ecg_results.groupby('dataset').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03670ca5-f3e9-4032-b958-be30ba5c2664",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f6c62b3a-074b-4f30-97c7-9f2a69c6ff67",
   "metadata": {},
   "source": [
    "### Power Demand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ea28e836-3741-4d78-8193-d54857d5d28d",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_scores = {'dataset': [], 'f1': [], 'pr_auc': [], 'roc_auc': []}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "da32bb00-93e2-4c99-919d-5d1ed0ed8fc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9a7a84650d7248cbafb8a6b13d427441",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "power_demand 0.2451253166374879 0.07203812556623504 0.13006826690588436\n"
     ]
    }
   ],
   "source": [
    "for loader in [load_power_demand]:\n",
    "    datasets = loader()\n",
    "    x_trains, x_tests, y_tests = datasets['x_train'], datasets['x_test'], datasets['y_test']\n",
    "    \n",
    "    for i in tqdm(range(len(x_trains))):\n",
    "        X_train = x_trains[i]\n",
    "        X_test = x_tests[i]\n",
    "        \n",
    "        clf = OC_SVM().fit(X_train)\n",
    "        # 1 -> 0 for inliers, -1 -> 1 for outliers.\n",
    "        X_test_rec = -clf.decision_function(X_test)\n",
    "        X_test_rec = X_test_rec.reshape(y_tests[i].shape)\n",
    "\n",
    "        \n",
    "        scores = evaluate(X_test, X_test_rec, y_tests[i], is_reconstructed=False)\n",
    "\n",
    "        total_scores['dataset'].append(loader.__name__.replace('load_', ''))\n",
    "        total_scores['f1'].append(np.max(scores['f1']))\n",
    "        total_scores['pr_auc'].append(scores['pr_auc'])\n",
    "        total_scores['roc_auc'].append(scores['roc_auc'])\n",
    "        print(loader.__name__.replace('load_', ''), np.max(scores['f1']), scores['pr_auc'], scores['roc_auc']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "17089905-c008-4799-a70c-1c8adcd7b4f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>f1</th>\n",
       "      <th>pr_auc</th>\n",
       "      <th>roc_auc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>power_demand</th>\n",
       "      <td>0.245125</td>\n",
       "      <td>0.072038</td>\n",
       "      <td>0.130068</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    f1    pr_auc   roc_auc\n",
       "dataset                                   \n",
       "power_demand  0.245125  0.072038  0.130068"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "power_results = pd.DataFrame(total_scores)\n",
    "power_results.groupby('dataset').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66f9779b-2791-4cca-b3d1-466160fedd21",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c5c244b6-fe82-46b3-9781-e80d5f1d2ad3",
   "metadata": {},
   "source": [
    "### 2D Gesture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4a686b0e-dd51-4306-bf9f-18129401ea7e",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_scores = {'dataset': [], 'f1': [], 'pr_auc': [], 'roc_auc': []}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bb477b1f-5481-4188-a9d8-73d5a618f07f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c3a9d70a9a604b0bba3ffe9ed46b20a8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gesture 0.5071769901395976 0.3945587169680225 0.3198546919809325\n"
     ]
    }
   ],
   "source": [
    "for loader in [load_gesture]:\n",
    "    datasets = loader()\n",
    "    x_trains, x_tests, y_tests = datasets['x_train'], datasets['x_test'], datasets['y_test']\n",
    "    \n",
    "    for i in tqdm(range(len(x_trains))):\n",
    "        X_train = x_trains[i]\n",
    "        X_test = x_tests[i]\n",
    "        \n",
    "        clf = OC_SVM().fit(X_train)\n",
    "        # 1 -> 0 for inliers, -1 -> 1 for outliers.\n",
    "        X_test_rec = -clf.decision_function(X_test)\n",
    "        X_test_rec = X_test_rec.reshape(y_tests[i].shape)\n",
    "\n",
    "        \n",
    "        scores = evaluate(X_test, X_test_rec, y_tests[i], is_reconstructed=False)\n",
    "\n",
    "        total_scores['dataset'].append(loader.__name__.replace('load_', ''))\n",
    "        total_scores['f1'].append(np.max(scores['f1']))\n",
    "        total_scores['pr_auc'].append(scores['pr_auc'])\n",
    "        total_scores['roc_auc'].append(scores['roc_auc'])\n",
    "        print(loader.__name__.replace('load_', ''), np.max(scores['f1']), scores['pr_auc'], scores['roc_auc'])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dc5a623d-0c0e-4f72-a169-2c711742416e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>f1</th>\n",
       "      <th>pr_auc</th>\n",
       "      <th>roc_auc</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>gesture</th>\n",
       "      <td>0.507177</td>\n",
       "      <td>0.394559</td>\n",
       "      <td>0.319855</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               f1    pr_auc   roc_auc\n",
       "dataset                              \n",
       "gesture  0.507177  0.394559  0.319855"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gesture_results = pd.DataFrame(total_scores)\n",
    "gesture_results.groupby('dataset').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ba9af1be-0c91-4e01-a530-e4efcbf4e032",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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