Baseline - CNN-AE.ipynb

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   "source": [
    "import os\n",
    "import random\n",
    "\n",
    "os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\";\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"1\"\n",
    "os.environ[\"TF_FORCE_GPU_ALLOW_GROWTH\"] = \"true\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "saving-behavior",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "\n",
    "from evaluator import evaluate\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 tensorflow import keras\n",
    "from tensorflow.keras import layers\n",
    "from tqdm.notebook import tqdm\n",
    "\n",
    "# THESE LINES ARE FOR REPRODUCIBILITY\n",
    "random.seed(0)\n",
    "np.random.seed(0)\n",
    "tf.random.set_seed(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "df48f9cd-3d69-4496-9823-0a66187dabc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def CNN_AE(X_train):\n",
    "    Conv1D = layers.Conv1D\n",
    "    Conv1DT = layers.Conv1DTranspose\n",
    "    Dropout = layers.Dropout\n",
    "\n",
    "    model = keras.Sequential(\n",
    "        [\n",
    "            layers.InputLayer(input_shape=(X_train.shape[1], X_train.shape[2])),\n",
    "            Conv1D(32, 7, padding='same', strides=2, activation='relu'),\n",
    "            Dropout(0.2),\n",
    "            Conv1D(16, 7, padding='same', strides=2, activation='relu'),\n",
    "            Conv1DT(16, 7, padding='same', strides=2, activation='relu'),\n",
    "            Dropout(0.2),\n",
    "            Conv1DT(32, 7, padding='same', strides=2, activation='relu'),\n",
    "            Conv1DT(1, 7, padding='same')\n",
    "        ]\n",
    "    )\n",
    "    model.compile(optimizer=keras.optimizers.Adam(learning_rate=0.001), loss=\"mse\")\n",
    "    model.fit(X_train, X_train, epochs=50, batch_size=128, validation_split=0.3, verbose=0, callbacks=[keras.callbacks.EarlyStopping(monitor=\"val_loss\", patience=5, mode=\"min\", restore_best_weights=True)])    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa97c067-d18d-4c06-ae1b-2cbfe88788b6",
   "metadata": {},
   "source": [
    "### Yahoo S5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "eight-partition",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_scores = {'dataset': [], 'f1': [], 'pr_auc': [], 'roc_auc': []}"
   ]
  },
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   "metadata": {
    "scrolled": true,
    "tags": []
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      "yahoo_A3 0.9999999494709021 0.9947089913665436 0.9999999990724933\n",
      "yahoo_A3 0.9759999484672026 0.9778188614702807 0.9986494330129203\n",
      "yahoo_A3 0.4312267300064981 0.20408702961009637 0.7023321761382679\n",
      "yahoo_A3 0.2812499757202169 0.014218947727946489 0.285259082069183\n",
      "yahoo_A3 0.9918698670896977 0.9740400138015646 0.9959037359694587\n",
      "yahoo_A3 0.7610618955595617 0.8032000874652826 0.9622860843405603\n",
      "yahoo_A3 0.45070419027970904 0.11648518535711627 0.3537795163144629\n",
      "yahoo_A3 0.6075948942156735 0.07942064696928905 0.19105424181534114\n",
      "yahoo_A3 0.4086798950024387 0.16111927024056658 0.47574129258100595\n",
      "yahoo_A3 0.9841269325648803 0.9789338708902098 0.9986629551539306\n",
      "yahoo_A3 0.9967637034006791 0.9916263870462358 0.9991145208436781\n",
      "yahoo_A3 0.87356316810969 0.7560012194842075 0.9476040473942638\n",
      "yahoo_A3 0.8872179940075782 0.8474900451003109 0.9835027749274002\n",
      "yahoo_A3 0.969696918763394 0.9861908930220834 0.9986802400527344\n",
      "yahoo_A3 0.975845360157766 0.9890334348997283 0.994969296245568\n",
      "yahoo_A3 0.8916876077927048 0.6641714900687788 0.36448582771272947\n",
      "yahoo_A3 0.8309178253484486 0.8337095956617853 0.7871415997385188\n",
      "yahoo_A3 0.5357736710795402 0.13731219510447787 0.01563871557255795\n",
      "yahoo_A3 0.998073167341971 0.9961243711550998 0.9999572640169444\n",
      "yahoo_A3 0.6075948942156735 0.5738599380640586 0.7225385033061351\n",
      "yahoo_A3 0.7356321371911775 0.513235246727015 0.531022641903672\n",
      "yahoo_A3 0.9973889834220724 0.9942880740998479 0.9995694715225751\n",
      "yahoo_A3 0.9752474742807103 0.9889237387461252 0.995565919134894\n",
      "yahoo_A3 0.45070419027970904 0.25177009913356446 0.4944848498679101\n",
      "yahoo_A3 0.8215487727465479 0.9234620026066537 0.8684180527895391\n",
      "yahoo_A3 0.8749999489583361 0.8255056726453949 0.9864464034657733\n",
      "yahoo_A3 0.9999999495726523 0.9957264929189362 0.9999999990872126\n",
      "yahoo_A3 0.9166666152638916 0.9631841227838059 0.9959684210051847\n",
      "yahoo_A3 0.323809496547848 0.007735943923552123 0.14065513954793943\n",
      "yahoo_A3 0.7153284209963267 0.6981925252511425 0.6812305124994434\n",
      "yahoo_A3 0.9361701624497034 0.9820496277152739 0.9749729915799593\n",
      "yahoo_A3 0.9810725049134755 0.9948468611137595 0.9954214953588731\n",
      "yahoo_A3 0.6846038411666818 0.5528030090629168 0.5729498167053285\n",
      "yahoo_A3 0.9999999484375026 0.9687499929863995 0.9999999981715427\n",
      "yahoo_A3 0.5367964874796244 0.4030019784343706 0.7027004236442576\n",
      "yahoo_A3 0.9960783805920825 0.9919498493444434 0.9998998386419373\n",
      "yahoo_A3 0.5874798941145886 0.002525254827719501 0.2334630343506266\n",
      "yahoo_A3 0.6199999496955596 0.6544493672890176 0.7924132278695457\n",
      "yahoo_A3 0.700147664776179 0.19311618248890167 0.30375344883390637\n",
      "yahoo_A3 0.6585365409949464 0.12925550817427062 0.39457770850515544\n",
      "yahoo_A3 0.9999999491803305 0.9918032738691552 0.9999999988658623\n",
      "yahoo_A3 0.9999999493865056 0.9938650268733292 0.9999999990254923\n",
      "yahoo_A3 0.9999999484375026 0.9687499930128896 0.9999999981715426\n",
      "yahoo_A3 0.7283236529018707 0.46781028482970866 0.45898972804403865\n",
      "yahoo_A3 0.9870800528520614 0.9909734247664416 0.994398748090163\n",
      "yahoo_A3 0.9999999492187527 0.9921874953450563 0.9999999988982372\n",
      "yahoo_A3 0.9741934977773179 0.9870234370860826 0.9968475867173497\n",
      "yahoo_A3 0.2544731387231303 0.1006419716357132 0.3927991896622583\n",
      "yahoo_A3 0.6943619722688439 0.09436986791762444 0.14727272455644155\n",
      "yahoo_A3 0.9915611310162215 0.9951462288533615 0.9992621220933355\n",
      "yahoo_A3 0.9822221717896321 0.9944457501319761 0.9988530916088887\n",
      "yahoo_A3 0.8167938426927366 0.8811248569499202 0.9553548840133054\n",
      "yahoo_A3 0.9999999496951245 0.9969512174144265 0.9999999988022648\n",
      "yahoo_A3 0.9922480115057214 0.9922898535245935 0.9944705843993998\n",
      "yahoo_A3 0.5217390848357789 0.3012210161847187 0.8293124484657595\n",
      "yahoo_A3 0.6236558703896433 0.5818150205435735 0.6143557573352523\n",
      "yahoo_A3 0.43694490352936993 0.12341958136072419 0.5276449093880653\n",
      "yahoo_A3 0.8539325339808765 0.9398140218171273 0.9520135261723331\n",
      "yahoo_A3 0.7215189375821216 0.7637358884153204 0.8616198474110746\n",
      "yahoo_A3 0.9999999484375026 0.984374991819945 0.9999999981715427\n",
      "yahoo_A3 0.25396823169564314 0.0013262609923276355 -0.0\n",
      "yahoo_A3 0.9999999492187527 0.9921874953568824 0.9999999988982372\n",
      "yahoo_A3 0.9999999491304373 0.9913043427536148 0.9999999988227425\n",
      "yahoo_A3 0.999999948591552 0.9859154853687733 0.9999999983205466\n",
      "yahoo_A3 0.8862558740751584 0.9382778742408555 0.9182353525004932\n",
      "yahoo_A3 0.8207791719588492 0.8216627738077881 0.7655193236545206\n",
      "yahoo_A3 0.9999999494413433 0.9944134043158066 0.999999999058199\n",
      "yahoo_A3 0.7231637924782184 0.802125966225386 0.7739646530857838\n",
      "yahoo_A3 0.9999999491935511 0.9919354791126205 0.9999999988770929\n",
      "yahoo_A3 0.9999999490909118 0.9909090856598246 0.9999999987878788\n",
      "yahoo_A3 0.9999999492957773 0.9929577421705947 0.9999999989602042\n",
      "yahoo_A3 0.5194804748786604 0.3497259439498366 0.7989539880907309\n",
      "yahoo_A3 0.7811633870811333 0.4341598389752874 0.3247019981121717\n",
      "yahoo_A3 0.3882783568463308 0.11690919954768202 0.22535940752377054\n",
      "yahoo_A3 0.45070419027970904 0.19419246549661118 0.38161259633519806\n",
      "yahoo_A3 0.6650123596352452 0.5156182953216365 0.7205711455784367\n",
      "yahoo_A3 0.8648648151100936 0.8585801438448043 0.9573798169036951\n"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yahoo_A4 0.8101265333974286 0.8189026420900345 0.8159884347383042\n",
      "yahoo_A4 0.2757111358464748 0.0810605536988216 0.1955534108692667\n",
      "yahoo_A4 0.41726615387920135 0.02436990568235497 0.289129429825014\n",
      "yahoo_A4 0.2970296775316166 0.08466546601403058 0.22520565550479696\n",
      "yahoo_A4 0.9974554202306287 0.9948719911884788 0.9999790890766338\n",
      "yahoo_A4 0.906148817004433 0.9498275969111786 0.9646029363635006\n",
      "yahoo_A4 0.9909090400082671 0.9913119437163782 0.9997987270416937\n",
      "yahoo_A4 0.9743589233609493 0.983626906114527 0.9977756485488377\n",
      "yahoo_A4 0.6835442570421438 0.465952224699498 0.6670674626884339\n",
      "yahoo_A4 0.9644268267149958 0.9882112632014334 0.9983473546692888\n",
      "yahoo_A4 0.7627627151187253 0.877755625573554 0.8719532279582499\n",
      "yahoo_A4 0.3882783568463308 0.009799171746010625 0.13214818059807854\n",
      "yahoo_A4 0.624390196387868 0.5955139846368949 0.7343365936046167\n",
      "yahoo_A4 0.46931403573095315 0.2891306410073324 0.6934711015939846\n",
      "yahoo_A4 0.07439824225732532 0.0035933378337895576 0.06533406297355433\n",
      "yahoo_A4 0.9999999484375026 0.9843749918224911 0.9999999981715426\n",
      "yahoo_A4 0.5953653758903948 0.4634370929140567 0.4349625866966947\n",
      "yahoo_A4 0.7702406523239308 0.6860079207206959 0.8228471778673415\n",
      "yahoo_A4 0.7721087955707143 0.8527720794346741 0.7667900672183596\n",
      "yahoo_A4 0.9303620667158103 0.9553364431437582 0.9550804923628013\n",
      "yahoo_A4 0.9473683696986855 0.9576027461475185 0.996405417061094\n",
      "yahoo_A4 0.45070419027970904 0.016439864055112437 0.030344785921196705\n",
      "yahoo_A4 0.7820068722500957 0.7922045675061861 0.7417107590093279\n",
      "yahoo_A4 0.9921259326926679 0.9834490150995169 0.9998337747675913\n",
      "yahoo_A4 0.9999999492187527 0.9921874953700389 0.9999999988982372\n",
      "yahoo_A4 0.6633662863738886 0.7318296162678238 0.8328827921977022\n",
      "yahoo_A4 0.8328911978596936 0.6795987841295968 0.51530498966694\n",
      "yahoo_A4 0.7785234398810894 0.576519685886672 0.9491823948679295\n",
      "yahoo_A4 0.9837837332534722 0.9895386091261207 0.9969960079002242\n",
      "yahoo_A4 0.8437499496772796 0.9161670810901243 0.8752401541998267\n",
      "yahoo_A4 0.9999999452380977 0.9523809329671135 0.9999999949994318\n",
      "yahoo_A4 0.772357673425213 0.7786963744101463 0.8966600388856021\n",
      "yahoo_A4 0.820143834025157 0.8188939020606081 0.9711008185401815\n",
      "yahoo_A4 0.999999949152545 0.9915254187249474 0.9999999988419835\n",
      "yahoo_A4 0.8267716046507871 0.8459854003969534 0.8699243555991434\n",
      "yahoo_A4 0.9753424152223708 0.950485749445944 0.9943316906472783\n",
      "yahoo_A4 0.7598565829935411 0.8590095267986155 0.8809766565697028\n",
      "yahoo_A4 0.7245508500932298 0.6624480343756526 0.7913775524962836\n",
      "yahoo_A4 0.4888059322809952 0.15616912480823075 0.43264527067757924\n",
      "yahoo_A4 0.4074073742112509 0.35078995118281364 0.6872547681658007\n",
      "yahoo_A4 0.9941519962039628 0.9937459500650718 0.9997712408716728\n",
      "yahoo_A4 0.9999999357142885 0.85714281602041 0.9999999854833388\n",
      "yahoo_A4 0.5806451198959446 0.13349519221651 0.2620084375201046\n",
      "yahoo_A4 0.9066666154933362 0.9621771411499943 0.993617568726437\n",
      "yahoo_A4 0.4878048294004939 0.2964196454397739 0.6914198718572488\n",
      "yahoo_A4 0.9770991851756917 0.9746164406860293 0.9985663213124618\n",
      "yahoo_A4 0.6564102106298519 0.6695217747641189 0.7320963533600702\n",
      "yahoo_A4 0.9999999484375026 0.984374991357483 0.9999999981715427\n",
      "yahoo_A4 0.46422334988015346 0.014905119917118159 0.029273049174514662\n",
      "yahoo_A4 0.45070419027970904 0.0900297521473658 0.11949276484115108\n",
      "yahoo_A4 0.9999999495983961 0.9959839330884007 0.9999999990748333\n",
      "yahoo_A4 0.9158878002503884 0.9136001413301913 0.8982960243436795\n",
      "yahoo_A4 0.7638888386091853 0.6469162994547879 0.9408426058122392\n",
      "yahoo_A4 0.9999999484375026 0.984374991749043 0.9999999981715428\n",
      "yahoo_A4 0.5852089616298454 0.07618323917422476 0.1553668565004208\n",
      "yahoo_A4 0.9999999490196104 0.9901960727927712 0.9999999987237499\n",
      "yahoo_A4 0.6444444000493856 0.6123419725924721 0.6894124981452356\n",
      "yahoo_A4 0.954248314767827 0.9770435324521994 0.9976632022691712\n",
      "yahoo_A4 0.6956521277882828 0.37484884842590976 0.8747922191451605\n",
      "yahoo_A4 0.5919999581337628 0.33636169802248284 0.46871042010155084\n",
      "yahoo_A4 0.45070419027970904 0.0024510341179937076 0.024038460622637328\n",
      "yahoo_A4 0.6982248064178453 0.5874601540385284 0.47183428620263956\n",
      "yahoo_A4 0.9943819719132709 0.9917949974426513 0.9985143561137094\n",
      "yahoo_A4 0.9999999495412869 0.9955357113706745 0.9999999990891805\n",
      "yahoo_A4 0.25396823169564314 0.061889236656012134 0.08654060424430565\n",
      "yahoo_A4 0.925925875514406 0.9461332025924716 0.9654749023406569\n",
      "yahoo_A4 0.9999999491869945 0.99186991390116 0.9999999988715345\n",
      "yahoo_A4 0.25396823169564314 0.05936873870607062 0.0650849821088085\n",
      "yahoo_A4 0.8111454607367109 0.8751348356812668 0.8766140107722389\n",
      "yahoo_A4 0.9999999495327129 0.995327099783696 0.9999999990902327\n",
      "yahoo_A4 0.8296295786227739 0.7192695269533436 0.970590710171951\n",
      "yahoo_A4 0.7535410293108873 0.7678979203887738 0.7041716907352722\n",
      "yahoo_A4 0.975609705569145 0.991476768155495 0.997770889013631\n",
      "yahoo_A4 0.8734176717522147 0.8459572447379152 0.9002254436694025\n",
      "yahoo_A4 0.45070419027970904 0.22925674322039657 0.49472805583313506\n",
      "yahoo_A4 0.618453815222546 0.3899664752608687 0.5272601386404862\n",
      "yahoo_A4 0.9922480112493266 0.9886979360502948 0.9986979155663385\n",
      "yahoo_A4 0.8050313971915697 0.7537132369746893 0.795843068656466\n",
      "yahoo_A4 0.7381702986396551 0.693662189041598 0.8732216803300078\n",
      "yahoo_A4 0.8713449795578154 0.7741840530752153 0.8970947824701498\n",
      "yahoo_A4 0.6419752648529217 0.12969421036955617 0.1560109276157793\n",
      "yahoo_A4 0.2608695424299727 0.001333334420998988 -0.0\n"
     ]
    }
   ],
   "source": [
    "for loader in [load_yahoo_A1, load_yahoo_A2, load_yahoo_A3, load_yahoo_A4]:\n",
    "    datasets = loader(64, 1)\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",
    "        model = CNN_AE(X_train)\n",
    "        scores = evaluate(X_test, model.predict(X_test), y_tests[i], is_reconstructed=True)\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": 6,
   "id": "bd9e37fd-124c-4f5c-8f1f-a9d61a4b0315",
   "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>0.888742</td>\n",
       "      <td>0.829842</td>\n",
       "      <td>0.905973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yahoo_A2</th>\n",
       "      <td>0.981968</td>\n",
       "      <td>0.649471</td>\n",
       "      <td>0.995692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yahoo_A3</th>\n",
       "      <td>0.771658</td>\n",
       "      <td>0.662304</td>\n",
       "      <td>0.745719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yahoo_A4</th>\n",
       "      <td>0.751491</td>\n",
       "      <td>0.655839</td>\n",
       "      <td>0.728629</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                f1    pr_auc   roc_auc\n",
       "dataset                               \n",
       "yahoo_A1  0.888742  0.829842  0.905973\n",
       "yahoo_A2  0.981968  0.649471  0.995692\n",
       "yahoo_A3  0.771658  0.662304  0.745719\n",
       "yahoo_A4  0.751491  0.655839  0.728629"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yahoo_results = pd.DataFrame(total_scores)\n",
    "yahoo_results.groupby('dataset').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76fd8e9e-5236-460e-bcc4-b5c99afc077d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8890a678-6116-4222-b0ff-395fcd7f41aa",
   "metadata": {},
   "source": [
    "### NASA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e4387a80-585a-488e-94a8-546f42d47632",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_scores = {'dataset': [], 'f1': [], 'pr_auc': [], 'roc_auc': []}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "106a238d-c5cb-49e9-9764-b1bc37f09436",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b0e1f96fe7ef44af9f0a6209eaa0d065",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "170f06edb325487b8df35ffaeb15dff2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D1 0.25300017462469454 0.13766862716749678 0.38522549868301587\n",
      "D2 0.2699530282282525 0.5840009300406523 0.5173913038147389\n"
     ]
    }
   ],
   "source": [
    "for loader in [load_nasa]:\n",
    "    datasets = loader(100, 100)\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",
    "        model = CNN_AE(X_train)\n",
    "        scores = evaluate(X_test, model.predict(X_test), y_tests[i], is_reconstructed=True)\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": 9,
   "id": "abb7a6a5-3924-4a33-bc54-871559819602",
   "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>0.253000</td>\n",
       "      <td>0.137669</td>\n",
       "      <td>0.385225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D2</th>\n",
       "      <td>0.269953</td>\n",
       "      <td>0.584001</td>\n",
       "      <td>0.517391</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               f1    pr_auc   roc_auc\n",
       "dataset                              \n",
       "D1       0.253000  0.137669  0.385225\n",
       "D2       0.269953  0.584001  0.517391"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nasa_results = pd.DataFrame(total_scores)\n",
    "nasa_results.groupby('dataset').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "10995024-fb1b-4ab9-8d85-36230b5a1383",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "41754734-5c75-4caf-9069-fb3b39c7a0da",
   "metadata": {},
   "source": [
    "### SMD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "60a26807-8359-42d4-82b4-9e1d37b6acaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_scores = {'dataset': [], 'f1': [], 'pr_auc': [], 'roc_auc': []}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "41af318e-1b08-4cc9-a680-6b4a978544f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ec915477c547487fb713ef47a506b30c",
       "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": "f833b14bfbdc4458aae67db743b37d4f",
       "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 0.20232181144029696 0.5753855920456366 0.521888680406877\n",
      "smd 0.47946607091009424 0.44955342114354546 0.9030968125116782\n",
      "smd 0.27509146250568944 0.20231886140329036 0.6860434886944784\n",
      "smd 0.28550125499121576 0.26944646088944874 0.6998155306392354\n",
      "smd 0.6894922795420774 0.7543820647819445 0.9604605779386108\n",
      "smd 0.721632979044796 0.7172255036672519 0.7808979685499313\n",
      "smd 0.28757856738135573 0.20055663567378268 0.5806954358516034\n",
      "smd 0.27698691100102835 0.24621629926606425 0.6217732767916516\n",
      "smd 0.4521223633297181 0.49493627270878787 0.8533029301151961\n",
      "smd 0.2596273980668859 0.2907512107322719 0.5346442447268345\n",
      "smd 0.6354114217912613 0.7106700874099949 0.8480261100537623\n",
      "smd 0.23641788958652887 0.2075002243402549 0.3100562181438564\n",
      "smd 0.4630716726006041 0.43856751904558683 0.85980375415138\n",
      "smd 0.5201026132652724 0.5219560657347636 0.711015863910138\n",
      "smd 0.3936269578700833 0.39479251554743977 0.8023227653823595\n",
      "smd 0.9433961761302981 0.9588987106619316 0.9857944131317462\n",
      "smd 0.7643821429023572 0.8097263108403387 0.8193801590346491\n",
      "smd 0.5670497675463134 0.6305868547133539 0.6981989658280073\n",
      "smd 0.7835112197998267 0.7900021175655687 0.9292536285922032\n",
      "smd 0.7834710264654082 0.7081279806718208 0.9362647795631585\n",
      "smd 0.1686561141956071 0.08653385884237623 0.5255170652203967\n",
      "smd 0.17522866902396475 0.07545172517461407 0.4353331182061198\n",
      "smd 0.7561450935640007 0.6954596352214003 0.8354925305582389\n",
      "smd 0.34515361920483006 0.2703158702478946 0.6966903631370632\n",
      "smd 0.8112139417889992 0.8276262071004532 0.9805536612052174\n",
      "smd 0.21666664729563664 0.2720542934795124 0.6551153327029366\n",
      "smd 0.48419225425138235 0.5542527507835233 0.9155294875788272\n",
      "smd 0.5584415180452045 0.5128829532038268 0.696866664358043\n"
     ]
    }
   ],
   "source": [
    "for loader in [load_smd]:\n",
    "    datasets = loader(64, 1)\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",
    "        model = CNN_AE(X_train)        \n",
    "        scores = evaluate(X_test, model.predict(X_test), y_tests[i], is_reconstructed=True)\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": "23474773-bac1-4145-ac75-c1a686bca5c6",
   "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>0.483427</td>\n",
       "      <td>0.488078</td>\n",
       "      <td>0.74228</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               f1    pr_auc  roc_auc\n",
       "dataset                             \n",
       "smd      0.483427  0.488078  0.74228"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smd_results = pd.DataFrame(total_scores)\n",
    "smd_results.groupby('dataset').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08c4fe00-b56d-4368-964b-ce337cecb61b",
   "metadata": {},
   "source": [
    "### ECG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8a2f4c6f-5b06-4a68-82c5-f12bdbc6e527",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_scores = {'dataset': [], 'f1': [], 'pr_auc': [], 'roc_auc': []}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "624fcf10-52db-43bb-b076-6324616e27b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0e7f3136f8e641f5984ec4c189e738e2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/9 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "173a79e206cd440db55eb4ce3ef53b52",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/9 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D1 0.6666666172839535 0.5287067732316802 0.6416972995475905\n",
      "D2 0.4444443950617328 0.3227746467562111 0.5713364930570336\n",
      "D3 0.10426539286179642 0.031830835018159885 0.29515458853807425\n",
      "D4 0.25806449047867025 0.11987751195222267 0.493926547264806\n",
      "D5 0.41025636765286383 0.2172037509032913 0.7438228359067329\n",
      "D6 0.24242419320478445 0.11312559835855264 0.549425107255294\n",
      "D7 0.24999997500000123 0.08985639743531602 0.49780569282665876\n",
      "D8 0.19753084615150285 0.08033499415600068 0.4321854815934826\n",
      "D9 0.39393936170799143 0.17883841324903912 0.34472812311541406\n"
     ]
    }
   ],
   "source": [
    "for loader in [load_ecg]:\n",
    "    datasets = loader(32, 16)\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",
    "        model = CNN_AE(X_train)        \n",
    "        scores = evaluate(X_test, model.predict(X_test), y_tests[i], is_reconstructed=True)\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": 15,
   "id": "a0b2faa2-35cf-49a7-ab64-6295c870e458",
   "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>0.666667</td>\n",
       "      <td>0.528707</td>\n",
       "      <td>0.641697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D2</th>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.322775</td>\n",
       "      <td>0.571336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D3</th>\n",
       "      <td>0.104265</td>\n",
       "      <td>0.031831</td>\n",
       "      <td>0.295155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D4</th>\n",
       "      <td>0.258064</td>\n",
       "      <td>0.119878</td>\n",
       "      <td>0.493927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D5</th>\n",
       "      <td>0.410256</td>\n",
       "      <td>0.217204</td>\n",
       "      <td>0.743823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D6</th>\n",
       "      <td>0.242424</td>\n",
       "      <td>0.113126</td>\n",
       "      <td>0.549425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D7</th>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.089856</td>\n",
       "      <td>0.497806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D8</th>\n",
       "      <td>0.197531</td>\n",
       "      <td>0.080335</td>\n",
       "      <td>0.432185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D9</th>\n",
       "      <td>0.393939</td>\n",
       "      <td>0.178838</td>\n",
       "      <td>0.344728</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               f1    pr_auc   roc_auc\n",
       "dataset                              \n",
       "D1       0.666667  0.528707  0.641697\n",
       "D2       0.444444  0.322775  0.571336\n",
       "D3       0.104265  0.031831  0.295155\n",
       "D4       0.258064  0.119878  0.493927\n",
       "D5       0.410256  0.217204  0.743823\n",
       "D6       0.242424  0.113126  0.549425\n",
       "D7       0.250000  0.089856  0.497806\n",
       "D8       0.197531  0.080335  0.432185\n",
       "D9       0.393939  0.178838  0.344728"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ecg_results = pd.DataFrame(total_scores)\n",
    "ecg_results.groupby('dataset').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41cd6896-bf51-421b-951f-9925fbeecfbf",
   "metadata": {},
   "source": [
    "### Power Demand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2f14a65f-50c1-4603-9221-3fc70fd12eff",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_scores = {'dataset': [], 'f1': [], 'pr_auc': [], 'roc_auc': []}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8c8caf36-0ab6-49d3-b367-9584f298416b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "93510055918646c3bb47bdfddd91df72",
       "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 0.3606206231571944 0.11922375084117279 0.2426117129441615\n"
     ]
    }
   ],
   "source": [
    "for loader in [load_power_demand]:\n",
    "    datasets = loader(64, 1)\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",
    "        model = CNN_AE(X_train)        \n",
    "        scores = evaluate(X_test, model.predict(X_test), y_tests[i], is_reconstructed=True)\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": 18,
   "id": "6d2f7903-a1ac-4b48-8af4-b98053444541",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</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>0.360621</td>\n",
       "      <td>0.119224</td>\n",
       "      <td>0.242612</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    f1    pr_auc   roc_auc\n",
       "dataset                                   \n",
       "power_demand  0.360621  0.119224  0.242612"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "power_results = pd.DataFrame(total_scores)\n",
    "power_results.groupby('dataset').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3148df3-815e-428d-bf3f-c74f8ea01cd1",
   "metadata": {},
   "source": [
    "### 2D Gesture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "33358f80-7d35-46cd-83a3-387982d50c17",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_scores = {'dataset': [], 'f1': [], 'pr_auc': [], 'roc_auc': []}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3876815f-1b4c-417a-8739-f3a8aea08430",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c819f93fb2ae44c29bf3772f16ac3609",
       "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 0.4203232924120907 0.35215139459909023 0.3754227420265823\n"
     ]
    }
   ],
   "source": [
    "for loader in [load_gesture]:\n",
    "    datasets = loader(64, 1)\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",
    "        model = CNN_AE(X_train)        \n",
    "        scores = evaluate(X_test, model.predict(X_test), y_tests[i], is_reconstructed=True)\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": 21,
   "id": "9a16c15d-745f-4bd0-b4e1-1d1775fc9e10",
   "metadata": {},
   "outputs": [
    {
     "data": {
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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>0.420323</td>\n",
       "      <td>0.352151</td>\n",
       "      <td>0.375423</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               f1    pr_auc   roc_auc\n",
       "dataset                              \n",
       "gesture  0.420323  0.352151  0.375423"
      ]
     },
     "execution_count": 21,
     "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": "1d9dd8de-a66e-45f8-ba83-b696d5f47939",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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