Baseline - GRU-VAE.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ee697e47-e8e0-4026-b895-3e9b7ee1264e",
   "metadata": {},
   "source": [
    "Ref: https://github.com/SchindlerLiang/VAE-for-Anomaly-Detection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a80a379b-932c-4401-8d76-b5ae5dedc6cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import random\n",
    "\n",
    "os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\";\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\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": "49c0a8e3-705d-429b-b7a0-c3c26a3a7b2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Sampling(layers.Layer):\n",
    "    \"\"\"Uses (z_mean, z_log_var) to sample z, the vector encoding a digit.\"\"\"\n",
    "\n",
    "    def call(self, inputs):\n",
    "        z_mean, z_log_var = inputs\n",
    "        batch = tf.shape(z_mean)[0]\n",
    "        dim = tf.shape(z_mean)[1]\n",
    "        epsilon = tf.keras.backend.random_normal(shape=(batch, dim))\n",
    "        return z_mean + tf.exp(0.5 * z_log_var) * epsilon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "24e86739-e767-4960-b7ea-9904d224a0d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "class VAE(keras.Model):\n",
    "    def __init__(self, encoder, decoder, **kwargs):\n",
    "        super(VAE, self).__init__(**kwargs)\n",
    "        self.encoder = encoder\n",
    "        self.decoder = decoder\n",
    "        self.total_loss_tracker = keras.metrics.Mean(name=\"total_loss\")\n",
    "        self.reconstruction_loss_tracker = keras.metrics.Mean(\n",
    "            name=\"reconstruction_loss\"\n",
    "        )\n",
    "        self.kl_loss_tracker = keras.metrics.Mean(name=\"kl_loss\")\n",
    "\n",
    "    @property\n",
    "    def metrics(self):\n",
    "        return [\n",
    "            self.total_loss_tracker,\n",
    "            self.reconstruction_loss_tracker,\n",
    "            self.kl_loss_tracker,\n",
    "        ]\n",
    "\n",
    "    def train_step(self, data):\n",
    "        with tf.GradientTape() as tape:\n",
    "            z_mean, z_log_var, z = self.encoder(data)\n",
    "            reconstruction = self.decoder(z)\n",
    "            reconstruction_loss = tf.reduce_mean(\n",
    "                tf.reduce_sum(\n",
    "                    keras.losses.binary_crossentropy(data, reconstruction), axis=1\n",
    "                )\n",
    "            )\n",
    "            kl_loss = -0.5 * (1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var))\n",
    "            kl_loss = tf.reduce_mean(tf.reduce_sum(kl_loss, axis=1))\n",
    "            total_loss = reconstruction_loss + kl_loss\n",
    "        grads = tape.gradient(total_loss, self.trainable_weights)\n",
    "        self.optimizer.apply_gradients(zip(grads, self.trainable_weights))\n",
    "        self.total_loss_tracker.update_state(total_loss)\n",
    "        self.reconstruction_loss_tracker.update_state(reconstruction_loss)\n",
    "        self.kl_loss_tracker.update_state(kl_loss)\n",
    "        return {\n",
    "            \"loss\": self.total_loss_tracker.result(),\n",
    "            \"reconstruction_loss\": self.reconstruction_loss_tracker.result(),\n",
    "            \"kl_loss\": self.kl_loss_tracker.result(),\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ac4f72c2-a0d7-481e-9993-fadd117b6a58",
   "metadata": {},
   "outputs": [],
   "source": [
    "def GRU_VAE(X_train):\n",
    "    latent_dim = 16\n",
    "\n",
    "    encoder_inputs = keras.Input(shape=(X_train.shape[1], X_train.shape[2]))\n",
    "    x = layers.GRU(64, return_sequences=True)(encoder_inputs)\n",
    "    x = layers.GRU(32)(x)\n",
    "    z_mean = layers.Dense(latent_dim, name=\"z_mean\")(x)\n",
    "    z_log_var = layers.Dense(latent_dim, name=\"z_log_var\")(x)\n",
    "    z = Sampling()([z_mean, z_log_var])\n",
    "    encoder = keras.Model(encoder_inputs, [z_mean, z_log_var, z], name=\"encoder\")\n",
    "\n",
    "    latent_inputs = keras.Input(shape=(latent_dim,))\n",
    "    x = layers.RepeatVector(X_train.shape[1])(latent_inputs)\n",
    "    x = layers.GRU(32, return_sequences=True)(x)\n",
    "    x = layers.GRU(64)(x)\n",
    "    x = layers.Dense(X_train.shape[1] *  X_train.shape[2])(x)\n",
    "    decoder_outputs = layers.Reshape([X_train.shape[1], X_train.shape[2]])(x)\n",
    "    decoder = keras.Model(latent_inputs, decoder_outputs, name=\"decoder\")\n",
    "\n",
    "    model = VAE(encoder, decoder)\n",
    "    model.compile(optimizer=keras.optimizers.Adam(learning_rate=0.001))\n",
    "    history = model.fit(X_train, epochs=50, batch_size=128, verbose=0)\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa97c067-d18d-4c06-ae1b-2cbfe88788b6",
   "metadata": {},
   "source": [
    "### Yahoo S5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eight-partition",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_scores = {'dataset': [], 'f1': [], 'pr_auc': [], 'roc_auc': []}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "71866d92-1163-4975-bb99-4302ccda66c3",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e8516fbde8e64df89c1f405e07a77fe1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/67 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "58749aa934e64f439ee6748e36f7092e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/56 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yahoo_A1 0.9924811515178951 0.9846206216760173 0.9999488105959181\n",
      "yahoo_A1 0.9599999424000027 0.9027291866049527 0.999530507820946\n",
      "yahoo_A1 0.9565216849716472 0.9294027075970502 0.9776921087106546\n",
      "yahoo_A1 0.999999930000003 0.7999999503333362 0.9999999797237571\n",
      "yahoo_A1 0.7692307076923109 0.6237454881308916 0.9878968087906828\n",
      "yahoo_A1 0.5454544892562023 0.31860867089890993 0.9281745880599177\n",
      "yahoo_A1 0.999999948484851 0.9848484768271686 0.999999998160173\n",
      "yahoo_A1 0.9714285206763874 0.9778746173646744 0.9866109727157787\n",
      "yahoo_A1 0.7763712574053335 0.8605467734597745 0.9078921450580624\n",
      "yahoo_A1 0.9999999487179513 0.9871794802018831 0.9999999983719279\n",
      "yahoo_A1 0.6467661244028643 0.6821671943259702 0.6944332256814504\n",
      "yahoo_A1 0.9999999487654349 0.9876543142436762 0.9999999984095603\n",
      "yahoo_A1 0.9999999483870992 0.9838709593577399 0.9999999980537634\n",
      "yahoo_A1 0.09473683303047171 0.00144927606610165 -0.0\n",
      "yahoo_A1 0.34146338616624533 0.03476337050829032 0.0891075263906907\n",
      "yahoo_A1 0.99999994761905 0.9523809426709138 0.9999999973104056\n",
      "yahoo_A1 0.559055077611758 0.37196985045529773 0.5325058993320829\n",
      "yahoo_A1 0.31870667052040613 0.16519098115635766 0.4944084463791641\n",
      "yahoo_A1 0.49795914607247255 0.022161695919950913 0.028239415526163616\n",
      "yahoo_A1 0.5101214192824038 0.39864739708909747 0.5728551863984314\n",
      "yahoo_A1 0.999999942307695 0.9230768956557769 0.999999992030684\n",
      "yahoo_A1 0.9411764096885841 0.7937739135649309 0.9846270816362092\n",
      "yahoo_A1 0.799999950480003 0.8708497242179448 0.9370268628142783\n",
      "yahoo_A1 0.9342560047652714 0.9852555253482702 0.9911230215395921\n",
      "yahoo_A1 0.999999930000003 0.7999999503333362 0.9999999797237571\n",
      "yahoo_A1 0.730923644231548 0.8185817537146705 0.8428409572231133\n",
      "yahoo_A1 0.4584269307511704 0.31641033118658857 0.48791320934504445\n",
      "yahoo_A1 0.7301586802609144 0.6548524158267864 0.8935521256221484\n",
      "yahoo_A1 0.37861912281189325 0.03717276347538201 0.15774677417874572\n",
      "yahoo_A1 0.9999999472222247 0.972222209301396 0.9999999969154738\n",
      "yahoo_A1 0.7228069711320436 0.30857373430062346 0.1840371656698786\n",
      "yahoo_A1 0.9935483358168599 0.9865502289249124 0.9998219356929541\n",
      "yahoo_A1 0.9999999482758646 0.9827586118285756 0.9999999979532813\n",
      "yahoo_A1 0.38241755131989136 0.1848426162246998 0.6150285181962876\n",
      "yahoo_A1 0.9999999484375026 0.984374991833314 0.9999999981085527\n",
      "yahoo_A1 0.49795914607247255 0.2497215704024144 0.5307197012342261\n",
      "yahoo_A1 0.37861912281189325 0.0925840233766845 0.34660850163445434\n",
      "yahoo_A1 0.9999999333333361 0.8333332883333356 0.9999999830563253\n",
      "yahoo_A1 0.37861912281189325 0.2170106033867467 0.5498480315968778\n",
      "yahoo_A1 0.5968991763746208 0.6247418370876906 0.768421658876562\n",
      "yahoo_A1 0.9934640009910745 0.9928717498969983 0.9998634982277486\n",
      "yahoo_A1 0.580896644715756 0.3377755607188527 0.5778031574609631\n",
      "yahoo_A1 0.580896644715756 0.430482479240544 0.6624503169782212\n",
      "yahoo_A1 0.49795914607247255 0.0020242931210648863 -0.0\n",
      "yahoo_A1 0.99999994761905 0.97619046472425 0.9999999973104056\n",
      "yahoo_A1 0.8571427948979622 0.668541324264585 0.8684523681029623\n",
      "yahoo_A1 0.9197079781767835 0.9171282120859913 0.9537528734370775\n",
      "yahoo_A1 0.0 0.0 -0.0\n",
      "yahoo_A1 0.16374267483328334 0.05260859666339335 0.4522683384793251\n",
      "yahoo_A1 0.3809523328798237 0.21036455685799949 0.9501983988602056\n",
      "yahoo_A1 0.8778280035134445 0.879896555459452 0.9401570026852873\n",
      "yahoo_A1 0.9999999490384642 0.9909090854127993 0.9999999986501491\n",
      "yahoo_A1 0.9999999473684236 0.9736841968149564 0.999999997059779\n",
      "yahoo_A1 0.43290039879312864 0.20016394724425926 0.4799506046809161\n",
      "yahoo_A1 0.9787233528695766 0.9801699610357855 0.998189822106924\n",
      "yahoo_A1 0.2961275372792815 0.018179121323137853 0.15082974901067644\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c97e497161b64fbfbbfe5fafb3e06773",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "55ec5f24a12445dab33061f1a83ebfe8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yahoo_A2 0.17493470983952586 0.012705632413852168 0.08772304980135551\n",
      "yahoo_A2 0.17493470983952586 0.008280099841383426 0.1301553428913529\n",
      "yahoo_A2 0.9999999485074653 0.9850746190029587 0.9999999983492348\n",
      "yahoo_A2 0.3111831163317063 0.22203001985868648 0.56329164399044\n",
      "yahoo_A2 0.004301074837784714 0.0010775860989057032 -0.0\n",
      "yahoo_A2 0.2178861593760345 0.08836021957300577 0.4281441107045056\n",
      "yahoo_A2 0.9952152600535729 0.9886862758768711 0.9997405361205594\n",
      "yahoo_A2 0.004464285266860649 0.001118568120555141 0.03779697245403993\n",
      "yahoo_A2 0.581005539103028 0.5703186309058171 0.9324343457118563\n",
      "yahoo_A2 0.5379309931795512 0.4854268079539126 0.6661342899011703\n",
      "yahoo_A2 0.015267174034147195 0.0038461534585799203 0.7224621306215055\n",
      "yahoo_A2 0.18823527645900873 0.09109566496635278 0.5787781457346384\n",
      "yahoo_A2 0.2482100020665201 0.058315409811966576 0.23995282930650208\n",
      "yahoo_A2 0.74074069012346 0.8018862518261567 0.963690475110884\n",
      "yahoo_A2 0.9999998500000123 0.0 0.9999998997840271\n",
      "yahoo_A2 0.17493470983952586 0.001642492283748465 0.07663330559868692\n",
      "yahoo_A2 0.834042503141696 0.8389178336273633 0.9781517083059169\n",
      "yahoo_A2 0.004301074837784714 0.0010775860989057032 -0.0\n",
      "yahoo_A2 0.955882301546283 0.9766333681624393 0.9991025866749181\n",
      "yahoo_A2 0.2482100020665201 0.02169648799900927 0.1476294969971165\n",
      "yahoo_A2 0.9999998500000123 0.0 0.9999998997840271\n",
      "yahoo_A2 0.9999999485074653 0.985074619003961 0.9999999983492349\n",
      "yahoo_A2 0.2482100020665201 0.042774220142405756 0.12501678216250667\n",
      "yahoo_A2 0.0044345893560012 0.0011111109997530974 0.03239740496060559\n",
      "yahoo_A2 0.9999998500000123 0.0 0.999999899784027\n",
      "yahoo_A2 0.3414633786060327 0.2589933292379516 0.7989269856315928\n",
      "yahoo_A2 0.6375545350164986 0.6453992742298938 0.9271291198404873\n",
      "yahoo_A2 0.010638296802851971 0.0026737965226343603 0.5993519917712042\n",
      "yahoo_A2 0.6626505534693024 0.6140202787394852 0.9509847896638524\n",
      "yahoo_A2 0.9999999490384642 0.9903846094895087 0.9999999988797316\n",
      "yahoo_A2 0.004301074837784714 0.0010775860989057032 -0.0\n",
      "yahoo_A2 0.9852940662089126 0.984196560916442 0.9999055339624647\n",
      "yahoo_A2 0.3647058344221516 0.2765187660676222 0.5050928088601039\n",
      "yahoo_A2 0.03076922764497042 0.007812499206543048 0.8639307989494834\n",
      "yahoo_A2 0.17493470983952586 0.02184416675650336 0.13493781975133826\n",
      "yahoo_A2 0.1851332229801596 0.06704259583739597 0.4559143645625258\n",
      "yahoo_A2 0.9952152600535729 0.990246366725514 0.9999771051068633\n",
      "yahoo_A2 0.9999998500000123 0.0 0.9999998997840271\n",
      "yahoo_A2 0.8489208121525832 0.8428356171695564 0.9863617025092888\n",
      "yahoo_A2 0.8326179756120052 0.8171292921789891 0.9781669708211623\n",
      "yahoo_A2 0.004301074837784714 0.0010775860989057032 -0.0\n",
      "yahoo_A2 0.8053690769424832 0.8670355154162844 0.9870938015067576\n",
      "yahoo_A2 0.9999999490384642 0.990384609890414 0.9999999988797315\n",
      "yahoo_A2 0.01801801619998377 0.004545454086776905 0.7645787570702928\n",
      "yahoo_A2 0.9999999485074653 0.9850746190035067 0.9999999983492349\n",
      "yahoo_A2 0.2482100020665201 0.14960630975211803 0.5657250415034146\n",
      "yahoo_A2 0.3079364802731191 0.13534440156956615 0.5374099061022151\n",
      "yahoo_A2 0.9999998500000123 0.0 0.9999998997840273\n",
      "yahoo_A2 0.23370783980002796 0.14535672071729183 0.6150228477714206\n",
      "yahoo_A2 0.9999999490384642 0.9807692260047703 0.9999999988797316\n",
      "yahoo_A2 0.004587155503535056 0.0011494251721495688 0.06263498292383748\n",
      "yahoo_A2 0.9999999485074653 0.9850746190040525 0.9999999983492348\n",
      "yahoo_A2 0.9999999490384642 0.9903846098886209 0.9999999988797316\n",
      "yahoo_A2 0.666666577777783 0.2499999625000044 0.9978400727866532\n",
      "yahoo_A2 0.19102747904859194 0.07620581210178293 0.29660426237793036\n",
      "yahoo_A2 0.2996941636057593 0.19112474262681362 0.5899002568160898\n",
      "yahoo_A2 0.007874014957529917 0.0019762843865706583 0.4557234964458525\n",
      "yahoo_A2 0.4999999375000028 0.16666664444444684 0.9956802457892796\n",
      "yahoo_A2 0.1814159070825855 0.08822153849499649 0.5629141073401777\n",
      "yahoo_A2 0.976525770706873 0.9885301715197268 0.9996871173172005\n",
      "yahoo_A2 0.9999998500000123 0.0 0.9999998997840271\n",
      "yahoo_A2 0.17493470983952586 0.008892982349325325 0.047779213073519546\n",
      "yahoo_A2 0.9999999490384642 0.9903846098913874 0.9999999988797316\n",
      "yahoo_A2 0.004301074837784714 0.0010775860989057032 -0.0\n",
      "yahoo_A2 0.9999999485074653 0.9850746189802435 0.9999999983492351\n",
      "yahoo_A2 0.2482100020665201 0.05352973823403487 0.20676985534175688\n",
      "yahoo_A2 0.9999998500000123 0.0 0.9999998997840271\n",
      "yahoo_A2 0.7534246068399357 0.7524094098654087 0.9747189668398628\n",
      "yahoo_A2 0.9999999485074653 0.985074618996672 0.9999999983492349\n",
      "yahoo_A2 0.24910177453992804 0.06185785195095756 0.18205956704062623\n",
      "yahoo_A2 0.9999998500000123 0.0 0.9999998997840271\n",
      "yahoo_A2 0.5859872114730862 0.595492500168074 0.9190828423139943\n",
      "yahoo_A2 0.9952152600535729 0.9901996900991745 0.9999694738492406\n",
      "yahoo_A2 0.004357298038266384 0.001091702947359901 0.012958961984242259\n",
      "yahoo_A2 0.18406591730278798 0.049631342305043805 0.21412866339729195\n",
      "yahoo_A2 0.9999999490384642 0.9903846098902566 0.9999999988797315\n",
      "yahoo_A2 0.004301074837784714 0.0010775860989057032 -0.0\n",
      "yahoo_A2 0.9999999485074653 0.9850746189992639 0.9999999983492349\n",
      "yahoo_A2 0.2482100020665201 0.00987983785073585 0.2177380720787066\n",
      "yahoo_A2 0.2482100020665201 0.040515482962422106 0.15146553202917862\n",
      "yahoo_A2 0.33333329444444565 0.09999998800000123 0.9913605917945323\n",
      "yahoo_A2 0.263565864428556 0.14434096694189685 0.6866611579335697\n",
      "yahoo_A2 0.9999999490384642 0.9903846098916662 0.9999999988797315\n",
      "yahoo_A2 0.9999998500000123 0.0 0.999999899784027\n",
      "yahoo_A2 0.9999999485074653 0.9850746190035062 0.9999999983492348\n",
      "yahoo_A2 0.29498522467782445 0.13198923601016696 0.4338026181466551\n",
      "yahoo_A2 0.08333332465277785 0.02173912816635183 0.9524837058418055\n",
      "yahoo_A2 0.9850745753954137 0.9833385446313666 0.9997992615273482\n",
      "yahoo_A2 0.6907630030096322 0.6927295185536926 0.9455509757417058\n",
      "yahoo_A2 0.9999998500000123 0.0 0.9999998997840271\n",
      "yahoo_A2 0.9999998500000123 0.0 0.999999899784027\n",
      "yahoo_A2 0.9999999485074653 0.985074619000248 0.9999999983492349\n",
      "yahoo_A2 0.4611650105782856 0.3224542295703845 0.8096688025117569\n",
      "yahoo_A2 0.9999998500000123 0.0 0.9999998997840271\n",
      "yahoo_A2 0.17493470983952586 0.007579767572084983 0.0923296720007434\n",
      "yahoo_A2 0.9999999490384642 0.9903846098893969 0.9999999988797315\n",
      "yahoo_A2 0.005917159169846993 0.0014836793764143531 0.2796975961598953\n",
      "yahoo_A2 0.9999999485074653 0.9850746190005751 0.9999999983492349\n",
      "yahoo_A2 0.5529009777213517 0.4813304478086117 0.8929716107212438\n",
      "yahoo_A2 0.11111109938271617 0.029411761591695812 0.9654426678260477\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2e868ee0d2834d57a6dbe42635df24bc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ccbc06fb0afa46dab4f948ffbd9d5af7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/92 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yahoo_A3 0.25396823169564314 0.0013440869727441144 0.013297871007101739\n",
      "yahoo_A3 0.442477841491114 0.1379907555093967 0.3141621673049646\n",
      "yahoo_A3 0.47750861400366645 0.08472034602769161 0.2095632137141159\n",
      "yahoo_A3 0.25396823169564314 0.0013262609306003033 -0.0\n",
      "yahoo_A3 0.6314151976538852 0.16010397552740746 0.1878220358906577\n",
      "yahoo_A3 0.41726615387920135 0.02950996360713502 0.05390614721571588\n",
      "yahoo_A3 0.5852089616298454 0.1751140472948915 0.21343771648294085\n",
      "yahoo_A3 0.45614031550631234 0.07857079497833565 0.20629850310886444\n",
      "yahoo_A3 0.5210083646578659 0.31196300292156764 0.5603471636275839\n",
      "yahoo_A3 0.7134502462945892 0.28918333409167984 0.32067438518250263\n",
      "yahoo_A3 0.637770854195864 0.20067344277560226 0.31605807536732144\n",
      "yahoo_A3 0.5783521396394755 0.2956132128764962 0.3922187462587988\n",
      "yahoo_A3 0.19301846297450495 0.043216702119184915 0.21687562217144915\n",
      "yahoo_A3 0.6766916843462069 0.17366922145504674 0.0937628408882275\n",
      "yahoo_A3 0.11563168070466742 0.0012077301518444415 -0.0\n",
      "yahoo_A3 0.6009538528423524 0.5236291731430771 0.6266821306536136\n",
      "yahoo_A3 0.25396823169564314 0.001996502086424655 0.005984042252397871\n",
      "yahoo_A3 0.25396823169564314 0.06842658645333044 0.16707483714670368\n",
      "yahoo_A3 0.2812499757202169 0.0013774114683232765 0.016304346395026162\n",
      "yahoo_A3 0.24701193044237577 0.07549739357800751 0.26607506895630345\n",
      "yahoo_A3 0.2258064314906365 0.018938123781096706 0.030414804302329387\n",
      "yahoo_A3 0.45070419027970904 0.00159744541016434 -0.0\n",
      "yahoo_A3 0.6075948942156735 0.002008033854540209 -0.0\n",
      "yahoo_A3 0.8271604429964975 0.8664201808781145 0.971010285712566\n",
      "yahoo_A3 0.25396823169564314 0.006075163258469144 0.15086593062856554\n",
      "yahoo_A3 0.5185184799351569 0.20392595989943688 0.4779730596749979\n",
      "yahoo_A3 0.43137251503268237 0.006929445730149544 0.02942040432568658\n",
      "yahoo_A3 0.25396823169564314 0.00132626090003508 -0.0\n",
      "yahoo_A3 0.3763837331436143 0.008755974208269152 0.048839175835749676\n",
      "yahoo_A3 0.637770854195864 0.018212514126389162 0.04601375555190935\n",
      "yahoo_A3 0.8916876077927048 0.07882081469414796 0.06623251714986744\n",
      "yahoo_A3 0.7726638296349493 0.5395492261230745 0.5291885961537075\n",
      "yahoo_A3 0.5357736710795402 0.06807121095394214 0.08056138865638648\n",
      "yahoo_A3 0.7428570959510233 0.6213467242639814 0.6028307683066703\n",
      "yahoo_A3 0.6075948942156735 0.17812956958254647 0.3287130079137602\n",
      "yahoo_A3 0.7356321371911775 0.15597398948855246 0.24785228664341086\n",
      "yahoo_A3 0.6075948942156735 0.3015372110961137 0.43425159950272035\n",
      "yahoo_A3 0.6228481572302212 0.6839262859011371 0.686098742480685\n",
      "yahoo_A3 0.45070419027970904 0.14343397328909246 0.3597052772349336\n",
      "yahoo_A3 0.7978141594762488 0.805389001577373 0.745554186457375\n",
      "yahoo_A3 0.5090908609586821 0.42258514875838715 0.9006221099130917\n",
      "yahoo_A3 0.6943619722688439 0.6534802823701055 0.6388137295231893\n",
      "yahoo_A3 0.25396823169564314 0.0013262609005301616 -0.0\n",
      "yahoo_A3 0.323809496547848 0.03623663195120054 0.2418383601471593\n",
      "yahoo_A3 0.7153284209963267 0.02025217520004524 0.05167015926374602\n",
      "yahoo_A3 0.7743732113825961 0.29227257309671906 0.21023834565517946\n",
      "yahoo_A3 0.8282289792174155 0.8765420512504003 0.7759390892574086\n",
      "yahoo_A3 0.6846038411666818 0.29609290251313625 0.37784334805483993\n",
      "yahoo_A3 0.25396823169564314 0.011203619918433709 0.09920247126026996\n",
      "yahoo_A3 0.40579706895610423 0.22919627245551374 0.5585198530433355\n",
      "yahoo_A3 0.45070419027970904 0.31356573323196696 0.6065057377053495\n",
      "yahoo_A3 0.5874798941145886 0.07871165265840772 0.15874806617115395\n",
      "yahoo_A3 0.5259631101380745 0.056127679360902696 0.19035805954178284\n",
      "yahoo_A3 0.700147664776179 0.3404106326754146 0.500824473065246\n",
      "yahoo_A3 0.702194309993026 0.6509283550099515 0.8124031938178119\n",
      "yahoo_A3 0.4341636669216475 0.0222169793958195 0.10539756763531019\n",
      "yahoo_A3 0.540630142792948 0.281561552051072 0.5131551691877735\n",
      "yahoo_A3 0.25396823169564314 0.1326187608458934 0.5628117150811599\n",
      "yahoo_A3 0.7283236529018707 0.18137478234584029 0.25830508179734796\n",
      "yahoo_A3 0.6646433545110223 0.33739120186493937 0.44353861524790716\n",
      "yahoo_A3 0.48351644002687255 0.36332614110029643 0.6444591677125869\n",
      "yahoo_A3 0.5657894235738444 0.5711022351660933 0.7029478086688846\n",
      "yahoo_A3 0.25396823169564314 0.07564081080915955 0.2058121044345455\n",
      "yahoo_A3 0.6943619722688439 0.5363439228992236 0.5581852882352709\n",
      "yahoo_A3 0.700147664776179 0.2996149464307538 0.3023550202307737\n",
      "yahoo_A3 0.6726998043237837 0.6478900732153903 0.6414421444258472\n",
      "yahoo_A3 0.442477841491114 0.0091161489553467 0.03294314997097864\n",
      "yahoo_A3 0.8541666175075981 0.9021109676053415 0.7696068796139534\n",
      "yahoo_A3 0.7301586835887245 0.5037474587854772 0.48044759255799446\n",
      "yahoo_A3 0.25049699588078084 0.0013227522729608557 -0.0\n",
      "yahoo_A3 0.45070419027970904 0.16468999294888514 0.43140734961974847\n",
      "yahoo_A3 0.43694490352936993 0.08290414904037806 0.1906888503750939\n",
      "yahoo_A3 0.7187499510528599 0.7772237220000255 0.8806836249182823\n",
      "yahoo_A3 0.5357736710795402 0.170920044341797 0.3680106671680807\n",
      "yahoo_A3 0.31858404263450785 0.17302137325542905 0.6779234839632494\n",
      "yahoo_A3 0.25396823169564314 0.012293924925164922 0.05431175212665903\n",
      "yahoo_A3 0.45070419027970904 0.013662401872419565 0.06822732051956781\n",
      "yahoo_A3 0.6198346611058028 0.5635221658099197 0.8313853662499578\n",
      "yahoo_A3 0.277886473028215 0.007093651691073434 0.1925550981918347\n",
      "yahoo_A3 0.6865671188861692 0.31913061237596313 0.34900362500871446\n",
      "yahoo_A3 0.6865671188861692 0.45113823131890635 0.4760294414411433\n",
      "yahoo_A3 0.6270269783769213 0.5590869579704206 0.7307559636199275\n",
      "yahoo_A3 0.6398763086823295 0.5977251149587484 0.6084516853491257\n",
      "yahoo_A3 0.7351778151634961 0.822361425944254 0.9027290610923188\n",
      "yahoo_A3 0.7410713781050735 0.6939391696149093 0.9138474041394042\n",
      "yahoo_A3 0.6802720589731166 0.641909443672886 0.8349356455010312\n",
      "yahoo_A3 0.3301707502968744 0.001412430463367102 -0.0\n",
      "yahoo_A3 0.7811633870811333 0.30855218167165255 0.2897560079746953\n",
      "yahoo_A3 0.3882783568463308 0.02207323536402844 0.052469601358841725\n",
      "yahoo_A3 0.45070419027970904 0.09709623255716493 0.2521307347817233\n",
      "yahoo_A3 0.516020197625476 0.1473465449908936 0.3365837891203296\n",
      "yahoo_A3 0.40579706895610423 0.17455646103383674 0.45663394449572675\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "10d4ec9247ac42d0b6947a7b5fe90ff5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/100 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a5c69fd7fc404dfa86ce162d9b3893ab",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/82 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "yahoo_A4 0.4853700147102329 0.25657498107801496 0.49332625438119043\n",
      "yahoo_A4 0.25396823169564314 0.044770499351400754 0.21725193406830898\n",
      "yahoo_A4 0.41726615387920135 0.055755066218204294 0.11679016066829237\n",
      "yahoo_A4 0.2945736181779962 0.05265633962775768 0.1493759594894746\n",
      "yahoo_A4 0.6850828225878368 0.7806503313212262 0.7872918848021806\n",
      "yahoo_A4 0.7349822816366827 0.8264313639880828 0.8701555013750565\n",
      "yahoo_A4 0.43694490352936993 0.3341806262531949 0.5732274134055915\n",
      "yahoo_A4 0.3673469086517006 0.010607232062204992 0.10479702453898934\n",
      "yahoo_A4 0.5942491593197877 0.16722972163773106 0.17894611908898933\n",
      "yahoo_A4 0.45070419027970904 0.21445981427106683 0.3850530805342578\n",
      "yahoo_A4 0.637770854195864 0.031129949794456806 0.06507821794702008\n",
      "yahoo_A4 0.3882783568463308 0.10804331585128504 0.4014503416980334\n",
      "yahoo_A4 0.45070419027970904 0.02380148439014991 0.1616334593335847\n",
      "yahoo_A4 0.3551401575753364 0.10951125947592956 0.3103227409262222\n",
      "yahoo_A4 0.07439824225732532 0.0011792458868547162 -0.0\n",
      "yahoo_A4 0.25396823169564314 0.0599129395564483 0.30047139942503975\n",
      "yahoo_A4 0.5942491593197877 0.15937112907479856 0.22603934363631334\n",
      "yahoo_A4 0.6075948942156735 0.09752249886727728 0.12525125584183555\n",
      "yahoo_A4 0.7675069552887851 0.4420682347553663 0.3683299158652281\n",
      "yahoo_A4 0.6075948942156735 0.4608337163478198 0.5452933079966684\n",
      "yahoo_A4 0.26495724185842845 0.09048301189451119 0.48857834474810197\n",
      "yahoo_A4 0.45070419027970904 0.05923358768293264 0.05321044152419166\n",
      "yahoo_A4 0.5714285304267189 0.17007009943206236 0.3233553831911022\n",
      "yahoo_A4 0.25396823169564314 0.10420974453848655 0.4017362908009511\n",
      "yahoo_A4 0.7698412197625378 0.7415001824272385 0.9101561063252412\n",
      "yahoo_A4 0.5454545056048113 0.052155118732080924 0.14720355306165178\n",
      "yahoo_A4 0.8328911978596936 0.7028678363185206 0.5088777809389297\n",
      "yahoo_A4 0.25396823169564314 0.027263466901465368 0.09994071837709359\n",
      "yahoo_A4 0.5897435479700884 0.5166606016113159 0.6242111713400001\n",
      "yahoo_A4 0.7726638296349493 0.3992242360889002 0.369559755106838\n",
      "yahoo_A4 0.11299433955759933 0.0326162017832012 0.49807903512744345\n",
      "yahoo_A4 0.4086798950024387 0.07961485425906559 0.2657922066497138\n",
      "yahoo_A4 0.25396823169564314 0.0013774114236444843 0.03856382720162337\n",
      "yahoo_A4 0.42293903459873594 0.2502204947189578 0.5656420092367921\n",
      "yahoo_A4 0.6666666220202049 0.24103051536975223 0.3321130544727257\n",
      "yahoo_A4 0.5897435479700884 0.3158114424457674 0.43402583262166744\n",
      "yahoo_A4 0.5620914626724793 0.5798676478352855 0.591767095201804\n",
      "yahoo_A4 0.6075948942156735 0.2085884213948119 0.40006155060969084\n",
      "yahoo_A4 0.4748699809545005 0.1131375722633702 0.2446431505892127\n",
      "yahoo_A4 0.3269961702410062 0.01901104145372668 0.034219770339495706\n",
      "yahoo_A4 0.557377008793338 0.4626524581062679 0.6131071352023605\n",
      "yahoo_A4 0.03131990741758405 0.0011655015139843017 0.015011545839489385\n",
      "yahoo_A4 0.5806451198959446 0.20022784002278993 0.47492745848394397\n",
      "yahoo_A4 0.3011582754610119 0.005270821654110601 0.061267153152037394\n",
      "yahoo_A4 0.3703703400548721 0.03588946916123442 0.1688132201731245\n",
      "yahoo_A4 0.4065933648653586 0.26424110549682045 0.7893683354172395\n",
      "yahoo_A4 0.47567563095982873 0.44765808423564085 0.5803080905389278\n",
      "yahoo_A4 0.25396823169564314 0.05185519607411375 0.41809642628596055\n",
      "yahoo_A4 0.46422334988015346 0.10223626078042747 0.10892898719482835\n",
      "yahoo_A4 0.45070419027970904 0.14341850942133574 0.2208863011606284\n",
      "yahoo_A4 0.7227866009475067 0.7147259466378832 0.6940754499994023\n",
      "yahoo_A4 0.6707691803947966 0.7302051846817398 0.7386119922002927\n",
      "yahoo_A4 0.25396823169564314 0.013061835163601987 0.06764623879139381\n",
      "yahoo_A4 0.6614172717961474 0.6375243350219983 0.9296016337090232\n",
      "yahoo_A4 0.5852089616298454 0.10450325517965857 0.2123535892162622\n",
      "yahoo_A4 0.41666661628147306 0.3635323570436712 0.6612001487928458\n",
      "yahoo_A4 0.4341636669216475 0.0072553430570974796 0.040048618226649166\n",
      "yahoo_A4 0.2978723149594655 0.02502343299692355 0.11794272070844934\n",
      "yahoo_A4 0.25396823169564314 0.005243870166809848 0.03716408818933074\n",
      "yahoo_A4 0.5919999581337628 0.020771986022075462 0.06426696450838822\n",
      "yahoo_A4 0.45070419027970904 0.030363543201679194 0.0246055879794261\n",
      "yahoo_A4 0.6982248064178453 0.10667998155710232 0.08240203012596493\n",
      "yahoo_A4 0.6009851711131106 0.657092407664649 0.7412655887927591\n",
      "yahoo_A4 0.674698750268547 0.3842607374757315 0.540868312759928\n",
      "yahoo_A4 0.25396823169564314 0.01332831646935403 0.04173291871288527\n",
      "yahoo_A4 0.4957264582716078 0.23575865647338942 0.48154775444634484\n",
      "yahoo_A4 0.43694490352936993 0.10268818526145852 0.3439683830400508\n",
      "yahoo_A4 0.25396823169564314 0.026106344691271027 0.06830403933684279\n",
      "yahoo_A4 0.5919999581337628 0.1466936325200908 0.2495227022424177\n",
      "yahoo_A4 0.6544342065351805 0.5600807484495374 0.5877313964550008\n",
      "yahoo_A4 0.25049699588078084 0.002395994310923006 0.036914232551732394\n",
      "yahoo_A4 0.7535410293108873 0.48965606396144135 0.45287935013851194\n",
      "yahoo_A4 0.599118899151937 0.4425132693730681 0.6205885867601341\n",
      "yahoo_A4 0.6544342065351805 0.2384455403774752 0.21167267965733738\n",
      "yahoo_A4 0.45070419027970904 0.13918885231745712 0.40256446984612926\n",
      "yahoo_A4 0.6075948942156735 0.12671817858087897 0.19888322765406033\n",
      "yahoo_A4 0.45070419027970904 0.09255315891410357 0.3381284609510873\n",
      "yahoo_A4 0.35205989595309467 0.06401559188966477 0.1868263960762081\n",
      "yahoo_A4 0.45070419027970904 0.11491608329451919 0.13883648155928494\n",
      "yahoo_A4 0.6626139372529846 0.5446382817586607 0.7064343570674148\n",
      "yahoo_A4 0.6419752648529217 0.12278626820661045 0.08415813179393093\n",
      "yahoo_A4 0.2608695424299727 0.0021226461364677075 0.05882352404844336\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",
    "        tf.keras.backend.clear_session()\n",
    "\n",
    "        X_train = x_trains[i]\n",
    "        X_test = x_tests[i]\n",
    "        \n",
    "        model = GRU_VAE(X_train)\n",
    "        \n",
    "        X_test_rec = model.decoder.predict(model.encoder.predict(X_test)[-1])\n",
    "        scores = evaluate(X_test, X_test_rec, 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": 8,
   "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>0.721876</td>\n",
       "      <td>0.611384</td>\n",
       "      <td>0.741474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yahoo_A2</th>\n",
       "      <td>0.548413</td>\n",
       "      <td>0.370599</td>\n",
       "      <td>0.683172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yahoo_A3</th>\n",
       "      <td>0.525552</td>\n",
       "      <td>0.253386</td>\n",
       "      <td>0.350647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yahoo_A4</th>\n",
       "      <td>0.478258</td>\n",
       "      <td>0.218550</td>\n",
       "      <td>0.333221</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                f1    pr_auc   roc_auc\n",
       "dataset                               \n",
       "yahoo_A1  0.721876  0.611384  0.741474\n",
       "yahoo_A2  0.548413  0.370599  0.683172\n",
       "yahoo_A3  0.525552  0.253386  0.350647\n",
       "yahoo_A4  0.478258  0.218550  0.333221"
      ]
     },
     "execution_count": 8,
     "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": "bb09ed4f-3e8f-4260-8996-a6811c7734be",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8890a678-6116-4222-b0ff-395fcd7f41aa",
   "metadata": {},
   "source": [
    "### NASA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e4387a80-585a-488e-94a8-546f42d47632",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_scores = {'dataset': [], 'f1': [], 'pr_auc': [], 'roc_auc': []}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "106a238d-c5cb-49e9-9764-b1bc37f09436",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "009e711a967f489aa6d9a8c565f88e28",
       "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": "bd781970f6c44b05a1c085f852940c9e",
       "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 0.1328442985983932 0.36693179617049704\n",
      "D2 0.2699530282282525 0.58400092990081 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",
    "        tf.keras.backend.clear_session()\n",
    "\n",
    "        X_train = x_trains[i]\n",
    "        X_test = x_tests[i]\n",
    "        \n",
    "        model = GRU_VAE(X_train)\n",
    "        \n",
    "        X_test_rec = model.decoder.predict(model.encoder.predict(X_test)[-1])\n",
    "        scores = evaluate(X_test, X_test_rec, 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": 11,
   "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>0.132844</td>\n",
       "      <td>0.366932</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  0.132844  0.366932\n",
       "D2       0.269953  0.584001  0.517391"
      ]
     },
     "execution_count": 11,
     "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": "ff85b8cf-8aa0-4712-8ddb-01d63ec0f497",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "41754734-5c75-4caf-9069-fb3b39c7a0da",
   "metadata": {},
   "source": [
    "### SMD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "60a26807-8359-42d4-82b4-9e1d37b6acaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_scores = {'dataset': [], 'f1': [], 'pr_auc': [], 'roc_auc': []}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41af318e-1b08-4cc9-a680-6b4a978544f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "403dc82f4a764ebc847a551b30a82436",
       "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": "b5b6be917d054612986ddfeaaa185383",
       "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 0.5556857798257031 0.521888680406877\n",
      "smd 0.6787003111164003 0.5490042926100768 0.9186154755807889\n",
      "smd 0.25058153864288274 0.22078452988844366 0.5955408869659128\n",
      "smd 0.2701578910395887 0.29703188922447676 0.5964809416824509\n",
      "smd 0.689655126355723 0.7311883672195485 0.9524332230177086\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",
    "        tf.keras.backend.clear_session()\n",
    "\n",
    "        X_train = x_trains[i]\n",
    "        X_test = x_tests[i]\n",
    "        \n",
    "        model = GRU_VAE(X_train)\n",
    "        \n",
    "        X_test_rec = model.decoder.predict(model.encoder.predict(X_test)[-1])\n",
    "        scores = evaluate(X_test, X_test_rec, 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": null,
   "id": "23474773-bac1-4145-ac75-c1a686bca5c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "smd_results = pd.DataFrame(total_scores)\n",
    "smd_results.groupby('dataset').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c72b6f33-19df-4d51-9144-e6604bb329ba",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "191def0b-8e38-4401-be4c-0d47e89eefa5",
   "metadata": {},
   "source": [
    "### ECG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9739a0bc-1fd1-48bc-86ad-d2fd75473bbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_scores = {'dataset': [], 'f1': [], 'pr_auc': [], 'roc_auc': []}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd9f21c4-27f5-412f-a213-60fc3c09acc7",
   "metadata": {},
   "outputs": [],
   "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 = GRU_VAE(X_train)\n",
    "        \n",
    "        X_test_rec = model.decoder.predict(model.encoder.predict(X_test)[-1])\n",
    "        scores = evaluate(X_test, X_test_rec, 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": null,
   "id": "4d23a70d-851c-497b-81ed-63c1369f4619",
   "metadata": {},
   "outputs": [],
   "source": [
    "ecg_results = pd.DataFrame(total_scores)\n",
    "ecg_results.groupby('dataset').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "681744af-e6bf-41c2-b1b8-523035e085a1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "b153349b-4e8c-45d3-90cd-a1c765412ee0",
   "metadata": {},
   "source": [
    "### Power Demand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d38db439-5db0-4021-bc17-5f65f36f3f2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_scores = {'dataset': [], 'f1': [], 'pr_auc': [], 'roc_auc': []}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7a1b4d2f-a3e8-4148-9590-eb9fa2a7def8",
   "metadata": {},
   "outputs": [],
   "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 = GRU_VAE(X_train)\n",
    "        \n",
    "        X_test_rec = model.decoder.predict(model.encoder.predict(X_test)[-1])\n",
    "        scores = evaluate(X_test, X_test_rec, 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": null,
   "id": "95679aba-14a8-4b08-aae0-27ce1f3b6407",
   "metadata": {},
   "outputs": [],
   "source": [
    "power_results = pd.DataFrame(total_scores)\n",
    "power_results.groupby('dataset').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc5a3d03-ea52-4958-b2d6-694be89b4bf5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "06b9409f-6b54-4ee1-9079-850794f4269a",
   "metadata": {},
   "source": [
    "### 2D Gesture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e787152b-f6d3-4ce7-a7b6-cbad809e2d9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_scores = {'dataset': [], 'f1': [], 'pr_auc': [], 'roc_auc': []}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8640d9ef-f1a3-40cd-ab83-c9e0c6cf561e",
   "metadata": {},
   "outputs": [],
   "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 = GRU_VAE(X_train)\n",
    "        \n",
    "        X_test_rec = model.decoder.predict(model.encoder.predict(X_test)[-1])\n",
    "        scores = evaluate(X_test, X_test_rec, 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": null,
   "id": "08d069d1-8830-4cc9-8e5b-2b8f48e0dc3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "gesture_results = pd.DataFrame(total_scores)\n",
    "gesture_results.groupby('dataset').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b67ea3f8-0113-4044-994c-7205c883be99",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}