Baseline - CNN-GAN.ipynb

{
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   "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\"] = \"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": "49c0a8e3-705d-429b-b7a0-c3c26a3a7b2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def train_gan(gan, dataset, batch_size, codings_size, dim=1, n_epochs=50):\n",
    "    generator, discriminator = gan.layers\n",
    "    for epoch in range(n_epochs):\n",
    "        for X_batch in dataset:\n",
    "            X_batch = tf.cast(X_batch, tf.float32)\n",
    "            # phase 1 - training the discriminator\n",
    "            noise = tf.random.normal(shape=[batch_size, codings_size, dim])\n",
    "            generated_images = generator(noise)\n",
    "            X_fake_and_real = tf.concat([generated_images, X_batch], axis=0)\n",
    "            y1 = tf.constant([[0.]] * batch_size + [[1.]] * batch_size)\n",
    "            discriminator.trainable = True\n",
    "            discriminator.train_on_batch(X_fake_and_real, y1)\n",
    "            # phase 2 - training the generator\n",
    "            noise = tf.random.normal(shape=[batch_size, codings_size, dim])\n",
    "            y2 = tf.constant([[1.]] * batch_size)\n",
    "            discriminator.trainable = False\n",
    "            gan.train_on_batch(noise, y2)\n",
    "            \n",
    "def get_gan(x_train):\n",
    "    generator = keras.models.Sequential([\n",
    "        layers.InputLayer(input_shape=(x_train.shape[1], x_train.shape[2])),\n",
    "        keras.layers.Conv1D(128, kernel_size=3, padding='same', activation=keras.layers.LeakyReLU(0.2)),\n",
    "        keras.layers.MaxPool1D(3, padding='same'),\n",
    "        keras.layers.Conv1D(64, kernel_size=3, padding='same', activation=keras.layers.LeakyReLU(0.2)),\n",
    "        keras.layers.MaxPool1D(3, padding='same'),\n",
    "        keras.layers.BatchNormalization(),\n",
    "        keras.layers.Conv1DTranspose(64, kernel_size=5, strides=2, padding=\"SAME\", activation=\"selu\"),\n",
    "        keras.layers.BatchNormalization(),\n",
    "        layers.Flatten(),\n",
    "        layers.Dense(x_train.shape[1] * x_train.shape[2]),\n",
    "        layers.Reshape([x_train.shape[1], x_train.shape[2]])\n",
    "    ])\n",
    "\n",
    "    discriminator = keras.models.Sequential([\n",
    "        layers.InputLayer(input_shape=(x_train.shape[1], x_train.shape[2])),\n",
    "        keras.layers.Conv1D(64, kernel_size=5, strides=2, padding=\"SAME\", activation=keras.layers.LeakyReLU(0.2)),\n",
    "        keras.layers.Dropout(0.4),\n",
    "        keras.layers.Conv1D(128, kernel_size=5, strides=2, padding=\"SAME\", activation=keras.layers.LeakyReLU(0.2)),\n",
    "        keras.layers.Dropout(0.4),\n",
    "        keras.layers.Flatten(),\n",
    "        keras.layers.Dense(1, activation=\"sigmoid\")\n",
    "    ])\n",
    "\n",
    "    gan = keras.models.Sequential([generator, discriminator])\n",
    "\n",
    "    discriminator.compile(loss=\"binary_crossentropy\", optimizer=\"adam\")\n",
    "    discriminator.trainable = False\n",
    "    gan.compile(loss=\"binary_crossentropy\", optimizer=\"adam\")\n",
    "    return gan"
   ]
  },
  {
   "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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   "execution_count": 5,
   "id": "71866d92-1163-4975-bb99-4302ccda66c3",
   "metadata": {
    "scrolled": true,
    "tags": []
   },
   "outputs": [
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      "yahoo_A4 0.4853700147102329 0.2154048927847556 0.4544414965022028\n",
      "yahoo_A4 0.25396823169564314 0.011902877405170258 0.12718266034552514\n",
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      "yahoo_A4 0.3673469086517006 0.07373759625699994 0.28189094906875767\n",
      "yahoo_A4 0.5942491593197877 0.12258306279970096 0.17033821026104493\n",
      "yahoo_A4 0.45070419027970904 0.1865850633674364 0.3163265024191134\n",
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      "yahoo_A4 0.45070419027970904 0.012090255496010744 0.020157950548058093\n",
      "yahoo_A4 0.5714285304267189 0.15943669481423278 0.2897195953060365\n",
      "yahoo_A4 0.25396823169564314 0.08006279916299296 0.26885115607935106\n",
      "yahoo_A4 0.5111110660987692 0.6038426118511137 0.7405448419872959\n",
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      "yahoo_A4 0.25396823169564314 0.0029184065354885213 0.3218084834915428\n",
      "yahoo_A4 0.5897435479700884 0.20337893300193682 0.4292416050029674\n",
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      "yahoo_A4 0.6874999505859404 0.6562626413094856 0.9270344667184596\n",
      "yahoo_A4 0.4086798950024387 0.029178224159439253 0.1346550340570262\n",
      "yahoo_A4 0.25396823169564314 0.0013262609136058562 -0.0\n",
      "yahoo_A4 0.42293903459873594 0.019795723191036743 0.18423254206047535\n",
      "yahoo_A4 0.6666666220202049 0.08569865944728702 0.1555902900316167\n",
      "yahoo_A4 0.5897435479700884 0.0997591610516333 0.14064921662004698\n",
      "yahoo_A4 0.5620914626724793 0.12398678310251954 0.17073868543150067\n",
      "yahoo_A4 0.6075948942156735 0.0242028631060653 0.08575364140137089\n",
      "yahoo_A4 0.4748699809545005 0.010385938475666427 0.06719895508055497\n",
      "yahoo_A4 0.32758616798454676 0.2541914046992938 0.2601488276198962\n",
      "yahoo_A4 0.557377008793338 0.11824718681102474 0.32903361305239065\n",
      "yahoo_A4 0.03131990741758405 0.0011520740865173299 -0.0\n",
      "yahoo_A4 0.5806451198959446 0.1355766597153127 0.5517834383177018\n",
      "yahoo_A4 0.3011582754610119 0.09249897691901898 0.3521669874441028\n",
      "yahoo_A4 0.3703703400548721 0.002652669808550484 0.00654411743398213\n",
      "yahoo_A4 0.25396823169564314 0.042424789386691586 0.36329724831097293\n",
      "yahoo_A4 0.45070419027970904 0.2087657200589924 0.4245842934003051\n",
      "yahoo_A4 0.25396823169564314 0.02323693763781675 0.29358541184159215\n",
      "yahoo_A4 0.46422334988015346 0.07752635146419891 0.06571849805373199\n",
      "yahoo_A4 0.45070419027970904 0.06060461715994453 0.1489993517553323\n",
      "yahoo_A4 0.744416824301612 0.859802928101412 0.8065284483953878\n",
      "yahoo_A4 0.5667752034992443 0.548082718932394 0.5583689126359812\n",
      "yahoo_A4 0.25396823169564314 0.0013262609021969535 -0.0\n",
      "yahoo_A4 0.25396823169564314 0.002410769568258501 0.012411346926737569\n",
      "yahoo_A4 0.5852089616298454 0.02097291057077636 0.07348578761489469\n",
      "yahoo_A4 0.3763837331436143 0.0895957086332125 0.2338796126515558\n",
      "yahoo_A4 0.4341636669216475 0.3751613022177369 0.4896717690349825\n",
      "yahoo_A4 0.2978723149594655 0.07329160033645327 0.28616233678089265\n",
      "yahoo_A4 0.25396823169564314 0.05946672000552643 0.21552656827719843\n",
      "yahoo_A4 0.5919999581337628 0.14012782346497674 0.2414671143877368\n",
      "yahoo_A4 0.45070419027970904 0.0015974453994661727 -0.0\n",
      "yahoo_A4 0.6982248064178453 0.04537191222744848 0.09526046849504159\n",
      "yahoo_A4 0.5806451198959446 0.5010057716967997 0.5219071387909106\n",
      "yahoo_A4 0.674698750268547 0.04714081977578387 0.11231009154350574\n",
      "yahoo_A4 0.25396823169564314 0.005102042148606386 0.7420212552022389\n",
      "yahoo_A4 0.4957264582716078 0.21838451411504292 0.3417854904451376\n",
      "yahoo_A4 0.43694490352936993 0.2971915072327872 0.36662271217442743\n",
      "yahoo_A4 0.25396823169564314 0.02144309196391083 0.011224828947503623\n",
      "yahoo_A4 0.5919999581337628 0.16402853025776143 0.24711820052043731\n",
      "yahoo_A4 0.6544342065351805 0.4347302294887143 0.5263705083130146\n",
      "yahoo_A4 0.25049699588078084 0.013364219473874372 0.15643843234886326\n",
      "yahoo_A4 0.7535410293108873 0.0789979344107979 0.11974523898277227\n",
      "yahoo_A4 0.5897435479700884 0.20038680936427206 0.3845512151044149\n",
      "yahoo_A4 0.6544342065351805 0.12757152255813053 0.13138460057482856\n",
      "yahoo_A4 0.45070419027970904 0.20112339508325489 0.43658626630250813\n",
      "yahoo_A4 0.6075948942156735 0.09571011749665324 0.2303007465278866\n",
      "yahoo_A4 0.45070419027970904 0.023712483579562474 0.1916220055977286\n",
      "yahoo_A4 0.35205989595309467 0.02961272809435716 0.1132958722071732\n",
      "yahoo_A4 0.45070419027970904 0.05799937314522154 0.06933626617652777\n",
      "yahoo_A4 0.6626139372529846 0.2883510362178367 0.7514447863557351\n",
      "yahoo_A4 0.6419752648529217 0.02601638846990633 0.026960701819807455\n",
      "yahoo_A4 0.2608695424299727 0.01478768119771381 0.07255821260840012\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",
    "        gan = get_gan(X_train)\n",
    "        dataset = tf.data.Dataset.from_tensor_slices(X_train)\n",
    "        dataset = dataset.batch(128, drop_remainder=True).prefetch(1)\n",
    "\n",
    "        train_gan(gan, dataset, 128, X_train.shape[1], X_train.shape[2])\n",
    "        \n",
    "        X_test_rec = gan.layers[0].predict(X_test)\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": 6,
   "id": "bd9e37fd-124c-4f5c-8f1f-a9d61a4b0315",
   "metadata": {},
   "outputs": [
    {
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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>yahoo_A1</th>\n",
       "      <td>0.710783</td>\n",
       "      <td>0.581876</td>\n",
       "      <td>0.668311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yahoo_A2</th>\n",
       "      <td>0.586948</td>\n",
       "      <td>0.396494</td>\n",
       "      <td>0.668050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yahoo_A3</th>\n",
       "      <td>0.510575</td>\n",
       "      <td>0.173903</td>\n",
       "      <td>0.257616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>yahoo_A4</th>\n",
       "      <td>0.469869</td>\n",
       "      <td>0.151107</td>\n",
       "      <td>0.268865</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                f1    pr_auc   roc_auc\n",
       "dataset                               \n",
       "yahoo_A1  0.710783  0.581876  0.668311\n",
       "yahoo_A2  0.586948  0.396494  0.668050\n",
       "yahoo_A3  0.510575  0.173903  0.257616\n",
       "yahoo_A4  0.469869  0.151107  0.268865"
      ]
     },
     "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": "6cae3622-9a89-4baf-95e0-363b54584199",
   "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": "d48e6c7ee1944838bd9f9c0f9be9ea05",
       "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": "c79074500edc4fe88ec8f020892774bc",
       "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 0.1372393423219817 0.5081281985260547\n",
      "D2 0.2699530282282525 0.1553041982995576 0.5280071554695182\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",
    "        gan = get_gan(X_train)\n",
    "        dataset = tf.data.Dataset.from_tensor_slices(X_train)\n",
    "        dataset = dataset.batch(128, drop_remainder=True).prefetch(1)\n",
    "\n",
    "        train_gan(gan, dataset, 128, X_train.shape[1], X_train.shape[2])\n",
    "        \n",
    "        X_test_rec = gan.layers[0].predict(X_test)\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": 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>0.137239</td>\n",
       "      <td>0.508128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D2</th>\n",
       "      <td>0.269953</td>\n",
       "      <td>0.155304</td>\n",
       "      <td>0.528007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               f1    pr_auc   roc_auc\n",
       "dataset                              \n",
       "D1       0.253000  0.137239  0.508128\n",
       "D2       0.269953  0.155304  0.528007"
      ]
     },
     "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": "aaab856a-8942-4bf6-94e7-0aa387023746",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "41754734-5c75-4caf-9069-fb3b39c7a0da",
   "metadata": {},
   "source": [
    "### SMD"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "60a26807-8359-42d4-82b4-9e1d37b6acaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_scores = {'dataset': [], 'f1': [], 'pr_auc': [], 'roc_auc': []}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "41af318e-1b08-4cc9-a680-6b4a978544f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "87180df4ec7b44f393177eb51f1ee8a7",
       "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": "7999af67eb424242b3f33e50632bbce0",
       "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 0.575072896662467 0.521888680406877\n",
      "smd 0.643300748602941 0.5466173976039415 0.919130748548558\n",
      "smd 0.15660441480072804 0.13596108774900337 0.5479628105973524\n",
      "smd 0.21419350200497705 0.21075818974883948 0.5818850916941399\n",
      "smd 0.689655126355723 0.7578630805913983 0.9600486201940233\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-925a3a0e2ccc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0mX_test_rec\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgan\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlayers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m         \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mevaluate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_test\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_test_rec\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_tests\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mis_reconstructed\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0mtotal_scores\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'dataset'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloader\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'load_'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m''\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Courses/CS570-Team4-Project/evaluator.py\u001b[0m in \u001b[0;36mevaluate\u001b[0;34m(x, rec_x, labels, is_reconstructed, n, scoring, x_val)\u001b[0m\n\u001b[1;32m     43\u001b[0m             \u001b[0;31m# if any part of the segment has an anomaly, we consider it as anomalous sequence\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 45\u001b[0;31m             \u001b[0mtrue_anomalies\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpred_anomalies\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwhere\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrec_errors\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mth\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     46\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpred_anomalies\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpred_anomalies\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mintersection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrue_anomalies\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "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",
    "        gan = get_gan(X_train)\n",
    "        dataset = tf.data.Dataset.from_tensor_slices(X_train)\n",
    "        dataset = dataset.batch(128, drop_remainder=True).prefetch(1)\n",
    "\n",
    "        train_gan(gan, dataset, 128, X_train.shape[1], X_train.shape[2])\n",
    "        \n",
    "        X_test_rec = gan.layers[0].predict(X_test)\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": "b8fb99a7-5554-43e1-a491-023334534380",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "c24a19b0-3072-4b5e-a500-ade737dc0e5f",
   "metadata": {},
   "source": [
    "### ECG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9cec61b-9422-4eca-ae5a-9ced594ec7d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_scores = {'dataset': [], 'f1': [], 'pr_auc': [], 'roc_auc': []}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9345004-df07-4f26-8e4d-109a3008765a",
   "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",
    "        tf.keras.backend.clear_session()\n",
    "\n",
    "        X_train = x_trains[i]\n",
    "        X_test = x_tests[i]\n",
    "        \n",
    "        gan = get_gan(X_train)\n",
    "        dataset = tf.data.Dataset.from_tensor_slices(X_train)\n",
    "        dataset = dataset.batch(128, drop_remainder=True).prefetch(1)\n",
    "\n",
    "        train_gan(gan, dataset, 128, X_train.shape[1], X_train.shape[2])\n",
    "        \n",
    "        X_test_rec = gan.layers[0].predict(X_test)\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": "8bcd9fe8-7266-4987-a08f-abf2ab2d6a7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "ecg_results = pd.DataFrame(total_scores)\n",
    "ecg_results.groupby('dataset').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a79a020-d6af-4625-b090-2003b7f7611f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "50445086-b227-45e5-9f6f-be55e3a3d9d4",
   "metadata": {},
   "source": [
    "### Power Demand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c7f442f-ff64-49f6-8059-faef927b5317",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_scores = {'dataset': [], 'f1': [], 'pr_auc': [], 'roc_auc': []}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0a58889-ce90-4689-ae48-125f80236863",
   "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",
    "        tf.keras.backend.clear_session()\n",
    "\n",
    "        X_train = x_trains[i]\n",
    "        X_test = x_tests[i]\n",
    "        \n",
    "        gan = get_gan(X_train)\n",
    "        dataset = tf.data.Dataset.from_tensor_slices(X_train)\n",
    "        dataset = dataset.batch(128, drop_remainder=True).prefetch(1)\n",
    "\n",
    "        train_gan(gan, dataset, 128, X_train.shape[1], X_train.shape[2])\n",
    "        \n",
    "        X_test_rec = gan.layers[0].predict(X_test)\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": "fbfc80f1-271a-4956-b7c9-4b7e0b07684a",
   "metadata": {},
   "outputs": [],
   "source": [
    "power_results = pd.DataFrame(total_scores)\n",
    "power_results.groupby('dataset').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a3671b6-f089-4402-9426-f2b95686f662",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "640debe7-c0f3-458d-a098-f7b56790e145",
   "metadata": {},
   "source": [
    "### 2D Gesture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9635a708-ecde-4e0e-8778-a2b33cbcc36f",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_scores = {'dataset': [], 'f1': [], 'pr_auc': [], 'roc_auc': []}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "467d0456-2733-424a-9033-ed6ffbccc6b7",
   "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",
    "        tf.keras.backend.clear_session()\n",
    "\n",
    "        X_train = x_trains[i]\n",
    "        X_test = x_tests[i]\n",
    "        \n",
    "        gan = get_gan(X_train)\n",
    "        dataset = tf.data.Dataset.from_tensor_slices(X_train)\n",
    "        dataset = dataset.batch(128, drop_remainder=True).prefetch(1)\n",
    "\n",
    "        train_gan(gan, dataset, 128, X_train.shape[1], X_train.shape[2])\n",
    "        \n",
    "        X_test_rec = gan.layers[0].predict(X_test)\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": "179706c4-4f1c-47c5-94f9-490c135ba258",
   "metadata": {},
   "outputs": [],
   "source": [
    "gesture_results = pd.DataFrame(total_scores)\n",
    "gesture_results.groupby('dataset').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd669dd1-acb8-4919-bb02-1037b368865a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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