IDB_drilling_monitoring_cvae.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mA_-eAkuEnLC"
   },
   "source": [
    "# IDB_drilling_monitoring"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "G6Dqfh0oC9d7"
   },
   "source": [
    "## Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "8KJudV78mY5m",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-24 15:48:22.510638: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fri Nov 24 15:48:24 2023       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 525.105.17   Driver Version: 525.105.17   CUDA Version: 12.0     |\n",
      "|-------------------------------+----------------------+----------------------+\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|                               |                      |               MIG M. |\n",
      "|===============================+======================+======================|\n",
      "|   0  NVIDIA GeForce ...  On   | 00000000:32:00.0 Off |                  Off |\n",
      "| 30%   27C    P8    18W / 450W |      2MiB / 24564MiB |      0%      Default |\n",
      "|                               |                      |                  N/A |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "                                                                               \n",
      "+-----------------------------------------------------------------------------+\n",
      "| Processes:                                                                  |\n",
      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
      "|        ID   ID                                                   Usage      |\n",
      "|=============================================================================|\n",
      "|  No running processes found                                                 |\n",
      "+-----------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import scale, StandardScaler, MinMaxScaler\n",
    "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n",
    "from math import sqrt\n",
    "from matplotlib import rcParams\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n",
    "from keras import backend as K\n",
    "from keras import metrics, optimizers, regularizers\n",
    "from keras.callbacks import ModelCheckpoint, TensorBoard, EarlyStopping\n",
    "from keras.layers import Input, Dense, Lambda, concatenate\n",
    "from keras.losses import categorical_crossentropy, mean_squared_error\n",
    "from keras.models import Model, load_model, Sequential, model_from_json\n",
    "\n",
    "# fix random seed for reproducibility\n",
    "tf.random.set_seed(123)\n",
    "np.random.seed(123)\n",
    "\n",
    "!nvidia-smi\n",
    "HOST_path = \"/root/autodl-tmp\"\n",
    "SAVE_path = os.path.join(HOST_path, \"IDB_drilling_monitoring/cvae\")\n",
    "\n",
    "if not os.path.exists(SAVE_path):\n",
    "    os.makedirs(SAVE_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "N4rlRDFk7mas"
   },
   "source": [
    "## Load signal data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "Ax7US8NWaZT8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (166, 8) 165 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (163, 8) 162 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (171, 8) 170 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (184, 8) 183 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (207, 8) 206 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (201, 8) 200 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (209, 8) 208 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (218, 8) 217 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (260, 8) 259 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (214, 8) 213 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (226, 8) 225 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (279, 8) 278 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (79, 8) 78 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (81, 8) 80 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (90, 8) 89 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (95, 8) 94 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (114, 8) 113 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (87, 8) 86 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (119, 8) 118 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (115, 8) 114 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (95, 8) 94 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (94, 8) 93 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (126, 8) 125 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (99, 8) 98 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (274, 8) 273 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (218, 8) 217 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (241, 8) 240 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (226, 8) 225 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (232, 8) 231 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (230, 8) 229 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (227, 8) 226 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (243, 8) 242 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (249, 8) 248 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (259, 8) 258 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (290, 8) 289 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (260, 8) 259 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (111, 8) 110 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (152, 8) 151 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (179, 8) 178 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (182, 8) 181 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (191, 8) 190 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (171, 8) 170 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (169, 8) 168 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (215, 8) 214 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (233, 8) 232 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (172, 8) 171 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (245, 8) 244 s\n",
      "WARNING *** OLE2 inconsistency: SSCS size is 0 but SSAT size is non-zero\n",
      "<class 'numpy.ndarray'> (292, 8) 291 s\n"
     ]
    }
   ],
   "source": [
    "def load_table(table_path, time_start, time_end):\n",
    "  SIGNAL_pd = pd.read_excel(table_path)\n",
    "  # display(SIGNAL_pd)\n",
    "  SIGNAL_arr = SIGNAL_pd.values[time_start-2+10 : time_end-2-10+1, 1:-1]\n",
    "  print(type(SIGNAL_arr), SIGNAL_arr.shape, len(SIGNAL_arr)-1, \"s\")\n",
    "  # print(SIGNAL_arr)\n",
    "  return SIGNAL_arr\n",
    "\n",
    "Y01A = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-1.xls\"), 140, 325)\n",
    "Y01B = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-1.xls\"), 609, 791)\n",
    "Y01C = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-1.xls\"), 1115, 1305)\n",
    "Y01D = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-1.xls\"), 1530, 1733)\n",
    "\n",
    "Y02A = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-1.xls\"), 3795, 4021)\n",
    "Y02B = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-1.xls\"), 4241, 4461)\n",
    "Y02C = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-1.xls\"), 4649, 4877)\n",
    "Y02D = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-1.xls\"), 5100, 5337)\n",
    "\n",
    "Y03A = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-1.xls\"), 5780, 6059)\n",
    "Y03B = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-1.xls\"), 6267, 6500)\n",
    "Y03C = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-1.xls\"), 6760, 7005)\n",
    "Y03D = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-1.xls\"), 7223, 7521)\n",
    "\n",
    "Y04A = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-2.xls\"), 123, 221)\n",
    "Y04B = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-2.xls\"), 463, 563)\n",
    "Y04C = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-2.xls\"), 773, 882)\n",
    "Y04D = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-2.xls\"), 1103, 1217)\n",
    "\n",
    "Y05A = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-2.xls\"), 1545, 1678)\n",
    "Y05B = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-2.xls\"), 1931, 2037)\n",
    "Y05C = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-2.xls\"), 2265, 2403)\n",
    "Y05D = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-2.xls\"), 2613, 2747)\n",
    "\n",
    "Y06A = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-2.xls\"), 3051, 3165)\n",
    "Y06B = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-2.xls\"), 3377, 3490)\n",
    "Y06C = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-2.xls\"), 3685, 3830)\n",
    "Y06D = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-2.xls\"), 4169, 4287)\n",
    "\n",
    "Y07A = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-3+4.xls\"), 146, 439)\n",
    "Y07B = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-3+4.xls\"), 692, 929)\n",
    "Y07C = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-3+4.xls\"), 1129, 1389)\n",
    "Y07D = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-3+4.xls\"), 1592, 1837)\n",
    "\n",
    "Y08A = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-3+4.xls\"), 2316, 2567)\n",
    "Y08B = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-3+4.xls\"), 2809, 3058)\n",
    "Y08C = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-3+4.xls\"), 3431, 3677)\n",
    "Y08D = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-3+4.xls\"), 3895, 4157)\n",
    "\n",
    "Y09A = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-3+4.xls\"), 4437, 4705)\n",
    "Y09B = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-3+4.xls\"), 4951, 5229)\n",
    "Y09C = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-3+4.xls\"), 5869, 6178)\n",
    "Y09D = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-3+4.xls\"), 6396, 6675)\n",
    "\n",
    "Y10A = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-3+4.xls\"), 8285, 8415)\n",
    "Y10B = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-3+4.xls\"), 8629, 8800)\n",
    "Y10C = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-3+4.xls\"), 9087, 9285)\n",
    "Y10D = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-3+4.xls\"), 9710, 9911)\n",
    "\n",
    "Y11A = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-3+4.xls\"), 10370, 10580)\n",
    "Y11B = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-3+4.xls\"), 10785, 10975)\n",
    "Y11C = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-3+4.xls\"), 11144, 11332)\n",
    "Y11D = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-3+4.xls\"), 11733, 11967)\n",
    "\n",
    "Y12A = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-3+4.xls\"), 12445, 12697)\n",
    "Y12B = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-3+4.xls\"), 12955, 13146)\n",
    "Y12C = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-3+4.xls\"), 13325, 13589)\n",
    "Y12D = load_table(os.path.join(HOST_path, \"IDB_drilling_monitoring/table/9.17-3+4.xls\"), 13768, 14079)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Y01A (166, 8) 165 s\n",
      "Y01B (163, 8) 162 s\n",
      "Y01C (171, 8) 170 s\n",
      "Y01D (184, 8) 183 s\n",
      "Y02A (207, 8) 206 s\n",
      "Y02B (201, 8) 200 s\n",
      "Y02C (209, 8) 208 s\n",
      "Y02D (218, 8) 217 s\n",
      "Y03A (260, 8) 259 s\n",
      "Y03B (214, 8) 213 s\n",
      "Y03C (226, 8) 225 s\n",
      "Y03D (279, 8) 278 s\n",
      "Y04A (79, 8) 78 s\n",
      "Y04B (81, 8) 80 s\n",
      "Y04C (90, 8) 89 s\n",
      "Y04D (95, 8) 94 s\n",
      "Y05A (114, 8) 113 s\n",
      "Y05B (87, 8) 86 s\n",
      "Y05C (119, 8) 118 s\n",
      "Y05D (115, 8) 114 s\n",
      "Y06A (95, 8) 94 s\n",
      "Y06B (94, 8) 93 s\n",
      "Y06C (126, 8) 125 s\n",
      "Y06D (99, 8) 98 s\n",
      "Y07A (274, 8) 273 s\n",
      "Y07B (218, 8) 217 s\n",
      "Y07C (241, 8) 240 s\n",
      "Y07D (226, 8) 225 s\n",
      "Y08A (232, 8) 231 s\n",
      "Y08B (230, 8) 229 s\n",
      "Y08C (227, 8) 226 s\n",
      "Y08D (243, 8) 242 s\n",
      "Y09A (249, 8) 248 s\n",
      "Y09B (259, 8) 258 s\n",
      "Y09C (290, 8) 289 s\n",
      "Y09D (260, 8) 259 s\n",
      "Y10A (111, 8) 110 s\n",
      "Y10B (152, 8) 151 s\n",
      "Y10C (179, 8) 178 s\n",
      "Y10D (182, 8) 181 s\n",
      "Y11A (191, 8) 190 s\n",
      "Y11B (171, 8) 170 s\n",
      "Y11C (169, 8) 168 s\n",
      "Y11D (215, 8) 214 s\n",
      "Y12A (233, 8) 232 s\n",
      "Y12B (172, 8) 171 s\n",
      "Y12C (245, 8) 244 s\n",
      "Y12D (292, 8) 291 s\n"
     ]
    }
   ],
   "source": [
    "table_list = [\"Y01A\", \"Y01B\", \"Y01C\", \"Y01D\",\n",
    "         \"Y02A\", \"Y02B\", \"Y02C\", \"Y02D\",\n",
    "         \"Y03A\", \"Y03B\", \"Y03C\", \"Y03D\",\n",
    "         \"Y04A\", \"Y04B\", \"Y04C\", \"Y04D\",\n",
    "         \"Y05A\", \"Y05B\", \"Y05C\", \"Y05D\",\n",
    "         \"Y06A\", \"Y06B\", \"Y06C\", \"Y06D\",\n",
    "         \"Y07A\", \"Y07B\", \"Y07C\", \"Y07D\",\n",
    "         \"Y08A\", \"Y08B\", \"Y08C\", \"Y08D\",\n",
    "         \"Y09A\", \"Y09B\", \"Y09C\", \"Y09D\",\n",
    "         \"Y10A\", \"Y10B\", \"Y10C\", \"Y10D\",\n",
    "         \"Y11A\", \"Y11B\", \"Y11C\", \"Y11D\",\n",
    "         \"Y12A\", \"Y12B\", \"Y12C\", \"Y12D\"]\n",
    "\n",
    "def check_table():\n",
    "    for table in table_list:\n",
    "        print(table, eval(table).shape, len(eval(table))-1,\"s\")\n",
    "check_table()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CVAE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data pretreatment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "INDEX_dict = [\"HEIGHT\", \"RPM\", \"TORQUE\", \"PUMPR\", \"PUMPP\", \"ROP\", \"WOB\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'> (8953, 8)\n",
      "[['10:24:25' '-354.00' '233.00' ... '919.00' '29.00' '813.00']\n",
      " ['10:24:26' '-352.00' '295.00' ... '919.00' '26.00' '800.00']\n",
      " ['10:24:27' '-351.00' '306.00' ... '1312.00' '28.00' '800.00']\n",
      " ...\n",
      " ['22:06:22' '-245.00' '302.00' ... '611.00' '19.00' '915.00']\n",
      " ['22:06:23' '-245.00' '302.00' ... '609.00' '19.00' '915.00']\n",
      " ['22:06:24' '-245.00' '302.00' ... '608.00' '19.00' '915.00']]\n",
      "<class 'numpy.ndarray'> (8953, 2)\n",
      "[['90.48' '29.00']\n",
      " ['92.63' '26.00']\n",
      " ['92.95' '28.00']\n",
      " ...\n",
      " ['54.76' '19.00']\n",
      " ['53.67' '19.00']\n",
      " ['54.25' '19.00']]\n",
      "<class 'numpy.ndarray'> (8953, 4)\n",
      "[['233.00' '71.00' '919.00' '813.00']\n",
      " ['295.00' '71.00' '919.00' '800.00']\n",
      " ['306.00' '71.00' '1312.00' '800.00']\n",
      " ...\n",
      " ['302.00' '50.00' '611.00' '915.00']\n",
      " ['302.00' '50.00' '609.00' '915.00']\n",
      " ['302.00' '50.00' '608.00' '915.00']]\n"
     ]
    }
   ],
   "source": [
    "X = np.concatenate((Y01A, Y01B, Y01C, Y01D,\n",
    "                    Y02A, Y02B, Y02C, Y02D,\n",
    "                    Y03A, Y03B, Y03C, Y03D,\n",
    "                    Y04A, Y04B, Y04C, Y04D,\n",
    "                    Y05A, Y05B, Y05C, Y05D,\n",
    "                    Y06A, Y06B, Y06C, Y06D,\n",
    "                    Y07A, Y07B, Y07C, Y07D,\n",
    "                    Y08A, Y08B, Y08C, Y08D,\n",
    "                    Y09A, Y09B, Y09C, Y09D,\n",
    "                    Y10A, Y10B, Y10C, Y10D,\n",
    "                    Y11A, Y11B, Y11C, Y11D,\n",
    "                    Y12A, Y12B, Y12C, Y12D), axis=0)\n",
    "print(type(X), X.shape)\n",
    "print(X)\n",
    "\n",
    "Y = X[:,[3,6]]\n",
    "print(type(Y), Y.shape)\n",
    "print(Y)\n",
    "\n",
    "X = X[:,[2,4,5,7]]\n",
    "print(type(X), X.shape)\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler_X = MinMaxScaler().fit(X)\n",
    "scaler_Y = MinMaxScaler().fit(Y)\n",
    "X = scaler_X.transform(X)  \n",
    "Y = scaler_Y.transform(Y)\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# np.save(os.path.join(SAVE_path, \"X.npy\"), X)\n",
    "# np.save(os.path.join(SAVE_path, \"Y.npy\"), Y)\n",
    "# np.save(os.path.join(SAVE_path, \"X_train.npy\"), X_train)\n",
    "# np.save(os.path.join(SAVE_path, \"X_test.npy\"), X_test)\n",
    "# np.save(os.path.join(SAVE_path, \"Y_train.npy\"), Y_train)\n",
    "# np.save(os.path.join(SAVE_path, \"Y_test.npy\"), Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model establishment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_x = X.shape[1]\n",
    "n_y = Y.shape[1]\n",
    "n_z = 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "VALUE = Input(shape=(n_x,))\n",
    "LABEL = Input(shape=(n_y,))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = concatenate([VALUE, LABEL])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-24 15:48:39.001486: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F AVX512_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-11-24 15:48:39.553518: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 22128 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 4090, pci bus id: 0000:32:00.0, compute capability: 8.9\n"
     ]
    }
   ],
   "source": [
    "encoder_1l = Dense(512, activation='relu')(inputs)\n",
    "encoder_2l = Dense(256, activation='relu')(encoder_1l)\n",
    "encoder_3l = Dense(128, activation='relu')(encoder_2l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "mu = Dense(n_z)(encoder_3l)\n",
    "l_sigma = Dense(n_z)(encoder_3l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sample_z(args):\n",
    "    mu, l_sigma = args\n",
    "    epsilon = K.random_normal(shape=(K.shape(mu)[0], n_z), mean=0., stddev=1.)\n",
    "    return mu + K.exp(l_sigma / 2) * epsilon"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "z = Lambda(sample_z, output_shape=(n_z,))([mu, l_sigma])\n",
    "z_cond = concatenate([z, LABEL])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "decoder_1 = Dense(128, activation='relu') \n",
    "decoder_2 = Dense(256, activation='relu')\n",
    "decoder_3 = Dense(512, activation='relu')\n",
    "decoder_r = Dense(n_x, activation='sigmoid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "decoder_1l = decoder_1(z_cond)\n",
    "decoder_2l = decoder_2(decoder_1l)\n",
    "decoder_3l = decoder_3(decoder_2l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "outputs = decoder_r(decoder_3l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "CVAE = Model([VALUE, LABEL], outputs)\n",
    "\n",
    "ENCODER = Model([VALUE, LABEL], z)\n",
    "\n",
    "d_input = Input(shape=(n_z,))\n",
    "d_input_cond = concatenate([d_input, LABEL])\n",
    "d_1l = decoder_1(d_input_cond)\n",
    "d_2l = decoder_2(d_1l)\n",
    "d_3l = decoder_3(d_2l)\n",
    "d_output = decoder_r(d_3l)\n",
    "DECODER = Model([d_input, LABEL], d_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, 4)]          0           []                               \n",
      "                                                                                                  \n",
      " input_2 (InputLayer)           [(None, 2)]          0           []                               \n",
      "                                                                                                  \n",
      " concatenate (Concatenate)      (None, 6)            0           ['input_1[0][0]',                \n",
      "                                                                  'input_2[0][0]']                \n",
      "                                                                                                  \n",
      " dense (Dense)                  (None, 512)          3584        ['concatenate[0][0]']            \n",
      "                                                                                                  \n",
      " dense_1 (Dense)                (None, 256)          131328      ['dense[0][0]']                  \n",
      "                                                                                                  \n",
      " dense_2 (Dense)                (None, 128)          32896       ['dense_1[0][0]']                \n",
      "                                                                                                  \n",
      " dense_3 (Dense)                (None, 2)            258         ['dense_2[0][0]']                \n",
      "                                                                                                  \n",
      " dense_4 (Dense)                (None, 2)            258         ['dense_2[0][0]']                \n",
      "                                                                                                  \n",
      " lambda (Lambda)                (None, 2)            0           ['dense_3[0][0]',                \n",
      "                                                                  'dense_4[0][0]']                \n",
      "                                                                                                  \n",
      " concatenate_1 (Concatenate)    (None, 4)            0           ['lambda[0][0]',                 \n",
      "                                                                  'input_2[0][0]']                \n",
      "                                                                                                  \n",
      " dense_5 (Dense)                (None, 128)          640         ['concatenate_1[0][0]']          \n",
      "                                                                                                  \n",
      " dense_6 (Dense)                (None, 256)          33024       ['dense_5[0][0]']                \n",
      "                                                                                                  \n",
      " dense_7 (Dense)                (None, 512)          131584      ['dense_6[0][0]']                \n",
      "                                                                                                  \n",
      " dense_8 (Dense)                (None, 4)            2052        ['dense_7[0][0]']                \n",
      "                                                                                                  \n",
      " tf.__operators__.add (TFOpLamb  (None, 2)           0           ['dense_4[0][0]']                \n",
      " da)                                                                                              \n",
      "                                                                                                  \n",
      " tf.math.square (TFOpLambda)    (None, 2)            0           ['dense_3[0][0]']                \n",
      "                                                                                                  \n",
      " tf.reshape_1 (TFOpLambda)      (None,)              0           ['dense_8[0][0]']                \n",
      "                                                                                                  \n",
      " tf.reshape (TFOpLambda)        (None,)              0           ['input_1[0][0]']                \n",
      "                                                                                                  \n",
      " tf.math.subtract (TFOpLambda)  (None, 2)            0           ['tf.__operators__.add[0][0]',   \n",
      "                                                                  'tf.math.square[0][0]']         \n",
      "                                                                                                  \n",
      " tf.math.exp (TFOpLambda)       (None, 2)            0           ['dense_4[0][0]']                \n",
      "                                                                                                  \n",
      " tf.convert_to_tensor (TFOpLamb  (None,)             0           ['tf.reshape_1[0][0]']           \n",
      " da)                                                                                              \n",
      "                                                                                                  \n",
      " tf.cast (TFOpLambda)           (None,)              0           ['tf.reshape[0][0]']             \n",
      "                                                                                                  \n",
      " tf.math.subtract_1 (TFOpLambda  (None, 2)           0           ['tf.math.subtract[0][0]',       \n",
      " )                                                                'tf.math.exp[0][0]']            \n",
      "                                                                                                  \n",
      " tf.math.squared_difference (TF  (None,)             0           ['tf.convert_to_tensor[0][0]',   \n",
      " OpLambda)                                                        'tf.cast[0][0]']                \n",
      "                                                                                                  \n",
      " tf.math.reduce_sum (TFOpLambda  (None,)             0           ['tf.math.subtract_1[0][0]']     \n",
      " )                                                                                                \n",
      "                                                                                                  \n",
      " tf.math.reduce_mean (TFOpLambd  ()                  0           ['tf.math.squared_difference[0][0\n",
      " a)                                                              ]']                              \n",
      "                                                                                                  \n",
      " tf.math.multiply (TFOpLambda)  (None,)              0           ['tf.math.reduce_sum[0][0]']     \n",
      "                                                                                                  \n",
      " tf.__operators__.add_1 (TFOpLa  (None,)             0           ['tf.math.reduce_mean[0][0]',    \n",
      " mbda)                                                            'tf.math.multiply[0][0]']       \n",
      "                                                                                                  \n",
      " tf.math.reduce_mean_1 (TFOpLam  ()                  0           ['tf.__operators__.add_1[0][0]'] \n",
      " bda)                                                                                             \n",
      "                                                                                                  \n",
      " add_loss (AddLoss)             ()                   0           ['tf.math.reduce_mean_1[0][0]']  \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 335,624\n",
      "Trainable params: 335,624\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# def cvae_loss(y_true, y_pred):\n",
    "#     re_loss = mean_squared_error(y_true, y_pred)\n",
    "#     kl_loss = - 0.5 * K.mean(1 + l_sigma - K.square(mu) - K.exp(l_sigma), axis=-1)\n",
    "#     return re_loss + kl_loss\n",
    "\n",
    "re_loss = mean_squared_error(K.flatten(VALUE), K.flatten(outputs))\n",
    "kl_loss = 1 + l_sigma - K.square(mu) - K.exp(l_sigma)\n",
    "kl_loss = -0.5 * K.sum(kl_loss, axis=-1)\n",
    "cvae_loss = K.mean(re_loss + kl_loss)\n",
    "CVAE.add_loss(cvae_loss)\n",
    "CVAE.compile(optimizer='rmsprop')\n",
    "CVAE.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      " 24/717 [>.............................] - ETA: 4s - loss: 0.0424 "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-24 15:48:41.871885: I tensorflow/stream_executor/cuda/cuda_blas.cc:1786] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "710/717 [============================>.] - ETA: 0s - loss: 0.0236\n",
      "Epoch 1: val_loss improved from inf to 0.01965, saving model to /root/autodl-tmp/IDB_drilling_monitoring/cvae/cvae_model_best.hdf5\n",
      "717/717 [==============================] - 7s 7ms/step - loss: 0.0236 - val_loss: 0.0197\n",
      "Epoch 2/100\n",
      "715/717 [============================>.] - ETA: 0s - loss: 0.0154\n",
      "Epoch 2: val_loss improved from 0.01965 to 0.01964, saving model to /root/autodl-tmp/IDB_drilling_monitoring/cvae/cvae_model_best.hdf5\n",
      "717/717 [==============================] - 6s 8ms/step - loss: 0.0154 - val_loss: 0.0196\n",
      "Epoch 3/100\n",
      "712/717 [============================>.] - ETA: 0s - loss: 0.0123\n",
      "Epoch 3: val_loss improved from 0.01964 to 0.01322, saving model to /root/autodl-tmp/IDB_drilling_monitoring/cvae/cvae_model_best.hdf5\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0123 - val_loss: 0.0132\n",
      "Epoch 4/100\n",
      "714/717 [============================>.] - ETA: 0s - loss: 0.0112\n",
      "Epoch 4: val_loss improved from 0.01322 to 0.01073, saving model to /root/autodl-tmp/IDB_drilling_monitoring/cvae/cvae_model_best.hdf5\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0112 - val_loss: 0.0107\n",
      "Epoch 5/100\n",
      "713/717 [============================>.] - ETA: 0s - loss: 0.0106\n",
      "Epoch 5: val_loss did not improve from 0.01073\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0106 - val_loss: 0.0112\n",
      "Epoch 6/100\n",
      "716/717 [============================>.] - ETA: 0s - loss: 0.0102\n",
      "Epoch 6: val_loss did not improve from 0.01073\n",
      "717/717 [==============================] - 5s 6ms/step - loss: 0.0102 - val_loss: 0.0112\n",
      "Epoch 7/100\n",
      "712/717 [============================>.] - ETA: 0s - loss: 0.0096\n",
      "Epoch 7: val_loss improved from 0.01073 to 0.00984, saving model to /root/autodl-tmp/IDB_drilling_monitoring/cvae/cvae_model_best.hdf5\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0096 - val_loss: 0.0098\n",
      "Epoch 8/100\n",
      "715/717 [============================>.] - ETA: 0s - loss: 0.0093\n",
      "Epoch 8: val_loss did not improve from 0.00984\n",
      "717/717 [==============================] - 6s 8ms/step - loss: 0.0093 - val_loss: 0.0100\n",
      "Epoch 9/100\n",
      "715/717 [============================>.] - ETA: 0s - loss: 0.0092\n",
      "Epoch 9: val_loss improved from 0.00984 to 0.00934, saving model to /root/autodl-tmp/IDB_drilling_monitoring/cvae/cvae_model_best.hdf5\n",
      "717/717 [==============================] - 6s 8ms/step - loss: 0.0092 - val_loss: 0.0093\n",
      "Epoch 10/100\n",
      "714/717 [============================>.] - ETA: 0s - loss: 0.0089\n",
      "Epoch 10: val_loss did not improve from 0.00934\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0090 - val_loss: 0.0097\n",
      "Epoch 11/100\n",
      "717/717 [==============================] - ETA: 0s - loss: 0.0088\n",
      "Epoch 11: val_loss did not improve from 0.00934\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0088 - val_loss: 0.0106\n",
      "Epoch 12/100\n",
      "716/717 [============================>.] - ETA: 0s - loss: 0.0087\n",
      "Epoch 12: val_loss did not improve from 0.00934\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0087 - val_loss: 0.0114\n",
      "Epoch 13/100\n",
      "716/717 [============================>.] - ETA: 0s - loss: 0.0085\n",
      "Epoch 13: val_loss improved from 0.00934 to 0.00840, saving model to /root/autodl-tmp/IDB_drilling_monitoring/cvae/cvae_model_best.hdf5\n",
      "717/717 [==============================] - 5s 8ms/step - loss: 0.0085 - val_loss: 0.0084\n",
      "Epoch 14/100\n",
      "717/717 [==============================] - ETA: 0s - loss: 0.0084\n",
      "Epoch 14: val_loss did not improve from 0.00840\n",
      "717/717 [==============================] - 6s 8ms/step - loss: 0.0084 - val_loss: 0.0094\n",
      "Epoch 15/100\n",
      "716/717 [============================>.] - ETA: 0s - loss: 0.0084\n",
      "Epoch 15: val_loss did not improve from 0.00840\n",
      "717/717 [==============================] - 6s 8ms/step - loss: 0.0084 - val_loss: 0.0095\n",
      "Epoch 16/100\n",
      "717/717 [==============================] - ETA: 0s - loss: 0.0082\n",
      "Epoch 16: val_loss did not improve from 0.00840\n",
      "717/717 [==============================] - 6s 8ms/step - loss: 0.0082 - val_loss: 0.0089\n",
      "Epoch 17/100\n",
      "715/717 [============================>.] - ETA: 0s - loss: 0.0081\n",
      "Epoch 17: val_loss did not improve from 0.00840\n",
      "717/717 [==============================] - 6s 9ms/step - loss: 0.0081 - val_loss: 0.0084\n",
      "Epoch 18/100\n",
      "715/717 [============================>.] - ETA: 0s - loss: 0.0081\n",
      "Epoch 18: val_loss did not improve from 0.00840\n",
      "717/717 [==============================] - 6s 8ms/step - loss: 0.0081 - val_loss: 0.0098\n",
      "Epoch 19/100\n",
      "713/717 [============================>.] - ETA: 0s - loss: 0.0081\n",
      "Epoch 19: val_loss improved from 0.00840 to 0.00829, saving model to /root/autodl-tmp/IDB_drilling_monitoring/cvae/cvae_model_best.hdf5\n",
      "717/717 [==============================] - 6s 9ms/step - loss: 0.0081 - val_loss: 0.0083\n",
      "Epoch 20/100\n",
      "711/717 [============================>.] - ETA: 0s - loss: 0.0080\n",
      "Epoch 20: val_loss improved from 0.00829 to 0.00796, saving model to /root/autodl-tmp/IDB_drilling_monitoring/cvae/cvae_model_best.hdf5\n",
      "717/717 [==============================] - 6s 9ms/step - loss: 0.0080 - val_loss: 0.0080\n",
      "Epoch 21/100\n",
      "712/717 [============================>.] - ETA: 0s - loss: 0.0079\n",
      "Epoch 21: val_loss did not improve from 0.00796\n",
      "717/717 [==============================] - 6s 9ms/step - loss: 0.0079 - val_loss: 0.0085\n",
      "Epoch 22/100\n",
      "714/717 [============================>.] - ETA: 0s - loss: 0.0079\n",
      "Epoch 22: val_loss did not improve from 0.00796\n",
      "717/717 [==============================] - 6s 9ms/step - loss: 0.0079 - val_loss: 0.0092\n",
      "Epoch 23/100\n",
      "716/717 [============================>.] - ETA: 0s - loss: 0.0078\n",
      "Epoch 23: val_loss improved from 0.00796 to 0.00784, saving model to /root/autodl-tmp/IDB_drilling_monitoring/cvae/cvae_model_best.hdf5\n",
      "717/717 [==============================] - 6s 9ms/step - loss: 0.0078 - val_loss: 0.0078\n",
      "Epoch 24/100\n",
      "715/717 [============================>.] - ETA: 0s - loss: 0.0078\n",
      "Epoch 24: val_loss did not improve from 0.00784\n",
      "717/717 [==============================] - 6s 9ms/step - loss: 0.0077 - val_loss: 0.0080\n",
      "Epoch 25/100\n",
      "714/717 [============================>.] - ETA: 0s - loss: 0.0077\n",
      "Epoch 25: val_loss did not improve from 0.00784\n",
      "717/717 [==============================] - 6s 8ms/step - loss: 0.0077 - val_loss: 0.0108\n",
      "Epoch 26/100\n",
      "711/717 [============================>.] - ETA: 0s - loss: 0.0077\n",
      "Epoch 26: val_loss did not improve from 0.00784\n",
      "717/717 [==============================] - 6s 9ms/step - loss: 0.0077 - val_loss: 0.0085\n",
      "Epoch 27/100\n",
      "712/717 [============================>.] - ETA: 0s - loss: 0.0077\n",
      "Epoch 27: val_loss did not improve from 0.00784\n",
      "717/717 [==============================] - 6s 8ms/step - loss: 0.0077 - val_loss: 0.0084\n",
      "Epoch 28/100\n",
      "713/717 [============================>.] - ETA: 0s - loss: 0.0076\n",
      "Epoch 28: val_loss did not improve from 0.00784\n",
      "717/717 [==============================] - 6s 9ms/step - loss: 0.0077 - val_loss: 0.0086\n",
      "Epoch 29/100\n",
      "711/717 [============================>.] - ETA: 0s - loss: 0.0075\n",
      "Epoch 29: val_loss improved from 0.00784 to 0.00783, saving model to /root/autodl-tmp/IDB_drilling_monitoring/cvae/cvae_model_best.hdf5\n",
      "717/717 [==============================] - 6s 9ms/step - loss: 0.0075 - val_loss: 0.0078\n",
      "Epoch 30/100\n",
      "715/717 [============================>.] - ETA: 0s - loss: 0.0075\n",
      "Epoch 30: val_loss did not improve from 0.00783\n",
      "717/717 [==============================] - 6s 8ms/step - loss: 0.0075 - val_loss: 0.0084\n",
      "Epoch 31/100\n",
      "716/717 [============================>.] - ETA: 0s - loss: 0.0076\n",
      "Epoch 31: val_loss did not improve from 0.00783\n",
      "717/717 [==============================] - 6s 9ms/step - loss: 0.0076 - val_loss: 0.0086\n",
      "Epoch 32/100\n",
      "715/717 [============================>.] - ETA: 0s - loss: 0.0075\n",
      "Epoch 32: val_loss did not improve from 0.00783\n",
      "717/717 [==============================] - 6s 9ms/step - loss: 0.0075 - val_loss: 0.0079\n",
      "Epoch 33/100\n",
      "715/717 [============================>.] - ETA: 0s - loss: 0.0075\n",
      "Epoch 33: val_loss did not improve from 0.00783\n",
      "717/717 [==============================] - 6s 9ms/step - loss: 0.0075 - val_loss: 0.0091\n",
      "Epoch 34/100\n",
      "713/717 [============================>.] - ETA: 0s - loss: 0.0075\n",
      "Epoch 34: val_loss improved from 0.00783 to 0.00770, saving model to /root/autodl-tmp/IDB_drilling_monitoring/cvae/cvae_model_best.hdf5\n",
      "717/717 [==============================] - 6s 9ms/step - loss: 0.0075 - val_loss: 0.0077\n",
      "Epoch 35/100\n",
      "715/717 [============================>.] - ETA: 0s - loss: 0.0075\n",
      "Epoch 35: val_loss did not improve from 0.00770\n",
      "717/717 [==============================] - 6s 9ms/step - loss: 0.0075 - val_loss: 0.0086\n",
      "Epoch 36/100\n",
      "711/717 [============================>.] - ETA: 0s - loss: 0.0073\n",
      "Epoch 36: val_loss did not improve from 0.00770\n",
      "717/717 [==============================] - 6s 9ms/step - loss: 0.0073 - val_loss: 0.0082\n",
      "Epoch 37/100\n",
      "717/717 [==============================] - ETA: 0s - loss: 0.0075\n",
      "Epoch 37: val_loss did not improve from 0.00770\n",
      "717/717 [==============================] - 6s 9ms/step - loss: 0.0075 - val_loss: 0.0078\n",
      "Epoch 38/100\n",
      "712/717 [============================>.] - ETA: 0s - loss: 0.0074\n",
      "Epoch 38: val_loss did not improve from 0.00770\n",
      "717/717 [==============================] - 6s 8ms/step - loss: 0.0074 - val_loss: 0.0085\n",
      "Epoch 39/100\n",
      "713/717 [============================>.] - ETA: 0s - loss: 0.0074\n",
      "Epoch 39: val_loss did not improve from 0.00770\n",
      "717/717 [==============================] - 6s 9ms/step - loss: 0.0074 - val_loss: 0.0085\n",
      "Epoch 40/100\n",
      "714/717 [============================>.] - ETA: 0s - loss: 0.0074\n",
      "Epoch 40: val_loss did not improve from 0.00770\n",
      "717/717 [==============================] - 6s 9ms/step - loss: 0.0074 - val_loss: 0.0081\n",
      "Epoch 41/100\n",
      "713/717 [============================>.] - ETA: 0s - loss: 0.0074\n",
      "Epoch 41: val_loss did not improve from 0.00770\n",
      "717/717 [==============================] - 6s 9ms/step - loss: 0.0074 - val_loss: 0.0079\n",
      "Epoch 42/100\n",
      "715/717 [============================>.] - ETA: 0s - loss: 0.0074\n",
      "Epoch 42: val_loss did not improve from 0.00770\n",
      "717/717 [==============================] - 6s 9ms/step - loss: 0.0073 - val_loss: 0.0081\n",
      "Epoch 43/100\n",
      "712/717 [============================>.] - ETA: 0s - loss: 0.0073\n",
      "Epoch 43: val_loss did not improve from 0.00770\n",
      "717/717 [==============================] - 6s 8ms/step - loss: 0.0073 - val_loss: 0.0078\n",
      "Epoch 44/100\n",
      "712/717 [============================>.] - ETA: 0s - loss: 0.0073\n",
      "Epoch 44: val_loss improved from 0.00770 to 0.00761, saving model to /root/autodl-tmp/IDB_drilling_monitoring/cvae/cvae_model_best.hdf5\n",
      "717/717 [==============================] - 6s 9ms/step - loss: 0.0073 - val_loss: 0.0076\n",
      "Epoch 45/100\n",
      "716/717 [============================>.] - ETA: 0s - loss: 0.0072\n",
      "Epoch 45: val_loss did not improve from 0.00761\n",
      "717/717 [==============================] - 6s 8ms/step - loss: 0.0073 - val_loss: 0.0089\n",
      "Epoch 46/100\n",
      "712/717 [============================>.] - ETA: 0s - loss: 0.0073\n",
      "Epoch 46: val_loss did not improve from 0.00761\n",
      "717/717 [==============================] - 6s 9ms/step - loss: 0.0073 - val_loss: 0.0078\n",
      "Epoch 47/100\n",
      "712/717 [============================>.] - ETA: 0s - loss: 0.0072\n",
      "Epoch 47: val_loss did not improve from 0.00761\n",
      "717/717 [==============================] - 6s 8ms/step - loss: 0.0072 - val_loss: 0.0097\n",
      "Epoch 48/100\n",
      "716/717 [============================>.] - ETA: 0s - loss: 0.0073\n",
      "Epoch 48: val_loss did not improve from 0.00761\n",
      "717/717 [==============================] - 6s 9ms/step - loss: 0.0073 - val_loss: 0.0078\n",
      "Epoch 49/100\n",
      "712/717 [============================>.] - ETA: 0s - loss: 0.0072\n",
      "Epoch 49: val_loss did not improve from 0.00761\n",
      "717/717 [==============================] - 6s 9ms/step - loss: 0.0073 - val_loss: 0.0083\n",
      "Epoch 50/100\n",
      "713/717 [============================>.] - ETA: 0s - loss: 0.0071\n",
      "Epoch 50: val_loss did not improve from 0.00761\n",
      "717/717 [==============================] - 6s 9ms/step - loss: 0.0071 - val_loss: 0.0100\n",
      "Epoch 51/100\n",
      "712/717 [============================>.] - ETA: 0s - loss: 0.0072\n",
      "Epoch 51: val_loss did not improve from 0.00761\n",
      "717/717 [==============================] - 6s 9ms/step - loss: 0.0072 - val_loss: 0.0083\n",
      "Epoch 52/100\n",
      "713/717 [============================>.] - ETA: 0s - loss: 0.0072\n",
      "Epoch 52: val_loss improved from 0.00761 to 0.00748, saving model to /root/autodl-tmp/IDB_drilling_monitoring/cvae/cvae_model_best.hdf5\n",
      "717/717 [==============================] - 6s 9ms/step - loss: 0.0073 - val_loss: 0.0075\n",
      "Epoch 53/100\n",
      "715/717 [============================>.] - ETA: 0s - loss: 0.0072\n",
      "Epoch 53: val_loss did not improve from 0.00748\n",
      "717/717 [==============================] - 6s 8ms/step - loss: 0.0072 - val_loss: 0.0079\n",
      "Epoch 54/100\n",
      "717/717 [==============================] - ETA: 0s - loss: 0.0072\n",
      "Epoch 54: val_loss did not improve from 0.00748\n",
      "717/717 [==============================] - 6s 9ms/step - loss: 0.0072 - val_loss: 0.0081\n",
      "Epoch 55/100\n",
      "717/717 [==============================] - ETA: 0s - loss: 0.0072\n",
      "Epoch 55: val_loss did not improve from 0.00748\n",
      "717/717 [==============================] - 6s 9ms/step - loss: 0.0072 - val_loss: 0.0075\n",
      "Epoch 56/100\n",
      "717/717 [==============================] - ETA: 0s - loss: 0.0072\n",
      "Epoch 56: val_loss did not improve from 0.00748\n",
      "717/717 [==============================] - 6s 9ms/step - loss: 0.0072 - val_loss: 0.0077\n",
      "Epoch 57/100\n",
      "714/717 [============================>.] - ETA: 0s - loss: 0.0071\n",
      "Epoch 57: val_loss did not improve from 0.00748\n",
      "717/717 [==============================] - 6s 9ms/step - loss: 0.0072 - val_loss: 0.0076\n",
      "Epoch 58/100\n",
      "717/717 [==============================] - ETA: 0s - loss: 0.0072\n",
      "Epoch 58: val_loss did not improve from 0.00748\n",
      "717/717 [==============================] - 6s 9ms/step - loss: 0.0072 - val_loss: 0.0078\n",
      "Epoch 59/100\n",
      "713/717 [============================>.] - ETA: 0s - loss: 0.0072\n",
      "Epoch 59: val_loss did not improve from 0.00748\n",
      "717/717 [==============================] - 6s 9ms/step - loss: 0.0072 - val_loss: 0.0081\n",
      "Epoch 60/100\n",
      "710/717 [============================>.] - ETA: 0s - loss: 0.0071\n",
      "Epoch 60: val_loss improved from 0.00748 to 0.00717, saving model to /root/autodl-tmp/IDB_drilling_monitoring/cvae/cvae_model_best.hdf5\n",
      "717/717 [==============================] - 6s 8ms/step - loss: 0.0071 - val_loss: 0.0072\n",
      "Epoch 61/100\n",
      "714/717 [============================>.] - ETA: 0s - loss: 0.0071\n",
      "Epoch 61: val_loss did not improve from 0.00717\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0071 - val_loss: 0.0077\n",
      "Epoch 62/100\n",
      "713/717 [============================>.] - ETA: 0s - loss: 0.0071\n",
      "Epoch 62: val_loss did not improve from 0.00717\n",
      "717/717 [==============================] - 6s 8ms/step - loss: 0.0071 - val_loss: 0.0077\n",
      "Epoch 63/100\n",
      "713/717 [============================>.] - ETA: 0s - loss: 0.0071\n",
      "Epoch 63: val_loss did not improve from 0.00717\n",
      "717/717 [==============================] - 6s 8ms/step - loss: 0.0071 - val_loss: 0.0080\n",
      "Epoch 64/100\n",
      "713/717 [============================>.] - ETA: 0s - loss: 0.0071\n",
      "Epoch 64: val_loss did not improve from 0.00717\n",
      "717/717 [==============================] - 6s 8ms/step - loss: 0.0071 - val_loss: 0.0077\n",
      "Epoch 65/100\n",
      "717/717 [==============================] - ETA: 0s - loss: 0.0071\n",
      "Epoch 65: val_loss did not improve from 0.00717\n",
      "717/717 [==============================] - 6s 8ms/step - loss: 0.0071 - val_loss: 0.0084\n",
      "Epoch 66/100\n",
      "715/717 [============================>.] - ETA: 0s - loss: 0.0071\n",
      "Epoch 66: val_loss did not improve from 0.00717\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0071 - val_loss: 0.0076\n",
      "Epoch 67/100\n",
      "714/717 [============================>.] - ETA: 0s - loss: 0.0071\n",
      "Epoch 67: val_loss did not improve from 0.00717\n",
      "717/717 [==============================] - 6s 8ms/step - loss: 0.0071 - val_loss: 0.0075\n",
      "Epoch 68/100\n",
      "714/717 [============================>.] - ETA: 0s - loss: 0.0071\n",
      "Epoch 68: val_loss did not improve from 0.00717\n",
      "717/717 [==============================] - 6s 8ms/step - loss: 0.0070 - val_loss: 0.0075\n",
      "Epoch 69/100\n",
      "716/717 [============================>.] - ETA: 0s - loss: 0.0070\n",
      "Epoch 69: val_loss improved from 0.00717 to 0.00710, saving model to /root/autodl-tmp/IDB_drilling_monitoring/cvae/cvae_model_best.hdf5\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0070 - val_loss: 0.0071\n",
      "Epoch 70/100\n",
      "712/717 [============================>.] - ETA: 0s - loss: 0.0071\n",
      "Epoch 70: val_loss did not improve from 0.00710\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0071 - val_loss: 0.0075\n",
      "Epoch 71/100\n",
      "712/717 [============================>.] - ETA: 0s - loss: 0.0070\n",
      "Epoch 71: val_loss did not improve from 0.00710\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0070 - val_loss: 0.0072\n",
      "Epoch 72/100\n",
      "715/717 [============================>.] - ETA: 0s - loss: 0.0069\n",
      "Epoch 72: val_loss did not improve from 0.00710\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0070 - val_loss: 0.0073\n",
      "Epoch 73/100\n",
      "717/717 [==============================] - ETA: 0s - loss: 0.0071\n",
      "Epoch 73: val_loss did not improve from 0.00710\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0071 - val_loss: 0.0073\n",
      "Epoch 74/100\n",
      "713/717 [============================>.] - ETA: 0s - loss: 0.0070\n",
      "Epoch 74: val_loss did not improve from 0.00710\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0070 - val_loss: 0.0072\n",
      "Epoch 75/100\n",
      "709/717 [============================>.] - ETA: 0s - loss: 0.0070\n",
      "Epoch 75: val_loss did not improve from 0.00710\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0070 - val_loss: 0.0071\n",
      "Epoch 76/100\n",
      "712/717 [============================>.] - ETA: 0s - loss: 0.0071\n",
      "Epoch 76: val_loss did not improve from 0.00710\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0071 - val_loss: 0.0085\n",
      "Epoch 77/100\n",
      "710/717 [============================>.] - ETA: 0s - loss: 0.0069\n",
      "Epoch 77: val_loss did not improve from 0.00710\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0069 - val_loss: 0.0083\n",
      "Epoch 78/100\n",
      "711/717 [============================>.] - ETA: 0s - loss: 0.0070\n",
      "Epoch 78: val_loss did not improve from 0.00710\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0070 - val_loss: 0.0084\n",
      "Epoch 79/100\n",
      "714/717 [============================>.] - ETA: 0s - loss: 0.0070\n",
      "Epoch 79: val_loss did not improve from 0.00710\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0070 - val_loss: 0.0075\n",
      "Epoch 80/100\n",
      "709/717 [============================>.] - ETA: 0s - loss: 0.0069\n",
      "Epoch 80: val_loss did not improve from 0.00710\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0070 - val_loss: 0.0092\n",
      "Epoch 81/100\n",
      "716/717 [============================>.] - ETA: 0s - loss: 0.0069\n",
      "Epoch 81: val_loss did not improve from 0.00710\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0069 - val_loss: 0.0091\n",
      "Epoch 82/100\n",
      "717/717 [==============================] - ETA: 0s - loss: 0.0070\n",
      "Epoch 82: val_loss did not improve from 0.00710\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0070 - val_loss: 0.0072\n",
      "Epoch 83/100\n",
      "712/717 [============================>.] - ETA: 0s - loss: 0.0070\n",
      "Epoch 83: val_loss did not improve from 0.00710\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0070 - val_loss: 0.0072\n",
      "Epoch 84/100\n",
      "710/717 [============================>.] - ETA: 0s - loss: 0.0070\n",
      "Epoch 84: val_loss improved from 0.00710 to 0.00683, saving model to /root/autodl-tmp/IDB_drilling_monitoring/cvae/cvae_model_best.hdf5\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0070 - val_loss: 0.0068\n",
      "Epoch 85/100\n",
      "713/717 [============================>.] - ETA: 0s - loss: 0.0069\n",
      "Epoch 85: val_loss did not improve from 0.00683\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0069 - val_loss: 0.0077\n",
      "Epoch 86/100\n",
      "709/717 [============================>.] - ETA: 0s - loss: 0.0070\n",
      "Epoch 86: val_loss did not improve from 0.00683\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0070 - val_loss: 0.0082\n",
      "Epoch 87/100\n",
      "715/717 [============================>.] - ETA: 0s - loss: 0.0070\n",
      "Epoch 87: val_loss did not improve from 0.00683\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0070 - val_loss: 0.0086\n",
      "Epoch 88/100\n",
      "713/717 [============================>.] - ETA: 0s - loss: 0.0071\n",
      "Epoch 88: val_loss did not improve from 0.00683\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0070 - val_loss: 0.0080\n",
      "Epoch 89/100\n",
      "717/717 [==============================] - ETA: 0s - loss: 0.0070\n",
      "Epoch 89: val_loss did not improve from 0.00683\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0070 - val_loss: 0.0069\n",
      "Epoch 90/100\n",
      "716/717 [============================>.] - ETA: 0s - loss: 0.0069\n",
      "Epoch 90: val_loss did not improve from 0.00683\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0069 - val_loss: 0.0089\n",
      "Epoch 91/100\n",
      "716/717 [============================>.] - ETA: 0s - loss: 0.0070\n",
      "Epoch 91: val_loss did not improve from 0.00683\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0069 - val_loss: 0.0074\n",
      "Epoch 92/100\n",
      "714/717 [============================>.] - ETA: 0s - loss: 0.0069\n",
      "Epoch 92: val_loss did not improve from 0.00683\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0069 - val_loss: 0.0082\n",
      "Epoch 93/100\n",
      "711/717 [============================>.] - ETA: 0s - loss: 0.0070\n",
      "Epoch 93: val_loss did not improve from 0.00683\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0070 - val_loss: 0.0074\n",
      "Epoch 94/100\n",
      "716/717 [============================>.] - ETA: 0s - loss: 0.0069\n",
      "Epoch 94: val_loss did not improve from 0.00683\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0069 - val_loss: 0.0071\n",
      "Epoch 95/100\n",
      "716/717 [============================>.] - ETA: 0s - loss: 0.0070\n",
      "Epoch 95: val_loss did not improve from 0.00683\n",
      "717/717 [==============================] - 5s 8ms/step - loss: 0.0070 - val_loss: 0.0078\n",
      "Epoch 96/100\n",
      "714/717 [============================>.] - ETA: 0s - loss: 0.0069\n",
      "Epoch 96: val_loss did not improve from 0.00683\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0069 - val_loss: 0.0076\n",
      "Epoch 97/100\n",
      "711/717 [============================>.] - ETA: 0s - loss: 0.0069\n",
      "Epoch 97: val_loss did not improve from 0.00683\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0069 - val_loss: 0.0070\n",
      "Epoch 98/100\n",
      "717/717 [==============================] - ETA: 0s - loss: 0.0069\n",
      "Epoch 98: val_loss did not improve from 0.00683\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0069 - val_loss: 0.0079\n",
      "Epoch 99/100\n",
      "717/717 [==============================] - ETA: 0s - loss: 0.0070\n",
      "Epoch 99: val_loss did not improve from 0.00683\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0070 - val_loss: 0.0075\n",
      "Epoch 100/100\n",
      "717/717 [==============================] - ETA: 0s - loss: 0.0069\n",
      "Epoch 100: val_loss did not improve from 0.00683\n",
      "717/717 [==============================] - 5s 7ms/step - loss: 0.0069 - val_loss: 0.0075\n"
     ]
    }
   ],
   "source": [
    "callbacks = [\n",
    "             ModelCheckpoint(os.path.join(SAVE_path, \"cvae_model_best.hdf5\"),\n",
    "                             monitor=\"val_loss\",\n",
    "                             mode=\"min\",\n",
    "                             verbose=1,\n",
    "                             save_best_only=True),\n",
    "             # EarlyStopping(monitor=\"val_loss\",\n",
    "             #               mode=\"min\",\n",
    "             #               verbose=1,\n",
    "             #               patience=5,\n",
    "             #               restore_best_weights=True),\n",
    "            ]\n",
    "\n",
    "history = CVAE.fit([X_train, Y_train], X_train,\n",
    "                   epochs=100,\n",
    "                   batch_size=10,\n",
    "                   shuffle=True,\n",
    "                   callbacks = callbacks,\n",
    "                   validation_data=([X_test, Y_test], X_test),\n",
    "                  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n",
      "WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
     ]
    }
   ],
   "source": [
    "CVAE.save(os.path.join(SAVE_path, \"cvae_model.hdf5\"))\n",
    "ENCODER.save(os.path.join(SAVE_path, \"encoder_model.hdf5\"))\n",
    "DECODER.save(os.path.join(SAVE_path, \"decoder_model.hdf5\"))\n",
    "np.save(os.path.join(SAVE_path, \"cvae_history.npy\"), history.history)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(CVAE.history.history['loss'])\n",
    "plt.plot(CVAE.history.history['val_loss'])\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend(['Train','Validation'],loc='upper right')\n",
    "plt.savefig(os.path.join(SAVE_path, \"cvae_loss.png\"), dpi=300, bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Latent space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_test_real = scaler_Y.inverse_transform(Y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "180/180 [==============================] - 0s 1ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_test_encoded = ENCODER.predict([X_test, Y_test], batch_size=10)\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.scatter(X_test_encoded[:, 0], X_test_encoded[:, 1], c=Y_test_real[:,0])\n",
    "plt.colorbar()\n",
    "plt.savefig(os.path.join(SAVE_path, \"latent_space_0.png\"), dpi=300, bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "180/180 [==============================] - 0s 2ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_test_encoded = ENCODER.predict([X_test, Y_test], batch_size=10)\n",
    "plt.figure(figsize=(6, 6))\n",
    "plt.scatter(X_test_encoded[:, 0], X_test_encoded[:, 1], c=Y_test_real[:,1])\n",
    "plt.colorbar()\n",
    "plt.savefig(os.path.join(SAVE_path, \"latent_space_1.png\"), dpi=300, bbox_inches='tight', transparent=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_of_samples = 10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 2)\n",
      "[[0.37454012 0.95071431]\n",
      " [0.73199394 0.59865848]\n",
      " [0.15601864 0.15599452]\n",
      " [0.05808361 0.86617615]\n",
      " [0.60111501 0.70807258]\n",
      " [0.02058449 0.96990985]\n",
      " [0.83244264 0.21233911]\n",
      " [0.18182497 0.18340451]\n",
      " [0.30424224 0.52475643]\n",
      " [0.43194502 0.29122914]\n",
      " [0.61185289 0.13949386]\n",
      " [0.29214465 0.36636184]\n",
      " [0.45606998 0.78517596]\n",
      " [0.19967378 0.51423444]\n",
      " [0.59241457 0.04645041]\n",
      " [0.60754485 0.17052412]\n",
      " [0.06505159 0.94888554]\n",
      " [0.96563203 0.80839735]\n",
      " [0.30461377 0.09767211]\n",
      " [0.68423303 0.44015249]]\n"
     ]
    }
   ],
   "source": [
    "Y_sampling = np.random.uniform(0, 1, (num_of_samples, 2))\n",
    "print(Y_sampling.shape)\n",
    "print(Y_sampling[:20])\n",
    "np.save(os.path.join(SAVE_path, \"Y_sampling.npy\"), Y_sampling)\n",
    "np.savetxt(os.path.join(SAVE_path, \"Y_sampling.csv\"), Y_sampling, delimiter=', ', fmt='%f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 2)\n",
      "[[ 75.80755931  82.95857313]\n",
      " [127.25588804  54.0899957 ]\n",
      " [ 44.35576292  17.79155067]\n",
      " [ 30.2599743   76.02644395]\n",
      " [108.41848364  63.06195138]\n",
      " [ 24.86272626  84.53260788]\n",
      " [141.71346929  22.41180708]\n",
      " [ 48.07006753  20.03916981]\n",
      " [ 65.68958603  48.03002739]\n",
      " [ 84.06984653  28.8807895 ]\n",
      " [109.96398714  16.43849657]\n",
      " [ 63.94837926  35.04167115]\n",
      " [ 87.54215283  69.38442883]\n",
      " [ 50.63904747  47.16722395]\n",
      " [107.1662289    8.80893384]\n",
      " [109.34393053  18.98297814]\n",
      " [ 31.26287578  82.80861405]\n",
      " [160.88341852  71.28858255]\n",
      " [ 65.7430598   13.00911335]\n",
      " [120.38165951  41.09250449]]\n"
     ]
    }
   ],
   "source": [
    "Y_sampling_real = scaler_Y.inverse_transform(Y_sampling)\n",
    "print(Y_sampling_real.shape)\n",
    "print(Y_sampling_real[:20])\n",
    "np.save(os.path.join(SAVE_path, \"Y_sampling_real.npy\"), Y_sampling_real)\n",
    "np.savetxt(os.path.join(SAVE_path, \"Y_sampling_real.csv\"), Y_sampling_real, delimiter=', ', fmt='%f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean = [0,0]\n",
    "cov = [[1, 0], [0, 1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10000, 2)\n",
      "[[ 0.49671415 -0.1382643 ]\n",
      " [ 0.64768854  1.52302986]\n",
      " [-0.23415337 -0.23413696]\n",
      " ...\n",
      " [ 0.95259051  0.38782734]\n",
      " [ 1.37736658  0.37835397]\n",
      " [ 1.71352973 -1.6199198 ]]\n"
     ]
    }
   ],
   "source": [
    "Z_sampling = np.random.multivariate_normal(mean, cov, num_of_samples)\n",
    "print(Z_sampling.shape)\n",
    "print(Z_sampling)\n",
    "np.save(os.path.join(SAVE_path, \"Z_sampling.npy\"), Z_sampling)\n",
    "np.savetxt(os.path.join(SAVE_path, \"Z_sampling.csv\"), Z_sampling, delimiter=', ', fmt='%f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 0s 1ms/step\n",
      "(10000, 4)\n",
      "[[0.86103886 0.7881538  0.5667508  0.5898881 ]\n",
      " [0.5992758  0.6463248  0.4034613  0.6414831 ]\n",
      " [0.03790224 0.7781202  0.61920464 0.6561036 ]\n",
      " ...\n",
      " [0.9341624  0.893605   0.7302389  0.51203257]\n",
      " [0.32596543 0.57214063 0.23534994 0.02539311]\n",
      " [0.03176623 0.7742019  0.51828796 0.66481024]]\n",
      "(10000, 4)\n",
      "[[ 410.98178    60.831383  838.0053    829.8039  ]\n",
      " [ 345.01752    54.02359   633.7301    887.95154 ]\n",
      " [ 203.55136    60.34977   903.625     904.4288  ]\n",
      " ...\n",
      " [ 429.40894    65.893036 1042.5289    742.0607  ]\n",
      " [ 276.14328    50.462753  423.4228    193.61803 ]\n",
      " [ 202.00508    60.161694  777.37823   914.24115 ]]\n"
     ]
    }
   ],
   "source": [
    "samples_scaled = DECODER.predict([Z_sampling, Y_sampling])\n",
    "print(samples_scaled.shape)\n",
    "print(samples_scaled)\n",
    "\n",
    "generated_samples = scaler_X.inverse_transform(samples_scaled)\n",
    "print(generated_samples.shape)\n",
    "print(generated_samples)\n",
    "np.save(os.path.join(SAVE_path, \"generated_samples.npy\"), generated_samples)\n",
    "np.savetxt(os.path.join(SAVE_path, \"generated_samples.csv\"), generated_samples, delimiter=', ', fmt='%f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(generated_samples[0:100,0])\n",
    "plt.plot(generated_samples[0:100,1])\n",
    "plt.plot(generated_samples[0:100,2])\n",
    "plt.plot(generated_samples[0:100,3])\n",
    "plt.savefig(os.path.join(SAVE_path, \"generated_distribution.png\"), dpi=300, bbox_inches='tight', transparent=True)"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "gpuType": "T4",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}