[SOL]kohi2022_ppf_bis.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yy0WHpW3X9oX"
      },
      "source": [
        "# 약물 효과 예측 인공지능 모델 실습\n",
        "프로포폴, 레미펜타닐 투여 후 마취 심도(bispectral index) 예측 모델"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9SwIjOX9frk6"
      },
      "source": [
        "## 본 프로그램에서 이용할 라이브러리 설치 및 import"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ch6czkFZfw_G",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6a904fe0-cd43-480d-fc6c-55f1106dcbb9"
      },
      "source": [
        "!pip install vitaldb\n",
        "\n",
        "import vitaldb\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting vitaldb\n",
            "  Downloading vitaldb-1.2.4-py3-none-any.whl (51 kB)\n",
            "\u001b[K     |████████████████████████████████| 51 kB 465 kB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from vitaldb) (2.23.0)\n",
            "Collecting wfdb\n",
            "  Downloading wfdb-4.0.0-py3-none-any.whl (161 kB)\n",
            "\u001b[K     |████████████████████████████████| 161 kB 31.7 MB/s \n",
            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from vitaldb) (1.3.5)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from vitaldb) (1.21.6)\n",
            "Collecting s3fs\n",
            "  Downloading s3fs-2022.8.2-py3-none-any.whl (27 kB)\n",
            "Requirement already satisfied: pyarrow in /usr/local/lib/python3.7/dist-packages (from vitaldb) (6.0.1)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas->vitaldb) (2022.2.1)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas->vitaldb) (2.8.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->vitaldb) (1.15.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->vitaldb) (2022.6.15)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->vitaldb) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->vitaldb) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->vitaldb) (2.10)\n",
            "Requirement already satisfied: fsspec==2022.8.2 in /usr/local/lib/python3.7/dist-packages (from s3fs->vitaldb) (2022.8.2)\n",
            "Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /usr/local/lib/python3.7/dist-packages (from s3fs->vitaldb) (3.8.1)\n",
            "Collecting aiobotocore~=2.4.0\n",
            "  Downloading aiobotocore-2.4.0-py3-none-any.whl (65 kB)\n",
            "\u001b[K     |████████████████████████████████| 65 kB 4.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: wrapt>=1.10.10 in /usr/local/lib/python3.7/dist-packages (from aiobotocore~=2.4.0->s3fs->vitaldb) (1.14.1)\n",
            "Collecting botocore<1.27.60,>=1.27.59\n",
            "  Downloading botocore-1.27.59-py3-none-any.whl (9.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 9.1 MB 64.8 MB/s \n",
            "\u001b[?25hCollecting aioitertools>=0.5.1\n",
            "  Downloading aioitertools-0.10.0-py3-none-any.whl (23 kB)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.7/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->s3fs->vitaldb) (6.0.2)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->s3fs->vitaldb) (1.8.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4 in /usr/local/lib/python3.7/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->s3fs->vitaldb) (4.1.1)\n",
            "Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->s3fs->vitaldb) (2.1.1)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.7/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->s3fs->vitaldb) (4.0.2)\n",
            "Requirement already satisfied: asynctest==0.13.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->s3fs->vitaldb) (0.13.0)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.7/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->s3fs->vitaldb) (1.2.0)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->s3fs->vitaldb) (22.1.0)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.7/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->s3fs->vitaldb) (1.3.1)\n",
            "Collecting jmespath<2.0.0,>=0.7.1\n",
            "  Downloading jmespath-1.0.1-py3-none-any.whl (20 kB)\n",
            "Collecting urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1\n",
            "  Downloading urllib3-1.25.11-py2.py3-none-any.whl (127 kB)\n",
            "\u001b[K     |████████████████████████████████| 127 kB 54.4 MB/s \n",
            "\u001b[?25hRequirement already satisfied: scipy<2.0.0,>=1.0.0 in /usr/local/lib/python3.7/dist-packages (from wfdb->vitaldb) (1.7.3)\n",
            "Requirement already satisfied: matplotlib<4.0.0,>=3.2.2 in /usr/local/lib/python3.7/dist-packages (from wfdb->vitaldb) (3.2.2)\n",
            "Requirement already satisfied: SoundFile<0.12.0,>=0.10.0 in /usr/local/lib/python3.7/dist-packages (from wfdb->vitaldb) (0.10.3.post1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib<4.0.0,>=3.2.2->wfdb->vitaldb) (3.0.9)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib<4.0.0,>=3.2.2->wfdb->vitaldb) (1.4.4)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib<4.0.0,>=3.2.2->wfdb->vitaldb) (0.11.0)\n",
            "Requirement already satisfied: cffi>=1.0 in /usr/local/lib/python3.7/dist-packages (from SoundFile<0.12.0,>=0.10.0->wfdb->vitaldb) (1.15.1)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.7/dist-packages (from cffi>=1.0->SoundFile<0.12.0,>=0.10.0->wfdb->vitaldb) (2.21)\n",
            "Installing collected packages: urllib3, jmespath, botocore, aioitertools, aiobotocore, wfdb, s3fs, vitaldb\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 1.24.3\n",
            "    Uninstalling urllib3-1.24.3:\n",
            "      Successfully uninstalled urllib3-1.24.3\n",
            "Successfully installed aiobotocore-2.4.0 aioitertools-0.10.0 botocore-1.27.59 jmespath-1.0.1 s3fs-2022.8.2 urllib3-1.25.11 vitaldb-1.2.4 wfdb-4.0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZjxFdZTXBZWb"
      },
      "source": [
        "# Data loading"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AabTJc6dflSy"
      },
      "source": [
        "##VitalDB Web API를 통해 데이터 로딩\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_trks = pd.read_csv('https://api.vitaldb.net/trks')\n",
        "df_cases = pd.read_csv(\"https://api.vitaldb.net/cases\")"
      ],
      "metadata": {
        "id": "HYLisyUb5TAU"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Track DataFrame확인    ## .head(), .tail()도 사용가능.\n",
        "df_trks"
      ],
      "metadata": {
        "id": "576gN5jL5WWw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "8d2e5f5a-56f2-4c5d-879e-119eb8e52939"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        caseid                  tname  \\\n",
              "0            1                BIS/BIS   \n",
              "1            1           BIS/EEG1_WAV   \n",
              "2            1           BIS/EEG2_WAV   \n",
              "3            1                BIS/EMG   \n",
              "4            1                BIS/SEF   \n",
              "...        ...                    ...   \n",
              "486444    6388     Solar8000/VENT_PIP   \n",
              "486445    6388   Solar8000/VENT_PPLAT   \n",
              "486446    6388      Solar8000/VENT_RR   \n",
              "486447    6388  Solar8000/VENT_SET_TV   \n",
              "486448    6388      Solar8000/VENT_TV   \n",
              "\n",
              "                                             tid  \n",
              "0       fd869e25ba82a66cc95b38ed47110bf4f14bb368  \n",
              "1       0aa685df768489a18a5e9f53af0d83bf60890c73  \n",
              "2       ad13b2c39b19193c8ae4a2de4f8315f18d61a57e  \n",
              "3       2525603efe18d982764dbca457affe7a45e766a9  \n",
              "4       1c91aec859304840dec75acf4a35da78be0e8ef0  \n",
              "...                                          ...  \n",
              "486444  2d63adbc7e2653f14348e219816673cde3358cf6  \n",
              "486445  6f6604255858ddc8f6a01b9f4774b0d43105f6da  \n",
              "486446  f34f3fae7fd963355c1c7060e1e876d20fa87536  \n",
              "486447  4a4a55b8aebf9c76a4a76f62a7c1ec6fcb80e618  \n",
              "486448  77453bd3c4bf24ce13e577781a51929281f3b7f2  \n",
              "\n",
              "[486449 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-fdeae0bd-8bda-43ee-8b0f-398df2f4ba74\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>caseid</th>\n",
              "      <th>tname</th>\n",
              "      <th>tid</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>BIS/BIS</td>\n",
              "      <td>fd869e25ba82a66cc95b38ed47110bf4f14bb368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>BIS/EEG1_WAV</td>\n",
              "      <td>0aa685df768489a18a5e9f53af0d83bf60890c73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>BIS/EEG2_WAV</td>\n",
              "      <td>ad13b2c39b19193c8ae4a2de4f8315f18d61a57e</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>BIS/EMG</td>\n",
              "      <td>2525603efe18d982764dbca457affe7a45e766a9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1</td>\n",
              "      <td>BIS/SEF</td>\n",
              "      <td>1c91aec859304840dec75acf4a35da78be0e8ef0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>486444</th>\n",
              "      <td>6388</td>\n",
              "      <td>Solar8000/VENT_PIP</td>\n",
              "      <td>2d63adbc7e2653f14348e219816673cde3358cf6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>486445</th>\n",
              "      <td>6388</td>\n",
              "      <td>Solar8000/VENT_PPLAT</td>\n",
              "      <td>6f6604255858ddc8f6a01b9f4774b0d43105f6da</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>486446</th>\n",
              "      <td>6388</td>\n",
              "      <td>Solar8000/VENT_RR</td>\n",
              "      <td>f34f3fae7fd963355c1c7060e1e876d20fa87536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>486447</th>\n",
              "      <td>6388</td>\n",
              "      <td>Solar8000/VENT_SET_TV</td>\n",
              "      <td>4a4a55b8aebf9c76a4a76f62a7c1ec6fcb80e618</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>486448</th>\n",
              "      <td>6388</td>\n",
              "      <td>Solar8000/VENT_TV</td>\n",
              "      <td>77453bd3c4bf24ce13e577781a51929281f3b7f2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>486449 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fdeae0bd-8bda-43ee-8b0f-398df2f4ba74')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-fdeae0bd-8bda-43ee-8b0f-398df2f4ba74 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fdeae0bd-8bda-43ee-8b0f-398df2f4ba74');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Track name 확인\n",
        "df_trks[df_trks.caseid == 1].tname\n",
        "\n",
        "## same as..\n",
        "#df_trks[df_trks['caseid'] == 1]['tname']"
      ],
      "metadata": {
        "id": "exb44MvU7iPN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "79bbb72d-2b7d-42e3-e620-45f0c0073199"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0                    BIS/BIS\n",
              "1               BIS/EEG1_WAV\n",
              "2               BIS/EEG2_WAV\n",
              "3                    BIS/EMG\n",
              "4                    BIS/SEF\n",
              "               ...          \n",
              "71      Solar8000/VENT_PPLAT\n",
              "72         Solar8000/VENT_RR\n",
              "73    Solar8000/VENT_SET_PCP\n",
              "74     Solar8000/VENT_SET_TV\n",
              "75         Solar8000/VENT_TV\n",
              "Name: tname, Length: 76, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 전체 목록 확인\n",
        "df_trks[df_trks.caseid == 1].tname.tolist()\n",
        "\n",
        "##same as,\n",
        "#print(df_trks[df_trks.caseid == 1].tname.tolist())"
      ],
      "metadata": {
        "id": "KHEI1_Hg8IDn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dc6a0ff8-5175-4154-fb69-d649511b2e0f"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['BIS/BIS',\n",
              " 'BIS/EEG1_WAV',\n",
              " 'BIS/EEG2_WAV',\n",
              " 'BIS/EMG',\n",
              " 'BIS/SEF',\n",
              " 'BIS/SQI',\n",
              " 'BIS/SR',\n",
              " 'BIS/TOTPOW',\n",
              " 'Primus/AWP',\n",
              " 'Primus/CO2',\n",
              " 'Primus/COMPLIANCE',\n",
              " 'Primus/ETCO2',\n",
              " 'Primus/EXP_DES',\n",
              " 'Primus/EXP_SEVO',\n",
              " 'Primus/FEN2O',\n",
              " 'Primus/FEO2',\n",
              " 'Primus/FIN2O',\n",
              " 'Primus/FIO2',\n",
              " 'Primus/INCO2',\n",
              " 'Primus/INSP_DES',\n",
              " 'Primus/INSP_SEVO',\n",
              " 'Primus/MAC',\n",
              " 'Primus/MAWP_MBAR',\n",
              " 'Primus/MV',\n",
              " 'Primus/PAMB_MBAR',\n",
              " 'Primus/PEEP_MBAR',\n",
              " 'Primus/PIP_MBAR',\n",
              " 'Primus/PPLAT_MBAR',\n",
              " 'Primus/RR_CO2',\n",
              " 'Primus/SET_AGE',\n",
              " 'Primus/SET_FIO2',\n",
              " 'Primus/SET_FRESH_FLOW',\n",
              " 'Primus/SET_INSP_PAUSE',\n",
              " 'Primus/SET_INSP_TM',\n",
              " 'Primus/SET_INTER_PEEP',\n",
              " 'Primus/SET_PIP',\n",
              " 'Primus/SET_RR_IPPV',\n",
              " 'Primus/SET_TV_L',\n",
              " 'Primus/TV',\n",
              " 'Primus/VENT_LEAK',\n",
              " 'SNUADC/ART',\n",
              " 'SNUADC/ECG_II',\n",
              " 'SNUADC/ECG_V5',\n",
              " 'SNUADC/PLETH',\n",
              " 'Solar8000/ART_DBP',\n",
              " 'Solar8000/ART_MBP',\n",
              " 'Solar8000/ART_SBP',\n",
              " 'Solar8000/BT',\n",
              " 'Solar8000/ETCO2',\n",
              " 'Solar8000/FEO2',\n",
              " 'Solar8000/FIO2',\n",
              " 'Solar8000/GAS2_EXPIRED',\n",
              " 'Solar8000/GAS2_INSPIRED',\n",
              " 'Solar8000/HR',\n",
              " 'Solar8000/INCO2',\n",
              " 'Solar8000/NIBP_DBP',\n",
              " 'Solar8000/NIBP_MBP',\n",
              " 'Solar8000/NIBP_SBP',\n",
              " 'Solar8000/PLETH_HR',\n",
              " 'Solar8000/PLETH_SPO2',\n",
              " 'Solar8000/RR_CO2',\n",
              " 'Solar8000/ST_AVF',\n",
              " 'Solar8000/ST_AVL',\n",
              " 'Solar8000/ST_AVR',\n",
              " 'Solar8000/ST_I',\n",
              " 'Solar8000/ST_II',\n",
              " 'Solar8000/ST_III',\n",
              " 'Solar8000/VENT_INSP_TM',\n",
              " 'Solar8000/VENT_MAWP',\n",
              " 'Solar8000/VENT_MV',\n",
              " 'Solar8000/VENT_PIP',\n",
              " 'Solar8000/VENT_PPLAT',\n",
              " 'Solar8000/VENT_RR',\n",
              " 'Solar8000/VENT_SET_PCP',\n",
              " 'Solar8000/VENT_SET_TV',\n",
              " 'Solar8000/VENT_TV']"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Cases DataFrame확인\n",
        "df_cases"
      ],
      "metadata": {
        "id": "aIUEfO8W5d3e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "outputId": "8801d1ff-1304-40f4-a857-5bddf9c68b08"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      caseid  subjectid  casestart  caseend  anestart   aneend  opstart  \\\n",
              "0          1       5955          0    11542      -552  10848.0     1668   \n",
              "1          2       2487          0    15741     -1039  14921.0     1721   \n",
              "2          3       2861          0     4394      -590   4210.0     1090   \n",
              "3          4       1903          0    20990      -778  20222.0     2522   \n",
              "4          5       4416          0    21531     -1009  22391.0     2591   \n",
              "...      ...        ...        ...      ...       ...      ...      ...   \n",
              "6383    6384       5583          0    15248      -260  15640.0     2140   \n",
              "6384    6385       2278          0    20643      -544  20996.0     2396   \n",
              "6385    6386       4045          0    19451      -667  19133.0     3533   \n",
              "6386    6387       5230          0    12025      -550  12830.0     1730   \n",
              "6387    6388       1306          0    10249       -79  10121.0     2321   \n",
              "\n",
              "      opend     adm      dis  ...  intraop_colloid  intraop_ppf  intraop_mdz  \\\n",
              "0     10368 -236220   627780  ...                0          120          0.0   \n",
              "1     14621 -221160  1506840  ...                0          150          0.0   \n",
              "2      3010 -218640    40560  ...                0            0          0.0   \n",
              "3     17822 -201120   576480  ...                0           80          0.0   \n",
              "4     20291  -67560  3734040  ...                0            0          0.0   \n",
              "...     ...     ...      ...  ...              ...          ...          ...   \n",
              "6383  14140 -215340   648660  ...                0          150          0.0   \n",
              "6384  19496 -225600  1675200  ...                0          100          0.0   \n",
              "6385  18233 -200460   836340  ...                0           70          0.0   \n",
              "6386  11030 -227760   377040  ...                0          120          0.0   \n",
              "6387   9221 -312060   379140  ...              500          120          0.0   \n",
              "\n",
              "     intraop_ftn  intraop_rocu  intraop_vecu  intraop_eph  intraop_phe  \\\n",
              "0            100            70             0           10            0   \n",
              "1              0           100             0           20            0   \n",
              "2              0            50             0            0            0   \n",
              "3            100           100             0           50            0   \n",
              "4              0           160             0           10          900   \n",
              "...          ...           ...           ...          ...          ...   \n",
              "6383           0            90             0           20            0   \n",
              "6384           0           100             0           25           30   \n",
              "6385           0           130             0           10            0   \n",
              "6386           0            50             0            0            0   \n",
              "6387           0            90             0           20            0   \n",
              "\n",
              "      intraop_epi intraop_ca  \n",
              "0               0          0  \n",
              "1               0          0  \n",
              "2               0          0  \n",
              "3               0          0  \n",
              "4               0       2100  \n",
              "...           ...        ...  \n",
              "6383            0          0  \n",
              "6384            0        300  \n",
              "6385            0          0  \n",
              "6386            0          0  \n",
              "6387            0          0  \n",
              "\n",
              "[6388 rows x 74 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e9d5ae2e-71ee-4edb-96f6-2a73fffa6a9f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>caseid</th>\n",
              "      <th>subjectid</th>\n",
              "      <th>casestart</th>\n",
              "      <th>caseend</th>\n",
              "      <th>anestart</th>\n",
              "      <th>aneend</th>\n",
              "      <th>opstart</th>\n",
              "      <th>opend</th>\n",
              "      <th>adm</th>\n",
              "      <th>dis</th>\n",
              "      <th>...</th>\n",
              "      <th>intraop_colloid</th>\n",
              "      <th>intraop_ppf</th>\n",
              "      <th>intraop_mdz</th>\n",
              "      <th>intraop_ftn</th>\n",
              "      <th>intraop_rocu</th>\n",
              "      <th>intraop_vecu</th>\n",
              "      <th>intraop_eph</th>\n",
              "      <th>intraop_phe</th>\n",
              "      <th>intraop_epi</th>\n",
              "      <th>intraop_ca</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>5955</td>\n",
              "      <td>0</td>\n",
              "      <td>11542</td>\n",
              "      <td>-552</td>\n",
              "      <td>10848.0</td>\n",
              "      <td>1668</td>\n",
              "      <td>10368</td>\n",
              "      <td>-236220</td>\n",
              "      <td>627780</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100</td>\n",
              "      <td>70</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>2487</td>\n",
              "      <td>0</td>\n",
              "      <td>15741</td>\n",
              "      <td>-1039</td>\n",
              "      <td>14921.0</td>\n",
              "      <td>1721</td>\n",
              "      <td>14621</td>\n",
              "      <td>-221160</td>\n",
              "      <td>1506840</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>2861</td>\n",
              "      <td>0</td>\n",
              "      <td>4394</td>\n",
              "      <td>-590</td>\n",
              "      <td>4210.0</td>\n",
              "      <td>1090</td>\n",
              "      <td>3010</td>\n",
              "      <td>-218640</td>\n",
              "      <td>40560</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>50</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>1903</td>\n",
              "      <td>0</td>\n",
              "      <td>20990</td>\n",
              "      <td>-778</td>\n",
              "      <td>20222.0</td>\n",
              "      <td>2522</td>\n",
              "      <td>17822</td>\n",
              "      <td>-201120</td>\n",
              "      <td>576480</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>80</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>50</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>4416</td>\n",
              "      <td>0</td>\n",
              "      <td>21531</td>\n",
              "      <td>-1009</td>\n",
              "      <td>22391.0</td>\n",
              "      <td>2591</td>\n",
              "      <td>20291</td>\n",
              "      <td>-67560</td>\n",
              "      <td>3734040</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>160</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>900</td>\n",
              "      <td>0</td>\n",
              "      <td>2100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6383</th>\n",
              "      <td>6384</td>\n",
              "      <td>5583</td>\n",
              "      <td>0</td>\n",
              "      <td>15248</td>\n",
              "      <td>-260</td>\n",
              "      <td>15640.0</td>\n",
              "      <td>2140</td>\n",
              "      <td>14140</td>\n",
              "      <td>-215340</td>\n",
              "      <td>648660</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>150</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>90</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6384</th>\n",
              "      <td>6385</td>\n",
              "      <td>2278</td>\n",
              "      <td>0</td>\n",
              "      <td>20643</td>\n",
              "      <td>-544</td>\n",
              "      <td>20996.0</td>\n",
              "      <td>2396</td>\n",
              "      <td>19496</td>\n",
              "      <td>-225600</td>\n",
              "      <td>1675200</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "      <td>25</td>\n",
              "      <td>30</td>\n",
              "      <td>0</td>\n",
              "      <td>300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6385</th>\n",
              "      <td>6386</td>\n",
              "      <td>4045</td>\n",
              "      <td>0</td>\n",
              "      <td>19451</td>\n",
              "      <td>-667</td>\n",
              "      <td>19133.0</td>\n",
              "      <td>3533</td>\n",
              "      <td>18233</td>\n",
              "      <td>-200460</td>\n",
              "      <td>836340</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>70</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>130</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6386</th>\n",
              "      <td>6387</td>\n",
              "      <td>5230</td>\n",
              "      <td>0</td>\n",
              "      <td>12025</td>\n",
              "      <td>-550</td>\n",
              "      <td>12830.0</td>\n",
              "      <td>1730</td>\n",
              "      <td>11030</td>\n",
              "      <td>-227760</td>\n",
              "      <td>377040</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>120</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>50</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6387</th>\n",
              "      <td>6388</td>\n",
              "      <td>1306</td>\n",
              "      <td>0</td>\n",
              "      <td>10249</td>\n",
              "      <td>-79</td>\n",
              "      <td>10121.0</td>\n",
              "      <td>2321</td>\n",
              "      <td>9221</td>\n",
              "      <td>-312060</td>\n",
              "      <td>379140</td>\n",
              "      <td>...</td>\n",
              "      <td>500</td>\n",
              "      <td>120</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>90</td>\n",
              "      <td>0</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6388 rows × 74 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e9d5ae2e-71ee-4edb-96f6-2a73fffa6a9f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e9d5ae2e-71ee-4edb-96f6-2a73fffa6a9f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e9d5ae2e-71ee-4edb-96f6-2a73fffa6a9f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Cases DataFrame의 column명들만 추출해서 확인\n",
        "df_cases.columns"
      ],
      "metadata": {
        "id": "IJiGFn7k9eGB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "47719063-4934-4516-da8f-133b56c5114d"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['caseid', 'subjectid', 'casestart', 'caseend', 'anestart', 'aneend',\n",
              "       'opstart', 'opend', 'adm', 'dis', 'icu_days', 'death_inhosp', 'age',\n",
              "       'sex', 'height', 'weight', 'bmi', 'asa', 'emop', 'department', 'optype',\n",
              "       'dx', 'opname', 'approach', 'position', 'ane_type', 'preop_htn',\n",
              "       'preop_dm', 'preop_ecg', 'preop_pft', 'preop_hb', 'preop_plt',\n",
              "       'preop_pt', 'preop_aptt', 'preop_na', 'preop_k', 'preop_gluc',\n",
              "       'preop_alb', 'preop_ast', 'preop_alt', 'preop_bun', 'preop_cr',\n",
              "       'preop_ph', 'preop_hco3', 'preop_be', 'preop_pao2', 'preop_paco2',\n",
              "       'preop_sao2', 'cormack', 'airway', 'tubesize', 'dltubesize', 'lmasize',\n",
              "       'iv1', 'iv2', 'aline1', 'aline2', 'cline1', 'cline2', 'intraop_ebl',\n",
              "       'intraop_uo', 'intraop_rbc', 'intraop_ffp', 'intraop_crystalloid',\n",
              "       'intraop_colloid', 'intraop_ppf', 'intraop_mdz', 'intraop_ftn',\n",
              "       'intraop_rocu', 'intraop_vecu', 'intraop_eph', 'intraop_phe',\n",
              "       'intraop_epi', 'intraop_ca'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0b_TyfelWg6e"
      },
      "source": [
        "# female은 1으로, male 은 0으로 치환\n",
        "df_cases['sex'] = (df_cases['sex'] == 'F').astype(int)  "
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xzuBetNgW5xv"
      },
      "source": [
        "##Case ID 선택 (inclusion, exclusion criteria)\n",
        "- 2시간 이상\n",
        "- 전신 마취\n",
        "- 18세 이상\n",
        "- PPF20_VOL, RFTN20_VOL, PPF20_CE, RFTN20_CE, BIS 트랙이 존재\n",
        "- 몸무게 35 kg 이상"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2qsz5TKVf6Nb"
      },
      "source": [
        "caseids = list(\n",
        "  set(df_trks.loc[df_trks['tname'] == 'Orchestra/PPF20_VOL', 'caseid']) & ## loc를 사용하는게 아래보다 빠름.\n",
        "  set(df_trks[df_trks['tname'] == 'Orchestra/RFTN20_VOL']['caseid']) &\n",
        "  set(df_trks[df_trks['tname'] == 'Orchestra/PPF20_CE']['caseid']) &\n",
        "  set(df_trks[df_trks['tname'] == 'Orchestra/RFTN20_CE']['caseid']) &\n",
        "  set(df_trks[df_trks['tname'] == 'BIS/BIS']['caseid']) & \n",
        "  set(df_cases[df_cases['age'] > 18]['caseid']) & \n",
        "  set(df_cases[df_cases['weight'] > 35]['caseid']) &\n",
        "  set(df_cases[df_cases['caseend'] > 7200]['caseid']) &   ## 2시간 이상의 수술.\n",
        "  set(df_cases[df_cases['ane_type'] == 'General']['caseid'])  ## 전신 마취\n",
        "  )\n",
        "np.random.shuffle(caseids)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Included cases numeber: {len(caseids)}')"
      ],
      "metadata": {
        "id": "mU7I7ybp-KDm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a445612e-bbc1-42b8-9c6d-b74a95c764c4"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Included cases numeber: 2225\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aBusNmsKpc2Z"
      },
      "source": [
        "# 데이터 전처리\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 하나의 Caseid로 먼저 실습 "
      ],
      "metadata": {
        "id": "Ibxg8nPY0sgE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "caseid = 5180 #5180"
      ],
      "metadata": {
        "id": "gBNmL8rs0_BZ"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 20초 간격으로 Propofol 및 BIS 데이터 추출\n",
        "tracknames = ['Orchestra/PPF20_VOL', 'BIS/BIS']\n",
        "vals = vitaldb.load_case(caseid, tracknames, 20)\n",
        "\n",
        "print(vals.shape) # Matrix shape 확인\n",
        "print()\n",
        "print(vals[:10]) # 데이터 일부 확인"
      ],
      "metadata": {
        "id": "sPOkPyQV067Q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7fcc239b-b2a0-4c1d-d65d-9e8dfea07b18"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(888, 2)\n",
            "\n",
            "[[ 0.          0.        ]\n",
            " [ 0.          0.        ]\n",
            " [ 0.          0.        ]\n",
            " [ 2.38199997  0.        ]\n",
            " [ 3.97900009  0.        ]\n",
            " [ 3.97900009 75.40000153]\n",
            " [ 3.97900009 60.90000153]\n",
            " [ 3.97900009 43.59999847]\n",
            " [ 4.0710001  44.20000076]\n",
            " [ 4.3039999  44.5       ]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 시각화\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(20,5))\n",
        "for idx, trk in enumerate(tracknames):\n",
        "  plt.plot(vals[:,idx],label=trk)\n",
        "plt.legend(loc='center right')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "LMKtjwIiG6my",
        "outputId": "9489b5e3-6582-40d2-a189-2a4cfa3d42b0"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 결측치 확인\n",
        "print(np.isnan(vals).sum(axis=0)) # track\n",
        "print()\n",
        "np.where(np.isnan(vals).sum(axis=1) > 0)[0] #time"
      ],
      "metadata": {
        "id": "JwQhjYp-4RSz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "27c4f9dd-abcd-4928-8258-345f07b95593"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[30  0]\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 12,  13,  14,  15, 862, 863, 864, 865, 866, 867, 868, 869, 870,\n",
              "       871, 872, 873, 874, 875, 876, 877, 878, 879, 880, 881, 882, 883,\n",
              "       884, 885, 886, 887])"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# track - time별 결측치 확인\n",
        "for idx, trk in enumerate(tracknames):\n",
        "  print(f' Missing values of {trk} track at : {np.where(np.isnan(vals[:, idx]))[0]}')\n",
        "  print()"
      ],
      "metadata": {
        "id": "QLhG3kzn6oDV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "716d7fe9-f00f-4e04-cece-b31260673684"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " Missing values of Orchestra/PPF20_VOL track at : [ 12  13  14  15 862 863 864 865 866 867 868 869 870 871 872 873 874 875\n",
            " 876 877 878 879 880 881 882 883 884 885 886 887]\n",
            "\n",
            " Missing values of BIS/BIS track at : []\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#  결측치 처리 방법1 - 전후 값으로 치환\n",
        "vals_ = vals[10:20]\n",
        "print(\"#\"*4, \"Original\", \"#\"*4)\n",
        "print(vals_)\n",
        "print()\n",
        "print(\"#\"*4, \"Forward filling\", \"#\"*4)\n",
        "print(pd.DataFrame(vals_).fillna(method='ffill').values)\n",
        "print()\n",
        "print(\"#\"*4, \"Backward filling\", \"#\"*4)\n",
        "print(pd.DataFrame(vals_).fillna(method='bfill').values)\n",
        "print()\n",
        "#  결측치 처리 방법2 - 전후 값으로 치환\n",
        "print(\"#\"*4, \"Interpolation\", \"#\"*4) \n",
        "print(pd.DataFrame(vals_).interpolate().values)\n"
      ],
      "metadata": {
        "id": "BS-bF0-V7eTX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5ea4dc56-5a1c-4ccf-a293-e94dadbf60b5"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "#### Original ####\n",
            "[[ 4.52199984 42.40000153]\n",
            " [ 4.71899986 46.90000153]\n",
            " [        nan 44.09999847]\n",
            " [        nan 44.5       ]\n",
            " [        nan 41.29999924]\n",
            " [        nan 39.70000076]\n",
            " [ 0.         50.5       ]\n",
            " [ 0.         43.90000153]\n",
            " [ 0.         51.70000076]\n",
            " [ 1.93900001 50.29999924]]\n",
            "\n",
            "#### Forward filling ####\n",
            "[[ 4.52199984 42.40000153]\n",
            " [ 4.71899986 46.90000153]\n",
            " [ 4.71899986 44.09999847]\n",
            " [ 4.71899986 44.5       ]\n",
            " [ 4.71899986 41.29999924]\n",
            " [ 4.71899986 39.70000076]\n",
            " [ 0.         50.5       ]\n",
            " [ 0.         43.90000153]\n",
            " [ 0.         51.70000076]\n",
            " [ 1.93900001 50.29999924]]\n",
            "\n",
            "#### Backward filling ####\n",
            "[[ 4.52199984 42.40000153]\n",
            " [ 4.71899986 46.90000153]\n",
            " [ 0.         44.09999847]\n",
            " [ 0.         44.5       ]\n",
            " [ 0.         41.29999924]\n",
            " [ 0.         39.70000076]\n",
            " [ 0.         50.5       ]\n",
            " [ 0.         43.90000153]\n",
            " [ 0.         51.70000076]\n",
            " [ 1.93900001 50.29999924]]\n",
            "\n",
            "#### Interpolation ####\n",
            "[[ 4.52199984 42.40000153]\n",
            " [ 4.71899986 46.90000153]\n",
            " [ 3.77519989 44.09999847]\n",
            " [ 2.83139992 44.5       ]\n",
            " [ 1.88759995 41.29999924]\n",
            " [ 0.94379997 39.70000076]\n",
            " [ 0.         50.5       ]\n",
            " [ 0.         43.90000153]\n",
            " [ 0.         51.70000076]\n",
            " [ 1.93900001 50.29999924]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#  결측치 처리 방법3 - 특정값으로 치환\n",
        "print(np.nan_to_num(vals_, 0))"
      ],
      "metadata": {
        "id": "55J1gSay_WX0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3295ec26-02e7-4edf-ecf6-4197b1cbc5fe"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 4.52199984 42.40000153]\n",
            " [ 4.71899986 46.90000153]\n",
            " [ 0.         44.09999847]\n",
            " [ 0.         44.5       ]\n",
            " [ 0.         41.29999924]\n",
            " [ 0.         39.70000076]\n",
            " [ 0.         50.5       ]\n",
            " [ 0.         43.90000153]\n",
            " [ 0.         51.70000076]\n",
            " [ 1.93900001 50.29999924]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 전체 실행 (TODO)\n",
        "- dataset을 담을 변수를 설정\n",
        "- propofol(ppf20), remifentanil(rftn20), bis를 로딩한다\n",
        "- 기록된 데이터가 짧거나, drug infusion이 실제 없었던 케이스, bis값이 적절하지 않은 케이스는 거르기\n",
        "- 결측값 및 음수 처리"
      ],
      "metadata": {
        "id": "euUUMCTRzuhm"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h6CHJpySrDxG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "384972b5-1414-41e7-ca20-ed52c684c776"
      },
      "source": [
        "######### TODO 1 #########\n",
        "\n",
        "# vital 파일로부터 dataset 을 만듬\n",
        "x_ppf_dose = []  # 각 레코드의 프로포폴 주입량\n",
        "x_rft_dose = []  # 각 레코드의 레미펜타닐 주입량\n",
        "x_aswh = []  # 각 레코드의 나이, 성별, 키, 몸무게\n",
        "x_caseid = []  # 각 레코드의 caseid\n",
        "y = []  # 각 레코드의 출력값 (bis)\n",
        "y_old = []  # 고전적 모델의 예측값\n",
        "\n",
        "LSTM_TIMEPOINTS = 180\n",
        "MAX_CASES = 50  # 본 예제에서 사용할 최대 case 수\n",
        "\n",
        "# 데이터 로딩했을 때 컬럼 순서\n",
        "PPF_DOSE = 0\n",
        "RFT_DOSE = 1\n",
        "PPF_CE = 2\n",
        "RFT_CE = 3\n",
        "BIS = 4\n",
        "\n",
        "icase = 0  # 현재 로딩 중인 케이스 번호\n",
        "ncase = min(MAX_CASES, len(caseids))\n",
        "for caseid in caseids:  # 본 연구에 사용할 각 case에 대하여\n",
        "    print('loading {} ({}/{})'.format(caseid, icase, ncase), end='...', flush=True)\n",
        "\n",
        "    ######### TODO 1 : Step 1 #########\n",
        "    # 10초 간격으로, propofol volume, remifentanil volume, propofol CE, remifentanil CE, BIS 데이터 추출\n",
        "    vals = vitaldb.load_case(caseid, ['Orchestra/PPF20_VOL', 'Orchestra/RFTN20_VOL', 'Orchestra/PPF20_CE', 'Orchestra/RFTN20_CE', 'BIS/BIS'], 10)\n",
        "\n",
        "    ######### TODO 1 : Step 2 #########\n",
        "    # 결측값은 측정된 마지막 값으로 대체\n",
        "    vals = pd.DataFrame(vals).fillna(method='ffill').values\n",
        "\n",
        "    ######### TODO 1 : Step 3 #########\n",
        "    # 마지막 남은 맨 앞 쪽 결측값은 0으로 대체\n",
        "    vals = np.nan_to_num(vals, 0)\n",
        "\n",
        "    # drug 주입을 하지 않은 case 혹은 bis를 사용하지 않은 case는 제외\n",
        "    if (np.max(vals, axis=0) <= 1).any():\n",
        "        print('no drug infusion or bis')\n",
        "        continue\n",
        "\n",
        "    # drug infusion 시작 시간을 구하고 그 이전을 삭제\n",
        "    first_ppf_idx = np.where(vals[:, PPF_DOSE] > 1)[0][0]\n",
        "    first_rft_idx = np.where(vals[:, RFT_DOSE] > 1)[0][0]\n",
        "    first_drug_idx = min(first_ppf_idx, first_rft_idx)\n",
        "    vals = vals[first_drug_idx:, :]\n",
        "\n",
        "    # volume 을 rate로 변경\n",
        "    vals[1:, PPF_DOSE] -= vals[:-1, PPF_DOSE]\n",
        "    vals[1:, RFT_DOSE] -= vals[:-1, RFT_DOSE]\n",
        "    vals[0, PPF_DOSE] = 0\n",
        "    vals[0, RFT_DOSE] = 0\n",
        "\n",
        "    ######### TODO 1 : Step 4 #########\n",
        "    # 음수 값을 0으로 대체\n",
        "    vals[vals < 0] = 0\n",
        "\n",
        "    # bis 값의 첫 값이 80 이하이거나 마지막 값이 70 이하인 case는 사용하지 않음\n",
        "    valid_bis_idx = np.where(vals[:, BIS] > 0)[0]\n",
        "    first_bis_idx = valid_bis_idx[0]\n",
        "    last_bis_idx = valid_bis_idx[-1]\n",
        "    if vals[first_bis_idx, BIS] < 80:\n",
        "        print('first bis < 80')\n",
        "        continue\n",
        "    if vals[last_bis_idx, BIS] < 70:\n",
        "        print('last bis < 70')\n",
        "        continue\n",
        "\n",
        "    # infusion 시작 전 LSTM_TIMEPOINTS 동안의 dose와 bis를 모두 0으로 세팅\n",
        "    vals = np.vstack((np.zeros((LSTM_TIMEPOINTS - 1, vals.shape[1])), vals))\n",
        "\n",
        "    ######### TODO 1 : Step 5 #########\n",
        "    # 현 case의 나이, 성별, 키, 몸무게를 가져옴\n",
        "    aswh = df_cases.loc[df_cases['caseid'] == caseid, ['age','sex','weight','height']]\n",
        "    aswh = aswh.values.astype(float).flatten()\n",
        "\n",
        "    # case 시작 부터 종료까지 dataset 에 넣음\n",
        "    for irow in range(1, vals.shape[0] - LSTM_TIMEPOINTS - 1):\n",
        "        bis = vals[irow + LSTM_TIMEPOINTS, BIS]\n",
        "        if bis == 0:\n",
        "            continue\n",
        "\n",
        "        # 데이터셋에 입력값을 넣음\n",
        "        x_ppf_dose.append(vals[irow:irow + LSTM_TIMEPOINTS, PPF_DOSE])\n",
        "        x_rft_dose.append(vals[irow:irow + LSTM_TIMEPOINTS, RFT_DOSE])\n",
        "\n",
        "        x_aswh.append(aswh)\n",
        "        x_caseid.append(caseid)\n",
        "        y.append(bis / 100)\n",
        "\n",
        "        ppf_ce = vals[irow + LSTM_TIMEPOINTS, PPF_CE]\n",
        "        rft_ce = vals[irow + LSTM_TIMEPOINTS, RFT_CE]\n",
        "         \n",
        "        ug = (ppf_ce / 4.47 + rft_ce / 19.3)**1.43\n",
        "        y_old.append(0.98 - 0.98 * (ug / (1 + ug)))\n",
        "\n",
        "\n",
        "    # 사용할 case\n",
        "    print('done')\n",
        "    icase += 1\n",
        "    if icase >= ncase:\n",
        "        break\n"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "loading 3244 (0/10)...done\n",
            "loading 1457 (1/10)...done\n",
            "loading 1824 (2/10)...first bis < 80\n",
            "loading 4356 (2/10)...done\n",
            "loading 4858 (3/10)...first bis < 80\n",
            "loading 3330 (3/10)...done\n",
            "loading 5520 (4/10)...last bis < 70\n",
            "loading 3423 (4/10)...done\n",
            "loading 4587 (5/10)...done\n",
            "loading 3781 (6/10)...done\n",
            "loading 378 (7/10)...done\n",
            "loading 3356 (8/10)...done\n",
            "loading 1474 (9/10)...first bis < 80\n",
            "loading 5999 (9/10)...done\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NwwLDouezDLc"
      },
      "source": [
        "## 데이터셋 포맷 및 차원 변환"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bHa2fte0zGEv"
      },
      "source": [
        "# 입력 데이터셋을 numpy array로 변경\n",
        "x_ppf_dose = np.array(x_ppf_dose)[..., None]  # LSTM 에 넣기 위해서는 3차원이어야 한다. 마지막 차원을 추가\n",
        "x_rft_dose = np.array(x_rft_dose)[..., None]\n",
        "x_aswh = np.array(x_aswh)\n",
        "y = np.array(y)\n",
        "y_old = np.array(y_old)\n",
        "x_caseid = np.array(x_caseid)\n",
        "\n",
        "# 최종적으로 로딩 된 caseid\n",
        "caseids = np.unique(x_caseid)\n",
        "\n",
        "# normalize data\n",
        "x_aswh = (x_aswh - np.mean(x_aswh, axis=0)) / np.std(x_aswh, axis=0)\n"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VpMowosc0Bzf"
      },
      "source": [
        "## 데이터를 학습(train)과 테스트(test)로 나누기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AjdvjsJ10JAe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "924f7931-08f5-45f7-97e7-8f9862d071fd"
      },
      "source": [
        "# train, test case로 나눔\n",
        "ntest = int(ncase * 0.1)\n",
        "ntrain = ncase - ntest\n",
        "train_caseids = caseids[:ntrain]\n",
        "test_caseids = caseids[ntrain:ncase]\n",
        "\n",
        "# train set과 test set 으로 나눔\n",
        "train_mask = np.array([caseid in train_caseids for caseid in x_caseid])\n",
        "test_mask = np.array([caseid in test_caseids for caseid in x_caseid])\n",
        "x_train = [x_ppf_dose[train_mask], x_rft_dose[train_mask], x_aswh[train_mask]]\n",
        "y_train = y[train_mask]\n",
        "x_test = [x_ppf_dose[test_mask], x_rft_dose[test_mask], x_aswh[test_mask]]\n",
        "y_test = y[test_mask]\n",
        "y_old = y_old[test_mask]\n",
        "\n",
        "print('train: {} cases {} samples, testing: {} cases {} samples'.format(len(train_caseids), np.sum(train_mask), len(test_caseids), np.sum(test_mask)))"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train: 9 cases 12675 samples, testing: 1 cases 1414 samples\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iPi6FIbB0NkF"
      },
      "source": [
        "# Model building\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import Model, load_model\n",
        "from keras.layers import Dense, Dropout, LSTM, Input, concatenate\n",
        "from keras.callbacks import EarlyStopping\n",
        "import tensorflow as tf\n",
        "\n",
        "# 모델 설계\n",
        "\n",
        "def build_model(LSTM_NODES, FNN_NODES, DROP_RATE):\n",
        "    \n",
        "    input_cov = Input(batch_shape=(None, 4))\n",
        "    input_ppf = Input(batch_shape=(None, LSTM_TIMEPOINTS, 1))\n",
        "    input_rft = Input(batch_shape=(None, LSTM_TIMEPOINTS, 1))\n",
        "    output_ppf = LSTM(LSTM_NODES, input_shape=(LSTM_TIMEPOINTS, 1), activation='tanh')(input_ppf)\n",
        "    output_rft = LSTM(LSTM_NODES, input_shape=(LSTM_TIMEPOINTS, 1), activation='tanh')(input_rft)\n",
        "    output = concatenate([output_ppf, output_rft, input_cov])\n",
        "    output = Dense(FNN_NODES)(output)\n",
        "    output = Dropout(DROP_RATE)(output)\n",
        "    output = Dense(1, activation='sigmoid')(output)\n",
        "    model = Model(inputs=[input_ppf, input_rft, input_cov], outputs=[output])\n",
        "    \n",
        "    return model\n"
      ],
      "metadata": {
        "id": "nstrZVa3WIez"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 모델 생성 및 구조 확인\n",
        "model = build_model(32, 32, 0.5)\n",
        "model.summary()"
      ],
      "metadata": {
        "id": "2QDgyGNwXBY3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d7c5b289-b61d-48bc-8ce8-d39167ad91c0"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                   Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            " input_2 (InputLayer)           [(None, 180, 1)]     0           []                               \n",
            "                                                                                                  \n",
            " input_3 (InputLayer)           [(None, 180, 1)]     0           []                               \n",
            "                                                                                                  \n",
            " lstm (LSTM)                    (None, 32)           4352        ['input_2[0][0]']                \n",
            "                                                                                                  \n",
            " lstm_1 (LSTM)                  (None, 32)           4352        ['input_3[0][0]']                \n",
            "                                                                                                  \n",
            " input_1 (InputLayer)           [(None, 4)]          0           []                               \n",
            "                                                                                                  \n",
            " concatenate (Concatenate)      (None, 68)           0           ['lstm[0][0]',                   \n",
            "                                                                  'lstm_1[0][0]',                 \n",
            "                                                                  'input_1[0][0]']                \n",
            "                                                                                                  \n",
            " dense (Dense)                  (None, 32)           2208        ['concatenate[0][0]']            \n",
            "                                                                                                  \n",
            " dropout (Dropout)              (None, 32)           0           ['dense[0][0]']                  \n",
            "                                                                                                  \n",
            " dense_1 (Dense)                (None, 1)            33          ['dropout[0][0]']                \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 10,945\n",
            "Trainable params: 10,945\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uytYU2Fu0rPi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7fa0815b-9166-4dc1-9d21-30c0ddabebc2"
      },
      "source": [
        "mse = tf.keras.losses.MeanSquaredError()\n",
        "mape = tf.keras.losses.MeanAbsolutePercentageError()\n",
        "\n",
        "model.compile(loss=mse, optimizer='sgd', metrics=[mape])\n",
        "hist = model.fit(x_train, y_train,\n",
        "                 batch_size=64,\n",
        "                 validation_split=0.2, \n",
        "                 epochs=1, \n",
        "                 callbacks=[EarlyStopping(monitor='val_loss', patience=3, verbose=0, mode='auto')])"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "159/159 [==============================] - 8s 21ms/step - loss: 0.0203 - mean_absolute_percentage_error: 25.4505 - val_loss: 0.0081 - val_mean_absolute_percentage_error: 16.7599\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SeVpuglt1W0k"
      },
      "source": [
        "# 결과 출력"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GNA1cUY71cxs",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 389
        },
        "outputId": "9b296135-f6e6-4c89-9c80-0d2863a96054"
      },
      "source": [
        "# 예측\n",
        "y_pred = model.predict(x_test).flatten()\n",
        "print(f\"Test MAPE (LSTM): {mape(y_test, y_pred):.1f}%\")\n",
        "print(f\"Test MAPE (Short): {mape(y_old, y_pred):.1f}%\")\n",
        "print()\n",
        "\n",
        "# 각 case에서 예측 결과를 그림으로 확인\n",
        "import matplotlib.pyplot as plt\n",
        "for caseid in test_caseids:\n",
        "    case_mask = (x_caseid[test_mask] == caseid)\n",
        "    case_len = np.sum(case_mask)\n",
        "    if case_len == 0:\n",
        "        continue\n",
        "\n",
        "    print('CaseID {}, LSTM MAPE={}, Short MAPE={}'.format(caseid, \n",
        "      mape(y_test[case_mask], y_pred[case_mask]), \n",
        "      mape(y_test[case_mask], y_old[case_mask])\n",
        "      ))\n",
        "\n",
        "    t = np.arange(0, case_len)\n",
        "    plt.figure(figsize=(20, 5))\n",
        "    plt.plot(t, y_test[case_mask], label='BIS')  # 측정 결과\n",
        "    plt.plot(t, y_pred[case_mask], label='LSTM')  # LSTM model\n",
        "    plt.plot(t, y_old[case_mask], label='Short')  # Short model\n",
        "    plt.legend()\n",
        "    plt.xlim([0, case_len])\n",
        "    plt.ylim([0, 1])\n",
        "    plt.show()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test MAPE (LSTM): 23.2%\n",
            "Test MAPE (Short): 11.2%\n",
            "\n",
            "CaseID 5999, LSTM MAPE=23.199962615966797, Short MAPE=28.437744140625\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 모델 학습 및 추론 (TODO)"
      ],
      "metadata": {
        "id": "zsjvmOFNcJ3r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "######### TODO 2 #########\n",
        "# 모델 설계 및 학습\n",
        "\n",
        "######### TODO 2 : Step 1 #########\n",
        "# LSTM의 node 수 8, FNN의 node 수 16, dropout rate는 0.2인 모델을 만들어주세요.\n",
        "model = build_model(8, 16, 0.2)\n",
        "\n",
        "######### TODO 2 : Step 2 #########\n",
        "# 손실함수는 mean absolute error, 옵티마이저는 Adam, 평가방법은 mean absolute percentage error로 모델을 컴파일 해주세요.\n",
        "mae = tf.keras.losses.MeanAbsoluteError()\n",
        "mape = tf.keras.losses.MeanAbsolutePercentageError()\n",
        "model.compile(loss=mae, optimizer='adam', metrics=[mape])\n",
        "\n",
        "######### TODO 2 : Step 3 #########\n",
        "# 모델 검증을 위해 10%로 split해서 미니배치 크기는 256으로 모델을 epoch은 10회로 학습해주세요.\n",
        "hist = model.fit(x_train, y_train,\n",
        "                 validation_split=0.1,\n",
        "                 epochs=10,\n",
        "                 batch_size=256)\n"
      ],
      "metadata": {
        "id": "oMruV58jYDh-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ce5a346e-0bd3-4683-96f0-ee428a380d81"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "45/45 [==============================] - 7s 77ms/step - loss: 0.0998 - mean_absolute_percentage_error: 22.4283 - val_loss: 0.1615 - val_mean_absolute_percentage_error: 48.6738\n",
            "Epoch 2/10\n",
            "45/45 [==============================] - 1s 19ms/step - loss: 0.0869 - mean_absolute_percentage_error: 19.2122 - val_loss: 0.1290 - val_mean_absolute_percentage_error: 38.8273\n",
            "Epoch 3/10\n",
            "45/45 [==============================] - 1s 17ms/step - loss: 0.0821 - mean_absolute_percentage_error: 18.0686 - val_loss: 0.1253 - val_mean_absolute_percentage_error: 37.7100\n",
            "Epoch 4/10\n",
            "45/45 [==============================] - 1s 16ms/step - loss: 0.0782 - mean_absolute_percentage_error: 17.2211 - val_loss: 0.1425 - val_mean_absolute_percentage_error: 42.9795\n",
            "Epoch 5/10\n",
            "45/45 [==============================] - 1s 16ms/step - loss: 0.0736 - mean_absolute_percentage_error: 16.2937 - val_loss: 0.1475 - val_mean_absolute_percentage_error: 44.5111\n",
            "Epoch 6/10\n",
            "45/45 [==============================] - 1s 16ms/step - loss: 0.0697 - mean_absolute_percentage_error: 15.6679 - val_loss: 0.1598 - val_mean_absolute_percentage_error: 48.1518\n",
            "Epoch 7/10\n",
            "45/45 [==============================] - 1s 16ms/step - loss: 0.0661 - mean_absolute_percentage_error: 15.0672 - val_loss: 0.1685 - val_mean_absolute_percentage_error: 50.6990\n",
            "Epoch 8/10\n",
            "45/45 [==============================] - 1s 16ms/step - loss: 0.0645 - mean_absolute_percentage_error: 14.7482 - val_loss: 0.1725 - val_mean_absolute_percentage_error: 51.9138\n",
            "Epoch 9/10\n",
            "45/45 [==============================] - 1s 24ms/step - loss: 0.0628 - mean_absolute_percentage_error: 14.3714 - val_loss: 0.1484 - val_mean_absolute_percentage_error: 44.9531\n",
            "Epoch 10/10\n",
            "45/45 [==============================] - 1s 17ms/step - loss: 0.0628 - mean_absolute_percentage_error: 14.3441 - val_loss: 0.1506 - val_mean_absolute_percentage_error: 45.4931\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 예측\n",
        "y_pred = model.predict(x_test).flatten()\n",
        "print(f\"Test MAPE (LSTM): {mape(y_test, y_pred):.1f}%\")\n",
        "print(f\"Test MAPE (Short): {mape(y_old, y_pred):.1f}%\")\n",
        "print()\n",
        "\n",
        "# 각 case에서 예측 결과를 그림으로 확인\n",
        "import matplotlib.pyplot as plt\n",
        "for caseid in test_caseids:\n",
        "    case_mask = (x_caseid[test_mask] == caseid)\n",
        "    case_len = np.sum(case_mask)\n",
        "    if case_len == 0:\n",
        "        continue\n",
        "\n",
        "    print('CaseID {}, LSTM MAPE={}, Short MAPE={}'.format(caseid, \n",
        "      mape(y_test[case_mask], y_pred[case_mask]), \n",
        "      mape(y_test[case_mask], y_old[case_mask])\n",
        "      ))\n",
        "\n",
        "    t = np.arange(0, case_len)\n",
        "    plt.figure(figsize=(20, 5))\n",
        "    plt.plot(t, y_test[case_mask], label='BIS')  # 측정 결과\n",
        "    plt.plot(t, y_pred[case_mask], label='LSTM')  # LSTM model\n",
        "    plt.plot(t, y_old[case_mask], label='Short')  # Short model\n",
        "    plt.legend()\n",
        "    plt.xlim([0, case_len])\n",
        "    plt.ylim([0, 1])\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "B1wIesXEI3oa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 389
        },
        "outputId": "05e8d869-7116-4289-d972-2202669e191e"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Test MAPE (LSTM): 18.8%\n",
            "Test MAPE (Short): 12.0%\n",
            "\n",
            "CaseID 5999, LSTM MAPE=18.767471313476562, Short MAPE=28.437744140625\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LwnmyGcFXm9l"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}