LSTM.ipynb

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      "name": "python3",
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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/heejjj/Sleeping_Project/blob/main/LSTM.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Applewatch data 불러오기"
      ],
      "metadata": {
        "id": "Q5MF7DkX0r7P"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-N1wWDdQ0dOE",
        "outputId": "29748384-8db8-4aee-aa40-d41abe67b25e"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "data = pd.read_csv('/content/drive/MyDrive/KDT_python/project/Hackathon/Sleep Analysis Data (1).csv')"
      ],
      "metadata": {
        "id": "aMk-NV4k0dwJ"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1kQrQRfy0dK0",
        "outputId": "1e8269d3-7428-4994-cc44-043125a7fa54"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2955, 5)"
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          "metadata": {},
          "execution_count": 3
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
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          "height": 206
        },
        "id": "lX3w5S0k0naR",
        "outputId": "a996a9b2-ebd3-428a-a65e-34a9f89b074a"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
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              "                 Start                  End  Duration (hr)  Value  \\\n",
              "0  2023-02-13 23:21:07  2023-02-14 08:47:48          9.440  InBed   \n",
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              "4  2023-02-14 00:30:25  2023-02-14 00:38:25          0.133   Deep   \n",
              "\n",
              "                 Source  \n",
              "0                   NHC  \n",
              "1  Sujeong의 Apple Watch  \n",
              "2  Sujeong의 Apple Watch  \n",
              "3  Sujeong의 Apple Watch  \n",
              "4  Sujeong의 Apple Watch  "
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\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",
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        "data.tail()"
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      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    Start                  End  Duration (hr)  Value  \\\n",
              "2950  2023-05-03 06:44:05  2023-05-03 07:09:05          0.417    REM   \n",
              "2951  2023-05-03 07:09:05  2023-05-03 07:29:05          0.333   Core   \n",
              "2952  2023-05-03 07:29:05  2023-05-03 07:30:05          0.017  Awake   \n",
              "2953  2023-05-03 07:30:05  2023-05-03 07:31:05          0.017   Core   \n",
              "2954  2023-05-03 07:30:05  2023-05-03 07:31:05          0.017  InBed   \n",
              "\n",
              "                    Source  \n",
              "2950  Sujeong의 Apple Watch  \n",
              "2951  Sujeong의 Apple Watch  \n",
              "2952  Sujeong의 Apple Watch  \n",
              "2953  Sujeong의 Apple Watch  \n",
              "2954  Sujeong의 Apple Watch  "
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              "                                                     [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",
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        "data.info()"
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      "metadata": {
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        },
        "id": "hGhrQgZx2GPg",
        "outputId": "7e8fb98a-3d7b-44d1-8a77-9e870f4966e0"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2955 entries, 0 to 2954\n",
            "Data columns (total 5 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Start          2955 non-null   object \n",
            " 1   End            2955 non-null   object \n",
            " 2   Duration (hr)  2955 non-null   float64\n",
            " 3   Value          2955 non-null   object \n",
            " 4   Source         2955 non-null   object \n",
            "dtypes: float64(1), object(4)\n",
            "memory usage: 115.6+ KB\n"
          ]
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터 저장.\n",
        "sleep = data"
      ],
      "metadata": {
        "id": "0QQq6QPS2HZD"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# EDA"
      ],
      "metadata": {
        "id": "x1YtbfTM1-sR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- \"data\"는 2023.02.13 저녁부터 2023.05.03까지의 수면시간 데이터가 저장되어 있음.\n",
        "\n",
        "- Start\n",
        "- End\n",
        "- Duration (hr)\n",
        "- Value : 수면 깊이. deep, core, rem, awake\n",
        "- Source : nhc(휴대폰), Sujeong의 Apple watch(워치)"
      ],
      "metadata": {
        "id": "t2X56C_K2A27"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- ['Source' == 'nhc'] 의 'Value'가 'InBed'인 경우는 한 번 수면한 총 시간 정보를 가지고 있으므로 따로 저장하여 도탈 시간과 비교하여 데이터가 정교한지(?)를 확인.\n",
        "- ['Source' == 'Sujeong의 Apple watch'] 의 'Value'가 'InBed'인 경우는 이상치로 구분하여 drop 하기로 결정.\n",
        "\n",
        "- 분석 구간 (2/17, 2/25, 3/20, 3/26, 4/22 에 워치를 착용하지 않고 수면함. 이상치 -> 제거 결정, 규칙적인 수면이 시작되는 2/26일부터 분석 시작)\n",
        "  - first - 2/26-3/25 (2/26-3/20, 3/22-3/25)\n",
        "  - second- 3/27~4/21\n",
        "  - third - 4/23~5/3 \n",
        "\n",
        "- deep: 1, core:2, rem:3, awake:4"
      ],
      "metadata": {
        "id": "Tepk5Lkb0rQo"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vWkj22WH4GZi"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# total 수면 시간 비교를 위한 \"nhc\" 데이터셋 생성\n",
        "InBed = data[data[\"Value\"]=='InBed']\n",
        "nhc = InBed[InBed[\"Source\"] == 'NHC']\n",
        "\n",
        "# 워치 데이터에서 InBed를 제외한 데이터인 \"watch\" 데이터셋 생성.\n",
        "watch = data[data[\"Source\"] != 'NHC']\n",
        "watch = watch[watch['Value'] != \"InBed\"]"
      ],
      "metadata": {
        "id": "PeQH7wLf3G9M"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nhc.head()"
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        "id": "mQP2dqRR4aLn",
        "outputId": "bd5fcb31-405e-408a-dcae-307245d26e92"
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      "execution_count": 9,
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            "text/plain": [
              "                   Start                  End  Duration (hr)  Value Source\n",
              "0    2023-02-13 23:21:07  2023-02-14 08:47:48           9.44  InBed    NHC\n",
              "36   2023-02-14 23:16:52  2023-02-15 09:00:24           9.73  InBed    NHC\n",
              "93   2023-02-15 23:50:07  2023-02-16 06:36:44           6.78  InBed    NHC\n",
              "138  2023-02-16 06:36:50  2023-02-16 09:00:17           2.39  InBed    NHC\n",
              "160  2023-02-17 00:40:33  2023-02-17 09:00:08           8.33  InBed    NHC"
            ],
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              "      <td>9.73</td>\n",
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              "      <td>2023-02-16 09:00:17</td>\n",
              "      <td>2.39</td>\n",
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              "      <td>2023-02-17 09:00:08</td>\n",
              "      <td>8.33</td>\n",
              "      <td>InBed</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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        "watch.head()"
      ],
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          "height": 206
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        "id": "jODP48g84cQk",
        "outputId": "ed407acf-7cfe-4f85-fe84-cc9387158a70"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Start                  End  Duration (hr) Value  \\\n",
              "1  2023-02-13 23:50:25  2023-02-14 00:07:25          0.283  Core   \n",
              "2  2023-02-14 00:07:25  2023-02-14 00:27:25          0.333  Deep   \n",
              "3  2023-02-14 00:27:25  2023-02-14 00:30:25          0.050  Core   \n",
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              "5  2023-02-14 00:38:25  2023-02-14 00:39:55          0.025  Core   \n",
              "\n",
              "                 Source  \n",
              "1  Sujeong의 Apple Watch  \n",
              "2  Sujeong의 Apple Watch  \n",
              "3  Sujeong의 Apple Watch  \n",
              "4  Sujeong의 Apple Watch  \n",
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              "            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",
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              "  "
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# \"Source\" 열의 데이터를 레이블링 해줌.\n",
        "# deep: 1, core:2, rem:3, awake:4\n",
        "watch['Value']= watch['Value'].replace('Deep',1)\n",
        "watch['Value']= watch['Value'].replace('Core',2)\n",
        "watch['Value']= watch['Value'].replace('REM',3)\n",
        "watch['Value']= watch['Value'].replace('Awake',4)"
      ],
      "metadata": {
        "id": "ruaVbQS4161b"
      },
      "execution_count": 11,
      "outputs": []
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        "watch.head()"
      ],
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        "id": "JsXoTID56oRF",
        "outputId": "39a675f3-f94d-477a-a736-29a06a830791"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 Start                  End  Duration (hr)  Value  \\\n",
              "1  2023-02-13 23:50:25  2023-02-14 00:07:25          0.283      2   \n",
              "2  2023-02-14 00:07:25  2023-02-14 00:27:25          0.333      1   \n",
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              "5  2023-02-14 00:38:25  2023-02-14 00:39:55          0.025      2   \n",
              "\n",
              "                 Source  \n",
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              "4  Sujeong의 Apple Watch  \n",
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              "      <td>2023-02-13 23:50:25</td>\n",
              "      <td>2023-02-14 00:07:25</td>\n",
              "      <td>0.283</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2023-02-14 00:07:25</td>\n",
              "      <td>2023-02-14 00:27:25</td>\n",
              "      <td>0.333</td>\n",
              "      <td>1</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2023-02-14 00:27:25</td>\n",
              "      <td>2023-02-14 00:30:25</td>\n",
              "      <td>0.050</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2023-02-14 00:30:25</td>\n",
              "      <td>2023-02-14 00:38:25</td>\n",
              "      <td>0.133</td>\n",
              "      <td>1</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2023-02-14 00:38:25</td>\n",
              "      <td>2023-02-14 00:39:55</td>\n",
              "      <td>0.025</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-225efa5a-118a-4821-be1e-c777f87db948')\"\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-225efa5a-118a-4821-be1e-c777f87db948 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-225efa5a-118a-4821-be1e-c777f87db948');\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": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Gf2_DRIu6pyf"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Series to datetime\n",
        "# 데이터 타입을 datetime으로 변경. 날짜와 시간 추출이 가능하도록 함.\n",
        "watch['Start'] = pd.to_datetime(watch['Start'], format='%Y-%m-%d %H:%M:%S', errors='raise')\n",
        "watch['End'] = pd.to_datetime(watch['End'], format='%Y-%m-%d %H:%M:%S', errors='raise')"
      ],
      "metadata": {
        "id": "qD4c4Odm4eC5"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "watch.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5We5T0614GWN",
        "outputId": "bde53fc8-c928-466f-e1a8-9dbabd1fbeb2"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 2267 entries, 1 to 2953\n",
            "Data columns (total 5 columns):\n",
            " #   Column         Non-Null Count  Dtype         \n",
            "---  ------         --------------  -----         \n",
            " 0   Start          2267 non-null   datetime64[ns]\n",
            " 1   End            2267 non-null   datetime64[ns]\n",
            " 2   Duration (hr)  2267 non-null   float64       \n",
            " 3   Value          2267 non-null   int64         \n",
            " 4   Source         2267 non-null   object        \n",
            "dtypes: datetime64[ns](2), float64(1), int64(1), object(1)\n",
            "memory usage: 106.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "iP8IMXbmDaYK"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Cep0Bgy0Cs0E"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 시각화"
      ],
      "metadata": {
        "id": "CrWchI6xQ4a8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "watch.columns"
      ],
      "metadata": {
        "id": "iyIpdj7mBzN8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5aa99247-552a-4e60-c591-20b634db99fa"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Start', 'End', 'Duration (hr)', 'Value', 'Source'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "date_time = pd.to_datetime(watch['Start'], format='%d.%m.%Y %H:%M:%S')"
      ],
      "metadata": {
        "id": "PC9nqE46PQDU"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "date_time"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lWb2xoiZP0_j",
        "outputId": "47923dd1-f802-4e1e-8e9b-7ed0d383f5ee"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1      2023-02-13 23:50:25\n",
              "2      2023-02-14 00:07:25\n",
              "3      2023-02-14 00:27:25\n",
              "4      2023-02-14 00:30:25\n",
              "5      2023-02-14 00:38:25\n",
              "               ...        \n",
              "2948   2023-05-03 06:16:05\n",
              "2950   2023-05-03 06:44:05\n",
              "2951   2023-05-03 07:09:05\n",
              "2952   2023-05-03 07:29:05\n",
              "2953   2023-05-03 07:30:05\n",
              "Name: Start, Length: 2267, dtype: datetime64[ns]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_features = watch[\"Value\"]\n",
        "plot_features.index"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tRB3W5mZP_ya",
        "outputId": "77e28eb2-c2d3-4e06-8ecf-671138897c16"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([   1,    2,    3,    4,    5,    6,    7,    8,    9,   10,\n",
              "            ...\n",
              "            2942, 2944, 2945, 2946, 2947, 2948, 2950, 2951, 2952, 2953],\n",
              "           dtype='int64', length=2267)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot_features = watch[\"Value\"]\n",
        "# plot_features.index = date_time\n",
        "# _ = plot_features.plot(subplots=True)\n",
        "\n",
        "plot_features = watch[\"Value\"][:100]\n",
        "plot_features.index = date_time[:100]\n",
        "_ = plot_features.plot(subplots=True)"
      ],
      "metadata": {
        "id": "mTmEWNbIBG7I",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "outputId": "38a635a3-e615-402d-c91b-6c1363b9b17f"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Gc638S92Qivm"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "FaHn1a_vQO36"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kljw7MKGBEKv"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "J2D4nLC712Nb"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "GXmsHR6N0qVs"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gWVhyxJS0lXI"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 시계열 데이터를 가지고 딥러닝 모델을 학습시키는 경우 train 과 test 데이터 셋은 어떻게 나누는가?? 하루 수면 데이터를 따로 저장해두고 test로 사용하면 되는건가?\n",
        "# training_set = df.iloc[:800, 1:2].values\n",
        "# test_set = df.iloc[800:, 1:2].values\n",
        "# 이와 같이 index를 랜덤으로 뽑는 것이 아니라 연속적으로 슬라이싱 해준다.\n",
        "# 훈련, 검증 및 테스트 세트에 (70%, 20%, 10%) 분할을 사용"
      ],
      "metadata": {
        "id": "y45f23xY0dH7"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Min-Max Scaler\n",
        "# 데이터 정규화는 어떤 col에 적용해주는가?\n",
        "# 우리의 경우 categorical이 사용되므로 pass"
      ],
      "metadata": {
        "id": "NgSOW2VR0dFa"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import math\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, LSTM, Dropout\n",
        "import keras\n",
        "from keras.layers import *\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
        "from keras.callbacks import EarlyStopping"
      ],
      "metadata": {
        "id": "eOoAHbR3ZpkO"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "watch.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V3ZvDPr6aBm2",
        "outputId": "18f91e4c-646e-4b10-bc8f-b8affdce1bde"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2267, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 훈련, 검증 및 테스트 세트에 (70%, 20%, 10%) 분할을 사용\n",
        "# 2267 * 0.7  = 1588\n",
        "# 2267 * 0.2 = 453\n",
        "# 2267 * 0.1 = 226\n",
        "1588 + 453 + 226"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q2JWLy0vaNK9",
        "outputId": "c7cd33ca-9aa8-4a40-c550-708b590ba3b6"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2267"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "watch.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "77cIyf7Jacol",
        "outputId": "2eeed035-6eb4-425b-edf7-8a4feb49f661"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Start                 End  Duration (hr)  Value  \\\n",
              "1 2023-02-13 23:50:25 2023-02-14 00:07:25          0.283      2   \n",
              "2 2023-02-14 00:07:25 2023-02-14 00:27:25          0.333      1   \n",
              "3 2023-02-14 00:27:25 2023-02-14 00:30:25          0.050      2   \n",
              "4 2023-02-14 00:30:25 2023-02-14 00:38:25          0.133      1   \n",
              "5 2023-02-14 00:38:25 2023-02-14 00:39:55          0.025      2   \n",
              "\n",
              "                 Source  \n",
              "1  Sujeong의 Apple Watch  \n",
              "2  Sujeong의 Apple Watch  \n",
              "3  Sujeong의 Apple Watch  \n",
              "4  Sujeong의 Apple Watch  \n",
              "5  Sujeong의 Apple Watch  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f9f33b82-d8ee-46b3-8634-9313e9056949\">\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>Start</th>\n",
              "      <th>End</th>\n",
              "      <th>Duration (hr)</th>\n",
              "      <th>Value</th>\n",
              "      <th>Source</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2023-02-13 23:50:25</td>\n",
              "      <td>2023-02-14 00:07:25</td>\n",
              "      <td>0.283</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2023-02-14 00:07:25</td>\n",
              "      <td>2023-02-14 00:27:25</td>\n",
              "      <td>0.333</td>\n",
              "      <td>1</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2023-02-14 00:27:25</td>\n",
              "      <td>2023-02-14 00:30:25</td>\n",
              "      <td>0.050</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2023-02-14 00:30:25</td>\n",
              "      <td>2023-02-14 00:38:25</td>\n",
              "      <td>0.133</td>\n",
              "      <td>1</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2023-02-14 00:38:25</td>\n",
              "      <td>2023-02-14 00:39:55</td>\n",
              "      <td>0.025</td>\n",
              "      <td>2</td>\n",
              "      <td>Sujeong의 Apple Watch</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "\n",
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              "          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",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "watch.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_VmwNSgAalYv",
        "outputId": "89a758ab-ac43-41df-8353-e11fb2272da3"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 2267 entries, 1 to 2953\n",
            "Data columns (total 5 columns):\n",
            " #   Column         Non-Null Count  Dtype         \n",
            "---  ------         --------------  -----         \n",
            " 0   Start          2267 non-null   datetime64[ns]\n",
            " 1   End            2267 non-null   datetime64[ns]\n",
            " 2   Duration (hr)  2267 non-null   float64       \n",
            " 3   Value          2267 non-null   int64         \n",
            " 4   Source         2267 non-null   object        \n",
            "dtypes: datetime64[ns](2), float64(1), int64(1), object(1)\n",
            "memory usage: 106.3+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train = watch.iloc[:1588, [0,3]]\n",
        "train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yruyyjisaCyW",
        "outputId": "2b607b27-0cd0-49fb-e3b4-4f706d3fe68e"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1588, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "QfjdKAzoavOV",
        "outputId": "7a01aec9-5313-4f3e-ae80-d925016cab75"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Start  Value\n",
              "1 2023-02-13 23:50:25      2\n",
              "2 2023-02-14 00:07:25      1\n",
              "3 2023-02-14 00:27:25      2\n",
              "4 2023-02-14 00:30:25      1\n",
              "5 2023-02-14 00:38:25      2"
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              "\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "      <th>1</th>\n",
              "      <td>2023-02-13 23:50:25</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2023-02-14 00:07:25</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2023-02-14 00:27:25</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2023-02-14 00:30:25</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2023-02-14 00:38:25</td>\n",
              "      <td>2</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "            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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "val = watch.iloc[1588:2041, [0,3]]\n",
        "val.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lxGEK_PzapJl",
        "outputId": "c6e54c64-19b2-42e6-eb3e-d8ee71351a4c"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(453, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test = watch.iloc[2041:, [0,3]]\n",
        "test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vRG_SZefbFsV",
        "outputId": "eb13257a-be93-466d-a959-22ebee58c30e"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(226, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a data structure with 60 time-steps and 1 output\n",
        "X_train = []\n",
        "y_train = []\n",
        "\n",
        "for i in range(60, 1588):\n",
        "\n",
        "   X_train.append(train.iloc[i-60:i, 1])\n",
        "   y_train.append(train.iloc[i, 1])\n",
        "\n",
        "X_train, y_train = np.array(X_train), np.array(y_train)\n",
        "X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))  # 형식(#값, #타임스텝, #1차원 출력)으로 재배열"
      ],
      "metadata": {
        "id": "9ZCjPUW2bNk4"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6w6y-G5RcwJW",
        "outputId": "c429bd2b-22f8-42a1-e621-7bb09eb86756"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1528, 60, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 50개의 뉴런과 4개의 숨겨진 층으로 LSTM을 만들 것이다.\n",
        "# 마지막으로, 우리는 주가를 예측하기 위해 출력층에 1개의 뉴런을 할당할 것이다. \n",
        "# MSE 손실 함수와 Adam stochastic gradient decent optimizer를 사용할 것이다."
      ],
      "metadata": {
        "id": "o9i4Ch9Oc3Zl"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Dropout을 사용해도 되는가?"
      ],
      "metadata": {
        "id": "Bwx4cDItdNIX"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "\n",
        "model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1],1)))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(LSTM(units=50, return_sequences=True))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(LSTM(units=50, return_sequences=True))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "model.add(LSTM(units=50))\n",
        "model.add(Dropout(0.2))\n",
        "\n",
        "# Adding the output layer\n",
        "model.add(Dense(units = 1))\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c5enk12bc2IM",
        "outputId": "3c728b55-9c88-438a-8920-d1425cc30e9e"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm (LSTM)                 (None, 60, 50)            10400     \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 60, 50)            0         \n",
            "                                                                 \n",
            " lstm_1 (LSTM)               (None, 60, 50)            20200     \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 60, 50)            0         \n",
            "                                                                 \n",
            " lstm_2 (LSTM)               (None, 60, 50)            20200     \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 60, 50)            0         \n",
            "                                                                 \n",
            " lstm_3 (LSTM)               (None, 50)                20200     \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 50)                0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 51        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 71,051\n",
            "Trainable params: 71,051\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer='adam', loss='mean_squared_error')"
      ],
      "metadata": {
        "id": "qD5EZ-vwdfLA"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train, epochs=100, batch_size=32)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YqM-67IMdpo8",
        "outputId": "49df5274-e3ff-4de1-b06a-1414bdb71fe5"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "48/48 [==============================] - 13s 15ms/step - loss: 1.6532\n",
            "Epoch 2/100\n",
            "48/48 [==============================] - 1s 14ms/step - loss: 0.9888\n",
            "Epoch 3/100\n",
            "48/48 [==============================] - 1s 14ms/step - loss: 0.9829\n",
            "Epoch 4/100\n",
            "48/48 [==============================] - 1s 14ms/step - loss: 0.9874\n",
            "Epoch 5/100\n",
            "48/48 [==============================] - 1s 14ms/step - loss: 0.9982\n",
            "Epoch 6/100\n",
            "48/48 [==============================] - 1s 15ms/step - loss: 0.9865\n",
            "Epoch 7/100\n",
            "48/48 [==============================] - 1s 14ms/step - loss: 0.9573\n",
            "Epoch 8/100\n",
            "48/48 [==============================] - 1s 14ms/step - loss: 0.9842\n",
            "Epoch 9/100\n",
            "48/48 [==============================] - 1s 17ms/step - loss: 0.9695\n",
            "Epoch 10/100\n",
            "48/48 [==============================] - 1s 21ms/step - loss: 0.9769\n",
            "Epoch 11/100\n",
            "48/48 [==============================] - 1s 20ms/step - loss: 0.9454\n",
            "Epoch 12/100\n",
            "48/48 [==============================] - 1s 20ms/step - loss: 0.9734\n",
            "Epoch 13/100\n",
            "48/48 [==============================] - 1s 23ms/step - loss: 0.9392\n",
            "Epoch 14/100\n",
            "48/48 [==============================] - 1s 24ms/step - loss: 0.9430\n",
            "Epoch 15/100\n",
            "48/48 [==============================] - 1s 18ms/step - loss: 0.9679\n",
            "Epoch 16/100\n",
            "48/48 [==============================] - 1s 20ms/step - loss: 0.9624\n",
            "Epoch 17/100\n",
            "48/48 [==============================] - 1s 14ms/step - loss: 0.9677\n",
            "Epoch 18/100\n",
            "48/48 [==============================] - 1s 15ms/step - loss: 0.9735\n",
            "Epoch 19/100\n",
            "48/48 [==============================] - 1s 14ms/step - loss: 0.9667\n",
            "Epoch 20/100\n",
            "48/48 [==============================] - 1s 14ms/step - loss: 0.9438\n",
            "Epoch 21/100\n",
            "48/48 [==============================] - 1s 15ms/step - loss: 0.9593\n",
            "Epoch 22/100\n",
            "48/48 [==============================] - 1s 14ms/step - loss: 0.9428\n",
            "Epoch 23/100\n",
            "48/48 [==============================] - 1s 14ms/step - loss: 0.9847\n",
            "Epoch 24/100\n",
            "48/48 [==============================] - 1s 14ms/step - loss: 0.9688\n",
            "Epoch 25/100\n",
            "48/48 [==============================] - 1s 14ms/step - loss: 0.9741\n",
            "Epoch 26/100\n",
            "48/48 [==============================] - 1s 15ms/step - loss: 0.9606\n",
            "Epoch 27/100\n",
            "48/48 [==============================] - 1s 21ms/step - loss: 0.9473\n",
            "Epoch 28/100\n",
            "48/48 [==============================] - 2s 35ms/step - loss: 0.9602\n",
            "Epoch 29/100\n",
            "48/48 [==============================] - 1s 21ms/step - loss: 0.9430\n",
            "Epoch 30/100\n",
            "48/48 [==============================] - 1s 20ms/step - loss: 0.9562\n",
            "Epoch 31/100\n",
            "48/48 [==============================] - 1s 14ms/step - loss: 0.9515\n",
            "Epoch 32/100\n",
            "48/48 [==============================] - 1s 14ms/step - loss: 0.9614\n",
            "Epoch 33/100\n",
            "48/48 [==============================] - 1s 14ms/step - loss: 0.9544\n",
            "Epoch 34/100\n",
            "48/48 [==============================] - 1s 14ms/step - loss: 0.9500\n",
            "Epoch 35/100\n",
            "48/48 [==============================] - 1s 14ms/step - loss: 0.9606\n",
            "Epoch 36/100\n",
            "48/48 [==============================] - 1s 14ms/step - loss: 0.9634\n",
            "Epoch 37/100\n",
            "48/48 [==============================] - 1s 15ms/step - loss: 0.9580\n",
            "Epoch 38/100\n",
            "48/48 [==============================] - 1s 14ms/step - loss: 0.9321\n",
            "Epoch 39/100\n",
            "48/48 [==============================] - 1s 14ms/step - loss: 0.8860\n",
            "Epoch 40/100\n",
            "48/48 [==============================] - 1s 21ms/step - loss: 0.9068\n",
            "Epoch 41/100\n",
            "48/48 [==============================] - 1s 14ms/step - loss: 0.8787\n",
            "Epoch 42/100\n",
            "48/48 [==============================] - 1s 14ms/step - loss: 0.8355\n",
            "Epoch 43/100\n",
            "48/48 [==============================] - 1s 14ms/step - loss: 0.8436\n",
            "Epoch 44/100\n",
            "48/48 [==============================] - 1s 15ms/step - loss: 0.8247\n",
            "Epoch 45/100\n",
            "48/48 [==============================] - 1s 19ms/step - loss: 0.8379\n",
            "Epoch 46/100\n",
            "48/48 [==============================] - 1s 26ms/step - loss: 0.8107\n",
            "Epoch 47/100\n",
            "48/48 [==============================] - 2s 36ms/step - loss: 0.8124\n",
            "Epoch 48/100\n",
            "48/48 [==============================] - 1s 31ms/step - loss: 0.8247\n",
            "Epoch 49/100\n",
            "48/48 [==============================] - 1s 21ms/step - loss: 0.8007\n",
            "Epoch 50/100\n",
            "48/48 [==============================] - 1s 14ms/step - loss: 0.8011\n",
            "Epoch 51/100\n",
            "48/48 [==============================] - 1s 29ms/step - loss: 0.8245\n",
            "Epoch 52/100\n",
            "48/48 [==============================] - 2s 32ms/step - loss: 0.7925\n",
            "Epoch 53/100\n",
            "48/48 [==============================] - 1s 21ms/step - loss: 0.7672\n",
            "Epoch 54/100\n",
            "48/48 [==============================] - 1s 19ms/step - loss: 0.7788\n",
            "Epoch 55/100\n",
            "48/48 [==============================] - 1s 21ms/step - loss: 0.7955\n",
            "Epoch 56/100\n",
            "48/48 [==============================] - 1s 18ms/step - loss: 0.7789\n",
            "Epoch 57/100\n",
            "48/48 [==============================] - 1s 22ms/step - loss: 0.7782\n",
            "Epoch 58/100\n",
            "48/48 [==============================] - 2s 33ms/step - loss: 0.7697\n",
            "Epoch 59/100\n",
            "48/48 [==============================] - 2s 33ms/step - loss: 0.7867\n",
            "Epoch 60/100\n",
            "48/48 [==============================] - 1s 26ms/step - loss: 0.7989\n",
            "Epoch 61/100\n",
            "48/48 [==============================] - 1s 23ms/step - loss: 0.7790\n",
            "Epoch 62/100\n",
            "48/48 [==============================] - 1s 20ms/step - loss: 0.7850\n",
            "Epoch 63/100\n",
            "48/48 [==============================] - 1s 25ms/step - loss: 0.7743\n",
            "Epoch 64/100\n",
            "48/48 [==============================] - 1s 22ms/step - loss: 0.7463\n",
            "Epoch 65/100\n",
            "48/48 [==============================] - 1s 18ms/step - loss: 0.7563\n",
            "Epoch 66/100\n",
            "48/48 [==============================] - 1s 21ms/step - loss: 0.7695\n",
            "Epoch 67/100\n",
            "48/48 [==============================] - 1s 19ms/step - loss: 0.7727\n",
            "Epoch 68/100\n",
            "48/48 [==============================] - 1s 22ms/step - loss: 0.7573\n",
            "Epoch 69/100\n",
            "48/48 [==============================] - 1s 25ms/step - loss: 0.7636\n",
            "Epoch 70/100\n",
            "48/48 [==============================] - 1s 29ms/step - loss: 0.7557\n",
            "Epoch 71/100\n",
            "48/48 [==============================] - 2s 39ms/step - loss: 0.7717\n",
            "Epoch 72/100\n",
            "48/48 [==============================] - 2s 35ms/step - loss: 0.7509\n",
            "Epoch 73/100\n",
            "48/48 [==============================] - 1s 30ms/step - loss: 0.7409\n",
            "Epoch 74/100\n",
            "48/48 [==============================] - 1s 23ms/step - loss: 0.7593\n",
            "Epoch 75/100\n",
            "48/48 [==============================] - 1s 25ms/step - loss: 0.7494\n",
            "Epoch 76/100\n",
            "48/48 [==============================] - 1s 28ms/step - loss: 0.7536\n",
            "Epoch 77/100\n",
            "48/48 [==============================] - 1s 26ms/step - loss: 0.7478\n",
            "Epoch 78/100\n",
            "48/48 [==============================] - 1s 25ms/step - loss: 0.7268\n",
            "Epoch 79/100\n",
            "48/48 [==============================] - 1s 26ms/step - loss: 0.7458\n",
            "Epoch 80/100\n",
            "48/48 [==============================] - 1s 30ms/step - loss: 0.7454\n",
            "Epoch 81/100\n",
            "48/48 [==============================] - 2s 31ms/step - loss: 0.7341\n",
            "Epoch 82/100\n",
            "48/48 [==============================] - 2s 39ms/step - loss: 0.7442\n",
            "Epoch 83/100\n",
            "48/48 [==============================] - 2s 34ms/step - loss: 0.7223\n",
            "Epoch 84/100\n",
            "48/48 [==============================] - 1s 31ms/step - loss: 0.7266\n",
            "Epoch 85/100\n",
            "48/48 [==============================] - 1s 29ms/step - loss: 0.7315\n",
            "Epoch 86/100\n",
            "48/48 [==============================] - 1s 25ms/step - loss: 0.7524\n",
            "Epoch 87/100\n",
            "48/48 [==============================] - 1s 17ms/step - loss: 0.7172\n",
            "Epoch 88/100\n",
            "48/48 [==============================] - 1s 14ms/step - loss: 0.7192\n",
            "Epoch 89/100\n",
            "48/48 [==============================] - 1s 14ms/step - loss: 0.7078\n",
            "Epoch 90/100\n",
            "48/48 [==============================] - 1s 14ms/step - loss: 0.7669\n",
            "Epoch 91/100\n",
            "48/48 [==============================] - 1s 14ms/step - loss: 0.7408\n",
            "Epoch 92/100\n",
            "48/48 [==============================] - 1s 14ms/step - loss: 0.7286\n",
            "Epoch 93/100\n",
            "48/48 [==============================] - 1s 14ms/step - loss: 0.7380\n",
            "Epoch 94/100\n",
            "48/48 [==============================] - 1s 14ms/step - loss: 0.7150\n",
            "Epoch 95/100\n",
            "48/48 [==============================] - 1s 15ms/step - loss: 0.7153\n",
            "Epoch 96/100\n",
            "48/48 [==============================] - 1s 18ms/step - loss: 0.7607\n",
            "Epoch 97/100\n",
            "48/48 [==============================] - 1s 23ms/step - loss: 0.7161\n",
            "Epoch 98/100\n",
            "48/48 [==============================] - 2s 35ms/step - loss: 0.7080\n",
            "Epoch 99/100\n",
            "48/48 [==============================] - 1s 27ms/step - loss: 0.7154\n",
            "Epoch 100/100\n",
            "48/48 [==============================] - 1s 15ms/step - loss: 0.7266\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f5b3b9d6470>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# val 데이터셋 준비\n",
        "total = pd.concat((train, val), axis=0)\n",
        "inputs = total[len(total) - len(val) - 60 : ].values"
      ],
      "metadata": {
        "id": "0LDFOKX3ds2x"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inputs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ruFUT907gg9_",
        "outputId": "891a31a0-b3ce-458c-83d9-50de3ba1d5b5"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[Timestamp('2023-04-06 04:17:34'), 3],\n",
              "       [Timestamp('2023-04-06 04:45:34'), 4],\n",
              "       [Timestamp('2023-04-06 04:46:34'), 3],\n",
              "       ...,\n",
              "       [Timestamp('2023-04-25 00:38:19'), 4],\n",
              "       [Timestamp('2023-04-25 00:38:49'), 2],\n",
              "       [Timestamp('2023-04-25 00:53:49'), 1]], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "r4z7S4-Sghez"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# inputs = inputs.reshape(-1, 1)"
      ],
      "metadata": {
        "id": "6TckyTQzeXKw"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inputs.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MpZ5kopOfkOQ",
        "outputId": "f540cc74-c06d-435e-8545-7c7753e65ecb"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(513, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_val = []\n",
        "\n",
        "for i in range(60, 513):\n",
        "  X_val.append(inputs[i-60:i, 1])\n",
        "\n",
        "X_val = np.array(X_val)\n",
        "X_val = np.reshape(X_val, (X_val.shape[0], X_val.shape[1], 1))\n",
        "\n",
        "print(X_val.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SgAfWdZheZcN",
        "outputId": "77232f8f-fd6f-4b1d-ce34-e81c92d8dbfd"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(453, 60, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 예측하기\n",
        "X_val = X_val.astype(np.float32)\n",
        "y_pred = model.predict(X_val)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OQGebmk3fskB",
        "outputId": "15c01e6f-093a-4342-e642-d6f78875f323"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "15/15 [==============================] - 2s 10ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "val.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mI31kTLljJ84",
        "outputId": "0f0594a0-be61-4617-e69b-0bd9b4d3c862"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(453, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_uve2nZgjmK_",
        "outputId": "e2ecb640-df49-4e5b-93da-71d5f0d587fc"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(453, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sc = MinMaxScaler(feature_range = (0, 1))\n",
        "training_set_scaled = train[\"Value\"]\n",
        "training_set_scaled = training_set_scaled.values.reshape(-1, 1)\n",
        "training_set_scaled = sc.fit_transform(training_set_scaled)"
      ],
      "metadata": {
        "id": "I-zIO9NxkDdt"
      },
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "1gWzGNPflLO8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hlnmIaHjkoHx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = sc.inverse_transform(y_pred)"
      ],
      "metadata": {
        "id": "lpyqQkR7jyOP"
      },
      "execution_count": 119,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "val.iloc[:40,1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T1CLa7GLl_eW",
        "outputId": "46a5b24a-2bc9-443c-a5fc-e826987e2828"
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2084    2\n",
              "2086    4\n",
              "2087    2\n",
              "2089    1\n",
              "2090    2\n",
              "2091    1\n",
              "2092    2\n",
              "2093    4\n",
              "2094    2\n",
              "2096    3\n",
              "2097    2\n",
              "2098    1\n",
              "2099    2\n",
              "2100    4\n",
              "2101    2\n",
              "2103    3\n",
              "2104    4\n",
              "2105    3\n",
              "2107    2\n",
              "2108    3\n",
              "2109    4\n",
              "2110    2\n",
              "2112    3\n",
              "2113    4\n",
              "2114    2\n",
              "2116    4\n",
              "2117    2\n",
              "2120    2\n",
              "2122    1\n",
              "2123    2\n",
              "2124    1\n",
              "2125    2\n",
              "2126    1\n",
              "2127    2\n",
              "2128    3\n",
              "2129    2\n",
              "2130    1\n",
              "2131    2\n",
              "2132    3\n",
              "2133    2\n",
              "Name: Value, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 시각화\n",
        "plt.plot(val.iloc[:40, 0], val.iloc[:40,1], color='red', label='Real')\n",
        "# plt.plot(val.iloc[:40, 0], y_pred[:40], color='blue', label='Predicted')\n",
        "plt.xticks(np.arange(0,459,50))\n",
        "\n",
        "plt.title('Prediction')\n",
        "\n",
        "\n",
        "plt.xlabel('Time')\n",
        "\n",
        "plt.ylabel('Value')\n",
        "\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "K8HezOS3hiVJ",
        "outputId": "829c7b3a-704f-49c3-dc57-453e8b77ffa2"
      },
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gSwMZMfnj3Ac"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "W1wZUeTHj1BJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8IouocgzjukA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Lhq43KREjsBl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "6JF5rqKTjgtC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "B1oa5HcnjQeV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "q4UgwCgbjHgj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "M7Ogy2n5hJGy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "V40073dmg68X"
      },
      "execution_count": null,
      "outputs": []
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