05_weld_keras.ipynb

{
  "cells": [
    {
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      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jjyjung/ai/blob/gh-pages/05_weld_keras.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')   # google drive를 google colab에 연결. 처음 실행 시, 인증 필요"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "urQChZqEcJST",
        "outputId": "41e62dae-372e-41f9-fce2-a68427806494"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
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          "height": 238
        },
        "id": "TW3XMmJUyqpK",
        "outputId": "76931b43-dad1-4e00-f136-830d9b14c8c9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Machine_Name      Item No working time  Thickness 1(mm)  Thickness 2(mm)  \\\n",
              "idx                                                                            \n",
              "1        Spot-01  65235-25800   2020-03-24              0.7              0.7   \n",
              "2        Spot-01  65235-25800   2020-03-24              0.7              0.7   \n",
              "3        Spot-01  65235-25800   2020-03-24              0.7              0.7   \n",
              "4        Spot-01  65235-25800   2020-03-24              0.7              0.7   \n",
              "5        Spot-01  65235-25800   2020-03-24              0.7              0.7   \n",
              "\n",
              "     weld force(bar)  weld current(kA)  weld Voltage(v)  weld time(ms)  \n",
              "idx                                                                     \n",
              "1               2.33             14.57            2.701           72.0  \n",
              "2               2.36             14.57            2.701           72.0  \n",
              "3               2.37             14.54            2.703           71.0  \n",
              "4               2.37             14.54            2.703           72.0  \n",
              "5               2.36             14.56            2.704           72.0  "
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              "        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": 3
        }
      ],
      "source": [
        "## 필요한 패키지 불러오기\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import sklearn.metrics as metric\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn import preprocessing\n",
        "from pandas import read_excel\n",
        "\n",
        "dir = \"/content/gdrive/My Drive/Colab Notebooks/kamp/05. Dataset_Welding/\"  ### [중요!!] Colab의 경우, 파일을 저장한 위치를 기록하시오.\n",
        "\n",
        "##pandas의 read_excel함수를 사용하여 데이터 불러옴, 또한 index_col = “열 이름” 을 사용하여 인덱스 열 설정\n",
        "welding_data = pd.read_excel(dir + \"/dataset/Welding Data Set_01.xlsx\", index_col =\"idx\")\n",
        "welding_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "hG2F693vyqpL",
        "outputId": "7d2613f3-5d9a-4d0d-fb6a-8d84cc62c3eb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Thickness 1(mm)  Thickness 2(mm)  weld force(bar)  weld current(kA)  \\\n",
              "count          11939.0          11939.0     11939.000000      11939.000000   \n",
              "mean               0.7              0.7         2.787925         14.711208   \n",
              "std                0.0              0.0         1.455966          0.099000   \n",
              "min                0.7              0.7         1.740000         14.520000   \n",
              "25%                0.7              0.7         2.310000         14.610000   \n",
              "50%                0.7              0.7         2.340000         14.730000   \n",
              "75%                0.7              0.7         2.370000         14.750000   \n",
              "max                0.7              0.7        10.540000         15.070000   \n",
              "\n",
              "       weld Voltage(v)  weld time(ms)  \n",
              "count     11939.000000   11939.000000  \n",
              "mean          2.704223      71.724123  \n",
              "std           0.024700       0.632049  \n",
              "min           2.464000      70.000000  \n",
              "25%           2.699000      71.000000  \n",
              "50%           2.702000      72.000000  \n",
              "75%           2.706000      72.000000  \n",
              "max           2.861000      73.000000  "
            ],
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              "      <th></th>\n",
              "      <th>Thickness 1(mm)</th>\n",
              "      <th>Thickness 2(mm)</th>\n",
              "      <th>weld force(bar)</th>\n",
              "      <th>weld current(kA)</th>\n",
              "      <th>weld Voltage(v)</th>\n",
              "      <th>weld time(ms)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>11939.0</td>\n",
              "      <td>11939.0</td>\n",
              "      <td>11939.000000</td>\n",
              "      <td>11939.000000</td>\n",
              "      <td>11939.000000</td>\n",
              "      <td>11939.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.7</td>\n",
              "      <td>0.7</td>\n",
              "      <td>2.787925</td>\n",
              "      <td>14.711208</td>\n",
              "      <td>2.704223</td>\n",
              "      <td>71.724123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.455966</td>\n",
              "      <td>0.099000</td>\n",
              "      <td>0.024700</td>\n",
              "      <td>0.632049</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.7</td>\n",
              "      <td>0.7</td>\n",
              "      <td>1.740000</td>\n",
              "      <td>14.520000</td>\n",
              "      <td>2.464000</td>\n",
              "      <td>70.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.7</td>\n",
              "      <td>0.7</td>\n",
              "      <td>2.310000</td>\n",
              "      <td>14.610000</td>\n",
              "      <td>2.699000</td>\n",
              "      <td>71.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.7</td>\n",
              "      <td>0.7</td>\n",
              "      <td>2.340000</td>\n",
              "      <td>14.730000</td>\n",
              "      <td>2.702000</td>\n",
              "      <td>72.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.7</td>\n",
              "      <td>0.7</td>\n",
              "      <td>2.370000</td>\n",
              "      <td>14.750000</td>\n",
              "      <td>2.706000</td>\n",
              "      <td>72.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.7</td>\n",
              "      <td>0.7</td>\n",
              "      <td>10.540000</td>\n",
              "      <td>15.070000</td>\n",
              "      <td>2.861000</td>\n",
              "      <td>73.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6f20b817-58c9-4a61-88cf-73ef181a60ed')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-6f20b817-58c9-4a61-88cf-73ef181a60ed 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-6f20b817-58c9-4a61-88cf-73ef181a60ed');\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",
              "\n",
              "\n",
              "<div id=\"df-9ebdbbfd-efb0-4865-b4b9-f60daac2972c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9ebdbbfd-efb0-4865-b4b9-f60daac2972c')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9ebdbbfd-efb0-4865-b4b9-f60daac2972c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "welding_data.describe()  #결과는 아래에서 확인 가능하다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "WzD6BpWRyqpM",
        "outputId": "6a1369dc-0892-4621-9739-22511d0f203e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     weld force(bar)  weld current(kA)  weld Voltage(v)  weld time(ms)\n",
              "idx                                                                   \n",
              "1               2.33             14.57            2.701           72.0\n",
              "2               2.36             14.57            2.701           72.0\n",
              "3               2.37             14.54            2.703           71.0\n",
              "4               2.37             14.54            2.703           72.0\n",
              "5               2.36             14.56            2.704           72.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-941b56ad-3807-4b17-80b9-8b09f4e9ee99\" 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>weld force(bar)</th>\n",
              "      <th>weld current(kA)</th>\n",
              "      <th>weld Voltage(v)</th>\n",
              "      <th>weld time(ms)</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>idx</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.33</td>\n",
              "      <td>14.57</td>\n",
              "      <td>2.701</td>\n",
              "      <td>72.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.36</td>\n",
              "      <td>14.57</td>\n",
              "      <td>2.701</td>\n",
              "      <td>72.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.37</td>\n",
              "      <td>14.54</td>\n",
              "      <td>2.703</td>\n",
              "      <td>71.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.37</td>\n",
              "      <td>14.54</td>\n",
              "      <td>2.703</td>\n",
              "      <td>72.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2.36</td>\n",
              "      <td>14.56</td>\n",
              "      <td>2.704</td>\n",
              "      <td>72.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-941b56ad-3807-4b17-80b9-8b09f4e9ee99')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-941b56ad-3807-4b17-80b9-8b09f4e9ee99 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-941b56ad-3807-4b17-80b9-8b09f4e9ee99');\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",
              "\n",
              "\n",
              "<div id=\"df-4adb6bc7-8f0d-4c48-bff8-cf041c0be436\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4adb6bc7-8f0d-4c48-bff8-cf041c0be436')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4adb6bc7-8f0d-4c48-bff8-cf041c0be436 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "## 용접기 데이터에서 필요없는 부분(생산품목, 작업시간, 소재두께)들을 제외\n",
        "new_welding_data = welding_data.iloc[:, 5:]\n",
        "new_welding_data.head()  #결과는 아래에서 확인 가능하다."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## sklearn의 preprocessing모듈에 들어있는 MinMaxScaler함수를 이용해 정규화 적용\n",
        "scaler = preprocessing.MinMaxScaler()\n",
        "scaler.fit(new_welding_data)\n",
        "scaled_data = scaler.transform(new_welding_data)\n",
        "scaled_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IerTdZdrLKhl",
        "outputId": "64472535-3306-4024-8aa1-5fc48e9c25ff"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.06704545, 0.09090909, 0.59697733, 0.66666667],\n",
              "       [0.07045455, 0.09090909, 0.59697733, 0.66666667],\n",
              "       [0.07159091, 0.03636364, 0.60201511, 0.33333333],\n",
              "       ...,\n",
              "       [0.07159091, 0.14545455, 0.62468514, 0.33333333],\n",
              "       [0.06931818, 0.01818182, 0.62468514, 0.33333333],\n",
              "       [0.06931818, 0.01818182, 0.62468514, 0.33333333]])"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test = train_test_split(scaled_data, test_size=0.2, random_state=42)\n",
        "print(scaled_data.shape)\n",
        "print(X_train.shape)\n",
        "print(X_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3hdae5xmUIgq",
        "outputId": "32f00831-523d-49b5-d849-dc5137a11987"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(11939, 4)\n",
            "(9551, 4)\n",
            "(2388, 4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 필요한 라이브러리 임포트\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from tensorflow.keras import Sequential  # 모듈(변수나 함수를 포함)만 불러오기\n",
        "from tensorflow.keras.layers import Dense, GaussianNoise\n",
        "\n",
        "\n",
        "# 오토인코더 모델 정의\n",
        "input_dim = len(X_train[0])\n",
        "hidden_dim = 3\n",
        "encoding_dim = 2  # 압축된 표현의 차원\n",
        "\n",
        "# # 모델 구조 정의하기\n",
        "# (1) AutoEncoder\n",
        "# model = Sequential()\n",
        "# model.add(Dense(encoding_dim, input_dim=input_dim,\n",
        "#                 activation='sigmoid'))            # Encoder 4 -> 2개\n",
        "# model.add(Dense(input_dim, activation='linear')), # Decoder 2 -> 4개,\n",
        "\n",
        "# (2) Stacked AutoEncoder (SAE)\n",
        "# model = Sequential()\n",
        "# model.add(Dense(hidden_dim, input_dim=input_dim,\n",
        "#                 activation='sigmoid'))              # Encoder 4 -> 3개\n",
        "# model.add(Dense(encoding_dim, activation='sigmoid')) # Encoder 3 -> 2개\n",
        "# model.add(Dense(hidden_dim, activation='linear'))   # Decoder 2 -> 3개\n",
        "# model.add(Dense(input_dim, activation='linear'))    # Decoder 3 -> 4개\n",
        "\n",
        "# (3) Denoising AutoEncoder (DAE)\n",
        "model = Sequential()\n",
        "model.add(GaussianNoise(0.5, input_dim=input_dim))   # Noise 추가 (평균 0, 표준편차 0.5)\n",
        "model.add(Dense(hidden_dim, activation='sigmoid'))   # Encoder 4 -> 3개\n",
        "model.add(Dense(encoding_dim, activation='sigmoid')) # Encoder 3 -> 2개\n",
        "model.add(Dense(hidden_dim, activation='linear'))    # Decoder 2 -> 3개\n",
        "model.add(Dense(input_dim, activation='linear'))     # Decoder 3 -> 4개\n",
        "\n",
        "model.compile(optimizer='adam', loss='mean_squared_error')\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZHfIg3M9cqms",
        "outputId": "f3ad77a8-7a3c-483d-8579-3796d89445e4"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " gaussian_noise (GaussianNo  (None, 4)                 0         \n",
            " ise)                                                            \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 3)                 15        \n",
            "                                                                 \n",
            " dense_8 (Dense)             (None, 2)                 8         \n",
            "                                                                 \n",
            " dense_9 (Dense)             (None, 3)                 9         \n",
            "                                                                 \n",
            " dense_10 (Dense)            (None, 4)                 16        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 48 (192.00 Byte)\n",
            "Trainable params: 48 (192.00 Byte)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 오토인코더 모델 학습\n",
        "hist = model.fit(X_train, X_train, epochs=50, batch_size=32, shuffle=True, validation_data=(X_test, X_test))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "etLO9QtNYQ3B",
        "outputId": "3b21f08e-9fcb-4af6-af74-fb3484e60203"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "299/299 [==============================] - 2s 3ms/step - loss: 0.1107 - val_loss: 0.0263\n",
            "Epoch 2/50\n",
            "299/299 [==============================] - 1s 3ms/step - loss: 0.0273 - val_loss: 0.0257\n",
            "Epoch 3/50\n",
            "299/299 [==============================] - 1s 2ms/step - loss: 0.0270 - val_loss: 0.0254\n",
            "Epoch 4/50\n",
            "299/299 [==============================] - 1s 3ms/step - loss: 0.0267 - val_loss: 0.0250\n",
            "Epoch 5/50\n",
            "299/299 [==============================] - 1s 3ms/step - loss: 0.0264 - val_loss: 0.0246\n",
            "Epoch 6/50\n",
            "299/299 [==============================] - 1s 3ms/step - loss: 0.0261 - val_loss: 0.0242\n",
            "Epoch 7/50\n",
            "299/299 [==============================] - 1s 3ms/step - loss: 0.0258 - val_loss: 0.0237\n",
            "Epoch 8/50\n",
            "299/299 [==============================] - 1s 4ms/step - loss: 0.0257 - val_loss: 0.0234\n",
            "Epoch 9/50\n",
            "299/299 [==============================] - 1s 4ms/step - loss: 0.0255 - val_loss: 0.0230\n",
            "Epoch 10/50\n",
            "299/299 [==============================] - 1s 4ms/step - loss: 0.0254 - val_loss: 0.0227\n",
            "Epoch 11/50\n",
            "299/299 [==============================] - 1s 4ms/step - loss: 0.0254 - val_loss: 0.0225\n",
            "Epoch 12/50\n",
            "299/299 [==============================] - 1s 3ms/step - loss: 0.0252 - val_loss: 0.0223\n",
            "Epoch 13/50\n",
            "299/299 [==============================] - 1s 3ms/step - loss: 0.0250 - val_loss: 0.0220\n",
            "Epoch 14/50\n",
            "299/299 [==============================] - 1s 3ms/step - loss: 0.0249 - val_loss: 0.0217\n",
            "Epoch 15/50\n",
            "299/299 [==============================] - 1s 3ms/step - loss: 0.0248 - val_loss: 0.0216\n",
            "Epoch 16/50\n",
            "299/299 [==============================] - 1s 3ms/step - loss: 0.0247 - val_loss: 0.0213\n",
            "Epoch 17/50\n",
            "299/299 [==============================] - 1s 3ms/step - loss: 0.0245 - val_loss: 0.0210\n",
            "Epoch 18/50\n",
            "299/299 [==============================] - 1s 2ms/step - loss: 0.0244 - val_loss: 0.0206\n",
            "Epoch 19/50\n",
            "299/299 [==============================] - 1s 3ms/step - loss: 0.0243 - val_loss: 0.0204\n",
            "Epoch 20/50\n",
            "299/299 [==============================] - 1s 3ms/step - loss: 0.0245 - val_loss: 0.0203\n",
            "Epoch 21/50\n",
            "299/299 [==============================] - 1s 2ms/step - loss: 0.0242 - val_loss: 0.0200\n",
            "Epoch 22/50\n",
            "299/299 [==============================] - 1s 3ms/step - loss: 0.0243 - val_loss: 0.0199\n",
            "Epoch 23/50\n",
            "299/299 [==============================] - 1s 3ms/step - loss: 0.0242 - val_loss: 0.0198\n",
            "Epoch 24/50\n",
            "299/299 [==============================] - 1s 2ms/step - loss: 0.0242 - val_loss: 0.0198\n",
            "Epoch 25/50\n",
            "299/299 [==============================] - 1s 3ms/step - loss: 0.0242 - val_loss: 0.0197\n",
            "Epoch 26/50\n",
            "299/299 [==============================] - 1s 3ms/step - loss: 0.0242 - val_loss: 0.0198\n",
            "Epoch 27/50\n",
            "299/299 [==============================] - 1s 3ms/step - loss: 0.0240 - val_loss: 0.0196\n",
            "Epoch 28/50\n",
            "299/299 [==============================] - 1s 3ms/step - loss: 0.0243 - val_loss: 0.0197\n",
            "Epoch 29/50\n",
            "299/299 [==============================] - 1s 3ms/step - loss: 0.0241 - val_loss: 0.0196\n",
            "Epoch 30/50\n",
            "299/299 [==============================] - 1s 3ms/step - loss: 0.0241 - val_loss: 0.0197\n",
            "Epoch 31/50\n",
            "299/299 [==============================] - 1s 2ms/step - loss: 0.0240 - val_loss: 0.0196\n",
            "Epoch 32/50\n",
            "299/299 [==============================] - 1s 3ms/step - loss: 0.0240 - val_loss: 0.0196\n",
            "Epoch 33/50\n",
            "299/299 [==============================] - 1s 3ms/step - loss: 0.0241 - val_loss: 0.0195\n",
            "Epoch 34/50\n",
            "299/299 [==============================] - 1s 2ms/step - loss: 0.0242 - val_loss: 0.0196\n",
            "Epoch 35/50\n",
            "299/299 [==============================] - 1s 3ms/step - loss: 0.0240 - val_loss: 0.0196\n",
            "Epoch 36/50\n",
            "299/299 [==============================] - 1s 2ms/step - loss: 0.0240 - val_loss: 0.0197\n",
            "Epoch 37/50\n",
            "299/299 [==============================] - 1s 2ms/step - loss: 0.0241 - val_loss: 0.0196\n",
            "Epoch 38/50\n",
            "299/299 [==============================] - 1s 2ms/step - loss: 0.0240 - val_loss: 0.0197\n",
            "Epoch 39/50\n",
            "299/299 [==============================] - 1s 2ms/step - loss: 0.0240 - val_loss: 0.0196\n",
            "Epoch 40/50\n",
            "299/299 [==============================] - 1s 2ms/step - loss: 0.0241 - val_loss: 0.0197\n",
            "Epoch 41/50\n",
            "299/299 [==============================] - 1s 3ms/step - loss: 0.0239 - val_loss: 0.0196\n",
            "Epoch 42/50\n",
            "299/299 [==============================] - 1s 2ms/step - loss: 0.0240 - val_loss: 0.0195\n",
            "Epoch 43/50\n",
            "299/299 [==============================] - 1s 2ms/step - loss: 0.0241 - val_loss: 0.0196\n",
            "Epoch 44/50\n",
            "299/299 [==============================] - 1s 3ms/step - loss: 0.0241 - val_loss: 0.0197\n",
            "Epoch 45/50\n",
            "299/299 [==============================] - 1s 3ms/step - loss: 0.0241 - val_loss: 0.0197\n",
            "Epoch 46/50\n",
            "299/299 [==============================] - 1s 3ms/step - loss: 0.0239 - val_loss: 0.0197\n",
            "Epoch 47/50\n",
            "299/299 [==============================] - 1s 3ms/step - loss: 0.0240 - val_loss: 0.0196\n",
            "Epoch 48/50\n",
            "299/299 [==============================] - 1s 3ms/step - loss: 0.0239 - val_loss: 0.0197\n",
            "Epoch 49/50\n",
            "299/299 [==============================] - 1s 3ms/step - loss: 0.0241 - val_loss: 0.0197\n",
            "Epoch 50/50\n",
            "299/299 [==============================] - 1s 2ms/step - loss: 0.0239 - val_loss: 0.0197\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def loss_graph():\n",
        "    fig, loss_ax = plt.subplots()\n",
        "    acc_ax = loss_ax.twinx()\n",
        "\n",
        "    loss_ax.plot(hist.history['loss'], 'y', label='train loss')\n",
        "    acc_ax.plot(hist.history['val_loss'], 'b', label='val loss')\n",
        "\n",
        "    loss_ax.set_xlabel('epoch')\n",
        "    loss_ax.set_ylabel('loss')\n",
        "    acc_ax.set_ylabel('mae')\n",
        "\n",
        "    loss_ax.legend(loc='lower left')\n",
        "    acc_ax.legend(loc='upper right')\n",
        "\n",
        "    plt.title(\"MSE\")\n",
        "    plt.show()\n",
        "\n",
        "loss_graph()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "UQJnnEwsc9Z-",
        "outputId": "856bc241-970b-44f7-8299-61d2bfeb98dd"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 학습된 오토인코더를 사용하여 이상치 검출\n",
        "reconstructed_data = model.predict(scaled_data)\n",
        "mse = np.mean(np.power(scaled_data - reconstructed_data, 2), axis=1)\n",
        "threshold = np.mean(mse) + 4 * np.std(mse)  # 임계값 설정\n",
        "print(\"Threshold :\", threshold)  #아래에서 확인 가능\n",
        "\n",
        "# 이상치 여부 확인\n",
        "outlier = list(mse >= threshold)\n",
        "outlier.count(True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SgQSA0dYPvqh",
        "outputId": "dae36541-c89e-444e-bb0c-4be32fff50dc"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "374/374 [==============================] - 1s 2ms/step\n",
            "Threshold : 0.13325100042967825\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "112"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "outlier_idx = [index for index, value in enumerate(outlier) if value]\n",
        "outlier_idx"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_tspetaIU8E0",
        "outputId": "813e7c53-072c-4ff0-cc4c-b42f9895e6b7"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1877,\n",
              " 1878,\n",
              " 1879,\n",
              " 1880,\n",
              " 1881,\n",
              " 1882,\n",
              " 1883,\n",
              " 1884,\n",
              " 1885,\n",
              " 1886,\n",
              " 1887,\n",
              " 1888,\n",
              " 1889,\n",
              " 1890,\n",
              " 1892,\n",
              " 1894,\n",
              " 2010,\n",
              " 2011,\n",
              " 2072,\n",
              " 2119,\n",
              " 2120,\n",
              " 2132,\n",
              " 2133,\n",
              " 2136,\n",
              " 2148,\n",
              " 2149,\n",
              " 2161,\n",
              " 2162,\n",
              " 4691,\n",
              " 4692,\n",
              " 4693,\n",
              " 4694,\n",
              " 4695,\n",
              " 4696,\n",
              " 4697,\n",
              " 4698,\n",
              " 4699,\n",
              " 4700,\n",
              " 4701,\n",
              " 4702,\n",
              " 4703,\n",
              " 4704,\n",
              " 4706,\n",
              " 4708,\n",
              " 4824,\n",
              " 4825,\n",
              " 4886,\n",
              " 4933,\n",
              " 4934,\n",
              " 4946,\n",
              " 4947,\n",
              " 4950,\n",
              " 4962,\n",
              " 4963,\n",
              " 4975,\n",
              " 4976,\n",
              " 7801,\n",
              " 7802,\n",
              " 7803,\n",
              " 7804,\n",
              " 7805,\n",
              " 7806,\n",
              " 7807,\n",
              " 7808,\n",
              " 7809,\n",
              " 7810,\n",
              " 7811,\n",
              " 7812,\n",
              " 7813,\n",
              " 7814,\n",
              " 7816,\n",
              " 7818,\n",
              " 7934,\n",
              " 7935,\n",
              " 7996,\n",
              " 8043,\n",
              " 8044,\n",
              " 8056,\n",
              " 8057,\n",
              " 8060,\n",
              " 8072,\n",
              " 8073,\n",
              " 8085,\n",
              " 8086,\n",
              " 10877,\n",
              " 10878,\n",
              " 10879,\n",
              " 10880,\n",
              " 10881,\n",
              " 10882,\n",
              " 10883,\n",
              " 10884,\n",
              " 10885,\n",
              " 10886,\n",
              " 10887,\n",
              " 10888,\n",
              " 10889,\n",
              " 10890,\n",
              " 10892,\n",
              " 10894,\n",
              " 11010,\n",
              " 11011,\n",
              " 11072,\n",
              " 11119,\n",
              " 11120,\n",
              " 11132,\n",
              " 11133,\n",
              " 11136,\n",
              " 11148,\n",
              " 11149,\n",
              " 11161,\n",
              " 11162]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "str = '{}'.format(outlier_idx)\n",
        "str"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "id": "6Fdc5I98U817",
        "outputId": "da2e0133-ca12-4647-f20b-a75d1e9f52ff"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'[1877, 1878, 1879, 1880, 1881, 1882, 1883, 1884, 1885, 1886, 1887, 1888, 1889, 1890, 1892, 1894, 2010, 2011, 2072, 2119, 2120, 2132, 2133, 2136, 2148, 2149, 2161, 2162, 4691, 4692, 4693, 4694, 4695, 4696, 4697, 4698, 4699, 4700, 4701, 4702, 4703, 4704, 4706, 4708, 4824, 4825, 4886, 4933, 4934, 4946, 4947, 4950, 4962, 4963, 4975, 4976, 7801, 7802, 7803, 7804, 7805, 7806, 7807, 7808, 7809, 7810, 7811, 7812, 7813, 7814, 7816, 7818, 7934, 7935, 7996, 8043, 8044, 8056, 8057, 8060, 8072, 8073, 8085, 8086, 10877, 10878, 10879, 10880, 10881, 10882, 10883, 10884, 10885, 10886, 10887, 10888, 10889, 10890, 10892, 10894, 11010, 11011, 11072, 11119, 11120, 11132, 11133, 11136, 11148, 11149, 11161, 11162]'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lcAgiKmjZoUf"
      },
      "execution_count": 22,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.8"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}