회전기계_고장진단_실습_v5.ipynb

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        "<a href=\"https://colab.research.google.com/github/jjyjung/ai/blob/gh-pages/%ED%9A%8C%EC%A0%84%EA%B8%B0%EA%B3%84_%EA%B3%A0%EC%9E%A5%EC%A7%84%EB%8B%A8_%EC%8B%A4%EC%8A%B5_v5.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "metadata": {
        "id": "hOynSnugegWD"
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      "source": [
        "# **회전기계 고장진단 실습**\n",
        "\n",
        "회전기계에서 수집된 진동센서 데이터를 전처리하고, 머신러닝 기법을 이용하여 회전기계의 고장 여부를 판단함\n",
        "\n",
        "본 자료는 정부에서 운영하는 아래의 KAMP(한국인공지능제조플랫폼)의 데이터와 설명자료를 활용하였고, 교육을 위한 머신러닝 코드를 새롭게 추가하였습니다.\n",
        "\n",
        " https://www.kamp-ai.kr/aidataDetail?DATASET_SEQ=18\n",
        "\n",
        "## 0. 회전기계 및 실험 환경\n",
        "\n",
        "* 산업 현장에서 사용되는 기계 구조물은 대부분 정상 상태로 가동되며, 수집되는 데이터 역시 정상 유형이 90% 이상을 차지하고 고장 데이터는 충분하지 않다.\n",
        "* Rotor testbed를 이용하여, 여러 고장유형에 대해 특징을 분석하고 정상 및 고장유형을 분류할 수 있는 충분한 양의 고장 데이터를 수집할 수 있다.\n",
        "* 많은 산업 현장(발전소 등)에서 주로 사용되는 회전 설비의 진동데이터를 수집하고 고장을 분석 및 진단하는 학습 모델을 구축하여, 이후 기술 확장을 통해 발전소나 대규모 공장의 자동화 라인 등에 활용하는 것을 목표로 한다.\n",
        "\n",
        "![fig.01.png](https://raw.githubusercontent.com/jjyjung/ai/gh-pages/img/fig.01.png)\n",
        "\n",
        "(1) Controller Module\n",
        "- 본 rotor testbed의 Controller에서 수동 모드를 이용하여 Motor 회전속도가 약 1500 RPM인 경우, 진동 데이터를 구축하였다. <br>\n",
        "<br>\n",
        "\n",
        "(2) DC Motor\n",
        "- 본 rotor testbed는 Brush가 없어서 기계적인 접촉이 없는 Brushless DC (BLDC) Motor를 사용한다.\n",
        "- Brushless DC Motor는 소음이 비교적 적으며, 출력을 세밀하게 조절할 수 있다는 장점이 있다.<br>\n",
        "![fig.02.png](https://raw.githubusercontent.com/jjyjung/ai/gh-pages/img/fig.02.png)\n",
        "- 본 가이드 북에서 사용된 rotor testbed는 총 2개의 aluminum disk가 있다.\n",
        "- 각각의 disk에는 회전 불균형을 구현할 수 있는 bolt 체결 홀이 있다.\n",
        "- 축의 중심으로부터 bolt 체결 홀의 거리는 약 60mm이며, Blot 체결 홀은 모두 36개이다.<br>\n",
        "<br>\n",
        "\n",
        "(4) Rotor Spec.\n",
        "- 본 rotor testbed의 사양은 다음과 같다.\n",
        "\n",
        "|    구분    |                 사양                 |\n",
        "|:----------:|:------------------------------------:|\n",
        "| Size       | 673 mm (W) x 280 mm (D) x 281 mm (H) |\n",
        "| Weight     |                 25kg                 |\n",
        "| Material   |               Aluminum               |\n",
        "| Bearing    |             6202ZZ x 2EA             |\n",
        "| Motor      |  DC 12V x 0.25 HP (0.2W), 0-3000 RPM  |\n",
        "| Main power |                AC 220V               |\n",
        "\n",
        "(5) 실험 환경\n",
        "- 회전 속도는 약 1500 RPM (Revolutions Per Minute)을 사용하였으며, 이 경우 기계의 최대 주파수는 25 Hz (= 1500 RPM / 60 sec)가 된다.\n",
        "- 나이퀴스트 (Nyquist) 이론에 따르면 기계의 최대 주파수의 2배 이상으로 sampling rate을 해야하지만, 실제 산업에서는 안정적인 신호 분석을 위해서 통상 최대 주파수의 5-10배 정도의 sampling rate를 수행한다.\n",
        "- 본 실험 데이터는 이보다 충분하게 최대 주파수의 40배 정도인 1 kHz의 진동 데이터(가속도)로 수집하였다.\n",
        "\n",
        "| Rotor testbed |                   |\n",
        "|:-------------:|:-----------------:|\n",
        "| RPM           |     about 1500    |\n",
        "| Sampling rate | 1 kHz (0.001 sec) |"
      ]
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      "source": [
        "## 1. 고장실험 데이터\n",
        "### 1-1. 고장 데이터 수집\n",
        "\n",
        "- 총 4개의 센서를 사용하고 sensor1~sensor4로 명명한다.<br>\n",
        "- 센서 데이터 취득 위치는 다음과 같다.<br>\n",
        "<br>\n",
        "\n",
        "![sensor1.png](https://raw.githubusercontent.com/jjyjung/ai/gh-pages/img/sensor1.png)<br>\n",
        "<center>[센서 설치 위치 (사진)]</center> <br>\n",
        "\n",
        "![sensor2.png](https://raw.githubusercontent.com/jjyjung/ai/gh-pages/img/sensor2.png)<br>\n",
        "<center>[센서 부착 위치 - Group 1(빨간색)]</center> <br>\n",
        "\n",
        "![sensor3.png](https://raw.githubusercontent.com/jjyjung/ai/gh-pages/img/sensor3.png)<br>\n",
        "<center>[센서 부착 위치 - Group 2(빨간색)]</center> <br>\n",
        "<br>\n",
        "\n",
        "- data는 데이터 취득 시간, 정상 유형(normal)과 고장유형 1, 2, 3 (type1, type2, type3) 경우의 센서값으로 구성된다. <br>\n",
        "- 고장유형 1, 2, 3은 다음의 사진과 표로 확인할 수 있다.<br>\n",
        "\n",
        "![fault_type.png](https://raw.githubusercontent.com/jjyjung/ai/gh-pages/img/fault_type.png)<br>\n",
        "\n",
        "\n",
        "| 구분   | 특징                                                |\n",
        "|--------|-----------------------------------------------------|\n",
        "| Type 1 | Disk 2에 회전 불균형 (270도 위치에 볼트, 너트 부착) |\n",
        "| Type 2 | Support 4에 지지 불균형                             |\n",
        "| Type 3 | Type 1 + Type 2                                     |\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1-2. 고장 데이터 업로드"
      ],
      "metadata": {
        "id": "akS33NCixVbN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        " * **[중요] Sensor 파일 4개 업로드**\n",
        "  * 파일 링크: https://github.com/jjyjung/ai/blob/gh-pages/rotor.zip\n",
        "  * **rotor.zip** 파일의 압축을 풀어서, Google Drive에 원하는 위치에 복사하시오.\n",
        "  * 파일 업로드가 끝나면 아래 코드를 실행시켜 보시오."
      ],
      "metadata": {
        "id": "Vzcw2hO70ZSf"
      }
    },
    {
      "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": "688fcad4-8a71-4af7-818d-91468922b39e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 데이터를 읽어 들이고 정규화하기\n",
        "dir = \"/content/gdrive/My Drive/Colab Notebooks/kamp/14. Dataset_Rotor/\"  ### [중요!!] Colab의 경우, 파일을 저장한 위치를 기록하시오."
      ],
      "metadata": {
        "id": "FHvw5IFEcL0N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9FAmYCHbegWG"
      },
      "outputs": [],
      "source": [
        "# 필요한 패키지(라이브러리) 로딩\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import random\n",
        "import seaborn as sns\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import scale\n",
        "\n",
        "# 4개 센서 데이터를 읽어들이기 위하여 아래 코드를 실행합니다.\n",
        "# group 1의 데이터를 사용할 경우, 아래 코드를 실행합니다.\n",
        "sensor1 = pd.read_csv(dir+'raw_data/g1_sensor1.csv', names = ['time', 'normal', 'type1', 'type2', 'type3'])\n",
        "sensor2 = pd.read_csv(dir+'raw_data/g1_sensor2.csv', names = ['time', 'normal', 'type1', 'type2', 'type3'])\n",
        "sensor3 = pd.read_csv(dir+'raw_data/g1_sensor3.csv', names = ['time', 'normal', 'type1', 'type2', 'type3'])\n",
        "sensor4 = pd.read_csv(dir+'raw_data/g1_sensor4.csv', names = ['time', 'normal', 'type1', 'type2', 'type3'])\n",
        "\n",
        "# # group 2의 데이터를 사용할 경우, 주석을 제거하고 아래 코드를 실행합니다.\n",
        "# sensor1 = pd.read_csv(dir+'raw_data/g2_sensor1.csv', names = ['time', 'normal', 'type1', 'type2', 'type3'])\n",
        "# sensor2 = pd.read_csv(dir+'raw_data/g2_sensor2.csv', names = ['time', 'normal', 'type1', 'type2', 'type3'])\n",
        "# sensor3 = pd.read_csv(dir+'raw_data/g2_sensor3.csv', names = ['time', 'normal', 'type1', 'type2', 'type3'])\n",
        "# sensor4 = pd.read_csv(dir+'raw_data/g2_sensor4.csv', names = ['time', 'normal', 'type1', 'type2', 'type3'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VQNWrTpwegWG"
      },
      "source": [
        "### 1-3. 데이터 확인\n",
        "* 불러온 데이터프레임의 변수 이름(예. sensor1, sensor2 등)을 입력하면 해당 내용을 확인할 수 있습니다.\n",
        "* **[TEST] 아래 코드에서 sensor 번호를 바꾸어가면서, sensor 2, sensor 3, sensor 4의 내용도 확인해보시오.**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('sensor 1의 데이터 크기 :', sensor1.shape)\n",
        "print('sensor 2의 데이터 크기 :', sensor2.shape)\n",
        "print('sensor 3의 데이터 크기 :', sensor3.shape)\n",
        "print('sensor 4의 데이터 크기 :', sensor4.shape)\n",
        "\n",
        "sensor1"
      ],
      "metadata": {
        "id": "CS3syvAVBp4A",
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          "height": 496
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        "outputId": "e2a946a7-837b-4e31-a84a-46ead9ecd20c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sensor 1의 데이터 크기 : (190218, 5)\n",
            "sensor 2의 데이터 크기 : (184211, 5)\n",
            "sensor 3의 데이터 크기 : (196079, 5)\n",
            "sensor 4의 데이터 크기 : (183969, 5)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
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              "              time    normal     type1     type2     type3\n",
              "0         0.000000 -0.853307 -3.464579  0.555219  3.919664\n",
              "1         0.000736 -0.740463 -2.448986 -0.234687  4.145351\n",
              "2         0.001472 -0.138630 -1.922383 -0.009000  2.941685\n",
              "3         0.002208  0.049443 -0.906790 -0.272301  2.603155\n",
              "4         0.002944 -0.289088 -0.568259 -0.986978  1.361874\n",
              "...            ...       ...       ...       ...       ...\n",
              "190213  139.996768 -1.156354 -2.696750  0.844491 -2.109427\n",
              "190214  139.997504 -0.930666 -1.380241  0.919720 -2.222270\n",
              "190215  139.998240 -0.554521 -2.132532  0.731647 -2.109427\n",
              "190216  139.998976 -1.419655 -2.433448  1.183022 -3.087405\n",
              "190217  139.999712 -0.441677 -2.245376  1.333480 -2.184656\n",
              "\n",
              "[190218 rows x 5 columns]"
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              "      <th>time</th>\n",
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              "      <th>1</th>\n",
              "      <td>0.000736</td>\n",
              "      <td>-0.740463</td>\n",
              "      <td>-2.448986</td>\n",
              "      <td>-0.234687</td>\n",
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              "      <th>2</th>\n",
              "      <td>0.001472</td>\n",
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              "      <th>3</th>\n",
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              "      <td>-0.272301</td>\n",
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              "      <th>4</th>\n",
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              "      <th>190213</th>\n",
              "      <td>139.996768</td>\n",
              "      <td>-1.156354</td>\n",
              "      <td>-2.696750</td>\n",
              "      <td>0.844491</td>\n",
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              "      <th>190214</th>\n",
              "      <td>139.997504</td>\n",
              "      <td>-0.930666</td>\n",
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              "      <td>139.998240</td>\n",
              "      <td>-0.554521</td>\n",
              "      <td>-2.132532</td>\n",
              "      <td>0.731647</td>\n",
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              "      <th>190216</th>\n",
              "      <td>139.998976</td>\n",
              "      <td>-1.419655</td>\n",
              "      <td>-2.433448</td>\n",
              "      <td>1.183022</td>\n",
              "      <td>-3.087405</td>\n",
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              "      <td>139.999712</td>\n",
              "      <td>-0.441677</td>\n",
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              "      <td>1.333480</td>\n",
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              "      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-00f60c88-7e0b-44ec-bde5-b78384f87bc9 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-00f60c88-7e0b-44ec-bde5-b78384f87bc9');\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-42ecb00d-21f2-4853-b8b3-df872a0f8fdd\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-42ecb00d-21f2-4853-b8b3-df872a0f8fdd')\"\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-42ecb00d-21f2-4853-b8b3-df872a0f8fdd 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
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PzfxFBbHegWH"
      },
      "source": [
        "* 140초 동안 수집된 데이터가 5개의 컬럼에 저장되어 있다.\n",
        "* 그러나 데이터 크기를 확인해보면 센서들의 미세한 sampling rate 차이로 데이터 개수가 조금씩 다른 것을 알 수 있다.\n",
        "* 따라서 동일한 time interval의 데이터를 취득하기 위해 데이터를 0.001 sec 간격으로 선형보간을 수행한다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CfY0653hegWH"
      },
      "source": [
        "### 1-4. 데이터 선형보간\n",
        "\n",
        "* 계측한 센서들의 계측 시간 간격이 일정하지 않은 경우, 데이터 간 정확한 비교가 어려우므로 계측 시간의 동일화를 위해 전처리(선형 보간)가 필요하다. 선형보간의 방법은 아래 그림과 같다.<br>\n",
        "\n",
        "![sensor3.png](https://raw.githubusercontent.com/jjyjung/ai/gh-pages/img/interpolation.png)<br>\n",
        "<br>\n",
        "\n",
        "* 각 센서의 데이터를 0.001 sec 간격으로 선형보간 한다 (총 140초). <br>\n",
        " - interpolate.interp1d() : [선형보간 함수](https://docs.scipy.org/doc/scipy/reference/generated/scipy.interpolate.interp1d.html)를 이용하였다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xv8s3t61egWH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "outputId": "fbb36a49-249b-49a2-db88-997852306e87"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "normal의 데이터 크기 : (140000, 4)\n",
            "type1의 데이터 크기 : (140000, 4)\n",
            "type2의 데이터 크기 : (140000, 4)\n",
            "type3의 데이터 크기 : (140000, 4)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              s1        s2        s3        s4\n",
              "0      -0.853307  0.048823 -0.437626 -1.116226\n",
              "1      -0.524588 -0.029477 -0.149817 -0.659694\n",
              "2      -0.003708 -0.004751  0.280889 -0.857345\n",
              "3      -0.297674  0.009673 -0.303281 -0.685318\n",
              "4      -0.091203  0.096215 -0.320263 -0.303108\n",
              "...          ...       ...       ...       ...\n",
              "139995 -1.057411  0.587429  0.275044  0.289837\n",
              "139996 -0.513635 -0.137874 -0.045973  0.131462\n",
              "139997 -1.085213  0.002242 -0.164593 -0.087380\n",
              "139998 -0.677177 -0.308897  0.288252  1.319862\n",
              "139999 -1.387764 -0.731303  0.395425 -0.125739\n",
              "\n",
              "[140000 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a394b318-5741-41ba-83a4-8e8d68a22b11\" 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>s1</th>\n",
              "      <th>s2</th>\n",
              "      <th>s3</th>\n",
              "      <th>s4</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.853307</td>\n",
              "      <td>0.048823</td>\n",
              "      <td>-0.437626</td>\n",
              "      <td>-1.116226</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.524588</td>\n",
              "      <td>-0.029477</td>\n",
              "      <td>-0.149817</td>\n",
              "      <td>-0.659694</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.003708</td>\n",
              "      <td>-0.004751</td>\n",
              "      <td>0.280889</td>\n",
              "      <td>-0.857345</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.297674</td>\n",
              "      <td>0.009673</td>\n",
              "      <td>-0.303281</td>\n",
              "      <td>-0.685318</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-0.091203</td>\n",
              "      <td>0.096215</td>\n",
              "      <td>-0.320263</td>\n",
              "      <td>-0.303108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139995</th>\n",
              "      <td>-1.057411</td>\n",
              "      <td>0.587429</td>\n",
              "      <td>0.275044</td>\n",
              "      <td>0.289837</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139996</th>\n",
              "      <td>-0.513635</td>\n",
              "      <td>-0.137874</td>\n",
              "      <td>-0.045973</td>\n",
              "      <td>0.131462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139997</th>\n",
              "      <td>-1.085213</td>\n",
              "      <td>0.002242</td>\n",
              "      <td>-0.164593</td>\n",
              "      <td>-0.087380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139998</th>\n",
              "      <td>-0.677177</td>\n",
              "      <td>-0.308897</td>\n",
              "      <td>0.288252</td>\n",
              "      <td>1.319862</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139999</th>\n",
              "      <td>-1.387764</td>\n",
              "      <td>-0.731303</td>\n",
              "      <td>0.395425</td>\n",
              "      <td>-0.125739</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>140000 rows × 4 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a394b318-5741-41ba-83a4-8e8d68a22b11')\"\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-a394b318-5741-41ba-83a4-8e8d68a22b11 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-a394b318-5741-41ba-83a4-8e8d68a22b11');\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-5a5cf776-a9cd-4130-8f4e-8d42cdf4f460\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5a5cf776-a9cd-4130-8f4e-8d42cdf4f460')\"\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-5a5cf776-a9cd-4130-8f4e-8d42cdf4f460 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": [
        "from scipy import interpolate\n",
        "\n",
        "x_new = np.arange(0, 140, 0.001)  # 0~140 구간을 0.001 간격으로 나눔\n",
        "\n",
        "y_new1 = []; y_new2 = []; y_new3 = []; y_new4 = []\n",
        "for item in ['normal', 'type1', 'type2', 'type3']:\n",
        "    f_linear1 = interpolate.interp1d(sensor1['time'], sensor1[item], kind='linear'); y_new1.append(f_linear1(x_new))\n",
        "    f_linear2 = interpolate.interp1d(sensor2['time'], sensor2[item], kind='linear'); y_new2.append(f_linear2(x_new))\n",
        "    f_linear3 = interpolate.interp1d(sensor3['time'], sensor3[item], kind='linear'); y_new3.append(f_linear3(x_new))\n",
        "    f_linear4 = interpolate.interp1d(sensor4['time'], sensor4[item], kind='linear'); y_new4.append(f_linear4(x_new))\n",
        "\n",
        "sensor1 = pd.DataFrame(np.array(y_new1).T, columns = ['normal', 'type1', 'type2', 'type3'])\n",
        "sensor2 = pd.DataFrame(np.array(y_new2).T, columns = ['normal', 'type1', 'type2', 'type3'])\n",
        "sensor3 = pd.DataFrame(np.array(y_new3).T, columns = ['normal', 'type1', 'type2', 'type3'])\n",
        "sensor4 = pd.DataFrame(np.array(y_new4).T, columns = ['normal', 'type1', 'type2', 'type3'])\n",
        "\n",
        "normal_ = pd.concat([sensor1['normal'],sensor2['normal'],sensor3['normal'],sensor4['normal']],axis=1)\n",
        "type1_  = pd.concat([sensor1['type1'], sensor2['type1'], sensor3['type1'], sensor4['type1']], axis=1)\n",
        "type2_  = pd.concat([sensor1['type2'], sensor2['type2'], sensor3['type2'], sensor4['type2']], axis=1)\n",
        "type3_  = pd.concat([sensor1['type3'], sensor2['type3'], sensor3['type3'], sensor4['type3']], axis=1)\n",
        "\n",
        "normal_.columns = ['s1', 's2', 's3', 's4']\n",
        "type1_.columns = ['s1', 's2', 's3', 's4']\n",
        "type2_.columns  = ['s1', 's2', 's3', 's4']\n",
        "type3_.columns = ['s1', 's2', 's3', 's4']\n",
        "\n",
        "print('normal의 데이터 크기 :', normal_.shape)\n",
        "print('type1의 데이터 크기 :', type1_.shape)\n",
        "print('type2의 데이터 크기 :', type2_.shape)\n",
        "print('type3의 데이터 크기 :', type3_.shape)\n",
        "\n",
        "normal_"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "as9le_xPegWL"
      },
      "source": [
        "* 정상 유형(normal)과 고장유형 1, 2, 3 (type1, type2, type3)별로 센서 데이터를 취합한다."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* **[TEST] 위 코드에서 Class 이름인 normal_을 type1_, type2_, type3_바꾸어서 다른 센서 데이터도 확인해보시오.**"
      ],
      "metadata": {
        "id": "LlZwHTRG8J4w"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5-jVf9KfegWM"
      },
      "source": [
        "### 1-5. 데이터 시각화\n",
        "* 특정 시간에서의 시계열 샘플을 그래프로 그려봅시다.\n",
        "* 아래는 Sensor 1에서 수집된 normal, type1, type2, type3의 상태 그래프를 보여주고 있습니다.\n",
        "* **[TEST] Sensor 2, Sensor 3, Sensor 4의 상태 그래프도 그려보고 비교해 보시오. ('s1'을 's2' 등으로 바꾸면 됨)**\n",
        "* **[질문] 네 가지 센서 중에서 어떤 센서가 가장 고장 진단에 가장 유용할 것으로 예상되는가?**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "B1j_5lJNegWM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "62252596-1130-4ff9-8ca9-e73450a8edbe"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# python 버전 : 3.7.3 (버전에 따라, jupyter notebook에서 아래 코드가 실행되지 않을 수 있습니다)\n",
        "# 실행이 되지 않는 경우, 아래 코드는 주석처리하고 넘어가도 고장진단 학습에는 영향을 주지 않습니다. (데이터 확인용)\n",
        "\n",
        "ss = 's2'\n",
        "\n",
        "plt.figure(figsize = (10, 4))\n",
        "plt.scatter(range(0,300), normal_[ss][:300], label=\"class = normal\", marker='o', s=5)\n",
        "plt.scatter(range(0,300), type1_[ss][:300], label=\"class = type1\", marker='o', s=5)\n",
        "plt.scatter(range(0,300), type2_[ss][:300], label=\"class = type2\", marker='o', s=5)\n",
        "plt.scatter(range(0,300), type3_[ss][:300], label=\"class = type3\", marker='o', s=5)\n",
        "\n",
        "plt.legend(loc=\"lower right\")\n",
        "plt.xlabel(\"Sensor\", fontsize=15)\n",
        "plt.ylabel(\"Sensor Value\", fontsize=15)\n",
        "plt.show()\n",
        "plt.close()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "itEJf4y5egWN"
      },
      "source": [
        "## 2. 데이터 전처리\n",
        "\n",
        "### 2-1. 데이터 필터링 (Filtering)\n",
        "\n",
        "* 센서 데이터는 계측 중에 많은 노이즈(Noise)가 포함되어 있습니다. 주위 데이터와 평균을 취하여 데이터 노이즈를 쉽게 제거할 수 있는데, 본 예제에서는 이동평균필터(Moving Average filter)를 사용합니다.\n",
        "\n",
        "* 이동평균필터 (Moving Average filter)는 python에서 [np.convolve()](https://numpy.org/doc/stable/reference/generated/numpy.convolve.html) 함수로 구현됩니다 (MATLAB의 filter 함수와 동일). <br>\n",
        "    * np.convolve() : 1차원 신호를 입력으로 받아, 최근 M개의 측정값들의 평균을 구합니다.\n",
        "    * 일반적으로 신호 데이터의 변동이 빠른 경우에는 M 값을 작게 하고, 신호 데이터의 변동이 느린 경우에는 M 값을 크게 합니다. <br>\n",
        "    * 자세한 이론은 [자료](https://www.analog.com/media/en/technical-documentation/dsp-book/dsp_book_Ch15.pdf)를 참고하세요."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Oe3qKgW3egWO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2e7c9ea5-3f3f-4043-b212-d1fa9b5e7f6d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(139986, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "M = 15\n",
        "\n",
        "normal_s1 = np.convolve(normal_['s1'], np.ones(M), 'valid') / M; normal_s1 = normal_s1.reshape(len(normal_s1),1)\n",
        "normal_s2 = np.convolve(normal_['s2'], np.ones(M), 'valid') / M; normal_s2 = normal_s2.reshape(len(normal_s2),1)\n",
        "normal_s3 = np.convolve(normal_['s3'], np.ones(M), 'valid') / M; normal_s3 = normal_s3.reshape(len(normal_s3),1)\n",
        "normal_s4 = np.convolve(normal_['s4'], np.ones(M), 'valid') / M; normal_s4 = normal_s4.reshape(len(normal_s4),1)\n",
        "\n",
        "type1_s1 = np.convolve(type1_['s1'], np.ones(M), 'valid') / M; type1_s1 = type1_s1.reshape(len(type1_s1),1)\n",
        "type1_s2 = np.convolve(type1_['s2'], np.ones(M), 'valid') / M; type1_s2 = type1_s2.reshape(len(type1_s2),1)\n",
        "type1_s3 = np.convolve(type1_['s3'], np.ones(M), 'valid') / M; type1_s3 = type1_s3.reshape(len(type1_s3),1)\n",
        "type1_s4 = np.convolve(type1_['s4'], np.ones(M), 'valid') / M; type1_s4 = type1_s4.reshape(len(type1_s4),1)\n",
        "\n",
        "type2_s1 = np.convolve(type2_['s1'], np.ones(M), 'valid') / M; type2_s1 = type2_s1.reshape(len(type2_s1),1)\n",
        "type2_s2 = np.convolve(type2_['s2'], np.ones(M), 'valid') / M; type2_s2 = type2_s2.reshape(len(type2_s2),1)\n",
        "type2_s3 = np.convolve(type2_['s3'], np.ones(M), 'valid') / M; type2_s3 = type2_s3.reshape(len(type2_s3),1)\n",
        "type2_s4 = np.convolve(type2_['s4'], np.ones(M), 'valid') / M; type2_s4 = type2_s4.reshape(len(type2_s4),1)\n",
        "\n",
        "type3_s1 = np.convolve(type3_['s1'], np.ones(M), 'valid') / M; type3_s1 = type3_s1.reshape(len(type3_s1),1)\n",
        "type3_s2 = np.convolve(type3_['s2'], np.ones(M), 'valid') / M; type3_s2 = type3_s2.reshape(len(type3_s2),1)\n",
        "type3_s3 = np.convolve(type3_['s3'], np.ones(M), 'valid') / M; type3_s3 = type3_s3.reshape(len(type3_s3),1)\n",
        "type3_s4 = np.convolve(type3_['s4'], np.ones(M), 'valid') / M; type3_s4 = type3_s4.reshape(len(type3_s4),1)\n",
        "\n",
        "normal_temp = np.concatenate((normal_s1,normal_s2,normal_s3,normal_s4), axis = 1)\n",
        "type1_temp = np.concatenate((type1_s1,type1_s2,type1_s3,type1_s4), axis = 1)\n",
        "type2_temp = np.concatenate((type2_s1,type2_s2,type2_s3,type2_s4), axis = 1)\n",
        "type3_temp = np.concatenate((type3_s1,type3_s2,type3_s3,type3_s4), axis = 1)\n",
        "\n",
        "normal_temp.shape"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "normal_s1 = normal_['s1'].to_numpy(); normal_s1 = normal_s1.reshape(len(normal_s1),1)\n",
        "normal_s2 = normal_['s2'].to_numpy(); normal_s2 = normal_s2.reshape(len(normal_s2),1)\n",
        "normal_s3 = normal_['s3'].to_numpy(); normal_s3 = normal_s3.reshape(len(normal_s3),1)\n",
        "normal_s4 = normal_['s4'].to_numpy(); normal_s4 = normal_s4.reshape(len(normal_s4),1)\n",
        "\n",
        "type1_s1 = type1_['s1'].to_numpy(); type1_s1 = type1_s1.reshape(len(type1_s1),1)\n",
        "type1_s2 = type1_['s2'].to_numpy(); type1_s2 = type1_s2.reshape(len(type1_s2),1)\n",
        "type1_s3 = type1_['s3'].to_numpy(); type1_s3 = type1_s3.reshape(len(type1_s3),1)\n",
        "type1_s4 = type1_['s4'].to_numpy(); type1_s4 = type1_s4.reshape(len(type1_s4),1)\n",
        "\n",
        "type2_s1 = type2_['s1'].to_numpy(); type2_s1 = type2_s1.reshape(len(type2_s1),1)\n",
        "type2_s2 = type2_['s2'].to_numpy(); type2_s2 = type2_s2.reshape(len(type2_s2),1)\n",
        "type2_s3 = type2_['s3'].to_numpy(); type2_s3 = type2_s3.reshape(len(type2_s3),1)\n",
        "type2_s4 = type2_['s4'].to_numpy(); type2_s4 = type2_s4.reshape(len(type2_s4),1)\n",
        "\n",
        "type3_s1 = type3_['s1'].to_numpy(); type3_s1 = type3_s1.reshape(len(type3_s1),1)\n",
        "type3_s2 = type3_['s2'].to_numpy(); type3_s2 = type3_s2.reshape(len(type3_s2),1)\n",
        "type3_s3 = type3_['s3'].to_numpy(); type3_s3 = type3_s3.reshape(len(type3_s3),1)\n",
        "type3_s4 = type3_['s4'].to_numpy(); type3_s4 = type3_s4.reshape(len(type3_s4),1)\n",
        "\n",
        "normal_temp = np.concatenate((normal_s1,normal_s2,normal_s3,normal_s4), axis = 1)\n",
        "type1_temp = np.concatenate((type1_s1,type1_s2,type1_s3,type1_s4), axis = 1)\n",
        "type2_temp = np.concatenate((type2_s1,type2_s2,type2_s3,type2_s4), axis = 1)\n",
        "type3_temp = np.concatenate((type3_s1,type3_s2,type3_s3,type3_s4), axis = 1)\n",
        "\n",
        "normal_temp.shape"
      ],
      "metadata": {
        "id": "BTls2BPMZALP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d68d2e7f-7220-4df2-f3d4-9d7eeafcb17d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(140000, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4xSUlfKwegWO"
      },
      "source": [
        "### 2-2. 데이터 정규화\n",
        "\n",
        "* 이동평균을 적용하였기 때문에 데이터의 크기가 M-1 만큼 축소되었습니다.\n",
        "* 데이터 (139986,4)의 센서 값들이 크기가 서로 다르면 SVM, 신경망 등 모형의 학습에 그대로 이용하기에 적절하지 않습니다. <br>\n",
        "* 일반적으로 입력값들을 정규화(Normalization)하는데, 본 예제에서는 Min-max normalization 활용하여 진행합니다. <br>\n",
        "* Min-max normalization는 데이터를 0과 1 사이의 값으로 scaling 합니다.\n",
        "\n",
        " 1) 대상 데이터들의 최대값(max)과 최소값(min)을 구한다. <br>\n",
        " 2) 정규화할 데이터 값(x)에 최대값(Xmax), 최소값(Xmin)을 적용하여 다음의 식과 같이 변환합니다.<br>\n",
        "![sensor1.png](https://raw.githubusercontent.com/jjyjung/ai/gh-pages/img/minmax.png)<br>\n",
        "\n",
        " 3) 위의 식은 sklearn.preprocessing의 MinMaxScaler에 구현되어 있습니다."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0rZQiqxxegWP"
      },
      "source": [
        "* rotor testbed 가동 후 정상 가동 전까지 일부 시간(여기에서는 30초)의 데이터를 탈락시키고(warm-up 시간), 이후의 안정적인 상태의 데이터(steady state)를 분석에 사용합니다.\n",
        "* 본 예제에서는 편의상 140초 중 30초 이후 100초 동안의 데이터를 (100000, 4)로 추출합니다.(sampling rate이 1 kHz이므로 100초 x 1000개)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Mfj_77PDegWO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5c4571f2-9641-4632-94e4-0853a9ac97e5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0.66521063 0.5095954  0.62137846 0.43836559]\n",
            " [0.63831288 0.34065529 0.6606747  0.39659356]\n",
            " [0.67475498 0.54801972 0.60896488 0.24213107]\n",
            " ...\n",
            " [0.73214189 0.62672439 0.48493925 0.76247388]\n",
            " [0.62888927 0.45714926 0.62430992 0.67200136]\n",
            " [0.77780298 0.54511241 0.51407894 0.63871357]]\n",
            "------------------------------------------------\n",
            "normal data size =  (100000, 4)\n"
          ]
        }
      ],
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "scaler = MinMaxScaler()\n",
        "scaler.fit(normal_temp)\n",
        "normal = scaler.transform(normal_temp)\n",
        "type1  = scaler.transform(type1_temp)\n",
        "type2  = scaler.transform(type2_temp)\n",
        "type3  = scaler.transform(type3_temp)\n",
        "\n",
        "normal = normal[30000:130000][:]\n",
        "type1  = type1[30000:130000][:]\n",
        "type2  = type2[30000:130000][:]\n",
        "type3  = type3[30000:130000][:]\n",
        "\n",
        "print(normal)\n",
        "print('------------------------------------------------')\n",
        "print('normal data size = ', normal.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IXtpvuUNegWQ"
      },
      "source": [
        "### 2-3. 데이터 분할\n",
        "\n",
        "* 공정한 평가를 위하여 Normal, type1, type2, type3 데이터를 학습용(training data)과 테스트용(test data)으로 나누어야 합니다.\n",
        "* 본 예제에서는 학습용 : 테스트용 데이터를 7:3 으로 분할합니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "J88Alfl5egWQ"
      },
      "outputs": [],
      "source": [
        "# 데이터 분할\n",
        "normal_train = normal[:][:70000]; normal_test = normal[:][70000:]\n",
        "type1_train  = type1[:][:70000];  type1_test  = type1[:][70000:]\n",
        "type2_train  = type2[:][:70000];  type2_test  = type2[:][70000:]\n",
        "type3_train  = type3[:][:70000];  type3_test  = type3[:][70000:]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2-4. 통계적 지표(SI) 추출\n",
        "\n",
        "* SVM, Random Forest 등 머신러닝 모형의 학습에 100000개 시계열 데이터를 직접 입력하면 학습이 잘 되지 않기 때문에, 시계열 데이터의 통계적 지표(SI)를 추출하여 입력으로 사용합니다.\n",
        "* 본 예제에서는 0.5초를 하나의 구간으로 보고, 각 구간에서 평균, 표준편차, 첨도, 왜도 등 10가지 통계적 지표를 추출하여 사용합니다.\n",
        "\n",
        "\n",
        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)"
      ],
      "metadata": {
        "id": "n9WgUb0cTkyK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* 통계적 지표(SI)를 추출하는 함수 정의\n",
        "* 0.5초씩 구간을 나누는 함수 정의\n",
        "* 두 가지 함수를 실행시켜, 네 가지 class의 데이터를 0.5초로 분할한 후, SI 지표를 추출함"
      ],
      "metadata": {
        "id": "uawmVmRADYuA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# 통계적 지표(SI) 함수 정의\n",
        "def get_mean(x):\n",
        "    avg = np.mean(x)\n",
        "    return(avg)\n",
        "\n",
        "def get_std(x):\n",
        "    std = np.std(x)\n",
        "    return(std)\n",
        "\n",
        "def get_skewness(x):\n",
        "    skewness = sum((x - np.mean(x))**3) / ((len(x)-1)*(np.std(x)**3))\n",
        "    return(skewness)\n",
        "\n",
        "def get_kurtosis(x):\n",
        "    kurtosis = sum((x - np.mean(x))**4) / ((len(x)-1)*(np.std(x)**4))\n",
        "    return (kurtosis)\n",
        "\n",
        "def get_p2p(x):\n",
        "    p2p = (np.max(x) - np.min(x))\n",
        "    return(p2p)\n",
        "\n",
        "def get_rms(x):\n",
        "    rms = np.sqrt(sum(x**2) / len(x))\n",
        "    return(rms)\n",
        "\n",
        "def get_crestFactor(x):\n",
        "    crestFactor = get_p2p(x) / get_rms(x)\n",
        "    return(crestFactor)\n",
        "\n",
        "def get_shapeFactor(x):\n",
        "    shapeFactor = get_rms(x) / get_mean(x)\n",
        "    return(shapeFactor)\n",
        "\n",
        "def get_marginFactor(x):\n",
        "    marginFactor = np.max(x) / (np.mean((np.sqrt(abs(x))))**2)\n",
        "    return(marginFactor)\n",
        "\n",
        "def get_impulseFactor(x):\n",
        "    impulseFactor = np.max(x) / np.mean(abs(x))\n",
        "    return(impulseFactor)\n",
        "\n",
        "def make_df(x, sensor_num):\n",
        "    mean_value = np.apply_along_axis(get_mean, 1, x)\n",
        "    std_value = np.apply_along_axis(get_std, 1, x)\n",
        "    skewness_value = np.apply_along_axis(get_skewness, 1, x)\n",
        "    kurtosis_value = np.apply_along_axis(get_kurtosis, 1, x)\n",
        "    p2p_value = np.apply_along_axis(get_p2p, 1, x)\n",
        "    rms_value = np.apply_along_axis(get_rms, 1, x)\n",
        "    crestFactor_value = np.apply_along_axis(get_crestFactor, 1, x)\n",
        "    shapeFactor_value = np.apply_along_axis(get_shapeFactor, 1, x)\n",
        "    marginFactor_value = np.apply_along_axis(get_marginFactor, 1, x)\n",
        "    impulseFactor_value = np.apply_along_axis(get_impulseFactor, 1, x)\n",
        "\n",
        "    # 통계치 선별하는 부분\n",
        "    df = pd.DataFrame({'std_value'+sensor_num:std_value})\n",
        "    # df = pd.DataFrame({'mean_'+sensor_num:mean_value, 'std_'+sensor_num:std_value})\n",
        "    # df = pd.DataFrame({'mean_'+sensor_num:mean_value, 'std_'+sensor_num:std_value, 'skewness_'+sensor_num:skewness_value,\n",
        "    #                    'kurtosis_'+sensor_num:kurtosis_value, 'p2p_'+sensor_num:p2p_value, 'rms_'+sensor_num:rms_value,\n",
        "    #                    'crest_'+sensor_num:crestFactor_value, 'shape_'+sensor_num:shapeFactor_value,\n",
        "    #                    'margin_'+sensor_num:marginFactor_value, 'impulse_'+sensor_num:impulseFactor_value})\n",
        "\n",
        "    return(df)\n",
        "\n",
        "# 0.5초 구간별로 분할하여 SI를 측정하는 함수 정의\n",
        "def generate_data(data):\n",
        "  data = pd.DataFrame(data)\n",
        "  df = pd.DataFrame()\n",
        "  i = 0\n",
        "  window_len = 500\n",
        "  while data.shape[0] >= window_len:\n",
        "    input = data.iloc[window_len*i:window_len*(i+1),:]\n",
        "    input_arr = np.array(input).reshape((1,-1))\n",
        "    df = pd.concat([df, pd.DataFrame(input_arr)], axis=0)\n",
        "    # print(df)\n",
        "    data = data.iloc[window_len*(i+1):,:]\n",
        "    # i += 1\n",
        "\n",
        "  df = df.reset_index(drop=True)\n",
        "  return df #.drop(index=df.index[-1])\n",
        "\n",
        "\n",
        "def extract_SI(data):\n",
        "  s1_tmp = generate_data(data[:,0])\n",
        "  s2_tmp = generate_data(data[:,1])\n",
        "  s3_tmp = generate_data(data[:,2])\n",
        "  s4_tmp = generate_data(data[:,3])\n",
        "\n",
        "  s1_ext = make_df(s1_tmp, 's1')\n",
        "  s2_ext = make_df(s2_tmp, 's2')\n",
        "  s3_ext = make_df(s3_tmp, 's3')\n",
        "  s4_ext = make_df(s4_tmp, 's4')\n",
        "\n",
        "  # 센서 개수 선별하는 부분\n",
        "  df = pd.concat([s2_ext], axis=1)\n",
        "  # df = pd.concat([s1_ext, s2_ext, s3_ext, s4_ext], axis=1)\n",
        "  return df\n",
        "\n",
        "\n",
        "# 0.5초 분할 및 SI 측정하는 코드 실행\n",
        "normal_train_ext = extract_SI(normal_train)\n",
        "type1_train_ext = extract_SI(type1_train)\n",
        "type2_train_ext = extract_SI(type2_train)\n",
        "type3_train_ext = extract_SI(type3_train)\n",
        "\n",
        "normal_test_ext = extract_SI(normal_test)\n",
        "type1_test_ext = extract_SI(type1_test)\n",
        "type2_test_ext = extract_SI(type2_test)\n",
        "type3_test_ext = extract_SI(type3_test)\n",
        "\n",
        "\n",
        "print('normal_train_ext의 데이터 크기 :', normal_train_ext.shape)\n",
        "print('normal_test_ext의 데이터 크기 :', normal_test_ext.shape)\n",
        "\n",
        "# print(normal_train_ext)\n",
        "\n",
        "\n",
        "# Normal, Type1, Type2, Type3에 대하여 0, 1, 2, 3으로 라벨링 부여\n",
        "normal_train_ext_labeled = np.append(normal_train_ext, np.repeat(0, normal_train_ext.shape[0]).reshape(normal_train_ext.shape[0], 1), axis=1)\n",
        "type1_train_ext_labeled = np.append(type1_train_ext, np.repeat(1, type1_train_ext.shape[0]).reshape(type1_train_ext.shape[0], 1), axis=1)\n",
        "type2_train_ext_labeled = np.append(type2_train_ext, np.repeat(2, type2_train_ext.shape[0]).reshape(type2_train_ext.shape[0], 1), axis=1)\n",
        "type3_train_ext_labeled = np.append(type3_train_ext, np.repeat(3, type3_train_ext.shape[0]).reshape(type3_train_ext.shape[0], 1), axis=1)\n",
        "\n",
        "normal_test_ext_labeled = np.append(normal_test_ext, np.repeat(0, normal_test_ext.shape[0]).reshape(normal_test_ext.shape[0], 1), axis=1)\n",
        "type1_test_ext_labeled = np.append(type1_test_ext, np.repeat(1, type1_test_ext.shape[0]).reshape(type1_test_ext.shape[0], 1), axis=1)\n",
        "type2_test_ext_labeled = np.append(type2_test_ext, np.repeat(2, type2_test_ext.shape[0]).reshape(type2_test_ext.shape[0], 1), axis=1)\n",
        "type3_test_ext_labeled = np.append(type3_test_ext, np.repeat(3, type3_test_ext.shape[0]).reshape(type3_test_ext.shape[0], 1), axis=1)\n",
        "\n",
        "combined_trn = np.concatenate((normal_train_ext_labeled, type1_train_ext_labeled, type2_train_ext_labeled, type3_train_ext_labeled))\n",
        "combined_test = np.concatenate((normal_test_ext_labeled, type1_test_ext_labeled, type2_test_ext_labeled, type3_test_ext_labeled))\n",
        "\n",
        "np.random.shuffle(combined_trn)\n",
        "np.random.shuffle(combined_test)\n",
        "\n",
        "\n",
        "print(combined_trn)\n",
        "print('------------------------------------------------')\n",
        "print(combined_trn.shape)\n",
        "print(combined_test.shape)"
      ],
      "metadata": {
        "id": "1F0K0Vt1TNl_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5217ca63-2ea4-47c7-8bb1-88debf4a1c72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "normal_train_ext의 데이터 크기 : (140, 1)\n",
            "normal_test_ext의 데이터 크기 : (60, 1)\n",
            "[[0.11468625 2.        ]\n",
            " [0.24282237 3.        ]\n",
            " [0.13503847 0.        ]\n",
            " ...\n",
            " [0.22362531 1.        ]\n",
            " [0.14284729 0.        ]\n",
            " [0.15174515 0.        ]]\n",
            "------------------------------------------------\n",
            "(560, 2)\n",
            "(240, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* 혼합된 학습데이터를 만들기 이전에 Normal, Type1, Type2, Type3 데이터의 클래스를 각각 0, 1, 2, 3으로 라벨링 부여합니다."
      ],
      "metadata": {
        "id": "ySPjI114Dv80"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* 골고루 학습하고 테스트하기 위하여, 학습 및 평가 데이터들에 Shuffling을 수행합니다."
      ],
      "metadata": {
        "id": "2sFMgxisD5-t"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6_It14bdegWS"
      },
      "source": [
        "## 3. 모델 학습 및 평가\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###3-1. 의사결정나무 (Decision Tree)\n",
        "* 의사결정나무(DT: Decision Tree)는 변수의 값에 따라 분할하는 규칙을 순차적으로 적용하여 데이터를 분류하는 기법\n",
        "\n",
        " ![dt_table.png](https://av-eks-blogoptimized.s3.amazonaws.com/905753.png)\n",
        " ![dt_table.png](https://av-eks-blogoptimized.s3.amazonaws.com/542834.png)"
      ],
      "metadata": {
        "id": "iv0_uHKOpZHI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* 성능 평가 함수 (여러 모델 공통)"
      ],
      "metadata": {
        "id": "8CEpSjfuck4s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "def show_confusion_matrix(y_pred):\n",
        "  accuracy = accuracy_score(combined_test[:,-1], y_pred)\n",
        "  precision = precision_score(combined_test[:,-1], y_pred, average='weighted')\n",
        "  recall = recall_score(combined_test[:,-1], y_pred, average='weighted')\n",
        "  f1 = f1_score(combined_test[:,-1], y_pred, average='weighted')\n",
        "  print('Accuracy: ', \"%.2f\" % (accuracy*100))\n",
        "  print('Precision: ', \"%.2f\" % (precision*100))\n",
        "  print('Recall: ', \"%.2f\" % (recall*100))\n",
        "  print('F1: ', \"%.2f\" % (f1*100))\n",
        "\n",
        "  # Confusion Matrix 생성\n",
        "  Confusion_Matrix = confusion_matrix(combined_test[:,-1], y_pred)\n",
        "\n",
        "  cm_df = pd.DataFrame(Confusion_Matrix,\n",
        "                      index = ['normal','type1','type2','type3'],\n",
        "                      columns = ['normal','type1','type2','type3'])\n",
        "\n",
        "  #Plotting the confusion matrix\n",
        "  plt.figure(figsize=(5,4))\n",
        "  sns.heatmap(cm_df, annot=True)\n",
        "  plt.title('Confusion Matrix')\n",
        "  plt.ylabel('Actual')\n",
        "  plt.xlabel('Predicted')\n",
        "  plt.show()"
      ],
      "metadata": {
        "id": "mXolcjOFr6KF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "* 모델 학습 및 평가 (Decision Tree)"
      ],
      "metadata": {
        "id": "Ez8dtG4SSVVJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "\n",
        "# 모델 학습\n",
        "dt_model = DecisionTreeClassifier()\n",
        "\n",
        "param_dist = {\"max_depth\": [3, 5, 7]}\n",
        "\n",
        "tuned_dt = GridSearchCV(dt_model, param_grid=param_dist, verbose=2, refit=True)\n",
        "tuned_dt.fit(combined_trn[:,:-1], combined_trn[:,-1])\n",
        "\n",
        "\n",
        "# 최적 파라미터 출력\n",
        "print(tuned_dt.best_params_)\n",
        "\n",
        "\n",
        "# 성능 평가\n",
        "y_pred = tuned_dt.predict(combined_test[:,:-1])\n",
        "show_confusion_matrix(y_pred)\n",
        "\n",
        "\n",
        "# Decision Tree 그리기\n",
        "from sklearn.tree import export_graphviz\n",
        "import pydotplus\n",
        "from IPython.display import Image\n",
        "\n",
        "x_list = list(normal_train_ext.columns)\n",
        "dot_data = export_graphviz(tuned_dt.best_estimator_, out_file=None, feature_names=x_list,\n",
        "                          filled=True, rounded=True, special_characters=True)\n",
        "graph = pydotplus.graph_from_dot_data(dot_data)\n",
        "Image(graph.create_png())"
      ],
      "metadata": {
        "id": "PrYE0D1lnnmH",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "1bcbf775-694c-4913-c6ee-83c0cb4a1633"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 3 candidates, totalling 15 fits\n",
            "[CV] END ........................................max_depth=3; total time=   0.0s\n",
            "[CV] END ........................................max_depth=3; total time=   0.0s\n",
            "[CV] END ........................................max_depth=3; total time=   0.0s\n",
            "[CV] END ........................................max_depth=3; total time=   0.0s\n",
            "[CV] END ........................................max_depth=3; total time=   0.0s\n",
            "[CV] END ........................................max_depth=5; total time=   0.0s\n",
            "[CV] END ........................................max_depth=5; total time=   0.0s\n",
            "[CV] END ........................................max_depth=5; total time=   0.0s\n",
            "[CV] END ........................................max_depth=5; total time=   0.0s\n",
            "[CV] END ........................................max_depth=5; total time=   0.0s\n",
            "[CV] END ........................................max_depth=7; total time=   0.0s\n",
            "[CV] END ........................................max_depth=7; total time=   0.0s\n",
            "[CV] END ........................................max_depth=7; total time=   0.0s\n",
            "[CV] END ........................................max_depth=7; total time=   0.0s\n",
            "[CV] END ........................................max_depth=7; total time=   0.0s\n",
            "{'max_depth': 3}\n",
            "Accuracy:  92.50\n",
            "Precision:  92.75\n",
            "Recall:  92.50\n",
            "F1:  92.48\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###3-2. 서포트 벡터 머신 (SVM)\n",
        "* 서포트 벡터 머신(SVM: Support vector machine)은 벡터 공간에서 두 클래스를 구분하는 가장 여유(margin) 있는 결정 경계(decision boundary)를 찾는 분류기\n",
        "\n",
        " ![svm.jpg](https://ars.els-cdn.com/content/image/3-s2.0-B978032385214200001X-f06-02-9780323852142.jpg)<br>"
      ],
      "metadata": {
        "id": "uQKBAWdEbv-B"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* 모델 학습 및 성능 평가 (SVM)"
      ],
      "metadata": {
        "id": "up5tf__GS5iC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import svm\n",
        "from scipy.stats import randint as sp_randint\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "\n",
        "# SVM\n",
        "SVM_model = svm.SVC()\n",
        "\n",
        "param_dist = {\"C\" : [10, 50, 100], \"kernel\": ['linear', 'rbf', 'poly']}\n",
        "\n",
        "tuned_svm = GridSearchCV(SVM_model, param_grid=param_dist, verbose=1, refit=True)\n",
        "tuned_svm.fit(combined_trn[:,:-1], combined_trn[:,-1])\n",
        "\n",
        "\n",
        "# 최적 파라미터 출력\n",
        "print(tuned_svm.best_params_)\n",
        "\n",
        "\n",
        "# 성능 평가\n",
        "y_pred = tuned_svm.predict(combined_test[:,:-1])\n",
        "show_confusion_matrix(y_pred)"
      ],
      "metadata": {
        "id": "YBbsMl7TBPDS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "outputId": "02c41d34-1232-4ec9-88bc-6a297090dcb9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 9 candidates, totalling 45 fits\n",
            "{'C': 10, 'kernel': 'linear'}\n",
            "Accuracy:  100.00\n",
            "Precision:  100.00\n",
            "Recall:  100.00\n",
            "F1:  100.00\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3-3. 랜덤 포레스트 (Random Forest)\n",
        "* 랜덤 포레스트(RF: Random Forest)는 훈련 데이터에서 복원 추출하여 생성한 다수의 데이터셋으로부터 다수의 의사결정나무(Decision Tree)를 학습시킨 후, 다수의 의사결정나무의 예측값의 평균으로 최종 예측하는 분류 또는 회귀 기법\n",
        "\n",
        " ![rf.svg](https://www.tibco.com/sites/tibco/files/media_entity/2021-05/random-forest-diagram.svg)<br>"
      ],
      "metadata": {
        "id": "oX5WhwGSbvrP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "* 모델 학습 및 성능 평가 (Random Forest)\n"
      ],
      "metadata": {
        "id": "rsbcTop-cOUS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "# Random Forest\n",
        "RF_model = RandomForestClassifier()\n",
        "\n",
        "param_dist = {\"n_estimators\": [100, 200],\n",
        "              \"max_depth\": [3, 5, 10]}\n",
        "\n",
        "tuned_rf = GridSearchCV(RF_model, param_grid=param_dist, verbose=1, refit=True)\n",
        "tuned_rf.fit(combined_trn[:,:-1], combined_trn[:,-1])\n",
        "\n",
        "# 최적 파라미터 값\n",
        "print(tuned_rf.best_params_)\n",
        "\n",
        "\n",
        "# 성능 평가\n",
        "y_pred = tuned_rf.predict(combined_test[:,:-1])\n",
        "show_confusion_matrix(y_pred)"
      ],
      "metadata": {
        "id": "piCZXZBgH7Vz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "outputId": "55b5acc6-6cdd-4b45-cf8d-06754040d1a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 6 candidates, totalling 30 fits\n",
            "{'max_depth': 3, 'n_estimators': 100}\n",
            "Accuracy:  92.50\n",
            "Precision:  92.75\n",
            "Recall:  92.50\n",
            "F1:  92.48\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.3"
    },
    "gpuClass": "standard"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}