Auto_ML_Anomaly_Detection.ipynb

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        "<a href=\"https://colab.research.google.com/github/cafechungkhoan/chu_gia/blob/master/Auto_ML_Anomaly_Detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "pip install pycaret"
      ],
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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "LXF6gLaJGY8c",
        "outputId": "23d16c78-2bb5-498b-f83f-85f99a53b01c"
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-a885e1e6-646c-468e-bd44-73d522adfd3c\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-a885e1e6-646c-468e-bd44-73d522adfd3c\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving anomaly detection vnindex.xls to anomaly detection vnindex (1).xls\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Upnu7D04G1-A",
        "outputId": "6ddd0eb0-d559-4a5e-9652-0e67b9da4795"
      },
      "source": [
        "import pandas as pd\n",
        "import io\n",
        "data = pd.read_excel(io.BytesIO(uploaded['anomaly detection vnindex.xls']))\n",
        "print(data)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "*** No CODEPAGE record, no encoding_override: will use 'ascii'\n",
            "            Date      Time        Close\n",
            "0     2021/07/08  09:00:00  1374.680054\n",
            "1     2021/07/07  09:00:00  1388.550000\n",
            "2     2021/07/06  09:00:00  1354.790000\n",
            "3     2021/07/05  09:00:00  1411.130000\n",
            "4     2021/07/02  09:00:00  1420.270000\n",
            "...          ...       ...          ...\n",
            "2997  2009/07/07  09:00:00   447.630000\n",
            "2998  2009/07/06  09:00:00   454.270000\n",
            "2999  2009/07/03  09:00:00   435.440000\n",
            "3000  2009/07/02  09:00:00   433.790000\n",
            "3001  2009/07/01  09:00:00   430.000000\n",
            "\n",
            "[3002 rows x 3 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "b4Gmb_SBHAC0",
        "outputId": "e59ccbab-37ba-41fb-d292-7d71384eaeb9"
      },
      "source": [
        "data['Date'] = pd.to_datetime(data['Date'])\n",
        "data = data.drop(['Time'],axis = 1)\n",
        "data = data.dropna()\n",
        "data"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Date</th>\n",
              "      <th>Close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2021-07-08</td>\n",
              "      <td>1374.680054</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2021-07-07</td>\n",
              "      <td>1388.550000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2021-07-06</td>\n",
              "      <td>1354.790000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2021-07-05</td>\n",
              "      <td>1411.130000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2021-07-02</td>\n",
              "      <td>1420.270000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2997</th>\n",
              "      <td>2009-07-07</td>\n",
              "      <td>447.630000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2998</th>\n",
              "      <td>2009-07-06</td>\n",
              "      <td>454.270000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2999</th>\n",
              "      <td>2009-07-03</td>\n",
              "      <td>435.440000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3000</th>\n",
              "      <td>2009-07-02</td>\n",
              "      <td>433.790000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3001</th>\n",
              "      <td>2009-07-01</td>\n",
              "      <td>430.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3002 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "           Date        Close\n",
              "0    2021-07-08  1374.680054\n",
              "1    2021-07-07  1388.550000\n",
              "2    2021-07-06  1354.790000\n",
              "3    2021-07-05  1411.130000\n",
              "4    2021-07-02  1420.270000\n",
              "...         ...          ...\n",
              "2997 2009-07-07   447.630000\n",
              "2998 2009-07-06   454.270000\n",
              "2999 2009-07-03   435.440000\n",
              "3000 2009-07-02   433.790000\n",
              "3001 2009-07-01   430.000000\n",
              "\n",
              "[3002 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mBqvVYmRHbHY"
      },
      "source": [
        "# set timestamp to index\n",
        "data.set_index('Date', drop=True, inplace=True)\n",
        "# creature features from date\n",
        "data['day'] = [i.day for i in data.index]\n",
        "data['day_name'] = [i.day_name() for i in data.index]\n",
        "data['day_of_year'] = [i.dayofyear for i in data.index]\n",
        "data['week_of_year'] = [i.weekofyear for i in data.index]\n",
        "data['hour'] = [i.hour for i in data.index]\n",
        "data['is_weekday'] = [i.isoweekday() for i in data.index]"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 431
        },
        "id": "KTFxZR0-IKhK",
        "outputId": "8b32d538-e5b3-470a-f982-30bb071f0496"
      },
      "source": [
        "data"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Close</th>\n",
              "      <th>day</th>\n",
              "      <th>day_name</th>\n",
              "      <th>day_of_year</th>\n",
              "      <th>week_of_year</th>\n",
              "      <th>hour</th>\n",
              "      <th>is_weekday</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2021-07-08</th>\n",
              "      <td>1374.680054</td>\n",
              "      <td>8</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>189</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-07-07</th>\n",
              "      <td>1388.550000</td>\n",
              "      <td>7</td>\n",
              "      <td>Wednesday</td>\n",
              "      <td>188</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-07-06</th>\n",
              "      <td>1354.790000</td>\n",
              "      <td>6</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>187</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-07-05</th>\n",
              "      <td>1411.130000</td>\n",
              "      <td>5</td>\n",
              "      <td>Monday</td>\n",
              "      <td>186</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-07-02</th>\n",
              "      <td>1420.270000</td>\n",
              "      <td>2</td>\n",
              "      <td>Friday</td>\n",
              "      <td>183</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-07-07</th>\n",
              "      <td>447.630000</td>\n",
              "      <td>7</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>188</td>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-07-06</th>\n",
              "      <td>454.270000</td>\n",
              "      <td>6</td>\n",
              "      <td>Monday</td>\n",
              "      <td>187</td>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-07-03</th>\n",
              "      <td>435.440000</td>\n",
              "      <td>3</td>\n",
              "      <td>Friday</td>\n",
              "      <td>184</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-07-02</th>\n",
              "      <td>433.790000</td>\n",
              "      <td>2</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>183</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009-07-01</th>\n",
              "      <td>430.000000</td>\n",
              "      <td>1</td>\n",
              "      <td>Wednesday</td>\n",
              "      <td>182</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3002 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Close  day   day_name  ...  week_of_year  hour  is_weekday\n",
              "Date                                     ...                                \n",
              "2021-07-08  1374.680054    8   Thursday  ...            27     0           4\n",
              "2021-07-07  1388.550000    7  Wednesday  ...            27     0           3\n",
              "2021-07-06  1354.790000    6    Tuesday  ...            27     0           2\n",
              "2021-07-05  1411.130000    5     Monday  ...            27     0           1\n",
              "2021-07-02  1420.270000    2     Friday  ...            26     0           5\n",
              "...                 ...  ...        ...  ...           ...   ...         ...\n",
              "2009-07-07   447.630000    7    Tuesday  ...            28     0           2\n",
              "2009-07-06   454.270000    6     Monday  ...            28     0           1\n",
              "2009-07-03   435.440000    3     Friday  ...            27     0           5\n",
              "2009-07-02   433.790000    2   Thursday  ...            27     0           4\n",
              "2009-07-01   430.000000    1  Wednesday  ...            27     0           3\n",
              "\n",
              "[3002 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "c4b2b0c4ef5c4bbb8694e670ef3fcf96",
            "915dffdb51fa4bd2a7882c78d4645793",
            "0263efa42fcb4a0598d4aa3035576533",
            "97e7fa33a2c1416f9169fe3caf620308",
            "1a930201e017485a953783564442c643",
            "b78652af511c4bb48e8a63048d8f62b1"
          ]
        },
        "id": "wL6xqXhcINEV",
        "outputId": "ba0ec445-1ecf-45ee-cb78-b5c5d96f4d3a"
      },
      "source": [
        "# init setup\n",
        "from pycaret.anomaly import *\n",
        "s = setup(data, session_id = 123)"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<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>Description</th>\n",
              "      <th>Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>session_id</td>\n",
              "      <td>123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Original Data</td>\n",
              "      <td>(3002, 7)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Missing Values</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Numeric Features</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Categorical Features</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Ordinal Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>High Cardinality Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>High Cardinality Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Transformed Data</td>\n",
              "      <td>(3002, 15)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>CPU Jobs</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Use GPU</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Log Experiment</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Experiment Name</td>\n",
              "      <td>anomaly-default-name</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>USI</td>\n",
              "      <td>a3e7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Imputation Type</td>\n",
              "      <td>simple</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Iterative Imputation Iteration</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Numeric Imputer</td>\n",
              "      <td>mean</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Iterative Imputation Numeric Model</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Categorical Imputer</td>\n",
              "      <td>mode</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Iterative Imputation Categorical Model</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Unknown Categoricals Handling</td>\n",
              "      <td>least_frequent</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Normalize</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Normalize Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Transformation</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Transformation Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>PCA</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>PCA Method</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>PCA Components</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>Ignore Low Variance</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Combine Rare Levels</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Rare Level Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Numeric Binning</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Remove Outliers</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>Outliers Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Remove Multicollinearity</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Multicollinearity Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Remove Perfect Collinearity</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>Clustering</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Clustering Iteration</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Polynomial Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Polynomial Degree</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Trignometry Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>Polynomial Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>Group Features</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Feature Selection</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>Feature Selection Method</td>\n",
              "      <td>classic</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>Features Selection Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>Feature Interaction</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>Feature Ratio</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>Interaction Threshold</td>\n",
              "      <td>None</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               Description                 Value\n",
              "0                               session_id                   123\n",
              "1                            Original Data             (3002, 7)\n",
              "2                           Missing Values                 False\n",
              "3                         Numeric Features                     4\n",
              "4                     Categorical Features                     3\n",
              "5                         Ordinal Features                 False\n",
              "6                High Cardinality Features                 False\n",
              "7                  High Cardinality Method                  None\n",
              "8                         Transformed Data            (3002, 15)\n",
              "9                                 CPU Jobs                    -1\n",
              "10                                 Use GPU                 False\n",
              "11                          Log Experiment                 False\n",
              "12                         Experiment Name  anomaly-default-name\n",
              "13                                     USI                  a3e7\n",
              "14                         Imputation Type                simple\n",
              "15          Iterative Imputation Iteration                  None\n",
              "16                         Numeric Imputer                  mean\n",
              "17      Iterative Imputation Numeric Model                  None\n",
              "18                     Categorical Imputer                  mode\n",
              "19  Iterative Imputation Categorical Model                  None\n",
              "20           Unknown Categoricals Handling        least_frequent\n",
              "21                               Normalize                 False\n",
              "22                        Normalize Method                  None\n",
              "23                          Transformation                 False\n",
              "24                   Transformation Method                  None\n",
              "25                                     PCA                 False\n",
              "26                              PCA Method                  None\n",
              "27                          PCA Components                  None\n",
              "28                     Ignore Low Variance                 False\n",
              "29                     Combine Rare Levels                 False\n",
              "30                    Rare Level Threshold                  None\n",
              "31                         Numeric Binning                 False\n",
              "32                         Remove Outliers                 False\n",
              "33                      Outliers Threshold                  None\n",
              "34                Remove Multicollinearity                 False\n",
              "35             Multicollinearity Threshold                  None\n",
              "36             Remove Perfect Collinearity                 False\n",
              "37                              Clustering                 False\n",
              "38                    Clustering Iteration                  None\n",
              "39                     Polynomial Features                 False\n",
              "40                       Polynomial Degree                  None\n",
              "41                    Trignometry Features                 False\n",
              "42                    Polynomial Threshold                  None\n",
              "43                          Group Features                 False\n",
              "44                       Feature Selection                 False\n",
              "45                Feature Selection Method               classic\n",
              "46            Features Selection Threshold                  None\n",
              "47                     Feature Interaction                 False\n",
              "48                           Feature Ratio                 False\n",
              "49                   Interaction Threshold                  None"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "id": "8U7f9eQ4I3JW",
        "outputId": "4c5983e5-5142-42a5-d20e-6ea57c4315cc"
      },
      "source": [
        "# check list of available models\n",
        "models()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Name</th>\n",
              "      <th>Reference</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>abod</th>\n",
              "      <td>Angle-base Outlier Detection</td>\n",
              "      <td>pyod.models.abod.ABOD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cluster</th>\n",
              "      <td>Clustering-Based Local Outlier</td>\n",
              "      <td>pyod.models.cblof.CBLOF</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cof</th>\n",
              "      <td>Connectivity-Based Local Outlier</td>\n",
              "      <td>pyod.models.cof.COF</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>iforest</th>\n",
              "      <td>Isolation Forest</td>\n",
              "      <td>pyod.models.iforest.IForest</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>histogram</th>\n",
              "      <td>Histogram-based Outlier Detection</td>\n",
              "      <td>pyod.models.hbos.HBOS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>knn</th>\n",
              "      <td>K-Nearest Neighbors Detector</td>\n",
              "      <td>pyod.models.knn.KNN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>lof</th>\n",
              "      <td>Local Outlier Factor</td>\n",
              "      <td>pyod.models.lof.LOF</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>svm</th>\n",
              "      <td>One-class SVM detector</td>\n",
              "      <td>pyod.models.ocsvm.OCSVM</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pca</th>\n",
              "      <td>Principal Component Analysis</td>\n",
              "      <td>pyod.models.pca.PCA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mcd</th>\n",
              "      <td>Minimum Covariance Determinant</td>\n",
              "      <td>pyod.models.mcd.MCD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sod</th>\n",
              "      <td>Subspace Outlier Detection</td>\n",
              "      <td>pyod.models.sod.SOD</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sos</th>\n",
              "      <td>Stochastic Outlier Selection</td>\n",
              "      <td>pyod.models.sos.SOS</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                        Name                    Reference\n",
              "ID                                                                       \n",
              "abod            Angle-base Outlier Detection        pyod.models.abod.ABOD\n",
              "cluster       Clustering-Based Local Outlier      pyod.models.cblof.CBLOF\n",
              "cof         Connectivity-Based Local Outlier          pyod.models.cof.COF\n",
              "iforest                     Isolation Forest  pyod.models.iforest.IForest\n",
              "histogram  Histogram-based Outlier Detection        pyod.models.hbos.HBOS\n",
              "knn             K-Nearest Neighbors Detector          pyod.models.knn.KNN\n",
              "lof                     Local Outlier Factor          pyod.models.lof.LOF\n",
              "svm                   One-class SVM detector      pyod.models.ocsvm.OCSVM\n",
              "pca             Principal Component Analysis          pyod.models.pca.PCA\n",
              "mcd           Minimum Covariance Determinant          pyod.models.mcd.MCD\n",
              "sod               Subspace Outlier Detection          pyod.models.sod.SOD\n",
              "sos             Stochastic Outlier Selection          pyod.models.sos.SOS"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225,
          "referenced_widgets": [
            "fa03da7289ae48fc92bc9423e8d0adb1",
            "4f690a20a00c43298c4b80a7a5b0680a",
            "40fdf3f76ac04f8c846271bea2eaa427"
          ]
        },
        "id": "AS4TD8dZI-RP",
        "outputId": "a6bd7747-247d-405a-fe98-a2d4802ecfb5"
      },
      "source": [
        "# train model\n",
        "iforest = create_model('iforest', fraction = 0.1)\n",
        "iforest_results = assign_model(iforest)\n",
        "iforest_results.head()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<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>Close</th>\n",
              "      <th>day</th>\n",
              "      <th>day_name</th>\n",
              "      <th>day_of_year</th>\n",
              "      <th>week_of_year</th>\n",
              "      <th>hour</th>\n",
              "      <th>is_weekday</th>\n",
              "      <th>Anomaly</th>\n",
              "      <th>Anomaly_Score</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2021-07-08</th>\n",
              "      <td>1374.680054</td>\n",
              "      <td>8</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>189</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>-0.002499</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-07-07</th>\n",
              "      <td>1388.550000</td>\n",
              "      <td>7</td>\n",
              "      <td>Wednesday</td>\n",
              "      <td>188</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0.026437</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-07-06</th>\n",
              "      <td>1354.790000</td>\n",
              "      <td>6</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>187</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0.014143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-07-05</th>\n",
              "      <td>1411.130000</td>\n",
              "      <td>5</td>\n",
              "      <td>Monday</td>\n",
              "      <td>186</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.037205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-07-02</th>\n",
              "      <td>1420.270000</td>\n",
              "      <td>2</td>\n",
              "      <td>Friday</td>\n",
              "      <td>183</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0.030987</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  Close  day   day_name  ...  is_weekday  Anomaly  Anomaly_Score\n",
              "Date                                     ...                                    \n",
              "2021-07-08  1374.680054    8   Thursday  ...           4        0      -0.002499\n",
              "2021-07-07  1388.550000    7  Wednesday  ...           3        1       0.026437\n",
              "2021-07-06  1354.790000    6    Tuesday  ...           2        1       0.014143\n",
              "2021-07-05  1411.130000    5     Monday  ...           1        1       0.037205\n",
              "2021-07-02  1420.270000    2     Friday  ...           5        1       0.030987\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "-EExCPwoJCRd",
        "outputId": "61bc4f84-8cb5-4eac-f5e5-3cb89fd5b9a4"
      },
      "source": [
        "# check anomalies\n",
        "iforest_results[iforest_results['Anomaly'] == 1].head()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    .dataframe tbody tr th {\n",
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              "    }\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>Close</th>\n",
              "      <th>day</th>\n",
              "      <th>day_name</th>\n",
              "      <th>day_of_year</th>\n",
              "      <th>week_of_year</th>\n",
              "      <th>hour</th>\n",
              "      <th>is_weekday</th>\n",
              "      <th>Anomaly</th>\n",
              "      <th>Anomaly_Score</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2021-07-07</th>\n",
              "      <td>1388.55</td>\n",
              "      <td>7</td>\n",
              "      <td>Wednesday</td>\n",
              "      <td>188</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>0.026437</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-07-06</th>\n",
              "      <td>1354.79</td>\n",
              "      <td>6</td>\n",
              "      <td>Tuesday</td>\n",
              "      <td>187</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0.014143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-07-05</th>\n",
              "      <td>1411.13</td>\n",
              "      <td>5</td>\n",
              "      <td>Monday</td>\n",
              "      <td>186</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.037205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-07-02</th>\n",
              "      <td>1420.27</td>\n",
              "      <td>2</td>\n",
              "      <td>Friday</td>\n",
              "      <td>183</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0.030987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-07-01</th>\n",
              "      <td>1417.08</td>\n",
              "      <td>1</td>\n",
              "      <td>Thursday</td>\n",
              "      <td>182</td>\n",
              "      <td>26</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>0.024050</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Close  day   day_name  ...  is_weekday  Anomaly  Anomaly_Score\n",
              "Date                                 ...                                    \n",
              "2021-07-07  1388.55    7  Wednesday  ...           3        1       0.026437\n",
              "2021-07-06  1354.79    6    Tuesday  ...           2        1       0.014143\n",
              "2021-07-05  1411.13    5     Monday  ...           1        1       0.037205\n",
              "2021-07-02  1420.27    2     Friday  ...           5        1       0.030987\n",
              "2021-07-01  1417.08    1   Thursday  ...           4        1       0.024050\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "gojeXTWpJHLW",
        "outputId": "f78387ce-a351-4684-c62e-3bc5f8f0c878"
      },
      "source": [
        "import plotly.graph_objects as go\n",
        "# plot value on y-axis and date on x-axis\n",
        "fig = px.line(iforest_results, x=iforest_results.index, y=\"Close\", title='VnIndex - UNSUPERVISED ANOMALY DETECTION', template = 'plotly_dark')\n",
        "# create list of outlier_dates\n",
        "outlier_dates = iforest_results[iforest_results['Anomaly'] == 1].index\n",
        "# obtain y value of anomalies to plot\n",
        "y_values = [iforest_results.loc[i]['Close'] for i in outlier_dates]\n",
        "fig.add_trace(go.Scatter(x=outlier_dates, y=y_values, mode = 'markers', \n",
        "                name = 'Anomaly', \n",
        "                marker=dict(color='red',size=10)))\n",
        "        \n",
        "fig.show()"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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