gnn_model.ipynb

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        "<a href=\"https://colab.research.google.com/github/TAUforPython/BioMedAI/blob/main/gnn_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220."
      ],
      "metadata": {
        "id": "IQjzGvScUtsz"
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YSGvAxRz1O0T"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "from tensorflow import keras\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bGB4LtWNkw_2"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "csv_url = 'https://github.com/TAUforPython/BioMedAI/blob/main/test_data_ECG.csv?raw=true'\n"
      ],
      "metadata": {
        "id": "qtaweYnt3rfG"
      },
      "execution_count": 151,
      "outputs": []
    },
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      "cell_type": "code",
      "execution_count": 152,
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        },
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10000"
            ]
          },
          "metadata": {},
          "execution_count": 152
        }
      ],
      "source": [
        "RawData = pd.read_csv(csv_url,index_col=0)\n",
        "len(RawData)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "RawData.head()"
      ],
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        "outputId": "7fef8446-b403-4fb8-c6a5-c4d4c6e3f8a3"
      },
      "execution_count": 153,
      "outputs": [
        {
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              "            Count_subj  study_id  cart_id  Healthy_Status eeg_time   \\\n",
              "subject_id                                                            \n",
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              "\n",
              "             eeg_date            report_0                           report_1  \\\n",
              "subject_id                                                                     \n",
              "19557662    27.06.2015       Sinus rhythm  Possible right atrial abnormality   \n",
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              "                              filtering rr_interval p_onset  p_end qrs_onset  \\\n",
              "subject_id                                                                     \n",
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      "metadata": {
        "id": "5jFC7zDtO-O6"
      },
      "outputs": [],
      "source": [
        "# выибираем 150 каких-то случайных образцов из выборки\n",
        "ecg_table = RawData.sample(n = 100, random_state = 13)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"data shape: \", ecg_table.shape)\n",
        "ecg_table.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 658
        },
        "id": "rID1ReoKGDev",
        "outputId": "a63e4bbe-2013-45dc-a2cf-8e774c36ca5f"
      },
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "data shape:  (100, 35)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Count_subj  study_id  cart_id  Healthy_Status eeg_time   \\\n",
              "subject_id                                                            \n",
              "10232096            57  40075514  6925986               0  10:13 AM   \n",
              "11823202             1  40022943  6049786               0   2:42 PM   \n",
              "12300375             1  40061431  6721756               0   4:11 PM   \n",
              "12730265            11  40001851  6632385               0   9:34 AM   \n",
              "18723386            19  40089987  6864237               0   3:33 PM   \n",
              "\n",
              "             eeg_date                     report_0  \\\n",
              "subject_id                                           \n",
              "10232096    11.02.2012                Sinus rhythm   \n",
              "11823202    23.11.2011           Sinus bradycardia   \n",
              "12300375    12.07.2014  Probable sinus tachycardia   \n",
              "12730265    27.06.2011              Atrial flutter   \n",
              "18723386    01.06.2010           Sinus tachycardia   \n",
              "\n",
              "                                                     report_1  \\\n",
              "subject_id                                                      \n",
              "10232096               Extensive ST-T changes are nonspecific   \n",
              "11823202    --- Suspect arm lead reversal - only aVF, V1-V...   \n",
              "12300375                           Marked left axis deviation   \n",
              "12730265                             Left bundle branch block   \n",
              "18723386    Extensive T wave changes may be due to myocard...   \n",
              "\n",
              "                                                    report_2  \\\n",
              "subject_id                                                     \n",
              "10232096                                                 NaN   \n",
              "11823202                                IV conduction defect   \n",
              "12300375    Probable anteroseptal infarct - age undetermined   \n",
              "12730265                                                 NaN   \n",
              "18723386                                                 NaN   \n",
              "\n",
              "                                                     report_3  ...  \\\n",
              "subject_id                                                     ...   \n",
              "10232096                                       Borderline ECG  ...   \n",
              "11823202    QRS changes V3/V4 may be due to LVH but cannot...  ...   \n",
              "12300375    Inferior ST elevation - repeat if myocardial i...  ...   \n",
              "12730265                                                  NaN  ...   \n",
              "18723386                                         Abnormal ECG  ...   \n",
              "\n",
              "                              filtering rr_interval p_onset  p_end qrs_onset  \\\n",
              "subject_id                                                                     \n",
              "10232096    60 Hz notch Baseline filter         983      40    156       220   \n",
              "11823202    60 Hz notch Baseline filter        1132      40    158       216   \n",
              "12300375    60 Hz notch Baseline filter         422   29999  29999        96   \n",
              "12730265                <not specified>         984     368  29999       503   \n",
              "18723386    60 Hz notch Baseline filter         540      40    146       194   \n",
              "\n",
              "           qrs_end t_end p_axis qrs_axis t_axis  \n",
              "subject_id                                       \n",
              "10232096       318   630     28       27      6  \n",
              "11823202       380   698    142     -160     40  \n",
              "12300375       216   422  29999      -50    102  \n",
              "12730265       648   973    -84      -37    109  \n",
              "18723386       280   524     20       57     22  \n",
              "\n",
              "[5 rows x 35 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-bb044e1b-e24d-4c95-aa4c-25462b6a7c93\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Count_subj</th>\n",
              "      <th>study_id</th>\n",
              "      <th>cart_id</th>\n",
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              "      <th>report_2</th>\n",
              "      <th>report_3</th>\n",
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              "      <th>p_end</th>\n",
              "      <th>qrs_onset</th>\n",
              "      <th>qrs_end</th>\n",
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              "      <th>p_axis</th>\n",
              "      <th>qrs_axis</th>\n",
              "      <th>t_axis</th>\n",
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              "    <tr>\n",
              "      <th>subject_id</th>\n",
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              "      <th></th>\n",
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              "      <th></th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>10232096</th>\n",
              "      <td>57</td>\n",
              "      <td>40075514</td>\n",
              "      <td>6925986</td>\n",
              "      <td>0</td>\n",
              "      <td>10:13 AM</td>\n",
              "      <td>11.02.2012</td>\n",
              "      <td>Sinus rhythm</td>\n",
              "      <td>Extensive ST-T changes are nonspecific</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Borderline ECG</td>\n",
              "      <td>...</td>\n",
              "      <td>60 Hz notch Baseline filter</td>\n",
              "      <td>983</td>\n",
              "      <td>40</td>\n",
              "      <td>156</td>\n",
              "      <td>220</td>\n",
              "      <td>318</td>\n",
              "      <td>630</td>\n",
              "      <td>28</td>\n",
              "      <td>27</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
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              "      <th>11823202</th>\n",
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              "      <td>6049786</td>\n",
              "      <td>0</td>\n",
              "      <td>2:42 PM</td>\n",
              "      <td>23.11.2011</td>\n",
              "      <td>Sinus bradycardia</td>\n",
              "      <td>--- Suspect arm lead reversal - only aVF, V1-V...</td>\n",
              "      <td>IV conduction defect</td>\n",
              "      <td>QRS changes V3/V4 may be due to LVH but cannot...</td>\n",
              "      <td>...</td>\n",
              "      <td>60 Hz notch Baseline filter</td>\n",
              "      <td>1132</td>\n",
              "      <td>40</td>\n",
              "      <td>158</td>\n",
              "      <td>216</td>\n",
              "      <td>380</td>\n",
              "      <td>698</td>\n",
              "      <td>142</td>\n",
              "      <td>-160</td>\n",
              "      <td>40</td>\n",
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              "      <td>6721756</td>\n",
              "      <td>0</td>\n",
              "      <td>4:11 PM</td>\n",
              "      <td>12.07.2014</td>\n",
              "      <td>Probable sinus tachycardia</td>\n",
              "      <td>Marked left axis deviation</td>\n",
              "      <td>Probable anteroseptal infarct - age undetermined</td>\n",
              "      <td>Inferior ST elevation - repeat if myocardial i...</td>\n",
              "      <td>...</td>\n",
              "      <td>60 Hz notch Baseline filter</td>\n",
              "      <td>422</td>\n",
              "      <td>29999</td>\n",
              "      <td>29999</td>\n",
              "      <td>96</td>\n",
              "      <td>216</td>\n",
              "      <td>422</td>\n",
              "      <td>29999</td>\n",
              "      <td>-50</td>\n",
              "      <td>102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12730265</th>\n",
              "      <td>11</td>\n",
              "      <td>40001851</td>\n",
              "      <td>6632385</td>\n",
              "      <td>0</td>\n",
              "      <td>9:34 AM</td>\n",
              "      <td>27.06.2011</td>\n",
              "      <td>Atrial flutter</td>\n",
              "      <td>Left bundle branch block</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>&lt;not specified&gt;</td>\n",
              "      <td>984</td>\n",
              "      <td>368</td>\n",
              "      <td>29999</td>\n",
              "      <td>503</td>\n",
              "      <td>648</td>\n",
              "      <td>973</td>\n",
              "      <td>-84</td>\n",
              "      <td>-37</td>\n",
              "      <td>109</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18723386</th>\n",
              "      <td>19</td>\n",
              "      <td>40089987</td>\n",
              "      <td>6864237</td>\n",
              "      <td>0</td>\n",
              "      <td>3:33 PM</td>\n",
              "      <td>01.06.2010</td>\n",
              "      <td>Sinus tachycardia</td>\n",
              "      <td>Extensive T wave changes may be due to myocard...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Abnormal ECG</td>\n",
              "      <td>...</td>\n",
              "      <td>60 Hz notch Baseline filter</td>\n",
              "      <td>540</td>\n",
              "      <td>40</td>\n",
              "      <td>146</td>\n",
              "      <td>194</td>\n",
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              "      <td>524</td>\n",
              "      <td>20</td>\n",
              "      <td>57</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 35 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-bb044e1b-e24d-4c95-aa4c-25462b6a7c93')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-bb044e1b-e24d-4c95-aa4c-25462b6a7c93 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-bb044e1b-e24d-4c95-aa4c-25462b6a7c93');\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-364e63c9-9ca4-48f5-8a06-a84f826808b1\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-364e63c9-9ca4-48f5-8a06-a84f826808b1')\"\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",
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              "    <g>\n",
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              "    </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-364e63c9-9ca4-48f5-8a06-a84f826808b1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "ecg_table"
            }
          },
          "metadata": {},
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7gHXiuLCECbu"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import LabelEncoder"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "LI-gObCrEhaj"
      },
      "outputs": [],
      "source": [
        "encoder = LabelEncoder()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jKuP_LckBSFd"
      },
      "outputs": [],
      "source": [
        "text_columns = ['subject_id',\t'Count_subj', 'study_id', 'Healthy_Status', 'eeg_date',\n",
        "                'report_0',\n",
        "                'report_1', 'report_2', 'report_3','report_4', 'report_5',\n",
        "                'report_6', 'report_7', 'report_8', 'report_9', 'report_10',\n",
        "                'report_11', 'report_12', 'report_13', 'report_14', 'report_15',\n",
        "                'report_16', 'report_17',\n",
        "                'bandwidth', 'filtering']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Q65-YrlWCnE8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "outputId": "7a683248-dd67-48de-dded-d810d507768a"
      },
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "'eeg_date'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3652\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3653\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3654\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'eeg_date'",
            "\nThe above exception was the direct cause of the following exception:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-17-06582f8622be>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtext_columns\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mecg_table\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mencoder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit_transform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mecg_table\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3759\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3760\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3761\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3762\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3763\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3653\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3654\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3655\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3656\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3657\u001b[0m             \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'eeg_date'"
          ]
        }
      ],
      "source": [
        "for col in text_columns:\n",
        "    ecg_table[col] = encoder.fit_transform(ecg_table[col])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "w7mkMG2KGJ3r",
        "outputId": "db6991fd-1a3d-43f0-bfea-980af65eadf1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       subject_id  Count_subj    study_id       cart_id  Healthy_Status  \\\n",
              "count  150.000000  150.000000  150.000000  1.500000e+02      150.000000   \n",
              "mean    74.026667   11.966667   74.500000  6.557152e+06        0.193333   \n",
              "std     43.012866   12.092716   43.445368  2.667335e+05        0.396235   \n",
              "min      0.000000    0.000000    0.000000  6.037973e+06        0.000000   \n",
              "25%     37.250000    3.000000   37.250000  6.381974e+06        0.000000   \n",
              "50%     74.500000    7.500000   74.500000  6.632385e+06        0.000000   \n",
              "75%    110.750000   17.750000  111.750000  6.749176e+06        0.000000   \n",
              "max    148.000000   45.000000  149.000000  6.966829e+06        1.000000   \n",
              "\n",
              "       report_15  report_16  report_17  rr_interval       p_onset  \\\n",
              "count        0.0        0.0        0.0   150.000000    150.000000   \n",
              "mean         NaN        NaN        NaN   808.413333   5487.346667   \n",
              "std          NaN        NaN        NaN   201.678600  11523.258975   \n",
              "min          NaN        NaN        NaN   394.000000     40.000000   \n",
              "25%          NaN        NaN        NaN   661.000000     40.000000   \n",
              "50%          NaN        NaN        NaN   794.500000     40.000000   \n",
              "75%          NaN        NaN        NaN   937.000000    348.000000   \n",
              "max          NaN        NaN        NaN  1276.000000  29999.000000   \n",
              "\n",
              "              p_end  qrs_onset     qrs_end        t_end        p_axis  \\\n",
              "count    150.000000  150.00000  150.000000   150.000000    150.000000   \n",
              "mean   11093.740000  253.26000  352.680000   648.626667   5657.320000   \n",
              "std    14432.945426  122.99574  124.940379   132.117476  11750.102787   \n",
              "min      102.000000   96.00000  194.000000   394.000000    -84.000000   \n",
              "25%      146.000000  190.00000  278.500000   562.000000     39.250000   \n",
              "50%      166.000000  201.00000  300.000000   626.000000     57.000000   \n",
              "75%    29999.000000  237.00000  357.500000   694.000000     70.000000   \n",
              "max    29999.000000  509.00000  666.000000  1004.000000  32767.000000   \n",
              "\n",
              "         qrs_axis      t_axis  \n",
              "count  150.000000  150.000000  \n",
              "mean    14.666667   41.513333  \n",
              "std     47.979535   50.324015  \n",
              "min   -160.000000 -169.000000  \n",
              "25%    -16.750000   18.000000  \n",
              "50%     17.000000   42.000000  \n",
              "75%     45.750000   64.750000  \n",
              "max    155.000000  180.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6f04ee19-ad29-4255-b1d4-538169a81f6c\" 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>subject_id</th>\n",
              "      <th>Count_subj</th>\n",
              "      <th>study_id</th>\n",
              "      <th>cart_id</th>\n",
              "      <th>Healthy_Status</th>\n",
              "      <th>report_15</th>\n",
              "      <th>report_16</th>\n",
              "      <th>report_17</th>\n",
              "      <th>rr_interval</th>\n",
              "      <th>p_onset</th>\n",
              "      <th>p_end</th>\n",
              "      <th>qrs_onset</th>\n",
              "      <th>qrs_end</th>\n",
              "      <th>t_end</th>\n",
              "      <th>p_axis</th>\n",
              "      <th>qrs_axis</th>\n",
              "      <th>t_axis</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>1.500000e+02</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.00000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>74.026667</td>\n",
              "      <td>11.966667</td>\n",
              "      <td>74.500000</td>\n",
              "      <td>6.557152e+06</td>\n",
              "      <td>0.193333</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>808.413333</td>\n",
              "      <td>5487.346667</td>\n",
              "      <td>11093.740000</td>\n",
              "      <td>253.26000</td>\n",
              "      <td>352.680000</td>\n",
              "      <td>648.626667</td>\n",
              "      <td>5657.320000</td>\n",
              "      <td>14.666667</td>\n",
              "      <td>41.513333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>43.012866</td>\n",
              "      <td>12.092716</td>\n",
              "      <td>43.445368</td>\n",
              "      <td>2.667335e+05</td>\n",
              "      <td>0.396235</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>201.678600</td>\n",
              "      <td>11523.258975</td>\n",
              "      <td>14432.945426</td>\n",
              "      <td>122.99574</td>\n",
              "      <td>124.940379</td>\n",
              "      <td>132.117476</td>\n",
              "      <td>11750.102787</td>\n",
              "      <td>47.979535</td>\n",
              "      <td>50.324015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.037973e+06</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>394.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>102.000000</td>\n",
              "      <td>96.00000</td>\n",
              "      <td>194.000000</td>\n",
              "      <td>394.000000</td>\n",
              "      <td>-84.000000</td>\n",
              "      <td>-160.000000</td>\n",
              "      <td>-169.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>37.250000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>37.250000</td>\n",
              "      <td>6.381974e+06</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>661.000000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>146.000000</td>\n",
              "      <td>190.00000</td>\n",
              "      <td>278.500000</td>\n",
              "      <td>562.000000</td>\n",
              "      <td>39.250000</td>\n",
              "      <td>-16.750000</td>\n",
              "      <td>18.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>74.500000</td>\n",
              "      <td>7.500000</td>\n",
              "      <td>74.500000</td>\n",
              "      <td>6.632385e+06</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>794.500000</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>166.000000</td>\n",
              "      <td>201.00000</td>\n",
              "      <td>300.000000</td>\n",
              "      <td>626.000000</td>\n",
              "      <td>57.000000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>42.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>110.750000</td>\n",
              "      <td>17.750000</td>\n",
              "      <td>111.750000</td>\n",
              "      <td>6.749176e+06</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>937.000000</td>\n",
              "      <td>348.000000</td>\n",
              "      <td>29999.000000</td>\n",
              "      <td>237.00000</td>\n",
              "      <td>357.500000</td>\n",
              "      <td>694.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>45.750000</td>\n",
              "      <td>64.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>148.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>149.000000</td>\n",
              "      <td>6.966829e+06</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1276.000000</td>\n",
              "      <td>29999.000000</td>\n",
              "      <td>29999.000000</td>\n",
              "      <td>509.00000</td>\n",
              "      <td>666.000000</td>\n",
              "      <td>1004.000000</td>\n",
              "      <td>32767.000000</td>\n",
              "      <td>155.000000</td>\n",
              "      <td>180.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6f04ee19-ad29-4255-b1d4-538169a81f6c')\"\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-6f04ee19-ad29-4255-b1d4-538169a81f6c 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-6f04ee19-ad29-4255-b1d4-538169a81f6c');\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-221c8ebd-11af-433c-a6ca-5c07f56a13bf\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-221c8ebd-11af-433c-a6ca-5c07f56a13bf')\"\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-221c8ebd-11af-433c-a6ca-5c07f56a13bf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"ecg_table\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"subject_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 53.62614905032757,\n        \"min\": 0.0,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          74.02666666666667,\n          74.5,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Count_subj\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 50.07067297646458,\n        \"min\": 0.0,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          11.966666666666667,\n          7.5,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"study_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 53.84281531474717,\n        \"min\": 0.0,\n        \"max\": 150.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          150.0,\n          74.5,\n          111.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cart_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2985350.356510686,\n        \"min\": 150.0,\n        \"max\": 6966829.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6557152.013333334,\n          6632385.0,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Healthy_Status\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.95384745716703,\n        \"min\": 0.0,\n        \"max\": 150.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.19333333333333333,\n          1.0,\n          0.39623507812467246\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"report_15\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"report_16\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"report_17\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"rr_interval\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 384.85717439804324,\n        \"min\": 150.0,\n        \"max\": 1276.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          808.4133333333333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"p_onset\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10552.158028749522,\n        \"min\": 40.0,\n        \"max\": 29999.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"p_end\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13119.968635962481,\n        \"min\": 102.0,\n        \"max\": 29999.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"qrs_onset\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 128.65401265195075,\n        \"min\": 96.0,\n        \"max\": 509.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          253.26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"qrs_end\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 171.05212784719103,\n        \"min\": 124.94037880138492,\n        \"max\": 666.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          352.68\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"t_end\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 292.0332174790983,\n        \"min\": 132.1174760281008,\n        \"max\": 1004.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          648.6266666666667\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"p_axis\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11502.680662492665,\n        \"min\": -84.0,\n        \"max\": 32767.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5657.32\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"qrs_axis\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 99.47197914319437,\n        \"min\": -160.0,\n        \"max\": 155.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          14.666666666666666\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"t_axis\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 104.31812616798096,\n        \"min\": -169.0,\n        \"max\": 180.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          41.513333333333335\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "ecg_table.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "If1aYawBx5l_"
      },
      "outputs": [],
      "source": [
        "ecg_table.loc[:,\"report_0\"] = ecg_table[\"report_0\"].astype(str)\n",
        "class_values = sorted(ecg_table[\"Healthy_Status\"].unique())\n",
        "class_idc = {name: id for id, name in enumerate(class_values)}\n",
        "\n",
        "paper_idc = {name: idx for idx, name in enumerate(sorted(ecg_table[\"study_id\"].unique()))}\n",
        "source_idc = {name: idx for idx, name in enumerate(sorted(ecg_table[\"Count_subj\"].unique()))}\n",
        "target_idc = {name: idx for idx, name in enumerate(sorted(ecg_table[\"subject_id\"].unique()))}\n",
        "ecg_table[\"Healthy_Status\"] = ecg_table[\"Healthy_Status\"].apply(lambda value: class_idc[value])\n",
        "ecg_table[\"study_id\"] = ecg_table[\"study_id\"].apply(lambda value: paper_idc[value])\n",
        "ecg_table[\"Count_subj\"] = ecg_table[\"Count_subj\"].apply(lambda value: source_idc[value])\n",
        "ecg_table[\"subject_id\"] = ecg_table[\"subject_id\"].apply(lambda value: target_idc[value])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 156,
      "metadata": {
        "id": "2dhUV_oXreGe"
      },
      "outputs": [],
      "source": [
        "import networkx as nx\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 8))\n",
        "ecg_graph = nx.from_pandas_edgelist(ecg_table, source = \"rr_interval\", target = \"report_0\")\n",
        "nx.draw_networkx(ecg_graph, pos=nx.spring_layout(ecg_graph), node_size=7, font_size = 5,edge_color ='grey')\n",
        "#nx.draw_networkx(ecg_graph, pos=nx.circular_layout(ecg_graph), node_size=7,\n",
        "#                 font_size = 7, edge_color ='grey')"
      ],
      "metadata": {
        "id": "JYGt-F9J6GoM",
        "outputId": "23c778a0-a76d-4ea0-c47e-94950b8c7770",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 653
        }
      },
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 499
        },
        "id": "sYqZ1P1QroWE",
        "outputId": "89c47fb6-2cfa-45ea-d0ae-24b29ececa35"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(6, 6))\n",
        "# цвет - это диагноз - надо доработать и сделать там классы ограниченные\n",
        "colors = ecg_table[\"Healthy_Status\"].tolist()\n",
        "\n",
        "# задаем матрицу смежности - здесь формируются ребра графа\n",
        "#ecg_graph = nx.from_pandas_edgelist(ecg_table.sample(n=50), source = \"study_id\", target = \"subject_id\")\n",
        "ecg_graph = nx.from_pandas_edgelist(ecg_table, source = \"study_id\", target = \"subject_id\")\n",
        "\n",
        "\n",
        "# subject - это диагноз, надо его привести к чему-то\n",
        "subjects = list(ecg_table[ecg_table[\"study_id\"].isin(list(ecg_graph.nodes))][\"Healthy_Status\"])\n",
        "\n",
        "#nx.draw_spring(ecg_graph, node_size=17, node_color=subjects)\n",
        "nx.draw_networkx(ecg_graph, pos=nx.spring_layout(ecg_graph), node_size=17, node_color=subjects)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aC3SelXpvqB2"
      },
      "outputs": [],
      "source": [
        "feature_names = set(ecg_table.columns) - {\"study_id\", \"report_0\"}\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JaW54Y_HwiNx",
        "outputId": "e5485835-348f-49cf-c368-7c80465924d8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Edges shape: (2, 100)\n"
          ]
        }
      ],
      "source": [
        "edges = ecg_table[[\"ecg_time\", \"subject_id\"]].to_numpy().T\n",
        "print(\"Edges shape:\", edges.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B7qBdF84xEzr",
        "outputId": "48c8364e-5e9e-40d0-a8ff-b3d252f15da7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[17,  8,  1, 66, 71, 14, 97, 88,  7, 69, 29, 72, 25, 42, 62, 56,\n",
              "        26, 47, 21, 70, 75, 90, 93, 44, 84, 11, 16, 80, 58, 87, 13, 91,\n",
              "        37,  3, 52, 64, 51,  6, 33, 24, 86,  4, 99, 10, 55, 67, 60, 22,\n",
              "        61, 19,  2, 23, 59, 95, 12, 15, 92, 73, 53, 39, 50,  0, 48, 98,\n",
              "        68, 32, 31, 82, 89, 79, 65, 96, 38, 49, 28, 77, 83, 81, 45, 35,\n",
              "        46, 74,  5,  9, 63, 36, 57, 43, 30, 27, 54, 85, 78, 40, 41, 94,\n",
              "        18, 20, 76, 34],\n",
              "       [ 5,  3, 47, 48, 16,  5, 78, 11, 28, 52, 65, 64, 56, 37, 68, 72,\n",
              "        33, 10, 80, 26, 38, 42,  0, 45, 32,  5,  5, 18, 71,  0, 63,  0,\n",
              "        59, 21, 43, 41, 13, 62, 12, 77, 14, 31, 19, 15, 24, 66, 70,  8,\n",
              "        74, 53, 29, 58, 39,  0,  5, 86,  0, 49, 69, 72, 35, 55, 57, 30,\n",
              "        46, 84,  1, 67,  0,  7, 22, 82,  6,  2, 40, 81, 17, 27,  9, 61,\n",
              "        54, 73, 50, 23, 76, 25, 83, 59, 51, 40, 79, 44, 75, 85, 60, 20,\n",
              "         4,  4, 34, 36]])"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ],
      "source": [
        "edges"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pjUPLauI9Epr",
        "outputId": "cc35823e-48b6-41f4-9d55-55f1dc644305"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Nodes shape: (100, 31)\n"
          ]
        }
      ],
      "source": [
        "node_features = tf.cast(ecg_table.sort_values(\"study_id\")[list(feature_names)].to_numpy(), dtype=tf.float32)\n",
        "print(\"Nodes shape:\", node_features.shape)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PJ8SRLPAHB-p",
        "outputId": "788350d6-2791-4caa-bb9b-a40333b3f01e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Edges_weights shape: (100,)\n"
          ]
        }
      ],
      "source": [
        "edge_weights = tf.ones(shape=edges.shape[1])\n",
        "print(\"Edges_weights shape:\", edge_weights.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3MypvHIfHbln"
      },
      "outputs": [],
      "source": [
        "class GNNNodeClassifier(tf.keras.Model):\n",
        "    def __init__(\n",
        "        self,\n",
        "        graph_info,\n",
        "        num_classes,\n",
        "        hidden_units,\n",
        "        aggregation_type=\"sum\",\n",
        "        combination_type=\"concat\",\n",
        "        dropout_rate=0.2,\n",
        "        normalize=True,\n",
        "        *args,\n",
        "        **kwargs,\n",
        "    ):\n",
        "        super().__init__(*args, **kwargs)\n",
        "\n",
        "        # Unpack graph_info to three elements: node_features, edges, and edge_weight.\n",
        "        node_features, edges, edge_weights = graph_info\n",
        "        self.node_features = node_features\n",
        "        self.edges = edges\n",
        "        self.edge_weights = edge_weights\n",
        "        # Set edge_weights to ones if not provided.\n",
        "        if self.edge_weights is None:\n",
        "            self.edge_weights = tf.ones(shape=edges.shape[1])\n",
        "        # Scale edge_weights to sum to 1.\n",
        "        self.edge_weights = self.edge_weights / tf.math.reduce_sum(self.edge_weights)\n",
        "\n",
        "        # Create a process layer.\n",
        "        self.preprocess = create_ffn(hidden_units, dropout_rate, name=\"preprocess\")\n",
        "        # Create the first GraphConv layer.\n",
        "        self.conv1 = GraphConvLayer(\n",
        "            hidden_units,\n",
        "            dropout_rate,\n",
        "            aggregation_type,\n",
        "            combination_type,\n",
        "            normalize,\n",
        "            name=\"graph_conv1\",\n",
        "        )\n",
        "         # Create the second GraphConv layer.\n",
        "        self.conv2 = GraphConvLayer(\n",
        "            hidden_units,\n",
        "            dropout_rate,\n",
        "            aggregation_type,\n",
        "            combination_type,\n",
        "            normalize,\n",
        "            name=\"graph_conv2\",\n",
        "        )\n",
        "        # Create a postprocess layer.\n",
        "        self.postprocess = create_ffn(hidden_units, dropout_rate, name=\"postprocess\")\n",
        "        # Create a compute logits layer.\n",
        "        self.compute_logits = layers.Dense(units=num_classes, name=\"logits\")\n",
        "\n",
        "    def call(self, input_node_indices):\n",
        "        # Preprocess the node_features to produce node representations.\n",
        "        x = self.preprocess(self.node_features)\n",
        "        # Apply the first graph conv layer.\n",
        "        x1 = self.conv1((x, self.edges, self.edge_weights))\n",
        "        # Skip connection.\n",
        "        x = x1 + x\n",
        "        # Apply the second graph conv layer.\n",
        "        x2 = self.conv2((x, self.edges, self.edge_weights))\n",
        "        # Skip connection.\n",
        "        x = x2 + x\n",
        "        # Postprocess node embedding.\n",
        "        x = self.postprocess(x)\n",
        "        # Fetch node embeddings for the input node_indices.\n",
        "        node_embeddings = tf.gather(x, input_node_indices)\n",
        "        # Compute logits\n",
        "        return self.compute_logits(node_embeddings)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dRH_mVpyHodH"
      },
      "outputs": [],
      "source": [
        "\"\"\"\n",
        "### Implement a graph convolution layer\n",
        "\n",
        "We implement a graph convolution module as a [Keras Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer?version=nightly).\n",
        "Our `GraphConvLayer` performs the following steps:\n",
        "\n",
        "1. **Prepare**: The input node representations are processed using a FFN to produce a *message*. You can simplify\n",
        "the processing by only applying linear transformation to the representations.\n",
        "2. **Aggregate**: The messages of the neighbours of each node are aggregated with\n",
        "respect to the `edge_weights` using a *permutation invariant* pooling operation, such as *sum*, *mean*, and *max*,\n",
        "to prepare a single aggregated message for each node. See, for example, [tf.math.unsorted_segment_sum](https://www.tensorflow.org/api_docs/python/tf/math/unsorted_segment_sum)\n",
        "APIs used to aggregate neighbour messages.\n",
        "3. **Update**: The `node_repesentations` and `aggregated_messages`—both of shape `[num_nodes, representation_dim]`—\n",
        "are combined and processed to produce the new state of the node representations (node embeddings).\n",
        "If `combination_type` is `gru`, the `node_repesentations` and `aggregated_messages` are stacked to create a sequence,\n",
        "then processed by a GRU layer. Otherwise, the `node_repesentations` and `aggregated_messages` are added\n",
        "or concatenated, then processed using a FFN.\n",
        "\n",
        "\n",
        "The technique implemented use ideas from [Graph Convolutional Networks](https://arxiv.org/abs/1609.02907),\n",
        "[GraphSage](https://arxiv.org/abs/1706.02216), [Graph Isomorphism Network](https://arxiv.org/abs/1810.00826),\n",
        "[Simple Graph Networks](https://arxiv.org/abs/1902.07153), and\n",
        "[Gated Graph Sequence Neural Networks](https://arxiv.org/abs/1511.05493).\n",
        "Two other key techniques that are not covered are [Graph Attention Networks](https://arxiv.org/abs/1710.10903)\n",
        "and [Message Passing Neural Networks](https://arxiv.org/abs/1704.01212).\n",
        "\"\"\"\n",
        "class GraphConvLayer(layers.Layer):\n",
        "    def __init__(\n",
        "        self,\n",
        "        hidden_units,\n",
        "        dropout_rate=0.2,\n",
        "        aggregation_type=\"mean\",\n",
        "        combination_type=\"concat\",\n",
        "        normalize=False,\n",
        "        *args,\n",
        "        **kwargs,\n",
        "    ):\n",
        "        super().__init__(*args, **kwargs)\n",
        "\n",
        "        self.aggregation_type = aggregation_type\n",
        "        self.combination_type = combination_type\n",
        "        self.normalize = normalize\n",
        "\n",
        "        self.ffn_prepare = create_ffn(hidden_units, dropout_rate)\n",
        "        if self.combination_type == \"gated\":\n",
        "            self.update_fn = layers.GRU(\n",
        "                units=hidden_units,\n",
        "                activation=\"tanh\",\n",
        "                recurrent_activation=\"sigmoid\",\n",
        "                dropout=dropout_rate,\n",
        "                return_state=True,\n",
        "                recurrent_dropout=dropout_rate,\n",
        "            )\n",
        "        else:\n",
        "            self.update_fn = create_ffn(hidden_units, dropout_rate)\n",
        "\n",
        "    def prepare(self, node_repesentations, weights=None):\n",
        "        # node_repesentations shape is [num_edges, embedding_dim].\n",
        "        messages = self.ffn_prepare(node_repesentations)\n",
        "        if weights is not None:\n",
        "            messages = messages * tf.expand_dims(weights, -1)\n",
        "        return messages\n",
        "\n",
        "    def aggregate(self, node_indices, neighbour_messages, node_repesentations):\n",
        "        # node_indices shape is [num_edges].\n",
        "        # neighbour_messages shape: [num_edges, representation_dim].\n",
        "        # node_repesentations shape is [num_nodes, representation_dim]\n",
        "        num_nodes = node_repesentations.shape[0]\n",
        "        if self.aggregation_type == \"sum\":\n",
        "            aggregated_message = tf.math.unsorted_segment_sum(\n",
        "                neighbour_messages, node_indices, num_segments=num_nodes\n",
        "            )\n",
        "        elif self.aggregation_type == \"mean\":\n",
        "            aggregated_message = tf.math.unsorted_segment_mean(\n",
        "                neighbour_messages, node_indices, num_segments=num_nodes\n",
        "            )\n",
        "        elif self.aggregation_type == \"max\":\n",
        "            aggregated_message = tf.math.unsorted_segment_max(\n",
        "                neighbour_messages, node_indices, num_segments=num_nodes\n",
        "            )\n",
        "        else:\n",
        "            raise ValueError(f\"Invalid aggregation type: {self.aggregation_type}.\")\n",
        "\n",
        "        return aggregated_message\n",
        "\n",
        "    def update(self, node_repesentations, aggregated_messages):\n",
        "        # node_repesentations shape is [num_nodes, representation_dim].\n",
        "        # aggregated_messages shape is [num_nodes, representation_dim].\n",
        "        if self.combination_type == \"gru\":\n",
        "            # Create a sequence of two elements for the GRU layer.\n",
        "            h = tf.stack([node_repesentations, aggregated_messages], axis=1)\n",
        "        elif self.combination_type == \"concat\":\n",
        "            # Concatenate the node_repesentations and aggregated_messages.\n",
        "            h = tf.concat([node_repesentations, aggregated_messages], axis=1)\n",
        "        elif self.combination_type == \"add\":\n",
        "            # Add node_repesentations and aggregated_messages.\n",
        "            h = node_repesentations + aggregated_messages\n",
        "        else:\n",
        "            raise ValueError(f\"Invalid combination type: {self.combination_type}.\")\n",
        "        # Apply the processing function.\n",
        "        node_embeddings = self.update_fn(h)\n",
        "        if self.combination_type == \"gru\":\n",
        "            node_embeddings = tf.unstack(node_embeddings, axis=1)[-1]\n",
        "\n",
        "        if self.normalize:\n",
        "            node_embeddings = tf.nn.l2_normalize(node_embeddings, axis=-1)\n",
        "        return node_embeddings\n",
        "\n",
        "    def call(self, inputs):\n",
        "        \"\"\"Process the inputs to produce the node_embeddings.\n",
        "\n",
        "        inputs: a tuple of three elements: node_repesentations, edges, edge_weights.\n",
        "        Returns: node_embeddings of shape [num_nodes, representation_dim].\n",
        "        \"\"\"\n",
        "\n",
        "        node_repesentations, edges, edge_weights = inputs\n",
        "        # Get node_indices (source) and neighbour_indices (target) from edges.\n",
        "        node_indices, neighbour_indices = edges[0], edges[1]\n",
        "        # neighbour_repesentations shape is [num_edges, representation_dim].\n",
        "        neighbour_repesentations = tf.gather(node_repesentations, neighbour_indices)\n",
        "\n",
        "        # Prepare the messages of the neighbours.\n",
        "        neighbour_messages = self.prepare(neighbour_repesentations, edge_weights)\n",
        "        # Aggregate the neighbour messages.\n",
        "        aggregated_messages = self.aggregate(\n",
        "            node_indices, neighbour_messages, node_repesentations\n",
        "        )\n",
        "        # Update the node embedding with the neighbour messages.\n",
        "        return self.update(node_repesentations, aggregated_messages)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "R1q2LwUhH-gV"
      },
      "outputs": [],
      "source": [
        "graph_info = (node_features, edges, edge_weights)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hhb2VlNUIEIt"
      },
      "outputs": [],
      "source": [
        "num_classes = len(class_idc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3if2_AkTIMv4"
      },
      "outputs": [],
      "source": [
        "def create_ffn(hidden_units, dropout_rate, name=None):\n",
        "    fnn_layers = []\n",
        "\n",
        "    for units in hidden_units:\n",
        "        fnn_layers.append(layers.BatchNormalization())\n",
        "        fnn_layers.append(layers.Dropout(dropout_rate))\n",
        "        fnn_layers.append(layers.Dense(units, activation=tf.nn.gelu))\n",
        "\n",
        "    return keras.Sequential(fnn_layers, name=name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "BcQLKk9NIOUC"
      },
      "outputs": [],
      "source": [
        "hidden_units = [64, 64]\n",
        "learning_rate = 0.01\n",
        "dropout_rate = 0.5\n",
        "num_epochs = 300\n",
        "batch_size = 256\n",
        "gnn_model = GNNNodeClassifier(\n",
        "    graph_info=graph_info,\n",
        "    num_classes=num_classes,\n",
        "    hidden_units=hidden_units,\n",
        "    dropout_rate=dropout_rate,\n",
        "    name=\"gnn_model\",\n",
        ")\n",
        "#gnn_model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j93Zzek_IbCh",
        "outputId": "f9db1bc2-d4ab-4aa1-b923-fa33c529d5ca"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Train data shape: (58, 33)\n",
            "Test data shape: (42, 33)\n"
          ]
        }
      ],
      "source": [
        "train_data, test_data = [], []\n",
        "\n",
        "for _, group_data in ecg_table.groupby(\"report_0\"):\n",
        "    # Select around 50% of the dataset for training.\n",
        "    random_selection = np.random.rand(len(group_data.index)) <= 0.5\n",
        "    train_data.append(group_data[random_selection])\n",
        "    test_data.append(group_data[~random_selection])\n",
        "\n",
        "train_data = pd.concat(train_data).sample(frac=1)\n",
        "test_data = pd.concat(test_data).sample(frac=1)\n",
        "\n",
        "print(\"Train data shape:\", train_data.shape)\n",
        "print(\"Test data shape:\", test_data.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HayzgIIoIqyg"
      },
      "outputs": [],
      "source": [
        "x_train = train_data.study_id.to_numpy()\n",
        "y_train = train_data[\"report_0\"]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test = test_data.study_id.to_numpy()\n",
        "y_test = test_data[\"report_0\"]"
      ],
      "metadata": {
        "id": "YfJYZCHn9YBf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "43qwG2znJGik"
      },
      "outputs": [],
      "source": [
        "def run_experiment(model, x_train, y_train, x_test, y_test):\n",
        "    # Compile the model.\n",
        "    model.compile(\n",
        "        optimizer=keras.optimizers.Adam(learning_rate),\n",
        "        loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "        metrics=[keras.metrics.SparseCategoricalAccuracy(name=\"acc\")],\n",
        "    )\n",
        "    # Create an early stopping callback.\n",
        "    early_stopping = keras.callbacks.EarlyStopping(\n",
        "        monitor=\"val_acc\", patience=100, restore_best_weights=True\n",
        "    )\n",
        "    # Fit the model.\n",
        "    history = model.fit(\n",
        "        x=x_train,\n",
        "        y=y_train,\n",
        "        epochs=num_epochs,\n",
        "        batch_size=batch_size,\n",
        "        validation_split=0.15,\n",
        "        callbacks=[early_stopping],\n",
        "    )\n",
        "\n",
        "    return history"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qxWsWITH9fJJ",
        "outputId": "591b92b8-39b1-4f3b-ceea-cb3b0bfe5edc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(58,)\n",
            "(58,)\n",
            "(42,)\n",
            "(42,)\n"
          ]
        }
      ],
      "source": [
        "print(x_train.shape)\n",
        "print(y_train.shape)\n",
        "print(x_test.shape)\n",
        "print(y_test.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "krwTUHFsJIx8",
        "outputId": "92981050-a518-4251-cc27-5e308f60aab5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/300\n",
            "1/1 [==============================] - 10s 10s/step - loss: 3.9810 - acc: 0.0000e+00 - val_loss: 28.8400 - val_acc: 0.0000e+00\n",
            "Epoch 2/300\n",
            "1/1 [==============================] - 0s 66ms/step - loss: 3.9139 - acc: 0.0408 - val_loss: 20.1806 - val_acc: 0.0000e+00\n",
            "Epoch 3/300\n",
            "1/1 [==============================] - 0s 66ms/step - loss: 3.3638 - acc: 0.1224 - val_loss: 17.1173 - val_acc: 0.0000e+00\n",
            "Epoch 4/300\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 3.1318 - acc: 0.0816 - val_loss: 16.7514 - val_acc: 0.0000e+00\n",
            "Epoch 5/300\n",
            "1/1 [==============================] - 0s 73ms/step - loss: 2.9210 - acc: 0.2449 - val_loss: 16.3380 - val_acc: 0.0000e+00\n",
            "Epoch 6/300\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 2.7871 - acc: 0.2653 - val_loss: 16.6744 - val_acc: 0.2222\n",
            "Epoch 7/300\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 2.5225 - acc: 0.3673 - val_loss: 17.7429 - val_acc: 0.2222\n",
            "Epoch 8/300\n",
            "1/1 [==============================] - 0s 71ms/step - loss: 2.4207 - acc: 0.3061 - val_loss: 19.1770 - val_acc: 0.2222\n",
            "Epoch 9/300\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 2.2653 - acc: 0.3673 - val_loss: 19.6666 - val_acc: 0.2222\n",
            "Epoch 10/300\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 2.3612 - acc: 0.2857 - val_loss: 20.3009 - val_acc: 0.2222\n",
            "Epoch 11/300\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 2.3047 - acc: 0.4490 - val_loss: 21.2823 - val_acc: 0.2222\n",
            "Epoch 12/300\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 2.3265 - acc: 0.4694 - val_loss: 22.1503 - val_acc: 0.2222\n",
            "Epoch 13/300\n",
            "1/1 [==============================] - 0s 66ms/step - loss: 2.1456 - acc: 0.5102 - val_loss: 23.4706 - val_acc: 0.2222\n",
            "Epoch 14/300\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 2.1490 - acc: 0.4490 - val_loss: 24.7146 - val_acc: 0.2222\n",
            "Epoch 15/300\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 2.2065 - acc: 0.4898 - val_loss: 25.6693 - val_acc: 0.2222\n",
            "Epoch 16/300\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 2.2799 - acc: 0.4898 - val_loss: 24.9470 - val_acc: 0.2222\n",
            "Epoch 17/300\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 2.3314 - acc: 0.3673 - val_loss: 24.1283 - val_acc: 0.2222\n",
            "Epoch 18/300\n",
            "1/1 [==============================] - 0s 54ms/step - loss: 2.2377 - acc: 0.4082 - val_loss: 23.6722 - val_acc: 0.2222\n",
            "Epoch 19/300\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 2.1276 - acc: 0.5102 - val_loss: 23.2622 - val_acc: 0.2222\n",
            "Epoch 20/300\n",
            "1/1 [==============================] - 0s 66ms/step - loss: 2.0039 - acc: 0.4490 - val_loss: 22.3891 - val_acc: 0.2222\n",
            "Epoch 21/300\n",
            "1/1 [==============================] - 0s 80ms/step - loss: 1.8780 - acc: 0.3878 - val_loss: 21.7258 - val_acc: 0.2222\n",
            "Epoch 22/300\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 2.2060 - acc: 0.2857 - val_loss: 20.8614 - val_acc: 0.2222\n",
            "Epoch 23/300\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 1.9993 - acc: 0.5102 - val_loss: 19.8322 - val_acc: 0.2222\n",
            "Epoch 24/300\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 1.8879 - acc: 0.4898 - val_loss: 18.8895 - val_acc: 0.2222\n",
            "Epoch 25/300\n",
            "1/1 [==============================] - 0s 44ms/step - loss: 1.9687 - acc: 0.4286 - val_loss: 18.3851 - val_acc: 0.2222\n",
            "Epoch 26/300\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 1.7877 - acc: 0.4694 - val_loss: 17.8928 - val_acc: 0.2222\n",
            "Epoch 27/300\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 1.8869 - acc: 0.4694 - val_loss: 17.5819 - val_acc: 0.2222\n",
            "Epoch 28/300\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 1.9280 - acc: 0.3878 - val_loss: 17.3004 - val_acc: 0.2222\n",
            "Epoch 29/300\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 1.7138 - acc: 0.4898 - val_loss: 17.2589 - val_acc: 0.2222\n",
            "Epoch 30/300\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 1.7199 - acc: 0.5102 - val_loss: 16.9878 - val_acc: 0.2222\n",
            "Epoch 31/300\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 1.8999 - acc: 0.4694 - val_loss: 17.0471 - val_acc: 0.2222\n",
            "Epoch 32/300\n",
            "1/1 [==============================] - 0s 67ms/step - loss: 1.6924 - acc: 0.5306 - val_loss: 17.0658 - val_acc: 0.2222\n",
            "Epoch 33/300\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 1.8836 - acc: 0.4898 - val_loss: 16.9586 - val_acc: 0.2222\n",
            "Epoch 34/300\n",
            "1/1 [==============================] - 0s 65ms/step - loss: 1.8270 - acc: 0.5102 - val_loss: 17.0774 - val_acc: 0.2222\n",
            "Epoch 35/300\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 1.7909 - acc: 0.3878 - val_loss: 17.1026 - val_acc: 0.2222\n",
            "Epoch 36/300\n",
            "1/1 [==============================] - 0s 69ms/step - loss: 1.4807 - acc: 0.5918 - val_loss: 17.3630 - val_acc: 0.2222\n",
            "Epoch 37/300\n",
            "1/1 [==============================] - 0s 67ms/step - loss: 1.5689 - acc: 0.5306 - val_loss: 17.3673 - val_acc: 0.2222\n",
            "Epoch 38/300\n",
            "1/1 [==============================] - 0s 55ms/step - loss: 1.6574 - acc: 0.4694 - val_loss: 17.1040 - val_acc: 0.2222\n",
            "Epoch 39/300\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 1.5308 - acc: 0.4898 - val_loss: 16.9331 - val_acc: 0.2222\n",
            "Epoch 40/300\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 1.7840 - acc: 0.4694 - val_loss: 16.5505 - val_acc: 0.2222\n",
            "Epoch 41/300\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 1.4646 - acc: 0.5714 - val_loss: 16.2932 - val_acc: 0.2222\n",
            "Epoch 42/300\n",
            "1/1 [==============================] - 0s 56ms/step - loss: 1.4683 - acc: 0.5510 - val_loss: 16.0530 - val_acc: 0.2222\n",
            "Epoch 43/300\n",
            "1/1 [==============================] - 0s 66ms/step - loss: 1.6270 - acc: 0.5306 - val_loss: 15.7221 - val_acc: 0.2222\n",
            "Epoch 44/300\n",
            "1/1 [==============================] - 0s 71ms/step - loss: 1.8416 - acc: 0.4082 - val_loss: 15.1724 - val_acc: 0.2222\n",
            "Epoch 45/300\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 1.5992 - acc: 0.5306 - val_loss: 14.5633 - val_acc: 0.2222\n",
            "Epoch 46/300\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 1.7348 - acc: 0.5102 - val_loss: 14.3198 - val_acc: 0.2222\n",
            "Epoch 47/300\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 1.8034 - acc: 0.5102 - val_loss: 14.3130 - val_acc: 0.2222\n",
            "Epoch 48/300\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 1.4479 - acc: 0.5306 - val_loss: 14.1177 - val_acc: 0.2222\n",
            "Epoch 49/300\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 1.5549 - acc: 0.6327 - val_loss: 14.1957 - val_acc: 0.2222\n",
            "Epoch 50/300\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 1.6455 - acc: 0.5714 - val_loss: 14.2949 - val_acc: 0.2222\n",
            "Epoch 51/300\n",
            "1/1 [==============================] - 0s 49ms/step - loss: 1.5683 - acc: 0.4694 - val_loss: 14.2574 - val_acc: 0.2222\n",
            "Epoch 52/300\n",
            "1/1 [==============================] - 0s 71ms/step - loss: 1.5014 - acc: 0.4694 - val_loss: 14.1909 - val_acc: 0.2222\n",
            "Epoch 53/300\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 1.4844 - acc: 0.5918 - val_loss: 13.9418 - val_acc: 0.2222\n",
            "Epoch 54/300\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 1.3812 - acc: 0.5306 - val_loss: 13.7619 - val_acc: 0.2222\n",
            "Epoch 55/300\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 1.4354 - acc: 0.5714 - val_loss: 13.5189 - val_acc: 0.2222\n",
            "Epoch 56/300\n",
            "1/1 [==============================] - 0s 65ms/step - loss: 1.5228 - acc: 0.5510 - val_loss: 13.1913 - val_acc: 0.2222\n",
            "Epoch 57/300\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 1.6398 - acc: 0.5510 - val_loss: 13.0831 - val_acc: 0.2222\n",
            "Epoch 58/300\n",
            "1/1 [==============================] - 0s 68ms/step - loss: 1.5645 - acc: 0.5510 - val_loss: 12.9113 - val_acc: 0.2222\n",
            "Epoch 59/300\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 1.7305 - acc: 0.5102 - val_loss: 12.5749 - val_acc: 0.2222\n",
            "Epoch 60/300\n",
            "1/1 [==============================] - 0s 65ms/step - loss: 1.3359 - acc: 0.5714 - val_loss: 12.2009 - val_acc: 0.2222\n",
            "Epoch 61/300\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 1.6284 - acc: 0.5510 - val_loss: 11.8901 - val_acc: 0.2222\n",
            "Epoch 62/300\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 1.3635 - acc: 0.5714 - val_loss: 11.7568 - val_acc: 0.2222\n",
            "Epoch 63/300\n",
            "1/1 [==============================] - 0s 53ms/step - loss: 1.5583 - acc: 0.5102 - val_loss: 11.8396 - val_acc: 0.2222\n",
            "Epoch 64/300\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 1.5304 - acc: 0.5102 - val_loss: 11.8358 - val_acc: 0.2222\n",
            "Epoch 65/300\n",
            "1/1 [==============================] - 0s 51ms/step - loss: 1.4496 - acc: 0.5306 - val_loss: 11.7601 - val_acc: 0.2222\n",
            "Epoch 66/300\n",
            "1/1 [==============================] - 0s 69ms/step - loss: 1.4348 - acc: 0.5306 - val_loss: 11.6653 - val_acc: 0.2222\n",
            "Epoch 67/300\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 1.2859 - acc: 0.5918 - val_loss: 11.4267 - val_acc: 0.2222\n",
            "Epoch 68/300\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 1.5626 - acc: 0.5306 - val_loss: 11.1805 - val_acc: 0.2222\n",
            "Epoch 69/300\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 1.3680 - acc: 0.5714 - val_loss: 10.8866 - val_acc: 0.2222\n",
            "Epoch 70/300\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 1.3472 - acc: 0.5306 - val_loss: 10.6154 - val_acc: 0.2222\n",
            "Epoch 71/300\n",
            "1/1 [==============================] - 0s 79ms/step - loss: 1.4362 - acc: 0.6327 - val_loss: 10.3524 - val_acc: 0.2222\n",
            "Epoch 72/300\n",
            "1/1 [==============================] - 0s 47ms/step - loss: 1.2959 - acc: 0.5714 - val_loss: 10.1626 - val_acc: 0.2222\n",
            "Epoch 73/300\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 1.5603 - acc: 0.5714 - val_loss: 10.1018 - val_acc: 0.2222\n",
            "Epoch 74/300\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 1.2536 - acc: 0.5918 - val_loss: 10.0062 - val_acc: 0.2222\n",
            "Epoch 75/300\n",
            "1/1 [==============================] - 0s 48ms/step - loss: 1.1482 - acc: 0.6327 - val_loss: 9.9055 - val_acc: 0.2222\n",
            "Epoch 76/300\n",
            "1/1 [==============================] - 0s 52ms/step - loss: 1.5573 - acc: 0.4694 - val_loss: 9.8841 - val_acc: 0.2222\n",
            "Epoch 77/300\n",
            "1/1 [==============================] - 0s 65ms/step - loss: 1.3710 - acc: 0.5510 - val_loss: 9.8497 - val_acc: 0.2222\n",
            "Epoch 78/300\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 1.2108 - acc: 0.6122 - val_loss: 9.6551 - val_acc: 0.2222\n",
            "Epoch 79/300\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 1.5179 - acc: 0.5510 - val_loss: 9.4457 - val_acc: 0.2222\n",
            "Epoch 80/300\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 1.1700 - acc: 0.6122 - val_loss: 9.3233 - val_acc: 0.2222\n",
            "Epoch 81/300\n",
            "1/1 [==============================] - 0s 57ms/step - loss: 1.4848 - acc: 0.5918 - val_loss: 9.2298 - val_acc: 0.2222\n",
            "Epoch 82/300\n",
            "1/1 [==============================] - 0s 61ms/step - loss: 1.3006 - acc: 0.5306 - val_loss: 9.1695 - val_acc: 0.2222\n",
            "Epoch 83/300\n",
            "1/1 [==============================] - 0s 45ms/step - loss: 1.4717 - acc: 0.4694 - val_loss: 9.2505 - val_acc: 0.2222\n",
            "Epoch 84/300\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 1.1722 - acc: 0.5510 - val_loss: 9.3177 - val_acc: 0.2222\n",
            "Epoch 85/300\n",
            "1/1 [==============================] - 0s 67ms/step - loss: 1.2828 - acc: 0.6531 - val_loss: 9.3729 - val_acc: 0.2222\n",
            "Epoch 86/300\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 1.2967 - acc: 0.6122 - val_loss: 9.3949 - val_acc: 0.2222\n",
            "Epoch 87/300\n",
            "1/1 [==============================] - 0s 66ms/step - loss: 1.4416 - acc: 0.5714 - val_loss: 9.3743 - val_acc: 0.2222\n",
            "Epoch 88/300\n",
            "1/1 [==============================] - 0s 50ms/step - loss: 1.2284 - acc: 0.5714 - val_loss: 9.3744 - val_acc: 0.2222\n",
            "Epoch 89/300\n",
            "1/1 [==============================] - 0s 62ms/step - loss: 1.1911 - acc: 0.6122 - val_loss: 9.4247 - val_acc: 0.2222\n",
            "Epoch 90/300\n",
            "1/1 [==============================] - 0s 66ms/step - loss: 1.2858 - acc: 0.5714 - val_loss: 9.4808 - val_acc: 0.2222\n",
            "Epoch 91/300\n",
            "1/1 [==============================] - 0s 64ms/step - loss: 1.1242 - acc: 0.5510 - val_loss: 9.5382 - val_acc: 0.2222\n",
            "Epoch 92/300\n",
            "1/1 [==============================] - 0s 73ms/step - loss: 1.2876 - acc: 0.5714 - val_loss: 9.6096 - val_acc: 0.2222\n",
            "Epoch 93/300\n",
            "1/1 [==============================] - 0s 46ms/step - loss: 1.4892 - acc: 0.5102 - val_loss: 9.6367 - val_acc: 0.2222\n",
            "Epoch 94/300\n",
            "1/1 [==============================] - 0s 65ms/step - loss: 1.4849 - acc: 0.4286 - val_loss: 9.6795 - val_acc: 0.2222\n",
            "Epoch 95/300\n",
            "1/1 [==============================] - 0s 81ms/step - loss: 1.3163 - acc: 0.5306 - val_loss: 9.7319 - val_acc: 0.2222\n",
            "Epoch 96/300\n",
            "1/1 [==============================] - 0s 70ms/step - loss: 1.2949 - acc: 0.5306 - val_loss: 9.8134 - val_acc: 0.2222\n",
            "Epoch 97/300\n",
            "1/1 [==============================] - 0s 91ms/step - loss: 1.1609 - acc: 0.4898 - val_loss: 9.8708 - val_acc: 0.2222\n",
            "Epoch 98/300\n",
            "1/1 [==============================] - 0s 94ms/step - loss: 1.2670 - acc: 0.6531 - val_loss: 9.9159 - val_acc: 0.2222\n",
            "Epoch 99/300\n",
            "1/1 [==============================] - 0s 63ms/step - loss: 1.5135 - acc: 0.5102 - val_loss: 9.9084 - val_acc: 0.2222\n",
            "Epoch 100/300\n",
            "1/1 [==============================] - 0s 87ms/step - loss: 1.3619 - acc: 0.5306 - val_loss: 9.8694 - val_acc: 0.2222\n",
            "Epoch 101/300\n",
            "1/1 [==============================] - 0s 94ms/step - loss: 1.3638 - acc: 0.5918 - val_loss: 9.8549 - val_acc: 0.2222\n",
            "Epoch 102/300\n",
            "1/1 [==============================] - 0s 84ms/step - loss: 1.1651 - acc: 0.5918 - val_loss: 9.8565 - val_acc: 0.2222\n",
            "Epoch 103/300\n",
            "1/1 [==============================] - 0s 85ms/step - loss: 1.0865 - acc: 0.6327 - val_loss: 9.9212 - val_acc: 0.2222\n",
            "Epoch 104/300\n",
            "1/1 [==============================] - 0s 88ms/step - loss: 1.0963 - acc: 0.6531 - val_loss: 9.9524 - val_acc: 0.2222\n",
            "Epoch 105/300\n",
            "1/1 [==============================] - 0s 82ms/step - loss: 1.3420 - acc: 0.5306 - val_loss: 9.8844 - val_acc: 0.2222\n",
            "Epoch 106/300\n",
            "1/1 [==============================] - 0s 107ms/step - loss: 1.3317 - acc: 0.5918 - val_loss: 9.8388 - val_acc: 0.2222\n"
          ]
        }
      ],
      "source": [
        "history = run_experiment(gnn_model, x_train, y_train, x_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "Vd5ll1V3Zgkr",
        "outputId": "5a6c4243-5f61-45f5-e35c-4207d6adeba6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Loss')"
            ]
          },
          "metadata": {},
          "execution_count": 59
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
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          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "fig, ax1 = plt.subplots(1, figsize=(15, 5))\n",
        "\n",
        "ax1.plot(history.history[\"loss\"])\n",
        "ax1.plot(history.history[\"val_loss\"])\n",
        "ax1.legend([\"train\", \"test\"], loc=\"upper right\")\n",
        "ax1.set_xlabel(\"Epochs\")\n",
        "ax1.set_ylabel(\"Loss\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 397
        },
        "id": "XEw4hS7PZ4yS",
        "outputId": "76d87fd6-ec4c-425b-b088-af3cabd02feb"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "fig, ax2 = plt.subplots(1, figsize=(15, 5))\n",
        "ax2.plot(history.history[\"acc\"])\n",
        "ax2.plot(history.history[\"val_acc\"])\n",
        "ax2.legend([\"train\", \"test\"], loc=\"upper right\")\n",
        "ax2.set_xlabel(\"Epochs\")\n",
        "ax2.set_ylabel(\"Accuracy\")\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}