GraphNodeClassification.ipynb

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            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "zip is already the newest version (3.0-12build2).\n",
            "unzip is already the newest version (6.0-26ubuntu3.2).\n",
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            "  inflating: stellargraph-master/tests/test_aaa_on_gpu.py  \n",
            "  inflating: stellargraph-master/tests/test_calibration.py  \n",
            "  inflating: stellargraph-master/tests/test_ensemble.py  \n",
            "  inflating: stellargraph-master/tests/test_losses.py  \n",
            "  inflating: stellargraph-master/tests/test_random.py  \n",
            "   creating: stellargraph-master/tests/test_utils/\n",
            "  inflating: stellargraph-master/tests/test_utils/__init__.py  \n",
            "  inflating: stellargraph-master/tests/test_utils/alloc.py  \n",
            "  inflating: stellargraph-master/tests/test_utils/graphs.py  \n",
            "   creating: stellargraph-master/tests/utils/\n",
            "  inflating: stellargraph-master/tests/utils/test_hyperbolic.py  \n",
            "  inflating: stellargraph-master/tests/utils/test_version_validation.py  \n",
            "finishing deferred symbolic links:\n",
            "  stellargraph-master/docs/README.md -> ../README.md\n",
            "  stellargraph-master/docs/demos/graph-classification/dgcnn_architecture.png -> ../../../demos/graph-classification/dgcnn_architecture.png\n",
            "  stellargraph-master/docs/demos/graph-classification/graph_classification_architecture.png -> ../../../demos/graph-classification/graph_classification_architecture.png\n",
            "  stellargraph-master/docs/demos/node-classification/Cora-features.png -> ../../../demos/node-classification/Cora-features.png\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install chardet"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-04-19T03:05:28.948150Z",
          "iopub.execute_input": "2023-04-19T03:05:28.948909Z",
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        },
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        "outputId": "0ea18df5-ac49-48eb-cb5c-252150cfbaa5"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: chardet in /usr/local/lib/python3.10/dist-packages (5.2.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import sys\n",
        "sys.path.insert(0, \"/content/sample_data/stellargraph-master\")\n",
        "\n",
        "import stellargraph as sg"
      ],
      "metadata": {
        "id": "ejDT3Y2kiBrW"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import os\n",
        "\n",
        "import stellargraph as sg\n",
        "from stellargraph.mapper import FullBatchNodeGenerator\n",
        "from stellargraph.layer import GCN, GraphSAGE, GAT\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "from tensorflow.keras import layers, optimizers, losses, metrics, Model\n",
        "from sklearn import preprocessing, model_selection\n",
        "from IPython.display import display, HTML\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-04-19T05:28:26.796012Z",
          "iopub.execute_input": "2023-04-19T05:28:26.797052Z",
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          "shell.execute_reply": "2023-04-19T05:28:26.807178Z"
        },
        "trusted": true,
        "id": "vZ2Te793bkvx"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importing the data"
      ],
      "metadata": {
        "id": "3GWZQtvzbkvx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = sg.datasets.Cora()\n",
        "display(HTML(dataset.description))\n",
        "G, node_subjects = dataset.load()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-04-19T03:05:54.878384Z",
          "iopub.execute_input": "2023-04-19T03:05:54.879081Z",
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        "trusted": true,
        "id": "nqvfezyLbkvx",
        "outputId": "24722d4f-082c-4f03-dc14-e06f21d1f827",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "The Cora dataset consists of 2708 scientific publications classified into one of seven classes. The citation network consists of 5429 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 1433 unique words."
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(G.info())"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-04-19T03:05:58.370112Z",
          "iopub.execute_input": "2023-04-19T03:05:58.370803Z",
          "iopub.status.idle": "2023-04-19T03:05:58.402140Z",
          "shell.execute_reply.started": "2023-04-19T03:05:58.370767Z",
          "shell.execute_reply": "2023-04-19T03:05:58.401088Z"
        },
        "trusted": true,
        "id": "XL9go5NJbkvy",
        "outputId": "b43a38ff-5be9-4111-a6b5-594b5ec9194e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "StellarGraph: Undirected multigraph\n",
            " Nodes: 2708, Edges: 5429\n",
            "\n",
            " Node types:\n",
            "  paper: [2708]\n",
            "    Features: float32 vector, length 1433\n",
            "    Edge types: paper-cites->paper\n",
            "\n",
            " Edge types:\n",
            "    paper-cites->paper: [5429]\n",
            "        Weights: all 1 (default)\n",
            "        Features: none\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "node_subjects.value_counts().to_frame()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-04-19T03:06:00.395931Z",
          "iopub.execute_input": "2023-04-19T03:06:00.396507Z",
          "iopub.status.idle": "2023-04-19T03:06:00.411353Z",
          "shell.execute_reply.started": "2023-04-19T03:06:00.396470Z",
          "shell.execute_reply": "2023-04-19T03:06:00.410320Z"
        },
        "trusted": true,
        "id": "Uap5TICgbkvy",
        "outputId": "d37e29c2-6a4b-4f28-f2a9-c15f3162a2ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                        count\n",
              "subject                      \n",
              "Neural_Networks           818\n",
              "Probabilistic_Methods     426\n",
              "Genetic_Algorithms        418\n",
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              "Case_Based                298\n",
              "Reinforcement_Learning    217\n",
              "Rule_Learning             180"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"node_subjects\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"subject\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Neural_Networks\",\n          \"Probabilistic_Methods\",\n          \"Reinforcement_Learning\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 211,\n        \"min\": 180,\n        \"max\": 818,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          818,\n          426,\n          217\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_subjects, test_subjects = model_selection.train_test_split(\n",
        "    node_subjects, train_size = None, test_size=1000, stratify=node_subjects)\n",
        "\n",
        "train_subjects, val_subjects = model_selection.train_test_split(\n",
        "    train_subjects, train_size = None, test_size=500, stratify=train_subjects)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-04-19T03:06:02.920026Z",
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        "id": "MB3tMHQJbkvy"
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      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_subjects.value_counts().to_frame()"
      ],
      "metadata": {
        "execution": {
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              "                        count\n",
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              "    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-95226483-4338-4dfe-a92a-ba85b5d0ac55 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"val_subjects\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"subject\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Neural_Networks\",\n          \"Probabilistic_Methods\",\n          \"Reinforcement_Learning\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39,\n        \"min\": 33,\n        \"max\": 151,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          151,\n          79,\n          40\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_subjects.value_counts().to_frame()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-04-19T03:06:05.732592Z",
          "iopub.execute_input": "2023-04-19T03:06:05.733297Z",
          "iopub.status.idle": "2023-04-19T03:06:05.743315Z",
          "shell.execute_reply.started": "2023-04-19T03:06:05.733258Z",
          "shell.execute_reply": "2023-04-19T03:06:05.742143Z"
        },
        "trusted": true,
        "id": "D93v_Aagbkvz",
        "outputId": "23031b8c-6a1c-4c47-cbf3-3064d2e29cc3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        }
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        count\n",
              "subject                      \n",
              "Neural_Networks           302\n",
              "Probabilistic_Methods     157\n",
              "Genetic_Algorithms        154\n",
              "Theory                    130\n",
              "Case_Based                110\n",
              "Reinforcement_Learning     80\n",
              "Rule_Learning              67"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ff6dd9e9-ff6c-4ece-bf24-a06905d62707\" 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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              "        vertical-align: top;\n",
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              "      <th>count</th>\n",
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              "      <th>Neural_Networks</th>\n",
              "      <td>302</td>\n",
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              "      <th>Probabilistic_Methods</th>\n",
              "      <td>157</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Genetic_Algorithms</th>\n",
              "      <td>154</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Theory</th>\n",
              "      <td>130</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Case_Based</th>\n",
              "      <td>110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Reinforcement_Learning</th>\n",
              "      <td>80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rule_Learning</th>\n",
              "      <td>67</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-ff6dd9e9-ff6c-4ece-bf24-a06905d62707')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\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-ff6dd9e9-ff6c-4ece-bf24-a06905d62707 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-ff6dd9e9-ff6c-4ece-bf24-a06905d62707');\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-b3f10862-e136-442a-9028-e22da663d794\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b3f10862-e136-442a-9028-e22da663d794')\"\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",
              "</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-b3f10862-e136-442a-9028-e22da663d794 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"test_subjects\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"subject\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"Neural_Networks\",\n          \"Probabilistic_Methods\",\n          \"Reinforcement_Learning\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 78,\n        \"min\": 67,\n        \"max\": 302,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          302,\n          157,\n          80\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "target_encoding = preprocessing.LabelBinarizer()\n",
        "\n",
        "train_targets = target_encoding.fit_transform(train_subjects)\n",
        "val_targets = target_encoding.transform(val_subjects)\n",
        "test_targets = target_encoding.transform(test_subjects)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-04-19T03:06:07.571469Z",
          "iopub.execute_input": "2023-04-19T03:06:07.572111Z",
          "iopub.status.idle": "2023-04-19T03:06:07.586459Z",
          "shell.execute_reply.started": "2023-04-19T03:06:07.572073Z",
          "shell.execute_reply": "2023-04-19T03:06:07.585378Z"
        },
        "trusted": true,
        "id": "A5a8xGpkbkvz"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# GCN"
      ],
      "metadata": {
        "id": "oHBnynqHbkvz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "generator = FullBatchNodeGenerator(G, method=\"gcn\")\n",
        "train_gen = generator.flow(train_subjects.index, train_targets)\n",
        "val_gen = generator.flow(val_subjects.index, val_targets)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-04-19T03:06:09.835605Z",
          "iopub.execute_input": "2023-04-19T03:06:09.836324Z",
          "iopub.status.idle": "2023-04-19T03:06:09.857536Z",
          "shell.execute_reply.started": "2023-04-19T03:06:09.836279Z",
          "shell.execute_reply": "2023-04-19T03:06:09.856206Z"
        },
        "trusted": true,
        "id": "fUcHhsUvbkv0",
        "outputId": "f50fa6ed-6ea1-413a-8f50-ab9ca887ad23",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Using GCN (local pooling) filters...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "\n",
        "gcn = GCN(layer_sizes=[256, 128, 32],\n",
        "          activations=[\"relu\", \"relu\", \"relu\"],\n",
        "          generator=generator,\n",
        "          dropout=0.3)\n",
        "x_inp, x_out = gcn.in_out_tensors()\n",
        "print(x_out)\n",
        "predictions = layers.Dense(units=train_targets.shape[1], activation=\"softmax\")(x_out)\n",
        "\n",
        "model = Model(inputs=x_inp, outputs=predictions)\n",
        "model.compile(\n",
        "    optimizer=optimizers.Adam(learning_rate=0.0001),\n",
        "    loss=losses.categorical_crossentropy,\n",
        "    metrics=[\"acc\"],\n",
        ")\n",
        "es_callback = EarlyStopping(monitor=\"val_acc\", patience=50, restore_best_weights=True)\n",
        "model.summary()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-04-18T21:55:59.281509Z",
          "iopub.execute_input": "2023-04-18T21:55:59.281913Z",
          "iopub.status.idle": "2023-04-18T21:55:59.463019Z",
          "shell.execute_reply.started": "2023-04-18T21:55:59.281877Z",
          "shell.execute_reply": "2023-04-18T21:55:59.462030Z"
        },
        "trusted": true,
        "id": "gE_S69Kgbkv0",
        "outputId": "e74f708a-096d-41d2-a844-d35faac82d24",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "KerasTensor(type_spec=TensorSpec(shape=(1, None, 32), dtype=tf.float32, name=None), name='gather_indices/GatherV2:0', description=\"created by layer 'gather_indices'\")\n",
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " input_1 (InputLayer)        [(1, 2708, 1433)]            0         []                            \n",
            "                                                                                                  \n",
            " input_3 (InputLayer)        [(1, None, 2)]               0         []                            \n",
            "                                                                                                  \n",
            " input_4 (InputLayer)        [(1, None)]                  0         []                            \n",
            "                                                                                                  \n",
            " dropout (Dropout)           (1, 2708, 1433)              0         ['input_1[0][0]']             \n",
            "                                                                                                  \n",
            " squeezed_sparse_conversion  (2708, 2708)                 0         ['input_3[0][0]',             \n",
            "  (SqueezedSparseConversion                                          'input_4[0][0]']             \n",
            " )                                                                                                \n",
            "                                                                                                  \n",
            " graph_convolution (GraphCo  (1, None, 256)               367104    ['dropout[0][0]',             \n",
            " nvolution)                                                          'squeezed_sparse_conversion[0\n",
            "                                                                    ][0]']                        \n",
            "                                                                                                  \n",
            " dropout_1 (Dropout)         (1, None, 256)               0         ['graph_convolution[0][0]']   \n",
            "                                                                                                  \n",
            " graph_convolution_1 (Graph  (1, None, 128)               32896     ['dropout_1[0][0]',           \n",
            " Convolution)                                                        'squeezed_sparse_conversion[0\n",
            "                                                                    ][0]']                        \n",
            "                                                                                                  \n",
            " dropout_2 (Dropout)         (1, None, 128)               0         ['graph_convolution_1[0][0]'] \n",
            "                                                                                                  \n",
            " graph_convolution_2 (Graph  (1, None, 32)                4128      ['dropout_2[0][0]',           \n",
            " Convolution)                                                        'squeezed_sparse_conversion[0\n",
            "                                                                    ][0]']                        \n",
            "                                                                                                  \n",
            " input_2 (InputLayer)        [(1, None)]                  0         []                            \n",
            "                                                                                                  \n",
            " gather_indices (GatherIndi  (1, None, 32)                0         ['graph_convolution_2[0][0]', \n",
            " ces)                                                                'input_2[0][0]']             \n",
            "                                                                                                  \n",
            " dense (Dense)               (1, None, 7)                 231       ['gather_indices[0][0]']      \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 404359 (1.54 MB)\n",
            "Trainable params: 404359 (1.54 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# GraphSage Network"
      ],
      "metadata": {
        "id": "mufVMi_xbkv0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from stellargraph.mapper import GraphSAGENodeGenerator\n",
        "batch_size = 50\n",
        "num_samples = [10, 8, 4]\n",
        "\n",
        "generator = GraphSAGENodeGenerator(G, batch_size, num_samples)\n",
        "train_gen = generator.flow(train_subjects.index, train_targets, shuffle=True)\n",
        "val_gen = generator.flow(val_subjects.index, val_targets)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-04-19T03:06:32.984879Z",
          "iopub.execute_input": "2023-04-19T03:06:32.985455Z",
          "iopub.status.idle": "2023-04-19T03:06:33.012333Z",
          "shell.execute_reply.started": "2023-04-19T03:06:32.985402Z",
          "shell.execute_reply": "2023-04-19T03:06:33.010663Z"
        },
        "trusted": true,
        "id": "H9zhd7PPbkv0"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from pathlib import Path\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "working_dir = Path('./')\n",
        "file_name= 'best-rnn-march.hdf5'\n",
        "filepath = os.path.join(working_dir, 'ckpt-cnt', file_name)\n",
        "!mkdir './ckpt-cnt'\n",
        "graphsage_model = GraphSAGE(layer_sizes=[256, 128, 32],\n",
        "                            generator=generator, bias=True,\n",
        "                            dropout=0.5)\n",
        "\n",
        "\n",
        "x_inp, x_out = graphsage_model.in_out_tensors()\n",
        "prediction = layers.Dense(units=train_targets.shape[1], activation=\"softmax\")(x_out)\n",
        "\n",
        "model = Model(inputs=x_inp, outputs=prediction)\n",
        "model.compile(\n",
        "    optimizer=optimizers.Adam(lr=0.0001),\n",
        "    loss=losses.categorical_crossentropy,\n",
        "    metrics=[\"acc\"])\n",
        "\n",
        "es_callback = EarlyStopping(monitor=\"val_acc\", patience=50, restore_best_weights=True)\n",
        "model_checkpoint_callback = ModelCheckpoint(\n",
        "     filepath=filepath,\n",
        "     save_weights_only=True,\n",
        "     monitor='val_acc',\n",
        "     mode='max',\n",
        "     save_best_only=True)\n",
        "model.summary()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-04-19T05:01:23.643971Z",
          "iopub.execute_input": "2023-04-19T05:01:23.645001Z",
          "iopub.status.idle": "2023-04-19T05:01:24.964478Z",
          "shell.execute_reply.started": "2023-04-19T05:01:23.644951Z",
          "shell.execute_reply": "2023-04-19T05:01:24.963562Z"
        },
        "trusted": true,
        "id": "hvO4LlSTbkv0",
        "outputId": "37b77028-3c2b-4ac5-e392-af27f70283d0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/initializers/initializers.py:120: UserWarning: The initializer GlorotUniform is unseeded and being called multiple times, which will return identical values each time (even if the initializer is unseeded). Please update your code to provide a seed to the initializer, or avoid using the same initializer instance more than once.\n",
            "  warnings.warn(\n",
            "WARNING:absl:`lr` is deprecated in Keras optimizer, please use `learning_rate` or use the legacy optimizer, e.g.,tf.keras.optimizers.legacy.Adam.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            " Layer (type)                Output Shape                 Param #   Connected to                  \n",
            "==================================================================================================\n",
            " input_6 (InputLayer)        [(None, 10, 1433)]           0         []                            \n",
            "                                                                                                  \n",
            " input_7 (InputLayer)        [(None, 80, 1433)]           0         []                            \n",
            "                                                                                                  \n",
            " input_8 (InputLayer)        [(None, 320, 1433)]          0         []                            \n",
            "                                                                                                  \n",
            " input_5 (InputLayer)        [(None, 1, 1433)]            0         []                            \n",
            "                                                                                                  \n",
            " reshape (Reshape)           (None, 1, 10, 1433)          0         ['input_6[0][0]']             \n",
            "                                                                                                  \n",
            " reshape_1 (Reshape)         (None, 10, 8, 1433)          0         ['input_7[0][0]']             \n",
            "                                                                                                  \n",
            " reshape_2 (Reshape)         (None, 80, 4, 1433)          0         ['input_8[0][0]']             \n",
            "                                                                                                  \n",
            " dropout_4 (Dropout)         (None, 1, 1433)              0         ['input_5[0][0]']             \n",
            "                                                                                                  \n",
            " dropout_3 (Dropout)         (None, 1, 10, 1433)          0         ['reshape[0][0]']             \n",
            "                                                                                                  \n",
            " dropout_6 (Dropout)         (None, 10, 1433)             0         ['input_6[0][0]']             \n",
            "                                                                                                  \n",
            " dropout_5 (Dropout)         (None, 10, 8, 1433)          0         ['reshape_1[0][0]']           \n",
            "                                                                                                  \n",
            " dropout_8 (Dropout)         (None, 80, 1433)             0         ['input_7[0][0]']             \n",
            "                                                                                                  \n",
            " dropout_7 (Dropout)         (None, 80, 4, 1433)          0         ['reshape_2[0][0]']           \n",
            "                                                                                                  \n",
            " mean_aggregator (MeanAggre  multiple                     367104    ['dropout_4[0][0]',           \n",
            " gator)                                                              'dropout_3[0][0]',           \n",
            "                                                                     'dropout_6[0][0]',           \n",
            "                                                                     'dropout_5[0][0]',           \n",
            "                                                                     'dropout_8[0][0]',           \n",
            "                                                                     'dropout_7[0][0]']           \n",
            "                                                                                                  \n",
            " reshape_3 (Reshape)         (None, 1, 10, 256)           0         ['mean_aggregator[1][0]']     \n",
            "                                                                                                  \n",
            " reshape_4 (Reshape)         (None, 10, 8, 256)           0         ['mean_aggregator[2][0]']     \n",
            "                                                                                                  \n",
            " dropout_10 (Dropout)        (None, 1, 256)               0         ['mean_aggregator[0][0]']     \n",
            "                                                                                                  \n",
            " dropout_9 (Dropout)         (None, 1, 10, 256)           0         ['reshape_3[0][0]']           \n",
            "                                                                                                  \n",
            " dropout_12 (Dropout)        (None, 10, 256)              0         ['mean_aggregator[1][0]']     \n",
            "                                                                                                  \n",
            " dropout_11 (Dropout)        (None, 10, 8, 256)           0         ['reshape_4[0][0]']           \n",
            "                                                                                                  \n",
            " mean_aggregator_1 (MeanAgg  multiple                     32896     ['dropout_10[0][0]',          \n",
            " regator)                                                            'dropout_9[0][0]',           \n",
            "                                                                     'dropout_12[0][0]',          \n",
            "                                                                     'dropout_11[0][0]']          \n",
            "                                                                                                  \n",
            " reshape_5 (Reshape)         (None, 1, 10, 128)           0         ['mean_aggregator_1[1][0]']   \n",
            "                                                                                                  \n",
            " dropout_14 (Dropout)        (None, 1, 128)               0         ['mean_aggregator_1[0][0]']   \n",
            "                                                                                                  \n",
            " dropout_13 (Dropout)        (None, 1, 10, 128)           0         ['reshape_5[0][0]']           \n",
            "                                                                                                  \n",
            " mean_aggregator_2 (MeanAgg  (None, 1, 32)                4128      ['dropout_14[0][0]',          \n",
            " regator)                                                            'dropout_13[0][0]']          \n",
            "                                                                                                  \n",
            " reshape_6 (Reshape)         (None, 32)                   0         ['mean_aggregator_2[0][0]']   \n",
            "                                                                                                  \n",
            " lambda_1 (Lambda)           (None, 32)                   0         ['reshape_6[0][0]']           \n",
            "                                                                                                  \n",
            " dense_1 (Dense)             (None, 7)                    231       ['lambda_1[0][0]']            \n",
            "                                                                                                  \n",
            "==================================================================================================\n",
            "Total params: 404359 (1.54 MB)\n",
            "Trainable params: 404359 (1.54 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "__________________________________________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(\n",
        "    train_gen,\n",
        "    epochs=300,\n",
        "    validation_data=val_gen,\n",
        "    verbose=2,\n",
        "    shuffle=False,  # this should be False, since shuffling data means shuffling the whole graph\n",
        "    callbacks=[es_callback, model_checkpoint_callback],\n",
        ")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-04-19T05:01:28.320671Z",
          "iopub.execute_input": "2023-04-19T05:01:28.321057Z",
          "iopub.status.idle": "2023-04-19T05:10:05.134771Z",
          "shell.execute_reply.started": "2023-04-19T05:01:28.321020Z",
          "shell.execute_reply": "2023-04-19T05:10:05.133755Z"
        },
        "trusted": true,
        "id": "aPnp51A5bkv1",
        "outputId": "661b852a-ab0d-496e-b8bc-e4678b859e45",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/300\n",
            "25/25 - 29s - loss: 1.7728 - acc: 0.3195 - val_loss: 1.5159 - val_acc: 0.6800 - 29s/epoch - 1s/step\n",
            "Epoch 2/300\n",
            "25/25 - 11s - loss: 1.4636 - acc: 0.7276 - val_loss: 1.2531 - val_acc: 0.8060 - 11s/epoch - 452ms/step\n",
            "Epoch 3/300\n",
            "25/25 - 11s - loss: 1.2108 - acc: 0.8709 - val_loss: 1.1202 - val_acc: 0.8080 - 11s/epoch - 455ms/step\n",
            "Epoch 4/300\n",
            "25/25 - 11s - loss: 1.0530 - acc: 0.8849 - val_loss: 1.0215 - val_acc: 0.8240 - 11s/epoch - 453ms/step\n",
            "Epoch 5/300\n",
            "25/25 - 9s - loss: 0.9387 - acc: 0.9139 - val_loss: 0.9344 - val_acc: 0.8440 - 9s/epoch - 352ms/step\n",
            "Epoch 6/300\n",
            "25/25 - 9s - loss: 0.8470 - acc: 0.9214 - val_loss: 0.8860 - val_acc: 0.8420 - 9s/epoch - 375ms/step\n",
            "Epoch 7/300\n",
            "25/25 - 10s - loss: 0.7751 - acc: 0.9214 - val_loss: 0.8319 - val_acc: 0.8500 - 10s/epoch - 418ms/step\n",
            "Epoch 8/300\n",
            "25/25 - 9s - loss: 0.7224 - acc: 0.9164 - val_loss: 0.7821 - val_acc: 0.8560 - 9s/epoch - 380ms/step\n",
            "Epoch 9/300\n",
            "25/25 - 9s - loss: 0.6573 - acc: 0.9354 - val_loss: 0.7593 - val_acc: 0.8520 - 9s/epoch - 358ms/step\n",
            "Epoch 10/300\n",
            "25/25 - 9s - loss: 0.6144 - acc: 0.9363 - val_loss: 0.7051 - val_acc: 0.8680 - 9s/epoch - 350ms/step\n",
            "Epoch 11/300\n",
            "25/25 - 9s - loss: 0.5644 - acc: 0.9429 - val_loss: 0.6900 - val_acc: 0.8580 - 9s/epoch - 380ms/step\n",
            "Epoch 12/300\n",
            "25/25 - 9s - loss: 0.5247 - acc: 0.9437 - val_loss: 0.6851 - val_acc: 0.8620 - 9s/epoch - 378ms/step\n",
            "Epoch 13/300\n",
            "25/25 - 11s - loss: 0.4960 - acc: 0.9454 - val_loss: 0.6617 - val_acc: 0.8620 - 11s/epoch - 449ms/step\n",
            "Epoch 14/300\n",
            "25/25 - 9s - loss: 0.4676 - acc: 0.9429 - val_loss: 0.6414 - val_acc: 0.8580 - 9s/epoch - 368ms/step\n",
            "Epoch 15/300\n",
            "25/25 - 9s - loss: 0.4362 - acc: 0.9437 - val_loss: 0.6309 - val_acc: 0.8420 - 9s/epoch - 374ms/step\n",
            "Epoch 16/300\n",
            "25/25 - 10s - loss: 0.4094 - acc: 0.9462 - val_loss: 0.6152 - val_acc: 0.8500 - 10s/epoch - 383ms/step\n",
            "Epoch 17/300\n",
            "25/25 - 9s - loss: 0.3749 - acc: 0.9644 - val_loss: 0.6175 - val_acc: 0.8400 - 9s/epoch - 359ms/step\n",
            "Epoch 18/300\n",
            "25/25 - 9s - loss: 0.3547 - acc: 0.9636 - val_loss: 0.6160 - val_acc: 0.8420 - 9s/epoch - 368ms/step\n",
            "Epoch 19/300\n",
            "25/25 - 9s - loss: 0.3345 - acc: 0.9627 - val_loss: 0.6118 - val_acc: 0.8540 - 9s/epoch - 379ms/step\n",
            "Epoch 20/300\n",
            "25/25 - 9s - loss: 0.3152 - acc: 0.9644 - val_loss: 0.6064 - val_acc: 0.8340 - 9s/epoch - 378ms/step\n",
            "Epoch 21/300\n",
            "25/25 - 9s - loss: 0.2865 - acc: 0.9752 - val_loss: 0.6014 - val_acc: 0.8360 - 9s/epoch - 354ms/step\n",
            "Epoch 22/300\n",
            "25/25 - 9s - loss: 0.2694 - acc: 0.9735 - val_loss: 0.5977 - val_acc: 0.8420 - 9s/epoch - 357ms/step\n",
            "Epoch 23/300\n",
            "25/25 - 9s - loss: 0.2663 - acc: 0.9669 - val_loss: 0.6196 - val_acc: 0.8300 - 9s/epoch - 374ms/step\n",
            "Epoch 24/300\n",
            "25/25 - 9s - loss: 0.2567 - acc: 0.9694 - val_loss: 0.6099 - val_acc: 0.8320 - 9s/epoch - 377ms/step\n",
            "Epoch 25/300\n",
            "25/25 - 9s - loss: 0.2380 - acc: 0.9719 - val_loss: 0.5930 - val_acc: 0.8420 - 9s/epoch - 356ms/step\n",
            "Epoch 26/300\n",
            "25/25 - 9s - loss: 0.2204 - acc: 0.9810 - val_loss: 0.5886 - val_acc: 0.8460 - 9s/epoch - 371ms/step\n",
            "Epoch 27/300\n",
            "25/25 - 9s - loss: 0.2078 - acc: 0.9818 - val_loss: 0.5762 - val_acc: 0.8460 - 9s/epoch - 379ms/step\n",
            "Epoch 28/300\n",
            "25/25 - 10s - loss: 0.1942 - acc: 0.9834 - val_loss: 0.5940 - val_acc: 0.8420 - 10s/epoch - 386ms/step\n",
            "Epoch 29/300\n",
            "25/25 - 9s - loss: 0.2034 - acc: 0.9752 - val_loss: 0.6062 - val_acc: 0.8360 - 9s/epoch - 351ms/step\n",
            "Epoch 30/300\n",
            "25/25 - 9s - loss: 0.1824 - acc: 0.9843 - val_loss: 0.5869 - val_acc: 0.8540 - 9s/epoch - 373ms/step\n",
            "Epoch 31/300\n",
            "25/25 - 10s - loss: 0.1736 - acc: 0.9826 - val_loss: 0.6032 - val_acc: 0.8400 - 10s/epoch - 382ms/step\n",
            "Epoch 32/300\n",
            "25/25 - 9s - loss: 0.1689 - acc: 0.9776 - val_loss: 0.6179 - val_acc: 0.8300 - 9s/epoch - 379ms/step\n",
            "Epoch 33/300\n",
            "25/25 - 9s - loss: 0.1551 - acc: 0.9843 - val_loss: 0.6119 - val_acc: 0.8340 - 9s/epoch - 349ms/step\n",
            "Epoch 34/300\n",
            "25/25 - 9s - loss: 0.1570 - acc: 0.9810 - val_loss: 0.6436 - val_acc: 0.8220 - 9s/epoch - 377ms/step\n",
            "Epoch 35/300\n",
            "25/25 - 9s - loss: 0.1614 - acc: 0.9793 - val_loss: 0.5926 - val_acc: 0.8380 - 9s/epoch - 379ms/step\n",
            "Epoch 36/300\n",
            "25/25 - 11s - loss: 0.1406 - acc: 0.9843 - val_loss: 0.6276 - val_acc: 0.8320 - 11s/epoch - 453ms/step\n",
            "Epoch 37/300\n",
            "25/25 - 11s - loss: 0.1360 - acc: 0.9868 - val_loss: 0.6471 - val_acc: 0.8380 - 11s/epoch - 454ms/step\n",
            "Epoch 38/300\n",
            "25/25 - 11s - loss: 0.1244 - acc: 0.9892 - val_loss: 0.6342 - val_acc: 0.8360 - 11s/epoch - 454ms/step\n",
            "Epoch 39/300\n",
            "25/25 - 9s - loss: 0.1239 - acc: 0.9851 - val_loss: 0.6664 - val_acc: 0.8240 - 9s/epoch - 348ms/step\n",
            "Epoch 40/300\n",
            "25/25 - 9s - loss: 0.1235 - acc: 0.9851 - val_loss: 0.6594 - val_acc: 0.8360 - 9s/epoch - 355ms/step\n",
            "Epoch 41/300\n",
            "25/25 - 10s - loss: 0.1199 - acc: 0.9818 - val_loss: 0.6132 - val_acc: 0.8460 - 10s/epoch - 382ms/step\n",
            "Epoch 42/300\n",
            "25/25 - 10s - loss: 0.1153 - acc: 0.9884 - val_loss: 0.6458 - val_acc: 0.8300 - 10s/epoch - 386ms/step\n",
            "Epoch 43/300\n",
            "25/25 - 9s - loss: 0.1093 - acc: 0.9868 - val_loss: 0.6495 - val_acc: 0.8300 - 9s/epoch - 373ms/step\n",
            "Epoch 44/300\n",
            "25/25 - 9s - loss: 0.1040 - acc: 0.9851 - val_loss: 0.6467 - val_acc: 0.8280 - 9s/epoch - 354ms/step\n",
            "Epoch 45/300\n",
            "25/25 - 12s - loss: 0.1037 - acc: 0.9851 - val_loss: 0.6557 - val_acc: 0.8260 - 12s/epoch - 490ms/step\n",
            "Epoch 46/300\n",
            "25/25 - 9s - loss: 0.0971 - acc: 0.9876 - val_loss: 0.6760 - val_acc: 0.8340 - 9s/epoch - 379ms/step\n",
            "Epoch 47/300\n",
            "25/25 - 9s - loss: 0.0922 - acc: 0.9892 - val_loss: 0.6758 - val_acc: 0.8300 - 9s/epoch - 376ms/step\n",
            "Epoch 48/300\n",
            "25/25 - 11s - loss: 0.0891 - acc: 0.9884 - val_loss: 0.6985 - val_acc: 0.8260 - 11s/epoch - 458ms/step\n",
            "Epoch 49/300\n",
            "25/25 - 9s - loss: 0.0864 - acc: 0.9901 - val_loss: 0.6767 - val_acc: 0.8240 - 9s/epoch - 357ms/step\n",
            "Epoch 50/300\n",
            "25/25 - 12s - loss: 0.0842 - acc: 0.9901 - val_loss: 0.6680 - val_acc: 0.8340 - 12s/epoch - 475ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# GAT"
      ],
      "metadata": {
        "id": "eqiH-7JZbkv1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "generator = FullBatchNodeGenerator(G, method=\"gat\")\n",
        "train_gen = generator.flow(train_subjects.index, train_targets)\n",
        "val_gen = generator.flow(val_subjects.index, val_targets)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-04-19T05:31:02.478788Z",
          "iopub.execute_input": "2023-04-19T05:31:02.479181Z",
          "iopub.status.idle": "2023-04-19T05:31:02.492363Z",
          "shell.execute_reply.started": "2023-04-19T05:31:02.479137Z",
          "shell.execute_reply": "2023-04-19T05:31:02.491201Z"
        },
        "trusted": true,
        "id": "4GieYx-Vbkv1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from pathlib import Path\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "working_dir = Path('./')\n",
        "file_name= 'gat_cora.hdf5'\n",
        "filepath = os.path.join(working_dir, 'ckpt-cnt', file_name)\n",
        "!mkdir './ckpt-cnt'\n",
        "gat = GAT(\n",
        "    layer_sizes=[64, 32, train_targets.shape[1]],\n",
        "    activations=[\"relu\",\"relu\", \"softmax\"],\n",
        "    attn_heads=32,\n",
        "    generator=generator,\n",
        "    in_dropout=0.5,\n",
        "    attn_dropout=0.5,\n",
        "    normalize=None,\n",
        ")\n",
        "\n",
        "\n",
        "x_inp, x_out = gat.in_out_tensors()\n",
        "#prediction = layers.Dense(units=train_targets.shape[1], activation=\"softmax\")(x_out)\n",
        "\n",
        "model = Model(inputs=x_inp, outputs=x_out)\n",
        "model.compile(\n",
        "    optimizer=optimizers.Adam(learning_rate = 0.0001),\n",
        "    loss=losses.categorical_crossentropy,\n",
        "    metrics=[\"acc\"])\n",
        "\n",
        "es_callback = EarlyStopping(monitor=\"val_acc\", patience=50, restore_best_weights=True)\n",
        "model_checkpoint_callback = ModelCheckpoint(\n",
        "     filepath=filepath,\n",
        "     save_weights_only=True,\n",
        "     monitor='val_acc',\n",
        "     mode='max',\n",
        "     save_best_only=True)\n",
        "model.summary()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-04-19T05:41:34.081839Z",
          "iopub.execute_input": "2023-04-19T05:41:34.082597Z",
          "iopub.status.idle": "2023-04-19T05:41:37.003468Z",
          "shell.execute_reply.started": "2023-04-19T05:41:34.082557Z",
          "shell.execute_reply": "2023-04-19T05:41:37.002308Z"
        },
        "trusted": true,
        "id": "kmInUEJ0bkv1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(\n",
        "    train_gen,\n",
        "    epochs=300,\n",
        "    validation_data=val_gen,\n",
        "    verbose=2,\n",
        "    shuffle=False,  # this should be False, since shuffling data means shuffling the whole graph\n",
        "    callbacks=[es_callback, model_checkpoint_callback],\n",
        ")\n",
        "\n",
        "plots = sg.utils.plot_history(history, return_figure=True)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-04-19T05:43:48.534691Z",
          "iopub.execute_input": "2023-04-19T05:43:48.535643Z",
          "iopub.status.idle": "2023-04-19T05:43:56.051085Z",
          "shell.execute_reply.started": "2023-04-19T05:43:48.535602Z",
          "shell.execute_reply": "2023-04-19T05:43:56.050074Z"
        },
        "trusted": true,
        "id": "eF8D-m5Zbkv1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plots.savefig('GAT_plots.png', bbox_inches='tight')"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-04-19T05:44:01.598036Z",
          "iopub.execute_input": "2023-04-19T05:44:01.598757Z",
          "iopub.status.idle": "2023-04-19T05:44:01.786141Z",
          "shell.execute_reply.started": "2023-04-19T05:44:01.598718Z",
          "shell.execute_reply": "2023-04-19T05:44:01.785166Z"
        },
        "trusted": true,
        "id": "IzADx0F2bkv1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.load_weights('/kaggle/working/ckpt-cnt/gat_cora.hdf5')"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-04-19T05:44:04.279344Z",
          "iopub.execute_input": "2023-04-19T05:44:04.280339Z",
          "iopub.status.idle": "2023-04-19T05:44:04.496581Z",
          "shell.execute_reply.started": "2023-04-19T05:44:04.280285Z",
          "shell.execute_reply": "2023-04-19T05:44:04.495564Z"
        },
        "trusted": true,
        "id": "Q4CWU4gTbkv2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_gen = generator.flow(test_subjects.index, test_targets)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-04-19T05:44:06.352709Z",
          "iopub.execute_input": "2023-04-19T05:44:06.353087Z",
          "iopub.status.idle": "2023-04-19T05:44:06.359705Z",
          "shell.execute_reply.started": "2023-04-19T05:44:06.353053Z",
          "shell.execute_reply": "2023-04-19T05:44:06.358553Z"
        },
        "trusted": true,
        "id": "icpGRTeXbkv2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_metrics = model.evaluate(test_gen)\n",
        "print(\"\\nTest Set Metrics:\")\n",
        "for name, val in zip(model.metrics_names, test_metrics):\n",
        "    print(\"\\t{}: {:0.4f}\".format(name, val))"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-04-19T05:44:09.291191Z",
          "iopub.execute_input": "2023-04-19T05:44:09.291568Z",
          "iopub.status.idle": "2023-04-19T05:44:09.487772Z",
          "shell.execute_reply.started": "2023-04-19T05:44:09.291536Z",
          "shell.execute_reply": "2023-04-19T05:44:09.486786Z"
        },
        "trusted": true,
        "id": "70b3X4lRbkv2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Making Node Predictions"
      ],
      "metadata": {
        "id": "lGpCmT05bkv2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "all_nodes = node_subjects.index\n",
        "all_gen = generator.flow(all_nodes)\n",
        "all_predictions = model.predict(all_gen)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-04-19T05:44:46.997163Z",
          "iopub.execute_input": "2023-04-19T05:44:46.997740Z",
          "iopub.status.idle": "2023-04-19T05:44:49.317657Z",
          "shell.execute_reply.started": "2023-04-19T05:44:46.997702Z",
          "shell.execute_reply": "2023-04-19T05:44:49.316918Z"
        },
        "trusted": true,
        "id": "8WH8QC8ebkv2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "node_predictions = target_encoding.inverse_transform(all_predictions.squeeze())"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-04-19T05:44:51.994691Z",
          "iopub.execute_input": "2023-04-19T05:44:51.995502Z",
          "iopub.status.idle": "2023-04-19T05:44:52.000875Z",
          "shell.execute_reply.started": "2023-04-19T05:44:51.995462Z",
          "shell.execute_reply": "2023-04-19T05:44:51.999649Z"
        },
        "trusted": true,
        "id": "-PVzFOphbkv2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame({\"Predicted\": node_predictions, \"True\": node_subjects})\n",
        "df.head(20)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-04-19T05:44:53.976551Z",
          "iopub.execute_input": "2023-04-19T05:44:53.977300Z",
          "iopub.status.idle": "2023-04-19T05:44:53.990676Z",
          "shell.execute_reply.started": "2023-04-19T05:44:53.977250Z",
          "shell.execute_reply": "2023-04-19T05:44:53.989503Z"
        },
        "trusted": true,
        "id": "QzMicx1Xbkv2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "embedding_model = Model(inputs=x_inp, outputs=x_out)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-04-19T05:45:00.239422Z",
          "iopub.execute_input": "2023-04-19T05:45:00.240482Z",
          "iopub.status.idle": "2023-04-19T05:45:00.251990Z",
          "shell.execute_reply.started": "2023-04-19T05:45:00.240419Z",
          "shell.execute_reply": "2023-04-19T05:45:00.250568Z"
        },
        "trusted": true,
        "id": "KhL_k2_Nbkv3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "emb = embedding_model.predict(all_gen)\n",
        "emb.shape"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-04-19T05:45:03.948098Z",
          "iopub.execute_input": "2023-04-19T05:45:03.948817Z",
          "iopub.status.idle": "2023-04-19T05:45:06.274984Z",
          "shell.execute_reply.started": "2023-04-19T05:45:03.948779Z",
          "shell.execute_reply": "2023-04-19T05:45:06.273976Z"
        },
        "trusted": true,
        "id": "xovMndR-bkv3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#GAT\n",
        "emb_layer = next(l for l in model.layers if l.name.startswith(\"graph_attention\"))\n",
        "print(\n",
        "    \"Embedding layer: {}, output shape {}\".format(emb_layer.name, emb_layer.output_shape)\n",
        ")"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-04-19T05:45:28.591612Z",
          "iopub.execute_input": "2023-04-19T05:45:28.591989Z",
          "iopub.status.idle": "2023-04-19T05:45:28.599115Z",
          "shell.execute_reply.started": "2023-04-19T05:45:28.591955Z",
          "shell.execute_reply": "2023-04-19T05:45:28.597707Z"
        },
        "trusted": true,
        "id": "OqJnIibobkv3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "embedding_model = Model(inputs=x_inp, outputs=emb_layer.output)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-04-19T05:45:38.690767Z",
          "iopub.execute_input": "2023-04-19T05:45:38.691180Z",
          "iopub.status.idle": "2023-04-19T05:45:38.701116Z",
          "shell.execute_reply.started": "2023-04-19T05:45:38.691121Z",
          "shell.execute_reply": "2023-04-19T05:45:38.700096Z"
        },
        "trusted": true,
        "id": "hEQrIw-Abkv3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "emb = embedding_model.predict(all_gen)\n",
        "emb.shape"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-04-19T05:45:46.430561Z",
          "iopub.execute_input": "2023-04-19T05:45:46.430931Z",
          "iopub.status.idle": "2023-04-19T05:45:47.617792Z",
          "shell.execute_reply.started": "2023-04-19T05:45:46.430898Z",
          "shell.execute_reply": "2023-04-19T05:45:47.616588Z"
        },
        "trusted": true,
        "id": "NydE_fSibkv3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.decomposition import PCA\n",
        "from sklearn.manifold import TSNE\n",
        "transform = TSNE  # or PCA\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.manifold import TSNE\n",
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-04-19T05:45:59.227227Z",
          "iopub.execute_input": "2023-04-19T05:45:59.228319Z",
          "iopub.status.idle": "2023-04-19T05:45:59.235676Z",
          "shell.execute_reply.started": "2023-04-19T05:45:59.228278Z",
          "shell.execute_reply": "2023-04-19T05:45:59.233850Z"
        },
        "trusted": true,
        "id": "gZKE6z-lbkv8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = emb.squeeze()\n",
        "y = np.argmax(target_encoding.transform(node_subjects), axis=1)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-04-19T05:46:12.399076Z",
          "iopub.execute_input": "2023-04-19T05:46:12.399794Z",
          "iopub.status.idle": "2023-04-19T05:46:12.409176Z",
          "shell.execute_reply.started": "2023-04-19T05:46:12.399755Z",
          "shell.execute_reply": "2023-04-19T05:46:12.407950Z"
        },
        "trusted": true,
        "id": "um4Dmm0sbkv8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if X.shape[1] > 2:\n",
        "    transform = TSNE  # PCA\n",
        "\n",
        "    trans = transform(n_components=2)\n",
        "    emb_transformed = pd.DataFrame(trans.fit_transform(X), index=list(G.nodes()))\n",
        "    emb_transformed[\"label\"] = y\n",
        "else:\n",
        "    emb_transformed = pd.DataFrame(X, index=list(G.nodes()))\n",
        "    emb_transformed = emb_transformed.rename(columns={\"0\": 0, \"1\": 1})\n",
        "    emb_transformed[\"label\"] = y"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-04-19T05:46:21.152065Z",
          "iopub.execute_input": "2023-04-19T05:46:21.152790Z",
          "iopub.status.idle": "2023-04-19T05:46:39.993584Z",
          "shell.execute_reply.started": "2023-04-19T05:46:21.152752Z",
          "shell.execute_reply": "2023-04-19T05:46:39.992657Z"
        },
        "trusted": true,
        "id": "HvFGcbFwbkv8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "alpha = 0.7\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(20, 7))\n",
        "scatter = ax.scatter(\n",
        "    emb_transformed[0],\n",
        "    emb_transformed[1],\n",
        "    c=emb_transformed[\"label\"].astype(\"category\"),\n",
        "    cmap=\"jet\",\n",
        "    alpha=alpha,\n",
        ")\n",
        "ax.set(aspect=\"equal\", xlabel=\"$X_1$\", ylabel=\"$X_2$\")\n",
        "ax.legend(handles = scatter.legend_elements()[0], labels = list(node_subjects.unique()), loc=\"lower left\", title=\"Classes\",bbox_to_anchor = (1.05, 0.6))\n",
        "plt.title(\n",
        "    \"{} visualization of GAT embeddings for cora dataset\".format(transform.__name__)\n",
        ")\n",
        "plt.savefig('GAT_embeddings.png', bbox_inches='tight')\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-04-19T05:47:21.802748Z",
          "iopub.execute_input": "2023-04-19T05:47:21.803345Z",
          "iopub.status.idle": "2023-04-19T05:47:22.508028Z",
          "shell.execute_reply.started": "2023-04-19T05:47:21.803306Z",
          "shell.execute_reply": "2023-04-19T05:47:22.507042Z"
        },
        "trusted": true,
        "id": "9VtRVzyvbkv8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "RMhZMdsvbkv9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "oqxGwat7bkv9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9TBvyGVDbkv9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# X = emb.squeeze(0)\n",
        "# X.shape\n",
        "# trans = transform(n_components=2)\n",
        "# X_reduced = trans.fit_transform(X)\n",
        "# X_reduced.shape"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-04-19T03:26:14.885890Z",
          "iopub.execute_input": "2023-04-19T03:26:14.886297Z",
          "iopub.status.idle": "2023-04-19T03:26:14.893892Z",
          "shell.execute_reply.started": "2023-04-19T03:26:14.886261Z",
          "shell.execute_reply": "2023-04-19T03:26:14.892738Z"
        },
        "trusted": true,
        "id": "1lTmhzakbkv9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = emb\n",
        "y = np.argmax(target_encoding.transform(node_subjects), axis=1)"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-04-19T03:27:35.572095Z",
          "iopub.execute_input": "2023-04-19T03:27:35.572805Z",
          "iopub.status.idle": "2023-04-19T03:27:35.582712Z",
          "shell.execute_reply.started": "2023-04-19T03:27:35.572767Z",
          "shell.execute_reply": "2023-04-19T03:27:35.581479Z"
        },
        "trusted": true,
        "id": "H-eAfy_mbkv9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "if X.shape[1] > 2:\n",
        "    transform = TSNE  # PCA\n",
        "\n",
        "    trans = transform(n_components=2)\n",
        "    emb_transformed = pd.DataFrame(trans.fit_transform(X), index=node_subjects.index)\n",
        "    emb_transformed[\"label\"] = y\n",
        "else:\n",
        "    emb_transformed = pd.DataFrame(X, index=node_subjects.index)\n",
        "    emb_transformed = emb_transformed.rename(columns={\"0\": 0, \"1\": 1})\n",
        "    emb_transformed[\"label\"] = y"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-04-19T03:28:14.243308Z",
          "iopub.execute_input": "2023-04-19T03:28:14.243673Z",
          "iopub.status.idle": "2023-04-19T03:28:29.202690Z",
          "shell.execute_reply.started": "2023-04-19T03:28:14.243640Z",
          "shell.execute_reply": "2023-04-19T03:28:29.201781Z"
        },
        "trusted": true,
        "id": "MLcS-scabkv9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "fig, ax = plt.subplots(figsize=(10, 7))\n",
        "\n",
        "#plt.figure(figsize=(7, 7))\n",
        "scatter = ax.scatter(\n",
        "    X_reduced[:, 0],\n",
        "    X_reduced[:, 1],\n",
        "    c=node_subjects.astype(\"category\").cat.codes,\n",
        "    cmap=\"jet\",\n",
        "    alpha=0.7,\n",
        ")\n",
        "ax.set(\n",
        "    #aspect=\"equal\",\n",
        "    xlabel=\"$X_1$\",\n",
        "    ylabel=\"$X_2$\",\n",
        "    title=f\"{transform.__name__} visualization of GCN embeddings for cora dataset\",\n",
        ")\n",
        "ax.legend(handles = scatter.legend_elements()[0], labels = list(node_subjects.unique()), loc=\"lower left\", title=\"Classes\")\n",
        "# legend1 = ax.legend(ax.scatter.legend_elements(),\n",
        "#  node_subjects.unique()                   loc=\"lower left\", title=\"Classes\")\n",
        "# ax.add_artist(legend1)\n",
        "plt.savefig('GCN_embeddings.png', bbox_inches='tight')\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-04-18T21:59:31.976777Z",
          "iopub.execute_input": "2023-04-18T21:59:31.977511Z",
          "iopub.status.idle": "2023-04-18T21:59:32.679841Z",
          "shell.execute_reply.started": "2023-04-18T21:59:31.977471Z",
          "shell.execute_reply": "2023-04-18T21:59:32.678731Z"
        },
        "trusted": true,
        "id": "sTzOkXiQbkv9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "alpha = 0.7\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(20, 7))\n",
        "scatter = ax.scatter(\n",
        "    emb_transformed[0],\n",
        "    emb_transformed[1],\n",
        "    c=emb_transformed[\"label\"].astype(\"category\"),\n",
        "    cmap=\"jet\",\n",
        "    alpha=alpha,\n",
        ")\n",
        "ax.set(aspect=\"equal\", xlabel=\"$X_1$\", ylabel=\"$X_2$\")\n",
        "ax.legend(handles = scatter.legend_elements()[0], labels = list(node_subjects.unique()), loc=\"lower left\", title=\"Classes\",bbox_to_anchor = (1.05, 0.6))\n",
        "plt.title(\n",
        "    \"{} visualization of GraphSAGE embeddings for cora dataset\".format(transform.__name__)\n",
        ")\n",
        "plt.savefig('GSage_embeddings.png', bbox_inches='tight')\n",
        "plt.show()"
      ],
      "metadata": {
        "execution": {
          "iopub.status.busy": "2023-04-19T04:15:41.519935Z",
          "iopub.execute_input": "2023-04-19T04:15:41.520322Z",
          "iopub.status.idle": "2023-04-19T04:15:42.157001Z",
          "shell.execute_reply.started": "2023-04-19T04:15:41.520289Z",
          "shell.execute_reply": "2023-04-19T04:15:42.156047Z"
        },
        "trusted": true,
        "id": "GFfctycqbkv-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "H1wJQU7Xbkv-"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}