10 Graph Neural Networks for Note Classification With DGL.ipynb

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      "provenance": [],
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      "machine_shape": "hm"
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
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    "accelerator": "GPU"
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      "cell_type": "markdown",
      "source": [
        "# Graph Neural Networks\n",
        "\n",
        "\\\n",
        "***Graph Neural Networks*** (GNNs) are a class of deep learning methods designed to perform inference on data described by graphs. GNNs are neural networks that can be directly applied to graphs, and provide an easy way to do node-level, edge-level, and graph-level prediction tasks.\n",
        "\n",
        "\\\n",
        "***Deep Graph Library*** (DGL) is a Python package built for easy implementation of graph neural network model family, on top of existing DL frameworks (currently supporting PyTorch, MXNet and TensorFlow).\n",
        "\n",
        "\\\n",
        "In this tutorial, we will learn about how to use DGL to classify node types using the famous Zachary's karate club data.\n",
        "\n",
        "\\\n",
        "Reading:\n",
        "[A Blitz Introduction to DGL](https://docs.dgl.ai/tutorials/blitz/index.html)"
      ],
      "metadata": {
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      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Zachary's Karate Club\n",
        "\n",
        "\\\n",
        "Zachary's karate club is a social network of a university karate club, described in the paper \"An Information Flow Model for Conflict and Fission in Small Groups\" by Wayne W. Zachary.\n",
        "\n",
        "\\\n",
        "A social network of a karate club was studied by Wayne W. Zachary for a period of three years from 1970 to 1972. The network captures 34 members of a karate club, documenting links between pairs of members who interacted outside the club. During the study a conflict arose between the administrator \"John A\" and instructor \"Mr. Hi\" (pseudonyms), which led to the split of the club into two. Half of the members formed a new club around Mr. Hi; members from the other part found a new instructor or gave up karate. Based on collected data Zachary correctly assigned all but one member of the club to the groups they actually joined after the split.\n",
        "\n",
        "\\\n",
        "Source: [Wikipedia page](https://en.wikipedia.org/wiki/Zachary%27s_karate_club)"
      ],
      "metadata": {
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    },
    {
      "cell_type": "markdown",
      "source": [
        "***A network representation of social relationships among the 34 individuals in the karate club studied by Zachary***\n",
        "\n",
        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)"
      ],
      "metadata": {
        "id": "dNgcE9ZG2oTM"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h7BhAZ6d0A6b",
        "outputId": "6d37bf84-1d80-4817-e551-723814155079"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('./interactions.csv', <http.client.HTTPMessage at 0x7f442f52d550>)"
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "# Read the dataset csv files, including the member (node) data and the interation (edge) data\n",
        "import urllib.request\n",
        "import pandas as pd\n",
        "urllib.request.urlretrieve(\n",
        "  'https://data.dgl.ai/tutorial/dataset/members.csv', './members.csv')\n",
        "urllib.request.urlretrieve(\n",
        "  'https://data.dgl.ai/tutorial/dataset/interactions.csv', './interactions.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* This tutorial treats the members as nodes and interactions as edges.\n",
        "\n",
        "* It takes age as a numeric feature of the nodes, affiliated club as the label of the nodes, and edge weight as a numeric feature of the edges."
      ],
      "metadata": {
        "id": "bMZgqpt5Qr0z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Member (node) data\n",
        "members = pd.read_csv('./members.csv')\n",
        "print(len(members))\n",
        "members.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "X042S9Dp4D8J",
        "outputId": "42b1fe2b-39a5-43d9-e624-4f8b77021df7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "34\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Id    Club  Age\n",
              "0   0  Mr. Hi   44\n",
              "1   1  Mr. Hi   37\n",
              "2   2  Mr. Hi   37\n",
              "3   3  Mr. Hi   40\n",
              "4   4  Mr. Hi   30"
            ],
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              "      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",
              "    [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-bff658d4-b051-422f-bcca-f96bc48d44d2 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-bff658d4-b051-422f-bcca-f96bc48d44d2');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make a numerical column for club memership for plotting by networkx\n",
        "# 0 - Mr. Hi; 1 - Officer\n",
        "members['Club_num'] = members['Club'].replace(['Mr. Hi', 'Officer'], [0, 1])"
      ],
      "metadata": {
        "id": "65PmmEylxL-T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Internation (edge) data\n",
        "interactions = pd.read_csv('./interactions.csv')\n",
        "print(len(interactions))\n",
        "interactions.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "a11K0zo74FWh",
        "outputId": "9d0a0b2d-c1c6-4536-fe14-5cadeb7fe57c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "156\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Src  Dst    Weight\n",
              "0    0    1  0.043591\n",
              "1    0    2  0.282119\n",
              "2    0    3  0.370293\n",
              "3    0    4  0.730570\n",
              "4    0    5  0.821187"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-71d00290-5ae0-468d-a3bb-2a53ad7e063f\">\n",
              "    <div 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>Src</th>\n",
              "      <th>Dst</th>\n",
              "      <th>Weight</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.043591</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.282119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0.370293</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0.730570</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>0.821187</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-71d00290-5ae0-468d-a3bb-2a53ad7e063f')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-71d00290-5ae0-468d-a3bb-2a53ad7e063f 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-71d00290-5ae0-468d-a3bb-2a53ad7e063f');\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",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Use networkx to visualize the network\n",
        "import networkx as nx\n",
        "\n",
        "# Create a networkx graph\n",
        "graph_nx = nx.from_pandas_edgelist(\n",
        "  df = interactions, \n",
        "  source = 'Src',\n",
        "  target = 'Dst', \n",
        "  edge_attr = ['Weight'])\n",
        "\n",
        "# Set node attributes\n",
        "nodes_attr = members.set_index('Id').to_dict(orient = 'index')\n",
        "nx.set_node_attributes(graph_nx, nodes_attr)\n",
        "\n",
        "# Plot the network\n",
        "nx.draw(\n",
        "  graph_nx, \n",
        "  # Note: node_color only works with a numerical column\n",
        "  node_color = [graph_nx.nodes[n]['Club_num'] for n in graph_nx.nodes],\n",
        "  with_labels = True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "UDOhaGXMsNKy",
        "outputId": "75994dd8-2987-488e-cfa1-aa8cb42e1cf5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Install DGL that allows the use of GPU\n",
        "!pip install dgl-cu111 --quiet"
      ],
      "metadata": {
        "id": "WtFQxxFHRn1Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from pandas.core.internals.managers import Dtype\n",
        "# Import DGL and other modules\n",
        "import dgl\n",
        "from dgl.data import DGLDataset\n",
        "import torch\n",
        "import os\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# Create a custimzed DGLDataset\n",
        "class KarateClubDataset(DGLDataset):\n",
        "  \n",
        "  # Define the constructor\n",
        "  def __init__(self, train_val_test_split = None):\n",
        "\n",
        "    # If the dataset is a node classification dataset, we will need to assign\n",
        "    # masks indicating whether a node belongs to training, validation, and test set.\n",
        "    # Therefore, we should determine how we want to split the data.\n",
        "    self.train_val_test_split = train_val_test_split\n",
        "    \n",
        "    # Inherit from the parent DGLDataset class\n",
        "    # Note: the argument name is required by the DGLDataset class\n",
        "    super().__init__(name = 'karate_club')\n",
        "    \n",
        "  # Define the process method\n",
        "  def process(self):\n",
        "\n",
        "    # Read the nodes data \n",
        "    nodes_data = pd.read_csv('./members.csv')\n",
        "\n",
        "    # Read the edges data\n",
        "    edges_data = pd.read_csv('./interactions.csv')\n",
        "\n",
        "    # Read the node features and convert them to a pytorch tensor\n",
        "    node_features = torch.from_numpy(nodes_data['Age'].to_numpy())\n",
        "\n",
        "    # Read the node labels and convert them to a pytorch long tensor\n",
        "    node_labels = (\n",
        "      torch.from_numpy(nodes_data['Club'].astype('category').cat.codes.to_numpy()).\n",
        "      type(torch.LongTensor))\n",
        "\n",
        "    # Read the edge features and convert them to a pytorch tensor\n",
        "    edge_features = torch.from_numpy(edges_data['Weight'].to_numpy())\n",
        "\n",
        "    # Read the sources and convert them to a pytorch tensor\n",
        "    # Note: The sources are the nodes that send the message\n",
        "    edges_src = torch.from_numpy(edges_data['Src'].to_numpy())\n",
        "\n",
        "    # Read the destinations and convert them to a pytorch tensor\n",
        "    # Note: The destinations are the nodes that receive the message\n",
        "    edges_dst = torch.from_numpy(edges_data['Dst'].to_numpy())\n",
        "\n",
        "    # If your dataset is a node classification dataset, you will need to assign\n",
        "    # masks indicating whether a node belongs to training, validation, and test set.\n",
        "    # Total number of nodes\n",
        "    n_nodes = nodes_data.shape[0]\n",
        "    # Number of nodes in the train set\n",
        "    n_train = int(n_nodes * self.train_val_test_split[0])\n",
        "    # Number of nodes in the val set\n",
        "    n_val = int(n_nodes * self.train_val_test_split[1])\n",
        "    # Create masks for train, val, and test sets \n",
        "    train_mask = torch.zeros(n_nodes, dtype = torch.bool)\n",
        "    val_mask = torch.zeros(n_nodes, dtype = torch.bool)\n",
        "    test_mask = torch.zeros(n_nodes, dtype = torch.bool)\n",
        "    train_mask[:n_train] = True\n",
        "    val_mask[n_train:n_train + n_val] = True\n",
        "    test_mask[n_train + n_val:] = True\n",
        "\n",
        "    # Standardize the node features\n",
        "    scaler = StandardScaler()\n",
        "    scaler.fit(node_features[train_mask].reshape(-1, 1))\n",
        "    node_features_standarized = scaler.transform(node_features.reshape(-1, 1))\n",
        "    node_features_standarized = torch.from_numpy(\n",
        "      node_features_standarized).to(torch.float32)\n",
        "\n",
        "    # Define the graph by providing the source nodes, destination nodes, and\n",
        "    # the total number of nodes in the graph\n",
        "    self.graph = dgl.graph(\n",
        "      (edges_src, edges_dst), \n",
        "      num_nodes = nodes_data.shape[0])\n",
        "    \n",
        "    # Assign standardized node features to the graph\n",
        "    # Note: Convert float64 to float32 using float()\n",
        "    self.graph.ndata['feat'] = node_features_standarized\n",
        "\n",
        "    # Assign node labels to the graph\n",
        "    self.graph.ndata['label'] = node_labels\n",
        "\n",
        "    # Assign edge features to the graph\n",
        "    self.graph.edata['weight'] = edge_features\n",
        "\n",
        "    # Add train, val, and test masks to the graph\n",
        "    self.graph.ndata['train_mask'] = train_mask\n",
        "    self.graph.ndata['val_mask'] = val_mask\n",
        "    self.graph.ndata['test_mask'] = test_mask\n",
        " \n",
        "  # Define the getitem method\n",
        "  def __getitem__(self, i):\n",
        "    return self.graph\n",
        "\n",
        "  # Define the len method\n",
        "  def __len__(self):\n",
        "   # Note: we only have a single graph \n",
        "   return 1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nM0TuB70RaHM",
        "outputId": "a32b5b58-f459-4c07-b4d5-553fbca57cf0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using backend: pytorch\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initiate the customized dataset\n",
        "dataset = KarateClubDataset(train_val_test_split = [0.6, 0.2, 0.2])\n",
        "\n",
        "# A DGL Dataset object may contain one or multiple graphs. \n",
        "# This customized dataset only consists of a single graph.\n",
        "graph = dataset[0]\n",
        "print(graph)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-JjQ1fRib7uu",
        "outputId": "c2cdea44-bcc8-4097-8bb8-160ee3ec08c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Graph(num_nodes=34, num_edges=156,\n",
            "      ndata_schemes={'feat': Scheme(shape=(1,), dtype=torch.float32), 'label': Scheme(shape=(), dtype=torch.int64), 'train_mask': Scheme(shape=(), dtype=torch.bool), 'val_mask': Scheme(shape=(), dtype=torch.bool), 'test_mask': Scheme(shape=(), dtype=torch.bool)}\n",
            "      edata_schemes={'weight': Scheme(shape=(), dtype=torch.float64)})\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:39: UserWarning: The given NumPy array is not writable, and PyTorch does not support non-writable tensors. This means writing to this tensor will result in undefined behavior. You may want to copy the array to protect its data or make it writable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  ../torch/csrc/utils/tensor_numpy.cpp:172.)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A DGL graph can store node features and edge features in two dictionary-like attributes called ndata and edata. In the customized dataset, the graph contains the following node features:\n",
        "\n",
        "* train_mask: A boolean tensor indicating whether the node is in the training set.\n",
        "\n",
        "* val_mask: A boolean tensor indicating whether the node is in the validation set.\n",
        "\n",
        "* test_mask: A boolean tensor indicating whether the node is in the test set.\n",
        "\n",
        "* label: The ground truth node category.\n",
        "\n",
        "* feat: The node features.\n",
        "\n",
        "* weight: The edge features."
      ],
      "metadata": {
        "id": "3Axrtx-dgN99"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Print node features, labels, and masks\n",
        "print(graph.ndata)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dLAb0HW3eyTo",
        "outputId": "3d3f5563-719f-47dc-f15c-6109fffd9bc7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'feat': tensor([[ 0.6520],\n",
            "        [-0.6336],\n",
            "        [-0.6336],\n",
            "        [-0.0826],\n",
            "        [-1.9192],\n",
            "        [-1.5519],\n",
            "        [-0.8173],\n",
            "        [ 1.2029],\n",
            "        [-1.0009],\n",
            "        [-0.6336],\n",
            "        [-1.0009],\n",
            "        [ 1.0193],\n",
            "        [ 1.0193],\n",
            "        [ 1.3866],\n",
            "        [ 0.1010],\n",
            "        [ 1.5703],\n",
            "        [ 1.0193],\n",
            "        [-0.4500],\n",
            "        [ 0.6520],\n",
            "        [ 0.1010],\n",
            "        [ 1.3866],\n",
            "        [-1.1846],\n",
            "        [ 0.4683],\n",
            "        [ 0.1010],\n",
            "        [-0.0826],\n",
            "        [-1.1846],\n",
            "        [-0.4500],\n",
            "        [ 0.2847],\n",
            "        [ 0.2847],\n",
            "        [ 0.6520],\n",
            "        [ 1.3866],\n",
            "        [ 0.1010],\n",
            "        [-1.0009],\n",
            "        [ 1.0193]]), 'label': tensor([0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1,\n",
            "        1, 1, 1, 1, 1, 1, 1, 1, 1, 1]), 'train_mask': tensor([ True,  True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
            "         True,  True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
            "        False, False, False, False, False, False, False, False, False, False,\n",
            "        False, False, False, False]), 'val_mask': tensor([False, False, False, False, False, False, False, False, False, False,\n",
            "        False, False, False, False, False, False, False, False, False, False,\n",
            "         True,  True,  True,  True,  True,  True, False, False, False, False,\n",
            "        False, False, False, False]), 'test_mask': tensor([False, False, False, False, False, False, False, False, False, False,\n",
            "        False, False, False, False, False, False, False, False, False, False,\n",
            "        False, False, False, False, False, False,  True,  True,  True,  True,\n",
            "         True,  True,  True,  True])}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Print edge features\n",
        "print(graph.edata)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_lfqb6algnZb",
        "outputId": "43ddc18f-82a5-4709-edfc-4b5a27d3c0e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'weight': tensor([0.0436, 0.2821, 0.3703, 0.7306, 0.8212, 0.0592, 0.1290, 0.5208, 0.9413,\n",
            "        0.8049, 0.5622, 0.1401, 0.3370, 0.0937, 0.1479, 0.5500, 0.5330, 0.8024,\n",
            "        0.5267, 0.2724, 0.8261, 0.8021, 0.5614, 0.7074, 0.4958, 0.0016, 0.7004,\n",
            "        0.9994, 0.8923, 0.5914, 0.7833, 0.0370, 0.3245, 0.2271, 0.4633, 0.2147,\n",
            "        0.2377, 0.7345, 0.1060, 0.0365, 0.1786, 0.0978, 0.6268, 0.5527, 0.9900,\n",
            "        0.9305, 0.9693, 0.6085, 0.2851, 0.7237, 0.8807, 0.3584, 0.4228, 0.5872,\n",
            "        0.4046, 0.4230, 0.2269, 0.2452, 0.9186, 0.7828, 0.6702, 0.9842, 0.6230,\n",
            "        0.8770, 0.7705, 0.0792, 0.2061, 0.1296, 0.2526, 0.0133, 0.3873, 0.9578,\n",
            "        0.3104, 0.6005, 0.9137, 0.9917, 0.6545, 0.9800, 0.0474, 0.6714, 0.7191,\n",
            "        0.5558, 0.3882, 0.2935, 0.2078, 0.0057, 0.2992, 0.8579, 0.1052, 0.1317,\n",
            "        0.8670, 0.2297, 0.1604, 0.0749, 0.8305, 0.5858, 0.6224, 0.5720, 0.8557,\n",
            "        0.0430, 0.4986, 0.3251, 0.3364, 0.1713, 0.9562, 0.3600, 0.0905, 0.3536,\n",
            "        0.0079, 0.9786, 0.3352, 0.2469, 0.6525, 0.4777, 0.2314, 0.6479, 0.2787,\n",
            "        0.2849, 0.4231, 0.0133, 0.4731, 0.7927, 0.9382, 0.3341, 0.9434, 0.5147,\n",
            "        0.8111, 0.5225, 0.4876, 0.0933, 0.1534, 0.0896, 0.0354, 0.9426, 0.7184,\n",
            "        0.5440, 0.1311, 0.3717, 0.3052, 0.3453, 0.0420, 0.6364, 0.5368, 0.2863,\n",
            "        0.8324, 0.7523, 0.2304, 0.7690, 0.0144, 0.1602, 0.3883, 0.2913, 0.8517,\n",
            "        0.9825, 0.3305, 0.2510], dtype=torch.float64)}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# We will build a three-layer Graph Convolutional Network (GCN). \n",
        "# Each layer computes new node representations by aggregating neighbor information.\n",
        "from dgl.nn import GraphConv\n",
        "from torch import nn\n",
        "from torch.nn import functional as F\n",
        "class GCN(nn.Module):\n",
        "  \n",
        "  # Define the constructor\n",
        "  def __init__(self, in_feats, h_feats, num_classes):\n",
        "    \n",
        "    # Inherit from the parent nn.Module\n",
        "    super(GCN, self).__init__()\n",
        "\n",
        "    # Define the first GNN layer\n",
        "    self.conv1 = GraphConv(in_feats, h_feats)\n",
        "\n",
        "    # Define the second GNN layer\n",
        "    self.conv2 = GraphConv(h_feats, h_feats)\n",
        "\n",
        "    # Define the last GNN layer\n",
        "    self.conv3 = GraphConv(h_feats, num_classes)\n",
        "\n",
        "  # Define the forward method\n",
        "  def forward(self, g, in_feat):\n",
        "\n",
        "    # Feed the graph and node features to the first GNN layer\n",
        "    h = self.conv1(g, in_feat)\n",
        "    # Apply the Relu activition function\n",
        "    h = F.relu(h)\n",
        "    # Feed to the second CNN layer\n",
        "    h = self.conv2(g, h)\n",
        "    # Apply the Relu activition function\n",
        "    h = F.relu(h)\n",
        "    # Feed to the third CNN layer\n",
        "    h = self.conv3(g, h)\n",
        "    return h"
      ],
      "metadata": {
        "id": "YyBZDzQrg-Pq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the model with given dimensions\n",
        "model = GCN(\n",
        "  # Input dimension, namely the number of node features\n",
        "  in_feats = graph.ndata['feat'].shape[1], \n",
        "  # Output dimension\n",
        "  h_feats = 16, \n",
        "  # Number of node classes\n",
        "  num_classes = 2)\n",
        "print(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VXFgjpD9l7Gv",
        "outputId": "8cee01fa-0a2a-44db-d5a6-96f1e3a740a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GCN(\n",
            "  (conv1): GraphConv(in=1, out=16, normalization=both, activation=None)\n",
            "  (conv2): GraphConv(in=16, out=16, normalization=both, activation=None)\n",
            "  (conv3): GraphConv(in=16, out=2, normalization=both, activation=None)\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the trainer\n",
        "def train(graph, model, epochs):\n",
        "\n",
        "  # Define the optimizer\n",
        "  optimizer = torch.optim.Adam(model.parameters(), lr = 0.01)\n",
        "  \n",
        "  # Keep track of validation and test accuracy\n",
        "  best_val_acc = 0\n",
        "  best_test_acc = 0\n",
        "\n",
        "  # Get node features, labels, and train, val, and test masks from the graph dataset\n",
        "  features = graph.ndata['feat']\n",
        "  labels = graph.ndata['label']\n",
        "  train_mask = graph.ndata['train_mask']\n",
        "  val_mask = graph.ndata['val_mask']\n",
        "  test_mask = graph.ndata['test_mask']\n",
        "  \n",
        "  # Define the training loop\n",
        "  for epoch in range(1, epochs + 1):\n",
        "    \n",
        "    # Forward pass\n",
        "    logits = model(graph, features)\n",
        "\n",
        "    # Compute prediction\n",
        "    pred = logits.argmax(axis = 1)\n",
        "\n",
        "    # Compute cross-entropy loss\n",
        "    # Note: we should only compute the losses of the nodes in the train set.\n",
        "    loss = F.cross_entropy(logits[train_mask], labels[train_mask])\n",
        "\n",
        "    # Compute accuracy on train, val, and test test\n",
        "    train_acc = (pred[train_mask] == labels[train_mask]).float().mean()\n",
        "    val_acc = (pred[val_mask] == labels[val_mask]).float().mean()\n",
        "    test_acc = (pred[test_mask] == labels[test_mask]).float().mean()\n",
        "\n",
        "    # Save the best val accuracy and the corresponding test accuracy.\n",
        "    if best_val_acc < val_acc:\n",
        "      best_val_acc = val_acc\n",
        "      best_test_acc = test_acc\n",
        "\n",
        "    # Zero out gradients\n",
        "    optimizer.zero_grad()\n",
        "    \n",
        "    # Backpropogation\n",
        "    loss.backward()\n",
        "    \n",
        "    # Update parameters using gradients\n",
        "    optimizer.step()\n",
        "\n",
        "    # Report results over epochs\n",
        "    if epoch % 10 == 0:\n",
        "      print(\n",
        "        'In epoch {}, loss: {:.3f}, val acc: {:.3f}, test acc: {:.3f}'.format(\n",
        "        epoch, loss, val_acc, best_val_acc, test_acc, best_test_acc))"
      ],
      "metadata": {
        "id": "ReleI0jsmgbd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Use GPU\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
      ],
      "metadata": {
        "id": "WaKKsAVnpHKY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Move graph and model to GPU\n",
        "graph = graph.to(device)\n",
        "model = model.to(device)"
      ],
      "metadata": {
        "id": "3OnTQqd_pIHX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Initiate training\n",
        "# Note: Possibly due to the small sample size, the training results are unstable\n",
        "train(graph, model, epochs = 100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rdbqo--BoPtC",
        "outputId": "020441da-7198-4f38-a186-be029f792b99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "In epoch 10, loss: 0.000, val acc: 1.000, test acc: 1.000\n",
            "In epoch 20, loss: 0.000, val acc: 1.000, test acc: 1.000\n",
            "In epoch 30, loss: 0.000, val acc: 1.000, test acc: 1.000\n",
            "In epoch 40, loss: 0.000, val acc: 1.000, test acc: 1.000\n",
            "In epoch 50, loss: 0.000, val acc: 1.000, test acc: 1.000\n",
            "In epoch 60, loss: 0.000, val acc: 1.000, test acc: 1.000\n",
            "In epoch 70, loss: 0.000, val acc: 1.000, test acc: 1.000\n",
            "In epoch 80, loss: 0.000, val acc: 1.000, test acc: 1.000\n",
            "In epoch 90, loss: 0.000, val acc: 1.000, test acc: 1.000\n",
            "In epoch 100, loss: 0.000, val acc: 1.000, test acc: 1.000\n"
          ]
        }
      ]
    }
  ]
}