main_update.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.12.0+cpu.html"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QBsDcDrRDVSu",
        "outputId": "008e56a1-a95d-470c-cb13-1e9fd9a9e774"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Looking in links: https://data.pyg.org/whl/torch-1.12.0+cpu.html\n",
            "Requirement already satisfied: torch-scatter in /usr/local/lib/python3.7/dist-packages (2.0.9)\n",
            "Requirement already satisfied: torch-sparse in /usr/local/lib/python3.7/dist-packages (0.6.15)\n",
            "Requirement already satisfied: torch-cluster in /usr/local/lib/python3.7/dist-packages (1.6.0)\n",
            "Requirement already satisfied: torch-spline-conv in /usr/local/lib/python3.7/dist-packages (1.2.1)\n",
            "Requirement already satisfied: torch-geometric in /usr/local/lib/python3.7/dist-packages (2.1.0.post1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from torch-sparse) (1.7.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from torch-geometric) (2.23.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torch-geometric) (1.21.6)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.7/dist-packages (from torch-geometric) (2.11.3)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from torch-geometric) (1.0.2)\n",
            "Requirement already satisfied: pyparsing in /usr/local/lib/python3.7/dist-packages (from torch-geometric) (3.0.9)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from torch-geometric) (4.64.1)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->torch-geometric) (2.0.1)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->torch-geometric) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->torch-geometric) (2022.6.15)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->torch-geometric) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->torch-geometric) (2.10)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->torch-geometric) (3.1.0)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->torch-geometric) (1.1.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fyjicU28DNC4",
        "outputId": "da418b1d-ee88-48b0-a8ef-fd8f08787b1c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data(x=[19717, 500], edge_index=[2, 88648], y=[19717], train_mask=[19717], val_mask=[19717], test_mask=[19717])\n"
          ]
        }
      ],
      "source": [
        "import torch_geometric\n",
        "import torch\n",
        "from torch_geometric.utils import train_test_split_edges\n",
        "\n",
        "data = dataset[0]\n",
        "print(data)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.is_undirected()\n",
        "dataset = dataset.shuffle()\n",
        "dataset = torch_geometric.datasets.Planetoid(root='data', name='PubMed')\n",
        "\n",
        "perm = torch.randperm(len(dataset))\n",
        "dataset = dataset[perm]\n",
        "print(dataset)\n",
        "\n",
        "train = dataset[:int(len(dataset)*0.8)]\n",
        "test = dataset[int(len(dataset)*0.8):]\n",
        "\n",
        "print(dataset.num_node_features)\n",
        "print(dataset.data)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lacyPqxBEO8e",
        "outputId": "cad186dc-3a67-4e7d-a12d-672a7e588358"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PubMed()\n",
            "500\n",
            "Data(x=[19717, 500], edge_index=[2, 88648], y=[19717], train_mask=[19717], val_mask=[19717], test_mask=[19717])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(data.x)\n",
        "print(data.x.shape)\n",
        "print(len(data.x[0]))\n",
        "print(data.edge_index)\n",
        "print(data.edge_attr) ##need to add the vector embeddings as the edge attribuets\n",
        "print(data.y) \n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "id": "gB1AeLyVJaey",
        "outputId": "cd0cdd6f-6e90-4b74-ccdd-bf079cca2eae"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
            "        [0.0000, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
            "        [0.1046, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
            "        ...,\n",
            "        [0.0000, 0.0194, 0.0080,  ..., 0.0000, 0.0000, 0.0000],\n",
            "        [0.1078, 0.0000, 0.0000,  ..., 0.0000, 0.0000, 0.0000],\n",
            "        [0.0000, 0.0266, 0.0000,  ..., 0.0000, 0.0000, 0.0000]])\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-107-11939ea347af>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0medge_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'Tensor' object has no attribute 'label'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from itertools import chain\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "edge_attributes = data.edge_index\n",
        "print(edge_attributes)\n",
        "num_edges = len(list(set(chain(*edge_attributes))))\n",
        "print(num_edges)\n",
        "\n",
        "all_nodes = len(data.x)\n",
        "print(all_nodes)\n",
        "\n",
        "np_table = np.zeros((all_nodes, all_nodes), dtype=int)\n",
        "print(np_table.shape)\n",
        "\n",
        "for idx in range(len(edge_attributes[0])):\n",
        "  idx_1 = edge_attributes[0][idx]\n",
        "  idx_2 = edge_attributes[1][idx]\n",
        "\n",
        "  if np_table[idx_1][idx_2] != 1:\n",
        "    np_table[idx_1][idx_2] = 1\n",
        "\n",
        "# np_table.tofile('adjacency.csv', sep = ',')\n",
        "# b = pd.DataFrame(np.asarray(data.x)).to_csv('features.csv')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VEadQluyJqX9",
        "outputId": "b71ee42b-7825-48b2-8aec-e87cba6430db"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tensor([[    0,     0,     0,  ..., 19714, 19715, 19716],\n",
            "        [ 1378,  1544,  6092,  ..., 12278,  4284, 16030]])\n",
            "177296\n",
            "19717\n",
            "(19717, 19717)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import networkx as nx\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "graph = [(edge_attributes[0][x], edge_attributes[1][x]) for x in range(len(edge_attributes[0]))]\n",
        "nodes = set(chain(*edge_attributes))\n",
        "\n",
        "\n",
        "\n",
        "def draw_graph(graph):\n",
        "  G = nx.Graph()\n",
        "\n",
        "  nodes = set([int(n1) for n1, n2 in graph] + [int(n2) for n1, n2 in graph])\n",
        "\n",
        "  for node in nodes: \n",
        "    G.add_node(node)\n",
        "  \n",
        "  for edge in graph:\n",
        "    G.add_edge(int(edge[0]), int(edge[1]))\n",
        "  \n",
        "    pos = nx.shell_layout(G)\n",
        "    nx.draw(G, pos)\n",
        "\n",
        "  plt.show()\n",
        "  \n",
        "\n",
        "draw_graph(graph)\n"
      ],
      "metadata": {
        "id": "VEt1byRLCrg3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import networkx as nx\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "def draw_graph(graph):\n",
        "\n",
        "    # extract nodes from graph\n",
        "    nodes = set([n1 for n1, n2 in graph] + [n2 for n1, n2 in graph])\n",
        "\n",
        "    # create networkx graph\n",
        "    G=nx.Graph()\n",
        "\n",
        "    # add nodes\n",
        "    for node in nodes:\n",
        "        G.add_node(node)\n",
        "\n",
        "    # add edges\n",
        "    for edge in graph:\n",
        "        G.add_edge(edge[0], edge[1])\n",
        "\n",
        "    # draw graph\n",
        "    pos = nx.shell_layout(G)\n",
        "    nx.draw(G, pos)\n",
        "\n",
        "    # show graph\n",
        "    plt.show()\n",
        "\n",
        "# draw example\n",
        "graph = [(20, 21),(21, 22),(22, 23), (23, 24),(24, 25), (25, 20)]\n",
        "draw_graph(graph)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "id": "M1-plALEFlL0",
        "outputId": "0a22bc4b-f674-4361-9547-2a1c90772ca7"
      },
      "execution_count": 111,
      "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": [
        "import torch\n",
        "\n",
        "data.train_mask = data.val_mask = data.test_mask = data.y = None\n",
        "data = train_test_split_edges(data)\n",
        "\n",
        "class Net(torch.nn.Module):\n",
        "  def __init__ (self):\n",
        "    super(Net, self).__init__()\n",
        "    self.conv1 = torch_geometric.nn.GCNConv(dataset.num_features, 256)\n",
        "    self.conv2 = torch_geometric.nn.GCNConv(256, 128)\n",
        "    self.conv3 = torch_geometric.nn.GCNConv(128, 64)\n",
        "\n",
        "  def encode(self):\n",
        "    x = self.conv1(data.x, data.pos_edge_index)\n",
        "    x = x.relu()\n",
        "    return self.conv2(x, data.pos_edge_index)\n",
        "  \n",
        "  def decode(self, z, pos_edge_index, neg_edge_index):\n",
        "    edge_index = torch.cat([pos_edge_index, neg_edge_index], dim=-1)\n",
        "    logits = (z[edge_index[0]] * z[edge_index[1]]).sum(dim=-1)\n",
        "    return logits\n",
        "\n",
        "  def decode_all(self, z):\n",
        "    prob_adj = z @ z.t()\n",
        "    return (prob_adj > 0).nonzero(as_tuple=False).t()"
      ],
      "metadata": {
        "id": "n_tZ9AArEW7X",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a4de86eb-e6b1-485a-b62a-11c180107986"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch_geometric/deprecation.py:12: UserWarning: 'train_test_split_edges' is deprecated, use 'transforms.RandomLinkSplit' instead\n",
            "  warnings.warn(out)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_auc_score\n",
        "from torch_geometric.utils import negative_sampling\n",
        "import torch.nn.functional as F\n",
        "\n",
        "data.train_mask = data.val_mask = data.test_mask = data.y = None\n",
        "data = train_test_split_edges(data)\n",
        "\n",
        "device = \"cpu\"\n",
        "\n",
        "\n",
        "model, data = Net().to(device), data.to(device)\n",
        "optimizer = torch.optim.Adam(params=model.parameters(), lr=0.01)\n",
        "\n",
        "def get_link_labels(pos_edge_index, neg_edge_index):\n",
        "    # returns a tensor:\n",
        "    # [1,1,1,1,...,0,0,0,0,0,..] with the number of ones is equel to the lenght of pos_edge_index\n",
        "    # and the number of zeros is equal to the length of neg_edge_index\n",
        "    E = pos_edge_index.size(1) + neg_edge_index.size(1)\n",
        "    link_labels = torch.zeros(E, dtype=torch.float, device=device)\n",
        "    link_labels[:pos_edge_index.size(1)] = 1.\n",
        "    return link_labels\n",
        "\n",
        "\n",
        "def train():\n",
        "    model.train()\n",
        "\n",
        "    neg_edge_index = negative_sampling(\n",
        "        edge_index=data.train_pos_edge_index, #positive edges\n",
        "        num_nodes=data.num_nodes, # number of nodes\n",
        "        num_neg_samples=data.train_pos_edge_index.size(1)) # number of neg_sample equal to number of pos_edges\n",
        "\n",
        "    optimizer.zero_grad()\n",
        "    \n",
        "    z = model.encode() #encode\n",
        "    link_logits = model.decode(z, data.train_pos_edge_index, neg_edge_index) # decode\n",
        "    \n",
        "    link_labels = get_link_labels(data.train_pos_edge_index, neg_edge_index)\n",
        "    loss = F.binary_cross_entropy_with_logits(link_logits, link_labels)\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "\n",
        "    return loss\n",
        "\n",
        "\n",
        "@torch.no_grad()\n",
        "def test():\n",
        "    model.eval()\n",
        "    perfs = []\n",
        "    for prefix in [\"val\", \"test\"]:\n",
        "        pos_edge_index = data[f'{prefix}_pos_edge_index']\n",
        "        neg_edge_index = data[f'{prefix}_neg_edge_index']\n",
        "\n",
        "        z = model.encode() # encode train\n",
        "        link_logits = model.decode(z, pos_edge_index, neg_edge_index) # decode test or val\n",
        "        link_probs = link_logits.sigmoid() # apply sigmoid\n",
        "        \n",
        "        link_labels = get_link_labels(pos_edge_index, neg_edge_index) # get link\n",
        "        \n",
        "        perfs.append(roc_auc_score(link_labels.cpu(), link_probs.cpu())) #compute roc_auc score\n",
        "    return perfs\n",
        "\n",
        "\n",
        "  \n",
        "best_val_perf = test_perf = 0\n",
        "for epoch in range(1, 101):\n",
        "    train_loss = train()\n",
        "    val_perf, tmp_test_perf = test()\n",
        "    if val_perf > best_val_perf:\n",
        "        best_val_perf = val_perf\n",
        "        test_perf = tmp_test_perf\n",
        "    log = 'Epoch: {:03d}, Loss: {:.4f}, Val: {:.4f}, Test: {:.4f}'\n",
        "    if epoch % 10 == 0:\n",
        "        print(log.format(epoch, train_loss, best_val_perf, test_perf))\n",
        "\n",
        "\n",
        "z = model.encode()\n",
        "final_edge_index = model.decode_all(z)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "Wu1nLK69VBS9",
        "outputId": "ce3a5cc9-b9ce-48ac-ea62-74a7fc309ed5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-97-786e52fd8351>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_mask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mval_mask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtest_mask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtrain_test_split_edges\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mdevice\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"cpu\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch_geometric/deprecation.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     11\u001b[0m                 f', {details}' if details is not None else '')\n\u001b[1;32m     12\u001b[0m             \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch_geometric/utils/train_test_split_edges.py\u001b[0m in \u001b[0;36mtrain_test_split_edges\u001b[0;34m(data, val_ratio, test_ratio)\u001b[0m\n\u001b[1;32m     39\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m     \u001b[0mnum_nodes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_nodes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m     \u001b[0mrow\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0medge_index\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     42\u001b[0m     \u001b[0medge_attr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0medge_attr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     43\u001b[0m     \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0medge_index\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0medge_attr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: cannot unpack non-iterable NoneType object"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "_YlGfMbEr99d"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}