GraphAutoencoder_.ipynb

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  "metadata": {
    "colab": {
      "name": "GraphAutoencoder .ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "URiQndrL5enj"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "import torch.nn\n",
        "import torch.nn.functional as F\n",
        "\n",
        "import numpy\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "def format_pytorch_version(version):\n",
        "  return version.split('+')[0]\n",
        "\n",
        "TORCH_version = torch.__version__\n",
        "TORCH = format_pytorch_version(TORCH_version)\n",
        "\n",
        "def format_cuda_version(version):\n",
        "  return 'cu' + version.replace('.', '')\n",
        "\n",
        "CUDA_version = torch.version.cuda\n",
        "CUDA = format_cuda_version(CUDA_version)\n",
        "\n",
        "!pip install torch-scatter     -f https://pytorch-geometric.com/whl/torch-{TORCH}+{CUDA}.html\n",
        "!pip install torch-sparse      -f https://pytorch-geometric.com/whl/torch-{TORCH}+{CUDA}.html\n",
        "!pip install torch-cluster     -f https://pytorch-geometric.com/whl/torch-{TORCH}+{CUDA}.html\n",
        "!pip install torch-spline-conv -f https://pytorch-geometric.com/whl/torch-{TORCH}+{CUDA}.html\n",
        "!pip install torch-geometric "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GrGzsj3X5qww",
        "outputId": "af7c2c93-6db8-4133-fc7b-3bf9c70749be"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in links: https://pytorch-geometric.com/whl/torch-1.10.0+cu111.html\n",
            "Collecting torch-scatter\n",
            "  Downloading https://data.pyg.org/whl/torch-1.10.0%2Bcu113/torch_scatter-2.0.9-cp37-cp37m-linux_x86_64.whl (7.9 MB)\n",
            "\u001b[K     |████████████████████████████████| 7.9 MB 5.5 MB/s \n",
            "\u001b[?25hInstalling collected packages: torch-scatter\n",
            "Successfully installed torch-scatter-2.0.9\n",
            "Looking in links: https://pytorch-geometric.com/whl/torch-1.10.0+cu111.html\n",
            "Collecting torch-sparse\n",
            "  Downloading https://data.pyg.org/whl/torch-1.10.0%2Bcu113/torch_sparse-0.6.12-cp37-cp37m-linux_x86_64.whl (3.5 MB)\n",
            "\u001b[K     |████████████████████████████████| 3.5 MB 5.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from torch-sparse) (1.4.1)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.7/dist-packages (from scipy->torch-sparse) (1.21.5)\n",
            "Installing collected packages: torch-sparse\n",
            "Successfully installed torch-sparse-0.6.12\n",
            "Looking in links: https://pytorch-geometric.com/whl/torch-1.10.0+cu111.html\n",
            "Collecting torch-cluster\n",
            "  Downloading https://data.pyg.org/whl/torch-1.10.0%2Bcu113/torch_cluster-1.5.9-cp37-cp37m-linux_x86_64.whl (2.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 2.3 MB 5.1 MB/s \n",
            "\u001b[?25hInstalling collected packages: torch-cluster\n",
            "Successfully installed torch-cluster-1.5.9\n",
            "Looking in links: https://pytorch-geometric.com/whl/torch-1.10.0+cu111.html\n",
            "Collecting torch-spline-conv\n",
            "  Downloading https://data.pyg.org/whl/torch-1.10.0%2Bcu113/torch_spline_conv-1.2.1-cp37-cp37m-linux_x86_64.whl (747 kB)\n",
            "\u001b[K     |████████████████████████████████| 747 kB 5.2 MB/s \n",
            "\u001b[?25hInstalling collected packages: torch-spline-conv\n",
            "Successfully installed torch-spline-conv-1.2.1\n",
            "Collecting torch-geometric\n",
            "  Downloading torch_geometric-2.0.3.tar.gz (370 kB)\n",
            "\u001b[K     |████████████████████████████████| 370 kB 5.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torch-geometric) (1.21.5)\n",
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            "Collecting rdflib\n",
            "  Downloading rdflib-6.1.1-py3-none-any.whl (482 kB)\n",
            "\u001b[K     |████████████████████████████████| 482 kB 41.8 MB/s \n",
            "\u001b[?25hRequirement already satisfied: googledrivedownloader in /usr/local/lib/python3.7/dist-packages (from torch-geometric) (0.4)\n",
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            "Collecting yacs\n",
            "  Downloading yacs-0.1.8-py3-none-any.whl (14 kB)\n",
            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from torch-geometric) (3.13)\n",
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            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas->torch-geometric) (1.15.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from rdflib->torch-geometric) (57.4.0)\n",
            "Collecting isodate\n",
            "  Downloading isodate-0.6.1-py2.py3-none-any.whl (41 kB)\n",
            "\u001b[K     |████████████████████████████████| 41 kB 555 kB/s \n",
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            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->torch-geometric) (1.1.0)\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",
            "Building wheels for collected packages: torch-geometric\n",
            "  Building wheel for torch-geometric (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for torch-geometric: filename=torch_geometric-2.0.3-py3-none-any.whl size=581968 sha256=fcaf514e581bdf98c3e00782f360e175abf638969a5ae8a86b4ca97bb58eb209\n",
            "  Stored in directory: /root/.cache/pip/wheels/c3/2a/58/87ce0508964d4def1aafb92750c4f3ac77038efd1b9a89dcf5\n",
            "Successfully built torch-geometric\n",
            "Installing collected packages: isodate, yacs, rdflib, torch-geometric\n",
            "Successfully installed isodate-0.6.1 rdflib-6.1.1 torch-geometric-2.0.3 yacs-0.1.8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch_geometric \n",
        "from torch_geometric.datasets import Planetoid\n",
        "import torch_geometric.transforms as T\n",
        "from torch_geometric.nn import GCNConv, VGAE, GAE\n",
        "from torch_geometric.utils import train_test_split_edges "
      ],
      "metadata": {
        "id": "Ey3RsPq3M-1w"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = Planetoid(\"\\..\", \"CiteSeer\", transform=T.NormalizeFeatures())\n",
        "dataset.data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "82ht4Z_sNcmP",
        "outputId": "a6bc2e1b-7568-4984-8500-90472a4e9041"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.citeseer.x\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.citeseer.tx\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.citeseer.allx\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.citeseer.y\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.citeseer.ty\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.citeseer.ally\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.citeseer.graph\n",
            "Downloading https://github.com/kimiyoung/planetoid/raw/master/data/ind.citeseer.test.index\n",
            "Processing...\n",
            "Done!\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Data(x=[3327, 3703], edge_index=[2, 9104], y=[3327], train_mask=[3327], val_mask=[3327], test_mask=[3327])"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = dataset[0]\n",
        "data.train_mask = data.val_mask = data.test_mask = None"
      ],
      "metadata": {
        "id": "RpDD0DhfONri"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "type(dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZnfZfsyWB62E",
        "outputId": "6bd6d105-5be8-4e44-f193-4c5c995714d8"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "torch_geometric.datasets.planetoid.Planetoid"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = train_test_split_edges(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lTC67DcMcmbA",
        "outputId": "bc361284-2175-425a-ff90-1d10f10ba526"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch_geometric/deprecation.py:13: UserWarning: 'train_test_split_edges' is deprecated, use 'transforms.RandomLinkSplit' instead\n",
            "  warnings.warn(out)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data\n",
        "#edge index = adjacency matrix, test_neg_edges -> edges in test set that are not in the graph, test_pos_edge_index -> edges in test set that are in graph, x -> feature matrix, y -> labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xYUbQKqxu6md",
        "outputId": "7c887ab1-9fe3-4eaa-deeb-76a488474e06"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Data(x=[3327, 3703], y=[3327], val_pos_edge_index=[2, 227], test_pos_edge_index=[2, 455], train_pos_edge_index=[2, 7740], train_neg_adj_mask=[3327, 3327], val_neg_edge_index=[2, 227], test_neg_edge_index=[2, 455])"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Define encoder\n",
        "#in_channels -> number of features \n",
        "#out_channels -> 2 * out_channels --> because we have 2 convolutional layers. We go from the input features to double of output features and then in 2nd layer we go from double of output features we go to out features (size of embedding we want to produce)\n"
      ],
      "metadata": {
        "id": "56tIGWQjvboM"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "class GCNEncoder(torch.nn.Module):\n",
        "    def __init__(self, in_channels, out_channels):\n",
        "        super(GCNEncoder, self).__init__()\n",
        "        self.conv1 = GCNConv(in_channels, 2 * out_channels, cached=True) # cached only for transductive learning\n",
        "        self.conv2 = GCNConv(2 * out_channels, out_channels, cached=True) # cached only for transductive learning\n",
        "\n",
        "    def forward(self, x, edge_index):\n",
        "        x = self.conv1(x, edge_index).relu()\n",
        "        return self.conv2(x, edge_index)\n"
      ],
      "metadata": {
        "id": "aqC48MM3whcG"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Define the autoencoder"
      ],
      "metadata": {
        "id": "rFSjJ-H-dm0I"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Parameters\n",
        "out_channels = 2 #embedding in 2 dimensions\n",
        "num_features = dataset.num_features\n",
        "epochs = 100\n",
        "\n",
        "#Model\n",
        "model = GAE(GCNEncoder(num_features, out_channels))\n",
        "\n",
        "#Move to GPU, if available\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "model = model.to(device)\n",
        "x = data.x.to(device)\n",
        "train_pos_edge_index = data.train_pos_edge_index.to(device)\n",
        "\n",
        "#Initialize the optimizer\n",
        "optimizer =torch.optim.Adam(model.parameters(), lr = 0.01)\n"
      ],
      "metadata": {
        "id": "8p0yi2LVdqsn"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FtY4uP2rtU9u",
        "outputId": "b7b8ac69-9e3a-4a84-f101-dbd3b77bdb05"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GAE(\n",
              "  (encoder): GCNEncoder(\n",
              "    (conv1): GCNConv(3703, 4)\n",
              "    (conv2): GCNConv(4, 2)\n",
              "  )\n",
              "  (decoder): InnerProductDecoder()\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dir(model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_elOWpXIupq3",
        "outputId": "e95ad488-f741-4f62-d375-bc12ea3ccdc1"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['T_destination',\n",
              " '__annotations__',\n",
              " '__call__',\n",
              " '__class__',\n",
              " '__delattr__',\n",
              " '__dict__',\n",
              " '__dir__',\n",
              " '__doc__',\n",
              " '__eq__',\n",
              " '__format__',\n",
              " '__ge__',\n",
              " '__getattr__',\n",
              " '__getattribute__',\n",
              " '__gt__',\n",
              " '__hash__',\n",
              " '__init__',\n",
              " '__init_subclass__',\n",
              " '__le__',\n",
              " '__lt__',\n",
              " '__module__',\n",
              " '__ne__',\n",
              " '__new__',\n",
              " '__reduce__',\n",
              " '__reduce_ex__',\n",
              " '__repr__',\n",
              " '__setattr__',\n",
              " '__setstate__',\n",
              " '__sizeof__',\n",
              " '__str__',\n",
              " '__subclasshook__',\n",
              " '__weakref__',\n",
              " '_apply',\n",
              " '_backward_hooks',\n",
              " '_buffers',\n",
              " '_call_impl',\n",
              " '_forward_hooks',\n",
              " '_forward_pre_hooks',\n",
              " '_get_backward_hooks',\n",
              " '_get_name',\n",
              " '_is_full_backward_hook',\n",
              " '_load_from_state_dict',\n",
              " '_load_state_dict_pre_hooks',\n",
              " '_maybe_warn_non_full_backward_hook',\n",
              " '_modules',\n",
              " '_named_members',\n",
              " '_non_persistent_buffers_set',\n",
              " '_parameters',\n",
              " '_register_load_state_dict_pre_hook',\n",
              " '_register_state_dict_hook',\n",
              " '_replicate_for_data_parallel',\n",
              " '_save_to_state_dict',\n",
              " '_slow_forward',\n",
              " '_state_dict_hooks',\n",
              " '_version',\n",
              " 'add_module',\n",
              " 'apply',\n",
              " 'bfloat16',\n",
              " 'buffers',\n",
              " 'children',\n",
              " 'cpu',\n",
              " 'cuda',\n",
              " 'decode',\n",
              " 'decoder',\n",
              " 'double',\n",
              " 'dump_patches',\n",
              " 'encode',\n",
              " 'encoder',\n",
              " 'eval',\n",
              " 'extra_repr',\n",
              " 'float',\n",
              " 'forward',\n",
              " 'get_buffer',\n",
              " 'get_extra_state',\n",
              " 'get_parameter',\n",
              " 'get_submodule',\n",
              " 'half',\n",
              " 'load_state_dict',\n",
              " 'modules',\n",
              " 'named_buffers',\n",
              " 'named_children',\n",
              " 'named_modules',\n",
              " 'named_parameters',\n",
              " 'parameters',\n",
              " 'recon_loss',\n",
              " 'register_backward_hook',\n",
              " 'register_buffer',\n",
              " 'register_forward_hook',\n",
              " 'register_forward_pre_hook',\n",
              " 'register_full_backward_hook',\n",
              " 'register_parameter',\n",
              " 'requires_grad_',\n",
              " 'reset_parameters',\n",
              " 'set_extra_state',\n",
              " 'share_memory',\n",
              " 'state_dict',\n",
              " 'test',\n",
              " 'to',\n",
              " 'to_empty',\n",
              " 'train',\n",
              " 'training',\n",
              " 'type',\n",
              " 'xpu',\n",
              " 'zero_grad']"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def train():\n",
        "    model.train()\n",
        "    optimizer.zero_grad()\n",
        "    z = model.encode(x, train_pos_edge_index)\n",
        "    loss = model.recon_loss(z, train_pos_edge_index)\n",
        "    #if args.variational:\n",
        "    #   loss = loss + (1 / data.num_nodes) * model.kl_loss()\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    return float(loss)\n",
        "\n",
        "\n",
        "def test(pos_edge_index, neg_edge_index):\n",
        "    model.eval()\n",
        "    with torch.no_grad():\n",
        "        z = model.encode(x, train_pos_edge_index)\n",
        "    return model.test(z, pos_edge_index, neg_edge_index)"
      ],
      "metadata": {
        "id": "cREhQP8FH6hu"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for epoch in range(1, epochs + 1):\n",
        "    loss = train()\n",
        "\n",
        "    auc, ap = test(data.test_pos_edge_index, data.test_neg_edge_index)\n",
        "    print('Epoch: {:03d}, AUC: {:.4f}, AP: {:.4f}'.format(epoch, auc, ap))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2OP5ue2Fw3So",
        "outputId": "7accdf95-3263-4b92-e201-75dd0b8f0a8c"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch: 001, AUC: 0.8129, AP: 0.8204\n",
            "Epoch: 002, AUC: 0.8140, AP: 0.8217\n",
            "Epoch: 003, AUC: 0.8154, AP: 0.8229\n",
            "Epoch: 004, AUC: 0.8167, AP: 0.8243\n",
            "Epoch: 005, AUC: 0.8181, AP: 0.8257\n",
            "Epoch: 006, AUC: 0.8199, AP: 0.8275\n",
            "Epoch: 007, AUC: 0.8212, AP: 0.8289\n",
            "Epoch: 008, AUC: 0.8224, AP: 0.8302\n",
            "Epoch: 009, AUC: 0.8238, AP: 0.8318\n",
            "Epoch: 010, AUC: 0.8252, AP: 0.8337\n",
            "Epoch: 011, AUC: 0.8267, AP: 0.8354\n",
            "Epoch: 012, AUC: 0.8281, AP: 0.8370\n",
            "Epoch: 013, AUC: 0.8295, AP: 0.8383\n",
            "Epoch: 014, AUC: 0.8307, AP: 0.8395\n",
            "Epoch: 015, AUC: 0.8317, AP: 0.8403\n",
            "Epoch: 016, AUC: 0.8328, AP: 0.8412\n",
            "Epoch: 017, AUC: 0.8340, AP: 0.8424\n",
            "Epoch: 018, AUC: 0.8352, AP: 0.8434\n",
            "Epoch: 019, AUC: 0.8362, AP: 0.8442\n",
            "Epoch: 020, AUC: 0.8372, AP: 0.8449\n",
            "Epoch: 021, AUC: 0.8382, AP: 0.8458\n",
            "Epoch: 022, AUC: 0.8387, AP: 0.8461\n",
            "Epoch: 023, AUC: 0.8396, AP: 0.8468\n",
            "Epoch: 024, AUC: 0.8403, AP: 0.8473\n",
            "Epoch: 025, AUC: 0.8410, AP: 0.8479\n",
            "Epoch: 026, AUC: 0.8417, AP: 0.8484\n",
            "Epoch: 027, AUC: 0.8424, AP: 0.8488\n",
            "Epoch: 028, AUC: 0.8429, AP: 0.8492\n",
            "Epoch: 029, AUC: 0.8433, AP: 0.8496\n",
            "Epoch: 030, AUC: 0.8436, AP: 0.8496\n",
            "Epoch: 031, AUC: 0.8439, AP: 0.8499\n",
            "Epoch: 032, AUC: 0.8445, AP: 0.8503\n",
            "Epoch: 033, AUC: 0.8451, AP: 0.8507\n",
            "Epoch: 034, AUC: 0.8453, AP: 0.8507\n",
            "Epoch: 035, AUC: 0.8454, AP: 0.8508\n",
            "Epoch: 036, AUC: 0.8452, AP: 0.8506\n",
            "Epoch: 037, AUC: 0.8453, AP: 0.8505\n",
            "Epoch: 038, AUC: 0.8453, AP: 0.8506\n",
            "Epoch: 039, AUC: 0.8460, AP: 0.8511\n",
            "Epoch: 040, AUC: 0.8463, AP: 0.8512\n",
            "Epoch: 041, AUC: 0.8464, AP: 0.8512\n",
            "Epoch: 042, AUC: 0.8464, AP: 0.8513\n",
            "Epoch: 043, AUC: 0.8461, AP: 0.8512\n",
            "Epoch: 044, AUC: 0.8460, AP: 0.8511\n",
            "Epoch: 045, AUC: 0.8462, AP: 0.8514\n",
            "Epoch: 046, AUC: 0.8462, AP: 0.8513\n",
            "Epoch: 047, AUC: 0.8463, AP: 0.8513\n",
            "Epoch: 048, AUC: 0.8463, AP: 0.8513\n",
            "Epoch: 049, AUC: 0.8463, AP: 0.8513\n",
            "Epoch: 050, AUC: 0.8461, AP: 0.8512\n",
            "Epoch: 051, AUC: 0.8461, AP: 0.8513\n",
            "Epoch: 052, AUC: 0.8462, AP: 0.8514\n",
            "Epoch: 053, AUC: 0.8463, AP: 0.8516\n",
            "Epoch: 054, AUC: 0.8462, AP: 0.8515\n",
            "Epoch: 055, AUC: 0.8462, AP: 0.8516\n",
            "Epoch: 056, AUC: 0.8461, AP: 0.8516\n",
            "Epoch: 057, AUC: 0.8457, AP: 0.8511\n",
            "Epoch: 058, AUC: 0.8457, AP: 0.8511\n",
            "Epoch: 059, AUC: 0.8457, AP: 0.8509\n",
            "Epoch: 060, AUC: 0.8458, AP: 0.8509\n",
            "Epoch: 061, AUC: 0.8459, AP: 0.8508\n",
            "Epoch: 062, AUC: 0.8460, AP: 0.8508\n",
            "Epoch: 063, AUC: 0.8459, AP: 0.8507\n",
            "Epoch: 064, AUC: 0.8459, AP: 0.8509\n",
            "Epoch: 065, AUC: 0.8460, AP: 0.8508\n",
            "Epoch: 066, AUC: 0.8459, AP: 0.8507\n",
            "Epoch: 067, AUC: 0.8460, AP: 0.8506\n",
            "Epoch: 068, AUC: 0.8462, AP: 0.8506\n",
            "Epoch: 069, AUC: 0.8463, AP: 0.8508\n",
            "Epoch: 070, AUC: 0.8463, AP: 0.8507\n",
            "Epoch: 071, AUC: 0.8462, AP: 0.8505\n",
            "Epoch: 072, AUC: 0.8459, AP: 0.8501\n",
            "Epoch: 073, AUC: 0.8458, AP: 0.8500\n",
            "Epoch: 074, AUC: 0.8463, AP: 0.8504\n",
            "Epoch: 075, AUC: 0.8466, AP: 0.8505\n",
            "Epoch: 076, AUC: 0.8470, AP: 0.8509\n",
            "Epoch: 077, AUC: 0.8471, AP: 0.8508\n",
            "Epoch: 078, AUC: 0.8468, AP: 0.8504\n",
            "Epoch: 079, AUC: 0.8459, AP: 0.8495\n",
            "Epoch: 080, AUC: 0.8456, AP: 0.8493\n",
            "Epoch: 081, AUC: 0.8460, AP: 0.8496\n",
            "Epoch: 082, AUC: 0.8465, AP: 0.8499\n",
            "Epoch: 083, AUC: 0.8469, AP: 0.8503\n",
            "Epoch: 084, AUC: 0.8469, AP: 0.8502\n",
            "Epoch: 085, AUC: 0.8466, AP: 0.8499\n",
            "Epoch: 086, AUC: 0.8461, AP: 0.8493\n",
            "Epoch: 087, AUC: 0.8458, AP: 0.8491\n",
            "Epoch: 088, AUC: 0.8457, AP: 0.8489\n",
            "Epoch: 089, AUC: 0.8458, AP: 0.8490\n",
            "Epoch: 090, AUC: 0.8460, AP: 0.8491\n",
            "Epoch: 091, AUC: 0.8459, AP: 0.8490\n",
            "Epoch: 092, AUC: 0.8461, AP: 0.8491\n",
            "Epoch: 093, AUC: 0.8460, AP: 0.8491\n",
            "Epoch: 094, AUC: 0.8457, AP: 0.8487\n",
            "Epoch: 095, AUC: 0.8454, AP: 0.8484\n",
            "Epoch: 096, AUC: 0.8449, AP: 0.8479\n",
            "Epoch: 097, AUC: 0.8451, AP: 0.8480\n",
            "Epoch: 098, AUC: 0.8453, AP: 0.8482\n",
            "Epoch: 099, AUC: 0.8454, AP: 0.8482\n",
            "Epoch: 100, AUC: 0.8454, AP: 0.8481\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Z = model.encode(x, train_pos_edge_index)\n",
        "Z"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IwTp3JV6IIXZ",
        "outputId": "89bbfebf-d026-41b1-842b-9cff6b0e1036"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[-0.7016,  0.4660],\n",
              "        [ 0.6916,  1.3677],\n",
              "        [-0.4613, -0.6089],\n",
              "        ...,\n",
              "        [ 0.0550,  1.3295],\n",
              "        [-0.8283,  0.1468],\n",
              "        [-1.0683, -0.2868]], device='cuda:0', grad_fn=<AddBackward0>)"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "MAcvHwATIPDY"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}