TRI_SQR_dataset.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/HxyScotthuang/CMPNN-Neptune/blob/withoutNeptune/TRI_SQR_dataset.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pGiRolLUNOvH",
        "outputId": "c60dc842-ddbd-4273-bd49-8beb1343d1f6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Looking in links: https://pytorch-geometric.com/whl/torch-2.0.1+cu118.html\n",
            "Collecting torch-scatter\n",
            "  Using cached torch_scatter-2.1.1.tar.gz (107 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: torch-scatter\n",
            "  Building wheel for torch-scatter (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for torch-scatter: filename=torch_scatter-2.1.1-cp310-cp310-linux_x86_64.whl size=3513298 sha256=762be1191353ae8cca6cdafacc22407310c5c835eb9afaca5dbb989eb01256b7\n",
            "  Stored in directory: /root/.cache/pip/wheels/ef/67/58/6566a3b61c6ec0f2ca0c2c324cd035ef2955601f0fb3197d5f\n",
            "Successfully built torch-scatter\n",
            "Installing collected packages: torch-scatter\n",
            "Successfully installed torch-scatter-2.1.1\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Looking in links: https://pytorch-geometric.com/whl/torch-2.0.1+cu118.html\n",
            "Collecting torch-sparse\n",
            "  Using cached torch_sparse-0.6.17.tar.gz (209 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from torch-sparse) (1.10.1)\n",
            "Requirement already satisfied: numpy<1.27.0,>=1.19.5 in /usr/local/lib/python3.10/dist-packages (from scipy->torch-sparse) (1.22.4)\n",
            "Building wheels for collected packages: torch-sparse\n",
            "  Building wheel for torch-sparse (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for torch-sparse: filename=torch_sparse-0.6.17-cp310-cp310-linux_x86_64.whl size=2738597 sha256=a11ad45c15c5866ff5a146903fdac8341886af0edfd1c6987477cb68f82d16d9\n",
            "  Stored in directory: /root/.cache/pip/wheels/67/25/e7/037b58fa47ba781444fd101a2f06c63a9d4e967ca6b910c53a\n",
            "Successfully built torch-sparse\n",
            "Installing collected packages: torch-sparse\n",
            "Successfully installed torch-sparse-0.6.17\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Looking in links: https://pytorch-geometric.com/whl/torch-2.0.1+cu118.html\n",
            "Collecting torch-cluster\n",
            "  Using cached torch_cluster-1.6.1.tar.gz (53 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from torch-cluster) (1.10.1)\n",
            "Requirement already satisfied: numpy<1.27.0,>=1.19.5 in /usr/local/lib/python3.10/dist-packages (from scipy->torch-cluster) (1.22.4)\n",
            "Building wheels for collected packages: torch-cluster\n",
            "  Building wheel for torch-cluster (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for torch-cluster: filename=torch_cluster-1.6.1-cp310-cp310-linux_x86_64.whl size=2061451 sha256=efc48e8aed21f6ac719f7886b8c4d60934f2a9ac17d2c882dc5cf271d3806f72\n",
            "  Stored in directory: /root/.cache/pip/wheels/2e/ed/ac/1df43a8ff5b9bd2f44042636b98a60b2b4027d3ce8e8a3185f\n",
            "Successfully built torch-cluster\n",
            "Installing collected packages: torch-cluster\n",
            "Successfully installed torch-cluster-1.6.1\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Looking in links: https://pytorch-geometric.com/whl/torch-2.0.1+cu118.html\n",
            "Collecting torch-spline-conv\n",
            "  Downloading torch_spline_conv-1.2.2.tar.gz (25 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: torch-spline-conv\n",
            "  Building wheel for torch-spline-conv (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for torch-spline-conv: filename=torch_spline_conv-1.2.2-cp310-cp310-linux_x86_64.whl size=512297 sha256=abe4d47c6fb1545aa27a27b1b9d8d53d424e3a3791c2d1cca343ec6d59638a50\n",
            "  Stored in directory: /root/.cache/pip/wheels/fc/34/be/187e4b5f5ccefecca2c1a5dfc8da244ec50baa1f33c7b8c9a1\n",
            "Successfully built torch-spline-conv\n",
            "Installing collected packages: torch-spline-conv\n",
            "Successfully installed torch-spline-conv-1.2.2\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting torch-geometric\n",
            "  Using cached torch_geometric-2.3.1.tar.gz (661 kB)\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from torch-geometric) (4.65.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from torch-geometric) (1.22.4)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from torch-geometric) (1.10.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch-geometric) (3.1.2)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from torch-geometric) (2.27.1)\n",
            "Requirement already satisfied: pyparsing in /usr/local/lib/python3.10/dist-packages (from torch-geometric) (3.0.9)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from torch-geometric) (1.2.2)\n",
            "Requirement already satisfied: psutil>=5.8.0 in /usr/local/lib/python3.10/dist-packages (from torch-geometric) (5.9.5)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch-geometric) (2.1.2)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->torch-geometric) (1.26.15)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->torch-geometric) (2022.12.7)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->torch-geometric) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->torch-geometric) (3.4)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->torch-geometric) (1.2.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->torch-geometric) (3.1.0)\n",
            "Building wheels for collected packages: torch-geometric\n",
            "  Building wheel for torch-geometric (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for torch-geometric: filename=torch_geometric-2.3.1-py3-none-any.whl size=910459 sha256=6cca8e36c5b8bbd300127cff4ec519cd425c5c607e5f8633d8d8dc2c48361cc3\n",
            "  Stored in directory: /root/.cache/pip/wheels/ac/dc/30/e2874821ff308ee67dcd7a66dbde912411e19e35a1addda028\n",
            "Successfully built torch-geometric\n",
            "Installing collected packages: torch-geometric\n",
            "Successfully installed torch-geometric-2.3.1\n"
          ]
        }
      ],
      "source": [
        "#@title [RUN] Install required libraries\n",
        "import torch\n",
        "torch.manual_seed(0)\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 "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "cellView": "form",
        "id": "tNRpVM-FNdee"
      },
      "outputs": [],
      "source": [
        "# @title [RUN] Import libraries and functions\n",
        "import numpy as np\n",
        "import torch.nn as nn\n",
        "from torch.nn import Linear\n",
        "from torch_geometric.nn.conv import MessagePassing\n",
        "from torch_geometric.nn.models import MLP\n",
        "import torch.nn.functional as F\n",
        "from torch_scatter import scatter\n",
        "from torch_geometric.utils import degree\n",
        "from torch_geometric.nn.pool import global_add_pool\n",
        "from torch.utils import data as torch_data\n",
        "import math\n",
        "import torch_geometric\n",
        "import random\n",
        "from torch_geometric.loader import DataLoader\n",
        "from torch_geometric.transforms.to_sparse_tensor import ToSparseTensor\n",
        "from torch_geometric.typing import Adj, OptTensor\n",
        "from torch_geometric.utils.random import erdos_renyi_graph"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oyfIuaQEOXJJ"
      },
      "source": [
        "#TRI-SQR Dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uu33UsSezcy5"
      },
      "source": [
        "We proposed a synthetic dataset TRI-SQR, which consists of multiple pairs of knowledge graphs in form $(G_1,G_2)$ s.t. $G_1 = (V_1,E_1,R,c_1)$, $G_2 = (V_2,E_2,R,c_2)$ where $R = \\{r_0,r_1,r_2\\}$. For each pair, we constructed as follow:\n",
        "\n",
        "1.   Generate an Erdos-Renyi graph $G_{\\text{init}}$ with $5$ nodes and a random probability $p$. We randomly select one of the nodes as the source node $u$.\n",
        "2.   $G_1$ is constructed by disjoint union $G_{\\text{init}}$ with two triangles, one with edges relation of $r_1$, and the other with $r_2$. The target query is $r_3(u,v)$ for all $v$ in a triangle with an edge relation of $r_1$.\n",
        "3.  Similarly, $G_2$ is constructed by disjoint union another copy of $G_{\\text{init}}$ with two squares, one with edges relation of $r_1$, and the other with $r_2$. The target query is $r_3(u,v)$ for all $v$ in a square with an edge relation of $r_2$.\n",
        "\n",
        "One example of such a pair can be shown in the following figure:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WqxJLqGIpVrs"
      },
      "source": [
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "Vo1Z2FCXe1PE"
      },
      "outputs": [],
      "source": [
        "from torch_geometric.data import Data\n",
        "\n",
        "edge_index_triangle_cluster = torch.tensor(\n",
        "    [\n",
        "        [0, 1, 2, 3, 4, 5],\n",
        "        [1, 2, 0, 4, 5, 3],\n",
        "    ],\n",
        "    dtype=torch.long,\n",
        ")\n",
        "triangle_cluster_num_nodes = edge_index_triangle_cluster.max() + 1\n",
        "edge_type_triangle_cluster = torch.tensor([1, 1, 1, 2, 2, 2], dtype=torch.long)\n",
        "\n",
        "edge_index_square_cluster = torch.tensor(\n",
        "    [\n",
        "        [0, 1, 2, 3, 4, 5, 6, 7],\n",
        "        [1, 2, 3, 0, 5, 6, 7, 4],\n",
        "    ],\n",
        "    dtype=torch.long,\n",
        ")\n",
        "square_cluster_num_nodes = edge_index_square_cluster.max() + 1\n",
        "edge_type_square_cluster = torch.tensor([1, 1, 1, 1, 2, 2, 2, 2], dtype=torch.long)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "z-xd67sbrzgB"
      },
      "outputs": [],
      "source": [
        "def concat_two_graph(index_1, type_1, index_2, type_2):\n",
        "    node_1 = torch.max(index_1.view(-1)) + 1\n",
        "    index = torch.cat([index_1, index_2 + node_1], dim=-1)\n",
        "    edge_type = torch.cat([type_1, type_2])\n",
        "    return index, edge_type\n",
        "\n",
        "\n",
        "def add_inverse_relation(index, edge_type):\n",
        "    num_relations = 2\n",
        "    index = torch.cat([index, index.flip(0)], dim=-1)\n",
        "    edge_type = torch.cat([edge_type, edge_type + num_relations])\n",
        "    return index, edge_type\n",
        "\n",
        "\n",
        "def generate_target_index_type(fact_index, fact_type, tri, init_edge_index):\n",
        "    # Generate r0\n",
        "    init_cluster_num_nodes = init_edge_index.max() + 1\n",
        "    if tri:\n",
        "        target = 1\n",
        "    else:\n",
        "        target = 2\n",
        "    target_type = []\n",
        "    # To avoid negative sampling\n",
        "    not_target_type = []\n",
        "    # for triangle, so we connect u with r_1 triangle\n",
        "    for index, item in enumerate(fact_index[0]):\n",
        "        # enumerate over all edges\n",
        "        if item >= init_cluster_num_nodes and fact_type[index] == target:\n",
        "            # Add an edge over\n",
        "            target_type.append([0, item.item()])\n",
        "        elif item >= init_cluster_num_nodes and fact_type[index] != target:\n",
        "            not_target_type.append([0, item.item()])\n",
        "    assert len(target_type) == len(not_target_type)\n",
        "    return (\n",
        "        torch.tensor(target_type).t().contiguous(),\n",
        "        torch.zeros(len(target_type), dtype=torch.int),\n",
        "        torch.tensor(not_target_type).t().contiguous(),\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "neazP0CDm0Bz"
      },
      "outputs": [],
      "source": [
        "def create_data_instances(num_node=5):\n",
        "    p = random.uniform(0.2, 1)\n",
        "    edge_index_init_cluster = erdos_renyi_graph(\n",
        "        num_nodes=num_node, edge_prob=p, directed=True\n",
        "    )\n",
        "    edge_type_init_cluster = torch.randint(\n",
        "        low=1, high=3, size=(edge_index_init_cluster.size(1),)\n",
        "    )\n",
        "\n",
        "    tri_fact_index, tri_fact_type = edge_index_init_cluster, edge_type_init_cluster\n",
        "    for i in range(1):\n",
        "        tri_fact_index, tri_fact_type = concat_two_graph(\n",
        "            tri_fact_index,\n",
        "            tri_fact_type,\n",
        "            edge_index_triangle_cluster,\n",
        "            edge_type_triangle_cluster,\n",
        "        )\n",
        "    (\n",
        "        tri_target_index,\n",
        "        tri_target_type,\n",
        "        tri_not_target_index,\n",
        "    ) = generate_target_index_type(\n",
        "        tri_fact_index, tri_fact_type, tri=True, init_edge_index=edge_index_init_cluster\n",
        "    )\n",
        "    tri_fact_index, tri_fact_type = add_inverse_relation(tri_fact_index, tri_fact_type)\n",
        "\n",
        "    tri_data = Data(\n",
        "        edge_index=tri_fact_index,\n",
        "        edge_type=tri_fact_type,\n",
        "        target_edge_index=tri_target_index,\n",
        "        target_edge_type=tri_target_type,\n",
        "        not_target_edge_index=tri_not_target_index,\n",
        "    )\n",
        "\n",
        "    sqr_fact_index, sqr_fact_type = edge_index_init_cluster, edge_type_init_cluster\n",
        "    for i in range(1):\n",
        "        sqr_fact_index, sqr_fact_type = concat_two_graph(\n",
        "            sqr_fact_index,\n",
        "            sqr_fact_type,\n",
        "            edge_index_square_cluster,\n",
        "            edge_type_square_cluster,\n",
        "        )\n",
        "    (\n",
        "        sqr_target_index,\n",
        "        sqr_target_type,\n",
        "        sqr_not_target_index,\n",
        "    ) = generate_target_index_type(\n",
        "        sqr_fact_index,\n",
        "        sqr_fact_type,\n",
        "        tri=False,\n",
        "        init_edge_index=edge_index_init_cluster,\n",
        "    )\n",
        "    sqr_fact_index, sqr_fact_type = add_inverse_relation(sqr_fact_index, sqr_fact_type)\n",
        "\n",
        "    square_data = Data(\n",
        "        edge_index=sqr_fact_index,\n",
        "        edge_type=sqr_fact_type,\n",
        "        target_edge_index=sqr_target_index,\n",
        "        target_edge_type=sqr_target_type,\n",
        "        not_target_edge_index=sqr_not_target_index,\n",
        "    )\n",
        "\n",
        "    tri_data.validate(raise_on_error=True)\n",
        "    square_data.validate(raise_on_error=True)\n",
        "    return tri_data, square_data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "YxZynODTykWk"
      },
      "outputs": [],
      "source": [
        "def generate_dataloader(length=10, seed=0, num_node=5):\n",
        "    tri_graphs = []\n",
        "    sqr_graphs = []\n",
        "    for _ in range(length):\n",
        "        tri_data, sqr_data = create_data_instances(num_node)\n",
        "\n",
        "        tri_graphs.append(tri_data)\n",
        "        sqr_graphs.append(sqr_data)\n",
        "    aggr_list = list(zip(tri_graphs, sqr_graphs))\n",
        "    random.Random(seed).shuffle(aggr_list)\n",
        "    tri_graphs, sqr_graphs = zip(*aggr_list)\n",
        "    split = int(len(aggr_list) * 0.7)\n",
        "    train_list = tri_graphs[:split] + sqr_graphs[:split]\n",
        "    test_list = tri_graphs[split:] + sqr_graphs[split:]\n",
        "\n",
        "    train_dataloader = DataLoader(train_list)\n",
        "    test_dataloader = DataLoader(test_list)\n",
        "    return train_dataloader, test_dataloader"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CEJX7jpt0I60"
      },
      "source": [
        "We generate $100$ graph pairs and assign $70$ pairs as the training set and the remaining $30$ pairs as the testing set ($140$ training graphs and $60$ testing graphs). In total, there are $490$ training triplets and $210$ testing triplets.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1F4b-zbw0Yt9"
      },
      "source": [
        " For all node $v \\notin G_{\\text{init}}$, we want a higher score for all the links $r_0(u,v)$ if either $v$ is in a triangle consists of $r_1$ relation or $v$ is in a square consists of $r_2$ relation, and a lower score otherwise. For negative sampling, we choose counterpart triplets for each graph, that is, we take $r_0(u,v)$ for all $v$ in a triangle with an edge relation of $r_2$ in $G_1$ and in a square with an edge relation of $r_1$ in $G_2$. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sDcZhW5N8jux"
      },
      "source": [
        "#Training Loop"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "LCFkq2SwA9bO"
      },
      "outputs": [],
      "source": [
        "def get_target_sampling(graph_data):\n",
        "    target_edge_index = graph_data.target_edge_index\n",
        "    target_edge_type = graph_data.target_edge_type\n",
        "    not_target_edge_index = graph_data.not_target_edge_index\n",
        "    h_index = torch.cat([target_edge_index[0], not_target_edge_index[0]]).unsqueeze(-1)\n",
        "    t_index = torch.cat([target_edge_index[1], not_target_edge_index[1]]).unsqueeze(-1)\n",
        "    r_index = torch.cat([target_edge_type, target_edge_type]).unsqueeze(-1)\n",
        "\n",
        "    target = torch.zeros(h_index.size(0))\n",
        "    target[: target_edge_index.size(-1)] = 1\n",
        "\n",
        "    return torch.stack([h_index, t_index, r_index], dim=-1), target.unsqueeze(-1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "61TJ_k8Z8oam"
      },
      "outputs": [],
      "source": [
        "def train_and_validate(\n",
        "    model,\n",
        "    train_dataloader,\n",
        "    num_epoch,\n",
        "    lr,\n",
        "):\n",
        "    if num_epoch == 0:\n",
        "        return\n",
        "\n",
        "    optimizer = torch.optim.Adam(model.parameters(), lr=lr)\n",
        "\n",
        "    batch_id = 0\n",
        "    for epoch in range(0, num_epoch):\n",
        "        model.train()\n",
        "        print(f\"Epoch {epoch} begin\")\n",
        "        losses = []\n",
        "        for graph_data in train_dataloader:\n",
        "            batch, target = get_target_sampling(graph_data)\n",
        "            pred = model(graph_data, batch)\n",
        "            loss = F.binary_cross_entropy_with_logits(pred, target)\n",
        "            neg_weight = torch.ones_like(pred)\n",
        "            with torch.no_grad():\n",
        "                neg_weight[:, 1:] = F.softmax(pred[:, 1:], dim=-1)\n",
        "\n",
        "            loss = (loss * neg_weight).sum(dim=-1) / neg_weight.sum(dim=-1)\n",
        "            loss = loss.mean()\n",
        "\n",
        "            loss.backward()\n",
        "            optimizer.step()\n",
        "            optimizer.zero_grad()\n",
        "            losses.append(loss.item())\n",
        "            batch_id += 1\n",
        "        avg_loss = sum(losses) / len(losses)\n",
        "        print(\"Epoch %d end\" % epoch)\n",
        "        print(\"average binary cross entropy: %g\" % avg_loss)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "DBl5yYP7Dmde"
      },
      "outputs": [],
      "source": [
        "@torch.no_grad()\n",
        "def test(model, test_dataloader):\n",
        "    model.eval()\n",
        "    corrects = []\n",
        "    totals = []\n",
        "    for graph_data in test_dataloader:\n",
        "        batch, target = get_target_sampling(graph_data)\n",
        "        pred = model(graph_data, batch)\n",
        "        output = (F.sigmoid(pred) > 0.5).float()\n",
        "        correct = (output == target).float().sum()\n",
        "        corrects.append(correct)\n",
        "        totals.append(target.size(0))\n",
        "    accuracy = 100 * sum(corrects) / sum(totals)\n",
        "    print(\"Accuracy = {}\".format(accuracy))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1MoCeuHwOS2h"
      },
      "source": [
        "#Model Definition"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "KtGpDM_qNQKr"
      },
      "outputs": [],
      "source": [
        "class CMPNNModule(nn.Module):\n",
        "    def __init__(\n",
        "        self,\n",
        "        input_dim,\n",
        "        hidden_dims,\n",
        "        num_relation,\n",
        "        message_func=\"distmult\",\n",
        "        aggregate_func=\"pna\",\n",
        "        short_cut=False,\n",
        "        layer_norm=False,\n",
        "        concat_hidden=False,\n",
        "        num_mlp_layer=2,\n",
        "        dependent=True,\n",
        "        readout=False,\n",
        "    ):\n",
        "        super(CMPNNModule, self).__init__()\n",
        "        self.dims = [input_dim] + list(hidden_dims)\n",
        "        self.num_relation = num_relation\n",
        "        self.short_cut = (\n",
        "            short_cut  # whether to use residual connections between GNN layers\n",
        "        )\n",
        "        self.concat_hidden = concat_hidden  # whether to compute final states as a function of all layer outputs or last\n",
        "        self.readout = readout\n",
        "        self.layers = nn.ModuleList()\n",
        "        for i in range(len(self.dims) - 1):\n",
        "            self.layers.append(\n",
        "                CMPNNLayer(\n",
        "                    self.dims[i],\n",
        "                    self.dims[i + 1],\n",
        "                    num_relation,\n",
        "                    self.dims[0],\n",
        "                    message_func,\n",
        "                    aggregate_func,\n",
        "                    layer_norm,\n",
        "                    dependent,\n",
        "                    readout,\n",
        "                )\n",
        "            )\n",
        "\n",
        "        feature_dim = (\n",
        "            sum(hidden_dims) if concat_hidden else hidden_dims[-1]\n",
        "        ) + input_dim\n",
        "\n",
        "        # additional relation embedding which serves as an initial 'query' for the NBFNet forward pass\n",
        "        # each layer has its own learnable relations matrix, so we send the total number of relations, too\n",
        "        self.query = nn.Embedding(num_relation, input_dim)\n",
        "        self.mlp = nn.Sequential()\n",
        "        mlp = []\n",
        "        for i in range(num_mlp_layer - 1):\n",
        "            mlp.append(nn.Linear(feature_dim, feature_dim))\n",
        "            mlp.append(nn.ReLU())\n",
        "        mlp.append(nn.Linear(feature_dim, 1))\n",
        "        self.mlp = nn.Sequential(*mlp)\n",
        "\n",
        "    def bellmanford(self, data, h_index, r_index, separate_grad=False):\n",
        "        batch_size = len(r_index)\n",
        "\n",
        "        # initialize queries (relation types of the given triples)\n",
        "        query = self.query(r_index)\n",
        "        index = h_index.unsqueeze(-1).expand_as(query)\n",
        "\n",
        "        # initial (boundary) condition - initialize all node states as zeros\n",
        "        boundary = torch.zeros(\n",
        "            batch_size, data.num_nodes, self.dims[0], device=h_index.device\n",
        "        )\n",
        "        # by the scatter operation we put query (relation) embeddings as init features of source (index) nodes\n",
        "        boundary.scatter_add_(1, index.unsqueeze(1), query.unsqueeze(1))\n",
        "        size = (data.num_nodes, data.num_nodes)\n",
        "        edge_weight = torch.ones(data.num_edges, device=h_index.device)\n",
        "\n",
        "        hiddens = []\n",
        "        edge_weights = []\n",
        "        layer_input = boundary\n",
        "\n",
        "        for layer in self.layers:\n",
        "            if separate_grad:\n",
        "                edge_weight = edge_weight.clone().requires_grad_()\n",
        "            # Bellman-Ford iteration, we send the original boundary condition in addition to the updated node states\n",
        "            hidden = layer(\n",
        "                layer_input, query, data.edge_index, data.edge_type, size, edge_weight\n",
        "            )\n",
        "            if self.short_cut and hidden.shape == layer_input.shape:\n",
        "                # residual connection here\n",
        "                hidden = hidden + layer_input\n",
        "            hiddens.append(hidden)\n",
        "            edge_weights.append(edge_weight)\n",
        "            layer_input = hidden\n",
        "\n",
        "        # original query (relation type) embeddings\n",
        "        node_query = query.unsqueeze(1).expand(\n",
        "            -1, data.num_nodes, -1\n",
        "        )  # (batch_size, num_nodes, input_dim)\n",
        "        if self.concat_hidden:\n",
        "            output = torch.cat(hiddens + [node_query], dim=-1)\n",
        "        else:\n",
        "            output = torch.cat([hiddens[-1], node_query], dim=-1)\n",
        "\n",
        "        return {\n",
        "            \"node_feature\": output,\n",
        "            \"edge_weights\": edge_weights,\n",
        "        }\n",
        "\n",
        "    def forward(self, data, batch):\n",
        "        h_index, t_index, r_index = batch.unbind(-1)\n",
        "        shape = h_index.shape\n",
        "\n",
        "        assert (h_index[:, [0]] == h_index).all()\n",
        "        assert (r_index[:, [0]] == r_index).all()\n",
        "\n",
        "        # message passing and updated node representations\n",
        "        output = self.bellmanford(\n",
        "            data, h_index[:, 0], r_index[:, 0]\n",
        "        )  # (num_nodes, batch_size, feature_dim）\n",
        "        feature = output[\"node_feature\"]\n",
        "        index = t_index.unsqueeze(-1).expand(-1, -1, feature.shape[-1])\n",
        "        # extract representations of tail entities from the updated node states\n",
        "        feature = feature.gather(\n",
        "            1, index\n",
        "        )  # (batch_size, num_negative + 1, feature_dim)\n",
        "\n",
        "        # probability logit for each tail node in the batch\n",
        "        # (batch_size, num_negative + 1, dim) -> (batch_size, num_negative + 1)\n",
        "        score = self.mlp(feature).squeeze(-1)\n",
        "        return score.view(shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "UWPe-iHENyjH"
      },
      "outputs": [],
      "source": [
        "class CMPNNLayer(MessagePassing):\n",
        "    eps = 1e-6\n",
        "\n",
        "    message2mul = {\n",
        "        \"transe\": \"add\",\n",
        "        \"distmult\": \"mul\",\n",
        "    }\n",
        "\n",
        "    def __init__(\n",
        "        self,\n",
        "        input_dim,\n",
        "        output_dim,\n",
        "        num_relation,\n",
        "        query_input_dim,\n",
        "        message_func=\"distmult\",\n",
        "        aggregate_func=\"pna\",\n",
        "        layer_norm=False,\n",
        "        dependent=True,\n",
        "        readout=False,\n",
        "    ):\n",
        "        super(CMPNNLayer, self).__init__()\n",
        "        self.input_dim = input_dim\n",
        "        self.output_dim = output_dim\n",
        "        self.num_relation = num_relation\n",
        "        self.query_input_dim = query_input_dim\n",
        "        self.message_func = message_func\n",
        "        self.aggregate_func = aggregate_func\n",
        "        self.dependent = dependent\n",
        "        self.readout = readout\n",
        "        if self.readout:\n",
        "            self.readout_transform = MLP(\n",
        "                [input_dim * 3] + [input_dim] + [output_dim], norm=None\n",
        "            )\n",
        "        if layer_norm:\n",
        "            self.layer_norm = nn.LayerNorm(output_dim)\n",
        "        else:\n",
        "            self.layer_norm = None\n",
        "\n",
        "        self.activation = nn.ReLU()\n",
        "\n",
        "        if self.aggregate_func == \"pna\":\n",
        "            self.linear = nn.Linear(input_dim * 13, output_dim)\n",
        "        else:\n",
        "            self.linear = nn.Linear(input_dim * 2, output_dim)\n",
        "\n",
        "        if dependent:\n",
        "            # obtain relation embeddings as a projection of the query relation\n",
        "            self.relation_linear = nn.Linear(query_input_dim, num_relation * input_dim)\n",
        "        else:\n",
        "            # relation embeddings as an independent embedding matrix per each layer\n",
        "            self.relation = nn.Embedding(num_relation, input_dim)\n",
        "\n",
        "    def forward(self, input, query, edge_index, edge_type, size, edge_weight=None):\n",
        "        batch_size = len(query)\n",
        "\n",
        "        if self.dependent:\n",
        "            # layer-specific relation features as a projection of input \"query\" (relation) embeddings\n",
        "            relation = self.relation_linear(query).view(\n",
        "                batch_size, self.num_relation, self.input_dim\n",
        "            )\n",
        "        else:\n",
        "            # layer-specific relation features as a special embedding matrix unique to each layer\n",
        "            relation = self.relation.weight.expand(batch_size, -1, -1)\n",
        "        if edge_weight is None:\n",
        "            edge_weight = torch.ones(len(edge_type), device=input.device)\n",
        "\n",
        "        # note that we send the initial boundary condition (node states at layer0) to the message passing\n",
        "        # correspond to Eq.6 on p5 in https://arxiv.org/pdf/2106.06935.pdf\n",
        "        output = self.propagate(\n",
        "            input=input,\n",
        "            relation=relation,\n",
        "            edge_index=edge_index,\n",
        "            edge_type=edge_type,\n",
        "            boundary=input,\n",
        "            size=size,\n",
        "            edge_weight=edge_weight,\n",
        "        )\n",
        "        return output\n",
        "\n",
        "    def message(self, input_j, boundary, relation, edge_type):\n",
        "        relation_j = relation.index_select(self.node_dim, edge_type)\n",
        "\n",
        "        if self.message_func == \"transe\":\n",
        "            message = input_j + relation_j\n",
        "        elif self.message_func == \"distmult\":\n",
        "            message = input_j * relation_j\n",
        "        elif self.message_func == \"rotate\":\n",
        "            x_j_re, x_j_im = input_j.chunk(2, dim=-1)\n",
        "            r_j_re, r_j_im = relation_j.chunk(2, dim=-1)\n",
        "            message_re = x_j_re * r_j_re - x_j_im * r_j_im\n",
        "            message_im = x_j_re * r_j_im + x_j_im * r_j_re\n",
        "            message = torch.cat([message_re, message_im], dim=-1)\n",
        "        else:\n",
        "            raise ValueError(\"Unknown message function `%s`\" % self.message_func)\n",
        "\n",
        "        # augment messages with the boundary condition\n",
        "        message = torch.cat(\n",
        "            [message, boundary], dim=self.node_dim\n",
        "        )  # (num_edges + num_nodes, batch_size, input_dim)\n",
        "\n",
        "        return message\n",
        "\n",
        "    def aggregate(self, input, edge_weight, index, dim_size):\n",
        "        # augment aggregation index with self-loops for the boundary condition\n",
        "        index = torch.cat(\n",
        "            [index, torch.arange(dim_size, device=input.device)]\n",
        "        )  # (num_edges + num_nodes,)\n",
        "        edge_weight = torch.cat(\n",
        "            [edge_weight, torch.ones(dim_size, device=input.device)]\n",
        "        )\n",
        "        shape = [1] * input.ndim\n",
        "        shape[self.node_dim] = -1\n",
        "        edge_weight = edge_weight.view(shape)\n",
        "\n",
        "        if self.aggregate_func == \"pna\":\n",
        "            mean = scatter(\n",
        "                input * edge_weight,\n",
        "                index,\n",
        "                dim=self.node_dim,\n",
        "                dim_size=dim_size,\n",
        "                reduce=\"mean\",\n",
        "            )\n",
        "            sq_mean = scatter(\n",
        "                input**2 * edge_weight,\n",
        "                index,\n",
        "                dim=self.node_dim,\n",
        "                dim_size=dim_size,\n",
        "                reduce=\"mean\",\n",
        "            )\n",
        "            max = scatter(\n",
        "                input * edge_weight,\n",
        "                index,\n",
        "                dim=self.node_dim,\n",
        "                dim_size=dim_size,\n",
        "                reduce=\"max\",\n",
        "            )\n",
        "            min = scatter(\n",
        "                input * edge_weight,\n",
        "                index,\n",
        "                dim=self.node_dim,\n",
        "                dim_size=dim_size,\n",
        "                reduce=\"min\",\n",
        "            )\n",
        "            std = (sq_mean - mean**2).clamp(min=self.eps).sqrt()\n",
        "            features = torch.cat(\n",
        "                [\n",
        "                    mean.unsqueeze(-1),\n",
        "                    max.unsqueeze(-1),\n",
        "                    min.unsqueeze(-1),\n",
        "                    std.unsqueeze(-1),\n",
        "                ],\n",
        "                dim=-1,\n",
        "            )\n",
        "            features = features.flatten(-2)\n",
        "            degree_out = degree(index, dim_size).unsqueeze(0).unsqueeze(-1)\n",
        "            scale = degree_out.log()\n",
        "            scale = scale / scale.mean()\n",
        "            scales = torch.cat(\n",
        "                [torch.ones_like(scale), scale, 1 / scale.clamp(min=1e-2)], dim=-1\n",
        "            )\n",
        "            output = (features.unsqueeze(-1) * scales.unsqueeze(-2)).flatten(-2)\n",
        "        else:\n",
        "            output = scatter(\n",
        "                input * edge_weight,\n",
        "                index,\n",
        "                dim=self.node_dim,\n",
        "                dim_size=dim_size,\n",
        "                reduce=self.aggregate_func,\n",
        "            )\n",
        "\n",
        "        return output\n",
        "\n",
        "    def update(self, update, input):\n",
        "        # node update as a function of old states (input) and this layer output (update)\n",
        "        if self.readout:\n",
        "            batch = torch.zeros(input.size(1), dtype=int)\n",
        "            pool = global_add_pool(input, batch).repeat(1, input.size(1), 1)\n",
        "            output = self.readout_transform(torch.cat([input, update, pool], dim=-1))\n",
        "        else:\n",
        "            output = self.linear(torch.cat([input, update], dim=-1))\n",
        "\n",
        "        if self.layer_norm:\n",
        "            output = self.layer_norm(output)\n",
        "        if self.activation:\n",
        "            output = self.activation(output)\n",
        "        return output"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "40kzeWpG8TLW"
      },
      "source": [
        "# Experiments"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Vly7ZlQ0iMj"
      },
      "source": [
        "We claim that C-MPNNs with sum readout can correctly predict all testing triplets, whereas C-MPNNs  without sum readout will fail to learn this pattern and achieve $50\\%$ as random guessing. Theoretically, the TRI-SQR dataset is designed in such a way that any R-MPNN model assigns identical node representations to nodes in triangles and squares with the same relation type. Consequently, any C-MPNN model without global readout will be unable to determine which graph between $G_1$ and $G_2$ in the graph pair is being predicted, making it challenging to learn the conditional representation $\\mathbf{h}_{u|v,q}$. However, we anticipate that a C-MPNN model with sum readout can differentiate between $G_1$ and $G_2$ in each pair, as it can access global information like the total number of nodes in the graph. This allows it to accurately identify the graph being predicted in the graph pair, even when the representations of the triangle and square with the same relation are identical. As a result, it can learn the target rules and achieve $100\\%$ accuracy."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "zFs3q79l8Sn1"
      },
      "outputs": [],
      "source": [
        "input_dim = 32\n",
        "hidden_dims = [32, 32, 32, 32]\n",
        "num_relation = 5\n",
        "num_epoch = 200\n",
        "lr = 1e-4\n",
        "length = 100\n",
        "num_node = 5"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eQSJu_ulE6jR",
        "outputId": "9c052f53-3213-450f-eac4-8396a97c0d40"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "140 60\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch_geometric/data/storage.py:304: UserWarning: Unable to accurately infer 'num_nodes' from the attribute set '{'edge_type', 'target_edge_type', 'target_edge_index', 'edge_index', 'not_target_edge_index'}'. Please explicitly set 'num_nodes' as an attribute of 'data' to suppress this warning\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "train_dataloader, test_dataloader = generate_dataloader(\n",
        "    length=length, num_node=num_node\n",
        ")\n",
        "print(len(train_dataloader), len(test_dataloader))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "xDFghhHVSK3N"
      },
      "outputs": [],
      "source": [
        "CMPNNModel_GR = CMPNNModule(\n",
        "    input_dim,\n",
        "    hidden_dims,\n",
        "    num_relation,\n",
        "    message_func=\"distmult\",\n",
        "    aggregate_func=\"sum\",\n",
        "    short_cut=False,\n",
        "    layer_norm=False,\n",
        "    concat_hidden=False,\n",
        "    num_mlp_layer=1,\n",
        "    dependent=False,\n",
        "    readout=True,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uw6ytaETGP7R",
        "outputId": "61adb09a-8173-4496-9dfc-ba3cefde7d1b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 0 begin\n",
            "Epoch 0 end\n",
            "average binary cross entropy: 0.654735\n",
            "Epoch 1 begin\n",
            "Epoch 1 end\n",
            "average binary cross entropy: 0.720568\n",
            "Epoch 2 begin\n",
            "Epoch 2 end\n",
            "average binary cross entropy: 0.695224\n",
            "Epoch 3 begin\n",
            "Epoch 3 end\n",
            "average binary cross entropy: 0.719236\n",
            "Epoch 4 begin\n",
            "Epoch 4 end\n",
            "average binary cross entropy: 0.68953\n",
            "Epoch 5 begin\n",
            "Epoch 5 end\n",
            "average binary cross entropy: 0.698431\n",
            "Epoch 6 begin\n",
            "Epoch 6 end\n",
            "average binary cross entropy: 0.694315\n",
            "Epoch 7 begin\n",
            "Epoch 7 end\n",
            "average binary cross entropy: 0.693591\n",
            "Epoch 8 begin\n",
            "Epoch 8 end\n",
            "average binary cross entropy: 0.69505\n",
            "Epoch 9 begin\n",
            "Epoch 9 end\n",
            "average binary cross entropy: 0.691501\n",
            "Epoch 10 begin\n",
            "Epoch 10 end\n",
            "average binary cross entropy: 0.692909\n",
            "Epoch 11 begin\n",
            "Epoch 11 end\n",
            "average binary cross entropy: 0.692991\n",
            "Epoch 12 begin\n",
            "Epoch 12 end\n",
            "average binary cross entropy: 0.692713\n",
            "Epoch 13 begin\n",
            "Epoch 13 end\n",
            "average binary cross entropy: 0.693392\n",
            "Epoch 14 begin\n",
            "Epoch 14 end\n",
            "average binary cross entropy: 0.692658\n",
            "Epoch 15 begin\n",
            "Epoch 15 end\n",
            "average binary cross entropy: 0.693007\n",
            "Epoch 16 begin\n",
            "Epoch 16 end\n",
            "average binary cross entropy: 0.69394\n",
            "Epoch 17 begin\n",
            "Epoch 17 end\n",
            "average binary cross entropy: 0.689261\n",
            "Epoch 18 begin\n",
            "Epoch 18 end\n",
            "average binary cross entropy: 0.6937\n",
            "Epoch 19 begin\n",
            "Epoch 19 end\n",
            "average binary cross entropy: 0.696484\n",
            "Epoch 20 begin\n",
            "Epoch 20 end\n",
            "average binary cross entropy: 0.690001\n",
            "Epoch 21 begin\n",
            "Epoch 21 end\n",
            "average binary cross entropy: 0.647244\n",
            "Epoch 22 begin\n",
            "Epoch 22 end\n",
            "average binary cross entropy: 0.766784\n",
            "Epoch 23 begin\n",
            "Epoch 23 end\n",
            "average binary cross entropy: 0.703877\n",
            "Epoch 24 begin\n",
            "Epoch 24 end\n",
            "average binary cross entropy: 0.691949\n",
            "Epoch 25 begin\n",
            "Epoch 25 end\n",
            "average binary cross entropy: 0.6902\n",
            "Epoch 26 begin\n",
            "Epoch 26 end\n",
            "average binary cross entropy: 0.699672\n",
            "Epoch 27 begin\n",
            "Epoch 27 end\n",
            "average binary cross entropy: 0.695782\n",
            "Epoch 28 begin\n",
            "Epoch 28 end\n",
            "average binary cross entropy: 0.692249\n",
            "Epoch 29 begin\n",
            "Epoch 29 end\n",
            "average binary cross entropy: 0.692287\n",
            "Epoch 30 begin\n",
            "Epoch 30 end\n",
            "average binary cross entropy: 0.693543\n",
            "Epoch 31 begin\n",
            "Epoch 31 end\n",
            "average binary cross entropy: 0.694907\n",
            "Epoch 32 begin\n",
            "Epoch 32 end\n",
            "average binary cross entropy: 0.692249\n",
            "Epoch 33 begin\n",
            "Epoch 33 end\n",
            "average binary cross entropy: 0.692528\n",
            "Epoch 34 begin\n",
            "Epoch 34 end\n",
            "average binary cross entropy: 0.691872\n",
            "Epoch 35 begin\n",
            "Epoch 35 end\n",
            "average binary cross entropy: 0.693018\n",
            "Epoch 36 begin\n",
            "Epoch 36 end\n",
            "average binary cross entropy: 0.69314\n",
            "Epoch 37 begin\n",
            "Epoch 37 end\n",
            "average binary cross entropy: 0.693595\n",
            "Epoch 38 begin\n",
            "Epoch 38 end\n",
            "average binary cross entropy: 0.691435\n",
            "Epoch 39 begin\n",
            "Epoch 39 end\n",
            "average binary cross entropy: 0.693422\n",
            "Epoch 40 begin\n",
            "Epoch 40 end\n",
            "average binary cross entropy: 0.691318\n",
            "Epoch 41 begin\n",
            "Epoch 41 end\n",
            "average binary cross entropy: 0.693065\n",
            "Epoch 42 begin\n",
            "Epoch 42 end\n",
            "average binary cross entropy: 0.691694\n",
            "Epoch 43 begin\n",
            "Epoch 43 end\n",
            "average binary cross entropy: 0.693085\n",
            "Epoch 44 begin\n",
            "Epoch 44 end\n",
            "average binary cross entropy: 0.69109\n",
            "Epoch 45 begin\n",
            "Epoch 45 end\n",
            "average binary cross entropy: 0.693555\n",
            "Epoch 46 begin\n",
            "Epoch 46 end\n",
            "average binary cross entropy: 0.692231\n",
            "Epoch 47 begin\n",
            "Epoch 47 end\n",
            "average binary cross entropy: 0.690255\n",
            "Epoch 48 begin\n",
            "Epoch 48 end\n",
            "average binary cross entropy: 0.697154\n",
            "Epoch 49 begin\n",
            "Epoch 49 end\n",
            "average binary cross entropy: 0.69128\n",
            "Epoch 50 begin\n",
            "Epoch 50 end\n",
            "average binary cross entropy: 0.691091\n",
            "Epoch 51 begin\n",
            "Epoch 51 end\n",
            "average binary cross entropy: 0.68891\n",
            "Epoch 52 begin\n",
            "Epoch 52 end\n",
            "average binary cross entropy: 0.700231\n",
            "Epoch 53 begin\n",
            "Epoch 53 end\n",
            "average binary cross entropy: 0.686508\n",
            "Epoch 54 begin\n",
            "Epoch 54 end\n",
            "average binary cross entropy: 0.693716\n",
            "Epoch 55 begin\n",
            "Epoch 55 end\n",
            "average binary cross entropy: 0.6884\n",
            "Epoch 56 begin\n",
            "Epoch 56 end\n",
            "average binary cross entropy: 0.692308\n",
            "Epoch 57 begin\n",
            "Epoch 57 end\n",
            "average binary cross entropy: 0.688448\n",
            "Epoch 58 begin\n",
            "Epoch 58 end\n",
            "average binary cross entropy: 0.686403\n",
            "Epoch 59 begin\n",
            "Epoch 59 end\n",
            "average binary cross entropy: 0.691466\n",
            "Epoch 60 begin\n",
            "Epoch 60 end\n",
            "average binary cross entropy: 0.67877\n",
            "Epoch 61 begin\n",
            "Epoch 61 end\n",
            "average binary cross entropy: 0.696728\n",
            "Epoch 62 begin\n",
            "Epoch 62 end\n",
            "average binary cross entropy: 0.683138\n",
            "Epoch 63 begin\n",
            "Epoch 63 end\n",
            "average binary cross entropy: 0.687469\n",
            "Epoch 64 begin\n",
            "Epoch 64 end\n",
            "average binary cross entropy: 0.684951\n",
            "Epoch 65 begin\n",
            "Epoch 65 end\n",
            "average binary cross entropy: 0.686034\n",
            "Epoch 66 begin\n",
            "Epoch 66 end\n",
            "average binary cross entropy: 0.681589\n",
            "Epoch 67 begin\n",
            "Epoch 67 end\n",
            "average binary cross entropy: 0.661225\n",
            "Epoch 68 begin\n",
            "Epoch 68 end\n",
            "average binary cross entropy: 0.715238\n",
            "Epoch 69 begin\n",
            "Epoch 69 end\n",
            "average binary cross entropy: 0.673166\n",
            "Epoch 70 begin\n",
            "Epoch 70 end\n",
            "average binary cross entropy: 0.683513\n",
            "Epoch 71 begin\n",
            "Epoch 71 end\n",
            "average binary cross entropy: 0.67679\n",
            "Epoch 72 begin\n",
            "Epoch 72 end\n",
            "average binary cross entropy: 0.671718\n",
            "Epoch 73 begin\n",
            "Epoch 73 end\n",
            "average binary cross entropy: 0.667718\n",
            "Epoch 74 begin\n",
            "Epoch 74 end\n",
            "average binary cross entropy: 0.668443\n",
            "Epoch 75 begin\n",
            "Epoch 75 end\n",
            "average binary cross entropy: 0.661233\n",
            "Epoch 76 begin\n",
            "Epoch 76 end\n",
            "average binary cross entropy: 0.669425\n",
            "Epoch 77 begin\n",
            "Epoch 77 end\n",
            "average binary cross entropy: 0.633403\n",
            "Epoch 78 begin\n",
            "Epoch 78 end\n",
            "average binary cross entropy: 0.627533\n",
            "Epoch 79 begin\n",
            "Epoch 79 end\n",
            "average binary cross entropy: 0.651712\n",
            "Epoch 80 begin\n",
            "Epoch 80 end\n",
            "average binary cross entropy: 0.623259\n",
            "Epoch 81 begin\n",
            "Epoch 81 end\n",
            "average binary cross entropy: 0.614712\n",
            "Epoch 82 begin\n",
            "Epoch 82 end\n",
            "average binary cross entropy: 0.606256\n",
            "Epoch 83 begin\n",
            "Epoch 83 end\n",
            "average binary cross entropy: 0.57295\n",
            "Epoch 84 begin\n",
            "Epoch 84 end\n",
            "average binary cross entropy: 0.561686\n",
            "Epoch 85 begin\n",
            "Epoch 85 end\n",
            "average binary cross entropy: 0.548293\n",
            "Epoch 86 begin\n",
            "Epoch 86 end\n",
            "average binary cross entropy: 0.537916\n",
            "Epoch 87 begin\n",
            "Epoch 87 end\n",
            "average binary cross entropy: 0.526\n",
            "Epoch 88 begin\n",
            "Epoch 88 end\n",
            "average binary cross entropy: 0.492865\n",
            "Epoch 89 begin\n",
            "Epoch 89 end\n",
            "average binary cross entropy: 0.469405\n",
            "Epoch 90 begin\n",
            "Epoch 90 end\n",
            "average binary cross entropy: 0.441915\n",
            "Epoch 91 begin\n",
            "Epoch 91 end\n",
            "average binary cross entropy: 0.421183\n",
            "Epoch 92 begin\n",
            "Epoch 92 end\n",
            "average binary cross entropy: 0.39143\n",
            "Epoch 93 begin\n",
            "Epoch 93 end\n",
            "average binary cross entropy: 0.365872\n",
            "Epoch 94 begin\n",
            "Epoch 94 end\n",
            "average binary cross entropy: 0.329508\n",
            "Epoch 95 begin\n",
            "Epoch 95 end\n",
            "average binary cross entropy: 0.305107\n",
            "Epoch 96 begin\n",
            "Epoch 96 end\n",
            "average binary cross entropy: 0.27118\n",
            "Epoch 97 begin\n",
            "Epoch 97 end\n",
            "average binary cross entropy: 0.255775\n",
            "Epoch 98 begin\n",
            "Epoch 98 end\n",
            "average binary cross entropy: 0.213417\n",
            "Epoch 99 begin\n",
            "Epoch 99 end\n",
            "average binary cross entropy: 0.203481\n",
            "Epoch 100 begin\n",
            "Epoch 100 end\n",
            "average binary cross entropy: 0.17338\n",
            "Epoch 101 begin\n",
            "Epoch 101 end\n",
            "average binary cross entropy: 0.163647\n",
            "Epoch 102 begin\n",
            "Epoch 102 end\n",
            "average binary cross entropy: 0.136867\n",
            "Epoch 103 begin\n",
            "Epoch 103 end\n",
            "average binary cross entropy: 0.129625\n",
            "Epoch 104 begin\n",
            "Epoch 104 end\n",
            "average binary cross entropy: 0.111537\n",
            "Epoch 105 begin\n",
            "Epoch 105 end\n",
            "average binary cross entropy: 0.109426\n",
            "Epoch 106 begin\n",
            "Epoch 106 end\n",
            "average binary cross entropy: 0.100532\n",
            "Epoch 107 begin\n",
            "Epoch 107 end\n",
            "average binary cross entropy: 0.0916666\n",
            "Epoch 108 begin\n",
            "Epoch 108 end\n",
            "average binary cross entropy: 0.0938813\n",
            "Epoch 109 begin\n",
            "Epoch 109 end\n",
            "average binary cross entropy: 0.0762683\n",
            "Epoch 110 begin\n",
            "Epoch 110 end\n",
            "average binary cross entropy: 0.0699313\n",
            "Epoch 111 begin\n",
            "Epoch 111 end\n",
            "average binary cross entropy: 0.0664099\n",
            "Epoch 112 begin\n",
            "Epoch 112 end\n",
            "average binary cross entropy: 0.0657006\n",
            "Epoch 113 begin\n",
            "Epoch 113 end\n",
            "average binary cross entropy: 0.0536976\n",
            "Epoch 114 begin\n",
            "Epoch 114 end\n",
            "average binary cross entropy: 0.0450456\n",
            "Epoch 115 begin\n",
            "Epoch 115 end\n",
            "average binary cross entropy: 0.039497\n",
            "Epoch 116 begin\n",
            "Epoch 116 end\n",
            "average binary cross entropy: 0.0359444\n",
            "Epoch 117 begin\n",
            "Epoch 117 end\n",
            "average binary cross entropy: 0.0318327\n",
            "Epoch 118 begin\n",
            "Epoch 118 end\n",
            "average binary cross entropy: 0.0291503\n",
            "Epoch 119 begin\n",
            "Epoch 119 end\n",
            "average binary cross entropy: 0.0271067\n",
            "Epoch 120 begin\n",
            "Epoch 120 end\n",
            "average binary cross entropy: 0.0256608\n",
            "Epoch 121 begin\n",
            "Epoch 121 end\n",
            "average binary cross entropy: 0.0248839\n",
            "Epoch 122 begin\n",
            "Epoch 122 end\n",
            "average binary cross entropy: 0.0229874\n",
            "Epoch 123 begin\n",
            "Epoch 123 end\n",
            "average binary cross entropy: 0.0227181\n",
            "Epoch 124 begin\n",
            "Epoch 124 end\n",
            "average binary cross entropy: 0.0210526\n",
            "Epoch 125 begin\n",
            "Epoch 125 end\n",
            "average binary cross entropy: 0.021674\n",
            "Epoch 126 begin\n",
            "Epoch 126 end\n",
            "average binary cross entropy: 0.0199737\n",
            "Epoch 127 begin\n",
            "Epoch 127 end\n",
            "average binary cross entropy: 0.0216271\n",
            "Epoch 128 begin\n",
            "Epoch 128 end\n",
            "average binary cross entropy: 0.0189003\n",
            "Epoch 129 begin\n",
            "Epoch 129 end\n",
            "average binary cross entropy: 0.0197423\n",
            "Epoch 130 begin\n",
            "Epoch 130 end\n",
            "average binary cross entropy: 0.0189469\n",
            "Epoch 131 begin\n",
            "Epoch 131 end\n",
            "average binary cross entropy: 0.0200774\n",
            "Epoch 132 begin\n",
            "Epoch 132 end\n",
            "average binary cross entropy: 0.0180647\n",
            "Epoch 133 begin\n",
            "Epoch 133 end\n",
            "average binary cross entropy: 0.0187245\n",
            "Epoch 134 begin\n",
            "Epoch 134 end\n",
            "average binary cross entropy: 0.0177942\n",
            "Epoch 135 begin\n",
            "Epoch 135 end\n",
            "average binary cross entropy: 0.0193998\n",
            "Epoch 136 begin\n",
            "Epoch 136 end\n",
            "average binary cross entropy: 0.0185111\n",
            "Epoch 137 begin\n",
            "Epoch 137 end\n",
            "average binary cross entropy: 0.0203634\n",
            "Epoch 138 begin\n",
            "Epoch 138 end\n",
            "average binary cross entropy: 0.0204086\n",
            "Epoch 139 begin\n",
            "Epoch 139 end\n",
            "average binary cross entropy: 0.0186018\n",
            "Epoch 140 begin\n",
            "Epoch 140 end\n",
            "average binary cross entropy: 0.0186388\n",
            "Epoch 141 begin\n",
            "Epoch 141 end\n",
            "average binary cross entropy: 0.0184331\n",
            "Epoch 142 begin\n",
            "Epoch 142 end\n",
            "average binary cross entropy: 0.0195437\n",
            "Epoch 143 begin\n",
            "Epoch 143 end\n",
            "average binary cross entropy: 0.0168731\n",
            "Epoch 144 begin\n",
            "Epoch 144 end\n",
            "average binary cross entropy: 0.0172951\n",
            "Epoch 145 begin\n",
            "Epoch 145 end\n",
            "average binary cross entropy: 0.0214885\n",
            "Epoch 146 begin\n",
            "Epoch 146 end\n",
            "average binary cross entropy: 0.0211327\n",
            "Epoch 147 begin\n",
            "Epoch 147 end\n",
            "average binary cross entropy: 0.015574\n",
            "Epoch 148 begin\n",
            "Epoch 148 end\n",
            "average binary cross entropy: 0.0149018\n",
            "Epoch 149 begin\n",
            "Epoch 149 end\n",
            "average binary cross entropy: 0.0142517\n",
            "Epoch 150 begin\n",
            "Epoch 150 end\n",
            "average binary cross entropy: 0.0169751\n",
            "Epoch 151 begin\n",
            "Epoch 151 end\n",
            "average binary cross entropy: 0.01592\n",
            "Epoch 152 begin\n",
            "Epoch 152 end\n",
            "average binary cross entropy: 0.018141\n",
            "Epoch 153 begin\n",
            "Epoch 153 end\n",
            "average binary cross entropy: 0.0160331\n",
            "Epoch 154 begin\n",
            "Epoch 154 end\n",
            "average binary cross entropy: 0.011312\n",
            "Epoch 155 begin\n",
            "Epoch 155 end\n",
            "average binary cross entropy: 0.00948117\n",
            "Epoch 156 begin\n",
            "Epoch 156 end\n",
            "average binary cross entropy: 0.00975788\n",
            "Epoch 157 begin\n",
            "Epoch 157 end\n",
            "average binary cross entropy: 0.00750961\n",
            "Epoch 158 begin\n",
            "Epoch 158 end\n",
            "average binary cross entropy: 0.00545279\n",
            "Epoch 159 begin\n",
            "Epoch 159 end\n",
            "average binary cross entropy: 0.00534583\n",
            "Epoch 160 begin\n",
            "Epoch 160 end\n",
            "average binary cross entropy: 0.00356493\n",
            "Epoch 161 begin\n",
            "Epoch 161 end\n",
            "average binary cross entropy: 0.00413743\n",
            "Epoch 162 begin\n",
            "Epoch 162 end\n",
            "average binary cross entropy: 0.00327993\n",
            "Epoch 163 begin\n",
            "Epoch 163 end\n",
            "average binary cross entropy: 0.00292724\n",
            "Epoch 164 begin\n",
            "Epoch 164 end\n",
            "average binary cross entropy: 0.0041086\n",
            "Epoch 165 begin\n",
            "Epoch 165 end\n",
            "average binary cross entropy: 0.00274535\n",
            "Epoch 166 begin\n",
            "Epoch 166 end\n",
            "average binary cross entropy: 0.00253367\n",
            "Epoch 167 begin\n",
            "Epoch 167 end\n",
            "average binary cross entropy: 0.00141854\n",
            "Epoch 168 begin\n",
            "Epoch 168 end\n",
            "average binary cross entropy: 0.000927991\n",
            "Epoch 169 begin\n",
            "Epoch 169 end\n",
            "average binary cross entropy: 0.00047567\n",
            "Epoch 170 begin\n",
            "Epoch 170 end\n",
            "average binary cross entropy: 0.000407098\n",
            "Epoch 171 begin\n",
            "Epoch 171 end\n",
            "average binary cross entropy: 0.000406695\n",
            "Epoch 172 begin\n",
            "Epoch 172 end\n",
            "average binary cross entropy: 0.000365083\n",
            "Epoch 173 begin\n",
            "Epoch 173 end\n",
            "average binary cross entropy: 0.000368285\n",
            "Epoch 174 begin\n",
            "Epoch 174 end\n",
            "average binary cross entropy: 0.000338422\n",
            "Epoch 175 begin\n",
            "Epoch 175 end\n",
            "average binary cross entropy: 0.000309685\n",
            "Epoch 176 begin\n",
            "Epoch 176 end\n",
            "average binary cross entropy: 0.00030919\n",
            "Epoch 177 begin\n",
            "Epoch 177 end\n",
            "average binary cross entropy: 0.000286804\n",
            "Epoch 178 begin\n",
            "Epoch 178 end\n",
            "average binary cross entropy: 0.000297111\n",
            "Epoch 179 begin\n",
            "Epoch 179 end\n",
            "average binary cross entropy: 0.000285191\n",
            "Epoch 180 begin\n",
            "Epoch 180 end\n",
            "average binary cross entropy: 0.000234775\n",
            "Epoch 181 begin\n",
            "Epoch 181 end\n",
            "average binary cross entropy: 0.000250961\n",
            "Epoch 182 begin\n",
            "Epoch 182 end\n",
            "average binary cross entropy: 0.000244946\n",
            "Epoch 183 begin\n",
            "Epoch 183 end\n",
            "average binary cross entropy: 0.000229572\n",
            "Epoch 184 begin\n",
            "Epoch 184 end\n",
            "average binary cross entropy: 0.000188191\n",
            "Epoch 185 begin\n",
            "Epoch 185 end\n",
            "average binary cross entropy: 0.000193046\n",
            "Epoch 186 begin\n",
            "Epoch 186 end\n",
            "average binary cross entropy: 0.000188391\n",
            "Epoch 187 begin\n",
            "Epoch 187 end\n",
            "average binary cross entropy: 0.000155502\n",
            "Epoch 188 begin\n",
            "Epoch 188 end\n",
            "average binary cross entropy: 0.000150838\n",
            "Epoch 189 begin\n",
            "Epoch 189 end\n",
            "average binary cross entropy: 0.000149508\n",
            "Epoch 190 begin\n",
            "Epoch 190 end\n",
            "average binary cross entropy: 0.000124299\n",
            "Epoch 191 begin\n",
            "Epoch 191 end\n",
            "average binary cross entropy: 0.000114567\n",
            "Epoch 192 begin\n",
            "Epoch 192 end\n",
            "average binary cross entropy: 0.000127123\n",
            "Epoch 193 begin\n",
            "Epoch 193 end\n",
            "average binary cross entropy: 0.000103965\n",
            "Epoch 194 begin\n",
            "Epoch 194 end\n",
            "average binary cross entropy: 0.000113678\n",
            "Epoch 195 begin\n",
            "Epoch 195 end\n",
            "average binary cross entropy: 0.000114522\n",
            "Epoch 196 begin\n",
            "Epoch 196 end\n",
            "average binary cross entropy: 0.00010399\n",
            "Epoch 197 begin\n",
            "Epoch 197 end\n",
            "average binary cross entropy: 8.11805e-05\n",
            "Epoch 198 begin\n",
            "Epoch 198 end\n",
            "average binary cross entropy: 7.08654e-05\n",
            "Epoch 199 begin\n",
            "Epoch 199 end\n",
            "average binary cross entropy: 5.7355e-05\n"
          ]
        }
      ],
      "source": [
        "train_and_validate(\n",
        "    CMPNNModel_GR,\n",
        "    train_dataloader,\n",
        "    num_epoch,\n",
        "    lr,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JW-fKzhBSOiJ",
        "outputId": "5f2373eb-b3c7-4aa6-a138-8920d3da18ea"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy = 98.57142639160156\n"
          ]
        }
      ],
      "source": [
        "test(CMPNNModel_GR, test_dataloader)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "31W2AOdTE3Vu"
      },
      "outputs": [],
      "source": [
        "CMPNNModel = CMPNNModule(\n",
        "    input_dim,\n",
        "    hidden_dims,\n",
        "    num_relation,\n",
        "    message_func=\"distmult\",\n",
        "    aggregate_func=\"sum\",\n",
        "    short_cut=False,\n",
        "    layer_norm=False,\n",
        "    concat_hidden=False,\n",
        "    num_mlp_layer=2,\n",
        "    dependent=False,\n",
        "    readout=False,\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JUBoiaL4FBTo",
        "outputId": "8432c9a8-0b88-45bd-d3d6-c423c1b52040"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 0 begin\n",
            "Epoch 0 end\n",
            "average binary cross entropy: 0.694234\n",
            "Epoch 1 begin\n",
            "Epoch 1 end\n",
            "average binary cross entropy: 0.693783\n",
            "Epoch 2 begin\n",
            "Epoch 2 end\n",
            "average binary cross entropy: 0.693684\n",
            "Epoch 3 begin\n",
            "Epoch 3 end\n",
            "average binary cross entropy: 0.693422\n",
            "Epoch 4 begin\n",
            "Epoch 4 end\n",
            "average binary cross entropy: 0.693454\n",
            "Epoch 5 begin\n",
            "Epoch 5 end\n",
            "average binary cross entropy: 0.693435\n",
            "Epoch 6 begin\n",
            "Epoch 6 end\n",
            "average binary cross entropy: 0.693356\n",
            "Epoch 7 begin\n",
            "Epoch 7 end\n",
            "average binary cross entropy: 0.693324\n",
            "Epoch 8 begin\n",
            "Epoch 8 end\n",
            "average binary cross entropy: 0.693301\n",
            "Epoch 9 begin\n",
            "Epoch 9 end\n",
            "average binary cross entropy: 0.69328\n",
            "Epoch 10 begin\n",
            "Epoch 10 end\n",
            "average binary cross entropy: 0.693264\n",
            "Epoch 11 begin\n",
            "Epoch 11 end\n",
            "average binary cross entropy: 0.69325\n",
            "Epoch 12 begin\n",
            "Epoch 12 end\n",
            "average binary cross entropy: 0.693236\n",
            "Epoch 13 begin\n",
            "Epoch 13 end\n",
            "average binary cross entropy: 0.693225\n",
            "Epoch 14 begin\n",
            "Epoch 14 end\n",
            "average binary cross entropy: 0.693215\n",
            "Epoch 15 begin\n",
            "Epoch 15 end\n",
            "average binary cross entropy: 0.693205\n",
            "Epoch 16 begin\n",
            "Epoch 16 end\n",
            "average binary cross entropy: 0.693196\n",
            "Epoch 17 begin\n",
            "Epoch 17 end\n",
            "average binary cross entropy: 0.693188\n",
            "Epoch 18 begin\n",
            "Epoch 18 end\n",
            "average binary cross entropy: 0.693182\n",
            "Epoch 19 begin\n",
            "Epoch 19 end\n",
            "average binary cross entropy: 0.693175\n",
            "Epoch 20 begin\n",
            "Epoch 20 end\n",
            "average binary cross entropy: 0.69317\n",
            "Epoch 21 begin\n",
            "Epoch 21 end\n",
            "average binary cross entropy: 0.693164\n",
            "Epoch 22 begin\n",
            "Epoch 22 end\n",
            "average binary cross entropy: 0.693161\n",
            "Epoch 23 begin\n",
            "Epoch 23 end\n",
            "average binary cross entropy: 0.693156\n",
            "Epoch 24 begin\n",
            "Epoch 24 end\n",
            "average binary cross entropy: 0.693157\n",
            "Epoch 25 begin\n",
            "Epoch 25 end\n",
            "average binary cross entropy: 0.69316\n",
            "Epoch 26 begin\n",
            "Epoch 26 end\n",
            "average binary cross entropy: 0.693154\n",
            "Epoch 27 begin\n",
            "Epoch 27 end\n",
            "average binary cross entropy: 0.693151\n",
            "Epoch 28 begin\n",
            "Epoch 28 end\n",
            "average binary cross entropy: 0.69315\n",
            "Epoch 29 begin\n",
            "Epoch 29 end\n",
            "average binary cross entropy: 0.69315\n",
            "Epoch 30 begin\n",
            "Epoch 30 end\n",
            "average binary cross entropy: 0.693152\n",
            "Epoch 31 begin\n",
            "Epoch 31 end\n",
            "average binary cross entropy: 0.693158\n",
            "Epoch 32 begin\n",
            "Epoch 32 end\n",
            "average binary cross entropy: 0.693153\n",
            "Epoch 33 begin\n",
            "Epoch 33 end\n",
            "average binary cross entropy: 0.693148\n",
            "Epoch 34 begin\n",
            "Epoch 34 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 35 begin\n",
            "Epoch 35 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 36 begin\n",
            "Epoch 36 end\n",
            "average binary cross entropy: 0.693181\n",
            "Epoch 37 begin\n",
            "Epoch 37 end\n",
            "average binary cross entropy: 0.69318\n",
            "Epoch 38 begin\n",
            "Epoch 38 end\n",
            "average binary cross entropy: 0.693153\n",
            "Epoch 39 begin\n",
            "Epoch 39 end\n",
            "average binary cross entropy: 0.693149\n",
            "Epoch 40 begin\n",
            "Epoch 40 end\n",
            "average binary cross entropy: 0.693148\n",
            "Epoch 41 begin\n",
            "Epoch 41 end\n",
            "average binary cross entropy: 0.693148\n",
            "Epoch 42 begin\n",
            "Epoch 42 end\n",
            "average binary cross entropy: 0.693152\n",
            "Epoch 43 begin\n",
            "Epoch 43 end\n",
            "average binary cross entropy: 0.693155\n",
            "Epoch 44 begin\n",
            "Epoch 44 end\n",
            "average binary cross entropy: 0.693152\n",
            "Epoch 45 begin\n",
            "Epoch 45 end\n",
            "average binary cross entropy: 0.693155\n",
            "Epoch 46 begin\n",
            "Epoch 46 end\n",
            "average binary cross entropy: 0.693063\n",
            "Epoch 47 begin\n",
            "Epoch 47 end\n",
            "average binary cross entropy: 0.693349\n",
            "Epoch 48 begin\n",
            "Epoch 48 end\n",
            "average binary cross entropy: 0.693227\n",
            "Epoch 49 begin\n",
            "Epoch 49 end\n",
            "average binary cross entropy: 0.693151\n",
            "Epoch 50 begin\n",
            "Epoch 50 end\n",
            "average binary cross entropy: 0.693148\n",
            "Epoch 51 begin\n",
            "Epoch 51 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 52 begin\n",
            "Epoch 52 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 53 begin\n",
            "Epoch 53 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 54 begin\n",
            "Epoch 54 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 55 begin\n",
            "Epoch 55 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 56 begin\n",
            "Epoch 56 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 57 begin\n",
            "Epoch 57 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 58 begin\n",
            "Epoch 58 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 59 begin\n",
            "Epoch 59 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 60 begin\n",
            "Epoch 60 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 61 begin\n",
            "Epoch 61 end\n",
            "average binary cross entropy: 0.693131\n",
            "Epoch 62 begin\n",
            "Epoch 62 end\n",
            "average binary cross entropy: 0.693317\n",
            "Epoch 63 begin\n",
            "Epoch 63 end\n",
            "average binary cross entropy: 0.693159\n",
            "Epoch 64 begin\n",
            "Epoch 64 end\n",
            "average binary cross entropy: 0.693148\n",
            "Epoch 65 begin\n",
            "Epoch 65 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 66 begin\n",
            "Epoch 66 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 67 begin\n",
            "Epoch 67 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 68 begin\n",
            "Epoch 68 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 69 begin\n",
            "Epoch 69 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 70 begin\n",
            "Epoch 70 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 71 begin\n",
            "Epoch 71 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 72 begin\n",
            "Epoch 72 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 73 begin\n",
            "Epoch 73 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 74 begin\n",
            "Epoch 74 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 75 begin\n",
            "Epoch 75 end\n",
            "average binary cross entropy: 0.693148\n",
            "Epoch 76 begin\n",
            "Epoch 76 end\n",
            "average binary cross entropy: 0.693154\n",
            "Epoch 77 begin\n",
            "Epoch 77 end\n",
            "average binary cross entropy: 0.692876\n",
            "Epoch 78 begin\n",
            "Epoch 78 end\n",
            "average binary cross entropy: 0.694163\n",
            "Epoch 79 begin\n",
            "Epoch 79 end\n",
            "average binary cross entropy: 0.693268\n",
            "Epoch 80 begin\n",
            "Epoch 80 end\n",
            "average binary cross entropy: 0.693202\n",
            "Epoch 81 begin\n",
            "Epoch 81 end\n",
            "average binary cross entropy: 0.693182\n",
            "Epoch 82 begin\n",
            "Epoch 82 end\n",
            "average binary cross entropy: 0.693169\n",
            "Epoch 83 begin\n",
            "Epoch 83 end\n",
            "average binary cross entropy: 0.693163\n",
            "Epoch 84 begin\n",
            "Epoch 84 end\n",
            "average binary cross entropy: 0.693158\n",
            "Epoch 85 begin\n",
            "Epoch 85 end\n",
            "average binary cross entropy: 0.693155\n",
            "Epoch 86 begin\n",
            "Epoch 86 end\n",
            "average binary cross entropy: 0.693153\n",
            "Epoch 87 begin\n",
            "Epoch 87 end\n",
            "average binary cross entropy: 0.693151\n",
            "Epoch 88 begin\n",
            "Epoch 88 end\n",
            "average binary cross entropy: 0.69315\n",
            "Epoch 89 begin\n",
            "Epoch 89 end\n",
            "average binary cross entropy: 0.693149\n",
            "Epoch 90 begin\n",
            "Epoch 90 end\n",
            "average binary cross entropy: 0.693149\n",
            "Epoch 91 begin\n",
            "Epoch 91 end\n",
            "average binary cross entropy: 0.693148\n",
            "Epoch 92 begin\n",
            "Epoch 92 end\n",
            "average binary cross entropy: 0.693148\n",
            "Epoch 93 begin\n",
            "Epoch 93 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 94 begin\n",
            "Epoch 94 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 95 begin\n",
            "Epoch 95 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 96 begin\n",
            "Epoch 96 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 97 begin\n",
            "Epoch 97 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 98 begin\n",
            "Epoch 98 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 99 begin\n",
            "Epoch 99 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 100 begin\n",
            "Epoch 100 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 101 begin\n",
            "Epoch 101 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 102 begin\n",
            "Epoch 102 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 103 begin\n",
            "Epoch 103 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 104 begin\n",
            "Epoch 104 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 105 begin\n",
            "Epoch 105 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 106 begin\n",
            "Epoch 106 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 107 begin\n",
            "Epoch 107 end\n",
            "average binary cross entropy: 0.693141\n",
            "Epoch 108 begin\n",
            "Epoch 108 end\n",
            "average binary cross entropy: 0.693192\n",
            "Epoch 109 begin\n",
            "Epoch 109 end\n",
            "average binary cross entropy: 0.693182\n",
            "Epoch 110 begin\n",
            "Epoch 110 end\n",
            "average binary cross entropy: 0.69315\n",
            "Epoch 111 begin\n",
            "Epoch 111 end\n",
            "average binary cross entropy: 0.693152\n",
            "Epoch 112 begin\n",
            "Epoch 112 end\n",
            "average binary cross entropy: 0.693148\n",
            "Epoch 113 begin\n",
            "Epoch 113 end\n",
            "average binary cross entropy: 0.693148\n",
            "Epoch 114 begin\n",
            "Epoch 114 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 115 begin\n",
            "Epoch 115 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 116 begin\n",
            "Epoch 116 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 117 begin\n",
            "Epoch 117 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 118 begin\n",
            "Epoch 118 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 119 begin\n",
            "Epoch 119 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 120 begin\n",
            "Epoch 120 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 121 begin\n",
            "Epoch 121 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 122 begin\n",
            "Epoch 122 end\n",
            "average binary cross entropy: 0.693138\n",
            "Epoch 123 begin\n",
            "Epoch 123 end\n",
            "average binary cross entropy: 0.693178\n",
            "Epoch 124 begin\n",
            "Epoch 124 end\n",
            "average binary cross entropy: 0.693649\n",
            "Epoch 125 begin\n",
            "Epoch 125 end\n",
            "average binary cross entropy: 0.693162\n",
            "Epoch 126 begin\n",
            "Epoch 126 end\n",
            "average binary cross entropy: 0.693177\n",
            "Epoch 127 begin\n",
            "Epoch 127 end\n",
            "average binary cross entropy: 0.693155\n",
            "Epoch 128 begin\n",
            "Epoch 128 end\n",
            "average binary cross entropy: 0.693151\n",
            "Epoch 129 begin\n",
            "Epoch 129 end\n",
            "average binary cross entropy: 0.693149\n",
            "Epoch 130 begin\n",
            "Epoch 130 end\n",
            "average binary cross entropy: 0.693148\n",
            "Epoch 131 begin\n",
            "Epoch 131 end\n",
            "average binary cross entropy: 0.693148\n",
            "Epoch 132 begin\n",
            "Epoch 132 end\n",
            "average binary cross entropy: 0.693148\n",
            "Epoch 133 begin\n",
            "Epoch 133 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 134 begin\n",
            "Epoch 134 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 135 begin\n",
            "Epoch 135 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 136 begin\n",
            "Epoch 136 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 137 begin\n",
            "Epoch 137 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 138 begin\n",
            "Epoch 138 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 139 begin\n",
            "Epoch 139 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 140 begin\n",
            "Epoch 140 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 141 begin\n",
            "Epoch 141 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 142 begin\n",
            "Epoch 142 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 143 begin\n",
            "Epoch 143 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 144 begin\n",
            "Epoch 144 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 145 begin\n",
            "Epoch 145 end\n",
            "average binary cross entropy: 0.693145\n",
            "Epoch 146 begin\n",
            "Epoch 146 end\n",
            "average binary cross entropy: 0.693161\n",
            "Epoch 147 begin\n",
            "Epoch 147 end\n",
            "average binary cross entropy: 0.693277\n",
            "Epoch 148 begin\n",
            "Epoch 148 end\n",
            "average binary cross entropy: 0.693152\n",
            "Epoch 149 begin\n",
            "Epoch 149 end\n",
            "average binary cross entropy: 0.693148\n",
            "Epoch 150 begin\n",
            "Epoch 150 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 151 begin\n",
            "Epoch 151 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 152 begin\n",
            "Epoch 152 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 153 begin\n",
            "Epoch 153 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 154 begin\n",
            "Epoch 154 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 155 begin\n",
            "Epoch 155 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 156 begin\n",
            "Epoch 156 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 157 begin\n",
            "Epoch 157 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 158 begin\n",
            "Epoch 158 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 159 begin\n",
            "Epoch 159 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 160 begin\n",
            "Epoch 160 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 161 begin\n",
            "Epoch 161 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 162 begin\n",
            "Epoch 162 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 163 begin\n",
            "Epoch 163 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 164 begin\n",
            "Epoch 164 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 165 begin\n",
            "Epoch 165 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 166 begin\n",
            "Epoch 166 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 167 begin\n",
            "Epoch 167 end\n",
            "average binary cross entropy: 0.693082\n",
            "Epoch 168 begin\n",
            "Epoch 168 end\n",
            "average binary cross entropy: 0.693291\n",
            "Epoch 169 begin\n",
            "Epoch 169 end\n",
            "average binary cross entropy: 0.693306\n",
            "Epoch 170 begin\n",
            "Epoch 170 end\n",
            "average binary cross entropy: 0.693166\n",
            "Epoch 171 begin\n",
            "Epoch 171 end\n",
            "average binary cross entropy: 0.693156\n",
            "Epoch 172 begin\n",
            "Epoch 172 end\n",
            "average binary cross entropy: 0.693153\n",
            "Epoch 173 begin\n",
            "Epoch 173 end\n",
            "average binary cross entropy: 0.693151\n",
            "Epoch 174 begin\n",
            "Epoch 174 end\n",
            "average binary cross entropy: 0.69315\n",
            "Epoch 175 begin\n",
            "Epoch 175 end\n",
            "average binary cross entropy: 0.693149\n",
            "Epoch 176 begin\n",
            "Epoch 176 end\n",
            "average binary cross entropy: 0.693159\n",
            "Epoch 177 begin\n",
            "Epoch 177 end\n",
            "average binary cross entropy: 0.693151\n",
            "Epoch 178 begin\n",
            "Epoch 178 end\n",
            "average binary cross entropy: 0.693148\n",
            "Epoch 179 begin\n",
            "Epoch 179 end\n",
            "average binary cross entropy: 0.693148\n",
            "Epoch 180 begin\n",
            "Epoch 180 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 181 begin\n",
            "Epoch 181 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 182 begin\n",
            "Epoch 182 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 183 begin\n",
            "Epoch 183 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 184 begin\n",
            "Epoch 184 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 185 begin\n",
            "Epoch 185 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 186 begin\n",
            "Epoch 186 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 187 begin\n",
            "Epoch 187 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 188 begin\n",
            "Epoch 188 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 189 begin\n",
            "Epoch 189 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 190 begin\n",
            "Epoch 190 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 191 begin\n",
            "Epoch 191 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 192 begin\n",
            "Epoch 192 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 193 begin\n",
            "Epoch 193 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 194 begin\n",
            "Epoch 194 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 195 begin\n",
            "Epoch 195 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 196 begin\n",
            "Epoch 196 end\n",
            "average binary cross entropy: 0.693147\n",
            "Epoch 197 begin\n",
            "Epoch 197 end\n",
            "average binary cross entropy: 0.693167\n",
            "Epoch 198 begin\n",
            "Epoch 198 end\n",
            "average binary cross entropy: 0.693161\n",
            "Epoch 199 begin\n",
            "Epoch 199 end\n",
            "average binary cross entropy: 0.693204\n"
          ]
        }
      ],
      "source": [
        "train_and_validate(CMPNNModel, train_dataloader, num_epoch, lr)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CrEOPXwzSCno",
        "outputId": "96289baf-c5d9-40d4-e516-8a4c4d985668"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Accuracy = 57.14285659790039\n"
          ]
        }
      ],
      "source": [
        "test(CMPNNModel, test_dataloader)"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "authorship_tag": "ABX9TyPERYekFGrrz5NybowlzFbY",
      "include_colab_link": true,
      "provenance": [],
      "toc_visible": true
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}