CS224W_Colab5.ipynb

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  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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    "language_info": {
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        "name": "ipython",
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      "pygments_lexer": "ipython3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/pipaber/GNN/blob/main/CS224W_Colab5.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XuXWJLEm2UWS"
      },
      "source": [
        "# **CS224W - Colab 5**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8gzsP50bF6Gb"
      },
      "source": [
        "In this Colab, we will shift our focus from homogenous graphs to heterogeneous graphs. Heterogeneous graphs extend the traditional homogenous graphs that we have been working with by incorperating different node and edge types. This additional information allows us to extend the graph neural nework models that we have worked with before. Namely, we can apply heterogenous message passing, where different message types now exist between different node and edge type relationships.\n",
        "\n",
        "In this notebook, we will first learn how to transform NetworkX graphs into DeepSNAP representations. Then, we will dive deeper into how DeepSNAP stores and represents heterogeneous graphs as PyTorch Tensors.\n",
        "\n",
        "Lastly, we will build our own heterogenous graph neural netowrk models using PyTorch Geometric and DeepSNAP. We will then apply our models for a node property prediction task; specifically, we will evaluate these models on the heterogeneous ACM node prediction dataset.\n",
        "\n",
        "**Note**: Make sure to **sequentially run all the cells in each section**, so that the intermediate variables / packages will carry over to the next cell\n",
        "\n",
        "Have fun and good luck on Colab 5 :)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MSaetj53YnT6"
      },
      "source": [
        "# Device\n",
        "You might need to use GPU for this Colab.\n",
        "\n",
        "Please click `Runtime` and then `Change runtime type`. Then set the `hardware accelerator` to **GPU**."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "67gOQITlCNQi"
      },
      "source": [
        "# Installation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J_m9l6OYCQZP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c820a81b-a476-4424-9edb-2ce556a75ec8"
      },
      "source": [
        "# Install torch geometric\n",
        "import os\n",
        "import torch\n",
        "if 'IS_GRADESCOPE_ENV' not in os.environ:\n",
        "  torch_version = str(torch.__version__)\n",
        "  scatter_src = f\"https://pytorch-geometric.com/whl/torch-{torch_version}.html\"\n",
        "  sparse_src = f\"https://pytorch-geometric.com/whl/torch-{torch_version}.html\"\n",
        "  !pip install torch-scatter -f $scatter_src\n",
        "  !pip install torch-sparse -f $sparse_src\n",
        "  !pip install torch-geometric\n",
        "  !pip install -q git+https://github.com/snap-stanford/deepsnap.git\n",
        "  !pip install -U -q PyDrive"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in links: https://pytorch-geometric.com/whl/torch-2.2.1+cu121.html\n",
            "Collecting torch-scatter\n",
            "  Downloading https://data.pyg.org/whl/torch-2.2.0%2Bcu121/torch_scatter-2.1.2%2Bpt22cu121-cp310-cp310-linux_x86_64.whl (10.9 MB)\n",
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            "\u001b[?25hInstalling collected packages: torch-scatter\n",
            "Successfully installed torch-scatter-2.1.2+pt22cu121\n",
            "Looking in links: https://pytorch-geometric.com/whl/torch-2.2.1+cu121.html\n",
            "Collecting torch-sparse\n",
            "  Downloading https://data.pyg.org/whl/torch-2.2.0%2Bcu121/torch_sparse-0.6.18%2Bpt22cu121-cp310-cp310-linux_x86_64.whl (5.0 MB)\n",
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            "\u001b[?25hRequirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from torch-sparse) (1.11.4)\n",
            "Requirement already satisfied: numpy<1.28.0,>=1.21.6 in /usr/local/lib/python3.10/dist-packages (from scipy->torch-sparse) (1.25.2)\n",
            "Installing collected packages: torch-sparse\n",
            "Successfully installed torch-sparse-0.6.18+pt22cu121\n",
            "Collecting torch-geometric\n",
            "  Downloading torch_geometric-2.5.3-py3-none-any.whl (1.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.1/1.1 MB\u001b[0m \u001b[31m12.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from torch-geometric) (4.66.4)\n",
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            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->torch-geometric) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->torch-geometric) (3.5.0)\n",
            "Installing collected packages: torch-geometric\n",
            "Successfully installed torch-geometric-2.5.3\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for deepsnap (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qpr0ThDgZmZV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "58ecf3f7-cbea-4b59-be91-2e596f391d80"
      },
      "source": [
        "if 'IS_GRADESCOPE_ENV' not in os.environ:\n",
        "  !nvcc --version\n",
        "  !python -c \"import torch; print(torch.version.cuda)\""
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "nvcc: NVIDIA (R) Cuda compiler driver\n",
            "Copyright (c) 2005-2023 NVIDIA Corporation\n",
            "Built on Tue_Aug_15_22:02:13_PDT_2023\n",
            "Cuda compilation tools, release 12.2, V12.2.140\n",
            "Build cuda_12.2.r12.2/compiler.33191640_0\n",
            "12.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PRfgbfTjCRD_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d73ce082-e2f6-4937-8470-43bf8d6d2cf1"
      },
      "source": [
        "if 'IS_GRADESCOPE_ENV' not in os.environ:\n",
        "  import torch\n",
        "  print(torch.__version__)\n",
        "  import torch_geometric\n",
        "  print(torch_geometric.__version__)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.2.1+cu121\n",
            "2.5.3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JuCXmtthnlB4"
      },
      "source": [
        "# DeepSNAP Basics\n",
        "\n",
        "In previous Colabs we used both of graph class (NetworkX) and tensor (PyG) representations of graphs separately. The graph class `nx.Graph` provides rich analysis and manipulation functionalities, such as the clustering coefficient and PageRank. To feed the graph into the model, we need to transform the graph into tensor representations including edge tensor `edge_index` and node attributes tensors `x` and `y`. But only using tensors (as the graphs formatted in PyG `datasets` and `data`) will make many graph manipulations and analysis less efficient and harder. So, in this Colab we will use DeepSNAP which combines both representations and offers a full pipeline for GNN training / validation / testing.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o6IJYWWonr9N"
      },
      "source": [
        "In general, [DeepSNAP](https://github.com/snap-stanford/deepsnap) is a Python library to assist efficient deep learning on graphs. DeepSNAP features in its support for flexible graph manipulation, standard pipeline, heterogeneous graphs and simple API.\n",
        "\n",
        "1. DeepSNAP is easy to be used for the sophisticated graph manipulations, such as feature computation, pretraining, subgraph extraction etc. during/before the training.\n",
        "2. In most frameworks, standard pipelines for node, edge, link, graph-level tasks under inductive or transductive settings are left to the user to code. In practice, there are additional design choices involved (such as how to split dataset for link prediction). DeepSNAP provides such a standard pipeline that greatly saves repetitive coding efforts, and enables fair comparision for models.\n",
        "3. Many real-world graphs are heterogeneous graphs. But packages support for heterogeneous graphs, including data storage and flexible message passing, is lacking. DeepSNAP provides an efficient and flexible heterogeneous graph that supports both the node and edge heterogeneity.\n",
        "\n",
        "[DeepSNAP](https://github.com/snap-stanford/deepsnap) is a newly released project and it is still under development. If you find any bugs or have any improvement ideas, feel free to raise issues or create pull requests on the GitHub directly :)\n",
        "\n",
        "In this Colab, we will focus on learning using Heterogeneous Graphs. Not many libraries are able to handle heterogeneous graphs, but DeepSNAP handles them quite elegantly, which is why we're introducing it here!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZoXlf4MtYrbz"
      },
      "source": [
        "# 1) DeepSNAP Heterogeneous Graph\n",
        "\n",
        "First, we will explore how to transform a NetworkX graph into the format supported by DeepSNAP.\n",
        "\n",
        "In DeepSNAP we have three levels of attributes. We can have **node level** attributes including `node_feature` and `node_label`. The other two levels of attributes are graph and edge attributes. The usage is similar to the node level one except that the feature becomes `edge_feature` or `graph_feature` and label becomes `edge_label` or `graph_label` etc.\n",
        "\n",
        "DeepSNAP extends its traditional graph representation to include heterogeneous graphs by including the following graph property features:  \n",
        "* `node_feature`: The feature of each node (`torch.tensor`)\n",
        "* `edge_feature`: The feature of each edge (`torch.tensor`)\n",
        "* `node_label`: The label of each node (`int`)\n",
        "* `node_type`: The node type of each node (`string`)\n",
        "* `edge_type`: The edge type of each edge (`string`)\n",
        "\n",
        "where the key **new** features we add are `node_type` and `edge_type`, which enables us to perform heterogenous message passing.\n",
        "\n",
        "For this first question we will work with the familiar [karate club graph](https://networkx.github.io/documentation/stable/auto_examples/graph/plot_karate_club.html) seen in Colab 1. To start, since each node in the graph belongs to one of two clubs (club \"Mr. Hi\" or club \"Officer\"), we will treat the club as the `node_type`. The code below demonstrates how to differentiate the nodes in the NetworkX graph.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8LQ_z5gcBVA1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 736
        },
        "outputId": "f4456800-b830-4e3d-d5fb-85dde6e3c674"
      },
      "source": [
        "import networkx as nx\n",
        "from networkx.algorithms.community import greedy_modularity_communities\n",
        "import matplotlib.pyplot as plt\n",
        "import copy\n",
        "\n",
        "if 'IS_GRADESCOPE_ENV' not in os.environ:\n",
        "  from pylab import show\n",
        "  G = nx.karate_club_graph()\n",
        "  community_map = {}\n",
        "  for node in G.nodes(data=True):\n",
        "    if node[1][\"club\"] == \"Mr. Hi\":\n",
        "      community_map[node[0]] = 0\n",
        "    else:\n",
        "      community_map[node[0]] = 1\n",
        "  node_color = []\n",
        "  color_map = {0: 0, 1: 1}\n",
        "  node_color = [color_map[community_map[node]] for node in G.nodes()]\n",
        "  pos = nx.spring_layout(G)\n",
        "  plt.figure(figsize=(7, 7))\n",
        "  nx.draw(G, pos=pos, cmap=plt.get_cmap('coolwarm'), node_color=node_color)\n",
        "  show()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1JpFb9fTw1lg"
      },
      "source": [
        "### Question 1.1: Assigning Node Type and Node Features\n",
        "\n",
        "Using the `community_map` dictionary and graph `G` from above, add node attributes `node_type` and `node_label` to the graph G. Namely, for `node_type` assign nodes in the \"Mr. Hi\" club to a node type `n0` and nodes in club \"Officer\" a node type `n1`. Note: the node type should be a `string` property.\n",
        "\n",
        "Then for `node_label`, assign nodes in \"Mr. Hi\" club to a `node_label` `0` and nodes in club \"Officer\" a `node_label` of `1`.\n",
        "\n",
        "Lastly, assign every node the *tensor* feature vector $[1, 1, 1, 1, 1]$.\n",
        "\n",
        "**Hint**: Look at the NetworkX function `nx.classes.function.set_node_attributes`.\n",
        "\n",
        "**Note**: This question is not specifically graded but is important for later questions."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zev_hMJHJXK1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "55504a55-e538-46c4-8de9-8f370057d2c8"
      },
      "source": [
        "import torch\n",
        "\n",
        "def assign_node_types(G, community_map):\n",
        "  # TODO: Implement a function that takes in a NetworkX graph\n",
        "  # G and community map assignment (mapping node id --> 0/1 label)\n",
        "  # and adds 'node_type' as a node_attribute in G.\n",
        "\n",
        "  ############# Your code here ############\n",
        "  ## (~2 line of code) It's alright if you take up more lines!\n",
        "  ## Note\n",
        "  ## 1. Look up NetworkX `nx.classes.function.set_node_attributes`\n",
        "  ## 2. Look above for the two node type values!\n",
        "\n",
        "  community_map = {key: \"n\"+str(value) if isinstance(value, (int, float)) else value for key, value in community_map.items()}\n",
        "\n",
        "  nx.classes.function.set_node_attributes(G, community_map, \"node_type\")\n",
        "\n",
        "  #########################################\n",
        "\n",
        "def assign_node_labels(G, community_map):\n",
        "  # TODO: Implement a function that takes in a NetworkX graph\n",
        "  # G and community map assignment (mapping node id --> 0/1 label)\n",
        "  # and adds 'node_label' as a node_attribute in G.\n",
        "\n",
        "  ############# Your code here ############\n",
        "  ## (~2 line of code) It's alright if you take up more lines!\n",
        "  ## Note\n",
        "  ## 1. Look up NetworkX `nx.classes.function.set_node_attributes`\n",
        "\n",
        "  nx.classes.function.set_node_attributes(G, community_map, 'node_label')\n",
        "\n",
        "  #########################################\n",
        "\n",
        "def assign_node_features(G):\n",
        "  # TODO: Implement a function that takes in a NetworkX graph\n",
        "  # G and adds 'node_feature' as a node_attribute in G. Each node\n",
        "  # in the graph has the same feature vector - a torchtensor with\n",
        "  # data [1., 1., 1., 1., 1.]\n",
        "\n",
        "  ############# Your code here ############\n",
        "  ## (~2 line of code) It's alright if you take up more lines!\n",
        "  ## Note\n",
        "  ## 1. Look up NetworkX `nx.classes.function.set_node_attributes`\n",
        "\n",
        "  nx.classes.function.set_node_attributes(G, torch.tensor([1., 1., 1., 1., 1.]), 'node_feature')\n",
        "\n",
        "  #########################################\n",
        "\n",
        "if 'IS_GRADESCOPE_ENV' not in os.environ:\n",
        "  assign_node_types(G, community_map)\n",
        "  assign_node_labels(G, community_map)\n",
        "  assign_node_features(G)\n",
        "\n",
        "  # Explore node properties for the node with id: 20\n",
        "  node_id = 20\n",
        "  print (f\"Node {node_id} has properties:\", G.nodes(data=True)[node_id])"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Node 20 has properties: {'club': 'Officer', 'node_type': 'n1', 'node_label': 1, 'node_feature': tensor([1., 1., 1., 1., 1.])}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mafN0P3EOhSb"
      },
      "source": [
        "### Question 1.2: Assigning Edge Types\n",
        "\n",
        "Next, we will assign three different `edge_types`:\n",
        "* Edges within club \"Mr. Hi\": `e0`\n",
        "* Edges within club \"Officer\": `e1`\n",
        "* Edges between the two clubs: `e2`\n",
        "\n",
        "**Hint**: Use the `community_map` from before and `nx.classes.function.set_edge_attributes`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rsbYWEVwSV5n",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "523599f4-c9a1-4274-c6f9-eaa5e10f5e40"
      },
      "source": [
        "def assign_edge_types(G, community_map):\n",
        "  # TODO: Implement a function that takes in a NetworkX graph\n",
        "  # G and community map assignment (mapping node id --> 0/1 label)\n",
        "  # and adds 'edge_type' as a edge_attribute in G.\n",
        "\n",
        "  ############# Your code here ############\n",
        "  ## (~5 line of code) It's alright if you take up more lines!\n",
        "  ## Note\n",
        "  ## 1. Create an edge assignment dict following rules above\n",
        "  ## 2. Look up NetworkX `nx.classes.function.set_edge_attributes`\n",
        "\n",
        "  clubs = nx.get_node_attributes(G,\"club\")\n",
        "\n",
        "  edge_types = {}\n",
        "\n",
        "  for u, v in G.edges():\n",
        "    if clubs[u] == clubs[v]:\n",
        "      if community_map[u] == 0:\n",
        "        edge_types[(u, v)] = 'e0'\n",
        "      else:\n",
        "        edge_types[(u, v)] = 'e1'\n",
        "    else:\n",
        "      edge_types[(u, v)] = 'e2'\n",
        "\n",
        "  nx.set_edge_attributes(G, edge_types, name='edge_type')\n",
        "\n",
        "\n",
        "\n",
        "  #########################################\n",
        "\n",
        "if 'IS_GRADESCOPE_ENV' not in os.environ:\n",
        "  assign_edge_types(G, community_map)\n",
        "\n",
        "  # Explore edge properties for a sampled edge and check the corresponding\n",
        "  # node types\n",
        "  edge_idx = 15\n",
        "  n1 = 0\n",
        "  n2 = 31\n",
        "  edge = list(G.edges(data=True))[edge_idx]\n",
        "  print (f\"Edge ({edge[0]}, {edge[1]}) has properties:\", edge[2])\n",
        "  print (f\"Node {n1} has properties:\", G.nodes(data=True)[n1])\n",
        "  print (f\"Node {n2} has properties:\", G.nodes(data=True)[n2])"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Edge (0, 31) has properties: {'weight': 2, 'edge_type': 'e2'}\n",
            "Node 0 has properties: {'club': 'Mr. Hi', 'node_type': 'n0', 'node_label': 0, 'node_feature': tensor([1., 1., 1., 1., 1.])}\n",
            "Node 31 has properties: {'club': 'Officer', 'node_type': 'n1', 'node_label': 1, 'node_feature': tensor([1., 1., 1., 1., 1.])}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VBsTdTPVTQ52"
      },
      "source": [
        "## Heterogeneous Graph Visualization\n",
        "\n",
        "Now we can visualize the Heterogeneous Graph we have generated."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n2sdufbODHtp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 836
        },
        "outputId": "e81e4315-1d4d-4802-a236-f2f7287774b0"
      },
      "source": [
        "if 'IS_GRADESCOPE_ENV' not in os.environ:\n",
        "  edge_color = {}\n",
        "  for edge in G.edges():\n",
        "    n1, n2 = edge\n",
        "    edge_color[edge] = community_map[n1] if community_map[n1] == community_map[n2] else 2\n",
        "    if community_map[n1] == community_map[n2] and community_map[n1] == 0:\n",
        "      edge_color[edge] = 'blue'\n",
        "    elif community_map[n1] == community_map[n2] and community_map[n1] == 1:\n",
        "      edge_color[edge] = 'red'\n",
        "    else:\n",
        "      edge_color[edge] = 'green'\n",
        "\n",
        "  G_orig = copy.deepcopy(G)\n",
        "  nx.classes.function.set_edge_attributes(G, edge_color, name='color')\n",
        "  colors = nx.get_edge_attributes(G,'color').values()\n",
        "  labels = nx.get_node_attributes(G, 'node_type')\n",
        "  plt.figure(figsize=(8, 8))\n",
        "  nx.draw(G, pos=pos, cmap=plt.get_cmap('coolwarm'), node_color=node_color, edge_color=colors, labels=labels, font_color='white')\n",
        "  show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QRANkF1jRxLV"
      },
      "source": [
        "where we differentiate edges within each clubs (2 types) and edges between the two clubs (1 type). Different types of nodes and edges are visualized in different colors. The NetworkX object `G` in following code can be transformed into `deepsnap.hetero_graph.HeteroGraph` directly."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4DW8L0hxbxw4"
      },
      "source": [
        "## Transforming to DeepSNAP representation\n",
        "\n",
        "We will now work through transforming the NetworkX object `G` into a `deepsnap.hetero_graph.HeteroGraph`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ZfHZ_eoVVGd",
        "collapsed": true
      },
      "source": [
        "from deepsnap.hetero_graph import HeteroGraph\n",
        "\n",
        "if 'IS_GRADESCOPE_ENV' not in os.environ:\n",
        "  hete = HeteroGraph(G_orig)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "izq4t_O9WxDH"
      },
      "source": [
        "## Question 1.3: How many nodes are of each type (10 Points)\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wRNSP6nnW78C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "253f99ff-df94-421f-f87b-4a24608ebc75"
      },
      "source": [
        "def get_nodes_per_type(hete):\n",
        "  # TODO: Implement a function that takes a DeepSNAP dataset object\n",
        "  # and return the number of nodes per `node_type`.\n",
        "\n",
        "\n",
        "\n",
        "  ############# Your code here ############\n",
        "  ## (~2 line of code)\n",
        "  ## Note\n",
        "  ## 1. Colab autocomplete functionality might be useful. Explore the attributes of HeteroGraph class.\n",
        "\n",
        "  num_nodes_n0 = hete.num_nodes('n0')\n",
        "  num_nodes_n1 = hete.num_nodes('n1')\n",
        "\n",
        "\n",
        "  #########################################\n",
        "\n",
        "  return num_nodes_n0, num_nodes_n1\n",
        "\n",
        "if 'IS_GRADESCOPE_ENV' not in os.environ:\n",
        "  num_nodes_n0, num_nodes_n1 = get_nodes_per_type(hete)\n",
        "  print(\"Node type n0 has {} nodes\".format(num_nodes_n0))\n",
        "  print(\"Node type n1 has {} nodes\".format(num_nodes_n1))"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Node type n0 has 17 nodes\n",
            "Node type n1 has 17 nodes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vEsHJp2ZYaE2"
      },
      "source": [
        "## Question 1.4: Message Types - How many edges are of each message type (10 Points)\n",
        "\n",
        "When working with heterogenous graphs, as we have discussed before, we now work with heterogenous message types (i.e. different message types for the different `node_type` and `edge_type` combinations). For example, an edge of type `e0` connecting two nodes in club \"Mr. HI\" would have a message type of (`n0`, `e0`, `n0`). In this problem we will analyze how many edges in our graph are of each message type.\n",
        "\n",
        "**Hint**: If you want to learn more about what the different message types are try the call `hete.message_types`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qobKuqbAYvJ7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c5e2643d-7ded-488b-ab8b-a07c4b6d8f52"
      },
      "source": [
        "def get_num_message_edges(hete):\n",
        "  # TODO: Implement this function that takes a DeepSNAP dataset object\n",
        "  # and return the number of edges for each message type.\n",
        "  # You should return a list of tuples as\n",
        "  # (message_type, num_edge)\n",
        "\n",
        "\n",
        "  ############# Your code here ############\n",
        "  ## (~2 line of code)\n",
        "  ## Note\n",
        "  ## 1. Colab autocomplete functionality might be useful. Explore the attributes of HeteroGraph class.\n",
        "\n",
        "  message_type_edges = list(hete.num_edges().items())\n",
        "\n",
        "  #########################################\n",
        "\n",
        "  return message_type_edges\n",
        "\n",
        "if 'IS_GRADESCOPE_ENV' not in os.environ:\n",
        "  message_type_edges = get_num_message_edges(hete)\n",
        "  for (message_type, num_edges) in message_type_edges:\n",
        "    print(\"Message type {} has {} edges\".format(message_type, num_edges))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Message type ('n0', 'e0', 'n0') has 35 edges\n",
            "Message type ('n0', 'e2', 'n1') has 11 edges\n",
            "Message type ('n1', 'e1', 'n1') has 32 edges\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bjMVik1JbJ76"
      },
      "source": [
        "## Question 1.5: Dataset Splitting - How many nodes are in each dataset split? (10 Points)\n",
        "\n",
        "DeepSNAP has built in Dataset creation and splitting methods for heterogeneous graphs. Here we will create train, validation, and test datasets for a node prediction task and inspect the resulting subgraphs. Specifically, write a function that computes the number of nodes with a known label in each dataset split.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ct10Oh4gcqgD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ff4deba2-bf64-4d2c-a668-91039517a8a6"
      },
      "source": [
        "from deepsnap.dataset import GraphDataset\n",
        "\n",
        "def compute_dataset_split_counts(datasets):\n",
        "  # TODO: Implement a function that takes a dict of datasets in the form\n",
        "  # {'train': dataset_train, 'val': dataset_val, 'test': dataset_test}\n",
        "  # and returns a dict mapping dataset names to the number of labeled\n",
        "  # nodes used for supervision in that respective dataset.\n",
        "\n",
        "  data_set_splits = {}\n",
        "\n",
        "  ############# Your code here ############\n",
        "  ## (~3 line of code)\n",
        "  ## Note\n",
        "  ## 1. The DeepSNAP `node_label_index` dictionary will be helpful.\n",
        "  ## 2. Remember to count both node_types\n",
        "  ## 3. Remember each dataset only has one graph that we need to access\n",
        "  ##    (i.e. dataset[0])\n",
        "\n",
        "  for key in datasets.keys():\n",
        "\n",
        "    tensors = list(datasets[key][0].node_label_index.values())\n",
        "\n",
        "    data_set_splits[key] = sum(list(map(lambda x: len(x), tensors)))\n",
        "\n",
        "\n",
        "  #########################################\n",
        "\n",
        "  return data_set_splits\n",
        "\n",
        "if 'IS_GRADESCOPE_ENV' not in os.environ:\n",
        "  dataset = GraphDataset([hete], task='node')\n",
        "  # Splitting the dataset\n",
        "  dataset_train, dataset_val, dataset_test = dataset.split(transductive=True, split_ratio=[0.4, 0.3, 0.3])\n",
        "  datasets = {'train': dataset_train, 'val': dataset_val, 'test': dataset_test}\n",
        "\n",
        "  data_set_splits = compute_dataset_split_counts(datasets)\n",
        "  for dataset_name, num_nodes in data_set_splits.items():\n",
        "    print(\"{} dataset has {} nodes\".format(dataset_name, num_nodes))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train dataset has 12 nodes\n",
            "val dataset has 10 nodes\n",
            "test dataset has 12 nodes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lFY2PaDbVKe4"
      },
      "source": [
        "## DeepSNAP Dataset Visualization\n",
        "\n",
        "We can now visualize the different nodes and edges used in each graph dataset split."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iiyEw-agbgV8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "collapsed": true,
        "outputId": "d8a89a3b-2e50-429d-b435-110448c0e52f"
      },
      "source": [
        "from deepsnap.dataset import GraphDataset\n",
        "\n",
        "if 'IS_GRADESCOPE_ENV' not in os.environ:\n",
        "  dataset = GraphDataset([hete], task='node')\n",
        "  # Splitting the dataset\n",
        "  dataset_train, dataset_val, dataset_test = dataset.split(transductive=True, split_ratio=[0.4, 0.3, 0.3])\n",
        "  titles = ['Train', 'Validation', 'Test']\n",
        "\n",
        "  for i, dataset in enumerate([dataset_train, dataset_val, dataset_test]):\n",
        "    n0 = hete._convert_to_graph_index(dataset[0].node_label_index['n0'], 'n0').tolist()\n",
        "    n1 = hete._convert_to_graph_index(dataset[0].node_label_index['n1'], 'n1').tolist()\n",
        "\n",
        "    plt.figure(figsize=(7, 7))\n",
        "    plt.title(titles[i])\n",
        "    nx.draw(G_orig, pos=pos, node_color=\"grey\", edge_color=colors, labels=labels, font_color='white')\n",
        "    nx.draw_networkx_nodes(G_orig.subgraph(n0), pos=pos, node_color=\"blue\")\n",
        "    nx.draw_networkx_nodes(G_orig.subgraph(n1), pos=pos, node_color=\"red\")\n",
        "    show()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e5LsVSRuI3hU"
      },
      "source": [
        "# 2) Heterogeneous Graph Node Property Prediction\n",
        "\n",
        "Now, we will use PyTorch Geometric and DeepSNAP to implement a GNN model for heterogeneous graph node property prediction (node classification). We will draw upon our understanding of heterogeneous graphs from lecture and previous work in implementing GNN layers using PyG (introduced in Colab 3).\n",
        "\n",
        "First let's take a look at the general structure of a heterogeneous GNN layer by working through an example:\n",
        "\n",
        "Let's assume we have a graph $G$, which contains two node types $a$ and $b$, and three message types $m_1=(a, r_1, a)$, $m_2=(a, r_2, b)$ and $m_3=(a, r_3, b)$. Note: during message passing we view each message as (src, relation, dst), where messages \"flow\" from src to dst node types. For example, during message passing, updating node type $b$ relies on two different message types $m_2$ and $m_3$.\n",
        "\n",
        "When applying message passing in heterogenous graphs, we seperately apply message passing over each message type. Therefore, for the graph $G$, a heterogeneous GNN layer contains three seperate Heterogeneous Message Passing layers (`HeteroGNNConv` in this Colab), where each `HeteroGNNConv` layer performs message passing and aggregation with respect to *only one message type*. Since a message type is viewed as (src, relation, dst) and messages \"flow\" from src to dst, each `HeteroGNNConv` layer only computes embeddings for the *dst* nodes of a given message type. For example, the `HeteroGNNConv` layer for message type $m_2$ outputs updated embedding representations *only* for node's with type b.\n",
        "\n",
        "---\n",
        "\n",
        "An overview of the heterogeneous layer we will create is shown below:\n",
        "\n",
        "![test](https://drive.google.com/uc?export=view&id=1mkp4OeRrvC4iNFTXSywrmI6Pfl5J__gA)\n",
        "\n",
        "where we highlight the following notation:\n",
        "\n",
        "- $H_a^{(l)[m_1]}$ is the intermediate matrix of of node embeddings for node type $a$, generated by the $l$th `HeteroGNNConv` layer for message type $m_1$.\n",
        "- $H_a^{(l)}$ is the matrix with current embeddings for nodes of type $a$ after the $l$th layer of our Heterogeneous GNN model. Note that these embeddings can rely on one or more intermediate `HeteroGNNConv` layer embeddings(i.e. $H_b^{(l)}$ combines $H_b^{(l)[m_2]}$ and $H_b^{(l)[m_3]}$).\n",
        "\n",
        "Since each `HeteroGNNConv` is only applied over a single message type, we additionally define a Heterogeneous GNN Wrapper layer (`HeteroGNNWrapperConv`). This wrapper manages and combines the output of each `HeteroGNNConv` layer in order to generate the complete updated node embeddings for each node type in layer $l$ of our model. More specifically, the $l^{th}$ `HeteroGNNWrapperConv` layer takes as input the node embeddings computed for each message type and node type (e.g. $H_b^{(l)[m_2]}$ and $H_b^{(l)[m_3]}$) and aggregates across message types with the same $dst$ node type. The resulting output of the $l^{th}$ `HeteroGNNWrapperConv` layer is the updated embedding matrix $H_i^{(l)}$ for each node type i.\n",
        "\n",
        "Continuing on our example above, to compute the node embeddings $H_b^{(l)}$ the wrapper layer aggregates output embeddings from the `HeteroGNNConv` layers associated with message types $m_2$ and $m_3$ (i.e. $H_b^{(l)[m_2]}$ and $H_b^{(l)[m_3]}$).\n",
        "\n",
        "---\n",
        "\n",
        "With the `HeteroGNNWrapperConv` module, we can now draw a \"simplified\" heterogeneous layer structure as follows:\n",
        "\n",
        "<br/>\n",
        "<center>\n",
        "<img src=\"http://web.stanford.edu/class/cs224w/images/colab4/hetero_conv_1.png\"/>\n",
        "</center>\n",
        "<br/>\n",
        "\n",
        "---\n",
        "**NOTE**:\n",
        "As reference, it may be helpful to additionally read through PyG's introduciton to heterogeneous graph representations and buidling heterogeneous GNN models: https://pytorch-geometric.readthedocs.io/en/latest/notes/heterogeneous.html"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rOTCyuRcJikS"
      },
      "source": [
        "<font color='red'>Looking ahead, we recommend you implement the heterogeneous GNN model in following steps:</font>\n",
        "\n",
        "1. Implement `HeteroGNNConv`.\n",
        "2. Implement **just** `mean` aggregation within `HeteroGNNWrapperConv`.\n",
        "3. Implement `generate_convs`.\n",
        "4. Implement the `HeteroGNN` model and the `train` function.\n",
        "5. Train the model with `mean` aggregation and test your model to make sure your model has reasonable performance.\n",
        "6. Once you are confident in your mean aggregation model, implement `attn` aggregation in `HeteroGNNWrapperConv`.\n",
        "7. Train the model with `attn` aggregation and test your model to make sure your model has reasonable performance.\n",
        "\n",
        "Note: The key point of advice is to work completely through implementing the mean aggregation heterogeneous GNN model before diving into the more difficult attention based model."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pkFjcktiJJLm"
      },
      "source": [
        "## Setup"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NAm9_OcJJJ-W"
      },
      "source": [
        "import copy\n",
        "import torch\n",
        "import deepsnap\n",
        "import numpy as np\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch_geometric.nn as pyg_nn\n",
        "\n",
        "from sklearn.metrics import f1_score\n",
        "from deepsnap.hetero_gnn import forward_op\n",
        "from deepsnap.hetero_graph import HeteroGraph\n",
        "from torch_sparse import SparseTensor, matmul"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n2prITo3JSbo"
      },
      "source": [
        "## Dataset\n",
        "\n",
        "You need to login to your Google account and enter the verification code below."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QvQwxJX4JTJX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d9ccfffe-cb25-482d-cc39-a122d3ba0794"
      },
      "source": [
        "if 'IS_GRADESCOPE_ENV' not in os.environ:\n",
        "  from pydrive.auth import GoogleAuth\n",
        "  from pydrive.drive import GoogleDrive\n",
        "  from google.colab import auth\n",
        "  from oauth2client.client import GoogleCredentials\n",
        "\n",
        "  # Authenticate and create the PyDrive client\n",
        "  auth.authenticate_user()\n",
        "  gauth = GoogleAuth()\n",
        "  gauth.credentials = GoogleCredentials.get_application_default()\n",
        "  drive = GoogleDrive(gauth)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:root:pydrive is deprecated and no longer maintained. We recommend that you migrate your projects to pydrive2, the maintained fork of pydrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Igoy4F_xJbVn"
      },
      "source": [
        "if 'IS_GRADESCOPE_ENV' not in os.environ:\n",
        "  id='1ivlxd6lJMcZ9taS44TMGG72x2V1GeVvk'\n",
        "  downloaded = drive.CreateFile({'id': id})\n",
        "  downloaded.GetContentFile('acm.pkl')"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VBlboS5kJmJL"
      },
      "source": [
        "## Implementing `HeteroGNNConv`\n",
        "\n",
        "Now let's start working on our own implementation of the heterogeneous message passing layer (`HeteroGNNConv`)! Just as in Colabs 3 and 4, we will implement the layer using PyTorch Geometric.\n",
        "\n",
        "At a high level, the `HeteroGNNConv` layer is equivalent to the homogenous GNN layers we implemented in Colab 3, but now applied to an individual heterogeous message type. Moreover, our heterogeneous GNN layer draws directly from the **GraphSAGE** message passing model ([Hamilton et al. (2017)](https://arxiv.org/abs/1706.02216)).\n",
        "\n",
        "We begin by defining the `HeteroGNNConv` layer with respect to message type $m$:\n",
        "\n",
        "\\begin{equation}\n",
        "m =(s, r, d)\n",
        "\\end{equation}\n",
        "\n",
        "where each message type is a tuple containing three elements: $s$ - the source node type, $r$ - the edge (relation) type, and $d$ - the destination node type.\n",
        "\n",
        "The message passing update rule that we implement is very similar to that of GraphSAGE, except we now need to include the node types and the edge relation type. The update rule for message type $m$ is described below:\n",
        "\n",
        "\\begin{equation}\n",
        "h_v^{(l)[m]} = W^{(l)[m]} \\cdot \\text{CONCAT} \\Big( W_d^{(l)[m]} \\cdot h_v^{(l-1)}, W_s^{(l)[m]} \\cdot AGG(\\{h_u^{(l-1)}, \\forall u \\in N_{m}(v) \\})\\Big)\n",
        "\\end{equation}\n",
        "\n",
        "where we compute $h_v^{(l)[m]}$, the node embedding representation for node $v$ after `HeteroGNNConv` layer $l$ with respect message type $m$. Further unpacking the forumla we have:\n",
        "- $W_s^{(l)[m]}$ - linear transformation matrix for the messages of neighboring source nodes of type $s$ along message type $m$.\n",
        "- $W_d^{(l)[m]}$ - linear transformation matrix for the message from the node $v$ itself of type $d$.\n",
        "- $W^{(l)[m]}$ - linear transformation matrix for the concatenated messages from neighboring node's and the central node.\n",
        "- $h_u^{(l-1)}$ - the hidden embedding representation for node $u$ after the $(l-1)$th `HeteroGNNWrapperConv` layer. Note, that this embedding is not associated with a particular message type (see layer diagrams above).\n",
        "- $N_{m}(v)$ - the set of neighbor source nodes $s$ for the node v that we are embedding along message type $m = (s, r, d)$.\n",
        "\n",
        "**NOTE**: We emphasize that each weight matrix is associated with a specific message type $[m]$ and additionally, the weight matrices applied to node messages are differentiated by node type (i.e. $W_s$ and $W_d$).\n",
        "\n",
        "Lastly, for simplicity, we use mean aggregations for $AGG$ where:\n",
        "\n",
        "\\begin{equation}\n",
        "AGG(\\{h_u^{(l-1)}, \\forall u \\in N_{m}(v) \\}) = \\frac{1}{|N_{m}(v)|} \\sum_{u\\in N_{m}(v)} h_u^{(l-1)}\n",
        "\\end{equation}"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Z1b0Mf8Jova"
      },
      "source": [
        "class HeteroGNNConv(pyg_nn.MessagePassing):\n",
        "    def __init__(self, in_channels_src, in_channels_dst, out_channels):\n",
        "        super(HeteroGNNConv, self).__init__(aggr=\"mean\")\n",
        "\n",
        "        self.in_channels_src = in_channels_src\n",
        "        self.in_channels_dst = in_channels_dst\n",
        "        self.out_channels = out_channels\n",
        "\n",
        "        # To simplify implementation, please initialize both self.lin_dst\n",
        "        # and self.lin_src out_features to out_channels\n",
        "\n",
        "\n",
        "        ############# Your code here #############\n",
        "        ## (~3 lines of code)\n",
        "        ## Note:\n",
        "        ## 1. Initialize the 3 linear layers.\n",
        "        ## 2. Think through the connection between the mathematical\n",
        "        ##    definition of the update rule and torch linear layers!\n",
        "\n",
        "        self.lin_dst = nn.Linear(in_channels_dst, out_channels)\n",
        "        self.lin_src = nn.Linear(in_channels_src, out_channels)\n",
        "\n",
        "        self.lin_update = nn.Linear(out_channels*2, out_channels)\n",
        "\n",
        "\n",
        "        ##########################################\n",
        "\n",
        "    def forward(\n",
        "        self,\n",
        "        node_feature_src,\n",
        "        node_feature_dst,\n",
        "        edge_index,\n",
        "        size=None):\n",
        "\n",
        "        ############# Your code here #############\n",
        "        ## (~1 line of code)\n",
        "        ## Note:\n",
        "        ## 1. Unlike Colabs 3 and 4, we just need to call self.propagate with\n",
        "        ## proper/custom arguments.\n",
        "\n",
        "        return self.propagate(edge_index=edge_index, size=size, node_feature_src=node_feature_src, node_feature_dst=node_feature_dst)\n",
        "\n",
        "\n",
        "\n",
        "        ##########################################\n",
        "\n",
        "    def message_and_aggregate(self, edge_index, node_feature_src):\n",
        "\n",
        "\n",
        "        ############# Your code here #############\n",
        "        ## (~1 line of code)\n",
        "        ## Note:\n",
        "        ## 1. Different from what we implemented in Colabs 3 and 4, we use message_and_aggregate\n",
        "        ##    to combine the previously seperate message and aggregate functions.\n",
        "        ##    The benefit is that we can avoid materializing x_i and x_j\n",
        "        ##    to make the implementation more efficient.\n",
        "        ## 2. To implement efficiently, refer to PyG documentation for message_and_aggregate\n",
        "        ##    and sparse-matrix multiplication:\n",
        "        ##    https://pytorch-geometric.readthedocs.io/en/latest/notes/sparse_tensor.html\n",
        "        ## 3. Here edge_index is torch_sparse SparseTensor. Although interesting, you\n",
        "        ##    do not need to deeply understand SparseTensor represenations!\n",
        "        ## 4. Conceptually, think through how the message passing and aggregation\n",
        "        ##    expressed mathematically can be expressed through matrix multiplication.\n",
        "\n",
        "        out = matmul(edge_index, node_feature_src, reduce=self.aggr)\n",
        "\n",
        "\n",
        "        ##########################################\n",
        "\n",
        "        return out\n",
        "\n",
        "    def update(self, aggr_out, node_feature_dst):\n",
        "\n",
        "        ############# Your code here #############\n",
        "        ## (~4 lines of code)\n",
        "        ## Note:\n",
        "        ## 1. The update function is called after message_and_aggregate\n",
        "        ## 2. Think through the one-one connection between the mathematical update\n",
        "        ##    rule and the 3 linear layers defined in the constructor.\n",
        "\n",
        "        aggr_out = torch.cat([self.lin_dst(node_feature_dst), self.lin_src(aggr_out)], dim=-1)\n",
        "\n",
        "        aggr_out = self.lin_update(aggr_out)\n",
        "\n",
        "\n",
        "\n",
        "        ##########################################\n",
        "\n",
        "        return aggr_out"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aKq8ScTiJthn"
      },
      "source": [
        "## Heterogeneous GNN Wrapper Layer\n",
        "\n",
        "After implementing the `HeteroGNNConv` layer for each message type, we need to manage and aggregate the node embedding results (with respect to each message types). Here we will implement two types of message type level aggregation.\n",
        "\n",
        "The first one is simply mean aggregation over message types:\n",
        "\n",
        "\\begin{equation}\n",
        "h_v^{(l)} = \\frac{1}{M}\\sum_{m=1}^{M}h_v^{(l)[m]}\n",
        "\\end{equation}\n",
        "\n",
        "where node $v$ has node type $d$ and we sum over the $M$ message types that have destination node type $d$. From our original example, for a node v of type $b$ we aggregate v's `HeteroGNNConv` embeddings for message types $m_2$ and $m_3$ (i.e. $h_v^{(l)[m_2]}$ and $h_v^{(l)[m_3]}$).\n",
        "\n",
        "The second method we implement is the semantic level attention introduced in **HAN** ([Wang et al. (2019)](https://arxiv.org/abs/1903.07293)). Instead of directly averaging on the message type aggregation results, we use attention to learn which message type result is more important, then aggregate across all the message types. Below are the equations for semantic level attention:\n",
        "\n",
        "\\begin{equation}\n",
        "e_{m} = \\frac{1}{|V_{d}|} \\sum_{v \\in V_{d}} q_{attn}^T \\cdot tanh \\Big( W_{attn}^{(l)} \\cdot h_v^{(l)[m]} + b \\Big)\n",
        "\\end{equation}\n",
        "\n",
        "where $m$ is the message type and $d$ refers to the destination node type for that message ($m = (s, r, d)$). Additionally, $V_{d}$ refers to the set of nodes v with type $d$. Lastly, the unormalized attention weight $e_m$ is a scaler computed for each message type $m$.\n",
        "\n",
        "Next, we can compute the normalized attention weights and update $h_v^{(l)}$:\n",
        "\n",
        "\\begin{equation}\n",
        "\\alpha_{m} = \\frac{\\exp(e_{m})}{\\sum_{m=1}^M \\exp(e_{m})}\n",
        "\\end{equation}\n",
        "\n",
        "\\begin{equation}\n",
        "h_v^{(l)} = \\sum_{m=1}^{M} \\alpha_{m} \\cdot h_v^{(l)[m]}\n",
        "\\end{equation}\n",
        "\n",
        ", where we emphasize that $M$ here is the number of message types associated with the destination node type $d$.\n",
        "\n",
        "**Note**: The implementation of the attention aggregation is tricky and nuanced. We strongly recommend working carefully through the math equations to undersatnd exactly what each notation refers to and how all the pieces fit together. If you can, try to connect the math to our original example, focusing on node type $b$, which depends on two different message types!\n",
        "\n",
        "**_We've implemented most of this for you but you'll need to initialize self.attn_proj in the initializer_**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0_bun02xJwFm"
      },
      "source": [
        "class HeteroGNNWrapperConv(deepsnap.hetero_gnn.HeteroConv):\n",
        "    def __init__(self, convs, args, aggr=\"mean\"):\n",
        "        super(HeteroGNNWrapperConv, self).__init__(convs, None)\n",
        "        self.aggr = aggr\n",
        "\n",
        "        # Map the index and message type\n",
        "        self.mapping = {}\n",
        "\n",
        "        # A numpy array that stores the final attention probability\n",
        "        self.alpha = None\n",
        "\n",
        "        self.attn_proj = None\n",
        "\n",
        "        if self.aggr == \"attn\":\n",
        "            ############# Your code here #############\n",
        "            ## (~1 line of code)\n",
        "            ## Note:\n",
        "            ## 1. Initialize self.attn_proj, where self.attn_proj should include\n",
        "            ##    two linear layers. Note, make sure you understand\n",
        "            ##    which part of the equation self.attn_proj captures.\n",
        "            ## 2. You should use nn.Sequential for self.attn_proj\n",
        "            ## 3. nn.Linear and nn.Tanh are useful.\n",
        "            ## 4. You can model a weight vector (rather than matrix) by using:\n",
        "            ##    nn.Linear(some_size, 1, bias=False).\n",
        "            ## 5. The first linear layer should have out_features as args['attn_size']\n",
        "            ## 6. You can assume we only have one \"head\" for the attention.\n",
        "            ## 7. We recommend you to implement the mean aggregation first. After\n",
        "            ##    the mean aggregation works well in the training, then you can\n",
        "            ##    implement this part.\n",
        "\n",
        "            self.attn_proj = nn.Sequential(nn.Linear(args['hidden_size'], args['attn_size']),\n",
        "                                           nn.Tanh(),\n",
        "                                           nn.Linear(args['attn_size'], 1, bias=False))\n",
        "\n",
        "            ##########################################\n",
        "\n",
        "    def reset_parameters(self):\n",
        "        super(HeteroConvWrapper, self).reset_parameters()\n",
        "        if self.aggr == \"attn\":\n",
        "            for layer in self.attn_proj.children():\n",
        "                layer.reset_parameters()\n",
        "\n",
        "    def forward(self, node_features, edge_indices):\n",
        "        message_type_emb = {}\n",
        "        for message_key, message_type in edge_indices.items():\n",
        "            src_type, edge_type, dst_type = message_key\n",
        "            node_feature_src = node_features[src_type]\n",
        "            node_feature_dst = node_features[dst_type]\n",
        "            edge_index = edge_indices[message_key]\n",
        "            message_type_emb[message_key] = (\n",
        "                self.convs[message_key](\n",
        "                    node_feature_src,\n",
        "                    node_feature_dst,\n",
        "                    edge_index,\n",
        "                )\n",
        "            )\n",
        "        node_emb = {dst: [] for _, _, dst in message_type_emb.keys()}\n",
        "        mapping = {}\n",
        "        for (src, edge_type, dst), item in message_type_emb.items():\n",
        "            mapping[len(node_emb[dst])] = (src, edge_type, dst)\n",
        "            node_emb[dst].append(item)\n",
        "        self.mapping = mapping\n",
        "        for node_type, embs in node_emb.items():\n",
        "            if len(embs) == 1:\n",
        "                node_emb[node_type] = embs[0]\n",
        "            else:\n",
        "                node_emb[node_type] = self.aggregate(embs)\n",
        "        return node_emb\n",
        "\n",
        "    def aggregate(self, xs):\n",
        "        # TODO: Implement this function that aggregates all message type results.\n",
        "        # Here, xs is a list of tensors (embeddings) with respect to message\n",
        "        # type aggregation results.\n",
        "\n",
        "        if self.aggr == \"mean\":\n",
        "\n",
        "            ############# Your code here #############\n",
        "            ## (~2 lines of code)\n",
        "            ## Note:\n",
        "            ## 1. Explore the function parameter `xs`!\n",
        "\n",
        "            return torch.stack(xs).mean(dim=0)\n",
        "\n",
        "            ##########################################\n",
        "\n",
        "        elif self.aggr == \"attn\":\n",
        "\n",
        "            N = xs[0].shape[0] # Number of nodes for that node type\n",
        "            M = len(xs) # Number of message types for that node type\n",
        "\n",
        "            x = torch.cat(xs, dim=0).view(M, N, -1) # M * N * D\n",
        "            z = self.attn_proj(x).view(M, N) # M * N * 1\n",
        "            z = z.mean(1) # M * 1\n",
        "            alpha = torch.softmax(z, dim=0) # M * 1\n",
        "\n",
        "            # Store the attention result to self.alpha as np array\n",
        "            self.alpha = alpha.view(-1).data.cpu().numpy()\n",
        "\n",
        "            alpha = alpha.view(M, 1, 1)\n",
        "            x = x * alpha\n",
        "            return x.sum(dim=0)"
      ],
      "execution_count": 89,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tn_pnCOKJw-d"
      },
      "source": [
        "## Initialize Heterogeneous GNN Layers\n",
        "\n",
        "Now let's put it all together and initialize the Heterogeneous GNN Layers. Different from the homogeneous graph case, heterogeneous graphs can be a little bit complex.\n",
        "\n",
        "In general, we need to create a dictionary of `HeteroGNNConv` layers where the keys are message types.\n",
        "\n",
        "* To get all message types, `deepsnap.hetero_graph.HeteroGraph.message_types` is useful.\n",
        "* If we are initializing the first conv layers, we need to get the feature dimension of each node type. Using `deepsnap.hetero_graph.HeteroGraph.num_node_features(node_type)` will return the node feature dimension of `node_type`. In this function, we will set each `HeteroGNNConv` `out_channels` to be `hidden_size`.\n",
        "* If we are not initializing the first conv layers, all node types will have the smae embedding dimension `hidden_size` and we still set `HeteroGNNConv` `out_channels` to be `hidden_size` for simplicity.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(hete.message_types)\n",
        "print(hete.num_node_features())\n",
        "print(hete.node_types)\n",
        "print(hete.num_node_labels())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BOoFBUO-z22Y",
        "outputId": "ffd039cc-cc5a-469d-c976-a0148ce825a2"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[('n0', 'e0', 'n0'), ('n0', 'e2', 'n1'), ('n1', 'e1', 'n1')]\n",
            "{'n0': 5, 'n1': 5}\n",
            "['n0', 'n1']\n",
            "{'n0': 1, 'n1': 1}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hSBImHClJzf4"
      },
      "source": [
        "def generate_convs(hetero_graph, conv, hidden_size, first_layer=False):\n",
        "    # TODO: Implement this function that returns a dictionary of `HeteroGNNConv`\n",
        "    # layers where the keys are message types. `hetero_graph` is deepsnap `HeteroGraph`\n",
        "    # object and the `conv` is the `HeteroGNNConv`.\n",
        "\n",
        "    convs = {}\n",
        "\n",
        "    ############# Your code here #############\n",
        "    ## (~9 lines of code)\n",
        "    ## Note:\n",
        "    ## 1. See the hints above!\n",
        "    ## 2. conv is of type `HeteroGNNConv`\n",
        "\n",
        "    for message_type in hetero_graph.message_types:\n",
        "\n",
        "        src_type, edge_type, dst_type = message_type\n",
        "\n",
        "        if first_layer:\n",
        "            in_channels = hetero_graph.num_node_features(src_type)\n",
        "            dst_channels = hetero_graph.num_node_features(dst_type)\n",
        "            out_channels = hidden_size\n",
        "\n",
        "        else:\n",
        "            in_channels = hidden_size\n",
        "            dst_channels = hidden_size\n",
        "            out_channels = hidden_size\n",
        "\n",
        "        convs[message_type] = conv(in_channels_src = in_channels, in_channels_dst=dst_channels, out_channels=out_channels)\n",
        "\n",
        "\n",
        "\n",
        "    ##########################################\n",
        "\n",
        "    return convs"
      ],
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U39dX8EpJ3FG"
      },
      "source": [
        "## HeteroGNN\n",
        "\n",
        "Now we will make a simple HeteroGNN model which contains only two `HeteroGNNWrapperConv` layers.\n",
        "\n",
        "For the forward function in `HeteroGNN`, the model is going to be run as following:\n",
        "\n",
        "$\\text{self.convs1} \\rightarrow \\text{self.bns1} \\rightarrow \\text{self.relus1} \\rightarrow \\text{self.convs2} \\rightarrow \\text{self.bns2} \\rightarrow \\text{self.relus2} \\rightarrow \\text{self.post_mps}$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rplknA8aJ6J5"
      },
      "source": [
        "class HeteroGNN(torch.nn.Module):\n",
        "    def __init__(self, hetero_graph, args, aggr=\"mean\"):\n",
        "        super(HeteroGNN, self).__init__()\n",
        "\n",
        "        self.aggr = aggr\n",
        "        self.hidden_size = args['hidden_size']\n",
        "\n",
        "        self.convs1 = None\n",
        "        self.convs2 = None\n",
        "\n",
        "        self.bns1 = nn.ModuleDict()\n",
        "        self.bns2 = nn.ModuleDict()\n",
        "        self.relus1 = nn.ModuleDict()\n",
        "        self.relus2 = nn.ModuleDict()\n",
        "        self.post_mps = nn.ModuleDict()\n",
        "\n",
        "        ############# Your code here #############\n",
        "        ## (~10 lines of code)\n",
        "        ## Note:\n",
        "        ## 1. For self.convs1 and self.convs2, call generate_convs at first and then\n",
        "        ##    pass the returned dictionary of `HeteroGNNConv` to `HeteroGNNWrapperConv`.\n",
        "        ## 2. For self.bns, self.relus and self.post_mps, the keys are node_types.\n",
        "        ##    `deepsnap.hetero_graph.HeteroGraph.node_types` will be helpful.\n",
        "        ## 3. Initialize all batchnorms to torch.nn.BatchNorm1d(hidden_size, eps=1).\n",
        "        ## 4. Initialize all relus to nn.LeakyReLU().\n",
        "        ## 5. For self.post_mps, each value in the ModuleDict is a linear layer\n",
        "        ##    where the `out_features` is the number of classes for that node type.\n",
        "        ##    `deepsnap.hetero_graph.HeteroGraph.num_node_labels(node_type)` will be\n",
        "        ##    useful.\n",
        "\n",
        "        self.convs1 = HeteroGNNWrapperConv(generate_convs(hetero_graph, HeteroGNNConv, self.hidden_size, first_layer=True), args, self.aggr)\n",
        "        self.convs2 = HeteroGNNWrapperConv(generate_convs(hetero_graph, HeteroGNNConv, self.hidden_size, first_layer=False), args, self.aggr)\n",
        "\n",
        "        for node_type in hetero_graph.node_types:\n",
        "          self.bns1[node_type] = nn.BatchNorm1d(self.hidden_size, eps=1)\n",
        "          self.bns2[node_type] = nn.BatchNorm1d(self.hidden_size, eps=1)\n",
        "          self.relus1[node_type] = nn.LeakyReLU()\n",
        "          self.relus2[node_type] = nn.LeakyReLU()\n",
        "          self.post_mps[node_type] = nn.Linear(self.hidden_size, hetero_graph.num_node_labels(node_type))\n",
        "\n",
        "\n",
        "        ##########################################\n",
        "\n",
        "    def forward(self, node_feature, edge_index):\n",
        "        # TODO: Implement the forward function. Notice that `node_feature` is\n",
        "        # a dictionary of tensors where keys are node types and values are\n",
        "        # corresponding feature tensors. The `edge_index` is a dictionary of\n",
        "        # tensors where keys are message types and values are corresponding\n",
        "        # edge index tensors (with respect to each message type).\n",
        "\n",
        "        x = node_feature\n",
        "\n",
        "        ############# Your code here #############\n",
        "        ## (~7 lines of code)\n",
        "        ## Note:\n",
        "        ## 1. `deepsnap.hetero_gnn.forward_op` can be helpful.\n",
        "\n",
        "        x = self.convs1(x, edge_index)\n",
        "        x = deepsnap.hetero_gnn.forward_op(x, self.bns1)\n",
        "        x = deepsnap.hetero_gnn.forward_op(x, self.relus1)\n",
        "\n",
        "        x = self.convs2(x, edge_index)\n",
        "        x = deepsnap.hetero_gnn.forward_op(x, self.bns2)\n",
        "        x = deepsnap.hetero_gnn.forward_op(x, self.relus2)\n",
        "\n",
        "        x = deepsnap.hetero_gnn.forward_op(x, self.post_mps)\n",
        "\n",
        "\n",
        "        ##########################################\n",
        "\n",
        "        return x\n",
        "\n",
        "    def loss(self, preds, y, indices):\n",
        "\n",
        "        loss = 0\n",
        "        loss_func = F.cross_entropy\n",
        "\n",
        "        ############# Your code here #############\n",
        "        ## (~3 lines of code)\n",
        "        ## Note:\n",
        "        ## 1. For each node type in preds, accumulate computed loss to `loss`\n",
        "        ## 2. Loss need to be computed with respect to the given index\n",
        "        ## 3. preds is a dictionary of model predictions keyed by node_type.\n",
        "        ## 4. indices is a dictionary of labeled supervision nodes keyed\n",
        "        ##    by node_type\n",
        "\n",
        "        for node_type in preds:\n",
        "          loss += loss_func(preds[node_type][indices[node_type]], y[node_type][indices[node_type]])\n",
        "\n",
        "\n",
        "        ##########################################\n",
        "\n",
        "        return loss"
      ],
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o9e7q_hUJ8zB"
      },
      "source": [
        "## Training and Testing\n",
        "\n",
        "Here we provide you with the functions to train and test. You only need to implement one line of code here.\n",
        "\n",
        "**Please do not modify other parts in `train` and `test` for grading purposes.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CI5Hl_5TJ_YL"
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "def train(model, optimizer, hetero_graph, train_idx):\n",
        "    model.train()\n",
        "    optimizer.zero_grad()\n",
        "    preds = model(hetero_graph.node_feature, hetero_graph.edge_index)\n",
        "\n",
        "    loss = None\n",
        "\n",
        "    ############# Your code here #############\n",
        "    ## Note:\n",
        "    ## 1. Compute the loss here\n",
        "    ## 2. `deepsnap.hetero_graph.HeteroGraph.node_label` is useful\n",
        "\n",
        "    loss = model.loss(preds, hetero_graph.node_label, train_idx)\n",
        "\n",
        "\n",
        "    ##########################################\n",
        "\n",
        "    loss.backward()\n",
        "    optimizer.step()\n",
        "    return loss.item()\n",
        "\n",
        "def test(model, graph, indices, best_model=None, best_val=0, save_preds=False, agg_type=None):\n",
        "    model.eval()\n",
        "    accs = []\n",
        "    for i, index in enumerate(indices):\n",
        "        preds = model(graph.node_feature, graph.edge_index)\n",
        "        num_node_types = 0\n",
        "        micro = 0\n",
        "        macro = 0\n",
        "        for node_type in preds:\n",
        "            idx = index[node_type]\n",
        "            pred = preds[node_type][idx]\n",
        "            pred = pred.max(1)[1]\n",
        "            label_np = graph.node_label[node_type][idx].cpu().numpy()\n",
        "            pred_np = pred.cpu().numpy()\n",
        "            micro = f1_score(label_np, pred_np, average='micro')\n",
        "            macro = f1_score(label_np, pred_np, average='macro')\n",
        "            num_node_types += 1\n",
        "\n",
        "        # Averaging f1 score might not make sense, but in our example we only\n",
        "        # have one node type\n",
        "        micro /= num_node_types\n",
        "        macro /= num_node_types\n",
        "        accs.append((micro, macro))\n",
        "\n",
        "        # Only save the test set predictions and labels!\n",
        "        if save_preds and i == 2:\n",
        "          print (\"Saving Heterogeneous Node Prediction Model Predictions with Agg:\", agg_type)\n",
        "          print()\n",
        "\n",
        "          data = {}\n",
        "          data['pred'] = pred_np\n",
        "          data['label'] = label_np\n",
        "\n",
        "          df = pd.DataFrame(data=data)\n",
        "          # Save locally as csv\n",
        "          df.to_csv('ACM-Node-' + agg_type + 'Agg.csv', sep=',', index=False)\n",
        "\n",
        "    if accs[1][0] > best_val:\n",
        "        best_val = accs[1][0]\n",
        "        best_model = copy.deepcopy(model)\n",
        "    return accs, best_model, best_val"
      ],
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DpNz9B5AKBUU"
      },
      "source": [
        "# Please do not change the following parameters\n",
        "args = {\n",
        "    'device': torch.device('cuda' if torch.cuda.is_available() else 'cpu'),\n",
        "    'hidden_size': 64,\n",
        "    'epochs': 100,\n",
        "    'weight_decay': 1e-5,\n",
        "    'lr': 0.003,\n",
        "    'attn_size': 32,\n",
        "}"
      ],
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pRHbWD4hKED8"
      },
      "source": [
        "## Dataset and Preprocessing\n",
        "\n",
        "In the next, we will load the data and create a tensor backend (without a NetworkX graph) `deepsnap.hetero_graph.HeteroGraph` object.\n",
        "\n",
        "We will use the `ACM(3025)` dataset in our node property prediction task, which is proposed in **HAN** ([Wang et al. (2019)](https://arxiv.org/abs/1903.07293)) and our dataset is extracted from [DGL](https://www.dgl.ai/)'s [ACM.mat](https://data.dgl.ai/dataset/ACM.mat).\n",
        "\n",
        "The original ACM dataset has three node types and two edge (relation) types. For simplicity, we simplify the heterogeneous graph to one node type and two edge types (shown below). This means that in our heterogeneous graph, we have one node type (paper) and two message types (paper, author, paper) and (paper, subject, paper).\n",
        "\n",
        "<br/>\n",
        "<center>\n",
        "<img src=\"http://web.stanford.edu/class/cs224w/images/colab4/cs224w-acm.png\"/>\n",
        "</center>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lJy03_IsKGh6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b2f40552-cfa1-4006-de69-863574983ff6"
      },
      "source": [
        "if 'IS_GRADESCOPE_ENV' not in os.environ:\n",
        "  print(\"Device: {}\".format(args['device']))\n",
        "\n",
        "  # Load the data\n",
        "  data = torch.load(\"acm.pkl\")\n",
        "\n",
        "  # Message types\n",
        "  message_type_1 = (\"paper\", \"author\", \"paper\")\n",
        "  message_type_2 = (\"paper\", \"subject\", \"paper\")\n",
        "\n",
        "  # Dictionary of edge indices\n",
        "  edge_index = {}\n",
        "  edge_index[message_type_1] = data['pap']\n",
        "  edge_index[message_type_2] = data['psp']\n",
        "\n",
        "  # Dictionary of node features\n",
        "  node_feature = {}\n",
        "  node_feature[\"paper\"] = data['feature']\n",
        "\n",
        "  # Dictionary of node labels\n",
        "  node_label = {}\n",
        "  node_label[\"paper\"] = data['label']\n",
        "\n",
        "  # Load the train, validation and test indices\n",
        "  train_idx = {\"paper\": data['train_idx'].to(args['device'])}\n",
        "  val_idx = {\"paper\": data['val_idx'].to(args['device'])}\n",
        "  test_idx = {\"paper\": data['test_idx'].to(args['device'])}\n",
        "\n",
        "  # Construct a deepsnap tensor backend HeteroGraph\n",
        "  hetero_graph = HeteroGraph(\n",
        "      node_feature=node_feature,\n",
        "      node_label=node_label,\n",
        "      edge_index=edge_index,\n",
        "      directed=True\n",
        "  )\n",
        "\n",
        "  print(f\"ACM heterogeneous graph: {hetero_graph.num_nodes()} nodes, {hetero_graph.num_edges()} edges\")\n",
        "\n",
        "  # Node feature and node label to device\n",
        "  for key in hetero_graph.node_feature:\n",
        "      hetero_graph.node_feature[key] = hetero_graph.node_feature[key].to(args['device'])\n",
        "  for key in hetero_graph.node_label:\n",
        "      hetero_graph.node_label[key] = hetero_graph.node_label[key].to(args['device'])\n",
        "\n",
        "  # Edge_index to sparse tensor and to device\n",
        "  for key in hetero_graph.edge_index:\n",
        "      edge_index = hetero_graph.edge_index[key]\n",
        "      adj = SparseTensor(row=edge_index[0], col=edge_index[1], sparse_sizes=(hetero_graph.num_nodes('paper'), hetero_graph.num_nodes('paper')))\n",
        "      hetero_graph.edge_index[key] = adj.t().to(args['device'])\n",
        "  print(hetero_graph.edge_index[message_type_1])\n",
        "  print(hetero_graph.edge_index[message_type_2])"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Device: cuda\n",
            "ACM heterogeneous graph: {'paper': 3025} nodes, {('paper', 'author', 'paper'): 26256, ('paper', 'subject', 'paper'): 2207736} edges\n",
            "SparseTensor(row=tensor([   0,    0,    0,  ..., 3024, 3024, 3024], device='cuda:0'),\n",
            "             col=tensor([   8,   20,   51,  ..., 2948, 2983, 2991], device='cuda:0'),\n",
            "             size=(3025, 3025), nnz=26256, density=0.29%)\n",
            "SparseTensor(row=tensor([   0,    0,    0,  ..., 3024, 3024, 3024], device='cuda:0'),\n",
            "             col=tensor([  75,  434,  534,  ..., 3020, 3021, 3022], device='cuda:0'),\n",
            "             size=(3025, 3025), nnz=2207736, density=24.13%)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hrmU5-QQKJv6"
      },
      "source": [
        "## Start Training!\n",
        "\n",
        "Now lets start training!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y0HplV9hKMkc"
      },
      "source": [
        "## Training the Mean Aggregation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZgwfyzLbKOUw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d11a002e-2cb1-4254-d4e9-d025af962968"
      },
      "source": [
        "if 'IS_GRADESCOPE_ENV' not in os.environ:\n",
        "  best_model = None\n",
        "  best_val = 0\n",
        "\n",
        "  model = HeteroGNN(hetero_graph, args, aggr=\"mean\").to(args['device'])\n",
        "  optimizer = torch.optim.Adam(model.parameters(), lr=args['lr'], weight_decay=args['weight_decay'])\n",
        "\n",
        "  for epoch in range(args['epochs']):\n",
        "      loss = train(model, optimizer, hetero_graph, train_idx)\n",
        "      accs, best_model, best_val = test(model, hetero_graph, [train_idx, val_idx, test_idx], best_model, best_val)\n",
        "      print(\n",
        "          f\"Epoch {epoch + 1}: loss {round(loss, 5)}, \"\n",
        "          f\"train micro {round(accs[0][0] * 100, 2)}%, train macro {round(accs[0][1] * 100, 2)}%, \"\n",
        "          f\"valid micro {round(accs[1][0] * 100, 2)}%, valid macro {round(accs[1][1] * 100, 2)}%, \"\n",
        "          f\"test micro {round(accs[2][0] * 100, 2)}%, test macro {round(accs[2][1] * 100, 2)}%\"\n",
        "      )\n",
        "  best_accs, _, _ = test(best_model, hetero_graph, [train_idx, val_idx, test_idx], save_preds=True, agg_type=\"Mean\")\n",
        "  print(\n",
        "      f\"Best model: \"\n",
        "      f\"train micro {round(best_accs[0][0] * 100, 2)}%, train macro {round(best_accs[0][1] * 100, 2)}%, \"\n",
        "      f\"valid micro {round(best_accs[1][0] * 100, 2)}%, valid macro {round(best_accs[1][1] * 100, 2)}%, \"\n",
        "      f\"test micro {round(best_accs[2][0] * 100, 2)}%, test macro {round(best_accs[2][1] * 100, 2)}%\"\n",
        "  )"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1: loss 1.09876, train micro 33.33%, train macro 16.67%, valid micro 33.33%, valid macro 16.67%, test micro 31.29%, test macro 15.89%\n",
            "Epoch 2: loss 1.09194, train micro 67.0%, train macro 56.1%, valid micro 66.33%, valid macro 54.35%, test micro 63.25%, test macro 51.91%\n",
            "Epoch 3: loss 1.06664, train micro 66.67%, train macro 55.12%, valid micro 66.33%, valid macro 54.81%, test micro 65.79%, test macro 54.37%\n",
            "Epoch 4: loss 1.01382, train micro 66.5%, train macro 54.86%, valid micro 66.0%, valid macro 54.82%, test micro 65.79%, test macro 54.52%\n",
            "Epoch 5: loss 0.92124, train micro 66.83%, train macro 55.7%, valid micro 66.0%, valid macro 54.82%, test micro 65.69%, test macro 54.45%\n",
            "Epoch 6: loss 0.78486, train micro 67.0%, train macro 56.06%, valid micro 66.0%, valid macro 54.82%, test micro 65.65%, test macro 54.42%\n",
            "Epoch 7: loss 0.62393, train micro 67.17%, train macro 56.35%, valid micro 66.0%, valid macro 54.82%, test micro 65.65%, test macro 54.42%\n",
            "Epoch 8: loss 0.47478, train micro 67.33%, train macro 56.78%, valid micro 66.0%, valid macro 54.82%, test micro 65.65%, test macro 54.43%\n",
            "Epoch 9: loss 0.35645, train micro 67.67%, train macro 57.48%, valid micro 66.33%, valid macro 55.56%, test micro 65.65%, test macro 54.42%\n",
            "Epoch 10: loss 0.2673, train micro 68.33%, train macro 58.81%, valid micro 66.33%, valid macro 55.39%, test micro 65.79%, test macro 54.54%\n",
            "Epoch 11: loss 0.20065, train micro 72.17%, train macro 65.95%, valid micro 67.0%, valid macro 56.74%, test micro 65.84%, test macro 54.58%\n",
            "Epoch 12: loss 0.15045, train micro 75.67%, train macro 71.39%, valid micro 69.67%, valid macro 61.38%, test micro 66.02%, test macro 54.91%\n",
            "Epoch 13: loss 0.11227, train micro 81.5%, train macro 79.43%, valid micro 76.0%, valid macro 71.82%, test micro 66.78%, test macro 56.49%\n",
            "Epoch 14: loss 0.08324, train micro 89.17%, train macro 88.55%, valid micro 81.33%, valid macro 79.14%, test micro 68.33%, test macro 59.64%\n",
            "Epoch 15: loss 0.06149, train micro 93.83%, train macro 93.66%, valid micro 86.33%, valid macro 85.32%, test micro 70.96%, test macro 64.52%\n",
            "Epoch 16: loss 0.04577, train micro 96.5%, train macro 96.45%, valid micro 88.0%, valid macro 87.34%, test micro 72.85%, test macro 67.63%\n",
            "Epoch 17: loss 0.03478, train micro 97.83%, train macro 97.82%, valid micro 89.33%, valid macro 88.85%, test micro 74.64%, test macro 70.67%\n",
            "Epoch 18: loss 0.02715, train micro 98.67%, train macro 98.66%, valid micro 91.0%, valid macro 90.69%, test micro 75.67%, test macro 72.3%\n",
            "Epoch 19: loss 0.02189, train micro 98.83%, train macro 98.83%, valid micro 92.0%, valid macro 91.82%, test micro 77.36%, test macro 74.73%\n",
            "Epoch 20: loss 0.01798, train micro 99.17%, train macro 99.16%, valid micro 94.67%, valid macro 94.62%, test micro 79.06%, test macro 76.98%\n",
            "Epoch 21: loss 0.01483, train micro 99.83%, train macro 99.83%, valid micro 95.0%, valid macro 94.96%, test micro 80.19%, test macro 78.47%\n",
            "Epoch 22: loss 0.01226, train micro 100.0%, train macro 100.0%, valid micro 96.33%, valid macro 96.32%, test micro 81.04%, test macro 79.52%\n",
            "Epoch 23: loss 0.01017, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 81.93%, test macro 80.65%\n",
            "Epoch 24: loss 0.00846, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 82.64%, test macro 81.5%\n",
            "Epoch 25: loss 0.007, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.25%, test macro 82.24%\n",
            "Epoch 26: loss 0.00566, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.48%, test macro 82.59%\n",
            "Epoch 27: loss 0.00457, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.95%, test macro 83.16%\n",
            "Epoch 28: loss 0.00372, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 84.24%, test macro 83.52%\n",
            "Epoch 29: loss 0.00306, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 84.75%, test macro 84.13%\n",
            "Epoch 30: loss 0.00255, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.66%, test micro 84.85%, test macro 84.25%\n",
            "Epoch 31: loss 0.00215, train micro 100.0%, train macro 100.0%, valid micro 98.0%, valid macro 98.0%, test micro 84.85%, test macro 84.29%\n",
            "Epoch 32: loss 0.00185, train micro 100.0%, train macro 100.0%, valid micro 98.0%, valid macro 98.0%, test micro 84.71%, test macro 84.14%\n",
            "Epoch 33: loss 0.00161, train micro 100.0%, train macro 100.0%, valid micro 98.0%, valid macro 98.0%, test micro 84.85%, test macro 84.34%\n",
            "Epoch 34: loss 0.00142, train micro 100.0%, train macro 100.0%, valid micro 98.0%, valid macro 98.0%, test micro 84.75%, test macro 84.26%\n",
            "Epoch 35: loss 0.00127, train micro 100.0%, train macro 100.0%, valid micro 98.33%, valid macro 98.33%, test micro 84.8%, test macro 84.33%\n",
            "Epoch 36: loss 0.00115, train micro 100.0%, train macro 100.0%, valid micro 98.33%, valid macro 98.33%, test micro 84.61%, test macro 84.15%\n",
            "Epoch 37: loss 0.00105, train micro 100.0%, train macro 100.0%, valid micro 98.33%, valid macro 98.33%, test micro 84.52%, test macro 84.06%\n",
            "Epoch 38: loss 0.00096, train micro 100.0%, train macro 100.0%, valid micro 98.33%, valid macro 98.33%, test micro 84.42%, test macro 83.97%\n",
            "Epoch 39: loss 0.00089, train micro 100.0%, train macro 100.0%, valid micro 98.33%, valid macro 98.33%, test micro 84.42%, test macro 83.98%\n",
            "Epoch 40: loss 0.00083, train micro 100.0%, train macro 100.0%, valid micro 98.0%, valid macro 98.0%, test micro 84.61%, test macro 84.21%\n",
            "Epoch 41: loss 0.00078, train micro 100.0%, train macro 100.0%, valid micro 98.0%, valid macro 98.0%, test micro 84.61%, test macro 84.22%\n",
            "Epoch 42: loss 0.00073, train micro 100.0%, train macro 100.0%, valid micro 98.0%, valid macro 98.0%, test micro 84.52%, test macro 84.16%\n",
            "Epoch 43: loss 0.00069, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.66%, test micro 84.56%, test macro 84.21%\n",
            "Epoch 44: loss 0.00065, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.66%, test micro 84.52%, test macro 84.18%\n",
            "Epoch 45: loss 0.00062, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.66%, test micro 84.52%, test macro 84.21%\n",
            "Epoch 46: loss 0.00059, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 84.56%, test macro 84.26%\n",
            "Epoch 47: loss 0.00057, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 84.47%, test macro 84.19%\n",
            "Epoch 48: loss 0.00055, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.66%, test micro 84.56%, test macro 84.29%\n",
            "Epoch 49: loss 0.00052, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.66%, test micro 84.52%, test macro 84.24%\n",
            "Epoch 50: loss 0.00051, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.66%, test micro 84.61%, test macro 84.35%\n",
            "Epoch 51: loss 0.00049, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.66%, test micro 84.61%, test macro 84.35%\n",
            "Epoch 52: loss 0.00047, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.66%, test micro 84.61%, test macro 84.37%\n",
            "Epoch 53: loss 0.00046, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.66%, test micro 84.42%, test macro 84.19%\n",
            "Epoch 54: loss 0.00045, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 84.38%, test macro 84.15%\n",
            "Epoch 55: loss 0.00043, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 84.19%, test macro 83.97%\n",
            "Epoch 56: loss 0.00042, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 84.24%, test macro 84.03%\n",
            "Epoch 57: loss 0.00041, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 84.28%, test macro 84.08%\n",
            "Epoch 58: loss 0.0004, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 84.14%, test macro 83.95%\n",
            "Epoch 59: loss 0.0004, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 84.14%, test macro 83.95%\n",
            "Epoch 60: loss 0.00039, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 84.19%, test macro 84.0%\n",
            "Epoch 61: loss 0.00038, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 84.05%, test macro 83.86%\n",
            "Epoch 62: loss 0.00037, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.95%, test macro 83.78%\n",
            "Epoch 63: loss 0.00037, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.95%, test macro 83.78%\n",
            "Epoch 64: loss 0.00036, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.95%, test macro 83.78%\n",
            "Epoch 65: loss 0.00035, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 84.05%, test macro 83.88%\n",
            "Epoch 66: loss 0.00035, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 84.14%, test macro 83.98%\n",
            "Epoch 67: loss 0.00034, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 84.14%, test macro 83.98%\n",
            "Epoch 68: loss 0.00034, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 84.05%, test macro 83.9%\n",
            "Epoch 69: loss 0.00033, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 84.0%, test macro 83.85%\n",
            "Epoch 70: loss 0.00033, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 84.0%, test macro 83.85%\n",
            "Epoch 71: loss 0.00032, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.95%, test macro 83.81%\n",
            "Epoch 72: loss 0.00032, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 84.0%, test macro 83.86%\n",
            "Epoch 73: loss 0.00031, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.91%, test macro 83.77%\n",
            "Epoch 74: loss 0.00031, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.91%, test macro 83.77%\n",
            "Epoch 75: loss 0.00031, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.86%, test macro 83.73%\n",
            "Epoch 76: loss 0.0003, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.81%, test macro 83.69%\n",
            "Epoch 77: loss 0.0003, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.76%, test macro 83.64%\n",
            "Epoch 78: loss 0.0003, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.81%, test macro 83.7%\n",
            "Epoch 79: loss 0.00029, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.76%, test macro 83.66%\n",
            "Epoch 80: loss 0.00029, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.81%, test macro 83.71%\n",
            "Epoch 81: loss 0.00029, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.81%, test macro 83.72%\n",
            "Epoch 82: loss 0.00029, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.81%, test macro 83.72%\n",
            "Epoch 83: loss 0.00028, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.76%, test macro 83.67%\n",
            "Epoch 84: loss 0.00028, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.81%, test macro 83.72%\n",
            "Epoch 85: loss 0.00028, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.81%, test macro 83.73%\n",
            "Epoch 86: loss 0.00027, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.86%, test macro 83.78%\n",
            "Epoch 87: loss 0.00027, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.81%, test macro 83.74%\n",
            "Epoch 88: loss 0.00027, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.72%, test macro 83.64%\n",
            "Epoch 89: loss 0.00027, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.76%, test macro 83.7%\n",
            "Epoch 90: loss 0.00027, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.76%, test macro 83.7%\n",
            "Epoch 91: loss 0.00026, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.76%, test macro 83.7%\n",
            "Epoch 92: loss 0.00026, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.72%, test macro 83.65%\n",
            "Epoch 93: loss 0.00026, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.58%, test macro 83.52%\n",
            "Epoch 94: loss 0.00026, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.58%, test macro 83.52%\n",
            "Epoch 95: loss 0.00025, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.53%, test macro 83.47%\n",
            "Epoch 96: loss 0.00025, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 83.53%, test macro 83.47%\n",
            "Epoch 97: loss 0.00025, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 83.53%, test macro 83.47%\n",
            "Epoch 98: loss 0.00025, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 83.48%, test macro 83.43%\n",
            "Epoch 99: loss 0.00025, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 83.48%, test macro 83.43%\n",
            "Epoch 100: loss 0.00024, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 83.44%, test macro 83.39%\n",
            "Saving Heterogeneous Node Prediction Model Predictions with Agg: Mean\n",
            "\n",
            "Best model: train micro 100.0%, train macro 100.0%, valid micro 98.33%, valid macro 98.33%, test micro 84.8%, test macro 84.33%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rtkKBI_nKS1T"
      },
      "source": [
        "## Question 2.1: What is your maximum test set **micro** F1 score for the best_model when using mean aggregation? (10 points)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AIvw51jMKTvn"
      },
      "source": [
        "## Question 2.2: What is your maximum test set **macro** F1 score for the best_model when using the mean aggregation? (10 points)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JBiYvwcuKd0z"
      },
      "source": [
        "## Training the Attention Aggregation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e6na5zyQKfvi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "80ad014c-0fe4-4c35-f9d9-d69551bd7324"
      },
      "source": [
        "if 'IS_GRADESCOPE_ENV' not in os.environ:\n",
        "  best_model = None\n",
        "  best_val = 0\n",
        "\n",
        "  output_size = hetero_graph.num_node_labels('paper')\n",
        "  model = HeteroGNN(hetero_graph, args, aggr=\"attn\").to(args['device'])\n",
        "  optimizer = torch.optim.Adam(model.parameters(), lr=args['lr'], weight_decay=args['weight_decay'])\n",
        "\n",
        "  for epoch in range(args['epochs']):\n",
        "      loss = train(model, optimizer, hetero_graph, train_idx)\n",
        "      accs, best_model, best_val = test(model, hetero_graph, [train_idx, val_idx, test_idx], best_model, best_val)\n",
        "      print(\n",
        "          f\"Epoch {epoch + 1}: loss {round(loss, 5)}, \"\n",
        "          f\"train micro {round(accs[0][0] * 100, 2)}%, train macro {round(accs[0][1] * 100, 2)}%, \"\n",
        "          f\"valid micro {round(accs[1][0] * 100, 2)}%, valid macro {round(accs[1][1] * 100, 2)}%, \"\n",
        "          f\"test micro {round(accs[2][0] * 100, 2)}%, test macro {round(accs[2][1] * 100, 2)}%\"\n",
        "      )\n",
        "  best_accs, _, _ = test(best_model, hetero_graph, [train_idx, val_idx, test_idx], save_preds=True, agg_type=\"Attention\")\n",
        "  print(\n",
        "      f\"Best model: \"\n",
        "      f\"train micro {round(best_accs[0][0] * 100, 2)}%, train macro {round(best_accs[0][1] * 100, 2)}%, \"\n",
        "      f\"valid micro {round(best_accs[1][0] * 100, 2)}%, valid macro {round(best_accs[1][1] * 100, 2)}%, \"\n",
        "      f\"test micro {round(best_accs[2][0] * 100, 2)}%, test macro {round(best_accs[2][1] * 100, 2)}%\"\n",
        "  )"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1: loss 1.09898, train micro 33.33%, train macro 16.67%, valid micro 33.33%, valid macro 16.67%, test micro 31.29%, test macro 15.89%\n",
            "Epoch 2: loss 1.09081, train micro 61.5%, train macro 51.52%, valid micro 59.33%, valid macro 50.08%, test micro 55.76%, test macro 45.29%\n",
            "Epoch 3: loss 1.06152, train micro 68.83%, train macro 59.4%, valid micro 67.0%, valid macro 56.44%, test micro 65.98%, test macro 54.68%\n",
            "Epoch 4: loss 1.00115, train micro 69.0%, train macro 59.94%, valid micro 67.33%, valid macro 57.6%, test micro 65.69%, test macro 54.59%\n",
            "Epoch 5: loss 0.89512, train micro 69.5%, train macro 60.92%, valid micro 67.33%, valid macro 57.44%, test micro 65.74%, test macro 54.56%\n",
            "Epoch 6: loss 0.73883, train micro 69.67%, train macro 60.95%, valid micro 67.67%, valid macro 57.18%, test micro 65.98%, test macro 54.75%\n",
            "Epoch 7: loss 0.55401, train micro 71.0%, train macro 63.36%, valid micro 68.67%, valid macro 59.05%, test micro 66.26%, test macro 55.2%\n",
            "Epoch 8: loss 0.38701, train micro 73.5%, train macro 67.87%, valid micro 70.67%, valid macro 62.74%, test micro 66.59%, test macro 56.06%\n",
            "Epoch 9: loss 0.26494, train micro 78.33%, train macro 75.35%, valid micro 73.0%, valid macro 67.42%, test micro 67.01%, test macro 57.27%\n",
            "Epoch 10: loss 0.18139, train micro 85.83%, train macro 84.95%, valid micro 81.33%, valid macro 79.93%, test micro 68.47%, test macro 60.5%\n",
            "Epoch 11: loss 0.12439, train micro 91.17%, train macro 90.9%, valid micro 86.33%, valid macro 85.83%, test micro 70.54%, test macro 64.35%\n",
            "Epoch 12: loss 0.08534, train micro 93.83%, train macro 93.72%, valid micro 89.33%, valid macro 89.11%, test micro 72.66%, test macro 67.92%\n",
            "Epoch 13: loss 0.05969, train micro 96.5%, train macro 96.47%, valid micro 91.0%, valid macro 90.82%, test micro 74.59%, test macro 70.98%\n",
            "Epoch 14: loss 0.04333, train micro 97.5%, train macro 97.49%, valid micro 91.67%, valid macro 91.52%, test micro 76.28%, test macro 73.41%\n",
            "Epoch 15: loss 0.03281, train micro 98.5%, train macro 98.5%, valid micro 92.33%, valid macro 92.19%, test micro 77.65%, test macro 75.29%\n",
            "Epoch 16: loss 0.02574, train micro 98.67%, train macro 98.66%, valid micro 93.33%, valid macro 93.21%, test micro 78.78%, test macro 76.76%\n",
            "Epoch 17: loss 0.02088, train micro 99.0%, train macro 99.0%, valid micro 94.33%, valid macro 94.23%, test micro 80.14%, test macro 78.49%\n",
            "Epoch 18: loss 0.01732, train micro 99.17%, train macro 99.17%, valid micro 94.33%, valid macro 94.25%, test micro 80.75%, test macro 79.27%\n",
            "Epoch 19: loss 0.01465, train micro 99.5%, train macro 99.5%, valid micro 95.0%, valid macro 94.94%, test micro 81.32%, test macro 79.97%\n",
            "Epoch 20: loss 0.01257, train micro 99.83%, train macro 99.83%, valid micro 95.33%, valid macro 95.28%, test micro 81.6%, test macro 80.37%\n",
            "Epoch 21: loss 0.0108, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 95.95%, test micro 81.93%, test macro 80.82%\n",
            "Epoch 22: loss 0.00916, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 95.95%, test micro 82.31%, test macro 81.28%\n",
            "Epoch 23: loss 0.00783, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.64%, test micro 82.54%, test macro 81.55%\n",
            "Epoch 24: loss 0.00675, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.64%, test micro 82.73%, test macro 81.81%\n",
            "Epoch 25: loss 0.00585, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.64%, test micro 83.2%, test macro 82.36%\n",
            "Epoch 26: loss 0.00511, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.11%, test macro 82.3%\n",
            "Epoch 27: loss 0.00443, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.2%, test macro 82.45%\n",
            "Epoch 28: loss 0.00361, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.29%, test macro 82.6%\n",
            "Epoch 29: loss 0.00291, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.34%, test macro 82.7%\n",
            "Epoch 30: loss 0.00238, train micro 100.0%, train macro 100.0%, valid micro 98.0%, valid macro 97.99%, test micro 83.06%, test macro 82.47%\n",
            "Epoch 31: loss 0.00197, train micro 100.0%, train macro 100.0%, valid micro 98.0%, valid macro 97.99%, test micro 83.11%, test macro 82.53%\n",
            "Epoch 32: loss 0.00166, train micro 100.0%, train macro 100.0%, valid micro 98.0%, valid macro 97.99%, test micro 83.25%, test macro 82.69%\n",
            "Epoch 33: loss 0.00143, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 83.11%, test macro 82.55%\n",
            "Epoch 34: loss 0.00125, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.67%, test micro 82.92%, test macro 82.38%\n",
            "Epoch 35: loss 0.00111, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 82.73%, test macro 82.2%\n",
            "Epoch 36: loss 0.001, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 97.0%, test micro 82.64%, test macro 82.13%\n",
            "Epoch 37: loss 0.00091, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 97.0%, test micro 82.59%, test macro 82.09%\n",
            "Epoch 38: loss 0.00084, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 97.0%, test micro 82.45%, test macro 81.95%\n",
            "Epoch 39: loss 0.00078, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 97.0%, test micro 82.21%, test macro 81.72%\n",
            "Epoch 40: loss 0.00073, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 96.0%, test micro 82.12%, test macro 81.63%\n",
            "Epoch 41: loss 0.00069, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 96.0%, test micro 81.98%, test macro 81.5%\n",
            "Epoch 42: loss 0.00065, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 96.0%, test micro 81.93%, test macro 81.46%\n",
            "Epoch 43: loss 0.00062, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 96.0%, test micro 81.84%, test macro 81.36%\n",
            "Epoch 44: loss 0.00059, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.67%, test micro 81.84%, test macro 81.36%\n",
            "Epoch 45: loss 0.00056, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.67%, test micro 81.93%, test macro 81.47%\n",
            "Epoch 46: loss 0.00054, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.67%, test micro 81.88%, test macro 81.42%\n",
            "Epoch 47: loss 0.00052, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.67%, test micro 81.88%, test macro 81.42%\n",
            "Epoch 48: loss 0.0005, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.67%, test micro 82.02%, test macro 81.58%\n",
            "Epoch 49: loss 0.00048, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.67%, test micro 82.07%, test macro 81.63%\n",
            "Epoch 50: loss 0.00046, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.67%, test micro 82.07%, test macro 81.64%\n",
            "Epoch 51: loss 0.00045, train micro 100.0%, train macro 100.0%, valid micro 95.33%, valid macro 95.35%, test micro 82.12%, test macro 81.69%\n",
            "Epoch 52: loss 0.00043, train micro 100.0%, train macro 100.0%, valid micro 95.33%, valid macro 95.35%, test micro 82.07%, test macro 81.64%\n",
            "Epoch 53: loss 0.00042, train micro 100.0%, train macro 100.0%, valid micro 95.33%, valid macro 95.35%, test micro 82.07%, test macro 81.64%\n",
            "Epoch 54: loss 0.00041, train micro 100.0%, train macro 100.0%, valid micro 95.33%, valid macro 95.35%, test micro 82.02%, test macro 81.6%\n",
            "Epoch 55: loss 0.0004, train micro 100.0%, train macro 100.0%, valid micro 95.33%, valid macro 95.35%, test micro 81.98%, test macro 81.55%\n",
            "Epoch 56: loss 0.00039, train micro 100.0%, train macro 100.0%, valid micro 95.33%, valid macro 95.35%, test micro 81.88%, test macro 81.46%\n",
            "Epoch 57: loss 0.00038, train micro 100.0%, train macro 100.0%, valid micro 95.33%, valid macro 95.35%, test micro 81.93%, test macro 81.51%\n",
            "Epoch 58: loss 0.00037, train micro 100.0%, train macro 100.0%, valid micro 95.33%, valid macro 95.35%, test micro 81.88%, test macro 81.47%\n",
            "Epoch 59: loss 0.00036, train micro 100.0%, train macro 100.0%, valid micro 95.33%, valid macro 95.35%, test micro 81.88%, test macro 81.47%\n",
            "Epoch 60: loss 0.00035, train micro 100.0%, train macro 100.0%, valid micro 95.33%, valid macro 95.35%, test micro 81.93%, test macro 81.52%\n",
            "Epoch 61: loss 0.00034, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 81.93%, test macro 81.52%\n",
            "Epoch 62: loss 0.00034, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 82.02%, test macro 81.62%\n",
            "Epoch 63: loss 0.00033, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 82.02%, test macro 81.63%\n",
            "Epoch 64: loss 0.00032, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 81.98%, test macro 81.58%\n",
            "Epoch 65: loss 0.00032, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 81.88%, test macro 81.48%\n",
            "Epoch 66: loss 0.00031, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 81.93%, test macro 81.54%\n",
            "Epoch 67: loss 0.00031, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 81.98%, test macro 81.59%\n",
            "Epoch 68: loss 0.0003, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 82.02%, test macro 81.64%\n",
            "Epoch 69: loss 0.0003, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 82.07%, test macro 81.69%\n",
            "Epoch 70: loss 0.00029, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 82.07%, test macro 81.69%\n",
            "Epoch 71: loss 0.00029, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 82.12%, test macro 81.74%\n",
            "Epoch 72: loss 0.00029, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 82.12%, test macro 81.74%\n",
            "Epoch 73: loss 0.00028, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 82.16%, test macro 81.79%\n",
            "Epoch 74: loss 0.00028, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 82.16%, test macro 81.79%\n",
            "Epoch 75: loss 0.00028, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 82.21%, test macro 81.84%\n",
            "Epoch 76: loss 0.00027, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 82.31%, test macro 81.94%\n",
            "Epoch 77: loss 0.00027, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 82.31%, test macro 81.94%\n",
            "Epoch 78: loss 0.00027, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 82.35%, test macro 81.99%\n",
            "Epoch 79: loss 0.00026, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 82.35%, test macro 81.99%\n",
            "Epoch 80: loss 0.00026, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 82.31%, test macro 81.96%\n",
            "Epoch 81: loss 0.00026, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 82.35%, test macro 82.01%\n",
            "Epoch 82: loss 0.00025, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 82.26%, test macro 81.91%\n",
            "Epoch 83: loss 0.00025, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 82.26%, test macro 81.91%\n",
            "Epoch 84: loss 0.00025, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 82.31%, test macro 81.96%\n",
            "Epoch 85: loss 0.00025, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 82.31%, test macro 81.96%\n",
            "Epoch 86: loss 0.00024, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 82.26%, test macro 81.92%\n",
            "Epoch 87: loss 0.00024, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 82.26%, test macro 81.92%\n",
            "Epoch 88: loss 0.00024, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 82.26%, test macro 81.92%\n",
            "Epoch 89: loss 0.00024, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 82.26%, test macro 81.92%\n",
            "Epoch 90: loss 0.00024, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 82.26%, test macro 81.92%\n",
            "Epoch 91: loss 0.00023, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 82.26%, test macro 81.92%\n",
            "Epoch 92: loss 0.00023, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 82.21%, test macro 81.88%\n",
            "Epoch 93: loss 0.00023, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 82.26%, test macro 81.93%\n",
            "Epoch 94: loss 0.00023, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 82.26%, test macro 81.93%\n",
            "Epoch 95: loss 0.00023, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 82.26%, test macro 81.93%\n",
            "Epoch 96: loss 0.00022, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 82.21%, test macro 81.88%\n",
            "Epoch 97: loss 0.00022, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 82.21%, test macro 81.88%\n",
            "Epoch 98: loss 0.00022, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 82.21%, test macro 81.88%\n",
            "Epoch 99: loss 0.00022, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 82.21%, test macro 81.88%\n",
            "Epoch 100: loss 0.00022, train micro 100.0%, train macro 100.0%, valid micro 95.67%, valid macro 95.68%, test micro 82.21%, test macro 81.89%\n",
            "Saving Heterogeneous Node Prediction Model Predictions with Agg: Attention\n",
            "\n",
            "Best model: train micro 100.0%, train macro 100.0%, valid micro 98.0%, valid macro 97.99%, test micro 83.06%, test macro 82.47%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mtAhFLQQKgbl"
      },
      "source": [
        "## Question 2.3: What is your maximum test set **micro** F1 score for the best_model when using the attention aggregation? (4 points)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_cnsMGbsqJG_"
      },
      "source": [
        "## Question 2.4: What is your maximum test set **macro** F1 score for the best_model when using the attention aggregation? (4 points)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aQgx5y4UqMHH"
      },
      "source": [
        "## Attention for each Message Type\n",
        "\n",
        "Through message type level attention we can learn which message type is more important to which layer.\n",
        "\n",
        "Here we will print out and show that each layer pay how much attention on each message type."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hvK58gijqN_C",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7f2ec605-224b-4c40-880b-9ff3d788b467"
      },
      "source": [
        "if 'IS_GRADESCOPE_ENV' not in os.environ:\n",
        "  if model.convs1.alpha is not None and model.convs2.alpha is not None:\n",
        "      for idx, message_type in model.convs1.mapping.items():\n",
        "          print(f\"Layer 1 has attention {model.convs1.alpha[idx]} on message type {message_type}\")\n",
        "      for idx, message_type in model.convs2.mapping.items():\n",
        "          print(f\"Layer 2 has attention {model.convs2.alpha[idx]} on message type {message_type}\")"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Layer 1 has attention 0.44633182883262634 on message type ('paper', 'author', 'paper')\n",
            "Layer 1 has attention 0.553668200969696 on message type ('paper', 'subject', 'paper')\n",
            "Layer 2 has attention 0.5026952624320984 on message type ('paper', 'author', 'paper')\n",
            "Layer 2 has attention 0.4973047375679016 on message type ('paper', 'subject', 'paper')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e7JXsMTBgeOI"
      },
      "source": [
        "# Submission\n",
        "\n",
        "You will need to submit three files on Gradescope to complete this notebook.\n",
        "\n",
        "1.   Your completed *CS224W_Colab5.ipynb*. From the \"File\" menu select \"Download .ipynb\" to save a local copy of your completed Colab.\n",
        "2.  *ACM-Node-MeanAgg.csv*\n",
        "3.  *ACM-Node-AttentionAgg.csv*\n",
        "\n",
        "Download the csv files by selecting the *Folder* icon on the left panel.\n",
        "\n",
        "To submit your work, zip the files downloaded in steps 1-3 above and submit to gradescope. **NOTE:** DO NOT rename any of the downloaded files."
      ]
    }
  ]
}