CS224W_Colab_4.ipynb

{
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  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "CS224W_Colab_4.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.8.8"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XuXWJLEm2UWS"
      },
      "source": [
        "# **CS224W - Colab 4**"
      ]
    },
    {
      "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 seen before by specifically 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, edge type relationships. \n",
        "\n",
        "At first, we will learn how to transform NetworkX graphs into DeepSNAP representations. Additionally, we will dive deeper into how DeepSNAP stores and represents heterogeneous graphs as PyTorch Tensors.\n",
        "\n",
        "Then, we will build our own heterogenous graph neural netowrk models using PyTorch Geonetric and DeepSNAP on node property prediction task. To evaluate these models, we will use our model on the heterogeneous ACM 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 on Colab 4 :)"
      ]
    },
    {
      "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": "_VkxWVavcGI4",
        "outputId": "fd33c437-549d-43c0-ee6a-9a86fe5b75b0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "!python -c \"import torch; print(torch.__version__)\"\n",
        "!python -c \"import torch; print(torch.version.cuda)\"\n"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.9.0+cu102\n",
            "10.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "10JpXxpfeP_W"
      },
      "source": [
        "### Attention! \n",
        "\n",
        "Please check the torch version and cuda version of google colab so you can install the appropriate version of torch-scatter and torch-sparse. \n",
        "\n",
        "The output from previous code chunk show that my Google Colab run with Pytorch 1.9.0 and cuda 10.2. So, my command for installing should be \n",
        "\n",
        "!pip install -q torch-scatter -f https://pytorch-geometric.com/whl/torch-1.9.0+cu102.html\n",
        "\n",
        "!pip install -q torch-sparse -f https://pytorch-geometric.com/whl/torch-1.9.0+cu102.html"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yI39c3egCyq3",
        "outputId": "74f7e26e-df98-4ba4-918b-3b94c02c0e41",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "# !pip install -q torch-scatter -f https://pytorch-geometric.com/whl/torch-1.9.0+cu102.html\n",
        "# !pip install -q torch-sparse -f https://pytorch-geometric.com/whl/torch-1.9.0+cu102.html\n",
        "# !pip install -q torch-geometric\n",
        "# !pip install -q git+https://github.com/snap-stanford/deepsnap.git\n",
        "# !pip install -U -q PyDrive"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[K     |████████████████████████████████| 2.6MB 6.2MB/s \n",
            "\u001b[K     |████████████████████████████████| 1.4MB 6.4MB/s \n",
            "\u001b[K     |████████████████████████████████| 225kB 8.6MB/s \n",
            "\u001b[K     |████████████████████████████████| 235kB 33.9MB/s \n",
            "\u001b[K     |████████████████████████████████| 51kB 8.1MB/s \n",
            "\u001b[?25h  Building wheel for torch-geometric (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for deepsnap (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "PRfgbfTjCRD_",
        "outputId": "0425b9d2-8e37-4460-dd4b-381b536af983"
      },
      "source": [
        "import torch_geometric\n",
        "torch_geometric.__version__"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'1.7.1'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZoXlf4MtYrbz"
      },
      "source": [
        "# 1 DeepSNAP Heterogeneous Graph\n",
        "\n",
        "First, we will explore how to transform NetworkX graphs into the format supported by DeepSNAP. \n",
        "\n",
        "In order to extend the traditional DeepSNAP representation of graphs to include heterogenous graphs, we include the following graph property features:\n",
        "* `node_feature`: The feature of each node (`torch.tensor`)\n",
        "* `edge_feature`: The feautre 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",
        "The key new features we add are `node_type` and `edge_type`, which enables us to perform heterogenous message passing.\n",
        "\n",
        "In 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 assignment one. 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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 535
        },
        "id": "8LQ_z5gcBVA1",
        "outputId": "215a8b8a-46fb-4f3b-bb39-44d2fca73217"
      },
      "source": [
        "from pylab import *\n",
        "import networkx as nx\n",
        "from networkx.algorithms.community import greedy_modularity_communities\n",
        "import matplotlib.pyplot as plt\n",
        "import copy\n",
        "\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",
        "        \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": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1JpFb9fTw1lg"
      },
      "source": [
        "### Question 1.1: Assigning Node Type and Node Features (Not Specifically Graded)\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`. \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 a 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"
      },
      "source": [
        "import torch\n",
        "\n",
        "def assign_node_types(G, community_map):\n",
        "    # TODO: Implement this function that takes in a NetworkX graph\n",
        "    # G and community map assignment (mapping node ids --> 0/1 labels)\n",
        "    # and adds 'node_type' as a node_attribute in G.\n",
        "\n",
        "    ############# Your code here ############\n",
        "    ## (~2 line of code)\n",
        "    ## Note\n",
        "    ## 1. Look up NetworkX `nx.classes.function.set_node_attributes`\n",
        "    node_type_map = {0:'n0', 1:'n1'}\n",
        "    node_types = {node:node_type_map[community_map[node]] for node in G.nodes()}\n",
        "    nx.set_node_attributes(G, node_types, 'node_type')\n",
        "    \n",
        "    #########################################\n",
        "\n",
        "def assign_node_labels(G, community_map):\n",
        "    # TODO: Implement this function that takes in a NetworkX graph\n",
        "    # G and community map assignment (mapping node ids --> 0/1 labels)\n",
        "    # and adds 'node_label' as a node_attribute in G.\n",
        "\n",
        "    ############# Your code here ############\n",
        "    ## (~2 line of code)\n",
        "    ## Note\n",
        "    ## 1. Look up NetworkX `nx.classes.function.set_node_attributes`\n",
        "    node_label_map = {0:0, 1:1}\n",
        "    node_labels = {node:node_label_map[community_map[node]] for node in G.nodes()}\n",
        "    nx.set_node_attributes(G, node_labels, 'node_label')\n",
        "    \n",
        "    #########################################\n",
        "\n",
        "def assign_node_features(G):\n",
        "    # TODO: Implement this 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 [1., 1., 1., 1., 1.]\n",
        "\n",
        "    ############# Your code here ############\n",
        "    ## (~2 line of code)\n",
        "    ## Note\n",
        "    ## 1. Look up NetworkX `nx.classes.function.set_node_attributes`\n",
        "    node_features = {node:[1., 1., 1., 1., 1.] for node in G.nodes()}\n",
        "    nx.set_node_attributes(G, node_features, 'node_feature')\n",
        "    \n",
        "    #########################################\n",
        "\n",
        "assign_node_types(G, community_map)\n",
        "assign_node_labels(G, community_map)\n",
        "assign_node_features(G)\n"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mafN0P3EOhSb"
      },
      "source": [
        "### Question 1.2: Assigning Edge Types (Not Specifically Graded)\n",
        "\n",
        "Next, we will assign three different types of `edge_type` to the edges: \n",
        "* Edges within club \"Mr. Hi\": `e0`\n",
        "* Edges within club \"Officer\": `e1`\n",
        "* Edges between clubs: `e2`\n",
        "\n",
        "**Hint**: Use the `community_map` from before and `nx.classes.function.set_edge_attributes`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rsbYWEVwSV5n"
      },
      "source": [
        "def assign_edge_types(G, community_map):\n",
        "    # TODO: Implement this function that takes in a NetworkX graph\n",
        "    # G and community map assignment (mapping node ids --> 0/1 labels)\n",
        "    # and adds 'edge_type' as a edge_attribute in G.\n",
        "\n",
        "    ############# Your code here ############\n",
        "    ## (~5 line of code)\n",
        "    ## Note\n",
        "    ## 1. Create an edge assignment dict following rules above\n",
        "    edge_type_map = {'00':'e0', '11':'e1', '01':'e2', '10':'e2'}\n",
        "    edge_types = {}\n",
        "    \n",
        "    for edge in G.edges:\n",
        "        class_node_0 = str(community_map[edge[0]])\n",
        "        class_node_1 = str(community_map[edge[1]])\n",
        "        edge_type = class_node_0 + class_node_1\n",
        "        edge_types[edge] = edge_type_map[edge_type]\n",
        "    \n",
        "    nx.set_edge_attributes(G, edge_types, 'edge_type')\n",
        "    #########################################\n",
        "\n",
        "assign_edge_types(G, community_map)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "n2sdufbODHtp",
        "outputId": "3a516004-d747-4fd1-9584-acbb37b4ed16"
      },
      "source": [
        "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": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QRANkF1jRxLV"
      },
      "source": [
        "which include edges within each clubs (2 types) and edges across 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",
        "The NetworkX object `G` in following code can be transformed into `deepsnap.hetero_graph.HeteroGraph` directly!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-ZfHZ_eoVVGd"
      },
      "source": [
        "from deepsnap.hetero_graph import HeteroGraph\n",
        "\n",
        "hete = HeteroGraph(G_orig)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "izq4t_O9WxDH"
      },
      "source": [
        "## Question 1.3: How many nodes are of each type (10 Points)\n",
        "\n",
        "Submit your answers on Gradescope.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wRNSP6nnW78C",
        "outputId": "cf5d754e-5bba-4055-dc44-de30615e87ae"
      },
      "source": [
        "def get_nodes_per_type(hete):\n",
        "    # TODO: Implement this function that takes a DeepSNAP dataset object\n",
        "    # and return the number of nodes per `node_type`.\n",
        "\n",
        "    num_nodes_n0 = 0\n",
        "    num_nodes_n1 = 0\n",
        "\n",
        "    ############# Your code here ############\n",
        "    ## (~2 line of code)\n",
        "    ## Note\n",
        "    ## 1. Colab autocomplete functionality might be useful.\n",
        "    num_nodes_n0 = len(hete.node_type['n0'])\n",
        "    num_nodes_n1 = len(hete.node_type['n1'])\n",
        "\n",
        "    #########################################\n",
        "\n",
        "    return num_nodes_n0, num_nodes_n1\n",
        "\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": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Node type n0 has 17 nodes\n",
            "Node type n1 has 17 nodes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vEsHJp2ZYaE2"
      },
      "source": [
        "## Question 1.4: Message Types: How many edges are of each message type (10 Points)\n",
        "\n",
        "Submit your answers on Gradescope.\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 `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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qobKuqbAYvJ7",
        "outputId": "5d91dc50-de7b-48c2-90de-b4daf59b8b82"
      },
      "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",
        "    message_type_edges = []\n",
        "\n",
        "    ############# Your code here ############\n",
        "    ## (~2 line of code)\n",
        "    ## Note\n",
        "    ## 1. Colab autocomplete functionality might be useful.\n",
        "    for mes_type in hete.message_types:\n",
        "        message_type_edges.append((mes_type, len(hete.edge_type[mes_type])))\n",
        "\n",
        "    #########################################\n",
        "\n",
        "    return message_type_edges\n",
        "\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))\n"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bjMVik1JbJ76"
      },
      "source": [
        "## Question 1.5: Dataset Splitting: How many nodes in each dataset split? (10 Points)\n",
        "\n",
        "Submit your answers on Gradescope.\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 in each dataset split.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ct10Oh4gcqgD",
        "outputId": "9ae77014-b301-49e4-e5a4-7685edb412ad"
      },
      "source": [
        "from deepsnap.dataset import GraphDataset\n",
        "\n",
        "def compute_dataset_split_counts(datasets):\n",
        "    # TODO: Implement this function that takes a dict of datasets\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. DeepSNAP `node_label_index` will be helpful.\n",
        "    ## 2. Remember to count both node_types\n",
        "    for set_name in datasets.keys():\n",
        "        num_node_n0 = datasets[set_name][0].node_label_index['n0'].shape[0]\n",
        "        num_node_n1 = datasets[set_name][0].node_label_index['n1'].shape[0]\n",
        "        data_set_splits[set_name] = num_node_n0 + num_node_n1\n",
        "    #########################################\n",
        "\n",
        "    return data_set_splits\n",
        "\n",
        "\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": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "train dataset has 12 nodes\n",
            "val dataset has 10 nodes\n",
            "test dataset has 12 nodes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lFY2PaDbVKe4"
      },
      "source": [
        "## DeepSNAP Dataset Visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "iiyEw-agbgV8",
        "outputId": "61fbe23c-1059-4fd0-c710-f85ab6a7268a"
      },
      "source": [
        "from deepsnap.dataset import GraphDataset\n",
        "\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": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e5LsVSRuI3hU"
      },
      "source": [
        "# 2 Heterogeneous Graph Node Property Prediction\n",
        "\n",
        "In this part we will use PyTorch Geometric and DeepSNAP to implement a GNN model for heterogeneous graph node property prediction (node classification). This part of Colab requires you having good understandings on the heterogeneous graph and how to implement the GNN layers by using PyG.\n",
        "\n",
        "At first let's take look at the general structure of a heterogeneous layer by 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 $m_3=(a, r_3, b)$.\n",
        "\n",
        "Thus, for $G$ a heterogeneous layer will contains three Heterogeneous GNN layers (`HeteroGNNConv` in this Colab) where each `HeteroGNNConv` layer will perform the message passing and aggregation with respect to only one message type. The overview of the heterogeneous layer is shown below:\n",
        "\n",
        "<br/>\n",
        "<center>\n",
        "<img src=\"https://web.stanford.edu/class/cs224w/images/colab4/hetero_conv.png\"/>\n",
        "</center>\n",
        "<br/>\n",
        "\n",
        "In this Colab, all the $l^{th}$ Heterogeneous GNN layers will be managed by a ($l^{th}$) Heterogeneous GNN Wrapper layer (the `HeteroGNNWrapperConv`). The $l^{th}$ Heterogeneous GNN Wrapper layer will take in the input node embeddings from $(l-1)^{th}$ layer and aggregate (across message types) the Heterogeneous GNN layers' results. For example, the wrapper layer will aggregate node type $b$'s node embeddings from Heterogeneous GNN layers for $m_2$ and $m_3$. The \"simplified\" heterogeneous layer structure is shown below:\n",
        "\n",
        "<br/>\n",
        "<center>\n",
        "<img src=\"http://web.stanford.edu/class/cs224w/images/colab4/hetero_conv_1.png\"/>\n",
        "</center>\n",
        "<br/>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rOTCyuRcJikS"
      },
      "source": [
        "<font color='red'>We recommend you implement the heterogeneous GNN model in following steps:</font>\n",
        "\n",
        "1. Implement the `HeteroGNNConv` first.\n",
        "2. Implement the `mean` aggregation in `HeteroGNNWrapperConv`.\n",
        "3. Implement the `generate_convs`.\n",
        "4. Implement the `HeteroGNN` model and the `train` function.\n",
        "5. Train the model with `mean` aggregation across the message types and make sure your model has reasonable performance.\n",
        "6. Implement the `attn` aggregation in `HeteroGNNWrapperConv`.\n",
        "7. Train the model with `attn` aggregation across the message types and make sure your model has reasonable performance."
      ]
    },
    {
      "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": 20,
      "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"
      },
      "source": [
        "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": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Igoy4F_xJbVn"
      },
      "source": [
        "id='1ivlxd6lJMcZ9taS44TMGG72x2V1GeVvk'\n",
        "downloaded = drive.CreateFile({'id': id})\n",
        "downloaded.GetContentFile('acm.pkl')"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VBlboS5kJmJL"
      },
      "source": [
        "## Heterogeneous GNN Layer\n",
        "\n",
        "Now let's start working on our own implementation of a heterogeneous layer (the `HeteroGNNConv`)! Similar to what we did in Colab 3, we will implement the layer using PyTorch Geometric. In general, our heterogeneous GNN layer draws ideas from the **GraphSAGE** ([Hamilton et al. (2017)](https://arxiv.org/abs/1706.02216)).\n",
        "\n",
        "At first, let's implement the GNN layer for each message type :\n",
        "\n",
        "\\begin{equation}\n",
        "m =(s, r, d)\n",
        "\\end{equation}\n",
        "\n",
        "Each message type is a tuple containing three elements where $s$ refers to the source node type, $r$ refers to the edge (relation) type and $d$ refers to the destination node type. The update rule is very similar to that of GraphSAGE but we need to include the node types and the edge type. The update rule is described as 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 $[m]$ indicates that the weight matrices or embeddings with respect to message type $m$, $W_s^{(l)[m]}$ computes the messages from neighboring nodes, $W_d^{(l)[m]}$ compute messages from the node itself, and $W^{(l)[m]}$ aggregates messages from both node types. In the equation above, $v$ has the node type $d$, and $u$ has the node type $s$.\n",
        "\n",
        "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": "HFFpqB99mmVQ"
      },
      "source": [
        "import torch_scatter\n",
        "from torch_sparse import matmul"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "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",
        "        self.lin_dst = None\n",
        "        self.lin_src = None\n",
        "\n",
        "        self.lin_update = None\n",
        "\n",
        "        ############# Your code here #############\n",
        "        ## (~3 lines of code)\n",
        "        self.lin_dst = nn.Linear(self.in_channels_dst, self.out_channels)\n",
        "        self.lin_src = nn.Linear(self.in_channels_src, self.out_channels)\n",
        "        self.lin_update = nn.Linear(self.out_channels*2, self.out_channels)\n",
        "        ##########################################\n",
        "\n",
        "    def forward(\n",
        "        self,\n",
        "        node_feature_src,\n",
        "        node_feature_dst,\n",
        "        edge_index,\n",
        "        size=None,\n",
        "        res_n_id=None,\n",
        "    ):\n",
        "        ############# Your code here #############\n",
        "        ## (~1 line of code)\n",
        "        return self.propagate(edge_index, size=size, \n",
        "        node_feature_src=node_feature_src, \n",
        "        node_feature_dst=node_feature_dst, \n",
        "        res_n_id=res_n_id)\n",
        "\n",
        "        ##########################################\n",
        "\n",
        "    def message_and_aggregate(self, edge_index, node_feature_src):\n",
        "\n",
        "        ############# Your code here #############\n",
        "        ## (~1 line of code)\n",
        "        ## Note:\n",
        "        ## 1. Different from what we implemented in Colab 3, we use message_and_aggregate\n",
        "        ## to replace the message and aggregate. The benefit is that we can avoid\n",
        "        ## materializing x_i and x_j, and make the implementation more efficient.\n",
        "        ## 2. To implement efficiently, following PyG documentation is helpful:\n",
        "        ## https://pytorch-geometric.readthedocs.io/en/latest/notes/sparse_tensor.html\n",
        "        ## 3. Here edge_index is torch_sparse SparseTensor.\n",
        "\n",
        "        out = matmul(edge_index, node_feature_src, reduce=\"mean\")\n",
        "        ##########################################\n",
        "\n",
        "        return out\n",
        "\n",
        "    def update(self, aggr_out, node_feature_dst, res_n_id):\n",
        "\n",
        "        ############# Your code here #############\n",
        "        ## (~4 lines of code)\n",
        "        aggr_out = self.lin_src(aggr_out)\n",
        "        node_feature_dst = self.lin_dst(node_feature_dst)\n",
        "\n",
        "        concat_features = torch.cat((node_feature_dst, aggr_out), dim=-1)\n",
        "        aggr_out = self.lin_update(concat_features)\n",
        "        ##########################################\n",
        "\n",
        "        return aggr_out"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aKq8ScTiJthn"
      },
      "source": [
        "## Heterogeneous GNN Wrapper Layer\n",
        "\n",
        "After implementing the GNN layer for each message type, we need to somehow aggregate the the node embedding results (with respect to each message types) together. Here we will implement two types of message type level aggregation.\n",
        "\n",
        "The first one is simply the mean aggregation:\n",
        "\n",
        "\\begin{equation}\n",
        "h_v^{(l)} = \\frac{1}{M}\\sum_{m=1}^{M}h_v^{(l)[m]}\n",
        "\\end{equation}\n",
        "\n",
        "Here node $v$ has the node type $d$ and $M$ is the total number of message types that the destination node type is $d$.\n",
        "\n",
        "The other one 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 can be more important, then aggregate from all the message types. Following 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 $m$ refers to message type and $d$ refers to the destination node type. Then we can compute the attention and update the $h_v^{(l)}$:\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}"
      ]
    },
    {
      "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 here.\n",
        "            ## 2. You should use nn.Sequential for self.attn_proj\n",
        "            ## 3. nn.Linear and nn.Tanh are useful.\n",
        "            ## 4. You can create a vector parameter 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",
        "            self.attn_proj = nn.Sequential(\n",
        "                nn.Linear(args['hidden_size'], args['attn_size']),\n",
        "                nn.Tanh(),\n",
        "                nn.Linear(args['attn_size'], 1, bias=False), # q_semantic_attention\n",
        "            )\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",
        "        if self.aggr == \"mean\":\n",
        "\n",
        "            ############# Your code here #############\n",
        "            ## (~2 lines of code)\n",
        "            out = torch.mean(torch.stack(xs), dim=0)\n",
        "            return out\n",
        "            ##########################################\n",
        "\n",
        "        elif self.aggr == \"attn\":\n",
        "\n",
        "            ############# Your code here #############\n",
        "            ## (~10 lines of code)\n",
        "            ## Note:\n",
        "            ## 1. Store the value of attention alpha (as a numpy array) to self.alpha,\n",
        "            ## which has the shape (len(xs), ) self.alpha will be not be used \n",
        "            ## to backpropagate etc. in the model. We will use it to see how much \n",
        "            ## attention the layer pays on different message types.\n",
        "            ## 2. torch.softmax and torch.cat are useful.\n",
        "            ## 3. You might need to reshape the tensors by using the \n",
        "            ## `view()` function https://pytorch.org/docs/stable/tensor_view.html\n",
        "            x = self.attn_proj(torch.stack(xs, dim=0))\n",
        "            x = torch.mean(x, dim=1)\n",
        "\n",
        "            self.alpha = torch.softmax(x, dim=0)\n",
        "            self.alpha = self.alpha.detach()\n",
        "\n",
        "            out = torch.stack(xs, dim=0)\n",
        "            out = self.alpha.unsqueeze(-1) * out\n",
        "\n",
        "            out = torch.sum(out, dim=0)\n",
        "            return out\n",
        "            ##########################################"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tn_pnCOKJw-d"
      },
      "source": [
        "## Initialize Heterogeneous GNN Layers\n",
        "\n",
        "Now let's initialize the Heterogeneous GNN Layers. Different from homogeneous graph case, heterogeneous case 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 same embedding dimension `hidden_size` and we still set `HeteroGNNConv` `out_channels` to be `hidden_size` for simplicity.\n",
        "\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",
        "    for mes_type in hetero_graph.message_types:\n",
        "        if first_layer:\n",
        "            num_node_feat_src = hetero_graph.num_node_features(mes_type[0])\n",
        "            num_node_feat_dst = hetero_graph.num_node_features(mes_type[-1])\n",
        "\n",
        "            convs[mes_type] = conv(num_node_feat_src, num_node_feat_dst,\n",
        "                                   hidden_size)\n",
        "            \n",
        "        else:\n",
        "            convs[mes_type] = conv(hidden_size, hidden_size, hidden_size)\n",
        "    ##########################################\n",
        "    \n",
        "    return convs"
      ],
      "execution_count": 30,
      "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": "Soui5IiT_6r9"
      },
      "source": [
        "from deepsnap.hetero_gnn import forward_op"
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "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=1.0).\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",
        "        self.convs1 = generate_convs(hetero_graph, HeteroGNNConv, self.hidden_size,\n",
        "                                     first_layer=True)\n",
        "        self.convs1 = HeteroGNNWrapperConv(self.convs1, args, self.aggr)\n",
        "\n",
        "        self.convs2 = generate_convs(hetero_graph, HeteroGNNConv, self.hidden_size,\n",
        "                                             first_layer=False)\n",
        "        self.convs2 = HeteroGNNWrapperConv(self.convs2, args, self.aggr)\n",
        "\n",
        "        for node_type in hetero_graph.node_types:\n",
        "            self.bns1[node_type] = torch.nn.BatchNorm1d(self.hidden_size, eps=1.0)\n",
        "            self.bns2[node_type] = torch.nn.BatchNorm1d(self.hidden_size, eps=1.0) \n",
        "            self.relus1[node_type] = nn.LeakyReLU()\n",
        "            self.relus2[node_type] = nn.LeakyReLU()\n",
        "            self.post_mps[node_type] = \\\n",
        "            nn.Linear(self.hidden_size, hetero_graph.num_node_labels(node_type))\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",
        "        x = self.convs1(x, edge_index)\n",
        "        x = forward_op(x, self.bns1)\n",
        "        x = forward_op(x, self.relus1)\n",
        "\n",
        "        x = self.convs2(x, edge_index)\n",
        "        x = forward_op(x, self.bns2)\n",
        "        x = forward_op(x, self.relus2)\n",
        "\n",
        "        x = forward_op(x, self.post_mps)\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",
        "        for node_type in preds:\n",
        "            idx = indices[node_type]\n",
        "            pred = preds[node_type][idx]\n",
        "            loss += loss_func(pred, y[node_type][idx])\n",
        "        ##########################################\n",
        "\n",
        "        return loss"
      ],
      "execution_count": 32,
      "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": [
        "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. `deepsnap.hetero_graph.HeteroGraph.node_label` is useful\n",
        "    ## 2. Compute the loss here\n",
        "    # print(train_idx)\n",
        "    loss = model.loss(preds, hetero_graph.node_label, train_idx)\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):\n",
        "    model.eval()\n",
        "    accs = []\n",
        "    for index in 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",
        "        # 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",
        "    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": 33,
      "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": 34,
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lJy03_IsKGh6",
        "outputId": "d060dd58-baf8-4ebc-c0c8-a2c5e789075b"
      },
      "source": [
        "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",
        "from deepsnap.hetero_graph import HeteroGraph\n",
        "\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": 35,
      "outputs": [
        {
          "output_type": "stream",
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZgwfyzLbKOUw",
        "outputId": "981b7603-967a-4fb3-fa33-b4594e4a4318"
      },
      "source": [
        "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])\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": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1: loss 1.10112, 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 1.09235, 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 3: loss 1.06329, train micro 37.0%, train macro 23.91%, valid micro 34.0%, valid macro 18.06%, test micro 32.33%, test macro 17.89%\n",
            "Epoch 4: loss 1.00568, train micro 70.83%, train macro 64.26%, valid micro 67.0%, valid macro 56.98%, test micro 62.12%, test macro 50.33%\n",
            "Epoch 5: loss 0.90913, train micro 74.0%, train macro 68.57%, valid micro 70.33%, valid macro 62.44%, test micro 66.59%, test macro 56.34%\n",
            "Epoch 6: loss 0.77159, train micro 73.33%, train macro 67.84%, valid micro 69.67%, valid macro 61.74%, test micro 66.45%, test macro 55.93%\n",
            "Epoch 7: loss 0.60947, train micro 74.5%, train macro 69.74%, valid micro 69.33%, valid macro 61.26%, test micro 66.45%, test macro 56.02%\n",
            "Epoch 8: loss 0.45422, train micro 76.0%, train macro 72.07%, valid micro 70.0%, valid macro 62.52%, test micro 66.45%, test macro 55.97%\n",
            "Epoch 9: loss 0.32887, train micro 78.33%, train macro 75.5%, valid micro 71.33%, valid macro 64.94%, test micro 66.73%, test macro 56.72%\n",
            "Epoch 10: loss 0.23798, train micro 83.83%, train macro 82.59%, valid micro 76.0%, valid macro 72.53%, test micro 67.48%, test macro 58.3%\n",
            "Epoch 11: loss 0.17451, train micro 88.83%, train macro 88.34%, valid micro 80.33%, valid macro 78.44%, test micro 68.66%, test macro 60.65%\n",
            "Epoch 12: loss 0.12951, train micro 93.83%, train macro 93.69%, valid micro 87.67%, valid macro 87.18%, test micro 70.78%, test macro 64.44%\n",
            "Epoch 13: loss 0.0968, train micro 96.83%, train macro 96.81%, valid micro 89.67%, valid macro 89.33%, test micro 72.99%, test macro 68.19%\n",
            "Epoch 14: loss 0.07291, train micro 98.17%, train macro 98.16%, valid micro 92.0%, valid macro 91.82%, test micro 75.62%, test macro 72.24%\n",
            "Epoch 15: loss 0.05585, train micro 99.0%, train macro 99.0%, valid micro 92.67%, valid macro 92.52%, test micro 78.21%, test macro 75.86%\n",
            "Epoch 16: loss 0.04361, train micro 99.33%, train macro 99.33%, valid micro 93.67%, valid macro 93.54%, test micro 79.81%, test macro 77.97%\n",
            "Epoch 17: loss 0.03476, train micro 99.67%, train macro 99.67%, valid micro 95.33%, valid macro 95.28%, test micro 81.27%, test macro 79.83%\n",
            "Epoch 18: loss 0.02806, train micro 99.67%, train macro 99.67%, valid micro 96.33%, valid macro 96.3%, test micro 82.73%, test macro 81.64%\n",
            "Epoch 19: loss 0.02287, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.66%, test micro 83.01%, test macro 82.08%\n",
            "Epoch 20: loss 0.01883, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.66%, test micro 83.53%, test macro 82.7%\n",
            "Epoch 21: loss 0.01568, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.66%, test micro 83.86%, test macro 83.1%\n",
            "Epoch 22: loss 0.01319, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 97.0%, test micro 84.33%, test macro 83.63%\n",
            "Epoch 23: loss 0.01115, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.34%, test micro 84.33%, test macro 83.63%\n",
            "Epoch 24: loss 0.00936, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 97.0%, test micro 84.42%, test macro 83.74%\n",
            "Epoch 25: loss 0.00779, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 97.0%, test micro 84.42%, test macro 83.76%\n",
            "Epoch 26: loss 0.00643, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 97.0%, test micro 84.56%, test macro 83.93%\n",
            "Epoch 27: loss 0.00532, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 97.0%, test micro 84.66%, test macro 84.03%\n",
            "Epoch 28: loss 0.00444, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.34%, test micro 84.71%, test macro 84.1%\n",
            "Epoch 29: loss 0.00375, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.34%, test micro 84.61%, test macro 84.0%\n",
            "Epoch 30: loss 0.00321, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.67%, test micro 84.66%, test macro 84.07%\n",
            "Epoch 31: loss 0.00278, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.67%, test micro 84.66%, test macro 84.08%\n",
            "Epoch 32: loss 0.00243, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.67%, test micro 84.56%, test macro 83.98%\n",
            "Epoch 33: loss 0.00215, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.66%, test micro 84.66%, test macro 84.08%\n",
            "Epoch 34: loss 0.00193, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.66%, test micro 84.71%, test macro 84.13%\n",
            "Epoch 35: loss 0.00174, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 84.56%, test macro 84.0%\n",
            "Epoch 36: loss 0.00158, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 84.52%, test macro 83.94%\n",
            "Epoch 37: loss 0.00145, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 84.38%, test macro 83.8%\n",
            "Epoch 38: loss 0.00133, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 84.19%, test macro 83.63%\n",
            "Epoch 39: loss 0.00124, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 84.33%, test macro 83.82%\n",
            "Epoch 40: loss 0.00115, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 84.28%, test macro 83.77%\n",
            "Epoch 41: loss 0.00108, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 84.19%, test macro 83.67%\n",
            "Epoch 42: loss 0.00101, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 84.24%, test macro 83.75%\n",
            "Epoch 43: loss 0.00095, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 84.24%, test macro 83.76%\n",
            "Epoch 44: loss 0.0009, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 84.33%, test macro 83.89%\n",
            "Epoch 45: loss 0.00085, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 84.19%, test macro 83.76%\n",
            "Epoch 46: loss 0.00081, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 84.28%, test macro 83.86%\n",
            "Epoch 47: loss 0.00078, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 84.24%, test macro 83.81%\n",
            "Epoch 48: loss 0.00074, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 84.19%, test macro 83.77%\n",
            "Epoch 49: loss 0.00071, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 84.19%, test macro 83.79%\n",
            "Epoch 50: loss 0.00068, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 84.19%, test macro 83.79%\n",
            "Epoch 51: loss 0.00066, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 84.19%, test macro 83.81%\n",
            "Epoch 52: loss 0.00064, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 84.24%, test macro 83.89%\n",
            "Epoch 53: loss 0.00061, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 84.14%, test macro 83.8%\n",
            "Epoch 54: loss 0.00059, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 84.19%, test macro 83.85%\n",
            "Epoch 55: loss 0.00058, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 84.05%, test macro 83.71%\n",
            "Epoch 56: loss 0.00056, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.95%, test macro 83.62%\n",
            "Epoch 57: loss 0.00054, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.95%, test macro 83.62%\n",
            "Epoch 58: loss 0.00053, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.86%, test macro 83.53%\n",
            "Epoch 59: loss 0.00052, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.95%, test macro 83.65%\n",
            "Epoch 60: loss 0.0005, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.95%, test macro 83.65%\n",
            "Epoch 61: loss 0.00049, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 84.0%, test macro 83.72%\n",
            "Epoch 62: loss 0.00048, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 84.0%, test macro 83.73%\n",
            "Epoch 63: loss 0.00047, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.95%, test macro 83.69%\n",
            "Epoch 64: loss 0.00046, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.91%, test macro 83.64%\n",
            "Epoch 65: loss 0.00045, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.95%, test macro 83.7%\n",
            "Epoch 66: loss 0.00044, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.86%, test macro 83.62%\n",
            "Epoch 67: loss 0.00044, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.91%, test macro 83.67%\n",
            "Epoch 68: loss 0.00043, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.95%, test macro 83.73%\n",
            "Epoch 69: loss 0.00042, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.95%, test macro 83.73%\n",
            "Epoch 70: loss 0.00041, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.95%, test macro 83.73%\n",
            "Epoch 71: loss 0.00041, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.95%, test macro 83.73%\n",
            "Epoch 72: loss 0.0004, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.91%, test macro 83.69%\n",
            "Epoch 73: loss 0.00039, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.86%, test macro 83.64%\n",
            "Epoch 74: loss 0.00039, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.86%, test macro 83.64%\n",
            "Epoch 75: loss 0.00038, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.86%, test macro 83.64%\n",
            "Epoch 76: loss 0.00038, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 83.81%, test macro 83.6%\n",
            "Epoch 77: loss 0.00037, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.65%, test micro 83.91%, test macro 83.7%\n",
            "Epoch 78: loss 0.00037, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.65%, test micro 83.91%, test macro 83.71%\n",
            "Epoch 79: loss 0.00036, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.65%, test micro 83.91%, test macro 83.71%\n",
            "Epoch 80: loss 0.00036, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.65%, test micro 83.95%, test macro 83.76%\n",
            "Epoch 81: loss 0.00035, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.65%, test micro 83.86%, test macro 83.67%\n",
            "Epoch 82: loss 0.00035, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.65%, test micro 83.86%, test macro 83.67%\n",
            "Epoch 83: loss 0.00035, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.65%, test micro 83.86%, test macro 83.67%\n",
            "Epoch 84: loss 0.00034, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.65%, test micro 83.81%, test macro 83.63%\n",
            "Epoch 85: loss 0.00034, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.65%, test micro 83.81%, test macro 83.63%\n",
            "Epoch 86: loss 0.00033, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.65%, test micro 83.76%, test macro 83.59%\n",
            "Epoch 87: loss 0.00033, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.65%, test micro 83.76%, test macro 83.59%\n",
            "Epoch 88: loss 0.00033, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.65%, test micro 83.81%, test macro 83.64%\n",
            "Epoch 89: loss 0.00032, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.65%, test micro 83.86%, test macro 83.68%\n",
            "Epoch 90: loss 0.00032, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.65%, test micro 83.91%, test macro 83.73%\n",
            "Epoch 91: loss 0.00032, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.65%, test micro 83.91%, test macro 83.73%\n",
            "Epoch 92: loss 0.00031, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.65%, test micro 83.86%, test macro 83.7%\n",
            "Epoch 93: loss 0.00031, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.65%, test micro 83.81%, test macro 83.65%\n",
            "Epoch 94: loss 0.00031, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.65%, test micro 83.91%, test macro 83.75%\n",
            "Epoch 95: loss 0.0003, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.65%, test micro 83.86%, test macro 83.7%\n",
            "Epoch 96: loss 0.0003, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.65%, test micro 83.86%, test macro 83.7%\n",
            "Epoch 97: loss 0.0003, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.65%, test micro 83.86%, test macro 83.7%\n",
            "Epoch 98: loss 0.0003, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.65%, test micro 83.81%, test macro 83.65%\n",
            "Epoch 99: loss 0.00029, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.65%, test micro 83.86%, test macro 83.7%\n",
            "Epoch 100: loss 0.00029, train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.65%, test micro 83.86%, test macro 83.7%\n",
            "Best model: train micro 100.0%, train macro 100.0%, valid micro 97.67%, valid macro 97.67%, test micro 84.66%, test macro 84.07%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3oITravjOVV4"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rtkKBI_nKS1T"
      },
      "source": [
        "## Question 2.1: What is the maximum **micro** F1 score you could get for the best_model on test set when using the mean aggregation? (10 points)\n",
        "\n",
        "Submit your answers on Gradescope."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AIvw51jMKTvn"
      },
      "source": [
        "## Question 2.2: What is the maximum **macro** F1 score you could get for the best_model on test set when using the mean aggregation? (10 points)\n",
        "\n",
        "Submit your answers on Gradescope."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JBiYvwcuKd0z"
      },
      "source": [
        "## Training the Attention Aggregation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e6na5zyQKfvi",
        "outputId": "04907abd-7d6c-400c-c7ce-fe3825b155cb"
      },
      "source": [
        "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])\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": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1: loss 1.10446, 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 1.09574, 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 3: loss 1.06575, train micro 39.33%, train macro 27.26%, valid micro 35.33%, valid macro 20.83%, test micro 32.56%, test macro 18.82%\n",
            "Epoch 4: loss 1.00498, train micro 79.0%, train macro 78.73%, valid micro 74.67%, valid macro 74.06%, test micro 68.89%, test macro 67.99%\n",
            "Epoch 5: loss 0.90145, train micro 88.17%, train macro 87.79%, valid micro 85.67%, valid macro 84.88%, test micro 70.82%, test macro 67.18%\n",
            "Epoch 6: loss 0.75591, train micro 79.33%, train macro 76.59%, valid micro 75.0%, valid macro 70.59%, test micro 68.09%, test macro 59.69%\n",
            "Epoch 7: loss 0.5953, train micro 75.83%, train macro 71.74%, valid micro 72.33%, valid macro 66.31%, test micro 66.87%, test macro 56.96%\n",
            "Epoch 8: loss 0.45296, train micro 75.5%, train macro 71.34%, valid micro 71.67%, valid macro 65.37%, test micro 66.64%, test macro 56.38%\n",
            "Epoch 9: loss 0.3399, train micro 76.83%, train macro 73.32%, valid micro 71.0%, valid macro 63.98%, test micro 66.64%, test macro 56.43%\n",
            "Epoch 10: loss 0.25457, train micro 78.83%, train macro 76.08%, valid micro 73.33%, valid macro 67.85%, test micro 66.87%, test macro 56.78%\n",
            "Epoch 11: loss 0.19201, train micro 84.33%, train macro 83.03%, valid micro 79.67%, valid macro 77.24%, test micro 67.72%, test macro 58.57%\n",
            "Epoch 12: loss 0.14597, train micro 90.0%, train macro 89.53%, valid micro 83.67%, valid macro 82.28%, test micro 69.08%, test macro 61.28%\n",
            "Epoch 13: loss 0.11133, train micro 94.33%, train macro 94.19%, valid micro 87.0%, valid macro 86.13%, test micro 71.53%, test macro 65.57%\n",
            "Epoch 14: loss 0.08541, train micro 96.0%, train macro 95.93%, valid micro 89.0%, valid macro 88.42%, test micro 73.36%, test macro 68.5%\n",
            "Epoch 15: loss 0.06667, train micro 97.83%, train macro 97.82%, valid micro 90.33%, valid macro 89.97%, test micro 75.39%, test macro 71.76%\n",
            "Epoch 16: loss 0.05293, train micro 98.67%, train macro 98.66%, valid micro 92.0%, valid macro 91.77%, test micro 76.75%, test macro 73.83%\n",
            "Epoch 17: loss 0.04258, train micro 98.83%, train macro 98.83%, valid micro 93.33%, valid macro 93.19%, test micro 77.98%, test macro 75.63%\n",
            "Epoch 18: loss 0.03459, train micro 99.5%, train macro 99.5%, valid micro 95.33%, valid macro 95.32%, test micro 79.34%, test macro 77.5%\n",
            "Epoch 19: loss 0.02833, train micro 99.5%, train macro 99.5%, valid micro 95.33%, valid macro 95.34%, test micro 80.56%, test macro 79.07%\n",
            "Epoch 20: loss 0.02339, train micro 99.83%, train macro 99.83%, valid micro 95.0%, valid macro 95.0%, test micro 80.99%, test macro 79.69%\n",
            "Epoch 21: loss 0.01939, train micro 100.0%, train macro 100.0%, valid micro 95.0%, valid macro 95.0%, test micro 81.18%, test macro 79.93%\n",
            "Epoch 22: loss 0.01609, train micro 100.0%, train macro 100.0%, valid micro 95.33%, valid macro 95.33%, test micro 81.6%, test macro 80.44%\n",
            "Epoch 23: loss 0.01341, train micro 100.0%, train macro 100.0%, valid micro 95.33%, valid macro 95.33%, test micro 82.21%, test macro 81.14%\n",
            "Epoch 24: loss 0.01123, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 96.0%, test micro 82.45%, test macro 81.46%\n",
            "Epoch 25: loss 0.00943, train micro 100.0%, train macro 100.0%, valid micro 96.33%, valid macro 96.33%, test micro 82.87%, test macro 81.97%\n",
            "Epoch 26: loss 0.00799, train micro 100.0%, train macro 100.0%, valid micro 96.0%, valid macro 96.0%, test micro 83.44%, test macro 82.6%\n",
            "Epoch 27: loss 0.00683, train micro 100.0%, train macro 100.0%, valid micro 96.33%, valid macro 96.33%, test micro 83.58%, test macro 82.8%\n",
            "Epoch 28: loss 0.00577, train micro 100.0%, train macro 100.0%, valid micro 96.33%, valid macro 96.33%, test micro 83.67%, test macro 82.96%\n",
            "Epoch 29: loss 0.00475, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.66%, test micro 84.14%, test macro 83.48%\n",
            "Epoch 30: loss 0.0039, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 84.61%, test macro 83.99%\n",
            "Epoch 31: loss 0.00321, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.66%, test micro 84.8%, test macro 84.21%\n",
            "Epoch 32: loss 0.00267, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.66%, test micro 84.99%, test macro 84.45%\n",
            "Epoch 33: loss 0.00226, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 85.13%, test macro 84.63%\n",
            "Epoch 34: loss 0.00195, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.66%, test micro 85.36%, test macro 84.94%\n",
            "Epoch 35: loss 0.0017, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.66%, test micro 85.41%, test macro 85.0%\n",
            "Epoch 36: loss 0.00151, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.66%, test micro 85.41%, test macro 85.04%\n",
            "Epoch 37: loss 0.00136, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.66%, test micro 85.55%, test macro 85.22%\n",
            "Epoch 38: loss 0.00123, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.66%, test micro 85.32%, test macro 84.99%\n",
            "Epoch 39: loss 0.00113, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 85.08%, test macro 84.76%\n",
            "Epoch 40: loss 0.00104, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 85.04%, test macro 84.72%\n",
            "Epoch 41: loss 0.00096, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 84.85%, test macro 84.54%\n",
            "Epoch 42: loss 0.0009, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 84.75%, test macro 84.46%\n",
            "Epoch 43: loss 0.00084, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 84.71%, test macro 84.43%\n",
            "Epoch 44: loss 0.00079, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 84.75%, test macro 84.49%\n",
            "Epoch 45: loss 0.00075, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 84.85%, test macro 84.59%\n",
            "Epoch 46: loss 0.00071, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.99%, test micro 84.8%, test macro 84.56%\n",
            "Epoch 47: loss 0.00067, train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.32%, test micro 84.85%, test macro 84.63%\n",
            "Epoch 48: loss 0.00064, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 84.85%, test macro 84.64%\n",
            "Epoch 49: loss 0.00062, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 84.8%, test macro 84.59%\n",
            "Epoch 50: loss 0.00059, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 84.8%, test macro 84.6%\n",
            "Epoch 51: loss 0.00057, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 84.66%, test macro 84.49%\n",
            "Epoch 52: loss 0.00055, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 84.66%, test macro 84.49%\n",
            "Epoch 53: loss 0.00053, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 84.66%, test macro 84.51%\n",
            "Epoch 54: loss 0.00051, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 84.61%, test macro 84.47%\n",
            "Epoch 55: loss 0.0005, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 84.71%, test macro 84.58%\n",
            "Epoch 56: loss 0.00048, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.64%, test micro 84.56%, test macro 84.45%\n",
            "Epoch 57: loss 0.00047, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.64%, test micro 84.47%, test macro 84.37%\n",
            "Epoch 58: loss 0.00046, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.64%, test micro 84.42%, test macro 84.32%\n",
            "Epoch 59: loss 0.00044, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.64%, test micro 84.33%, test macro 84.24%\n",
            "Epoch 60: loss 0.00043, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.64%, test micro 84.38%, test macro 84.29%\n",
            "Epoch 61: loss 0.00042, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.64%, test micro 84.28%, test macro 84.2%\n",
            "Epoch 62: loss 0.00041, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.64%, test micro 84.33%, test macro 84.25%\n",
            "Epoch 63: loss 0.00041, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.64%, test micro 84.38%, test macro 84.31%\n",
            "Epoch 64: loss 0.0004, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.64%, test micro 84.33%, test macro 84.26%\n",
            "Epoch 65: loss 0.00039, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.64%, test micro 84.42%, test macro 84.37%\n",
            "Epoch 66: loss 0.00038, train micro 100.0%, train macro 100.0%, valid micro 96.67%, valid macro 96.64%, test micro 84.61%, test macro 84.57%\n",
            "Epoch 67: loss 0.00038, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.97%, test micro 84.52%, test macro 84.48%\n",
            "Epoch 68: loss 0.00037, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.97%, test micro 84.52%, test macro 84.48%\n",
            "Epoch 69: loss 0.00036, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.97%, test micro 84.47%, test macro 84.45%\n",
            "Epoch 70: loss 0.00036, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.97%, test micro 84.52%, test macro 84.5%\n",
            "Epoch 71: loss 0.00035, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.97%, test micro 84.52%, test macro 84.5%\n",
            "Epoch 72: loss 0.00035, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.97%, test micro 84.52%, test macro 84.51%\n",
            "Epoch 73: loss 0.00034, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.97%, test micro 84.38%, test macro 84.37%\n",
            "Epoch 74: loss 0.00034, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.97%, test micro 84.33%, test macro 84.33%\n",
            "Epoch 75: loss 0.00033, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.97%, test micro 84.33%, test macro 84.33%\n",
            "Epoch 76: loss 0.00033, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.97%, test micro 84.33%, test macro 84.33%\n",
            "Epoch 77: loss 0.00033, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.97%, test micro 84.38%, test macro 84.39%\n",
            "Epoch 78: loss 0.00032, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.97%, test micro 84.42%, test macro 84.43%\n",
            "Epoch 79: loss 0.00032, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.97%, test micro 84.42%, test macro 84.43%\n",
            "Epoch 80: loss 0.00031, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.97%, test micro 84.38%, test macro 84.39%\n",
            "Epoch 81: loss 0.00031, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.97%, test micro 84.28%, test macro 84.3%\n",
            "Epoch 82: loss 0.00031, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.97%, test micro 84.19%, test macro 84.2%\n",
            "Epoch 83: loss 0.0003, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.97%, test micro 84.19%, test macro 84.2%\n",
            "Epoch 84: loss 0.0003, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.97%, test micro 84.24%, test macro 84.26%\n",
            "Epoch 85: loss 0.0003, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 84.24%, test macro 84.26%\n",
            "Epoch 86: loss 0.00029, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 84.24%, test macro 84.26%\n",
            "Epoch 87: loss 0.00029, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 84.28%, test macro 84.3%\n",
            "Epoch 88: loss 0.00029, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 84.24%, test macro 84.26%\n",
            "Epoch 89: loss 0.00029, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 84.14%, test macro 84.17%\n",
            "Epoch 90: loss 0.00028, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 84.05%, test macro 84.08%\n",
            "Epoch 91: loss 0.00028, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.91%, test macro 83.94%\n",
            "Epoch 92: loss 0.00028, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.86%, test macro 83.9%\n",
            "Epoch 93: loss 0.00028, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.86%, test macro 83.9%\n",
            "Epoch 94: loss 0.00027, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.86%, test macro 83.9%\n",
            "Epoch 95: loss 0.00027, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.86%, test macro 83.9%\n",
            "Epoch 96: loss 0.00027, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.86%, test macro 83.9%\n",
            "Epoch 97: loss 0.00027, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.86%, test macro 83.9%\n",
            "Epoch 98: loss 0.00026, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.86%, test macro 83.9%\n",
            "Epoch 99: loss 0.00026, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.86%, test macro 83.9%\n",
            "Epoch 100: loss 0.00026, train micro 100.0%, train macro 100.0%, valid micro 97.0%, valid macro 96.98%, test micro 83.86%, test macro 83.9%\n",
            "Best model: train micro 100.0%, train macro 100.0%, valid micro 97.33%, valid macro 97.33%, test micro 85.04%, test macro 84.72%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mtAhFLQQKgbl"
      },
      "source": [
        "## Question 2.3: What is the maximum **micro** F1 score you could get for the best_model on test set when using the attention aggregation? (4 points)\n",
        "\n",
        "Submit your answers on Gradescope."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_cnsMGbsqJG_"
      },
      "source": [
        "## Question 2.4: What is the maximum **macro** F1 score you could get for the best_model on test set when using the attention aggregation? (4 points)\n",
        "\n",
        "Submit your answers on Gradescope."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aQgx5y4UqMHH"
      },
      "source": [
        "## Attention for each Message Type\n",
        "\n",
        "Through message type level attention we can learn that 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": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hvK58gijqN_C",
        "outputId": "ef5bbbea-b194-4b29-d776-e1c99d2a1fce"
      },
      "source": [
        "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": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Layer 1 has attention tensor([0.3901], device='cuda:0') on message type ('paper', 'author', 'paper')\n",
            "Layer 1 has attention tensor([0.6099], device='cuda:0') on message type ('paper', 'subject', 'paper')\n",
            "Layer 2 has attention tensor([0.5140], device='cuda:0') on message type ('paper', 'author', 'paper')\n",
            "Layer 2 has attention tensor([0.4860], device='cuda:0') on message type ('paper', 'subject', 'paper')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e7JXsMTBgeOI"
      },
      "source": [
        "# Submission\n",
        "\n",
        "In order to get credit, you must go submit your answers on Gradescope.\n",
        "\n",
        "Also, you need to submit the `ipynb` file of Colab 4, by clicking `File` and `Download .ipynb`. Please make sure that your output of each cell is available in your `ipynb` file."
      ]
    }
  ]
}