36_E(3)_invariant_graph_neural_networks.ipynb

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      "gpuType": "T4",
      "authorship_tag": "ABX9TyNU7b5gnlUzE6sRMHkHPM2j",
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/shumshersubashgautam/Computer-Aided-Drug-Discovery-Learning/blob/main/36_E(3)_invariant_graph_neural_networks.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZRwMpwBf2udq",
        "outputId": "3f72d28e-392a-4ee3-cef9-35d4c112e062"
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      "outputs": [
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          "output_type": "stream",
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          "text": [
            "Cloning into 'teachopencadd'...\n",
            "remote: Enumerating objects: 17115, done.\u001b[K\n",
            "remote: Counting objects: 100% (2094/2094), done.\u001b[K\n",
            "remote: Compressing objects: 100% (971/971), done.\u001b[K\n",
            "remote: Total 17115 (delta 1311), reused 1784 (delta 1121), pack-reused 15021\u001b[K\n",
            "Receiving objects: 100% (17115/17115), 849.73 MiB | 32.24 MiB/s, done.\n",
            "Resolving deltas: 100% (11503/11503), done.\n",
            "Updating files: 100% (468/468), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/volkamerlab/teachopencadd.git"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install rdkit"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "_GFN3noT5euO",
        "outputId": "b133892e-5c90-4120-cb57-c6b3a183e8f4"
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      "execution_count": 3,
      "outputs": [
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting rdkit\n",
            "  Downloading rdkit-2023.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (29.7 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m29.7/29.7 MB\u001b[0m \u001b[31m50.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from rdkit) (1.22.4)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from rdkit) (8.4.0)\n",
            "Installing collected packages: rdkit\n",
            "Successfully installed rdkit-2023.3.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Contents in Theory**\n",
        "\n",
        "Why 3D coordinates?\n",
        "\n",
        "Representing molecules as point clouds\n",
        "\n",
        "Equivariance and Invariance in euclidean space and why we care\n",
        "\n",
        "**How to construct **\n",
        "\n",
        "-invariant and equivariant models\n",
        "\n",
        "The QM9 dataset\n",
        "\n",
        "Contents in Practical\n",
        "\n",
        "Visualization of point clouds\n",
        "\n",
        "Set up and inspect the QM9 dataset\n",
        "\n",
        "**Preprocessing**\n",
        "\n",
        "Atomic number distribution and point cloud size\n",
        "\n",
        "Data split, distribution of regression target electronic spatial extent\n",
        "\n",
        "Model implementation\n",
        "\n",
        "Plain \"naive Euclidean\" GNN\n",
        "\n",
        "Demo: Plain GNNs are not E(3)-invariant\n",
        "\n",
        "EGNN model\n",
        "\n",
        "Demo: Our EGNN is E(3)-invariant\n",
        "\n",
        "Training and evaluation\n",
        "\n",
        "Setup\n",
        "\n",
        "Training the EGNN\n",
        "\n",
        "Training the plain GNN\n",
        "\n",
        "Comparative evaluation"
      ],
      "metadata": {
        "id": "ViVd9MhSSFlp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import math\n",
        "import operator\n",
        "from itertools import chain, product\n",
        "from functools import partial\n",
        "from pathlib import Path\n",
        "from typing import Any, Optional, Callable, Tuple, Dict, Sequence, NamedTuple\n",
        "\n",
        "import numpy as np\n",
        "\n",
        "from tqdm import tqdm\n",
        "\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from torch import Tensor, LongTensor"
      ],
      "metadata": {
        "id": "6jKBnaypRt2q"
      },
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import sys\n",
        "if sys.platform.startswith((\"linux\", \"darwin\")):\n",
        "    !mamba install -q -y -c pyg pyg\n",
        "    !mamba install -q -y -c conda-forge pytorch_scatter"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "saLpUH-xSUgT",
        "outputId": "e0c73a30-caaf-408c-ee16-4286162c29f2"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
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            "            / _/                       \\_____/  `\n",
            "            |/\n",
            "        ███╗   ███╗ █████╗ ███╗   ███╗██████╗  █████╗\n",
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            "        ██║ ╚═╝ ██║██║  ██║██║ ╚═╝ ██║██████╔╝██║  ██║\n",
            "        ╚═╝     ╚═╝╚═╝  ╚═╝╚═╝     ╚═╝╚═════╝ ╚═╝  ╚═╝\n",
            "\n",
            "        mamba (0.27.0) supported by @QuantStack\n",
            "\n",
            "        GitHub:  https://github.com/mamba-org/mamba\n",
            "        Twitter: https://twitter.com/QuantStack\n",
            "\n",
            "█████████████████████████████████████████████████████████████\n",
            "\n",
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            "                /    \\/    \\/    \\/    \\\n",
            "███████████████/  /██/  /██/  /██/  /████████████████████████\n",
            "              /  / \\   / \\   / \\   / \\  \\____\n",
            "             /  /   \\_/   \\_/   \\_/   \\    o \\__,\n",
            "            / _/                       \\_____/  `\n",
            "            |/\n",
            "        ███╗   ███╗ █████╗ ███╗   ███╗██████╗  █████╗\n",
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            "        ██║╚██╔╝██║██╔══██║██║╚██╔╝██║██╔══██╗██╔══██║\n",
            "        ██║ ╚═╝ ██║██║  ██║██║ ╚═╝ ██║██████╔╝██║  ██║\n",
            "        ╚═╝     ╚═╝╚═╝  ╚═╝╚═╝     ╚═╝╚═════╝ ╚═╝  ╚═╝\n",
            "\n",
            "        mamba (0.27.0) supported by @QuantStack\n",
            "\n",
            "        GitHub:  https://github.com/mamba-org/mamba\n",
            "        Twitter: https://twitter.com/QuantStack\n",
            "\n",
            "█████████████████████████████████████████████████████████████\n",
            "\n",
            "  Package            Version  Build                  Channel                  Size\n",
            "────────────────────────────────────────────────────────────────────────────────────\n",
            "  Install:\n",
            "────────────────────────────────────────────────────────────────────────────────────\n",
            "\n",
            "\u001b[32m  + pytorch_scatter\u001b[00m    2.0.9  cuda112py37hacf7e2f_0  conda-forge/linux-64      5MB\n",
            "\n",
            "  Summary:\n",
            "\n",
            "  Install: 1 packages\n",
            "\n",
            "  Total download: 5MB\n",
            "\n",
            "────────────────────────────────────────────────────────────────────────────────────\n",
            "\n",
            "Preparing transaction: ...working... done\n",
            "Verifying transaction: ...working... done\n",
            "Executing transaction: ...working... done\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch_geometric\n",
        "from torch_geometric.transforms import BaseTransform, Compose\n",
        "from torch_geometric.datasets import QM9\n",
        "from torch_geometric.data import Data\n",
        "from torch_geometric.loader import DataLoader\n",
        "from torch_geometric.data import Dataset\n",
        "from torch_geometric.nn.aggr import SumAggregation\n",
        "import torch_geometric.nn as geom_nn\n",
        "\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "# from torch_scatter import scatter"
      ],
      "metadata": {
        "id": "neFSJC9SSW2v"
      },
      "execution_count": 109,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade torch\n",
        "!pip install --upgrade torch-scatter"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PywzYe-WS0Na",
        "outputId": "1f896762-a22b-4704-ba55-e4f992b06910"
      },
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.7/site-packages (1.13.1)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/site-packages (from torch) (4.6.3)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu11==11.7.99 in /usr/local/lib/python3.7/site-packages (from torch) (11.7.99)\n",
            "Requirement already satisfied: nvidia-cudnn-cu11==8.5.0.96 in /usr/local/lib/python3.7/site-packages (from torch) (8.5.0.96)\n",
            "Requirement already satisfied: nvidia-cublas-cu11==11.10.3.66 in /usr/local/lib/python3.7/site-packages (from torch) (11.10.3.66)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu11==11.7.99 in /usr/local/lib/python3.7/site-packages (from torch) (11.7.99)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/site-packages (from nvidia-cublas-cu11==11.10.3.66->torch) (67.7.2)\n",
            "Requirement already satisfied: wheel in /usr/local/lib/python3.7/site-packages (from nvidia-cublas-cu11==11.10.3.66->torch) (0.40.0)\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0mLooking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: torch-scatter in /usr/local/lib/python3.7/site-packages (2.0.9)\n",
            "Collecting torch-scatter\n",
            "  Using cached torch_scatter-2.1.1.tar.gz (107 kB)\n",
            "\u001b[31mERROR: Operation cancelled by user\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Set path to this notebook\n",
        "HERE = Path(_dh[-1])\n",
        "DATA = HERE / \"data\""
      ],
      "metadata": {
        "id": "PVBoPA5qSbkJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Visualization of point clouds\n",
        "def to_perceived_brightness(rgb: np.ndarray) -> np.ndarray:\n",
        "    \"\"\"\n",
        "    Auxiliary function, useful for choosing label colors\n",
        "    with good visibility\n",
        "    \"\"\"\n",
        "    r, g, b = rgb\n",
        "    return 0.1 * r + 0.8 * g + 0.1\n",
        "\n",
        "\n",
        "def plot_point_cloud_3d(\n",
        "    fig: mpl.figure.Figure,\n",
        "    ax_pos: int,\n",
        "    color: np.ndarray,\n",
        "    pos: np.ndarray,\n",
        "    cmap: str = \"plasma\",\n",
        "    point_size: float = 180.0,\n",
        "    label_axes: bool = False,\n",
        "    annotate_points: bool = True,\n",
        "    remove_axes_ticks: bool = True,\n",
        "    cbar_label: str = \"\",\n",
        ") -> mpl.axis.Axis:\n",
        "    \"\"\"Visualize colored 3D point clouds.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    fig : mpl.figure.Figure\n",
        "        The figure for which a new axis object is added for plotting\n",
        "    ax_pos : int\n",
        "        Three-digit integer specifying axis layout and position\n",
        "        (see docs for `mpl.figure.Figure.add_subplot`)\n",
        "    color : np.ndarray\n",
        "        The point colors as a float array of shape `(N,)`\n",
        "    pos : np.ndarray\n",
        "        The point xyz-coordinates as an array\n",
        "    cmap : str, optional\n",
        "        String identifier for a matplotlib colormap.\n",
        "        Is used to map the values in `color` to rgb colors.\n",
        "        , by default \"plasma\"\n",
        "    point_size : float, optional\n",
        "        The size of plotted points, by default 180.0\n",
        "    label_axes : bool, optional\n",
        "        whether to label x,y and z axes by default False\n",
        "    annotate_points : bool, optional\n",
        "        whether to label points with their index, by default True\n",
        "    cbar_label : str, optional\n",
        "        label for the colorbar, by default \"\"\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    mpl.axis.Axis\n",
        "        The new axis object for the 3D point cloud plot.\n",
        "    \"\"\"\n",
        "    cmap = mpl.cm.get_cmap(cmap)\n",
        "    ax = fig.add_subplot(ax_pos, projection=\"3d\")\n",
        "    x, y, z = pos\n",
        "    if remove_axes_ticks:\n",
        "        ax.set_xticklabels([])\n",
        "        ax.set_yticklabels([])\n",
        "        ax.set_zticklabels([])\n",
        "    if label_axes:\n",
        "        ax.set_xlabel(\"$x$ coordinate\")\n",
        "        ax.set_ylabel(\"$y$ coordinate\")\n",
        "        ax.set_zlabel(\"$z$ coordinate\")\n",
        "    sc = ax.scatter(x, y, z, c=color, cmap=cmap, s=point_size)\n",
        "    plt.colorbar(sc, location=\"bottom\", shrink=0.6, anchor=(0.5, 2), label=cbar_label)\n",
        "    if annotate_points:\n",
        "        _colors = sc.cmap(color)\n",
        "        rgb = _colors[:, :3].transpose()\n",
        "        brightness = to_perceived_brightness(rgb)\n",
        "        for i, (xi, yi, zi, li) in enumerate(zip(x, y, z, brightness)):\n",
        "            ax.text(xi, yi, zi, str(i), None, color=[1 - li] * 3, ha=\"center\", va=\"center\")\n",
        "    return ax\n",
        "\n",
        "\n",
        "# testing\n",
        "fig = plt.figure(figsize=(8, 8))\n",
        "\n",
        "for ax_pos in [221, 222, 223, 224]:\n",
        "    pos = np.random.rand(3, 5)\n",
        "    color = np.random.rand(5)\n",
        "    plot_point_cloud_3d(fig, ax_pos, color, pos)\n",
        "\n",
        "fig.suptitle(\"Random test point clouds\")\n",
        "fig.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 694
        },
        "id": "_pBzKQNsSdwZ",
        "outputId": "94b09322-f876-4306-96ed-d22000f70014"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_model_input(data: Data, fig: mpl.figure.Figure, ax_pos: int) -> mpl.axis.Axis:\n",
        "    \"\"\"\n",
        "    Plots 3D point cloud model input represented by a torch geometric\n",
        "    `Data` object. Use atomic numbers as colors.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    data : Data\n",
        "        The 3D point cloud. Must have atomic numbers `z` and 2D coordinates `pos`\n",
        "        properties that are not `None`.\n",
        "    fig: mpl.figure.Figure\n",
        "        The maptlotlib figure to plot on.\n",
        "    ax_pos:\n",
        "        Three-digit integer specifying axis layout and position\n",
        "        (see docs for `mpl.figure.Figure.add_subplot`).\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    mpl.axis.Axis\n",
        "        The newly created axis object.\n",
        "    \"\"\"\n",
        "    color, pos = data.z, data.pos\n",
        "    color = color.flatten().detach().numpy()\n",
        "    pos = pos.T.detach().numpy()\n",
        "    return plot_point_cloud_3d(fig, ax_pos, color, pos, cbar_label=\"Atomic number\")\n",
        "\n",
        "\n",
        "def plot_model_embedding(\n",
        "    data: Data, model: Callable[[Data], Tensor], fig: mpl.figure.Figure, ax_pos: int\n",
        ") -> mpl.axis.Axis:\n",
        "    \"\"\"\n",
        "    Same as `plot_model_input` but instead of node features as color,\n",
        "    first apply a GNN model to obtain colors from node embeddings.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    data : Data\n",
        "        the model input. Must have 3D coordinates `pos`\n",
        "        an atomic number `z` properties that are not `None`.\n",
        "    model : Callable[[Data], Tensor]\n",
        "        the model must take Data objects as input and return node embeddings\n",
        "        as a Tensor output.\n",
        "    fig: mpl.figure.Figure\n",
        "        The maptlotlib figure to plot on.\n",
        "    ax_pos:\n",
        "        Three-digit integer specifying axis layout and position\n",
        "        (see docs for `mpl.figure.Figure.add_subplot`).\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    mpl.axis.Axis\n",
        "        The newly created axis object.\n",
        "    \"\"\"\n",
        "    x = model(data)\n",
        "    pos = data.pos\n",
        "    color = x.flatten().detach().numpy()\n",
        "    pos = pos.T.detach().numpy()\n",
        "    return plot_point_cloud_3d(fig, ax_pos, color, pos, cbar_label=\"Atom embedding (1D)\")"
      ],
      "metadata": {
        "id": "0wqBN80DTVeF"
      },
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def num_heavy_atoms(qm9_data: Data) -> int:\n",
        "    \"\"\"Count the number of heavy atoms in a torch geometric\n",
        "    Data object.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    qm9_data : Data\n",
        "        A pytorch geometric qm9 data object representing a small molecule\n",
        "         where atomic numbers are stored in a\n",
        "        tensor-valued attribute `qm9_data.z`\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    int\n",
        "        The number of heavy atoms in the molecule.\n",
        "    \"\"\"\n",
        "    # every atom with atomic number other than 1 is heavy\n",
        "    return (qm9_data.z != 1).sum()\n",
        "\n",
        "\n",
        "def complete_edge_index(n: int) -> LongTensor:\n",
        "    \"\"\"\n",
        "    Constructs a complete edge index.\n",
        "\n",
        "    NOTE: representing complete graphs\n",
        "    with sparse edge tensors is arguably a bad idea\n",
        "    due to performance reasons, but for this tutorial it'll do.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    n : int\n",
        "        the number of nodes in the graph.\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    LongTensor\n",
        "        A PyTorch `edge_index` represents a complete graph with n nodes,\n",
        "        without self-loops. Shape (2, n).\n",
        "    \"\"\"\n",
        "    # filter removes self loops\n",
        "    edges = list(filter(lambda e: e[0] != e[1], product(range(n), range(n))))\n",
        "    return torch.tensor(edges, dtype=torch.long).T\n",
        "\n",
        "\n",
        "def add_complete_graph_edge_index(data: Data) -> Data:\n",
        "    \"\"\"\n",
        "    On top of any edge information already there,\n",
        "    add a second edge index that represents\n",
        "    the complete graph corresponding to a  given\n",
        "    torch geometric data object\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    data : Data\n",
        "        The torch geometric data object.\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    Data\n",
        "        The torch geometric `Data` object with a new\n",
        "        attribute `complete_edge_index` as described above.\n",
        "    \"\"\"\n",
        "    data.complete_edge_index = complete_edge_index(data.num_nodes)\n",
        "    return data\n",
        "\n",
        "\n",
        "#\n",
        "dataset = QM9(\n",
        "    DATA,\n",
        "    # Filter out molecules with more than 8 heavy atoms\n",
        "    pre_filter=lambda data: num_heavy_atoms(data) < 9,\n",
        "    # implement point cloud adjacency as a complete graph\n",
        "    pre_transform=add_complete_graph_edge_index,\n",
        ")\n",
        "\n",
        "print(f\"Num. examples in QM9 restricted to molecules with at most 8 heavy atoms: {len(dataset)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "x_VYs37CUwxy",
        "outputId": "4070f73f-b48c-4aab-8708-a0f8e6299ace"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Num. examples in QM9 restricted to molecules with at most 8 heavy atoms: 130831\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = dataset[0]\n",
        "# This displays all named data attributes, and their shapes (in the case of tensors), or values (in the case of other data).\n",
        "data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XnRQzqLvU0tb",
        "outputId": "7e5dc43c-96c1-4e7a-8bcb-4928a9165c9f"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Data(x=[5, 11], edge_index=[2, 8], edge_attr=[8, 4], y=[1, 19], pos=[5, 3], z=[5], name='gdb_1', idx=[1])"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.z"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hhm0VK8_U4qs",
        "outputId": "491f8d30-5310-457f-acf5-e0982b8a66a9"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([6, 1, 1, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.pos.round(decimals=2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "te_eA28QU7Th",
        "outputId": "4265ff4e-715a-4888-9413-47626f10ba3d"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([[-0.0100,  1.0900,  0.0100],\n",
              "        [ 0.0000, -0.0100,  0.0000],\n",
              "        [ 1.0100,  1.4600,  0.0000],\n",
              "        [-0.5400,  1.4500, -0.8800],\n",
              "        [-0.5200,  1.4400,  0.9100]])"
            ]
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure()\n",
        "ax = plot_model_input(data, fig, 111)\n",
        "_ = ax.set_title(\"CH$_4$ (Methane)\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "id": "OslKxAxcU9eH",
        "outputId": "6068400f-e082-46df-faff-f2908316fb3d"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, (ax_atoms, ax_graph_size) = plt.subplots(1, 2, figsize=(8, 5))\n",
        "\n",
        "# ax_atoms.hist(dataset.data.z[dataset.data.z != 1])\n",
        "ax_atoms.hist(dataset.data.z)\n",
        "ax_atoms.set_xlabel(\"Atomic number $z$\")\n",
        "ax_atoms.set_ylabel(\"Count\")\n",
        "num_nodes = [dataset[i].num_nodes for i in range(len(dataset))]\n",
        "ax_graph_size.hist(num_nodes)\n",
        "ax_graph_size.set_xlabel(\"Graph size (#nodes)\")\n",
        "fig.suptitle(\"Aggregated molecular point cloud properties\")\n",
        "fig.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 509
        },
        "id": "EIheisgQU_mE",
        "outputId": "652ab166-321c-4a29-b24d-3c64efc34102"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class QM9DataModule:\n",
        "    def __init__(\n",
        "        self,\n",
        "        train_ratio: float = 0.8,\n",
        "        val_ratio: float = 0.1,\n",
        "        test_ratio: float = 0.1,\n",
        "        target_idx: int = 5,\n",
        "        seed: float = 420,\n",
        "    ) -> None:\n",
        "        \"\"\"Encapsulates everything related to the dataset\n",
        "\n",
        "        Parameters\n",
        "        ----------\n",
        "        train_ratio : float, optional\n",
        "            fraction of data used for training, by default 0.8\n",
        "        val_ratio : float, optional\n",
        "            fraction of data used for validation, by default 0.1\n",
        "        test_ratio : float, optional\n",
        "            fraction of data used for testing, by default 0.1\n",
        "        target_idx : int, optional\n",
        "            index of the target (see torch geometric docs), by default 5 (electronic spatial extent)\n",
        "            (https://pytorch-geometric.readthedocs.io/en/latest/modules/datasets.html?highlight=qm9#torch_geometric.datasets.QM9)\n",
        "        seed : float, optional\n",
        "            random seed for data split, by default 420\n",
        "        \"\"\"\n",
        "        assert sum([train_ratio, val_ratio, test_ratio]) == 1\n",
        "        self.target_idx = target_idx\n",
        "        self.num_examples = len(self.dataset())\n",
        "        rng = np.random.default_rng(seed)\n",
        "        self.shuffled_index = rng.permutation(self.num_examples)\n",
        "        self.train_split = self.shuffled_index[: int(self.num_examples * train_ratio)]\n",
        "        self.val_split = self.shuffled_index[\n",
        "            int(self.num_examples * train_ratio) : int(\n",
        "                self.num_examples * (train_ratio + val_ratio)\n",
        "            )\n",
        "        ]\n",
        "        self.test_split = self.shuffled_index[\n",
        "            int(self.num_examples * (train_ratio + val_ratio)) : self.num_examples\n",
        "        ]\n",
        "\n",
        "    def dataset(self, transform=None) -> QM9:\n",
        "        dataset = QM9(\n",
        "            DATA,\n",
        "            pre_filter=lambda data: num_heavy_atoms(data) < 9,\n",
        "            pre_transform=add_complete_graph_edge_index,\n",
        "        )\n",
        "        dataset.data.y = dataset.data.y[:, self.target_idx].view(-1, 1)\n",
        "        return dataset\n",
        "\n",
        "    def loader(self, split, **loader_kwargs) -> DataLoader:\n",
        "        dataset = self.dataset()[split]\n",
        "        return DataLoader(dataset, **loader_kwargs)\n",
        "\n",
        "    def train_loader(self, **loader_kwargs) -> DataLoader:\n",
        "        return self.loader(self.train_split, shuffle=True, **loader_kwargs)\n",
        "\n",
        "    def val_loader(self, **loader_kwargs) -> DataLoader:\n",
        "        return self.loader(self.val_split, shuffle=False, **loader_kwargs)\n",
        "\n",
        "    def test_loader(self, **loader_kwargs) -> DataLoader:\n",
        "        return self.loader(self.test_split, shuffle=False, **loader_kwargs)"
      ],
      "metadata": {
        "id": "QW3mnOa2VGa-"
      },
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data_module = QM9DataModule()\n",
        "\n",
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 6), sharey=True)\n",
        "target = data_module.dataset().data.y.flatten().numpy()\n",
        "ax1.boxplot(\n",
        "    [\n",
        "        target[data_module.train_split],\n",
        "        target[data_module.val_split],\n",
        "        target[data_module.test_split],\n",
        "    ]\n",
        ")\n",
        "ax1.set_xticklabels([\"Train\", \"Val\", \"Test\"])\n",
        "ax1.set_ylabel(\"Electronic spatial extent $\\langle R^2 \\\\rangle$\")\n",
        "\n",
        "for label, split in {\n",
        "    \"Train\": data_module.train_split,\n",
        "    \"Val\": data_module.val_split,\n",
        "    \"Test\": data_module.test_split,\n",
        "}.items():\n",
        "    ax2.scatter(split, target[split], label=label, s=1)\n",
        "\n",
        "ax2.set_xlabel(\"Example index\")\n",
        "ax2.legend()\n",
        "fig.suptitle(\"Random data split - target distribution\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "-VC-xszVVMjj",
        "outputId": "e92c418c-ab49-47ea-8d7f-267ac8fe4dd2"
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 0.98, 'Random data split - target distribution')"
            ]
          },
          "metadata": {},
          "execution_count": 119
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "class NaiveEuclideanGNN(nn.Module):\n",
        "    def __init__(\n",
        "        self,\n",
        "        hidden_channels: int,\n",
        "        num_layers: int,\n",
        "        num_spatial_dims: int,\n",
        "        final_embedding_size: Optional[int] = None,\n",
        "        act: nn.Module = nn.ReLU(),\n",
        "    ) -> None:\n",
        "        super().__init__()\n",
        "        # NOTE nn.Embedding acts like a lookup table.\n",
        "        # Here we use it to store each atomic number in [0,100]\n",
        "        # a learnable, fixed-size vector representation\n",
        "        self.f_initial_embed = nn.Embedding(100, hidden_channels)\n",
        "        self.f_pos_embed = nn.Linear(num_spatial_dims, hidden_channels)\n",
        "        self.f_combine = nn.Sequential(nn.Linear(2 * hidden_channels, hidden_channels), act)\n",
        "\n",
        "        if final_embedding_size is None:\n",
        "            final_embedding_size = hidden_channels\n",
        "\n",
        "        # Graph isomorphism network as main GNN\n",
        "        # (see Talktorial 034)\n",
        "        # takes care of message passing and\n",
        "        # Learning node-level embeddings\n",
        "        self.gnn = geom_nn.models.GIN(\n",
        "            in_channels=hidden_channels,\n",
        "            hidden_channels=hidden_channels,\n",
        "            out_channels=final_embedding_size,\n",
        "            num_layers=num_layers,\n",
        "            act=act,\n",
        "        )\n",
        "\n",
        "        # modules required for aggregating node embeddings\n",
        "        # into graph embeddings and making graph-level predictions\n",
        "        self.aggregation = geom_nn.aggr.SumAggregation()\n",
        "        self.f_predict = nn.Sequential(\n",
        "            nn.Linear(final_embedding_size, final_embedding_size),\n",
        "            act,\n",
        "            nn.Linear(final_embedding_size, 1),\n",
        "        )\n",
        "\n",
        "    def encode(self, data: Data) -> Tensor:\n",
        "        # initial atomic number embedding and embedding od positional information\n",
        "        atom_embedding = self.f_initial_embed(data.z)\n",
        "        pos_embedding = self.f_pos_embed(data.pos)\n",
        "\n",
        "        # treat both as plain node-level features and combine into initial node-level\n",
        "        # embedddings\n",
        "        initial_node_embed = self.f_combine(torch.cat((atom_embedding, pos_embedding), dim=-1))\n",
        "\n",
        "        # message passing\n",
        "        # NOTE in contrast to the EGNN implemented later, this model does use bond information\n",
        "        # i.e., data.egde_index stems from the bond adjacency matrix\n",
        "        node_embed = self.gnn(initial_node_embed, data.edge_index)\n",
        "        return node_embed\n",
        "\n",
        "    def forward(self, data: Data) -> Tensor:\n",
        "        node_embed = self.encode(data)\n",
        "        aggr = self.aggregation(node_embed, data.batch)\n",
        "        return self.f_predict(aggr)"
      ],
      "metadata": {
        "id": "mt9WlGJUVWZF"
      },
      "execution_count": 120,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Plain GNNs are not E3\n",
        "-invariant"
      ],
      "metadata": {
        "id": "tXX3B-IKVfir"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# use rotations along z-axis as demo e(3) transformation\n",
        "def rotation_matrix_z(theta: float) -> Tensor:\n",
        "    \"\"\"Generates a rotation matrix and returns\n",
        "    a corresponing tensor. The rotation is about the $z$-axis.\n",
        "    (https://en.wikipedia.org/wiki/Rotation_matrix)\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    theta : float\n",
        "        the angle of rotation.\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    Tensor\n",
        "        the rotation matrix as float tensor.\n",
        "    \"\"\"\n",
        "    return torch.tensor(\n",
        "        [\n",
        "            [math.cos(theta), -math.sin(theta), 0],\n",
        "            [math.sin(theta), math.cos(theta), 0],\n",
        "            [0, 0, 1],\n",
        "        ]\n",
        "    )"
      ],
      "metadata": {
        "id": "-nichCLQVbac"
      },
      "execution_count": 121,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Some data points from qm9\n",
        "sample_data = dataset[800].clone()\n",
        "\n",
        "# apply an E(3) transformation\n",
        "rotated_sample_data = sample_data.clone()\n",
        "rotated_sample_data.pos = rotated_sample_data.pos @ rotation_matrix_z(45)\n",
        "\n",
        "# initialize a model with 2 hidden layers, 32 hidden channels,\n",
        "# that outputs 1-dimensional node embeddings\n",
        "model = NaiveEuclideanGNN(\n",
        "    hidden_channels=32,\n",
        "    num_layers=2,\n",
        "    num_spatial_dims=3,\n",
        "    final_embedding_size=1,\n",
        ")\n",
        "\n",
        "# make a plot that demonstrates non-equivariance\n",
        "# fig, axes = plt.subplots(2, 2, figsize=(8,8), sharex=True, sharey=True)\n",
        "fig = plt.figure(figsize=(8, 8))\n",
        "\n",
        "ax1 = plot_model_input(sample_data, fig, 221)\n",
        "ax1.set_title(\"Sample input $(X, Z)$\")\n",
        "\n",
        "ax2 = plot_model_input(rotated_sample_data, fig, 222)\n",
        "ax2.set_title(\"Rotated input $(X, g(Z))$\")\n",
        "\n",
        "ax3 = plot_model_embedding(sample_data, model.encode, fig, 223)\n",
        "ax3.set_title(\"Model output for $(X, Z)$\")\n",
        "\n",
        "ax4 = plot_model_embedding(rotated_sample_data, model.encode, fig, 224)\n",
        "ax4.set_title(\"Model output for $(X, g(Z))$\")\n",
        "fig.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "1ZbTlKewVi9d",
        "outputId": "3ca457cb-59e9-4b1b-c701-bd490fac3024"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 8 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **EGNN model**"
      ],
      "metadata": {
        "id": "sPN3YBw2Vo11"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "class EquivariantMPLayer(nn.Module):\n",
        "    def __init__(\n",
        "        self,\n",
        "        in_channels: int,\n",
        "        hidden_channels: int,\n",
        "        act: nn.Module,\n",
        "    ) -> None:\n",
        "        super().__init__()\n",
        "        self.act = act\n",
        "        self.residual_proj = nn.Linear(in_channels, hidden_channels, bias=False)\n",
        "\n",
        "        # Messages will consist of two (source and target) node embeddings and a scalar distance\n",
        "        message_input_size = 2 * in_channels + 1\n",
        "\n",
        "        # equation (3) \"phi_l\" NN\n",
        "        self.message_mlp = nn.Sequential(\n",
        "            nn.Linear(message_input_size, hidden_channels),\n",
        "            act,\n",
        "        )\n",
        "        # equation (4) \"psi_l\" NN\n",
        "        self.node_update_mlp = nn.Sequential(\n",
        "            nn.Linear(in_channels + hidden_channels, hidden_channels),\n",
        "            act,\n",
        "        )\n",
        "\n",
        "    def node_message_function(\n",
        "        self,\n",
        "        source_node_embed: Tensor,  # h_i\n",
        "        target_node_embed: Tensor,  # h_j\n",
        "        node_dist: Tensor,  # d_ij\n",
        "    ) -> Tensor:\n",
        "        # implements equation (3)\n",
        "        message_repr = torch.cat((source_node_embed, target_node_embed, node_dist), dim=-1)\n",
        "        return self.message_mlp(message_repr)\n",
        "\n",
        "    def compute_distances(self, node_pos: Tensor, edge_index: LongTensor) -> Tensor:\n",
        "        row, col = edge_index\n",
        "        xi, xj = node_pos[row], node_pos[col]\n",
        "        # relative squared distance\n",
        "        # implements equation (2) ||X_i - X_j||^2\n",
        "        rsdist = (xi - xj).pow(2).sum(1, keepdim=True)\n",
        "        return rsdist\n",
        "\n",
        "    def forward(\n",
        "        self,\n",
        "        node_embed: Tensor,\n",
        "        node_pos: Tensor,\n",
        "        edge_index: Tensor,\n",
        "    ) -> Tensor:\n",
        "        row, col = edge_index\n",
        "        dist = self.compute_distances(node_pos, edge_index)\n",
        "\n",
        "        # compute messages \"m_ij\" from  equation (3)\n",
        "        node_messages = self.node_message_function(node_embed[row], node_embed[col], dist)\n",
        "\n",
        "        # message sum aggregation in equation (4)\n",
        "        aggr_node_messages = scatter(node_messages, col, dim=0, reduce=\"sum\")\n",
        "\n",
        "        # compute new node embeddings \"h_i^{l+1}\"\n",
        "        # (implements rest of equation (4))\n",
        "        new_node_embed = self.residual_proj(node_embed) + self.node_update_mlp(\n",
        "            torch.cat((node_embed, aggr_node_messages), dim=-1)\n",
        "        )\n",
        "\n",
        "        return new_node_embed\n",
        "\n",
        "\n",
        "class EquivariantGNN(nn.Module):\n",
        "    def __init__(\n",
        "        self,\n",
        "        hidden_channels: int,\n",
        "        final_embedding_size: Optional[int] = None,\n",
        "        target_size: int = 1,\n",
        "        num_mp_layers: int = 2,\n",
        "    ) -> None:\n",
        "        super().__init__()\n",
        "        if final_embedding_size is None:\n",
        "            final_embedding_size = hidden_channels\n",
        "\n",
        "        # non-linear activation func.\n",
        "        # usually configurable, here we just use Relu for simplicity\n",
        "        self.act = nn.ReLU()\n",
        "\n",
        "        # equation (1) \"psi_0\"\n",
        "        self.f_initial_embed = nn.Embedding(100, hidden_channels)\n",
        "\n",
        "        # create stack of message passing layers\n",
        "        self.message_passing_layers = nn.ModuleList()\n",
        "        channels = [hidden_channels] * (num_mp_layers) + [final_embedding_size]\n",
        "        for d_in, d_out in zip(channels[:-1], channels[1:]):\n",
        "            layer = EquivariantMPLayer(d_in, d_out, self.act)\n",
        "            self.message_passing_layers.append(layer)\n",
        "\n",
        "        # modules required for readout of a graph-level\n",
        "        # representation and graph-level property prediction\n",
        "        self.aggregation = SumAggregation()\n",
        "        self.f_predict = nn.Sequential(\n",
        "            nn.Linear(final_embedding_size, final_embedding_size),\n",
        "            self.act,\n",
        "            nn.Linear(final_embedding_size, target_size),\n",
        "        )\n",
        "\n",
        "    def encode(self, data: Data) -> Tensor:\n",
        "        # theory, equation (1)\n",
        "        node_embed = self.f_initial_embed(data.z)\n",
        "        # message passing\n",
        "        # theory, equation (3-4)\n",
        "        for mp_layer in self.message_passing_layers:\n",
        "            # NOTE here we use the complete edge index defined by the transform earlier on\n",
        "            # to implement the sum over $j \\neq i$ in equation (4)\n",
        "            node_embed = mp_layer(node_embed, data.pos, data.complete_edge_index)\n",
        "        return node_embed\n",
        "\n",
        "    def _predict(self, node_embed, batch_index) -> Tensor:\n",
        "        aggr = self.aggregation(node_embed, batch_index)\n",
        "        return self.f_predict(aggr)\n",
        "\n",
        "    def forward(self, data: Data) -> Tensor:\n",
        "        node_embed = self.encode(data)\n",
        "        pred = self._predict(node_embed, data.batch)\n",
        "        return pred"
      ],
      "metadata": {
        "id": "a-tYKB8HVlSG"
      },
      "execution_count": 123,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "EGNN is E(3)\n",
        "-invariant"
      ],
      "metadata": {
        "id": "19rAfma4VuqC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = EquivariantGNN(hidden_channels=32, final_embedding_size=1, num_mp_layers=2)"
      ],
      "metadata": {
        "id": "tkiGRfutVyUa"
      },
      "execution_count": 124,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Some data points from qm9\n",
        "sample_data = dataset[800].clone()\n",
        "\n",
        "# apply E(3) transformation\n",
        "rotated_sample_data = sample_data.clone()\n",
        "rotated_sample_data.pos = rotated_sample_data.pos @ rotation_matrix_z(120)\n",
        "\n",
        "fig = plt.figure(figsize=(8, 8))\n",
        "\n",
        "ax1 = plot_model_input(sample_data, fig, 221)\n",
        "ax1.set_title(\"Sample input $(X, Z)$\")\n",
        "\n",
        "ax2 = plot_model_input(rotated_sample_data, fig, 222)\n",
        "ax2.set_title(\"Rotated input $(X, g(Z))$\")\n",
        "\n",
        "ax3 = plot_model_embedding(sample_data, model.encode, fig, 223)\n",
        "ax3.set_title(\"Model output for $(X, Z)$\")\n",
        "\n",
        "ax4 = plot_model_embedding(rotated_sample_data, model.encode, fig, 224)\n",
        "ax4.set_title(\"Model output for $(X, g(Z))$\")\n",
        "fig.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 757
        },
        "id": "Wd3ub6XIVsy7",
        "outputId": "cfab8a38-786a-403b-8b50-8744b46ed659"
      },
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.7/site-packages/torch_geometric/data/storage.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m     77\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 78\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     79\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/site-packages/torch_geometric/data/storage.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    102\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mapping\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'complete_edge_index'",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-125-9c84f8846a79>\u001b[0m in \u001b[0;36m<cell line: 16>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0max2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Rotated input $(X, g(Z))$\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0max3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplot_model_embedding\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample_data\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m223\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0max3\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_title\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Model output for $(X, Z)$\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-111-d37b5a94e42d>\u001b[0m in \u001b[0;36mplot_model_embedding\u001b[0;34m(data, model, fig, ax_pos)\u001b[0m\n\u001b[1;32m     52\u001b[0m         \u001b[0mThe\u001b[0m \u001b[0mnewly\u001b[0m \u001b[0mcreated\u001b[0m \u001b[0maxis\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     53\u001b[0m     \"\"\"\n\u001b[0;32m---> 54\u001b[0;31m     \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     55\u001b[0m     \u001b[0mpos\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     56\u001b[0m     \u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflatten\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-123-9cc7ee7f91df>\u001b[0m in \u001b[0;36mencode\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m    109\u001b[0m             \u001b[0;31m# NOTE here we use the complete edge index defined by the transform earlier on\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    110\u001b[0m             \u001b[0;31m# to implement the sum over $j \\neq i$ in equation (4)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 111\u001b[0;31m             \u001b[0mnode_embed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmp_layer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode_embed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpos\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomplete_edge_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    112\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnode_embed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/site-packages/torch_geometric/data/data.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    439\u001b[0m                 \u001b[0;34m\"dataset, remove the 'processed/' directory in the dataset's \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    440\u001b[0m                 \"root folder and try again.\")\n\u001b[0;32m--> 441\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_store\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    442\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    443\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/site-packages/torch_geometric/data/storage.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m     78\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     79\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m             raise AttributeError(\n\u001b[0m\u001b[1;32m     81\u001b[0m                 f\"'{self.__class__.__name__}' object has no attribute '{key}'\")\n\u001b[1;32m     82\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'GlobalStorage' object has no attribute 'complete_edge_index'"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# We will be using mean absolute error\n",
        "# as a metric for validation and testing\n",
        "def total_absolute_error(pred: Tensor, target: Tensor, batch_dim: int = 0) -> Tensor:\n",
        "    \"\"\"Total absolute error, i.e. sums over batch dimension.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    pred : Tensor\n",
        "        batch of model predictions\n",
        "    target : Tensor\n",
        "        batch of ground truth / target values\n",
        "    batch_dim : int, optional\n",
        "        dimension that indexes batch elements, by default 0\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    Tensor\n",
        "        total absolute error\n",
        "    \"\"\"\n",
        "    return (pred - target).abs().sum(batch_dim)"
      ],
      "metadata": {
        "id": "m3Dm-OWtV2Db"
      },
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def run_epoch(\n",
        "    model: nn.Module,\n",
        "    loader: DataLoader,\n",
        "    criterion: Callable[[Tensor, Tensor], Tensor],\n",
        "    pbar: Optional[Any] = None,\n",
        "    optim: Optional[torch.optim.Optimizer] = None,\n",
        "):\n",
        "    \"\"\"Run a single epoch.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    model : nn.Module\n",
        "        the NN used for regression\n",
        "    loader : DataLoader\n",
        "        an iterable over data batches\n",
        "    criterion : Callable[[Tensor, Tensor], Tensor]\n",
        "        a criterion (loss) that is optimized\n",
        "    pbar : Optional[Any], optional\n",
        "        a tqdm progress bar, by default None\n",
        "    optim : Optional[torch.optim.Optimizer], optional\n",
        "        a optimizer that is optimizing the criterion, by default None\n",
        "    \"\"\"\n",
        "\n",
        "    def step(\n",
        "        data_batch: Data,\n",
        "    ) -> Tuple[float, float]:\n",
        "        \"\"\"Perform a single train/val step on a data batch.\n",
        "\n",
        "        Parameters\n",
        "        ----------\n",
        "        data_batch : Data\n",
        "\n",
        "        Returns\n",
        "        -------\n",
        "        Tuple[float, float]\n",
        "            Loss (mean squared error) and validation critierion (absolute error).\n",
        "        \"\"\"\n",
        "        pred = model.forward(data_batch)\n",
        "        target = data_batch.y\n",
        "        loss = criterion(pred, target)\n",
        "        if optim is not None:\n",
        "            optim.zero_grad()\n",
        "            loss.backward()\n",
        "            optim.step()\n",
        "        return loss.detach().item(), total_absolute_error(pred.detach(), target.detach())\n",
        "\n",
        "    if optim is not None:\n",
        "        model.train()\n",
        "        # This enables pytorch autodiff s.t. we can compute gradients\n",
        "        model.requires_grad_(True)\n",
        "    else:\n",
        "        model.eval()\n",
        "        # disable autodiff: when evaluating we do not need to track gradients\n",
        "        model.requires_grad_(False)\n",
        "\n",
        "    total_loss = 0\n",
        "    total_mae = 0\n",
        "    for data in loader:\n",
        "        loss, mae = step(data)\n",
        "        total_loss += loss * data.num_graphs\n",
        "        total_mae += mae\n",
        "        if pbar is not None:\n",
        "            pbar.update(1)\n",
        "\n",
        "    return total_loss / len(loader.dataset), total_mae / len(loader.dataset)\n",
        "\n",
        "\n",
        "def train_model(\n",
        "    data_module: QM9DataModule,\n",
        "    model: nn.Module,\n",
        "    num_epochs: int = 30,\n",
        "    lr: float = 3e-4,\n",
        "    batch_size: int = 32,\n",
        "    weight_decay: float = 1e-8,\n",
        "    best_model_path: Path = DATA.joinpath(\"trained_model.pth\"),\n",
        ") -> Dict[str, Any]:\n",
        "    \"\"\"Takes data and model as input and runs training, collecting additional validation metrics\n",
        "    while doing so.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    data_module : QM9DataModule\n",
        "        a data module as defined earlier\n",
        "    model : nn.Module\n",
        "        a gnn model\n",
        "    num_epochs : int, optional\n",
        "        number of epochs to train for, by default 30\n",
        "    lr : float, optional\n",
        "        \"learning rate\": optimizer SGD step size, by default 3e-4\n",
        "    batch_size : int, optional\n",
        "        number of examples used for one training step, by default 32\n",
        "    weight_decay : float, optional\n",
        "        L2 regularization parameter, by default 1e-8\n",
        "    best_model_path : Path, optional\n",
        "        path where the model weights with lowest val. error should be stored\n",
        "        , by default DATA.joinpath(\"trained_model.pth\")\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    Dict[str, Any]\n",
        "        a training result, ie statistics and info about the model\n",
        "    \"\"\"\n",
        "    # create data loaders\n",
        "    train_loader = data_module.train_loader(batch_size=batch_size)\n",
        "    val_loader = data_module.val_loader(batch_size=batch_size)\n",
        "\n",
        "    # setup optimizer and loss\n",
        "    optim = torch.optim.Adam(model.parameters(), lr, weight_decay=1e-8)\n",
        "    loss_fn = nn.MSELoss()\n",
        "\n",
        "    # keep track of the epoch with the best validation mae\n",
        "    # st we can save the \"best\" model weights\n",
        "    best_val_mae = float(\"inf\")\n",
        "\n",
        "    # Statistics that will be plotted later on\n",
        "    # and model info\n",
        "    result = {\n",
        "        \"model\": model,\n",
        "        \"path_to_best_model\": best_model_path,\n",
        "        \"train_loss\": np.full(num_epochs, float(\"nan\")),\n",
        "        \"val_loss\": np.full(num_epochs, float(\"nan\")),\n",
        "        \"train_mae\": np.full(num_epochs, float(\"nan\")),\n",
        "        \"val_mae\": np.full(num_epochs, float(\"nan\")),\n",
        "    }\n",
        "\n",
        "    # Auxiliary functions for updating and reporting\n",
        "    # Training progress statistics\n",
        "    def update_statistics(i_epoch: int, **kwargs: float):\n",
        "        for key, value in kwargs.items():\n",
        "            result[key][i_epoch] = value\n",
        "\n",
        "    def desc(i_epoch: int) -> str:\n",
        "        return \" | \".join(\n",
        "            [f\"Epoch {i_epoch + 1:3d} / {num_epochs}\"]\n",
        "            + [\n",
        "                f\"{key}: {value[i_epoch]:8.2f}\"\n",
        "                for key, value in result.items()\n",
        "                if isinstance(value, np.ndarray)\n",
        "            ]\n",
        "        )\n",
        "\n",
        "    # main training loop\n",
        "    for i_epoch in range(0, num_epochs):\n",
        "        progress_bar = tqdm(total=len(train_loader) + len(val_loader))\n",
        "        try:\n",
        "            # tqdm for reporting progress\n",
        "            progress_bar.set_description(desc(i_epoch))\n",
        "\n",
        "            # training epoch\n",
        "            train_loss, train_mae = run_epoch(model, train_loader, loss_fn, progress_bar, optim)\n",
        "            # validation epoch\n",
        "            val_loss, val_mae = run_epoch(model, val_loader, loss_fn, progress_bar)\n",
        "\n",
        "            update_statistics(\n",
        "                i_epoch,\n",
        "                train_loss=train_loss,\n",
        "                val_loss=val_loss,\n",
        "                train_mae=train_mae,\n",
        "                val_mae=val_mae,\n",
        "            )\n",
        "\n",
        "            progress_bar.set_description(desc(i_epoch))\n",
        "\n",
        "            if val_mae < best_val_mae:\n",
        "                best_val_mae = val_mae\n",
        "                torch.save(model.state_dict(), best_model_path)\n",
        "        finally:\n",
        "            progress_bar.close()\n",
        "\n",
        "    return result"
      ],
      "metadata": {
        "id": "9nd6Rxi5V8MG"
      },
      "execution_count": 127,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "@torch.no_grad()\n",
        "def test_model(model: nn.Module, data_module: QM9DataModule) -> Tuple[float, Tensor, Tensor]:\n",
        "    \"\"\"\n",
        "    Test a model.\n",
        "\n",
        "    Parameters\n",
        "    ----------\n",
        "    model : nn.Module\n",
        "        a trained model\n",
        "    data_module : QM9DataModule\n",
        "        a data module as defined earlier\n",
        "        from which we'll get the test data\n",
        "\n",
        "    Returns\n",
        "    -------\n",
        "    _Tuple[float, Tensor, Tensor]\n",
        "        Test MAE, and model predictions & targets for further processing\n",
        "    \"\"\"\n",
        "    test_mae = 0\n",
        "    preds, targets = [], []\n",
        "    loader = data_module.test_loader()\n",
        "    for data in loader:\n",
        "        pred = model(data)\n",
        "        target = data.y\n",
        "        preds.append(pred)\n",
        "        targets.append(target)\n",
        "        test_mae += total_absolute_error(pred, target).item()\n",
        "\n",
        "    test_mae = test_mae / len(data_module.test_split)\n",
        "\n",
        "    return test_mae, torch.cat(preds, dim=0), torch.cat(targets, dim=0)"
      ],
      "metadata": {
        "id": "cHfSXNYnV_Wk"
      },
      "execution_count": 128,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = EquivariantGNN(hidden_channels=64, num_mp_layers=2)\n",
        "\n",
        "egnn_train_result = train_model(\n",
        "    data_module,\n",
        "    model,\n",
        "    num_epochs=25,\n",
        "    lr=2e-4,\n",
        "    batch_size=32,\n",
        "    weight_decay=1e-8,\n",
        "    best_model_path=DATA.joinpath(\"/content/teachopencadd/teachopencadd/talktorials/T036_e3_equivariant_gnn/data/trained_egnn.pth\"),\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "PP5vY8JyWBnL",
        "outputId": "7cb71d95-7dc9-4088-ada2-23ebb349691e"
      },
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch   1 / 25 | train_loss:      nan | val_loss:      nan | train_mae:      nan | val_mae:      nan:   0%|          | 0/3680 [00:00<?, ?it/s]\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.7/site-packages/torch_geometric/data/storage.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m     77\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 78\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     79\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/site-packages/torch_geometric/data/storage.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    102\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mapping\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    104\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'complete_edge_index'",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-130-cd8310e584ba>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mEquivariantGNN\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhidden_channels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m64\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_mp_layers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m egnn_train_result = train_model(\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mdata_module\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-127-c3d6340283e8>\u001b[0m in \u001b[0;36mtrain_model\u001b[0;34m(data_module, model, num_epochs, lr, batch_size, weight_decay, best_model_path)\u001b[0m\n\u001b[1;32m    148\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m             \u001b[0;31m# training epoch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m             \u001b[0mtrain_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_mae\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrun_epoch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprogress_bar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptim\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m             \u001b[0;31m# validation epoch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m             \u001b[0mval_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_mae\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrun_epoch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mval_loader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mloss_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprogress_bar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-127-c3d6340283e8>\u001b[0m in \u001b[0;36mrun_epoch\u001b[0;34m(model, loader, criterion, pbar, optim)\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0mtotal_mae\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mdata\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mloader\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 59\u001b[0;31m         \u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmae\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     60\u001b[0m         \u001b[0mtotal_loss\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mloss\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_graphs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m         \u001b[0mtotal_mae\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mmae\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-127-c3d6340283e8>\u001b[0m in \u001b[0;36mstep\u001b[0;34m(data_batch)\u001b[0m\n\u001b[1;32m     36\u001b[0m             \u001b[0mLoss\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmean\u001b[0m \u001b[0msquared\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mvalidation\u001b[0m \u001b[0mcritierion\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mabsolute\u001b[0m \u001b[0merror\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m         \"\"\"\n\u001b[0;32m---> 38\u001b[0;31m         \u001b[0mpred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     39\u001b[0m         \u001b[0mtarget\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata_batch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m         \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpred\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-123-9cc7ee7f91df>\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mData\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 119\u001b[0;31m         \u001b[0mnode_embed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    120\u001b[0m         \u001b[0mpred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_predict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode_embed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    121\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mpred\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-123-9cc7ee7f91df>\u001b[0m in \u001b[0;36mencode\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m    109\u001b[0m             \u001b[0;31m# NOTE here we use the complete edge index defined by the transform earlier on\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    110\u001b[0m             \u001b[0;31m# to implement the sum over $j \\neq i$ in equation (4)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 111\u001b[0;31m             \u001b[0mnode_embed\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmp_layer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode_embed\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpos\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomplete_edge_index\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    112\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnode_embed\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    113\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/site-packages/torch_geometric/data/data.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    439\u001b[0m                 \u001b[0;34m\"dataset, remove the 'processed/' directory in the dataset's \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    440\u001b[0m                 \"root folder and try again.\")\n\u001b[0;32m--> 441\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_store\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    442\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    443\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/site-packages/torch_geometric/data/storage.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m     78\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     79\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 80\u001b[0;31m             raise AttributeError(\n\u001b[0m\u001b[1;32m     81\u001b[0m                 f\"'{self.__class__.__name__}' object has no attribute '{key}'\")\n\u001b[1;32m     82\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'GlobalStorage' object has no attribute 'complete_edge_index'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gcn_baseline = NaiveEuclideanGNN(64, 4, 3)\n",
        "\n",
        "gcn_train_result = train_model(\n",
        "    data_module,\n",
        "    gcn_baseline,\n",
        "    num_epochs=100,\n",
        "    lr=3e-4,\n",
        "    batch_size=32,\n",
        "    best_model_path=DATA.joinpath(\"/content/teachopencadd/teachopencadd/talktorials/T036_e3_equivariant_gnn/data/trained_gnn.pth\"),\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LzDjdYNlWDb9",
        "outputId": "02cab283-6f07-415b-f4c6-b115d9feaf4b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Epoch   1 / 100 | train_loss: 63277.25 | val_loss: 14573.95 | train_mae:   168.90 | val_mae:    91.05: 100%|██████████| 3680/3680 [01:26<00:00, 42.42it/s]\n",
            "Epoch   2 / 100 | train_loss: 11986.86 | val_loss: 10382.88 | train_mae:    78.10 | val_mae:    76.41: 100%|██████████| 3680/3680 [01:02<00:00, 59.16it/s]\n",
            "Epoch   3 / 100 | train_loss:      nan | val_loss:      nan | train_mae:      nan | val_mae:      nan:   3%|▎         | 112/3680 [00:02<00:51, 69.42it/s]"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gcn_num_params = sum(p.numel() for p in gcn_train_result[\"model\"].parameters())\n",
        "egnn_num_params = sum(p.numel() for p in egnn_train_result[\"model\"].parameters())\n",
        "\n",
        "for key, value in {\"GCN\": gcn_num_params, \"EGNN\": egnn_num_params}.items():\n",
        "    print(f\"{key} has {value} parameters\")"
      ],
      "metadata": {
        "id": "hUa-cABSWTRQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, (loss_ax, mae_ax) = plt.subplots(1, 2, figsize=(8, 4))\n",
        "\n",
        "loss_ax.set_title(\"Loss (MSE)\")\n",
        "mae_ax.set_title(\"MAE\")\n",
        "loss_ax.set_xlabel(\"Epoch\")\n",
        "mae_ax.set_xlabel(\"Epoch\")\n",
        "\n",
        "for metric in [\"train_loss\", \"val_loss\", \"train_mae\", \"val_mae\"]:\n",
        "    split = metric.split(\"_\")[0]\n",
        "    ax = loss_ax if \"loss\" in metric else mae_ax\n",
        "\n",
        "    ax.plot(egnn_train_result[metric], label=f\"EGNN {split}\")\n",
        "    ax.plot(gcn_train_result[metric], label=f\"GNN {split}\")\n",
        "\n",
        "mae_ax.legend()\n",
        "mae_ax.set_yscale(\"log\")\n",
        "loss_ax.set_yscale(\"log\")"
      ],
      "metadata": {
        "id": "D014cK65Wa1D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gcn_model = gcn_train_result[\"model\"]\n",
        "gcn_model.load_state_dict(torch.load(gcn_train_result[\"path_to_best_model\"]))\n",
        "gcn_test_mae, gcn_preds, gcn_targets = test_model(gcn_model, data_module)\n",
        "\n",
        "egnn_model = egnn_train_result[\"model\"]\n",
        "egnn_model.load_state_dict(torch.load(egnn_train_result[\"path_to_best_model\"]))\n",
        "egnn_test_mae, egnn_preds, egnn_targets = test_model(egnn_model, data_module)\n",
        "\n",
        "print(f\"EGNN test MAE: {egnn_test_mae}\")\n",
        "print(f\"GNN test MAE: {gcn_test_mae}\")"
      ],
      "metadata": {
        "id": "4jkwVerwWfF0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots()\n",
        "ax.plot(gcn_targets, gcn_targets, \"--\", color=\"grey\")\n",
        "ax.scatter(gcn_targets, gcn_preds, s=1, label=\"GNN\")\n",
        "ax.scatter(egnn_targets, egnn_preds, s=1, label=\"EGNN\")\n",
        "ax.set_ylabel(\"Model prediction\")\n",
        "ax.set_xlabel(\"Ground truth $\\langle R^2 \\\\rangle$\")\n",
        "ax.set_title(\"Test performance\")\n",
        "ax.legend()"
      ],
      "metadata": {
        "id": "98MPWmynWf1m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "You have now seen, theoretically and practically, why we need S(E)(3)\n",
        " to work with point cloud representations of molecules and how to implement, train and evaluate them. The dataset used here is not directly relevant to CADD, but the practical importance of\n",
        " equi-/invariance definitely carries over to more relevant applications such as protein ligand docking. Recent work on molecular representation learning also suggests that 3D point clouds are favored for a broad range of property prediction tasks more relevant to CADD such as toxicity prediction."
      ],
      "metadata": {
        "id": "gS8ElvfPWoyD"
      }
    }
  ]
}