teachopencadd/talktorials/T036_e3_equivariant_gnn/talktorial.ipynb

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    "# T036 · An introduction to E(3)-invariant graph neural networks\n",
    "\n",
    "**Note:** This talktorial is a part of TeachOpenCADD, a platform that aims to teach domain-specific skills and to provide pipeline templates as starting points for research projects.\n",
    "\n",
    "Authors:\n",
    "\n",
    "- Joschka Groß, 2022, [Chair for Modelling and Simulation](https://mosi.uni-saarland.de/), [NextAID](https://nextaid.cs.uni-saarland.de/) project, Saarland University"
   ]
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    "## Aim of this talktorial\n",
    "\n",
    "This talktorial is supposed to serve as an introduction to machine learning on point cloud representations of molecules with 3D conformer information, i.e., molecular graphs that are embedded into Euclidean space (see **Talktorial 033**). You will learn why Euclidean equivariance and invariance are important properties of neural networks (NNs) that take point clouds as input and learn how to implement and train such NNs. In addition to discussing them in theory, this notebook also aims to demonstrate the shortcomings of plain graph neural networks (GNNs) when working with point clouds practically."
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    "### Contents in *Theory*\n",
    "\n",
    "* Why 3D coordinates?\n",
    "* Representing molecules as point clouds\n",
    "* Equivariance and Invariance in euclidean space and why we care\n",
    "* How to construct $\\text{E}(n)$-invariant and equivariant models\n",
    "* The QM9 dataset"
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    "### Contents in *Practical*\n",
    "\n",
    "* Visualization of point clouds\n",
    "* Set up and inspect the QM9 dataset\n",
    "  * Preprocessing\n",
    "  * Atomic number distribution and point cloud size\n",
    "  * Data split, distribution of regression target electronic spatial extent\n",
    "* Model implementation\n",
    "  * Plain \"naive Euclidean\" GNN\n",
    "  * Demo: Plain GNNs are not E(3)-invariant\n",
    "  * EGNN model\n",
    "  * Demo: Our EGNN is E(3)-invariant\n",
    "* Training and evaluation\n",
    "  * Setup\n",
    "  * Training the EGNN\n",
    "  * Training the plain GNN\n",
    "  * Comparative evaluation"
   ]
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    "### References\n",
    "\n",
    "#### Theoretical\n",
    "* **Quantum chemistry structures and properties of 134k molecules (QM9)**: [<i>Scientific data</i> (2014)](https://www.nature.com/articles/sdata201422/?ref=https://githubhelp.com)\n",
    "* **MoleculeNet: a benchmark for molecular machine learning**: [<i>Chem. Sci.</i>, 2018, <b>9</b>, 513-530](https://pubs.rsc.org/en/content/articlehtml/2018/sc/c7sc02664a)\n",
    "* **E(n)-Equivariant Graph Neural Networks**: [<i>International conference on machine learning</i> (2021), <b>139</b>, 99323-9332](https://proceedings.mlr.press/v139/satorras21a.html)\n",
    "* **SE(3)-transformers: 3D roto-translation equivariant attention networks**: [<i>Advances in Neural Information Processing Systems</i> (2021), <b>33</b>, 1970-1981](https://proceedings.neurips.cc/paper/2020/file/15231a7ce4ba789d13b722cc5c955834-Paper.pdf)\n",
    "* **TorchMD-NET: Equivariant Transformers for Neural Network based Molecular Potentials**: [<i>arXiv preprint (2022)</i>](https://arxiv.org/abs/2202.02541)\n",
    "* **DiffDock**: [<i>arXiv preprint</i> (2022)](https://arxiv.org/abs/2210.01776)\n",
    "\n",
    "#### Practical\n",
    "* [Pytorch Geometric QM9 version](https://pytorch-geometric.readthedocs.io/en/latest/modules/datasets.html#torch_geometric.datasets.QM9)"
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    "## Theory"
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    "### Why 3D coordinates?\n",
    "\n",
    "* Some properties are more easily derived when 3D coordinates are known.\n",
    "* Sometimes the task is to predict properties that are directly linked to Euclidean space, e.g. future atom positions or forces that apply to atoms.\n",
    "* Compared to molecular graph representations, we in principle only gain information. Covalent bonds can still be inferred from atom types and positions because they can be attributed to overlapping atomic orbitals. Note that one could still include structural information s.t. the model does not have to learn this information itself\n",
    "\n",
    "\n",
    "An example CADD application that may require the use of 3D coordinates is protein-ligand docking (see **Talktorial 015**). [Recent work from 2022](https://arxiv.org/abs/2210.01776) uses E(3) equivariant graph neural networks as the backbone for a generative model that learns to predict potential ligand docking positions (3D coordinates for the atoms of a given ligand) when additionally given protein structures with 3D information as input."
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    "### Molecules as point clouds: mathematical background\n",
    "In this talktorial we will focus on atoms and their 3D positions and ignore structural (bond) information. Our mathematical representations of a molecule is thus a point cloud (also see **Talktorial T033**), i.e., a tuple $(X, Z)$ where $Z \\in \\mathbb{R}^{m \\times d}$ is a matrix of $m$ atoms represented by $d$ features each and $X \\in \\mathbb{R}^{m \\times 3}$ captures the atom 3D coordinates. We will assume that the coordinates correspond to a specific molecular conformation (see **Talktorial T033**) of the molecule."
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    "### Equivariance and Invariance in Euclidean space and why we care\n",
    "When representing molecules as graphs equi- and/or invariance w.r.t. to node permutations are desirable model properties (**Talktorial T033/T035**). When working with point clouds, i.e., when atoms/nodes are embedded into Euclidean space, we should also be concerned about Euclidean symmetry groups. These are groups of transformations $g: \\mathbb{R}^n \\to \\mathbb{R}^n$ that preserve distance, i.e., translations, rotations, reflections, or combinations thereof. For the Euclidean space $\\mathbb{R}^n$ with $n$ spatial dimensions, one typically distinguishes between\n",
    "\n",
    "* the Euclidean group $\\text{E}(n)$, which consists of *all* distance-preserving transformations, and\n",
    "* the special Euclidean group $\\text{SE}(n)$, which consists only of translations and rotations.\n",
    "  \n",
    "Say $\\theta$ is a model that learns atom embeddings $H = \\theta(X, Z) \\in \\mathbb{R}^{m \\times q}$ where $q$ is the number of embedding dimensions. We call $\\theta$ $\\text{E}(n)$-*invariant*, if for all $g \\in \\text{E}(n)$\n",
    "$$\n",
    "\\theta(g(X), Z) = \\theta(X, Z),\n",
    "$$\n",
    "where $g$ is applied row-wise to $X$. Put simply the output of $\\theta$ remains unaffected, no matter how we rotate, translate, or reflect the molecule.\n",
    "\n",
    "If we consider a model that makes predictions about objects which are coupled to the Euclidean space $X' = \\theta(X, Z) \\in \\mathbb{R}^{m \\times n}$ (e.g. future atom positions in a dynamical system), we can define $\\text{E}(n)$-*equivariance* as\n",
    "$$\n",
    "\\theta(g(X), Z) = g(\\theta(X, Z)),\n",
    "$$\n",
    "for all $g \\in \\text{E}(n)$ applied in row-wise fashion. This is saying that the output of $\\theta$ is transformed in the same way as its input. Note that this definition can easily be extended to arbitrary Euclidean features (velocities, electromagnetic forces, ...).\n",
    "\n",
    "So, why do we care about these properties?\n",
    "\n",
    "Let's assume our goal was to train a model that predicts the docking position of a ligand when given a fixed protein structure, also with 3D coordinates. Would you trust a model that predicted different relative positions for the ligand atoms when the protein was simply rotated by 180 degrees? If your answer is no, then you should consider using a model that is at least $\\text{SE}(3)$-equivariant.\n",
    "In addition to being a \"natural\" choice given such considerations, euclidean equivariance empirically also increases the sample complexity (efficiency) of training and improves the model's ability to generalize to unseen data."
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    "**To sum up** it may be helpful to address the problem from a slightly different point of view: Point clouds as representations for molecular conformations are *not unique*. In fact, for one molecular conformation, there are *infinitely many* valid point cloud representations. If $(X, Z)$ is such a representation then $(g(X), Z)$ with $g \\in \\text{E}(3)$ is too and there are infinitely many such $g$. All $\\text{E}(3)$-invariance and equivariance are thus saying, is that our machine learning models *should not care* which of these representations we end up using.\n"
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    "<code>![Figure title](images/2d_rotation_equivariance.png)</code>\n",
    "\n",
    "*Figure 1:* \n",
    "An illustration of a 2D-rotationally equi- and invariant transformation $\\phi$.\n",
    "Taken from [the EGNN paper by Satoras et. al.](https://proceedings.mlr.press/v139/satorras21a.html)"
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    "### How to construct $\\text{E}(n)$-invariant and equivariant models\n",
    "\n",
    "Constructing such models is simple if we focus on the fact that all $g \\in \\text{E}(n)$ are *distance-preserving*. We will not give a [fully-fledged proof](https://proceedings.mlr.press/v139/satorras21a.html), but it should not come as a great surprise that a model which *only considers relative distances between atoms* for computing node (atom) embeddings is guaranteed to be $E(n)$-invariant. We can thus define a *message passing network* $\\theta(Z, X)$ with $l=1,\\ldots,L$ layers where\n",
    "\n",
    "$$h_{i}^0 = \\psi_0(Z_i)$$\n",
    "$$d_{ij} = ||X_i - X_j||^2 $$\n",
    "$$m_{ij}^{l} = \\phi_{l}(h_i^l, h_j^l, d_{ij})  \\quad \\quad \\text{for}~l=0,\\ldots,L-1$$\n",
    "$$h_{i}^{l+1} = \\psi_l(h_{i}^l, \\sum_{j \\neq i} m_{ij}^l) \\quad \\quad \\text{for}~l=0,\\ldots,L-1$$\n",
    "\n",
    "and $\\psi_0$ computes the initial node embeddings, the $\\phi_l$ MLPs $\\text{}^1$ construct messages and $\\psi_l$ MLPs take care of combining previous embeddings and aggregated messages into new embeddings. The final node embeddings $H = (h_1^L \\ldots h_n^L)^t$ computed by this scheme are $E(n)$-invariant.\n",
    "\n",
    "\n",
    "In the practical part, we will only predict properties that are not directly linked to the Euclidean space, so this kind of network suffices for our purposes. If your goal is to predict e.g. atom positions, you will need to define additional, slightly more sophisticated transformations to ensure that they are $E(3)$-equivariant, but they usually follow the same principle of only using distances in their computations. If you want to read up on this you can take a look at these papers\n",
    "\n",
    "* [E(n) Equivariant Graph Neural Networks](https://proceedings.mlr.press/v139/satorras21a.html)\n",
    "* [SE(3) Transformer](https://proceedings.neurips.cc/paper/2020/file/15231a7ce4ba789d13b722cc5c955834-Paper.pdf)\n",
    "* [TorchMD-Net](https://arxiv.org/abs/2202.02541)\n",
    "\n",
    "\\\n",
    "\\\n",
    "$~^1$ multi-layer perceptrons (MLPS) are stacks of multiple fully connected layers with non-linear activation functions (also see **Talktorial T022**)"
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    "### The QM9 dataset\n",
    "\n",
    "The QM9 dataset [[1]](https://www.nature.com/articles/sdata201422/?ref=https://githubhelp.com) [[2]](https://pytorch-geometric.readthedocs.io/en/latest/modules/datasets.html#torch_geometric.datasets.QM9) is part of the [MoleculeNet benchmark](https://pubs.rsc.org/en/content/articlehtml/2018/sc/c7sc02664a) and consists of ~130k small, organic molecules with up to 9 heavy atoms. It also includes targets for various geometric, energetic, electronic and thermodynamic properties. Crucially, it also includes atom 3D coordinates, which makes it suitable for this talktorial."
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    "## Practical"
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    "For the practical part, we will be working with a [version of QM9 that is already included in PyTorch Geometric](https://pytorch-geometric.readthedocs.io/en/latest/modules/datasets.html#torch_geometric.datasets.QM9), as implementing the dataset from scratch would go beyond the scope of this talktorial. We will just inspect the data and briefly discuss how point clouds can be represented by several tensors. Then we will demonstrate how one could use plain GNNs to work with point clouds and why this approach would yield models that are not $\\text{E}(3)$ invariant/equivariant. Finally, you will learn how to implement, train and evaluate equivariant GNNs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set path to this notebook\n",
    "HERE = Path(_dh[-1])\n",
    "DATA = HERE / \"data\""
   ]
  },
  {
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   "source": [
    "### Visualization of point clouds\n",
    "The following auxiliary function `plot_point_cloud_3d` will see heavy use later on for the visualization of model input and model outputs.\n",
    "Note that to visualize molecules rather than their tensor representations used for machine learning, it would be better to use e.g. `RDKit` or `NGLview`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_51907/441942460.py:53: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
      "  cmap = mpl.cm.get_cmap(cmap)\n",
      "/tmp/ipykernel_51907/441942460.py:84: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations.\n",
      "  fig.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "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)\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set up and inspect the QM9 dataset"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Preprocessing\n",
    "For the sake of this tutorial, we will restrict ourselves to small molecules with at most 8 heavy atoms. Due to our decision to ignore structural information and treat molecules as point clouds, where every atom interacts with every other atom, we also need to extend the torch geometric `Data` objects with additional adjacency information that represents a complete graph without self-loops.\n",
    "\n",
    "For performance reasons, both of these steps are performed *once* when pre-processing the raw data using the `pre_filter` and `pre_transform` keyword arguments of the dataset class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Num. examples in QM9 restricted to molecules with at most 8 heavy atoms: 21800\n"
     ]
    }
   ],
   "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)}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NOTE: executing the above cell for the first time first downloads and then processes the raw data, which **might take a while**."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indexing the dataset we just created returns a single Pytorch Geometric `Data` object representing one molecular graph/point cloud. You can think of these objects as dictionaries with some extra utility methods already implemented."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "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], complete_edge_index=[2, 20])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For index 0 (name `gdb_1`) this should be the molecule CH4. We can check this by looking into the atomic numbers stored in the attributed named `z`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([6, 1, 1, 1, 1])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.z"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For molecules with `N` atoms and `M` (covalent) bonds, the data objects also contain named tensors of shape\n",
    "\n",
    "* `Data.x`: `(N, d_node)` node-level features (e.g. formal charge, membership to aromatic rings, chirality, ...), but we will ignore them here and just use atomic numbers.\n",
    "* `Data.y`: `(19,)` regression targets\n",
    "* `Data.edge_index`: `(2, M)` edges between atoms derived from covalent bonds, stored as source and target node index pairs.\n",
    "* `Data.edge_attr`: `(M, d_edge)` contains bond features (e.g. bond type, ring-membership, ...)\n",
    "* `Data.pos`: `(N, 3)` most interesting to us, atom 3D coordinates.\n",
    "* `Data.complete_edge_index`: `(2, (N-1)^2)`: the complete graph edge index (without self-loops) we added earlier.\n",
    "\n",
    "The input to our (point cloud) model we will implement later can be visualized using just `Data.z` as color and `Data.pos` as scatter plot positions. Note: the alpha channel of colors is used to convey depth-information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "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]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.pos.round(decimals=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_51907/441942460.py:53: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
      "  cmap = mpl.cm.get_cmap(cmap)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = plot_model_input(data, fig, 111)\n",
    "_ = ax.set_title(\"CH$_4$ (Methane)\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Atomic number distribution and point cloud size\n",
    "Now that our dataset is set up, and we have a basic understanding of how molecules are represented, we can try to visualize the properties of the entire dataset.\n",
    "Let us first look at the distribution of node-level features (atomic numbers) and the point cloud size (number of atoms) aggregated over the entire dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "~/.miniconda3/envs/teachopencadd/lib/python3.9/site-packages/torch_geometric/data/in_memory_dataset.py:157: UserWarning: It is not recommended to directly access the internal storage format `data` of an 'InMemoryDataset'. The data of the dataset is already cached, so any modifications to `data` will not be reflected when accessing its elements. Clearing the cache now by removing all elements in `dataset._data_list`. If you are absolutely certain what you are doing, access the internal storage via `InMemoryDataset._data` instead to suppress this warning. Alternatively, you can access stacked individual attributes of every graph via `dataset.{attr_name}`.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "image/png": 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/oJqFhQWsra3v2M76XLhwAVu3bq2xv969ewP4v/NdaGgoVq9ejbNnz+L555+Hg4MD+vXrh+TkZIP2T0SAcUtXgKitW716NYQQ+O9//4v//ve/NZavW7cOc+bMgZGREezs7HD48OEaMVqtVjZf/SWq+sO5vthqt3/Rqe59mDFjBkaPHl3rOu7u7tL+7mZfe/bswfnz57F3716plwIASkpKat1+bezt7aFQKPDLL79IXw5vVV1WfQxqa69Wq73jjbmGqt5/YWFhjWXnz5+X9e7crnrZf//733p7Vzp16gTg5g399TEzM6t15J67+aU1Li4Orq6u2Lhxo+w9UtdIQA19bkJdrw/wf8fQzs6uzuMIoN5jea906tQJlZWV0Gq1DUqm7OzscOjQIQghZMeqqKgIN27ckNpS3ftw+3G+/Uu+jY0NFApFvcfxbtT3/3t7Unb7a3yn93qHDh1q9GJptVp07txZmr9x4wYuXbokbetu/wfqqlNj2Nvbo0+fPpg7d26ty6sTWQD4xz/+gX/84x+4evUq9u/fj48//hhBQUE4depUg3pEiUiOPRZEBqisrMS6devQvXt3/PzzzzWm6dOno7CwULrswt/fH1euXKkxslFCQoJs3t3dHWq1usboTfn5+Th48OBd1c3d3R1ubm44fvw4fHx8ap2srKykeu3Zs0f2RbWqqgrfffedbJvVH/63JwQrVqyosf+6flEOCgqCEAJ//vlnrXXy9PQEcHMoSTMzM8THx8vWP3jwYKMvf2kIPz8/mJubIy4uTlZ+7tw57NmzB4MHD65z3WHDhsHY2Bh//PFHncceAHr06IHu3btj9erV9Q756eLigqKiItmXx/LycuzcufOO7VAoFDA1NZV9cdNqtbWOCtUYWVlZOH78uKxsw4YNsLKywqOPPgoAGDx4ME6ePCldGlXt22+/hUKhwKBBg5qkLkql8q57MKovj1m2bFmD9jF48GCUlZVhy5YtsvJvv/1WWg7c7EEwMzPDr7/+Kou7/bhbWlri8ccfx6ZNm2Q9SFeuXMHWrVvvul5XrlypMZrVhg0b0KFDBzz11FP1ruvu7o7OnTtjw4YNEEJI5VevXsX3338vjRR1q9v/L//zn//gxo0b0sME7/Z/4E4a8poGBQUhMzMT3bt3r3V/tyYW1SwtLREYGIiZM2eivLwcWVlZd7UvIqodeyyIDPDTTz/h/Pnz+Oyzz2p9Oq+HhweWLl2KVatWISgoCGFhYVi0aBFeffVVzJkzBw899BB++ukn6Qti9fXNHTp0wKxZszBhwgS88MILeP3111FSUoJZs2bByclJdh10fVasWIHAwEAMGzYM4eHh6Ny5My5fvozs7GwcPXpUShxmzpyJrVu3YvDgwZg5cybMzc2xfPlyaQjZ6v31798fNjY2ePPNN/Hxxx/DxMQE8fHxNb5YApAShM8++wyBgYEwMjJCnz59MGDAALzxxhv4xz/+gSNHjuCpp56CpaUlCgsLceDAAXh6euKtt96CjY0NoqOjMWfOHIwfPx4vvvgiCgoKEBMT0+DLVxrjgQcewIcffogPPvgAr732Gl5++WVcunQJs2bNgpmZGT7++OM613VxccHs2bMxc+ZMnDlzBsOHD4eNjQ0uXLiAw4cPw9LSUnp44FdffYXg4GD4+vpi6tSp6Nq1K/Lz87Fz507py9tLL72Ejz76CGPHjsU777yD69ev48svv5QNJVqXoKAgbNq0CZGRkXjhhRdQUFCATz75BE5OTrL7fxpLo9Fg5MiRiImJgZOTE+Li4pCcnIzPPvtM+jI6depUfPvttxgxYgRmz54NZ2dnbN++HV9//TXeeust9OjRw+B6ADffcwkJCdi4cSO6desGMzMz6X14uyeffBKhoaGYM2cOLly4gKCgICiVShw7dgwWFhaYPHlyreu99tpr+OqrrxAWFoa8vDx4enriwIEDiI2NxTPPPIMhQ4YAgHQf0erVq9G9e3f07dsXhw8fxoYNG2ps85NPPsHw4cMxdOhQTJ8+HZWVlfjss89gaWmJy5cv31Xb7ezs8NZbbyE/Px89evTAjh07sHLlSrz11lvo2rVrvet26NAB8+fPxyuvvIKgoCBMmDABer0en3/+OUpKSvDpp5/WWGfTpk0wNjbG0KFDkZWVhQ8//BB9+/aV7stqyP9AfTw9PbFp0yYsW7YM3t7e6NChQ51JyezZs5GcnIz+/ftjypQpcHd3x/Xr15GXl4cdO3Zg+fLlePDBBxEREQFzc3MMGDAATk5O0Gq1mDdvHlQqFR577LG7ONpEVKcWvHGcqM0bNWqUMDU1FUVFRXXGjB07VhgbG0vDqObn54vRo0eLjh07CisrK/H888+LHTt21DqqyzfffCMeeughYWpqKnr06CFWr14tnn32WdloPtUjvdw6vOetjh8/LsaMGSMcHByEiYmJUKvV4umnn5ZGsKr2yy+/iH79+gmlUinUarV45513pJFuSkpKpLiDBw8KPz8/YWFhITp16iTGjx8vjh49WmMEHL1eL8aPHy86deokFApFjdGlVq9eLfr16ycsLS2Fubm56N69u3jttdfEkSNHpJiqqioxb9480aVLF2Fqair69Okjtm7dWmOknrrgtuE26zteP//8swAgvvvuO1n5v//9b9GnTx9hamoqVCqVePbZZ2VDZQpR++hMQtwcgWrQoEHC2tpaKJVK4ezsLF544QWxa9cuWVxKSooIDAwUKpVKKJVK0b179xqj7uzYsUM88sgjwtzcXHTr1k0sXbr0rkeF+vTTT4WLi4tQKpWiZ8+eYuXKlbWuW9vxqo+zs7MYMWKE+O9//yt69+4tTE1NhYuLi1i4cGGN2LNnz4qQkBBhZ2cnTExMhLu7u/j8889loxBV16G2UaHS0tJkcdWv188//yyV5eXliYCAAGFlZSUNiVqfyspKsWjRIuHh4SG9vn5+fmLr1q1STG3vtUuXLok333xTODk5CWNjY+Hs7CxmzJghDadcTafTifHjxwtHR0dhaWkpgoODRV5eXo02CiHEjz/+KL3PunbtKj799NM631e38/f3F7179xZ79+4VPj4+QqlUCicnJ/HBBx/IRoS707liy5Ytol+/fsLMzExYWlqKwYMHi//973+ymOo6paeni+DgYOk89vLLL4sLFy7Uus07/Q+EhYUJS0vLWut0+fJl8cILL4gHHnhAOo9Uq+04Xrx4UUyZMkW4uroKExMTYWtrK7y9vcXMmTOl0eHWrVsnBg0aJBwdHYWpqanQaDRizJgx0ohwRNR4CiFu6fckohYRGxuLf/7zn8jPz6/3Rt6SkhL06NEDo0aNwjfffHPP6xUQEIC8vDycOnXqnu+L2h4XFxd4eHhg27ZtLV2V+9rAgQPx119/ITMz857vKyYmBrNmzcLFixdb5N4YImrdeCkUUTNbunQpAODhhx9GRUUF9uzZgy+//BKvvvqqLKnQarWYO3cuBg0aBDs7O5w9exaLFi3ClStX8Pbbbzd5vaZNmwYvLy906dIFly9fRnx8PJKTk6UnihMRERHVh4kFUTOzsLDAokWLkJeXB71ej65du+K9997DP//5T1mcUqlEXl4eIiMjcfnyZVhYWMDX1xfLly+Xhk9sSpWVlfjoo4+g1WqhUCjQq1cvrF+/Hq+++mqT74uIiIjaH14KRUREREREBuNws0REREREZDAmFkREREREZDAmFkREREREZDAmFkREREREZDAmFkREREREZDAmFkREREREZDAmFkREREREZDAmFkREREREZDAmFkREREREZDAmFkREREREZDAmFkREREREZDAmFkREREREZDAmFkREREREZDAmFkREREREZDAmFkREREREZDAmFkREREREZDAmFkREREREZDAmFkREREREZDDjlq7A/aaqqgrnz5+HlZUVFApFS1eHiKhJCSFw5coVaDQadOjQ/n+74jmdiNq7hpzXmVg0s/Pnz6NLly4tXQ0ionuqoKAADz74YEtX457jOZ2I7hd3c15nYtHMrKysANx8caytrVu4NkRETau0tBRdunSRznXtHc/pRNTeNeS8zsSimVV3lVtbW/NDiIjarfvlsiCe04nofnE35/X2fwEsERERERHdc0wsiIiIiIjIYEwsiIioVXFxcYFCoagxTZw4EcDNEUpiYmKg0Whgbm6OgQMHIisrS7YNvV6PyZMnw97eHpaWlhg5ciTOnTsniykuLkZoaChUKhVUKhVCQ0NRUlLSXM0kImp3mFgQEVGrkpaWhsLCQmlKTk4GALz44osAgPnz52PhwoVYunQp0tLSoFarMXToUFy5ckXaRlRUFDZv3oyEhAQcOHAAZWVlCAoKQmVlpRQTEhKCjIwMJCYmIjExERkZGQgNDW3exhIRtSMKIYRo6UrcT0pLS6FSqaDT6XijHxG1O/fiHBcVFYVt27bh9OnTAACNRoOoqCi89957AG72Tjg6OuKzzz7DhAkToNPp0KlTJ6xfvx4vvfQSgP8bFnbHjh0YNmwYsrOz0atXL6SmpqJfv34AgNTUVPj5+eG3336Du7t7i7WXiKg1ach5jj0WRETUapWXlyMuLg6vv/46FAoFcnNzodVqERAQIMUolUr4+/vj4MGDAID09HRUVFTIYjQaDTw8PKSYlJQUqFQqKakAAF9fX6hUKimGiIgahsPNEhFRq7VlyxaUlJQgPDwcAKDVagEAjo6OsjhHR0ecPXtWijE1NYWNjU2NmOr1tVotHBwcauzPwcFBiqmNXq+HXq+X5ktLSxveKCKidoo9FkRE1GqtWrUKgYGB0Gg0svLbx1MXQtxxjPXbY2qLv9N25s2bJ93srVKp+NRtIqJbMLEgIqJW6ezZs9i1axfGjx8vlanVagCo0atQVFQk9WKo1WqUl5ejuLi43pgLFy7U2OfFixdr9IbcasaMGdDpdNJUUFDQuMYREbVDTCyIiKhVWrNmDRwcHDBixAipzNXVFWq1WhopCrh5H8a+ffvQv39/AIC3tzdMTExkMYWFhcjMzJRi/Pz8oNPpcPjwYSnm0KFD0Ol0UkxtlEql9JRtPm2biEiO91gQEVGrU1VVhTVr1iAsLAzGxv/3UaVQKBAVFYXY2Fi4ubnBzc0NsbGxsLCwQEhICABApVJh3LhxmD59Ouzs7GBra4vo6Gh4enpiyJAhAICePXti+PDhiIiIwIoVKwAAb7zxBoKCgu56RCgiIpJjYkFERK3Orl27kJ+fj9dff73GsnfffRfXrl1DZGQkiouL0a9fPyQlJcHKykqKWbRoEYyNjTFmzBhcu3YNgwcPxtq1a2FkZCTFxMfHY8qUKdLoUSNHjsTSpUvvfeOIiNopPseimXHMcyJqz+63c9z91l4iuv/wORZERERERNSsmFgQEREREZHBmFgQEREREZHBePN2G+Ly/vZm3V/epyPuHERERETtHr+D0N1gjwURERERERmMiQURERERERmMiQURERERERmMiQURERERERmMiQURERERERmMiQURERERERmMiQURERERERmsRROL/fv3Izg4GBqNBgqFAlu2bJEtVygUtU6ff/65FDNw4MAay8eOHSvbTnFxMUJDQ6FSqaBSqRAaGoqSkhJZTH5+PoKDg2FpaQl7e3tMmTIF5eXlspgTJ07A398f5ubm6Ny5M2bPng0hRJMeEyIiIiKitqhFH5B39epV9O3bF//4xz/w/PPP11heWFgom//pp58wbty4GrERERGYPXu2NG9ubi5bHhISgnPnziExMREA8MYbbyA0NBRbt24FAFRWVmLEiBHo1KkTDhw4gEuXLiEsLAxCCCxZsgQAUFpaiqFDh2LQoEFIS0vDqVOnEB4eDktLS0yfPt3wg0FERERE1Ia1aGIRGBiIwMDAOper1WrZ/A8//IBBgwahW7dusnILC4sasdWys7ORmJiI1NRU9OvXDwCwcuVK+Pn5IScnB+7u7khKSsLJkydRUFAAjUYDAFiwYAHCw8Mxd+5cWFtbIz4+HtevX8fatWuhVCrh4eGBU6dOYeHChZg2bRoUCoUhh4KIiIiIqE1rM/dYXLhwAdu3b8e4ceNqLIuPj4e9vT169+6N6OhoXLlyRVqWkpIClUolJRUA4OvrC5VKhYMHD0oxHh4eUlIBAMOGDYNer0d6eroU4+/vD6VSKYs5f/488vLymrq5RERERERtSov2WDTEunXrYGVlhdGjR8vKX3nlFbi6ukKtViMzMxMzZszA8ePHkZycDADQarVwcHCosT0HBwdotVopxtHRUbbcxsYGpqamshgXFxdZTPU6Wq0Wrq6utdZbr9dDr9dL86WlpQ1oNRERERFR29BmEovVq1fjlVdegZmZmaw8IiJC+tvDwwNubm7w8fHB0aNH8eijjwJArZcpCSFk5Y2Jqb5xu77LoObNm4dZs2bV1zQiIiIiojavTVwK9csvvyAnJwfjx4+/Y+yjjz4KExMTnD59GsDN+zQuXLhQI+7ixYtSj4NarZZ6JqoVFxejoqKi3piioiIAqNHbcasZM2ZAp9NJU0FBwR3bQERERETU1rSJxGLVqlXw9vZG37597xiblZWFiooKODk5AQD8/Pyg0+lw+PBhKebQoUPQ6XTo37+/FJOZmSkbhSopKQlKpRLe3t5SzP79+2VD0CYlJUGj0dS4ROpWSqUS1tbWsomIiIiIqL1p0cSirKwMGRkZyMjIAADk5uYiIyMD+fn5UkxpaSm+++67Wnsr/vjjD8yePRtHjhxBXl4eduzYgRdffBFeXl4YMGAAAKBnz54YPnw4IiIikJqaitTUVERERCAoKAju7u4AgICAAPTq1QuhoaE4duwYdu/ejejoaEREREiJQEhICJRKJcLDw5GZmYnNmzcjNjaWI0IREREREaGFE4sjR47Ay8sLXl5eAIBp06bBy8sLH330kRSTkJAAIQRefvnlGuubmppi9+7dGDZsGNzd3TFlyhQEBARg165dMDIykuLi4+Ph6emJgIAABAQEoE+fPli/fr203MjICNu3b4eZmRkGDBiAMWPGYNSoUfjiiy+kGJVKheTkZJw7dw4+Pj6IjIzEtGnTMG3atHtxaIiIiIiI2hSF4KOjm1VpaSlUKhV0Ol2DL4tyeX/7PapV7fI+HdGs+yOits+Qc1xbdL+1l+5f/A5y/2rIea5N3GNBREREREStGxMLIiIiIiIyGBMLIiIiIiIyGBMLIiIiIiIyGBMLIiIiIiIyGBMLIiIiIiIyGBMLIiIiIiIyGBMLIiIiIiIyGBMLIiIiIiIyGBMLIiIiIiIyGBMLIiIiIiIyGBMLIiIiIiIyGBMLIiIiIiIyGBMLIiIiIiIyGBMLIiIiIiIyGBMLIiIiIiIyGBMLIiIiIiIyGBMLIiIiIiIyGBMLIiIiIiIyGBMLIiIiIiIyGBMLIiIiIiIyGBMLIiIiIiIyGBMLIiJqdf7880+8+uqrsLOzg4WFBR555BGkp6dLy4UQiImJgUajgbm5OQYOHIisrCzZNvR6PSZPngx7e3tYWlpi5MiROHfunCymuLgYoaGhUKlUUKlUCA0NRUlJSXM0kYio3WFiQURErUpxcTEGDBgAExMT/PTTTzh58iQWLFiABx54QIqZP38+Fi5ciKVLlyItLQ1qtRpDhw7FlStXpJioqChs3rwZCQkJOHDgAMrKyhAUFITKykopJiQkBBkZGUhMTERiYiIyMjIQGhranM0lImo3jFu6AkRERLf67LPP0KVLF6xZs0Yqc3Fxkf4WQmDx4sWYOXMmRo8eDQBYt24dHB0dsWHDBkyYMAE6nQ6rVq3C+vXrMWTIEABAXFwcunTpgl27dmHYsGHIzs5GYmIiUlNT0a9fPwDAypUr4efnh5ycHLi7uzdfo4mI2gH2WBARUavy448/wsfHBy+++CIcHBzg5eWFlStXSstzc3Oh1WoREBAglSmVSvj7++PgwYMAgPT0dFRUVMhiNBoNPDw8pJiUlBSoVCopqQAAX19fqFQqKYaIiO4eEwsiImpVzpw5g2XLlsHNzQ07d+7Em2++iSlTpuDbb78FAGi1WgCAo6OjbD1HR0dpmVarhampKWxsbOqNcXBwqLF/BwcHKeZ2er0epaWlsomIiG7ipVBERNSqVFVVwcfHB7GxsQAALy8vZGVlYdmyZXjttdekOIVCIVtPCFGj7Ha3x9QWX9925s2bh1mzZt11W4iI7ifssSAiolbFyckJvXr1kpX17NkT+fn5AAC1Wg0ANXoVioqKpF4MtVqN8vJyFBcX1xtz4cKFGvu/ePFijd6QajNmzIBOp5OmgoKCRrSQiKh9YmJBREStyoABA5CTkyMrO3XqFJydnQEArq6uUKvVSE5OlpaXl5dj37596N+/PwDA29sbJiYmspjCwkJkZmZKMX5+ftDpdDh8+LAUc+jQIeh0OinmdkqlEtbW1rKJiIhu4qVQRETUqkydOhX9+/dHbGwsxowZg8OHD+Obb77BN998A+Dm5UtRUVGIjY2Fm5sb3NzcEBsbCwsLC4SEhAAAVCoVxo0bh+nTp8POzg62traIjo6Gp6enNEpUz549MXz4cERERGDFihUAgDfeeANBQUEcEYqIqBGYWBARUavy2GOPYfPmzZgxYwZmz54NV1dXLF68GK+88ooU8+677+LatWuIjIxEcXEx+vXrh6SkJFhZWUkxixYtgrGxMcaMGYNr165h8ODBWLt2LYyMjKSY+Ph4TJkyRRo9auTIkVi6dGnzNZaIqB1RCCFES1fiflJaWgqVSgWdTtfgLnSX97ffo1rVLu/TEc26PyJq+ww5x7VF91t76f7F7yD3r4ac53iPBRERERERGYyJBRERERERGYyJBRERERERGYyJBRERERERGYyjQhERERG1Ic19IzXR3WKPBRERERERGYyJBRERERERGYyJBRERERERGaxFE4v9+/cjODgYGo0GCoUCW7ZskS0PDw+HQqGQTb6+vrIYvV6PyZMnw97eHpaWlhg5ciTOnTsniykuLkZoaChUKhVUKhVCQ0NRUlIii8nPz0dwcDAsLS1hb2+PKVOmoLy8XBZz4sQJ+Pv7w9zcHJ07d8bs2bPB5wsSEREREbVwYnH16lX07dsXS5curTNm+PDhKCwslKYdO3bIlkdFRWHz5s1ISEjAgQMHUFZWhqCgIFRWVkoxISEhyMjIQGJiIhITE5GRkYHQ0FBpeWVlJUaMGIGrV6/iwIEDSEhIwPfff4/p06dLMaWlpRg6dCg0Gg3S0tKwZMkSfPHFF1i4cGETHhEiIiIiorapRUeFCgwMRGBgYL0xSqUSarW61mU6nQ6rVq3C+vXrMWTIEABAXFwcunTpgl27dmHYsGHIzs5GYmIiUlNT0a9fPwDAypUr4efnh5ycHLi7uyMpKQknT55EQUEBNBoNAGDBggUIDw/H3LlzYW1tjfj4eFy/fh1r166FUqmEh4cHTp06hYULF2LatGlQKBRNeGSIiIiIiNqWVn+Pxd69e+Hg4IAePXogIiICRUVF0rL09HRUVFQgICBAKtNoNPDw8MDBgwcBACkpKVCpVFJSAQC+vr5QqVSyGA8PDympAIBhw4ZBr9cjPT1divH394dSqZTFnD9/Hnl5efek7UREREREbUWrTiwCAwMRHx+PPXv2YMGCBUhLS8PTTz8NvV4PANBqtTA1NYWNjY1sPUdHR2i1WinGwcGhxrYdHBxkMY6OjrLlNjY2MDU1rTemer46pjZ6vR6lpaWyiYiIiIiovWnVD8h76aWXpL89PDzg4+MDZ2dnbN++HaNHj65zPSGE7NKk2i5TaoqY6hu367sMat68eZg1a1ady4mIiIiI2oNW3WNxOycnJzg7O+P06dMAALVajfLychQXF8viioqKpN4EtVqNCxcu1NjWxYsXZTG39zoUFxejoqKi3pjqy7Ju78m41YwZM6DT6aSpoKCgIU0mIiIiImoT2lRicenSJRQUFMDJyQkA4O3tDRMTEyQnJ0sxhYWFyMzMRP/+/QEAfn5+0Ol0OHz4sBRz6NAh6HQ6WUxmZiYKCwulmKSkJCiVSnh7e0sx+/fvlw1Bm5SUBI1GAxcXlzrrrFQqYW1tLZuIiIiIiNqbFk0sysrKkJGRgYyMDABAbm4uMjIykJ+fj7KyMkRHRyMlJQV5eXnYu3cvgoODYW9vj+eeew4AoFKpMG7cOEyfPh27d+/GsWPH8Oqrr8LT01MaJapnz54YPnw4IiIikJqaitTUVERERCAoKAju7u4AgICAAPTq1QuhoaE4duwYdu/ejejoaEREREiJQEhICJRKJcLDw5GZmYnNmzcjNjaWI0IREREREaGF77E4cuQIBg0aJM1PmzYNABAWFoZly5bhxIkT+Pbbb1FSUgInJycMGjQIGzduhJWVlbTOokWLYGxsjDFjxuDatWsYPHgw1q5dCyMjIykmPj4eU6ZMkUaPGjlypOzZGUZGRti+fTsiIyMxYMAAmJubIyQkBF988YUUo1KpkJycjIkTJ8LHxwc2NjaYNm2aVGciIiIiovuZQvDR0c2qtLQUKpUKOp2uwZdFuby//R7VqnZ5n45o1v0RUdtnyDmuLbrf2kutQ3N/H2gJ/A7SejTkPNem7rEgIiIiIqLWiYkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFERG1KjExMVAoFLJJrVZLy4UQiImJgUajgbm5OQYOHIisrCzZNvR6PSZPngx7e3tYWlpi5MiROHfunCymuLgYoaGhUKlUUKlUCA0NRUlJSXM0kYioXWJiQURErU7v3r1RWFgoTSdOnJCWzZ8/HwsXLsTSpUuRlpYGtVqNoUOH4sqVK1JMVFQUNm/ejISEBBw4cABlZWUICgpCZWWlFBMSEoKMjAwkJiYiMTERGRkZCA0NbdZ2EhG1J8YtXQEiIqLbGRsby3opqgkhsHjxYsycOROjR48GAKxbtw6Ojo7YsGEDJkyYAJ1Oh1WrVmH9+vUYMmQIACAuLg5dunTBrl27MGzYMGRnZyMxMRGpqano168fAGDlypXw8/NDTk4O3N3dm6+xRETtBHssiIio1Tl9+jQ0Gg1cXV0xduxYnDlzBgCQm5sLrVaLgIAAKVapVMLf3x8HDx4EAKSnp6OiokIWo9Fo4OHhIcWkpKRApVJJSQUA+Pr6QqVSSTFERNQw7LEgIqJWpV+/fvj222/Ro0cPXLhwAXPmzEH//v2RlZUFrVYLAHB0dJSt4+joiLNnzwIAtFotTE1NYWNjUyOmen2tVgsHB4ca+3ZwcJBiaqPX66HX66X50tLSxjWSiKgdYmJBREStSmBgoPS3p6cn/Pz80L17d6xbtw6+vr4AAIVCIVtHCFGj7Ha3x9QWf6ftzJs3D7NmzbqrdhAR3W94KRQREbVqlpaW8PT0xOnTp6X7Lm7vVSgqKpJ6MdRqNcrLy1FcXFxvzIULF2rs6+LFizV6Q241Y8YM6HQ6aSooKDCobURE7QkTCyIiatX0ej2ys7Ph5OQEV1dXqNVqJCcnS8vLy8uxb98+9O/fHwDg7e0NExMTWUxhYSEyMzOlGD8/P+h0Ohw+fFiKOXToEHQ6nRRTG6VSCWtra9lEREQ38VIoIiJqVaKjoxEcHIyuXbuiqKgIc+bMQWlpKcLCwqBQKBAVFYXY2Fi4ubnBzc0NsbGxsLCwQEhICABApVJh3LhxmD59Ouzs7GBra4vo6Gh4enpKo0T17NkTw4cPR0REBFasWAEAeOONNxAUFMQRoYiIGomJBRERtSrnzp3Dyy+/jL/++gudOnWCr68vUlNT4ezsDAB49913ce3aNURGRqK4uBj9+vVDUlISrKyspG0sWrQIxsbGGDNmDK5du4bBgwdj7dq1MDIykmLi4+MxZcoUafSokSNHYunSpc3bWCKidkQhhBAtXYn7SWlpKVQqFXQ6XYO70F3e336PalW7vE9HNOv+iKjtM+Qc1xbdb+2l1qG5vw+0BH4HaT0acp7jPRZERERERGQwJhZERERERGQwJhZERERERGQwJhZERERERGQwJhZERERERGSwFk0s9u/fj+DgYGg0GigUCmzZskVaVlFRgffeew+enp6wtLSERqPBa6+9hvPnz8u2MXDgQCgUCtk0duxYWUxxcTFCQ0OhUqmgUqkQGhqKkpISWUx+fj6Cg4NhaWkJe3t7TJkyBeXl5bKYEydOwN/fH+bm5ujcuTNmz54NDqpFRERERNTCicXVq1fRt2/fWscN//vvv3H06FF8+OGHOHr0KDZt2oRTp05h5MiRNWIjIiJQWFgoTdUPO6oWEhKCjIwMJCYmIjExERkZGQgNDZWWV1ZWYsSIEbh69SoOHDiAhIQEfP/995g+fboUU1paiqFDh0Kj0SAtLQ1LlizBF198gYULFzbhESEiIiIiapta9AF5gYGBCAwMrHWZSqVCcnKyrGzJkiV4/PHHkZ+fj65du0rlFhYWUKvVtW4nOzsbiYmJSE1NRb9+/QAAK1euhJ+fH3JycuDu7o6kpCScPHkSBQUF0Gg0AIAFCxYgPDwcc+fOhbW1NeLj43H9+nWsXbsWSqUSHh4eOHXqFBYuXIhp06ZBoVA0xSEhIiIiImqT2tQ9FjqdDgqFAg888ICsPD4+Hvb29ujduzeio6Nx5coVaVlKSgpUKpWUVACAr68vVCoVDh48KMV4eHhISQUADBs2DHq9Hunp6VKMv78/lEqlLOb8+fPIy8u7B60lIiIiImo7WrTHoiGuX7+O999/HyEhIbKn/r3yyitwdXWFWq1GZmYmZsyYgePHj0u9HVqtFg4ODjW25+DgAK1WK8U4OjrKltvY2MDU1FQW4+LiIoupXker1cLV1bXWeuv1euj1emm+tLS0gS0nIiIiImr92kRiUVFRgbFjx6Kqqgpff/21bFlERIT0t4eHB9zc3ODj44OjR4/i0UcfBYBaL1MSQsjKGxNTfeN2fZdBzZs3D7NmzaqveUREREREbV6rvxSqoqICY8aMQW5uLpKTk2W9FbV59NFHYWJigtOnTwMA1Go1Lly4UCPu4sWLUo+DWq2WeiaqFRcXo6Kiot6YoqIiAKjR23GrGTNmQKfTSVNBQcEdWkxERERE1Pa06sSiOqk4ffo0du3aBTs7uzuuk5WVhYqKCjg5OQEA/Pz8oNPpcPjwYSnm0KFD0Ol06N+/vxSTmZmJwsJCKSYpKQlKpRLe3t5SzP79+2VD0CYlJUGj0dS4ROpWSqUS1tbWsomIiIiIqL1p0cSirKwMGRkZyMjIAADk5uYiIyMD+fn5uHHjBl544QUcOXIE8fHxqKyshFarhVarlb7c//HHH5g9ezaOHDmCvLw87NixAy+++CK8vLwwYMAAAEDPnj0xfPhwREREIDU1FampqYiIiEBQUBDc3d0BAAEBAejVqxdCQ0Nx7Ngx7N69G9HR0YiIiJASgZCQECiVSoSHhyMzMxObN29GbGwsR4QiIiIiIkILJxZHjhyBl5cXvLy8AADTpk2Dl5cXPvroI5w7dw4//vgjzp07h0ceeQROTk7SVD2ak6mpKXbv3o1hw4bB3d0dU6ZMQUBAAHbt2gUjIyNpP/Hx8fD09ERAQAACAgLQp08frF+/XlpuZGSE7du3w8zMDAMGDMCYMWMwatQofPHFF1JM9fC3586dg4+PDyIjIzFt2jRMmzatmY4WEREREVHr1aI3bw8cOLDeJ1ff6anWXbp0wb59++64H1tbW8TFxdUb07VrV2zbtq3eGE9PT+zfv/+O+yMiIiIiut+06nssiIiIiIiobWBiQUREREREBmNiQUREREREBmsTD8gjorbF5f3tzbq/vE9HNOv+iIiIqCb2WBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARUas2b948KBQKREVFSWVCCMTExECj0cDc3BwDBw5EVlaWbD29Xo/JkyfD3t4elpaWGDlyJM6dOyeLKS4uRmhoKFQqFVQqFUJDQ1FSUtIMrSIian+YWBARUauVlpaGb775Bn369JGVz58/HwsXLsTSpUuRlpYGtVqNoUOH4sqVK1JMVFQUNm/ejISEBBw4cABlZWUICgpCZWWlFBMSEoKMjAwkJiYiMTERGRkZCA0Nbbb2ERG1J0wsiIioVSorK8Mrr7yClStXwsbGRioXQmDx4sWYOXMmRo8eDQ8PD6xbtw5///03NmzYAADQ6XRYtWoVFixYgCFDhsDLywtxcXE4ceIEdu3aBQDIzs5GYmIi/v3vf8PPzw9+fn5YuXIltm3bhpycnBZpMxFRW8bEgoiIWqWJEydixIgRGDJkiKw8NzcXWq0WAQEBUplSqYS/vz8OHjwIAEhPT0dFRYUsRqPRwMPDQ4pJSUmBSqVCv379pBhfX1+oVCop5nZ6vR6lpaWyiYiIbjJu6QoQERHdLiEhAUePHkVaWlqNZVqtFgDg6OgoK3d0dMTZs2elGFNTU1lPR3VM9fparRYODg41tu/g4CDF3G7evHmYNWtWwxtE7ZrL+9tbugpErQJ7LIiIqFUpKCjA22+/jbi4OJiZmdUZp1AoZPNCiBplt7s9prb4+rYzY8YM6HQ6aSooKKh3f0RE9xMmFkRE1Kqkp6ejqKgI3t7eMDY2hrGxMfbt24cvv/wSxsbGUk/F7b0KRUVF0jK1Wo3y8nIUFxfXG3PhwoUa+7948WKN3pBqSqUS1tbWsomIiG5iYkFERK3K4MGDceLECWRkZEiTj48PXnnlFWRkZKBbt25Qq9VITk6W1ikvL8e+ffvQv39/AIC3tzdMTExkMYWFhcjMzJRi/Pz8oNPpcPjwYSnm0KFD0Ol0UgwREd29Rt1j0a1bN6SlpcHOzk5WXlJSgkcffRRnzpxpksoREdH9x8rKCh4eHrIyS0tL2NnZSeVRUVGIjY2Fm5sb3NzcEBsbCwsLC4SEhAAAVCoVxo0bh+nTp8POzg62traIjo6Gp6endDN4z549MXz4cERERGDFihUAgDfeeANBQUFwd3dvxhYTEbUPjUos8vLyZOOAV9Pr9fjzzz8NrhQREVF93n33XVy7dg2RkZEoLi5Gv379kJSUBCsrKylm0aJFMDY2xpgxY3Dt2jUMHjwYa9euhZGRkRQTHx+PKVOmSKNHjRw5EkuXLm329hARtQcNSix+/PFH6e+dO3dCpVJJ85WVldi9ezdcXFyarHJEREQAsHfvXtm8QqFATEwMYmJi6lzHzMwMS5YswZIlS+qMsbW1RVxcXBPVkojo/tagxGLUqFEAbp7Qw8LCZMtMTEzg4uKCBQsWNFnliIiIiIiobWjQzdtVVVWoqqpC165dUVRUJM1XVVVBr9cjJycHQUFBd729/fv3Izg4GBqNBgqFAlu2bJEtF0IgJiYGGo0G5ubmGDhwILKysmQxer0ekydPhr29PSwtLTFy5EicO3dOFlNcXIzQ0FCoVCqoVCqEhoaipKREFpOfn4/g4GBYWlrC3t4eU6ZMQXl5uSzmxIkT8Pf3h7m5OTp37ozZs2dDCHHX7SUiIiIiaq8aNSpUbm4u7O3tDd751atX0bdv3zqvZ50/fz4WLlyIpUuXIi0tDWq1GkOHDsWVK1ekmKioKGzevBkJCQk4cOAAysrKEBQUJLsHJCQkBBkZGUhMTERiYiIyMjIQGhoqLa+srMSIESNw9epVHDhwAAkJCfj+++8xffp0Kaa0tBRDhw6FRqNBWloalixZgi+++AILFy40+DgQEREREbV1jX7y9u7du7F7926p5+JWq1evvqttBAYGIjAwsNZlQggsXrwYM2fOxOjRowEA69atg6OjIzZs2IAJEyZAp9Nh1apVWL9+vTTKR1xcHLp06YJdu3Zh2LBhyM7ORmJiIlJTU9GvXz8AwMqVK+Hn54ecnBy4u7sjKSkJJ0+eREFBATQaDQBgwYIFCA8Px9y5c2FtbY34+Hhcv34da9euhVKphIeHB06dOoWFCxdi2rRpd3woExERERFRe9aoHotZs2YhICAAu3fvxl9//YXi4mLZ1BRyc3Oh1WqlkTqAmw8m8vf3x8GDBwHcfIhSRUWFLEaj0cDDw0OKSUlJgUqlkpIKAPD19YVKpZLFeHh4SEkFAAwbNgx6vR7p6elSjL+/P5RKpSzm/PnzyMvLa5I2ExERERG1VY3qsVi+fDnWrl0ru5yoqVU/UfX2p586Ojri7NmzUoypqSlsbGxqxFSvr9Vq4eDgUGP7Dg4Ospjb92NjYwNTU1NZzO0jXt369FdXV9da26HX66HX66X50tLSuhtNRERERNRGNarHory8vNmeSnr7JUZCiDtednR7TG3xTRFTfeN2ffWZN2+edNO4SqVCly5d6q07EREREVFb1KjEYvz48diwYUNT10VGrVYD+L+ei2pFRUVST4FarUZ5eXmNy69uj7lw4UKN7V+8eFEWc/t+iouLUVFRUW9MUVERgJq9KreaMWMGdDqdNBUUFNTfcCIiIiKiNqhRicX169excOFC+Pv7Y/LkyZg2bZpsagqurq5Qq9VITk6WysrLy7Fv3z6pt8Tb2xsmJiaymMLCQmRmZkoxfn5+0Ol0OHz4sBRz6NAh6HQ6WUxmZiYKCwulmKSkJCiVSnh7e0sx+/fvlw1Bm5SUBI1GU+9DAZVKJaytrWUTEREREVF706h7LH799Vc88sgjAIDMzEzZsoaMjlRWVobff/9dms/NzUVGRgZsbW3RtWtXREVFITY2Fm5ubnBzc0NsbCwsLCwQEhICAFCpVBg3bhymT58OOzs72NraIjo6Gp6entIoUT179sTw4cMRERGBFStWAADeeOMNBAUFwd3dHQAQEBCAXr16ITQ0FJ9//jkuX76M6OhoRERESIlASEgIZs2ahfDwcHzwwQc4ffo0YmNj8dFHH3FEKCIiIiK67zUqsfj555+bZOdHjhzBoEGDpPnq3o6wsDCsXbsW7777Lq5du4bIyEgUFxejX79+SEpKgpWVlbTOokWLYGxsjDFjxuDatWsYPHgw1q5dCyMjIykmPj4eU6ZMkUaPGjlypOzZGUZGRti+fTsiIyMxYMAAmJubIyQkBF988YUUo1KpkJycjIkTJ8LHxwc2NjZN2kNDRERERNSWKQQfHd2sSktLoVKpoNPpGnxZlMv72+9RrWqX9+mIZt0ftR98r96/DDnHtUX3W3upds19zrsf8LzeejTkPNeoHotBgwbVe/nPnj17GrNZIiIiIiJqoxqVWFTfX1GtoqICGRkZyMzMRFhYWFPUi4iIiIiI2pBGJRaLFi2qtTwmJgZlZWUGVYiIiIiIiNqeRg03W5dXX30Vq1evbspNEhERERFRG9CkiUVKSgrMzMyacpNERERERNQGNOpSqNGjR8vmhRAoLCzEkSNH8OGHHzZJxYiIiIiIqO1oVGKhUqlk8x06dIC7uztmz54tPSuCiIiIiIjuH41KLNasWdPU9SAiIiIiojasUYlFtfT0dGRnZ0OhUKBXr17w8vJqqnoREREREVEb0qjEoqioCGPHjsXevXvxwAMPQAgBnU6HQYMGISEhAZ06dWrqehIRERERUSvWqFGhJk+ejNLSUmRlZeHy5csoLi5GZmYmSktLMWXKlKauIxERERERtXKN6rFITEzErl270LNnT6msV69e+Oqrr3jzNhERERHRfahRPRZVVVUwMTGpUW5iYoKqqiqDK0VERERERG1LoxKLp59+Gm+//TbOnz8vlf3555+YOnUqBg8e3GSVIyIiIiKitqFRicXSpUtx5coVuLi4oHv37njooYfg6uqKK1euYMmSJU1dRyIiIiIiauUadY9Fly5dcPToUSQnJ+O3336DEAK9evXCkCFDmrp+RERERETUBjSox2LPnj3o1asXSktLAQBDhw7F5MmTMWXKFDz22GPo3bs3fvnll3tSUSIiIiIiar0alFgsXrwYERERsLa2rrFMpVJhwoQJWLhwYZNVjoiIiIiI2oYGJRbHjx/H8OHD61weEBCA9PR0gytFRERERERtS4MSiwsXLtQ6zGw1Y2NjXLx40eBKERERERFR29KgxKJz5844ceJEnct//fVXODk5GVwpIiIiIiJqWxqUWDzzzDP46KOPcP369RrLrl27ho8//hhBQUFNVjkiIiIiImobGjTc7D//+U9s2rQJPXr0wKRJk+Du7g6FQoHs7Gx89dVXqKysxMyZM+9VXYmIiIiIqJVqUGLh6OiIgwcP4q233sKMGTMghAAAKBQKDBs2DF9//TUcHR3vSUWJiIiIiKj1avAD8pydnbFjxw4UFxfj999/hxACbm5usLGxuRf1IyIiIiKiNqBRT94GABsbGzz22GNNWRciIiIiImqjGnTzNhERERERUW2YWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARUauybNky9OnTB9bW1rC2toafnx9++uknabkQAjExMdBoNDA3N8fAgQORlZUl24Zer8fkyZNhb28PS0tLjBw5EufOnZPFFBcXIzQ0FCqVCiqVCqGhoSgpKWmOJhIRtUtMLIiIqFV58MEH8emnn+LIkSM4cuQInn76aTz77LNS8jB//nwsXLgQS5cuRVpaGtRqNYYOHYorV65I24iKisLmzZuRkJCAAwcOoKysDEFBQaisrJRiQkJCkJGRgcTERCQmJiIjIwOhoaHN3l4iovai0Q/IIyIiuheCg4Nl83PnzsWyZcuQmpqKXr16YfHixZg5cyZGjx4NAFi3bh0cHR2xYcMGTJgwATqdDqtWrcL69esxZMgQAEBcXBy6dOmCXbt2YdiwYcjOzkZiYiJSU1PRr18/AMDKlSvh5+eHnJwcuLu7N2+jiYjaAfZYEBFRq1VZWYmEhARcvXoVfn5+yM3NhVarRUBAgBSjVCrh7++PgwcPAgDS09NRUVEhi9FoNPDw8JBiUlJSoFKppKQCAHx9faFSqaSY2uj1epSWlsomIiK6iYkFERG1OidOnEDHjh2hVCrx5ptvYvPmzejVqxe0Wi0AwNHRURbv6OgoLdNqtTA1NYWNjU29MQ4ODjX26+DgIMXUZt68edI9GSqVCl26dDGonURE7QkTCyIianXc3d2RkZGB1NRUvPXWWwgLC8PJkyel5QqFQhYvhKhRdrvbY2qLv9N2ZsyYAZ1OJ00FBQV32yQionaPiQUREbU6pqameOihh+Dj44N58+ahb9+++Ne//gW1Wg0ANXoVioqKpF4MtVqN8vJyFBcX1xtz4cKFGvu9ePFijd6QWymVSmm0quqJiIhuYmJBREStnhACer0erq6uUKvVSE5OlpaVl5dj37596N+/PwDA29sbJiYmspjCwkJkZmZKMX5+ftDpdDh8+LAUc+jQIeh0OimGiIgahqNCERFRq/LBBx8gMDAQXbp0wZUrV5CQkIC9e/ciMTERCoUCUVFRiI2NhZubG9zc3BAbGwsLCwuEhIQAAFQqFcaNG4fp06fDzs4Otra2iI6OhqenpzRKVM+ePTF8+HBERERgxYoVAIA33ngDQUFBHBGKiKiRWn2PhYuLCxQKRY1p4sSJAIDw8PAay3x9fWXbaKoHJeXn5yM4OBiWlpawt7fHlClTUF5efk/bT0R0v7lw4QJCQ0Ph7u6OwYMH49ChQ0hMTMTQoUMBAO+++y6ioqIQGRkJHx8f/Pnnn0hKSoKVlZW0jUWLFmHUqFEYM2YMBgwYAAsLC2zduhVGRkZSTHx8PDw9PREQEICAgAD06dMH69evb/b2EhG1F62+xyItLU32QKPMzEwMHToUL774olQ2fPhwrFmzRpo3NTWVbSMqKgpbt25FQkIC7OzsMH36dAQFBSE9PV36kAkJCcG5c+eQmJgI4OYvV6Ghodi6dSuAm0MejhgxAp06dcKBAwdw6dIlhIWFQQiBJUuW3LP2ExHdb1atWlXvcoVCgZiYGMTExNQZY2ZmhiVLltR7fra1tUVcXFxjq0lERLdp9YlFp06dZPOffvopunfvDn9/f6lMqVRKN/TdrqkelJSUlISTJ0+ioKAAGo0GALBgwQKEh4dj7ty5vIGPiIiIiO5rrf5SqFuVl5cjLi4Or7/+umw4wL1798LBwQE9evRAREQEioqKpGVN9aCklJQUeHh4SEkFAAwbNgx6vR7p6en3rM1ERERERG1Bq++xuNWWLVtQUlKC8PBwqSwwMBAvvvginJ2dkZubiw8//BBPP/000tPToVQqm+xBSVqttsYQhDY2NjA1Na33YUp6vR56vV6a51NaiYiIiKg9alOJxapVqxAYGCjrNXjppZekvz08PODj4wNnZ2ds374do0ePrnNbjXlQUmMepjRv3jzMmjWr7kYREREREbUDbeZSqLNnz2LXrl0YP358vXFOTk5wdnbG6dOnATTdg5LUanWNnoni4mJUVFTU+zAlPqWViIiIiO4HbSaxWLNmDRwcHDBixIh64y5duoSCggI4OTkBaLoHJfn5+SEzMxOFhYVSTFJSEpRKJby9veusD5/SSkRERET3gzaRWFRVVWHNmjUICwuDsfH/Xb1VVlaG6OhopKSkIC8vD3v37kVwcDDs7e3x3HPPAZA/KGn37t04duwYXn311ToflJSamorU1FRERETIHpQUEBCAXr16ITQ0FMeOHcPu3bsRHR2NiIgIJgtEREREdN9rE4nFrl27kJ+fj9dff11WbmRkhBMnTuDZZ59Fjx49EBYWhh49eiAlJaXJH5RkZGSE7du3w8zMDAMGDMCYMWMwatQofPHFF/f+ABARERERtXJt4ubtgIAACCFqlJubm2Pnzp13XL+pHpTUtWtXbNu27c4VJiIiIiK6z7SJHgsiIiIiImrdmFgQEREREZHBmFgQEREREZHBmFgQEREREZHBmFgQEREREZHBmFgQEREREZHBmFgQEREREZHBmFgQEREREZHBmFgQEREREZHBmFgQEREREZHBmFgQEREREZHBmFgQEREREZHBmFgQEREREZHBmFgQEREREZHBmFgQEREREZHBmFgQEREREZHBmFgQEREREZHBmFgQEREREZHBmFgQEREREZHBmFgQEREREZHBmFgQEREREZHBmFgQEREREZHBmFgQEREREZHBmFgQEREREZHBmFgQEREREZHBmFgQEREREZHBmFgQEREREZHBmFgQEREREZHBmFgQEREREZHBmFgQEREREZHBmFgQEREREZHBmFgQEREREZHBmFgQEREREZHBmFgQEREREZHBmFgQEREREZHBmFgQEVGrMm/ePDz22GOwsrKCg4MDRo0ahZycHFmMEAIxMTHQaDQwNzfHwIEDkZWVJYvR6/WYPHky7O3tYWlpiZEjR+LcuXOymOLiYoSGhkKlUkGlUiE0NBQlJSX3uolERO2ScUtXgIiI6Fb79u3DxIkT8dhjj+HGjRuYOXMmAgICcPLkSVhaWgIA5s+fj4ULF2Lt2rXo0aMH5syZg6FDhyInJwdWVlYAgKioKGzduhUJCQmws7PD9OnTERQUhPT0dBgZGQEAQkJCcO7cOSQmJgIA3njjDYSGhmLr1q0t03gymMv721u6CkT3LSYWRETUqlR/ya+2Zs0aODg4ID09HU899RSEEFi8eDFmzpyJ0aNHAwDWrVsHR0dHbNiwARMmTIBOp8OqVauwfv16DBkyBAAQFxeHLl26YNeuXRg2bBiys7ORmJiI1NRU9OvXDwCwcuVK+Pn5IScnB+7u7s3bcCKiNo6XQhERUaum0+kAALa2tgCA3NxcaLVaBAQESDFKpRL+/v44ePAgACA9PR0VFRWyGI1GAw8PDykmJSUFKpVKSioAwNfXFyqVSoq5nV6vR2lpqWwiIqKbmFgQEVGrJYTAtGnT8MQTT8DDwwMAoNVqAQCOjo6yWEdHR2mZVquFqakpbGxs6o1xcHCosU8HBwcp5nbz5s2T7sdQqVTo0qWLYQ0kImpHeCkUERG1WpMmTcKvv/6KAwcO1FimUChk80KIGmW3uz2mtvj6tjNjxgxMmzZNmi8tLWVyQXQPtMS9Mnmfjmj2fbY3rbrHIiYmBgqFQjap1WppeXOOCpKfn4/g4GBYWlrC3t4eU6ZMQXl5+T1rOxHR/W7y5Mn48ccf8fPPP+PBBx+Uyqs/B27vVSgqKpJ6MdRqNcrLy1FcXFxvzIULF2rs9+LFizV6Q6oplUpYW1vLJiIiuqlVJxYA0Lt3bxQWFkrTiRMnpGXVo4IsXboUaWlpUKvVGDp0KK5cuSLFREVFYfPmzUhISMCBAwdQVlaGoKAgVFZWSjEhISHIyMhAYmIiEhMTkZGRgdDQUGl5ZWUlRowYgatXr+LAgQNISEjA999/j+nTpzfPQSAiuo8IITBp0iRs2rQJe/bsgaurq2y5q6sr1Go1kpOTpbLy8nLs27cP/fv3BwB4e3vDxMREFlNYWIjMzEwpxs/PDzqdDocPH5ZiDh06BJ1OJ8UQEdHda/WXQhkbG8t6Kao156ggSUlJOHnyJAoKCqDRaAAACxYsQHh4OObOnctfrIiImtDEiROxYcMG/PDDD7CyspJ6JlQqFczNzaFQKBAVFYXY2Fi4ubnBzc0NsbGxsLCwQEhIiBQ7btw4TJ8+HXZ2drC1tUV0dDQ8PT2lz4OePXti+PDhiIiIwIoVKwDcHG42KCiII0IRETVCq++xOH36NDQaDVxdXTF27FicOXMGQPOOCpKSkgIPDw8pqQCAYcOGQa/XIz09/d41nojoPrRs2TLodDoMHDgQTk5O0rRx40Yp5t1330VUVBQiIyPh4+ODP//8E0lJSdIzLABg0aJFGDVqFMaMGYMBAwbAwsICW7dulZ5hAQDx8fHw9PREQEAAAgIC0KdPH6xfv75Z20tE1F606h6Lfv364dtvv0WPHj1w4cIFzJkzB/3790dWVla9o4KcPXsWQNONCqLVamvsx8bGBqampnWOHFJNr9dDr9dL8xyakIiofkKIO8YoFArExMQgJiamzhgzMzMsWbIES5YsqTPG1tYWcXFxjakmERHdplUnFoGBgdLfnp6e8PPzQ/fu3bFu3Tr4+voCaL5RQRo6cki1efPmYdasWfXGEBERERG1da3+UqhbWVpawtPTE6dPn27WUUHUanWN/RQXF6OioqLOkUOqzZgxAzqdTpoKCgoa0GIiIiIiorahTSUWer0e2dnZcHJyatZRQfz8/JCZmYnCwkIpJikpCUqlEt7e3vXWmUMTEhEREdH9oFVfChUdHY3g4GB07doVRUVFmDNnDkpLSxEWFtaso4IEBASgV69eCA0Nxeeff47Lly8jOjoaERERTBSIiIiIiNDKE4tz587h5Zdfxl9//YVOnTrB19cXqampcHZ2BnBzVJBr164hMjISxcXF6NevX62jghgbG2PMmDG4du0aBg8ejLVr19YYFWTKlCnS6FEjR47E0qVLpeVGRkbYvn07IiMjMWDAAJibmyMkJARffPFFMx0JIiIiIqLWTSHuZvgNajKlpaVQqVTQ6XQN7u1o7sfb89H21Fh8r96/DDnHtUX3W3vbguY+/1D7wc+S2jXkPNem7rEgIiIiIqLWiYkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFEREREREZjIkFERG1Ovv370dwcDA0Gg0UCgW2bNkiWy6EQExMDDQaDczNzTFw4EBkZWXJYvR6PSZPngx7e3tYWlpi5MiROHfunCymuLgYoaGhUKlUUKlUCA0NRUlJyT1uHRFR+8TEgoiIWp2rV6+ib9++WLp0aa3L58+fj4ULF2Lp0qVIS0uDWq3G0KFDceXKFSkmKioKmzdvRkJCAg4cOICysjIEBQWhsrJSigkJCUFGRgYSExORmJiIjIwMhIaG3vP2ERG1R8YtXQEiorbI5f3tzbq/vE9HNOv+WlpgYCACAwNrXSaEwOLFizFz5kyMHj0aALBu3To4Ojpiw4YNmDBhAnQ6HVatWoX169djyJAhAIC4uDh06dIFu3btwrBhw5CdnY3ExESkpqaiX79+AICVK1fCz88POTk5cHd3b57GEhG1E626x2LevHl47LHHYGVlBQcHB4waNQo5OTmymPDwcCgUCtnk6+sri2mq7vD8/HwEBwfD0tIS9vb2mDJlCsrLy+9J24mIqHa5ubnQarUICAiQypRKJfz9/XHw4EEAQHp6OioqKmQxGo0GHh4eUkxKSgpUKpWUVACAr68vVCqVFENERHevVScW+/btw8SJE5Gamork5GTcuHEDAQEBuHr1qixu+PDhKCwslKYdO3bIljdFd3hlZSVGjBiBq1ev4sCBA0hISMD333+P6dOn39uDQEREMlqtFgDg6OgoK3d0dJSWabVamJqawsbGpt4YBweHGtt3cHCQYm6n1+tRWloqm4iI6KZWfSlUYmKibH7NmjVwcHBAeno6nnrqKalcqVRCrVbXuo2m6g5PSkrCyZMnUVBQAI1GAwBYsGABwsPDMXfuXFhbW9+LQ0BERHVQKBSyeSFEjbLb3R5TW3x925k3bx5mzZrViNoSEbV/rbrH4nY6nQ4AYGtrKyvfu3cvHBwc0KNHD0RERKCoqEha1lTd4SkpKfDw8JCSCgAYNmwY9Ho90tPTm76xRERUq+ofkm7vVSgqKpJ6MdRqNcrLy1FcXFxvzIULF2ps/+LFizV6Q6rNmDEDOp1OmgoKCgxuDxFRe9FmEgshBKZNm4YnnngCHh4eUnlgYCDi4+OxZ88eLFiwAGlpaXj66aeh1+sBNF13uFarrfFBY2NjA1NT0zq7zAF2mxMRNTVXV1eo1WokJydLZeXl5di3bx/69+8PAPD29oaJiYksprCwEJmZmVKMn58fdDodDh8+LMUcOnQIOp1OirmdUqmEtbW1bCIiopta9aVQt5o0aRJ+/fVXHDhwQFb+0ksvSX97eHjAx8cHzs7O2L59uzRaSG0a0x3e0C5zgN3mRESNUVZWht9//12az83NRUZGBmxtbdG1a1dERUUhNjYWbm5ucHNzQ2xsLCwsLBASEgIAUKlUGDduHKZPnw47OzvY2toiOjoanp6e0mWxPXv2xPDhwxEREYEVK1YAAN544w0EBQVxRCgiokZoEz0WkydPxo8//oiff/4ZDz74YL2xTk5OcHZ2xunTpwE0XXe4Wq2u0TNRXFyMioqKOrvMAXabExE1xpEjR+Dl5QUvLy8AwLRp0+Dl5YWPPvoIAPDuu+8iKioKkZGR8PHxwZ9//omkpCRYWVlJ21i0aBFGjRqFMWPGYMCAAbCwsMDWrVthZGQkxcTHx8PT0xMBAQEICAhAnz59sH79+uZtLBFRO9GqeyyEEJg8eTI2b96MvXv3wtXV9Y7rXLp0CQUFBXBycgIg7w4fM2YMgP/rDp8/fz4AeXf4448/DqBmd7ifnx/mzp2LwsJCadtJSUlQKpXw9vausz5KpRJKpbLxB4GI6D40cOBACCHqXK5QKBATE4OYmJg6Y8zMzLBkyRIsWbKkzhhbW1vExcUZUlUiIvr/terEYuLEidiwYQN++OEHWFlZST0GKpUK5ubmKCsrQ0xMDJ5//nk4OTkhLy8PH3zwAezt7fHcc89JsU3RHR4QEIBevXohNDQUn3/+OS5fvozo6GhERETwGlsiIiIiuu+16kuhli1bBp1Oh4EDB8LJyUmaNm7cCAAwMjLCiRMn8Oyzz6JHjx4ICwtDjx49kJKS0uTd4UZGRti+fTvMzMwwYMAAjBkzBqNGjcIXX3zRfAeEiIiIiKiVatU9FvV1gwOAubk5du7cecftNFV3eNeuXbFt27Y77o+IiIiI6H7TqnssiIiIiIiobWBiQUREREREBmvVl0IREd0Nl/e3t3QViIiI7nvssSAiIiIiIoMxsSAiIiIiIoMxsSAiIiIiIoMxsSAiIiIiIoMxsSAiIiIiIoNxVCgiIiIiuu819wiDeZ+OaNb9NQf2WBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGYWBARERERkcGMW7oCRERE1H65vL+9patARM2EPRZERERERGQwJhZERERERGQwXgpFRERERNTMWuIywbxPR9zT7bPHgoiIiIiIDMbEgoiIiIiIDMbEgoiIiIiIDMbEohG+/vpruLq6wszMDN7e3vjll19aukpERGQAnteJiAzHxKKBNm7ciKioKMycORPHjh3Dk08+icDAQOTn57d01YiIqBF4XiciahpMLBpo4cKFGDduHMaPH4+ePXti8eLF6NKlC5YtW9bSVSMiokbgeZ2IqGlwuNkGKC8vR3p6Ot5//31ZeUBAAA4ePNhCtSIiosa6387rfAo2Ed1LTCwa4K+//kJlZSUcHR1l5Y6OjtBqtbWuo9frodfrpXmdTgcAKC0tbfD+q/R/N3gdQzSmjkRA879X7wddp37X7PvMnDWswetUnzeEEE1dnXuioef1pjynA4DHxzsbtR4RUWM05lzVkPM6E4tGUCgUsnkhRI2yavPmzcOsWbNqlHfp0uWe1K0pqRa3dA2IqCUZcg64cuUKVCpVk9XlXrvb83pbPqcTEd3r8zoTiwawt7eHkZFRjV+xioqKavzaVW3GjBmYNm2aNF9VVYXLly/Dzs6uzmSktSstLUWXLl1QUFAAa2vrlq5Oq8PjUzcem7q1l2MjhMCVK1eg0Whauip3paHn9bZ0Tm8v76mGYrvZ7vtBc7a7Ied1JhYNYGpqCm9vbyQnJ+O5556TypOTk/Hss8/Wuo5SqYRSqZSVPfDAA/eyms3G2tr6vvonbigen7rx2NStPRybttRT0dDzels8p7eH91RjsN33F7b73rrb8zoTiwaaNm0aQkND4ePjAz8/P3zzzTfIz8/Hm2++2dJVIyKiRuB5nYioaTCxaKCXXnoJly5dwuzZs1FYWAgPDw/s2LEDzs7OLV01IiJqBJ7XiYiaBhOLRoiMjERkZGRLV6PFKJVKfPzxxzUuB6CbeHzqxmNTNx6bltUez+v363uK7Wa77wettd0K0VbGBCQiIiIiolaLT94mIiIiIiKDMbEgIiIiIiKDMbEgIiIiIiKDMbGguzZv3jw89thjsLKygoODA0aNGoWcnJyWrlarNG/ePCgUCkRFRbV0VVqNP//8E6+++irs7OxgYWGBRx55BOnp6S1drRZ348YN/POf/4SrqyvMzc3RrVs3zJ49G1VVVS1dNWqjYmJioFAoZJNarW7pajW5/fv3Izg4GBqNBgqFAlu2bJEtF0IgJiYGGo0G5ubmGDhwILKyslqmsk3oTu0ODw+v8fr7+vq2TGWbyN18/2iPr/fdtLu1vd5MLOiu7du3DxMnTkRqaiqSk5Nx48YNBAQE4OrVqy1dtVYlLS0N33zzDfr06dPSVWk1iouLMWDAAJiYmOCnn37CyZMnsWDBglb/YLHm8Nlnn2H58uVYunQpsrOzMX/+fHz++edYsmRJS1eN2rDevXujsLBQmk6cONHSVWpyV69eRd++fbF06dJal8+fPx8LFy7E0qVLkZaWBrVajaFDh+LKlSvNXNOmdad2A8Dw4cNlr/+OHTuasYZN726+f7TH1/tuv3e1qtdbEDVSUVGRACD27dvX0lVpNa5cuSLc3NxEcnKy8Pf3F2+//XZLV6lVeO+998QTTzzR0tVolUaMGCFef/11Wdno0aPFq6++2kI1orbu448/Fn379m3pajQrAGLz5s3SfFVVlVCr1eLTTz+Vyq5fvy5UKpVYvnx5C9Tw3ri93UIIERYWJp599tkWqU9zuf37x/3yetf2vau1vd7ssaBG0+l0AABbW9sWrknrMXHiRIwYMQJDhgxp6aq0Kj/++CN8fHzw4osvwsHBAV5eXli5cmVLV6tVeOKJJ7B7926cOnUKAHD8+HEcOHAAzzzzTAvXjNqy06dPQ6PRwNXVFWPHjsWZM2daukrNKjc3F1qtFgEBAVKZUqmEv78/Dh482II1ax579+6Fg4MDevTogYiICBQVFbV0lZrU7d8/7pfXu67vXa3p9eYD8qhRhBCYNm0annjiCXh4eLR0dVqFhIQEHD16FGlpaS1dlVbnzJkzWLZsGaZNm4YPPvgAhw8fxpQpU6BUKvHaa6+1dPVa1HvvvQedToeHH34YRkZGqKysxNy5c/Hyyy+3dNWojerXrx++/fZb9OjRAxcuXMCcOXPQv39/ZGVlwc7OrqWr1yy0Wi0AwNHRUVbu6OiIs2fPtkSVmk1gYCBefPFFODs7Izc3Fx9++CGefvpppKent7qHqTVGbd8/7ofXu67vXa3t9WZiQY0yadIk/Prrrzhw4EBLV6VVKCgowNtvv42kpCSYmZm1dHVanaqqKvj4+CA2NhYA4OXlhaysLCxbtuy+Tyw2btyIuLg4bNiwAb1790ZGRgaioqKg0WgQFhbW0tWjNigwMFD629PTE35+fujevTvWrVuHadOmtWDNmp9CoZDNCyFqlLU3L730kvS3h4cHfHx84OzsjO3bt2P06NEtWLOmUd/3j/b8etfV7tb2evNSKGqwyZMn48cff8TPP/+MBx98sKWr0yqkp6ejqKgI3t7eMDY2hrGxMfbt24cvv/wSxsbGqKysbOkqtignJyf06tVLVtazZ0/k5+e3UI1aj3feeQfvv/8+xo4dC09PT4SGhmLq1KmYN29eS1eN2glLS0t4enri9OnTLV2VZlM9Clb1L9nVioqKavyq3d45OTnB2dm5Xbz+dX3/aO+vd0O+d7X0683Egu6aEAKTJk3Cpk2bsGfPHri6urZ0lVqNwYMH48SJE8jIyJAmHx8fvPLKK8jIyICRkVFLV7FFDRgwoMYQeadOnYKzs3ML1aj1+Pvvv9Ghg/xUbGRkxOFmqcno9XpkZ2fDycmppavSbFxdXaFWq5GcnCyVlZeXY9++fejfv38L1qz5Xbp0CQUFBW369b/T94/2+no35ntXS7/evBSK7trEiROxYcMG/PDDD7CyspJ+GVCpVDA3N2/h2rUsKyurGveaWFpaws7OjvegAJg6dSr69++P2NhYjBkzBocPH8Y333yDb775pqWr1uKCg4Mxd+5cdO3aFb1798axY8ewcOFCvP766y1dNWqjoqOjERwcjK5du6KoqAhz5sxBaWlpu7u0rqysDL///rs0n5ubi4yMDNja2qJr166IiopCbGws3Nzc4ObmhtjYWFhYWCAkJKQFa224+tpta2uLmJgYPP/883ByckJeXh4++OAD2Nvb47nnnmvBWhvmTt8/qp8b1d5e7zu1u6ysrPW93i03IBW1NQBqndasWdPSVWuVONys3NatW4WHh4dQKpXi4YcfFt98801LV6lVKC0tFW+//bbo2rWrMDMzE926dRMzZ84Uer2+patGbdRLL70knJychImJidBoNGL06NEiKyurpavV5H7++edaP5PCwsKEEDeHIP3444+FWq0WSqVSPPXUU+LEiRMtW+kmUF+7//77bxEQECA6deokTExMRNeuXUVYWJjIz89v6Wob5G6+f7TH1/tO7W6Nr7fi/684ERERERFRo/EeCyIiIiIiMhgTCyIiIiIiMhgTCyIiIiIiMhgTCyIiIiIiMhgTCyIiIiIiMhgTCyIiIiIiMhgTCyIiIiIiMhgTCyIiIiIiMhgTCyIiIqJ6DBw4EFFRUU22vZiYGDzyyCNNtr26hIaGIjY29p7vp6FcXFywePHiJtnWtm3b4OXlhaqqqibZHhmGiQVRK9HUH1zNra3Xn4haJ61Wi7fffhsPPfQQzMzM4OjoiCeeeALLly/H33//3dLVa5To6Gjs3r37nu7j119/xfbt2zF58uRal6elpUGj0QAAzp8/D3Nzc5SXl9/TOt0LQUFBUCgU2LBhQ0tXhcDEgggHDx6EkZERhg8fXmNZc35Z3rRpEz755JNm2RcRUVtw5swZeHl5ISkpCbGxsTh27Bh27dqFqVOnYuvWrdi1a1ed61ZUVDRjTRumY8eOsLOzu6f7WLp0KV588UVYWVnVujwlJQUDBgwAAPzyyy/w8fGBqanpPa3TvfKPf/wDS5YsaelqEJhYEGH16tWYPHkyDhw4gPz8/Barh62tbZ0fAPertvjrGRE1ncjISBgbG+PIkSMYM2YMevbsCU9PTzz//PPYvn07goODpViFQoHly5fj2WefhaWlJebMmYPKykqMGzcOrq6uMDc3h7u7O/71r3/J9hEeHo5Ro0Zh1qxZcHBwgLW1NSZMmFDj/FNVVYV3330Xtra2UKvViImJqbfue/fuxeOPPw5LS0s88MADGDBgAM6ePQug5qVQCoWixuTi4iItP3nyJJ555hl07NgRjo6OCA0NxV9//VXnvquqqvDdd99h5MiRdcYcPHhQSiwOHDgg/V0tLy8PCoUCmzZtwqBBg2BhYYG+ffsiJSVFFvf999+jd+/eUCqVcHFxwYIFC2TLi4qKEBwcDHNzc7i6uiI+Pr5GXXQ6Hd544w3p+D/99NM4fvy4tPz48eMYNGgQrKysYG1tDW9vbxw5ckRaPnLkSBw+fBhnzpyps73UTATRfaysrExYWVmJ3377Tbz00kti1qxZ0rKwsDABQDbl5uaK69evi8mTJ4tOnToJpVIpBgwYIA4fPizbrr+/v5g0aZJ4++23xQMPPCAcHBzEihUrRFlZmQgPDxcdO3YU3bp1Ezt27JCt8/bbb0vzlZWV4tNPPxXdu3cXpqamokuXLmLOnDl1tsXf319MnjxZvPPOO8LGxkY4OjqKjz/+WBbj7OwsFi1aJCvr27evLK4xda9eb+LEiWLixIlCpVIJW1tbMXPmTFFVVSWEEKKqqkp89tlnwtXVVZiZmYk+ffqI7777rtZtTJ06VdjZ2Ymnnnqq1rbOnTu3xmsDQCxYsKDO40NEbctff/0lFAqFmDdv3l3FAxAODg5i1apV4o8//hB5eXmivLxcfPTRR+Lw4cPizJkzIi4uTlhYWIiNGzdK64WFhYmOHTuKl156SWRmZopt27aJTp06iQ8++ECK8ff3F9bW1iImJkacOnVKrFu3TigUCpGUlFRrXSoqKoRKpRLR0dHi999/FydPnhRr164VZ8+eFUII8fHHH4u+fftK8YWFhdL0+++/i4ceekiEhoYKIYQ4f/68sLe3FzNmzBDZ2dni6NGjYujQoWLQoEF1Hotjx44JAEKr1crKf/nlF6FSqYRKpRJGRkbCwsJCqFQqYWxsLMzNzYVKpZKOd25urgAgHn74YbFt2zaRk5MjXnjhBeHs7CwqKiqEEEIcOXJEdOjQQcyePVvk5OSINWvWCHNzc7FmzRppn4GBgcLDw0McPHhQHDlyRPTv31+Ym5tLn0VVVVViwIABIjg4WKSlpYlTp06J6dOnCzs7O3Hp0iUhhBC9e/cWr776qsjOzhanTp0S//nPf0RGRoasbQ4ODmLt2rV1HhNqHkws6L62atUq4ePjI4QQYuvWrcLFxUX6IlxSUiL8/PxERESEdMK/ceOGmDJlitBoNGLHjh0iKytLhIWFCRsbG+kEKMTNDyErKyvxySefiFOnTolPPvlEdOjQQQQGBopvvvlGnDp1Srz11lvCzs5OXL16VVrn1sTi3XffFTY2NmLt2rXi999/F7/88otYuXJlnW25mw++u00sGlr36vU6duwo3n77bfHbb79JH+DffPONEEKIDz74QDz88MMiMTFR/PHHH2LNmjVCqVSKvXv31tjGO++8I3777TeRnZ1da1tLS0tlH8RvvfWWcHZ2FgUFBXUeHyJqW1JTUwUAsWnTJlm5nZ2dsLS0FJaWluLdd9+VygGIqKioO243MjJSPP/889J8WFiYsLW1lZ3Pli1bJjp27CgqKyuFEDfPTU888YRsO4899ph47733at3HpUuXBADZ+e1WtycW1aqqqsRzzz0nvL29xd9//y2EEOLDDz8UAQEBsriCggIBQOTk5NS6/c2bNwsjIyPp86zatWvXRG5urvjpp5+EjY2NOHPmjDhy5IgwNTUV2dnZIjc3VxQXFwsh/i+x+Pe//y2tn5WVJQBI5+aQkBAxdOhQ2T7eeecd0atXLyGEEDk5OQKASE1NlZZnZ2cLANJn0e7du4W1tbW4fv26bDvdu3cXK1asEEIIYWVldcekwcvLS8TExNQbQ/ceEwu6r/Xv318sXrxYCHHzFyZ7e3uRnJwsLb/9y35ZWZkwMTER8fHxUll5ebnQaDRi/vz5svVu/RC6ceOGsLS0lH6BEuLmL1QAREpKSo19lZaWCqVSWW8icbu7+eC728SioXWvXq9nz56yD7L33ntP9OzZU5SVlQkzMzNx8OBB2b7HjRsnXn75Zdk2HnnkkbtusxBCxMTECGdnZ5GXl9eg9YiodatOLDZv3iwr/+OPP8Tp06fF448/Ljs/AxBxcXE1trNs2TLh7e0t7O3thaWlpTAxMRGPPfaYtDwsLKzGr/8ZGRkCgHRe8ff3F5GRkbKYkSNHin/84x911j88PFwolUoRFBQkFi9eLM6fPy8tqyuxeP/994VarZb9SPLMM88IExMTKZmqngDU6DmutmHDBmFhYVFn3T777DMREhIihBAiLi5ODB48uEZMdWJxa4/85cuXBQCxb98+IUTtX+a3bNkiTExMxI0bN8SWLVuEsbGxuHHjhizmgQcekD6L5s+fLzp06FCjfR06dJASx48//lgYGxuLwYMHi3nz5onff/+9Rn379+8vSzSpZRg3zwVXRK1PTk4ODh8+jE2bNgEAjI2N8dJLL2H16tUYMmRIrev88ccfqKiokF2LamJigscffxzZ2dmy2D59+kh/GxkZwc7ODp6enlKZo6MjgJvXn94uOzsber0egwcPblCbbt0nADg5OdW6/YZspyF19/X1hUKhkOb9/PywYMECZGZm4vr16xg6dKgsvry8HF5eXrIyHx+fu67nrFmzsGbNGuzbtw/Ozs53vR4RtX4PPfQQFAoFfvvtN1l5t27dAADm5uY11rG0tJTN/+c//8HUqVOxYMEC+Pn5wcrKCp9//jkOHTp0V3W49XxmYmJSY1l9Q5yuWbMGU6ZMQWJiIjZu3Ih//vOfSE5Ohq+vb63xcXFxWLRoEfbu3YsHH3xQKq+qqkJwcDA+++yzGus4OTnVui17e3v8/fffKC8vl92Q3bFjRwCAXq9Hhw4d8MMPP6C8vBxCCHTs2BFPPvkkfvrpJ9m2bm139fGobrcQQnaMqstu//v2mFtVVVXByckJe/furbHsgQceAHDznpSQkBBs374dP/30Ez7++GMkJCTgueeek2IvX76MTp061bkfah5MLOi+tWrVKty4cQOdO3eWyoQQMDExQXFxMWxsbGqsU9dJsraTa20fQvWdoG9V2wfm3bjTB1+HDh1kJ32g9pFTDKl7fbZv3y473gCgVCpl87d/MagLkwqi9s3Ozg5Dhw7F0qVLMXny5Ls+N9zql19+Qf/+/REZGSmV/fHHHzXijh8/jmvXrknn3tTUVHTs2FH2Bb8xvLy84OXlhRkzZsDPzw8bNmyoNbFISUnB+PHjsWLFihrLH330UXz//fdwcXGBsfHdfW2rvjH85MmTspvEMzIycOPGDTzyyCPYtWsX1Go1nnzySXz99dfw9PRs8GdPr169cODAAVnZwYMH0aNHDxgZGaFnz564ceMGjhw5gscffxzAzR/1SkpKZO3TarUwNjaW3bB+ux49eqBHjx6YOnUqXn75ZaxZs0ZKLK5fv44//vijxg9V1Pw4KhTdl27cuIFvv/0WCxYsQEZGhjQdP34czs7O0qgVpqamqKyslNZ76KGHYGpqKjuRVlRU4MiRI+jZs2eT1c/NzQ3m5uZNPs55p06dUFhYKM2XlpYiNze3ybafmppaY97NzQ29evWCUqlEfn4+HnroIdnUpUuXBu+HSQXR/eHrr7/GjRs34OPjg40bNyI7Oxs5OTmIi4vDb7/9BiMjo3rXf+ihh3DkyBHs3LkTp06dwocffoi0tLQaceXl5Rg3bhxOnjwp/SI+adIkdOjQuK9Jubm5mDFjBlJSUnD27FkkJSXh1KlTtX5OaLVaPPfccxg7diyGDRsGrVYLrVaLixcvAgAmTpyIy5cv4+WXX5ZGPkpKSsLrr78u+3y6VadOnfDoo4/W+NL/0EMPoaSkRHoWiKmpKa5cuYKgoCA89NBDNX74uZPp06dj9+7d+OSTT3Dq1CmsW7cOS5cuRXR0NADA3d0dw4cPR0REBA4dOoT09HSMHz9elsAMGTIEfn5+GDVqFHbu3Im8vDwcPHgQ//znP3HkyBFcu3YNkyZNwt69e3H27Fn873//Q1pamuxYpqamQqlUws/Pr0H1p6bHHgu6L23btg3FxcUYN24cVCqVbNkLL7yAVatWYdKkSXBxccGhQ4eQl5eHjh07wtbWFm+99Rbeeecd2NraomvXrpg/fz7+/vtvjBs3rsnqZ2Zmhvfeew/vvvsuTE1NMWDAAFy8eBFZWVkG7efpp5/G2rVrERwcDBsbG3z44Yd3/GBuiIKCAkybNg0TJkzA0aNHsWTJEixYsABWVlaIjo7G1KlTUVVVhSeeeAKlpaU4ePAgOnbsiLCwsLvex5w5c7B06VJs27YNSqUSWq0WAGBjY1Oj94OI2rbu3bvj2LFjiI2NxYwZM3Du3DkolUr06tUL0dHRsp6I2rz55pvIyMjASy+9BIVCgZdffhmRkZE1LvcZPHgw3Nzc8NRTT0Gv12Ps2LF3HE62PhYWFvjtt9+wbt06XLp0CU5OTpg0aRImTJhQI/a3337DhQsXsG7dOqxbt04qd3Z2Rl5eHjQaDf73v//hvffew7Bhw6DX6+Hs7Izhw4fXm/i88cYbWLt2LSZNmiQr37t3L5566ikAwL59++Dn51ejl/puPfroo/jPf/6Djz76CJ988gmcnJwwe/ZshIeHSzFr1qzB+PHj4e/vD0dHR8yZMwcffvihtFyhUGDHjh2YOXMmXn/9dVy8eBFqtRpPPfUUHB0dYWRkhEuXLuG1117DhQsXYG9vj9GjR2PWrFnSNv7f//t/eOWVV2BhYdGodlATarnbO4haTlBQkHjmmWdqXZaeni4AiPT0dJGTkyN8fX2Fubm5NNzstWvXxOTJk4W9vX29w83eelOhELXfOI1bbkysbbjZOXPmCGdnZ2FiYiK6du0qYmNj62xTbft89tlnRVhYmDSv0+nEmDFjhLW1tejSpYtYu3ZtrTdvN7Tu1etFRkaKN998U1hbWwsbGxvx/vvvy4ab/de//iXc3d2FiYmJ6NSpkxg2bJh0E2Bd+75VVVWVsLa2rnWo2VtHHSEiulthYWHi2WefbelqNLlr166Jrl271hg0o70pKioStra24syZMy1dFRJCKIS47YJrIiIiovtEeHg4SkpKsGXLlpauSpPbt28fSktLZQ8SbG8OHz6M3NxcvPTSSy1dFQIvhSIiIiJql/z9/Vu6Cvfc448/Lt0YTi2PPRZERERERGQwjgpFREREREQGY2JBREREREQGY2JBREREREQGY2JBREREREQGY2JBREREREQGY2JBREREREQGY2JBREREREQGY2JBREREREQGY2JBREREREQGY2JBREREREQG+/8ApL7keP3RgKkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that while fluorine atoms (number 9) show up in the data, they are heavily underrepresented (the bar at $z=9$ is barely visible), which is not a nice property that is likely since we shrunk the dataset. The number of atoms seems to be roughly normally distributed, which is nice."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data split, distribution of regression target electronic spatial extent\n",
    "Next, we will implement data splitting, choose a regression target and visualize the split w.r.t. to this target. \n",
    "Out of the 19 regression targets included in QM9, we'll focus on *electronic spatial extent*, which, simply put, describes the volume of a molecule, so it should be a good fit for methods that use 3D information.\n",
    "Let us now start with implementing a *data module* that takes care of train/val/test splits and of indexing the correct target."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "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)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can easily plot the target across the data split."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "~/.miniconda3/envs/teachopencadd/lib/python3.9/site-packages/torch_geometric/data/in_memory_dataset.py:157: UserWarning: It is not recommended to directly access the internal storage format `data` of an 'InMemoryDataset'. If you are absolutely certain what you are doing, access the internal storage via `InMemoryDataset._data` instead to suppress this warning. Alternatively, you can access stacked individual attributes of every graph via `dataset.{attr_name}`.\n",
      "  warnings.warn(msg)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Random data split - target distribution')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You should be able to observe that random splits are typically very homogenous, which means measuring generalization capabilities with them can yield deceivingly good results."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model implementation"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plain \"naive Euclidean\" GNN\n",
    "A naive way to incorporate 3D coordinates into a GNN for molecular graphs would be to interpret them as atom-level features that are simply combined with the other features.\n",
    "It is easy to implement a simple baseline model which does exactly this (see **Talktorial T035**).\n",
    "For its message-passing topology, our implementation uses the edges induced by bonds between atoms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "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)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Demo: Plain GNNs are not $\\text{E(3)}$-invariant\n",
    "However, this approach is problematic because the corresponding atom embeddings of a regular GNN (from which we would also derive our final predictions) will not be $\\text{E}(3)$-invariant. This can be demonstrated easily:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "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",
    "    )"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*NOTE*: you may need to run the cell below multiple times to find a model initialization for which non-invariance can easily be observed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_51907/441942460.py:53: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
      "  cmap = mpl.cm.get_cmap(cmap)\n",
      "/tmp/ipykernel_51907/2912899843.py:32: UserWarning: Tight layout not applied. The left and right margins cannot be made large enough to accommodate all axes decorations.\n",
      "  fig.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When executing the above cells a few times, we can observe that rotating the molecule may *significantly* alter the atom embeddings obtained from the plain GNN model."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### EGNN model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now implement an $\\text{E}(n)$-invariant GNN based on the principles outlined in the theory section."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "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"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Demo: Our EGNN is $E(3)$-invariant\n",
    "We can collect evidence that this model is indeed $\\text{E}(n)$-invariant by repeating the experiment we conducted earlier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = EquivariantGNN(hidden_channels=32, final_embedding_size=1, num_mp_layers=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_51907/441942460.py:53: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed two minor releases later. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap(obj)`` instead.\n",
      "  cmap = mpl.cm.get_cmap(cmap)\n",
      "/tmp/ipykernel_51907/1099465595.py:21: UserWarning: Tight layout not applied. tight_layout cannot make axes width small enough to accommodate all axes decorations\n",
      "  fig.tight_layout()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can execute the above cells as often as you like, with whatever input you choose, the atom embeddings will always be unaffected by the rotation applied to the model input."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training and evaluation\n",
    "Now that we have set up our data and implemented two different models for point clouds, we can start implementing a training and evaluation pipeline.\n",
    "\n",
    "We will follow the ubiquitous ML principle of also monitoring a validation loss in addition to the training loss. The validation loss acts as an estimate for how well the model generalizes and can be used for selecting a final model to be tested."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "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)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Training the EGNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "~/.miniconda3/envs/teachopencadd/lib/python3.9/site-packages/torch_geometric/data/in_memory_dataset.py:157: UserWarning: It is not recommended to directly access the internal storage format `data` of an 'InMemoryDataset'. If you are absolutely certain what you are doing, access the internal storage via `InMemoryDataset._data` instead to suppress this warning. Alternatively, you can access stacked individual attributes of every graph via `dataset.{attr_name}`.\n",
      "  warnings.warn(msg)\n",
      "Epoch   1 / 25 | train_loss: 83816.98 | val_loss:  8249.58 | train_mae:   177.73 | val_mae:    62.58: 100%|█████████████████████████████████████████████████| 614/614 [00:28<00:00, 21.82it/s]\n",
      "Epoch   2 / 25 | train_loss:  5193.82 | val_loss:  2884.07 | train_mae:    51.31 | val_mae:    34.33: 100%|█████████████████████████████████████████████████| 614/614 [00:21<00:00, 28.63it/s]\n",
      "Epoch   3 / 25 | train_loss:  1847.79 | val_loss:   932.89 | train_mae:    29.56 | val_mae:    20.27: 100%|█████████████████████████████████████████████████| 614/614 [00:19<00:00, 31.40it/s]\n",
      "Epoch   4 / 25 | train_loss:   853.45 | val_loss:   786.19 | train_mae:    20.28 | val_mae:    21.96: 100%|█████████████████████████████████████████████████| 614/614 [00:18<00:00, 32.92it/s]\n",
      "Epoch   5 / 25 | train_loss:   619.10 | val_loss:   389.76 | train_mae:    17.42 | val_mae:    13.32: 100%|█████████████████████████████████████████████████| 614/614 [00:18<00:00, 32.35it/s]\n",
      "Epoch   6 / 25 | train_loss:   479.23 | val_loss:   343.42 | train_mae:    15.32 | val_mae:    12.68: 100%|█████████████████████████████████████████████████| 614/614 [00:19<00:00, 31.53it/s]\n",
      "Epoch   7 / 25 | train_loss:   383.88 | val_loss:   285.21 | train_mae:    13.70 | val_mae:    11.90: 100%|█████████████████████████████████████████████████| 614/614 [00:21<00:00, 29.10it/s]\n",
      "Epoch   8 / 25 | train_loss:   306.98 | val_loss:   201.01 | train_mae:    12.24 | val_mae:     9.67: 100%|█████████████████████████████████████████████████| 614/614 [00:19<00:00, 30.92it/s]\n",
      "Epoch   9 / 25 | train_loss:   259.13 | val_loss:   387.28 | train_mae:    11.30 | val_mae:    16.76: 100%|█████████████████████████████████████████████████| 614/614 [00:19<00:00, 30.84it/s]\n",
      "Epoch  10 / 25 | train_loss:   265.28 | val_loss:   231.35 | train_mae:    11.62 | val_mae:    11.55: 100%|█████████████████████████████████████████████████| 614/614 [00:18<00:00, 33.07it/s]\n",
      "Epoch  11 / 25 | train_loss:   187.75 | val_loss:   148.88 | train_mae:     9.50 | val_mae:     8.89: 100%|█████████████████████████████████████████████████| 614/614 [00:18<00:00, 32.69it/s]\n",
      "Epoch  12 / 25 | train_loss:   168.02 | val_loss:   303.56 | train_mae:     9.35 | val_mae:    15.35: 100%|█████████████████████████████████████████████████| 614/614 [00:18<00:00, 32.65it/s]\n",
      "Epoch  13 / 25 | train_loss:   153.95 | val_loss:    86.36 | train_mae:     9.01 | val_mae:     6.31: 100%|█████████████████████████████████████████████████| 614/614 [00:20<00:00, 30.39it/s]\n",
      "Epoch  14 / 25 | train_loss:   136.87 | val_loss:    73.61 | train_mae:     8.50 | val_mae:     5.95: 100%|█████████████████████████████████████████████████| 614/614 [00:23<00:00, 25.83it/s]\n",
      "Epoch  15 / 25 | train_loss:   111.18 | val_loss:   144.24 | train_mae:     7.67 | val_mae:    10.03: 100%|█████████████████████████████████████████████████| 614/614 [00:18<00:00, 32.98it/s]\n",
      "Epoch  16 / 25 | train_loss:   110.45 | val_loss:   127.40 | train_mae:     7.69 | val_mae:     9.73: 100%|█████████████████████████████████████████████████| 614/614 [00:20<00:00, 30.23it/s]\n",
      "Epoch  17 / 25 | train_loss:    88.18 | val_loss:    87.97 | train_mae:     6.83 | val_mae:     7.15: 100%|█████████████████████████████████████████████████| 614/614 [00:21<00:00, 28.76it/s]\n",
      "Epoch  18 / 25 | train_loss:    96.38 | val_loss:    69.41 | train_mae:     7.21 | val_mae:     6.59: 100%|█████████████████████████████████████████████████| 614/614 [00:22<00:00, 27.20it/s]\n",
      "Epoch  19 / 25 | train_loss:    66.19 | val_loss:   213.51 | train_mae:     5.91 | val_mae:    13.43: 100%|█████████████████████████████████████████████████| 614/614 [00:21<00:00, 28.50it/s]\n",
      "Epoch  20 / 25 | train_loss:    97.42 | val_loss:    30.31 | train_mae:     6.86 | val_mae:     3.56: 100%|█████████████████████████████████████████████████| 614/614 [00:21<00:00, 29.20it/s]\n",
      "Epoch  21 / 25 | train_loss:    66.18 | val_loss:    31.33 | train_mae:     5.98 | val_mae:     3.84: 100%|█████████████████████████████████████████████████| 614/614 [00:18<00:00, 32.92it/s]\n",
      "Epoch  22 / 25 | train_loss:    64.28 | val_loss:    42.74 | train_mae:     5.82 | val_mae:     4.67: 100%|█████████████████████████████████████████████████| 614/614 [00:21<00:00, 28.31it/s]\n",
      "Epoch  23 / 25 | train_loss:    65.13 | val_loss:   102.03 | train_mae:     5.96 | val_mae:     8.89: 100%|█████████████████████████████████████████████████| 614/614 [00:18<00:00, 32.82it/s]\n",
      "Epoch  24 / 25 | train_loss:    71.31 | val_loss:   142.06 | train_mae:     6.10 | val_mae:     8.94: 100%|█████████████████████████████████████████████████| 614/614 [00:18<00:00, 33.01it/s]\n",
      "Epoch  25 / 25 | train_loss:    63.18 | val_loss:    28.93 | train_mae:     5.71 | val_mae:     3.54: 100%|█████████████████████████████████████████████████| 614/614 [00:18<00:00, 32.71it/s]\n"
     ]
    }
   ],
   "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(\"trained_egnn.pth\"),\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Training the plain GNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Epoch   1 / 100 | train_loss: 131605.16 | val_loss: 51881.71 | train_mae:   257.84 | val_mae:   178.73: 100%|███████████████████████████████████████████████| 614/614 [00:10<00:00, 59.30it/s]\n",
      "Epoch   2 / 100 | train_loss: 46252.38 | val_loss: 33482.20 | train_mae:   159.96 | val_mae:   139.05: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 86.94it/s]\n",
      "Epoch   3 / 100 | train_loss: 31016.87 | val_loss: 25729.41 | train_mae:   131.66 | val_mae:   118.91: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 81.91it/s]\n",
      "Epoch   4 / 100 | train_loss: 19575.64 | val_loss: 16162.15 | train_mae:   104.25 | val_mae:    95.53: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 82.16it/s]\n",
      "Epoch   5 / 100 | train_loss: 13219.30 | val_loss: 18588.67 | train_mae:    85.73 | val_mae:   111.78: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 78.85it/s]\n",
      "Epoch   6 / 100 | train_loss: 10594.13 | val_loss:  9926.80 | train_mae:    76.39 | val_mae:    69.72: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 86.72it/s]\n",
      "Epoch   7 / 100 | train_loss:  8526.29 | val_loss:  9002.18 | train_mae:    67.99 | val_mae:    67.95: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 86.48it/s]\n",
      "Epoch   8 / 100 | train_loss:  7094.07 | val_loss:  6584.26 | train_mae:    62.25 | val_mae:    56.78: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 86.69it/s]\n",
      "Epoch   9 / 100 | train_loss:  6621.97 | val_loss:  6924.00 | train_mae:    59.52 | val_mae:    58.06: 100%|████████████████████████████████████████████████| 614/614 [00:06<00:00, 87.96it/s]\n",
      "Epoch  10 / 100 | train_loss:  6078.34 | val_loss:  6341.89 | train_mae:    56.62 | val_mae:    56.60: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 86.94it/s]\n",
      "Epoch  11 / 100 | train_loss:  5703.69 | val_loss:  6478.72 | train_mae:    55.66 | val_mae:    58.99: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 85.74it/s]\n",
      "Epoch  12 / 100 | train_loss:  5338.27 | val_loss:  5886.17 | train_mae:    53.40 | val_mae:    54.12: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 84.17it/s]\n",
      "Epoch  13 / 100 | train_loss:  5155.95 | val_loss:  5749.83 | train_mae:    52.08 | val_mae:    52.79: 100%|████████████████████████████████████████████████| 614/614 [00:06<00:00, 87.90it/s]\n",
      "Epoch  14 / 100 | train_loss:  4964.06 | val_loss:  5171.14 | train_mae:    51.32 | val_mae:    49.19: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 85.07it/s]\n",
      "Epoch  15 / 100 | train_loss:  4871.12 | val_loss:  5170.29 | train_mae:    50.80 | val_mae:    49.65: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 86.82it/s]\n",
      "Epoch  16 / 100 | train_loss:  4644.91 | val_loss:  4882.15 | train_mae:    49.54 | val_mae:    46.48: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 85.09it/s]\n",
      "Epoch  17 / 100 | train_loss:  4630.13 | val_loss:  6608.02 | train_mae:    49.50 | val_mae:    58.90: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 81.44it/s]\n",
      "Epoch  18 / 100 | train_loss:  4616.51 | val_loss:  5037.78 | train_mae:    49.50 | val_mae:    49.18: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 80.24it/s]\n",
      "Epoch  19 / 100 | train_loss:  4216.14 | val_loss:  5161.73 | train_mae:    46.83 | val_mae:    47.26: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 77.75it/s]\n",
      "Epoch  20 / 100 | train_loss:  4141.20 | val_loss:  4744.50 | train_mae:    46.76 | val_mae:    46.50: 100%|████████████████████████████████████████████████| 614/614 [00:08<00:00, 76.75it/s]\n",
      "Epoch  21 / 100 | train_loss:  4027.40 | val_loss:  4649.81 | train_mae:    46.30 | val_mae:    45.15: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 81.51it/s]\n",
      "Epoch  22 / 100 | train_loss:  3948.61 | val_loss:  4409.36 | train_mae:    45.76 | val_mae:    45.78: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 81.78it/s]\n",
      "Epoch  23 / 100 | train_loss:  3980.70 | val_loss:  4948.89 | train_mae:    45.73 | val_mae:    48.64: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 87.25it/s]\n",
      "Epoch  24 / 100 | train_loss:  3621.07 | val_loss:  4880.79 | train_mae:    43.77 | val_mae:    49.11: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 86.39it/s]\n",
      "Epoch  25 / 100 | train_loss:  3692.66 | val_loss:  6213.45 | train_mae:    44.01 | val_mae:    55.11: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 87.52it/s]\n",
      "Epoch  26 / 100 | train_loss:  3850.76 | val_loss:  4153.96 | train_mae:    45.27 | val_mae:    44.38: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 86.56it/s]\n",
      "Epoch  27 / 100 | train_loss:  3442.45 | val_loss:  4037.86 | train_mae:    42.52 | val_mae:    42.73: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 86.44it/s]\n",
      "Epoch  28 / 100 | train_loss:  3333.53 | val_loss:  4577.04 | train_mae:    41.92 | val_mae:    47.31: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 85.67it/s]\n",
      "Epoch  29 / 100 | train_loss:  3418.91 | val_loss:  3798.06 | train_mae:    42.22 | val_mae:    40.75: 100%|████████████████████████████████████████████████| 614/614 [00:06<00:00, 88.26it/s]\n",
      "Epoch  30 / 100 | train_loss:  3373.68 | val_loss:  4033.93 | train_mae:    42.03 | val_mae:    41.66: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 87.06it/s]\n",
      "Epoch  31 / 100 | train_loss:  3111.80 | val_loss:  3682.38 | train_mae:    40.34 | val_mae:    40.78: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 87.05it/s]\n",
      "Epoch  32 / 100 | train_loss:  3117.61 | val_loss:  3806.91 | train_mae:    40.66 | val_mae:    41.78: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 87.66it/s]\n",
      "Epoch  33 / 100 | train_loss:  3206.69 | val_loss:  3581.51 | train_mae:    40.87 | val_mae:    40.26: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 86.08it/s]\n",
      "Epoch  34 / 100 | train_loss:  3079.73 | val_loss:  4547.30 | train_mae:    40.19 | val_mae:    45.41: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 79.78it/s]\n",
      "Epoch  35 / 100 | train_loss:  3149.01 | val_loss:  4522.56 | train_mae:    40.80 | val_mae:    45.55: 100%|████████████████████████████████████████████████| 614/614 [00:06<00:00, 90.56it/s]\n",
      "Epoch  36 / 100 | train_loss:  2918.20 | val_loss:  3964.84 | train_mae:    39.13 | val_mae:    41.33: 100%|████████████████████████████████████████████████| 614/614 [00:06<00:00, 95.45it/s]\n",
      "Epoch  37 / 100 | train_loss:  2908.17 | val_loss:  4062.00 | train_mae:    39.27 | val_mae:    42.20: 100%|████████████████████████████████████████████████| 614/614 [00:06<00:00, 92.70it/s]\n",
      "Epoch  38 / 100 | train_loss:  2858.89 | val_loss:  3632.95 | train_mae:    39.15 | val_mae:    39.16: 100%|████████████████████████████████████████████████| 614/614 [00:06<00:00, 93.83it/s]\n",
      "Epoch  39 / 100 | train_loss:  2911.77 | val_loss:  4292.41 | train_mae:    39.19 | val_mae:    45.72: 100%|████████████████████████████████████████████████| 614/614 [00:06<00:00, 91.97it/s]\n",
      "Epoch  40 / 100 | train_loss:  2774.28 | val_loss:  4351.61 | train_mae:    38.52 | val_mae:    44.17: 100%|████████████████████████████████████████████████| 614/614 [00:08<00:00, 74.30it/s]\n",
      "Epoch  41 / 100 | train_loss:  2828.55 | val_loss:  3983.95 | train_mae:    38.97 | val_mae:    43.67: 100%|████████████████████████████████████████████████| 614/614 [00:08<00:00, 71.27it/s]\n",
      "Epoch  42 / 100 | train_loss:  2639.83 | val_loss:  3555.43 | train_mae:    37.39 | val_mae:    39.24: 100%|████████████████████████████████████████████████| 614/614 [00:06<00:00, 89.57it/s]\n",
      "Epoch  43 / 100 | train_loss:  2802.29 | val_loss:  3784.69 | train_mae:    38.28 | val_mae:    40.87: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 84.90it/s]\n",
      "Epoch  44 / 100 | train_loss:  2669.42 | val_loss:  4253.03 | train_mae:    37.75 | val_mae:    42.04: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 80.92it/s]\n",
      "Epoch  45 / 100 | train_loss:  2543.59 | val_loss:  3882.21 | train_mae:    36.53 | val_mae:    41.58: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 84.11it/s]\n",
      "Epoch  46 / 100 | train_loss:  2563.37 | val_loss:  3912.68 | train_mae:    36.96 | val_mae:    41.34: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 85.72it/s]\n",
      "Epoch  47 / 100 | train_loss:  2559.09 | val_loss:  5493.43 | train_mae:    37.02 | val_mae:    51.46: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 85.71it/s]\n",
      "Epoch  48 / 100 | train_loss:  2576.10 | val_loss:  4768.31 | train_mae:    36.85 | val_mae:    52.41: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 86.25it/s]\n",
      "Epoch  49 / 100 | train_loss:  2398.73 | val_loss:  3751.79 | train_mae:    35.63 | val_mae:    38.73: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 86.18it/s]\n",
      "Epoch  50 / 100 | train_loss:  2565.22 | val_loss:  3555.04 | train_mae:    37.00 | val_mae:    37.67: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 86.84it/s]\n",
      "Epoch  51 / 100 | train_loss:  2360.89 | val_loss:  3545.71 | train_mae:    35.47 | val_mae:    40.19: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 87.31it/s]\n",
      "Epoch  52 / 100 | train_loss:  2467.34 | val_loss:  3806.81 | train_mae:    36.17 | val_mae:    40.76: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 87.50it/s]\n",
      "Epoch  53 / 100 | train_loss:  2312.26 | val_loss:  3497.13 | train_mae:    35.16 | val_mae:    37.88: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 86.50it/s]\n",
      "Epoch  54 / 100 | train_loss:  2167.41 | val_loss:  3487.33 | train_mae:    34.01 | val_mae:    36.67: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 86.55it/s]\n",
      "Epoch  55 / 100 | train_loss:  2185.56 | val_loss:  3963.23 | train_mae:    34.25 | val_mae:    40.11: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 77.66it/s]\n",
      "Epoch  56 / 100 | train_loss:  2280.32 | val_loss:  3320.05 | train_mae:    34.80 | val_mae:    36.47: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 85.87it/s]\n",
      "Epoch  57 / 100 | train_loss:  2211.96 | val_loss:  3721.73 | train_mae:    34.26 | val_mae:    39.31: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 87.31it/s]\n",
      "Epoch  58 / 100 | train_loss:  2284.40 | val_loss:  3462.30 | train_mae:    34.87 | val_mae:    38.11: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 85.90it/s]\n",
      "Epoch  59 / 100 | train_loss:  2304.63 | val_loss:  3297.33 | train_mae:    35.03 | val_mae:    36.64: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 87.13it/s]\n",
      "Epoch  60 / 100 | train_loss:  2117.74 | val_loss:  3451.63 | train_mae:    33.53 | val_mae:    38.31: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 85.60it/s]\n",
      "Epoch  61 / 100 | train_loss:  2247.47 | val_loss:  3426.57 | train_mae:    34.57 | val_mae:    39.07: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 87.00it/s]\n",
      "Epoch  62 / 100 | train_loss:  2037.65 | val_loss:  3057.25 | train_mae:    32.89 | val_mae:    36.22: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 86.02it/s]\n",
      "Epoch  63 / 100 | train_loss:  2076.61 | val_loss:  3299.59 | train_mae:    33.09 | val_mae:    35.54: 100%|████████████████████████████████████████████████| 614/614 [00:06<00:00, 88.46it/s]\n",
      "Epoch  64 / 100 | train_loss:  2004.54 | val_loss:  3806.01 | train_mae:    32.72 | val_mae:    41.34: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 86.80it/s]\n",
      "Epoch  65 / 100 | train_loss:  1974.01 | val_loss:  3369.84 | train_mae:    32.42 | val_mae:    35.64: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 83.84it/s]\n",
      "Epoch  66 / 100 | train_loss:  1968.44 | val_loss:  3482.79 | train_mae:    32.30 | val_mae:    38.24: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 85.11it/s]\n",
      "Epoch  67 / 100 | train_loss:  1977.37 | val_loss:  3171.34 | train_mae:    32.50 | val_mae:    36.01: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 86.51it/s]\n",
      "Epoch  68 / 100 | train_loss:  1863.63 | val_loss:  3157.46 | train_mae:    31.64 | val_mae:    34.47: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 86.52it/s]\n",
      "Epoch  69 / 100 | train_loss:  1961.02 | val_loss:  3296.61 | train_mae:    32.31 | val_mae:    37.72: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 81.34it/s]\n",
      "Epoch  70 / 100 | train_loss:  1943.04 | val_loss:  3274.01 | train_mae:    32.25 | val_mae:    34.72: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 84.12it/s]\n",
      "Epoch  71 / 100 | train_loss:  1889.30 | val_loss:  3842.83 | train_mae:    31.79 | val_mae:    40.47: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 81.92it/s]\n",
      "Epoch  72 / 100 | train_loss:  1896.79 | val_loss:  4150.15 | train_mae:    31.78 | val_mae:    43.21: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 82.86it/s]\n",
      "Epoch  73 / 100 | train_loss:  1860.80 | val_loss:  3174.58 | train_mae:    31.54 | val_mae:    35.24: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 87.59it/s]\n",
      "Epoch  74 / 100 | train_loss:  1870.97 | val_loss:  3089.24 | train_mae:    31.62 | val_mae:    36.22: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 77.78it/s]\n",
      "Epoch  75 / 100 | train_loss:  1869.31 | val_loss:  3397.72 | train_mae:    31.83 | val_mae:    37.43: 100%|████████████████████████████████████████████████| 614/614 [00:08<00:00, 71.10it/s]\n",
      "Epoch  76 / 100 | train_loss:  1723.28 | val_loss:  3294.50 | train_mae:    30.57 | val_mae:    34.38: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 82.95it/s]\n",
      "Epoch  77 / 100 | train_loss:  2041.00 | val_loss:  3031.84 | train_mae:    32.94 | val_mae:    34.34: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 76.83it/s]\n",
      "Epoch  78 / 100 | train_loss:  1670.29 | val_loss:  3470.53 | train_mae:    29.81 | val_mae:    38.44: 100%|████████████████████████████████████████████████| 614/614 [00:08<00:00, 74.45it/s]\n",
      "Epoch  79 / 100 | train_loss:  1795.03 | val_loss:  3402.04 | train_mae:    31.12 | val_mae:    38.37: 100%|████████████████████████████████████████████████| 614/614 [00:09<00:00, 65.54it/s]\n",
      "Epoch  80 / 100 | train_loss:  1769.94 | val_loss:  3149.87 | train_mae:    30.62 | val_mae:    33.53: 100%|████████████████████████████████████████████████| 614/614 [00:08<00:00, 74.66it/s]\n",
      "Epoch  81 / 100 | train_loss:  1682.90 | val_loss:  3412.26 | train_mae:    30.09 | val_mae:    38.59: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 80.33it/s]\n",
      "Epoch  82 / 100 | train_loss:  1737.01 | val_loss:  3396.11 | train_mae:    30.41 | val_mae:    35.16: 100%|████████████████████████████████████████████████| 614/614 [00:08<00:00, 69.65it/s]\n",
      "Epoch  83 / 100 | train_loss:  1967.99 | val_loss:  2990.12 | train_mae:    32.48 | val_mae:    33.36: 100%|████████████████████████████████████████████████| 614/614 [00:08<00:00, 74.43it/s]\n",
      "Epoch  84 / 100 | train_loss:  1722.19 | val_loss:  3358.17 | train_mae:    30.33 | val_mae:    35.79: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 77.35it/s]\n",
      "Epoch  85 / 100 | train_loss:  1633.18 | val_loss:  3178.61 | train_mae:    29.64 | val_mae:    34.94: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 83.83it/s]\n",
      "Epoch  86 / 100 | train_loss:  1691.06 | val_loss:  3589.25 | train_mae:    30.34 | val_mae:    39.16: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 80.62it/s]\n",
      "Epoch  87 / 100 | train_loss:  1573.87 | val_loss:  3321.35 | train_mae:    29.09 | val_mae:    34.73: 100%|████████████████████████████████████████████████| 614/614 [00:08<00:00, 70.68it/s]\n",
      "Epoch  88 / 100 | train_loss:  1624.47 | val_loss:  3328.78 | train_mae:    29.65 | val_mae:    36.73: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 80.53it/s]\n",
      "Epoch  89 / 100 | train_loss:  1566.87 | val_loss:  2961.29 | train_mae:    28.97 | val_mae:    34.25: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 83.01it/s]\n",
      "Epoch  90 / 100 | train_loss:  1622.22 | val_loss:  3082.79 | train_mae:    29.63 | val_mae:    35.45: 100%|████████████████████████████████████████████████| 614/614 [00:08<00:00, 68.98it/s]\n",
      "Epoch  91 / 100 | train_loss:  1658.47 | val_loss:  2969.16 | train_mae:    30.08 | val_mae:    33.32: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 79.57it/s]\n",
      "Epoch  92 / 100 | train_loss:  1495.98 | val_loss:  3389.66 | train_mae:    28.39 | val_mae:    34.39: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 77.54it/s]\n",
      "Epoch  93 / 100 | train_loss:  1512.13 | val_loss:  3102.99 | train_mae:    28.54 | val_mae:    34.93: 100%|████████████████████████████████████████████████| 614/614 [00:08<00:00, 73.15it/s]\n",
      "Epoch  94 / 100 | train_loss:  1489.28 | val_loss:  3190.44 | train_mae:    28.52 | val_mae:    35.37: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 85.53it/s]\n",
      "Epoch  95 / 100 | train_loss:  1651.04 | val_loss:  2940.62 | train_mae:    29.94 | val_mae:    32.10: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 86.13it/s]\n",
      "Epoch  96 / 100 | train_loss:  1529.36 | val_loss:  3137.99 | train_mae:    28.90 | val_mae:    34.13: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 86.59it/s]\n",
      "Epoch  97 / 100 | train_loss:  1531.24 | val_loss:  3489.23 | train_mae:    28.92 | val_mae:    38.00: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 87.50it/s]\n",
      "Epoch  98 / 100 | train_loss:  1580.89 | val_loss:  2815.52 | train_mae:    29.14 | val_mae:    32.56: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 78.90it/s]\n",
      "Epoch  99 / 100 | train_loss:  1518.98 | val_loss:  3225.39 | train_mae:    28.69 | val_mae:    32.25: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 85.77it/s]\n",
      "Epoch 100 / 100 | train_loss:  1555.37 | val_loss:  3180.20 | train_mae:    29.11 | val_mae:    35.67: 100%|████████████████████████████████████████████████| 614/614 [00:07<00:00, 86.09it/s]\n"
     ]
    }
   ],
   "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(\"trained_gnn.pth\"),\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comparative evaluation\n",
    "\n",
    "Let us now compare the trained EGNN and GNN baseline model. First note that in terms of capacity (measured by the number of trainable parameters) the models are very similar. But be aware that the comparison is still not completely fair, because\n",
    "\n",
    "* the EGNN is a *message-passing* neural network while the baseline GNN is a type of *graph convolutional* neural network\n",
    "* the EGNN is run on complete graphs, whereas the baseline GNN uses the bond adjacency info, which could also be a disadvantage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GCN has 52417 parameters\n",
      "EGNN has 51969 parameters\n"
     ]
    }
   ],
   "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\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plotting the loss and validation MAE for each epoch, we can observe that the EGNN training progresses *much* faster and yields *much* better results,\n",
    "even though it is trained for a smaller number of epochs (note that loss and MAE are in log-scale).\n",
    "\n",
    "Surprisingly the validation loss/MAE for the EGNN is sometimes *lower* than the train loss/MAE. This might be explained by the fact that the data split is *very* homogenous, and the validation data contains fewer outliers than the train data (see box plots from the section on distribution of regression target across splits)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This performance improvement can also be observed in the held-out test data. For testing, we select the best model as the model that had the lowest validation MAE each."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EGNN test MAE: 3.4511184202421696\n",
      "GNN test MAE: 31.881833081726633\n"
     ]
    }
   ],
   "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}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f663bf0f700>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "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()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These findings support our initial hypothesis that $\\text{E}(3)$-invariant models lead to faster learning and improved generalization performance."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Discussion\n",
    "\n",
    "### Summary\n",
    "You have now seen, theoretically and practically, why we need $(S)E(3)$ 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 $(S)E(3)$ 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.\n",
    "\n",
    "### Caveats of our approach\n",
    "At this point, we should also go over some final caveats with the EGNN presented here and our approach in general:\n",
    "\n",
    "1. Our model assumes that every atom interacts with every other atom, i.e. the neighborhood of node $i$, $N(i) = \\{j \\neq i\\}$ is complete. This approach has quadratic complexity meaning its more computationally expensive (go back to the model training and compare how long one epoch takes compared to the plain GNN) and thus might not be scalable to larger molecules. In this case we could restrict interactions by instead using \n",
    "   * $k$-nearest neighborhoods, i.e. $|N(i)| = k$ contains the $k$ nodes with the smallest euclidean distance to $i$,\n",
    "   * or spherical neighborhoods with a fixed radius $\\delta$ instead, i.e. $N(i) = \\{j \\mid ||X_i - X_j||^2 \\leq \\delta\\}$\n",
    "3. Our EGNN model is $E(3)$-invariant. Note that some molecular properties are *sensitive* to reflection, In such settings, $SE(3)$-invariance should be the preferred model property (see **Talktorial T033**).\n",
    "4. Random data splits are considered bad practice for measuring the capability of a molecular machine learning model to generalize to unseen data (see [this paper](https://jcheminf.biomedcentral.com/articles/10.1186/s13321-019-0391-2) which analyzes and discusses this issue in-depth for QM9)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quiz\n",
    "\n",
    "1. In addition to 3D coordinates, what is strictly required for inference of covalent bonds between atoms?\n",
    "2. What is the difference between equivariance and invariance?\n",
    "3. True or false? $SE(3)$ contains transformations which are not included in $E(3)$.\n",
    "4. True or false? The atom embeddings $h$ computed by iterating the following message passing scheme for a fixed number of steps are $E(3)$-invariant\n",
    "$$m_{ij}^{l} = \\phi_{l}(h_i^l, h_j^l, X_i - X_j)$$\n",
    "$$h_{i}^{l+1} = \\psi_l(h_{i}^l, \\sum_{j \\neq i} m_{ij}^l)$$"
   ]
  }
 ],
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