gns_colab_jx_v2.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y0EtE29uWUv2",
        "outputId": "b525ede6-ac47-4774-b548-3a8205e6a33f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "PyTorch has version 2.3.1+cpu with cuda None\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import torch\n",
        "import math\n",
        "print(f\"PyTorch has version {torch.__version__} with cuda {torch.version.cuda}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "EGdduR5yDsAO",
        "outputId": "9d797cbf-882d-46af-c5b9-609d755de7f8"
      },
      "outputs": [],
      "source": [
        "#!pip install torch_geometric\n",
        "\n",
        "# Optional dependencies:\n",
        "#!pip install pyg_lib torch_scatter torch_sparse torch_cluster torch_spline_conv -f https://data.pyg.org/whl/torch-2.3.0+cu121.html"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-sYTZtP6DoL7",
        "outputId": "f6b787a0-b3eb-439d-c367-cfb39c6594cd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "PyTorch has version 2.3.1+cpu with cuda None\n"
          ]
        }
      ],
      "source": [
        "from torch_geometric.data import Data\n",
        "from torch_geometric.nn import MessagePassing, radius_graph\n",
        "from torch_geometric.utils import add_self_loops\n",
        "import torch_cluster\n",
        "import torch_scatter\n",
        "print(f\"PyTorch has version {torch.__version__} with cuda {torch.version.cuda}\")\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QrY6KUfsXIIv",
        "outputId": "8dc6eccd-4970-4f64-fdb6-b287f61fb730"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1.26.4\n"
          ]
        }
      ],
      "source": [
        "print(np.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "I8VjFiaeXU0P"
      },
      "outputs": [],
      "source": [
        "# start coding with numpy==1.23.1\n",
        "# !pip uninstall numpy -y\n",
        "# !pip install numpy==1.23.1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "OTaYxnvVIEid"
      },
      "outputs": [],
      "source": [
        "import torch.nn as nn\n",
        "from torch.utils.data import Dataset, DataLoader\n",
        "from tqdm import tqdm\n",
        "from torch_geometric.data import Data\n",
        "from torch_geometric.nn import radius_graph\n",
        "from torch_geometric.utils import add_self_loops\n",
        "from torch_geometric.utils import to_networkx\n",
        "import networkx as nx"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LSLBKX1KBYxW"
      },
      "source": [
        "First, create a function to generate a graph from raw data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 659
        },
        "id": "o3j7BB9KBPX3",
        "outputId": "fca22234-9115-400c-d4b3-e7d093a6a7cc"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "tensor([[ 1.0000, -1.0000,  0.0000,  7.6481],\n",
            "        [ 1.0000,  1.0000,  0.0000,  1.8623],\n",
            "        [ 1.0000, -1.0000,  0.0000,  0.9722],\n",
            "        [ 1.0000,  1.0000,  0.0000,  2.7172],\n",
            "        [ 1.0000, -1.0000,  0.0000,  3.8846],\n",
            "        [ 1.0000,  1.0000,  0.0000,  2.4550],\n",
            "        [ 1.0000, -1.0000,  0.0000,  5.6662],\n",
            "        [ 1.0000,  1.0000,  0.0000,  8.9766]])\n"
          ]
        }
      ],
      "source": [
        "\n",
        "\n",
        "def create_gnn_graph(coordinates, metadata):\n",
        "    \"\"\"\n",
        "    Create a graph data object for a GNN model.\n",
        "\n",
        "    Args:\n",
        "    - coordinates (list of tuples): A list of 2D coordinates for each node.\n",
        "    - E_initial (float): The initial value for the edge attribute.\n",
        "\n",
        "    Returns:\n",
        "    - data (Data): A PyG Data object representing the graph.\n",
        "    \"\"\"\n",
        "\n",
        "    # Convert coordinates to tensor\n",
        "    node_features = torch.tensor(coordinates, dtype=torch.float)\n",
        "\n",
        "    # Add the load as a node attribute\n",
        "    load = torch.rand(node_features.size(0), 1) * metadata[\"P_initial\"]\n",
        "    dist_boundary = torch.norm(node_features - torch.tensor([[0, 0]], dtype=torch.float), dim=1).unsqueeze(1)\n",
        "\n",
        "    # Create edges\n",
        "    num_nodes = len(coordinates)\n",
        "    edge_index = radius_graph(node_features, r=metadata[\"default_connectivity_radius\"], loop=False)\n",
        "\n",
        "    # Calculate edge attributes (e.g., Euclidean distance)\n",
        "    distance = torch.norm(node_features[edge_index[0]] - node_features[edge_index[1]], dim=1).unsqueeze(1)\n",
        "    # Calculate displacement as vector at each edge\n",
        "    displacement = (node_features[edge_index[1]] - node_features[edge_index[0]])\n",
        "\n",
        "    # Convert E_initial to a PyTorch tensor\n",
        "    E_initial = torch.tensor(metadata[\"E_initial\"], dtype=torch.float)\n",
        "    # Correctly generate a tensor with random values uniformly distributed between 0 and 1, then scale by E_initial\n",
        "    E_edge = torch.rand(edge_index.size(1), 1) * E_initial\n",
        "\n",
        "    # Now, both tensors have shape [number_of_edges, 1] and can be concatenated\n",
        "    edge_attr = torch.cat([distance, displacement, E_edge], dim=1)\n",
        "\n",
        "    # Create a graph data object\n",
        "    graph_data = Data(pos=node_features, edge_index=edge_index, edge_attr=edge_attr, node_attr=load)\n",
        "\n",
        "    return graph_data\n",
        "\n",
        "# Example usage\n",
        "metadata = {\"default_connectivity_radius\": 1.6, \"E_initial\": 10, \"P_initial\": 0}\n",
        "coordinates = [(0.5, 0), (1.5, 0), (2.5, 0), (3.5, 0), (4.5, 0)]\n",
        "graph_data = create_gnn_graph(coordinates, metadata)\n",
        "\n",
        "graph = to_networkx(graph_data)\n",
        "nx.draw(graph, with_labels=True)\n",
        "plt.show()\n",
        "\n",
        "print(graph_data.edge_attr)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lw_n4CbFDC3C",
        "outputId": "ebb39e5b-6e27-4fd7-b970-bdbe29686d1c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.3.1+cpu\n",
            "None\n"
          ]
        }
      ],
      "source": [
        "# Check version of cuda\n",
        "import torch\n",
        "print(torch.__version__)\n",
        "print(torch.version.cuda)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1VM7XVk9Fohd"
      },
      "source": [
        "MLP class function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "gFMVulxFC9wp"
      },
      "outputs": [],
      "source": [
        "# class MLP(nn.Module):\n",
        "#     \"\"\"\n",
        "#     Create a simple MLP\n",
        "#     \"\"\"\n",
        "#     def __init__(\n",
        "#         self,\n",
        "#         input_size,\n",
        "#         layer_sizes,\n",
        "#         output_size=None,\n",
        "#         output_activation=torch.nn.Identity,\n",
        "#         activation=torch.nn.ReLU,\n",
        "#         layernorm=True\n",
        "#     ):\n",
        "#         super(MLP, self).__init__()\n",
        "#         sizes = [input_size] + layer_sizes\n",
        "#         if output_size is not None:\n",
        "#             sizes.append(output_size)\n",
        "#         layers = []\n",
        "#         for i in range(len(sizes) - 1):\n",
        "#             layers.append(torch.nn.Linear(sizes[i], sizes[i + 1]))\n",
        "#             if layernorm and i < len(sizes) - 2:\n",
        "#                 layers.append(torch.nn.LayerNorm(sizes[i + 1]))\n",
        "#             if i < len(sizes) - 2:\n",
        "#                 layers.append(activation())\n",
        "#             else:\n",
        "#                 layers.append(output_activation())\n",
        "#         self.model = torch.nn.Sequential(*layers)\n",
        "\n",
        "#     def forward(self, x):\n",
        "#         return self.model(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "FIFg__aOhuyX"
      },
      "outputs": [],
      "source": [
        "class MLP(nn.Module):\n",
        "    \"\"\"\n",
        "    Create a simple MLP\n",
        "    \"\"\"\n",
        "    def __init__(\n",
        "        self,\n",
        "        input_size,\n",
        "        layer_sizes,\n",
        "        output_size=None,\n",
        "        output_activation=torch.nn.Identity,\n",
        "        activation=torch.nn.ReLU,\n",
        "        layernorm=True\n",
        "    ):\n",
        "        super(MLP, self).__init__()\n",
        "        sizes = [input_size] + layer_sizes\n",
        "        if output_size is not None:\n",
        "            sizes.append(output_size)\n",
        "        layers = []\n",
        "        for i in range(len(sizes) - 1):\n",
        "            layers.append(torch.nn.Linear(sizes[i], sizes[i + 1]))\n",
        "            if layernorm and i < len(sizes) - 2:\n",
        "                layers.append(torch.nn.LayerNorm(sizes[i + 1]))\n",
        "            if i < len(sizes) - 2:\n",
        "                layers.append(activation())\n",
        "            else:\n",
        "                layers.append(output_activation())\n",
        "        self.model = torch.nn.Sequential(*layers)\n",
        "\n",
        "    def forward(self, x):\n",
        "        return self.model(x)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vv1aF3njFqhb"
      },
      "source": [
        "## Encoder"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "iM-Fa5kC_Vtq"
      },
      "outputs": [],
      "source": [
        "class Encoder(nn.Module):\n",
        "    def __init__(\n",
        "        self,\n",
        "        node_in, # same shape as the data vertices (nodes) WARNING\n",
        "        node_out, # shape for the processor block\n",
        "        edge_in, # same shape as the data edges (elements)\n",
        "        edge_out, # shape for the processor block\n",
        "        mlp_num_layers,\n",
        "        mlp_hidden_dim,\n",
        "    ):\n",
        "        super(Encoder, self).__init__()\n",
        "\n",
        "        ############################################################################\n",
        "        # TODO: Implement the encoder.\n",
        "        # Hint: The node_fn and edge_fn are of the same structure, which is a MLP layer followed by a layer norm\n",
        "        ############################################################################\n",
        "        self.node_fn = nn.Sequential(*[MLP(node_in, [mlp_hidden_dim for _ in range(mlp_num_layers)], node_out)])\n",
        "        self.edge_fn = nn.Sequential(*[MLP(edge_in, [mlp_hidden_dim for _ in range(mlp_num_layers)], edge_out)])\n",
        "        ############################################################################\n",
        "\n",
        "    def forward(self, x, e_features): # global_features\n",
        "        '''\n",
        "        x: (E, node_in)\n",
        "        edge_index: (2, E)\n",
        "        e_features: (E, edge_in)\n",
        "        '''\n",
        "        return self.node_fn(x), self.edge_fn(e_features)\n",
        "\n",
        "class Decoder(nn.Module):\n",
        "    def __init__(\n",
        "        self,\n",
        "        node_in, # directly from the processor\n",
        "        node_out, # this shape needs to be (num_nodes of real problem x 2). The first column should be u, the second should be p.\n",
        "        edge_in, # directly from the processor\n",
        "        edge_out, # this shape needs to be (num_elements of real problem x 1). The first column should be ei.\n",
        "        mlp_num_layers,\n",
        "        mlp_hidden_dim,\n",
        "    ):\n",
        "        super(Decoder, self).__init__()\n",
        "        ############################################################################\n",
        "        # Implement the decoder.\n",
        "        # The decoder outputs both node and edge information.\n",
        "        ############################################################################\n",
        "        self.node_fn = MLP(node_in, [mlp_hidden_dim for _ in range(mlp_num_layers)], node_out)\n",
        "        self.edge_fn = MLP(edge_in, [mlp_hidden_dim for _ in range(mlp_num_layers)], edge_out)\n",
        "        ############################################################################\n",
        "\n",
        "    def forward(self, x, e_features):\n",
        "        '''\n",
        "        x: (E, node_in)\n",
        "        e_features: (E, edge_in)\n",
        "        '''\n",
        "        ############################################################################\n",
        "        # Implement the forward pass.\n",
        "        ############################################################################\n",
        "        return self.node_fn(x), self.edge_fn(e_features)\n",
        "        ############################################################################\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q4_Qn_eRZhX9"
      },
      "source": [
        "Sanity check the encoder decoder implementation with the node AND edge outputs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4qy55GmEF-Hh"
      },
      "source": [
        "## Message passing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "1s865OW1F9qX"
      },
      "outputs": [],
      "source": [
        "class InteractionNetwork(MessagePassing):\n",
        "    \"\"\"Interaction Network as proposed in this paper:\n",
        "    https://proceedings.neurips.cc/paper/2016/hash/3147da8ab4a0437c15ef51a5cc7f2dc4-Abstract.html\"\"\"\n",
        "    def __init__(self,node_fn,edge_fn):\n",
        "        super().__init__()\n",
        "        self.lin_edge = node_fn\n",
        "        self.lin_node = edge_fn\n",
        "\n",
        "    def forward(self, x, edge_index, edge_feature):\n",
        "        edge_out, aggr = self.propagate(edge_index, x=(x, x), edge_feature=edge_feature)\n",
        "        print(edge_out.shape, aggr.shape)\n",
        "        node_out = self.lin_node(torch.cat((x, aggr), dim=-1))\n",
        "        edge_out = edge_feature + edge_out\n",
        "        node_out = x + node_out\n",
        "        return node_out, edge_out\n",
        "\n",
        "    def message(self, x_i, x_j, edge_feature):\n",
        "        x = torch.cat((x_i, x_j, edge_feature), dim=-1)\n",
        "        x = self.lin_edge(x)\n",
        "        return x\n",
        "\n",
        "    def aggregate(self, inputs, index, dim_size=None):\n",
        "        out = torch_scatter.scatter(inputs, index, dim=self.node_dim, dim_size=dim_size, reduce=\"sum\")\n",
        "        return (inputs, out)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ssnOxwogZx73"
      },
      "source": [
        "The processer has $M$ layers of InteractionNetworks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "8lQlVqBlT8G2"
      },
      "outputs": [],
      "source": [
        "class Processor(MessagePassing):\n",
        "    def __init__(\n",
        "        self,\n",
        "        node_in,\n",
        "        node_out,\n",
        "        edge_in,\n",
        "        edge_out,\n",
        "        num_message_passing_steps,\n",
        "        mlp_num_layers,\n",
        "        mlp_hidden_dim,\n",
        "    ):\n",
        "        super(Processor, self).__init__(aggr='max')\n",
        "        self.gnn_stacks = nn.ModuleList([\n",
        "            InteractionNetwork(\n",
        "                node_fn = nn.Sequential(*[MLP(node_in+edge_out, [mlp_hidden_dim for _ in range(mlp_num_layers)], node_out)]),\n",
        "                edge_fn = nn.Sequential(*[MLP(node_in+node_in+edge_in, [mlp_hidden_dim for _ in range(mlp_num_layers)], edge_out)])\n",
        "            ) for _ in range(num_message_passing_steps)])\n",
        "\n",
        "    def forward(self, x, edge_index, e_features):\n",
        "        for gnn in self.gnn_stacks:\n",
        "            x, e_features = gnn(x, edge_index, e_features)\n",
        "        return x, e_features"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {},
      "outputs": [],
      "source": [
        "num_nodes=100\n",
        "num_edges=99\n",
        "node_dimension=1\n",
        "edge_dimension=2\n",
        "node_in=1\n",
        "edge_in=2\n",
        "latent_dim=3\n",
        "num_message_passing_steps=2\n",
        "\n",
        "mlp_num_layers=2\n",
        "mlp_hidden_dim=3\n",
        "connectivity_radius=0.1011"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "tensor([[0.5000, 0.0000],\n",
              "        [1.5000, 0.0000],\n",
              "        [2.5000, 0.0000],\n",
              "        [3.5000, 0.0000],\n",
              "        [4.5000, 0.0000]])"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "graph_data.pos"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {},
      "outputs": [
        {
          "ename": "SyntaxError",
          "evalue": "incomplete input (3155447518.py, line 1)",
          "output_type": "error",
          "traceback": [
            "\u001b[1;36m  Cell \u001b[1;32mIn[16], line 1\u001b[1;36m\u001b[0m\n\u001b[1;33m    model = InteractionNetwork(\u001b[0m\n\u001b[1;37m                               ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m incomplete input\n"
          ]
        }
      ],
      "source": [
        "model = InteractionNetwork("
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6dG5XLWUZ4Wr"
      },
      "source": [
        "Combining the encoder, processor, and decoder into a single network."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nbmwx-L_Z2-Z"
      },
      "outputs": [],
      "source": [
        "class EncodeProcessDecode(nn.Module):\n",
        "    def __init__(\n",
        "        self,\n",
        "        node_in,\n",
        "        node_out,\n",
        "        edge_in,\n",
        "        edge_out,\n",
        "        latent_dim,\n",
        "        num_message_passing_steps,\n",
        "        mlp_num_layers,\n",
        "        mlp_hidden_dim,\n",
        "    ):\n",
        "        super(EncodeProcessDecode, self).__init__()\n",
        "        ############################################################################\n",
        "        # Implementation of the encoder, processor, and decoder\n",
        "        ############################################################################\n",
        "        self._encoder = Encoder(\n",
        "            node_in=node_in,\n",
        "            node_out=latent_dim,\n",
        "            edge_in=edge_in,\n",
        "            edge_out=latent_dim,\n",
        "            mlp_num_layers=mlp_num_layers,\n",
        "            mlp_hidden_dim=mlp_hidden_dim,\n",
        "        )\n",
        "\n",
        "        self._processor = Processor(\n",
        "            node_in=latent_dim,\n",
        "            node_out=latent_dim,\n",
        "            edge_in=latent_dim,\n",
        "            edge_out=latent_dim,\n",
        "            num_message_passing_steps=num_message_passing_steps,\n",
        "            mlp_num_layers=mlp_num_layers,\n",
        "            mlp_hidden_dim=mlp_hidden_dim,\n",
        "        )\n",
        "\n",
        "        self._decoder = Decoder(\n",
        "            node_in=latent_dim,\n",
        "            node_out=node_out,\n",
        "            edge_in=latent_dim,\n",
        "            edge_out=edge_out,\n",
        "            mlp_num_layers=mlp_num_layers,\n",
        "            mlp_hidden_dim=mlp_hidden_dim,\n",
        "        )\n",
        "        ############################################################################\n",
        "\n",
        "    def forward(self, x, edge_index, e_features):\n",
        "        ''' forward pass of the architecture\n",
        "          x: (E, node_in) -> e.g., (5, 3) if there are 5 nodes each with 3 features\n",
        "          edge_index: (2, num_edges) -> e.g., (2, 4) if there are 4 edges\n",
        "          e_features: (E, edge_in) -> e.g., (5, 4) if there are 5 edges each with 4 features\n",
        "        '''\n",
        "        ############################################################################\n",
        "        # TODO: implementation of making a forward pass through the encoder,\n",
        "        # processor, and decoder blocks\n",
        "        ############################################################################\n",
        "        x, e_features = self._encoder(x, e_features)\n",
        "        x, e_features = self._processor(x, edge_index, e_features)\n",
        "        node_decoded, edge_decoded = self._decoder(x, e_features)\n",
        "        return node_decoded, edge_decoded\n",
        "        ############################################################################\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qXSUG_peZ_eB"
      },
      "source": [
        "The simulator imports the training trajectories. For the static case, it is just a single 1-D array of values that are position, $EI$, $u$, $p$. We put these into the graph."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Hh-PA6PxaL5a"
      },
      "outputs": [],
      "source": [
        "class Simulator(nn.Module):\n",
        "    def __init__(\n",
        "        self,\n",
        "        num_nodes,\n",
        "        num_edges,\n",
        "        node_dimension,\n",
        "        edge_dimension,\n",
        "        node_in,\n",
        "        edge_in,\n",
        "        latent_dim, # this is what we choose for the size of the network\n",
        "        num_message_passing_steps, # this is M\n",
        "        mlp_num_layers,\n",
        "        mlp_hidden_dim,\n",
        "        connectivity_radius,\n",
        "        # device='cpu',\n",
        "    ):\n",
        "        super(Simulator, self).__init__()\n",
        "        self._connectivity_radius = connectivity_radius\n",
        "\n",
        "        self._encode_process_decode = EncodeProcessDecode(\n",
        "            node_in=node_in,\n",
        "            node_out=node_dimension,\n",
        "            edge_in=edge_in,\n",
        "            edge_out=edge_dimension,\n",
        "            latent_dim=latent_dim,\n",
        "            num_message_passing_steps=num_message_passing_steps,\n",
        "            mlp_num_layers=mlp_num_layers,\n",
        "            mlp_hidden_dim=mlp_hidden_dim,\n",
        "        )\n",
        "\n",
        "        # self._device = device\n",
        "\n",
        "    def forward(self):\n",
        "        pass\n",
        "\n",
        "    def _create_gnn_graph(self, coordinates):\n",
        "        \"\"\"\n",
        "        Create a graph data object for a GNN model.\n",
        "\n",
        "        Args:\n",
        "        - coordinates (list of tuples): A list of 2D coordinates for each node.\n",
        "\n",
        "        Returns:\n",
        "        - data (Data): A PyG Data object representing the graph, with edge and node features purley from the coordinates\n",
        "        \"\"\"\n",
        "\n",
        "        # Convert coordinates to tensor\n",
        "        pos = torch.tensor(coordinates, dtype=torch.float)\n",
        "\n",
        "        # Concat node features and load\n",
        "        node_features = torch.cat([pos], dim=1)\n",
        "        # Create edges\n",
        "        num_nodes = len(coordinates)\n",
        "        edge_index = radius_graph(pos, r=self._connectivity_radius, loop=False)\n",
        "        # Calculate edge attributes (e.g., Euclidean distance)\n",
        "        distance = torch.norm(pos[edge_index[0]] - pos[edge_index[1]], dim=1).unsqueeze(1)\n",
        "        # Calculate displacement as vector at each edge\n",
        "        displacement = (pos[edge_index[1]] - pos[edge_index[0]])\n",
        "\n",
        "        edge_attr = torch.cat([distance, displacement], dim=1)\n",
        "        # Create a graph data object\n",
        "        data = Data(x=node_features, edge_index=edge_index, edge_attr=edge_attr)\n",
        "\n",
        "        return node_features,edge_index, edge_attr\n",
        "\n",
        "    def _decoder_post_processor(self, node_decoded, edge_decoded):\n",
        "      ''' TODO: We want to use the decoded edges and features to feed into the next iteration of gradient step\n",
        "      '''\n",
        "      #         edge_decoded = edge_attr = torch.cat([distance, displacement, E_edge], dim=1)\n",
        "      #         node_decoded = node_features = torch.cat([pos, load], dim=1)\n",
        "      # extract the nodal information\n",
        "      u = node_decoded[:,0] # deflection at each node, (num_nodes x 1), one deflection value in meters for each node\n",
        "      # p = node_decoded[:,1] # force applied at each node, (num_nodes x 1), one force magnitude value in meters for each node\n",
        "\n",
        "      # ei = edge_decoded[:,0] # flexural rigidity for each edge, (num_edges x 1), one value for flexural rigidity\n",
        "\n",
        "      return u#,p,ei\n",
        "\n",
        "    def predict_nodes_edges(self, coordinates):\n",
        "      ''' TODO: Change this to predict the new features of u, EI, and p\n",
        "\n",
        "      '''\n",
        "      node_features, edge_index, e_features = self._create_gnn_graph(coordinates)\n",
        "      node_decoded, edge_decoded = self._encode_process_decode(node_features, edge_index, e_features)\n",
        "      u = self._decoder_post_processor(node_decoded, edge_decoded)\n",
        "\n",
        "      # u,p,ei = self._decoder_post_processor(node_decoded, edge_decoded)\n",
        "      return u#,p,ei  # to be used in loss calculation\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qu4-Iynmtu0d"
      },
      "source": [
        "Coding the training function"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9UBjHWyMupqm"
      },
      "outputs": [],
      "source": [
        "from tqdm import tqdm\n",
        "\n",
        "def train(lr, epoch, simulator, x_data, y_data):\n",
        "  ''' TODO: Implement the training loop.\n",
        "    Args:\n",
        "      simulator: the simulator model\n",
        "      x_data: our features\n",
        "      y_data: our labels\n",
        "  '''\n",
        "  loss_fn = torch.nn.MSELoss() # pure data loss for now\n",
        "  optimizer = torch.optim.Adam(simulator.parameters(), lr=lr)\n",
        "  scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.1 ** (1 / 5e6))\n",
        "\n",
        "  # recording loss curve\n",
        "  train_loss_list = []\n",
        "  pred_history_list = []\n",
        "  total_step = 0\n",
        "\n",
        "  # first use initial guess of data\n",
        "  data = x_data\n",
        "\n",
        "  for i in range(epoch):\n",
        "      simulator.train()\n",
        "      total_loss = 0\n",
        "      batch_count = 0\n",
        "\n",
        "      optimizer.zero_grad()\n",
        "      pred = simulator.predict_nodes_edges(data)\n",
        "      loss_data = loss_fn(pred, y_data)\n",
        "      loss_data.backward()\n",
        "      optimizer.step()\n",
        "      scheduler.step()\n",
        "      print({\"data loss\": loss_data.item(), \"lr\": optimizer.param_groups[0][\"lr\"]})\n",
        "      total_step += 1\n",
        "\n",
        "      train_loss_list.append((total_step, loss_data.item()))\n",
        "      pred_history_list.append(pred)\n",
        "\n",
        "  return train_loss_list, pred_history_list # return loss and prediction history"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WXMp0TwLw5sp",
        "outputId": "70fadfb1-ce5a-4923-ba77-917674161659"
      },
      "outputs": [],
      "source": [
        "# Load Dataset\n",
        "\n",
        "from torch_geometric.data import Dataset, Data\n",
        "from torch_geometric.loader import DataLoader\n",
        "\n",
        "class BeamDataset(Dataset):\n",
        "    \"\"\"\n",
        "    A PyTorch Geometric Dataset class for the beam data.\n",
        "    \"\"\"\n",
        "    def __init__(self, df_input, transform=None):\n",
        "        super().__init__()\n",
        "        self.input = df_input\n",
        "        self.transform = transform\n",
        "\n",
        "    def transform(self):\n",
        "        self.input = np.array([np.array(x) in x for x in self.input])\n",
        "        return self.input\n",
        "\n",
        "    def len(self):\n",
        "        return len(self.input)\n",
        "\n",
        "    def get(self, idx):\n",
        "        # Extract node features, edge indices, edge attributes from self.df\n",
        "        self.input = self.transform()\n",
        "        graph_data = create_gnn_graph(self.input[idx], metadata)\n",
        "        node_features = graph_data.x\n",
        "        edge_index = graph_data.edge_index\n",
        "        edge_attr = graph_data.edge_attr\n",
        "        # Create and return a PyTorch Geometric Data object\n",
        "        data = Data(x=node_features, edge_index=edge_index, edge_attr=edge_attr)\n",
        "        if self.transform:\n",
        "            self.data = self.transform(data)\n",
        "        return data\n",
        "\n",
        "# Create a PyTorch Geometric Dataset from your DataFrame\n",
        "dataset = BeamDataset(data_df[1])\n",
        "\n",
        "# Modify the DataLoader to use the Dataset\n",
        "data_loader = DataLoader(dataset, batch_size=1)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4DTMaPB_b2-X"
      },
      "outputs": [],
      "source": [
        "# build model\n",
        "simulator = Simulator(\n",
        "    num_nodes=100,\n",
        "    num_edges=99,\n",
        "    node_dimension=1,\n",
        "    edge_dimension=2,\n",
        "    node_in=1,\n",
        "    edge_in=2,\n",
        "    latent_dim=3,\n",
        "    num_message_passing_steps=2,\n",
        "    mlp_num_layers=2,\n",
        "    mlp_hidden_dim=3,\n",
        "    connectivity_radius=0.1011,\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ZGW5VuahjKVF"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "torch.Size([100, 1])\n",
            "torch.Size([100, 1])\n"
          ]
        }
      ],
      "source": [
        "data_df = pd.read_csv(r\"C:\\Users\\kurt-\\Documents\\GitHub\\dsa-2024\\data\\n=100_l=5_varying_p\\node-disp_l=5_n=100_p=1.csv\")\n",
        "\n",
        "data_df.head()\n",
        "data_x = torch.tensor(data_df['position'].values).view(100, 1).float()\n",
        "\n",
        "print(data_x.shape)\n",
        "data_y_np = data_df['displacement'].values\n",
        "data_y = torch.tensor(data_df['displacement']).view(100, 1).float()\n",
        "print(data_y.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "pYkjH1Puf5MD",
        "outputId": "89702501-aa9d-4376-8bca-562391c2c298"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\kurt-\\AppData\\Local\\Temp\\ipykernel_25268\\1412386375.py:48: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n",
            "  pos = torch.tensor(coordinates, dtype=torch.float)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'data loss': 2.3492217948728467e-08, 'lr': 0.009999995394830874}\n",
            "{'data loss': 0.00170885119587183, 'lr': 0.00999999078966387}\n",
            "{'data loss': 4.900033218291355e-06, 'lr': 0.009999986184498984}\n",
            "{'data loss': 0.0023078403901308775, 'lr': 0.009999981579336221}\n",
            "{'data loss': 0.0021822326816618443, 'lr': 0.009999976974175578}\n",
            "{'data loss': 0.0006189711857587099, 'lr': 0.009999972369017056}\n",
            "{'data loss': 0.00010599543747957796, 'lr': 0.009999967763860654}\n",
            "{'data loss': 0.00148826336953789, 'lr': 0.009999963158706373}\n",
            "{'data loss': 0.002178749069571495, 'lr': 0.009999958553554213}\n",
            "{'data loss': 0.0014014432672411203, 'lr': 0.009999953948404174}\n",
            "{'data loss': 0.00033740769140422344, 'lr': 0.009999949343256254}\n",
            "{'data loss': 1.1509669093356933e-05, 'lr': 0.009999944738110457}\n",
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            "{'data loss': 1.3241565177679604e-08, 'lr': 0.009977244153860398}\n",
            "{'data loss': 1.4263076275611297e-09, 'lr': 0.009977239559170724}\n",
            "{'data loss': 2.423378830940237e-08, 'lr': 0.009977234964483167}\n",
            "{'data loss': 3.2677753836196644e-08, 'lr': 0.009977230369797724}\n",
            "{'data loss': 1.194121512781976e-08, 'lr': 0.009977225775114397}\n",
            "{'data loss': 1.3751853822352444e-10, 'lr': 0.009977221180433187}\n",
            "{'data loss': 1.3129254128330103e-08, 'lr': 0.009977216585754093}\n",
            "{'data loss': 2.1940957495303337e-08, 'lr': 0.009977211991077114}\n",
            "{'data loss': 1.0468541589148117e-08, 'lr': 0.009977207396402251}\n",
            "{'data loss': 7.452623934645075e-11, 'lr': 0.009977202801729505}\n",
            "{'data loss': 6.429961452170119e-09, 'lr': 0.009977198207058875}\n",
            "{'data loss': 1.42536942249194e-08, 'lr': 0.00997719361239036}\n",
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            "{'data loss': 1.1733836124960817e-09, 'lr': 0.009977166044423705}\n",
            "{'data loss': 7.842835825222494e-10, 'lr': 0.009977161449770003}\n",
            "{'data loss': 4.925075902662002e-09, 'lr': 0.009977156855118414}\n",
            "{'data loss': 5.3985100834097466e-09, 'lr': 0.009977152260468943}\n",
            "{'data loss': 1.6234287247840484e-09, 'lr': 0.009977147665821588}\n",
            "{'data loss': 8.786356364698378e-11, 'lr': 0.009977143071176349}\n",
            "{'data loss': 2.449612290789105e-09, 'lr': 0.009977138476533224}\n",
            "{'data loss': 3.7853280510091736e-09, 'lr': 0.009977133881892216}\n",
            "{'data loss': 1.8042932703465908e-09, 'lr': 0.009977129287253325}\n",
            "{'data loss': 2.3229421858284027e-11, 'lr': 0.00997712469261655}\n",
            "{'data loss': 9.877285656045842e-10, 'lr': 0.009977120097981889}\n",
            "{'data loss': 2.3926367553883665e-09, 'lr': 0.009977115503349345}\n",
            "{'data loss': 1.7006055452739588e-09, 'lr': 0.009977110908718917}\n",
            "{'data loss': 2.143215188654679e-10, 'lr': 0.009977106314090604}\n",
            "{'data loss': 2.660877074234236e-10, 'lr': 0.009977101719464407}\n",
            "{'data loss': 1.3292454914548557e-09, 'lr': 0.009977097124840326}\n",
            "{'data loss': 1.3965210099442515e-09, 'lr': 0.009977092530218362}\n",
            "{'data loss': 4.235875539215783e-10, 'lr': 0.009977087935598513}\n",
            "{'data loss': 1.7946418656711316e-11, 'lr': 0.00997708334098078}\n",
            "{'data loss': 6.018056608603217e-10, 'lr': 0.009977078746365163}\n",
            "{'data loss': 9.85154069432781e-10, 'lr': 0.009977074151751662}\n",
            "{'data loss': 5.282500548098312e-10, 'lr': 0.009977069557140276}\n",
            "{'data loss': 2.299444835929254e-11, 'lr': 0.009977064962531007}\n",
            "{'data loss': 1.927093762343901e-10, 'lr': 0.009977060367923854}\n",
            "{'data loss': 5.91359905488531e-10, 'lr': 0.009977055773318817}\n",
            "{'data loss': 5.114159651320449e-10, 'lr': 0.009977051178715895}\n",
            "{'data loss': 1.1620958362268397e-10, 'lr': 0.00997704658411509}\n",
            "{'data loss': 2.316244591960004e-11, 'lr': 0.0099770419895164}\n",
            "{'data loss': 2.7631014143381094e-10, 'lr': 0.009977037394919825}\n",
            "{'data loss': 3.920144486802002e-10, 'lr': 0.009977032800325367}\n",
            "{'data loss': 1.9001557272080305e-10, 'lr': 0.009977028205733025}\n",
            "{'data loss': 5.091560419806518e-12, 'lr': 0.0099770236111428}\n",
            "{'data loss': 8.551658686739572e-11, 'lr': 0.009977019016554688}\n",
            "{'data loss': 2.3951413075096184e-10, 'lr': 0.009977014421968694}\n",
            "{'data loss': 2.047589320319787e-10, 'lr': 0.009977009827384815}\n",
            "{'data loss': 4.910318679440806e-11, 'lr': 0.009977005232803053}\n",
            "{'data loss': 7.354761764888362e-12, 'lr': 0.009977000638223406}\n"
          ]
        }
      ],
      "source": [
        "train_loss_list, pred_history_list = train(lr = 1e-2,\n",
        "      epoch = 5000,\n",
        "      simulator = simulator,\n",
        "      x_data = data_x,\n",
        "      y_data = data_y,\n",
        "      );"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mDxbvtUVe3NZ"
      },
      "outputs": [],
      "source": [
        "def plot_loss_curves(loss_tuple):\n",
        "  ''' function to plot the loss curve\n",
        "    Args:\n",
        "      loss_tuple: the tuple that comes directly out of the training function\n",
        "  '''\n",
        "  # extract the default tuple\n",
        "  epochs = [item[0] for item in loss_tuple]\n",
        "  loss = [item[1] for item in loss_tuple]\n",
        "  # Creating the plot\n",
        "  fig, ax = plt.subplots(figsize=(10,5))\n",
        "\n",
        "  # Plotting the loss curve\n",
        "  ax.plot(epochs, loss, linestyle='-', alpha=0.25,label='Data loss')\n",
        "\n",
        "  # Adding title and labels\n",
        "  ax.set_title('Loss Curve')\n",
        "  ax.set_xlabel('Epoch')\n",
        "  ax.set_ylabel('Loss')\n",
        "\n",
        "  # Adding a legend\n",
        "  ax.legend()\n",
        "\n",
        "  # Displaying the plot\n",
        "  plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "e8iJMbBsnoqC",
        "outputId": "cdc6baed-2359-4744-a579-d15e304f08df"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_loss_curves(loss_tuple=train_loss_list)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "xaUuRPC0pz6Z",
        "outputId": "d143f688-f581-48b2-844a-63d8ede84c92"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_loss_curves(loss_tuple=train_loss_list[0:100])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K8P3ZJxbqCeK",
        "outputId": "431c64ab-8bd3-4461-906f-137970e86c65"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[-1.09256804e-03 -3.86551023e-04 -9.59634781e-06  2.76267529e-05\n",
            "  5.02020121e-05  6.37769699e-05  7.17043877e-05  7.58320093e-05\n",
            "  7.76350498e-05  7.75158405e-05  7.65621662e-05  7.51018524e-05\n",
            "  7.29411840e-05  7.07060099e-05  6.82920218e-05  6.59078360e-05\n",
            "  6.36130571e-05  6.10798597e-05  5.87403774e-05  5.65052032e-05\n",
            "  5.44041395e-05  5.23328781e-05  5.02914190e-05  4.84585762e-05\n",
            "  4.66704369e-05  4.48375940e-05  4.32878733e-05  4.16785479e-05\n",
            "  4.03970480e-05  3.88324261e-05  3.74615192e-05  3.62694263e-05\n",
            "  3.49879265e-05  3.39150429e-05  3.26633453e-05  3.16351652e-05\n",
            "  3.06516886e-05  2.97427177e-05  2.87443399e-05  2.78800726e-05\n",
            "  2.68667936e-05  2.61664391e-05  2.52872705e-05  2.45720148e-05\n",
            "  2.39163637e-05  2.31862068e-05  2.24858522e-05  2.18600035e-05\n",
            "  2.13235617e-05  2.07573175e-05  2.01165676e-05  1.94907188e-05\n",
            "  1.88797712e-05  1.84178352e-05  1.80453062e-05  1.75237656e-05\n",
            "  1.70767307e-05  1.66296959e-05  1.60932541e-05  1.56760216e-05\n",
            "  1.53034925e-05  1.49607658e-05  1.45882368e-05  1.42306089e-05\n",
            "  1.37239695e-05  1.35004520e-05  1.30385160e-05  1.28298998e-05\n",
            "  1.24573708e-05  1.20103359e-05  1.18762255e-05  1.15185976e-05\n",
            "  1.12056732e-05  1.10119581e-05  1.07437372e-05  1.03563070e-05\n",
            "  1.02221966e-05  9.87946987e-06  9.74535942e-06  9.28342342e-06\n",
            "  9.22381878e-06  8.91089439e-06  8.76188278e-06  8.44895840e-06\n",
            "  8.28504562e-06  8.03172588e-06  7.95722008e-06  7.61449337e-06\n",
            "  7.46548176e-06  7.27176666e-06  7.04824924e-06  6.85453415e-06\n",
            "  6.73532486e-06  6.57141209e-06  6.39259815e-06  6.22868538e-06\n",
            "  6.03497028e-06  5.90085983e-06 -3.10659409e-04 -8.88273120e-04]\n"
          ]
        }
      ],
      "source": [
        "print(pred_history_list[0].cpu().detach().numpy())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "In6x0zwHqOCq",
        "outputId": "660bd2ec-543e-42d3-af4c-2ed7f9c2ea90"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[ 0.00000e+00 -2.86431e-11 -1.14185e-10 -2.56046e-10 -4.53645e-10\n",
            " -7.06401e-10 -1.01373e-09 -1.37506e-09 -1.78981e-09 -2.25739e-09\n",
            " -2.77722e-09 -3.34873e-09 -3.97133e-09 -4.64444e-09 -5.36749e-09\n",
            " -6.13988e-09 -6.96105e-09 -7.83041e-09 -8.74737e-09 -9.71137e-09\n",
            " -1.07218e-08 -1.17781e-08 -1.28797e-08 -1.40260e-08 -1.52165e-08\n",
            " -1.64504e-08 -1.77274e-08 -1.90467e-08 -2.04078e-08 -2.18102e-08\n",
            " -2.32532e-08 -2.47362e-08 -2.62588e-08 -2.78202e-08 -2.94200e-08\n",
            " -3.10575e-08 -3.27321e-08 -3.44433e-08 -3.61906e-08 -3.79732e-08\n",
            " -3.97907e-08 -4.16424e-08 -4.35279e-08 -4.54464e-08 -4.73974e-08\n",
            " -4.93803e-08 -5.13947e-08 -5.34397e-08 -5.55150e-08 -5.76199e-08\n",
            " -5.97538e-08 -6.19162e-08 -6.41064e-08 -6.63239e-08 -6.85681e-08\n",
            " -7.08385e-08 -7.31344e-08 -7.54553e-08 -7.78005e-08 -8.01696e-08\n",
            " -8.25618e-08 -8.49768e-08 -8.74138e-08 -8.98722e-08 -9.23516e-08\n",
            " -9.48513e-08 -9.73707e-08 -9.99093e-08 -1.02466e-07 -1.05042e-07\n",
            " -1.07634e-07 -1.10244e-07 -1.12869e-07 -1.15511e-07 -1.18167e-07\n",
            " -1.20838e-07 -1.23523e-07 -1.26221e-07 -1.28932e-07 -1.31656e-07\n",
            " -1.34390e-07 -1.37136e-07 -1.39893e-07 -1.42659e-07 -1.45434e-07\n",
            " -1.48218e-07 -1.51011e-07 -1.53811e-07 -1.56617e-07 -1.59431e-07\n",
            " -1.62250e-07 -1.65074e-07 -1.67903e-07 -1.70736e-07 -1.73572e-07\n",
            " -1.76412e-07 -1.79253e-07 -1.82097e-07 -1.84941e-07 -1.87786e-07]\n"
          ]
        }
      ],
      "source": [
        "print(data_y_np)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FbCqNKuuqRPx"
      },
      "outputs": [],
      "source": [
        "def plot_prediction(prediction, data_y_np, title='Prediction comparison',length=1, num_nodes=100):\n",
        "  ''' function to plot the prediction curve\n",
        "  '''\n",
        "  # Creating the plot\n",
        "  fig, ax = plt.subplots(figsize=(10,5))\n",
        "\n",
        "  beam_length = np.linspace(0, length, num_nodes)\n",
        "\n",
        "  # Plotting the prediction curve\n",
        "  ax.plot(beam_length, prediction, linestyle='-', label='Prediction')\n",
        "  # Plotting true curve\n",
        "  ax.plot(beam_length, data_y_np, linestyle='-', label='True')\n",
        "\n",
        "\n",
        "  # Adding title and labels\n",
        "  ax.set_title('Prediction comparison')\n",
        "  ax.set_xlabel('Length')\n",
        "  ax.set_ylabel('Deflection')\n",
        "\n",
        "  # Adding a legend\n",
        "  ax.legend()\n",
        "\n",
        "  # Displaying the plot\n",
        "  plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "u0Y0QZYUrftQ",
        "outputId": "de239353-a0f2-4cdc-909b-ae70eae8f7bb"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_prediction(prediction=pred_history_list[-1].cpu().detach().numpy(),\n",
        "                data_y_np=data_y_np,\n",
        "                title='Prediction comparison (only data loss)',\n",
        "                length=10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w3hMXfeXroDc"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.12.4"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}