Tutorial-5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# <center>Tutorial-5</center>\n",
    "## <center>Transductive vs Inductive Learning on GNNs for Node Classification</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents\n",
    "1. Introduction\n",
    "2. Setup and Data Loading\n",
    "3. Understanding Inductive vs Transductive Learning\n",
    "4. Model Implementations\n",
    "\n",
    "      4.1 Graph Convolutional Network (GCN)\n",
    "      \n",
    "      4.2 GraphSAGE\n",
    "5. Training and Evaluation\n",
    "6. Results and Visualizations\n",
    "\n",
    "## 1. Introduction\n",
    "\n",
    "In this tutorial, we'll implement and compare three popular Graph Neural Network (GNN) architectures:\n",
    "- Graph Convolutional Network (GCN)\n",
    "- GraphSAGE\n",
    "\n",
    "We'll use the Protein-Protein Interaction (PPI) dataset, which is a multi-label node classification task. This dataset is particularly interesting because it requires inductive learning, allowing us to highlight the differences between inductive and transductive approaches.\n",
    "\n",
    "## 2. Setup and Data Loading\n",
    "\n",
    "First, let's import the necessary libraries and load the PPI dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of training graphs: 20\n",
      "Number of validation graphs: 2\n",
      "Number of test graphs: 2\n",
      "Number of features: 50\n",
      "Number of classes: 121\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn.functional as F\n",
    "from torch_geometric.nn import GCNConv, SAGEConv\n",
    "from torch_geometric.datasets import PPI\n",
    "from torch_geometric.loader import DataLoader\n",
    "from sklearn.metrics import f1_score\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "\n",
    "# Check if CUDA is available\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "# Load PPI dataset\n",
    "train_dataset = PPI(root='/tmp/PPI', split='train')\n",
    "val_dataset = PPI(root='/tmp/PPI', split='val')\n",
    "test_dataset = PPI(root='/tmp/PPI', split='test')\n",
    "\n",
    "train_loader = DataLoader(train_dataset, batch_size=1, shuffle=True)\n",
    "val_loader = DataLoader(val_dataset, batch_size=2, shuffle=False)\n",
    "test_loader = DataLoader(test_dataset, batch_size=2, shuffle=False)\n",
    "\n",
    "num_features = train_dataset.num_features\n",
    "num_classes = train_dataset.num_classes\n",
    "\n",
    "print(f\"Number of training graphs: {len(train_dataset)}\")\n",
    "print(f\"Number of validation graphs: {len(val_dataset)}\")\n",
    "print(f\"Number of test graphs: {len(test_dataset)}\")\n",
    "print(f\"Number of features: {num_features}\")\n",
    "print(f\"Number of classes: {num_classes}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Understanding Inductive vs Transductive Learning\n",
    "\n",
    "Before we implement the models, let's discuss the key differences between inductive and transductive learning in the context of GNNs.\n",
    "\n",
    "<img title=\"Tranductive Vs. Inductive\" alt=\"Alt text\" src=\"tvsi.png\">\n",
    "\n",
    "### Transductive Learning:\n",
    "- The entire graph (including test nodes) is available during training.\n",
    "- The model learns to make predictions for specific nodes in a single, fixed graph.\n",
    "- Cannot generalize to unseen nodes or graphs.\n",
    "- Typical for tasks on a single, large graph (e.g., citation networks).\n",
    "\n",
    "### Inductive Learning:\n",
    "- The model learns to generalize to unseen nodes or entirely new graphs.\n",
    "- Training is done on a set of graphs, and testing is performed on different, unseen graphs.\n",
    "- Can handle dynamic graphs and generalizes to new nodes/graphs.\n",
    "- Suitable for tasks involving multiple graphs or where test nodes are not available during training.\n",
    "\n",
    "The PPI dataset and inductive learning:\n",
    "1. It consists of multiple graphs (24 for training, 2 for validation, 2 for testing).\n",
    "2. The test graphs are completely separate from the training graphs.\n",
    "3. The model must generalize to unseen proteins (nodes) in new cellular contexts (graphs).\n",
    "\n",
    "This inductive nature of the PPI task influences our model design and training procedure, as we'll see in the following sections."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 4.1 Graph Convolutional Network (GCN)\n",
    "\n",
    "\n",
    "class GCN(torch.nn.Module):\n",
    "    def __init__(self, in_channels, hidden_channels, out_channels):\n",
    "        super(GCN, self).__init__()\n",
    "        self.conv1 = GCNConv(in_channels, hidden_channels)\n",
    "        self.conv2 = GCNConv(hidden_channels, hidden_channels)\n",
    "        self.conv3 = GCNConv(hidden_channels, out_channels)\n",
    "\n",
    "    def forward(self, x, edge_index, edge_attr=None):\n",
    "        x = F.relu(self.conv1(x, edge_index))\n",
    "        x = F.relu(self.conv2(x, edge_index))\n",
    "        return self.conv3(x, edge_index)\n",
    "\n",
    "\n",
    "\n",
    "### 4.2 GraphSAGE\n",
    "\n",
    "\n",
    "class GraphSAGE(torch.nn.Module):\n",
    "    def __init__(self, in_channels, hidden_channels, out_channels):\n",
    "        super(GraphSAGE, self).__init__()\n",
    "        self.conv1 = SAGEConv(in_channels, hidden_channels)\n",
    "        self.conv2 = SAGEConv(hidden_channels, hidden_channels)\n",
    "        self.conv3 = SAGEConv(hidden_channels, out_channels)\n",
    "\n",
    "    def forward(self, x, edge_index, edge_attr=None):\n",
    "        x = F.relu(self.conv1(x, edge_index))\n",
    "        x = F.relu(self.conv2(x, edge_index))\n",
    "        return self.conv3(x, edge_index)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that all three models have a similar structure with three layers. This design allows them to capture multi-hop neighborhood information, which is crucial for the PPI task. The main differences lie in how they process and aggregate neighborhood information:\n",
    "\n",
    "- GCN uses a simple neighborhood averaging approach.\n",
    "- GraphSAGE uses a sample and aggregate approach, which is particularly suitable for inductive tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 5. Training and Evaluation\n",
    "\n",
    "'''Now, let's define our training and evaluation procedures.'''\n",
    "\n",
    "def train(model, loader, optimizer, criterion):\n",
    "    model.train()\n",
    "    total_loss = 0\n",
    "    for data in loader:\n",
    "        data = data.to(device)\n",
    "        optimizer.zero_grad()\n",
    "        out = model(data.x, data.edge_index)\n",
    "        loss = criterion(out, data.y)\n",
    "        total_loss += loss.item() * data.num_graphs\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "    return total_loss / len(loader.dataset)\n",
    "\n",
    "def test(model, loader):\n",
    "    model.eval()\n",
    "    ys, preds = [], []\n",
    "    for data in loader:\n",
    "        data = data.to(device)\n",
    "        with torch.no_grad():\n",
    "            out = model(data.x, data.edge_index)\n",
    "        pred = (out > 0).float().cpu()\n",
    "        y = data.y.cpu()\n",
    "        ys.append(y)\n",
    "        preds.append(pred)\n",
    "    y, pred = torch.cat(ys, dim=0).numpy(), torch.cat(preds, dim=0).numpy()\n",
    "    return f1_score(y, pred, average='micro') if pred.sum() > 0 else 0\n",
    "\n",
    "def train_and_evaluate(model, train_loader, val_loader, test_loader, optimizer, criterion, epochs=100):\n",
    "    best_val_f1 = 0\n",
    "    train_losses, val_f1s, test_f1s = [], [], []\n",
    "    training_time = 0\n",
    "    \n",
    "    for epoch in range(1, epochs + 1):\n",
    "        start_time = time.time()\n",
    "        loss = train(model, train_loader, optimizer, criterion)\n",
    "        epoch_time = time.time() - start_time\n",
    "        training_time += epoch_time\n",
    "        \n",
    "        val_f1 = test(model, val_loader)\n",
    "        test_f1 = test(model, test_loader)\n",
    "        \n",
    "        train_losses.append(loss)\n",
    "        val_f1s.append(val_f1)\n",
    "        test_f1s.append(test_f1)\n",
    "        \n",
    "        if val_f1 > best_val_f1:\n",
    "            best_val_f1 = val_f1\n",
    "            torch.save(model.state_dict(), 'best_model.pth')\n",
    "        \n",
    "        print(f'Epoch: {epoch:03d}, Loss: {loss:.4f}, Val F1: {val_f1:.4f}, Test F1: {test_f1:.4f}, Time: {epoch_time:.2f}s')\n",
    "    \n",
    "    # Load best model\n",
    "    model.load_state_dict(torch.load('best_model.pth'))\n",
    "    return model, train_losses, val_f1s, test_f1s, training_time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize models\n",
    "gcn = GCN(num_features, 256, num_classes).to(device)\n",
    "sage = GraphSAGE(num_features, 256, num_classes).to(device)\n",
    "\n",
    "# Define optimizers and criterion\n",
    "criterion = torch.nn.BCEWithLogitsLoss()\n",
    "gcn_optimizer = torch.optim.Adam(gcn.parameters(), lr=0.01)\n",
    "sage_optimizer = torch.optim.Adam(sage.parameters(), lr=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training GCN...\n",
      "Epoch: 001, Loss: 0.6042, Val F1: 0.4180, Test F1: 0.4192, Time: 0.92s\n",
      "Epoch: 002, Loss: 0.5794, Val F1: 0.4340, Test F1: 0.4353, Time: 0.33s\n",
      "Epoch: 003, Loss: 0.5673, Val F1: 0.4763, Test F1: 0.4831, Time: 0.32s\n",
      "Epoch: 004, Loss: 0.5538, Val F1: 0.4979, Test F1: 0.5079, Time: 0.31s\n",
      "Epoch: 005, Loss: 0.5392, Val F1: 0.3914, Test F1: 0.4039, Time: 0.31s\n",
      "Epoch: 006, Loss: 0.5329, Val F1: 0.4956, Test F1: 0.5075, Time: 0.32s\n",
      "Epoch: 007, Loss: 0.5254, Val F1: 0.4941, Test F1: 0.5051, Time: 0.32s\n",
      "Epoch: 008, Loss: 0.5184, Val F1: 0.4964, Test F1: 0.5044, Time: 0.33s\n",
      "Epoch: 009, Loss: 0.5115, Val F1: 0.4706, Test F1: 0.4841, Time: 0.31s\n",
      "Epoch: 010, Loss: 0.5110, Val F1: 0.4747, Test F1: 0.4848, Time: 0.31s\n",
      "Epoch: 011, Loss: 0.5104, Val F1: 0.5348, Test F1: 0.5481, Time: 0.32s\n",
      "Epoch: 012, Loss: 0.4988, Val F1: 0.5291, Test F1: 0.5404, Time: 0.32s\n",
      "Epoch: 013, Loss: 0.4961, Val F1: 0.5301, Test F1: 0.5414, Time: 0.31s\n",
      "Epoch: 014, Loss: 0.4916, Val F1: 0.5441, Test F1: 0.5573, Time: 0.33s\n",
      "Epoch: 015, Loss: 0.4881, Val F1: 0.5529, Test F1: 0.5634, Time: 0.32s\n",
      "Epoch: 016, Loss: 0.4876, Val F1: 0.5411, Test F1: 0.5514, Time: 0.32s\n",
      "Epoch: 017, Loss: 0.4898, Val F1: 0.4987, Test F1: 0.5081, Time: 0.31s\n",
      "Epoch: 018, Loss: 0.4845, Val F1: 0.5368, Test F1: 0.5454, Time: 0.32s\n",
      "Epoch: 019, Loss: 0.4785, Val F1: 0.5316, Test F1: 0.5428, Time: 0.32s\n",
      "Epoch: 020, Loss: 0.4752, Val F1: 0.5482, Test F1: 0.5567, Time: 0.31s\n",
      "Epoch: 021, Loss: 0.4725, Val F1: 0.5619, Test F1: 0.5713, Time: 0.32s\n",
      "Epoch: 022, Loss: 0.4722, Val F1: 0.5796, Test F1: 0.5870, Time: 0.32s\n",
      "Epoch: 023, Loss: 0.4721, Val F1: 0.5179, Test F1: 0.5325, Time: 0.31s\n",
      "Epoch: 024, Loss: 0.4745, Val F1: 0.5751, Test F1: 0.5813, Time: 0.31s\n",
      "Epoch: 025, Loss: 0.4735, Val F1: 0.5664, Test F1: 0.5780, Time: 0.31s\n",
      "Epoch: 026, Loss: 0.4710, Val F1: 0.5769, Test F1: 0.5878, Time: 0.32s\n",
      "Epoch: 027, Loss: 0.4657, Val F1: 0.5849, Test F1: 0.5903, Time: 0.31s\n",
      "Epoch: 028, Loss: 0.4633, Val F1: 0.5598, Test F1: 0.5674, Time: 0.32s\n",
      "Epoch: 029, Loss: 0.4596, Val F1: 0.5520, Test F1: 0.5593, Time: 0.32s\n",
      "Epoch: 030, Loss: 0.4575, Val F1: 0.5183, Test F1: 0.5278, Time: 0.32s\n",
      "Epoch: 031, Loss: 0.4625, Val F1: 0.5747, Test F1: 0.5826, Time: 0.32s\n",
      "Epoch: 032, Loss: 0.4605, Val F1: 0.5154, Test F1: 0.5285, Time: 0.32s\n",
      "Epoch: 033, Loss: 0.4587, Val F1: 0.5851, Test F1: 0.5976, Time: 0.31s\n",
      "Epoch: 034, Loss: 0.4519, Val F1: 0.5705, Test F1: 0.5818, Time: 0.32s\n",
      "Epoch: 035, Loss: 0.4524, Val F1: 0.5653, Test F1: 0.5793, Time: 0.32s\n",
      "Epoch: 036, Loss: 0.4520, Val F1: 0.5938, Test F1: 0.6045, Time: 0.32s\n",
      "Epoch: 037, Loss: 0.4479, Val F1: 0.6084, Test F1: 0.6202, Time: 0.32s\n",
      "Epoch: 038, Loss: 0.4498, Val F1: 0.5756, Test F1: 0.5873, Time: 0.32s\n",
      "Epoch: 039, Loss: 0.4479, Val F1: 0.5573, Test F1: 0.5685, Time: 0.31s\n",
      "Epoch: 040, Loss: 0.4472, Val F1: 0.5660, Test F1: 0.5759, Time: 0.32s\n",
      "Epoch: 041, Loss: 0.4472, Val F1: 0.5941, Test F1: 0.6021, Time: 0.31s\n",
      "Epoch: 042, Loss: 0.4525, Val F1: 0.5765, Test F1: 0.5895, Time: 0.31s\n",
      "Epoch: 043, Loss: 0.4432, Val F1: 0.5889, Test F1: 0.5977, Time: 0.31s\n",
      "Epoch: 044, Loss: 0.4429, Val F1: 0.6089, Test F1: 0.6194, Time: 0.32s\n",
      "Epoch: 045, Loss: 0.4392, Val F1: 0.5854, Test F1: 0.5975, Time: 0.31s\n",
      "Epoch: 046, Loss: 0.4389, Val F1: 0.5311, Test F1: 0.5459, Time: 0.32s\n",
      "Epoch: 047, Loss: 0.4448, Val F1: 0.5698, Test F1: 0.5866, Time: 0.32s\n",
      "Epoch: 048, Loss: 0.4408, Val F1: 0.6082, Test F1: 0.6197, Time: 0.31s\n",
      "Epoch: 049, Loss: 0.4383, Val F1: 0.6160, Test F1: 0.6262, Time: 0.32s\n",
      "Epoch: 050, Loss: 0.4376, Val F1: 0.6007, Test F1: 0.6106, Time: 0.31s\n",
      "Epoch: 051, Loss: 0.4333, Val F1: 0.5849, Test F1: 0.5940, Time: 0.31s\n",
      "Epoch: 052, Loss: 0.4346, Val F1: 0.6166, Test F1: 0.6248, Time: 0.31s\n",
      "Epoch: 053, Loss: 0.4301, Val F1: 0.5900, Test F1: 0.6003, Time: 0.31s\n",
      "Epoch: 054, Loss: 0.4306, Val F1: 0.6169, Test F1: 0.6242, Time: 0.31s\n",
      "Epoch: 055, Loss: 0.4343, Val F1: 0.6048, Test F1: 0.6129, Time: 0.31s\n",
      "Epoch: 056, Loss: 0.4442, Val F1: 0.6153, Test F1: 0.6249, Time: 0.31s\n",
      "Epoch: 057, Loss: 0.4403, Val F1: 0.6026, Test F1: 0.6137, Time: 0.32s\n",
      "Epoch: 058, Loss: 0.4358, Val F1: 0.5906, Test F1: 0.6051, Time: 0.32s\n",
      "Epoch: 059, Loss: 0.4354, Val F1: 0.6290, Test F1: 0.6401, Time: 0.31s\n",
      "Epoch: 060, Loss: 0.4271, Val F1: 0.5934, Test F1: 0.6019, Time: 0.31s\n",
      "Epoch: 061, Loss: 0.4227, Val F1: 0.6202, Test F1: 0.6257, Time: 0.31s\n",
      "Epoch: 062, Loss: 0.4231, Val F1: 0.6331, Test F1: 0.6439, Time: 0.32s\n",
      "Epoch: 063, Loss: 0.4251, Val F1: 0.6121, Test F1: 0.6228, Time: 0.31s\n",
      "Epoch: 064, Loss: 0.4200, Val F1: 0.5826, Test F1: 0.5949, Time: 0.32s\n",
      "Epoch: 065, Loss: 0.4195, Val F1: 0.6225, Test F1: 0.6316, Time: 0.31s\n",
      "Epoch: 066, Loss: 0.4197, Val F1: 0.6186, Test F1: 0.6303, Time: 0.31s\n",
      "Epoch: 067, Loss: 0.4250, Val F1: 0.6303, Test F1: 0.6386, Time: 0.31s\n",
      "Epoch: 068, Loss: 0.4215, Val F1: 0.6171, Test F1: 0.6262, Time: 0.31s\n",
      "Epoch: 069, Loss: 0.4208, Val F1: 0.6412, Test F1: 0.6502, Time: 0.31s\n",
      "Epoch: 070, Loss: 0.4210, Val F1: 0.6178, Test F1: 0.6312, Time: 0.31s\n",
      "Epoch: 071, Loss: 0.4181, Val F1: 0.6044, Test F1: 0.6163, Time: 0.31s\n",
      "Epoch: 072, Loss: 0.4250, Val F1: 0.6200, Test F1: 0.6322, Time: 0.31s\n",
      "Epoch: 073, Loss: 0.4192, Val F1: 0.6297, Test F1: 0.6420, Time: 0.32s\n",
      "Epoch: 074, Loss: 0.4131, Val F1: 0.6320, Test F1: 0.6446, Time: 0.31s\n",
      "Epoch: 075, Loss: 0.4116, Val F1: 0.6182, Test F1: 0.6314, Time: 0.31s\n",
      "Epoch: 076, Loss: 0.4108, Val F1: 0.6315, Test F1: 0.6443, Time: 0.31s\n",
      "Epoch: 077, Loss: 0.4127, Val F1: 0.6147, Test F1: 0.6278, Time: 0.31s\n",
      "Epoch: 078, Loss: 0.4120, Val F1: 0.6418, Test F1: 0.6532, Time: 0.31s\n",
      "Epoch: 079, Loss: 0.4103, Val F1: 0.6542, Test F1: 0.6662, Time: 0.32s\n",
      "Epoch: 080, Loss: 0.4098, Val F1: 0.6003, Test F1: 0.6184, Time: 0.32s\n",
      "Epoch: 081, Loss: 0.4124, Val F1: 0.6170, Test F1: 0.6332, Time: 0.32s\n",
      "Epoch: 082, Loss: 0.4129, Val F1: 0.6466, Test F1: 0.6593, Time: 0.32s\n",
      "Epoch: 083, Loss: 0.4466, Val F1: 0.5414, Test F1: 0.5703, Time: 0.32s\n",
      "Epoch: 084, Loss: 0.4399, Val F1: 0.6244, Test F1: 0.6397, Time: 0.31s\n",
      "Epoch: 085, Loss: 0.4232, Val F1: 0.6246, Test F1: 0.6418, Time: 0.31s\n",
      "Epoch: 086, Loss: 0.4136, Val F1: 0.6335, Test F1: 0.6482, Time: 0.32s\n",
      "Epoch: 087, Loss: 0.4102, Val F1: 0.6186, Test F1: 0.6360, Time: 0.31s\n",
      "Epoch: 088, Loss: 0.4109, Val F1: 0.6436, Test F1: 0.6562, Time: 0.31s\n",
      "Epoch: 089, Loss: 0.4205, Val F1: 0.6132, Test F1: 0.6339, Time: 0.31s\n",
      "Epoch: 090, Loss: 0.4068, Val F1: 0.6098, Test F1: 0.6280, Time: 0.32s\n",
      "Epoch: 091, Loss: 0.4036, Val F1: 0.6178, Test F1: 0.6357, Time: 0.31s\n",
      "Epoch: 092, Loss: 0.4016, Val F1: 0.6523, Test F1: 0.6652, Time: 0.31s\n",
      "Epoch: 093, Loss: 0.4014, Val F1: 0.6213, Test F1: 0.6416, Time: 0.31s\n",
      "Epoch: 094, Loss: 0.4124, Val F1: 0.6088, Test F1: 0.6264, Time: 0.31s\n",
      "Epoch: 095, Loss: 0.4081, Val F1: 0.6334, Test F1: 0.6508, Time: 0.31s\n",
      "Epoch: 096, Loss: 0.4031, Val F1: 0.6426, Test F1: 0.6574, Time: 0.31s\n",
      "Epoch: 097, Loss: 0.4093, Val F1: 0.6359, Test F1: 0.6522, Time: 0.31s\n",
      "Epoch: 098, Loss: 0.4046, Val F1: 0.6496, Test F1: 0.6639, Time: 0.32s\n",
      "Epoch: 099, Loss: 0.3994, Val F1: 0.6365, Test F1: 0.6530, Time: 0.32s\n",
      "Epoch: 100, Loss: 0.3972, Val F1: 0.6381, Test F1: 0.6551, Time: 0.31s\n",
      "\n",
      "Training GraphSAGE...\n",
      "Epoch: 001, Loss: 0.6051, Val F1: 0.4523, Test F1: 0.4537, Time: 0.21s\n",
      "Epoch: 002, Loss: 0.5495, Val F1: 0.3861, Test F1: 0.3860, Time: 0.21s\n",
      "Epoch: 003, Loss: 0.5353, Val F1: 0.4166, Test F1: 0.4181, Time: 0.22s\n",
      "Epoch: 004, Loss: 0.5124, Val F1: 0.5117, Test F1: 0.5194, Time: 0.21s\n",
      "Epoch: 005, Loss: 0.4840, Val F1: 0.5645, Test F1: 0.5739, Time: 0.21s\n",
      "Epoch: 006, Loss: 0.4577, Val F1: 0.5447, Test F1: 0.5576, Time: 0.22s\n",
      "Epoch: 007, Loss: 0.4378, Val F1: 0.5757, Test F1: 0.5902, Time: 0.21s\n",
      "Epoch: 008, Loss: 0.4275, Val F1: 0.6281, Test F1: 0.6416, Time: 0.21s\n",
      "Epoch: 009, Loss: 0.4073, Val F1: 0.6400, Test F1: 0.6556, Time: 0.21s\n",
      "Epoch: 010, Loss: 0.3927, Val F1: 0.6360, Test F1: 0.6555, Time: 0.21s\n",
      "Epoch: 011, Loss: 0.3814, Val F1: 0.6461, Test F1: 0.6649, Time: 0.21s\n",
      "Epoch: 012, Loss: 0.3685, Val F1: 0.6877, Test F1: 0.7067, Time: 0.22s\n",
      "Epoch: 013, Loss: 0.3626, Val F1: 0.6571, Test F1: 0.6774, Time: 0.21s\n",
      "Epoch: 014, Loss: 0.3499, Val F1: 0.6850, Test F1: 0.7041, Time: 0.21s\n",
      "Epoch: 015, Loss: 0.3446, Val F1: 0.6886, Test F1: 0.7091, Time: 0.21s\n",
      "Epoch: 016, Loss: 0.3419, Val F1: 0.6948, Test F1: 0.7164, Time: 0.21s\n",
      "Epoch: 017, Loss: 0.3297, Val F1: 0.6978, Test F1: 0.7191, Time: 0.21s\n",
      "Epoch: 018, Loss: 0.3223, Val F1: 0.7096, Test F1: 0.7315, Time: 0.21s\n",
      "Epoch: 019, Loss: 0.3224, Val F1: 0.7155, Test F1: 0.7388, Time: 0.21s\n",
      "Epoch: 020, Loss: 0.3149, Val F1: 0.7285, Test F1: 0.7502, Time: 0.22s\n",
      "Epoch: 021, Loss: 0.3082, Val F1: 0.7287, Test F1: 0.7512, Time: 0.21s\n",
      "Epoch: 022, Loss: 0.3056, Val F1: 0.7393, Test F1: 0.7617, Time: 0.21s\n",
      "Epoch: 023, Loss: 0.3060, Val F1: 0.7333, Test F1: 0.7564, Time: 0.21s\n",
      "Epoch: 024, Loss: 0.3014, Val F1: 0.7379, Test F1: 0.7598, Time: 0.21s\n",
      "Epoch: 025, Loss: 0.2949, Val F1: 0.7466, Test F1: 0.7694, Time: 0.21s\n",
      "Epoch: 026, Loss: 0.2904, Val F1: 0.7418, Test F1: 0.7643, Time: 0.21s\n",
      "Epoch: 027, Loss: 0.2859, Val F1: 0.7441, Test F1: 0.7663, Time: 0.21s\n",
      "Epoch: 028, Loss: 0.2884, Val F1: 0.7477, Test F1: 0.7701, Time: 0.21s\n",
      "Epoch: 029, Loss: 0.2843, Val F1: 0.7477, Test F1: 0.7700, Time: 0.21s\n",
      "Epoch: 030, Loss: 0.2795, Val F1: 0.7445, Test F1: 0.7672, Time: 0.21s\n",
      "Epoch: 031, Loss: 0.2807, Val F1: 0.7559, Test F1: 0.7774, Time: 0.22s\n",
      "Epoch: 032, Loss: 0.2747, Val F1: 0.7556, Test F1: 0.7791, Time: 0.21s\n",
      "Epoch: 033, Loss: 0.2732, Val F1: 0.7505, Test F1: 0.7733, Time: 0.21s\n",
      "Epoch: 034, Loss: 0.2810, Val F1: 0.7621, Test F1: 0.7846, Time: 0.21s\n",
      "Epoch: 035, Loss: 0.2700, Val F1: 0.7613, Test F1: 0.7835, Time: 0.21s\n",
      "Epoch: 036, Loss: 0.2704, Val F1: 0.7595, Test F1: 0.7824, Time: 0.21s\n",
      "Epoch: 037, Loss: 0.2666, Val F1: 0.7683, Test F1: 0.7908, Time: 0.21s\n",
      "Epoch: 038, Loss: 0.2671, Val F1: 0.7744, Test F1: 0.7969, Time: 0.21s\n",
      "Epoch: 039, Loss: 0.2718, Val F1: 0.7666, Test F1: 0.7894, Time: 0.21s\n",
      "Epoch: 040, Loss: 0.2659, Val F1: 0.7675, Test F1: 0.7912, Time: 0.21s\n",
      "Epoch: 041, Loss: 0.2594, Val F1: 0.7740, Test F1: 0.7973, Time: 0.21s\n",
      "Epoch: 042, Loss: 0.2593, Val F1: 0.7755, Test F1: 0.7994, Time: 0.21s\n",
      "Epoch: 043, Loss: 0.2546, Val F1: 0.7758, Test F1: 0.7994, Time: 0.21s\n",
      "Epoch: 044, Loss: 0.2556, Val F1: 0.7699, Test F1: 0.7930, Time: 0.21s\n",
      "Epoch: 045, Loss: 0.2604, Val F1: 0.7692, Test F1: 0.7915, Time: 0.22s\n",
      "Epoch: 046, Loss: 0.2554, Val F1: 0.7786, Test F1: 0.8017, Time: 0.21s\n",
      "Epoch: 047, Loss: 0.2512, Val F1: 0.7741, Test F1: 0.7978, Time: 0.21s\n",
      "Epoch: 048, Loss: 0.2467, Val F1: 0.7811, Test F1: 0.8042, Time: 0.21s\n",
      "Epoch: 049, Loss: 0.2576, Val F1: 0.7858, Test F1: 0.8075, Time: 0.21s\n",
      "Epoch: 050, Loss: 0.2545, Val F1: 0.7714, Test F1: 0.7950, Time: 0.21s\n",
      "Epoch: 051, Loss: 0.2518, Val F1: 0.7772, Test F1: 0.8001, Time: 0.21s\n",
      "Epoch: 052, Loss: 0.2554, Val F1: 0.7749, Test F1: 0.7990, Time: 0.22s\n",
      "Epoch: 053, Loss: 0.2455, Val F1: 0.7804, Test F1: 0.8044, Time: 0.21s\n",
      "Epoch: 054, Loss: 0.2429, Val F1: 0.7901, Test F1: 0.8128, Time: 0.21s\n",
      "Epoch: 055, Loss: 0.2429, Val F1: 0.7893, Test F1: 0.8121, Time: 0.21s\n",
      "Epoch: 056, Loss: 0.2454, Val F1: 0.7768, Test F1: 0.8004, Time: 0.21s\n",
      "Epoch: 057, Loss: 0.2477, Val F1: 0.7665, Test F1: 0.7913, Time: 0.21s\n",
      "Epoch: 058, Loss: 0.2457, Val F1: 0.7872, Test F1: 0.8097, Time: 0.21s\n",
      "Epoch: 059, Loss: 0.2433, Val F1: 0.7784, Test F1: 0.8012, Time: 0.21s\n",
      "Epoch: 060, Loss: 0.2457, Val F1: 0.7841, Test F1: 0.8076, Time: 0.21s\n",
      "Epoch: 061, Loss: 0.2442, Val F1: 0.7853, Test F1: 0.8088, Time: 0.21s\n",
      "Epoch: 062, Loss: 0.2386, Val F1: 0.7872, Test F1: 0.8105, Time: 0.21s\n",
      "Epoch: 063, Loss: 0.2437, Val F1: 0.7838, Test F1: 0.8090, Time: 0.21s\n",
      "Epoch: 064, Loss: 0.2405, Val F1: 0.7906, Test F1: 0.8146, Time: 0.21s\n",
      "Epoch: 065, Loss: 0.2353, Val F1: 0.7943, Test F1: 0.8179, Time: 0.21s\n",
      "Epoch: 066, Loss: 0.2368, Val F1: 0.7944, Test F1: 0.8178, Time: 0.21s\n",
      "Epoch: 067, Loss: 0.2339, Val F1: 0.7937, Test F1: 0.8173, Time: 0.21s\n",
      "Epoch: 068, Loss: 0.2349, Val F1: 0.7851, Test F1: 0.8087, Time: 0.21s\n",
      "Epoch: 069, Loss: 0.2382, Val F1: 0.7839, Test F1: 0.8077, Time: 0.21s\n",
      "Epoch: 070, Loss: 0.2322, Val F1: 0.7901, Test F1: 0.8139, Time: 0.21s\n",
      "Epoch: 071, Loss: 0.2324, Val F1: 0.7910, Test F1: 0.8149, Time: 0.21s\n",
      "Epoch: 072, Loss: 0.2351, Val F1: 0.7924, Test F1: 0.8166, Time: 0.21s\n",
      "Epoch: 073, Loss: 0.2382, Val F1: 0.7822, Test F1: 0.8054, Time: 0.21s\n",
      "Epoch: 074, Loss: 0.2464, Val F1: 0.7840, Test F1: 0.8075, Time: 0.21s\n",
      "Epoch: 075, Loss: 0.2369, Val F1: 0.7816, Test F1: 0.8047, Time: 0.21s\n",
      "Epoch: 076, Loss: 0.2348, Val F1: 0.7958, Test F1: 0.8179, Time: 0.21s\n",
      "Epoch: 077, Loss: 0.2307, Val F1: 0.7943, Test F1: 0.8177, Time: 0.21s\n",
      "Epoch: 078, Loss: 0.2305, Val F1: 0.7982, Test F1: 0.8213, Time: 0.21s\n",
      "Epoch: 079, Loss: 0.2292, Val F1: 0.7901, Test F1: 0.8137, Time: 0.21s\n",
      "Epoch: 080, Loss: 0.2294, Val F1: 0.7959, Test F1: 0.8198, Time: 0.21s\n",
      "Epoch: 081, Loss: 0.2311, Val F1: 0.7824, Test F1: 0.8084, Time: 0.21s\n",
      "Epoch: 082, Loss: 0.2312, Val F1: 0.7943, Test F1: 0.8192, Time: 0.21s\n",
      "Epoch: 083, Loss: 0.2242, Val F1: 0.7980, Test F1: 0.8231, Time: 0.21s\n",
      "Epoch: 084, Loss: 0.2244, Val F1: 0.8017, Test F1: 0.8256, Time: 0.21s\n",
      "Epoch: 085, Loss: 0.2230, Val F1: 0.7972, Test F1: 0.8224, Time: 0.21s\n",
      "Epoch: 086, Loss: 0.2244, Val F1: 0.7961, Test F1: 0.8212, Time: 0.21s\n",
      "Epoch: 087, Loss: 0.2222, Val F1: 0.8016, Test F1: 0.8255, Time: 0.21s\n",
      "Epoch: 088, Loss: 0.2226, Val F1: 0.8020, Test F1: 0.8253, Time: 0.21s\n",
      "Epoch: 089, Loss: 0.2244, Val F1: 0.7944, Test F1: 0.8181, Time: 0.21s\n",
      "Epoch: 090, Loss: 0.2244, Val F1: 0.7933, Test F1: 0.8167, Time: 0.21s\n",
      "Epoch: 091, Loss: 0.2234, Val F1: 0.7996, Test F1: 0.8238, Time: 0.21s\n",
      "Epoch: 092, Loss: 0.2296, Val F1: 0.7997, Test F1: 0.8217, Time: 0.21s\n",
      "Epoch: 093, Loss: 0.2293, Val F1: 0.7931, Test F1: 0.8179, Time: 0.21s\n",
      "Epoch: 094, Loss: 0.2259, Val F1: 0.7921, Test F1: 0.8154, Time: 0.21s\n",
      "Epoch: 095, Loss: 0.2257, Val F1: 0.7940, Test F1: 0.8177, Time: 0.21s\n",
      "Epoch: 096, Loss: 0.2182, Val F1: 0.8061, Test F1: 0.8280, Time: 0.21s\n",
      "Epoch: 097, Loss: 0.2184, Val F1: 0.8043, Test F1: 0.8270, Time: 0.21s\n",
      "Epoch: 098, Loss: 0.2209, Val F1: 0.8010, Test F1: 0.8238, Time: 0.21s\n",
      "Epoch: 099, Loss: 0.2200, Val F1: 0.8040, Test F1: 0.8279, Time: 0.21s\n",
      "Epoch: 100, Loss: 0.2197, Val F1: 0.8048, Test F1: 0.8278, Time: 0.21s\n"
     ]
    }
   ],
   "source": [
    "# Train and evaluate models\n",
    "print(\"Training GCN...\")\n",
    "gcn, gcn_losses, gcn_val_f1s, gcn_test_f1s, gcn_time = train_and_evaluate(gcn, train_loader, val_loader, test_loader, gcn_optimizer, criterion)\n",
    "\n",
    "\n",
    "print(\"\\nTraining GraphSAGE...\")\n",
    "sage, sage_losses, sage_val_f1s, sage_test_f1s, sage_time = train_and_evaluate(sage, train_loader, val_loader, test_loader, sage_optimizer, criterion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Test F1 Scores:\n",
      "GCN: 0.6551\n",
      "GraphSAGE: 0.8278\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Total Training Times:\n",
      "GCN: 32.09 seconds\n",
      "GraphSAGE: 21.17 seconds\n"
     ]
    }
   ],
   "source": [
    "def plot_results(gcn_results, sage_results, title, ylabel):\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    epochs = range(1, len(gcn_results) + 1)\n",
    "    plt.plot(epochs, gcn_results, label='GCN')\n",
    "    plt.plot(epochs, sage_results, label='GraphSAGE')\n",
    "    plt.title(title)\n",
    "    plt.xlabel('Epochs')\n",
    "    plt.ylabel(ylabel)\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "# Plot training loss\n",
    "plot_results(gcn_losses, sage_losses, 'Training Loss', 'Loss')\n",
    "\n",
    "# Plot validation F1 score\n",
    "plot_results(gcn_val_f1s, sage_val_f1s, 'Validation F1 Score', 'F1 Score')\n",
    "\n",
    "# Plot test F1 score\n",
    "plot_results(gcn_test_f1s, sage_test_f1s, 'Test F1 Score', 'F1 Score')\n",
    "\n",
    "# Print final test F1 scores\n",
    "print(f\"Final Test F1 Scores:\")\n",
    "print(f\"GCN: {gcn_test_f1s[-1]:.4f}\")\n",
    "print(f\"GraphSAGE: {sage_test_f1s[-1]:.4f}\")\n",
    "\n",
    "# Plot training times\n",
    "plt.figure(figsize=(10, 6))\n",
    "models = ['GCN', 'GraphSAGE']\n",
    "times = [gcn_time, sage_time]\n",
    "plt.bar(models, times)\n",
    "plt.title('Total Training Time Comparison')\n",
    "plt.xlabel('Models')\n",
    "plt.ylabel('Training Time (seconds)')\n",
    "for i, v in enumerate(times):\n",
    "    plt.text(i, v, f'{v:.2f}s', ha='center', va='bottom')\n",
    "plt.show()\n",
    "\n",
    "# Print total training times\n",
    "print(f\"\\nTotal Training Times:\")\n",
    "print(f\"GCN: {gcn_time:.2f} seconds\")\n",
    "print(f\"GraphSAGE: {sage_time:.2f} seconds\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of nodes: 2708\n",
      "Number of edges: 10556\n",
      "Number of classes: 7\n",
      "Number of node features: 1433\n",
      "Training GCN...\n",
      "Epoch: 010, Loss: 1.8806, Train: 0.8643, Val: 0.7060, Test: 0.7190, Time: 0.0055s\n",
      "Epoch: 020, Loss: 1.7570, Train: 0.9286, Val: 0.6880, Test: 0.7110, Time: 0.0054s\n",
      "Epoch: 030, Loss: 1.6388, Train: 0.9429, Val: 0.7300, Test: 0.7660, Time: 0.0052s\n",
      "Epoch: 040, Loss: 1.4113, Train: 0.9571, Val: 0.7660, Test: 0.7900, Time: 0.0052s\n",
      "Epoch: 050, Loss: 1.2488, Train: 0.9571, Val: 0.7520, Test: 0.7800, Time: 0.0052s\n",
      "Epoch: 060, Loss: 1.0889, Train: 0.9643, Val: 0.7740, Test: 0.8050, Time: 0.0051s\n",
      "Epoch: 070, Loss: 0.9330, Train: 0.9786, Val: 0.7820, Test: 0.8060, Time: 0.0052s\n",
      "Epoch: 080, Loss: 0.8196, Train: 0.9714, Val: 0.7660, Test: 0.8000, Time: 0.0053s\n",
      "Epoch: 090, Loss: 0.7361, Train: 0.9929, Val: 0.7920, Test: 0.8100, Time: 0.0046s\n",
      "Epoch: 100, Loss: 0.6728, Train: 0.9857, Val: 0.7680, Test: 0.8060, Time: 0.0049s\n",
      "Epoch: 110, Loss: 0.6482, Train: 0.9929, Val: 0.7880, Test: 0.8220, Time: 0.0052s\n",
      "Epoch: 120, Loss: 0.6059, Train: 0.9929, Val: 0.7820, Test: 0.8220, Time: 0.0052s\n",
      "Epoch: 130, Loss: 0.5387, Train: 0.9929, Val: 0.7800, Test: 0.8170, Time: 0.0051s\n",
      "Epoch: 140, Loss: 0.4804, Train: 0.9929, Val: 0.7820, Test: 0.8170, Time: 0.0052s\n",
      "Epoch: 150, Loss: 0.4768, Train: 0.9929, Val: 0.7840, Test: 0.8120, Time: 0.0051s\n",
      "Epoch: 160, Loss: 0.4788, Train: 0.9929, Val: 0.7880, Test: 0.8240, Time: 0.0052s\n",
      "Epoch: 170, Loss: 0.4420, Train: 0.9929, Val: 0.7840, Test: 0.8150, Time: 0.0054s\n",
      "Epoch: 180, Loss: 0.4855, Train: 1.0000, Val: 0.7920, Test: 0.8230, Time: 0.0053s\n",
      "Epoch: 190, Loss: 0.4083, Train: 0.9929, Val: 0.7840, Test: 0.8170, Time: 0.0052s\n",
      "Epoch: 200, Loss: 0.4106, Train: 0.9929, Val: 0.7880, Test: 0.8220, Time: 0.0051s\n",
      "\n",
      "Training GraphSAGE...\n",
      "Epoch: 010, Loss: 1.8235, Train: 0.8786, Val: 0.4620, Test: 0.4500, Time: 0.0085s\n",
      "Epoch: 020, Loss: 1.5968, Train: 0.8571, Val: 0.5200, Test: 0.5170, Time: 0.0085s\n",
      "Epoch: 030, Loss: 1.1711, Train: 0.9286, Val: 0.5860, Test: 0.5790, Time: 0.0085s\n",
      "Epoch: 040, Loss: 0.8668, Train: 1.0000, Val: 0.7500, Test: 0.7470, Time: 0.0086s\n",
      "Epoch: 050, Loss: 0.6199, Train: 1.0000, Val: 0.7640, Test: 0.7720, Time: 0.0085s\n",
      "Epoch: 060, Loss: 0.4613, Train: 1.0000, Val: 0.7940, Test: 0.7970, Time: 0.0085s\n",
      "Epoch: 070, Loss: 0.4512, Train: 1.0000, Val: 0.7880, Test: 0.7970, Time: 0.0089s\n",
      "Epoch: 080, Loss: 0.3127, Train: 1.0000, Val: 0.8000, Test: 0.8060, Time: 0.0085s\n",
      "Epoch: 090, Loss: 0.3055, Train: 1.0000, Val: 0.7900, Test: 0.7930, Time: 0.0085s\n",
      "Epoch: 100, Loss: 0.2656, Train: 1.0000, Val: 0.7820, Test: 0.8070, Time: 0.0085s\n",
      "Epoch: 110, Loss: 0.2582, Train: 1.0000, Val: 0.7980, Test: 0.8120, Time: 0.0085s\n",
      "Epoch: 120, Loss: 0.2155, Train: 1.0000, Val: 0.7860, Test: 0.7910, Time: 0.0086s\n",
      "Epoch: 130, Loss: 0.2683, Train: 1.0000, Val: 0.8020, Test: 0.8120, Time: 0.0084s\n",
      "Epoch: 140, Loss: 0.2312, Train: 1.0000, Val: 0.7940, Test: 0.8100, Time: 0.0085s\n",
      "Epoch: 150, Loss: 0.2060, Train: 1.0000, Val: 0.7880, Test: 0.8070, Time: 0.0085s\n",
      "Epoch: 160, Loss: 0.2117, Train: 1.0000, Val: 0.8000, Test: 0.8120, Time: 0.0087s\n",
      "Epoch: 170, Loss: 0.1641, Train: 1.0000, Val: 0.7780, Test: 0.8040, Time: 0.0085s\n",
      "Epoch: 180, Loss: 0.2018, Train: 1.0000, Val: 0.7920, Test: 0.8070, Time: 0.0085s\n",
      "Epoch: 190, Loss: 0.1654, Train: 1.0000, Val: 0.7860, Test: 0.8010, Time: 0.0085s\n",
      "Epoch: 200, Loss: 0.1751, Train: 1.0000, Val: 0.7840, Test: 0.8010, Time: 0.0085s\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Final Test Accuracies:\n",
      "GCN: 0.8270\n",
      "GraphSAGE: 0.8130\n",
      "\n",
      "Total Training Times:\n",
      "GCN: 1.04 seconds\n",
      "GraphSAGE: 1.71 seconds\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# GCN vs GraphSAGE Comparison on Cora Dataset (Transductive Learning)\n",
    "\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "from torch_geometric.nn import GCNConv, SAGEConv\n",
    "from torch_geometric.datasets import Planetoid\n",
    "from torch_geometric.transforms import NormalizeFeatures\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "\n",
    "# Set random seed for reproducibility\n",
    "torch.manual_seed(50)\n",
    "\n",
    "# Check if CUDA is available\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "# Load the Cora dataset\n",
    "dataset = Planetoid(root='/tmp/Cora', name='Cora', transform=NormalizeFeatures())\n",
    "data = dataset[0].to(device)\n",
    "\n",
    "print(f\"Number of nodes: {data.num_nodes}\")\n",
    "print(f\"Number of edges: {data.num_edges}\")\n",
    "print(f\"Number of classes: {dataset.num_classes}\")\n",
    "print(f\"Number of node features: {dataset.num_features}\")\n",
    "\n",
    "# Define the GCN model\n",
    "class GCN(torch.nn.Module):\n",
    "    def __init__(self, in_channels, hidden_channels, out_channels):\n",
    "        super(GCN, self).__init__()\n",
    "        self.conv1 = GCNConv(in_channels, hidden_channels)\n",
    "        self.conv2 = GCNConv(hidden_channels, out_channels)\n",
    "\n",
    "    def forward(self, x, edge_index):\n",
    "        x = F.dropout(x, p=0.5, training=self.training)\n",
    "        x = F.relu(self.conv1(x, edge_index))\n",
    "        x = F.dropout(x, p=0.5, training=self.training)\n",
    "        x = self.conv2(x, edge_index)\n",
    "        return F.log_softmax(x, dim=1)\n",
    "\n",
    "# Define the GraphSAGE model\n",
    "class GraphSAGE(torch.nn.Module):\n",
    "    def __init__(self, in_channels, hidden_channels, out_channels):\n",
    "        super(GraphSAGE, self).__init__()\n",
    "        self.conv1 = SAGEConv(in_channels, hidden_channels)\n",
    "        self.conv2 = SAGEConv(hidden_channels, out_channels)\n",
    "\n",
    "    def forward(self, x, edge_index):\n",
    "        x = F.dropout(x, p=0.5, training=self.training)\n",
    "        x = F.relu(self.conv1(x, edge_index))\n",
    "        x = F.dropout(x, p=0.5, training=self.training)\n",
    "        x = self.conv2(x, edge_index)\n",
    "        return F.log_softmax(x, dim=1)\n",
    "\n",
    "# Training function\n",
    "def train(model, data, optimizer):\n",
    "    model.train()\n",
    "    optimizer.zero_grad()\n",
    "    out = model(data.x, data.edge_index)\n",
    "    loss = F.nll_loss(out[data.train_mask], data.y[data.train_mask])\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    return loss.item()\n",
    "\n",
    "# Evaluation function\n",
    "@torch.no_grad()\n",
    "def test(model, data):\n",
    "    model.eval()\n",
    "    out = model(data.x, data.edge_index)\n",
    "    pred = out.argmax(dim=1)\n",
    "    train_correct = pred[data.train_mask] == data.y[data.train_mask]\n",
    "    val_correct = pred[data.val_mask] == data.y[data.val_mask]\n",
    "    test_correct = pred[data.test_mask] == data.y[data.test_mask]\n",
    "    train_acc = int(train_correct.sum()) / int(data.train_mask.sum())\n",
    "    val_acc = int(val_correct.sum()) / int(data.val_mask.sum())\n",
    "    test_acc = int(test_correct.sum()) / int(data.test_mask.sum())\n",
    "    return train_acc, val_acc, test_acc\n",
    "\n",
    "# Function to train and evaluate a model\n",
    "def train_and_evaluate(model, data, optimizer, epochs=200):\n",
    "    best_val_acc = 0\n",
    "    final_test_acc = 0\n",
    "    train_accs, val_accs, test_accs = [], [], []\n",
    "    times = []\n",
    "    \n",
    "    for epoch in range(1, epochs + 1):\n",
    "        start_time = time.time()\n",
    "        loss = train(model, data, optimizer)\n",
    "        train_acc, val_acc, test_acc = test(model, data)\n",
    "        epoch_time = time.time() - start_time\n",
    "        \n",
    "        train_accs.append(train_acc)\n",
    "        val_accs.append(val_acc)\n",
    "        test_accs.append(test_acc)\n",
    "        times.append(epoch_time)\n",
    "        \n",
    "        if val_acc > best_val_acc:\n",
    "            best_val_acc = val_acc\n",
    "            final_test_acc = test_acc\n",
    "        \n",
    "        if epoch % 10 == 0:\n",
    "            print(f'Epoch: {epoch:03d}, Loss: {loss:.4f}, Train: {train_acc:.4f}, '\n",
    "                  f'Val: {val_acc:.4f}, Test: {test_acc:.4f}, Time: {epoch_time:.4f}s')\n",
    "    \n",
    "    total_time = sum(times)\n",
    "    return train_accs, val_accs, test_accs, final_test_acc, total_time\n",
    "\n",
    "# Initialize models\n",
    "gcn = GCN(dataset.num_features, 16, dataset.num_classes).to(device)\n",
    "sage = GraphSAGE(dataset.num_features, 16, dataset.num_classes).to(device)\n",
    "\n",
    "# Define optimizers\n",
    "gcn_optimizer = torch.optim.Adam(gcn.parameters(), lr=0.01, weight_decay=5e-4)\n",
    "sage_optimizer = torch.optim.Adam(sage.parameters(), lr=0.01, weight_decay=5e-4)\n",
    "\n",
    "# Train and evaluate models\n",
    "print(\"Training GCN...\")\n",
    "gcn_train_accs, gcn_val_accs, gcn_test_accs, gcn_final_test_acc, gcn_time = train_and_evaluate(gcn, data, gcn_optimizer)\n",
    "\n",
    "print(\"\\nTraining GraphSAGE...\")\n",
    "sage_train_accs, sage_val_accs, sage_test_accs, sage_final_test_acc, sage_time = train_and_evaluate(sage, data, sage_optimizer)\n",
    "\n",
    "# Plotting results\n",
    "plt.figure(figsize=(15, 5))\n",
    "\n",
    "# Training Accuracy\n",
    "plt.subplot(131)\n",
    "plt.plot(gcn_train_accs, label='GCN')\n",
    "plt.plot(sage_train_accs, label='GraphSAGE')\n",
    "plt.title('Training Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "\n",
    "# Validation Accuracy\n",
    "plt.subplot(132)\n",
    "plt.plot(gcn_val_accs, label='GCN')\n",
    "plt.plot(sage_val_accs, label='GraphSAGE')\n",
    "plt.title('Validation Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "\n",
    "# Test Accuracy\n",
    "plt.subplot(133)\n",
    "plt.plot(gcn_test_accs, label='GCN')\n",
    "plt.plot(sage_test_accs, label='GraphSAGE')\n",
    "plt.title('Test Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Print final results\n",
    "print(\"\\nFinal Test Accuracies:\")\n",
    "print(f\"GCN: {gcn_final_test_acc:.4f}\")\n",
    "print(f\"GraphSAGE: {sage_final_test_acc:.4f}\")\n",
    "\n",
    "print(\"\\nTotal Training Times:\")\n",
    "print(f\"GCN: {gcn_time:.2f} seconds\")\n",
    "print(f\"GraphSAGE: {sage_time:.2f} seconds\")\n",
    "\n",
    "# Plot training times\n",
    "plt.figure(figsize=(8, 6))\n",
    "models = ['GCN', 'GraphSAGE']\n",
    "times = [gcn_time, sage_time]\n",
    "plt.bar(models, times)\n",
    "plt.title('Total Training Time Comparison')\n",
    "plt.xlabel('Models')\n",
    "plt.ylabel('Training Time (seconds)')\n",
    "for i, v in enumerate(times):\n",
    "    plt.text(i, v, f'{v:.2f}s', ha='center', va='bottom')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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