Initial commit

skills/torch_geometric/scripts/visualize_graph.py

@@ -0,0 +1,313 @@
+#!/usr/bin/env python3
+"""
+Visualize PyTorch Geometric graph structures using networkx and matplotlib.
+
+This script provides utilities to visualize Data objects, including:
+- Graph structure (nodes and edges)
+- Node features (as colors)
+- Edge attributes (as edge colors/widths)
+- Community/cluster assignments
+
+Usage:
+    python visualize_graph.py --dataset Cora --output graph.png
+
+Or import and use:
+    from scripts.visualize_graph import visualize_data
+    visualize_data(data, title="My Graph", show_labels=True)
+"""
+
+import argparse
+import matplotlib.pyplot as plt
+import networkx as nx
+import torch
+from typing import Optional, Union
+import numpy as np
+
+
+def visualize_data(
+    data,
+    title: str = "Graph Visualization",
+    node_color_attr: Optional[str] = None,
+    edge_color_attr: Optional[str] = None,
+    show_labels: bool = False,
+    node_size: int = 300,
+    figsize: tuple = (12, 10),
+    layout: str = "spring",
+    output_path: Optional[str] = None,
+    max_nodes: Optional[int] = None,
+):
+    """
+    Visualize a PyTorch Geometric Data object.
+
+    Args:
+        data: PyTorch Geometric Data object
+        title: Plot title
+        node_color_attr: Data attribute to use for node colors (e.g., 'y', 'train_mask')
+        edge_color_attr: Data attribute to use for edge colors
+        show_labels: Whether to show node labels
+        node_size: Size of nodes in visualization
+        figsize: Figure size (width, height)
+        layout: Graph layout algorithm ('spring', 'circular', 'kamada_kawai', 'spectral')
+        output_path: Path to save figure (if None, displays interactively)
+        max_nodes: Maximum number of nodes to visualize (samples if exceeded)
+    """
+    # Sample nodes if graph is too large
+    if max_nodes and data.num_nodes > max_nodes:
+        print(f"Graph has {data.num_nodes} nodes. Sampling {max_nodes} nodes for visualization.")
+        node_indices = torch.randperm(data.num_nodes)[:max_nodes]
+        data = data.subgraph(node_indices)
+
+    # Convert to networkx graph
+    G = nx.Graph() if is_undirected(data.edge_index) else nx.DiGraph()
+
+    # Add nodes
+    G.add_nodes_from(range(data.num_nodes))
+
+    # Add edges
+    edge_index = data.edge_index.cpu().numpy()
+    edges = list(zip(edge_index[0], edge_index[1]))
+    G.add_edges_from(edges)
+
+    # Setup figure
+    fig, ax = plt.subplots(figsize=figsize)
+
+    # Choose layout
+    if layout == "spring":
+        pos = nx.spring_layout(G, k=0.5, iterations=50)
+    elif layout == "circular":
+        pos = nx.circular_layout(G)
+    elif layout == "kamada_kawai":
+        pos = nx.kamada_kawai_layout(G)
+    elif layout == "spectral":
+        pos = nx.spectral_layout(G)
+    else:
+        raise ValueError(f"Unknown layout: {layout}")
+
+    # Determine node colors
+    if node_color_attr and hasattr(data, node_color_attr):
+        node_colors = getattr(data, node_color_attr).cpu().numpy()
+        if node_colors.dtype == bool:
+            node_colors = node_colors.astype(int)
+        if len(node_colors.shape) > 1:
+            # Multi-dimensional features - use first dimension
+            node_colors = node_colors[:, 0]
+    else:
+        node_colors = 'skyblue'
+
+    # Determine edge colors
+    if edge_color_attr and hasattr(data, edge_color_attr):
+        edge_colors = getattr(data, edge_color_attr).cpu().numpy()
+        if len(edge_colors.shape) > 1:
+            edge_colors = edge_colors[:, 0]
+    else:
+        edge_colors = 'gray'
+
+    # Draw graph
+    nx.draw_networkx_nodes(
+        G, pos,
+        node_color=node_colors,
+        node_size=node_size,
+        cmap=plt.cm.viridis,
+        ax=ax
+    )
+
+    nx.draw_networkx_edges(
+        G, pos,
+        edge_color=edge_colors,
+        alpha=0.3,
+        arrows=isinstance(G, nx.DiGraph),
+        arrowsize=10,
+        ax=ax
+    )
+
+    if show_labels:
+        nx.draw_networkx_labels(G, pos, font_size=8, ax=ax)
+
+    ax.set_title(title, fontsize=16, fontweight='bold')
+    ax.axis('off')
+
+    # Add colorbar if using numeric node colors
+    if node_color_attr and isinstance(node_colors, np.ndarray):
+        sm = plt.cm.ScalarMappable(
+            cmap=plt.cm.viridis,
+            norm=plt.Normalize(vmin=node_colors.min(), vmax=node_colors.max())
+        )
+        sm.set_array([])
+        cbar = plt.colorbar(sm, ax=ax, fraction=0.046, pad=0.04)
+        cbar.set_label(node_color_attr, rotation=270, labelpad=20)
+
+    plt.tight_layout()
+
+    if output_path:
+        plt.savefig(output_path, dpi=300, bbox_inches='tight')
+        print(f"Figure saved to {output_path}")
+    else:
+        plt.show()
+
+    plt.close()
+
+
+def is_undirected(edge_index):
+    """Check if graph is undirected."""
+    row, col = edge_index
+    num_edges = edge_index.size(1)
+
+    # Create a set of edges and reverse edges
+    edges = set(zip(row.tolist(), col.tolist()))
+    reverse_edges = set(zip(col.tolist(), row.tolist()))
+
+    # Check if all edges have their reverse
+    return edges == reverse_edges
+
+
+def plot_degree_distribution(data, output_path: Optional[str] = None):
+    """Plot the degree distribution of the graph."""
+    from torch_geometric.utils import degree
+
+    row, col = data.edge_index
+    deg = degree(col, data.num_nodes).cpu().numpy()
+
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))
+
+    # Histogram
+    ax1.hist(deg, bins=50, edgecolor='black', alpha=0.7)
+    ax1.set_xlabel('Degree', fontsize=12)
+    ax1.set_ylabel('Frequency', fontsize=12)
+    ax1.set_title('Degree Distribution', fontsize=14, fontweight='bold')
+    ax1.grid(alpha=0.3)
+
+    # Log-log plot
+    unique_degrees, counts = np.unique(deg, return_counts=True)
+    ax2.loglog(unique_degrees, counts, 'o-', alpha=0.7)
+    ax2.set_xlabel('Degree (log scale)', fontsize=12)
+    ax2.set_ylabel('Frequency (log scale)', fontsize=12)
+    ax2.set_title('Degree Distribution (Log-Log)', fontsize=14, fontweight='bold')
+    ax2.grid(alpha=0.3)
+
+    plt.tight_layout()
+
+    if output_path:
+        plt.savefig(output_path, dpi=300, bbox_inches='tight')
+        print(f"Degree distribution saved to {output_path}")
+    else:
+        plt.show()
+
+    plt.close()
+
+
+def plot_graph_statistics(data, output_path: Optional[str] = None):
+    """Plot various graph statistics."""
+    from torch_geometric.utils import degree, contains_self_loops, is_undirected as check_undirected
+
+    # Compute statistics
+    row, col = data.edge_index
+    deg = degree(col, data.num_nodes).cpu().numpy()
+
+    stats = {
+        'Nodes': data.num_nodes,
+        'Edges': data.num_edges,
+        'Avg Degree': deg.mean(),
+        'Max Degree': deg.max(),
+        'Self-loops': contains_self_loops(data.edge_index),
+        'Undirected': check_undirected(data.edge_index),
+    }
+
+    if hasattr(data, 'num_node_features'):
+        stats['Node Features'] = data.num_node_features
+    if hasattr(data, 'num_edge_features') and data.edge_attr is not None:
+        stats['Edge Features'] = data.num_edge_features
+    if hasattr(data, 'y'):
+        if data.y.dim() == 1:
+            stats['Classes'] = int(data.y.max().item()) + 1
+
+    # Create text plot
+    fig, ax = plt.subplots(figsize=(8, 6))
+    ax.axis('off')
+
+    text = "Graph Statistics\n" + "=" * 40 + "\n\n"
+    for key, value in stats.items():
+        text += f"{key:20s}: {value}\n"
+
+    ax.text(0.1, 0.5, text, fontsize=14, family='monospace',
+            verticalalignment='center', transform=ax.transAxes)
+
+    plt.tight_layout()
+
+    if output_path:
+        plt.savefig(output_path, dpi=300, bbox_inches='tight')
+        print(f"Statistics saved to {output_path}")
+    else:
+        plt.show()
+
+    plt.close()
+
+    # Print to console as well
+    print("\n" + text)
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Visualize PyTorch Geometric graphs")
+    parser.add_argument('--dataset', type=str, default='Cora',
+                        help='Dataset name (e.g., Cora, CiteSeer, ENZYMES)')
+    parser.add_argument('--output', type=str, default=None,
+                        help='Output file path for visualization')
+    parser.add_argument('--node-color', type=str, default='y',
+                        help='Attribute to use for node colors')
+    parser.add_argument('--layout', type=str, default='spring',
+                        choices=['spring', 'circular', 'kamada_kawai', 'spectral'],
+                        help='Graph layout algorithm')
+    parser.add_argument('--show-labels', action='store_true',
+                        help='Show node labels')
+    parser.add_argument('--max-nodes', type=int, default=500,
+                        help='Maximum nodes to visualize')
+    parser.add_argument('--stats', action='store_true',
+                        help='Show graph statistics')
+    parser.add_argument('--degree', action='store_true',
+                        help='Show degree distribution')
+
+    args = parser.parse_args()
+
+    # Load dataset
+    print(f"Loading dataset: {args.dataset}")
+
+    try:
+        # Try Planetoid datasets
+        from torch_geometric.datasets import Planetoid
+        dataset = Planetoid(root=f'/tmp/{args.dataset}', name=args.dataset)
+        data = dataset[0]
+    except:
+        try:
+            # Try TUDataset
+            from torch_geometric.datasets import TUDataset
+            dataset = TUDataset(root=f'/tmp/{args.dataset}', name=args.dataset)
+            data = dataset[0]
+        except Exception as e:
+            print(f"Error loading dataset: {e}")
+            print("Supported datasets: Cora, CiteSeer, PubMed, ENZYMES, PROTEINS, etc.")
+            return
+
+    print(f"Loaded {args.dataset}: {data.num_nodes} nodes, {data.num_edges} edges")
+
+    # Generate visualizations
+    if args.stats:
+        stats_output = args.output.replace('.png', '_stats.png') if args.output else None
+        plot_graph_statistics(data, stats_output)
+
+    if args.degree:
+        degree_output = args.output.replace('.png', '_degree.png') if args.output else None
+        plot_degree_distribution(data, degree_output)
+
+    # Main visualization
+    visualize_data(
+        data,
+        title=f"{args.dataset} Graph",
+        node_color_attr=args.node_color,
+        show_labels=args.show_labels,
+        layout=args.layout,
+        output_path=args.output,
+        max_nodes=args.max_nodes
+    )
+
+
+if __name__ == '__main__':
+    main()