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import plotly.graph_objects as go
import plotly.express as px
import networkx as nx
import torch
import numpy as np
class GraphVisualizer:
"""Graph visualization utilities"""
@staticmethod
def create_graph_plot(data, max_nodes=500):
"""Create interactive graph visualization"""
try:
# Limit nodes for performance
num_nodes = min(data.num_nodes, max_nodes)
# Create NetworkX graph
G = nx.Graph()
edge_list = data.edge_index.t().cpu().numpy()
# Filter edges to include only first max_nodes
edge_list = edge_list[
(edge_list[:, 0] < num_nodes) & (edge_list[:, 1] < num_nodes)
]
if len(edge_list) > 0:
G.add_edges_from(edge_list)
# Add isolated nodes
G.add_nodes_from(range(num_nodes))
# Layout
if len(G.nodes()) > 100:
pos = nx.spring_layout(G, k=0.5, iterations=20)
else:
pos = nx.spring_layout(G, k=1, iterations=50)
# Node colors
if hasattr(data, 'y') and data.y is not None:
node_colors = data.y.cpu().numpy()[:num_nodes]
else:
node_colors = [0] * num_nodes
# Create edge traces
edge_x, edge_y = [], []
for edge in G.edges():
if edge[0] in pos and edge[1] in pos:
x0, y0 = pos[edge[0]]
x1, y1 = pos[edge[1]]
edge_x.extend([x0, x1, None])
edge_y.extend([y0, y1, None])
# Create node traces
node_x = [pos[node][0] for node in G.nodes() if node in pos]
node_y = [pos[node][1] for node in G.nodes() if node in pos]
fig = go.Figure()
# Add edges
if edge_x:
fig.add_trace(go.Scatter(
x=edge_x, y=edge_y,
line=dict(width=0.5, color='#888'),
hoverinfo='none',
mode='lines',
name='Edges'
))
# Add nodes
fig.add_trace(go.Scatter(
x=node_x, y=node_y,
mode='markers',
hoverinfo='text',
text=[f'Node {i}' for i in range(len(node_x))],
marker=dict(
size=8,
color=node_colors[:len(node_x)],
colorscale='Viridis',
line=dict(width=1)
),
name='Nodes'
))
fig.update_layout(
title=f'Graph Visualization ({num_nodes} nodes)',
showlegend=False,
hovermode='closest',
margin=dict(b=20, l=5, r=5, t=40),
xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
yaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
plot_bgcolor='white'
)
return fig
except Exception as e:
# Return error plot
fig = go.Figure()
fig.add_annotation(
text=f"Visualization error: {str(e)}",
x=0.5, y=0.5,
xref="paper", yref="paper",
showarrow=False
)
return fig
@staticmethod
def create_metrics_plot(metrics):
"""Create metrics visualization"""
try:
metric_names = []
metric_values = []
for key, value in metrics.items():
if isinstance(value, (int, float)) and key != 'error':
metric_names.append(key.replace('_', ' ').title())
metric_values.append(value)
if metric_names:
fig = go.Figure(data=[
go.Bar(
x=metric_names,
y=metric_values,
marker_color='lightblue'
)
])
fig.update_layout(
title='Model Performance Metrics',
xaxis_title='Metric',
yaxis_title='Value',
yaxis=dict(range=[0, 1])
)
else:
fig = go.Figure()
fig.add_annotation(
text="No metrics to display",
x=0.5, y=0.5,
xref="paper", yref="paper",
showarrow=False
)
return fig
except Exception as e:
fig = go.Figure()
fig.add_annotation(
text=f"Metrics plot error: {str(e)}",
x=0.5, y=0.5,
xref="paper", yref="paper",
showarrow=False
)
return fig |