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Heatmap Component
Gradio component for displaying attack vulnerability heatmaps.
"""
import logging
from typing import Any, List, Optional
import gradio as gr
import plotly.graph_objects as go
from dashboard.schemas import HeatmapData
from dashboard.utils import log_dashboard_event
logger = logging.getLogger(__name__)
def create_heatmap_chart() -> gr.Plot:
"""
Create heatmap chart component.
Returns:
Plot component
"""
plot = gr.Plot(
label="Attack Vulnerability Heatmap",
)
return plot
def update_heatmap_chart(
heatmap_data: Optional[HeatmapData],
) -> Any:
"""
Update heatmap chart with data.
Args:
heatmap_data: Heatmap data
Returns:
Plotly figure
"""
if heatmap_data is None or not heatmap_data.attack_types:
# Return empty chart
fig = go.Figure()
fig.update_layout(
title="No Data Available",
xaxis=dict(title="Metrics"),
yaxis=dict(title="Attack Types"),
)
return fig
log_dashboard_event("DASHBOARD_VIEW_HEATMAP", run_id=heatmap_data.run_id)
# Create heatmap
fig = go.Figure(
data=go.Heatmap(
z=heatmap_data.values,
x=heatmap_data.metrics,
y=heatmap_data.attack_types,
colorscale="RdYlGn_r", # Red (high) to Green (low) - reversed
zmin=0,
zmax=1,
colorbar=dict(
title=dict(text="Metric Value", side="right"),
),
hovertemplate=(
"<b>Attack:</b> %{y}<br>"
"<b>Metric:</b> %{x}<br>"
"<b>Value:</b> %{z:.3f}<extra></extra>"
),
)
)
fig.update_layout(
title="Attack Vulnerability Heatmap",
xaxis=dict(title="Metrics"),
yaxis=dict(
title="Attack Types",
autorange="reversed", # Top to bottom
),
height=500,
width=700,
)
return fig
def create_comparison_heatmap(
heatmap_data_list: List[HeatmapData],
model_names: List[str],
) -> Any:
"""
Create comparison heatmap across multiple runs.
Args:
heatmap_data_list: List of heatmap data
model_names: List of model names
Returns:
Plotly figure
"""
if not heatmap_data_list:
# Return empty chart
fig = go.Figure()
fig.update_layout(title="No Data Available")
return fig
# For comparison, we'll show the average vulnerability
import numpy as np
all_attack_types = set()
for hd in heatmap_data_list:
all_attack_types.update(hd.attack_types)
attack_types = sorted(list(all_attack_types))
metrics = heatmap_data_list[0].metrics if heatmap_data_list else []
# Calculate average values
avg_values = []
for attack_type in attack_types:
row = []
for metric in metrics:
values = []
for hd in heatmap_data_list:
if attack_type in hd.attack_types and metric in hd.metrics:
idx = hd.attack_types.index(attack_type)
m_idx = hd.metrics.index(metric)
values.append(hd.values[idx][m_idx])
if values:
row.append(np.mean(values))
else:
row.append(0.0)
avg_values.append(row)
fig = go.Figure(
data=go.Heatmap(
z=avg_values,
x=metrics,
y=attack_types,
colorscale="RdYlGn_r",
zmin=0,
zmax=1,
colorbar=dict(
title=dict(text="Avg Value", side="right"),
),
)
)
fig.update_layout(
title="Average Attack Vulnerability Across Models",
xaxis=dict(title="Metrics"),
yaxis=dict(
title="Attack Types",
autorange="reversed",
),
height=500,
width=700,
)
return fig
class HeatmapChart:
"""
Heatmap chart component with state management.
"""
def __init__(self):
"""Initialize heatmap chart."""
self._current_data: Optional[HeatmapData] = None
def set_data(self, data: HeatmapData) -> None:
"""
Set heatmap data.
Args:
data: Heatmap data
"""
self._current_data = data
def get_data(self) -> Optional[HeatmapData]:
"""
Get current heatmap data.
Returns:
Current heatmap data or None
"""
return self._current_data
def get_figure(self) -> Any:
"""
Get Plotly figure.
Returns:
Plotly figure
"""
return update_heatmap_chart(self._current_data)
@staticmethod
def create_empty() -> Any:
"""
Create empty heatmap chart.
Returns:
Empty Plotly figure
"""
fig = go.Figure()
fig.update_layout(
title="Select a run to view vulnerability heatmap",
xaxis=dict(title="Metrics"),
yaxis=dict(title="Attack Types"),
height=400,
width=600,
)
return fig
def get_heatmap_tooltip() -> str:
"""
Get heatmap tooltip explanation.
Returns:
Tooltip string
"""
return (
"Heatmap shows mean metric values for each attack type.\n\n"
"Color Scale:\n"
"• Red (1.0) = High vulnerability (bad)\n"
"• Yellow (0.5) = Medium vulnerability\n"
"• Green (0.0) = Low vulnerability (good)\n\n"
"Formula: M_ij = mean(metric_j | attack_i)"
)
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