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def generate_agent_actions(fig, feature_map, red_nodes, central_node, scores, threshold=0.7):
"""
Suggests actions based on node anomaly scores and graph structure.
Args:
fig (matplotlib.Figure): The plotted graph figure.
feature_map (list): List mapping node indices to feature names.
red_nodes (list): Indices of detected anomalous nodes.
central_node (int): The node with highest anomaly score.
scores (np.ndarray): Anomaly score array per node.
threshold (float): Minimum score to trigger an action.
Returns:
list of str: Recommended actions for inspection.
"""
actions = []
# Get name of central node
try:
central_name = feature_map[central_node]
except IndexError:
central_name = f"Node {central_node}"
central_score = scores[central_node]
actions.append(f"🔍 Investigate central anomaly node: {central_name} (score: {central_score:.2f})")
for node in red_nodes:
if node == central_node:
continue
try:
node_name = feature_map[node]
except IndexError:
node_name = f"Node {node}"
node_score = scores[node]
if node_score > threshold:
actions.append(f"🔧 Inspect connected node: {node_name} (score: {node_score:.2f})")
else:
actions.append(f"ℹ️ Monitor node: {node_name} (score: {node_score:.2f})")
# Sort actions by score
actions.sort(key=lambda x: float(x.split("score:")[-1].rstrip(")").strip()), reverse=True)
return actions
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