mengzhx/semantica
1
0
Fork 0
mirror of https://github.com/semantica-agi/semantica.git synced 2026-08-29 04:26:20 +00:00
Code Issues Packages Projects Releases Wiki Activity

fix: Resolve centrality result misread in DecisionQuery

Browse Source 
- Fix centrality access to properly read nested 'centrality' dictionary structure
- Update calculate_degree_centrality result access from centrality.get(decision_id) to centrality.get('centrality', {}).get(decision_id)
- Fix calculate_all_centrality result access to extract measures from nested wrapper structure
- Correct influence score calculation to use proper centrality measure keys
- Ensure centrality boosts and influence values are calculated correctly
This commit is contained in:
KaifAhmad1
2026-02-13 16:55:23 +05:30
parent 7a25a7791e
commit 7a24273f41
1 changed files with 16 additions and 7 deletions
Download Patch File Download Diff File Expand all files Collapse all files
semantica/context/decision_query.py
+16 -7
View File
@@ -783,7 +783,7 @@ class DecisionQuery:
# Calculate degree centrality
centrality = self.kg_components["centrality_calculator"].calculate_degree_centrality(subgraph)
boost = centrality.get(decision_id, 0.0)
boost = centrality.get('centrality', {}).get(decision_id, 0.0)
# Cache the result
self._cache[cache_key] = boost
@@ -882,16 +882,25 @@ class DecisionQuery:
# Calculate centrality measures
if "centrality_calculator" in self.kg_components:
subgraph = self._get_decision_subgraph(decision_id, max_depth)
centrality_measures = self.kg_components["centrality_calculator"].calculate_all_centrality(subgraph)
analysis["centrality_measures"] = centrality_measures.get(decision_id, {})
centrality_results = self.kg_components["centrality_calculator"].calculate_all_centrality(subgraph)
# Extract centrality measures for this decision from nested structure
decision_measures = {}
centrality_measures = centrality_results.get('centrality_measures', {})
for measure_type, measure_data in centrality_measures.items():
if isinstance(measure_data, dict) and 'centrality' in measure_data:
decision_measures[measure_type] = measure_data['centrality'].get(decision_id, 0.0)
analysis["centrality_measures"] = decision_measures
# Calculate overall influence score
measures = analysis["centrality_measures"]
analysis["influence_score"] = (
0.3 * measures.get("degree_centrality", 0.0) +
0.3 * measures.get("betweenness_centrality", 0.0) +
0.2 * measures.get("closeness_centrality", 0.0) +
0.2 * measures.get("eigenvector_centrality", 0.0)
0.3 * measures.get("degree", 0.0) +
0.3 * measures.get("betweenness", 0.0) +
0.2 * measures.get("closeness", 0.0) +
0.2 * measures.get("eigenvector", 0.0)
)
# Community detection