switch shap endpoints to offline cache

backend/services/shap_service.py

@@ -1,3 +1,7 @@
+import json
+import os
+
+import config
 from core.shap_analysis import SHAPAnalyzer
 
 
@@ -11,21 +15,60 @@ class SHAPService:
         if self._analyzer is None:
             self._analyzer = SHAPAnalyzer()
 
+    def _get_cache_path(self, model_type):
+        return os.path.join(config.SHAP_CACHE_DIR, f'{model_type}.json')
+
+    def _load_cache(self, model_type):
+        cache_path = self._get_cache_path(model_type)
+        if not os.path.exists(cache_path):
+            return None
+        try:
+            with open(cache_path, 'r', encoding='utf-8') as fp:
+                return json.load(fp)
+        except Exception:
+            return None
+
     def get_global_importance(self, model_type='random_forest'):
-        self._ensure_analyzer()
-        return self._analyzer.global_shap_values(model_type)
+        cache = self._load_cache(model_type)
+        if not cache:
+            return {
+                'error': f'SHAP cache not found for {model_type}. '
+                         f'Run backend/core/generate_shap_cache.py first.'
+            }
+        return cache.get('global', {'error': f'Invalid SHAP cache for {model_type}'})
 
     def get_local_explanation(self, data, model_type='random_forest'):
         self._ensure_analyzer()
         return self._analyzer.local_shap_values(data, model_type)
 
     def get_interactions(self, model_type='random_forest', top_n=10):
-        self._ensure_analyzer()
-        return self._analyzer.shap_interaction(model_type, top_n)
+        cache = self._load_cache(model_type)
+        if not cache:
+            return {
+                'error': f'SHAP cache not found for {model_type}. '
+                         f'Run backend/core/generate_shap_cache.py first.'
+            }
+        data = cache.get('interaction')
+        if not data:
+            return {'error': f'Interaction cache missing for {model_type}'}
+        if top_n and data.get('top_interactions'):
+            result = dict(data)
+            result['top_interactions'] = data['top_interactions'][:top_n]
+            return result
+        return data
 
     def get_dependence(self, feature_name, model_type='random_forest'):
-        self._ensure_analyzer()
-        return self._analyzer.shap_dependence(feature_name, model_type)
+        cache = self._load_cache(model_type)
+        if not cache:
+            return {
+                'error': f'SHAP cache not found for {model_type}. '
+                         f'Run backend/core/generate_shap_cache.py first.'
+            }
+        dependence_map = cache.get('dependence', {})
+        data = dependence_map.get(feature_name)
+        if data:
+            return data
+        return {'error': f'Dependence cache missing for feature {feature_name}'}
 
 
 shap_service = SHAPService()