reuse global shap cache for detail views

backend/core/shap_analysis.py

@@ -25,6 +25,7 @@ class SHAPAnalyzer:
         self.label_encoders = {}
         self.background_data = {}
         self.global_result_cache = {}
+        self.global_matrix_cache = {}
         self._initialized = False
 
     def _ensure_initialized(self):
@@ -168,6 +169,10 @@ class SHAPAnalyzer:
             shap_values = explainer.shap_values(X)
             if isinstance(shap_values, list):
                 shap_values = shap_values[0]
+            self.global_matrix_cache[model_type] = {
+                'X': X,
+                'shap_values': shap_values,
+            }
 
             mean_abs_shap = np.abs(shap_values).mean(axis=0)
             feature_names = self.selected_features or self.feature_names or []
@@ -303,35 +308,33 @@ class SHAPAnalyzer:
             return {'error': str(exc)}
 
     def shap_interaction(self, model_type='random_forest', top_n=10):
-        """计算 SHAP 交互值"""
+        """计算近似 SHAP 交互强度"""
         if not SHAP_AVAILABLE:
             return {'error': 'SHAP library not installed'}
 
         self._ensure_initialized()
-        explainer = self._get_tree_explainer(model_type)
+        if model_type not in self.global_matrix_cache:
-        if explainer is None:
+            result = self.global_shap_values(model_type)
-            return {'error': f'No tree model available for {model_type}'}
+            if result.get('error'):
-
+                return result
-        X = self._get_background_sample(n_samples=12)
-        if X is None:
-            return {'error': 'Failed to prepare background data'}
 
         try:
-            interaction_values = explainer.shap_interaction_values(X)
+            cached = self.global_matrix_cache.get(model_type)
-            if isinstance(interaction_values, list):
+            if not cached:
-                interaction_values = interaction_values[0]
+                return {'error': 'Failed to prepare SHAP cache'}
-
+            shap_values = np.asarray(cached['shap_values'])
-            mean_interaction = np.abs(interaction_values).mean(axis=0)
             feature_names = self.selected_features or self.feature_names or []
+            if shap_values.ndim != 2 or shap_values.shape[0] < 2:
+                return {'error': 'Not enough SHAP samples for interaction analysis'}
 
-            # 获取 top_n 特征的交互
+            corr_matrix = np.nan_to_num(np.corrcoef(shap_values, rowvar=False))
-            mean_abs = np.abs(interaction_values.mean(axis=0))
+            strength_matrix = np.abs(corr_matrix)
-            np.fill_diagonal(mean_abs, 0)
+            np.fill_diagonal(strength_matrix, 0)
-            flat_idx = np.argsort(mean_abs.ravel())[::-1][:top_n * 2]
+            flat_idx = np.argsort(strength_matrix.ravel())[::-1][:top_n * 2]
             top_pairs = []
             seen = set()
             for idx in flat_idx:
-                i, j = divmod(idx, mean_abs.shape[1])
+                i, j = divmod(idx, strength_matrix.shape[1])
                 if i >= j:
                     continue
                 pair_key = (min(i, j), max(i, j))
@@ -346,7 +349,7 @@ class SHAPAnalyzer:
                     'feature_1_cn': name_map.get(fi, fi),
                     'feature_2': fj,
                     'feature_2_cn': name_map.get(fj, fj),
-                    'strength': round(float(mean_interaction[i, j]), 4),
+                    'strength': round(float(strength_matrix[i, j]), 4),
                 })
                 if len(top_pairs) >= top_n:
                     break
@@ -364,24 +367,22 @@ class SHAPAnalyzer:
             return {'error': 'SHAP library not installed'}
 
         self._ensure_initialized()
-        explainer = self._get_tree_explainer(model_type)
+        if model_type not in self.global_matrix_cache:
-        if explainer is None:
+            result = self.global_shap_values(model_type)
-            return {'error': f'No tree model available for {model_type}'}
+            if result.get('error'):
-
+                return result
-        X = self._get_background_sample(n_samples=24)
-        if X is None:
-            return {'error': 'Failed to prepare background data'}
 
         try:
+            cached = self.global_matrix_cache.get(model_type)
+            if not cached:
+                return {'error': 'Failed to prepare SHAP cache'}
             feature_names = self.selected_features or self.feature_names or []
             if feature_name not in feature_names:
                 return {'error': f'Feature {feature_name} not found'}
 
             col_idx = list(feature_names).index(feature_name)
-            shap_values = explainer.shap_values(X)
+            X = np.asarray(cached['X'])
-            if isinstance(shap_values, list):
+            shap_values = np.asarray(cached['shap_values'])
-                shap_values = shap_values[0]
-
             feature_vals = X[:, col_idx].tolist()
             shap_vals = shap_values[:, col_idx].tolist()