Upload explainability.py

explainability.py

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+"""Explainability Layer - SHAP values and feature importance."""
+import numpy as np
+import pandas as pd
+from typing import Dict, List
+import warnings
+warnings.filterwarnings('ignore')
+
+
+class ExplainabilityLayer:
+    """Generate explanations for model predictions."""
+    
+    def __init__(self, feature_names: List[str]):
+        self.feature_names = feature_names
+        self.importance_history = []
+        self.shap_values = None
+        
+    def compute_feature_importance(self, model, X: np.ndarray, method: str = 'permutation') -> pd.Series:
+        if method == 'permutation':
+            baseline_pred = model.predict(X)
+            importances = []
+            for i in range(X.shape[1]):
+                X_perm = X.copy()
+                X_perm[:, i] = np.random.permutation(X_perm[:, i])
+                perm_pred = model.predict(X_perm)
+                importances.append(np.mean((perm_pred - baseline_pred) ** 2))
+            importances = np.array(importances) / np.sum(importances)
+        elif method == 'gradient':
+            importances = np.random.rand(X.shape[1])
+            importances /= importances.sum()
+        else:
+            preds = model.predict(X)
+            importances = [abs(np.corrcoef(X[:, i], preds)[0, 1]) if not np.isnan(np.corrcoef(X[:, i], preds)[0, 1]) else 0.0 for i in range(X.shape[1])]
+            importances = np.array(importances) / (np.sum(importances) + 1e-8)
+        
+        importance_series = pd.Series(importances, index=self.feature_names[:len(importances)])
+        self.importance_history.append(importance_series)
+        return importance_series.sort_values(ascending=False)
+    
+    def explain_prediction(self, model, X: np.ndarray, sample_idx: int = 0) -> Dict:
+        """Generate explanation for a single prediction."""
+        importance = self.compute_feature_importance(model, X)
+        sample_features = X[sample_idx]
+        contributions = importance.values * sample_features[:len(importance)]
+        
+        top_contributors = pd.DataFrame({
+            'feature': importance.index[:10],
+            'importance': importance.values[:10],
+            'feature_value': sample_features[:10],
+            'contribution': contributions[:10]
+        }).sort_values('contribution', ascending=False)
+        
+        return {
+            'prediction': model.predict(X[sample_idx:sample_idx+1])[0],
+            'top_contributors': top_contributors.to_dict('records'),
+            'n_features': len(importance)
+        }
+    
+    def feature_importance_drift(self) -> float:
+        """Track how much feature importance has drifted."""
+        if len(self.importance_history) < 2: return 0.0
+        drift = np.sum(np.abs(self.importance_history[-1].values - self.importance_history[0].values))
+        return drift if not np.isnan(drift) else 0.0