Update src/streamlit_app.py

src/streamlit_app.py

@@ -617,17 +617,42 @@ with tabs[4]:
             selector = SelectKBest(f_regression, k=min(40, X_scaled.shape[1]))
             X_sel = pd.DataFrame(selector.fit_transform(X_scaled, y), columns=[X.columns[i] for i in selector.get_support(indices=True)])
 
+            # --- Safe stacking ensemble build ---
             kf = KFold(n_splits=5, shuffle=True, random_state=42)
-            oof_preds, base_models = pd.DataFrame(index=X_sel.index), []
-            for fam, entry in [(r["family"], r) for r in tuned_results if r.get("model_obj")]:
+            oof_preds = pd.DataFrame(index=X_sel.index)
+            base_models = []
+            
+            # Explicitly filter valid models (no truthiness eval)
+            valid_results = []
+            for r in tuned_results:
+                m = r.get("model_obj", None)
+                if isinstance(m, object) and hasattr(m, "fit") and callable(getattr(m, "fit", None)):
+                    valid_results.append((r["family"], r))
+            
+            # Train each base model safely
+            for fam, entry in valid_results:
                 model = entry["model_obj"]
                 preds = np.zeros(X_sel.shape[0])
                 for tr, va in kf.split(X_sel):
-                    model.fit(X_sel.iloc[tr], y.iloc[tr])
-                    preds[va] = model.predict(X_sel.iloc[va])
+                    try:
+                        model.fit(X_sel.iloc[tr], y.iloc[tr])
+                        preds[va] = model.predict(X_sel.iloc[va])
+                    except Exception as e:
+                        st.warning(f"⚠️ {fam} failed in fold: {e}")
                 oof_preds[f"{fam}_oof"] = preds
-                model.fit(X_sel, y)
-                base_models.append({"family": fam, "model": model})
+                try:
+                    model.fit(X_sel, y)
+                    base_models.append({"family": fam, "model": model})
+                except Exception as e:
+                    st.warning(f"⚠️ {fam} full-fit failed: {e}")
+            
+            # Meta model on OOF predictions
+            meta = LinearRegression(positive=True)
+            meta.fit(oof_preds, y)
+            y_pred = meta.predict(oof_preds)
+            final_r2 = r2_score(y, y_pred)
+            st.success(f"Stacked Ensemble R² = {final_r2:.4f}")
+
 
             meta = LinearRegression(positive=True)
             meta.fit(oof_preds, y)