Identified problem with features

explicability/shap_vals.py

@@ -143,7 +143,7 @@ if __name__ == "__main__":
     # Shap value generation
     # --------------------------------------------------------------------------------------------------------
     for i, group in enumerate(['pre', 'post']):
-        # Get test dataset based on group
+        # Get test dataset based on group, add column names
         X_test = pd.DataFrame(data_dic['X_test_' + group], columns=attribute_names)
         y_test = data_dic['y_test_' + group]
         for j, method in enumerate(['', '', 'over_', 'under_']):
@@ -157,14 +157,15 @@ if __name__ == "__main__":
             # --------------------------------------------------------------------------------------------------------
             # Fit model with training data
             fitted_model = model.fit(X_train[:500], y_train[:500])
-            # Check if we are dealing with a tree vs nn model
+            # # Check if we are dealing with a tree vs nn model
             if is_tree:
-                explainer = shap.TreeExplainer(fitted_model, X_test[:500])
-            else:
-                explainer = shap.KernelExplainer(fitted_model.predict_proba, X_test[:500])
+                 explainer = shap.TreeExplainer(fitted_model)
+            # else:
+            #     explainer = shap.KernelExplainer(fitted_model.predict_proba, X_test[:500])
             # Compute shap values
             shap_vals = explainer.shap_values(X_test[:500], check_additivity=False) # Change to true for final results
             # ---------------------------------------------------------------------------------------------------------
             # Save results
             np.save(f"./output/shap_values/{group}_{method_names[j]}", shap_vals)
+            print(f'Shape of numpy array: {shap_vals.shape}')
     # --------------------------------------------------------------------------------------------------------
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