script ready for shap vals computation

explicability/shap_vals.py

@@ -86,11 +86,13 @@ if __name__ == "__main__":
         "post_OVER": (None,None),
         "post_UNDER": (None,None),
     }
+    # # Retrieve attribute names in order
+    # df = pd.read_csv("..\gen_train_data\data\input\pre_dataset.csv")
+    # attribute_names = list(df.columns.values)
     # --------------------------------------------------------------------------------------------------------
 
     # Shap value generation
     # --------------------------------------------------------------------------------------------------------
-    shap_values = {} # Mapping group-method -> shap values
     for i, group in enumerate(['pre', 'post']):
         # Get test dataset based on group
         X_test = data_dic['X_test_' + group]
@@ -105,8 +107,14 @@ if __name__ == "__main__":
             is_tree = model_info[0]
             model = model_info[1]
             # Fit model with training data
-            fitted_model = model.fit(X_train, y_train) # [:500]?
+            fitted_model = model.fit(X_train[:500], y_train[:500])
             # Check if we are dealing with a tree vs nn model
             if is_tree:
-                explainer = shap.TreeExplainer(fitted_model, X_test) # [:500]?
+                explainer = shap.TreeExplainer(fitted_model, X_test[:500])
+            else:
+                explainer = shap.KernelExplainer(fitted_model.predict, 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"shap_values/{group}_{method_names[j]}", shap_vals)
     # --------------------------------------------------------------------------------------------------------
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