Progress made on shap (still pending to see predict_proba and X_train vs test)

explicability/shap_vals.py

@@ -110,8 +110,10 @@ def get_chosen_model(group_str, method_str, model_name):
 
     # Initialize the model with the parameters
     chosen_model = model_class(**parameters)
+    # Return if it is a tree model, for SHAP
+    is_tree = model_name not in ['LR', 'SVM', 'MLP']
     
-    return chosen_model
+    return chosen_model, is_tree
 # --------------------------------------------------------------------------------------------------------
 
 if __name__ == "__main__":
@@ -133,42 +135,36 @@ if __name__ == "__main__":
         "OVER": "XGB",
         "UNDER": "XGB"
     }
-    # # Retrieve attribute names in order
-    # df = pd.read_csv("..\gen_train_data\data\input\pre_dataset.csv")
-    # attribute_names = list(df.columns.values)
+    # 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
     # --------------------------------------------------------------------------------------------------------
     for i, group in enumerate(['pre', 'post']):
         # Get test dataset based on group
-        X_test = data_dic['X_test_' + group]
+        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_']):
             print(f"{group}-{method_names[j]}")
             # Get train dataset based on group and method
-            X_train = data_dic['X_train_' + method + group]
+            X_train = pd.DataFrame(data_dic['X_train_' + method + group], columns=attribute_names)
             y_train = data_dic['y_train_' + method + group]
             method_name = method_names[j]
             # Get chosen tuned model for this group and method context
-            model = get_chosen_model(group_str=group, method_str=method_name, model_name=model_choices[method_name])
-            print(f'Name: {model_choices[method_name]}')
-            print(model.get_params())
-            # # --------------------------------------------------------------------------------------------------------
-            # # Retrieve best model for this group-method context
-            # model_info = models[group + '_' + method_names[j]]
-            # is_tree = model_info[0]
-            # model = model_info[1]
-            # # 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
-            # if is_tree:
-            #     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
-            # # ---------------------------------------------------------------------------------------------------------
+            model, is_tree = get_chosen_model(group_str=group, method_str=method_name, model_name=model_choices[method_name])
+            # --------------------------------------------------------------------------------------------------------
+            # 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
+            if is_tree:
+                explainer = shap.TreeExplainer(fitted_model, X_test[:500])
+            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"shap_values/{group}_{method_names[j]}", shap_vals)
+            np.save(f"./output/shap_values/{group}_{method_names[j]}", shap_vals)
     # --------------------------------------------------------------------------------------------------------
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