Completed comments

explainability/compute_shap_inter_vals.py

+# Computing SHAP Interaction Values
+# Author: Joaquín Torres Bravo
+"""
+    Script to compute SHAP interaction values for chosen models.
+"""
+
 # Libraries
 # --------------------------------------------------------------------------------------------------------
 import pandas as pd
 import numpy as np
-import shap
-import pickle
+import shap # Explainability
+import pickle # Loading/saving models
+# Models
 from xgboost import XGBClassifier
 from sklearn.ensemble import RandomForestClassifier, BaggingClassifier, AdaBoostClassifier
 from sklearn.neural_network import MLPClassifier
@@ -60,10 +67,6 @@ if __name__ == "__main__":
         X_test = data_dic['X_test_' + group]
         y_test = data_dic['y_test_' + group]
         for j, method in enumerate(['', '', 'over_', 'under_']):
-            # Remove (used to isolate RF)
-            # if j != 1:
-            #     print('Skip')
-            #     continue
             print(f"{group}-{method_names[j]}")
             method_name = method_names[j]
             model_name = model_choices[method_name]
@@ -76,7 +79,7 @@ if __name__ == "__main__":
             if is_tree:
                  explainer = shap.TreeExplainer(fitted_model)
             # else:
-            #     explainer = shap.KernelExplainer(fitted_model.predict_proba, X_test[:500])
+            #     explainer = shap.KernelExplainer...
             # Compute shap values
             shap_interaction_values = explainer.shap_interaction_values(X_test)
             # ---------------------------------------------------------------------------------------------------------