Script to compute shap interaction values

explicability/compute_shap_inter_vals.py

+# Libraries
+# --------------------------------------------------------------------------------------------------------
+import pandas as pd
+import numpy as np
+import shap
+import pickle
+from xgboost import XGBClassifier
+from sklearn.ensemble import RandomForestClassifier, BaggingClassifier, AdaBoostClassifier
+from sklearn.neural_network import MLPClassifier
+from sklearn.svm import SVC
+from sklearn.linear_model import  LogisticRegression
+from sklearn.tree import DecisionTreeClassifier
+# --------------------------------------------------------------------------------------------------------
+
+# Reading test data
+# --------------------------------------------------------------------------------------------------------
+def read_test_data(attribute_names):
+    # Load test data
+    X_test_pre = np.load('../gen_train_data/data/output/pre/X_test_pre.npy', allow_pickle=True)
+    y_test_pre = np.load('../gen_train_data/data/output/pre/y_test_pre.npy', allow_pickle=True)
+    X_test_post = np.load('../gen_train_data/data/output/post/X_test_post.npy', allow_pickle=True)
+    y_test_post = np.load('../gen_train_data/data/output/post/y_test_post.npy', allow_pickle=True)
+
+    # Type conversion needed    
+    data_dic = {
+        "X_test_pre": pd.DataFrame(X_test_pre, columns=attribute_names).convert_dtypes(),
+        "y_test_pre": y_test_pre,
+        "X_test_post": pd.DataFrame(X_test_post, columns=attribute_names).convert_dtypes(),
+        "y_test_post": y_test_post,
+    }
+    return data_dic
+# --------------------------------------------------------------------------------------------------------
+
+if __name__ == "__main__":
+
+    # Setup
+    # --------------------------------------------------------------------------------------------------------
+    # Retrieve attribute names in order
+    attribute_names = list(np.load('../gen_train_data/data/output/attributes.npy', allow_pickle=True))
+    # Reading data
+    data_dic = read_test_data(attribute_names)
+    method_names = {
+        0: "ORIG",
+        1: "ORIG_CW",
+        2: "OVER",
+        3: "UNDER"
+    }
+    model_choices = {
+        "ORIG": "XGB",
+        "ORIG_CW": "RF",
+        "OVER": "XGB",
+        "UNDER": "XGB"
+    }
+    # --------------------------------------------------------------------------------------------------------
+
+    # Shap value generation
+    # --------------------------------------------------------------------------------------------------------
+    for i, group in enumerate(['pre', 'post']):
+        # Get test dataset based on group, add column names
+        X_test = data_dic['X_test_' + group]
+        y_test = data_dic['y_test_' + group]
+        for j, method in enumerate(['', '', 'over_', 'under_']):
+            print(f"{group}-{method_names[j]}")
+            method_name = method_names[j]
+            model_name = model_choices[method_name]
+            model_path = f"./output/fitted_models/{group}_{method_names[j]}_{model_name}.pkl"
+            # Load the fitted model from disk
+            with open(model_path, 'rb') as file:
+                fitted_model = pickle.load(file)
+            # Check if we are dealing with a tree vs nn model
+            is_tree = model_name not in ['LR', 'SVM', 'MLP']
+            if is_tree:
+                 explainer = shap.TreeExplainer(fitted_model)
+            # else:
+            #     explainer = shap.KernelExplainer(fitted_model.predict_proba, X_test[:500])
+            # Compute shap values
+            shap_interaction_values = explainer.shap_interaction_values(X_test)
+            # ---------------------------------------------------------------------------------------------------------
+            # Save results
+            np.save(f"./output/shap_inter_values/{group}_{method_names[j]}", shap_interaction_values)
+    # --------------------------------------------------------------------------------------------------------
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